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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Ren, Liang Cao, Zhiqiang Tan, Min Zhao, Peng Chen, Xuechao University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China 21st Research Institute of China Electronics Technology Group Corporation Shanghai China BIT Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China

ISBN: (纸本)9781728163215

The vision-based target recognition and tracking have received much attention in the field of robotics. Existing methods mainly focus on the vision perception of individual robot with a single view, however, the performance is susceptible to illumination and occlusion. Multi-robot collaborative perception provides a potential solution to deal with the limitation of single-view observation, however, the challenging of environmental adaptability for multi-robot collaborative decision still remains unsolved. To solve this problem, this paper proposes a two-level adaptive target recognition and tracking method based on vision for multi-robot system. The problem of multi-robot target recognition and tracking is solved under a two-level framework, which contains the features fusion level of individual robot and the cooperation level of multi-robot system. In the first level, the features measuring results that influence the visual perception of individual robot are fused, while the second level combines the voting of each robot together to determine the target for multi-robot system. Both the features measuring weights and robots voting weights are adaptively updated according to their evaluation, which lead to a beneficial result where the features and robots with higher accuracy play major roles in the first and second levels, respectively. Therefore, a good adaptability to the environments can be guaranteed. The experimental results show that the proposed approach can realize the coordination of multi-robot system in target recognition and tracking with an effective performance. © 2019 IEEE.

关键词： Multipurpose robots

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Review on Development of Smart Education

Review on Development of Smart Education

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Shi, Wanruo Liu, Xiwei Gong, Xiaoyan Niu, Xiaojie Wang, Xinzhu Jing, Sifeng Lu, Hao Zhang, Nan Luo, Jie State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Education Beijing Normal University Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China Chinese Association of Automation Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China

ISBN: (纸本)9781728128535

Smart education is leading the development direction of Chinese education informatization and becoming a main theme of education development in the era of which technology changes education. There are seven main branches of smart education, namely intelligent Tutoring System (ITS), smart campus, Big Data in Education (BDE), knowledge graph, educational robots, virtual teachers, and personalized education. Based on literature survey and market research, this paper gives a comprehensive overview of the research and application of smart education from above seven aspects and proposes its development based on the status of smart education development. © 2019 IEEE.

关键词： intelligent vehicle highway systems

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A Fast Quantity and Position Detection Method Based on Monocular Vision for a Workpieces Counting and Sorting System

A Fast Quantity and Position Detection Method Based on Monoc...

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第三十八届中国控制会议

作者： Xin Wang Baokui Li Hongbin Ma Man Luo School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

With the rapid development of machine vision, many technologies have been applied to the robots for improving the efficiency in the industrial field. This paper concerns the industrial sorting and counting technology problems in a workpieces counting and sorting system, and puts forward a solution using monocular vision. The main process consists of three parts. The rough positioning is accomplished first by using the pixel intensity comparison-based object detection(PICO). Then, image preprocessing and extracting geometric features are established, composing of binarization, morphological operation, optimizing the foreground,finding inner as well as outer contours, and calculating areas. Finally, the center coordinates and categories of workpieces are obtained. We choose nuts and gears as experimental objects, and complete the fast detection. The results of counting nuts and locating gears illustrate that the proposal solution not only has high speed, but also can ensure a high accuracy.

关键词： Monocular Vision the PICO Detector Workpieces Counting Fast Positioning

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Deep-learning based Optimal PMU Placement and Fault Classification for Power System

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Expert systems with Applications 2025年

作者： Xin Lei Zhen Li Huaiguang Jiang Samson S. Yu Yu Chen Bin Liu Peng Shi School of Future Technology South China University of Technology Panyu District Guangdong China School of Automation & Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing 100081 China School of Engineering Deakin University 75 Pigdon Rd Waurn Ponds Victoria 3216 Australia College of Information Science and Engineering China University of Petroleum (Beijing) Beijing China School of Electrical and Electronic Engineering University of Adelaide 5005 SA Australia

Phasor measurement units (PMUs) are vital for power grid monitoring, yet their high cost restricts widespread adoption. PMU measurement data is also crucial for fault analysis in power systems. However, existing research seldom explores the interplay between optimal PMU placement (OPP) and fault analysis, impeding advancements in grid economy and security. This study introduces a perception-driven, deep learning-based optimization approach that integrates OPP, multi-task learning, and fault data augmentation. First, deep reinforcement learning optimizes PMU placement, balancing cost-effectiveness with observability requirements. Next, multi-task learning, enhanced by Bayesian optimization, improves fault classification efficiency using PMU data. Finally, pre-trained models paired with k -means clustering augment fault data, boosting classification accuracy. Extensive simulations across four IEEE standard test systems validate the proposed method’s effectiveness.

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An Adaptive Disturbance Rejection controller for Artificial Pancreas

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IFAC-PapersOnLine 2020年第2期53卷 16372-16379页

作者： Deheng Cai Wei Liu Eyal Dassau Francis J. Doyle Iii Xiaoling Cai Junzheng Wang Linong Ji Dawei Shi State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing 100081 China Department of Endocrine and Metabolism Peking University People’s Hospital Beijing China. Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA.

