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Cyber-Physical-Social systems for Smart City: An Implementation Based on intelligent Loop

IFAC-PapersOnLine 2020年第5期53卷 501-506页

作者： Gang Xiong Xiaoyu Chen Nan Shuo Yisheng Lv Fenghua Zhu Tianci Qu Peijun Ye State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China Graduate School of System Design Tokyo Metropolitan University Tokyo 191-0065 Japan

Cyber-Physical-Social systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-System (HMTS), focusing on the fusion of ternary space: social network of human society, network of machines and the Internet of things. In this paper, we propose a specific implementation framework of CPSS for Smart City based on intelligent loops, including basic modeling and interactive fusion, state perception and cognition, and adaptive learning. On this basis, an overall architecture of the CPSS platform is designed, which is applied in the urban transportation management in Hangzhou. The application results demonstrate that the intelligent loop could optimize the control and management strategies for actual urban transportation.

关键词： CPSS intelligent Loop Human-Cyber-Physical Ternary Space

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Collaborative Optimization of Cyber Physical Social systems for Urban Transportation Based on Knowledge Automation

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IFAC-PapersOnLine 2020年第5期53卷 572-577页

The integration of signals from physical, social and cyber spaces, known as Cyber-Physical-Social systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M) is collaborative optimized among the three sub-systems. Though some technologies and optimization methods have been studied since its proposition, there is a lack of a systemic architecture as well as an overall implementation about how to efficiently exploit the social signals. For this reason, this paper proposes a general framework of CPSS for urban transportation and presents a feasible solution for traffic optimization based on knowledge automation. The specific implementation includes basic modeling of CPSS, knowledge evolution and reasoning, and collaborative optimization of C&P strategies. As a remarkable highlight, the influence of both individual activities and social learning is concerned during knowledge evolution and reasoning part. A case study from the application in the city of Dongguan is also given to validate our proposed framework and methods, showing that they can efficiently improve the average speed of the actual transportation.

关键词： CPSS knowledge automation urban transportation Collaborative Optimization

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Automatic Cell Assembly by Two-fingered Microhand

Automatic Cell Assembly by Two-fingered Microhand

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2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Junnan Chen Xiaoming Liu Shengnan Dong Pengyun Li Xiaoqing Tang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Intelligent Robotics Institute School of Mechatronical Engineering Key Laboratory of Biomimetic Robots and Systems Ministry of Education Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing CHINA Department of Systems Innovation Graduate school of Engineering Science Osaka University Toyonaka Osaka Japan Global Alliance Lab The University of Electro-Communications Tokyo Japan

We have successfully achieved manipulation and assembly of microbeads having the size of 100μm diameter by hemispherical end-effectors with high stability and accuracy. The motivation of achieving assembly of actual cells lies in the great significance of it in tissue regeneration and cell analysis. Firstly, the most difficult problem we need to solve is the releasing problem caused by adhesion force. The viscosity on cell surface is much larger than the microbeads which makes cell releasing challenging. Secondly, the cell can generate its deformation, then contact area with end-effector will change during grasping process. This may influence the adhesion force and also bring problem to releasing. Thirdly, cell is much smaller, around 15μm in diameter, so we need to fabricate smaller end-effector to achieve successful manipulation and ensure the stability in the meantime. In this paper, we realize the manipulation by decreasing the adhesion forces and apply vibration to release a cell stably. We found the appropriate scale size for the end-effector is around 10μm diameter. It can not only grasp a 15μm cell but also bring little interference to the environment. As a demonstration of the proposed manipulation method, the repeated experiments were conducted to explore the dependence of adhesion force on the grasping distance, which can be helpful in the improvement of successful rate. Finally, we achieved automatic cell assembly using Hela cells.

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Traffic Congestion Prediction using decision Tree, Logistic Regression and Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 512-517页

作者： Tariku Sinshaw Tamir Gang Xiong Zhishuai Li Hao Tao Zhen Shen Bin Hu Heruye Mulugeta Menkir State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China China Ship Development and Design Center Wuhan 430064 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Debremarkos University Institute of Technology Debremarkos 269 Ethiopia

Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.

关键词： Confusion matrix Data preprocessing Python TensorFlow Clementine environment ITS

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Distributed auction-based coverage motion planning for multiple cooperative robots 8

Distributed auction-based coverage motion planning for multi...

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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Gao, Guanqiang Xin, Bin School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

The multi-robot coverage motion planning (MCMP) problem refers to a typical task where every point of the target area must be covered by robots at least once. This paper designs a novel efficient offline algorithm to solve the MCMP problem, which is extended from the spiral-spanning tree coverage algorithm. A distributed auction mechanism is designed for the cooperative task allocation in the proposed algorithm. In this mechanism, each robot will be an auctioneer which chooses a suitable cell as an auction item form its neighbor vertexes and determines a winner robot. According to the connectedness and the balance of each robot’s graph, bidders will submit proper biddings to the auctioneer. After the auction processes, acceptable coverage trajectories can be planned rapidly. Computational experiments validate the effectiveness of the proposed MCMP algorithm. Meanwhile, the proposed algorithm is compared with the state-of-the-art algorithm named DARP. The comparative results show that the auction-based MCMP algorithm has apparent advantages in the running time and the makespans for large crowded configuration spaces. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Multipurpose robots

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A Blockchain-based Educational Digital Assets Management System

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IFAC-PapersOnLine 2020年第5期53卷 47-52页

