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IEEE Transactions on Cybernetics
Journal Prestige (SJR): 3.274  Citation Impact (citeScore): 9 Number of Followers: 16  Hybrid journal (It can contain Open Access articles)ISSN (Print) 2168-2267 Published by IEEE  [228 journals]
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- IEEE SYSTEMS, MAN, AND CYBERNETICS SOCIETY
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Pages: C2 - C2
PubDate: FRI, 15 SEP 2023 14:06:21 -04
Issue No: Vol. 53, No. 10 (2023)
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- IEEE Transactions on Cybernetics
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Pages: C3 - C3
PubDate: FRI, 15 SEP 2023 14:06:21 -04
Issue No: Vol. 53, No. 10 (2023)
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- IEEE Transactions on Cybernetics
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Pages: C4 - C4
PubDate: FRI, 15 SEP 2023 14:06:21 -04
Issue No: Vol. 53, No. 10 (2023)
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- Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern
Classification-
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Authors: Gonzalo Nápoles;Yamisleydi Salgueiro;Isel Grau;Maikel Leon Espinosa;
Pages: 6083 - 6094
Abstract: Machine-learning solutions for pattern classification problems are nowadays widely deployed in society and industry. However, the lack of transparency and accountability of most accurate models often hinders their safe use. Thus, there is a clear need for developing explainable artificial intelligence mechanisms. There exist model-agnostic methods that summarize feature contributions, but their interpretability is limited to predictions made by black-box models. An open challenge is to develop models that have intrinsic interpretability and produce their own explanations, even for classes of models that are traditionally considered black boxes like (recurrent) neural networks. In this article, we propose a long-term cognitive network (LTCN) for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. For supporting the interpretability without affecting the performance, the model incorporates more flexibility through a quasi-nonlinear reasoning rule that allows controlling nonlinearity. Besides, we propose a recurrence-aware decision model that evades the issues posed by the unique fixed point while introducing a deterministic learning algorithm to compute the tunable parameters. The simulations show that our interpretable model obtains competitive results when compared to state-of-the-art white and black-box models.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Neural Network-Based Hybrid Three-Dimensional Position Control for a
Flapping Wing Aerial Vehicle-
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Authors: Chen Qian;Yongchun Fang;Youpeng Li;
Pages: 6095 - 6108
Abstract: This article presents a novel neural network-based hybrid mode-switching control strategy, which successfully stabilizes the flapping wing aerial vehicle (FWAV) to the desired 3-D position. First, a novel description for the dynamics, resolved in the proposed vertical frame, is proposed to facilitate further position loop controller design. Then, a radial base function neural network (RBFNN)-based adaptive control strategy is proposed, which employs a switching strategy to keep the system away from dangerous flight conditions and achieve efficient flight. The learning process of the neural network pauses, resumes, or alternates its update strategy when switching between different modes. Moreover, saturation functions and barrier Lyapunov functions (BLFs) are introduced to constrain the lateral velocity within proper ranges. The closed-loop system is theoretically guaranteed to be semiglobally uniformly ultimately bounded with arbitrarily small bound, based on Lyapunov techniques and hybrid system analysis. Finally, experimental results demonstrate the excellent reliability and efficiency of the proposed controller. Compared to existing works, the innovations are the put forward of the vertical frame and the cooperative switching learning and control strategies.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Resilient Corrective Control of Asynchronous Sequential Machines Against
Intermittent Loss of Actuator Outputs-
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Authors: Jung-Min Yang;Seong Woo Kwak;
Pages: 6109 - 6121
Abstract: This article proposes a resilient corrective control scheme for input/state asynchronous sequential machines (ASMs) against a class of actuator faults in which certain actuator outputs cannot be generated temporarily. We first present a mathematical formulation to describe the reachability of the controlled ASM damaged by the intermittent loss of actuator outputs. Based on the mathematical formulation, we address the existence condition and design procedure for a state-feedback corrective controller and a diagnoser that achieve resilience, that is, to make the closed-loop system exhibit normal input/state behaviors despite the intermittent loss of actuator outputs. To validate the applicability of the proposed concept and methodology, the closed-loop system of a practical asynchronous digital system is implemented on a field-programmable gate array (FPGA) and experimental verifications are provided.
PubDate: FRI, 15 SEP 2023 14:07:51 -04
Issue No: Vol. 53, No. 10 (2023)
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- Novel General Regression Neural Networks for Improving Control Accuracy of
Nonlinear MIMO Discrete-Time Systems-
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Authors: Ahmad Jobran Al-Mahasneh;Sreenatha G. Anavatti;
Pages: 6122 - 6132
Abstract: In this article, a novel version of the general regression neural network (Imp_GRNN) is developed to control a class of multiinput and multioutput (MIMO) nonlinear discrete-time (DT) systems. The improvements retain the features of the original GRNN along with a significant improvement of the control accuracy. The enhancements include developing a method to set the input-hidden weights of GRNN using the inputs recursive statistical means, introducing a new output layer and adaptable forward weighted connections from the inputs to the new layer, and suggesting an interval-type smoothing parameter to eradicate the need for selecting the parameter beforehand or adapting it online. Also, controller stability is studied using Lyapunov’s method for DT systems. The controller performance is tested with different simulation examples and compared with the original GRNN to verify its superiority over it. Also, Imp_GRNN performance is compared with an adaptive radial basis function network controller, an adaptive feedforward neural-network (NN) controller, and a proportional–integral–derivative (PID) controller, where it demonstrated higher accuracy in comparison with them. In comparison with the formerly proposed control methods for MIMO DT systems, our controller is capable of producing high control accuracy while it is model free, does not require complex mathematics, has low computational complexity, and can be utilized for a wide range of DT dynamic systems. Also, it is one of the few methods that aims to improve the control system accuracy by improving the NN structure.
PubDate: FRI, 15 SEP 2023 14:07:48 -04
Issue No: Vol. 53, No. 10 (2023)
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- Three-Dimensional Maneuver Control of Multiagent Systems With Constrained
Input-
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Authors: Yu-Wen Chen;Ming-Li Chiang;Li-Chen Fu;
Pages: 6133 - 6145
Abstract: In this article, we propose a new 3-D maneuver controller for a class of nonlinear multiagent systems (MASs) with nonholonomic constraint and saturated control. The system is designed under a distributed communication topology and the controller is more flexible and efficient for general formation maneuver tasks. The saturation design generates control inputs within pregiven bounds, which makes the system more applicable in practice. Moreover, based on the nonholonomic model, the proposed control also considers the heading angles of the agents. Thus, the maneuver controller can achieve a more natural tracking movement where the heading of the formation will align to the direction of the reference trajectory during the tracking motion. Several simulation examples are given to validate our results and demonstrate the competence for various maneuver tasks of MASs.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Graph Influence Network
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Authors: Yong Shi;Pei Quan;Yang Xiao;Minglong Lei;Lingfeng Niu;
Pages: 6146 - 6159
Abstract: Due to the extraordinary abilities in extracting complex patterns, graph neural networks (GNNs) have demonstrated strong performances and received increasing attention in recent years. Despite their prominent achievements, recent GNNs do not pay enough attention to discriminate nodes when determining the information sources. Some of them select information sources from all or part of neighbors without distinction, and others merely distinguish nodes according to either graph structures or node features. To solve this problem, we propose the concept of the Influence Set and design a novel general GNN framework called the graph influence network (GINN), which discriminates neighbors by evaluating their influences on targets. In GINN, both topological structures and node features of the graph are utilized to find the most influential nodes. More specifically, given a target node, we first construct its influence set from the corresponding neighbors based on the local graph structure. To this aim, the pairwise influence comparison relations are extracted from the paths and a HodgeRank-based algorithm with analytical expression is devised to estimate the neighbors’ structure influences. Then, after determining the influence set, the feature influences of nodes in the set are measured by the attention mechanism, and some task-irrelevant ones are further dislodged. Finally, only neighbor nodes that have high accessibility in structure and strong task relevance in features are chosen as the information sources. Extensive experiments on several datasets demonstrate that our model achieves state-of-the-art performances over several baselines and prove the effectiveness of discriminating neighbors in graph representation learning.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Scalable Transfer Evolutionary Optimization: Coping With Big Task