Artificial pancreas (AP) systems are designed to automate glucose management for patients with type 1 diabetes. In this work, we propose an adaptive disturbance rejection control approach for AP systems to achieve safe and effective glucose regulation. The controller is built within the framework of active disturbance rejection control, but incorporates safety operation constraints, and glucose- and velocity-dependent parameter adaptation modules for the key control parameters. In silico performance comparison between the proposed controller and an adaptive zone model predictive controller (MPC) (Shi, Dassau, and Doyle III, 2019a) is conducted on the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator. For both announced and unannounced meals, the controller achieves comparable glucose regulation performance in terms of mean glucose (134.9 mg/dL vs. 135.4 mg/dL, p < 0.001; 149.7 mg/dL vs. 151.7 mg/dL, p < 0.001, respectively) and percentage time in [70, 180] mg/dL (93.8% vs. 92.4%, p < 0.001; 76.0% vs. 72.4%, p < 0.001, respectively) without increasing the risk of hypoglycemia. The results indicate the feasibility of achieving comparable glucose regulation performance through a non-optimization control law for AP systems.

关键词： Artificial pancreas Active disturbance rejection control Adaptive control Glucose regulation

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Youla Parameterization Based control Performance Degradation Online Recovery of Single Inverter control systems in Microgrids: a PnP Approach

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IFAC-PapersOnLine 2020年第2期53卷 12080-12085页

作者： Y. Liu C. Hu H. Lu S.X. Ding L. Li X. Peng Y. Wang Institute for Automatic Control and Complex Systems University of Duisburg-Essen Bismarkstr. 81 BB 47057 Duisburg Germany College of Electrical and Control Engineering North China University of Technology 100144 Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing China Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology200237 Shanghai China

In an inverter control system of smart microgrids, the control performance degradation due to load fluctuations, unbalanced loads and other factors, is one of the considerable factors affecting the output power quality. In this paper, a plug-and-play (PnP) technology based on Youla parameterization is proposed to achieve system performance recovery without modifying the existing controller. By ‘plugging-in’ a residual generator and a compensator, a dynamic feed-back of residual signals is embedded into the original inverter control system. In order to realize PnP, an online parameter estimation scheme of the compensator is developed. Finally, an inverter control system is applied to show the effectiveness of the proposed approach.

关键词： Youla parameterization performance degradation online recovery power quality microgrids

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An Efficient Restart-Enhanced Genetic Algorithm for the Coalition Formation Problem 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Guo, Miao Xin, Bin Chen, Jie Wang, Yipeng School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

In multi-agent system (MAS), the coalition formation (CF) is an important problem focusing on allocating agents to different tasks. In this paper, the single-task single-coalition (STSC) formation problem is considered. The mathematical model of the STSC problem is built with the objective of minimizing the total cost with the ability constraint. Besides, an efficient restart-enhanced genetic algorithm (REGA) is designed to solve the STSC problem. Furthermore, this paper constructs a comparison experiment, employing a random sampling method, an estimation of distribution algorithm and a genetic algorithm without restart strategy as competitors. The results of statistical analysis by the Wilcoxon’s rank-sum test demonstrate that the designed REGA performs better than its competitors in solving the STSC cases of different scales. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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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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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

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

ISBN: (纸本)9781728128535

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. © 2019 IEEE.

关键词： 3D printers

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Design of a low-cost miniature robot to assist the COVID-19 nasopharyngeal swab sampling

arXiv

引用

arXiv 2020年

作者： Wang, Shuangyi Wang, Kehao Liu, Hongbin Hou, Zengguang The State Key Laboratory of Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University China The School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom

Nasopharyngeal (NP) swab sampling is an effective approach for the diagnosis of coronavirus disease 2019 (COVID-19). Medical staffs carrying out the task of collecting NP specimens are in close contact with the suspected patient, thereby posing a high risk of cross-infection. We propose a low-cost miniature robot that can be easily assembled and remotely controlled. The system includes an active end-effector, a passive positioning arm, and a detachable swab gripper with integrated force sensing capability. The cost of the materials for building this robot is 55 USD and the total weight of the functional part is 0.23kg. The design of the force sensing swab gripper was justified using Finite Element (FE) modeling and the performances of the robot were validated with a simulation phantom and three pig noses. FE analysis indicated a 0.5mm magnitude displacement of the gripper's sensing beam, which meets the ideal detecting range of the optoelectronic sensor. Studies on both the phantom and the pig nose demonstrated the successful operation of the robot during the collection task. The average forces were found to be 0.35N and 0.85N, respectively. It is concluded that the proposed robot is promising and could be further developed to be used in vivo. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Adaboost-kNN for dynamic emotion recognition 8

Adaboost-kNN for dynamic emotion recognition

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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： Li, Min Su, Wanjuan Chen, Luefeng Wu, Min Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Tokyo Institute of Technology Yokohama226-8502 Japan

— K-nearest Neighbor based adaptive boosting (AdaBoost-KNN) is proposed for emotion understanding in human-robot interaction (HRI), where the real-time dynamic emotion is recognized according to facial expression. It can make robots capable of understanding human emotions and make HRI run smoothly. The Candide-3 model was adopted to extract the two-dimensional coordinate values of the key points of facial emotion, as the basis for emotion classification. The emotion classification is based on AdaBoost-KNN, which sets up seven basic KNN classifier. Then it iterates seven times to update the weight of samples, and combines the weak classifier with coefficient to establish the final classifier. Finally, the performance of the proposal is verified by using K-fold cross validation. Results show that the proposal achieves higher accuracy than that of a single KNN. © 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： Adaptive boosting

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