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Higher-order temporal interactions promote the cooperation in the multiplayer snowdrift game

Science China(Information Sciences) 2023年第12期66卷 138-153页

作者： Yan XU Juan WANG Chengyi XIA Zhen WANG School of Computer Science and Engineering Tianjin University of Technology School of Electrical Engineering and Automation Tianjin University of Technology School of Artificial Intelligence Tiangong University School of Cyberspace and Center for OPTical IMagery Analysis and Learning(OPTIMAL) Northwestern Polytechnical University

To explore evolutionary dynamics of collective behaviors within the interconnected population,previous studies usually map non-pairwise interactions to higher-order static networks. However, from human communications to chemical reactions and biological systems, interactions often change over time, which cannot be simply described by higher-order static networks. In this study, we introduce time effects into higher-order networks and correspondingly investigate the evolutionary dynamics of multiplayer snowdrift games on higher-order temporal networks. Specifically, extensive simulations from four empirical datasets reveal that(1) the temporal effect of higher-order networks can facilitate the evolution of cooperation;(2) the higher-order topology can enhance the emergence of cooperation within a certain range of parameters;(3) the contribution of temporal burstiness and participants burstiness to cooperation is reversed. Furthermore,we theoretically demonstrate that the higher-order structure will suppress the propagation of defection in temporal networks. Our findings offer a new avenue for studying the evolution of altruistic behaviors in realistic complex networks.

关键词： collective behaviors non-pairwise interactions multiplayer snowdrift games temporal networks higher-order complex networks

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Vehicle and pedestrian detection method based on improved YOLOv4-tiny

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Optoelectronics Letters 2023年第10期19卷 623-628页

作者： LI Jing XU Zhengjun XU Liang School of Computer Science and Engineering Tianjin University of TechnologyTianjin300384China School of Electrical Engineering and Automation Tianjin University of TechnologyTianjin300384China

Aiming at the problem of low detection accuracy of vehicle and pedestrian detection models,this paper proposes an improved you only look once v4(YOLOv4)-tiny vehicle and pedestrian target detection *** block attention module(CBAM)is introduced into cross stage partial Darknet-53(CSPDarknet53)-tiny module to enhance feature extraction *** addition,the cross stage partial dense block layer(CSP-DBL)module is used to replace the original simple convolutional module superposition,which compensates for the high-resolution characteristic information and further improves the detection accuracy of the ***,the test results on the BDD100K traffic dataset show that the mean average precision(mAP)value of the final network of the proposed method is 88.74%,and the detection speed reaches 63 frames per second(FPS),which improves the detection accuracy of the network and meets the real-time detection speed.

关键词： replace network method

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A deep convolutional neural network for COVID-19 detection using chest X-rays

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Research on Biomedical engineering 2022年第1期38卷 139-148页

作者： Bassi, Pedro R. A. S. Attux, Romis Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Campinas Brazil

Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia, and normal. Methods: We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-ray14 dataset as an intermediate step. We also suggested a novelty called output neuron keeping, which changes the twice transfer learning technique. In order to clarify the modus operandi of the models, we used Layer-wise Relevance Propagation (LRP) to generate heatmaps. Results: We were able to reach test accuracy of 100% on our test dataset. Twice transfer learning and output neuron keeping showed promising results improving performances, mainly in the beginning of the training process. Although LRP revealed that words on the X-rays can influence the networks’ predictions, we discovered this had only a very small effect on accuracy. Conclusion: Although clinical studies and larger datasets are still needed to further ensure good generalization, the state-of-the-art performances we achieved show that, with the help of artificial intelligence, chest X-rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis. Heatmaps generated by LRP improve the interpretability of the deep neural networks and indicate an analytical path for future research on diagnosis. Twice transfer learning with output neuron keeping improved DNN performance. © 2021, Sociedade Brasileira de Engenharia Biomedica.

关键词： COVID-19

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Terminal Configuration Principles for Achieving Various Electromagnetic Integration Units with Flexible Multilayer Foil Technique

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IEEE Electromagnetic Compatibility Magazine 2023年第2期12卷 47-61页

作者： Jiang, Shiqi Wang, Panbao Wang, Wei Liu, Yitao Xu, Dianguo School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150001 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China

Owing to the greater integration capability, flexible multilayer foil (FMLF) technique has a great potential to promote the evolution of passive integration techniques. To understand the mechanism of this technique is essential for its applications and popularization. To this end, this paper reveals the fundamentals and terminal configuration principles to realize various functional electromagnetic integration units with FMLF technique. A regular FMLF winding is taken as the research object, from theoretical analyses on the associated distributed electromagnetic component (DEMC) circuits, the lumped electromagnetic component (LEMC) circuits with different terminal configuration modes are derived. Moreover, A FMLF winding prototype is built for experimental verifications. The LEMC circuits are demonstrated through the comparative analyses between practically measured and LTspice based simulated impedances or port characteristics. © 2021 IEEE.

关键词： Timing circuits

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Trajectory Tracking control of Autonomous Vehicles based on Improved Model-free Adaptive control 3

Trajectory Tracking Control of Autonomous Vehicles based on ...

