This study investigates a scheduling problem involving dual-arm cluster tools (CTs) that simultaneously handle two types of wafers, considering both wafer priority and residency time constraints. The two types of wafers have their own processing routes and processing times at each step. To fully utilize the resources of the CTs, we use the fewest processing modules (PMs) to produce one type of wafers with maximum productivity, and use the available PMs to produce the other type of wafers. Based on this, we introduce a swap sequence for scheduling a dual-arm robot, which is simple to implement and supports periodic operations. Without affecting the priority wafer production, we provide the necessary and sufficient conditions for scheduling a CT that processes two types of wafers, and present the optimal PM configuration. A high-performance algorithm is developed to determine an optimal periodic schedule, with its practicality and feasibility illustrated through several examples.

Due to their graphical representation and capability for property analysis, Petri nets (PNs) have been widely used in developing automated manufacturing systems (AMS). When designing them, it is important to perform their state reachability analysis and verify their functionality. The most common approach is to traverse a reachability tree of their PN models. However, it has the problem of state space explosion. A way to determine a state’s reachability is to find a firing sequence (FS) that corresponds to nonnegative integer solutions (NISs) of a state equation. Our prior work has given an algorithm to decide the existence of FS corresponding to an NIS in polynomial time. Yet it is impossible to decide the reachability of a marking given an infinite number of NISs of a state equation. This work studies the relationship between the PN model and NIS count of any state equation. An innovative method is proposed to modify a PN structure such that, for any given initial state and destination one, its state equation has no more than one NIS. Given an initial PN model of an AMS, by analyzing the relationship between initial PN properties and the modified one’s, we have that the proposed method can maintain the functionality of the modeled AMS. As a result, by using the modified PN as the AMS’s final model, the reachability of any marking can be determined in polynomial time, which can be viewed as a breakthrough result in the field of PN analysis. The proposed method is illustrated via case studies.

Scheduling single-robotic-arm cluster tools subject to wafer residency time constraints has received much attention. Compared to some scheduling strategies that use all processing modules (PMs) to process wafers, it is much more challenging to schedule a more general case whose optimal scheduling strategy is not limited to the case of using all PMs. The strategy of only adjusting the robot’s waiting time may fail to produce a desired schedule. When a tool using all PMs is not schedulable, it may become schedulable if only some PMs of a type are used. Therefore, it is very important to select an appropriate number of PMs to process wafers. This work studies the cyclic scheduling problem of wafer-residency-time-constrained single-robotic-arm cluster tools by simultaneously adjusting the number of PMs and robot waiting time. We build a virtual cell that includes an appropriate number of PMs to process wafers with the maximal productivity. We establish sufficient and necessary conditions under which the system is schedulable. The schedulability conditions are less conservative than the state-of-the-art one. A polynomial algorithm is developed to find the optimal cyclic schedule, a virtual cell’s configuration, and robot waiting time. We illustrate the practicability of the proposed algorithm via several examples, and its superiority over the existing one.Note to Practitioners—This paper addresses the optimal cyclic scheduling problem of wafer-residency-time-constrained single-robotic-arm cluster tools that are used in every wafer fabrication factory. This work for the first time simultaneously adjusts the number of PMs and the robot waiting time given such a cluster tool such that it can be scheduled with the highest productivity. It presents the optimal scheduling method with polynomial complexity. We confirm that the proposed approach can improve schedulability of single-robotic-arm cluster tools over existing methods by using multiple examples. It can thus be readily applied to industrial wafer fabrication.

During the past two decades or so, many researchers devoted themselves to avoiding the deadlocks of automated manufacturing systems (AMSs). Many deadlock control policies have been developed under the assumption that AMSs do not contain unreliable resources and assembly operations. This work focuses on the deadlock control of failure-prone AMS with assembly operations and a single unreliable resource. At first, an automata model is developed to characterize the studied AMS. Then, four properties that a robust deadlock control policy for the studied AMS should satisfy are proposed and rigorously formulated for the first time. Finally, a new robust deadlock control policy based on the modified Banker’s algorithms is proposed. Compared with existing control policies, only the proposed one can well satisfy the newly proposed properties, thus representing a significant contribution to the field of robust control of failure-prone manufacturing systems.