Instances-
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Authors: Mojtaba Shakeri;Erfan Miahi;Abhishek Gupta;Yew-Soon Ong;
Pages: 6160 - 6172
Abstract: In today’s digital world, we are faced with an explosion of data and models produced and manipulated by numerous large-scale cloud-based applications. Under such settings, existing transfer evolutionary optimization (TrEO) frameworks grapple with simultaneously satisfying two important quality attributes, namely: 1) scalability against a growing number of source tasks and 2) online learning agility against sparsity of relevant sources to the target task of interest. Satisfying these attributes shall facilitate practical deployment of transfer optimization to scenarios with big task instances, while curbing the threat of negative transfer. While applications of existing algorithms are limited to tens of source tasks, in this article, we take a quantum leap forward in enabling more than two orders of magnitude scale-up in the number of tasks; that is, we efficiently handle scenarios beyond 1000 source task instances. We devise a novel TrEO framework comprising two co-evolving species for joint evolutions in the space of source knowledge and in the search space of solutions to the target problem. In particular, co-evolution enables the learned knowledge to be orchestrated on the fly, expediting convergence in the target optimization task. We have conducted an extensive series of experiments across a set of practically motivated discrete and continuous optimization examples comprising a large number of source task instances, of which only a small fraction indicate source–target relatedness. The experimental results show that not only does our proposed framework scale efficiently with a growing number of source tasks but is also effective in capturing relevant knowledge against sparsity of related sources, fulfilling the two salient features of scalability and online learning agility.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Adaptively Weighted k-Tuple Metric Network for Kinship Verification
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Authors: Sheng Huang;Jingkai Lin;Luwen Huangfu;Yun Xing;Junlin Hu;Daniel Dajun Zeng;
Pages: 6173 - 6186
Abstract: Facial image-based kinship verification is a rapidly growing field in computer vision and biometrics. The key to determining whether a pair of facial images has a kin relation is to train a model that can enlarge the margin between the faces that have no kin relation while reducing the distance between faces that have a kin relation. Most existing approaches primarily exploit duplet (i.e., two input samples without cross pair) or triplet (i.e., single negative pair for each positive pair with low-order cross pair) information, omitting discriminative features from multiple negative pairs. These approaches suffer from weak generalizability, resulting in unsatisfactory performance. Inspired by human visual systems that incorporate both low-order and high-order cross-pair information from local and global perspectives, we propose to leverage high-order cross-pair features and develop a novel end-to-end deep learning model called the adaptively weighted $k$ -tuple metric network (AW $k$ -TMN). Our main contributions are three-fold. First, a novel cross-pair metric learning loss based on $k$ -tuplet loss is introduced. It naturally captures both the low-order and high-order discriminative features from multiple negative pairs. Second, an adaptively weighted scheme is formulated to better highlight hard negative examples among multiple negative pairs, leading to enhanced performance. Third, the model utilizes multiple levels of convolutional features and jointly optimizes feature and metric learning to further exploit the low-order and high-order representational power. Extensive experimental results on three popular kinship verification datasets demonstrate the effectiveness of our proposed AW $k$ -TMN approach compared with several state-of-the-art approaches. The source codes and models are released.1
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Fast and Effective: A Novel Sequential Single-Path Search for
Mixed-Precision-Quantized Networks-
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Authors: Qigong Sun;Xiufang Li;Licheng Jiao;Yan Ren;Fanhua Shang;Fang Liu;
Pages: 6187 - 6199
Abstract: Model quantization can reduce the model size and computational latency, it has been successfully applied for many applications of mobile phones, embedded devices, and smart chips. Mixed-precision quantization models can match different bit precision according to the sensitivity of different layers to achieve great performance. However, it is difficult to quickly determine the quantization bit precision of each layer in deep neural networks under some constraints (for example, hardware resources, energy consumption, model size, and computational latency). In this article, a novel sequential single-path search (SSPS) method for mixed-precision model quantization is proposed, in which some given constraints are introduced to guide the searching process. A single-path search cell is proposed to combine a fully differentiable supernet, which can be optimized by gradient-based algorithms. Moreover, we sequentially determine the candidate precisions according to the selection certainties to exponentially reduce the search space and speed up the convergence of the searching process. Experiments show that our method can efficiently search the mixed-precision models for different architectures (for example, ResNet-20, 18, 34, 50, and MobileNet-V2) and datasets (for example, CIFAR-10, ImageNet, and COCO) under given constraints, and our experimental results verify that SSPS significantly outperforms their uniform-precision counterparts.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- MARF: Multiscale Adaptive-Switch Random Forest for Leg Detection With 2-D
Laser Scanners-
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Authors: Tianxi Wang;Feng Xue;Yu Zhou;Anlong Ming;
Pages: 6200 - 6210
Abstract: For the 2-D laser-based tasks, e.g., people detection and people tracking, leg detection is usually the first step. Thus, it carries great weight in determining the performance of people detection and people tracking. However, many leg detectors ignore the inevitable noise and the multiscale characteristics of the laser scan, which makes them sensitive to the unreliable features of point cloud and further degrades the performance of the leg detector. In this article, we propose a multiscale adaptive-switch random forest (MARF) to overcome these two challenges. First, the adaptive-switch decision tree is designed to use noise-sensitive features to conduct weighted classification and noise-invariant features to conduct binary classification, which makes our detector perform more robust to noise. Second, considering the multiscale property that the sparsity of the 2-D point cloud is proportional to the length of laser beams, we design a multiscale random forest structure to detect legs at different distances. Moreover, the proposed approach allows us to discover a sparser human leg from point clouds than others. Consequently, our method shows an improved performance compared to other state-of-the-art leg detectors on the challenging Moving Legs dataset and retains the entire pipeline at a speed of 60+ FPS on low-computational laptops. Moreover, we further apply the proposed MARF to the people detection and tracking system, achieving a considerable gain in all metrics.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- An Intelligent Collaborative System for Robot Dynamics
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Authors: Jia Guo;Dongyu Li;Bo He;Shuzhi Sam Ge;
Pages: 6211 - 6221
Abstract: In this article, we propose an intelligent collaborative system for robotic navigation and control (CNaC) governed by the Euler–Lagrange equation. First, a state reconstruction based on neural networks navigation (SR-NNN) law is designed to estimate the current position of the robot for intelligent CNaC. The SR-NNN makes full use of partial truth information and the mighty local fitting ability of neural networks. In the absence of landmark, SR-NNN still exhibits navigation performance with high precision. The maximum root-mean-squared error (RMSE) of DR is 0.096 and the maximum RMSE of SR-NNN is 0.053, which has been improved by 55%. In addition, the motion model obtained by SR-NNN online training can avoid the error introduced by the predetermined motion model and overcome the interference of the external environment. The intelligent CNaC still can achieve satisfactory control performance based on the estimated position given by the SR-NNN rather than the ground truth which is formed by postprocessing. The intelligent CNaC has been demonstrated by simulation tracking sample and real experiments, which verifies the effectiveness of the intelligent CNaC.
