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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Xinhui Cheng, Meiqi Shi, Dawei Shi, Ling Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

ISBN: (纸本)9798350316339

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of E(varepsilon)-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement. © 2024 IEEE.

关键词： Scheduling algorithms

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Object Pose Estimation Based on Improved YOLOX Algorithm 11

Object Pose Estimation Based on Improved YOLOX Algorithm

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Zhou, Yanhong Liu, Shan Polytechnic Institute Zhejiang University Hangzhou310015 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

This paper proposes a deep learning method for object recognition and pose estimation only through a single RGB image. We extend the YOLOX algorithm, which has excellent performance in the field of 2D object detection, making it suitable for 6DoF pose estimation scenarios in a natural way. The model is designed to predict the position of key points in the image, which are used to establish the correspondence between the object model and the scene image. After that, the object pose can be obtained by PnP algorithm. In order to obtain labeled data for model training effectively, this paper designs a data augmentation method combining offline expansion and online augmentation, which can improve the generalization ability of the model. The results of experiments on the LINEMOD dataset demonstrate the effectiveness of our method, which is significantly competitive with other methods using similar implementation ideas. © 2022 IEEE.

关键词： Object recognition

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$H_{\infty}$ Antidisturbance Event-Triggered control for Aeroengine Systems via Switched Affine Models

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IEEE Transactions on Aerospace and Electronic Systems 2024年第2期61卷 1600-1609页

作者： Ying Zhao Xinyu Ni Sixin Wen Ben Niu College of Marine Electrical Engineering Dalian Maritime University Dalian China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

This study is focused on the issue of $H_{\infty }$ antidisturbance event-triggered (ADET) control for aeroengine control systems (AECS) under multiple disturbances. First, a switched affine model is employed to describe the AECS, taking into account the degradation of aeroengine performance and the phenomenon of multimode operation. Second, a disturbance observer is established to compensate for disturbances that arise from the modeling error, uncertainties, and other factors. Then, based on the disturbance observer, a dynamic event-triggered mechanism with respect to the affine term and an $H_{\infty }$ ADET controller are built to optimize network transmission resources and fuel flow efficiency. Furthermore, a sufficient condition is developed to enable the solvability of the $H_{\infty }$ ADET control issue for AECSs. Finally, a simulation platform of hardware-in-the-loop for the AECS based on switched affine model is implemented to illustrate the effectiveness of the present $H_{\infty }$ ADET control methodology.

关键词： Switches control systems Engines Aircraft propulsion Degradation Switched systems Fuels

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Formation control of Second-Order Nonholonomic Systems with a Novel Model-Based Event-Triggered Mechanism 7th

Formation Control of Second-Order Nonholonomic Systems with...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Xiao, Feng Sun, Sijia Wang, Xiaoyu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9789819733392

The formation control problem of second-order nonholonomic systems formulated by a force-balance equation is investigated with network communication constraints. A novel event-triggered mechanism based on polynomial interpolation predictive models is presented. To address the stabilization obstacles imposed by the nonholonomic constraints, persistently exciting (PE) functions are utilized to design the distributed control protocols. Under the designed event-triggered mechanism, the system does not exhibit Zeno behavior. Sufficient conditions for second-order nonholonomic systems to achieve stable formation are obtained based on Barbalat’s lemma. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed parameter control systems

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： Image registration

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Federated Learning With Meta-Layers Training for Privacy-Preserving in Vehicular Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年

作者： Shen, Xiaoyang Li, Haibin Li, Yaqian Zhang, Wenming Alenazi, Mohammed J.F. Agarwal, Kadambri Yanshan University College of Electrical Engineering Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia ABES Engineering College Department of Computer Science and Engineering Ghaziabad201009 India

