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第32届中国过程控制会议(CPCC2021)

作者： Lu Lv Qian Zhang Jianzhong Su Wenhai Wang Jianquan Yuan Zijian Tian Zhenghui Zhang Long Xiao Zeyin Zhang Xinggao Liu Zhejiang University NGICS Platform State Key Laboratory of Industrial Control TechnologyControl DepartmentZhejiang University Tianjin Jinhang Institute of Technical Physics Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Mathematics Department Zhejiang University

Dynamic optimization for hypersonic reentry vehicle(HRV) is a challenge for complex nonlinear constraints during a global strike *** to the strong nonlinearity of HRV,it is difficult to guarantee the precision of complex constraints on non-collocation points with traditional Gauss pseudospectral method(GPM).A novel approach which provides an optimal control strategy considering the above challenges is then ***,an adaptive mesh refinement strategy based on curvature information is presented to improve the accuracy of GPM.A collocation point redistribution method based on error analysis is then proposed to further improve the accuracy of the ***,a detection mechanism is proposed to correct the results on-line when the path constraint is not *** proposed approaches are finally applied to a classic HRV problem and compared with other literature work in *** research results validate the effectiveness of the proposed methods.

关键词： nonlinear optimal control Gauss pseudospectral method adaptive mesh refinement hypersonic reentry vehicle

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Cyber-physical -social systems for smart cities: An overview 1

Cyber-physical -social systems for smart cities: An overview

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Anwar, Noreen Xiong, Gang Lu, Wanze Ye, Peijun Zhao, Hongxia Wei, Qinglai Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation The Cloud Computing Center Chinese Academy of Sciences China School of Artificial Intelligence and Automation Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781665433372

Cyber-physical social systems (CPSS) include cyberspace, physical space, and social space. Since the introduction of CPSS, human relationships with machinery and the physical world have altered as a new paradigm. Recent estimates indicate that approximately 60 percent of the world's population will live in cities by 2030. Large, dense cities can be highly productive, innovative, and very green per capita, hence ideal for our future. However, the huge influx of new people poses enormous challenges to their administrations. Smart cities are becoming a reality, therefore, to achieve higher efficiency, optimum use of resources, management and to strengthen several features of modern towns, several elements of modern cities are automated and integrated into Information and Communications Technology (ICT). Cities and infrastructure are growing all over the world, in providing end-users with increased performance, comfort, visibility and convenience, the Smart Cities concept is critical. This paper will discuss smart cities and the CPSS concept intensively with some of its key technologies, challenges, and further development by using CPSS. © 2021 IEEE.

关键词： Smart city

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UNMAS: Multi-Agent Reinforcement Learning for Unshaped Cooperative Scenarios

arXiv

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arXiv 2022年

作者： Chai, Jiajun Li, Weifan Zhu, Yuanheng Zhao, Dongbin Ma, Zhe Sun, Kewu Ding, Jishiyu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Xlab The Second Academy of CASIC Beijing100854 China

Multi-agent reinforcement learning methods such as VDN, QMIX, and QTRAN that adopt centralized training with decentralized execution (CTDE) framework have shown promising results in cooperation and competition. However, in some multi-agent scenarios, the number of agents and the size of action set actually vary over time. We call these unshaped scenarios, and the methods mentioned above fail in performing satisfyingly. In this paper, we propose a new method called Unshaped Networks for Multi-Agent systems (UNMAS) that adapts to the number and size changes in multi-agent systems. We propose the self-weighting mixing network to factorize the joint action-value. Its adaption to the change in agent number is attributed to the nonlinear mapping from each-agent Q value to the joint action-value with individual weights. Besides, in order to address the change in action set, each agent constructs an individual action-value network that is composed of two streams to evaluate the constant environment-oriented subset and the varying unit-oriented subset. We evaluate UNMAS on various StarCraft II micro-management scenarios and compare the results with several state-of-the-art MARL algorithms. The superiority of UNMAS is demonstrated by its highest winning rates especially on the most difficult scenario 3s5z-vs-3s6z. The agents learn to perform effectively cooperative behaviors while other MARL algorithms fail in. Copyright © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

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Digital twin based validation platform for smart metro scenarios 1

