Sensor-equipped tool condition monitoring (TCM) is crucial for automated machining, but missing data poses a significant challenge. Existing methods struggle with the complex patterns and substantial data loss common in these dynamic processes. This paper introduces a novel duration-aware part attention mechanism for robust TCM. Unlike existing attention mechanisms, ours explicitly models time-duration dependencies within sensor signals, capturing multi-scale representations of tool degradation even with incomplete data. The part-attention operator, adapted from the Swin Transformer, can dynamically weight different time segments based on their duration and relevance. We further incorporate a cross-dimensional self-attention mechanism to fuse information across multiple sensors and time steps, capturing complex relationships indicative of tool wear. We evaluate our method on real-world machining datasets with varying levels of missing data, demonstrating its superior ability to accurately monitor tool condition compared to existing methods. The results show that the duration-aware part-attention effectively captures crucial temporal dependencies, leading to robust TCM even with substantial data loss.

This paper introduces a novel transfer learning framework for time series forecasting that uses Concept Echo State Network (CESN) and a multi-objective optimization strategy. Our approach addresses the challenges of feature extraction and knowledge transfer in heterogeneous data environments. By optimizing CESN for each data source, we extract targeted features that capture the unique characteristics of individual datasets. Additionally, our multi-network architecture enables effective knowledge sharing among different ESNs, leading to improved forecasting performance. To further enhance efficiency, CESN reduces the need for extensive hyperparameter tuning by focusing on optimizing only the concept matrix and output weights. Our proposed framework offers a promising solution for forecasting problems where data is diverse, limited, or missing.

Dynamic events, such as machine fault and rush order insertion, are fairly common in the job shop scheduling, which may lead to significant delay in order delivery and low production efficiency. Under such circumstance, it is urgent to consider more perspectives in the scheduling, such as delay time and equipment load rate. In this article, a dynamic many-objective flexible job shop scheduling problem (DMaFJSP) is founded to simultaneously optimize the completion time, delay time, total equipment load and energy consumption. Canonical many-objective optimization algorithms are seeing difficulties in maintaining population diversity and enduring poor adaptability in dynamic scheduling problems. The paper proposes a two-stage individual feedback non-dominated sorting genetic algorithm-III (TSIF-NSGA-III), where a new population diversity strategy and an individual feedback strategy are added to expand the global search faculty and stronger dynamic adaptability. Numerical study in many-objective problem and dynamic many-objective problem are conducted. The final results illustrate that the proposed algorithm can with effect dispose of the DMaFJSP.Note to Practitioners—This paper was motivated by the flexible job shop scheduling problem (FJSP) in practical dynamic situations. In the actual production procedure, however, FJSP is a more challenging issue. Not only operation sequencing and machine allocation matters, but also uncertain factors in the environment, such as machine fault, rush order insertion, etc., are important. In addition, the majority of current researchers formulate the FJSP simply focusing on maximum completion time. However, low carbon and high efficient manufacturing calls for more objectives. In this paper, two dynamic incidents, machine stoppage and rush order insertion, are considered. In addition, the model of DMaFJSP is established with many objectives such as total energy consumption, completion time, equipment load and delay time. To resolve foregoing problems, this article proposes a TSIF-NSGA-III algorithm, which adopts a diversity generation strategy and an individual feedback strategy to strengthen the search ability and dynamic adaptability of this algorithm. Preliminary simulation outcomes illuminate that this algorithm has certain advantages. In addition, the algorithm can also be applied to other multi-objective workshop scheduling problems, such as mixed flow workshop, distributed workshop, etc.

This paper presents a novel and cost-effective approach for structural health monitoring using smartphones. By using built-in accelerometers, smartphones can collect data on building motion, facilitating the detection of potential damage. Traditional methods often rely on classification techniques, requiring extensive training data encompassing both damaged and undamaged scenarios. However, this proves impractical for smartphones due to their limited computational resources for complex classification tasks. We propose a paradigm shift, transforming the classification problem into a regression problem. This enables robust structural health assessment using a neural network specifically designed for this purpose: the echo state network (ESN). ESNs offer inherent robustness to noise and perturbations, making them ideal for real-world applications with sensor data. Compared to traditional methods, the proposed smartphone-based system offers significant advantages in terms of cost-effectiveness, user-friendliness, and computational efficiency. The effectiveness of the proposed method is evaluated through several experiments, demonstrating its capability in identifying structural damage.

