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Group Cooperation in Intergroup Conflicting Networks: An Evolutionary Game Approach

IFAC-PapersOnLine 2023年第2期56卷 3459-3464页

作者： Aixin Liu Lin Wang Guanrong Chen Jing Wu Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Department of Electrical Engineering City University of Hong Kong Hong Kong China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Most intergroup conflicts arise from rivalry over limited resources, malicious disturbance, or hostile attitudes; therefore, it is critical to investigate individuals’ behaviors involved in intergroup conflicting contexts. Focusing on cooperation issues among individuals, in this study we establish an evolutionary game framework for analyzing cooperation and conflicts that arise within and inter-groups of intergroup conflicting networks. We first model the intergroup conflicting networked evolutionary games (ICNEGs). Then, we analyze the ICNEGs and prove that the evolution of ICNEGs can be expressed as a logical dynamic system. Finally, we apply the obtained results to a simplified Israeli-Palestinian conflict scenario. Our case study demonstrates that only by adopting suitable initial strategy profiles can a certain scale of group cooperation be continuously generated without suffering casualties.

关键词： Coupled networks intergroup conflicting cooperation evolutionary game approach

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Entity Representation Learning with Multimodal Neighbors for Link Prediction in Knowledge Graph 7

Entity Representation Learning with Multimodal Neighbors for...

引用

7th International Conference on Computer and Communications, ICCC 2021

作者： Liu, Wenxuan Duan, Hao Li, Zeng Liu, Jingdong Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Intelligent Control and Management China

ISBN: (纸本)9781665409506

Entity representation learning is both fundamental and crucial for link prediction in knowledge graph. Existing entity representation learning approaches mainly focus on learning from knowledge triples with single modal text data which could only describe the things or concepts in words. However, multimodal data commonly exists in the real worlds and may describe things or concepts from different aspects. Therefore, multimodal entities have emerged in knowledge graph in recent years, which greatly enrich the knowledge graph by different information. This paper focuses on how to learn more robust and comprehensive representations of multimodal entities for better link prediction. A new representation learning method for multimodal entities is proposed, called Graph Attention with Bi-pooling Representation Learning (GBRL), where multimodal fusion and knowledge embedding are combined for learning representations of an entity with consideration of its multimodal neighbors. Firstly, feature vectors are obtained by pre-trained models for each modality. Then, the bilinear pooling is utilized for combining feature vectors of multimodal entities into multimodal feature vectors. A two-layer graph attention module is followed to generate multimodal representations of an entity from its multimodal neighbors. Finally, the inception decoder uses multimodal knowledge embedding to calculate scores for link prediction. The experimental results have shown that the proposed method performs well on link prediction by consideration of multimodal entities comparing with traditional methods that using only single modal text data. © 2021 IEEE.

关键词： Knowledge graph

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Arbitrary State Transition of Open Qubit system Based on Switching control

arXiv

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arXiv 2024年

作者： Wu, Guangpu Xue, Shibei Ma, Shan Kuang, Sen Dong, Daoyi Petersen, Ian R. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Automation Central South University Changsha410083 China Department of Automation University of Science and Technology of China Hefei230027 China School of Engineering Australian National University CanberraACT2601 Australia

We present a switching control strategy based on Lyapunov control for arbitrary state transitions in open qubit systems. With coherent vector representation, we propose a switching control strategy, which can prevent the state of the qubit from entering invariant sets and singular value sets, effectively driving the system ultimately to a sufficiently small neighborhood of target states. In comparison to existing works, this control strategy relaxes the strict constraints on system models imposed by special target states. Furthermore, we identify conditions under which the open qubit system achieves finite-time stability (FTS) and finite-time contractive stability (FTCS), respectively. This represents a critical improvement in quantum state transitions, especially considering the asymptotic stability of arbitrary target states is unattainable in open quantum systems. The effectiveness of our proposed method is convincingly demonstrated through its application in a qubit system affected by various types of decoherence, including amplitude, dephasing and polarization decoherence. Copyright © 2024, The Authors. All rights reserved.

关键词： Asymptotic stability

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Optimal control Synthesis of Markov Decision Processes for Efficiency with Surveillance Tasks

arXiv

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arXiv 2024年

作者： Chen, Yu Yin, Xuanyuan Li, Shaoyuan Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University 62 Nanyang Drive Singapore

We investigate the problem of optimal control synthesis for Markov Decision Processes (MDPs), addressing both qualitative and quantitative objectives. Specifically, we require the system to fulfill a qualitative surveillance task in the sense that a specific region of interest can be visited infinitely often with probability one. Furthermore, to quantify the performance of the system, we consider the concept of efficiency, which is defined as the ratio between rewards and costs. This measure is more general than the standard long-run average reward metric as it aims to maximize the reward obtained per unit cost. Our objective is to synthesize a control policy that ensures the surveillance task while maximizes the efficiency. We provide an effective approach to synthesize a stationary control policy achieving ϵ-optimality by integrating state classifications of MDPs and perturbation analysis in a novel manner. Our results generalize existing works on efficiency-optimal control synthesis for MDP by incorporating qualitative surveillance tasks. A robot motion planning case study is provided to illustrate the proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

