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Chinese Automation Congress (CAC)

作者： Zhao, Xiaoyu Liu, Xiwei Gong, Xiaoyan Jing, Sifeng Lie, Jian Beijing Univ Technol Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Qingdao Acad Intelligent IndInst Smart Educ Syst Beijing Peoples R China Beijing Univ Technol Beijing Peoples R China

ISBN: (纸本)9781728140940

Due to the increasing frequency of terrorist attacks all over the world, we designed an emergency evacuation model under the situation of the terrorist attacks. This model uses the Ant Colony Optimization (ACO) as a macro solution, the Cellular Automata (CA) as a micro-control method, and the Queuing Theory Model (QTM) to solve the queuing problem of escape exits. Based on the model we built, the optimized evacuation plan under different conditions were obtained.

关键词： evacuation queuing theory model improved cellular automaton ant colony optimization

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Research and practice of lightweight digital twin speeding up the implementation of flexible manufacturing systems

Research and practice of lightweight digital twin speeding u...

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

作者： Xiaodong Zhang Bin Hu Gang Xiong Xinjie Liu Xisong Dong Dongjia Li School of Information Engineering Yantai institute of Technology Yantai China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijing China Yantai University Yantai China School of Artificial Intelligence Yantai Institute of Technology Yantai China

Parallel manufacturing in Industry 5.0 requires digital twin to digitize physical systems, building virtual models to open up channels connecting physical systems, information systems, and social systems, and transforming the physical models of the existing production environment to achieve two-way feedback of virtual and real is the current research direction. This paper proposes the modeling idea of lightweight digital twin, extracts core dimensions and performs digital virtual simulation, so as to quickly realize the complete process of two-way feedback, and realize a set of chess flexible parallel manufacturing production lines as a practice for the design of complete lightweight digital twin.

关键词： Industries Digital twin Conferences Buildings Production Manufacturing Flexible manufacturing systems

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A Learner Model Integrating Cognitive and Metacognitive And Its Application on Scratch Programming Projects

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IFAC-PapersOnLine 2020年第5期53卷 644-649页

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao 266044 China Department of Electrical and Computer Engineering Rowan University Glassboro NJ 08028 USA

learner’s cognitive and metacognitive are key personal profile for individualized teaching. To evaluate learner’s comprehensive characteristics, existing learner model were reviewed. Two challenges of constructing an accurate and comprehensive learner model integrating cognitive and metacognitive were summarized. A plan of constructing a comprehensive learner model was made based on analysis of existing massive online learning environment, sensor information technology and educational data-mining. As a case study, a method of how to map learning data onto learners’ cognitive and metacognitive was proposed based on an analysis of a number of pupils’ Scratch projects. Three mapping table were established. Pupil’s cognitive skill could be evaluated from technology shown from Scratch project, namely, data structure, algorithm, computational practices and overall evaluation. Content shown from Scratch project were used to infer pupil’s cognitive style. Meta-cognitive ability can be measured from computational practices and behavior in programming process.

关键词： learner model cognitive state metacognitive ability individualized teaching

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Calibration of Agent-Based Model Using Reinforcement Learning

Calibration of Agent-Based Model Using Reinforcement Learnin...

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

作者： Bing Song Gang Xiong Songmin Yu Peijun Ye Xisong Dong Yisheng Lv The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China Fraunhofer Institute for Systems and Innovation Research Breslauer Germany

In the research and application of Agent-Based Models (ABM), parameter calibration is an important content. Based on the existing state transfer equations that link the micro-parameters and macro-states of the multi-agent system, this paper further proposes to introduce Reinforcement Learning when calibrating the parameters. The state transfer of the agent after learning is used to calibrate the micro-parameters of ABM, and the interaction between each agent and multiple other agents is expressed as the parameters of the agent. The application case study of population migration demonstrates that our method can achieve high accuracy and low computational complexity.

关键词： Learning systems Microscopy Digital twin Sociology Reinforcement learning Mathematical models Calibration

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Learning Network-Invariant and lab.l-Discriminative Representations for Cross-Network Node Classification

Learning Network-Invariant and Label-Discriminative Represen...

