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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal control

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1702-1704页

作者： Jingwei Lu Lefei Li Qinglai Wei Fei-Yue Wang IEEE the Department of Industrial Engineering Tsinghua UniversityBeijing 100084China The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

关键词： Deep letter enable

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Parallel Systems Framework: A Safe and Efficient Testbed for Unmanned Ships 4

Parallel Systems Framework: A Safe and Efficient Testbed for...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Fu, Long Shen, Zhen Dong, Xisong Han, Yunjun Yang, Shuguang Xiong, Gang Wang, Fei-Yue University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing100190 China Chinese Academy of Sciences Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Shandong Jiaotong University Schoool of Rail Transportation Jinan250399 China Chinese Academy of Sciences State Key Laboratory for Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

In this study, unmanned ships test platform based on the parallel systems framework is proposed to improve the test efficiency and accuracy of unmanned ships in complex ocean environment. The parallel intelligence theory and ACP approach, the efficient interaction between virtual and real environment is achieved, which provides more comprehensive and accurate data for unmanned ships testing. Future research will further expand the scope of virtual environment simulation and improve systems accuracy and response speed to a wider range of unmanned ships test requirements. © 2024 IEEE.

关键词： Virtual environments

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Towards intelligent and Trustworthy Crowdsourcing Systems with Cyber-physical Social Intelligence via Retrieval-augmented Generation

Towards Intelligent and Trustworthy Crowdsourcing Systems wi...

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Yang, Jing Wang, Xingxia Ni, Qinghua Liu, Yuhang Wang, Jing Wang, Fei-Yue Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering 999078 China Macau University of Science and Technology 999078 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing100190 China

ISBN: (纸本)9798350376739

In recent years, crowdsourcing systems that tackle complex tasks through the collective efforts of many individuals have garnered substantial attention. However, the existing crowd-sourcing systems face some challenges such as overreliance on human experience and effort, information overload, and risks of privacy breaches. To overcome these challenges, this paper proposes an intelligent and trustworthy crowdsourcing frame-work based on blockchain and retrieval-augmented generation. The framework centers around foundational models to integrate the three distinct and sequentially interdependent phases: task decomposition, task allocation or recommendation, and task execution, enabling the automatic provision of a personalized, comfortable, and efficient user experience. Blockchain and smart contracts are utilized to protect user privacy and interests without leaning on any third trusted authority. Additionally, the operational process of the framework is explained and validated by taking printed circuit board design and manufacturing as a case study and ChatGPT as its generator. © 2024 IEEE.

关键词： Embedded systems

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Multimodal Multilevel Attention for Semi-Supervised Skeleton-Based Gesture Recognition

SSRN

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SSRN 2024年

作者： Liu, Jinting Gan, Ming-Gang He, Yuxuan Guo, Jia Hu, Kang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

Although skeleton-based gesture recognition based on supervised learning has made promising achievements, the reliance on large amounts of annotation for training poses a significant cost. This paper addresses semi-supervised gesture recognition, aiming to learn feature representations effectively from unlabeled data. To resolve this problem, we propose a novel multimodal multilevel attention network to achieve semi-supervised learning. This model uses an attention mechanism to polymerize multimodal and multilevel complementary semantic information of the hand skeleton, designing a multimodal multilevel contrastive loss to measure the similarity of features for semi-supervised learning. Specifically, our method explores the relationships between joint, bone, and motion to learn more discriminative feature representations. Considering the hierarchy of the hand skeleton, the skeleton data is divided into multilevel (i.e., hand-level, finger-level, and joint-level) to capture complementary semantic information. Furthermore, a multimodal contrastive loss measure similarity among multilevel representations. The proposed method outperforms the semi-supervised skeleton-based gesture recognition tasks, according to extensive experimentation on two challenging datasets from SHREC-17 and DHG 14/28 datasets. © 2024, The Authors. All rights reserved.

