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

作者： Yixiang Liu Xiaolin Dai Kai Guo Jiang Wu Rui Song Jie Zhao Yibin Li School of Control Science and Engineering Shandong University Jinan China State Key Laboratory of Mechanical Transmissions Chongqing University Chongqing China Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou Shenhao Technology Co. Ltd Hangzhou China Engineering Research Center of Intelligent Unmanned System of Ministry of Education School of Control Science and Engineering Shandong University Jinan China School of Mechanical Engineering Shandong University Jinan China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Existing in-pipe robots have insufficient adaptability when dealing with accidents in unfamiliar pipe environments. Developing a pipe robot that can be designed and manufactured quickly is one solution. The tensegrity structure is a self-stressing spatial structure formed by the interaction of rigid members and flexible cables, which has the advantages of simple structure, good flexibility, deformability, and impact resistance. Inspired by this structure, we design a novel worm-like tensegrity robot for different pipe environments, which can be manufactured rapidly at low cost. Firstly, a robotic module based on the tensegrity structure is designed inspired by the motion patterns of worm-like organisms. Then, the design process of the module is presented based on the mathematical analysis of the deformation. Finally, a prototype of the tensegrity robot is developed using simple and low-cost parts in less than an hour. To test the motion performance, load performance, and inspection capability of the tensegrity robot, we designed a series of experiments on horizontal pipes, vertical pipes, elbows, and steel pipes. Experimental results show that the worm-like tensegrity robot is simple in structure, easy to manufacture, low in cost, and good in performance.

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A Real-time Framework of Multi-Camera Vehicle Tracking system*

A Real-time Framework of Multi-Camera Vehicle Tracking Syste...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Hanyang Zhuang Xingyuan Chen Jianglin Lan Ying Wang Zhaoxuan Lu Chunxiang Wang Ming Yang University of Michigan - Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China James Watt School of Engineering University of Glasgow Glasgow United Kingdom School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Intelligent and Connected Vehicle R&D Center Co. LTD Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Multi-camera vehicle tracking (MCVT) system is a key technology to build an intelligent city and intelligent transportation system. The MCVT system utilizes roadside monitoring devices and computing platforms to achieve city-level road traffic perception at a finer scale by tracking each vehicle on the road to obtain the trajectory. The existing MCVT system mainly focuses on dealing with the algorithm modules separately, without studying the real-time performance and system architecture of the entire pipeline. However, in real-world applications, many of the tricks in improving evaluation metrics are not applicable since there is endless traffic flow running all the time. Therefore, this paper proposes a real-time framework of the MCVT system to streamline all the algorithm modules with proper data flow. Firstly, a multi-threaded multi-vehicle tracking (MVT) module is developed to detect vehicles in each camera. Re-identification is implemented during MVT to provide the feature for tracking in the same camera and across different cameras. Then, after the vehicle leaves the camera field of view, the feature of vehicle trajectory is calculated by averaging the feature at each frame. Meanwhile, a background thread runs continuously to match the incoming feature of vehicle trajectory with past features by conducting a topology searching method. In the end, the vehicle trajectories with well-matched features are considered to be the same vehicle. The entire pipeline has been validated on two different datasets, the precision is 0.64 in the CityFlow dataset which uses 6 cameras simultaneously. In the customized dataset, the same method can still reach real-time when using 16 cameras at the same time with smaller network modules.

关键词： Visualization Target tracking Roads Human-machine systems Pipelines Urban areas Cameras

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Distributed Shadow Dynamic Consensus Optimization Framework for Power Market with Parameterization Bidding Strategy of GENCOs

Distributed Shadow Dynamic Consensus Optimization Framework ...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Liying Yu Peng Wang Zhe Chen Dewen Li Ning Li Rachid Cherkaoui Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Electricity and Electronic Engineering École Polytechnique Fédérale de Lausanne Switzerland

This paper presents a distributed shadow dynamic consensus (DSDC) framework to address the generation company (GENCO) strategic bidding problem in the independent system operator (ISO) operation. First, each sub-group is defined according to the communication principle. The global resource allocation can be solved by introducing the local shadow power demand. Then, for the general market clearing optimization problem, a distributed dynamic consensus algorithm is presented to estimate the general equality constraint. The optimal locational marginal price (LMP) and power allocation in each sub-group are obtained in this step. Finally, the optimal solution is corrected when considering the transmission constraint. A theoretical analysis of the DSDC framework is provided. The algorithm and model are tested by the IEEE 30-bus test system to support the proposed DSDC framework.

