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Multi-Channel Based on Attention Network for Infrared Small Target Detection

Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 414-427页

作者：张彦军王碧云蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Aerospace Control Technology Shanghai 201109China Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai 201109China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking ***,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning ***,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target ***,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target *** addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion ***,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

关键词： infrared image small target detection deep learning attention mechanism feature fusion

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3D measurement of scattering media based on self-supervised fringe domain transformation

3D measurement of scattering media based on self-supervised ...

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2024 International Conference on Optical and Photonic Engineering, icOPEN 2024

作者： Tan, Ji Niu, Haipeng Luo, Suofan Liu, Jia He, Zhaoshui School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong University of Technology Key Laboratory for IoT Intelligent Information Processing and System Integration Ministry of Education Guangzhou510006 China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

The scattering media measurement for structured light techniques remains challenging due to the subsurface scattering that destroys fringe patterns and introduces phase error. Recently, deep learning has been successfully applied to fringe analysis. Unfortunately, collecting a large scale of labeled data is difficult, especially in scattering media measurement scenarios. Inspired by image style transfer, we proposed a high-precision scattering medium measurement method based on self-supervised fringe domain transformation. Specifically, by treating degraded fringe and ideal fringe as two pattern styles, a cycle generation strategy is developed to achieve fringe enhancement by translating between two fringe domains. Two generators and two discriminators are used to form cycle-consistency constraints for self-supervised learning with unpaired dataset. Additionally, we combine numerical and physical constraints to jointly optimize network parameters, effectively suppressing phase-shift errors and improving phase quality. Since the proposed approach eliminates the need for label data, it exhibits great potential for applying deep learning to scattering medium measurement. Experiments verify that our method can obtain high-quality 3D morphology of translucent objects. © 2025 SPIE.

关键词： Velocity measurement

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Multi-source information fusion:Progress and future

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Chinese Journal of Aeronautics 2024年第7期37卷 24-58页

作者： Xinde LI Fir DUNKIN Jean DEZERT School of Automation Southeast UniversityNanjing 210008China Nanjing Center for Applied Mathematics Nanjing 211135China Key Laboratory of Measurement and Control of CSE Ministry of EducationNanjing 210096China Southeast University Shenzhen Research Institute Shenzhen 518063China Information Modeling and Processing Department ONERAPalaiseau 92320France

Multi-Source information Fusion(MSIF),as a comprehensive interdisciplinary field based on modern information technology,has gained significant research value and extensive application prospects in various domains,attracting high attention and interest from scholars,engineering experts,and practitioners *** achieving fruitful results in both theoretical and applied aspects over the past five decades,there remains a lack of comprehensive and systematic review articles that provide an overview of recent development in *** light of this,this paper aims to assist researchers and individuals interested in gaining a quick understanding of the relevant theoretical techniques and development trends in MSIF,which conducts a statistical analysis of academic reports and related application achievements in the field of MSIF over the past two decades,and provides a brief overview of the relevant theories,methodologies,and application domains,as well as key issues and challenges currently ***,an analysis and outlook on the future development directions of MSIF are presented.

关键词： Multi-sensor system information fusion Artificial intelligence Pattern recognition Human-machine integration

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Fixed-Time Consensus-Based Nash Equilibrium Seeking

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 267-269页

作者： Mengwei Sun Jian Liu Lu Ren Changyin Sun the Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China IEEE the School of Automation and the Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China the School of Artificial Intelligence and the Engineering Research Center of Autonomous Unmanned System Technology Ministry of EducationAnhui UniversityHefei 230601China

Dear Editor,This letter examines the fixed-time stability of the Nash equilibrium(NE)in non-cooperative *** propose a consensus-based NE seeking algorithm for situations where players do not have perfect information and communicate via a topology *** proposed algorithm can achieve NE in a fixed time that does not depend on initial conditions and can be adjusted in advance.

关键词： Fixed seeking letter

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Diagnosis of Occupational Pneumoconiosis Based on Adaptive Feature Fusion and Multi-class Loss Function

Diagnosis of Occupational Pneumoconiosis Based on Adaptive F...

