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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第5期8卷 3224-3227页

作者： Chen, Jicheng Zhang, Yongkang Teng, Siyu Chen, Yuanyuan Zhang, Hui Wang, Fei-Yue Beihang Univ Sch Transportat Sci & Engn Beijing 100091 Peoples R China Hong Kong Baptist Univ Kowloon Hong Kong 999077 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China

Reducing vehicle exhaust pollution and energy consumption is of great significance for improving the sustainability of social development. Currently, most energy-efficient and regenerative energy recovery methods are from a vehicle control perspective, ignoring the impact on the overall traffic environment. An important reason is that the transportation system's large scale, complexity and social nature restrict its energy-efficient development. Hence, this letter proposes energy-efficient and regenerative energy recovery schemes for sustainable intelligent transportation system using the Artificial societies, Computational experiments, Parallel execution (ACP) framework. The framework includes three parts: energy-efficient oriented intelligent road infrastructure design, transportation traffic flow and vehicle velocity profile planning co-design, and cloud-based vehicle engine parameter calibration. This letter is the second part of Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL) and aims to enhance the sustainability of transportation system from the energy-efficient perspective.

关键词： Transportation Roads Energy efficiency Engines Intelligent vehicles Vibrations Planning Sustainability intelligent vehicles parallel transportation system energy efficiency transportation regenerative energy carbon neutrality green transportation

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Development of a High-Speed Swimming Robot With the Capability of Fish-Like Leaping

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第5期27卷 3579-3589页

作者： Chen, Di Wu, Zhengxing Meng, Yan Tan, Min Yu, Junzhi 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 Peking Univ Beijing Innovat Ctr Engn Sci & Adv Technol Dept Adv Mfg & Robot Coll EngnState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Achieving fast and agile swimming still remains extremely challenging for a self-propelled robotic fish due to the constraint of actuator's propulsion capability. In this article, we report an untethered bioinspired robotic fish, which combines a high-frequency oscillation and a compliant passive mechanism to realize fast swimming, high pitch maneuvers, and even the leaping motion. For pursuing the explosive propulsion of the robotic fish, we propose an actuation system with a powerful output and a compact structure. A dynamic model is established and indicates that the compliant joint is able to modulate the power transmitted to a caudal fin to affect its velocity in the return stroke for generating more peak thrust. The design is validated with extensive experimental results. Namely, the robotic fish can surprisingly reach up to a speed of 3.8 body lengths per second (BL/s). Compared to the case with a rigid joint, dramatic improvements, involving a speed of 1.2 BL/s and a swimming distance of 141.2 m (70.6%), have been obtained, which reveal that besides the high-frequency oscillation, the compliant passive mechanism is also of great significance to perform high-speed swimming. Additionally, the robotic fish demonstrates its high pitch maneuvers by performing an agile front flip motion with a radius of 0.4 BL and an average angular velocity of 439 degrees/s. Most importantly, with a simple control strategy, our robotic fish can remarkably leap out of water completely. Results from this study provide significant insights into the innovative designs of next-generation robotic fishes, which require high speed and maneuverability.

关键词： Bioinspired robotic fish compliant joint dynamic modeling high-frequency propulsion high swimming performance

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When Blockchain Meets Auction: A Comprehensive Survey

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第3期11卷 4242-4254页

作者： Liu, Xuan Liu, Lu Yuan, Yong Long, Yong-Hong Li, San-Xi Wang, Fei-Yue Renmin Univ China Sch Math Beijing 100872 Peoples R China Renmin Univ China Sch Econ Beijing 100872 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macau Peoples R China

Recent years have witnessed remarkable developments and increasingly deepened integrations between blockchain as a decentralized computing architecture and auction as an efficient resource allocation approach. Typically, blockchain can help provide a secured and trusted distributed environment for various auction scenarios, while auction is particularly suitable for designing resource allocation and pricing mechanisms in blockchain systems. As such, integrative research on blockchain and auction developed rapidly and attracted widespread attention in various fields ranging from academia to financial, industrial, and social services. However, a comprehensive survey on this interdisciplinary topic is still nonexistent, which motivates our work. In this article, we aim to fill this important research gap by reviewing the related literature. We first conducted a brief overview of blockchain technology and auction theory, and then systematically discussed the research progress on the existing blockchain research based on auction theory as well as auction research enabled by blockchain. Toward the end, we presented several open research issues and directions, aiming to provide useful guidance and reference for future research efforts.

