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第41届中国控制会议

作者： Qian Wan Huiming Wang Xia Sun Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing Institute of Engineering

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this article,a robust finite-time control strategy using continuous terminal sliding mode control(CTSMC) and sliding mode observer(SMO) is proposed to solve the problem of position tracking of permanent magnet synchronous motor(PMSM) servo *** control performance of such a system is always affected by internal and external *** this reason,a continuous sliding mode control(CSMC) method with fast convergence and strong anti-disturbance is firstly ***,considering that the disturbance of the actual system is generally in various forms and time-varying,to further improve the disturbance rejection ability of the system,an SMO based CTSMC method is proposed,where the SMO is devised to estimate time-varying disturbances and the estimations are employed to the position controller through feedforward compensation for disturbance *** present control method can not only improve the tracking rate,but also enhance the disturbance suppression ability of the *** with the PD control,CSMC,and CTSMC,simulation results demonstrate that the proposed control method can achieve the promised control performance for the studied system.

关键词： Continuous terminal sliding mode(CTSM) sliding mode observer(SMO) position control permanent magnet synchronous motor(PMSM)

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Optimized Hybrid control for Attitude Adjustment of Downhole Directional Drilling Tools 50

Optimized Hybrid Control for Attitude Adjustment of Downhole...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Xiao, Jing Wang, She Cai, Zhen School of Information Engineering Wuhan City Polytechnic University Wuhan430064 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (纸本)9781665464543

This study delves into the critical role of attitude control in downhole directional drilling tools, focusing on the complex coupling between inclination and azimuth in the motion model, particularly the time-varying nature of azimuth adjustments. By implementing separate control strategies for inclination and azimuth, the research integrates a comprehensive hybrid control system. It rigorously examines the stability of the closed-loop systems and validates the effectiveness and applicability of the hybrid control method through extensive simulations. The system demonstrates significant robustness and adaptability, enhancing drilling precision and efficiency. © 2024 IEEE.

关键词： State feedback

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optimization of Wide-Baseline Stereo Matching Based on Multi-view Geometry

Optimization of Wide-Baseline Stereo Matching Based on Multi...

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2024 China Automation Congress, CAC 2024

作者： Xu, Yinhui Shan, Shuo Han, Gongben Fang, Shixiong Zhang, Kanjian Wei, Haikun Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China Shenzhen Research Institute Southeast University Shenzhen China

ISBN: (纸本)9798350368604

Stereo vision plays a crucial role in scientific expeditions and facility maintenance. However, fixed-baseline stereo vision lacks accuracy for long-distance measurements, while wide-baseline stereo vision encounters occlusion issues at short distances. In this study, a wide-baseline stereo matching based on multi-view geometric constraints is proposed. Initially, the camera poses of multiple wide-baseline images are estimated, followed by image rectification and depth estimation. Subsequently, the disparity searching is constrained using geometric relationships among the views, resulting in more precise depth maps. Experiments were conducted to verify the superiority of the proposed method. The results indicate that the proposed method achieves an RMSE as low as 0.15 meters, even with invalid depth points. Compared to traditional stereo vision methods, accuracy is improved by approximately 35%, and robustness is significantly enhanced. In the aircraft runway scenario, overall depth map accuracy is higher, and occluded areas are significantly reduced. © 2024 IEEE.

关键词： Stereo vision

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Facial expression recognition under occlusion conditions based on multi-feature cross-attention

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Journal of Intelligent and Fuzzy systems 2024年第4期46卷 9841-9856页

作者： Guo, Sheng Tan, Mian Cai, Shan Zhang, Zaijun Liang, Yihui Feng, Hongxi Zou, Xue Wang, Lin School of Data Science and Information Engineering Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Mathematics and Statistics Qiannan Normal University for Nationalities Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Aerospace Tianma Electromechanical Technology Co LTD Zunyi China

Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. © 2024 – IOS Press.

关键词： Convolution

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LRDE:Long-Range Dependency and Salient Feature Enhancement for Co-SOD 41

LRDE:Long-Range Dependency and Salient Feature Enhancement f...

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第41届中国控制会议

作者： LongSheng Wei SiYuan Guo GuanYu Zong Jiu Huang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Different from other salient object detection(SOD) for the task of extracting salient obj ects in a image,collaborative saliency object detection(Co-SOD) targets a set of related image groups,and uses algorithms to find and locate the common collaborative salient objects in related image *** current SOD has achieved remarkable results,but most of the previous researches focus on how to obtain the consistency between images,while the contextual semantic information of images and the features of salient objects are less *** solve these problems,we propose a novel network to capture long-range dependencies on images and enhance(LRDE) the representation of salient ***,In LRDE,we capture the longrange dependencies by the Non-local module to obtain contextual semantic ***,in order to highlight the features of salient objects and enhance the representation of salient objects,we propose the Coordinate Attention module,which embeds the location-attention channel attention and encodes the feature maps as orientation-aware and position-aware attention maps,and It is applied complementary to the input feature map,thereby enhancing the representation of salient *** show that the method outperforms twelve improved methods on four publicly available datasets.