Anchor user identification across social networks is a classification task which determines whether a pair of accounts from different social networks belong to the same user. It is a fundamental research of information dissemination across social networks. Based on the observation that users prefer to use similar or identical display names in different social network, some researchers utilized the similarity between display names to build models. However, due to Chinese social network setting and pronunciation and font characteristics of Chinese display names, these methods do not perform well in Chinese social network datasets. To address this problem, we analyze the display name pairs of Chinese anchor users which are obtained by a crawler build in this paper. Then we define 4 special features to extract the pronunciation and font similarities. Finally, we use Gradient Boosting to establish the identification model. The experiments based on the ground-truth datasets we obtained show that these features can improve the performance of display name-based anchor user identification between Chinese social networks.

Reinforcement learning (RL) is an effective method to design robust control. Uncertainty in the worst case requires large state-action learning space. The continuous time RL can solve this computational problem. In this paper, we modify the classical continuous time RL. Compared with the actor-critic (AC) algorithm, our method is more simple and more robust under the worst-case uncertainty.

Autonomous navigation in unknown environment is a big challenge. In this paper, we combine the SLAM (simultaneous localization and mapping) with the path planning method. We first modify the classical SLAM with sliding mode technique, such that it is robust in the unknown environment. Then we analyze the algorithm using the “known space” and “free space” conditions, and propose the polar histogram path planning based on these conditions. We use Monte Carlo method to evaluate the performance of our algorithms. Simulation results show that our autonomous navigation algorithms are better than the others in unknown environment.

This paper investigated a dynamic rescheduling problem for a flexible machining system with random machine breakdown and urgent job arrivals. The energy consumption characteristics of the machining system is explicitly analyzed by considering multiple flexibilities with related to process routes and machine tool selection as well as dynamic events. Then a multi-objective optimization model of dynamic rescheduling is presented to take minimum energy consumption and minimum makespan as objectives, which is solved by a MOGSA algorithm. Case studies with random urgent job arrival and machine breakdown are implemented and the experimental results show that the proposed approach is effective for energy saving through rescheduling.

Critical Infrastructure Systems (CISs) play an essential role in our life, when disasters, attacks, failures happen, such complex systems are expected to be reliable and safety, even react to undesirable accidents. Modeling CISs and developing methods to analyze their safety and dependability is of utmost importance. CIS modeling formalisms must be able to describing both discrete and continuous quantities, a hybrid system modelling approach is natural. In this work, CISs are modeled from two aspects: logic and continuous; Adaptive fuzzy Petri nets (AFPN) and neural networks are combined in our framework, where AFPN is adopted to model the logic parts, and dynamic neural networks are applied to continuous parts. Two hybrid system examples are illustrated to show the effectiveness of the proposed approach.

This paper takes advantages from probability theory and fuzzy modeling. We use probability theory to overcome some common problems in data based modeling methods. A probability based clustering method is proposed to partition the hidden features, and extract fuzzy rules with probability measurement. An optimization method are applied to train the consequent part of the fuzzy rules and the probability parameters. The proposed method is validated with two benchmark problems.

Many cascade processes, such as wastewater treatment plant, include complex nonlinear sub-systems and many variables. The normal input-output relation only represent the first block and the last block of the cascade process. In order to model the whole process. We use hierarchical dynamic neural networks to identify the cascade process. The internal variables of the cascade process are estimated. Two stable learning algorithms and theoretical analysis are given. Real operational data of a wastewater treatment plant are applied to illustrate this new neural modeling approach.

With fuzzy set theory, the uncertainty nonlinear systems can be modeled with fuzzy equations or fuzzy differential equations (FDEs). The solutions of them are applied to analyze many engineering problems. However, it is very difficult to obtain solutions of FDEs. In this paper, the solutions of FDEs are approximated by two type of Bernstein neural networks. We first transform the FDE into four ordinary differential equation (ODEs) with Hukuhara differentiability. Then we construct neural models with the structure of ODEs. With modified backpropagation method for fuzzy variables, the neural networks are trained. The theory analysis and simulation results show that these new models, Bernstein neural networks, are effective to estimate the solutions of FDEs.

