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European control Conference (ECC)

作者： Xin Mao Dan Wang Wei Chen Li Qiu School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Southern University of Science and Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong S.A.R. China

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, the output synchronization in large-scale discrete-time networks is examined by utilizing the novel phase tool, where the agent dynamics are allowed to be significantly heterogeneous. The synchronization synthesis problem is formulated and thoroughly investigated, with the goal of characterizing the allowable heterogeneity among the agents to ensure synchronization under a uniform controller. The solvability condition is provided in terms of the phases of the residue matrices of the agents at the persistent modes. When the condition is satisfied, a design procedure is given, producing a low-gain synchronizing controller. Numerical examples are given to illustrate the results.

关键词： Sufficient conditions Europe Heterogeneous networks Synchronization Multi-agent systems

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A Human-Robot Skill Transfer Framework of Mobile Medical Robots for Autonomous Motion with Teaching by Demonstration

A Human-Robot Skill Transfer Framework of Mobile Medical Rob...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Jiehao Li Junzheng Wang Shoukun Wang Hui Peng Qi Wen Longbin Zhang Meina Lin Politecnico di Milano Milan Italy State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China College of Intelligence Science and Technology National University of Defense Technology Changsha China BioMEx Center KTH Mechanics KTH Royal Institute of Technology Stockholm Sweden Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangzhou China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

The accuracy of autonomous motion is the main challenge for mobile medical robots, especially running in narrow space conditions to transport the casualties in the hospital. This article mainly centers on the human-robot transmission of the mobile medical robot for narrow space autonomous motion. In order to enable mobile medical robots to autonomously and smoothly transport hospital casualties through narrow aisles, in this paper, we present a robot skill transfer technology, namely a human-robot skill transfer framework of mobile medical robots for autonomous motion with teaching by demonstration, which is that a smooth optimal path is planned through the human motion, and then the Kinect sensor is used to detect human bone movement to learn its motion trajectory so as to achieve trajectory tracking control. Meanwhile, using the Remote Center of Motion (RCM) constraint to minimize the error between the actual trajectory of the medical robot and the tracking reference path to control the motion trajectory accurately, human-computer interactive mobile medical robots can smoothly pass through narrow channels. Through research and analysis, it is demonstrated that the mobile medical robot proposed in this paper has great feasibility, which is of great reference value in the field of medical rescue.

关键词： Medical robotics Education Trajectory Mobile robots Tracking Hospitals

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Cyber-Physical -Social systems for Smart Cities: An Overview

Cyber-Physical -Social Systems for Smart Cities: An Overview

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

作者： Noreen Anwar Gang Xiong Wanze Lu Peijun Ye Hongxia Zhao Qinglai Wei The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation and with The Cloud Computing Center Chinese Academy of Sciences China School of Artificial Intelligence and Automation Beijing University of Technology Beijing China

Cyber-physical social systems (CPSS) include cyberspace, physical space, and social space. Since the introduction of CPSS, human relationships with machinery and the physical world have altered as a new paradigm. Recent estimates indicate that approximately 60 percent of the world's population will live in cities by 2030. Large, dense cities can be highly productive, innovative, and very green per capita, hence ideal for our future. However, the huge influx of new people poses enormous challenges to their administrations. Smart cities are becoming a reality, therefore, to achieve higher efficiency, optimum use of resources, management and to strengthen several features of modern towns, several elements of modern cities are automated and integrated into Information and Communications Technology (ICT). Cities and infrastructure are growing all over the world, in providing end-users with increased performance, comfort, visibility and convenience, the Smart Cities concept is critical. This paper will discuss smart cities and the CPSS concept intensively with some of its key technologies, challenges, and further development by using CPSS.

关键词： Smart cities Digital twin Sociology Information and communication technology Security Resource management Statistics

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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Parameter identification of plant growth models with stochastic development

Parameter identification of plant growth models with stochas...

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International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA)

作者： Mengzhen Kang Jing Hua Philippe de Reffye Marc Jaeger Qingdao Academy of Intelligent Sciences Qingdao China State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China AMAP Unit CIRAD Montpellier Languedoc-Roussillon FR AMAP Unit CIRAD Montpellier Languedoc-Roussillon France LIRMM ICAR University of Montpellier France

ISBN: (纸本)9781509016600

Plant architectures generally display structural variations among individuals. Stochastic FSPMs have been developed to capture such feature, but calibrating such models is a challenging issue. For GreenLab model, parameter identification has been achieved on several crops and trees, but the estimation of functional parameters is mostly limited to plants with deterministic development. In this work, we propose a methodological framework allowing the efficient FSPM parameter estimation for stochastic ramified plants. We focus on the randomness in three kinds of meristem activities in plant development: growth, death and branching. Concepts of organic series and potential structure are introduced to build the fitting target as well as corresponding model output. We show that, with a limited set of sampled plants (here from simulation), using a few organic series, the inverse method retrieves well the parameter values (the original parameter set being known here). Requiring the concept of physiological age and the assumption of common biomass pool, the proposed approach provides a solution of solving source-sink functions of complex plant architectures, with a novel simplified way of plant sampling. The proposed parameter estimation frame is promising, since this in silico process mimics the procedure of calibrating model for real plants in a stand. Estimating parameters on stochastic plant architectures opens a new range of coming applications.

