Crowdsourcing is a common approach for training data-hungry models by collecting high-quality labeled data with human labor. With crowdsourcing data, the end-to-end learning paradigm is rising, where the classifier is concatenated with annotator-specific confusion layers and the two parts are co-trained in a parameter-coupled manner. However, learning with the size of a very large set of annotations is a challenge when computation or energy is limited. In this paper, we analyze and refine the coresets for end-to-end learning from crowds under the sensitivity sampling framework. This coreset is a small possible subset of annotations, so one can efficiently optimize the Coupled Cross-Entropy Minimization problem with guaranteed approximation. We first prove the lower bound, which shows no coresets smaller than complete data with confusion layers. Then, with workers' transition matrices, we show that with the regularization term, this lower bound can be prevented. Our main result is that under mild assumptions, a smaller coreset exists for the regularized Coupled Cross-Entropy Minimization problem. An upper bound of sensitivity is proposed for designing a sampling algorithm called CrowdCore. The experimental results on synthetic and real-world datasets demonstrate the effectiveness of our analysis.

International audience

Fuzzy relation models play an important role in describing the complex relationships between the antecedent and consequent parts, but suffer from insufficient interpretability and accuracy, and rely on centralized modeling. This paper proposes a granular fuzzy relation model based on horizontal federated learning to enable distributed modeling of fuzzy relation models with privacy protection, while improving both interpretability and accuracy. In the first phase, fuzzy sets are designed with the aid of federated fuzzy clustering. In the second phase, the max-min fuzzy logic operation is employed to develop fuzzy relation models in federated learning. Based on the initial fuzzy relation issued by the global server, each client establishes the local fuzzy relation model using two federated learning strategies, namely gradient-based and average-based approaches, respectively. In the third phase, granular fuzzy relation models are generalized by introducing a granular parameter in specifying the level of information granularity, which is optimized by applying the Differential Evolution algorithm. The overall performance is measured by the product of two conflicting criteria, namely, coverage and specificity. The originality of proposed model lies in the realization of horizontal federated learning for granular fuzzy relation models. In this study, the granular version of fuzzy relation models incorporates richer semantic information, thereby enhancing both the interpretability and accuracy of the model. Meanwhile, each client can complete the training by simply interacting gradients or parameters instead of the original data and location, thus realizing privacy-protecting distributed modeling. Experimental results indicate better performance of the proposed method than that of centralized learning.

Deep convolutional neural networks can use hierarchical information to progressively extract structural information to recover high-quality images. However, preserving the effectiveness of the obtained structural information is important in image super-resolution. In this paper, we propose a cosine network for image super-resolution (CSRNet) by improving a network architecture and optimizing the training strategy. To extract complementary homologous structural information, odd and even heterogeneous blocks are designed to enlarge the architectural differences and improve the performance of image super-resolution. Combining linear and non-linear structural information can overcome the drawback of homologous information and enhance the robustness of the obtained structural information in image super-resolution. Taking into account the local minimum of gradient descent, a cosine annealing mechanism is used to optimize the training procedure by performing warm restarts and adjusting the learning rate. Experimental results illustrate that the proposed CSRNet is competitive with state-of-the-art methods in image super-resolution.

This study tackles the challenge of robust fault diagnosis in discrete event systems (DESs) that experience permanent observation losses using labeled Petri nets (LPNs). We consider the scenario that the initially observable transitions may become unobservable before their firings. Especially, the case that some, instead of all, of the transitions with a shared label may become unobservable is also taken into account. In such a scenario, the diagnosers in the existing methods may not report correct diagnostic results. This article presents a novel notion to ensure robust diagnosability for LPNs, aimed at overcoming the issue of permanent observation loss. To avert enumerating all the reachable markings, a structure called a tagged basis reachability graph (t-BRG) is developed, based on which all subsets of observable transitions, called diagnosis transition sets (DTSs), that ensure the diagnosability of the plant independently are calculated. Then, a special class of verifiers to assess the robust diagnosability of a system experiencing permanent observation loss is developed. Finally, an online diagnosis method performed by a set of diagnosers is presented and demonstrated by examples.

This article deals with an asynchronous supervisory control problem that enforces generalized mutual exclusion constraints (GMECs) to partially observed discrete-event systems (DESs) with intermittent faults, which are modeled by labeled Petri nets (LPNs) whose initial markings belong to a convex set. The developed control laws are asynchronous and robust with respect to the intermittent loss of observations. Given a plant, a dilated operation is introduced due to the existence of intermittent faults, leading to a dilated net system. Technically, we first develop an asynchronous supervisory control mechanism, aiming to enforce the predefined control constraints based on observations of the dilated net system. Then, an algorithm is designed to construct a directed graph, called an asynchronous basis reachability graph (ABRG), to describe the dilated system, in which a state includes a set of basis markings consistent with an observation. Based on the graph, an asynchronous observer is constructed to perform state estimation of the dilated net system. Finally, it is shown that the asynchronous control laws are maximally permissive after removing all illegal and potentially illegal states from the established asynchronous observer. A manufacturing example illustrates the proposed results.

In the data-driven era, collecting high-quality labeled data requiring human labor is a common approach for training data-hungry models, called crowdsourcing. Recently, end-to-end learning from crowds has shown its flexibility and practicality. However, existing works in an end-to-end manner focus on learning after collecting labels, which results in noisy annotations and also requires cost. Inspired by computerized adaptive testing, we argue that the characteristics of workers should be mined as soon as possible to make the best use of talents. To this end, we propose an adaptive learning from crowds method, AdaCrowd, as a cost-effective solution. Specifically, we propose a probabilistic model to capture the informativeness of possible instances for each worker. The informativeness is considered to be the uncertainty of the annotation prediction model output in its current status. The adaptive learning procedure is optimized by maximizing data likelihood and can be used with existing crowdsourcing models. Extensive experiments are conducted on real-world datasets, LabelMe and CIFAR-10H. The experimental results, e.g., the reduction of annotations without performance degradation, demonstrate the effectiveness.

Reliability assessment is essential for ensuring the stable operation of industrial systems. This study presents a hybrid reliability assessment framework that integrates mechanism-based and data-driven approaches for online degradation evaluation. A representative degradation mode— impact fatigue—is investigated to demonstrate the method. The mechanistic component is formulated by using crack propagation theory and Miner’s cumulative damage rule; while the data-driven one employs a generalized power-law Wiener process with an additive nonlinear drift. Both sources of information are fused through an evolving unscented Kalman filter that performs optimal and unbiased online state estimation via prediction and observation updates. In the proposed framework, the Wiener process serves as the state equation, and the mechanistic model functions as the measurement equation. The reliability of impact fatigue is further evaluated by using the inverse Gaussian distribution, which is inherently associated with the Wiener process. Application to a thrust bearing in the main pump of a pressurized water reactor demonstrates the effectiveness and superiority of the proposed method over existing ones.

Existing studies on traffic flow prediction primarily rely on on-board devices to collect vehicle trajectory data , which can potentially infringe upon the privacy of users and limit the applicability of the method. Additionally, traffic flow prediction remains challenging due to the complex spatial and temporal dependencies within real-world traffic networks. To address these limitations, this paper introduces a framework for analyzing discrete vehicle trajectory data at urban intersections. By incorporating various external physical factors into traffic flow prediction, this framework derives embedding vectors from vehicle trajectory sequences and road network topology , modeling their spatio-temporal dependencies using Skip-Gram and GraphSAGE, respectively. Additionally, the intersection similarity is introduced to capture and integrate traffic flow patterns between the target intersection and similar intersections. A Spatio-Temporal Graph Convolutional Neural Network (ST-GCN) algorithm, which combines Graph Convolutional Networks (GCN) with Long Short-Term Memory (LSTM), is developed to achieve precise traffic flow prediction. Extensive experiments on a real-world traffic flow dataset from Qingdao, China, validate that the proposed method outperforms state-of-the-art baseline methods .

This article formalizes and solves the problem of diagnosability verification and enforcement of discrete event systems (DESs) that are modeled by Petri nets with unknown initial markings, i.e., the number of tokens in part or all of the places is unknown or uncertain at an initial marking. A revised topology of the original net with a certain initial marking, called an acceptable initial-model (AIM), is constructed to verify the diagnosability of such a system without testing diagnosability for each possible initial marking. The process applies a diagnosability verification agent, called an extended verifier that is obtained from the proposed AIM. We prove that a Petri net with unknown initial markings is diagnosable if and only if there does not exist any uncertain fault path in the verifier. The result also provides sufficient and necessary conditions for enforcing diagnosability by developing an online supervisor. Examples are presented to demonstrate the proposed method.

This paper addresses the problems of diagnosability verification and enforcement of discrete event systems modeled with unbounded Petri nets. Diagnosability in such systems is critical for ensuring reliability and maintaining operational integrity, yet current methods often struggle with the complexity introduced by unboundedness and potential deadlocks. Given an unbounded labeled Petri net that may reach deadlocks, a quiescent basis coverability graph is established to verify the diagnosability of the considered system. This procedure employs a deterministic finite state automaton, called an extended verifier, derived from the proposed quiescent basis coverability graph. It is shown that an unbounded Petri net is diagnosable if and only if the verifier does not contain a class of cycles, called repetitive F-cycles. This result also provides necessary and sufficient conditions for diagnosability enforcement by developing an online supervisor. Further, the designed supervisor is maximally permissive and also circumvents a plant entering deadlocks by firing non-fault sequences. Examples are presented to demonstrate the proposed method. Note to Practitioners—Fault diagnosis and diagnosability enforcement are critical for the development and operation of highly automated systems covering computer-integrated production processes, intelligent traffic, computer and communication networks, smart gird, etc. This work touches upon this problem from the perspective of discrete event systems that are modeled with unbounded labeled Petri nets. The feasibility and applicability of the reported method stem from the usage of a structurally compact representation of a considered plant such that the computational cost of a real-world system is acceptable. The graphical representation of Petri nets as well as the proposed quiescent basis coverability graph make the method easy to use and manipulate. Moreover the sufficient and necessary conditions of diagnosability enforcement can be readily verified by the supervisory theory, facilitating its adoption by practitioners.

Human-cyber-physical system has emerged as a pivotal tool for intelligent production throughout the whole lifecycle. In this paper, a health state assessment approach based on hierarchical data fusion is proposed under human-cyber-physical system architecture. The human-cyber-physical system emphasizes the flexibility of dynamic operating states and the evolvability of the enabling models. First of all, a multiple hierarchy consisting of equipment, systems and function is established as a physical basis for human-cyber-physical system. Regarding the equipment-level assessment characterized by extensive input parameters, a dynamic weighting method is exploited to highlight the abnormal parameters. Thus, the degraded device can be significantly reflected in the system-level assessment by fusing equipment parameters. To evaluate the operation function of a plant, a Nearest Class Mean classifiers-based Random Forests algorithm is explored considering the small-scale and imbalanced instances in different health levels. However, the conventional fixed and changeable models threaten the accuracy and rationality of the human-cyber-physical system. We introduce incremental learning to reform Random Forests that can update promptly with new data and knowledge. Finally, the proposed scheme and technologies are applied in a nuclear power plant to demonstrate their effectiveness.Note to Practitioners—The modern production desires to be further intelligent for improvement of economic benefits but with the consistent requirements of high safety and reliability. The trades-off between efficiency and safety bring the greater challenges to the operation and maintenance for production. This paper proposes an evolvable human-cyber-physical system scheme that not only integrates more intelligent elements but also attaches some solution to the abovementioned problem. The scheme is implemented on the multi-hierarchy health states assessment which is the core in production maintenance against lagging. This equipment-system-function fusion and assessment provide a unified platform for multi-departments to collaborate and self-manage. The novel weighting method abandons the conventional subjective evaluation criteria with high time consumption. Computers are convenient to possess sensing data efficiently and highlight the anomaly obviously. In particular, it is mentioned that the intelligent models in human-cyber-physical systems are always fixed and unable to alter with the physical system changes. Incremental learning, a promising branch of machine learning, is introduced to reform the assessment model to be stainable developed with new data adding. The scheme takes a nuclear plant as example because it is more corresponding to these issues. The future studies can extend the scheme to other scenarios.

Opacity of a discrete event system is a confidentiality property that characterizes instances when the secret behavior of the system cannot be revealed. This paper considers current-state opacity with respect to a set of secret states, and addresses the opacity enforcement problem via modification functions that are capable of replacing in real-time the output of the underlying system with another output, aiming to prevent the intruder from inferring the secret. We study modification functions that are constrained, i.e., they may not be able to modify certain outputs or they may have restrictions in how they modify a particular output. A system is said to be emc-enforceable if opacity can be enforced via modification functions under the given event modification constraints (emc). To verify whether a system is emc-enforceable, we construct a verifier, based on which a necessary and sufficient condition is obtained. If a system is emc-enforceable, an algorithm is developed to compute a modification strategy based on the verifier to enforce opacity. For m-enforceability, a special case of emc-enforceability without constraints, two necessary and sufficient conditions to verify and enforce opacity with reduced complexity are presented, one based on a system estimator and the other based on a detector. Note to Practitioners—Discrete event systems provide appropriate mathematical models for analysing information-flow properties of cyber-physical systems, such as intelligent transportation systems, command-control systems, and networked communication systems, in dynamically changing environments. Opacity is a general property capable of capturing safe information-flow in a cyber-physical system. This article targets the enforcement of opacity using modification functions that mask the output of the system into another output in a manner that appears consistent to an external observer as the system evolves. The designed modification function can be realized via triggers equipped with digital logic and storage functionality. As an output is generated by a system, a trigger displays a fake output based on the stored real and fake states.

This paper deals with the online fault diagnosis problem of discrete event systems under malicious external attacks. We consider a scenario where an attacker can intercept certain sensor measurements and alter them arbitrarily, potentially causing a diagnoser to malfunction. In the framework of labeled Petri nets, a novel integer linear programming problem is formulated by introducing binary variables to estimate the possible transition sequences of an observation that may have been tampered with by an attacker. The proposed approach makes two main contributions. The first one is that, by specifying two different objective functions to the integer linear programming problem, we can obtain the diagnosis results in the presence of attacks, which classic diagnosers may fail to achieve; the second is computational efficiency. In the absence of attacks, the proposed approach is experimentally verified to have lower computational overhead compared with the existing results that are based on integer linear programming and those using basis markings. Finally, the proposed approach is illustrated through a manufacturing system for assembling brake valves. Note to Practitioners—Fault diagnosis is critical for highly automated systems such as manufacturing systems, power plants and smart grids. Engineers are familiar with various fault diagnosis techniques in their community. However, malicious attacks in a system are not fully considered when performing fault diagnosis. This research elaborates upon a novel approach to fault diagnosis of discrete event systems modeled by labeled Petri nets, which can perform online fault diagnosis both in the presence and absence of attacks. The approach enables faults to be detected even after certain labels are tampered with by an attacker. The proposed diagnostic vehicle is presented in terms of integer linear programming problems, which facilitates its applications to real systems by practitioners.

Selective maintenance has a significant impact on the sustainable management of maintenance operations. The collaboration of multiple maintenance teams/operators is helpful to achieve sustainability for selective maintenance sequence planning. For products with a large number of components, a single maintenance team/operator is inefficient due to a long completion time which is not acceptable for emergency planning. Providing specific and efficient maintenance sequence planning is critical to effectively handle different types of emergencies (e.g., wartime) while avoiding vague task assignments to multiple maintenance teams/operators. For scheduling many maintenance jobs while improving the efficiency and quality of maintenance operations, this study proposes a collaborative maintenance planning based on the concept of imperfect maintenance. In this regard, this study develops a multi-objective optimization model to optimize parallel maintenance sequences considering maintenance profit, maintenance cost, maintenance team, and resource limitations. We show the feasibility of the proposed multi-objective optimization model through a real case of maintenance practice for the components of an assistor device. For analyzing the complexity of the proposed maintenance sequence planning problem, this study introduces a new multi-objective metaheuristic algorithm which is an enhanced multi-objective gravitational search algorithm (EMOGSA) to find high-quality Pareto solutions for the proposed problem. Different multi-objective evaluation metrics are used to study the performance of the proposed algorithm. From the results, the proposed model and developed solution algorithm can help maintenance decision-makers to determine complex maintenance planning.Note to Practitioners—This paper deals product with a maintenance and proposes gravitational search algorithm based on only maintenance task, which maintenance task. The goal of this paper is to analyze the maintenance problem from the perspective of collaboration of multiple maintenance teams/operators.

This paper addresses the property of critical observability in labeled Petri nets. A system is deemed critically observable if its state estimate (formed by observing the system output) can be determined to belong to a set of either critical markings or non-critical markings. More generally, for a critically observable system, there may be multiple types of critical markings and the operator may desire to know the type to which the current state estimate belongs. We propose a generalized version of critical observability, namely generalized critical observability, and provide two necessary and sufficient conditions to check it based on basis markings and integer linear programs, effectively circumventing the need for an exhaustive enumeration of all reachable markings of the net. In addition, we investigate (generalized) critical observability of Petri nets under nondeterministic observations, which may arise due to the loss of transmission signals or due to interference with sensor measurement outputs. To handle this, an augmented system is constructed from the original Petri net by adding new transitions that capture the effect of transitions associated with nondeterministic labels. Thus, we transform the verification of (generalized) critical observability of a Petri net under nondeterministic observations to that of the augmented model. Two real-world examples illustrate the applicability of the presented notion and methodology.

Design for disassembly (DFD) is a basic design technology serving the scrap and recycling stages, which can relieve the environmental pressure and improve the economic benefits. Considering the wide variety of mechanical products and the shortening of product lifecycle, the product design integrating customer requirements can effectively improve the market competitiveness of products. Thus, this paper proposes a QFD-based human-centric decision-making approach for disassembly scheme selection. The theory of the interval 2-tuple q-rung orthopair fuzzy sets (I2q-ROFSs) is proposed to better describe the ambiguity of the environment and avoid information loss/distortion in the information aggregation stage. A scheme evaluation system based on the disassembly technical features is established. A hybrid multi-attribute decision-making (MADM) method combing BWM-FQFD (best worst method and fuzzy quality function deployment) and RT-EDAS (the regret theory based on distance from average solution) is presented to obtain the optimal alternative. A case study, i.e., four refrigerator DFD schemes, is applied to verify the effectiveness of the proposed method. The result demonstrates that this work provides an effective tool to select the optimal scheme of disassembly considering customer requirements and some references for designersNote to Practitioners—The selection of DFD schemes is the last task of the disassembly design. The decision information of the disassembly design scheme has insufficient evaluation complexity ability and the information distortion of the evaluation information in the aggregation stage. To end this, this paper proposes I2q-ROFSs which is a fuzzy set with the higher level of fuzzy description and avoiding information distortion. At present, the disassembly design research lacks the correlation between customer demand and disassembly technology features. Therefore, a fuzzy QFD method can well assist manufacturing enterprises to improve the market competitiveness of their products. Then, RT-EDAS can describe consumer psychology, which can choose a more market-appropriate DFD scheme.

This paper focuses on the modeling and analysis of systems with various temporal behaviors in the framework of discrete event systems modeled with time-dependent Petri nets. While Petri nets have been extensively studied, few existing formalisms effectively represent the coordination and synchronization among temporal behaviors. To this end, we first introduce a dual-time Petri net that captures both state-driven and event-driven behaviors by integrating temporal attributes associated with places and transitions, respectively. To describe the dynamic evolution of systems, a structural representation of the state space, abstracted as an extended state class graph, is presented. This representation serves as a foundation for analyzing critical system properties. Moreover, we show the efficiency of the proposed method to address the complexity in temporal dynamics, offering a more powerful modeling capability and a more compact structure compared with the existing time-dependent Petri net models. Finally, a case study on a semiconductor manufacturing system is provided to illustrate the practical application of the established model and reported analysis methods.

In this paper we investigate a supervisory control problem of discrete event systems under attacks. Specially, we consider a type of sensor deception attacks, called replacement attacks, under which the intruder confuses the observation of events by substituting the occurrence of an observation with another. We use labeled Petri nets as the reference formalism to model a discrete event system and represent control specifications in terms of generalized mutual exclusion constraints (GMECs). The concept of a monitor function is proposed to describe the satisfiability of GMECs given an observation by counting the number of occurrences of each label. Due to the existence of attacks, some labels generated by a plant are prone to be altered by an attacker, interfering with a supervisor such that it cannot make correct control decisions. For assisting the monitor function to estimate the number of occurrences of those altered-prone labels, generated by the plant, the notion of label dependency is introduced. Accordingly, a monitor-based supervisor is designed with low online computational effort, avoiding the marking estimation or the reachability analysis of the system. It is verified that the proposed supervisor not only enforces all GMECs no matter whether or not replacement attacks occur, but also keeps the system's behavior as permissive as possible. Note to Practitioners—Cyber physical systems (CPSs) have exhibited multifaceted applications in various fields such as process control systems, smart grids, distributed robotics, autonomous vehicles. Due to the over-reliance on communication networks, CPSs are vulnerable to attacks that can tamper the data collection processes and interfere safety critical decision making processes, resulting in catastrophic damages to the systems. In the frame of discrete event systems, most of existing supervisory control strategies of CPSs under attacks rely on an exhaustive reachability analysis, which is computationally expensive, making such approaches hardly applicable to large systems. In order to address this issue, this work considers a type of sensor deception attacks, called replacement attacks, and proposes a monitor-based supervisor policy, enforcing the control specifications of the systems in the presence of replacement attacks. Without requiring tedious analysis, the designed online supervisor has low computational effort and control decisions only depend on a direct analysis of the observation sequence.

As Internet technology undergoes rapid development and widespread adoption, e-commerce emerges as a pivotal component of the platform economy, permeating various facets of daily life. However, due to the influence of time, space, and other factors, the problem of integrity becomes severe in the real trading environment. As the platforms, sellers, and consumers are the main participants and their decision-making is restricted by historical experiences and contextual conditions, they exhibit constrained rationality. Utilizing evolutionary game theory, the study constructs a tripartite game model that analyses the influence of relevant parameters on the behavior of the participants. To deal with the behaviors of the participants, we built a simulation system on MATLAB to demonstrate the effects of beginning circumstances and associated parameter adjustments on the evolution outcomes for participants. Through theoretical analysis and numerical simulation analysis, we identify that the e-commerce platforms should standardize the good faith behavior of sellers by increasing the punishment, which can reduce the malicious return behavior of consumers. Sellers can mitigate the probability of fraud by improving production technology. Consumers can improve their learning to avoid returning products. This research provides a theoretical framework and decision support for e-commerce platforms, and it also promotes the long-term growth of online transactions.

Nowadays, social media continually influences people’s opinions and behaviors. The spread of unchecked rumors has a detrimental impact on society. It is crucial to develop effective strategies to block the dissemination of rumors as much as possible. However, current approaches for influence blocking overlook the fairness among diverse communities, which are fundamental structures in social networks, leading to a disproportionate exclusion of marginalized communities from intervention benefits and resulting in fairness disparities. This article addresses such a critical issue in influence blocking and proposes a fairness-aware blocking strategy that balances the effectiveness of influence blocking and fairness. A sampling-based approximate algorithm called fairness-aware influence blocking maximization based on martingales (FIBMM), which is applicable to large networks, is presented. We conduct a series of experiments on six real networks, showing that the FIBMM can effectively reduce fairness disparities between diverse communities while maintaining blocking effectiveness.

Fuzzy rule-based models constructed in the presence of numeric data are nonlinear numeric models producing for any input some numeric output. There are no ideal models so the obtained numeric output could create a false illusion of achieved accuracy. A desirable approach is to augment the results with some measure of confidence (credibility) by admitting a granular rather than numeric format of the produced output values of the model. Our focus of this study is on fuzzy Takagi–Sugeno rule-based models whose conclusions are constant. The ultimate objective is to extend such models to the generalized granular structure with the conclusions formed as information granules. We study information granules described by intervals and fuzzy sets as well as probabilistic Gaussian information granules. The original design of the granular model is realized by involving the principle of justifiable granularity. Using this principle, we also show how to determine the equivalence between information granules. The construction of probabilistic information granules of the model is completed with the aid of optimized Gaussian process models. The granular models built in this way constitute a substantial and application-oriented departure from the numeric fuzzy models by offering a comprehensive insight into the quality of the produced results. The experimental studies based on synthetic and publicly available data demonstrate the design process and discuss the quality of the obtained results.

In this article, we propose a novel methodology for determining accurate positions of characteristic points encountered in the analysis of bioimpedance signals. The proposed approach fully utilizes two fundamental modeling pursuits based on fuzzy rule-based models and neural networks. We take advantages of the unique capabilities of fuzzy rule-based models to characterize the nonlinear relationship between the acquired bioimpedance signals and their temporal coordinates. The fuzzy modeling approach is used to approximate the process that generates the bioimpedance signals through a collection of rules (if–then statements). In the sequel, the parameters of the models are used as the inputs of neural network models to determine the position of characteristic points. We further augment the numeric neural network to its granular counterpart to accommodate the uncertainty in the available experimental evidence by allocating a certain level of information granularity across the parameter space. The resulting granular outputs (intervals) become reflective of the quality and level of confidence associated with the prediction results. The quality of the prediction results is quantified in terms of the coverage and specificity criteria. The performance index is also enhanced to deal with the situation when the positions provided by experts are also information granules (intervals). The performance of the proposed approach is justified through a collection of experiments carried out on the collected real-world bioimpedance signals. Experimental results show that the proposed approach achieved higher accuracy in determining the position of characteristic points in comparison with other existing methods.