This work investigates how multiple unmanned aerial vehicles (UAVs) assist the large-scale IoT devices (its count$\geq$100) in the edge computing system in accomplishing their tasks. The UAVs serve the latter as edge servers, and fly to footholds to collect task data from the latter, execute tasks locally and return results to the latter. The goal of this work is to minimize overall energy consumption by jointly optimizing the association between each UAV and ground-based IoT devices, deployments of UAVs, and their flight trajectories. To achieve this, this work proposes a joint optimization approach (JOA). It has three parts: 1) an improved k-means method is designed to handle the association between each UAV and ground-based IoT devices, where the number of clusters is equal to that of UAVs, which means that each UAV is responsible for the IoT devices within a cluster; 2) for the deployments of UAVs, an improved fireworks algorithm (IFWA) with variable-length encoding strategy and population size update strategy is proposed to optimize the number and locations of footholds of each UAV, where each member of the population symbolizes a UAV foothold, and each firework and its offspring are considered as the deployment of UAV. Also, the population size update strategy is employed to dynamically change the number of footholds; and 3) regarding UAV flight trajectory, a pre-computed greedy algorithm based on the footholds of UAVs obtained by IFWA is proposed to minimize the total UAV distance. The proposed approach is verified on ten large-scale instances, and the results demonstrate its effectiveness in achieving minimal energy consumption when compared to other state-of-the-art methods.

As an increasingly mature business model, crowdsourcing, especially spatial crowdsourcing, has played an important role in data collection, disaster response, urban planning and other fields. However, the rapid growth of user scale and massive data collected inevitably brings serious challenges to computing and storage resources. The emergence of cloud computing provides an opportunity to handle such challenges. Its nearly unlimited resource provision capability can provide reliable services for different crowdsourcing applications. Nevertheless, considering the risks of privacy leakage and vendor lock-in using only a single cloud, as well as the additional restrictions caused by the wide geographical distribution of data and associations among workers, the use of multi-cloud seems to be a better choice. In this article, we define a problem to find an effective data placement scheme for spatial crowdsourcing in multi-cloud environment to achieve the cost-effectiveness and minimal latency. We take full account of the interval pricing strategy. Then we analyze the geographical distribution characteristics of data centers through a clustering algorithm, and propose an effective data initialization strategy. Finally, we use a genetic algorithm to further optimize the results. Through experiments on real-world data from cloud providers, the efficiency and effectiveness of our proposed method is verified. Compared with some existing algorithms, the proposed method can significantly reduce the system cost and latency, among which the cost reduction is up to 150 times and the latency reduction is up to twice.

Multitask optimization (MTO) is a new optimization paradigm that leverages useful information contained in multiple tasks to help solve each other. It attracts increasing attention in recent years and gains significant performance improvements. However, the solutions of distinct tasks usually obey different distributions. To avoid that individuals after intertask learning are not suitable for the original task due to the distribution differences and even impede overall solution efficiency, we propose a novel multitask evolutionary framework that enables knowledge aggregation and online learning among distinct tasks to solve MTO problems. Our proposal designs a domain adaptation-based mapping strategy to reduce the difference across solution domains and find more genetic traits to improve the effectiveness of information interactions. To further improve the algorithm performance, we propose a smart way to divide initial population into different subpopulations and choose suitable individuals to learn. By ranking individuals in target subpopulation, worse-performing individuals can learn from other tasks. The significant advantage of our proposed paradigm over the state of the art is verified via a series of MTO benchmark studies.

Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective when high-dimension data are handled, because it results in a huge search space, as well as a large amount of training time and memory overhead. In this article, we propose a length-adaptive genetic algorithm with Markov blanket (LAGAM), which adopts a length-variable individual encoding and enables individuals to evolve in their own search space. In LAGAM, features are rearranged decreasingly based on their relevance, and an adaptive length changing operator is introduced, which extends or shortens an individual to guide it to explore in a better search space. Local search based on Markov blanket (MB) is embedded to further improve individuals. Experiments are conducted on 12 high-dimensional datasets and results reveal that LAGAM performs better than existing methods. Specifically, it achieves a higher classification accuracy by using fewer features.

For tracking suspicious objects using intelligent robots, Multiple Object Tracking (MOT) has gained great attention. MOT is easily affected by long-term severe occlusion. This work proposes a joint MOT algorithm to handle such occlusion. Pairs of frames in complicated environments are taken as input. A center-based feature extraction framework is designed for precisely detecting objects and extracting their feature maps. A ConvGRU module is applied to learn permanent representations by using historical spatio-temporal information of objects. A Hungarian matching method is applied to match the detected objects and predicted predictions. The proposed algorithm is compared with several representative methods on two public multi-object tracking benchmarks. Furthermore, this work constructs a database with videos captured from street scenarios and uses it to test the proposed algorithm and its peers. Experimental results demonstrate that the proposed algorithm outperforms its peers, especially under long-term severe occlusion, thus advancing the field of MOT.

