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第34届中国控制与决策会议

作者： Tian Gao Qinglin Wang Yaping Dai School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

In order to solve the problem that the clustering number in Fuzzy C-Means(FCM) needs to be set manually in advance,a two-phase hybrid fuzzy clustering approach using membership fusion(TPHFC) is *** the first phase,conventional FCM is used for *** the second phase,the results obtained by pre-clustering are fused according to the relationship between the membership of samples to different clusters and the membership threshold.A density-based clustering validity measurement is established for this *** proposed method obtains better clustering effect with setting fewer *** on synthetic datasets conforming to Gaussian distribution and UCI datasets demonstrate the effectiveness of the proposed clustering *** clustering number and clustering centers can be obtained adaptively.

关键词： Fuzzy Clustering Fuzzy C-Means Two-Stage Clustering Adaptive Parameter Adjustment

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Towards Pose Estimation for Large UAV in Close Range

Towards Pose Estimation for Large UAV in Close Range

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Ni Ou Junzheng Wang Shangfei Liu Jiehao Li State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665465373

This paper deals with the problem of 4D pose estimation for large unmanned aerial vehicles (UAVs) in close range. A sensor system consisting of one single point laser range-finder and two cameras is designed and a novel pose estimation method based on vision fusion and point cloud registration is proposed. Our approach works on one-shot mode and only requires 10 samples with real poses for template construction. Through V-rep simulation environment, we generate two 200-sample datasets of different difficulty for evaluation. Error quantiles, 5cm5deg and 10cml0deg are three evaluation metrics used in our ablation experiments. It is illustrated that our method outperforms in robustness and precision due to proposed dimension extension modification and fusion of vision sensors.

关键词： Point cloud compression Measurement Cloud computing Pose estimation Vision sensors Autonomous aerial vehicles Laser modes

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Integrated Inspection of QoM, QoP, and QoS for AOI Industries in Metaverses

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2071-2078页

作者： Yutong Wang Yonglin Tian Jiangong Wang Yansong Cao Shixing Li Bin Tian IEEE The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China North Automatic Control Technology Institute in Taiyuan ShanxiChina Qingdao Academy of Intelligent Industries Shandong 266109China

With the rapid development of information technologies such as digital twin, extended reality, and blockchain,the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection(Auto QI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing(Qo M), Quality of Products, Quality of Processes(Qo P), Quality of systems, and Quality of Services(Qo S). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottomup inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection,we propose Quality Transformers to complete detection task,while federated learning is integrated to regulate data *** summary, we point out the development directions of quality inspection under metaverse tide.

关键词： Automated Optical Inspection Metaverses Quality Inspection Transformers

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Prior area searching for energy-based sound source localization

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Science China(Information Sciences) 2022年第12期65卷 220-231页

作者： Feng GAO Yeyun CAI Fang DENG Chengpu YU Jie CHEN Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Guohao Academy Tongji University

Traditional energy-based sound source localization methods have the problems of the large solution space and time-consuming calculation. Accordingly, this paper proposes to use the data collected by each acoustic sensor and their corresponding weights to adaptively initialize the prior area of a target. In this way, the potential existence range of the target is reduced and the location estimate can be determined in a small area. Specifically, we first determine the initial search point based on the current sound data and the set rules. Then, the prior location of the target is iteratively searched according to different sound energy circles' weights. Next, the prior area of the target is determined around the prior location. Finally, the precise location of the target is further traversed to minimize the objective function, which is constructed by the weighted nonlinear least squares location(WNLS) algorithm. A series of indoor experiments are *** results show that our method can effectively improve the positioning accuracy by approximately 13%and greatly reduce the calculation time.

关键词： prior area searching sound source localization adaptively initialization iterative search indoor environment

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Temperature control System of Thermoelectric Cooler Based on Improved ADRC

Temperature Control System of Thermoelectric Cooler Based on...

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第34届中国控制与决策会议

作者： Xiyuan Shan Tao Cai Zhicheng Zong Hongbo Mu School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Aiming at the problems containing complex working conditions,modeling difficulties and long-time delay of thermoelectric cooler temperature control system,an improved ADRC control method combining ADRC and Smith predictor is proposed in this *** deals with the disturbances and uncertain dynamics in the system,SP compensates the time delay to improve the control *** with the traditional PID controller,the proposed control method has faster response speed and stronger anti-disturbance ability,meanwhile,overcomes the dependence of Smith predictor on object parameters to a certain *** simulation and experimental verification,a good control effect is obtained,which provides a positive guidance of the related application of thermoelectric cooler.

关键词： Thermoelectric cooler(TEC) Active Disturbance Rejection control(ADRC) Smith Predictor(SP) Temperature control System

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An Improved Active Disturbance Rejection control Method for Crane System

An Improved Active Disturbance Rejection Control Method for ...

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第34届中国控制与决策会议

作者： Yubo Sun Xiyuan Shan Zhicheng Zong School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

In order to improve the dynamic performance of the underdriven crane system,an improved linear active disturbance rejection controller(LADRC) based on the new error was *** improved LADRC takes the error value between the disturbance and its observed value multiplied by a coefficient as the basis for adjusting the linear extended state observer(LESO).The improved method has two ***,the new error can prevent the traditional LESO from choosing larger parameter adjustment disturbances,which will limit the performance of the ***,the pole can be configured by adjusting the coefficient to obtain better dynamic ***,the effectiveness of the proposed method is verified by simulation and *** proposed method can effectively restrain the swing of the payload and it is robust to system parameters perturbation as well.