Deep learning techniques have been successfully used for pattern classification. These advantage methods are still not applied in nonlinear systems identification. In this paper, the neural model has deep architecture which is obtained by a random search method. The initial weights of this deep neural model is obtained from the denoising autoencoders model. We propose special unsupervised learning methods for this deep learning model with input data. The normal supervised learning is used to train the weights with the output data. The deep learning identification algorithms are validated with three benchmark examples.

Gasoline blending is an important unit operation in gasoline industry. A good model for the blending system is beneficial for supervision operation, prediction of the gasoline qualities and performing model-based optimal control. Gasoline blending process involves two types of proprieties: static blending and dynamic in the blending tanks. The blending process cannot be modeled exactly, because it does not follow ideal mixing rules in practice. In this paper we propose a hybrid neural network, which uses static and dynamic neural networks to approximate the blending properties. Numerical simulations are provided to illustrate the neuro modeling approach.

The increasing usage of on-line social networks has open new research areas such social network analysis. In this survey we review the literature and concepts around this topics with a special emphasis on the hierarchical representation of the graph structure of the social networks. There have been concepts and techniques, of data mining, social networks, and graphs, adapted to this context, taking into consideration that the community structure of a network can be defined as a dendrogram that shows how a network is organized from smaller communities at lower levels to larger ones at higher levels.

Many real data sets are imbalanced and contain a large number of a certain type of patterns, but a very small number of another type of patterns. Normal classification methods, such as support vector machine (SVM), do not work well for these imbalanced data sets (IDS). It is difficult for SVMs to get the optimal separation hyperplane when they are trained with imbalanced data. In this paper, we propose a genetic algorithm (GA)-based classification method. A draft hyperplane and support vectors are first generated by SVMs. Then, GA is applied to compensate the imbalanced data. Finally, SVM is used again to find the best hyperplane from the generated data points. Compared with the other popular classification algorithms, our method has better classification accuracy for several IDS.

Adaptive Fuzzy Petri Nets (AFPN) were proposed for knowledge reasoning and learning. They have advantage on learning dynamical knowledge, i.e., weights of an AFPN model are adjustable dynamically according to knowledge update. In this paper, an evolutionary algorithm called Adaptive Weights Evolutionary Algorithm (AWEA) is introduced which is capable of guaranteeing convergence of AFPN weights. Simulation results show effectiveness of AWEA. Comparing with the original back propagation learning algorithm of AFPN, AWEA does not depend on initial parameters to achieve convergence, so it avoids of getting trapped in local minimum. Additionally, AWEA converges faster than Back propagation algorithms.

Many real data sets are imbalanced, which contain a large number of certain type objects and a very small number of opposite type objects. Normal classification methods, such as support vector machine (SVM), do not work well for these skewed data sets. In this paper we propose a genetic algorithm (GA) based classification method. We first use SVM to generate a draft hyper plane and support vectors. Then GA is applied to find new data points in the sensible region or classification margin. Finally, SVM is used again to find the best hyper plane from the generated data points. Compared with the other popular classification algorithms, the proposed method has better classification accuracy for several skewed data sets.

In this paper we develop a wireless communication human machine interface (HMI) system, which uses a smart-phone. This HMI has some advantages over the other HMIs, such as it is small, cheap, and space sensing. It uses the accelerometer and the gyroscope of the smartphone to generate six commands to control a robot via Wi-Fi network. The experiment results show that our HMI is convenient and effective for remote control.

Support Vector Machine (SVM) is an important classification method used in a many areas. The training of SVM is almost O(n^{2}) in time and space. Some methods to reduce the training complexity have been proposed in last years. Data selection methods for SVM select most important examples from training data sets to improve its training time. This paper introduces a novel data reduction method that works detecting clusters and then selects some examples from them. Different from other state of the art algorithms, the novel method uses a decision tree to form partitions that are treated as clusters, and then executes a guided random selection of examples. The clusters discovered by a decision tree can be linearly separable, taking advantage of the Eidelheit separation theorem, it is possible to reduce the size of training sets by carefully selecting examples from training sets. The novel method was compared with LibSVM using public available data sets, experiments demonstrate an important reduction of the size of training sets whereas showing only a slight decreasing in the accuracy of classifier.