PubDate: FRI, 15 SEP 2023 14:07:48 -04
Issue No: Vol. 53, No. 10 (2023)
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- Semisupervised Graph Neural Networks for Graph Classification
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Authors: Yu Xie;Yanfeng Liang;Maoguo Gong;A. K. Qin;Yew-Soon Ong;Tiantian He;
Pages: 6222 - 6235
Abstract: Graph classification aims to predict the label associated with a graph and is an important graph analytic task with widespread applications. Recently, graph neural networks (GNNs) have achieved state-of-the-art results on purely supervised graph classification by virtue of the powerful representation ability of neural networks. However, almost all of them ignore the fact that graph classification usually lacks reasonably sufficient labeled data in practical scenarios due to the inherent labeling difficulty caused by the high complexity of graph data. The existing semisupervised GNNs typically focus on the task of node classification and are incapable to deal with graph classification. To tackle the challenging but practically useful scenario, we propose a novel and general semisupervised GNN framework for graph classification, which takes full advantage of a slight amount of labeled graphs and abundant unlabeled graph data. In our framework, we train two GNNs as complementary views for collaboratively learning high-quality classifiers using both labeled and unlabeled graphs. To further exploit the view itself, we constantly select pseudo-labeled graph examples with high confidence from its own view for enlarging the labeled graph dataset and enhancing predictions on graphs. Furthermore, the proposed framework is investigated on two specific implementation regimes with a few labeled graphs and the extremely few labeled graphs, respectively. Extensive experimental results demonstrate the effectiveness of our proposed semisupervised GNN framework for graph classification on several benchmark datasets.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Discriminative Geometric-Structure-Based Deep Hashing for Large-Scale
Image Retrieval-
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Authors: Guohua Dong;Xiang Zhang;Xiaobo Shen;Long Lan;Zhigang Luo;Xiaomin Ying;
Pages: 6236 - 6247
Abstract: Deep hashing reaps the benefits of deep learning and hashing technology, and has become the mainstream of large-scale image retrieval. It generally encodes image into hash code with feature similarity preserving, that is, geometric-structure preservation, and achieves promising retrieval results. In this article, we find that existing geometric-structure preservation manner inadequately ensures feature discrimination, while improving feature discrimination of hash code essentially determines hash learning retrieval performance. This fact principally spurs us to propose a discriminative geometric-structure-based deep hashing method (DGDH), which investigates three novel loss terms based on class centers to induce the so-called discriminative geometrical structure. In detail, the margin-aware center loss assembles samples in the same class to the corresponding class centers for intraclass compactness, then a linear classifier based on class center serves to boost interclass separability, and the radius loss further puts different class centers on a hypersphere to tentatively reduce quantization errors. An efficient alternate optimization algorithm with guaranteed desirable convergence is proposed to optimize DGDH. We theoretically analyze the robustness and generalization of the proposed method. The experiments on five popular benchmark datasets demonstrate superior image retrieval performance of the proposed DGDH over several state of the arts.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- A Brain-Inspired Approach for Probabilistic Estimation and Efficient
Planning in Precision Physical Interaction-
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Authors: Dengpeng Xing;Yiming Yang;Tielin Zhang;Bo Xu;
Pages: 6248 - 6262
Abstract: This article presents a novel structure of spiking neural networks (SNNs) to simulate the joint function of multiple brain regions in handling precision physical interactions. This task desires efficient movement planning while considering contact prediction and fast radial compensation. Contact prediction demands the cognitive memory of the interaction model, and we novelly propose a double recurrent network to imitate the hippocampus, addressing the spatiotemporal property of the distribution. Radial contact response needs rich spatial information, and we use a cerebellum-inspired module to achieve temporally dynamic prediction. We also use a block-based feedforward network to plan movements, behaving like the prefrontal cortex. These modules are integrated to realize the joint cognitive function of multiple brain regions in prediction, controlling, and planning. We present an appropriate controller and planner to generate teaching signals and provide a feasible network initialization for reinforcement learning, which modifies synapses in accordance with reality. The experimental results demonstrate the validity of the proposed method.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Performance Indicator-Based Adaptive Model Selection for Offline
Data-Driven Multiobjective Evolutionary Optimization-
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Authors: Zhening Liu;Handing Wang;Yaochu Jin;
Pages: 6263 - 6276
Abstract: A number of real-world multiobjective optimization problems (MOPs) are driven by the data from experiments or computational simulations. In some cases, no new data can be sampled during the optimization process and only a certain amount of data can be sampled before optimization starts. Such problems are known as offline data-driven MOPs. Although multiple surrogate models approximating each objective function are able to replace the real fitness evaluations in evolutionary algorithms (EAs), their approximation errors are easily accumulated and therefore, mislead the solution ranking. To mitigate this issue, a new surrogate-assisted indicator-based EA for solving offline data-driven multiobjective problems is proposed. The proposed algorithm adopts an indicator-based selection EA as the baseline optimizer due to its selection robustness to the approximation errors of surrogate models. Both the Kriging models and radial basis function networks (RBFNs) are employed as surrogate models. An adaptive model selection mechanism is designed to choose the right type of models according to a maximum acceptable approximation error that is less likely to mislead the indicator-based search. The main idea is that when the uncertainty of the Kriging models exceeds the acceptable error, the proposed algorithm selects RBFNs as the surrogate models. The results comparing with state-of-the-art algorithms on benchmark problems with up to ten objectives indicate that the proposed algorithm is effective on offline data-driven optimization problems with up to 20 and 30 decision variables.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- The Event-Triggered Impulsive Controls for Quasisynchronization of the
Leader-Following Heterogeneous Dynamical Networks-
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Authors: Wen Sun;Biwen Li;Ailong Wu;Wanli Guo;Xiaoqun Wu;
Pages: 6277 - 6288
Abstract: The time-triggered impulsive controls were widely used to study the collective behavior of homogeneous dynamical networks due to their low control cost, which was a bit conservative in the occupation of communication channels. This article addresses designing the event-triggered impulsive controls for the quasisynchronization, namely, a weak cooperative behavior with the synchronization error no more than a positive constant in the leader-following heterogeneous dynamical network, which thus can reduce the occupation of resources significantly. The centralized and distributed impulsive controls are designed to lead the followers to synchronize approximately to the leader within a nonzero bound, where the impulsive instants are triggered, respectively, by the global or local state-dependent conditions. Numerical results are put forward to verify the effectiveness of the proposed methods.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Adapting Decomposed Directions for Evolutionary Multiobjective
Optimization-
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Authors: Yuchao Su;Qiuzhen Lin;Zhong Ming;Kay Chen Tan;
Pages: 6289 - 6302
Abstract: Decomposition methods have been widely employed in evolutionary algorithms for tackling multiobjective optimization problems (MOPs) due to their good mathematical explanation and promising performance. However, most decomposition methods only use a single ideal or nadir point to guide the evolution, which are not so effective for solving MOPs with extremely convex/concave Pareto fronts (PFs). To solve this problem, this article proposes an effective method to adapt decomposed directions (ADDs) for solving MOPs. Instead of using one single ideal or nadir point, each weight vector has one exclusive ideal point in our method for decomposition, in which the decomposed directions are adapted during the search process. In this way, the adapted decomposed directions can evenly and entirely cover the PF of the target MOP. The effectiveness of our method is analyzed theoretically and verified experimentally when embedding it into three representative multiobjective evolutionary algorithms (MOEAs), which can significantly improve their performance. When compared to seven competitive MOEAs, the experiments also validate the advantages of our method for solving 39 artificial MOPs with various PFs and one real-world MOP.
PubDate: FRI, 15 SEP 2023 14:07:48 -04
Issue No: Vol. 53, No. 10 (2023)
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- Hierarchical One-Class Model With Subnetwork for Representation Learning
and Outlier Detection-
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Authors: Wandong Zhang;Q. M. Jonathan Wu;W. G. Will Zhao;Haojin Deng;Yimin Yang;
Pages: 6303 - 6316
Abstract: The multilayer one-class classification (OCC) frameworks have gained great traction in research on anomaly and outlier detection. However, most multilayer OCC algorithms suffer from loosely connected feature coding, affecting the ability of generated latent space to properly generate a highly discriminative representation between object classes. To alleviate this deficiency, two novel OCC frameworks, namely: 1) OCC structure using the subnetwork neural network (OC-SNN) and 2) maximum correntropy-based OC-SNN (MCOC-SNN), are proposed in this article. The novelties of this article are as follows: 1) the subnetwork is used to build the discriminative latent space; 2) the proposed models are one-step learning networks, instead of stacking feature learning blocks and final classification layer to recognize the input pattern; 3) unlike existing works which utilize mean square error (MSE) to learn low-dimensional features, the MCOC-SNN uses maximum correntropy criterion (MCC) for discriminative feature encoding; and 4) a brand-new OCC dataset, called CO-Mask, is built for this research. Experimental results on the visual classification domain with a varying number of training samples from 6131 to 513 061 demonstrate that the proposed OC-SNN and MCOC-SNN achieve superior performance compared to the existing multilayer OCC models. For reproducibility, the source codes are available at https://github.com/W1AE/OCC.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Optimal Trajectory Planning Method for the Navigation of WIP Vehicles in
Unknown Environments: Theory and Experiment-
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Authors: Yigao Ning;Ming Yue;Jinyong Shangguan;Jian Zhao;
Pages: 6317 - 6328
Abstract: Navigation of underactuated wheeled inverted pendulum (WIP) vehicles in unknown environments is still facing great difficulties, especially when the optimal motion is required. This article proposes an optimal trajectory planning method for the navigation of WIP vehicles in unknown environments, where various performance demands, such as security, smoothness, efficiency, etc., are all considered. First, a map-building algorithm based on the improved Rao–Blackwellized particle filter is applied for the WIP vehicle to construct the environmental map. Then, a multiobjective optimization using the genetic algorithm is performed to find an optimized path between the given start and target point with path length, path curvature, and safe distance being taken into consideration simultaneously. Moreover, on the basis of kinematical and dynamical analysis, velocity, and acceleration constraints are parameterized with a path parameter, and the minimum-time trajectory along the optimized path is further planned with a sequence of maximum acceleration and deceleration trajectories. Finally, a WIP vehicle platform based on the robot operating system is designed, and related experiments in a real obstacle environment are conducted to validate the feasibility of the proposed method.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Multiview Deep Graph Infomax to Achieve Unsupervised Graph Embedding
-
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Authors: Zhichao Zhou;Yu Hu;Yue Zhang;Jiazhou Chen;Hongmin Cai;
Pages: 6329 - 6339
Abstract: Unsupervised graph embedding aims to extract highly discriminative node representations that facilitate the subsequent analysis. Converging evidence shows that a multiview graph provides a more comprehensive relationship between nodes than a single-view graph to capture the intrinsic topology. However, little attention has been paid to excavating discriminative representations of each node from multiview heterogeneous networks in an unsupervised manner. To that end, we propose a novel unsupervised multiview graph embedding method, called multiview deep graph infomax (MVDGI). The backbone of our proposed model sought to maximize the mutual information between the view-dependent node representations and the fused unified representation via contrastive learning. Specifically, the MVDGI first uses an encoder to extract view-dependent node representations from each single-view graph. Next, an aggregator is applied to fuse the view-dependent node representations into the view-independent node representations. Finally, a discriminator is adopted to extract highly discriminative representations via contrastive learning. Extensive experiments demonstrate that the MVDGI achieves better performance than the benchmark methods on five real-world datasets, indicating that the obtained node representations by our proposed approach are more discriminative than by its competitors for classification and clustering tasks.
