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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Gao, Changxin Zhang, Yingya Zhao, Deli Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China

Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they generally suffer from two limitations: i) the matching procedure between local frames tends to be inaccurate due to the lack of guidance to force long-range temporal perception;ii) explicit motion learning is usually ignored, leading to partial information loss. To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder. Specifically, the long-short contrastive objective is to endow local frame features with long-form temporal awareness by maximizing their agreement with the global token of videos belonging to the same class. The motion autodecoder is a lightweight architecture to reconstruct pixel motions from the differential features, which explicitly embeds the network with motion dynamics. By this means, MoLo can simultaneously learn long-range temporal context and motion cues for comprehensive few-shot matching. To demonstrate the effectiveness, we evaluate MoLo on five standard benchmarks, and the results show that MoLo favorably outperforms recent advanced methods. The source code is available at https://***/alibaba-mmai-research/MoLo. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Demand-Side Frequency control in Power Network Based on Multi-Agent Deep Reinforcement Learning

Demand-Side Frequency Control in Power Network Based on Mult...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Xianggang Liu Zhi-Wei Liu Ming Chi Yifan Zhang Key Laboratory of Image Processing and Intelligent Control Ministry of Education College of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

The integration of renewable energy sources into the power grid has led to new challenges in maintaining the stability of the system frequency. This paper proposes a novel approach to address the Optimal Demand Side Frequency control (ODFC) problem using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method. The proposed method models the ODFC problem as a Markov game, with centralized training based on multi-agent cooperative self-learning and associative storage service. In the decentralized execution stage, each agent independently outputs control actions to the controlled plant using local observations. Numerical simulations show that the proposed method effectively addresses the ODFC problem with superior performance compared to traditional methods.

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Neural-Network-Based Nonlinear Model Predictive Tracking control of a Pneumatic Muscle Actuator-Driven Exoskeleton

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IEEE/CAA Journal of Automatica Sinica 2020年第6期7卷 1478-1488页

作者： Yu Cao Jian Huang IEEE Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

Pneumatic muscle actuators(PMAs)are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries,such as strokes,spinal cord injuries,etc.,to accomplish rehabilitation ***,because PMAs have nonlinearities,hysteresis,and uncertainties,etc.,complex mechanisms are rarely involved in the study of PMA-driven robotic *** this paper,we use nonlinear model predictive control(NMPC)and an extension of the echo state network called an echo state Gaussian process(ESGP)to design a tracking controller for a PMA-driven lower limb *** dynamics of the system include the PMA actuation and mechanism of the leg orthoses;thus,the system is represented by two nonlinear uncertain *** facilitate the design of the controller,joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP.A gradient descent algorithm is employed to solve the optimization problem and generate the control *** stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system *** and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.

关键词： Echo state Gaussian process model predictive control neural network pneumatic muscle actuators-driven exoskeleton

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Semi-Supervised Generalized Source-Free Domain Adaptation (SSG-SFDA)

Semi-Supervised Generalized Source-Free Domain Adaptation (S...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiayu An Changming Zhao Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Continual learning aims to learn on a sequence of new tasks while maintaining the performance on previous tasks. Source-free domain adaptation (SFDA), which adapts a pretrained source model to a target domain, is useful in protecting the source domain data privacy. Generalized SFDA (G-SFDA) combines continual learning and SFDA to achieve outstanding performance on both the source and the target domains. This paper proposes semi-supervised G-SFDA (SSG-SFDA) for domain incremental learning, where a pre-trained source model (instead of the source data), few labeled target data, and plenty of unlabeled target data, are available. The goal is to achieve good performance on all domains. To cope with domain-ID agnostic, SSG-SFDA trains a conditional variational auto-encoder (CVAE) for each domain to learn its feature distribution, and a domain discriminator using virtual shallow features generated by CVAE to estimate the domain ID. To cope with catastrophic forgetting, SSG-SFDA uses soft domain attention to improve the sparse domain attention in G-SFDA. To cope with insufficient labeled target data, SSG-SFDA uses MixMatch to augment the unlabeled target data and better exploit the few labeled target data. Experiments on three datasets demonstrated the effectiveness of SSG-SFDA.

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Global Stability of Bidirectional Associative Memory Neural Networks With Multiple Time-Varying Delays

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第6期52卷 4095-4104页

作者： Sheng, Yin Zeng, Zhigang Huang, Tingwen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Texas A&M Univ Qatar Dept Sci Doha Qatar

This article investigates the global stability of bidirectional associative memory neural networks with discrete and distributed time-varying delays (DBAMNNs). By employing the comparison strategy and inequality techniques, global asymptotic stability (GAS) and global exponential stability (GES) of the underlying DBAMNNs are of concern in terms of p-norm (p >= 2). Meanwhile, GES of the addressed DBAMNNs is also analyzed in terms of 1-norm. When distributed time delay is neglected, the GES of the corresponding bidirectional associative memory neural networks is presented as an M-matrix, which includes certain existing outcomes as special cases. Two examples are finally provided to substantiate the validity of theories.

关键词： Bidirectional associative memory neural networks (BAMNNs) discrete-time delay distributed time delay global stability

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Intralayer Synchronization in Heterogeneous Multiplex Dynamical Networks Based on Spectral Graph Theory

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2023年第3期13卷 646-657页

作者： Liu, Hui Zhang, Shiman Wu, Chai Wah Wu, Xiaoqun Li, Zengyang Xu, Jiangqiao Huazhong Univ Sci & Technol Minist China Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain Inspired Intelligent Syst Wuhan 430074 Peoples R China IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Wuhan Univ Sch Math & Stat Wuhan 430072 Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan 430079 Peoples R China Cent China Normal Univ Hubei Prov Key Lab Artificial Intelligence & Smar Wuhan 430079 Peoples R China

This paper studies a heterogeneous multiplex network model that allows different dynamics in different layers. We explore intralayer synchronization of the multiplex network under distinct types of interlayer connections. From the perspective of spectral graph theory, we propose a set of edge weight requirements to synchronize the multiplex network. Focusing on the effect of interlayer connections to intralayer synchronization, it is found that a multiplex network can achieve intralayer synchronization with a large enough interlayer coupling strength even if a single network of one layer cannot synchronize by itself. In fact, the synchronizability of the multiplex network is found to be stronger than that of the single-layer network. These results provide insights into the practical application of multiplex network theory in engineering networks.

