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13th International Conference on Computer Supported Collaborative Learning - A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, CSCL 2019

作者： Deng, Xiaozhou Wang, Danli Jin, Qiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781732467231

In this paper, we present Co Pro Story, a collaborative tangible programming tool to support children learning programming. In recent years, many collaborative tangible programming tools were developed for children. Compared to the other tools, Sync Blocks which were designed to coordinate children’s programming were used in our tool. The tool consists of two parts: Tangible programming blocks and game tasks. Game tasks are provided with 3D cartoon story scenes on computer screen and need to be finished by collaborative programming with our programming blocks. To evaluate our tool, a contrast user study was conducted with 28 children aged 6 to 10. The results demonstrated that CoProStory could support children to program collaboratively and children had a more positive attitude toward programming with Sync Blocks. © ISLS.

关键词： Computer games

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Consider payoff or emphasize learning? Exploring the effect of new dual-credit policy on electric vehicle diffusion in complex network

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Energy Economics 2025年 148卷

作者： Zhao, Dan Wang, Jian Li, Wen-wu Tang, Jin-huan Huang, Shuai School of Management Science and Engineering Anhui University of Technology Ma'anshan243032 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan243002 China School of Economics and Management Shenyang Aerospace University Shenyang110136 China School of Business Qingdao University Qingdao266071 China School of Systems Engineering National University of Defense Technology Changsha410003 China

In recent years, although the overall quality of the new energy vehicle (NEV) industry has shown an upward trend, it is difficult to ignore the turbulence in the production and sales of NEV. The impact caused by dual credit policy during the transition needs further analysis. In order to clarify the internal operating mechanism of the new dual-credit policy parameters and stably promote the booming development of the automobile industry, we construct a complex network evolutionary game model to analyze the impact of the new dual-credit policy on the electric vehicle (EV) diffusion, and further discuss the influence of different update strategies on EV diffusion. The results indicate that with the increase of NEV credit accounting coefficient, the upper limit of threshold interval of NEV credit trading price remains unchanged, while lower limit decreases. With the decrease of ratio of standard CAFC to target CAFC or the increase of requirement of NEV credits for fuel vehicle manufacturer, the upper and lower limits of threshold interval of NEV credit price are reduced. With the increasing network scale, the driving effect of Fermi function is gradually superior to EWA algorithm. These results not only reveal the complex mechanism of policy parameters on EV diffusion, but also provide theoretical basis and practical guidance for optimizing NEV industry policies. © 2024

关键词： complex network evolutionary game theory EV diffusion New dual-credit policy Synergistic effect Update strategy

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Deep reinforcement learning based automatic exploration for navigation in unknown environment

arXiv

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arXiv 2020年

作者： Li, Haoran Zhang, Qichao Zhao, Dongbin Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

This paper investigates the automatic exploration problem under the unknown environment, which is the key point of applying the robotic system to some social tasks. The solution to this problem via stacking decision rules is impossible to cover various environments and sensor properties. Learning based control methods are adaptive for these scenarios. However, these methods are damaged by low learning efficiency and awkward transferability from simulation to reality. In this paper, we construct a general exploration framework via decomposing the exploration process into the decision, planning, and mapping modules, which increases the modularity of the robotic system. Based on this framework, we propose a deep reinforcement learning based decision algorithm which uses a deep neural network to learning exploration strategy from the partial map. The results show that this proposed algorithm has better learning efficiency and adaptability for unknown environments. In addition, we conduct the experiments on the physical robot, and the results suggest that the learned policy can be well transfered from simulation to the real robot. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Guided Crowd Evacuation:Approaches and Challenges

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1081-1094页

作者： Min Zhou Hairong Dong Petros A.Ioannou Yanbo Zhao Fei-Yue Wang the State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China the Department of Electrical Engineering University of Southern CaliforniaLos AngelesCA 90089-2560 USA the Center for Advanced Transportation Technologies University of Southern CaliforniaLos AngelesCA 90089 USA the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the Research Center of Computational Experiments and Parallel Systems the National University of Defense TechnologyChangsha 410073China

Evacuation leaders and/or equipment provide route and exit information for people and guide them to the expected destinations, which could make crowd evacuation more efficient in case of emergency. The purpose of this paper is to provide an overview of recent advances in guided crowd evacuation. Different guided crowd evacuation approaches are classified according to guidance approaches and technologies. A comprehensive analysis and comparison of crowd evacuation with static signage, dynamic signage, trained leader, mobile devices, mobile robot and wireless sensor networks are presented based on a single guidance mode perspective. In addition, the different evacuation guidance systems that use high-tech means such as advanced intelligent monitoring techniques, AI techniques, computer technology and intelligent inducing algorithms are reviewed from a system’s perspective. Future researches in the area of crowd evacuation are also discussed.

关键词： Auxiliary equipment evacuation guidance system (EGS) guided crowd evacuation leader signage

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Turning control of a Tuna-Like BAUV for a Minimum Turning Radius

Turning Control of a Tuna-Like BAUV for a Minimum Turning Ra...

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第40届中国控制会议

作者： Sheng Du Junzhi Yu Zhengxing Wu Di Chen Ming Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Dept.Adv.Manuf.Robot. BIC-ESAT College of Engineering Peking University School of Information and Electrical Engineering Shandong Jianzhu University

In order to break the bottleneck of conventional autonomous underwater vehicles(AUVs), biologically-inspired AUVs(BAUVs) have drawn great attention. This paper explores the turning control issue of a tuna-like BAUV with the purpose of seeking a minimum turning radius. Specifically, for better understanding of the turning motion, a dynamical model is established and the force acting on the tail fin is taken as the main source. Because of the complex nonlinear relationship and physical restrictions, it is hard to optimize the turning radius directly. To tackle with this problem, the input signals of joints are generated by sine function so as to satisfy physical constraints, and simulations under different parameter combinations are ***, a fixed tail joint method and a half-cycle flapping method are chosen and compared in an indoor pool. Finally,the experimental results verify the effectiveness of proposed methods, and a minimum turning radius of the tuna-like BAUV is achieved as small as 0.37 body lengths.