PubDate: FRI, 15 SEP 2023 14:07:48 -04
Issue No: Vol. 53, No. 10 (2023)
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- Attention and Prediction-Guided Motion Detection for Low-Contrast Small
Moving Targets-
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Authors: Hongxin Wang;Jiannan Zhao;Huatian Wang;Cheng Hu;Jigen Peng;Shigang Yue;
Pages: 6340 - 6352
Abstract: Small target motion detection within complex natural environments is an extremely challenging task for autonomous robots. Surprisingly, the visual systems of insects have evolved to be highly efficient in detecting mates and tracking prey, even though targets occupy as small as a few degrees of their visual fields. The excellent sensitivity to small target motion relies on a class of specialized neurons, called small target motion detectors (STMDs). However, existing STMD-based models are heavily dependent on visual contrast and perform poorly in complex natural environments, where small targets generally exhibit extremely low contrast against neighboring backgrounds. In this article, we develop an attention-and-prediction-guided visual system to overcome this limitation. The developed visual system comprises three main subsystems, namely: 1) an attention module; 2) an STMD-based neural network; and 3) a prediction module. The attention module searches for potential small targets in the predicted areas of the input image and enhances their contrast against a complex background. The STMD-based neural network receives the contrast-enhanced image and discriminates small moving targets from background false positives. The prediction module foresees future positions of the detected targets and generates a prediction map for the attention module. The three subsystems are connected in a recurrent architecture, allowing information to be processed sequentially to activate specific areas for small target detection. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of the proposed visual system for detecting small, low-contrast moving targets against complex natural environments.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Safety-Critical Model Reference Adaptive Control of Switched Nonlinear
Systems With Unsafe Subsystems: A State-Dependent Switching Approach-
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Authors: Chunxiao Huang;Lijun Long;
Pages: 6353 - 6362
Abstract: In this article, a novel safety-critical model reference adaptive control approach is established to solve the safety control problem of switched uncertain nonlinear systems, where the safety of subsystems is unnecessary. The considered switched reference model consists of submodels possessing safe system behaviors that are governed by switching signals to achieve satisfactory performances. A state-dependent switching control technique based on the time-varying safe sets is proposed by utilizing the multiple Lyapunov functions method, which guarantees the state of the subsystem is within the corresponding safe set when the subsystem is activated. To deal with uncertainties, a switched adaptive controller with different update laws is constructed by resorting to the projection operator, which reduces the conservatism caused by the common update law adopted in all subsystems. Moreover, a sufficient condition is obtained by structuring a switched time-varying safety function, which ensures the safety of switched systems and the boundedness of error systems in the presence of uncertainties. As a special case, the safety control problem under arbitrary switching is considered and a corollary is deduced. Finally, a numerical example and a wing rock dynamics model are provided to verify the effectiveness of the developed approach.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Dual Multiscale Mean Teacher Network for Semi-Supervised Infection
Segmentation in Chest CT Volume for COVID-19-
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Authors: Liansheng Wang;Jiacheng Wang;Lei Zhu;Huazhu Fu;Ping Li;Gary Cheng;Zhipeng Feng;Shuo Li;Pheng-Ann Heng;
Pages: 6363 - 6375
Abstract: Automated detecting lung infections from computed tomography (CT) data plays an important role for combating coronavirus 2019 (COVID-19). However, there are still some challenges for developing AI system: 1) most current COVID-19 infection segmentation methods mainly relied on 2-D CT images, which lack 3-D sequential constraint; 2) existing 3-D CT segmentation methods focus on single-scale representations, which do not achieve the multiple level receptive field sizes on 3-D volume; and 3) the emergent breaking out of COVID-19 makes it hard to annotate sufficient CT volumes for training deep model. To address these issues, we first build a multiple dimensional-attention convolutional neural network (MDA-CNN) to aggregate multiscale information along different dimension of input feature maps and impose supervision on multiple predictions from different convolutional neural networks (CNNs) layers. Second, we assign this MDA-CNN as a basic network into a novel dual multiscale mean teacher network (DM ${^{2}}text{T}$ -Net) for semi-supervised COVID-19 lung infection segmentation on CT volumes by leveraging unlabeled data and exploring the multiscale information. Our DM ${^{2}}text{T}$ -Net encourages multiple predictions at different CNN layers from the student and teacher networks to be consistent for computing a multiscale consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from multiple predictions of MDA-CNN. Third, we collect two COVID-19 segmentation datasets to evaluate our method. The experimental results show that our network consistently outperforms the compared state-of-the-art methods.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Event-Triggered Communication-Based Control for Strict-Feedback Nonlinear
Systems-
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Authors: Shuai Liu;Jing Zhang;Xianfu Zhang;Huaicheng Yan;Bo Sun;
Pages: 6376 - 6385
Abstract: In this article, we shall investigate the event-triggered communication control problem for strict-feedback nonlinear systems with measurement outputs. First, two event-triggered communication schemes are designed. Based on both event-triggered schemes, the measurement output and control input signals are only transmitted at triggering time instants, which saves communication costs from the sensor to the controller and from the controller to the actuator. Meanwhile, Zeno behavior can be excluded under the proposed triggering schemes. Second, since the full-state information is not available to the controller, by developing an observer, the system state is estimated and a controller based on estimated state information is designed. Due to the irregular sampling of information communication and state estimation error affects each other, the parameters of the state observer, the controller, and the event-triggering mechanism should be jointly designed. It is proved that the closed-loop system state converges to the origin. Finally, a simulation example verifies the validity of the obtained theoretical result.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Periodic Event-Triggered Control for Networked Control Systems With
External Disturbance and Input and Output Delays-
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Authors: Jiankun Sun;Zhigang Zeng;
Pages: 6386 - 6394
Abstract: This article investigates the problem of periodic event-triggered output-feedback control for networked control systems in the presence of external disturbance and input and output delays. With the aid of the prediction technique, we first develop the predictor-based-extended state observer to reconstruct the system information, including the unknown state and disturbance. The periodic event-triggered output-feedback control law is then designed via the disturbance/uncertainty estimation and attenuation (DUEA) method, such that the communication times can be remarkably reduced and, at the same time, the disturbance rejection ability can be effectively enhanced. Under the predictor-based event-triggered control method, the influence of the time delays is effectively attenuated, and the effect of external disturbance is considerably attenuated due to the prediction technique and the DUEA method. By using the small-gain arguments, this article gives some sufficient stability conditions for the overall control system, and the explicit computations of sampling/updating period and time delays are presented as well. Finally, we employ a practical example and show some comparative simulation results to demonstrate the advantages of the predictor-based event-triggered control method proposed in this article.
