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arXiv 2023年

作者： He, Yong Yu, Hongshan Yang, Zhengeng Sun, Wei Feng, Mingtao Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu Dist. Changsha410082 China The School of Computer Science and Technology Xidian University Xi’an710071 China The Department of Computer Science The University of Western Australia PerthWA6009 Australia

Local features and contextual dependencies are crucial for 3D point cloud analysis. Many works have been devoted to designing better local convolutional kernels that exploit the contextual dependencies. However, current point convolutions lack robustness to varying point cloud density. Moreover, contextual modeling is dominated by non-local or self-attention models which are computationally expensive. To solve these problems, we propose density adaptive convolution, coined DAConv. The key idea is to adaptively learn the convolutional weights from geometric connections obtained from the point density and position. To extract precise context dependencies with fewer computations, we propose an interactive attention module (IAM) that embeds spatial information into channel attention along different spatial directions. DAConv and IAM are integrated in a hierarchical network architecture to achieve local density and contextual direction-aware learning for point cloud analysis. Experiments show that DAConv is significantly more robust to point density compared to existing methods and extensive comparisons on challenging 3D point cloud datasets show that our network achieves state-of-the-art classification results of 93.6% on ModelNet40, competitive semantic segmentation results of 68.71% mIoU on S3DIS and part segmentation results of 86.7% mIoU on ShapeNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Fault Diagnosis Strategy for Few Shot Industrial Process Based on Data Augmentation and Depth Information Extraction

SSRN

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SSRN 2022年

作者： Tian, Ying Xiang, Xin Peng, Xin Zhong, Yin Zhang, Wei Department of control science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Data based intelligent fault diagnosis method is an important tool for ensuring the stability of industrial process. However, in the actual industrial process, due to the difficulty of feature extraction and the lack of labeled fault data, it is hard to form a fault diagnosis model with good performance. To address this problem, the Self-Attention embedded Generative Adversarial Networks combined with the Residual Network(SAGAN-ResNet) is proposed in this study. Firstly, to handle the lack of the fault data, the data enhancement method which consists of Self-Attention embedded generator and discriminator is adopted. Then, to extract the feature for better diagnosis performance, based on the augmented training dataset, Residual Network (ResNet) is introduced. Finally, the proposed method is compared with others, the results show that proposed method has advantages in the case of complex process fault diagnosis with few shot industrial data. © 2022, The Authors. All rights reserved.

关键词： Failure analysis

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Sensing With Random Communication Signals

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IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

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Goods Anomaly Detection Based on Multi-Branch Feature Extraction and Foreground Mining

Goods Anomaly Detection Based on Multi-Branch Feature Extrac...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Zhiqian Jia Wei Li Zuoyong Li Zhipeng Mu Lihui Lin Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Xiamen Air Traffic Management Station of CAAC Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science MinjiangUniversity Fuzhou China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Wuyi University Fujian China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

In recent years, significant advancements have been made in the field of anomaly detection. Expanding anomaly detection tasks to new scenarios has become a development avenue. Anomaly detection for the outer packaging of supermarket goods is a challenging task due to the diversity of goods’ appearances, which results in some detection methods suffering from limited sensitivity to anomalies and inadequate target mining. To address these issues, we propose a novel unsupervised anomaly detection method based on multi-branch feature extraction and foreground cosine structure mining strategy. Firstly, we use anomaly synthesis technique to enhance the model’s perception of anomalies and construct more precise decision boundaries in the feature space. Secondly, a multi-branch feature extractor extracts and integrates multi-level and multi-scale features, providing rich texture and semantic information to improve the model’s adaptability in real-world scenarios. Finally, the foreground cosine loss focuses on target mining while supplementing local structural information, further enhancing the model’s ability to detect subtle anomalies. To validate the effectiveness of the proposed method, extensive experiments were conducted on the GoodsAD dataset, sampled from supermarket scenarios. Experimental results show that this method achieves significant performance in image-level detection and pixel-level localization.

