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Asynchronous Impulsive Protocols With Asymmetric Feedback Saturation on Leader-Based Formation control of Multiagent Systems

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 9931-9942页

作者： Han, Yiyan Zeng, Zhigang Huazhong Univ Sci & Technol Inst Artificial Intelligence Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Saturation phenomena often exist due to limited system resources, and impulsive protocols can lead to a reduction in communication cost. From these issues, this article investigates a leader-based formation control problem of multiagent systems via asynchronous impulsive protocols with saturated feedback. General linear system models with and without finite time-varying time delays under asymmetric saturated feedback control are concurrently considered. The asynchronous impulsive protocols only permit communication at impulsive instants and each agent has its own communication instants independently. Moreover, to improve system performance, an offset only containing desired formation information is introduced. Finally, because the feedbacks are saturated, admissible regions are proved to exist, which are also estimated by a mean of optimization. Numerical simulations are presented to demonstrate the validity of the proposed schemes.

关键词： Protocols Multi-agent systems Target tracking Optimization Numerical stability Nonlinear dynamical systems Linear systems Admissible region asynchronous impulsive protocols feedback saturation formation control linear time-delayed multiagent systems

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Battery state-of-charge estimation combining extended Kalman filter and RLS with adaptive directional forgetting 17

Battery state-of-charge estimation combining extended Kalman...

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IEEE 17th International Conference on control and Automation (ICCA)

作者： Zhu, Kun Zhang, Cong Zhang, Sihang Wan, Yiming Huazhong Univ Sci & Technol Sch Articial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Beijing Inst Control Engn Beijing 100094 Peoples R China

ISBN: (数字)9781665495721

ISBN: (纸本)9781665495721

Accurate estimation of battery state-of-charge (SOC) is important for safe and reliable operations of batteries. To account for time-varying model parameters, recursive least squares and extended Kalman filter (EKF) are usually combined to jointly estimate model parameters and SOC. However, in practice, the level of excitation in the online data varies with the working condition. In case that the online data are not persistently exciting, the recursive parameter estimation with various forgetting strategies suffers from covariance blowup, To address this issue, a novel adaptive directional forgetting strategy is proposed and used in combination with EKF. Different from forgetting all old data as in the conventional forgetting methods, only the direction with sufficient excitation is forgotten. The forgetting factor is also adjusted adaptively according to the prediction error, which enhances the capability to capture the parameters variations. Using experimental data on three different working conditions, the proposed approach demonstrates improved SOC estimation accuracy compared to using the conventional exponential forgetting.

关键词： Employee welfare Adaptation models Parameter estimation Automation Estimation Batteries State of charge

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A Dual-branch Model for Early Detection of Alzheimer’s Disease Using Resting-State fMRI

A Dual-branch Model for Early Detection of Alzheimer’s Dise...

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Yixuan Wang Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

Alzheimer’s disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical imaging technique, offers rich temporal and spatial data, which has led researchers to explore various feature extraction methods based on rs-fMRI images for AD identification. However, the related work still suffers from insufficient utilization of temporal and spatial information which leads to unsatisfactory early diagnosis. In this study, we propose a dual-branch fusion model to extract spatial-temporal features from rs-fMRI images. Our proposed model can extract temporal features at different levels. We developed a Class Activation Sequence (CAS) branch, which is a structure that emphasizes the function of each temporal node throughout the whole time series. Additionally, we created a time-domain local branch for local feature extraction. Further, we designed a fusion module for the model to describe temporal contextual relationships and fuse features at various levels. We tested the performance of the model on the ADNI dataset, and the experimental results show that compared with other algorithms, the dual-branch fusion model achieves higher classification accuracy on several classification tasks including early diagnosis, which proves the advantage of the dual-branch fusion model in temporal and spatial feature extraction for rs-fMRI images, and our work also provides a foundation for the temporal domain characterization of rs-fMRI images.

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Spatiotemporal distance embedded hybrid ant colony algorithm for a kind of vehicle routing problem with constraints

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Frontiers of Information Technology & Electronic Engineering 2023年第7期24卷 1062-1079页

作者： Zhenhui FENG Renbin XIAO School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Institute of Artificial Intelligence Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