PubDate: FRI, 15 SEP 2023 14:07:51 -04
Issue No: Vol. 53, No. 10 (2023)
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- Flexible and Generalized Real Photograph Denoising Exploiting Dual Meta
Attention-
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Authors: Ruijun Ma;Shuyi Li;Bob Zhang;Leyuan Fang;Zhengming Li;
Pages: 6395 - 6407
Abstract: Supervised deep learning techniques have been widely explored in real photograph denoising and achieved noticeable performances. However, being subject to specific training data, most current image denoising algorithms can easily be restricted to certain noisy types and exhibit poor generalizability across testing sets. To address this issue, we propose a novel flexible and well-generalized approach, coined as dual meta attention network (DMANet). The DMANet is mainly composed of a cascade of the self-meta attention blocks (SMABs) and collaborative-meta attention blocks (CMABs). These two blocks have two forms of advantages. First, they simultaneously take both spatial and channel attention into account, allowing our model to better exploit more informative feature interdependencies. Second, the attention blocks are embedded with the meta-subnetwork, which is based on metalearning and supports dynamic weight generation. Such a scheme can provide a beneficial means for self and collaborative updating of the attention maps on-the-fly. Instead of directly stacking the SMABs and CMABs to form a deep network architecture, we further devise a three-stage learning framework, where different blocks are utilized for each feature extraction stage according to the individual characteristics of SMAB and CMAB. On five real datasets, we demonstrate the superiority of our approach against the state of the art. Unlike most existing image denoising algorithms, our DMANet not only possesses a good generalization capability but can also be flexibly used to cope with the unknown and complex real noises, making it highly competitive for practical applications.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Two-Stage Sparse Representation Clustering for Dynamic Data Streams
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Authors: Jie Chen;Zhu Wang;Shengxiang Yang;Hua Mao;
Pages: 6408 - 6420
Abstract: Data streams are a potentially unbounded sequence of data objects, and the clustering of such data is an effective way of identifying their underlying patterns. Existing data stream clustering algorithms face two critical issues: 1) evaluating the relationship among data objects with individual landmark windows of fixed size and 2) passing useful knowledge from previous landmark windows to the current landmark window. Based on sparse representation techniques, this article proposes a two-stage sparse representation clustering (TSSRC) method. The novelty of the proposed TSSRC algorithm comes from evaluating the effective relationship among data objects in the landmark windows with an accurate number of clusters. First, the proposed algorithm evaluates the relationship among data objects using sparse representation techniques. The dictionary and sparse representations are iteratively updated by solving a convex optimization problem. Second, the proposed TSSRC algorithm presents a dictionary initialization strategy that seeks representative data objects by making full use of the sparse representation results. This efficiently passes previously learned knowledge to the current landmark window over time. Moreover, the convergence and sparse stability of TSSRC can be theoretically guaranteed in continuous landmark windows under certain conditions. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of TSSRC.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Anti-Martingale Proximal Policy Optimization
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Authors: Yang Gu;Yuhu Cheng;Kun Yu;Xuesong Wang;
Pages: 6421 - 6432
Abstract: Since the sample data after one exploration process can only be used to update network parameters once in on-policy deep reinforcement learning (DRL), a high sample efficiency is necessary to accelerate the training process of on-policy DRL. In the proposed method, a submartingale criterion is proposed on the basis of the equivalence relationship between the optimal policy and martingale, and then an advanced value iteration (AVI) method is proposed to conduct value iteration with a high accuracy. Based on this foundation, an anti-martingale (AM) reinforcement learning framework is established to efficiently select the sample data that is conducive to policy optimization. In succession, an AM proximal policy optimization (AMPPO) method, which combines the AM framework with proximal policy optimization (PPO), is proposed to reasonably accelerate the updating process of state value that satisfies the submartingale criterion. Experimental results on the Mujoco platform show that AMPPO can achieve better performance than several state-of-the-art comparative DRL methods.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Two-Phase Performance Adjustment Approach for Distributed Neuroadaptive
Consensus Control of Strict-Feedback Multiagent Systems-
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Authors: Libei Sun;Yongduan Song;
Pages: 6433 - 6442
Abstract: This article addresses the practical prescribed-time leaderless consensus problem for multiple networked strict-feedback systems under directed topology. Different from most existing protocols for finite-time consensus that rely on the signum function or fractional power state feedback (thus, the finite convergence time is contingent upon the initial positions of the agents or other design parameters), the proposed distributed neuroadaptive consensus solution is based on a two-phase performance adjustment approach, which exhibits several salient features: 1) the consensus error is ensured to converge to a preassigned arbitrarily small residual set within prescribed time; 2) the tunable transient behavior and desired steady-state control performance of the consensus error is maintained under any unknown initial conditions; and 3) the control scheme involves only one parameter estimation, significantly reducing the design complexity and online computation. Furthermore, we extend the result to practical prescribed-time leader-following consensus control under directed communication topology. Numerical simulation verifies the benefits and efficiency of the proposed method.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Empirical Policy Optimization for
n -Player Markov Games-
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Authors: Yuanheng Zhu;Weifan Li;Mengchen Zhao;Jianye Hao;Dongbin Zhao;
Pages: 6443 - 6455
Abstract: In single-agent Markov decision processes, an agent can optimize its policy based on the interaction with the environment. In multiplayer Markov games (MGs), however, the interaction is nonstationary due to the behaviors of other players, so the agent has no fixed optimization objective. The challenge becomes finding equilibrium policies for all players. In this research, we treat the evolution of player policies as a dynamical process and propose a novel learning scheme for Nash equilibrium. The core is to evolve one’s policy according to not just its current in-game performance, but an aggregation of its performance over history. We show that for a variety of MGs, players in our learning scheme will provably converge to a point that is an approximation to Nash equilibrium. Combined with neural networks, we develop an empirical policy optimization algorithm, which is implemented in a reinforcement-learning framework and runs in a distributed way, with each player optimizing its policy based on own observations. We use two numerical examples to validate the convergence property on small-scale MGs, and a pong example to show the potential on large games.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Fully Distributed Event-Driven Coordination With Actuator Faults
-
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Authors: Jiayue Sun;Zilong Tan;Shu Liu;Huaguang Zhang;Wenyu Chuo;
Pages: 6456 - 6464
Abstract: This article investigates the event-driven fault-tolerance (ETFT) consensus problem for general linear multiagent systems (MASs) with partial loss of effectiveness (PLOE) and bias faults of actuators in leader–follower networks. Each agent’s controller is only updated relatively infrequently at its event moments. A desirable feature of this article is that the proposed event-driven algorithm is fully distributed also independent of global information and additive fault boundaries. Based on this, a consensus error prediction model is used to avoid the limitation that each agent needs to monitor its neighbors’ state under event-driven conditions continuously. We further excluded the Zeno behavior by proving that any adjacent event interval for each agent is greater than zero. The simulations verify our results.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Model Fusion and Multiscale Feature Learning for Fault Diagnosis of
Industrial Processes-
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Authors: Kai Liu;Ningyun Lu;Feng Wu;Ridong Zhang;Furong Gao;
Pages: 6465 - 6478
Abstract: The data generated by modern industrial processes often exhibit high-dimensional, nonlinear, timing, and multiscale characteristics. Presently, most of the fault diagnosis methods based on deep learning only consider the part of the characteristics of industrial data, which will cause the loss of part of the feature information during training, thereby affecting the final diagnosis effect. In order to solve the above problems, this article proposes an end-to-end multiscale feature learning method based on model fusion, which can simultaneously extract multiscale spatial features and temporal features of data, effectively reducing the loss of feature information. First, this article combines the convolutional neural network (CNN) with residual learning and designs a multiscale residual network (MRCNN) to extract high-dimensional nonlinear spatial features of different scales in the data. Then, the extracted features are input into the long and short-term memory (LSTM) network to further extract the temporal features of the data. After the fully connected layer, it is input into the classifier for final fault classification. The residual learning in MRCNN can effectively avoid the problem of model degradation and improve the training efficiency of the model. Through the fusion of MRCNN and LSTM, we can significantly improve the feature extraction ability of the model, thereby greatly improving the diagnosis effect. In the final case experiment, the method improved the comprehensive diagnostic accuracy of the Tennessee-Eastman (TE) process and industrial coking furnace datasets to 94.43% and 97.80%, respectively, which was significantly better than the existing deep learning model and proves the effectiveness and superiority of this method.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Event-Triggered Impulsive Quasisynchronization of Coupled Dynamical
Networks With Proportional Delay-
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Authors: Yufeng Zhou;Zhigang Zeng;
Pages: 6479 - 6490