The fault diagnosis problem in discrete event systems consists in detecting the occurrences of faults in a plant, which is essential for ensuring the reliability of the plant. In the literature, this problem has been widely studied by assuming that the communications between a plant and a diagnostic agent, i.e., a diagnoser, are reliable and instantaneous. However, for a networked system, the information generated by a plant is transmitted through a shared communication network such that communication delays and losses are inevitable. This article formulates and studies the fault diagnosis problem in networked discrete event systems (NDESs) modeled by labeled Petri nets, where communication delays and losses are considered. Such a problem is also calledrobust fault diagnosisin this article. An important notion closely related to robust diagnosis, namednetworked diagnosability, is introduced, indicating that any occurred fault in a NDES is definitely determined after a limited number of observations. For a NDES, a tool called anetworked basis diagnoseris excogitated to solve the robust diagnosis problem. A necessary and sufficient condition for verifying the networked diagnosability of a plant is derived by using the developed diagnoser. Finally, a manufacturing system is presented to illustrate the developed approach.

This article formalizes and solves the problems of diagnosability verification and enforcement in discrete event systems modeled with labeled Petri nets (LPNs) under sensor attacks. Given a plant, attackers work as a group in the framework of a coordinated distributed architecture and have the ability to edit some sensor readings to conceal the faults to confuse the operator. Furthermore, attackers necessarily remain furtive, i.e., their presence should not be discovered by the operator. In order to describe the set of all possible furtive attacks, a joint furtive diagnoser is established. We prove that an LPN under the above attacks is diagnosable if and only if its joint furtive diagnoser does not have the cycles composed of pairs of either faulty states and normal states, or faulty states and uncertain states. A new labeling function is proposed to enforce a plant to be diagnosable against as many attacks as possible. Examples are provided to illustrate the proposed method.

In this article, we introduce a novel weighted digraph, namely, a resource requirement graph, to address deadlock problems in flexible manufacturing systems (FMSs) modeled with Petri nets. A liveness-enforcing supervisor consisting of recovery transitions designed to be enabled at pre-partial deadlocks is synthesized. First, a resource requirement graph is directly derived from a Petri net model of an FMS and is used to represent competition relations for shared resources by various processes. Subsequently, the notion of pre-partial deadlock, described as a set of linear inequalities, is introduced by analyzing a resource requirement graph. Then, an algorithm is presented to develop a supervisor consisting of recovery transitions that are only enabled at the identified pre-partial deadlocks. The controlled Petri net model of an FMS under the designed supervisor is shown to be live with all initial reachable markings retained. The method proposed in this article offers computational efficiency since the resource requirement graph is structurally compact compared to its counterpart net model. Finally, several examples are provided to demonstrate the developed techniques.

To cope with the problems of high-computational complexity and multiple locally optimal solutions induced by the coupling of multiple subproblems, conflicting objectives, and integration of resource constraints of the energy-efficient lot-streaming scheduling of multi-resource constrained flexible job shop (γ-shop for short), an energy-efficient lot-streaming scheduling optimization approach based on the knowledge-based lot-splitting method (KLSM) and the improved multiobjective evolutionary algorithm (IMOEA) is presented. First, a flexible job shop lot-splitting scheduling model with the optimization objectives of total energy consumption, makespan, and total processing cost is formulated. Second, a hybrid approach of the KLSM and the IMOEA is designed to solve the model The solution space of the problem is fully explored based on the moth-flame operator. Co-evolutionary operators are performed to promote information interaction among populations, hence both the population diversity and the convergence effect of the algorithm are improved. Moreover, a post-adjustment strategy based on adjacent processes is developed to reduce unnecessary fixture changes. Finally, extended experiments between some KLSM-based well-known and novel algorithms, including the proposed IMOEA, MOEA/D, NSGA-II, MOPSO, SGECF, SCEA, and SLMEA are conducted in benchmark problems and a real-world case of machine tool plant. The results show that the proposed method outperforms its competitors on co-optimization of lot-splitting, machine allocation, operation sequencing, and fixture assignment of the γ-shop scheduling, which can effectively reduce total energy consumption, makespan, and total processing cost.

This article investigates the state estimation for a networked timed discrete event system, where a plant communicates with a supervisor via a multichannel network characterized by bounded delays and losses. To address delays and losses in observation channels, we augment the plant by integrating the dynamics of these channels, thus capturing the system’s open-loop behavior. To tackle delays and losses in control channels, we augment the supervisor by considering all control decisions with potential impact on the system’s behavior. By integrating the augmented plant and supervisor, we introduce a compensated system that enables the derivation of an over-approximation of the closed-loop system’s behavior. Ultimately, we devise an online over-approximation state estimation algorithm for the closed-loop system, to compute all possible system states under communication delays and losses. We provide a simulation example to illustrate the efficacy of the proposed method.

Learning from crowds aims to train a robust and generalizable model with a noisy crowdsourced dataset from multiple annotators. Due to its simplicity and practicality, the target model and label correction mechanism are co-trained in a parameter-coupled manner. However, this end-to-end training suffers from the inherent flaws in data. The crowdsourced dataset is naturally noisy and redundant: 1) some annotations may violate the annotator’s transition matrix and mislead the training procedure and 2) the size of the annotation set is much larger than the instance set, and some instances may be backpropagated multiple times without performance gain. To address these issues, we propose a sample selection method called CrowdSketch for deep learning from crowds. Specifically, to mitigate the annotation noise, we find possibly high-quality data with small local contrastive loss (clean) and high divergence loss between prediction probabilities (important) on a two-branch network. After that, to alleviate redundant data, an importance score is developed based onl2-norm of errors. The originality of this work stems from the designed selection criteria and specified two-branch architecture for crowdsourcing. Besides, by removing the noisy data, the risk of nonoptimum in dataset pruning is reduced. Extensive experiments are conducted on real-world crowdsourcing datasets. The experimental results, which show average accuracy improvements of 1.27% on LabelMe and 1.52% on CIFAR-10H, demonstrate the effectiveness of CrowdSketch.

This study addresses an energy-efficient multiobjective distributed assembly permutation flowshop scheduling problem with sequence dependent setup time. The objectives are to minimize the maximum completion time (makespan), mean of earliness and tardiness, and total carbon emission, simultaneously. First, a mathematical model is established. Second, the double-learning-strategy-based Jaya algorithms are developed to address the problems. According to problem-specific nature, one Q-learning state-action strategy is designed to guide nondominated solutions choosing appropriate machine speed adjustment strategies for achieving a satisfactory tradeoff among the three objectives. Third, eight neighborhood structures are designed and embedded in the proposed Jaya algorithms to discover high-quality solutions in local spaces. Fourth, another three novel Q-learning state-action design strategies are proposed to dynamically select the appropriate neighborhood structures during iterations, which introduce the searching directions and improve the convergence of the proposed Jaya. Finally, 81 benchmark instances are solved and the effectiveness of improved strategies is demonstrated. The proposed Jaya algorithm with the best double Q-learning strategies is compared to a solver, Gurobi, to verify the developed mathematical model. The experimental analysis demonstrates that the improved Jaya algorithm with both the Q-learning based machine speed adjustment and the Q-learning-based neighborhood selection strategies shows the best performance.

A deadlock prevention policy for discrete event systems (DESs) modeled as unbounded Petri nets (UPNs) is developed in this article. A reachability tree, an essential instrument, shows the evolution of UPNs is fundamental for the description and analysis of several characteristics, including liveness and reversibility. Deadlocks are an undesirable situation within the realm of DESs, particularly those represented by Petri nets. This article introduces a method implemented through integer linear programming for UPNs. We explore the modified reachability trees of UPNs from a new lens by defining two classes of circuits, and exploit an algorithm to engineer a supervisor that ensures liveness and maximum permissiveness. The proposed approach can supervise and control all deadlock nodes in a reachability tree such that the closed-loop unbounded net is live. Finally, several typical cases are shown to demonstrate the reported methods.

This paper addresses the problem of fault diagnosis and diagnosability enforcement of discrete event systems. Given a nondiagnosable discrete event system modeled with Petri nets, which may enter deadlocks and unobservable live-locks, a digital twin system (derived from the original Petri net model) can be established as a particular Petri net such that the closed-loop system established from the original system under the control of its digital twin counterpart is diagnosable. This procedure employs a deterministic finite automaton (derived from the digital twin system) to diagnose the closed-loop system and provides necessary and sufficient conditions for diagnosability enforcement. In this way, we do not need to compute and analyse the reachability graph or basis reachability graph of the original Petri net, which in practice reduces the computational overheads as exposed by experimental studies. Examples are presented to demonstrate the proposed method.Note to Practitioners—Fault diagnosis and diagnosability enforcement are critical for the development and operation of highly automated systems covering computer-integrated production processes, intelligent traffic, computer and communication networks, smart gird, etc. This work touches upon this problem from the perspective of discrete event systems that are modeled with Petri nets. The feasibility and applicability of the reported method stem from the usage of a digital twin system derived from a plant to be investigated such that the computational cost of a real-world system is acceptable. The graphical representation of Petri nets as well as the digital twin system make the method easy to use and manipulate. Moreover the sufficient and necessary conditions of diagnosability enforcement can be readily verified by the basics of graph theory, facilitating its adoption by practitioners.

In this paper, we study the nonblocking supervisor synthesis problem in partially observed discrete event systems modeled by finite-state automata. We consider a particular type of supervisors that can observe not only the execution of observable events in a plant but also the quiescence of it. We first define a q-observer to characterize the behavior of a plant with observable quiescence. Comparing with the classical observer structure, the q-observer contains the quiescence information of a plant, which can be used to improve state estimation. Then we propose a method to detect the blocking states in a$q$-observer. Finally, we develop an iterative method to synthesize a nonblockingness enforcement supervisor from the$q$-observer. Since quiescence provides additional information on state estimation, the supervisor synthesized by the proposed method is in general more permissive than those synthesized by the existing approaches that do not monitor the quiescence. A manufacturing system example is also given to elucidate the effectiveness of the developed approachNote to Practitioners—A discrete event system is a discrete-state and event-driven system, covering a deluge of contemporary computer-integrated man-made constructs, such as automated manufacturing systems, smart urban transportation systems and computer communication networks. Such a system is in general partially observed due to the limited sensor deployment, which complicates its controller design. This research studies the typical supervisory control problem for a partially observed discrete event system. A supervisor is designed to restrict the dynamics of the system in order to guarantee a safe operation through a different control scheme by using the quiescence information that can be usually provided by a real-world system. The practitioners in control and automation community are capable of practicing the formulated control scheme for engineering applications.

This work addresses the verification of current-state opacity with respect to a real-time observation generated from time-dependent systems. The secret behavior of a time-dependent system is defined as a set of states in a time labeled Petri net. The current-state opacity of a real-time observation means that a given secret remains opaque to the intruder who can partially observe the system behavior in the framework of a time labeled Petri net at a given time instant. We introduce a novel directed graph, called a parallel state class graph, to represent the parallel evolution of time-dependent systems exhaustively. Based on the parallel state class graph, we design an algorithm for the construction of a critical observer and show that the current-state opacity of a real-time observation in time labeled Petri nets can be efficiently solved by the critical observer. This approach is computationally competitive since the critical observer can be constructed by solving a number of linear programming problems.Note to Practitioners—In computer-integrated real-time systems, their correctness-related properties usually depend on time constraints. System opacity guarantees that particular system information cannot be disclosed or inferred by external observers, which is a generalization of many information flow properties associated with system safety, particularly in aerospace, automotive, and healthcare domains. This research addresses the verification problem of this property for a discrete event system modeled with time labeled Petri nets. By developing a graph-theoretical structure that can be readily managed by security engineers, a type of diagram called a critical observer is derived which is able to detect the privacy of the system at a given time instant. This research provides a systematic and efficient approach for practitioners to check a desired privacy strategy of time-constrained safety systems.

The development of lot streaming technology in flexible production systems has greatly benefited the aerospace, semiconductor, automotive, pharmaceutical and other discrete manufacturing industries. In this paper, we address a lot-splitting optimization problem of flexible job shop. To handle the issues of large performance gap and low searching efficiency of optimal scheme caused by strong randomness of lot-splitting, a knowledge-based method is proposed. First, under the equal lot-splitting strategy and variable minimum sub-lots constraint, a lot-splitting scheduling model of flexible job shop is constructed. Second, the effectiveness of lot-splitting schemes is assessed by appropriate data-driven algorithms according to the different scales of the problem. Moreover, a neighborhood search strategy based on the number of sub-lots is introduced to improve the search ability of the method. Then, by analyzing the characteristics of the problem, a parameter adjustment strategy based on problem-specific knowledge is presented to balance the search time and performance. Next, sensitivity analyses are carried out to calibrate the parameters based on the design of experiment Taguchi method. Finally, extensive experiments are conducted to evaluate the effectiveness of the proposed method based on the benchmark problems with single or multiple objectives. The results show that the proposed method can obtain high-quality optimized lot-splitting schemes.Note to Practitioners—In discrete manufacturing industries, the flexible production mode in multi-variety and small-batch has become the mainstream. The flexible job shop lot-splitting optimization problem in such a mode is of great significance to practitioners. We mathematically characterize the lot-splitting problem in a flexible job shop considering energy consumption and solve it through a new general multi-objective optimization framework by integrating the lot-splitting and job shop scheduling that is activated from problem-specific knowledge. Existing lot-splitting optimization approaches fail to solve the concerned problem. This work introduces appropriate data-driven algorithms to assess the scheduling schemes after lot-splitting. By updating the minimum lot-size of jobs and the archive set, the search of the optimal lot-splitting scheme is directional. Furthermore, the global and local iterations of the method are adjusted according to the problem size and the number of sub-lots to balance the performance and search time. The results of comparative study show that the proposed method outperforms other algorithms well and can greatly help practitioners manage the lot-splitting scheme of jobs.

This paper proposes an adaptive supervisory control policy for an automated manufacturing system (AMS) with multiple types of unreliable resources. The considered AMS is a generalized system of simple sequential process with resources (GS$^{3}$PR). In order to model resource failures and recoveries, modified recovery subnets are developed, which model more complex resource failure scenarios compared with traditional recovery subnets. For the purpose of guaranteeing the execution of a system’s production successfully, from the perspective of the structural analysis of the system, a siphon control-based technique is introduced and a type of controller is developed. It is verified that the proposed adaptive control policy assures the deadlock-freeness of the controlled system no matter if there exist resource failures, and retains all behavior of the original system. Finally, examples are presented to demonstrate the proposed method.Note to Practitioners—Resources failures are ubiquitous in an automated manufacturing system (AMS). Their occurrences can lead partial or global system stagnation, which causes unnecessary downtime and even catastrophic results. Most of the existing robust deadlock control strategies for AMSs with unreliable resources usually restrict system evolution in an unnecessarily conservative way such that part of permissive behavior is excluded if no resource failure occurs, which affects the utilization of resources and reduces the productivity of the system. In order to address the issue, this work considers a type of AMSs where each processing stage may require an arbitrary number of units, but all from a single resource type. For such an AMS, we propose an adaptive deadlock control policy that makes the controlled system deadlock-free, keeping all the system behavior as in the case that unreliable resources do not break down. The proposed method is realized by dynamically adjusting the working modes of a system to adapt to resource failures and recoveries, which is readily accessible to practitioners from a technical viewpoint.

Since a noisy image has inferior characteristics, the direct use of Fuzzy C -Means (FCM) to segment it often produces poor image segmentation results. Intuitively, using its ideal value (noise-free image) benefits FCM's robustness enhancement. Therefore, the realization of accurate noise estimation in FCM is a new and important task. To date, only two noise-estimation-based FCM algorithms have been proposed for image segmentation, that is: 1) deviation-sparse FCM (DSFCM) and 2) our earlier proposed residual-driven FCM (RFCM). In this article, we make a thorough comparative study of DSFCM and RFCM. We demonstrate that an RFCM framework can realize more accurate noise estimation than DSFCM when different types of noise are involved. It is mainly thanks to its utilization of noise distribution characteristics instead of noise sparsity used in DSFCM. We show that DSFCM is a particular case of RFCM, thus signifying that they are the same when only impulse noise is involved. With a spatial information constraint, we demonstrate RFCM's superior effectiveness and efficiency over DSFCM in terms of supporting experiments with different levels of single, mixed, and unknown noise.

In this study, we establish a new design methodology of granular models realized by augmenting the existing numeric models through analyzing and modeling their associated prediction error. Several novel approaches to the construction of granular architectures through augmenting existing numeric models by incorporating modeling errors are proposed in order to improve and quantify the numeric models' prediction abilities. The resulting construct arises as a granular model that produces granular outcomes generated as a result of the aggregation of the outputs produced by the numeric model (or its granular counterpart) and the corresponding error terms. Three different architectural developments are formulated and analyzed. In comparison with the numeric models, which strive to achieve the highest accuracy, granular models are developed in a way such that they produce comprehensive prediction outcomes realized as information granules. In virtue of the granular nature of results, the coverage and specificity of the constructed information granules express the quality of the results of prediction in a more descriptive and comprehensive manner. The performance of the granular constructs is evaluated using the criteria of coverage and specificity, which are pertinent to granular outputs produced by the granular models.

Pattern recognition plays an important role in the process of knowledge discovery. The construction of easily describable and interpretable classification rules is of vital importance in pattern recognition. In this study, we propose a development of fuzzy rule-based classifier for multiclass classification problems and elaborate on a privacy-preserving realization of the proposed methodology in the presence of decentralized datasets. Fuzzy rule-based models provide an effective and efficient alternative for characterizing the complex relationship between the input variables and target classes. An overall design process of the proposed classifier consists of two main phases: (a) formation of information granules (clusters) to reveal the underlying structure of the training data, and (b) construction of local classification rules whose outputs reflect the probability distribution of the input data over all the classes. The constructed information granules form a backbone of the architecture of the classifier while the optimization of the parameters of local rules is carried out through using a gradient descent method with the guidance of the cross-entropy loss function. Furthermore, a federated gradient-based optimization mechanism is utilized to construct fuzzy classifier in a privacy-preserving approach. The originalities of the proposed methodology are twofold: first, a design of fuzzy classifier through the synergy of cluster-centric architecture and the cross-entropy loss function is presented. Second, we augment the proposed fuzzy classifier based on the concept of federated learning such that it can learn from distributed data without sacrificing data security and confidentiality. Experiments are carried out on a two-dimensional synthetic dataset and a number of real-world datasets. Experimental results show the excellent classification capability of the proposed classifier realized in the centralized way and in the federated learning environment.

This study focuses on the construction of granular Gaussian process models completed at different levels of granularity and the emergence of higher-type granular outputs through aggregating the individual prediction results. Each Gaussian process model is instantiated utilizing granular data (or information granules) to enhance algorithmic efficiency and can be tailored to specific levels of precision (granularity). The overall design methodology emphasizes human centricity in system modeling by focusing on both the interpretability and accuracy of the resulting models. First, clustering algorithms are applied to construct information granules that provide a comprehensive overview of the experimental evidence. As the number of information granules grows, the existing knowledge imbedded within data could be perceived and described at increased levels of details. Information granules are built in an augmented feature space constructed by concatenating the input and output variables. Next, Gaussian process models are constructed on a basis of the information granules formed at different levels of abstraction. Subsequently, the confidence intervals are transformed to intervals and the reconciliation of the predictions produced by individual models, which offer different perspectives on the system, leads to the emergence of more abstract entities (such as type-2 intervals/fuzzy sets, etc.) rather than plain numbers. The efficacy of the comprehensive model is measured by the coverage and specificity criteria of the granular outputs. Experimental studies conducted on a synthetic dataset and a number of real-world datasets validated the effectiveness and adaptability of the proposed methodology.

Rule–based fuzzy models have modular architectures and come with well-developed design methodologies such that they can build accurate models with good interpretabilities in system modeling. However, a large amount of private data needs to be used for statistical analysis and forecasting in rule–based fuzzy models. The purpose of this study is to build an intelligent model with high accuracy and versatility under the premise of data privacy and model security. To mitigate the risk of malicious attacks on privacy data during the analysis process, we have employed highly regarded differential privacy techniques to devise a novel rule-based fuzzy modeling approach. We propose a function approximation mechanism to reconstruct the objective function and add a perturbation mechanism to the objective function in Takagi-Sugeno rules model. Taking into account the delicate balance between data privacy and utility, we have innovatively introduced a Takagi-Sugeno rule-based model based on differential privacy. This model is applicable to both linear and nonlinear systems, offering protection to sensitive data privacy and model security within the system. We investigate the relationship between the interpretability of the model and the degree of privacy protection. By constructing a reasonable rule base, we achieve higher accuracy than other system modeling methods based on differential privacy. This paper compares the influence of the number of rules on differential privacy, and considers the algorithm performance under various noise distributions. It is shown that the Takagi-Sugeno rules model based on differential privacy has a strong ability to predict and analyze data.

This article presents a design and realization of fuzzy rule-based regression models based on standard decision trees. A two-phase design of rule-based model is offered in this study to provide a good alternative to cope with high dimensional data. We first build a standard decision tree on the basis of variables in order to discover homogeneous subsets of the data. Subsequently, a collection of fuzzy rules is induced by the decision tree with the aim of reflecting the underlying phenomenon. The calculation of membership degrees and the refinement of fuzzy rules on the basis of data located in each partition exhibit a substantial level of originality and innovation. The introduction of fuzziness into decision rules helps to characterize and quantify the continuous change of output values near the boundary areas. The constructed fuzzy rules could efficiently handle the ambiguity and vagueness in the experimental evidence and offer an accurate characterization of the nonlinearities of the input–output relationships. The developed fuzzy models could achieve much higher prediction accuracy in comparison with traditional decision trees of the same size and fuzzy rule-based models with the same number of rules. Another advantage of the proposed methodology comes with the evident readability of the formed fuzzy rules. A series of experiments is reported to demonstrate the superiority of the proposed architecture of fuzzy rule-based models over traditional fuzzy rule-based models and decision trees.

Currently, the healthcare of elderly people arouses widespread concerns since the sharp increase of aging population puts severe stress on public medical resources. Home health care (HHC) is regarded as an alternative answer to hospitalization, while it plays an important role in reducing healthcare cost and improving service satisfaction. This work addresses a service resource routing and scheduling problem with sharing strategy among multiple HHC centers for given customers. Two objective functions are involved: minimizing the total operation cost including the fixed usage cost of centers, caregiver usage cost and service cost, and minimizing the total tardiness caused by delay service. Firstly, a mixed integer programming model is formulated to describe the concerned problem. Secondly, a multi-objective artificial bee colony algorithm with problem-specific knowledge (MABC-PK) is proposed. Three problem-specific knowledge-based heuristics are designed to initialize population. A crossover operation and a self-learning neighborhood selection method are developed to prompt collaborative search of population and external archive. Furthermore, two knowledge-based local search methods are proposed for refining solutions in the external archive via employing some observations and priority properties derived from the problem characteristics. Finally, extensive experiments are conducted by comparing the proposed approach with four widely-acknowledged multi-objective optimization methods and a mathematical programming solver CPLEX. The comparative results and statistical analysis confirm the strong competitiveness of MABC-PK for solving the concerned problem.

This article addresses the problem of optimal sensor selection for diagnosability enforcement of discrete event systems modeled with Petri nets. Given a nondiagnosable labeled Petri netlabeled Petri net (LPN) that may reach deadlocks, it can be enforced to be diagnosable by a novel systematic strategy through a mask labeling function. The proposed strategy employs a special nondeterministic finite automaton, namely, a simplified verifierSV, obtained from the original labeled Petri netLPN and a particular set of labels. Since we only need to silence sensors of some particular transitions rather than add or replace new sensors to transitions, it is more computationally efficient compared with other documented methods in the literature. In addition, we formulate and solve an integer linear programming problemILPP to optimize a set of given mask labeling functions. Finally, an algorithm is constructed to calculate the minimum value of$K$for a$K$-diagnosable system with multiple faults under the mask labeling function. Examples are presented to demonstrate the proposed method.

This research focuses on the forbidden state problem in the framework of labeled Petri nets (LPNs), i.e., to design a supervisor for a plant modeled by an LPN such that the closed-loop system cannot reach a set of predefined forbidden markings and does not contain any deadlock. Different from the traditional control scheme, the supervisor derived by this work can not only observe the observable transitions, but also the quiescence information. First, a new structure named an extended basis reachability graph (EBRG) is introduced to describe the reachability space of an LPN without computing all reachable markings. Based on an EBRG, a basis observer is then excogitated to represent the behavior of an LPN. Some states in the basis observer are defined as bad states and control-induced deadlocks, which relates to the undesirable behavior of the plant. Finally, an algorithm is introduced to compute a supervisor based on the basis observer. The consideration of system quiescence provides extra information on the marking estimation of the closed-loop system such that certain disabled transitions are re-enabled. Consequently, the developed supervisor in this article is generally more permissive than those do not observe the quiescence.