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Selection for Schottky Barrier Diodes with Various On-Resistance in Full-SiC Voltage Source Converters

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IEEE Transactions on Industry Applications 2025年

作者： Chen, Yuzhi Li, Chi Liu, Jianwei Wu, Yifan Liu, Zicheng Zheng, Zedong Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Wuhan430074 China

In Silicon Carbide based voltage source converters (VSCs), pairing Schottky barrier diodes (SBDs) presents trade-offs. While SBDs reduce the freewheeling loss and mitigate the adverse effects of bipolar conduction, their junction capacitance also contributes to increased switching loss. This paper proposes a systematic selection strategy for SBDs aimed at enhancing converter efficiency, using on-resistance as the design variable. A three-phase VSC with 1.2 kV MOSFETs and four sets of 1.2 kV SBDs is demonstrated as the case study. The static and switching characteristics for MOSFET/SBD pair are measured and analyzed over a wide temperature range. VSC efficiency is evaluated in both inverter and rectifier modes under different operating conditions. It appears that VSC using SBDs with 126% of the MOSFETs' resistance exhibits an overall superior performance. A 4 kW full-SiC VSC prototype is developed to validate the proposed selection strategy. Compared to other SBDs, using the recommended selection enhances VSC efficiency by up to 2% (± 0.5%) under light loads while maintaining high performance under medium and heavy loads. Compared to freewheeling by the body diode, using the recommended selection enhances VSC efficiency by an average of 0.4% (± 0.1%) across nearly the entire load range. © 1972-2012 IEEE.

关键词： Schottky barrier diodes

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Pose Estimation of Moving Vehicles Based on Heuristic Rules for Autonomous Driving

Pose Estimation of Moving Vehicles Based on Heuristic Rules ...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： He, Yuesheng Zhang, Wei Yang, Ming The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Shenzhen Deeproute.ai Co. Ltd China

ISBN: (纸本)9781665481090

Estimating the pose information on moving vehi-cles is one of the most fundamental functions of autonomous driving for detecting and tracking moving objects. The current methods are often based on the prior information of the point cloud distribution, however, the distribution in the actual scene changes dynamically, and is easy not to meet a priori. This paper proposes a 3D pose estimation method based on heuristic rules. In a single frame point cloud, the edge length and edge visibility are used to heuristically screen the candidate orientations established by the convex hull sampling method. On consecutive frame point clouds, tracking heuristics are employed to obtain smoother orientation estimation results. The experimental results on the KITTI dataset and the data from the actual scene of vehicles show that the proposed method reduces the average object-orientation error and improves the accuracy of estimation effectively. © 2022 IEEE.

关键词： Autonomous vehicles

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Event-Triggered Consensus on Matrix-weighted Networks

Event-Triggered Consensus on Matrix-weighted Networks

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2021 China automation Congress, CAC 2021

作者： Pan, Lulu Wang, Chongzhi Shao, Haibin Li, Dewei Xi, Yugeng The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9781665426473

In this work, we consider the consensus problem of the multi-agent system over matrix-weighted networks using an event-triggered mechanism. An event-triggered coordination strategy is proposed to steer this generalized category of multi-agent systems towards the bipartite consensus state. The triggering condition is periodically checked and distributed in the sense that the event detector only uses neighbors' information and local computation at discrete sampling instants. Moreover, it is shown that the triggering condition is closely related to the spectrum of matrix-valued weights corresponding to the edges in the matrix-weighted network. In the meantime, the Zeno phenomenon can be avoided automatically under the proposed event trigger approach. © 2021 IEEE

关键词： Multi agent systems

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An Integrated Scheduling Algorithm of Satellite Observation and Data Download Based on Multi-Agent Deep Reinforcement Learning

An Integrated Scheduling Algorithm of Satellite Observation ...

引用

Chinese automation Congress (CAC)

作者： Kang Chen Shaochong Du Hong Huo Tao Fang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

The earth observation satellite scheduling has always been critical for the maximum use of limited satellite resources, which basically includes scheduling ground target observation and observation data downloading. Due to the complexity of the mathematical model and its NP-hardness, current research usually splits it into two problems to simplify the model. In this paper, we propose a model of the integrated scheduling problem of satellite observation and data downloading (ISP-SODD) based on the decentralized partially observable Markov decision process (Dec-POMDP), treating each satellite as an agent with autonomous decision-making capability. A multi-agent deep reinforcement learning-based (MADRL-based) algorithm is proposed to solve it. A series of simulation experiments of different scales validate the effectiveness of the method and its advantages compared with the traditional heuristic algorithms. To the best of our knowledge, this study is the first attempt to apply multi-agent reinforcement learning to a multi-satellite scheduling scenario with downloading considerations.

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Sensor-based Multi-Robot Coverage control with Spatial Separation in Unstructured Environments

Sensor-based Multi-Robot Coverage Control with Spatial Separ...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Xinyi Wang Jiwen Xu Chuanxiang Gao Yizhou Chen Jihan Zhang Chenggang Wang Yulong Ding Ben M. Chen Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information system), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others.

关键词： Point cloud compression system recovery Robot sensing systems Sensors Safety Multi-robot systems Task analysis

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