关键词： Power demand Heuristic algorithms ISO Consensus algorithm Dynamic scheduling Power markets Smart grids

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Structural Optimization of a Magnetically Operated Actuator Mechanism for 10kV Circuit Breaker

Structural Optimization of a Magnetically Operated Actuator ...

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system Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Chao Liang He Tian Guanqiao Zhao Jiangping Li Ziliang Zhu Ruixuan Shen Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) Tianjin China Tianjin Sanyuan Power Equipment Manufacturing Co. Ltd Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

As the demand for reliable power supply in power systems grows, ensuring the performance of circuit breaker operating mechanisms is crucial. This performance is pivotal to the stability of the power grid, the speed of fault recovery, and the equipment’s lifespan. In this paper, we introduce a structural optimization plan for a new magnetic control operating mechanism designed for 10kV circuit breakers. Employing three-dimensional and mathematical modeling, complemented by finite element analysis with Maxwell software, we delved into an in-depth study of the mechanism’s structure, performance, and materials. We further conducted a parametric design and comparative analysis of key components. The simulation results, which exclude the spring load, demonstrate that the optimized circuit breaker can achieve a closing velocity of 3.07 m/s, an electromagnetic force peaking at 2.7 kN, and a closure time minimized to 8.5 ms. These enhancements notably improve the mechanism’s closing efficiency. Consequently, our research introduces a novel perspective to the field of reliability studies for magnetic control operating mechanisms.

关键词： Magnetic cores Circuit breakers Magnetic domains Power system stability Power system reliability Magnetic analysis Magnetosphere Integrated circuit reliability Magnetic circuits Optimization

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Indicators of Learning Ability of Autonomous Vehicles

Indicators of Learning Ability of Autonomous Vehicles

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2021 China Automation Congress, CAC 2021

作者： Su, Yimin Zhou, Junjie Huang, Hongrong Wang, Lin Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai Jiao Tong University Shanghai China Saic Volkswagen Automotive Co. Ltd. Shanghai China

ISBN: (纸本)9781665426473

With the increase of research on autonomous vehicles, the evaluation research of autonomous vehicles has gradually attracted attention. Effective evaluation of autonomous vehicles can ensure the safety of autonomous vehicles and improve their level of intelligence. The current related research focuses on the evaluation of vehicles in terms of safety and comfort, while there are few studies on the evaluation of the learning ability of autonomous vehicles from the time dimension. Therefore, this article proposes three learning indicators: learning time, learning speed and learning capacity. These three indicators are used to measure the learning ability of autonomous driving algorithms. By analyzing the learning curve of the vehicle algorithm, the evaluation results of three learning indicators can be obtained. Thus the learning ability of the automatic driving algorithm can be evaluated. The differences in different vehicle algorithms can also be compared. In this article, taking the parking algorithm based on reinforcement learning as an example, simulation examples and analysis of the three learning indicators proposed are carried out. © 2021 IEEE

关键词： Reinforcement learning

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Enhancing Autism Spectrum Disorder Early Detection with the Parent-Child Dyads Block-Play Protocol and an Attention-enhanced GCN-xLSTM Hybrid Deep Learning Framework

arXiv

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arXiv 2024年

作者： Li, Xiang Fan, Lizhou Wu, Hanbo Chen, Kunping Yu, Xiaoxiao Che, Chao Cai, Zhifeng Niu, Xiuhong Cao, Aihua Ma, Xin School of Control Science and Engineering Shandong University Shandong Jinan250061 China Engineering Research Center of Intelligent Unmanned System Shandong University Shandong Jinan250061 China School of Information University of Michigan Ann Arbor48109 United States Department of Pediatrics Qilu Hospital of Shandong University Shandong Jinan250061 China

Autism Spectrum Disorder (ASD) is a rapidly growing neurodevelopmental disorder. Performing a timely intervention is crucial for the growth of young children with ASD, but traditional clinical screening methods lack objectivity. This study introduces an innovative approach to early detection of ASD. The contributions are threefold. First, this work proposes a novel Parent-Child Dyads Block-Play (PCB) protocol, grounded in kinesiological and neuroscientific research, to identify behavioral patterns distinguishing ASD from typically developing (TD) toddlers. Second, we have compiled a substantial video dataset, featuring 40 ASD and 89 TD toddlers engaged in block play with parents. This dataset exceeds previous efforts on both the scale of participants and the length of individual sessions. Third, our approach to action analysis in videos employs a hybrid deep learning framework, integrating a two-stream graph convolution network with attention-enhanced xLSTM (2sGCN-AxLSTM). This framework is adept at capturing dynamic interactions between toddlers and parents by extracting spatial features correlated with upper body and head movements and focusing on global contextual information of action sequences over time. By learning these global features with spatio-temporal correlations, our 2sGCN-AxLSTM effectively analyzes dynamic human behavior patterns and demonstrates an unprecedented accuracy of 89.6% in early detection of ASD. Our approach shows strong potential for enhancing early ASD diagnosis by accurately analyzing parent-child interactions, providing a critical tool to support timely and informed clinical decision-making. © 2024, CC BY-NC-ND.