In recent years, vertical partitioning techniques have been employed in multimedia databases to achieve efficient retrieval of multimedia objects. These techniques are static because the input to the partitioning process, which includes queries accessing database and their frequency as well as the database schema, is obtained from an earlier analysis stage. This implies that when the system undergoes sufficient changes, a new analysis stage is carried out to re-run the partitioning process. Multimedia databases are accessed by many users simultaneously, therefore queries and their frequency tend to quickly change over time. In this context, dynamic vertical partitioning can significantly improve performance. In this paper we present an active system called DYMOND (DYnamic Multimedia ON line Distribution), which performs a dynamic vertical partitioning in multimedia databases to improve query performance. Experimental results on benchmark multimedia databases clarify the validness of our system.

Support Vector Machines (SVMs) are high-accuracy classifiers. However, normal SVM algorithms are unsuitable for classification of large data sets because of their training complexity. In this paper, we propose a novel SVM classification approach for

Active rules are the mechanism by which some systems can behave automatically. Rule validation is a mandatory step to guarantee those systems work properly. One of the most used validation techniques is based on test cases. In this paper we introduce a new metric through the Conditional Colored Petri Net model of the rule base, to determine the number of test cases.

Vertical and horizontal partitioning are physical database design techniques that can considerably improve query response time in distributed database system. Although most current database management systems support horizontal partitioning, they do not implement vertical partitioning bacause it is based on user queries and it is necessary to monitor queries in order to generate a good vertical partitioning solution. In this paper, we use active rules to develop an active system for dynamic vertical partitioning of distributed database. The system vertically fragment and re-fragment a database without intervention of a database administrator. Experiments on a Benchmark database TPC-H demonstrate acceptable query response time.

Active rules are a powerful mechanism to represent reactive behavior. Constructing an active rule base is not an easy work since errors may be (unnoticed) introduced during rule development. In this paper we describe ECAPNVer, a software tool that supports active systems development by automatically verifying an active rule base based on an extension of Petri nets CCPN. ECAPNVer can detected and correct structural errors as well as potential errors such as redundancy and partial redundancy, inconsistency and partial inconsistency, incompleteness and circularity. In this paper, an example of inconsistency analysis is used to demonstrate ECAPNVer tool functionality.

This paper, presents a novel identification approach using fuzzy neural networks. It focuses on structure and parameters uncertainties which have been widely explored in the literatures. The main contribution of this paper is that an integrated analytic framework is proposed for automated structure selection and parameter identification. A kernel smoothing technique is used to generate a model structure automatically in a fixed time interval. To cope with structural change, a hysteresis strategy is proposed to guarantee finite times switching and desired performance.

Combination of neural networks and sliding mode control (SMC) can reduce chattering, because the upper bound of uncertainties becomes smaller when neural networks are used to model unknwn nolinear systems. The tracking error of normal neural sliding mode control is asymptotically stable, while neural control and SMC are applied at same time. In this paper, neural control and SMC are connected serially: first a deadzone neural control assures that the tracking error is bounded, then super-twisting secondorder slidingmode is used to guarantee finite time convergence of the contoller.

Support vector machines (SVMs) are known to be excellent algorithms for classification problems. The principal disadvantage of SVMs is due to its excessive training time in large data set, such as DNA sequences. This paper presents a novel SVMs classification method which reduces significantly the input data set using Bayesian technique. Using this system, we are able to predict with a high accuracy huge data sets in a reasonable time. The system has been tested successfully on large splice-junction gene sequences (DNA). Experimental results show that the accuracy obtained by the proposed algorithm is comparable (98.2) with other SVMs implementations such as SMO (98.4%), LibSVM (98.4%), and Simple SVM (97.6%). Furthermore the proposed approach is scalable to large data sets with high classification accuracy.

Active rules allow software systems behave automatically when relevant events take place. Due to unstructured rule processing, it is necessary to inspect behavior characteristics such as termination which guarantees that rule processing finishes. In this paper we introduce potential termination concept which gives valuable information about those rules whose processing may not terminate during execution time. It is very useful to manage possible bad scenarios. We also describe our Petri net-based approach to effectively detect termination and potential termination problems.

In this paper, we propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.

In recent years support vector machines (SVM) has received considerable attention due to its high generalization ability and performance for a wide range of applications. However, the most important problem of this method is slow training for classification problems with a large data sets because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. This paper presents a novel SVM classification approach for large data sets by reducing training data and train the support vector machine using only these data. In this algorithm, a first stage uses SVM classification on a small data set in order to gets a sketch of classes distribution and labels the support vectors as a data set with label +1 and the other points as a data set with label -1. We call this change of classes. Then the algorithm obtains the classification hyperplane and classify the original input data set, the data points obtained with label +1 constitute the data points in the boundary of each original class and represent the most important data points, these data points are used as training data for a posterior SVM classification. The effectiveness of the approach proposed is supported by experimental results.