关键词： Location awareness Adaptation models Sensitivity Semantics Education Packaging Feature extraction Information technology Anomaly detection Biomedical imaging

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A Vision Based Hardware-Software Real-Time control System for the Autonomous Landing of an UAV 1

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International Conference on computer Vision and Graphics, ICCVG 2020

作者： Blachut, Krzysztof Szolc, Hubert Wasala, Mateusz Kryjak, Tomasz Gorgon, Marek Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper we present a vision based hardware-software control system enabling the autonomous landing of a multirotor unmanned aerial vehicle (UAV). It allows for the detection of a marked landing pad in real-time for a 1280 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption. © 2020, Springer Nature Switzerland AG.

关键词： System-on-chip

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Path Planning for Coverage Spraying of Plants in Outdoor Wheeled Mobile Robots

Path Planning for Coverage Spraying of Plants in Outdoor Whe...

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Robotics, control and Automation (ICRCA), International Conference on

作者： Weixin Zhang Zhengxin Yu Shixin Mao Xuefeng Zhou Jia Pan Haifei Zhu School of ElectRomechanical Engineering Guangdong University of Technology Guangzhou China Jiutian Innovation (Guangdong) Intelligent Technology Co Ltd Foshan China Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou China Department of Computer Science The University of Hong Kong Hong Kong China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

In contemporary agricultural practices and pest management, the deployment of outdoor wheeled mobile robots for precise and efficient plant spraying is crucial to optimize chemical application and mitigate environmental impact. This study introduces an integrated path planning framework designed to systematically tackle the challenges of obstacle avoidance, adherence to kinematic constraints, and optimization of spraying operations, while ensuring complete coverage of target plants. The framework comprises three key components: a local route generator utilizing principal component analysis to minimize movement required for spraying, thereby enhancing efficiency; a global path optimizer that effectively sequences and connects the generated routes to produce collision-free paths; and a path regulator that fine-tunes the resulting paths to comply with the robot's kinematic constraints. Extensive simulations across various outdoor scenarios demonstrate that the proposed framework achieves comprehensive coverage, reduces path lengths, and improves operational efficiency, presenting a viable solution for advanced robotic spraying applications.

关键词： Regulators Robot kinematics Spraying Kinematics Path planning Real-time systems Mobile robots Collision avoidance Optimization Principal component analysis

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Policy and Value Deep RL for Temporal Language-Agnostic Street Image Captioning 3

Policy and Value Deep RL for Temporal Language-Agnostic Stre...

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3rd IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2021

作者： Chan, Christopher Chun Ki Chen, Chih-Cheng Delaney, Steven Yang, Cheng-Fu Department of Information Management Chaoyang University of Technology Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung Taiwan Department of Computer Science Ryerson University Canada Department of Chemical and Materials Engineering National University of Kaohsiung Kaohsiung Taiwan

ISBN: (纸本)9781728193045

Dashcams and street cameras capture a huge amount of street and traffic video data. Temporal image captioning for such video data has been approached with an encoder-decoder framework and achieved substantial success in captioning accuracy. Most recently, policy and value deep reinforcement learning (PVRL) emerged as an outperformer over other decision-making frameworks for image captioning. In this paper, we design a framework that utilizes PVRL on an in-house dataset containing temporal images of East Asia streets as a step towards designing a language-agnostic street image captioning framework that is capable of captioning temporal images of any street regardless of location. For language-invariance, the framework includes cross-modal retrieval at the character level so that similar words in different languages but in the same word-embedding space are grouped together. Our results show that PVRL can be applied successfully to temporal video captured the streets and achieve natural semantic captions;preliminary studies on our dataset suggest that the framework is capable of use in multi-language scenarios. © 2021 ECBIOS 2021. All rights reserved.

关键词： Deep learning

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Short-term Traffic Flow Prediction by Graph Deep Learning with Spatial Temporal Modeling 2

Short-term Traffic Flow Prediction by Graph Deep Learning wi...