关键词： Gesture recognition

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3D Point Cloud-Based Lithium Battery Surface Defects Detection Using Region Growing Proposal Approach 8th

3D Point Cloud-Based Lithium Battery Surface Defects Detecti...

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8th International Workshop on Advanced Computational Intelligence and intelligent Informatics, IWACIII 2023

作者： Rehman, Zia Ur Wang, Xin Alsumeri, Abdulrahman Abdo Ali Khan, Malak Abid Ali Ma, Hongbin State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819975891

Detecting the lithium battery surface defects is a difficult task due to the illumination reflection from the surface. To overcome the issue related to labeling and training big data by using 2D techniques, a 3D point cloud-based technique has been proposed in this paper. The 3D point cloud-based defect detection of lithium batteries used feature-based techniques to downscale the point clouds to reduce the computational cost, extracting the normals of the points and calculating their differences to detect the defects of the battery which assure the quality of the product. This paper offers a novel strategy using 3D point clouds to get beyond the labeling and training challenges involved with conventional 2D approaches. This 3D point cloud-based approach for lithium battery fault identification makes use of feature-based methods to improve the point cloud data and lessen the computing burden. In our work, the experiments show that the feature-based technique precisely detects the affected surface of the battery. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithium batteries

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Hypersonic Re-entry Vehicle Fault-Tolerant control Against Actuator Failure via Integral Sliding Mode

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Journal of Beijing institute of Technology (English Edition) 2021年第ZK期30卷 111-120页

作者： Guo, Fuhui Lu, Pingli School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

A fault-tolerant control (FTC) scheme is proposed based on integral sliding mode(ISM) for attitude control of hypersonic re-entry vehicle (HRV) under partial loss of actuator effectiveness. First, the inner/outer loop mathematical model of HRV in the present of actuator failure is given with specific analysis on torque. Then prescribed performance approach is introduced to the ISM controller design by error transformation. Specifically, the transformation error rather than tracking error is chosen as the sliding variable to establish the sliding mode surface and reaching law. As a result, the attitude tracking error of HRV is limited into the expected range. Meanwhile, the transient process and the steady state behavior are both considered. Considering that the unknown fault information of actuator can cause adverse effect on HRV, an extended state observer(ESO) is adopted to estimate the unknown actuator failure. Thus the observer result is served as compensation for the loss of the controlled system performance. Simulation results show that the proposed scheme can fulfil the tracking task with prescribed performance. © 2020 Journal of Beijing institute of Technology

关键词： Actuators

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Civilian Autonomous Carriers for Peaceful Services: intelligent Vehicle Carries Enabled Future Transportation and Logistics

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2023年 1-5页

作者： Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

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Model Predictive control for Nonlinear Bilateral Teleoperation Systems With Time Delay 41

Model Predictive Control for Nonlinear Bilateral Teleoperati...

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第41届中国控制会议

作者： Yu Liu Chuanzhi Zang Yuqi Liu Peng Zeng School of Artificial Intelligence Shenyang University of Technology Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Bilateral teleoperation systems have received extensive attention as a substitute for the human to perform tasks in remote and hazardous *** paper proposes a model predictive control strategy for nonlinear bilateral teleoperation systems based on Long-Short Term Memory(LSTM) ***,model predictive control is used to achieve good tracking performance in the presence of input constraints in the *** the same time,considering the influence of different proficiency operators operating the master manipulator on the system performance,the error between the actual master manipulator trajectory and the reference trajectory is introduced into a linear feedback term to compensate for the decrease in system control accuracy caused by the error of ***,the LSTM network is used to predict the trajectory of the master manipulator in the future time equivalent to the network transmission *** predictions are used to control the remote slave manipulator,thereby eliminating the impact of communication delay on the ***,in slave sides,the trajectory creators are applied to generate the desired trajectories to achieve good force feedback *** experiments are carried out to verify the proposed control strategy,which can guarantee good performance with both position tracking and force feedback under time delay.

关键词： Model predictive control bilateral teleoperation long-short term memory network communication delay

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