Vehicular consumer electronics, such as autonomous vehicles (AVs), need collecting large amounts of private user information, which face the risk of privacy leakage. To protect the privacy of consumers, researchers have proposed to apply federated learning (FL) to privacy-preserving vehicular consumer electronics, that is, leveraging FL for collaborative training of a decision model without exchanging the sensitive information generated by AVs. However, FL generally needs to exchange the gradients of client models periodically with the central server, which can be attacked by adversaries to infer user information. Thereby, it may still face the risk of privacy leakage. To solve that challenge, we put forward a novel FL framework, called FL with Meta-Layers Training (FL-MLT). Instead of exchanging the gradients of the client models, it exchanges, meta-layers with the central servers. Since meta-layers are only a slice of the client models, exchanging them helps protect privacy. On the other hand, they contain meta-knowledge to help the FL training process. In the experiments, we conduct extensive visual classification simulation to evaluate FL-MLT, and the experimental results demonstrate the superior performance of FL-MLT. © 1975-2011 IEEE.

关键词： Federated learning

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A Data Fusion Method for Pipeline Defect Depth Quantitative Evaluation in Gas Gathering Station

A Data Fusion Method for Pipeline Defect Depth Quantitative ...

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第35届中国过程控制会议

作者： Ye Zheng Jie Wang Baiyu Zhu Minglv Jin Zheng Liu Pingjie Huang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University School of Information Science and Engineering Zhejiang Sci-Tech University

This paper presents a quantitative evaluation method based on Dempster-Shafer(D-S) evidence theory and Gradient Boosting Regression Tree(GBRT) for defects in gas gathering station. The detection experiments were conducted using pulsed eddy current and ultrasound for defects with different depth. Through the experiments, an optimal feature selection method based on D-S evidence is proposed, which combines the Fisher Score and Pearson correlation coefficient to select the optimal features. The optimal features are then fed into GBRT to predict the depth of defects. The optimal feature selection method and GBRT are compared with other method. The results show that the proposed method in this paper selects better feature and has better performance in the quantitative evaluation of defect depth.

关键词： gas gathering station feature selection defect detection composite detection

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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Quantized impulsive consensus of nonlinear Leader-following multi-agent systems 41

Quantized impulsive consensus of nonlinear Leader-following ...

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第41届中国控制会议

作者： Gang Li Xiaowei Jiang Le You Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This work looks into the quantized impulsive consensus challenges of nonlinear leader-following multi-agent *** graph theory,principle of differential dynamics,logarithmic quantizer,matrix theory,and the Lyapunov function,among other things,are used to investigate the key discoveries on *** appropriate consensus protocols which contain quantized all agents' relative state information and quantized a part of agents' relative state *** the sake of reducing communication consumption and energy,we combine two control strategies and design two control protocols according to the characteristics of quantized control and impulsive *** a series of deductions,we find that under quantized impulsive control,multi-agent systems can achieve consensus as long as certain sufficient conditions are ***,the simulation example demonstrates the validity and correctness of this papers theoretical approach.

关键词： Multi-agent systems Leader-following Quantized impulsive consensus

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Distributed Nash equilibrium for pursuit-evasion game with one evader and multiple pursuers

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Science China(Information Sciences) 2025年

作者： Libing WANG Zhixin LIU Shuo YUAN Zhiqiang PU Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering Wayne State University Institute of Automation Chinese Academy of Sciences

This paper investigates the pursuit-evasion problem involving one evader and multiple pursuers with limited sensing capability, where the evader tries to maximize the distance with the pursuers, while the pursuers have different objectives based on whether they can receive the information of the evader. The subgroup of pursuers who can observe the evader(called leaders) tries to be close to the evader, and the other subgroup of pursuers(called followers) tries to synchronize with their neighbors. When the subgraph formed by all leaders is complete, sufficient conditions are given to guarantee that the pursuers capture the evader and the pursuit-evasion game composed of the evader and leaders reaches Nash equilibrium. Furthermore, for the incomplete subgraph case, the distributed observers are proposed to estimate the relative positions between the evader and all leaders. It is shown that the distributed control strategy based on the observers converges exponentially to the Nash equilibrium solution, and makes the pursuers capture the evader. Finally, simulation examples are provided to verify the effectiveness of the proposed strategies.

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