This study focuses on distributed no-wait permutation flow shop scheduling problems that have many practical engineering backgrounds. The objective is to dispatch jobs optimally to multiple processing centers and ordering them for minimizing the maximum completion time (makespan). First, to solve the problems, a mathematical model is established. Second, a novel evolutionary algorithm is proposed, in which a two-dimensional (2-D) array is designed for solution representation. Based on the problem-specific knowledge, a factory assign strategy and jigsaw puzzle inspired algorithm (JPA) are employed for initializing the population of the evolutionary algorithm. Furthermore, a relative local search is used to improve the performance of the proposed algorithm. Finally, 120 instances with different scales are solved and the results are recorded. Comparisons and discussions show the proposed algorithm has computational competitiveness in solving the concerned problems with makespan criteria.

Deep networks can usually depend on extracting more structural information to improve denoising results. However, they may ignore correlation between pixels from an image to pursue better-denoising performance. Window Transformer can use long- and short-distance modeling to interact pixels to address mentioned problem. To make a tradeoff between distance modeling and denoising time, we propose a heterogeneous window Transformer (HWformer) for image denoising. HWformer first designs heterogeneous global windows to capture global context information for improving denoising effects. To build a bridge between long and short-distance modeling, global windows are horizontally and vertically shifted to facilitate diversified information without increasing denoising time. To prevent the information loss phenomenon of independent patches, sparse idea is guided a feed-forward network to extract local information of neighboring patches. The proposed HWformer only takes 30% of popular restoration Transformer in terms of denoising time. Its codes can be obtained athttps://github.com/hellloxiaotian/HWformer.

This article proposes a new adaptive neural control scheme with guaranteed performance for mechanical systems under dynamic uncertainties and uncertain initial conditions. Employing the novel time-varying neuron (TVN) approach and a shifting function, the control method developed in this article can systematically solve two crucial problems: one is how to construct a variable structure network to improve the approximation ability while the online tuning parameters do not increase with the number of neurons, and the other is how to achieve the predetermined tracking performance for multi-input multi-output (MIMO) mechanical systems under any bounded initial tracking errors. To approximate uncertain dynamics, the TVN approach is first presented to instruct the process of adding new neurons for better-learning capability, where the online updating parameters in the neural network (NN) unit are compressed by the vector projection technique, yielding an NN approximator with low-computational burden. By virtue of a shifting function, the uncertain initial tracking error is converted to zero such that a speed function with predetermined convergence performance can be efficiently employed to constrain the tracking trajectory without considering the initial condition. Moreover, to obviate the differentiation operation for the virtual stabilizing function, the dynamic surface technique is adopted to derive the presented control scheme for facilitating practical implementation. Finally, the effectiveness and benefits of the presented control are verified via theoretical analysis and a two-link manipulator.

Nowadays, biochemical and biomedical laboratories, and pharmaceutical industries widely adopt high throughput screening (HTS) systems for the development of new drugs and detection of new viruses. Different from a flow shop in manufacturing systems, in operating an HTS system, microplates are often required to be processed multiple times by some devices, resulting in complex microplate flows. Such flows make the system deadlock-prone and very difficult to deal with. Also, consistency is essential for its operation, requiring a one-microplate cyclic schedule for its operation. Generally, the scheduling problem of such systems is NP-hard due to their combinatorial nature. Thus, scheduling an HTS system is challenging. Antibody detection is an important complement to the nucleic acid test for response to the coronavirus disease pandemic and is adopted in many laboratories, which can be implemented by the enzyme-linked immunoassay (ELISA) via an HTS system. This article focuses on the problem of scheduling an HTS for ELISA and explores the possibility of a polynomial algorithm for finding an optimal one-microplate cyclic schedule. To do so, we model the system via a type of Petri nets and develop an optimal deadlock avoidance policy. As a result, the optimal activity sequence can be simply obtained. With the obtained sequence, the scheduling problem is converted to a continuous optimization problem that can be solved by determining the robot waiting time at the devices. Then, a one-microplate cyclic schedule can be efficiently found by solving a small-sized linear programming (LP) problem. Moreover, it is shown that, for many cases, one can quickly get an optimal solution by simply setting the robot waiting time without solving an LP problem. Consequently, we successfully present a polynomial algorithm to get an optimal cyclic schedule and show that the addressed problem is polynomial-time solvable. A practical example is used to show its applications.

In this study, we elaborate on a realization of fuzzy clustering and the construction of fuzzy rule-based models on the basis of vertically partitioned datasets in a privacy-preserving federated learning approach. The main focus of the overall design process is to construct a family of information granules (clusters) and the corresponding fuzzy rules in the presence of a collection of vertically partitioned datasets without compromising data privacy. These datasets are composed of the same data but are described by different features, and due to security considerations, data cannot be shared. The vertical federated fuzzy clustering can be realized as an iterative optimization process composed of successive cycles: 1) computation (update) of the prototypes and partition matrices performed on the basis of local datasets and 2) an integration of the local sources of knowledge carried out on a central coordinator-server. The update of the partition matrices can be completed using a distance-based or gradient-based approach. The communication of findings between local clients and the coordinator-server is realized through exchanging partition matrices, which are more general than numeric data and can avoid leakage of data privacy. Fuzzy models are optimized in a similar manner through exchanging the gradients of the performance index computed with respect to the parameters between the clients and the global coordinator. The proposed mechanism exhibits significant originality since the realization of fuzzy modeling in a vertical federated learning environment has not been studied. Experimental studies show that the proposed federated clustering and fuzzy model design could effectively reveal the structure of the entire dataset and achieve high performance compared with the results obtained in a centralized manner.

Industrial Internet of Things (IIoT) is changing the way in which factories operate with the help of various industrial applications. However, the execution uncertainty of computing tasks has always been ignored in IIoT applications. In this article, we define a novel conditional task graph to describe the execution uncertainty and present a generation algorithm to obtain all task scenario graphs and corresponding occurrence probabilities. Then, a new IIoT-oriented multitask scheduling model under execution uncertainty is built. This model is simplified by reformulating the nonlinear constraints and subsequently decomposed into several small-scale models using the Lagrange multipliers, from which a decomposition-based algorithm is derived to solve the decomposed small-scale models and progressively acquire a well-optimized solution of the initial model. Furthermore, a patching algorithm is constructed to improve the obtained solution. Finally, many test cases are generated, and four selected algorithms are taken for comparison to evaluate the performance of our algorithms. The results demonstrate that our algorithms remarkably outperform the others. Besides, the solutions of the proposed algorithms can completely satisfy the execution deadline constraints of different task scenarios.

Modular supervisory control may lead to conflicts among the modular supervisors for large-scale discrete event systems. The existing methods for ensuring nonblocking control of modular supervisors either exploit favorable structures in the system model to guarantee the nonblocking property of modular supervisors or employ hierarchical model abstraction methods for reducing the computational complexity of designing a nonblocking coordinator. The nonblocking modular control problem is, in general, NP-hard. This study integrates supervisory control theory and a model-based deep reinforcement learning method to synthesize a nonblocking coordinator for the modular supervisors. The deep reinforcement learning method significantly reduces the computational complexity by avoiding the computation of synchronization of multiple modular supervisors and the plant models. The supervisory control function is approximated by the deep neural network instead of a large-sized finite automaton. Furthermore, the proposed model-based deep reinforcement learning method is more efficient than the standard deep Q network algorithm.

This paper proposes an iterative approach to separate a set of admissible markings of a nonlinear constraint into a number of subsets. At each iteration, we find a maximal subset of admissible markings that are separated from inadmissible markings by linear constraints. Then, the union of all the obtained subsets constitutes the set of all admissible markings. For each subset of admissible markings, we obtain a set of conjunctive linear constraints. Accordingly, we can equivalently transform a given nonlinear constraint to be a set of disjunctive/conjunctive linear constraints, which can deal with the case that both admissible and inadmissible marking spaces of a nonlinear constraint cannot be separated by linear constraints from each other. Furthermore, we propose a method to design a Petri net supervisor for a derived set of disjunctive/conjunctive constraints. Some examples are used to demonstrate the proposed approach. Note to Practitioners—Linear constraints on Petri nets have been widely studied in the literature. However, not all control specifications can be represented as linear constraints. This paper proposes to enforce nonlinear constraints on Petri nets. A maximally permissive supervisor is designed to make the controlled net live with all admissible markings with respect to a given nonlinear constraint. Meanwhile, the structural complexity is also considered by compressing the number of control places in the supervisor. Experimental results show that the proposed approach can implement the control specifications represented by nonlinear constraints.

A time Petri net is said to be critically observable at a given time instant if the markings consistent with any observation at the time instant are included either in the set of critical markings or non-critical markings. This work studies the verification problem of critical observability for timed discrete event systems modeled by bounded labeled time Petri nets. The proposed method is a two-fold process: a preliminary verification of critical observability for the underlying logic labeled Petri net and a further verification considering the time constraint associated with each transition. The first step is based on the concurrent composition of a reachability graph of the logic net. If the logic net is critically observable, then the time net is also critically observable at any given time instant. Otherwise, the second step is to design an algorithm to compute all pairs of transition-class sequences that violate critical observability at the given time instant, and then a set of linear programming problems is exploited to check critical observability for the corresponding timed system.Note to Practitioners—Timed discrete event systems provide a theoretical model for safety-critical real applications such as air traffic management, smart grid, and industrial control, which are vulnerable to malicious attack and destruction at some particular time instants such that a system may be misled to dangerous states. Critical observability of a timed discrete event system is a property with which the predefined dangerous states can be determined and detected from the observation by an observer at a given time instant. This research aims to offer a systematic approach to check critical observability for timed discrete event systems modeled by bounded labeled time Petri nets, which can also present some new ideas and insights for practitioners in the field of safety-critical systems.

In this paper, we revisit state estimation and weak detectability verification for discrete event systems (DES) from a span-new perspective. Specifically, using the semi-tensor product (STP) technique, we construct two new matrix-based information structures called a current-state estimator (C-estimator) and an initial-state estimator (I-estimator) for computing three fundamental types of state estimates, namely, current-state estimate (CSE), initial-state estimate (ISE), and delayed-state estimate (DSE). The complexity of building C-estimator and I-estimator is polynomial time with respect to the size of a plant. A notion of weak delayed detectability is introduced, which captures that, after observing a$k_{1}$-length sequence/string, whether or not one can always accurately determine the state of a plant at this moment after at most$k_{2}$steps of delays for some trajectories. Further, using the proposed C-estimator and I-estimator, we discuss the different types of detectability verification problems, including, but not restricted to, weak current-state detectability (C-detectability), weak initial-state detectability (I-detectability), and weak delayed detectability. Accordingly, several necessary and sufficient criteria are derived for verifying the aforementioned different types of detectability. Our approaches are numerically tractable and only involve some basic matrix manipulations. Finally, some examples are given to illustrate the obtained results. Note to Practitioners—State estimation is one of the most fundamental problems in many practical engineering systems. For instance, one needs to infer the state of a manufacturing system before a failure occurs. For a communication system, can we guarantee that whether important information remains secret to outsiders for security requirements? Finding an alternative and efficient approach to capture the state of a plant based on imperfect observations is still crucial for engineers. To solve these problems, in this paper we develop a novel methodology to tackle simultaneously three fundamental categories of state estimation for practical engineering systems that are inherently abstracted as partially-observed discrete-event systems. Our approaches are technically quite different from the existing ones. The novel results obtained in this paper are all of matrix-based characterization, which can be implemented algorithmically by means of the user-friendly STP software package. We believe that the alternative methodology provides an innovative insight for engineers in the field of automatic control.

This paper addresses a distributed lot-streaming permutation flow shop scheduling problem that has various applications in real-life manufacturing systems. We aim to optimally assign jobs to multiple distributed factories and sequence them to minimize the maximum completion time (Makespan). A mathematic model is first developed to describe the considered problem. Then, five meta-heuristics are executed to solve it, including particle swarm optimization, genetic algorithm, harmony search, artificial bee colony, and Jaya algorithm. To improve the performance of these meta-heuristics, we employ Nawaz-Enscore-Ham (NEH) heuristic to initialize populations and propose improved strategies based on the problem’s feature. Finally, experiments are carried out based on 120 instances. The performance of improved strategies is verified. Comparisons and discussions show that the artificial bee colony algorithm with improved strategies has the best competitiveness for solving the proposed problem with makespan criteria. Note to Practitioners—In contemporary manufacturing industry, the traditional single-factory environment is being replaced by a distributed multi-factory environment, as a distributed pattern can effectively improve the production efficiency through the reasonable resource allocation strategies. The distributed lot-streaming permutation flow shop scheduling problem in such a pattern is of significance to practitioners. Although intelligent optimization can provide an effective tool to solve such problems, most of the algorithms are parameter-sensitive. A challenge for engineers is parameter selection, which greatly impacts the algorithm performance. To ensure the robustness of the algorithms, we develop five improved meta-heuristics by employing some strategies. Furthermore, parameter setting test is carried out to select the appropriate parameter values. As a result, the proposed algorithms can obtain resource allocation schemes with high-quality. It is shown that the artificial bee colony algorithm with improved strategies outperforms other algorithms well. The proposed methodology can be readily applied to real distributed scheduling problems.

A distributed flow-shop scheduling problem with lot-streaming that considers completion time and total energy consumption is addressed. It requires to optimally assign jobs to multiple distributed factories and, at the same time, sequence them. A biobjective mathematic model is first developed to describe the considered problem. Then, an improved Jaya algorithm is proposed to solve it. The Nawaz-Enscore-Ham (NEH) initializing rule, a job-factory assignment strategy, the improved strategies for makespan and energy efficiency are designed based on the problem's characteristic to improve the Jaya's performance. Finally, experiments are carried out on 120 instances of 12 scales. The performance of the improved strategies is verified. Comparisons and discussions show that the Jaya algorithm improved by the designed strategies is highly competitive for solving the considered problem with makespan and total energy consumption criteria.

Designing effective and efficient classifiers is a challenging task given the facts that data may exhibit different geometric structures and complex intrarelationships may exist within data. As a fundamental component of granular computing, information granules play a key role in human cognition. Therefore, it is of great interest to develop classifiers based on information granules such that highly interpretable human-centric models with higher accuracy can be constructed. In this study, we elaborate on a novel design methodology of granular classifiers in which information granules play a fundamental role. First, information granules are formed on the basis of labeled patterns following the principle of justifiable granularity. The diversity of samples embraced by each information granule is quantified and controlled in terms of the entropy criterion. This design implies that the information granules constructed in this way form sound homogeneous descriptors characterizing the structure and the diversity of available experimental data. Next, granular classifiers are built in the presence of formed information granules. The classification result for any input instance is determined by summing the contents of the related information granules weighted by membership degrees. The experiments concerning both synthetic data and publicly available datasets demonstrate that the proposed models exhibit better prediction abilities than some commonly encountered classifiers (namely, linear regression, support vector machine, Naïve Bayes, decision tree, and neural networks) and come with enhanced interpretability.

Generally, fuzzy models, especially rule-based models, are designed in a monolithic manner, meaning that all data are useden blocto design the model. At the same time, there is a visible need to cope with the ever-increasing volumes of data (both in terms of the number of data and their dimensionality) as well as being faced with distributed data located at various locations. The objective of this article is to develop a concept and provide a design framework as well as assess its performance for constructing a collection of rule-based models on a basis of a randomly sampled repository of data and then realize their aggregation. More specifically, for the sampled data, the design of each model is carried out in a standard way as commonly encountered in the case of Takagi–Sugeno (TS) rule-based models and next augmented by gradient boosting. The aggregation is realized by optimizing a weighting scheme applied to the results of the individual models. Our intent is also to carefully demonstrate the performance offered by the mechanisms of machine learning applied in the setting of rule-based models, which is an original task completed before. A number of high-dimensional data are used in the experimental studies to complete a thorough assessment. A comparative performance analysis is reported with respect to the monolithically developed TS models.

Understanding the rationale behind the predictions produced by machine learning models is a necessary prerequisite for human to build confidence and trust for the intelligent systems. To tackle the problem of interpretability faced by black-box models, a fuzzy local surrogate model is proposed in this study to articulate the rationale for predictions to enhance the interpretability of the results of machine learning models. Fuzzy rule-based model comes with high interpretability since it is composed of a collection of readable rules, and thus is suitable for prediction interpretation. The general scheme of fuzzy local surrogate model is composed of the following phases: i) select data points around the instance of interest, for which we wish to explain the prediction result produced by the predictive model; ii) generate predictions for these newly selected data and weight the selected data based on the distance from the instance of interest; and iii) a fuzzy rule-based model composed a collection of interpretable is constructed to approximate the weighted data and offer meaningful interpretation to the prediction result of the given instance. The proposed fuzzy model for explaining predictions is model-agnostic and could provide high estimation accuracy. The proposed methodology offers a significant original contribution to the interpretation of machine learning models. Experimental studies demonstrate the usefulness of the proposed fuzzy local surrogate model in providing local explanations.

Planning a disassembly sequence of product structure in a remanufacturing-oriented manner is a newly emerging and important problem, which has attracted considerable attention owing to high demand for sustainable development. However, current approaches cannot lead to a reliable disassembly plan focused on the product structure itself, starting from functional properties to application demand stimulated by remanufacturing. First, they fail to focus on the role that EOL products play in the promotion of remanufacturing. Second, determining a disassembly solution for advanced and sophisticated products is complicated and time-consuming. Third, the uncertainty of mixed information triggered by the intercrossing of multiple subjects is neglected. In this study, an improved DSP method is first proposed by integrating the remanufacturing properties of EOL products, an improved QICA, and the measure and update of the reliability of experts’ decision-making to perform DSP. The functional recoverability of EOL product parts is marked to identify the remanufacturing value of a disassembly plan. The expected reproduction probability is adopted to increase the population diversity and improve the ability of QICA to search for feasible solutions. The model and update mechanism of experts’ recognition degree are constructed to narrow the score deviation. Finally, we provide a case for a sewing machine to verify the superiority of the improved QICA. The results show that it is capable of obtaining satisfactory solutions and outperforms the quantum genetic algorithm and particle swarm optimizer.

A disassembly line is an industrialized and automated production line which should be scheduled with high production efficiency. Although many disassembly line balancing optimization studies are contributed recently, they increase or reduce the number of workstations to balance the disassembly line. From real-world managerial settings, an increase or decrease workstations, is too expensive and not realistic. The bucket brigades’ disassembly line is self-balancing and self-organizing, which is not constrained by the workstation beat time and only needs to distribute workers on the line according to certain rules to achieve line balancing after a period of time. In this article, a bucket brigades disassembly line balancing optimization method considering uncertainty is proposed, in which a cloud model is used to represent the uncertain disassembly time. The proposed model handles multiple objectives including smoothness, disassembly cost and disassembly energy consumption to be minimized. To solve this complex problem, this article innovates a new heuristic method based on the social engineering optimizer as an enhanced local search metaheuristic. Finally, a ball collector is used to verify the effectiveness of the proposed method and extensive analysis is done to compare the performance of proposed model with other recent algorithms.

To confront the great challenge of industrial big data, the software-defined industrial networks (SDINs) are introduced to dynamically coordinate these data flows among the heterogeneous and distributed computing resources. Deciding how to more efficiently schedule many homogeneous computing tasks, which extensively appear in SDIN, becomes of critical importance. To this end, this article first illustrates some related notations and assumptions of the homogeneous computing tasks and computing networks, from which a new targeted optimization model is formulated. Then, the model is significantly enhanced by reformulating all the nonlinear constraints and inventively establishing the symmetry-breaking constraints and computation time cuts. Furthermore, considering the computational complexity, the scheduling process with many homogeneous computing tasks is further viewed as two associated phases: computing nodes assignment and tasks sequencing. As a result, a novel bilevel decomposition algorithm is proposed using Lagrangian decomposition and a new form of Lagrangian relaxation. Finally, a real industrial scenario is chosen, and three comparison algorithms are used to demonstrate the preponderant performance of the proposed algorithm.

Rumor spreads fast in social networks and may seriously damage our society. In this article, we present a mathematical programming formulation based on integer linear programming (ILP) to minimize rumor spread by blocking a subset of nodes (called blockers) in complex social networks modeled as a linear threshold model. We also propose a modified approach which solves the top-$k$blockers problem with a reduced computational effort and formally proves that its performance is still optimal. Then, the presented method is evaluated for its effectiveness of containing rumor spread in four different networks and its performance is compared with a greedy-based and two centrality-based approaches. The experimental analysis shows that the ILP-based method outperforms the other three approaches and is applicable to large-scale networks.

As an effective way for knowledge representation and processing, fuzzy rule-based models have been extensively studied and widely used in practice. In many circumstances, a very limited amount of data or insufficient computational resources make the construction of accurate models a genuine challenge. In this study, a granular augmentation of fuzzy rule-based models is proposed with intent to realize knowledge transfer in system modeling. This research mainly focuses on how to effectively exploit the existing fuzzy model, which has been constructed on extensive previously acquired experimental evidence and could be regarded as source of knowledge, in a new environment where only very limited experimental evidence is available. Rather than constructing a new model from scratch, knowledge conveyed by the existing model could be retained and reused in the target domain. The originality and innovation of this study lies in the adaption of the existing model to the new environment through optimal allocation of information granularity to produce granular fuzzy models, which are more abstract and general than the original numeric constructs. The granular fuzzy models yield results in a granular form whose quality is evaluated using the coverage and specificity criteria.

This paper deals with the problem of diagnosability enforcement of discrete event systems. Given a non-diagnosable discrete event system modelled with Petri nets, which may enter a deadlock state or an unobservable live-lock (that each composed transition is unobservable), a digital twin system (derived from the original labeled Petri net model) can be established as a particular Petri net such that the original system under the control of its digital twin system is diagnosable. An example is given to illustrate the proposed method and the correctness of the method is proved by theoretical proof.

This paper addresses the liveness enforcement problem of time Petri nets through the control of time intervals of transitions. We design a structure named a critical state class graph, whose nodes contain the critical constraints of the time intervals of transitions. Then, we propose an algorithm to control the time intervals of controllable transitions such that a non-live time Petri net can be transformed into a live time Petri net. The results show that a maximally permissive solution of liveness enforcement can be computed if it exists.

This research analyzes current-state-based opacity in discrete event systems, which is a type of state-based opacity verification. We consider an unbounded Petri net to model this system, in which we assume that each transition is unobservable and partial places are observable (the number of tokens can be measured). A novel coverability graph based on partial markings is proposed, which is used to construct an estimator for current-state estimation. Current-state opacity is verified in this work employing the proposed new coverability graph. This paper proposes a sufficient and necessary condition for the verification of current-state opacity, as well as a concrete example to explain the approach.

With the widespread application of the Industrial Internet of Things (IIoT), industrial control systems (ICSs) greatly improve industrial productivity, efficiency, and product quality. However, IIoT protocols as the bridge of different parts of ICSs are vulnerable to be attacked due to their vulnerabilities. To reduce cyberattack threats, we need to find the vulnerabilities of IIoT protocols by using efficient vulnerability mining methods, such as fuzzing. Fuzzing is often used to mine vulnerabilities for IIoT protocols. However, the traditional fuzzing methods for IIoT protocols have a low passing rate and low code coverage. To solve these problems, we propose a generative adversarial network (GAN), here referred to as coverage-guided GANs (CovGAN), which aims to generate test cases with a high passing rate and code coverage by learning IIoT protocol specifications. Based on the CovGAN, we construct a fuzzing framework (CGFuzzer) for IIoT protocols. Finally, we design a protocol simulator to verify the CovGAN performance. Experimental results show that the proposed methodology outperforms approximately 5%, 7%, and 39% of the passing rate of GANFuzz, SeqFuzzer, and Peach, respectively. In addition, CGFuzzer has a significant improvement in code coverage, which is about 17%, 24%, and 31% higher than GANFuzz, SeqFuzzer, and Peach, respectively.

Deadlocks are a highly undesirable situation in flexible manufacturing systems (FMSs). This article presents a direct and novel method to detect such markings by constructing the resource flow graph of a Petri net that models an FMS and to recover such markings by adding a set of recovery transitions. First, an algorithm is developed to build a new kind of directed graph called the resource flow graph of a Petri net. Resource flow graphs can well represent the competition for shared resources by different processes. Second, based on the resource flow graph, loop graphs can be found. Furthermore, partial deadlock markings can be easily detected due to their relationship with loop graphs. Then, we propose an algorithm to design a set of recovery transitions for loop graphs that are enabled at partial deadlock markings. The proposed approach can detect partial deadlock markings without generating a complete reachability graph of a Petri net and the resulting net is deadlock-free with all reachable markings by adding the obtained recovery transitions. Finally, some widely used examples are provided to demonstrate the proposed approach.Note to Practitioners—The occurrence of deadlocks in an FMS tends to cause unnecessary productivity costs and even catastrophic results. In the framework of Petri nets, reachability graph analysis can usually obtain a maximally permissive supervisor of a plant. However, it is rather inefficient since it suffers from the state explosion problem. In this particular research, we develop an off-line deadlock detection and recovery policy by setting a group of virtual events that are not present in a physical model. The proposed approach is computationally efficient since it does not require to generate a reachability graph. It guarantees that the resulted system is deadlock-free with its all original reachable markings.

