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第41届中国控制会议

作者： Chao Ma Hang Fu Yidao Ji Wei Wu School of Mechanical Engineering University of Science and Technology State Key Lab of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

In this paper,the L-gain based filtering problem for nonlinear positive semi-Markov jump systems is investigated by proposing a novel asynchronous design *** precisely,the mode-dependent filters are designed in terms of practical observed modes instead of true system modes,such that less conservatism can be *** addition,the effect of time-varying delays is taken into account for more robustness and *** selecting suitable stochastic Lyapunov-Krasovskii functions and applying the linear programming method,sufficient conditions are established to fulfill the desired L-gain ***,the illustrative simulation is performed to verify the effectiveness of our developed control scheme.

关键词： Positive semi-Markov jump systems nonlinear semi-Markov jump systems asynchronous filtering L1-gain

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Learning Continuous 3-DoF Air-to-Air Close-in Combat Strategy using Proximal Policy Optimization

Learning Continuous 3-DoF Air-to-Air Close-in Combat Strateg...

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IEEE Conference on Games (CoG)

作者： Li, Luntong Zhou, Zhiming Chai, Jiajun Liu, Zhen Zhu, Yuanheng Yi, Jianqiang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Integrated Infoimat Syst Res Ctr Inst Automat Beijing 100190 Peoples R China

ISBN: (数字)9781665459891

ISBN: (纸本)9781665459891

Air-to-air close-in combat is based on many basic fighter maneuvers and can be largely modeled as an algorithmic function of inputs. This paper studies autonomous close-in combat, to learn new strategy that can adapt to different circumstances to fight against an opponent. Current methods for learning close-in combat strategy are largely limited to discrete action sets whether in the form of rules, actions or sub-polices. In contrast, we consider one-on-one air combat game with continuous action space and present a deep reinforcement learning method based on proximal policy optimization (PPO) that learns close-in combat strategy from observations in an end-to-end manner. The state space is designed to promote the learning efficiency of PPO. We also design a minimax strategy for the game. Simulation results show that the learned PPO agent is able to defeat the minimax opponent with about 97% win rate.

关键词： air-combat reinforcement learning proximal policy optimization flight simulation

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Current Problems and Solutions of Industrial control Network Intrusion Detection: A Brief Survey

Current Problems and Solutions of Industrial Control Network...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Jiang, Tianyu Zhang, Chen Wang, Rui Chai, Senchun Institute of Automation Chinese Academy of Sciences and University of Science and Technology Beijing China Beijing Institute of Technology China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems China Institute of Intelligent Information Processing and Control Beijing Institute of Technology China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

With the advancement of industrial intelligence, the Industrial Internet has been widely used in energy, manufacturing and other important industries. In recent years, security incidents have occurred frequently in industrial control network, which means that maintaining industrial control network security has become more and more important. Intrusion detection technology can actively detect abnormal behavior in the network, and is an important means to ensure the security of industrial control networks. Presently, the intrusion detection technology of industrial control network has problems such as extreme imbalance of classes and redundant interference of traffic characteristics. This paper analyzes these problems and clarifies the existing solutions for different problems in principle. © 2022 IEEE.

关键词： Network security

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A Spatiotemporal Hybrid Model for Airspace complexity Prediction

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IEEE INTELLIGENT TRANSPORTATION systems MAGAZINE 2023年第2期15卷 217-224页

作者： Du, Wenbo Li, Biyue Chen, Jun Lv, Yisheng Li, Yumeng Beihang Univ Sch Elect & Informat Engn Natl Engn Lab Big Data Applicat Technol Comprehen Beijing 100191 Peoples R China Queen Mary Univ London Sch Engn & Mat Sci London E1 4NS England Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Airspace complexity is a key indicator that reflects the safety of airspace operations in air traffic management systems. Furthermore, to achieve efficient air traffic control, it is necessary to accurately predict the airspace complexity. In this article, we propose a novel spatiotemporal hybrid deep learning model for airspace complexity prediction to efficiently capture spatial correlations as well as temporal dependencies pertaining to the airspace complexity data. Specifically, we apply convolutional networks to discover the short-term temporal patterns and skip long short-term memory networks to model the longterm temporal patterns of airspace complexity data. Furthermore, it is observed that the graph attention network in our proposed model, which emphasizes capturing the spatial correlations of the airspace sectors, can significantly improve the prediction accuracy. Extensive experiments are conducted on the real data of six airspace sectors in Southwest China. The experimental results show that our spatiotemporal deep learning approach is superior to state-of-the-art methods.