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Chinese Automation Congress (CAC)

作者： Linyao Yang Yancai Xu Jiachen Hou Yuxin Dai Chen Lv The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Institute of Systems Engineering Macau University of Science and Technology Macau China School of Electrical Engineering and Automation Wuhan University Wuhan China China Electric Power Research Institute Beijing China

ISBN: (纸本)9781665426480

Networks are ubiquitous data structures in the real world. The accurate and efficient analysis of networks is critical to realizing many intelligent network-based services. However, most existing network analysis methods are developed for single networks and require a lot of lab.led data, which is costly and time-consuming to acquire. Transfer learning has been widely accepted as an effective paradigm for tackling this problem by reusing the model trained on a supervised task. However, transfer learning on the non-euclidean network data has been investigated by no more than a few studies. To realize accurate node classification based on the knowledge learned from the lab.led source network, this paper proposes to learn network-invariant and lab.l-discriminative representations based on graph embedding and linear discriminant analysis. Specifically, we embed the source and target networks into adjacent vector spaces based on the graph attention network by minimizing the Sinkhorn distributional distances between their embeddings. To obtain lab.l-discriminative features for learning better classification models, we then utilize a transferable linear discriminative analysis method to project the embeddings into lab.l-discriminative subspaces. In the end, a support vector machine model trained on the lab.led source network is utilized to classify the target nodes. Experiments on two pairs of networks illustrate that our method achieves the best performance and evaluates the effectiveness of the proposed modules.

关键词： Support vector machines Knowledge engineering Analytical models Automation Transfer learning Network analyzers Data structures

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Social Energy: Emerging Token Economy for Energy Production and Consumption

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2019年第3期6卷 388-393页

作者： Wang, Fei-Yue Zhang, Jun Jason Qin, Rui Yuan, Yong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China

Welcome to the third issue of the IEEE Transactions on Computational Social systems (TCSS) in 2019. Thanks to the efforts of the editors, reviewers, authors, and readers of TCSS, the influence of TCSS is rapidly increasing. According to the latest statistics released by Elsevier, the CiteScore of TCSS in 2018 reaches 4.00, and ranks eighth out of the 255 journals (top 3%) in the field of social sciences. This is a solid improvement compared with the corresponding data in 2017 (CiteScore: 2.36, Rank: 17/226, and top 8%). Thanks and congratulations to our authors, reviewers, and members of our editorial board. The current issue includes 20 regular papers and a brief discussion on social energy.

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Parallel Vision for Long-Tail Regularization: Initial Results From IVFC Autonomous Driving Testing

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IEEE TRANSACTIONS ON intelligent VEHICLES 2022年第2期7卷 286-299页

作者： Wang, Jiangong Wang, Xiao Shen, Tianyu Wang, Yutong Li, Li Tian, Yonglin Yu, Hui Chen, Long Xin, Jingmin Wu, Xiangbin Zheng, Nanning 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 Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Tsinghua Univ Dept Automat BNRist Beijing 100084 Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth PO1 2DJ Hants England Sun Yat Sen Univ Guangzhou 510275 Peoples R China Waytous Inc Qingdao 266109 Shandong Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot IAIR Xian 710049 Peoples R China Intel Corp Intel Labs China Beijing 100190 Peoples R China

Long-tail effect, characterized by highly frequent occurrence of normal scenarios and the scarce appearance of extreme "long-tail" scenarios, ubiquitously exists in the vision-related problems in the real-world applications. Though many computer vision methods to date have already achieved feasible performance for most of the normal scenarios, it is still challenging for existing vision systems to accurately perceive the long-tail scenarios. This deficiency largely hinders the practical application of computer vision systems, since long-tail problems may incur fatal consequences, such as traffic accidents, taking the vision systems of autonomous vehicles as an example. In this paper, we firstly propose a theoretical framework named Long-tail Regularization (LoTR), for analyzing and tackling the long-tail problems in the vision perception of autonomous driving. LoTR is able to regularize the scarcely occurred long-tail scenarios to be frequently encountered. Then we present a Parallel Vision Actualization System (PVAS), which consists of closed-loop optimization and virtual-real interaction, to search for challenging long-tail scenarios and produce large-scale long-tail driving scenarios for autonomous vehicles. In addition, we introduce how to perform PVAS in intelligent Vehicle Future Challenge of China (IVFC), the most durable autonomous driving competition around the world. Results over the past decade demonstrate that PVAS can effectively guide the collection of long-tail data to diminish the cost in the real world, and thus promote the capability of vision systems to adapt to complex environments, alleviating the impact of long-tail effect.