This article proposes a risk assessment method based on interval intuitionistic integrated cloud Petri net (IIICPN). The cloud model is widely used in data mining and knowledge discovery, especially in risk assessment problems with linguistic variables. However, the cloud models proposed in the literature do not express interval-valued intuitionistic linguistic satisfactorily, and the reasoning methods based on the cloud models cannot perform risk assessment well. The work in this article includes the definition of IIIC and IIICPN, the method of converting the interval-valued intuitionistic uncertain linguistic numbers into IIIC, and the reasoning method of IIICPN. As proofs, a subway fire accident model is adopted to confirm the feasibility of the proposed method, and comparison experiments between the IIICPN with general fuzzy Petri net and the trapezium cloud model are conducted to verify the superiority of the proposed model.Note to Practitioners—This work deals with the subway fire risk assessment problem. It proposes a cloud model based on interval-valued intuitionistic uncertain linguistic and builds a cloud-based Petri net model. The methods of fire risk assessment use the existing fault trees or aggregation operators to combine all the factors into consideration, but they do not take the interaction of factors. The goal of this work is to assess the risk of subway fire accident of subway, using fuzzy linguistic decision variables. The simulation results indicate that the proposed method is highly effective. The obtained results can help assessors better determine which factors may cause the disaster.

Information granulation and degranulation play a fundamental role in granular computing (GrC). Given a collection of information granules (referred to as reference information granules), the essence of the granulation process (encoding) is to represent each data (either numeric or granular) in terms of these reference information granules. The degranulation process (decoding) that realizes the reconstruction of original data is associated with a certain level of reconstruction error. An important issue is how to reduce the reconstruction error such that the data could be reconstructed more accurately. In this study, the granulation process is realized by involving fuzzy clustering. A novel neural network is leveraged in the consecutive degranulation process, which could help significantly reduce the reconstruction error. We show that the proposed degranulation architecture exhibits improved capabilities in reconstructing original data in comparison with other methods. A series of experiments with the use of synthetic data and publicly available datasets coming from the machine-learning repository demonstrates the superiority of the proposed method over some existing alternatives.

This article provides a solution to tube-based output feedback robust model predictive control (RMPC) for discrete-time linear parameter varying (LPV) systems with bounded disturbances and noises. The proposed approach synthesizes an offline optimization problem to design a look-up table and an online tube-based output feedback RMPC with tightened constraints and scaled terminal constraint sets. In the offline optimization problem, a sequence of nested robust positively invariant (RPI) sets and robust control invariant (RCI) sets, respectively, for estimation errors and control errors is optimized and stored in the look-up table. In the online optimization problem, real-time control parameters are searched based on the bounds of time-varying estimation error sets. Considering the characteristics of the uncertain scheduling parameter in LPV systems, the online tube-based output feedback RMPC scheme adopts one-step nominal system prediction with scaled terminal constraint sets. The formulated simple and efficient online optimization problem with fewer decision variables and constraints has a lower online computational burden. Recursive feasibility of the optimization problem and robust stability of the controlled LPV system are guaranteed by ensuring that the nominal system converges to the terminal constraint set, and uncertain state trajectories are constrained within robust tubes with the center of the nominal system. A numerical example is given to verify the approach.

In this article, we elaborate on a Kullback-Leibler (KL) divergence-based Fuzzy C -Means (FCM) algorithm by incorporating a tight wavelet frame transform and morphological reconstruction (MR). To make membership degrees of each image pixel closer to those of its neighbors, a KL divergence term on the partition matrix is introduced as a part of FCM, thus resulting in KL divergence-based FCM. To make the proposed FCM robust, a filtered term is augmented in its objective function, where MR is used for image filtering. Since tight wavelet frames provide redundant representations of images, the proposed FCM is performed in a feature space constructed by tight wavelet frame decomposition. To further improve its segmentation accuracy (SA), a segmented feature set is reconstructed by minimizing the inverse process of its objective function. Each reconstructed feature is reassigned to the closest prototype, thus modifying abnormal features produced in the reconstruction process. Moreover, a segmented image is reconstructed by using tight wavelet frame reconstruction. Finally, supporting experiments coping with synthetic, medical, and real-world images are reported. The experimental results exhibit that the proposed algorithm works well and comes with better segmentation performance than other peers. In a quantitative fashion, its average SA improvements over its peers are 4.06%, 3.94%, and 4.41%, respectively, when segmenting synthetic, medical, and real-world images. Moreover, the proposed algorithm requires less time than most of the FCM-related algorithms.

Rule-based fuzzy models play a dominant role in fuzzy modeling and come with extensive applications in the system modeling area. Due to the presence of system modeling error, it is impossible to construct a model that fits exactly the experimental evidence and, at the same time, exhibits high generalization capabilities. To alleviate these problems, in this study, we elaborate on a realization of granular outputs for rule-based fuzzy models with the aim of effectively quantifying the associated modeling errors. Through analyzing the characteristics of modeling errors, an error model is constructed to characterize deviations among the estimated outputs and the expected ones. The resulting granular model comes into play as an aggregation of the regression model and the error model. Information granularity plays a central role in the construction of granular outputs (intervals). The quality of the produced interval estimates is quantified in terms of the coverage and specificity criteria. The optimal allocation of information granularity is determined through a combined index involving these two criteria pertinent to the evaluation of interval outputs. A series of experimental studies is provided to demonstrate the effectiveness of the proposed approach and show its superiority over the traditional statistical-based method.

In this article, we are concerned with the formation of type-2 information granules in a two-stage approach. We present a comprehensive algorithmic framework which gives rise to information granules of a higher type (type-2, to be specific) such that the key structure of the local granular data, their topologies, and their diversities become fully reflected and quantified. In contrast to traditional collaborative clustering where local structures (information granules) are obtained by running algorithms on the local datasets and communicating findings across sites, we propose a way of characterizing granular data (formed) by forming a suite of higher type information granules to reveal an overall structure of a collection of locally available datasets. Information granules built at the lower level on a basis of local sources of data are weighted by the number of data they represent while the information granules formed at the higher level of hierarchy are more abstract and general, thus facilitating a formation of a hierarchical description of data realized at different levels of detail. The construction of information granules is completed by resorting to fuzzy clustering algorithms (more specifically, the well-known Fuzzy C-Means). In the formation of information granules, we follow the fundamental principle of granular computing, viz., the principle of justifiable granularity. Experimental studies concerning selected publicly available machine-learning datasets are reported.

This article is concerned with a problem of relational factorization which engages fuzzy relational calculus. It forms an interesting alternative to the method of nonnegative matrix factorization that has been commonly discussed and found in numerous applications. The relational factorization takes originaln-dimensional data located in the unit hypercube and factorizes it into data of lower dimensionality and some fuzzy relations. Owing to the logic nature of processing delivered by relational calculus, the dimensionality reduction exhibits transparency as the reduction mechanism itself is described in terms of logic expressions. Two types of factorizations mechanisms are investigated by usings–tandt–scomposition operators wheretandsare triangular norms and conorms, respectively. A two-level process of factorization is designed. A gradient-based learning scheme is developed. The quantification of the performance of the factorization process is realized by bringing a concept of information granularity: The obtained fuzzy relations are formed based on granular constructs and the quality of the produced factorization is assessed in terms of the coverage and specificity of the obtained granular results. A collection of experiments is included to present the performance of factorization and its parametric analysis. In addition, the proposed algorithm comes with sound interpretability in terms of both the structure of the model and an intuitive meaning of the fuzzy relations being the result of factorization.

For Takagi–Sugeno fuzzy systems subject to inexact membership functions, bounded disturbances, and noises, an output feedback robust model predictive control (RMPC) approach with time-varying robust tubes is investigated. The membership functions errors are bounded within convex sets via the properties of zonotopes and interval matrices. An offline table stores a series of structures that include nested robust positive invariant sets with the corresponding nominal feedback controller gains, ancillary controller gains, and observer gains. According to bounds of real-time estimation error sets, the time-varying structures in the offlined table are searched. Then, the output feedback RMPC problem with time-varying tightened constraints on inputs and states is optimized to stabilize the nominal system. The output feedback RMPC approach can not only update bounds of the estimation errors and uncertain terms resulting from inexact membership functions, but also reduce the computational burden. The proposed RMPC algorithm with recursive feasibility guarantees the robust stability of the controlled systems.

In this article, we elaborate on a design and realization of fuzzy rule-based model in the horizontal federated learning framework. Traditional machine learning in distributed environment often involves sharing sensitive information with other sites or transferring data to a central server on which a global model is trained. These situations increase the communication overhead and pose serious threats to the privacy of sensitive data. Federated learning opens up the possibility for collaboratively training a global model on a basis of distributed on-site data without sacrificing data privacy. While fuzzy rule-based models have been used in system modeling due to their substantial modeling abilities and good interpretability, the implementation of fuzzy rule-based models in a distributed environment without compromising data privacy still requires careful consideration. This article proposes a two-step federated learning approach to train a global model on a basis of private data located across different sites without their centralization. The first step concerns the determination of the structure of the data through federated collaborative clustering. Subsequently, a shared global model is trained jointly by all the participating clients. An advantage of the proposed method is that it achieves high accuracy without violating data privacy. A series of experimental studies are conducted to gain a detailed insight into the realization steps and demonstrate the effectiveness of the proposed method.

Transportation services play an increasingly significant role for people’s daily lives and bring a lot of benefits to individuals and economic development. The randomness and volatility of traffic flows, however, constrains the effective provision of transportation services to a certain extent. Precise traffic flow forecasting becomes the key and primary task to realize the stability of intelligent transport systems and ensure efficient scheduling of traffic. This paper investigates the application of an ensemble approach based on deep belief networks for short-term traffic flow forecasting. Traffic flow data, collected from the real world, is decomposed into several Intrinsic Mode Functions (IMFs) and a residue with EEMD (Ensemble Empirical Mode Decomposition). Then, for each component, the essential feature subset is extracted by the mRMR (minimum Redundancy Maximum Relevance Feature Selection) method considering weather conditions and day properties. Furthermore, each component is trained by DBN (Deep belief networks) and their forecasting results are summed up as the output of the ensemble model at last. Results indicate that the proposed approach achieves significant performance improvement over the single DBN and other selected methods.

With integer linear programming problems (ILPPs) being formulated and solved, the existing approaches design optimal Petri-net supervisors via nonpure net structures, including self-loops and data inhibitor arcs. Nonpure net structures are powerful for control of Petri-net-modeled discrete-event systems. However, in the existing work, the formulated ILPPs contain a large number of constraints, which is computationally inefficient. In this article, we propose approaches that formulate ILPPs with fewer constraints such that the computational efficiency is significantly improved. To do so, in formulating ILPPs for optimal Petri-net controllers by using self-loops and data inhibitor arcs, we remove the reachability conditions for legal markings. By doing so, an obtained solution may result in some legal markings unreachable. To solve this problem, a novel technique is developed to design an optimal controller by modifying the initial marking and structure of the obtained supervisor. It is shown that, by the reduced ILPPs, one can find the same feasible solutions as that obtained by the existing work. Finally, the proposed approaches are demonstrated by examples.

This article focuses on the issue of checking critical observability for labeled Petri nets. Critical observability is a property related to the safety concern of cyber-physical systems. With the aim of checking this property of a net system, it is required to detect whether a set of markings consistent with any observed word of the net system is a subset of a set of critical states representing undesirable operations or a set of noncritical states. In this work, we prove a necessary and sufficient condition to check critical observability when the critical state set is described by an arbitrary subset of reachable markings. Then, the result is extended to the case when a critical state set is modeled by all the reachable markings that satisfy disjunctions of generalized mutual exclusion constraints. The proposed method is derived from the solutions of integer linear programming problems and is applicable to net systems with liveness and boundness. Several case studies show the performance of the presented methodology for discrete-event systems.

Given a discrete event system, it is said to be opaque if an intruder who can partially observe system behavior cannot infer that the system is necessarily in a predefined secret. A discrete event system is current-state opaque if an intruder is never able to verify whether the current state of the system is within the secret states. This article addresses the verification of current-state opacity of a discrete event system modeled with Petri nets, where the secret is defined as a set of states. The existing methods cannot deal with large-sized systems due to the curse of dimensionality. Multi-valued decision diagrams (MDDs) are widely accepted as an efficient and compact data structure for representing the extremely large reachability sets of Petri nets. We show that MDDs can be used in Petri nets with partial observable transitions and propose an approach for the verification of current-state opacity for bounded Petri nets. It is shown by experimental studies that the symbolic approach is practically more efficient than traditional techniques such as basis reachability graphs (BRGs).

This article proposes a semi-structural approach to verify thenonblockingnessof a Petri net. We construct a structure, called minimal-maximal basis reachability graph (min-max-BRG): it provides an abstract description of the reachability set of a net while preserving all information needed to test if the net isblocking. We prove that a bounded deadlock-free Petri net isnonblockingif and only if its min-max-BRG isunobstructed, which can be verified by solving a set of integer constraints and then examining the min-max-BRG. For Petri nets that are not deadlock-free, one needs to determine the set of dead markings. This can be done with an approach based on the computation ofmaximal implicit firing sequencesenabled by the markings in the min-max-BRG. The approach we developed does not require the construction of the reachability graph and has wide applicability.

Nowadays, high-throughput screening (HTS) systems are widely used in pharmaceutical industries and laboratories for the discovery of new drugs and biomedical substances. It is important to efficiently schedule them so as to reduce the cost. In the operation of an HTS system, a microplate may visit some resources more than once and complex time window constraints are imposed on some activities and activity sequences. Moreover, with the consistency requirement, a one-microplate cyclic schedule is necessary. Thus, its scheduling problem is very challenging. This article studies the scheduling problem of an HTS system for an enzymatic assay, a typical application of HTSs, from the perspective of control theory. The system is modeled by resource-oriented Petri nets (ROPNs). With the model, necessary and sufficient conditions under which a feasible cyclic schedule exists are established. Then, we determine how many microplates should be in the system for concurrent processing and the transition firing sequence to obtain the activity sequence for an optimal and feasible schedule. In this way, a feasible and optimal cyclic schedule can be found by very simple computations, which shows that polynomial algorithms for an optimal schedule exist, while the existing studies apply mathematical programming with exponential computation complexity. Also, its efficient implementation is given.

In an automated manufacturing system (AMS), resources are, in general, subject to unpredictable failures, which invalidate many existing deadlock control strategies. In this article, we propose an adaptive deadlock control policy for an AMS with multiple types of unreliable resources. The considered AMS is modeled with a system of simple sequential processes with resources. First, based on an elementary siphon control method, monitors are added for elementary siphons and some particular dependent siphons to ensure the liveness of a system if there are no resource failures. By considering the fact that an unreliable resource may fail in a system, recovery subnets are added to describe the resource failures and recoveries. Since a monitor added for a siphon may not be able to guarantee that the corresponding siphon is always marked if the failure of a resource in the siphon occurs, the concept of switch controllers is presented so as to make the siphon always remarked if it is emptied by resource failures. It is verified that the adaptive controller proposed in this article can guarantee the liveness of the controlled system no matter whether unreliable resources break down or not. More importantly, if there is no resource failure, the system can maintain predefined production without degrading planned system performance. Finally, examples are presented to illustrate the validity of the proposed method.

While granular computing has experienced rapid growth in the past decades and some milestones have been reached, a comprehensive study of the representation capabilities delivered by numeric prototypes and granular prototypes produced by different techniques still calls for comprehensive research and a comparative analysis. Well-constructed information granules are reflective of the nature of the numeric evidence and serve as backbones of granular classifiers and granular models. The objective of this study is to review a number of clustering paradigms aimed at the construction of information granules, discuss the development of granular prototypes, and conduct a comprehensive evaluation of quality of numeric prototypes and their corresponding augmentations coming in the form of granular prototypes. We have been witnessing many studies devoted to the construction of information granules, but a comparative analysis of the quality of information granules constructed on a basis of prototypes produced by different clustering algorithms is still lacking. In this regard, the review of the clustering algorithms supporting the formation of information granules and the comprehensive comparative study of their usefulness in classification and modeling tasks offered in this study make sense. This will promote the usage of information granules in various future works, especially classification problem and system modeling.

This work addresses the problem of diagnosability of discrete event systems using labeled Petri nets. The possible faults in a system are modeled as faulty transitions (a subset of unobservable transitions) of a Petri net system. Moreover, the observable transitions are indistinguishable (i.e., two or more transitions may share the same label). We first construct two compact diagnosers, called a redundant basis reachability diagnoser and a basis reachability diagnoser, of a net system involving faulty transitions. Then, a necessary and sufficient condition for diagnosability is provided by checking whether each cycle in the two diagnosers satisfies particular conditions. The approach is efficient since the exhaustive enumeration of the reachability space is avoided thanks to a compact representation of the state space of a discrete event system.

This article deals with reconfigurable uniprocessor systems powered by a renewable energy source under real-time and resource sharing constraints. A reconfigurable system is defined as a set of implementations, each of which is encoded by real-time periodic software tasks. Reconfiguration is a flexible runtime scenario that adapts the current system’s implementation to any related environment evolution under well-defined conditions. A task is characterized by an effective calculated deadline that should be less than a maximum deadline defined in user requirements. The main problem is how to calculate the effective deadlines of the different periodic tasks in the different implementations under possibly the predicted renewable energy source and the sharing of resource constraints. We propose an offline method based on three solutions to calculate the deadlines of tasks. The first serves to compute the deadlines ensuring the real-time system feasibility and also minimizes the number of context switches by assigning the highest priority to the task with the smallest maximum deadline. The second computes the deadlines ensuring the respect of energy constraints, and the third computes the deadlines ensuring the respect of resource sharing constraints. These three solutions calculate the possible deadlines of each task in the hyperperiod of the corresponding implementations. We develop a new simulator called DEAD-CALC, that integrates a new tool called RANDOM-TASK for applying and evaluating the proposed solutions. The conducted experimentation proves that this methodology provides deadlines with affecting neither the load nor the processor speed while reducing the calculation time.Note to Practitioners—Real-time software is particularly difficult to design since, in addition to ever more complex functional constraints, it has to satisfy a set of stringent nonfunctional requirements, such as deadlines. In fact, the violation of deadlines can lead to breakdowns, data loss, and so on, which can be catastrophic for critical applications. This article presents the DEAD-CALC project for computing efficiently the deadlines of reconfigurable real-time devices to run possibly under energy and resource sharing constraints in all the system implementations. A device, in the software level, is a superset of implementations, each of which (i.e., a set of periodic software tasks) is activated and executed at a particular time according to user requirements. DEAD-CALC reduces the development time by computing the deadlines to be certainly respected without any additional feasibility analysis of the device. The calculation can only consider the real-time aspects, or both the harvesting energy and resource sharing constraints, giving the user the ability to configure the system under different conditions. DEAD-CALC is a visual environment that can be simply used by designers to compute and display these deadlines, with a few clicks, in arranged tables, and in a short time. This project can be a future reference for industrial partners who will be focusing on various real-time applications design, such as the intelligent manufacturing industry, intelligent transportation systems, embedded systems, wireless sensor networks, smart grids, medical control devices, and military platforms.

This article deals with improved formal verification of reconfigurable discrete-event systems (DESs) modeled by reconfigurable timed net condition event systems (R-TNCESs). An R-TNCES consists of a set of timed net condition event systems, each of which represents a particular behavior of a DES, and a reconfiguration scenario is a switching mode from a timed net condition event system to another. However, the verification with the classical computation tree logic (CTL) as well as the related extensions increases the number of properties for complete verification of a complex R-TNCES. We propose reconfigurable CTL as a new extension of CTL to reduce such a number. New connectors of reconfigurable CTL are proposed, with their formal syntax and semantics, and a set of new algorithms is proposed to control the complexity of model checking. We use a benchmark production system for the performance evaluation of the proposed approach. Reduction in the number of properties to be checked is shown, and consequently, the related validation time is reduced.Note to Practitioners—This research represents a new orientation for guiding efficiently the model checking of reconfigurable discrete-event systems. A classification of properties described in computation tree logic (CTL), according to their dominance and equivalence relations, allows one to conduct an efficient verification by avoiding inefficient calculation due to redundant properties. In this case, giving a verification order for these properties allows one to shorten their verification time. An extension named reconfigurable CTL describes the new syntax of the proposed classification. This approach can be applied in modeling and verification of advanced reconfigurable systems arising from smart grids, adaptive sensor networks, intelligent transportation, reconfigurable manufacturing, and embedded systems.

In this paper, we elaborate on a new design approach to the development and analysis of granular input spaces and ensuing granular modeling. Given a numeric model (no matter what specific design methodology has been used to construct it and what architecture has been adopted), we form a granular input space through allocating a certain level of information granularity across the input variables. The formation of granular input space helps us gain a better insight into the ranking of input variables with respect to their precision (the variables with a lower level of information granularity need to be specified in a precise way when estimating the inputs). As a consequence, for granular inputs, the outputs of the granular model are also information granules (say, intervals, fuzzy sets, rough sets, etc.). It is shown that the process of forming granular input space can be sought as an optimization of allocation of information granularity across the input variables so that the specificity of the corresponding granular outputs of the granular model becomes the highest while coverage of data becomes maximized. The construction of granular input space dwells upon two fundamental principles of granular computing-the principle of justifiable granularity and the optimal allocation of information granularity. The quality of the granular input space is quantified in terms of the two conflicting criteria, that is, the specificity of the results produced by the granular model and the coverage of experimental data delivered by this model. In the ensuing optimization problem, one maximizes a product of specificity and coverage. Differential evolution is engaged in this optimization task. The experimental studies involve both synthetic dataset and data coming from the machine learning repository.

In the design of fuzzy rule-based models, in the presence of high-dimensional data, we are faced with conceptual and algorithmic challenges. Conceptually, as the dimensionality of data increases, the notion of distance starts to be questionable, resulting in the well-known concentration effect. This has a direct detrimental effect given the fact that the distance is used in fuzzy clustering, using which the condition parts of the rules are being formed. Computationally, with the increase of dimensionality, the computing overhead becomes significant and has to be carefully addressed. In this article, we advocate a construction of distributed fuzzy rule-based models, where instead of a single monolithic (multivariable) rule-based model, we construct a collection of low-dimensional (in particular, 1- or 2-D) rule-based models and aggregate their results through some linear transformations. A suite of experimental studies realized using publicly available data are reported along with a comparative analysis engaging accuracy criteria and computing overhead. Interestingly, building distributed models exhibits some tangible benefits over the construction of the monolithic rule-based models. In terms of accuracy and computing costs, the gains of 1-D rule-based models with optimal linkage matrix (on average) are around 43.46% and 98.85%, respectively.

G-images refer to image data defined on irregular graph domains. This article elaborates on a similarity-preserving FuzzyC-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback–Leibler divergence term on partition matrix is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a wavelet space, the proposed FCM is performed in this space rather than the Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art segmentation algorithms. Moreover, it requires less computation than most of them.

In this article, we develop a residual-sparse FuzzyC-Means (FCM) algorithm for image segmentation, which furthers FCM's robustness by realizing the favorable estimation of the residual (e.g., unknown noise) between an observed image and its ideal version (noise-free image). To achieve a sound tradeoff between detail preservation and noise suppression, morphological reconstruction is used to filter the observed image. By combining the observed and filtered images, a weighted sum image is generated. Tight wavelet frame decomposition is used to transform the weighted sum image into its corresponding feature set. Taking such feature set as data for clustering, we impose an$\ell _0$regularization term on residual to FCM's objective function, thus resulting in residual-sparse FCM, where spatial information is introduced for improving its robustness and making residual estimation more reliable. To further enhance segmentation accuracy of the proposed FCM, we employ morphological reconstruction to smoothen the labels generated by clustering. Finally, based on the prototypes and smoothed labels, a segmented image is reconstructed by using tight wavelet frame reconstruction. Experimental results regarding synthetic, medical, and real-world images show that the proposed algorithm is effective and efficient, and outperforms its peers.