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2024 China Automation Congress, CAC 2024

作者： Zhao, Qian Guo, Yinan Gong, Dunwei China University of Mining and Technology Xuzhou China China University of Mining and Technology Beijing China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Qingdao University of Science and Technology Qingdao China

ISBN: (纸本)9798350368604

Pneumoconiosis, as the most prevalent and severe occupational disease in China, is categorized into four stages based on the area of small shadows in lungs. Previous diagnostic methods have limited accuracy for identifying different stages due to complex and diverse lesions of pneumoconiosis, as well as the difficulty of collecting rich X-ray images of patients. To address the issues, a novel diagnostic method based on adaptive feature fusion and multi-class loss function is proposed. First, the prototype network is introduced to establish a diagnostic model under the insufficient images, with the purpose of distinguishing the disease stage by the distance between the features of various samples. Following that, the multi-channel and multi-level feature fusion modules are proposed, respectively. These modules incorporate a gate-based adaptive selection mechanism to efficiently integrate the most important features from different channels and levels. Finally, the multi-class loss function is designed to train the diagnostic model of pneumoconiosis. The experimental results on a self-built dataset of chest X-ray radiographs demonstrate that the proposed method outperforms ten comparative methods in terms of comprehensive performance. © 2024 IEEE.

关键词： X ray radiography

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Cross-Modality Guided Multi-Scale Feature Representation Based Image Compression for Industrial Scene Understanding 22

Cross-Modality Guided Multi-Scale Feature Representation Bas...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Tu, Jingzheng Yan, Yan Lu, Ying Chen, Cailian Zhou, Shiru China Academy of Aerospace Science and Innovation Beijing100088 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9798331527471

Industrial surveillance videos capture subsequent video frames and deliver them to processing servers for video analytics. In such a case, image compression is a promising technology in industrial scene understanding due to its reduced transmission burden. Existing works focus on establishing macroscopic visual feature maps that contain compact image information. In this article, a cross-modality guided multi-scale feature representation based image compression method is proposed for understanding industrial scenes. Firstly, an image compressing framework with multi-grained representation (MGRIC) is proposed for industrial scene understanding, which builds image patch, structure, semantic, and signal levels feature maps. Secondly, an encoder-decoder architecture is designed for image compression with diverse granularities. Thirdly, an interaction policy between different levels is developed to ensure the subsequent level’s decoding. Experiments on the CUB-200-2011 dataset demonstrate that the proposed MGRIC method achieves competitive performance, which verifies its effectiveness. © 2024 IEEE.

关键词： Decoding

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Improved control Strategies for Totem-Pole PFC With True Full Range ZVS Operation

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IEEE Transactions on Industrial Electronics 2023年第3期70卷 2419-2430页

作者： Chen, Jie Tai, Weiyu Xun, Benxin Gong, Chunying Chen, Jiawei Nanjing University of Aeronautics and Astronautics Center for More-Electric-Aircraft Power System Ministry of Industry and Information Technology College of Automation Engineering Nanjing211106 China Chongqing University Key Laboratory of Complex System Safety and Control Ministry of Education College of Automation Chongqing400044 China

Zero voltage switching (ZVS) of power semiconductor devices is an effective way to improve the efficiency and power density of the power electronic converters with extremely high switching frequency. This article aims to analyze and realize the true full-range ZVS operation of a totem-pole power factor correction circuit. The available ZVS control strategies are investigated, and the conditions for the realization of full-range ZVS operation are analyzed. It is revealed that the existed control strategies did not consider the influence of the dead-time, resulting in the failure of ZVS operation in some input voltage ranges. To address this issue, two feasible retrofitted control strategies, namely the turn-off current optimization control and the adaptive dead-time control, are proposed. The merits and drawbacks of the two control strategies are carefully analyzed and compared. It is found that the adaptive dead-time control can not only achieve the true full range ZVS but also significantly reduce the loss of the freewheeling diodes. The design consideration of key parameters is discussed. Finally, the feasibility of the proposed control and the correctness of the theoretical analyses are verified by simulation and experiment. © 1982-2012 IEEE.