Formulating a cooperative autonomous vehicle group is challenging in an urban scene that has complex road networks and diverse disturbance. Existing methods of vehicle cluster cooperation in a vehicular ad-hoc network cannot be applied to autonomous vehicles because the latter have different requirements for a vehicle group structure and communication quality. Existing studies focus on autonomous vehicle group cooperation in closed and highway scenes only. Their outcomes cannot be directly applied to an urban scene because of its complex road conditions, incomplete cooperation properties, and lack of a vehicle group size control strategy. In this work, we formulate a cooperation model for autonomous vehicle groups in such scene. First, we analyze cooperation criteria based on the non-colliding aggregate motion of flocks and deduce the connectivity, coupling, timeliness, evolvability, and adaptivity of a vehicle group, based on which we propose a cooperation model. Next, we solve our model by using a modified distributed evolutionary multi-objective optimization method, prove its convergence, and analyze its computational complexity. Finally, we conduct simulations on synthetic and real roads to show its performance in terms of average connectivity, coupling, timeliness, evolvability, and adaptivity of vehicle groups.

Locating multiple sources in an unknown environment based on their signal strength is called a multi-source location problem. In recent years, there has been great interest in deploying autonomous devices to solve it. A particle swarm optimizer (PSO) is a widely employed source location method. Yet most work in this field focuses on a flat search space while ignoring height information. An unmanned aerial vehicle (UAV) has a coarser but wider view as it flies higher. Inspired by such facts, this paper focuses on improving the efficiency of locating sources by utilizing height information through UAVs. A novel source location model is designed where their sensing range gradually increases as their flying height rises, but their obtained signal strength fades away. It can be directly deployed to existing PSO-based multi-source location methods and improve their performance, especially in a large-scale environment with sparse sources. UAVs can spontaneously switch their search schemes between a rough search at a higher height and a fine one at a lower height. Experimental results of three PSO-based methods show their significant improvement after deploying our model. Given the same computation resources, its deployment leads to over 30% hike in both location accuracy and speed. This represents a great advance to the field of source location.

Recently, using a particle swarm optimizer (PSO) to guide robots in a source location problem has attracted widespread interest. While being navigated by PSO, robots are easily trapped into U-shape-like concave obstacles such that they move back and forth cyclically and fail to locate a correct source. Existing obstacle avoidance strategies perform well when robots have information about all obstacles. Yet in many real scenes, robots have no prior information. This work proposes a novel PSO based on Tabu Search (PSO-TS) for robots to locate multiple sources. Instead of traditionally setting obstacles as tabu objects, PSO-TS innovatively sets trapping areas as tabu objects such that robots do not need prior knowledge or expensive hardware and much time to obtain obstacle information. The weighted average velocity of a robot is employed to determine if it is stuck inside an obstacle-induced area. If so, a rectangular tabu area is set to push robots out of the area and prevents robots from searching the same area again. The proposed method can be embedded into various source location algorithms to improve their performance. Its obstacle avoidance capability is proved. Finally, experimental results show the algorithmic compatibility, environmental adaptability and obstacle avoidance performance of the proposed method.

Feature selection can be used to solve imbalanced classification problems encountered in big data projects. There often exist multiple feature subsets achieving the same accuracy. These subsets tend to exhibit different acquisition difficulty and reliability, thus offering decision-makers with multiple choices if they can be well-identified. This work formulates feature selection as a Multimodal Multiobjective Problem (MMOP), where a point on Pareto front in objective space has multiple equivalent feature subsets in decision space. To seek more equivalent feature subsets, this work proposes a new multiobjective fireworks algorithm. It extends a latest single-objective fireworks algorithm to a multiobjective version such that it becomes suitable for solving MMOP. An adaptive strategy and special archive guidance are newly designed to improve its performance. A weighted extreme learning machine is chosen to classify datasets and return classification accuracy due to its fast learning speed. Experimental results show that the proposed algorithm outperforms its compared ones on 15 imbalanced classification datasets including 5 low-dimensional, 5 high-dimensional feature selection problems and 5 large-scale problems with larger imbalanced ratio, and its runtime is the least among them. Also, fault diagnosis in self-organizing cellular networks, as an important imbalance classification problem, is performed by the proposed algorithm and the results show that it can perform fault diagnosis well.