The conventional fuzzy C-means (FCM) algorithm is not robust to noise and its rate of convergence is generally impacted by data distribution. Consequently, it is challenging to develop FCM-related algorithms that have good performance and require less computing time. In this article, we elaborate on a comprehensive FCM-related algorithm for image segmentation. To make FCM robust, we first utilize a morphological grayscale reconstruction (MGR) operation to filter observed images before clustering, which guarantees noise-immunity and image detail-preservation. Since real images can generally be approximated by sparse coefficients in a tight wavelet frame system, feature spaces of observed and filtered images can be obtained. Taking such features to be clustered, we investigate an improved FCM model in which a sparse regularization term is introduced into the objective function of FCM. We design a three-step iterative algorithm to solve the sparse regularization-based FCM model, which is constructed by the Lagrangian multiplier method, hard-threshold operator, and normalization operator, respectively. Such an algorithm can not only perform well for image segmentation, but also come with high computational efficiency. To further enhance the segmentation accuracy, we use MGR to filter the label set generated by clustering. Finally, a large number of supporting experiments and comparative studies with other FCM-related algorithms available in the literature are provided. The obtained results for synthetic, medical and color images indicate that the proposed algorithm has good ability for multiphase image segmentation, and performs better than other alternative FCM-related algorithms. Moreover, the proposed algorithm requires less time than most of the existing algorithms.

In this article, we elaborate on a design methodology and the detailed realization of hierarchically structured granular models by engaging the fundamental principles and concepts of granular computing. The existing models are elevated to a more abstract (general) level by allocating a certain level of information granularity throughout the parameter space. In a concise way, the essence of the overall architecture of the proposed modeling mechanism could be generalized as follows: Numeric model (granular model of type-0)→granular model of type-1→granular model of type-2→…→granular model of higher type. The results of the granular models come in the form of type-0, type-1, or higher type information granules, which are decided by the overall level of hierarchy of the corresponding granular model. The specificity of granular outputs becomes a more comprehensive and sound quantification of the prediction accuracy and precision of the model and the quality of the specific prediction outputs. The proposed method facilitates effective communication with humans, who could get actively involved in the modeling process and determine the suitable level of abstraction depending upon the requirements of the problem. The determination of a suitable level of information granularity is realized with the guidance of the principle of justifiable granularity. A number of experimental studies concerning publicly available datasets are presented to illustrate the development methodology and show the effectiveness of the approach to form hierarchically structured solutions (reflecting different levels of abstraction) to the problem.

Successful product development relies on the enterprise system (ES) to manage product lifecycle data and support decision-making in various levels. Since the amount of data generated in the industrial Internet of Things is increasing dramatically, new paradigms of the ES are required to realize the distributed intelligence. Recent progress in edge computing has enabled advances in decentralized decision support systems. Although the product design is a key stage of new product development, none of the recent studies in edge-based ES have shed light on this stage. Therefore, an edge-based ES framework for design scheme evaluation is proposed in this article. It not only accomplishes most of the decision-making tasks within the edge mesh of an enterprise, but also solves the information island of multiple decision makers from different geographical regions. Moreover, a multigroup decision-making algorithm is proposed to enable collaborative design scheme evaluation. The evaluation processes of designers, experts, and customers are analyzed systematically in this article. For one advantage, the trapezium cloud model is applied to convert the qualitative evaluation information given by designers and experts into qualitative values. It decreases the cognitive discrepancy and solves the information distortion. For another, EEG data are utilized to explore the implicit psychological states of customers during the product operation. All the evaluation results of multiple groups of decision makers are integrated by the fuzzy measure and Choquet integral to determine the optimal design scheme. A case study is conducted to illustrate the feasibility of the method proposed in this article.

Failure mode and effect analysis (FMEA), as a proactive reliability management technique, has been widely employed to reduce the risk of systems and assure the quality of products in various industries. Nevertheless, the traditional FMEA method shows many weaknesses when used in real-life scenarios. This causes the dilemma for practitioners that it is not great as expected. Many alternative risk priority models have been developed to enhance the capacity of FMEA, but the majority of them focus on how to acquire a complete ranking of failure modes. In this article, we propose a novel FMEA approach using hesitant uncertain linguistic Z numbers (HULZNs) and density-based spatial clustering of applications with noise (DBSCAN) algorithm to assess and cluster the risk of failure modes. The HULZNs are adopted to represent the uncertain and hesitant risk evaluation information of FMEA team members. The normal DBSCAN algorithm is improved to cluster the recognized failure modes into different risk classes. Moreover, the weights of FMEA experts are dynamically obtained with a weight adjustment method. Finally, a practical case of a geothermal power plant is given to demonstrate the effectiveness and validity of our introduced FMEA approach.

This paper deals with the medium between two reconfigurable sensor nodes characterized by radio interfaces that support multiple channels for exchanging real-time messages under energy constraints. These constraints are violated if the consumed energy in transmission is higher than the remaining quantity of energy. A reconfiguration, i.e., any addition or removal of tasks in devices and consequently of messages on the medium, can cause the violation of real-time or energy constraints at run time. To achieve a feasible scheduling in time (i.e., message deadlines will be respected) and energy (i.e., there is available energy) on the medium, we propose new dynamic solutions: Balance, Dilute, and a Combination of them to manage any addition or removal of messages. The proposed approach utilizes the energy harvesting techniques and the PowerControl algorithm to reduce the nonharvested consumed energy. The proposed strategies achieve significant improvement over existing methods and provide the highest percentage of adding messages, with a lower average in response time and energy consumption. They reach a percentage of success in adding the highest priority messages while meeting deadlines up to 85%.

A maximally permissive (or optimal) supervisory control of an automated manufacturing system (AMS) modeled by Petri nets (PNs) can be usually implemented by imposing constraints in the form of a set of linear inequalities. To find such a set of linear constraints, in the existing work, an integer linear programming (ILP) problem is generally formulated and solved for some dominant markings obtained by reachability analysis and vector covering theory, which is computationally inefficient due to the combinatorial nature of solving ILPs to decide the coefficients of optimal linear constraints. This paper addresses the deadlock prevention problem for AMSs by developing efficient methods to reduce the computational overhead through the establishment of conditions on deciding the maximal permissiveness of linear constraints imposed on a system. By taking the advantage of structural properties of a PN model under consideration, we identify the most part of minimal covered illegal markings that can be optimally controlled via policies obtained by specific linear inequality constraints. Also, algorithms are developed to implement the proposed policies. The proposed approach can verify the optimality of a linear constraint efficiently without solving ILP problems. A linear programming method is developed to deal with the markings that cannot be processed by the proposed structural analysis. It is shown that for the considered class of PNs, called system of simple sequential processes with resources (S3PR), no mixed ILP problem needs to solve and the computational burden is dramatically reduced. Two examples are employed to demonstrate the efficiency of the developed method.

In this paper, a neural network (NN)-based distributed cooperative identification strategy with event-triggered communication is studied for a group of coupled identical nonlinear systems. We develop a distributed cooperative learning law in the context of event-triggered communication, where an agent will transmit its NN weights to its neighbors only when its weight trigger error norm exceeds an exponentially decreasing threshold. It is proven that the estimated weights of all radial basis function NNs converge to a small neighborhood of their optimal values. Therefore, the unknown nonlinear function is approximated along the union of all the system trajectories. It is further proven that there exists a positive minimum interevent interval and Zeno behavior can be avoided. Finally, we give a simulation example to demonstrate these features.

A kind of facilities for wafer fabrication, cluster tools (CTs) need to close down to an idle state from time to time because of periodical maintenance and switches from one type of lots to another, which is called a normal close-down process (NCDP). It is crucial to optimize such a transient process since it tends to occur more and more frequently due to customization. Also, process modules (PMs) in CTs are known to be failure-prone. Once a PM failure occurs, a tool needs to close down to an idle state as well, which is different from NCDP and is called a failure close-down process (FCDP). With wafer residency time constraints (WRTCs) being imposed, close-down process optimization for such a tool is challenging, since one needs to not only finish this process as soon as possible but also meet WRTCs during this transient process. In order to tackle this problem, this article first introduces steady state scheduling problems. Then, with a presented backward robot task sequence, a linear programming model is first proposed to optimize NCDP. To deal with the PM failures, efficient PM failure response policies are formulated for the cases in which a PM fails. Then, four linear programs are proposed to optimize an FCDP. Finally, industrial case studies are given to show the usefulness of the proposed approaches.

In semiconductor manufacturing, a process module (PM) failure in cluster tools (CTs) happens from time to time. To effectively operate a CT, such a failure should be handled in a proper and timely manner. This issue becomes much more complicated because wafer residency time constraints (WRTCs) must be met to ensure the quality for some wafer fabrication processes. With such constraints, if a tool is operated under a periodic schedule and a PM fails, it is desired that the tool can still operate under a periodic schedule if it is possible. Nevertheless, the periodic schedule after a PM failure must be different from that before its failure since in this case the tool is degraded. Thus, there must be a transient process between them. It is a great challenge to operate a tool such that it can go through such a transient process with WRTCs being always satisfied. This paper aims to solve this problem by proposing PM failure response policies which can successfully transfer a CT to the feasible schedule after failure from the one before a failure. Then, efficient algorithms are developed to improve these response policies. The proposed policies are composed of simple control laws such that they can be realized in real time and online. Illustrative examples are presented to show their applications.

Multicriteria decision making (MCDM) problems are often encountered in complex system design. Most of them need to be evaluated with a large number of interactive and qualitative indices, which are difficult to be addressed effectively through the existing methods. In this paper, a novel fuzzy Choquet integral-based grey comprehensive evaluation (GCE) method, called fuzzy grey Choquet integral (FGCI), is proposed to evaluate MCDM problems with many interactive and qualitative indices. In this method, expert evaluation of qualitative indices is represented through fuzzy linguistic values. Fuzzy values are defuzzified and standardized to obtain the original evaluation matrix. The original values are replaced by the correlation coefficients, which, to a certain extent, eliminate the influence of experts' subjective preference. An improved teaching-learning-based optimization algorithm is employed to identify λ-fuzzy-measures following the weights given by experts in order to enhance the consistency of weights. Then the correlation coefficients are aggregated through Choquet integral among λ-fuzzy-measures, which can reflect interactions among indices. In addition, according to the characteristics of λ-fuzzy-measures, the construction guidelines for a corresponding index system are given to overcome the limitations of FGCI. Finally, the performance of the proposed method is demonstrated via a practical example of green design evaluation and compared with the GCE method. The results validate its feasibility and effectiveness.

In this paper we consider timed automata endowed with a single clock that is reset at each event occurrence. A time interval is associated with each transition specifying at which clock values it may occur. We assume that the (logical and timed) structure of a timed automaton is known, and event labels associated with transitions are not observable. The problem addressed in this paper is to estimate and update the set of possible current discrete states as time elapses without any observation being received. By a partitioning of time intervals into regions, we design a λ-observer (λ is the null observed sequence) that, for a given state and a current time value, estimates the set of possible current states and the corresponding regions to which the timer value associated with each possible current state may belong.

With the expansion of the sharing economy, growing urban traffic, and increasing environmental pollution, bike-sharing systems (BSSs) are developing rapidly all over the world. A major operational issue in BSS is to reposition the bikes over time such that enough bikes and open parking slots are available to users. Especially during peak hours, it is essential to stabilize BSS in use. To cope with the issue, this article proposes a new approach integrating multiobjective optimization and a weighting factor based on the shortage event types of each station. In addition, the multiobjective artificial bee colony algorithm is modified according to the features of this work to find optimal solutions. The proposed approach is applied to the real-life repositioning of a BSS during peak hours to verify its feasibility and effectiveness. Also, the algorithm is compared with other frequently used multiobjective algorithms. For the comparative study, convergence metric and spacing are adopted to further measure the algorithm performance. The scalability of the proposed approach in addressing the multiobjective repositioning problems during peak hours is also verified by multiple trials. Note to Practitioners-This work deals with bike repositioning in bike-sharing systems (BSSs) during peak hours, which has major significance in the efficient operation of such systems. It builds a multiobjective optimization model and solves it through a modified multiobjective artificial bee colony algorithm. The existing single-objective optimization methods fail to solve the concerned problem. This work can find the optimal routes of the repositioning vehicles along with the number of desired parked bikes of corresponding stations. The experimental results indicate that the proposed method is highly effective and can greatly and readily help decision-makers better manage the BSS of a practical size.

The paper presents an approach to the control of black-box embedded systems by integrating automaton learning and supervisory control theory (SCT) of discrete-event systems (DES), where automaton models of both the system and requirements are unavailable or hard to obtain. First, the system is tested against the requirements. If all the requirements are satisfied, no supervisor is needed and the process terminates. Otherwise, a supervisor is synthesized to enforce the system to satisfy the requirements. To apply SCT and automaton learning technologies efficiently, the system is abstracted to be a finite-discrete model. Then, a C* learning algorithm is proposed based on the classical L* algorithm to infer a Moore automaton describing both the behavior of the system and the conjunctive behavior of the system and the requirements. Subsequently, a supervisor for the system is derived from the learned Moore automaton and patched on the system. Finally, the controlled system is tested again to check the correctness of the supervisor. If the requirements are still not satisfied, a larger Moore automaton is learned and a refined supervisor is synthesized. The whole process iterates until the requirements hold in the controlled system. The effectiveness of the proposed approach is manifested through two realistic case studies.

In recent years, image processing in a Euclidean domain has been well studied. Practical problems in computer vision and geometric modeling involve image data defined in irregular domains, which can be modeled by huge graphs. In this paper, a wavelet frame-based fuzzy C -means (FCM) algorithm for segmenting images on graphs is presented. To enhance its robustness, images on graphs are first filtered by using spatial information. Since a real image usually exhibits sparse approximation under a tight wavelet frame system, feature spaces of images on graphs can be obtained. Combining the original and filtered feature sets, this paper uses the FCM algorithm for segmentation of images on graphs contaminated by noise of different intensities. Finally, some supporting numerical experiments and comparison with other FCM-related algorithms are provided. Experimental results reported for synthetic and real images on graphs demonstrate that the proposed algorithm is effective and efficient, and has a better ability for segmentation of images on graphs than other improved FCM algorithms existing in the literature. The approach can effectively remove noise and retain feature details of images on graphs. It offers a new avenue for segmenting images in irregular domains.

A novel risk analysis approach is developed by merging interval-valued fuzzy sets (IVFSs), improved Dempster-Shafer (D-S) evidence theory, and fuzzy Bayesian networks (BNs), acting as a systematic decision support approach for safety insurance for the entire life cycle of a complex system under uncertainty. Aiming to alleviate the problem of insufficient and imprecise data collected from the complicated environment, the expert judgment in linguistic expressions is employed to describe the risk levels for all risk factors, which are represented by IVFSs using Gaussian membership function to fully consider such fuzziness and uncertainty. In regard to interval fusion and highly conflicting data, an improved combination rule based on the D-S evidence theory is developed. Then, fuzzy prior probability for each risk factor can be generated from fused intervals and fed into a fuzzy BN model for fuzzy-based Bayesian inference, including predictive, sensitivity, and diagnosis analysis. Furthermore, a case study is used to demonstrate the feasibility of the proposed risk analysis. A comparison of risk analysis based upon the hybrid improved D-S, classical D-S, and arithmetic average method is illustrated to show the outstanding performance of the developed approach in fusing multisource information with ubiquitous uncertainty and conflicts in an efficient manner, leading to more reliable risk evaluation. It is concluded that the proposed risk analysis provides a deep insight on risk control, especially for complex project environment, which enables to not only reduce the likelihood of failure ahead of time but also mitigate risk magnitudes to some degree after the occurrence of a failure.

State-tree structure (STS) is a powerful tool for the modeling and control of large-scale discrete-event systems (DES) whose structure is organized in a top-down hierarchy. The notorious state-explosion problem can be managed effectively in the framework of STS. Priority is an important concept and exists in a wide range of DES, such as manufacturing systems, traffic systems, and logistic (service) systems. In this article, a conditional-preemption matrix is used to describe the preemption relations among events, which can represent the priority intuitively. In order to augment STS with priority, we propose a novel STS framework: STS with conditional-preemption matrices (STSM), which aims to solve different kinds of issues related to priority. The optimal nonblocking supervisory control is deployed in the framework of STSM. Symbolic synthesis algorithms are implemented and integrated in a software tool, STSLib, which exploits binary decision diagrams as a basis for efficient computation. The developed approach is demonstrated by (a) manufacturing systems using automatic guided vehicles and (b) real-time systems.

With the increasing speed of railway vehicles, deciding how to reasonably distribute impact energy to each vehicle has been a widespread concern in safety protection systems. This article formulates a three-dimensional train-track coupling dynamics model using MAthematical DYnamic MOdels (MADYMO) multibody dynamics software. A train-to-train collision is then simulated using this model. A hybrid solution methodology that combines the non-dominated sorting genetic algorithm II (NSGA-II), modified best and worst method with cloud model theory and grey relational analysis is proposed. The optimization parameters and objectives are determined based on the EN15227 crashworthiness requirements for railway vehicles. An empirical case of an existing train with eight vehicles that have been in operation in China is applied to verify this dynamics model derived from a high-speed train and solution methodology. Analysis and discussion are conducted to monitor the robustness of the results and the practical implications for rail transportation are summarized. The results prove that the obtained optimal solution by this research has better crashworthiness than an existing solution.

Image denoising technologies in a Euclidean domain have achieved good results and are becoming mature. However, in recent years, many real-world applications encountered in computer vision and geometric modeling involve image data defined in irregular domains modeled by huge graphs, which results in the problem on how to solve image denoising problems defined on graphs. In this paper, we propose a novel model for removing mixed or unknown noise in images on graphs. The objective is to minimize the sum of a weighted fidelity term and a sparse regularization term that additionally utilizes wavelet frame transform on graphs to retain feature details of images defined on graphs. Specifically, the weighted fidelity term with ℓ1-norm and ℓ2-norm is designed based on a analysis of the distribution of mixed noise. The augmented Lagrangian and accelerated proximal gradient methods are employed to achieve the optimal solution to the problem. Finally, some supporting numerical results and comparative analyses with other denoising algorithms are provided. It is noted that we investigate image denoising with unknown noise or a wide range of mixed noise, especially the mixture of Poisson, Gaussian, and impulse noise. Experimental results reported for synthetic and real images on graphs demonstrate that the proposed method is effective and efficient, and exhibits better performance for the removal of mixed or unknown noise in images on graphs than other denoising algorithms in the literature. The method can effectively remove mixed or unknown noise and retain feature details of images on graphs. It delivers a new avenue for denoising images in irregular domains.

Automated guided vehicles (AGVs) are being extensively used for transportation and distribution of materials due to their high-efficiency. However, the vehicle-collision free problem is challenging since, when modeling these systems, there are indistinguishable and uncontrollable events due to the limited sensors and actuators. This paper proposes an approach to the design of a maximally permissive (optimal) controller to prevent vehicles from any collision based on Petri nets (PNs). For a typical class of AGV systems, a system modeling algorithm is presented using labeled PN, where indistinguishable events are represented by a set of transitions carrying the same label, and an uncontrollable event by an uncontrollable transition. By virtue of the PN model, the collision-free problem is formalized as a conjunction of linear constraints that are converted into admissible ones by an algorithm such that the computational overhead due to uncontrollable events is significantly reduced. In turn, a method is developed to compute the set of consistent markings for an observed sequence of labels that represent signals generated by sensors. Finally, given an observed sequence, a maximally permissive control action is computed to enforce a conjunction of admissible linear constraints based on the set of consistent markings. The approach well addresses the challenging issues caused by indistinguishable and uncontrollable events. A typical AGV system is utilized to illustrate and verify the theoretical results throughout the work.

In this paper, an event-based distributed cooperative learning (DCL) law is proposed for a group of adaptive neural control systems. The plants to be controlled have identical structures, but reference signals for each plant are different. During control process, each agent intermittently broadcasts its neural network (NN) weight estimation to its neighboring agents under an event-triggered condition that is only based on its own estimated NN weights. If communication topology is connected and undirected, the NN weights of all neural control systems can converge to a small neighborhood of their optimal values. The generalization ability of NNs is guaranteed in the event-triggered context, that is, the approximation domain of each NN is the union of all system trajectories. Furthermore, a strictly positive lower bound on the interevent intervals is also guaranteed to avoid the Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the proposed learning law.

In this article, we propose a design and evaluation framework of granular neural networks realized in the presence of information granules. Neural networks realized in this manner are able to process both nonnumerical data, such as information granules as well as numerical data. Information granules are meaningful and semantically sound entities formed by organizing existing knowledge and available experimental data. The directional nature of mapping between the input and output data needs to be considered when building information granules. The development of neural networks advocated in this article is realized as a two-phase process. First, a collection of information granules is formed through granulation of numeric data in the input and output spaces. Second, neural networks are constructed on the basis of information granules rather than original (numeric) data. The proposed method leads to the construction of neural networks in a completely new way. In comparison with traditional (numeric) neural networks, the networks developed in the presence of granular data require shorter learning time. They also produce the results (outputs) that are information granules rather than numeric entities. The quality of granular outputs generated by our neural networks is evaluated in terms of the coverage and specificity criteria that are pertinent to the characterization of the information granules.

Wireless sensor networks (WSNs) can be seriously impacted by several changes in behavior. As a measure of optimality, a network should react in real-time. However, even if some reactions can make the system flexible, they can cause significant damages when they are not well-structured. Moreover, the scenarios of reconfigurations can affect several parameters within a wireless sensor network. Among these parameters it is possible to name the energy efficiency, the memory limitation within a node, the real-time constraints at the level of the nodes and the network in general. Either through the application or the transmission of the reconfiguration scenarios, some parameters are severely altered. A metamorphosis of the internal architecture of the nodes is proposed in this research work as well as a policy of communication between several nodes aiming to adapt the network to any change. The resulting proposition offers general efficiency in energy and real-time constraints. The efficiency is realized by the application of a pipelined approach, dealing with the incoming reconfiguration scenarios, and a communication protocol, based on a priority energy and deadline aware scheduling algorithm (a multicriteria scheduling algorithm). This protocol is capable of optimizing the transmission of both reconfiguration scenarios and sensed data simultaneously. The solution is tested on a network connecting several cars during an automobile journey and the results validate the efficiency of this proposition.

This paper deals with software validation of flexible discrete-event systems. A reconfiguration scenario is any run-time adaptation of the software execution according to user requirements. Nevertheless, since several behaviors can be redundant from an execution to another, using the object constraint language (OCL) is not useful to specify all constraints that should be satisfied by a system. We propose an extension of OCL named reconfigurable OCL for improving the specification and validation of constraints related to different execution scenarios of a system. An ROCL metamodel is proposed with formal syntax and semantics. This solution gains in terms of validation time and quick expressions of constraints. We apply the proposed extended language to reconfigurable wireless sensor networks to highlight the benefits of this contribution.

This paper deals with the formal verification of reconfigurable discrete event control systems (RDECSs) using reconfigurable timed net condition/event systems (R-TNCESs) formalism. A reconfigurable system switches from a mode to another during its working process to adapt its behavior to the related environment. By including such a feature, RDECSs become complex and their verification is often expensive in terms of computation time and memory. In this paper, a new methodology for formal verification of RDECSs is proposed in order to ensure the correctness of these systems with a reduced cost (decreasing the verification time and memory occupation). The proposed contribution includes an improved modeling and verification of RDECSs. The modeling with R-TNCESs is enriched with all reconfiguration forms, and the verification involves an improvement method that avoids any redundancy and cancels unnecessary calculations. In addition, a visual tool called Rec-AG based on the proposed methodology is developed. The performance evaluation of this paper is achieved by measuring computation time and memory for several systems and different sizes of the problem. This paper's contribution is applied to the benchmark production system FESTO.

State space techniques are one of the main approaches deployed for the analysis of concurrent systems. However, state space construction is stalled by a common phenomenon called the state explosion problem which makes it a tough task or even impossible when the state space computation demands prohibitive cost (time and memory). We limit general resource allocation systems (RASs) to a certain class whose state space can be hierarchically constructed, yet it comprises various enough types of real-world discrete event systems, such as automated manufacturing systems. This paper focuses on a class of RASs modeled with Petri nets (PNs), where, through pure algebraic operations, a novel method to compute the state spaces is proposed, which is motivated by the superposition property. Given a PN model of a system and a target resource configuration, we first propose a special initial marking called the initial basis marking and compute the corresponding reachability graph. Then, we increase the capacity of the resource places in an incremental way and generate the reachability graphs by taking advantage of the PN structure and the previously computed reachability graph until the capacity function of resources reaches the target resource configuration. A complete enumeration of reachable states can be obtained by a recursive scheme. Experimental studies also demonstrate the efficiency of the proposed approach in terms of computational cost and its high-potential to cope with the state-explosion problem.

A public room in buildings could hold a number of persons who may prefer dissimilar thermal environment. Different areas in such rooms may have different temperatures. Facility layout in such a room has effect on the distribution of the people in the room, thus it may affect its thermal environment. It is meaningful and challenging to effectively operate an air-conditioning control (ACC) system by taking the above mentioned factors into account such that the thermal environment is improved and energy is saved. This work aims at optimally operating an ACC system and designing the facility layout so as to maximize the total thermal satisfaction rate (TSR) as well as energy saving. To do so, with a large library room at Macau University of Science and Technology (MUST) as a case scenario, an investigation with a large number of experiments is conducted to collect necessary data. Based on the data, regression analysis is done to predict its indoor temperatures in different areas and TSR at a given temperature. Then, we propose a particle swarm optimization (PSO) algorithm such that an optimal or near-optimal solution can be efficiently found. By numerical results, it is observed that the proposed method can significantly improve the total TSR and save energy.