关键词： Multiplexing Synchronization Couplings Graph theory Network topology Laplace equations Mathematical models Multiplex network intralayer synchronization spectral graph theory edge weight allocation

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Deep Reinforcement Learning for Smart Grid Operations: Algorithms, Applications, and Prospects

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PROCEEDINGS OF THE IEEE 2023年第9期111卷 1055-1096页

作者： Li, Yuanzheng Yu, Chaofan Shahidehpour, Mohammad Yang, Tao Zeng, Zhigang Chai, Tianyou Huazhong Univ Sci & Technol Autonomous Intelligent Unmanned Syst Engn Res Ctr Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligence ControlMinist E Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain Inspired Intelligent Syst & Be Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China IIT Robert W Galvin Ctr Elect Innovat Chicago IL 60616 USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

With the increasing penetration of renewable energy and flexible loads in smart grids, a more complicated power system with high uncertainty is gradually formed, which brings about great challenges to smart grid operations. Traditional optimization methods usually require accurate mathematical models and parameters and cannot deal well with the growing complexity and uncertainty. Fortunately, the widespread popularity of advanced meters makes it possible for smart grid to collect massive data, which offers opportunities for data-driven artificial intelligence methods to address the optimal operation and control issues. Therein, deep reinforcement learning (DRL) has attracted extensive attention for its excellent performance in operation problems with high uncertainty. To this end, this article presents a comprehensive literature survey on DRL and its applications in smart grid operations. First, a detailed overview of DRL, from fundamental concepts to advanced models, is conducted in this article. Afterward, we review various DRL techniques as well as their extensions developed to cope with emerging issues in the smart grid, including optimal dispatch, operational control, electricity market, and other emerging areas. In addition, an application-oriented survey of DRL in smart grid is presented to identify difficulties for future research. Finally, essential challenges, potential solutions, and future research directions concerning the DRL applications in smart grid are also discussed.

关键词： Deep reinforcement learning (DRL) electricity market operational control optimal dispatch smart grid (SG)

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On Pinning Linear and Adaptive Synchronization of Multiple Fractional-Order Neural Networks With Unbounded Time-Varying Delays

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第4期53卷 2402-2411页

作者： Liu, Peng Xu, Minglin Sun, Junwei Zeng, Zhigang Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Peoples R China Zhengzhou Univ Light Ind Henan Key Lab Informat Based Elect Appliances Zhengzhou 450002 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

In this article, the synchronization of multiple fractional-order neural networks with unbounded time-varying delays (FNNUDs) is investigated. By introducing a pinning linear control, sufficient conditions are provided for achieving the synchronization of multiple FNNUDs via an extended Halanay inequality. Moreover, a new effective adaptive control which applies to the fractional differential equations with unbounded time-varying delays is designed, under which sufficient criteria are presented to ensure the synchronization of multiple FNNUDs. The introduced control in this article is also workable in traditional integer-order neural networks. Finally, the validity of obtained results is demonstrated by a numerical example.

关键词： Synchronization Fuzzy control Delays Adaptive control Neural networks Time-varying systems Delay effects Adaptive control fractional-order synchronization unbounded time-varying delays

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Multiagent System With Periodic and Event-Triggered Communications for Solving Distributed Resource Allocation Problem

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第10期53卷 6245-6256页

作者： Li, Kaixuan Liu, Qingshan Zeng, Zhigang Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This article mainly investigates how to reduce the communication cost in multiagent system (MAS) for distributed optimization. First, a continuous-time distributed optimization model based on MAS is proposed for resource allocation (RA) with periodic communication. All agents in the system do not need to be in constant contact with their neighbors, but contact at set intervals. This will greatly reduce the communication consumption of the system. Second, to further reduce the communication cost, MAS with event-triggered communication is proposed based on periodic communication. It is proved that the system is convergent to an optimal solution of the investigated problem subject to bound and equality constraints. Finally, two examples with simulations are given to verify the performance of the proposed system.

关键词： Costs Optimization Convergence Resource management Multi-agent systems Linear programming Technological innovation Distributed optimization event-triggered communication multiagent system (MAS) resource allocation (RA)

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Finite-Time Stabilization of Competitive Neural Networks With Time-Varying Delays

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 11325-11334页

作者： Sheng, Yin Zeng, Zhigang Huang, Tingwen Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China Texas A&M Univ Qatar Sci Program Doha Qatar

This article investigates finite-time stabilization of competitive neural networks with discrete time-varying delays (DCNNs). By virtue of comparison strategies and inequality techniques, finite-time stabilization of the underlying DCNNs is analyzed by designing a discontinuous state feedback controller, which simplifies the controller design and proof processes of some existing results. Meanwhile, global exponential stabilization of the DCNNs is provided under a continuous state feedback controller. In addition, global exponential stability of the DCNNs is shown as an M-matrix, which contains some published outcomes as special cases. Finally, three examples are given to illuminate the validity of the theories.

关键词： Stability analysis Delays State feedback Biological neural networks Stability control theory Asymptotic stability Competitive neural networks (CNNs) discrete time delay finite-time stabilization global exponential stability global exponential stabilization

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