关键词： complexity theory Atmospheric modeling Spatiotemporal phenomena Predictive models Deep learning Correlation Air traffic control

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Integration of Train control and Online Rescheduling for High-Speed Railways in Case of Emergencies

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第5期9卷 1574-1582页

作者： Dong, Hairong Liu, Xuan Zhou, Min Zheng, Wei Xun, Jing Gao, Shigen Song, Haifeng Li, Yidong Wang, Fei-Yue Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Beijing Jiaotong Univ Natl Res Ctr Railway Safety Assessment Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Chinese Acad Sci CASIA Inst Automat State Key Lab Management & Control Complex Syst S Beijing 100190 Peoples R China

The high-speed train control system is essential to the safety and efficiency of train operation. With the rapid increase of high-speed railway (HSR) operating mileage and development of information technology, the disposal flow and methods in emergency response are still based on dispatchers and drivers' experience within the ``layered'' architecture of current system. There is a certain gap between current processing methods and effective resolution, which may even cause the spread of delay along with the railway networks. Therefore, we propose an integration system of operation control and online rescheduling to improve the recovery ability of HSR carrying capacity. We first describe the framework, information flow, and disposal process of the current system and analyze the shortcomings in handling emergencies. Then, the basic concept, system structure, and framework of the integration system are introduced. Finally, taking temporary speed restriction caused by strong wind as an example, we also analyze the principle of why and how the integration system can promote the recovery ability of HSR carrying capacity.

关键词： Rail transportation control systems Delays Dispatching Trajectory Wind Optimization Carrying capacity emergencies high-speed railways (HSRs) integration online rescheduling train control

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Traditional Chinese medicine diagnosis and treatment based on systematics

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iLIVER 2023年第4期2卷 181-187页

作者： Zhanhong Wang Danli Wang Wen Liu Zhongxia Wang The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Hospital of Traditional Chinese Medicine Capital Medical UniversityBeijing 100010China Department of Nutrition Fifth Medical CenterPLA General HospitalBeijing 100039China

Background:Traditional Chinese Medicine(TCM)is a well-established medical system with a long ***,the overall concept and systematic thinking in TCM are not comprehensively understood and applied in its inheritance and *** This study aims to provide a basic theory for TCM diagnosis using systematics as the guiding *** modern scientific and technological achievements,we aim to explore a new TCM diagnosis ***:We analyzed previous studies on TCM diagnosis and treatment,and reviewed clinical research on TCM diagnosis and treatment from the viewpoint of *** propose a new process model based on systematics for TCM diagnosis and *** is a generalized model that summarizes the process of“establishing an image to express meaning”.Results:The proposed model was implemented in the clinical practice of *** monitored the detailed treatment process of patients in the Department of Liver Diseases at Beijing *** patient underwent a treatment program that lasted 1 year and 45 days,consisting of 12 iterative cycles,each guided by the proposed diagnostic model and tailored to the patient's evolving *** case study validates the effectiveness of the proposed model in the diagnosis and treatment of liver disease in *** therapeutic efficacy has been validated through the examination of both TCM indicators and Western medical auxiliary *** these,the TCM indicators consist of 2 components:tongue diagnosis and pulse ***,the Western medical auxiliary indicators encompass a range of assessments,including whole blood cell analysis,professional liver function examination,a series of liver function assessments,a high-sensitivity hepatitis B pentathlete test,as well as color Doppler ultrasound evaluations of the liver,bile duct,pancreas,spleen,and assessments of liver elasticity,among other related examination *** conclusion,it is evident that the syndrome of l

关键词： Traditional Chinesemedicine Systematics Diagnosis and treatment model Liver disease

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META-IMITATION LEARNING BY WATCHING VIDEO DEMONSTRATIONS 10

META-IMITATION LEARNING BY WATCHING VIDEO DEMONSTRATIONS

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10th International Conference on Learning Representations, ICLR 2022

作者： Li, Jiayi Lu, Tao Cao, Xiaoge Cai, Yinghao Wang, Shuo School of Artificial Intelligence University of Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China

Meta-Imitation Learning is a promising technique for the robot to learn a new task from observing one or a few human demonstrations. However, it usually requires a significant number of demonstrations both from humans and robots during the meta-training phase, which is a laborious and hard work for data collection, especially in recording the actions and specifying the correspondence between human and robot. In this work, we present an approach of meta-imitation learning by watching video demonstrations from humans. In comparison to prior works, our approach is able to translate human videos into practical robot demonstrations and train the meta-policy with adaptive loss based on the quality of the translated data. Our approach relies only on human videos and does not require robot demonstration, which facilitates data collection and is more in line with human imitation behavior. Experiments reveal that our method achieves the comparable performance to the baseline on fast learning a set of vision-based tasks through watching a single video demonstration. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Robots