This paper deals with formal modeling and verification of reconfigurable hierarchical discrete-event control systems (RDECSs). The system's behavior is with a dynamic structure for being adapted to related environment. We propose an extension of timed automata named reconfigurable hierarchical timed automata (RHTA) to consider hierarchy and reconfigurability of the considered system. Since the verification becomes an expensive task in terms of computation time and memory, an improved verification methodology is proposed for RHTA where redundancies and similarities between system's configurations are considered which controls verification cost. The proposed contribution is applied to an example to evaluate the related performance where gains in term of verification time are marked.

Cyber-physical systems have emerged as a key technology in the development of distributed and autonomous large scale systems. However, one of their undesirable side effects is the fact that they are particularly exposed to cyber attacks carried out by malicious intruders. Therefore, efficient strategies for cyber security are in high demand. In this paper, we consider the issue of attack detection in the framework of partially observable discrete event systems modeled by finite automata. We assume that the observation produced by a plant can be corrupted by an intruder which, through one or more attack dictionaries, can change events into different strings. The problem we address is that of detecting if a plant has been attacked and, if such is the case, of identifying the nature of the attack, i.e., which attack dictionaries have been used. We show that the problem of attack detection can be reduced to a classical problem of state estimation or fault diagnosis for a new structure which describes the behavior of the plant under attack.

The paper focuses on robust scheduling for flexible manufacturing systems (FMSs) with unreliable resources modelled by timed Petri nets (TPNs). For this purpose, the paper shows that schedulers based on beam search method can be combined with liveness-enforcing supervisors in a straightforward way. This combination is motivated by some properties in the set of reachable markings of a TPN. The use of the liveness-enforcing supervisor improves the robustness of the scheduling without degrading the performance for a particular class of job-shop systems. An example is given to illustrate the proposed strategy.

To guarantee a better power availability in complex power systems, this paper provides a new strategy that deals with smart grids rebuilding for faults recovery. We propose a heuristic that schedules technical teams interventions and allows to carry favour clients with no recovery sources compared with those that are covered by renewable sources, and the ones recovered by pertinent emergency lines. Thus, we overcome this complexity by providing an oriented cloud architecture based on effective computing sources for assisting technical teams in recovery, it permits to obtain gains in term of total energy availability and to reduce loss time. We implement a simulation tool for smart grid rebuilding based on a cloud system called SGCS-RB which is experimented on different smart grids, and we evaluate the number of recovered failures which is improved thanks to the proposed contribution.

With growing environmental and sustainability-related concerns, recovery optimization of mechanical products has been gaining increased exposure. It facilitates environmental sustainability through the improvement in the life-cycle material efficiency and reduction in environmental impact with disassembly sequence planning, component reuse, and material recycling. Traditional product recovery separates end-of-life (EOL) products into components and selects EOL options of components. However, there are many practical cases in which the recovery of a set of subassemblies and components leads to better net revenue than that of a complete set of single components. This paper proposes to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. Finally, a case study is described to illustrate the proposed method and the influence on decision variables of the tradeoff between the recovery profit and environmental impact is discussed. Note to Practitioners-This paper deals with the process planning and EOL decision-making problem of product recovery. Based on hybrid disassembly, this paper proposes a flexible process planning and EOL decision-making method for product recovery. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. The goal of this paper is to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. The results demonstrate that the proposed method can leads to small environmental impact and low cost. Subassemblies and components with high reliability and expensive price are suggested to be destined for reuse. Minimizing transportation distances is more effective to reduce product recovery cost. Such results can help decision makers to perform better judgments when a disassembly process of an EOL product is executed.

Supervisory control theory (SCT) of a discrete-event system (DES) is well developed to find its maximally permissive supervisor. As an extension of SCT, a new framework, state-tree structures (STS), has been deployed to manage the state explosion problem of SCT. The supervisory control withpartial observationis investigated in SCT, of which the state explosion issue remains to be explored. This paper dwells upon an approach to the synthesis of supervisory control with partial observation in thenormalSTS framework, requiring that the conjunction of any two projected subpredicates should be false. First, following the definition of the observability based on STS, it is proven that there does not exist the supremal observable subpredicate. Second, we construct a subclass of observable subpredicates of a given predicate (obtained as a specification) on the basis ofnormalSTS. Third, a largest element is proven to exist in the subclass, and thus aweakly controllable, coreachable, and observablesubpredicate is computed to solve the supervisory control problem of thenormalSTS by an iterative algorithm. An illustrative example with state size over 109is given to show that the proposed algorithm based on STS is superior to the approach based on SCT, which leads to the program crashes in SCT.Note to Practitioners—DES is a collection of many contemporary computer-integrated industrial and social service systems. For the sake of technology or cost considerations, the occurrences of some events are not fully observed, rendering the supervisory control algorithmically complicated and exacerbating the originally notorious state explosion problem. This paper aims to ameliorate the state explosion problem using a novel formalism, namely, state-tree structures, that can be applied to real-world systems thanks to the computational efficiency that is demonstrated by an industrial system. Compared with the traditional SCT of DES, the significance of the proposed methodology will intrigue academic researchers and industrial practitioners.

IEC 61499 provides a standardized approach for the development of distributed control systems. The standard introduces a component architecture, based on function blocks that are event-triggered components processing data and signals. However, it gives only limited support for the design of reconfigurable architectures. In particular, handling of several reconfiguration scenarios is quite heavy on this level since a scenario changes the execution model of the system due to requirements. To this end, a new IEC 61499-based model named reconfigurable function blocks (RFBs) is proposed. An RFB processes the reconfiguration events and switches directly to the suitable configuration using a hierarchical state machine model. The latter represents the reconfiguration model which reacts on changes in the environment in order to find an adequate reconfiguration scenario to be executed. Each scenario presents a particular sequence of algorithms, encapsulated in another execution control chart slave which represents the control model of an RFB. This hierarchy simplifies the design and separates the reconfiguration logic from control models. To verify its correctness and alleviate its state space explosion problem in model checking, this paper translates an RFB system automatically into a generalized model of reconfigurable timed net condition/event systems (GR-TNCES), a Petri net class that preserves the semantics of an RFB system. In this paper, along with verification of deterministic properties, we also propose to quantify and analyze some probabilistic properties. As a case study, we consider a smart-grid system, interpreting permanent faults in it as reconfiguration events, and we characterize them with the expected occurrence probability and the corresponding repair time. A tool chain ZiZo is developed to support the proposed approach.

The design of information granules assumes a central position in the discipline of Granular Computing and its applications. The principle of justifiable granularity offers a conceptually and algorithmically attractive way of designing information granule completed on a basis of some experimental evidence (especially present in the form of numeric data). This paper builds upon the existing principle and presents its significant generalization, referred here as an adaptive principle of justifiable information granularity. The method supports a granular data aggregation producing an optimal information granule (with the optimality expressed in terms of the criteria of coverage and specificity commonly used when characterizing quality of information granules). The flexibility of the method stems from an introduction of the adaptive weighting scheme of the data leading to a vector of weights used in the construction of the optimal information granule. A detailed design procedure is provided along with the required optimization vehicle (realized with the aid of the population-based optimization techniques, such as particle swarm optimization and differential evolution). Two direct application areas in which the principle becomes of direct usage include prediction of time series and prediction of spatial data. In both cases, it is advocated that the results formed by the principle are reflective of the precision (quality) of the prediction process.

Failure mode and effect analysis (FMEA) is a prospective reliability analysis technique used in a wide range of industries for enhancing the safety and reliability of systems, products, processes, and services. However, the conventional FMEA method has been criticized for inherent drawbacks that limit effectiveness and applications. In this paper, a novel integrated FMEA model based on cloud model theory and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS) method is developed to assess and rank the risk of failure modes. First, individual linguistic assessments of failure modes are converted into normal clouds. Then, FMEA team members' weights are calculated based on the subjective weighting information. Finally, the risk priority of failure modes is determined by using the cloud hierarchical TOPSIS. The newly proposed FMEA method combines the advantages of the cloud model in coping with fuzziness and randomness of linguistic assessments and the merits of hierarchical TOPSIS in solving complex decision making problems. Two empirical examples to illustrate the feasibility and effectiveness of the proposed FMEA are presented together with a comparison to existing methods.

Clustering offers a general methodology and comes with a remarkably rich conceptual and algorithmic framework for data analysis and data interpretation. As one of the most representative algorithms of fuzzy clustering, fuzzy C-means (FCM) is a widely used objective function-based clustering method exploited in various applications. In this study, a virtual-based fuzzy clustering algorithm is proposed to improve the classification performance coming as a result of using fuzzy clustering. This improvement is achieved by forming a virtual space based on the original data space. First, we construct a piecewise linear transformation function to modify the similarity matrix of the original data and build the so-called virtual similarity matrix (VSM). Considering the VSM, the effect of closeness becomes amplified; in other words, high similarity values (say, larger than α which is a cutoff value of the large and small similarity in this paper) present in the original similarity matrix are made higher, whereas lower similarity levels (say, smaller than α) are further reduced. In addition, data with high similarity (say, larger than a certain threshold value) observed in the original space will overlap (the attributes of the samples are exactly the same) significantly in the virtual space; the overlapping samples can be treated as one sample. This modification makes possible easier to identify clusters. Second, we build a relationship matrix between the original dataset and the determined similarity values and present two closed-form solutions to the problem of building the relationship matrix. Subsequently, a virtual space of the original data space is derived through the modified similarity matrix and the introduced relationship matrix. We offer a thorough analysis behind the developed clustering algorithm. The experimental results are in agreement with the underlying conceptual basis. Furthermore, the resulting classification performance is significantly improved compared with the results produced by the FCM and the kernel-based fuzzy C-means.

Disassembly planning aims to perform the optimal disassembly sequence given a used or obsolete product in terms of cost and environmental impact. This paper presents a new multiobjective programming model for the target disassembly sequencing. It proposes an improved multiobjective ant colony algorithm to derive optimal target disassembly sequences. This work also establishes some indices on disassembly scheme evaluation and a fuzzy integral method to evaluate the obtained disassembly scheme. A CNC machine tool example is given to illustrate the proposed models and the effectiveness of the proposed algorithm. Both theoretical and simulation results demonstrate that the proposed approach can perform the quantitative analysis of a disassembly process effectively. Such results can help decision makers select the best plans and sequences when executing a disassembly process of a product.

This paper deals with the reconfigurable real-time systems that should be adapted to their environment under real-time constraints. The reconfiguration allows moving from one implementation to another by adding/removing/modifying parameters of real-time software tasks which should meet related deadlines. Implementing those systems as threads generates a complex system code due to the large number of threads, which may lead to a reconfiguration time overhead as well as the energy consumption and the memory allocation increase. Thus this paper proposes a multiobjective optimization approach for reconfigurable systems called MO2R2S for the development of a reconfigurable real-time system. Given a specification, the proposed approach aims to produce an optimal design while ensuring the system feasibility. We focus on three optimization criteria: 1) response time; 2) memory allocation; and 3) energy consumption. To address the portability issue, the optimal design is then transformed to an abstract code that may in turn be transformed to a concrete code which is specific to a procedural programming (i.e., POSIX) or an object-oriented language (i.e., RT-Java). The MO2R2S approach allows reducing the number of threads by minimizing the redundancy between the implementation sets. By an experimental study, such optimization permits to decrease the memory allocation by 28.89%, the energy consumption by 40.2%, and the response time by 61.32%.

Resource failures may happen in automated manufacturing systems (AMSs) because of different reasons in the real world, making most existing deadlock control policies unapplicable. This paper develops methods for the robust deadlock control of AMSs with unreliable resources based on Petri nets. The considered AMSs are modeled with generalized systems of simple sequential processes with resources (GS3PR). First, a method based on reachability graph partition technique is provided to analyze the robust legal markings and the forbidden ones in an unreliable GS3PR (U-GS3PR), in which resource failures and recovery procedures are modeled with recovery subnets. Then, the control problem for such a system is converted into a problem for controlling the forbidden states in a U-GS3PR and control places can be designed by solving the maximal number of forbidden markings problems. Since the robust legal reachability spaces computed may be nonconvex and such a system cannot be optimally controlled by the conjunctions of linear constraints, we propose an interval-inhibitor-arc-based robust deadlock control policy for a system with nonconvex legal reachability spaces by solving the maximal number of tq-critical marking/transition separation instances problems (MNTMPs(tq)). Finally, examples are presented to demonstrate the proposed methods.

This paper deals with the identification problem of faulty behavior in a discrete event system, assuming that the fault-free model of a system is given in terms of Petri nets, where the set of transitions is divided into two disjoint subsets: 1) observable and 2) unobservable ones. The observed system output is defined as a transition-marking sequence, i.e., each transition is followed by a marking. First, a nonlinear integer programming model that characterizes the faults modeled by fault transitions is built according to the abnormal behavior extracted from the observed sequence. Then, it is converted into an integer linear programming (ILP) problem and a faulty net that preserves the structure of the fault-free one is obtained by solving this ILP model. In addition, an algorithm is developed to ensure acyclicity of the resulting unobservable subnet whose transition set is composed of the unobservable transitions of the fault-free net and the identified fault transitions.

The problem of stealthy attacks for partially-observed discrete event systems is considered. An operator observes the plant through an observation mask that does not allow him to detect the occurrence of certain events (silent events). The observation is corrupted by an intruder who can insert and erase some sensor readings. We construct an attack structure A which guarantees that, when the plant reaches an unsafe state, A can make the operator think that the plant is in a safe state. Based on A, we can obtain a stealthy attack structure As by simply removing from A all the exposing states, all the weakly exposing states and their input and output arcs. The stealthy attack structure As only contains the possible attacks that can not be detected by the operator.

This paper deals with the test of a reconfigurable hardware system (RHS). The latter is a hardware device that allows to change the hardware resources at runtime in order to modify the system functions and therefore to dynamically adapt the system to its environment. The increasing functional complexity of embedded systems and the transition to the RHS make the hardware testing a challenging task, especially under the confine of providing a high quality with a low cost. Considering the fact that the hardware test represents a key cost factor in a production process, an optimal test strategy can be advantageous in the competitive industrial market. Accordingly, this paper introduces a new methodology for an efficient hardware test of RHS. For an RHS, the number of stuck-at faults can be very large, which leads to a significant slowdown in the testing process. Because of the redundancy of faults between the different circuits composing an RHS, the proposed methodology aims at minimizing the number of faults using the inter-circuits relationships and consequently at providing an optimal fault set that can be effectively used for testing. Efficient techniques for test generation and test set validation are proposed to provide the test patterns for faults reduced by inter-circuits fault collapsing. The application of the generated test patterns is typically sufficient to provide an overall fault coverage. The proposed methodology is implemented in a new visual environment named TnTest. An experimental study confirms and validates the expected findings. Note to Practitioners-This paper addresses possible challenges for future generations of adaptive embedded systems. It proposes an original methodology for an efficient reconfigurable hardware system (RHS) hardware test. The main objective is to significantly reduce time and cost needed for the testing process. For an RHS, the number of stuck-at faults can be very large, which can cause a major slowdown in the hardware test. Based on the inter-circuits relations existing between the different circuits composing an RHS, the proposed methodology decreases considerably not only the number of the faults but also the test patterns needed for testing. The application of the generated test patterns is typically sufficient to provide an overall fault coverage. The proposed methodology is implemented in a new visual software environment named TnTest, which is capable of providing the smallest fault set as well as the efficient test set that can be effectively used for testing. This environment can be applied to test any embedded device that can be deployed in any new application based on flexible technologies. It can also be useful in manufacturing industries for a required improvement of the production process in relation to time and cost.

A fundamental requirement of the supervisory control theory (SCT) of discrete-event systems is a finite automaton model of the plant. The requirement does not hold for black-box systems whose source code and logical model are not accessible. To apply SCT to black-box systems, we integrate automaton learning technology with SCT and apply the new method to improve the requirements conformance of software reuse. If the reused software component does not satisfy a requirement, the method adds a supervisor component to prevent the black-box system from reaching “faulty sections.” The method employs learning-based testing (LBT) to verify whether the reused software meets all requirements in the new context. LBT generates a large number of test cases and iteratively constructs an automaton model of the system under test. If the system fails the test, the learned model is applied as the plant model for control synthesis using SCT. Then, the supervisor is implemented as an executable program to monitor and control the system to follow the requirement. Finally, the integrated system, including the supervisory program and the reused component, is tested by LBT to assure the satisfiability of the requirement. This paper makes two contributions. First, we innovatively integrate LBT and SCT for the control synthesis of black-box reactive systems. Second, software component reuse is still possible even if it does not satisfy user requirements at the outset.

Fuzzy clustering has emerged as one of the fundamental conceptual and algorithmic frameworks supporting the development of information granules. Generic fuzzy clustering such as fuzzy C-means (FCM) has been utilized in a broad range of applications. However, the constructs resulting from fuzzy clustering, namely a partition matrix and prototypes, are numeric and as such are not capable of fully capturing the essence of the overall data. In this study, we propose an alternative augmented way of building information granules by generating hypercube-like information granules. A collection of hypercubes is referred to as a family of ε-information granules. This family is constructed around numeric prototypes generated through a modified version of the FCM algorithm whose running time is linear with respect to the number of clusters. By admitting a certain level of information granularity (ε), a collection of hypercubes is formed around the prototypes. The quality of information granules realized in this way is assessed by involving them in the granulation-degranulation process as well as determining a value of the coverage criterion. The level of information granularity and the number of the granular prototypes in the family of ε-information granules form an important design asset directly impacting the obtained coverage level of the data. The computational facet of the approach is stressed. It has been demonstrated that the granular enhancements of the description of data come with a very limited computing overhead. Experimental studies involve synthetic data as well as data coming from the UCI Machine Learning repository. The granular reconstruction capabilities delivered by the family of ε-information granules are discussed.

Fuzzy models have been commonly used in system modeling and model-based control. Among various fuzzy models, Takagi-Sugeno (TS) fuzzy models form one of the intensively studied and applied categories of models. In this study, we are concerned with a development of a granular TS fuzzy model realized on a basis of numerical evidence and completed through a combination of fuzzy subspace clustering and the principle of optimal allocation of information granularity. The TS fuzzy models are built with the use of the fuzzy subspace clustering algorithm. Information granularity is regarded as a crucial design asset whose optimal allocation gives rise to granular fuzzy models and makes the constructed models to become better in rapport with experimental data. In comparison with fuzzy models, granular fuzzy models produce results (outputs) that are information granules rather than numeric entities being encountered in fuzzy models. In contrast with the commonly used optimization criteria, which emphasize the highest accuracy encountered at the numeric level, the performance of the granular TS fuzzy model is quantified in terms of the coverage and specificity criteria where such criteria are of interest in the evaluation of quality of information granules vis-à-vis experimental (numeric) data. Experimental results are reported for both synthetic datasets and publicly available data sets coming from the UCI machine learning repository.

In this study, we propose a new design methodology of granular fuzzy models, introduce its further generalization in the form of granular fuzzy models of higher type, and discuss detection and characterization of outliers expressed with regard to the constructed information granules. In recent years, various models that describe the system from different perspectives have been built to resolve the growing challenges brought on by real-world systems. These models usually aim to achieve the highest accuracy at the cost of model interpretability. To improve the interpretability of models, a concept of granular models has been developed in the setting of granular computing. We focus on the formation of a general granular model at the higher level of hierarchy by taking advantage of existing models developed at the lower (numeric) level. Here, information granularity is regarded as an important design asset whose optimal allocation across the parameters of the original model gives rise to granular models. Next, through an allocation of information granularity to the existing type-1 granular model, we create an interesting and useful augmentation of the granular fuzzy model by forming a granular fuzzy model of type-2. Higher type granular models are also realized through the optimal allocation of information granularity. We examine the problem of outlier detection in granular models where outliers are expressed with regard to the constructed information granules. Experimental results demonstrate that granular fuzzy models provide significant improvement to the model's interpretability, and the proposed outlier detection method based on granular models of higher type is effective.

With the development of communication and control technology, intelligent transportation systems (ITS) have received increasing attention from both industry and academia. However, plenty of studies providing different formulations for ITS depend on Master Control Center and require a high level of hardware configuration. The systematized technologies for distributed architectures are still not explored in detail. In this paper, we proposed a novel distributed cyber–physical system for connected and automated vehicles, and related methodologies are illustrated. Every vehicle in this system is modeled as a double-integrator and supposed to travel along a desired trajectory for maintaining a rigid formation geometry. The desired trajectory is generated by reference leading vehicles using information from multiple sources, while ordinary following vehicles use velocity and position information from their nearest neighbors and sensor information from on-board sensors to correct their own performance. Information graphs are used to illustrate the interaction topology between connected and automated vehicles. Edge computing technology is used to analyze and process information, such that the risk of privacy leaks can be greatly reduced. The performance scaling laws for the network with a one-dimensional information graph are generalized to networks withD-dimensional information graphs, and the results of the experiments show that the performance of the connected and automated vehicles matches very well with analytic predictions. Some design guidelines and open questions are provided for the future study.

Fuzzy Petri nets (FPNs) are a vital modeling technique for the construction of knowledge-based systems, which have been commonly used in many fields, such as fault diagnosis, risk assessment, workflow management, and disassembly process planning. However, the conventional FPNs have been blamed for the following reasons: 1) the representation parameters in FPNs cannot precisely model experts' experience since it is difficult to manage the fuzziness and randomness of knowledge assessments simultaneously, and 2) the weight coefficients in the existing approximate reasoning algorithms are hardly enough to reflect the associated weights of reordered places. In response, we propose a new type of FPNs, called cloud reasoning Petri nets (CRPNs) based on the concept of interval clouds and the hybrid averaging operator. The cloud production rules in a knowledge-based system are modeled by CRPNs, where the truth degrees of places, the certainty factors of rules, and the thresholds of transitions are represented by interval clouds. Moreover, a matrix operation-based reasoning algorithm is proposed to improve the efficiency of calculating final truth degrees, in which both local and ordered weight coefficients are taken into consideration. Finally, a practical example concerning a power system is provided to demonstrate the usefulness and advantages of the proposed CRPN model.

This paper proposes a novel decentralized on-line fault diagnosis approach based on the solution of some integer linear programming problems for discrete event systems in a Petri net framework. The decentralized architecture consists of a set of local sites communicating with a coordinator that decides whether the system behavior is normal or subject to some possible faults. To this aim, some results allow defining the rules applied by the coordinator and the local sites to provide the global diagnosis results. Moreover, two protocols for the detection and diagnosis of faults are proposed: they differ for the information exchanged between local sites and coordinator and the diagnostic capability. In addition, a sufficient and necessary condition under which the second presented protocol can successfully diagnose a fault in the decentralized architecture is proved. Finally, some examples are presented to show the efficiency of the proposed approach.

The pipeline of reconfiguration is a novel approach that gives a decision making mechanism for any system of tasks when several reconfiguration scenarios are required. These scenarios represent the run-time operations that allow automatic functional modification of the system behavior. In the case of their frequent income, the pipeline follows a specific set of steps in order to figure out the scenarios that need to be applied without altering its own performance. The steps are represented by a sequence of modules. The first module checks the availability of the resources needed by the tasks. The second maintains the stability by rejecting any insupportable flow of software reconfigurations that can destabilize a system. The third module checks the availability of memory for each software reconfiguration request, whereas the fourth verifies the availability of energy for the execution of a new system software configuration. The last module checks the schedulability of this configuration by changing the priorities of the finally accepted tasks in order to reduce their response and blocking times, and to meet their deadlines. The feasibility of the reconfiguration through this approach allows its implementation in wireless sensor nodes. Known by their important requirements in terms of memory and energy, this proposition is adequate when it comes to guaranteeing a maximum lifespan of the nodes. Therefore, the pipeline of reconfiguration is integrated as a middleware within the software architecture of a node. The functionality of this middleware is coordinated by the mean of a set of threads that manage the communication between the pipeline of reconfiguration and the operating system. A tool has been developed within LISI Laboratory to encode this proposition and show its impact on a large range of real devices under diversified scenarios of reconfigurations.

This paper deals with reconfigurable wireless sensor networks (RWSNs) that should be adapted to their environment in order to minimize the energy consumption during the communication among nodes and consequently to maximize the lifetime of the network as much as possible. An RWSN is assumed to be composed of a set of nodes located in distributed zones such that each node executes reconfigurable software tasks to control local sensors. We propose in a previous work a zone-based multiagent architecture for an RWSN where a communication protocol is well-defined to optimize distributed reconfigurations. This architecture combines all possible reconfiguration forms of the network to be adapted to its environment under energy constraints. With the multiagent architecture we gain in term of energy to be consumed by each node in the network. After applying a set of reconfiguration scenarios, the total charge of the network decreases. In order to preserve this charge as much as possible before the next recharging operation, in this particular study, we tend to resize geographically the zones in an RWSN. We propose also to move the mobile nodes in order to increase the network lifetime. A new run-time power oriented methodology is reported, which manages the zones and their mobile nodes in order to control the energy consumption. Two dynamic solutions are applied: 1) the resizing of zones and 2) the mobility of nodes. For the new proposed methodology, we present a set of equation systems to model the resizing of zones and the mobility of nodes. The solution of these systems allows us to conclude that we can conserve more energy during the communication among network elements (nodes, stations, and agents). The contributions are applied to a case study that we simulate with the reconfigurable wireless network environment1to analyze the research originality.