关键词： Allocation auction blockchain decentralized pricing

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Distributed Event-Triggered Formation control for a Multi-robotic Fish System

Distributed Event-Triggered Formation Control for a Multi-ro...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Dai, Shijie Wu, Zhengxing Tan, Min Yu, Junzhi School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9789811966125

This paper investigates the formation control problem for a multi-robotic fish system and a distributed event-triggered-based formation control framework is proposed. The framework consists of a communication topology, a distributed formation control law, and a dynamic event-triggered communication mechanism. In particular, a position-based formation control law is presented to drive the multi-robotic fish system to an anticipated configuration based on local measurements while keeping pace with a moving target. Meanwhile, a dynamic event-triggered mechanism is utilized to measure importance of the interactive information and decide the communication timing. The proposed formation control method shows its merits to greatly reduce communication consumption with a limited loss of formation control performance. Finally, simulations with quantitative comparisons are presented to verify the effectiveness of the proposed framework. This formation control framework provides a solid foundation for future marine cooperative control of the multi-robotic fish system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Development and control of a Bioinspired Robotic Remora for Hitchhiking

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第5期27卷 2852-2862页

作者： Zhang, Pengfei Wu, Zhengxing Meng, Yan Dong, Huijie Tan, Min Yu, Junzhi 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 100049 Peoples R China Peking Univ Beijing Innovat Ctr Engn Sci & Adv Technol Dept Adv Mfg & Robot Coll EngnState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Remora, a bony fish, is well known for the remarkable hitchhiking behavior, by which it can transport itself over large distances without too much effort. In this article, a novel robotic remora with a stable adhesion system and its motion control methods are proposed for transferring the hitchhiking behavior to the engineered system. In terms of the mechatronic design, a robotic prototype with the uncoupled planar and vertical motion mechanisms is created to realize the precise and agile movement. Furthermore, a reliable adhesion system possessing low preload demand, considerable adhesive force (similar to 250 N), and reversible adhesion ability is developed, and then, the design principle of the adhesive disc is investigated. In the aspect of motion control, a mode-switch-based planar pose (position and orientation) control method and an antidisturbance depth control method are designed. The combination of these two controllers endows the robotic remora with the ability to precisely arrive at any position in a 3-D space, which paves the way for the hitchhiking task. Extensive experimental results are presented to support the performance benefits of the proposed robotic remora and control methods. Finally, the hitchhiking behavior is implemented successfully by robotic remora. To the best of our knowledge, this is the first engineered implementation of hitchhiking for a fin-actuated underwater robot. The results obtained will promote the design and control of the future underwater adhesion robot and offer valuable insights into the long-endurance robotic fish and mother-son multirobot system.

关键词： Bioinspired adhesion depth control pose control remora robotic fish

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iCyberGuard: A FlipIt Game for Enhanced Cybersecurity in IIoT

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第6期11卷 8005-8014页

作者： Chen, Xiaoguang Cao, Wenyuan Chen, Lili Han, Jinpeng Yang, Manzhi Wang, Zhen Wang, Fei-Yue Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China Hangzhou Dianzi Univ Sch Cyberspace Hangzhou 310018 Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Social manufacturing has significantly advanced the industrial Internet of Things (IIoT), integrating information technology and operation technology to enhance production efficiency and quality, and to foster new business models. This integration, however, introduces novel risks, including advanced persistent threats, which demand robust security measures to safeguard IIoT systems. This article proposes an iCyberGuard game model, tailored for IIoT environments, designed to imitate the cyber and physical attacks for information and operation technologies. Then, we used a reinforcement learning algorithm to compute the optimal strategy. We conducted comprehensive simulation experiments, which demonstrate that our model the strategic interactions between attackers and defenders. Participants are enabled to learn adaptively, discerning optimal strategies based on the intelligence of their adversaries. Finally, we explain the practical significance of the best strategy of defenders or attackers, and how users can rely on these best strategies to strengthen the security performance of the network.

关键词： Industrial Internet of Things Games Security Game theory Costs Reinforcement learning Manufacturing Advanced persistent threat (APT) industrial Internet of Things (IIoT) reinforcement learning social manufacturing

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Modeling and Simulation of Crowd Evacuation With Signs at Subway Platform: A Case Study of Beijing Subway Stations

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第2期23卷 1492-1504页

作者： Zhou, Min Dong, Hairong Wang, Xiao Hu, Xiaoming Ge, Shichao Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China V1Pioneers Inc Qingdao 266109 Peoples R China KTH Royal Inst Technol Dept Math SE-10044 Stockholm Sweden