Active rules are widely used in modern reactive software systems, such as active data base management systems, smart homes, etc. Determining if an active rule base is free of errors is an important process for both rule base design and maintenance. In this paper, we originally define the basic errors in an active rule-based system by extending the conceptions which are used generally in production rule base. Furthermore, a Petri net-based approach is proposed for active rule-base verification. An example on smart homes design is used as an application.

Despite of good theoretic foundations and high classification accuracy of support vector machine (SVM), normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is very high. This paper presents a novel two stages SVM classification approach for large data sets by randomly selecting training data. The first stage SVM classification gets a sketch of support vector distribution. Then the neighbors of these support vectors in original data set are used as training data for the second stage SVM classification. Experimental results demonstrate that our approach have good classification accuracy while the training is significantly faster than other SVM classifiers.

Reactive behavior of active database systems is achieved through the definition ofevent-condition-action(ECA) rules. Generally, ECA rule representation and processing are separated in the majority of existing active database systems. In this paper, we propose a conditional colored Petri net (CCPN) approach to model and simulate ECA rules. CCPN can not only integrate rule representation and processing in only one model, but is also independent of the actual database system. Furthermore, we have developed a software platform named ECAPNSim, which can generate a CCPN model automatically from a text file of ECA rule description, and communicate with a traditional database system when an event is detected from the database or an action command is generated by the CCPN simulator.

In this paper we propose a novel online clustering approach which can be applied for nonlinear system modeling. Fuzzy neural networks are used as models whose structure and parameters are updated online. The new idea for the structure identification is that the input (precondition) and the output (consequent) spaces partitioning are carried out in the same time index. This idea gives better explanation for input-output mapping of nonlinear system. An application on modeling of crude oil blending is proposed.

Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of these networks be stable. The objective of the paper is to develop sufficient conditions for stability of the dynamic neural networks with different time scales. Lyapunov function and singularly perturbed technique are combined to access several new stable properties of different time-scales neural networks. Exponential stability and asymptotic stability are obtained by sector and bound conditions. Compared to other papers, these conditions are simpler. Numerical examples are given to demonstrate the effectiveness of the theoretical results.

This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compensated effectively. This new neural control can resolve the two problems for overhead crane control: 1) decrease steady-state error of normal PD control. 2) guarantee stability via neural compensation. Lyapunov method and input-to-state stability technique, we prove that these robust controllers with neural compensators are stable. Real-time experiments are presented to show the applicability of the approach presented in this paper.

Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence. In this paper, Kalman filter is modified with a risk-sensitive cost criterion, we call it as risk-sensitive Kalman filter. This new algorithm is applied to train recurrent neural networks for nonlinear system identification. Input-to-state stability is used to prove that the risk-sensitive Kalman filter training is stable. The contributions of this paper are: 1) the risk-sensitive Kalman filter is used for the state-space recurrent neural networks training, 2) the stability of the risk-sensitive Kalman filter is proved.

Nonlinear system online identification via dynamic neural networks is studied in this paper. The main contribution of the paper is that the passivity approach is applied to access several new stable properties of neuro identification. The conditions for passivity, stability, asymptotic stability, and input-to-state stability are established in certain senses. We conclude that the gradient descent algorithm for weight adjustment is stable in an L(infinity) sense and robust to any bounded uncertainties.

Since knowledge in an expert system is vague and modified frequently, expert systems are fuzzy and dynamic. It is very important to design a dynamic knowledge inference framework which is adjustable according to knowledge variation as human cognition and thinking. A generalized fuzzy Petri net model, called adaptive fuzzy Petri net (AFPN), is proposed with this object in mind. AFPN not only has the descriptive advantages of the fuzzy Petri net, it also has learning ability like a neural network. Just as other fuzzy Petri net (FPN) models, AFPN can be used for knowledge representation and reasoning, but AFPN has one important advantage: it is suitable for dynamic knowledge, i.e., the weights of AFPN are adjustable. Based on the AFPN transition firing rule, a modified backpropagation learning algorithm is developed to assure the convergence of the weights.