关键词： Turning control biologically-inspired autonomous underwater vehicles tuna-like robot

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A Safety Joint with Passive Compliant and Manual Override Mechanisms for Medical Robotics

A Safety Joint with Passive Compliant and Manual Override Me...

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IEEE International Conference on Intelligence and Safety for Robotics (ISR)

作者： Jia Zheng Shuangyi Wang James Housden Zeng-Guang Hou Davinder Singh Kawal Rhode School of General Engineering Beihang University Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Biomedical Engineering and Imaging Sciences King's College London London UK Xtronics Ltd. Gravesend UK

Force and collision control is a primary concern to guarantee the safe use of medical robots as such systems normally need to interact with clinicians and patients, while at the same time cooperate with other devices. Among different strategies, passive features working with intrinsically safety components are treated as one of the most effective approaches and therefore deserve in-depth study. In this study, we focus on the design of a novel back-drivable safety joint that incorporates a torque limiter with passive compliance and a manual override mechanism to disconnect the robotic joint from its drive train. The design and working principle of the proposed joint are explained, followed by the mathematical analysis of its performance and the impacts of parameters. An example of the design was manufactured and tested experimentally to validate the working concepts. It is concluded that the proposed multi-functional safety joint provides more versatility and customization to the design of bespoke medical robots and would limit the maximum torque that can be exserted onto the patient, allow the clinician to push the joint back, and enable the operator to switch back to manual override.

关键词： Torque Medical robotics Mathematical analysis Manuals Switches Drives Back

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A Neural-Network-Based controller for Piezoelectric-Actuated Stick-Slip Devices

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2598-2607页

作者： Cheng, Long Liu, Weichuan Yang, Chenguang Huang, Tingwen Hou, Zeng-Guang Tan, Min State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Zienkiewicz Centre for Computational Engineering Swansea University Swansea U.K. Texas A&M University at Qatar Doha Qatar State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that is composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the endeffector can slip on the surface of the driving object, the PASSD is capable of realizing the macrolevel motion with the microlevel precision. Due to the following two reasons: the complicated relative motion between the end-effector and the driving object, and the inherent hysteresis nonlinearity in the PEA, the ultraprecision displacement control of the end-effector of PASSDs raises a real challenge, which is rarely reported in the literature. Toward solving this challenge, a neural-network-based controller is proposed in this paper. First, a neural-network-based model is proposed to capture the relative motion between the end-effector and the driving object. Second, a neural-network-based inversion model is developed to online calculate the desired position of the PEA under the predesigned reference of the end-effector. Third, a dynamic linearized neural-network-based model predictive control method, which can effectively handle the hysteresis nonlinearity, is employed to implement the displacement control of the PEA, which finally results in an overall high-precision controller of the end-effector. Finally, a PASSD prototype has been implemented and tested through experimental studies to demonstrate the effectiveness of the proposed approach.

关键词： End-effector neural network piezoelectric actuator (PEA) stick-slip

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

arXiv

引用

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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An Interpretable Neural Network Model Through Piecewise Linear Approximation

arXiv

引用

arXiv 2020年

作者： Guo, Mengzhuo Zhang, Qingpeng Liao, Xiuwu Zeng, Daniel Dajun School of Management Xi’an Jiaotong University School of Data Science City University of Hong Kong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Management Information Systems Eller College of Management University of Arizona

Most existing interpretable methods explain a black-box model in a post-hoc manner, which uses simpler models or data analysis techniques to interpret the predictions after the model is learned. However, they (a) may derive contradictory explanations on the same predictions given different methods and data samples, and (b) focus on using simpler models to provide higher descriptive accuracy at the sacrifice of prediction accuracy. To address these issues, we propose a hybrid interpretable model that combines a piecewise linear component and a nonlinear component. The first component describes the explicit feature contributions by piecewise linear approximation to increase the expressiveness of the model. The other component uses a multi-layer perceptron to capture feature interactions and implicit nonlinearity, and increase the prediction performance. Different from the post-hoc approaches, the interpretability is obtained once the model is learned in the form of feature shapes. We also provide a variant to explore higher-order interactions among features to demonstrate that the proposed model is flexible for adaptation. Experiments demonstrate that the proposed model can achieve good interpretability by describing feature shapes while maintaining state-of-the-art accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Piecewise linear techniques

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A Tracking control Method based on Event-Triggered Adaptive Dynamic Programming 38

A Tracking Control Method based on Event-Triggered Adaptive ...

引用

第三十八届中国控制会议

作者： Ziyang Wang Qinglai Wei Derong Liu School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation Guangdong University of Technology

ISBN: (纸本)9781728123295;9789881563972

In this paper, an event-triggered adaptive dynamic programming(ADP) algorithm is developed to solve optimal tracking control problems. The event-triggered control law will be updated only when the triggering conditions are ***, the computational cost can be reduced and the celerity of tracking can be improved. Compared to existing works,novel triggering conditions are designed in this paper. Besides, the stability is guaranteed with less assumption. Neural networks are used to implement the tracking control algorithm. Finally, an example is employed to show the effectiveness of the algorithm.

关键词： Adaptive dynamic programming event-triggered control optimal tracking control

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