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Fixed-time stabilization of discontinuous spatiotemporal neural networks with time-varying coefficients via aperiodically switching control

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Science China(Information Sciences) 2023年第5期66卷 182-195页

作者： Xiaofang HU Leimin WANG Chuan-Ke ZHANG Xiongbo WAN Yong HE School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper focuses on the challenge of fixed-time control for spatiotemporal neural networks(SNNs) with discontinuous activations and time-varying coefficients. A novel fixed-time convergence lemma is proposed, which facilitates the handling of time-varying coefficients of SNNs and relaxes the restriction on the non-positive definiteness of the derivative of the Lyapunov function. Besides, a more flexible and economical aperiodically switching control technique is presented to stabilize SNNs within a fixed time,efectively reducing the amount of information transmission and control costs. Under the newly established fixed-time convergence lemma and aperiodically switching controller, many more general algebraic conditions are deduced to ensure the fixed-time stabilization of SNNs. Numerical examples are provided to manifest the validity of the results.

关键词： spatiotemporal neural networks discontinuous activations time-varying coefficients fixed-time stabilization aperiodically switching control

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A Hybrid Data-Driven Granular Model for Battery Remaining Useful Life Prediction

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jing, TaiLong Du, Sheng Wang, Cong Wu, Chao Pedrycz, Witold Sharaf, Mohamed Li, ZhiWu Luoyang Institute of Science and Technology School of Electrical Engineering and Automation Luoyang471023 China China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Xi’an710072 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland King Saud University College of Engineering Department of Industrial Engineering Riyadh11421 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology Macau China

Accurate and meaningful prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) provides a significant guide for the maintenance and management of these LIBs. A singular numerical predictive result by numerous conventional models is insufficient to furnish decision-makers with comprehensive or nuanced insights. To address this issue, predictive RUL results in the form of information granules are essential. In this study, a hybrid data-driven granular model is established for battery RUL prediction with granular (interval-based) quantification for LIBs. This granular model is designed and formulated in a way that it produces comprehensive prediction outcomes realized as information granules, which are expressed in a more descriptive and comprehensive manner. Thus, the resulting information granules can effectively reflect and communicate the confidence associated with the predictions. The granular model is realized in two steps: 1) probabilistic prediction results are produced by a hybrid numeric predictive model and 2) a comprehensive granulation of the probabilistic results is achieved under the principle of information granularity. The granular predictive results are holistically evaluated and optimized by the two associated criteria of granularity (coverage and specificity). This transformation of information granules provides an intuitive and interpretable display for battery maintenance and safe work. The proposed granular model is verified by using an open-access LIBs dataset, and the experimental results demonstrate that the proposed granular model outperforms other comparative methods. © 2025 IEEE. All rights reserved.

关键词： Prediction models

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Extended Dissipativity Analysis for Delayed Markovian Jump Neural Networks via a Delay-Interval-Adjustable-Based Lyapunov-Krasovskii Functional

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IEEE transactions on cybernetics 2025年 PP卷 PP页

作者： Xu-Kang Chang Yong He China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China

The issue of extended dissipativity analysis (EDA) for delayed Markovian jump neural networks (MJNNs) is investigated in this article. First, a delay-interval-adjustable-based Lyapunov-Krasovskii functional (LKF) is proposed, in which the delay interval is adjusted by a tunable parameter to obtain an optimal extended dissipativity result, offering a new idea to enhance the consideration of delay information. Furthermore, to take into account more effective information, the LKF is augmented with both single and quadratic integral variables. Accordingly, the LKF derivative becomes a higher-order term of the time-varying delay. To solve this nonlinear problem, a variable-augmented-based free-weighting-matrices approach is employed to transform the nonlinear term into a linear form and provides more freedom in obtaining enhanced EDA results. Then, two novel extended dissipativity criteria of delayed MJNNs are derived. Meanwhile, to show the general applicability of the proposed methods, the derived criteria are applied to the EDA and stability analysis for delayed neural networks (NNs). Lastly, the merits and effectiveness of the proposed techniques are demonstrated through three numerical examples and a real-world application of a quadruple-tank process system. Additionally, the proposed methods can be effectively applied to the practical fields of power system stability control, robot motion control, and image processing, while reducing the conservatism of system performance results.

关键词： Extended dissipativity free-weighting matrices Lyapunov–Krasovskii functional (LKF) Markovian jump neural networks (MJNNs) time-varying delay

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Path Planning for Distribution Network Robots with Adaptive Simulated Annealing-Enhanced Artificial Potential Field Method

Path Planning for Distribution Network Robots with Adaptive ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Baichao Zhang Xin Chen Jinge Chen Jinbin Li Xiaokai Wang Xu Jian School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan P. R. China Hubei Fangyuan Dongli Electric Power Science Research Co. Ltd Wuhan P. R. China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

In this paper, we propose a hybrid algorithm that combines an improved Artificial Potential Field (APF) method with the Simulated Annealing (SA) algorithm for path planning of an electric power operation robot manipulator in complex distribution grid environments. To address the unreachable target issue inherent in traditional APF, we introduce a distance regulation factor to optimize the repulsive function. This modification allows the manipulator to smoothly approach the target point as it nears, while the repulsion from obstacles gradually decreases. Additionally, to overcome the limitations of the traditional SA algorithm, such as its tendency to get trapped in local minimum solutions and its inefficiency in complex environments, we propose an adaptive temperature rise strategy. This strategy increases the temperature, enhancing the probability of escaping local optimal solutions. When the APF algorithm becomes trapped in a local optimum, the improved SA algorithm is applied to escape the local minimum. Once the local optimum is avoided, the algorithm switches back to APF to continue the path planning process. Simulation results demonstrate that the proposed improved APF-SA algorithm adapts effectively to various complex environments, achieving shorter planning times and higher success rates compared to traditional APF and SA algorithms. It successfully resolves the unreachable target and local minimum problems associated with APF. Finally, the feasibility of the proposed APF-SA fusion algorithm is validated through experiments conducted on an electric power operation robot experimental platform for distribution grid applications.