Digital twin based validation platform for smart metro scena...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Wang, Xiaoyong Song, Haifeng Zha, Wei Li, Jing Dong, Hairong CASCO Signal Ltd Science and Technology Management Dept SHANGHAI China Key Laboratory of Road Ana Traffic Engineeering Ministry of Education Tongji University Shanghai China College of Electronical and Information Beijing Jiaotong University Beijing China CASCO Signal Ltd Urban System Integration Dept SHANGHAI China Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China

ISBN: (纸本)9781665433372

Operation scenario is very important to smart metro, but it is difficult to validated with real environment. Digital twin achieves the digitization of metro facilities, interface with related control systems, operation as a real metro system, and digital twin based validation platform can be used to validate most of smart metro scenarios. This paper focus on smart metro and its scenarios validation platform with digital twin technology, included conception, architecture, modeling, and data processing etc. © 2021 IEEE.

关键词： Data handling

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Muscle Synergy Analysis Based on NMF for Lower Limb Motor Function Assessment

Muscle Synergy Analysis Based on NMF for Lower Limb Motor Fu...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xiang, Kexin Wang, Weiqun Hou, Zeng-Guang Zhang, Chutian Wang, Jiaxing Shi, Weiguo Jiao, Yuze Lin, Tianyu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Intelligence University of Chinese Academy of Sciences Beijing100049 China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology 999078 China Macau Institute of Systems Engineering Macau University of Science and Technology China The CASIAMUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

ISBN: (纸本)9781665481090

Accurate rehabilitation assessments are essential for designing effective rehabilitation methods and helping patients recover better. It's well known that commonly used scale assessment methods for neurorehabilitation suffer from the issue of subjectivity, thus investigation of objective assessment methods is very necessary. Muscle synergy analysis can be uesd to assess limb motor functions from the perspective of neuromuscular control. In this paper, a method for evaluation of human lower limb motor functions based on muscle synergy analysis is presented. Muscle synergy modules are designed using surface electromyography (sEMG) signals of the subjects' lower limbs by non-negative matrix factorizations (NMF). By comparing the cosine similarities of these synergy modules, it can be seen that muscle synergies of healthy subjects and patients are significantly different, while they are similar among healthy subjects. Therefore, a reference synergy module (RSM) is designed by averaging the muscle synergy modules for healthy subjects, and the similarities can be calculated by comparing the synergy modules for healthy subjects or patients with the RSM. In the experiment carried out in this study, average similarities of the three synergy modules for healthy subjects are respectively 0.97166, 0.87368 and 0.84932, and on the other hand, the average similarities for the three synergy modules for patients are respectively 0.59979, 0.56426 and 0.69042. Therefore, the similarities for healthy subjects are much higher than those for SCI patients, which denotes that the similarity between an individual synergy module and the RSM can be used as an objective assessing index for evaluating patients' motor function. © 2022 IEEE.

关键词： Non-negative matrix factorization

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Development and path planning of a novel unmanned surface vehicle system and its application to exploitation of Qarhan Salt Lake

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Science China(Information Sciences) 2019年第8期62卷 195-197页

作者： Zhibin XUE Jincun LIU Zhengxing WU Sheng DU Shihan KONG Junzhi YU College of Chemical Engineering Qinghai University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

Qarhan Salt Lake is rich in mineral resources, such as sodium chloride, potassium, and magnesium [1].At present, liquid mine exploiting is the main exploitation form for the Qarhan Salt Lake. Salt mining ships with mining equipments are widely used to exploit carnallite which is located at the bottom of pools. Conventionally, workmen utilize rude ferric sampler to collect carnallite samples

关键词： USV simulation Development and path planning of a novel unmanned surface vehicle system and its application to exploitation of Qarhan Salt Lake

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An Improved Chimp Optimization Algorithm for Short-term Hydrothermal Scheduling

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第3期49卷 666页

作者： Chen, Gonggui Wang, Shumin He, Yongsheng Shang, Wanfeng Long, Hongyu Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing400065 China State Grid Chongqing Electric Power Company Chongqing400015 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shenzhen518055 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