Reachability is the basis for studying other dynamic properties of Petri nets (PNs). When a state equation is used to determine the reachability of a marking, we need to judge whether there is a corresponding legal firing sequence (LFS) for a non-negative integer solution (NIS), i.e., a firing count vector, of the state equation. The search for an LFS is an NP-hard problem, and previous work cannot always find an LFS for any NISs. This article proposes that transition-dependent circuits or firing-dependent circuits are the root cause that a state equation has an NIS but the marking is nonreachable, i.e., there is no LFS corresponding to an NIS in PNs. Based on this, we propose a state-equation-based backward algorithm (SBA) to determine whether there is an LFS corresponding to an NIS of the state equation in a PN. The correctness and effectiveness of SBA are verified by a case study on a PN-based flexible manufacturing system and through simulation on an S4PR net. The experimental results show that the time required for SBA to determine the existence of an LFS increases linearly with the transition firing count in NISs. When the number of NISs of a state equation is finite, we can efficiently determine the reachability of a marking. This represents an important result in theory and applications of PNs.

Resource allocation systems (RASs) exist in various fields of modern society. The deadlock control problem is a crucial issue in control theory of RAS. This work is concentrated on a special class of shared resource and process-oriented Petri nets whose initial marking can have only a token in every resource place. Using mixed-integer programming (MIP) and iterative siphon control, we present a two-stage deadlock prevention policy. In particular, a modified MIP technique is developed for the first stage to compute a specific type of emptiable siphons and a siphon control method introducing monitors with related arcs whose weights all equal to one is established in the second stage. This policy leads to a maximally permissive liveness-enforcing supervisor and such an obtained controlled net is ordinary. Moreover, it avoids the exhaustive enumeration of siphons and the reachability analysis. Examples are provided to explain the policy.

Accurately processing dynamic evolution events is extremely challenging for autonomous vehicle groups in an urban scene, which can be disturbed by manned vehicles, roadside obstacles, traffic lights, and pedestrians. Existing work focuses on a dynamic evolution method for such groups in a highway scene only. Its outcomes cannot be directly used to an urban scene due to different environmental factors, incomplete dynamic evolution events, and lack of simulation evaluation with real road networks. In this work, we present a dynamic evolution method for such groups in an urban scene. First, we analyze their dynamic evolution reasons. Then, we abstract five dynamic evolution events, i.e., joining, leaving, merging, splitting, and disappearing, and introduce a dynamic evolution method to process them. Finally, we deduce the evolvability that can reflect dynamic evolution states of a vehicle group. The simulation results in synthetic and real urban scenes show that the connectivity, coupling, timeliness, and evolvability of vehicle groups using the proposed dynamic evolution method are higher than those of using a dynamic evolution method for a highway scene.

Vehicle groups that are composed of autonomous vehicles can increase the perception range of vehicles, and their dynamic evolution can provide guidance for the operation of autonomous vehicles. Most existing studies on vehicle group formation neither propose a standard vehicle group model, nor consider vehicle mobility and dynamic topology of vehicle groups. Instead, they focus on detecting dynamic evolution without predicting it. This work proposes a dynamic evolution method for autonomous vehicle groups. It first defines five vehicle states and their transitions. Then, it proposes an autonomous vehicle group formation method based on vehicle states and formulates an autonomous vehicle group model. Next, it uses meta vehicle group sequences to manage vehicle groups at different times. Finally, it gives detection and prediction methods of vehicle group dynamic evolution. Extensive simulation results show that the proposed method can be used to establish interconnection among autonomous vehicle nodes, detect dynamic evolution characteristics inside a vehicle group precisely, and predict dynamic evolution trends of vehicle groups effectively.

In a distributed heterogeneous fog environment, fog nodes may change their state at any time. Their reliability changes accordingly. A dynamic analysis of state changes can help one detect fault-tolerant fog nodes, which is conducive to promoting the reliability of fog services. This article proposes a fault-tolerant model based on a Markov chain for a fog system’s performance optimization. The real-time reliability of fog nodes is analyzed by using dynamic distributed parameters. Thus, the state transition process of fog nodes is modeled with a continuous-time Markov chain. The steady-state probability of a fog system is analyzed. Then, a fault-tolerant strategy and its algorithms are designed to select nodes with the minimum cost based on their steady-state probabilities. The proposed method can predict the number of faulty ones of a fog system via the steady-state probability. An intelligent optimization method called simulated annealing (ISA) is designed and used to select the most appropriate fog nodes to substitute faulty ones. The experimental results show that the method is feasible and effective for selecting the right fault-tolerant nodes according to different performance requirements. ISA can well outperform such methods as random selection, discrete differential evolution, and simulated annealing in terms of cost and time.