关键词： Stochastic processes Biological systems Physiology Biological system modeling Computational modeling Vegetation Biomass

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CMFN: Cross-Modal Fusion Network for Irregular Scene Text Recognition

arXiv

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arXiv 2024年

作者： Zheng, Jinzhi Ji, Ruyi Zhang, Libo Wu, Yanjun Zhao, Chen Intelligent Software Research Center Institute of Software Chinese Academy of Sciences Beijing100190 China The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China

Scene text recognition, as a cross-modal task involving vision and text, is an important research topic in computer vision. Most existing methods use language models to extract semantic information for optimizing visual recognition. However, the guidance of visual cues is ignored in the process of semantic mining, which limits the performance of the algorithm in recognizing irregular scene text. To tackle this issue, we propose a novel cross-modal fusion network (CMFN) for irregular scene text recognition, which incorporates visual cues into the semantic mining process. Specifically, CMFN consists of a position self-enhanced encoder, a visual recognition branch and an iterative semantic recognition branch. The position self-enhanced encoder provides character sequence position encoding for both the visual recognition branch and the iterative semantic recognition branch. The visual recognition branch carries out visual recognition based on the visual features extracted by CNN and the position encoding information provided by the position self-enhanced encoder. The iterative semantic recognition branch, which consists of a language recognition module and a cross-modal fusion gate, simulates the way that human recognizes scene text and integrates cross-modal visual cues for text recognition. The experiments demonstrate that the proposed CMFN algorithm achieves comparable performance to stateof- the-art algorithms, indicating its effectiveness. © 2024, CC BY.

关键词： Optical character recognition

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Design of a curled hyper-redundant manipulator and the motion control with tip-following algorithm

Design of a curled hyper-redundant manipulator and the motio...

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Chinese control and decision Conference, CCDC

作者： Aoshun Zhang En Li Feng Zhang Rui Guo Mingrui Luo Yuwei Zhang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Laboratory for Intelligent Industrial Vision CAS Beijing China State Grid Intelligent Technology Co. Ltd. Shandong China State Grid Shandong Electric Power Company Jinan Power Supply Company Shandong China

In order to meet the operational needs in some narrow environments, the hyper-redundant snake-shaped manipulator has received extensive attention and has been widely studied due to its compact shape and multiple degrees of freedom. At present, the research of hyper-redundant manipulator mainly focuses on the straight push platform. Considering the limitation of working space, this paper designed a hyper-redundant manipulator delivered by a curled turntable. According to the characteristics of turntable delivery, a tip-following algorithm based on curve fitting and a tip-following strategy based on terminal posture record are proposed. Finally, with the algorithm applied to the physical robot we made, we successfully realized the tip-following motion control of the hyper-redundant manipulator.

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Two Intersections Traffic Signal control Method Based on ADHDP

Two Intersections Traffic Signal Control Method Based on ADH...

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2016年IEEE汽车电子与安全国际会议

作者： Lin Cao Bin Hu Xisong Dong Gang Xiong Fenghua Zhu Zhen Shen Dong Shen Yuliang Liu The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Dongguan Research Institute of CASIA Cloud Computing CenterChinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences

With the rapid development of Chinese economy and automotive industry,urban traffic congestion has become increasingly ***,how to effectively alleviate the traffic congestion and improve the efficiency of vehicles has become the main *** signal control is one of the effective ways to solve urban traffic *** this paper,a traffic signal control method based on Action-Dependent Heuristic Dynamic Programming(ADHDP) is *** control algorithm is simulated on two intersections,both of which have two phases with four entrance *** computer simulation results show that the control method has the better ability of on-line learning compared with traditional Fix-Time control,and can effectively improve the average speed of vehicles,and reduce travel time and alleviate the traffic pressure.

关键词： ADHDP Traffic Signal control Adaptive control intelligent Transportation systems

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Social Manufacturing Pattern in the intelligent Reform of the Shoes and Clothing Industry

Social Manufacturing Pattern in the Intelligent Reform of th...

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

作者： Yansong Cao Yutong Wang Juwu Dai Xihao Zhu Fei-Yue Wang Macau Institute of Systems Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Ningbo Sanbody Intelligent Technology co. LTD Ningbo China Faculty of Science and Engineering University of Nottingham Ningbo China

Along with economic globalization, the shoes and clothing market is undergoing huge changes and is facing increasing individualized demands. To realize personalized customization, we propose a social manufacturing pattern for the shoes and clothing industry. On the customer side, we collect costumer information with the help of 3D scanning and virtual fitting. On the production side, we replace human labor with intelligent production equipment. On the enterprise management side, we construct a cloud platform to realize source maximization. Through this social manufacturing pattern, we expect to accelerate the intelligent reform of the shoes and clothing industry.

关键词： Product customization Cloud computing Three-dimensional displays Social networking (online) Production equipment Footwear Clothing industry

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