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2nd International Conference on Electronic Information technology and Smart Agriculture, ICEITSA 2022

作者： Yuan, Lilian Fang, Weilin Xiao, Hongbo Xiao, Jianhua Shi, Yuanquan Yang, Yujun School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Hunan Huaihua418000 China Wuling-Mountain Ecological Agriculture in Hunan Province Key Laboratory of Intelligent Control Technology for Huaihua Hunan418000 China

ISBN: (纸本)9781665464017

An extensive amount of research applications on traffic flow prediction models taking into account spatio-temporal modeling have been undertaken in recent years. Relative to traditional spatio-temporal modeling approaches, good application results have been achieved in predicting traffic flow. However, in real life, traffic flow proves to be time- and space-dependent, which poses difficulties for accurate modeling of traffic flow prediction. To address these problems, we propose the ST-GNN-GRU model, which mainly contains S-GNN and T-GRU modules. The model uses a combination of spatio-temporal modeling and deep learning, which can not only cope with the spatio-temporal modeling problem, but also improve the traffic flow prediction accuracy. The experiments are conducted using the METR-LA dataset, and the outcomes show that the proposed method performs more effectively than other baseline approaches. © 2022 IEEE.

关键词： Graph neural networks

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Structural and functional analysis of a potent human neutralizing antibody against enterovirus A71

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science China(Life sciences) 2022年第12期65卷 2517-2526页

作者： Zhe Chen Linlin Bao Bin Zhu Hua Fu Shuangli Zhu Tianjiao Ji Ying Xue Chuan Liu Xurong Wang Fengdi Li Qi Lv Feifei Qi Pin Yu Wei Deng Wenbo Xu Chuan Qin Hongrong Liu Qi Jin MOH Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases NHC Key Laboratory of Human Disease Comparative MedicineInstitute of Laboratory Animal ScienceChinese Academy of Medical Sciences and Comparative Medicine CenterPeking Union Medical CollegeBeijing 100021China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China National Institute for Viral Disease Control and Prevention China CDCBeijing 102206China Ministry of Education Key Laboratory of Protein Sciences Beijing Advanced Innovation Center for Structural BiologyTsinghua UniversityBeijing 100084China Key Laboratory for Matter Microstructure and Function of Hunan Province Key Laboratory of Low-dimensional Quantum Structures and Quantum ControlSchool of Physics and ElectronicsHunan Normal UniversityChangsha 410081China Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases Hangzhou 310058China

Enterovirus A71(EV-A71) causes major outbreaks of hand,foot,and mouth disease(HFMD) in many countries,most frequently affecting children,and a small proportion of cases may lead to ***,no vaccine is available in most endemic regions,and no licenced treatments for EV-A71 infection are ***,we characterize a human monoclonal antibody(Hu MAb),E1,by screening a Fab antibody phage library derived from patients who recovered from EV-A71 infection.E1 exhibits strong neutralizing activity against EV-A71 virus in *** cryo-electron microscopy(cryo-EM) structures of the EV-A71 virion in complex with E1 Fab fragments demonstrated that E1 recognized an epitope formed by residues in the BC and HI loops of *** a mouse model,E1 effectively protected against lethal EV-A71 challenge in both prophylactic and therapeutic *** particular,E1 significantly reduces virus titers and muscle damage.E1 might represent a potential adjunct to EV-A71 treatment.

关键词： enterovirus A71 antibody mouse models cryo-electron microscopy

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Point Gated Attention

Point Gated Attention

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2022 Chinese Automation Congress, CAC 2022

作者： Wang, Haibin Wang, Tian Zhang, Mengyi Zhang, Baochang Shi, Peng Snoussi, Hichem Beihang University School of Automation Science and Electrical Engineering Beijing China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understandingof Scoial Safety Nanjing China Institute of Artificial Intellgence Beihang University Beijing China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China Fujian Normal University School of Mathematics and Computer Science Fujian China University of Technology of Troyes Institute Charles Delaunay-LM2S Troyes France

ISBN: (纸本)9781665465335

Self-attention networks have achieved great success in traditional language processing, and have also made great progress in image classification and object detection. When analyzing the previous work, it is found that some point cloud information is lost in their feature extraction work, so we design a network to reduce the loss when extracting features. And through the feature extraction network - Gated Attention Unit instead of Transformer, to make the extraction of point cloud features more efficient. Based on Point Transformer, a new network structure named Point Gated Attention is used to classify point clouds. The overallacc on the dataset ModelNet40 can reach 91.4%, and the average accuracy can reach 93.9%. © 2022 IEEE.

关键词： Feature extraction

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