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Cooperative Localization in Transportation 5.0

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4259-4264页

作者： Gao, Letian Xia, Xin Zheng, Zhaoliang Xiang, Hao Meng, Zonglin Han, Xu Zhou, Zewei He, Yi Wang, Yutong Li, Zhaojian Zhang, Yubiao Ma, Jiaqi Univ Calif Los Angeles UCLA UCLA Mobil Lab Los Angeles CA 90095 USA Univ Calif Los Angeles UCLA FHWA Ctr Excellence New Mobil & Automated Vehicle Los Angeles CA 90095 USA Wuhan Univ Technol Intelligent Transportat Syst Res Ctr Wuhan 430063 Peoples R China Wuhan Univ Technol Engn Res Ctr Transportat Safety Minist Educ Wuhan 430063 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA Gen Motors Control & Learning Syst Grp Global R&D Warren MI 48090 USA

In the era of future mobility within Transportation 5.0, autonomy and cooperation across all road users and smart infrastructure stand as the key features to enhance transportation safety, efficiency, and sustainability, supported by cooperative perception, decision-making and planning, and control. An accurate and robust localization system plays a vital role in enabling these modules for future mobility and is constrained by environmental uncertainties and sensing limitations. To achieve precise and resilient localization in this new era, this letter introduces emerging technologies including edge computing, hybrid data-driven and physical model approaches, foundation models as well as parallel intelligence, that are beneficial for next-generation localization systems. On top of these key technologies, by integrating real-world testing and digital twin technology, we further put forward a Decentralized Autonomous Service (DAS)-based cooperative localization framework for future mobility systems to enhance the resilience, robustness, and safety of transportation systems.

关键词： Location awareness Transportation Roads Robot sensing systems Safety Digital twins Computational modeling Cooperative driving cooperative localization localization service smart infrastructure transportation 5.0

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Inductive Representation Learning on Dynamic Stock Co-Movement Graphs for Stock Predictions

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INFORMS JOURNAL ON COMPUTING 2022年第4期34卷 1940-1957页

作者： Tian, Hu Zheng, Xiaolong Zhao, Kang Liu, Maggie Wenjing Zeng, Daniel Dajun Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Univ Iowa Tippie Coll Business Dept Business Analyt Iowa City IA 52242 USA Tsinghua Univ Sch Econ & Management Beijing 100084 Peoples R China

Co-movement among individual firms' stock prices can reflect complex inter-firm relationships. This paper proposes a novel method to leverage such relationships for stock price predictions by adopting inductive graph representation learning on dynamic stock graphs constructed based on historical stock price co-movement. To learn node representations from such dynamic graphs for better stock predictions, we propose the hybrid-attention dynamic graph neural network, an inductive graph representation learning method. We also extended mini-batch gradient descent to inductive representation learning on dynamic stock graphs so that the model can update parameters over mini-batch stock graphs with higher training efficiency. Extensive experiments on stocks from different markets and trading simulations demonstrate that the proposed method significantly improves stock predictions. The proposed method can have important implications for the management of financial portfolios and investment risk.

关键词： graph representation learning deep learning predictive models business intelligence

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A DAOs-Based Publishing System for Advancing Open Access

A DAOs-Based Publishing System for Advancing Open Access

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Siji Ma Juanjuan Li Sangtian Guan Qinghua Ni Fei Lin Tengchao Zhang Jun Huang Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

The development of the scientific publishing system has remarkably enhanced global accessibility to research findings and substantially increased the visibility and dissemination of academic publications. However, significant challenges still exist in effectively safeguarding the intellectual property rights of contributors, such as the unauthorized usage of materials, the complexity of enforcing intellectual property rights across various legal jurisdictions, and high instances of plagiarism and content misuse. Additionally, financial barriers related to open access may restrict the participation of economically disadvantaged researchers, potentially biasing scientific records towards more affluent research initiatives. To address these issues, a novel decentralized framework is formulated to ensure truly open access. This framework leverages blockchain for immutable record-keeping and clear attribution of authorship, to prevent unauthorized usage and plagiarism. Besides, it also utilizes a copyright-sharing model based on decentralized autonomous organizations and operations (DAOs), where smart contracts automatically enforce copyright and access policies to ensure fair compensation for authors and researchers. Furthermore, the copyright sharing model based on non-fungible tokens (NFT) and gradual ownership optimization (GOO) mechanism is proposed to ensure fair and accurate recognition and compensation for scholarly contributions.

关键词： Open Access Law Plagiarism Smart contracts Intellectual property Research initiatives Scientific publishing Nonfungible tokens Remuneration Optimization

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