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature.

The green design of electromechanical products is a pivotal link to manufacturing industry. The question on how to design green products must be answered by excellent designers using both advanced design methods and effective assessment techniques of design alternatives. Making an objective and precise assessment of green designs is of increasing importance to ensure sustainable development. This work proposes a framework based on the combination of analytical hierarchy process (AHP), gray correlation (GC), and technique for order performance by similarity to ideal solution (TOPSIS) to evaluate the performance of design alternatives. AHP is used to determine the weights of performance indices and a nonlinear programming model with constraints is proposed to obtain the integrated closeness index based on the similarity closeness index from GC and distance closeness index from TOPSIS. A case study, i.e., three kinds of refrigerators, is illustrated to verify the proposed method. By comparing with existing methods, i.e., AHP-TOPSIS and AHP-GC, the effectiveness of the proposed method has been confirmed. In addition, sensitivity analysis is also provided in order to assess the robustness of the proposed method. Also, the following implication can be obtained from our results: 1) chloro- fluoro-carbons (mg/m3) (C1), production noise (dB) (C6), and environmental cost (C16) have a large impact on refrigerator's green design since these factors carry relatively larger weights. Results of the sensitivity analysis from different cases demonstrate that the best alternative may change when different weights are assigned to the evaluation criteria. This finding means the importance of establishing a qualified group of experts/designers in design evaluation and 2) the selection of design alternatives to produce a green product is critical to product development. The main contribution of this paper is the definition and development of an effective evaluation framework to guide managers to assess product design alternatives. The results show that it overcomes the one-sidedness of AHP-TOPSIS and AHP-GC, and makes the evaluation results more objective and realistic. It provides an accurate, effective, and systematic decision support tool for green performance evaluation of product design alternatives.

Control of a large-scale automated manufacturing system is an important and challenging issue. Its discrete event system model represented by Petri nets tends to become highly complicated in structure, especially when there exist uncontrollable or unobservable events. The existing approaches are nontrivial to design both efficient and maximally permissive supervisors to impose constraints on an overall system. In this paper, instead of considering the control problem from an overall system perspective, we intend to transform an overall control problem into the one designing multiple controllers in parallel, each of which is much simpler in structure. A Petri net structure is decomposed via integer linear programming or a polynomial decomposition method to obtain multiple state-machine subnets that constitute a decentralized system. A necessary and sufficient condition for preserving the equivalence in terms of states and behaviors between the overall system and its decentralized version is reported. Constraints representing control requirements are further converted and enforced in the respective subnets. Then, supervisors are generated via a generalized mutual exclusion constraint method. By considering the deviations between the subnet control and overall control, this paper formulates a communication mechanism to guarantee that the decentralized system runs in an appropriate manner. Finally, two examples are presented to demonstrate the proposed approach.

This paper presents new challenges for the development of reconfigurable wireless sensor networks (RWSNs) that adapt dynamically their behaviors to their environment under different properties. An RWSN is a set of networked nodes that execute reconfigurable software tasks for the control of local sensors. We propose a new design methodology named RWiN of an RWSN using unified modeling language (UML) to analyze, construct, develop, and verify easily RWSN architectures. For that, we formulate a metamodel of RWSN based on UML to describe a zone-based architecture that uses a communication protocol for the optimization of distributed reconfigurations. To control the design complexity, we model each agent of this architecture by nested state machines. To verify the temporal constraints by communicating agents, each one is modeled by a timed automaton. The paper's contribution is applied to a case study, which is simulated with TRMSim-WSN and UPPAAL environment to expose the originality of this new architecture.

Timed marked graphs, a special class of Petri nets, are extensively used to model and analyze cyclic manufacturing systems. Weighted marked graphs are convenient to model systems with bulk services and arrivals. We consider two problems of practical importance for this class of nets. The marking optimization problem consists in finding an initial marking to minimize the weighted sum of tokens in places, while the average cycle time is less than or equal to a given value. The cycle time optimization problem consists in finding an initial marking to minimize the average cycle time, while the weighted sum of tokens in places is less than or equal to a given value. We propose two heuristic algorithms to solve these problems. Several simulation studies show that the proposed approach is significantly more efficient than existing ones.

Granular computing (GrC) has emerged as a unified conceptual and processing framework. Information granules are fundamental constructs that permeate concepts and models of GrC. This paper is concerned with a design of a collection of meaningful, easily interpretable ellipsoidal information granules with the use of the principle of justifiable granularity by taking into consideration reconstruction abilities of the designed information granules. The principle of justifiable granularity supports designing of information granules based on numeric or granular evidence, and aims to achieve a compromise between justifiability and specificity of the information granules to be constructed. A two-stage development strategy behind the construction of justifiable information granules is considered. First, a collection of numeric prototypes is determined with the use of fuzzy clustering. Second, the lengths of the semi-axes of ellipsoidal information granules to be formed around such prototypes are optimized. Two optimization criteria are introduced and studied. Experimental studies involving synthetic data set and data sets coming from the machine learning repository are reported.

Information granules are generic building blocks supporting the processing realized in granular computing and facilitating communication with the environment. In this paper, we are concerned with a fundamental problem of encoding-decoding of information granules. The essence of the problem is outlined as follows: given a finite collection of granular data X1, X2,...,XN (sets, fuzzy sets, etc.), construct an optimal codebook composed of information granules A 1, A2, ..., Ac, where typically c <;<; N, so that any Xk represented in terms of A i's and then decoded (reconstructed) with the help of this codebook leads to the lowest decoding error. A fundamental result is established, which states that in the proposed encoders and decoders, when encoding-decoding error is present, the information granule coming as a result of decoding is of a higher type than the original information granules (say, if Xk is information granule of type-1, then its decoded version becomes information granule of type-2). It would be beneficial to note that as the encoding-decoding process is not lossless (in general, with an exception of a few special cases), the lossy nature of the method is emphasized by the emergence of information granules of higher type (in comparison with the original data being processed). For instance, when realizing encoding-decoding of numeric data (viz., information granules of type-0), the losses occur and they are quantified in terms of intervals, fuzzy sets, probabilities, rough sets, etc., where, in fact, the result becomes an information granule of type-1. In light of the nature of the constructed result when Xkis an interval or a fuzzy set, an optimized performance index engages a distance between the bounds of the interval-valued membership function. We develop decoding and encoding mechanisms by engaging the theory of possibility and fuzzy relational calculus and show that the decoded information granule is either a granular interval or interval-valued fuzzy set. The optimization mechanism is realized with the aid of the particle swarm optimization (PSO). A series of experiments are reported with intent to illustrate the details of the encoding-decoding mechanisms and show that the PSO algorithm can efficiently optimize the granular codebook.

This work proposes a novel structure in Petri nets, namely data inhibitor arcs, and their application to the optimal supervisory control of Petri nets. A data inhibitor arc is an arc from a place to a transition labeled with a set of integers. A transition is disabled by a data inhibitor arc if the number of tokens in the place is in the set of integers labeled on it. Its formal definitions and properties are given. Then, we propose a method to design an optimal Petri net supervisor with data inhibitor arcs to prevent a system from reaching illegal markings with respect to control specifications. Two techniques are developed to reduce the supervisor structure by compressing the number of control places. Finally, a number of examples are used to illustrate the proposed approaches and experimental results show that they can obtain optimal Petri net supervisors for the net models that cannot be optimally controlled by pure net supervisors. A significant result is that the proposed approach can always lead to an optimal supervisor with only one control place for bounded Petri nets on the premise that such a supervisor exists.

With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.

Failure propagation in smart grids (SGs) complicates and prolongs the recovery time as the faults to be resolved increase. This paper presents the design and implementation of a framework for SGs modeling, simulation, and recovery. The proposed approach is based on a multiagent system composed of static and mobile agents to ensure local and remote resolutions. The efficiency and completeness of the power system recovery protocol are proved as all the existing solutions are analyzed. The use of fault classification and a performing communication process as well as the deployment of local distributed databases updated at run-time ensure the effectiveness of the proposed fault recovery strategy. An experimental study confirms and validates the expected results.

This paper presents a general discrete-event system (DES)-based hard periodic real-time task model. Based on supervisory control theory (SCT), an optimal priority-free real-time scheduling technique is proposed to process all the tasks running in uniprocessor or multiprocessor real-time systems (RTS). The preemption relation in this paper generalizes priority-based preemption. First, regular languages are utilized to describe the processor behavior related to each task's execution. Thereafter, the languages are represented by DES generators. Finally, the global processor behavior is generated as the synchronous product of these DES generators. By discarding the priorities, a novel preemption policy, namely conditional-preemption, is developed. Two sets of conditional-preemption specifications are developed, on the processor level and task level, respectively. Moreover, in order to control the system to be nonblocking and also limit the worst-case response time of the tasks, two corresponding sets of specifications are presented. After generating the global specification as the synchronous product, by implementing SCT the calculated supervisor can provide all the safe real-time execution sequences. The supervisor calculation can be sped up by a three-step algorithm. Finally, the real-time scheduling is implemented for real-world examples.

In semiconductor manufacturing, there are wafer fabrication processes with wafer revisiting. Some of them must meet wafer residency time constraints. Taking atomic layer deposition (ALD) as a typical wafer revisiting process, this paper studies the challenging scheduling problem of single-arm cluster tools for the ALD process with wafer residency time constraints. It is found that there are only several scheduling strategies that are applicable to this problem and one needs to apply each of them to decide whether a feasible schedule can be found or not. This work, for each applicable strategy, performs the schedulability analysis and derives the schedulability conditions for such tools for the first time. It proposes scheduling algorithms to obtain an optimal schedule efficiently if such conditions are met. It finally gives illustrative examples to show the application of the proposed concepts and approach.

For modelling and analysis of automated production systems as batch or high throughput systems, timed Petri nets are commonly used. In this paper, we deal with the marking optimization problem of a timed weighted marked graphs under infinite server semantics. The problem consists in finding an initial marking to minimize the weighted sum of tokens in places while the average cycle time is less than or equal to a given value. We propose two different heuristic approaches to solve the optimization problem.

Formed as generic building blocks being reflective of domain knowledge and experimental numeric evidence, information granules play a pivotal role in processing realized in Granular Computing and facilitating communication with the environment. In this study, we are concerned with a fundamental problem of constructing a collection of meaningful, easily interpretable spherical information granules with the use of the principle of justifiable granularity. The design process is formulated as an optimization problem. First, a series of numeric prototypes are determined around which information granules are constructed. Second, the values of radii of these information granules are optimized aiming at maximizing a certain performance index. Two alternatives of determining centers of information granules are compared, i.e., randomly selected numeric prototypes and prototypes generated with the aid of clustering. Two optimization criteria are also introduced and studied. Experimental studies involving synthetic data as well as data coming from the UCI Machine Learning repository are reported.

Based on the supervisory control theory (SCT) of timed discrete-event systems (TDES), this study presents a dynamic reconfiguration technique for real-time scheduling of real-time systems running on uni-processors. A new formalism is developed to assign periodic tasks with multiple-periods. By implementing SCT, a real-time system (RTS) is dynamically reconfigured when its initial safe execution sequence set is empty. During the reconfiguration process, based on the multiple-periods, the supervisor proposes different safe execution sequences. Two real-world examples illustrate that the presented approach provides an increased number of safe execution sequences as compared with the earliest-deadline-first (EDF) scheduling algorithm.

This work studies the scheduling problem of a single-arm multicluster tool with a linear topology and process-bound bottleneck individual tool. The objective is to find a one-wafer cyclic schedule such that the lower bound of cycle time is reached by optimally configuring spaces in buffering modules that link individual cluster tools. A Petri net (PN) model is developed to describe the dynamic behavior of the system by extending resource-oriented PNs such that a schedule can be parameterized by robots' waiting time. Based on this model, conditions are presented under which a one-wafer cyclic schedule with the lower bound of cycle time can be found. With the derived conditions, an algorithm is developed to find such a schedule and optimally configure buffer spaces. The algorithm requires only simple calculation to set the robots' waiting time and buffer size. Illustrative examples are presented to demonstrate the proposed method.

This paper deals with the dynamic low-power reconfiguration of a real-time system. It processes periodic and probabilistic tasks that have hard/soft deadlines corresponding to internal/external events. A runtime event-based reconfiguration scenario is a dynamic operation allowing the addition/removal of the assumed periodic/probabilistic tasks. Thereafter, some tasks may miss their hard deadlines and the power consumption may increase. In order to reconfigure the system to be feasible, i.e., satisfying its real-time constraints with low-power consumption, this research presents a software-agent-based architecture. An intelligent agent is developed, which provides four solutions to reconfigure the system at runtime. For these solutions, in order to reconfigure the probabilistic tasks to be feasible, the agent modifies their temporal parameters dynamically; moreover, in order to feasibly serve the probabilistic tasks and reduce the system's power consumption, the agent provides three virtual processors by dynamically extending the periods of the periodic tasks. A simulation study verifies the effectiveness of the agent.

In the above paper [8, p. 733], there are errors inTheorem 1:

This paper develops a decentralized supervision policy for a Petri net through collaboration between a coordinator and subnet controllers. The coordinator is chosen from the subnet controllers by solving an integer linear programming problem. An optimal objective function is used to minimize the communication cost between the subnet controllers and the coordinator. Furthermore, a protocol to reach an agreement on the firing conditions of common transitions among the subnet controllers is proposed. Observation agreement and control agreement can be achieved by the “AND” operator in logic algebra. Control agreement is used to decide the firing conditions of common transitions in the next step. The firing of common transitions, which will lead to a new marking that violates the given constraints, will be forbidden by the control agreement. A feasibility analysis of the proposed decentralized control framework is discussed. Finally, four examples are presented to illustrate the proposed approach.

Supervisory control is usually considered as an external control mechanism to a system by controlling the occurrences of its controllable events. There exist Petri net models whose legal reachability spaces are nonconvex. In this case, they cannot be optimally controlled by the conjunctions of linear constraints. For Petri net models of flexible manufacturing systems, this work proposes a method to classify the legal markings into several subsets. Each subset is associated with a linear constraint that can forbid all first-met bad markings. Then, the disjunctions of the obtained constraints can make all legal markings reachable and forbid all first-met bad markings, i.e., the controlled net is live and maximally permissive. An integer linear programming model is formulated to minimize the number of the constraints. A supervisory structure is also proposed to implement the disjunctions of the constraints. Finally, examples are provided to illustrate the proposed method.

Some numerals inare found to be incorrect. Also,Definition 7inis unclear and should be stated as follows.

This paper presents a new Petri net structure, namely, an interval inhibitor arc, and its application to the optimal supervisory control of Petri nets. An interval inhibitor arc is an arc from a place to a transition labeled with an integer interval. The transition is disabled by the place if the number of tokens in the place is between the labeled interval. The formal definition and the firing rules of Petri nets with interval inhibitor arcs are developed. Then, an optimal Petri net supervisor based on the interval inhibitor arcs is designed to prevent a system from reaching illegal markings. Two techniques are developed to simplify the supervisory structure by compressing the number of control places. The proposed approaches are general since they can be applied to any bounded Petri net models. A marking reduction approach is also introduced if they are applied to Petri net models of flexible manufacturing systems. Finally, a number of examples are provided to demonstrate the proposed approaches and the experimental results show that they can obtain optimal Petri net supervisors for some net models that cannot be optimally controlled by pure net supervisors. Furthermore, the obtained supervisor is structurally simple.

In this paper a type of specifications called OR-GMEC for place/transition nets is defined. Such a specification consists of a set of disjunctive Generalized Mutual Exclusion Constraint, i.e. the requirement is that, at any given time, the controlled system should satisfy at least one of them. We show that a bounded OR-GMEC can be enforced by a special control structure composed by a set of monitor places (one for each constraint) plus a switcher that determines the current active constraint. We also show that such a simple control structure is not maximally permissive, and characterize this problem identifying a special subset of transitions that may be over-restricted. A modified controller that ensures maximal permissiveness is also presented. Finally, we discuss a particular control problem, that consists in preventing the firing of a given set of transitions and show that it can be reduced to an OR-GMEC problem.

Observation structures usually considered for Petri nets assume that the firing of transitions may be observed through static labels and that the marking of some places may be measurable. These observation structures are rather limited and we consider in this paper more general ones, correspondingly defining two new classes of nets: labeled Petri nets with outputs and adaptive labeled Petri nets. Several examples of nets in these classes are considered. Furthermore we also show the possibility to convert a labeled Petri net into an adaptive Petri net or a labeled Petri net.

In the framework of automated manufacturing systems (AMS), Petri nets are widely used to model, analyze, and control them. Resolving deadlocks is of paramount significance because their emergence reduces the system throughput to zero. Supervisory control technique is the most widely adopted method to resolve them. A control policy can be converted into satisfying a set of inequalities, each of which corresponds to a siphon in a Petri net structure. The number of siphons can be exponential in the worst case, so does the number of inequalities. Taking into account the independent and dependent inequalities, this paper proposes a method to remove all the dependent inequalities while preserving only the independent ones. This method can significantly reduce the size of a supervisory controller. Examples are presented to illustrate the effectiveness and efficiency of this method.

Sensors and actuators are two indispensable parts in the paradigm of feedback control. Their implementation cost should be properly evaluated and constrained. In the previous work, a Petri net monitor with the least cost is synthesized through integer programming formulation. Despite its technical correctness, the existing method may lead to undesirable results when the net structure contains some shared or unshared resource places of a manufacturing-oriented net model. A necessary and sufficient condition is established to show that certain structures can lead to deadlock-prone supervisors. An efficient algorithm is developed to identify such structures. Furthermore, it is shown that if one can identify such structures at the initial stage, it is possible to achieve desirable controllers for the original systems. The theoretical correctness of the proposed algorithm is discussed. A manufacturing example is provided to illustrate the proposed approach.

No abstract available.

No abstract available.

In the context of automated manufacturing systems (AMS), Petri nets are widely adopted to solve the modeling, analysis, and control problems. So far, nearly all known approaches to liveness enforcing supervisory control investigate AMS with either flexible routes or assembly operations, whereas little work investigates them with both. In this paper, we propose a novel class of systems, which can well deal with both features so as to facilitate the control of more complex AMS. Using structural analysis, we show that liveness of their Petri net model can be attributed to the absence of undermarked siphons, which is realizable by synthesizing a proper supervisory controller. Moreover, an efficient method is developed and verified via AMS examples.

This paper illustrates that Petri nets with self-loops are more powerful than pure nets in modeling and control of flexible manufacturing systems. A self-loop in a Petri net cannot be mathematically represented by its incidence matrix. This paper presents a mathematical method to design a maximally permissive Petri net supervisor that is expressed by a set of control places with self-loops. A control place with a self-loop can be represented by a constraint and a self-loop associated with a transition whose firing may lead to an illegal marking. The constraint is designed to ensure that all legal markings are reachable and the self-loop is used to prevent the system from reaching illegal markings by disabling the transition at a specific marking. A marking-reduction approach is developed in order to cut down the considered markings, which can greatly decrease the computational overhead of the proposed method. An integer linear programming model is developed to compress the number of control places, aiming to reduce the structural complexity of the resulting supervisors. Finally, illustrative examples are used to validate the proposed method and to demonstrate that it can obtain an optimal supervisor for some cases that cannot be optimally controlled by pure net supervisors.

In each of Fig. 1(a), (c), (e), and (f) in the above-named article [ibid., vol. 42, no. 1, pp. 178-191, Jan. 2012], there is an arc from transition t3 to place p6, which is incorrect. The arc between the transition and the place should be from p6 to t3. The Petri net depicted in Fig. 1(a) is a well-known example that has been widely utilized in the literature.

This paper proposes a hybrid liveness-enforcing method for a class of Petri nets, which can well model many flexible manufacturing systems. The proposed method combines elementary siphons with a characteristic structure-based method to prevent deadlocks and enforce liveness to the net class under consideration. The characteristic structure-based method is further advanced in this work. It unveils and takes a full advantage of an intrinsically live structure of generalized Petri nets, which hides behind the arc weights, to achieve the liveness enforcement without any external control agent such as monitors. This hybrid method can identify and remove redundant monitors from a liveness-enforcing supervisor designed according to existing policies, improve the permissiveness, reduce the structural complexity of a controlled system, and consequently save the control implementation cost. Several examples are used to illustrate this method.

This paper proposes a new modified reachability tree (NMRT) approach for a class of unbounded generalized Petri nets called ω-independent nets. The NMRT of an ω-independent net consists of all and only reachable markings from its initial marking. Moreover, the applications of the NMRT to deadlock analysis for ω-independent nets are developed. The proposed method has a larger application scope than all the existing methods. Several examples are provided to show its superiority over them.

This study deals with the formal modeling and verification of reconfigurable discrete event control systems (RDECSs). The behavior of an RDECS is represented by that of control components (CCs) and the communication among them. A new formalism, called Reconfigurable Timed Net Condition/Event Systems (TNCES) (R-TNCES), is proposed for the optimal functional and temporal specification of RDECS, which is defined by a behavior module and a control module. The former is a union of various superposed TNCESs, where TNCES-based CC modules are basic units. The latter is a set of reconfiguration functions dealing with the automatic transformations of these TNCESs in response to errors or user requirements by enabling or disabling CC modules, changing condition signals and/or event signals among them, and also treating the state feasibility before and after reconfigurations. To control the verification complexity of R-TNCES, a layer-by-layer verification method is developed, where the similarities of different TNCESs in the behavior module are considered. The contribution of this original paper is applied to a benchmark production system.

Behavioral permissiveness is one of the well-accepted criteria for evaluating the performance of a liveness-enforcing Petri net supervisor. However, a maximally permissive liveness-enforcing pure net supervisor does not always exist for any Petri net. This paper focuses on the nonexistence of maximally permissive liveness-enforcing pure net supervisors for a subclass of Petri nets. It shows that if a net contains a siphon that cannot be optimally controlled by a P-invariant, the net does not have a maximally permissive liveness-enforcing pure net supervisor. For a class of Petri nets that can model flexible manufacturing systems, sufficient conditions are proposed to decide whether there exists a siphon that cannot be optimally controlled. This paper also shows that a Petri net system does not have a maximally permissive liveness-enforcing pure net supervisor if it contains some special reachable markings.

Presents corrections to the second paragraph of Section V-B in the above titled paper (ibid., vol. 8, no. 2, pp. 374-393, Apr. 2011).

This paper presents two iterative deadlock prevention policies for flexible manufacturing systems (FMSs). Both can find a maximally permissive liveness-enforcing supervisor with a small number of control places. A vector covering approach is used to obtain a minimal covering set of legal markings and a minimal covered set of first-met bad markings (FBMs), which are much smaller than the sets of legal markings and FBMs, respectively. At each iteration, by solving an integer linear programming problem (ILPP), a place invariant (PI) associated with a control place is designed to forbid as many FBMs as possible, and no markings in the minimal covering set of legal markings are forbidden. The objective function of the ILPP maximizes the number of FBMs that are forbidden by the PI. Then, the forbidden FBMs by the PI are removed from the minimal covered set of FBMs. This process is carried out until all FBMs are forbidden. Two ILPP design techniques are developed for the control policies. Though the proposed methods cannot in general guarantee the minimality of the supervisory structure, they reduce the overall computational time greatly, which is shown by numerical studies. Finally, a number of FMS examples from the literature are presented to illustrate the proposed methods.

In automated manufacturing systems (AMSs), Petri nets are widely adopted to solve supervisory control problems. Among them, how to enforce liveness and fairness for AMS constitutes an important problem. Enforcing liveness avoids the occurrence of deadlock situations to inhibit the emergence of partial or complete blockness for processing jobs in AMS. Fairness requires the determination of a reasonable regulation scheme for orderly resolution such that a desired ratio can be assigned among different processes. This paper proposes an efficient method to design supervisors that enforce both, which is based on the invariance property of Petri nets. A novel approach is proposed to iteratively identify empty siphons as solutions to a set of linear inequalities. Supervisors are then designed to control these siphons. The applicability of the proposed methodology is illustrated through examples. Its comparison with existing works is presented to demonstrate its advantages.

Deadlocks are an undesirable situation in automated flexible manufacturing systems (FMS). Their occurrences often deteriorate the utilization of resources and may lead to catastrophic results. Finding an optimal supervisor is NP-hard. A computationally efficient method often ends up with a suboptimal one. This paper develops a deadlock prevention method that makes a good tradeoff between optimality and computational tractability for a class of Petri nets, which can model many FMS. The theory of regions guides our efforts toward the development of near-optimal solutions for deadlock prevention. Given a plant net, a minimal initial marking is first decided by structural analysis, and an optimal live controlled system is computed. Then, a set of inequality constraints is derived with respect to the markings of monitors and the places in the model such that no siphon can be insufficiently marked. A method is proposed to identify the redundancy condition for constraints. For a new initial marking of the plant net, a deadlock-free controlled system can be obtained by regulating the markings of the monitors such that the inequality constraints are satisfied, without changing the structure of the controlled system. The near-optimal performance of a controlled net system via the proposed method is shown through several examples.

