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Impact of Automated Driving under Virtual Traffic Signals on Traffic Resilience of Urban Road Networks

Impact of Automated Driving under Virtual Traffic Signals on...

International Conference on Transportation and Development 2024: Transportation Safety and Emerging Technologies, ICTD 2024

作者： Zhang, Yankai Chen, Jiajing Liang, Bo Xu, Zihuan Mo, Lei Zhang, Zhao Bian, Ruijie Key Laboratory of Intelligent Transportation Technology and System Ministry of Education School of Transportation Science and Engineering Beihang Univ. Beijing China Louisiana Transportation Research Center Dept. of Civil and Environmental Engineering Louisiana State University Baton RougeLA United States

ISBN: (纸本)9780784485514

With the rapid development of autonomous driving technology, it is imperative to study the impact of this technology on the resilience of urban road transportation networks. This study focuses on virtual traffic signal control (VTS) as a representative of autonomous driving technology and proposes a multi-dimensional resilience evaluation system to compare the impacts of three different signal-timing strategies on the resilience of urban traffic networks under various scenarios. Simulation results demonstrate that the average vehicle speed under VTS control is 38.1% and 40.1% higher than the other two control modes, respectively. In addition, the VTS approach significantly reduces delays and minimizes fuel consumption compared to the other two approaches. Moreover, this paper delves into the intrinsic mechanisms by which VTS enhances traffic resilience. It highlights the importance of integrating autonomous driving technology into traffic strategies to achieve carbon emissions reduction and promote sustainable transportation. The network resilience enhanced by VTS enables cities to effectively respond to unexpected events, thereby ensuring smooth traffic flow and safety. This research provides valuable insights into existing knowledge and promotes further research in the field of traffic resilience. © ASCE.

关键词： Autonomous vehicles

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Gas Scheduling Optimization Algorithm Integrating Knowledge and Dynamic Programming 24

Gas Scheduling Optimization Algorithm Integrating Knowledge ...

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8th International Conference on Computing and Data Analysis, ICCDA 2024

作者： Sheng, Gang Sun, Yanguang Hao, Xiaodong Wang, Yushi Zhang, Yixuan State Key Laboratory of Metallurgical Intelligent Manufacturing System Beijing China Automation Research and Design Institute of Metallurgical Industry Co. Ltd. Beijing China Central Iron & Steel Research Institute Beijing China China Iron & Steel Research Institute Group Beijing China

ISBN: (纸本)9798400710445

Regarding the gas scheduling issue in the coal gas systems of steel enterprises, a gas scheduling optimization model is proposed based on the multi-input and multi-output characteristics of the gas system. This model combines knowledge-based and dynamic programming approaches, utilizing production experience to establish scheduling knowledge. The model calculates the scheduling results under two scenarios: non-optimized and optimized, across various time dimensions (1 minute, 15 minutes, 30 minutes) and different production conditions (with and without blast furnace maintenance). The results from simulation experiments with field data demonstrate the model’s timeliness and practicality, providing a reasonable guide for developing gas system schemes. This model is particularly suitable for steel production enterprises with no gas holders and significant pipeline pressure fluctuations, offering a scientific and effective guidance method. This research can be applied to solve energy optimization problems in complex production systems and scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Blast furnaces

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Forming Circle Formations of Multi-agent systems Under Actuator Faults 5th

Forming Circle Formations of Multi-agent Systems Under Actu...

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Liu, Jiajun Ru, Xinfeng Xia, Weiguo Wang, Chen China National Aeronautical Radio Electronic Research Institute and National Key Laboratory of Science and Technology on Avionics System Integration Shanghai China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian China National Engineering Research Center of Software Engineering and the State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

ISBN: (纸本)9789811939976

A distributed fault-tolerant formation control law is designed in this paper for multi-agent systems under external disturbances and actuator faults including time-varying loss of effectiveness faults. The initial position of each agent is already on a circle and the control purpose is to form a desired formation on this circle. Based on this local information, a distributed adaptive formation control law is designed by employing the congelation of variables method to deal with time-varying actuator faults. Finally, a simulation example is given to verify the effectiveness of the adaptive fault-tolerant control design method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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A Survey of Output Feedback Robust MPC for Linear Parameter Varying systems