关键词： Crane LADRC New Error Underdriven System

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Dynamic state Estimation for Power System with Communication Constraint Using Event-Triggered Cubature Kalman Filter

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Journal of Beijing Institute of Technology (English Edition) 2021年第ZK期30卷 129-140页

作者： Wei, Minfeng Xu, Min Zhang, Fengdi School of Automation and Key Laboratory for Intelligent Control and Decision on Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing100854 China

Accurate dynamic state estimation plays an important role in power systems. Although various filtering algorithms, such as unscented Kalman filter (UKF) and particle filter (PF), have been proposed based on phasor measurement units (PMUs), they occupy a huge communication bandwidth without specific concern, which puts heavy burden on the communication network especially when wireless communications are widely applied in smart grids. In order to relieve this communication burden, the event-triggered cubature Kalman filter (ETCKF) is proposed based on the stochastic event-triggered schedule in this paper, which guarantees that only the measurements containing innovational information are transmitted and has advantages over other event-triggered schedules, such as the deterministic event-triggered schedule, since it can maintain the Gaussian property of the conditional distribution of the system state. Based on the developed nonlinear event-triggered schedule, the cubature Kalman filter (CKF), using the third-degree spherical-radial cubature rule, further provides more accurate estimation than UKF and has lower computational complexity than PF. The proposed filter can effectively reduce the communication rate while ensuring the accuracy of filtering. Finally, the standard IEEE 39-bus system is utilized to verify the feasibility and performance of the proposed method. © 2020 Journal of Beijing Institute of Technology

关键词： Phasor measurement units

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GAT-Net:A Network using Grid Attention and Transformer for Dynamic Facial Expression Recognition

GAT-Net:A Network using Grid Attention and Transformer for D...

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第34届中国控制与决策会议

作者： Wenxin Dai Yaping Dai Kaoru Hirota Shuai Shao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Dynamic facial expression recognition(DFER) in the wild has received widespread attention *** are complex factors such as face occlusion and pose variation in the *** expression recognition has a subtle competition between capturing local features of a human face and obtaining a global feature *** paper proposes an end-to-end DFER network GAT-Net based on the grid attention module and Transformer,which improves the robustness and accuracy of DFER in the ***,GAT-Net is divided into two components:spatial feature extraction and temporal feature *** grid attention module of the spatial feature extraction component guides the network to pay attention to the local salient features of the face,which reduces the interference of field occlusion and non-frontal *** Transformer in the temporal feature processing component guides the network to learn the temporal relationship of high-level semantic features and the global representation of facial expression *** two components balance the subtle competition between local features and global feature representations of facial *** ablation experiment has proved the effectiveness of the grid attention module and *** demonstrate that our GAT-Net outperforms state-of-the-art methods on DFEW and AFEW benchmarks with accuracies of 67.53%,and 50.14% respectively.

关键词： Facial Expression Recognition Deep Learning Attention Mechanism Transformer

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Study on Disturbance Propagation Characteristics of Power Grid and Evaluation of Temporal and Spatial Correlation Characteristics of Voltage Phase Trajectory 41

Study on Disturbance Propagation Characteristics of Power Gr...

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第41届中国控制会议

作者： Yibo Kong Changyou Feng Hongying Yang Liping Yan Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Xinjiang Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. National Electric Power Dispatching and Control Center of State Grid Corporation of China China Electric Power Research Institute

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the access of large-scale new energy and power electronic equipment,as well as the influence of various random uncertain factors,the analysis and control of dynamic security of large power grids become particularly difficult,so it is of great practical significance to study the disturbance propagation characteristics of complex power *** is an important parameter that affects the security of power systems,and the voltage phase trajectory contains abundant power grid ***,based on the measured data,this paper quantitatively describes the characteristics of dynamic space-time distribution of the voltage,and numerically reveals the different influences of disturbance on different nodes and the characteristics of disturbance ***,the geometric characteristics of the measured voltage phase trajectory are mined,the new trajectory distance is defined,the characteristic trajectory plane is constructed,and the spatio-temporal evolution law of voltage phase trajectory is *** the sequel,through the density-based DBSCAN clustering method,the characteristic tracks of voltage movement are clustered and analyzed,and the temporal and spatial correlation characteristics of voltage movement trends of each node are measured,and the law of disturbance propagation is ***,based on the measured data of IEEE 39-bus system,the simulation analysis is carried out,and the effectiveness of the presented algorithm is verified.

关键词： Disturbance propagation Voltage phase trajectory Temporal and spatial correlation characteristics Trajectory clustering

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An Improved Machine Learning Model for Pure Component Property Estimation

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Engineering 2024年第8期39卷 61-73页

作者： Xinyu Cao Ming Gong Anjan Tula Xi Chen Rafiqul Gani Venkat Venkatasubramanian State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Physics Bard College at Simon’s RockGreat BarringtonMA 01230USA PSE for SPEED Company Charlottenlund DK-2920Denmark Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 510000China Department of Applied Sustainability Széchenyi István UniversityGyör 9026Hungary Complex Resilient Intelligent Systems Laboratory Department of Chemical EngineeringColumbia UniversityNew YorkNJ 10027USA

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product ***,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these ***,accuracy and predictive capabilities still limit the scope and applicability of most property estimation *** paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different *** selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research *** framework is assessed using datasets of varying sizes for 20 pure component *** 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.

关键词： Group contribution Gaussian process Warping function Prior predictive checking

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