Abstract: The quasisynchronization of nonidentically coupled dynamical networks (NCDNs) with proportional delay is achieved by the event-triggered mechanism (ETM). Heterogeneity and proportional delay greatly increase the difficulty on synchronization of NCDNs. As an unbounded delay in the coupling term, proportional delay is dealt with by the comparison principle, constructing parameter equations, and contradiction method. Moreover, different impulsive effects based on ETM are taken into account to reduce the burden of communication, and the quasisynchronization criteria for NCDNs are derived by the impulsive comparison principle and extended variable parameter formula. The synchronization errors and the exponential convergence rates under different impulsive effects are obtained. It is proven that the proposed ETM can avoid Zeno behavior. Finally, examples show the effectiveness of the proposed control scheme.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Fuzzy Small-Gain Approach for the Distributed Optimization of T–S Fuzzy
Cyber–Physical Systems-
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Authors: Zhenghong Jin;Xingjian Sun;Zhengyan Qin;Choon Ki Ahn;
Pages: 6491 - 6502
Abstract: The distributed optimization problem (DOP) of Takagi–Sugeno (T–S) fuzzy cyber–physical systems is studied under the framework of weight-balanced graphs and quasistrongly connected characteristics. The objective is to drive the outputs of all agents to the optimal solution of a given global objective function regarded as the desired output, based on the partial information of the local objective functions. To this end, distributed optimal coordinators (DOCs) are used to generate optimal solutions of local objective functions that converge to the desired output, and fuzzy reference-tracking controllers are designed to ensure that all agents can track the optimal solutions. As novel technical results, two Lyapunov-based fuzzy input-to-state stability (ISS) small-gain theorems are proposed for the T–S fuzzy interconnected system. Thus, the overall closed-loop system is an interconnected system involving the modules of optimal coordinators and fuzzy tracking controllers with T–S fuzzy subsystems. The fuzzy ISS cyclic-small-gain theorem is applied to analyze the system stability. The DOP of T–S fuzzy cyber–physical systems is solved using the DOCs and fuzzy reference-tracking controllers through the fuzzy small-gain approach. A numerical example is presented to demonstrate the effectiveness and superiority of the proposed method.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Event-Triggered SMC for Networked Markov Jumping Systems With Channel
Fading and Applications: Genetic Algorithm-
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Authors: Wenhai Qi;Ning Zhang;Guangdeng Zong;Shun-Feng Su;Huaicheng Yan;Ruey-Huei Yeh;
Pages: 6503 - 6515
Abstract: The event-triggered sliding-mode control (SMC) for discrete-time networked Markov jumping systems (MJSs) with channel fading is investigated by means of a genetic algorithm. In order to reduce resource consumption in the transmission process, an event-triggered protocol is adopted for networked MJSs. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to delay, random noise, and amplitude attenuation in a networked environment. With the aid of a common sliding surface, an event-triggered SMC law is designed by adjusting the system network mode. Under the framework of stochastic Lyapunov stability, sufficient conditions are constructed to ensure the mean-square stability of the closed-loop networked MJSs, and the sliding region is reached around the specified sliding surface. Moreover, based on the iteration optimizing accessibility of objective function, an effective SMC approach under genetic algorithm is proposed to minimize the convergence region around the sliding surface. Finally, the effectiveness of the proposed method is proved by the F-404 aircraft model.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Cooperative Tracking Control of Unknown Discrete-Time Linear Multiagent
Systems Subject to Unknown External Disturbances-
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Authors: Ruohan Yang;Lu Liu;Gang Feng;
Pages: 6516 - 6528
Abstract: This article studies the tracking problem of a class of heterogeneous linear minimum-phase discrete-time multiagent systems (MASs) with unknown agent parameters in the presence of bounded disturbances. By introducing a distributed adaptive observer and a reference model, a novel framework is proposed to convert the complicated cooperative tracking problem of unknown heterogeneous MASs into a cooperative tracking problem of the reference models to the leader and a local robust model reference adaptive control problem. It is shown that under the adaptive controller designed based on the proposed framework, the tracking errors between the outputs of all the agents and the output of the leader converge to a residual set. It is also shown that the tracking errors will converge to zero asymptotically when the disturbances are absent. Compared with the existing related works, our main contribution is that the proposed framework could deal with the unknown MASs with arbitrary individual relative degrees and do not rely on any global graph information. Finally, the effectiveness of the proposed controller is illustrated by an example.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Global Consensus Tracking Control for High-Order Nonlinear Multiagent
Systems With Prescribed Performance-
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Authors: Zeqiang Li;Yujuan Wang;Yongduan Song;Wei Ao;
Pages: 6529 - 6537
Abstract: In this article, we investigate the prescribed performance tracking control problem for high-order nonlinear multiagent systems (MASs) under directed communication topology and unknown control directions. Different from most existing prescribed performance consensus control methods where certain initial conditions are needed to be satisfied, here the restriction related to the initial conditions is removed and global tracking result irrespective of initial condition is established. Furthermore, output consensus tracking is achieved asymptotically with arbitrarily prescribed transient performance in spite of the directed topology and unknown control directions. Our development benefits from the performance function and prescribed-time observer. Both theoretical analysis and numerical simulation confirm the validity of the developed control scheme.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Predefined-Time Adaptive Neural Tracking Control of Switched Nonlinear
Systems-
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Authors: Huanqing Wang;Miao Tong;Xudong Zhao;Ben Niu;Man Yang;
Pages: 6538 - 6548
Abstract: This article investigates the neural-network-based adaptive predefined-time tracking control problem for switched nonlinear systems. Neural networks are employed to approximate the unknown part of nonlinear functions. The finite-time differentiators are introduced to estimate the first derivative of the virtual controllers. Then, a novel adaptive predefined-time controller is proposed by utilizing the backstepping control technique and the common Lyapunov function (CLF) method. It is explained by the theoretical analysis that the developed controller guarantees that all signals of the switched closed-loop systems are bounded under arbitrary switchings and the tracking error converges to zero within the predefined time. A simulation is shown to verify the validity of the developed predefined-time control approach.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Robust H∞ Pinning Synchronization for Multiweighted Coupled
Reaction–Diffusion Neural Networks-
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Authors: Lin-Hao Zhao;Shiping Wen;Song Zhu;Zhenyuan Guo;Tingwen Huang;
Pages: 6549 - 6561
Abstract: This article focuses on the robust $mathcal {H}_{infty }$ synchronization of two types of coupled reaction–diffusion neural networks with multiple state and spatial diffusion couplings by utilizing pinning adaptive control strategies. First, based on the Lyapunov functional combined with inequality techniques, several sufficient conditions are formulated to ensure $mathcal {H}_{infty }$ synchronization for these two networks with parameter uncertainties. Moreover, node-based pinning adaptive control strategies are devised to address the robust $mathcal {H}_{infty }$ synchronization problem. In addition, some criteria of $mathcal {H}_{infty }$ synchronization for these two networks under parameter uncertainties are developed via edge-based pinning adaptive controllers. Finally, two numerical examples are presented to verify our results.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Switching Event-Triggered Adaptive Resilient Dynamic Surface Control for
Stochastic Nonlinear CPSs With Unknown Deception Attacks-
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Authors: Ben Niu;Wendi Chen;Wei Su;Huanqing Wang;Ding Wang;Xudong Zhao;
Pages: 6562 - 6570
Abstract: This work concentrates on the adaptive resilient dynamic surface controller design problem for uncertain nonlinear lower triangular stochastic cyber–physical systems (CPSs) subject to unknown deception attacks based on a switching threshold event-triggered mechanism. The adverse effect of deception attacks on the stochastic CPSs is that the exact system state variables become unavailable. Furthermore, it should be emphasized that the coexistence of unknown nonlinearities, stochastic perturbations, and unknown sensor and actuator attacks makes it a very difficult and challenging event to implement the control design. To get the desired controller, radial basis function (RBF) neural networks (NNs) are introduced so that the design obstacle caused from the unknown nonlinearities can be easily solved. On this basis, in order to save resources and effectively transmit, the event-triggered control scheme based on a switching threshold strategy is further considered. In the backstepping design process, the dynamic surface control (DSC) technique is presented to deal with the issue of “explosion of complexity.” By skillfully designing a new coordinate transformation and the attack compensators, the problem of unknown deception attacks is successfully handled. Under our proposed control scheme, all the closed-loop signals are bounded in probability and the stabilization errors converge to an adjustable neighborhood of the origin in probability. Finally, the simulation results on the double chemical reactor show the validity of the proposed design scheme.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Dynamic Periodic Event-Triggered Synchronization of Complex Networks: The
Discrete-Time Scenario-
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Authors: Sanbo Ding;Zhanshan Wang;Xiangpeng Xie;
Pages: 6571 - 6576
Abstract: This article reports the synchronization control of discrete-time complex networks using an event-triggered method. The main contributions are twofold: 1) a discrete-time scenario of the dynamic periodic event-triggered mechanism is developed to schedule the transmissions of measurements. The proposed mechanism monitors the synchronization error in a periodic manner, which is beneficial to reduce the calculation resources of sensors. Simultaneously, the proposed mechanism increases the triggering threshold so that it contributes to enlarging the average interevent interval and 2) a new Lyapunov functional is developed to deal with the periodic samplings. On the one hand, the proposed functional involves a delay-dependent term, which is convenient to formulate the synchronization criterion by the delay analysis technique. On the other hand, the functional takes the sawtooth constraint of periodic samplings into consideration by introducing a piecewise functional. Finally, a succinct criterion is derived such that the considered networks are synchronized with a predetermined error level. A simulation example is provided to show our advantages in comparison with the existing approaches.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- A Dynamic-Memory Event-Triggered Protocol to Multiarea Power Systems With