关键词： Zero voltage switching

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ACBL:Attentive CNN-BiLSTM Model For Trajectory Prediction 43

ACBL:Attentive CNN-BiLSTM Model For Trajectory Prediction

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43rd Chinese control Conference, CCC 2024

作者： Yang, Gan Wang, Guangwei Li, Yichen Yu, Wenbin Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Tianjin Navigation Instruments Research Institute Tianjin300131 China

ISBN: (纸本)9789887581581

Predicting the future trajectory of a target becomes particularly challenging while dealing with occlusion conditions, where current information is unavailable, and historical data becomes the only basis for prediction. In this paper, we propose a trajectory prediction model that utilizes scaled dot product attention mechanism, a convolution neural network, and a bidirectional long short term memory network. The scaled dot product attention mechanism gauges the impact of historical sequences, while the convolution neural network extracts intricate feature information from these sequences. Subsequently, these data is fed into the bidirectional long short term memory network to forecast the target's navigation path. Experimental results highlight that the Attentive CNN-BiLSTM (ACBL) network which we proposed has lower root mean square error (RMSE) by 23 % on average than current methods. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolutional neural networks

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Multi-Branch Spatial-Temporal Attention Graph Convolution Network for Skeleton-based Action Recognition 41

Multi-Branch Spatial-Temporal Attention Graph Convolution Ne...

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第41届中国控制会议

作者： Daoshuai Wang Dewei Li Yaonan Guan Gang Wang Haibin Shao Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Skeleton-based human action reco gnition has attracted considerable research interest due to its robustness to dynamic environments and complex *** based on graph neural networks have achieved great success in this ***,most of these graph neural network based methods adopt a single-branch or two-stream structure with limited input *** to the separation of space and time,many methods cannot pay attention to the critical information of human skeleton sequence,and multi-scale spatial-temporal dependent attention modeling becomes the key to *** paper proposes a novel multi-branch Spatio-temporal attention graph convolutional neural network(MB-STAGCN) to recognize human actions from skeleton *** proposed model adopts a multi-branch structure,fuses three-stream features,and then inputs them into the backbone network to extract features from multiple perspectives to a greater ***,we design a new temporal convolutional block for multi-scale extraction of information in the backbone *** also propose an attention block named Spatio-temporal Concat Attention(STCatAtt) to capture critical *** on benchmark datasets show remarkable performance for human action reco gnition,demonstrating the effectiveness of our method.

关键词： Skeleton-based human action recognition MB-STAGCN STCatAtt

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Learning Knowledge Evolution with Time Duration from Finer-Time-Granularity Temporal Knowledge Graph

Learning Knowledge Evolution with Time Duration from Finer-T...

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2023 China Automation Congress, CAC 2023

作者： Du, Shaochong Chen, Kang Yang, Shijie Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation Shanghai China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350303759

How to represent the temporal information corresponding to each fact in temporal knowledge graphs (TKGs) effectively is always challenging. Most existing representation learning methods usually map the timelines of knowledges into multiple points and ignore the underlying continuous time property and periodic characteristics behind those dense timestamps in finer-time granularity temporal knowledge graphs. A novel Knowledge Evolution with Time Duration (KETD) model is proposed for knowledge representation of temporal knowledge graphs with finer-time granularities. It represents the entity embedding as a time-varying nonlinear function to find the specific continuous time property from dense timestamps. To the best of our knowledge, it is the first attempt to learn embeddings of time durations and combines them into embeddings of entities and relations to predict potential periodic facts. It also uses the temporal point process to capture the impact of historical facts on the current fact. The experiments on self-built finer-time granularity TKG of Satellite-to-Earth Communication (STEC) and two public datasets have demonstrated the superiority of KETD compared to some baseline approaches. © 2023 IEEE.

关键词： Knowledge graph

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