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1717-1751页

作者： Xubin Ping Jianchen Hu Tingyu Lin Baocang Ding Peng Wang Zhiwu Li the Department of Automation School of Electro-Mech-anical EngineeringXidian UniversityXi’an 710071China the Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringFaculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China the State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China the College of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China the Information and Navigation College Air Force Engineering UniversityXi’an 710077China IEEE the School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071 the Institute of System Engineering Macao University of Science and TechnologyMacao SARChina

For constrained linear parameter varying(LPV)systems,this survey comprehensively reviews the literatures on output feedback robust model predictive control(OFRMPC)over the past two decades from the aspects on motivations,main contributions,and the related *** to the types of state observer systems and scheduling parameters of LPV systems,different kinds of OFRMPC approaches are summarized and *** extensions of OFRMPC for LPV systems to other related uncertain systems are also *** methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are *** issues on OFRMPC optimizations for LPV systems are ***,the future research directions on OFRMPC for LPV systems are suggested.

关键词： Linear parameter varying(LPV)systems model predictive control(MPC) output feedback robust control

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Large-scale non-real-time Simulation Platform for Power systems Based on Cloud Computing 4

Large-scale non-real-time Simulation Platform for Power Syst...

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4th International Conference on Energy, Power and Electrical Engineering, EPEE 2024

作者： Zhang, Jingyue Lu, Yuanhong Hu, Yufeng Zhang, Jie Guo, Haiping Huang, Libin Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China China Southern Power Grid Co. Ltd Guangzhou China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798331518066

The rapid evolution of smart grids has increased the complexity of power system structures and operational characteristics, presenting new challenges in system management and reliability. Traditional power system simulation tools struggle with non-real-time data processing and scalability due to limitations in their architectural designs. Moreover, existing cloud simulation platforms are constrained by closed hardware architectures and lack sufficient non-real-time capabilities. As a solution, we propose a novel large-scale non-real-time simulation platform for power systems based on cloud computing, designed with a modular architecture. By leveraging advanced technologies in cloud computing, parallel computing, and non-real-time simulation, our platform addresses critical needs for scalability, efficiency, and cost-effectiveness. We discuss how integrating these technologies enhances computational performance and supports complex simulation tasks, ultimately improving decision-making and operational efficiency in power system management. © 2024 IEEE.

关键词： Cloud platforms

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Automatic Cell Rotation Method Based on Deep Reinforcement Learning

Automatic Cell Rotation Method Based on Deep Reinforcement L...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Huiying Gong Yujie Zhang Yaowei Liu Qili Zhao Xin Zhao Mingzhu Sun Tianjin Key Laboratory of Intelligent Robotic (tjKLIR) Institute of Robotics and Automatic Information System (IRAIS) Nankai University Tianjin China the Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

Cell rotation is widely used to adjust cell posture in sub-cellular micromanipulations. The trajectory planning of the injection micropipette is needed, so that the cells can be rotated with the minimum deformation to reduce cell damage and keep cell viability. Due to the uncertainty of cell properties and manipulation environment, it is difficult to identify the parameters of the mechanical models in traditional robotic cell rotation methods. In this paper, deep reinforcement learning is introduced into cell manipulation for the first time to perform trajectory planning of the micropipette. We first abstract the cell rotation process by using the mechanical model and microscopic vision techniques and build a cell rotation simulation environment. Then we design a reward function by combining various factors of cell rotation and implement a reinforcement learning framework based on deep Q-learning (DQL). Finally, we train the cell rotation process based on the deep reinforcement learning algorithm. The simulation results indicate the proposed DQL agent achieved an average success rate of 97% without useless exploration. Moreover, the proposed method rotated the cells in a way that causes less mechanical damage than humans, demonstrating the DRL ability for cell rotation with high efficiency and low cell damage.

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An Acoustic Dataset Construction Method of Chinese Traditional Musical Instrument Guqin

An Acoustic Dataset Construction Method of Chinese Tradition...