With the rapid development of Internet of Things (IoT), much data has been produced, and new requirements have been posed for data mining. Clustering plays an essential role in discovering the underlying patterns of IoT data. It is widely used in health prognoses, pattern recognition, information retrieval, and computer vision. Density clustering is crucial to find arbitrary-shaped clusters and noise points without knowing the number of clusters in advance. However, its efficiency and applicability are reduced sharply when there exists mutual interference among parameters. In this article, a new algorithm called density-center-based automatic clustering (DAC) is proposed. First, this work presents a nonparametric density computing method. Second, it proposes to use an adaptive neighborhood whose radius is automatically calculated based on all the points in a data set. Finally, it selects appropriate density centers from a decision graph, which merge their surrounding points into the same groups. Experiments are conducted to show that DAC has higher accuracy than six classic and updated algorithms. Its effectiveness is shown via data from photovoltaic power and oil extraction systems. As an outstanding feature that its compared peers lack, it can determine parameters automatically. Thus this work greatly advances the state-of-the-art of clustering algorithms in the field of IoT data analysis.

Full-view coverage realized by camera sensor networks (CSNs) is highly demanded for monitoring and recognizing objects appearing at target points. However, it aggravates the energy shortage in CSNs as caused by the need to generate and process much sensed data. Undoubtedly, enabling CSN nodes to be rechargeable and harvest energy from their surroundings is an effective method to overcome the energy limitation of a CSN and ensuring its perpetual operation. Moreover, using rechargeable nodes can avoid the replacement of batteries, and thus can reduce network maintenance cost. In this article, we investigate how to design and deploy a rechargeable CSN with the fewest nodes to achieve full-view coverage of all target points while guaranteeing its connectivity and perpetual operation. We first formulate the problem as an integer linear program and prove its NP-hardness, and then propose a greedy heuristic and a differential evolution algorithm to solve it. Extensive simulation results reveal that the latter is able to achieve a larger success rate and higher solution quality but spends more time than the former.

System scheduling is a decision-making process that plays an important role in improving the performance of robotic cellular manufacturing (RCM) systems. Timed Petri nets (PNs) are a formalism suitable for graphically and concisely modeling such systems and obtaining their reachable state graphs. Within their reachability graphs, timed PNs’ evolution and intelligent search algorithms can be combined to find an efficient operation sequence from an initial state to a goal one for the underlying systems of the nets. To schedule RCM systems, this work proposes an A* search with a new heuristic function based on timed PNs. When compared with related approaches, the proposed one can deal with token remaining time, weighted arcs, and multiple resource copies commonly seen in the PN models of RCM systems. The admissibility of the proposed heuristic function is proved. Finally, experimental results are given to show the effectiveness and efficiency of the proposed method and heuristic function.Note to Practitioners—Robotic cellular manufacturing (RCM) systems are among the most common and complicated discrete-event dynamic systems, which provide a great number of choices of resources and processing routes to allow high system productivity. Timed Petri nets (PNs) and intelligent search algorithms on their reachability graphs are ideal tools to handle the RCM scheduling problem. This work proposes an A* search method based on the evolutions of timed PNs to optimally schedule RCM systems. The proposed method can deal with token remaining time, weighted arcs, and multiple resource copies often encountered in the PN models of RCM systems.

Surrogate-assisted evolutionary algorithms (EAs) have been intensively used to solve computationally expensive problems with some success. However, traditional EAs are not suitable to deal with high-dimensional expensive problems (HEPs) with high-dimensional search space even if their fitness evaluations are assisted by surrogate models. The recently proposed autoencoder-embedded evolutionary optimization (AEO) framework is highly appropriate to deal with high-dimensional problems. This work aims to incorporate surrogate models into it to further boost its performance, thus resulting in surrogate-assisted AEO (SAEO). It proposes a novel model management strategy that can guarantee reasonable amounts of re-evaluations; hence, the accuracy of surrogate models can be enhanced via being updated with new evaluated samples. Moreover, to ensure enough data samples before constructing surrogates, a problem-dimensionality-dependent activation condition is developed for incorporating surrogates into the SAEO framework. SAEO is tested on seven commonly used benchmark functions and compared with state-of-the-art algorithms for HEPs. The experimental results show that SAEO can further enhance the performance of AEO on most cases and SAEO performs significantly better than other algorithms. Therefore, SAEO has great potential to deal with HEPs.