关键词： Electric potential Adaptation models Force Distribution networks Simulated annealing Manipulators Path planning Planning Collision avoidance Robots

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan 430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China University of Alberta Department of Electrical and Computer Engineering Edmonton T6R 2G7 AB Canada Tokyo Institute of Technology Yokohama 226-8502 Japan Tokyo Institute of Technology Tokyo 226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： cross-modal attention Multi-scale features multimodal emotion recognition

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Simplified outlier detection for improving the robustness of a fuzzy model

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Science China(Information Sciences) 2020年第4期63卷 218-220页

作者： Yali JIN Weihua CAO Min WU Yan YUAN School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences

Dear editor,Fuzzy models have been widely employed in various fields, such as complex industrial process modeling, classification, function approximation, and time series prediction. Extracting fuzzy rules is very important as they determine the capabilities of the fuzzy model. In recent years, data-based methods such as fuzzy clustering, genetic algorithm, and neural networks [1–3], have been pro-

关键词： Simplified outlier detection for improving the robustness of a fuzzy model Gaussian

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Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive control

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 723-733页

作者： Xiongbo Wan Chaoling Zhang Fan Wei Chuan-Ke Zhang Min Wu IEEE the School of Automation China University of Geosciencesthe Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systemsand the Engineering Research Center of Intelligent Technology for Geo-ExplorationMinistry of EducationWuhan 430074China

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic *** addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI *** the designed controller,the involved fuzzy system is ensured to be asymptotically *** examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

关键词： Dynamic event-triggered mechanism(DETM) hybrid dynamic variables model predictive control(MPC) robust positive invariant(RPI)set T-S fuzzy systems

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Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

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Computer Modeling in Engineering & Sciences 2023年第1期134卷 221-239页

作者： Tianyuan Jia Xiangyong Chen Xiurong Yao Feng Zhao Jianlong Qiu School of Automation and Electrical Engineering and Key Laboratory of Complex Systems and Intelligent Computing in Universities of ShandongLinyi UniversityLinyi276005China The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and School of AutomationChina University of GeosciencesWuhan430074China

Fixed-time synchronization(FTS)of delayed memristor-based neural networks(MNNs)with discontinuous activations is studied in this *** continuous and discontinuous activations are considered *** the mixed delays which are closer to reality are taken into the ***,two kinds of control schemes are proposed,including feedback and adaptive control *** on some lemmas,mathematical inequalities and the designed controllers,a few synchronization criteria are ***,the upper bound of settling time(ST)which is independent of the initial values is ***,the feasibility of our theory is attested by simulation examples.

关键词： Memristor-based neural networks fixed-time synchronization mixed delays discontinuous activations adaptive control

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An automated pulmonary parenchyma segmentation method based on an improved region growing algorithm in PET-CT imaging

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Frontiers of Computer Science 2016年第1期10卷 189-200页

作者： Juanjuan ZHAO Guohua JI Xiaohong HAN Yan QIANG Xiaolei LIAO College of Computer Science and Technology Taiyuan University of Technology Taiyuan 030024 China Key Laboratory of Advanced Transducers and Intelligent Control Systems Taiyuan University of Technology Taiyuan 030024 China

To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages： image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography （PET-CT） data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.

关键词： pulmonary parenchyma segmentation bot-tom region of lung image binarization iterative threshold seeded region growing four-corner rotating and scanning,denoising contour refining PET-CT

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Development of electric cart for improving walking ability — application of control theory to assistive technology

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Science China(Information Sciences) 2017年第12期60卷 262-270页

作者： Jinhua SHE Yasuhiro OHYAMA Min WU Hiroshi HASHIMOTO School of Engineering Tokyo University of Technology School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Master Program of Innovation for Design & Engineering Advanced Institute of Industrial Technology

This paper explains the development of an electric cart that helps the elderly maintain or improve their physical strength. Unlike commercially available ones, it has a pedal unit that provides some exercise for a user in training his lower limbs. An impedance model describes the feeling of pushing the pedals. The largest pedal load is determined based on a pedaling experiment. An H∞ controller is designed for each of the largest pedal load and virtually no load. A control law, which is based on the concept of dynamic parallel distributed compensation, is designed using the rating of perceived exertion of a driver as a criterion to choose a pedal load between the largest and almost zero. Five university students and twelve elderly people participated experiments to verify the system design and the validity of the system.

关键词： aging Borg’s scale dynamic parallel distributed compensation electrical cart H_∞ control Karvonen formula lower limbs pedaling rating of perceived exertion

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