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2024 International Conference on Culture-Oriented Science and Technology, CoST 2024

作者： Liu, Jingyu Yang, Chen Li, Zijin Jiang, Wei State Key Laboratory of Media Convergence and Communication Communication University of China 100024 China Ministry of Culture and Tourism Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System 100024 China Communication University of China School of Information and Communication Engineering 100024 China Central Conservatory of Music Ai Music and Music Information Technology Department 100032 China

ISBN: (纸本)9798350380347

With the acceleration of modernization and the rapid development of popular music, the splendid culture of Guqin, a type of Chinese traditional musical instrument which has lasted for nearly 1,000 years, is facing a serious inheritance crisis. Therefore, how to make full use of modern technical means to digitally store and manage the valuable traditional music cultural resources is the current *** response to the aforementioned challenges, this study focuses on the Guqin, an emblematic Chinese traditional musical instrument. By employing advanced audio feature extraction techniques, this research systematically analyzes the diverse playing techniques of Guqin, thereby establishing a comprehensive acoustic dataset. This endeavor not only facilitates the digital preservation and management of the Guqin's musical heritage but also pioneers a standardized framework for the creation of acoustic datasets. This framework serves as a benchmark for the development of similar datasets, thereby promoting the conservation and standardization of resources associated with Chinese traditional musical instruments. This will provide a scientific basis for the inheritance, standardized management and construction of more acoustic datasets of ethnic musical instruments in the future. © 2024 IEEE.

关键词： Musical instruments

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Event-Triggered Mechanism-Based MPC for Path-Tracking Control of Four-Wheel Steering Vehicles 22

Event-Triggered Mechanism-Based MPC for Path-Tracking Contro...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhang, Xiangyu Xu, Guoyan Li, Han Chen, Peng Xia, Qi Cai, Han School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

In this study, we tackle the path-tracking problem of a nonlinear four-wheel steering vehicle dynamics model subject to model mismatches and propose a model predictive control (MPC) algorithm based on an event-triggered mechanism (ET-MPC). The goal is to maintain closed-loop control performance while reducing the computational and communication burdens of traditional MPC. We introduce an ET-MPC framework utilizing a model-free reinforcement learning agent with proximal policy optimization (PPO). This agent interacts with the MPC system, progressively learning to determine the optimal event-triggered mechanism. To enhance exploration and training efficiency, we incorporate the Long Short-Term Memory (LSTM) technique into PPO. Experimental results show that the proposed ET-MPC framework, combined with reinforcement learning for reward optimization, demonstrates superior overall performance in path-tracking control of four-wheel steering vehicles. © 2024 IEEE.

关键词： Four wheel steering

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A grid-assisted 3D printing method for magnetically driven micro soft robot 9

A grid-assisted 3D printing method for magnetically driven m...

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9th International Conference on Advanced Manufacturing Technology and Materials Science, AMTMS 2021

作者： Gao, Jian Gao, Peng Rong, Weibin Li, Dongjie Wang, Lefeng State Key Laboratory of Robotics and System Harbin Institute of Technology Heilongjiang Harbin150001 China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology Heilongjiang Harbin150080 China

The magnetically driven micro soft robot is one of the research hotspots in the field of micro robots. A 3D printing method for manufacturing micro soft robots with the help of auxiliary grids is proposed. The shape flexibility of the micro soft robots manufactured by the micro 3D printing method can be improved, and internal structures composed of different materials inside the micro robots can be printed using this method. The technical details of the grid-assisted 3D printing process are introduced in this article. Micro soft robots with specific patterns were 3D printed, and the deformation and movement capabilities of the robots were verified in magnetically driven motion experiments. The experimental results prove that the grid-assisted 3D printing technology can not only manufacture magnetically driven micro soft robots, but also adjust the internal structure and motion performance of the robots. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： 3D printers

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Active Support Performance Test of Grid-connected Photovoltaic system Based on Grid-forming Control 3

Active Support Performance Test of Grid-connected Photovolta...

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3rd International Conference on intelligent Power and systems, ICIPS 2023

作者： Xi, Xinze Hu, Yun Xing, Chao Guo, Qi Xu, Shoudong Tu, Liang He, Peng Zhu, Yihua Su, Mingzhang Luo, Chao Yunnan Electric Power Research Institute Kunming China National Energy Power Grid Technology R&D Centre Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798350328691

With the continuous increase penetration of new energy, the integration of grid-following (GFL) converter is prone to frequency stability issue, while the utilization of grid-forming (GFM) converter with active support capability has become an inevitable trend in development. In this paper, the model of grid-connected photovoltaic system using GFL/GFM control is established. The support characteristic of GFM is compared and analyzed in different testing scenarios with GFL as the reference: (1) Small disturbance test (2) Large disturbance test (3) Island capability test. Finally, the active support performance of the system is quantitatively analyzed based on corresponding evaluation indexes. ©2023 IEEE.

关键词： Power converters

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