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Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks

IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2020年第4期21卷 1624-1630页

作者： Chen, Yuanyuan Lv, Yisheng Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Shandong 266109 Peoples R China

Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently proposed method of using synthetic and real data for data mining and data-driven process, in which we apply GANs to generate synthetic traffic data. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: 1) using the real data or the corrupted ones instead of random vectors as latent codes to generator within GANs and 2) introducing a representation loss to measure discrepancy between the synthetic data and the real data. The experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic data imputation.

关键词： Generators Data models Gallium nitride Generative adversarial networks Training Loss measurement Biological system modeling Parallel data generative adversarial networks traffic flow imputation data augmentation deep learning

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Historical temporal difference learning: Some initial results

Historical temporal difference learning: Some initial result...

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First International Multi- Symposiums on Computer and Computational Sciences, IMSCCS'06

作者： Hengshuai, Yao Diao, Dongcui Zengqi, Sun State Key Lab. of Intelligent Control and Systems Tsinghua University Mathematical Department Shandong University of Technology Department of Computer Science and Technology Tsinghua University

ISBN: (纸本)0769525814

In this paper, we develop a multi-step prediction algorithm that is guaranteed to converge when using general function approximation. Besides, the new algorithm should satisfy the following requirements: First, it does not have to be faster than TD(0) in the look-up table representation;however, the new algorithm should be faster than residual gradient method. Second, the new algorithm should learn optimally. © 2006 IEEE.

关键词： Learning systems

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Generative Adversarial Networks for Parallel Transportation systems

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IEEE intelligent TRANSPORTATION systems MAGAZINE 2018年第3期10卷 4-10页

作者： Lv, Yisheng Chen, Yuanyuan Li, Li Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao Peoples R China Univ Chinese Acad Sci Control Theory & Control Engn Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

Generative Adversarial Networks (GANs) have emerged as a promising and effective mechanism for machine learning due to its recent successful applications. GANs share the same idea of producing, testing, acquiring, and utilizing data as well as knowledge based on artificial systems, computational experiments, and parallel execution of actual and virtual scenarios, as outlined in the theory of parallel transportation. Clearly, the adversarial concept is embedded implicitly or explicitly in both GANs and parallel transportation systems. In this article, we first introduce basics of GANs and parallel transportation systems, and then present an approach of using GANs in parallel transportation systems for traffic data generation, traffic modeling, traffic prediction and traffic control. Our preliminary investigation indicates that GANs have a great potential and provide specific algorithm support for implement n.g parallel transportation systems.

关键词： Generative adversarial networks Computational modeling Machine learning Predictive models Traffic control Integrated circuit modeling

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Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-control for intelligent Vehicles

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1658-1672页

作者： Hong Mo Yinghui Meng Fei-Yue Wang Dongrui Wu the College of Electric and Information Engineering Changsha University of Science and TechnologyChangsha 410114China the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Qingdao Academy of Intelligent Industries Qingda 266109 the Center of China Economic and Social Security University of Chinese Academy of SciencesBeijing 100190China the Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control the School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

intelligent vehicles can effectively improve traffic congestion and road traffic *** cruise followingcontrol(ACFC)is a vital part of intelligent *** this paper,a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model,type-2 fuzzy control,feedforward+fuzzy proportion integration(PI)feedback(F+FPIF)control,and inverse longitudinal dynamics model of ***,a traditional variable time headway model is improved considering the acceleration of the lead ***,an interval type-2 fuzzy logic controller(IT2 FLC)is designed for the upper structure of the ACFC system to simulate the driver's operating *** reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration,the control strategy of F+FPIF is given for the lower control ***,the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method(no lower controller for tracking desired acceleration)***,the proportion integration differentiation(PID),linear quadratic regulator(LQR),subsection function control(SFC)and type-1 fuzzy logic control(T1 FLC)are respectively compared with the IT2 FLC in control performance under different ***,the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.

关键词： Adaptive cruise following-control(ACFC) feedforward+fuzzy proportion integration(PI)feedback(F+FPIF) type-2 fuzzy control variable time headway

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An Immersive Learning System with Multimodal Cognitive Processes Inference

An Immersive Learning System with Multimodal Cognitive Proce...

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2022 Chinese Automation Congress, CAC 2022

作者： Bian, Siyu Liu, Xiwei Zhao, Hongxia Gong, Xiaoyan Jing, Sifeng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China

ISBN: (纸本)9781665465335

Using multimodal technology to infer learners' cognitive processes is an important means to support personalized learning. Previous cognitive process analysis is more about subjective reasoning, and less about combining physiological signals. With the rapid development of virtual reality technology, new requirements and more immersive schemes for learner's cognitive process inferring have been proposed. Based on this, this paper proposes an immersive learning system with multimodal cognitive processes inference, which is composed of macro, mesoscopic and micro environments. The system analyzes learner's actions, emotions and attention to infer learner's cognitive process through more comprehensive and objective multimodal data. The system also combines eye movement, brainwaves and other data, and proposes a multimodal data acquisition and analysis scheme using HMD-based VR. This paper also provides an immersive learning cognitive processes inference scheme for individual online learning, offline classroom learning scenarios. © 2022 IEEE.

关键词： Learning systems

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System Dynamics-Based Analysis on Factors Influencing Comprehensive Competence of Primary and Secondary School Students

System Dynamics-Based Analysis on Factors Influencing Compre...