Deadlocks are a rather undesirable situation in a highly automated flexible manufacturing system. Their occurrences often deteriorate the utilization of resources and may lead to catastrophic results in safety-critical systems. Graph theory, automata, and Petri nets are three important mathematical tools to handle deadlock problems in resource allocation systems. Particularly, Petri nets are considered as a popular formalism because of their inherent characteristics. They received much attention over the past decades to deal with deadlock problems, leading to a variety of deadlock-control policies. This study surveys the state-of-the-art deadlock-control strategies for automated manufacturing systems by reviewing the principles and techniques that are involved in preventing, avoiding, and detecting deadlocks. The focus is deadlock prevention due to its large and continuing stream of efforts. A control strategy is evaluated in terms of computational complexity, behavioral permissiveness, and structural complexity of its deadlock-free supervisor. This study provides readers with a conglomeration of the updated results in this area and facilitates engineers in finding a suitable approach for their industrial scenarios. Future research directions are finally discussed.

This study deals with deadlock problems in a system with uncontrollable and unobservable events, which is modeled with a subclass of Petri nets. We design monitors to enforce constraints for minimal siphons in a Petri net model. A key point of this paper is how to select the input and output arcs of a monitor when there exist uncontrollable and unobservable transitions. After the enforcement of constraints, an iterative algorithm is developed to obtain a controlled system with liveness. A sufficient and necessary condition is established to decide the existence of a monitor enforcing a constraint in a plant with uncontrollable and unobservable transitions.

Deadlocks should be eliminated in resource allocation systems such as flexible manufacturing systems. An iterative deadlock control policy is usually considered to be a natural solution with reasonable computational cost for a large-scale system where direct methods would be prohibitively expensive (and in some cases impossible) even with the best available computing power. This paper reviews the existing iterative deadlock prevention policies for discrete event systems that are modeled with Petri nets. A number of technical problems in the existing iterative deadlock control approaches are formulated and discussed. Their solutions are illustrated through case studies. We conclude that the suitability, effectiveness, and efficiency of an iterative deadlock control approach are sensitive to specific examples, and no general algorithm is found in the literature, which works well for all cases.

Strict minimal siphons (SMS) play an important role in the development of deadlock control policies for flexible manufacturing systems (FMS). For a class of Petri nets called Systems of Simple Sequential Processes with Resources (S3PR), the resource circuit-based method is an effective way to compute SMS. In this paper, a more effective one to compute SMS is proposed. First, the concepts of loop resource subsets and their characteristic resource subnets are proposed. Next, sufficient and necessary conditions for loop resource subsets to generate SMS are established. Finally, an algorithm is given to find all the SMS based on loop resource subsets. Since the number of loop resource subsets is much less than that of resource circuits and their combinations, the computational efficiency of the SMS enumeration task is significantly improved by the proposed method. An FMS example is used to illustrate the application of the proposed method, and computational time comparisons are provided on several S3PRs to show its superior efficiency.

This paper develops a place invariant based deadlock prevention method to obtain an optimal Petri net supervisor by considering its implemental cost. A supervisor is expressed by control places and the arcs connecting control places to transitions. We assign an implemental cost for each control place and arc. Maximally permissiveness can be achieved by designing place invariants that make all legal markings reachable while all first-met bad marking unreachable. By solving an integer linear programming problem (ILPP), a set of optimal control places are obtained and the objective function is used to minimize the implemental cost of the final supervisor. A vector covering approach is used to reduce the number of constraints and variables in the ILPP, aiming to reduce the computational overhead of the proposed method. Finally, a number of examples are used to illustrate the proposed approach.

In the context of automated manufacturing systems (AMS), Petri nets are widely adopted to solve the modeling, analysis, and control problems. So far, nearly all known approaches to liveness-enforcing supervisory control study AMS with either flexible routes or assembly operations, whereas little work investigates them with both. In this paper, we propose a novel class of systems, which can well deal with both features so as to facilitate the investigation of more complex systems. Using structural analysis, we show that liveness of such systems can be attributed to the absence of undermarked siphons, which is realizable by synthesizing a proper supervisory controller.

Given a set of forbidden states in a plant model, an optimal Petri net supervisor is developed. The optimality is twofold. First, it provides a minimal supervisory control structure in the sense of the number of monitors that are used to prevent the occurrences of the forbidden states in a plant model. A monitor is designed by associating a P-semiflow with other places in the plant. Second, the supervisor is maximally permissive, i.e., no admissible state is excluded. If a maximally permissive supervisor does not exist, a most permissive supervisor can be similarly designed. A partial reachability graph of a plant is generated by considering the given forbidden states, from which first-met bad markings are then identified. Integer linear programming problems are employed to offer monitor solutions to ensure that the first-met bad markings are not reachable. Generalized mutual exclusion constraints are considered in this work.

Deadlock prevention plays an important role in the modeling and control of flexible manufacturing systems (FMS). This paper presents a novel and computationally efficient method to design optimal control places, and an iteration approach that only computes the reachability graph of a plant Petri net model once in order to obtain a maximally permissive liveness-enforcing supervisor for an FMS. By using a vector covering approach, a minimal covering set of legal markings and a minimal covered set of first-met bad markings (FBM) are computed. At each iteration, an FBM from the minimal covered set is selected. By solving an integer linear programming problem, a place invariant is designed to prevent the FBM from being reached and no marking in the minimal covering set of legal markings is forbidden. This process is carried out until no FBM can be reached. In order to make the considered problem computationally tractable, binary decision diagrams (BDD) are used to compute the sets of legal markings and FBM, and solve the vector covering problem to get a minimal covering set of legal markings and a minimal covered set of FBM. Finally, a number of FMS examples are presented to illustrate the proposed approaches.

For automated manufacturing systems (AMSs), deadlock resolution in terms of Petri nets remains an attractive topic to which many approaches are dedicated. However, few of them can quantitatively optimize certain indices during their supervisor synthesis process. This causes unnecessary control limitations and often leads to high implementation cost. In the framework of Petri nets, this paper proposes a method to synthesize a cost-effective supervisor with the aid of a set of mathematical programming formulations. Along the same vein, we also show some results by investigating timed Petri nets, which can be utilized to make a good tradeoff between implementation cost and system cycle time. Examples are used to validate the effectiveness of our result.

The paper deals with reconfigurable embedded control systems following different component-based technologies and/or Architecture Description Languages used today in industry. We define a Control Component as a software unit to support control tasks of the system, which is assumed to be a network of components with precedence constraints. We define an agent-based architecture to handle automatic reconfigurations under well-defined conditions by creating, deleting, or updating components to bring the whole system into safe and optimal behaviors. To cover all possible reconfiguration forms, we model the agent by nested state machines according to the formalism Net Condition/Event Systems (NCESs), which is an extension of Petri nets. In addition, we apply a model checking to verify functional and extra-functional properties according to the temporal logic "Computation Tree Logic” (CTL). The goal is to check the agent reactivity after any evolution of the environment. Several complex networks can implement the system, where each one is executed at a given time when a corresponding reconfiguration scenario is automatically applied by the agent. To check the correctness of each one of them, we apply in several steps a refinement-based approach that automatically specifies feasible Control Components according to NCES. The model checker SESA is automatically applied in each step to verify deadlock properties of new generated components, and it is manually used to verify CTL-based properties according to user requirements. We implement the reconfiguration agent by three modules that allow interpretations of environment evolutions, decisions of useful reconfiguration scenarios, and finally, their applications. Two Industrial Benchmark Production Systems FESTO and EnAS available in our research laboratory are applied to explain the contributions of the paper.

This paper proposes a new Petri net class, namely, Ratio-enforced weighted Augmented Marked Graphs (RAMGs), and solves ratio control and liveness-enforcing supervision problems for automated manufacturing systems. RAMGs can ensure the required product ratios as demanded by production planners. Since the deadlock of such a system can be attributed to improper acquisition of finite shared resources, a supervisor is introduced such that they are properly allocated. This paper proves that ratio and supervisory controllers for an RAMG can be separately designed. Their design methods are presented. Examples are given to illustrate them.

This paper addresses a component-based approach using the Event-B method to develop automated systems. These systems are composed of two parts: the control part (controller) and the operative part (controlled component). The first is a software component which controls the operative part that models the physical device and its environment. We propose in this paper the use of the formal Event-B method to develop automated systems applying a codesign technique, where the two components are developed separately, and then, a composition is defined with the Event-B method to prove the automated system correctness. First of all, we define a specification for the composition of these two components in the Event-B method. Second, we give refinement semantics for a component-based system before proposing a method to verify the refinement of a whole system from that of its components.

For automated manufacturing systems (AMS), deadlock resolution in terms of their Petri net models remains an attractive topic to which many approaches are dedicated. However, few of them can quantitatively optimize certain indices during their supervisor synthesis process. This causes unnecessary control limitations and often leads to unnecessary implementation cost. In the framework of Petri nets, this paper proposes a method to synthesize a cost-effective supervisor with the aid of a set of mixed integer programming (MIP) formulations. Examples are used to validate the legality of the proposed method.

For practical automated manufacturing systems (AMSs), the time dimension is of great significance and should be integrated in their plant models. Reasonably, many of the realistic general mutual exclusion constraints (GMECs) imposed on these discrete models should be timed rather than merely algebraic or logic. In the past, such a problem was studied on the basis of the Ramadge-Wonham supervisory control technique (SCT) and the theory of regions. It proves to be NP-hard since it necessitates the generation of reachability graphs. This paper shows that it can be solvable in polynomial time by using generalized linear constraints, which are originally proposed to increase the expressive power of the linear marking constraints. By dividing each constraint into marking, firing vector, and Parikh terms, its respective control place can be synthesized algebraically without considering the separation of dangerous states and events. Several examples are used to validate the effectiveness and efficiency of the proposed approach.

In the context of automated manufacturing, this work proposes a new special class of timed Petri nets, namely, Timed ratio-enforced Augmented Marked Graph (TAMG) and its low-cost and high-performance supervisor synthesis methodology. A supervisor is composed of a set of control places (monitors), each of which is easy to be algebraically specified by a generalized mutual exclusion constraint (GMEC) to prevent certain siphons from being undermarked. In order to make a good tradeoff between the supervisor implementation cost and system performance, a mixed integer programming (MIP) approach is formulated to synthesize the monitors. An example is used to validate the effectiveness and efficiency of the proposed method. The results show that the proposed method remarkably outperforms any existing ones.

Generalized mutual exclusion constraints (GMECs) are a typical class of specifications for the supervisory control of discrete event systems in a Petri net formalism. This paper classifies the given constraints into elementary and dependent ones according to the linear dependency of their characteristic transition vectors that indicate the token count change of the concerned places. The dependent constraints are further divided into strongly and weakly dependent ones. A constraint is usually enforced by explicitly adding a monitor to a plant model. This research develops the conditions under which a dependent constraint is enforced due to the enforcement of the elementary constraints. The results developed in this paper are applied to the existing manufacturing-oriented Petri net classes. An algorithm is also proposed to identify a set of elementary constraints. Examples are used to demonstrate the proposed methods. Some potential extensions and applications are also discussed. This research improves the existing methods in computational efficiency and structure simplification of the supervisor, given a set of GMECs.

This paper presents a class of Petri nets that can well model ratio-enforced resource allocation systems (RASs). Such RASs are required to enforce certain ratio among their processes to meet a desired production plan. A ratio-enforced system of sequential systems with shared resources (RS4R) can model an RAS whose processes are described by state machines. Both a ratio-enforcing controller and liveness-enforcing supervisor must be designed to fulfil the deadlock-free and ratio-constrained production of products. We theoretically prove that their design can be separately performed.

In previous work, an acceptable transformation of an unacceptable specification on a Petri net can be implemented through a set of mathematical programming formulations. However, such a method becomes invalid when no solution exists in practice. In this paper, we reestablish these formulations and show their correctness.

Internet-motivated video streaming systems face such complicated issues as a high degree of network-resource sharing amongst many flows, which potentially leads to deadlocks. Using siphons and their corresponding dangerous markings, this work investigates a method to enforce control iteratively. At each iteration, a generalized mutual exclusion constraint is produced to keep only those markings under which liveness is enforced. Furthermore, a generalized elementary siphon control method is proposed such that the final supervisor is structurally simple. Examples are used to illustrate the proposed approach.

In the above paper, it proposes a deadlock prevention policy for a flexible manufacturing system (FMS), which needs the complete state enumeration of the FMS that is modeled with Petri nets. The reachability graph of a Petri-net model is divided into two parts: the live zone (LZ) and the deadlock zone (DZ). The states in the LZ of the reachability graph of a Petri net constitute the legal behavior of the net from the viewpoint of deadlock prevention. The concept of first-met bad markings is proposed. A first-met bad marking is a node in DZ, whose father nodes are in LZ. The deadlock prevention policy is used in an iterative way. At each iteration, a first-met bad marking is identified from the reachability graph of a Petri net to be controlled. The reachability of a first-met bad marking is prohibited by adding a monitor, establishing a marking invariance relationship between the additional monitor and the activity places that are marked under the first-met bad marking. To achieve this, without a formal proof, [Lemma 1] is developed as shown in this article.

Petri nets are a popular mathematical tool to investigate the deadlock problems in resource allocation systems. As an important problem solution paradigm in computer science, the divide-and-conquer strategy is used in this paper to investigate the deadlock prevention for flexible manufacturing systems (FMSs) that are modeled with Petri nets. Based on the concept of resource circuits, a plant net model is divided into an idle subnet, an autonomous subnet, and a number of small but independent subnets, called toparchies, from the viewpoint of deadlock control. A liveness-enforcing supervisor, called toparch, is designed for each toparchy. If a particular separation condition holds in a plant net model, the computational complexity of toparches is significantly reduced. This research shows that the resultant net, called monarch, by composing the toparches derived for the toparchies can serve as a liveness-enforcing Petri net supervisor for the whole plant model. FMS examples are given to illustrate the proposed method.

As a structural object of Petri nets, siphons play a key role in the development of deadlock prevention policies for resource allocation systems. Elementary siphons are a novel concept in net theory. Based on graph theory, this paper proposes an effective algorithm with polynomial complexity to find a set of elementary siphons for a linear system of simple sequential processes with resources ($\hbox{LS}^{3} \hbox{PR}$), a subclass of Petri nets, which can model many flexible manufacturing systems. The algorithm is established through the use of a resource directed graph and complementary sets of strict minimal siphons (SMS) of the net. The upper bound of the number of SMS in such a net is identified. A running example is used to demonstrate the proposed method.

This paper considers the deadlock prevention problem for a class of conjunctive/disjunctive resource allocation systems (C/D-RAS) in which multiple resource acquisitions and flexible routings are allowed. The proposed policy is developed in a generalized Petri net framework by an iterative algorithm. Each iteration uses a mixed integer programming (MIP) formulation to find both a bad marking and insufficiently marked siphon. Two new strategies are then presented. One aims to maximize the permissiveness of a liveness-enforcing supervisor while another to minimize the computational and supervisor's complexity. Experimental results validate the effectiveness of these two strategies.

This paper addresses the deadlock problems in flexible manufacturing systems (FMS) by using a Petri net siphon control method and the theory of regions. The proposed policy consists of two stages. The first one, called siphons control, is to add, for every siphon that we identify, a monitor to the original net model such that it is optimally invariant controlled. In the second stage, the theory of regions is utilized to derive the net supervisors such that deadlocks can be prevented. The first-stage work significantly lowers the computational cost compared with the approach where the theory of regions is used alone. An FMS example is presented to illustrate the technique. By varying the markings of given net structures, this paper shows its computational advantages.

The importance of siphons is well recognized in the analysis and control of deadlocks in a Petri net. To minimize the number of siphons that have to be explicitly controlled, siphons in a net are divided in a net into elementary and dependent ones. The concepts of token-rich, token-poor, and equivalent siphons are newly presented. More general conditions under which a dependent siphon can be always marked are established. The existence of dependent siphons in a Petri net is investigated. An algorithm is developed to find the set of elementary siphons in a net system for deadlock control purposes. The application of the proposed elementary siphon concept to the existing deadlock control policies is discussed. A few different-sized manufacturing examples are used to demonstrate the advantages of elementary siphon-based policies. The significant value of the proposed theory via a particular deadlock control policy is shown. Finally, some interesting and open problems are discussed.

A fair amount of research has shown the importance of siphons in the analysis and control of deadlocks in a variety of resource allocation systems by using a Petri net formalism. In this paper, siphons in a generalized Petri net are classified into elementary and dependent ones, as done for ordinary nets in our previous work. Conditions are derived under which a dependent siphon is controlled by properly supervising its elementary siphons, which indicates that the controllability of dependent siphons in an ordinary Petri net is a special case of that in a generalized one. The application of the controllability of dependent siphons is shown by considering the deadlock prevention problem for a class of resource allocation systems, namely, G-system that allows multiple resource acquisitions and flexible routings in a flexible manufacturing system with machining, assembly, and disassembly operations. We develop a monitor-based deadlock prevention policy that first adds monitors for elementary siphons only to a G-system plant model such that the resultant net system satisfies the maximal controlled-siphon property (maximal cs-property). Then, by linear programming, initial tokens in the additional monitors are decided such that liveness is enforced to the supervised system. Also, a simplified live marking relationship for a G-system between the initial tokens of the source places and those of the resource places is derived. Finally, the proposed deadlock prevention methods are illustrated by using an example.

As a structural object, siphons are well recognized in the analysis and control of deadlocks in resource allocation systems modeled with Petri nets. Many deadlock prevention policies characterize the deadlock behavior of the systems in terms of siphons and utilize this characterization to avoid deadlocks. This paper develops a novel methodology to find interesting siphons for deadlock control purposes in a class of Petri nets, i.e., a system of simple sequential processes with resources . Resource circuits in an are first detected, from which, in general, a small portion of emptiable minimal siphons can be derived. The remaining emptiable ones can be found by their composition. A polynomial-time algorithm for finding the set of elementary siphons is proposed, which avoids complete siphon enumeration. It is shown that a dependent siphon can always be controlled by properly supervising its elementary siphons. A computationally efficient deadlock control policy is accordingly developed. Experimental study shows the efficiency of the proposed siphon computation approach.

Over the last two decades, a great deal of research has been focused on solving deadlock problems in resource allocation systems such as computer communication systems, workflow systems, and flexible manufacturing systems, resulting in a wide variety of approaches. As a well-defined problem in resource allocation systems, deadlock prevention based on a Petri net formalism has received an enormous amount of attention in the literature. This paper intends to review and compare a variety of Petri net-based deadlock prevention policies reported in the literature. Their comparison is done in terms of structural complexity, behavior permissiveness, and computational complexity. This paper should facilitate engineers in choosing a suited method for their industrial application cases.

In many flexible assembly systems, base components are transported with pallets; parts to be mounted onto the base ones are transported by trays with no pallets. When an assembly operation is performed by using some parts in a tray but not all, the tray with the remaining parts still occupies a buffer space. In this way, an assembly/disassembly material flow is formed. In such a material flow, deadlock can occur both in the base component and part flow. Furthermore, the assembly operations can also result in a deadlock. Thus, it is a great challenge to tackle deadlocks in such processes. This paper models them using resource-oriented Petri nets. Based on the models, a deadlock control policy is proposed and proved to be computationally efficient and less conservative than the existing policies in the literature. An industrial case study is used to show the results.

This paper develops a two-stage approach to synthesizing liveness-enforcing supervisors for flexible manufacturing systems (FMS) that can be modeled by a class of Petri nets. First, we find siphons that need to be controlled using a mixed integer programming (MIP) method. This way avoids complete siphon enumeration that is more time-consuming for a sizable plant model than the MIP method. Monitors are added for only those siphons that require them. Second, we rearrange the output arcs of the monitors on condition that liveness is still preserved. The liveness is verified by an MIP-based deadlock detection method instead of much time-consuming reachability analysis. Experimental studies show that the proposed approach is more efficient than the existing ones and can result in more permissive and structurally simpler liveness-enforcing supervisors than all the known existing methods. This paper makes the application of siphon-based deadlock control methods to industrial-size FMS possible

The concept of elementary siphons of Petri nets is first proposed in our previous work. However, their definitions can cause confusion when there exist weakly independent siphons in a net. In this correspondence, we redefine elementary siphons and show the significance of this improvement

The competition for limited resources can produce deadlocks in flexible manufacturing systems (FMS). Petri net is an effective way to model, analyze, and control deadlocks in FMS. Proposed by our previous work, elementary siphons are an efficient method to investigate the deadlock problems in Petri nets. An elementary siphon based deadlock prevention policy is developed in this paper. Also this research presents an analysis to show the advantages of elementary siphons in the design of liveness-enforcing Petri net supervisors. By analyzing the deadlock control problems in three different scale FMS examples, this paper shows that our policy always leads to structurally simple liveness-enforcing net supervisors. Moreover, the superiority of our approach can be clearly shown by large scale FMS.

This paper develops a formal design methodology for the deadlock prevention problems in flexible manufacturing systems (FMS). We target the system that can be modelled by a particular subclass Petri net where the deadlocks in the Peri nets models resulting from insufficiently marked siphons. Deadlock prevention in a system is achieved by adding control places to make elementary siphons max-controlled. Conditions are developed under which a dependent siphon satisfies max-controlled when its elementary siphons are max-controlled. In this way the subclass is enforced to be live. Comparing with the existing methods, this policy requires a much smaller number of control places, especially for large-scale Petri nets. Finally, an application of this technique to a realistic FMS is proposed.

This paper largely presents a deadlock prevention policy for automated flexible manufacturing systems. Based on Petri nets and some investigations on siphons, this policy is targeted at one modeled manufacturing system extended from system of simple sequential process. Given a net, elementary and dependent siphons can be obtained from the strict minimal siphons. Our deadlock control method is an iterative one by adding two kinds of control places called ordinary control place and weighted control place to the original net to prevent elementary siphons from being unmarked, under some conditions the dependent siphons can be prevented from being emptied as well. Finally, a flexible manufacturing example is presented to illustrate our deadlock prevention policy.

Based on siphons, a variety of techniques are developed for the deadlock problems in flexible manufacturing systems. The major disadvantage of the existing approaches is that the number of siphons that have to be considered is large or grows fast as these methods proceed. In this paper, we investigate the deadlock prevention problem in a class of Petri net (PN), S/sup 3/PR, where siphons are distinguished by elementary and redundant ones. For each elementary siphon, a monitor is added to prevent it from being emptied without generating emptiable control-induced siphons. The results show that a more permissive liveness enforcing Petri net supervisor with a small number of additional monitors can be obtained by our deadlock control policy.

A variety of important Petri net-based methods to prevent deadlocks arising in flexible manufacturing systems (FMS) are to add some control places and related arcs to strict minimal siphons (SMS) such that no siphon can be emptied. Since the number of minimal siphons grows in general exponentially with respect to a Petri net size, their disadvantages lie in that they often add too many additional places to the net, thereby making the resulting net model much more complex than the original one. This paper explores ways to minimize the new additions of places while achieving the same control purpose. It proposes for the first time the concept of elementary siphons that are a special class of siphons. The set of elementary siphons in a Petri net is generally a proper subset of the set of all SMS. Its smaller cardinality becomes evident in large Petri net models. This paper proves that by adding a control place for each elementary siphon to make sure that it is marked, deadlock can be successfully prevented. Compared with the existing methods, the new method requires a much smaller number of control places and, therefore, is suitable for large-scale Petri nets. An FMS example is used to illustrate the proposed concepts and policy, and show the significant advantage over the previous methods.

An important Petri net-based method prevents deadlocks arising in flexible manufacturing systems (FMS) is to add control places and related arcs to strict minimal siphons (SMS) such that no siphon can be emptied. It advantages lies in that the method often adds too any additional places to the net. This paper explores ways to minimize the new additions of places while achieving the same control purpose. It proposes the concept of elementary siphons that are a special class of SMS and proves that by adding a control place for each elementary siphon to make sure that it is always marked, deadlock can be successfully prevented. Compared with the existing methods, the new method requires a much smaller number of control places and, therefore, is suitable for large-scale Petri nets. An FMS example is used to illustrate the proposed concepts and policy.

An important Petri net-based method to prevent deadlocks arising in flexible manufacturing systems (FMS) is to add control places and related arcs to strict minimal siphons (SMS) such that no siphon can be emptied. Its disadvantage lies in that the method often adds too many additional places to the net. This paper explores ways to minimize the new additions of places while achieving the same control purpose. It proposes the concept of elementary siphons that are a special class of SMS and proves that by adding a control place for each elementary siphon to make sure that it is always marked, deadlock can be successfully prevented. Compared with the existing methods, the new method requires a much smaller number of control places and, therefore, is suitable for large-scale Petri nets. An FMS example is used to illustrate the proposed concepts and policy.