Semi-Markov Jumping Parameter-
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Authors: Lifei Xie;Jun Cheng;Yanli Zou;Zheng-Guang Wu;Huaicheng Yan;
Pages: 6577 - 6587
Abstract: This work deals with the dynamic-memory event-triggered-based load frequency control issue for interconnected multiarea power systems (IMAPSs) associated with random abrupt variations and deception attacks. To facilitate the transient faults, a semi-Markov process is addressed to model the dynamic behavior of IMAPSs. In order to modulate transmission frequency, a novel area-dependent dynamic-memory event-triggered protocol (DMETP) is scheduled by resorting to a set of the historically released packets (HRPs), which ensures better dynamic performance. From the viewpoint of the defender, the randomly occurring deception attack is taken into account, which is regulated by a Bernoulli-distributed scalar. Benefitting from the DMETP scheduling, a novel framework of the memory-based asynchronous control strategy is formulated, in which the hidden semi-Markov model is adopted to reveal the mode mismatches. Based on the Lyapunov theory, sufficient conditions are established to ensure the stochastic stability of the resulting systems. In the end, the simulation result is presented to reveal the efficiency of the proposed dynamic-memory event-triggered-based approach.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Multi-ASV Coordinated Tracking With Unknown Dynamics and Input
Underactuation via Model-Reference Reinforcement Learning Control-
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Authors: Wenbo Hu;Fei Chen;Linying Xiang;Guanrong Chen;
Pages: 6588 - 6597
Abstract: This article studies coordinated tracking of underactuated and uncertain autonomous surface vehicles (ASVs) via model-reference reinforcement learning control. It considered how model-reference control can be incorporated with reinforcement learning to address the challenges caused by model uncertainties and input underactuation, and how existing results may be employed to realize adaptive communication amongst ASVs. It is demonstrated that the proposed algorithm has a better performance over baseline control and effectively improves the training efficiency over reinforcement learning.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Edge–Cloud Co-Evolutionary Algorithms for Distributed Data-Driven
Optimization Problems-
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Authors: Xiao-Qi Guo;Wei-Neng Chen;Feng-Feng Wei;Wen-Tao Mao;Xiao-Min Hu;Jun Zhang;
Pages: 6598 - 6611
Abstract: Surrogate-assisted evolutionary algorithms (EAs) have been proposed in recent years to solve data-driven optimization problems. Most existing surrogate-assisted EAs are for centralized optimization and do not take into account the challenges brought by the distribution of data at the edge of networks in the era of the Internet of Things. To this end, we propose edge–cloud co-EAs (ECCoEAs) to solve distributed data-driven optimization problems, where data are collected by edge servers. Specifically, we first propose a distributed framework of ECCoEAs, which consists of a communication mechanism, edge model management, and cloud model management. This communication mechanism is to avoid deadlock during the collaboration of edge servers and the cloud server. In edge model management, the edge models are trained based on local historical data and data composed of new solutions generated by co-evolutionary and their real evaluation values. In cloud model management, the black-box prediction functions received from edge models are used to find promising solutions to guide the edge model management. Moreover, two ECCoEAs are implemented, which proves the generality of the framework. To verify the performance of algorithms for distributed data-driven optimization problems, we design a novel benchmark test suite. The performance on the benchmarks and practical distributed clustering problems shows the effectiveness of ECCoEAs.
PubDate: FRI, 15 SEP 2023 14:07:51 -04
Issue No: Vol. 53, No. 10 (2023)
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- A Minimum Cost Consensus-Based Failure Mode and Effect Analysis Framework
Considering Experts’ Limited Compromise and Tolerance Behaviors-
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Authors: Hengjie Zhang;Shenghua Liu;Yucheng Dong;Francisco Chiclana;Enrique Enrique Herrera-Viedma;
Pages: 6612 - 6625
Abstract: This study proposes a minimum cost consensus-based failure mode and effect analysis (MCC-FMEA) framework considering experts’ limited compromise and tolerance behaviors, where the first behavior indicates that a failure mode and effect analysis (FMEA) expert might not tolerate modifying his/her risk assessment without limitations, and the second behavior indicates that an FMEA expert will accept risk assessment suggestions without being paid for any cost if the suggested risk assessments fall within his/her tolerance threshold. First, an MCC-FMEA with limited compromise behaviors is presented. Second, experts’ tolerance behaviors are added to the MCC-FMEA with limited compromise behaviors. Theoretical results indicate that in some cases, this MCC-FMEA with limited compromise and tolerance behaviors has no solution. Thus, a minimum compromise adjustment consensus model and a maximum consensus model with limited compromise behaviors are developed and analyzed, and an interactive MCC-FMEA framework, resulting in an FMEA problem consensual collective solution, is designed. A case study, regarding the assessment of COVID-19-related risk in radiation oncology, and a detailed sensitivity and comparative analysis with the existing FMEA approaches are provided to verify the effectiveness of the proposed approach to FMEA consensus-reaching.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- H∞ Bipartite Synchronization Control of Markov Jump
Cooperation–Competition Networks With Reaction–Diffusions-
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Authors: Hao Shen;Xuelian Wang;Peiyong Duan;Jinde Cao;Jing Wang;
Pages: 6626 - 6635
Abstract: This article is concerned with the bipartite synchronization problem of coupled switching neural networks with cooperative–competitive interactions and reaction–diffusion terms. Different from the existing literature, the networked systems under investigation possess the relationship of cooperation and competition among nodes. Notably, the switching topology is described by a signed graph subject to the Markov jump process with the coexistence of positive and negative interaction weights. Specifically, a positive weight indicates an alliance relationship between two nodes and a negative one shows an adversary relationship. This article aims to design a bipartite synchronization controller for the aforementioned networks with the switching topology such that a prescribed $mathcal {H}_{infty }$ bipartite synchronization is satisfied. Then, some sufficient criteria to ensure the stochastic stability of bipartite synchronization error systems are established in view of an appropriate Lyapunov function. Finally, two simulation examples are presented to verify the validity of the proposed bipartite synchronization control method.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Multiparty Secure Broad Learning System for Privacy Preserving
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Authors: Xiao-Kai Cao;Chang-Dong Wang;Jian-Huang Lai;Qiong Huang;C. L. Philip Chen;
Pages: 6636 - 6648
Abstract: Multiparty learning is an indispensable technique to improve the learning performance via integrating data from multiple parties. Unfortunately, directly integrating multiparty data could not meet the privacy-preserving requirements, which then induces the development of privacy-preserving machine learning (PPML), a key research task in multiparty learning. Despite this, the existing PPML methods generally cannot simultaneously meet multiple requirements, such as security, accuracy, efficiency, and application scope. To deal with the aforementioned problems, in this article, we present a new PPML method based on the secure multiparty interactive protocol, namely, the multiparty secure broad learning system (MSBLS) and derive its security analysis. To be specific, the proposed method employs the interactive protocol and random mapping to generate the mapped features of data, and then uses efficient broad learning to train the neural network classifier. To the best of our knowledge, this is the first attempt for privacy computing method that jointly combines secure multiparty computing and neural network. Theoretically, this method can ensure that the accuracy of the model will not be reduced due to encryption, and the calculation speed is very fast. Three classical datasets are adopted to verify our conclusion.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- NSCKL: Normalized Spectral Clustering With Kernel-Based Learning for
Semisupervised Hyperspectral Image Classification-
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Authors: Yuanchao Su;Lianru Gao;Mengying Jiang;Antonio Plaza;Xu Sun;Bing Zhang;
Pages: 6649 - 6662
Abstract: Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI) classification. However, most SSC methods mainly consider local information, so that some correlations may not be effectively discovered when they appear in regions that are not contiguous. Although many SSC methods can acquire spatial-contextual characteristics via spatial filtering, they lack the ability to consider correlations in non-Euclidean spaces. To address the aforementioned issues, we develop a new semisupervised HSI classification approach based on normalized spectral clustering with kernel-based learning (NSCKL), which can aggregate local-to-global correlations to achieve a distinguishable embedding to improve HSI classification performance. In this work, we propose a normalized spectral clustering (NSC) scheme that can learn new features under a manifold assumption. Specifically, we first design a kernel-based iterative filter (KIF) to establish vertices of the undirected graph, aiming to assign initial connections to the nodes associated with pixels. The NSC first gathers local correlations in the Euclidean space and then captures global correlations in the manifold. Even though homogeneous pixels are distributed in noncontiguous regions, our NSC can still aggregate correlations to generate new (clustered) features. Finally, the clustered features and a kernel-based extreme learning machine (KELM) are employed to achieve the semisupervised classification. The effectiveness of our NSCKL is evaluated by using several HSIs. When compared with other state-of-the-art (SOTA) classification approaches, our newly proposed NSCKL demonstrates very competitive performance. The codes will be available at https://github.com/yuanchaosu/TCYB-nsckl.