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3rd International Conference on Testing Technology and automation engineering, TTAE 2023

作者： Wang, Wanting Wang, Siming Han, Taoguo School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou730070 China College of Computer and control engineering Northeast Forestry University Harbin150000 China

ISBN: (纸本)9781510674769

This paper presents a novel approach to address the lateral control issue in trajectory tracking for autonomous cars. Traditional model-free adaptive control algorithms have some limitations, prompting the development of an enhanced data-driven model-free adaptive control scheme. More precisely, inside a specified trajectory, the vehicle's issue of lateral control is converted into a problem of tracking the preview-deviation-angle. A dynamic linearized data model is developed for the lateral control of autonomous cars, taking into account the input and output data. To enhance the steering angle control rate, a time-varying proportional control term is incorporated. An enhanced trajectory tracking control technique for the MFAC system is proposed, followed by a demonstration of its convergence. The simulation findings demonstrate that the IMFAC scheme exhibits a superior output response compared to both MFAC and PID in both low-speed and high-speed scenarios. Furthermore, the IMFAC system is capable of achieving autonomous vehicle path tracking. © 2024 SPIE.

关键词： Autonomous vehicles

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A Data-Based Feedback Relearning Algorithm for Uncertain Nonlinear Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1288-1303页

作者： Chaoxu Mu Yong Zhang Guangbin Cai Ruijun Liu Changyin Sun the School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China the College of Missile Engineering Rocket Force University of EngineeringXi’an 710025China the School of Computer and Information Engineering Beijing Technology and Business UniversityBeijing 100048China the School of Automation Southeast UniversityNanjing 210096China

In this paper,a data-based feedback relearning algorithm is proposed for the robust control problem of uncertain nonlinear *** by the classical on-policy and off-policy algorithms of reinforcement learning,the online feedback relearning(FR)algorithm is developed where the collected data includes the influence of disturbance *** FR algorithm has better adaptability to environmental changes(such as the control channel disturbances)compared with the off-policy algorithm,and has higher computational efficiency and better convergence performance compared with the on-policy *** processing based on experience replay technology is used for great data efficiency and convergence *** experiments are presented to illustrate convergence stability,optimality and algorithmic performance of FR algorithm by comparison.

关键词： Data episodes experience replay neural networks reinforcement learning(RL) uncertain systems

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Flexible prescribed performance-based approaching control for UAV aerial recovery under multiwind disturbances

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Chinese Journal of Aeronautics 2025年第4期38卷 420-436页

作者： Yanxiang WANG Honglun WANG Junfan ZHU Yiheng LIU Menghua ZHANG Zikang SU School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China The Science and Technology on Aircraft Control Laboratory Beihang UniversityBeijing 100191China Shenyuan Honors College Beihang UniversityBeijing 100191China Beijing Institute of Astronautical Systems Engineering Beijing 100076China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

This article investigates the approaching control for fixed-wing Unmanned Aerial Vehi-cle(UAV)aerial recovery in the presence of pre-specified performance requirements,complex air-flows,maneuvering flight of transport aircraft,and different initial ***,a novelcontrol-oriented Six-Degree-Of-Freedom(6-DOF)UAV model considering airflow disturbancesis established for better consistency with the actual UAV ***,to achieve satisfactory per-formance in the approaching process,a Flexible Appointed-time Prescribed Performance control(FAPPC)algorithm,with the features of user-specified time convergence,no overshoot,indepen-dence from the initial value,and singularity-free,is ***,to solve the singularityissue encountered by the existing PPC methods in dealing with sudden disturbances,an adaptiveadjustment signal is introduced in FAPPC to perceive the threat of increasing error and relax thepreset boundaries ***,minimum learning parameter-based neural networkestimators are developed to approximate unknown lumped disturbances at a low ***,the stability of the closed system is analyzed via Lyapunov synthesis,and the effective-ness and advantages of the proposed control scheme are demonstrated via simulation andHardware-In-the-Loop(HIL)experimental validation.

关键词： Approaching control UAV aerial recovery Flexible prescribed performance control Sudden disturbances Neural network estimator

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Deep Reinforcement Learning Based Joint Optimization of Task Migration and Resource Allocation for Mobile Edge Computing

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IEEE Internet of Things Journal 2025年第13期12卷 24431-24440页

作者： Li, Juncai Jiang, Qi Leung, Victor C. M. Ma, Zhuo Abrokwa, Kofi Kwarteng Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Task migration and resource allocation are essential to integrate available resources for improving the efficiency of mobile edge computing to support various computation-intensive and delay-sensitive Internet of Things applications, which cooperative optimization has yet been well addressed to achieve desirable performance. In this paper, a deep reinforcement learning (DRL) based adaptive cooperative optimization strategy is presented to fill this gap. Task migration and resource allocation are jointly employed to adaptively integrate available resources within cooperative edge nodes for processing various randomly offloaded tasks. The policy optimization to minimize the system cost and task dropout rates is formulated as Markov decision processes. A DRL algorithm that enhances deep deterministic policy gradient with a dual experience pool is proposed to jointly optimize the task migration and resource allocation in unknown stochastic application environments. Simulation experiments have been conducted to evaluate the performance of the presented strategy, and the results illustrate that it increases system rewards by 17.9 61.5% and reduces task dropout rate by 5.2 31.3% comparing with benchmarks. © 2025 IEEE.

关键词： Deep reinforcement learning

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control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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Optimal Cooperative Secondary control for Islanded DC Microgrids via a Fully Actuated Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 405-417页

作者： Yi Yu Guo-Ping Liu Yi Huang Peng Shi IEEE Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and TechnologyShenzhen 518055China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China School of Electrical and Mechanical Engineering the University of AdelaideSA 5005Australia Research and Innovation Centre Obuda University1034 BudapestHungary

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

关键词： DC microgrids distributed control high-order fully actuated system approach linear quadratic regulator microgrid modeling secondary control

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