Emergency signage systems provide effective route guidance and evacuation information for pedestrians in case of emergencies such as fire, blackout, and explosion. This paper proposes a modified social force (SF) model to investigate crowd evacuation dynamics taking into account the influence of emergency signs. The perceiving probability model is formulated for the quantitative description of the probability that pedestrians can successfully notice the sign and clearly perceive the guidance information. Simulation experiments and controlled experiments are designed to calibrate the parameters of the proposed model. The effectiveness of the modified SF model is preliminarily verified by comparing the simulation results with experimental data and/or empirical results such as fundamental diagrams and self-organization phenomena. A case study of crowd evacuation simulations at a typical Beijing subway station is conducted to evaluate evacuation performance of three signage distribution schemes, i.e., Maximal Covering (MaxCover), Uniform, and Random, which are proposed by the maximal covering location and empirical approaches, as well as contrasted scheme without emergency signs. The effects of the quantity and distribution of emergency signs on crowd evacuation efficiency are studied quantitatively by simulations. The results show that installing emergency signs can improve evacuation efficiency no matter what distribution scheme is adopted. By choosing an appropriate distribution scheme i.e., MaxCover, the evacuation performance can be further improved and the evacuation time can be significantly reduced.

关键词： Public transportation Data models Safety Solid modeling Force Rails Crowd evacuation modeling emergency sign social force model distribution scheme navigational force

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Airport Capacity Prediction With Multisource Features: A Temporal Deep Learning Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第1期24卷 615-630页

作者： Du, Wenbo Chen, Shenwen Li, Haitao Li, Zhishuai Cao, Xianbin Lv, Yisheng Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Adv Technol Near Space Informat Syst Beijing 100191 Peoples R China 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 100049 Peoples R China

Accurate airport capacity estimation is crucial for the secure and orderly operation of the aviation system. However, such estimation is a non-trivial task as capacity depends on various meteorological and operational features. The complex coupling characteristics among these multi-source features have proved to be challenging for most of the traditional regression models. Recently, enhanced by its excellent ability to mine nonlinear relationships, the machine learning methods trigger widely applications. However, due to the imbalance of features scatter and the neglect of temporal dependences in aviation systems, existing machine learning methods for airport capacity prediction still have room for improvement. In light of these, this paper presents a novel airport capacity prediction method based on the multi-channel fusion Transformer model (MF-Transformer). Besides the commonly used aviation features, we unprecedentedly harness the power of the high-dimensional meteorological feature for accurate prediction. As to the model, we construct a multi-channel feature fusion structure, which includes a three-channel network for multi-source features extraction and an attention-based feature fusion module between channels. In each channel, the Transformer-based model is utilized to capture the temporal dependences of features. We conduct experiments on the capacity prediction tasks of the Beijing Capital International Airport which is the largest airport in China and verify that the proposed MF-Transformer outperforms benchmarks under different prediction horizons.

关键词： Airport capacity predictive model multi-channel fusion structure machine learning deep learning

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Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks 17

Signal Detection Method Based on Social Relationship Strengt...

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17th IFAC Symposium on control of Transportation systems (CTS)

作者： Li, Yi Han, Shuangshuang Bai, Yongqiang Wang, Fei-Yue Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China MNR Key Lab Ocean Observat Technol Tianjin Peoples R China Beijing Huairou Acad Parallel Sensing Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields. Copyright (c) 2024 The Authors.

关键词： VANETs Social relation strength Image data transmission MMSE detection Intelligent transportation Social networking

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VGN: Value Decomposition With Graph Attention Networks for Multiagent Reinforcement Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2024年第1期35卷 182-195页

作者： Wei, Qinglai Li, Yugu Zhang, Jie Wang, Fei-Yue 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 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

Although value decomposition networks and the follow on value-based studies factorizes the joint reward function to individual reward functions for a kind of cooperative multiagent reinforcement problem, in which each agent has its local observation and shares a joint reward signal, most of the previous efforts, however, ignored the graphical information between agents. In this article, a new value decomposition with graph attention network (VGN) method is developed to solve the value functions by introducing the dynamical relationships between agents. It is pointed out that the decomposition factor of an agent in our approach can be influenced by the reward signals of all the related agents and two graphical neural network-based algorithms (VGN-Linear and VGN-Nonlinear) are designed to solve the value functions of each agent. It can be proved theoretically that the present methods satisfy the factorizable condition in the centralized training process. The performance of the present methods is evaluated on the StarCraft Multiagent Challenge (SMAC) benchmark. Experiment results show that our method outperforms the state-of-the-art value-based multiagent reinforcement algorithms, especially when the tasks are with very hard level and challenging for existing methods.

关键词： Mathematical models Task analysis Games Q-learning Neural networks Behavioral sciences Training Deep learning graph attention networks (GATs) multiagent systems reinforcement learning

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