关键词： Co-SOD Long-Range Dependency Feature Enhancement Non-local Module Coordinate Attention Module

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Position Attention Network for Object Tracking 41

Position Attention Network for Object Tracking

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第41届中国控制会议

作者： Mingzhong Du Longsheng Wei Shiyu Liu School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In recent years,Siamese-based trackers have achieved excellent performance,most of them usually calculate the similarity of each position on the search region to the object through the cross-correlation layer for tracking obj *** solve the problem that the above method neglects the correspondence of the local information between the object and the search region and cannot adapt to the object deformation well,we propose a Siamese network-based tracker with position attention network(SiamPA).First,we use Siamese backbone network to extract template and search region ***,we adopt the boxguided object feature selection strategy to avoid similarity calculations for background *** addition,we introduce the position attention network instead of the cross-correlation layer to learn the part-level relationship between the object and the search region ***,the classification-regression sub-network is used to decode the similarity respond map obtained by the position attention network and predict the position of the *** contribution,one is to propose a box-guided method for refining object features,and the other is to introduce a position attention network for information *** on three challenging benchmarks including GOT-10 k,UAV123 and OTB-100 demonstrate that our SiamPA achieves excellent tracking performance with a real-time speed.

关键词： Object tracking Siamese network Box-guided Position attention network

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Tracking and Mapping Strategy for Indoor UAV Based on Entropy Theory:An ORB-SLAM3 Extension 41

Tracking and Mapping Strategy for Indoor UAV Based on Entrop...

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第41届中国控制会议

作者： Jian Xu Haoyu Qi Min Xu Yunge Zang Zhen Li Xi Zhang Xiangdong Liu School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

For indoor application,which is an entirely GPS-denied environment,visual simultaneous localization and mapping(SLAM) facilitates the real autonomy of unmanned aerial vehicle(UAV) but raises the challenging requirement on accurate localization with low computational *** address this difficulty,a stereo-camera based SLAM system is proposed by applying Entropy theory to *** an extension to ORB-SLAM3,the additional entropy decision module and map processor are specifically *** decision module can improve computing efficiency by deciding whether keyframes or extra optimization should be ***,the map processor is targeted at loading and maintaining the prior map whenever *** results in indoor laboratory environment show that the developed system can achieve the superior localization accuracy in more efficient computation manner with smaller size of mapping compared with ***,the map can be effectively expanded and corrected even when prior information is invalid,greatly increasing the robustness of SLAM system.

关键词： UAV stereo-camera ORB-SLAM3 entropy theory prior map

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Tea Bud Recognition Based on Machine Learning 41

Tea Bud Recognition Based on Machine Learning

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第41届中国控制会议

作者： Guangjun Wang Zhi Wang Yi Zhao Yangzheng Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Due to the low efficiency and high cost of manual tea picking,as well as the development and application of machine vision and image recognition technology,the mechanization and intelligence of tea picking will become a *** of all,this paper analyzed the advantages and disadvantages of several common target detection methods by using Matlab ***,considering cost,accuracy and real-time performance,the method of combining K-means clustering and image morphology processing is finally selected to extract tea ***,the method is reproduced on STM32 single chip ***,the effect was verified in the actual tea garden,which laid a foundation for the subsequent intelligent picking.

关键词： Machine learning K-means clustering STM32

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Non-fragile Joint State and Fault Estimation for complex Networks with Missing Measurements and Switching Topology 41

Non-fragile Joint State and Fault Estimation for Complex Net...

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第41届中国控制会议

作者： Pingping Gao Jun Hu Chaoqing Jia Zhihui Wu Department of Mathematics Harbin University of Science and Technology School of Automation Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a non-fragile joint state and fault estimation problem is investigated for time-varying complex networks with switching topology and randomly occurring nonlinearities under the missing *** phenomena of the switching topology,randomly occurring nonlinearities and missing measurements are described by three mutually independent Bernoulli random variables,where the uncertain occurrence probabilities are *** addition,the parameter perturbations of the estimator gain matrix are characterized by a set of zero-mean multiplicative noises.A novel time-varying estimation method is designed and an upper bound of estimation error covariance matrix is obtained by solving two recursive matrix ***,the desired estimator gain matrix is parameterized by minimizing the trace of the obtained upper ***,a sufficient condition is given to ensure the boundedness of the upper bound matrix by using the mathematical induction ***,a simulation study is given to verify the feasibility of the proposed estimation scheme.

关键词： Time-varying complex networks switching topology randomly occurring nonlinearities missing measurements uncertain occurrence probabilities joint estimation

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Decomposing Visual and Semantic Correlations for Both Fully Supervised and Few-Shot Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1658-1668页

作者： Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Most image classification methods are designed to either boost the classification accuracies with abundant supervision, or cope with the shortage of supervision information. This is often achieved by using the visual and semantic information of other sources. However, these methods use the visual and semantic information within the same class independently, leaving their intrinsic correlations unconsidered. Objects and disturbing components as well as noise exist on the same image. Besides, semantic representations also contain noisy information. To solve the problems mentioned above, we propose a novel method for both fully supervised image classification and few-shot image classification by decomposing visual and semantic correlations. We jointly explore the intrinsic correlations of visual and semantic information of images within the same class. For each class, we decompose its visual and semantic correlations using low-rank and sparse constraint respectively. The decomposed low-rank parts character the intrinsic correlations of images can be used in a linear transformation way. Using the decomposed parts of each class, Classification can be achieved by reconstruction error minimization. We conduct experiments on several datasets for both fully supervised image classification and few-shot image classification. Experimental results well show the effectiveness of the proposed method. © 2020 IEEE.

关键词： Semantics

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