We investigate a kind of vehicle routing problem with constraints(VRPC)in the car-sharing mobility environment,where the problem is based on user orders,and each order has a reservation time limit and two location point transitions,origin and *** is a typical extended vehicle routing problem(VRP)with both time and space *** consider the VRPC problem characteristics and establish a vehicle scheduling model to minimize operating costs and maximize user(or passenger)*** solve the scheduling model more accurately,a spatiotemporal distance representation function is defined based on the temporal and spatial properties of the customer,and a spatiotemporal distance embedded hybrid ant colony algorithm(HACA-ST)is *** algorithm can be divided into two ***,through spatiotemporal clustering,the spatiotemporal distance between users is the main measure used to classify customers in categories,which helps provide heuristic information for problem ***,an improved ant colony algorithm(ACO)is proposed to optimize the solution by combining a labor division strategy and the spatiotemporal distance function to obtain the final scheduling *** analysis is carried out based on existing data sets and simulated urban *** with other heuristic algorithms,HACA-ST reduces the length of the shortest route by 2%–14%in benchmark *** VRPC testing instances,concerning the combined cost,HACA-ST has competitive cost compared to existing VRP-related ***,we provide two actual urban scenarios to further verify the effectiveness of the proposed algorithm.

关键词： Vehicle routing problem with constraints(VRPC) Spatiotemporal distance function labor division strategy Ant colony algorithm(ACO)

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Source-free Unsupervised Domain Adaptation for Privacy-Preserving intelligent Fault Diagnosis 3

Source-free Unsupervised Domain Adaptation for Privacy-Prese...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Zhu, Mengliang Zeng, Xiangyu Liu, Jie Zhou, Kaibo MOE Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Huazhong University of Science and Technology Wuhan China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665492812

Rolling bearing is of vital significance in industrial applications and intelligent fault diagnosis (IFD) have been widely exploited in this field. However, cross-machine variations hinder the model performance across different machines in varying working conditions. To solve this issue, many unsupervised domain adaptation (UDA) approaches have been proposed recently, requiring direct access to the fully labeled source domain. However, privacy concerns are raised in traditional UDA scenario, since raw signals contain various private information. To address this issue, the source-free unsupervised domain adaptation (SFUDA) scenario is considered, where only the pre-trained source model is required, instead of the fully labeled source domain. In this paper, a SFUDA approach for intelligent fault diagnosis (IFD) is proposed. It consists of two steps: 1) source model generalization, where virtual adversarial training, R-Drop and label smoothing techniques are adopted to improve the generalization ability of the source model;and 2) target model adaptation, where information maximization is used for cluster assumption and mean teacher training paradigm is utilized to alleviate the catastrophic forgetting phenomenon. The proposed approach is verified on the Case Western Reserve University (CWRU) dataset. Experimental results on the show that the proposed SFUDA approach outperforms several typical UDA approaches, and its effectiveness is verified. © 2022 IEEE.

关键词： intelligent fault diagnosis privacy concerns Rolling bearing source-free unsupervised domain adaptation

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Distributed Moving Target Fencing in a Regular Polygon Formation

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2022年第1期9卷 210-218页

作者： Hu, Bin-Bin Chen, Zhiyong Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Tech Wuhan 430074 Peoples R China Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia

This article proposes a distributed controller for a network of agents of second-order dynamics to fence a moving target of unknown velocity within their convex hull. Moreover, the agents form a regular polygon formation and avoid collision during the entire evolution. In the control scheme, the agents are not necessarily labeled and the nearest angle rules are applied. Each controller is composed of the functionalities of estimation of the target's velocity, regulation of distance between the agents and the target, and angle repulsion among agents resulting in an equal distribution. The latter two also guarantee collision avoidance. The asymptotical behavior of the closed-loop system is rigorously analyzed, especially subject to the velocity estimation error. The effectiveness of the controller is also demonstrated by numerical simulation.

关键词： Autonomous agents collaborative control collision avoidance formation multiagent systems target fencing

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Adhesive and Hydrophobic Bilayer Hydrogel Enabled On-Skin Biosensors for High-Fidelity Classification of Human Emotion

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ADVANCED FUNCTIONAL MATERIALS 2022年第29期32卷 2200457-2200457页

作者： Yang, Ganguang Zhu, Kanhao Guo, Wei Wu, Dongrui Quan, Xueliang Huang, Xin Liu, Shaoyu Li, Yangyang Fang, Han Qiu, Yuqi Zheng, Qingyang Zhu, Mengliang Huang, Jian Zeng, Zhigang Yin, Zhouping Wu, Hao Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Flexible Elect Res Ctr Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Traditional human emotion recognition is based on electroencephalogram (EEG) data collection technologies which rely on plenty of rigid electrodes and lack anti-interference, wearing comfort, and portability. Moreover, a significant distribution difference in EEG data also results in low classification accuracy. Here, on-skin biosensors with adhesive and hydrophobic bilayer hydrogel (AHBH) as interfaces for high accuracy emotion classification are proposed. The AHBH achieves remarkable adhesion (59.7 N m(-1)) by combining the adhesion mechanism of catechol groups and electrostatic attraction. Meanwhile, based on the synergistic effects of hydrophobic group rearrangements and surface energy reduction, the AHB-hydrophobic layer exhibits 133.87 degrees water contact angles through hydrophobic treatment of only 0.5 h. Hydrogen and electrostatic bonds are also introduced to form a seamless adhesive-hydrophobic hydrogel interface and inhibit adhesion attenuation, respectively. With the AHBH as an ideal device/skin interface, the biosensor can reliably collect high-quality electrophysiological signals even under vibration, sweating, and long-lasting monitoring condition. Furthermore, the on-skin electrodes, data processing, and wireless modules are integrated into a portable headband for EEG-based emotion classification. A domain adaptive neural network based on the transfer learning technique is introduced to alleviate the effect of domain shift and achieve high classification accuracy.