PubDate: FRI, 15 SEP 2023 14:07:51 -04
Issue No: Vol. 53, No. 10 (2023)
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- Learning-Based Cuckoo Search Algorithm to Schedule a Flexible Job Shop
With Sequencing Flexibility-
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Authors: ChengRan Lin;ZhengCai Cao;MengChu Zhou;
Pages: 6663 - 6675
Abstract: This work considers an extended version of flexible job-shop problem from a postprinting or semiconductor manufacturing environment, which needs a directed acyclic graph rather than a linear order to describe the precedences among operations. To obtain its reliable and high-quality schedule in a reasonable time, a learning-based cuckoo search (LCS) algorithm is presented. In it, cuckoo search is selected as an optimizer. To produce promising solutions in a high-dimensional solution space, a sparse autoencoder is introduced to compress a high-dimensional solution into an informative low-dimensional one. It extends the application area of autoencoder-embedded evolutionary optimization methods into combinational optimization by developing an improved one-hot encoding method. Then, in order to reveal the linkages among decision variables and enhance the explore ability of the proposed method, a factorization machine (FM) is used, for the first time, to capture the relevant and complementary features of population. Hence, a parallel framework involving three co-evolved subpopulations is constructed. The first one is an autoencoder embedded subpopulation, the second one is assisted by an FM, and the last one undergoes a regular iteration process. To balance the exploration and exploitation of the proposed framework and avoid unnecessary computation, a reinforcement learning algorithm is used to adaptively adjust the proportion of subpopulations and tune parameters of each subpopulation iteratively. Numerical simulations with benchmarks are performed to compare it with CPLEX, some classical heuristics, and several recently developed methods. The results shows that it well outperforms them.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Differential Evolution With Duplication Analysis for Feature Selection in
Classification-
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Authors: Peng Wang;Bing Xue;Jing Liang;Mengjie Zhang;
Pages: 6676 - 6689
Abstract: By selecting a small subset of relevant features, feature selection can reduce the dimensionality of the problem while maintaining or increasing the discriminating ability of the data. However, many existing feature selection approaches ignore the fact that there are multiple optimal solutions to a feature selection problem. Multiple feature subsets with different features selected can achieve very similar or the same classification accuracy. To search for multiple optimal feature subsets, a niching-based differential evolution (DE) method with duplication analysis is proposed. In the proposed method, the duplicated feature subsets in the population are modified by the proposed subset repairing scheme which can produce unique feature subsets. Second, the mutation operator in DE is improved, which uses both the niche and global information to produce promising feature subsets. Third, a new selection method considering the diversity among feature subsets is adopted to form a new population for the next-generation. In the experiments, the proposed method is compared with seven evolutionary feature selection algorithms and two typical feature selection methods on 18 datasets. The results show that the proposed algorithm achieves higher classification accuracy than the compared methods on most of the used datasets. Furthermore, the proposed method can find different feature subsets with very similar or the same classification accuracy.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Event-Triggered Adaptive Output-Feedback Control for Nonlinearly
Parameterized Uncertain Systems With Quantization and Input Delay-
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Authors: Wenhui Liu;Qian Ma;Shengyuan Xu;
Pages: 6690 - 6699
Abstract: An output-feedback-based event-triggered control issue of a class of uncertain nonlinear systems considering state quantization and input delay is investigated. In this study, by constructing the state observer and adaptive estimation function, a discrete adaptive control scheme is designed based on the dynamic sampled and quantized mechanism. With the aid of the Lyapunov–Krasovskii functional method and a stability criterion, the global stability of the time-delay nonlinear systems is ensured. Additionally, the Zeno behavior will not happen in the event-triggering. Finally, a numerical example and a practical example are presented to verify the effectiveness of the designed discrete control algorithm with input time-varying delay.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Maximal Margin Support Vector Machine for Feature Representation and
Classification-
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Authors: Zhihui Lai;Xi Chen;Junhong Zhang;Heng Kong;Jiajun Wen;
Pages: 6700 - 6713
Abstract: High-dimensional small sample size data, which may lead to singularity in computation, are becoming increasingly common in the field of pattern recognition. Moreover, it is still an open problem how to extract the most suitable low-dimensional features for the support vector machine (SVM) and simultaneously avoid singularity so as to enhance the SVM’s performance. To address these problems, this article designs a novel framework that integrates the discriminative feature extraction and sparse feature selection into the support vector framework to make full use of the classifiers’ characteristics to find the optimal/maximal classification margin. As such, the extracted low-dimensional features from high-dimensional data are more suitable for SVM to obtain good performance. Thus, a novel algorithm, called the maximal margin SVM (MSVM), is proposed to achieve this goal. An alternatively iterative learning strategy is adopted in MSVM to learn the optimal discriminative sparse subspace and the corresponding support vectors. The mechanism and the essence of the designed MSVM are revealed. The computational complexity and convergence are also analyzed and validated. Experimental results on some well-known databases (including breastmnist, pneumoniamnist, colon-cancer, etc.) show the great potential of MSVM against classical discriminant analysis methods and SVM-related methods, and the codes can be available on https://www.scholat.com/laizhihui.
PubDate: FRI, 15 SEP 2023 14:07:51 -04
Issue No: Vol. 53, No. 10 (2023)
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- Event-Based Optimal Stealthy False Data-Injection Attacks Against Remote
State Estimation Systems-
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Authors: Haibin Guo;Jian Sun;Zhong-Hua Pang;Guo-Ping Liu;
Pages: 6714 - 6724
Abstract: Security is a crucial issue for cyber–physical systems, and has become a hot topic up to date. From the perspective of malicious attackers, this article aims to devise an efficient scheme on false data-injection (FDI) attacks such that the performance on remote state estimation is degraded as much as possible. First, an event-based stealthy FDI attack mechanism is introduced to selectively inject false data while evading a residual-based anomaly detector. Compared with some existing methods, the main advantage of this mechanism is that it decides when to launch the FDI attacks dynamically according to real-time residuals. Second, the state estimation error covariance of the compromised system is used to evaluate the performance degradation under FDI attacks, and the larger the state estimation error covariance, the more the performance degradation. Moreover, under attack stealthiness constraints, an optimal strategy is presented to maximize the trace of the state estimation error covariance. Finally, simulation experiments are carried out to illustrate the superiority of the proposed method compared with some existing ones.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Differentially Private Average Consensus With Logarithmic Dynamic
Encoding–Decoding Scheme-
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Authors: Wei Chen;Zidong Wang;Jun Hu;Guo-Ping Liu;
Pages: 6725 - 6736
Abstract: This article is concerned with the differentially private average consensus (DPAC) problem for a class of multiagent systems with quantized communication. By constructing a pair of auxiliary dynamic equations, a logarithmic dynamic encoding–decoding (LDED) scheme is developed and then utilized during the process of data transmission, thereby eliminating the effect of quantization errors on the consensus accuracy. The primary purpose of this article is to establish a unified framework that integrates the convergence analysis, the accuracy evaluation, and the privacy level for the developed DPAC algorithm under the LDED communication scheme. By means of the matrix eigenvalue analysis method, the Jury stability criterion, and the probability theory, a sufficient condition (with respect to the quantization accuracy, the coupling strength, and the communication topology) is first derived to ensure the almost sure convergence of the proposed DPAC algorithm, and the convergence accuracy and privacy level are thoroughly investigated by resorting to the Chebyshev inequality and $epsilon $ -differential privacy index. Finally, simulation results are provided to illustrate the correctness and validity of the developed algorithm.
PubDate: FRI, 15 SEP 2023 14:07:50 -04
Issue No: Vol. 53, No. 10 (2023)
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- Distributed Observer-Based Robust Fault Estimation Design for
Discrete-Time Interconnected Systems With Disturbances-
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Authors: Yunfei Mu;Huaguang Zhang;Yuqing Yan;Xiangpeng Xie;
Pages: 6737 - 6747
Abstract: This article focuses on the distributed robust fault estimation problem for a kind of discrete-time interconnected systems with input and output disturbances. For each subsystem, by letting the fault as a special state, an augmented system is constructed. Particularly, the dimensions of system matrices after augmentation are lower than some existing related results, which may help to reduce calculation amount, especially, for linear matrix inequality-based conditions. Then, a distributed fault estimation observer design scheme that utilizes the associated information among subsystems is presented to not only reconstruct faults, but also suppress disturbances in the sense of robust $H_{infty }$ optimization. Besides, to improve the fault estimation performance, a common Lyapunov matrix-based multiconstrained design method is first given to solve the observer gain, which is further extended to the different Lyapunov matrices-based multiconstrained calculation method. Thus, the conservatism is reduced. Finally, simulation experiments are shown to verify the validity of our distributed fault estimation scheme.
PubDate: FRI, 15 SEP 2023 14:07:49 -04
Issue No: Vol. 53, No. 10 (2023)
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- Connect. Support. Inspire.
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Pages: 6748 - 6748
PubDate: FRI, 15 SEP 2023 14:06:22 -04
Issue No: Vol. 53, No. 10 (2023)
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