The short-term hydrothermal scheduling (STHS) is a complex non-linear, non-convex and high-dimensional mathematical optimization problem. Due to the fact that the original chimp optimization algorithm (ChOA) is likely to fall into local optimum and has the disadvantage of low population diversity, an improved chimp optimization algorithm (IChOA) is proposed to solve the STHS problem. Firstly, Logistic-Tent chaotic mapping is employed to initialize the population and increase the population diversity. Then a new nonlinear convergence factor is introduced to make the optimization search process more applicable to STHS problems. Next, particle swarm algorithm (PSO) and gravitational search algorithm (GSA) are combined to enhance the shortcoming and balance the global search ability and local exploration ability of ChOA. Moreover, Cauchy mutation and opposition-based learning strategy are supplemented as perturbation interference when the optimal position is not updated to improve the ability to leap out of the local optimum. Finally, a graded optimization strategy is adopted for the constraint handing of dynamic reservoir balance and thermal power balance. Three standard hydrothermal power systems are utilized to verify the practicability and validity of the proposed method. Results of simulations reveal that IChOA has significant competitive advantages in addressing the STHS problem. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Particle swarm optimization (PSO)

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A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios

A Reinforcement Learning Benchmark for Autonomous Driving in...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Yuqi Liu Qichao Zhang Dongbin Zhao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728190495

In recent years, control under urban intersection scenarios has become an emerging research topic. In such scenarios, the autonomous vehicle confronts complicated situations since it must deal with the interaction with social vehicles timely while obeying the traffic rules. Generally, the autonomous vehicle is supposed to avoid collisions while pursuing better efficiency. The existing work fails to provide a framework that emphasizes the integrity of the scenarios while deploying and testing reinforcement learning(RL) methods. Specifically, we propose a benchmark for training and testing RL-based autonomous driving agents in complex intersection scenarios, which is called RL-CIS. Then, a set of baselines consisting various algorithms are deployed. The test benchmark and baselines provide a fair and comprehensive training and testing platform for the study of RL for autonomous driving in the intersection scenario, advancing RL-based methods for autonomous driving control. The code of our proposed framework can be found at https://***/liuyufi123/complexUrbanScenarios.

关键词： Training Codes Stochastic processes Reinforcement learning Benchmark testing Sampling methods Concrete

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A reinforcement learning benchmark for autonomous driving in intersection scenarios

arXiv

引用

arXiv 2021年

作者： Liu, Yuqi Zhang, Qichao Zhao, Dongbin The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In recent years, control under urban intersection scenarios becomes an emerging research topic. In such scenarios, the autonomous vehicle confronts complicated situations since it must deal with the interaction with social vehicles timely while obeying the traffic rules. Generally, the autonomous vehicle is supposed to avoid collisions while pursuing better efficiency. The existing work fails to provide a framework that emphasizes the integrity of the scenarios while being able to deploy and test reinforcement learning(RL) methods. Specifically, we propose a benchmark for training and testing RL-based autonomous driving agents in complex intersection scenarios, which is called RL-CIS. Then, a set of baselines are deployed consists of various algorithms. The test benchmark and baselines are to provide a fair and comprehensive training and testing platform for the study of RL for autonomous driving in the intersection scenario, advancing the progress of RL-based methods for intersection autonomous driving control. The code of our proposed framework can be found at https://***/liuyuqi123/complexUrbanScenarios. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

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Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs 28

Knowledge-Enhanced Natural Language Inference Based on Knowl...

引用

28th International Conference on Computational Linguistics, COLING 2020

作者： Wang, Zikang Li, Linjing Zeng, Daniel State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen China

ISBN: (纸本)9781952148279

Natural Language Inference (NLI) is a vital task in natural language processing. It aims to identify the logical relationship between two sentences. Most of the existing approaches make such inference based on semantic knowledge obtained through training corpus. The adoption of background knowledge is rarely seen or limited to a few specific types. In this paper, we propose a novel Knowledge Graph-enhanced NLI (KGNLI) model to leverage the usage of background knowledge stored in knowledge graphs in the field of NLI. KGNLI model consists of three components: a semantic-relation representation module, a knowledge-relation representation module, and a label prediction module. Different from previous methods, various kinds of background knowledge can be flexibly combined in the proposed KGNLI model. Experiments on four benchmarks, SNLI, MultiNLI, SciTail, and BNLI, validate the effectiveness of our model. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Knowledge graph

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