作者： Junqing Li Mo Lan Ying Tang Shichao Chen Fei-Yue Wang Wei Wei Shandong University of Science and Technology Qingdao China 266590 The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China 100190 Southwest University Chongqing China 400715 Beijing Achievelearn Education Technology Group Beijing China 100095 Electrical and Computer Engineering Rowan University Glassboro NJ 08003 USA Qingdao Academy of Intelligent Industries Qingdao China 266042 3E Smart Education Beijing China 100083

A variety of heterogeneous information is produced after the application of various emerging technologies in education, which is of great research value as educational assets. The traditional educational asset management system has emerged. In view of the disadvantages of traditional system centralization process, people begin to use decentralized blockchain technology for innovation. This paper proposes an educational digital asset management system architecture based on blockchain 3.0 technology system, which forms various data formed by students in and out of class learning process, courseware and experience data generated by teachers in the process of teaching into educational digital assets, and carries out the right confirmation and storage on the blockchain. The system can use a variety of heterogeneous data in digital assets to analyze the development status of school users, achieve the teaching purpose of teaching students according to their aptitude and different people.

关键词： blockchain blockchain system digital assets educational technology decentralized systems

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Secure analysis of dynamic networks under pinning attacks against synchronization

arXiv

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arXiv 2019年

作者： Li, Yuzhe Shi, Dawei Chen, Tongwen State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110004 State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada

In this paper, we first consider a pinning node selection and control gain co-design problem for complex networks. A necessary and sufficient condition for the synchronization of the pinning controlled networks at a homogeneous state is provided. A quantitative model is built to describe the pinning costs and to formulate the pinning node selection and control gain design problem for different scenarios into the corresponding optimization problems. Algorithms to solve these problems efficiently are presented. Based on the developed results, we take the existence of a malicious attacker into consideration and a resource allocation model for the defender and the malicious attacker is described. We set up a leader-follower Stackelberg game framework to study the behaviour of both sides and the equilibrium of this security game is investigated. Numerical examples and simulations are presented to demonstrate the main results. Copyright © 2019, The Authors. All rights reserved.

关键词： complex networks

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The Distributed System of Smart Wearable Energy Harvesting Based on Human Body

The Distributed System of Smart Wearable Energy Harvesting B...

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第37届中国控制会议

作者： Yeyun Cai Fang Deng Jiachen Zhao Huangbin Qiu Xinyu Fan Zelang Liang School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems

Wearable devices have appeared in every aspect of our lives, from small items such as wristbands and eyeglasses to finished garments as shoes and clothing. With the promoting of scientific development, intelligent technology is also combined with wearable devices to form a unique network that depends on the human body. The increasing demand for equipment has made smart wearable devices a research hotspot. Such equipment is usually small, light-weight, good flexibility, and can be adapted to the complex use of the environment. But these advantages just become the limitations of power supply for ***, the fixed charger can not be used to charge the device during use, and the volume of such a special battery can not be increased in order to increase the storage capacity. Therefore, how to solve the problem of power suppling for the wearable device effectively is a big challenge. Based on this, the paper proposes a distributed system of smart wearable energy harvesting, which considers the human body as the core and uses a variety of ubiquitous energy sources such as solar energy, thermal energy and mechanical energy. The system makes a combination of wearable and distributed design to form a wireless body area network,and provides a stable power output for smart wearable devices.

关键词： wearable energy harvesting distributed system human body

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Off-Policy Q-Learning for Infinite Horizon LQR Problem with Unknown Dynamics

Off-Policy Q-Learning for Infinite Horizon LQR Problem with ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Xinxing Li Zhihong Peng Li Liang Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

In this paper, a novel online Q-Iearning approach is proposed to solve the Infinite Horizon Linear Quadratic Regulator (IHLQR) problem for continuous-time (CT) linear time-invariant (LMI) systems. The proposed Q-Iearning algorithm employing off-policy reinforcement learning (RL) technology improves the exploration ability of Q-Iearning to the state space. During the learning process, the Q-Iearning algorithm can be implemented just using the data sets which just contains the information of the behavior policy and the corresponding system state, thus is data- driven. Moreover, the data sets can be used repeatedly, which is computationally efficient. A mild condition on probing noise is established to ensure the converge of the proposed Q-Iearning algorithm. Simulation results demonstrate the effectiveness of the developed algorithm.

关键词： Silicon Optimal control Convergence Approximation algorithms Manganese Regulators Adaptation models

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3D Deep Learning for 3D Printing of Tooth Model

3D Deep Learning for 3D Printing of Tooth Model

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yiwen Liu Xiuqin Shang Zhen Shen Bin Hu Zhihai Wang Gang Xiong Faculty of Information The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing University of Technology Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Faculty of Information Beijing University of Technology Beijing China

Recently, with the development of both 3D sensors and 3D virtual network that bring the needs of interaction with the real world, many 3D applications burst out. However, it is difficult to understanding these three-dimensional scenes with a fixed program. Then, a data-driven method is required to process these 3D data, which brings a strong demand of 3D Deep Learning in 3D data. Towards this goal, with an end-to-end deep learning, the experiment is based on PointNet++, a well proposed method for feature extraction. The experiment optimizes the network structure and parameters to improve the classification results. Finally, the network is applied to tooth model for classification and identification so that the dental model can be found from different perspectives.

关键词： Three-dimensional displays Deep learning Transforms Cameras Object segmentation Image segmentation Sensors

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