In modern logistics and smart warehouse, a terminal exists as a hub to connect multiple transportation modes and exchange goods. Solving a block relocation problem (BRP) arising from block retrieval processes in a terminal is fundamentally important to enhance the terminal’s overall efficiency and save its energy. In this paper, we improve the state-of-the-art mixed integer programming (MIP) formulation of an unrestricted BRP by extracting valid inequalities from structural properties and proposing a new variable fixation method to solve it. Computational results show that the improved model can be optimally solved much more easily than the original model. Among over five-hundred benchmark instances with height limits, the improved model can solve 12.3% more than the original one. For the instances that can be optimally solved by the latter, the improved model shows six times faster speed than the latter. This work represents a significant advance in this important area.

This work proposes a decision tree (DT)-based method for initializing a dendritic neuron model (DNM). Neural networks become larger and larger, thus consuming more and more computing resources. This calls for a strong need to prune neurons that do not contribute much to their network's output. Pruning those with low contribution may lead to a loss of accuracy of DNM. Our proposed method is novel because 1) it can reduce the number of dendrites in DNM while improving training efficiency without affecting accuracy and 2) it can select proper initialization weight and threshold of neurons. The Adam algorithm is used to train DNM after its initialization with our proposed DT-based method. To verify its effectiveness, we apply it to seven benchmark datasets. The results show that decision-tree-initialized DNM is significantly better than the original DNM, k-nearest neighbor, support vector machine, back-propagation neural network, and DT classification methods. It exhibits the lowest model complexity and highest training speed without losing any accuracy. The interactions among attributes can also be observed in its dendritic neurons.

Text classification is a fundamental and important area of natural language processing for assigning a text into at least one predefined tag or category according to its content. Most of the advanced systems are either too simple to get high accuracy or centered on using complex structures to capture the genuinely required category information, which requires long time to converge during their training stage. In order to address such challenging issues, we propose a dynamic embedding projection-gated convolutional neural network (DEP-CNN) for multi-class and multi-label text classification. Its dynamic embedding projection gate (DEPG) transforms and carries word information by using gating units and shortcut connections to control how much context information is incorporated into each specific position of a word-embedding matrix in a text. To our knowledge, we are the first to apply DEPG over a word-embedding matrix. The experimental results on four known benchmark datasets display that DEP-CNN outperforms its recent peers.

The object of this paper is to simulate and optimize small scale concentrating solar power tower (CSP) built and operationalized at King Abdulaziz University, Jeddah, Saudi Arabia, through analytic hierarchy process (AHP) technique. The aim is to facilitate cost effective integration of solar power coupled with energy generation technologies subjected to challenging climatic conditions; and also to present the effects of changing in parameters such as receiver, heliostats, storage tanks or power generation subsystem on the cost and system performance. This study adopts the AHP technique to obtain the most appropriate receiver shape out of three possible shapes; spherical, cubic, and cylindrical. The used criteria in this in this optimization are reliability, manufacturing in the vicinity, manufacturing cost, service and maintain cost, lower operation risks, and high performance. Based on the results of AHP analysis, square shape is selected. A finite element analysis via ANSYS is performed to compute the through analytic division of temperature in the receiver. The highest temperature from the simulation is 503°C. The thermal power, dispensed by the molten is 12.52 kW during the heat exchanger. However, 13 kW is the design thermal power; while about 3.7% is the percentage error in the thermal power. The findings of this research will provide the needed knowledge and scientific background to assist the authorities concerned in the energy sector in establishing a commercial-scale plant. At the end, Artificial Neural Network algorithms/Fuzzy system is modeled to optimize the process.

Smart mobile devices (SMDs) can meet users' high expectations by executing computational intensive applications but they only have limited resources, including CPU, memory, battery power, and wireless medium. To tackle this limitation, partial computation offloading can be used as a promising method to schedule some tasks of applications from resource-limited SMDs to high-performance edge servers. However, it brings communication overhead issues caused by limited bandwidth and inevitably increases the latency of tasks offloaded to edge servers. Therefore, it is highly challenging to achieve a balance between high-resource consumption in SMDs and high communication cost for providing energy-efficient and latency-low services to users. This work proposes a partial computation offloading method to minimize the total energy consumed by SMDs and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of SMDs, allocated bandwidth of available channels, and transmission power of each SMD in each time slot. It jointly considers the execution time of tasks performed in SMDs and edge servers, and transmission time of data. It also jointly considers latency limits, CPU speeds, transmission power limits, available energy of SMDs, and the maximum number of CPU cycles and memories in edge servers. Considering these factors, a nonlinear constrained optimization problem is formulated and solved by a novel hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSP) to produce a close-to-optimal solution. GSP achieves joint optimization of computation offloading between a cloud data center and the edge, and resource allocation in the data center. Real-life data-based experimental results prove that it achieves lower energy consumption in less convergence time than its three typical peers.