关键词： Testing Machine vision Autonomous vehicles Visualization Accidents Task analysis Reliability theory Parallel vision long tail autonomous vehicles

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FCM-RDpA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, droprule, and powerball adabelief

arXiv

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arXiv 2020年

作者： Shi, Zhenhua Wu, Dongrui Guo, Chenfeng Zhao, Changming Cui, Yuqi Wang, Fei-Yue Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which integrates recently proposed Powerball gradient and AdaBelief to further expedite and stabilize parameter optimization. Extensive experiments on 22 regression datasets with various sizes and dimensionalities validated the superiority of FCM-RDpA over MBGD-RDA, especially when the feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that further improves FCM-RDpA by using augmented features in both the antecedents and consequents of the rules. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

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Research on Trajectory Planning and Motion control of 7-DoF Sawyer Manipulator

Research on Trajectory Planning and Motion Control of 7-DoF ...

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

作者： Kun Luo Gang Xiong Yuxiao Wang Bin Hu Sheng Liu Shichao Chen The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijng China School of Artificial Intelligence University of Chinese Academy of Sciences Beijng China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Beijng China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China

Regarding human-machine collab.rative task, generated trajectories of a robot manipulator should be able to avoid joint limits and singularities, but the 6 Degrees of Freedom(DoF) or lower DoF manipulators encounter difficulties in many schemes. In this paper, research is implemented on the methods of trajectory planning and motion control on the newly human-machine collab.rative 7-DoF Sawyer manipulator. We try to pay more attention to these approaches and find a better combination between trajectory planners and motion controllers for Sawyer manipulator. First of all, comparison is proposed between the methods about trajectory planning in task space and joint space, drawing a conclusion that the latter has the advantage of quickness and smoothness on trajectories in real time. Next, we choose to plan trajectory in the joint space by interpolating positions and orientations. Three approaches, i.e., trapezoidal velocity trajectory, cubic polynomial trajectory and basic spline trajectory, are applied to interpolate positions. Experimental results show that it results in the smoothest trajectory using basic spline trajectory planning. We also compare linear and nonlinear orientation interpolations to address the angle wrapping issue, which leads to difficulties in smooth orientation interpolation between several waypoints. Combining polynomial trajectory planning methods with the proportional-derivative (PD) motion controller and the computed-torque motion controller, we focus on the performance of the two different combinations. It is found that trajectories with computed-torque controller have shorter path with a congruous convergence on joint position, and more accurate trajectories with less overshoots in position than others.

关键词： Interpolation Trajectory planning Collab.ration Aerospace electronics Manipulators Trajectory Task analysis

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Weakly Supervised Person Search 7

Weakly Supervised Person Search

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7th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

作者： Yan, Lan Zheng, Wenbo Wang, Fei-Yue Gou, Chao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China

ISBN: (数字)9781728182063

ISBN: (纸本)9781728182063

While existing person search methods have achieved good performance, they require the images used for training contain lab.ls about the identity and bounding box location of each person. However, it is expensive and difficult to manually annotate these lab.ls in the large scale scenario. To overcome this issue, we consider weakly supervised person search. The weakly supervised setting means during training we only know which identities appear in the image set and how many individuals present in each image, without any identity or location information on the image. Facing this challenge, we propose a clustering and patch based weakly supervised learning (CPBWSL) framework, which separately addresses two sub-tasks including pedestrian detection and person re-identification. Particularly, we introduce multiple detectors to provide more detection results as well as fuzzy c-means clustering algorithm to cluster these results and remove low membership ones. Moreover, a patch based learning network is designed to generate different patches and learn discriminative patch features. Extensive experiments on two benchmarks indicate that the proposed weakly supervised setting is feasible and our method can achieve performance comparable to some fully supervised person search methods.

关键词： Person search Pedestrian detection Person re-identification Weakly supervised learning

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