关键词： electrophysiological signals human emotion classification hydrogel interfaces on-skin biosensors

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Hybrid Relation Guided Set Matching for Few-shot Action Recognition

Hybrid Relation Guided Set Matching for Few-shot Action Reco...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Tang, Mingqian Zuo, Zhengrong Gao, Changxin Jin, Rong Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current few-shot action recognition methods reach impressive performance by learning discriminative features for each video via episodic training and designing various temporal alignment strategies. Nevertheless, they are limited in that (a) learning individual features without considering the entire task may lose the most relevant information in the current episode, and (b) these alignment strategies may fail in misaligned instances. To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric. The purpose of the hybrid relation module is to learn task-specific embeddings by fully exploiting associated relations within and cross videos in an episode. Built upon the task-specific features, we reformulate distance measure between query and support videos as a set matching problem and further design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. By this means, the proposed HyRSM can be highly informative and flexible to predict query categories under the few-shot settings. We evaluate HyRSM on six challenging benchmarks, and the experimental results show its superiority over the state-of-the-art methods by a convincing margin. Project page: https://***/.

关键词： Measurement Training Semantics Benchmark testing Pattern recognition Kinetic theory Task analysis

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A Bayesian Approach for Joint Discriminative Dictionary and Classifier Learning

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第12期52卷 7858-7870页

作者： Zhou, Wei Wu, Yue Li, Junlin Wang, Maolin Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China

Sparse representation has been widely applied to image classification, where the key issue is to extract a suitable discriminative dictionary. To this end, we propose a joint dictionary and classifier learning algorithm based on a parameterized Bayesian model. Therein, the Gaussian priors of a dictionary endow it with the capability of discrimination and representation. Moreover, we introduce a multivariate Gaussian prior for the sparse codes to achieve group sparsity, thereby substantially improving the classification performance. Furthermore, the sparse codes are estimated by a group-sparse Bayesian learning (GSBL) method, and the dictionary atoms are updated sequentially by maximizing a posterior. Moreover, to avoid manual parameter adjustment, the hyperparameters are optimized by an evidence maximization method. Accordingly, we develop a classification scheme via GSBL. Finally, extensive experiments are conducted on six benchmark datasets of face classification, object recognition, handwritten recognition, and scene categorization to substantiate the effectiveness and superiority of the proposed method.

关键词： Dictionaries Bayes methods Codes Machine learning Sparse matrices Training Face recognition Dictionary learning image classification sparse Bayesian learning sparse representation (SR)

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Novel fixed-time stability criteria of nonlinear systems and applications in fuzzy competitive neural network and Chua's oscillator

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NEURAL COMPUTING & APPLICATIONS 2023年第22期35卷 16527-16542页

作者： Ren, Fangmin Wang, Xiaoping Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol KeyLaboratory Image Proc & Intelligent Control ofE Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain inspired Intelligent Syst Wuhan 430074 Peoples R China

Since the fixed-time stability forms of nonlinear systems satisfy strict conditions, there are few general forms for nonlinear systems to achieve fixed-time stability. This work proposes a new class of more general fixed-time stability criteria. It is worth mentioning that, compared with the traditional method of estimating the convergence time, this paper obtains a more conservative stable time estimation formula through the integration method of the generalized integral mean theorem. In addition, given that the fixed-time stabilization of neural networks and chaotic oscillators have attracted extensive attention in recent years, and there are still many fixed-time stabilizations of nonlinear systems that have not been studied. Therefore, a discontinuous controller is designed in this paper. The above stability theory results are applied to the fixed-time stabilization of the Takagi-Sugeno (T-S) fuzzy competitive neural network and chaotic system (coupled Chua's oscillator). Finally, the validity and applicability of the theoretical results are verified by examples.

关键词： Fixed-time stability Competitive neural network Chua's oscillator Discontinuous controller

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