One of the important Internet-of-Things applications is to use image and video to realize automatic people monitoring, surveillance, tracking, and reidentification (Re-ID). Despite some recent advances, pedestrian Re-ID remains a challenging task. Existing algorithms based on fully supervised learning for it usually requires numerous labeled image and video data, while often ignoring the problem of data imbalance. This work proposes a method based on unlabeled samples generated by cycle generative adversarial networks. For a newly generated unlabeled sample, it learns its pseudorelationship between unlabeled samples and labeled ones in a low-dimensional space by using a self-paced learning approach. Then, these unlabeled ones having pseudo-relationship with labeled ones are added in a training set to better mine discriminative information between positive and negative samples, which is in turn used to learn a more effective metric. We name this method as a semi-supervised learning approach based on the built pseudopairwise relations between labeled data and unlabeled one. It can greatly enhance the performance of pedestrian Re-ID in case of insufficient labeled images. By using only about 10% labeled images in a given database, the proposed method obtains higher accuracy than state-of-the-art supervised learning methods using all labeled ones, e.g., deep-learning ones, thus greatly advancing the field of pedestrian Re-ID.

The performance of face recognition has been greatly improved by deep neural network algorithms when a dataset is large. However, when face data are insufficient as in practical Internet of Things (IoT) applications and captured by IoT devices under the same intrasubject variation, both data quantity and quality bring big challenges to construct a model or representation, and most of the time it becomes infeasible to build a deep neural network model. This work proposes a sparse individual low-rank component-based representation (SILR) such that the representation of testing images can be based on individual subjects’ low-rank component. Theoretically, we put the$l_{2}$-norm constraint on intrasubject coefficients to represent testing images, thus making intrasubject coefficients dense. Hence, we alleviate the impact of an undersampled training dataset and its same intersubject variation on classification performance. We solve a convex minimization problem in polynomial time via an augmented lagrange multiplier scheme to get the solution of SILR. The scheme can reduce the influences from the same intersubject variation and contribute to an accurate recognition of the undersampled training dataset. We adopt sparse individual low-rank component representation and minimum reconstruction residual to recognize testing images. Extensive results on various databases show that SILR outperforms the other state-of-the-art methods for face recognition.

A model can be easily influenced by unseen factors in nonstationary environments and fail to fit dynamic data distribution. In a classification scenario, this is known as a concept drift. For instance, the shopping preference of customers may change after they move from one city to another. Therefore, a shopping website or application should alter recommendations based on its poorer predictions of such user patterns. In this article, we propose a novel approach called the multiscale drift detection test (MDDT) that efficiently localizes abrupt drift points when feature values fluctuate, meaning that the current model needs immediate adaption. MDDT is based on a resampling scheme and a paired student t -test. It applies a detection procedure on two different scales. Initially, the detection is performed on a broad scale to check if recently gathered drift indicators remain stationary. If a drift is claimed, a narrow scale detection is performed to trace the refined change time. This multiscale structure reduces the massive time of constantly checking and filters noises in drift indicators. Experiments are performed to compare the proposed method with several algorithms via synthetic and real-world datasets. The results indicate that it outperforms others when abrupt shift datasets are handled, and achieves the highest recall score in localizing drift points.

Pedestrian re-identification (Re-ID) is a very challenging and unavoidable problem in the field of multi-camera surveillance in smart transportation. Among many ways to solve this problem, keep it simple and straightforward (KISS) metric learning (KISSME) stands out since it has unbeatable advantages in running time while maintaining highly acceptable matching rate. It can be used to realize effective pedestrian Re-ID in an open world. Although it has achieved highly acceptable performance in some applications, it encounters a small sample size (S3) problem that causes too small eigenvalues of its covariance matrix, thus resulting in an instability issue. Its large eigenvalues are overestimated; while its small ones are underestimated. In order to solve this problem, we use an orthogonal basis vector to generate virtual samples to overcome the S3problem. The resulting algorithm named KISS+ is experimentally shown to have the eigenvalues of its covariance matrix significantly larger than those of the original KISSME. In order to show its advantage in pedestrian Re-ID, this work uses multi-feature fusion to extract more discriminant features, and obtain a low-dimensional expression of features through dimension reduction. Experiments based on several well-known databases show that our method can improve the matching rate, while maintaining the advantage of fast computation. Compared with deep learning algorithms, our algorithm does not achieve their matching rate, but it is highly suitable for real-time pedestrian Re-ID of an open world due to its simplicity, easy operation and fast execution.