关键词： Diseases

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Scalable No-wait Scheduling with Flow-aware Model Conversion in Time-Sensitive Networking

Scalable No-wait Scheduling with Flow-aware Model Conversion...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yanzhou Zhang Qimin Xu Shouliang Wang Yingxiu Chen Lei Xu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665435413

The development of the Industrial Internet of Things (1IoT) has given rise to massive information from the networked controllers, sensors and actuators, leading to the increasing demands for real-time and reliable transmission. Time-Sensitive Networking (TSN) provides the deterministic mechanism guar-antee for these demands, but with an open scheduling problem. For the low-latency and low-jitter traffic, it is hard to schedule in a scalable way, that is, increasing the scheduling speed under the Quality-of-Service (QoS) requirements. Therefore, this paper constructs a no-wait forwarding (NW-TAS) model with a time-aware shaper to eliminate the queuing delay and jitter, and further converts it into a flow-aware model by divisibility theory for scheduling simplification. With the converted model, an interval transformation-based method is proposed to get the analytical expression of feasible scheduling for each flow. Then, a flow-aware NW- TAS scheduling algorithm (FANS) with cyclic interval searching is designed to compress invalid search spaces. The evaluation results show that our method decreases the transmission latency of 1000 flows by more than 23 % while increasing the scheduling speed by 43x than the existing works.

关键词： Fans Schedules Job shop scheduling Scheduling algorithms Quality of service Jitter Delays

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Sensor-based Multi-agent Coverage control with Spatial Separation in Unstructured Environments

arXiv

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arXiv 2024年

作者： Wang, Xinyi Xu, Jiwen Gao, Chuanxiang Chen, Yizhou Zhang, Jihan Wang, Chenggang Ding, Yulong Chen, Ben M. Department of Mechanical and Automation Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information system), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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A Hierarchy Early Warning Method based on RBF Neural Network for BOD in WWTP

A Hierarchy Early Warning Method based on RBF Neural Network...

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第34届中国控制与决策会议

作者： Junkai Zhang Wenjing Li Faculty of Information Technology Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education

As an indicator of the organic pollution of waste water,the early warning for the Biochemical Oxygen Demand(BOD) is vital and could prevent substandard waste water from environment *** paper presents a BOD early warning method combining multi-step time series prediction and hierarchy ***,an MIMO strategy based on an RBF neural network is designed to predict BOD concentration with multiple steps ***,a hierarchy alarm method based on a double-check from limit-value and tendency is proposed to recognize alarms from the predicted BOD time series ***,the BOD data from the Benchmark Simulation Model 1(BSM1) platform is used to test the effectiveness of the proposed *** experimental results show that the proposed method has a reliable early warning performance in different alarming level.

关键词： Early warning method Multi-step Prediction RBF neural network Biochemical Oxygen Demand(BOD) Waste Water Treatment Process

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Event-Triggered Fault-tolerant Model Predictive control of Nonlinear Multi-agent system with Time Delay and Parameter Uncertainty

Event-Triggered Fault-tolerant Model Predictive Control of N...

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第40届中国控制会议

作者： Linglong Wang Jianxiong Li Xinjian Liu Yiming Fang Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University The Key Lab of Industrial Computer Control Engineering of Yanshan University

This paper proposes a compound control method combining with robust model predictive control(RMPC) method and the event-triggered mechanism for a class of nonlinear multi-agent system(NMAS) with time delay, uncertainties, actuator faults and input constraints. Specifically, at each sampling time, the control action is calculated by solving an online constrained optimization problem,using Lyapunov-Krasovskii functions to design state feedback control laws to ensure that the consensus error of the NMAS tends to zero and the event-triggered mechanism can save communication resources. Finally, the effectiveness of the proposed event-triggered fault-tolerant RMPC method is verified by an example.

关键词： robust model predictive control fault-tolerant control non-linear multi-agent system event-triggered mechanism

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