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2022 Chinese Automation Congress, CAC 2022

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Zhao, Hongxia Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao266044 China

ISBN: (纸本)9781665465335

With rapid progress of artificial intelligence (AI), it is urgent to promote educational reform to improve comprehensive competence level of primary and secondary school students (CCLPSSS). However, educational reform is a system engineering involving multiple and intertwined factors. In this research, a system dynamics-based model of education reform is developed. Then, based on this model, a series of simulation experiments are carried out to find main factors affecting improvement of CCLPSSS. Results show that: 1) this model could simulate trends of CCLPSSS under different educational reform measures;2) Measures including teaching mode reform, development and application of AI-based new teaching method, implementation of Double Reduction in compulsory education, implementation of deepening education evaluation reform policy, and development and application of AI-based education evaluation technology could play significant roles in promoting CCLPSSS and further strengthening cultivation of AI compound talents. © 2022 IEEE.

关键词： Students

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Research on intelligent methods in virtual reality games

Research on intelligent methods in virtual reality games

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2022 Chinese Automation Congress, CAC 2022

作者： Wang, JiaWei Liu, Xiwei Gong, Xiaoyan Chinese Academy of Sciences The State Key Laboratory for Management Control of Complex Systems Institute of Automation Beijing100190 China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao266044 China

ISBN: (纸本)9781665465335

Introduction of virtual reality technology enriches the expression of traditional games and greatly improves the sense of substitution and interactive experience of users. Realization of virtual reality is inseparable from artificial intelligence technology. In order to improve immersion and interactivity of virtual reality games, it is necessary to propose more intelligent solutions to existing technologies. This paper aims to discuss some intelligent methods of virtual reality games. Firstly, it discusses artificial intelligence means to enhance the virtual reality experience, which can improve the tracking and force / tactile interaction system of existing virtual reality games from software and hardware aspects;Then it proposes the concept of human-machine hybrid enhanced intelligence: by introducing human cognitive model to improve perception and decision-making ability of virtual reality games, so as to make interaction of virtual reality games more friendly and more real. © 2022 IEEE.

关键词： Virtual reality

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Limit-Cycle-Based Design of Formation control for Mobile Agents

Limit-Cycle-Based Design of Formation Control for Mobile Age...

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作者： Wang, Chen Xia, Weiguo Xie, Guangming National Engineering Research Center for Software Engineering Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian116024 China State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this article, we study the formation problem for a group of mobile agents in a plane, in which the agents are required to maintain a distribution pattern, as well as to rotate around or remain static relative to a static/moving target. The prescribed distribution pattern is a class of general formations where the distances between neighboring agents or the distances between each agent and the target do not need to be equal. Each agent is modeled as a double integrator and can merely perceive the relative information of the target and its neighbors, and the acceleration of the target. In order to solve the formation problem, a limit-cycle-based controller design is delivered. We divide the overall control objective into two subobjectives, where the first is target circling that each agent keeps its own desired distance to the static/moving target as well as rotating around or remaining static relative to the target as expected, and the second is distribution adjustment that each agent maintains the desired distance to its neighbors. Then, we propose a controller comprised of two parts, where a limit cycle oscillator named a converging part is designed to deal with the first subobjective, while a layout part is introduced to address the second subobjective. One key merit of the controller is that it can be implemented by each agent in its local frame so that only local information is utilized without knowing global information. Theoretical analysis of the convergence to the desired formation, of which the agents are required to be evenly distributed on a circle around the target, is provided for the multiagent system under the proposed controller. Numerical simulations are given to validate the effectiveness of the proposed controller for the cases of general formations, and to show that no collision between agents ever takes place throughout the system's evolution. © 1963-2012 IEEE.

关键词： Multi agent systems

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Parallel Factories for Smart Industrial Operations:From Big AI Models to Field Foundational Models and Scenarios Engineering

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2079-2086页

作者： Jingwei Lu Xingxia Wang Xiang Cheng Jing Yang Oliver Kwan Xiao Wang Qingdao Academy of Intelligent Industries Qingdao 266114 The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Motion G Inc.Singapore 068898Singapore IEEE School of Artificial Intelligence Anhui UniversityHefei 230031

The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence,internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems,QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called Eu Artisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.

关键词： Cyber-physical-social system(CPSS) Industry5.0 Metaverses Parallel factories Parallel intelligence

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Photoelectric intelligent Detection Platform Based on Multifunctional Edge Computing Equipment

Photoelectric Intelligent Detection Platform Based on Multif...

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第43届中国控制会议

作者： Qianjing Xu Wenjie Chen Zhijun Xue State Key Lab of Intelligent Control and Decision of Complex Systems

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In order to study to design a photoelectric intelligent platform based on multifunctional edge computing equipment by using photoelectric detection equipment TC505C, RK-Series Development Kits computing platform and Nvidia Jetson *** photoelectric intelligent platform can be used to run multiple target detection algorithms after design and *** as *** of all, after the first stage of the photoelectric intelligent platform is completed, the host computer can establish communication with the photoelectric detection equipment, carry out automatic or manual servo control on the photoelectric detection equipment, switch its visible and infrared field of view and view it in real time through the monitor, and realize simple tracking function through the image processing ***, after the second stage of the photoelectric intelligent platform is built, under the premise of ensuring the normal availab.lity of servo function, a variety of target detection algorithms can be debugged on multifunctional edge computing ***, this paper uses the modular design concept to design, debug and implement the system in stages. Pay attention to the synchronous debugging of hardware and software functions of each module to ensure the stability of each module. Finally completed the design goals of each stage.

关键词： Multifunctional Edge Computing Equipment Photoelectric intelligent Platform Object Detection

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