In the field of computer vision, without sufficient labeled images, it is challenging to train an accurate model. However, through visual adaptation from source to target domains, a relevant labeled dataset can help solve such problem. Many methods apply adversarial learning to diminish cross-domain distribution difference. They are able to greatly enhance the performance on target classification tasks. Generative adversarial network (GAN) loss is widely used in adversarial adaptation learning methods to reduce an across-domain distribution difference. However, it becomes difficult to decline such distribution difference if generator or discriminator in GAN fails to work as expected and degrades its performance. To solve such cross-domain classification problems, we put forward a novel adaptation framework called generative adversarial distribution matching (GADM). In GADM, we improve the objective function by taking cross-domain discrepancy distance into consideration and further minimize the difference through the competition between a generator and discriminator, thereby greatly decreasing cross-domain distribution difference. Experimental results and comparison with several state-of-the-art methods verify GADM's superiority in image classification across domains.

In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace. This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace. It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching. We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations. Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time. The results can be used in social cognition, person reidentification, and human-machine interactions.

Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This article summarizes the recently proposed methods to solve an aspect-based sentiment analysis problem. At present, there are three mainstream methods: lexicon-based, traditional machine learning, and deep learning methods. In this survey article, we provide a comparative review of state-of-the-art deep learning methods. Several commonly used benchmark data sets, evaluation metrics, and the performance of the existing deep learning methods are introduced. Finally, existing problems and some future research directions are presented and discussed.

In this paper a novel modulation technique is proposed to eliminate instabilities such as subharmonic and chaotic oscillations in dc-dc switching converters. This modulation technique injects a stabilizing signal which is generated internally from its own state variables so that the system is free from the problems due to externally injected signal, such as frequency mismatch, phase shift, etc. Stability analysis of the system is carried out using Floquet theory taking into account its switching nature. Our results show that the system has larger stable region in the parameter space compared to the conventional modulation techniques. Numerical simulations illustrate the performance of the proposed technique under line and load disturbances.

Power electronic systems exhibit different types of fast- and slow-scale instabilities which limit the stable operating range of the parameters. It has been shown that the stability of complex power electronic systems can be fruitfully investigated using the Filippov method, where the stability of the system is given by the eigenvalues of the monodromy matrix, which is a combination of the state transition matrices through each subsystem and those across the switching events, called saltation matrix. In this paper we show that the components of the saltation matrix can be used to change the stability status of the system, and propose three specific techniques, which can be used individually or together to extend the range of stability significantly. The performance of these techniques are shown using line and load disturbances.

In this paper, a typical intermediate bus architecture is modeled from circuit theory and nonlinear dynamics point of view. In the studied system a regulated DC-DC buck converter in the first level supplies two parallel connected regulated buck converters in the second level through an intermediate bus. The loads connected to the second level converters are resistive. The complexity of the system is due to the interaction of the output voltage of the first converter with the downstream converters. Unlike the earlier studies where averaged or simplified reduced-order model were used only for two cascaded converters, we have done our study using the exact switching model of the DC-DC converters. This paper shows different mechanisms of instability when the parallel converters are fed through another converters instead of constant voltage. This knowledge will help in designing more reliable intermediate bus architecture for different application under different operating conditions.

In this paper, a single-inductor single-input boost-type dual-output dc-dc converter is studied. Peak current-mode control is used for the main switch whereas the output switch is controlled by voltage-mode controller. To regulate both the output voltages proportional-integral compensator is introduced. In the state-space the five-dimensional system is divided into four subsystems by four switching surfaces. Due to the large number of subsystems, Filippov's method is used to obtain the stable operating region in the design parameter space. Coexisting attractors, period-doubling and Neimark-Sacker bifurcations are identified as the major causes of the possible instabilities. Detailed studies of the possible bifurcation scenarios (smooth as well as nonsmooth) have been done by detecting the stable as well as unstable orbits and by calculating their eigenvalues.