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Continuous Wrist Angle Estimation Under Different Resistance Based on Dynamic EMG Decomposition

IEEE Transactions on Biomedical engineering 2025年

作者： Yang, Xinhao Xu, Baoguo Gao, Zelin Ren, Shipeng Li, Huijun Song, Aiguo Southeast University State Key Laboratory of Digital Medical Engineering the Jiangsu Key Laboratory of Robot Perception and Control the School of Instrument Science and Engineering Nanjing210096 China

Estimating wrist movements through neural drives is crucial in human-machine interface (HMI). However, studies on wrist movements mostly focused on isometric contractions, while research on dynamic EMG decomposition during non-stationary movements is notably scarce. Moreover, the impact of different resistance on the motor unit (MU) decomposition and wrist angle estimation remains unexplored. To address these gaps, this paper proposed a novel framework to decode neural drives from EMG signals during dynamic wrist movements. Specifically, the EMG signals were divided into short segments firstly. Next, progressive FastICA peel-off (PFP) algorithm was utilized to decompose each EMG segment into motor unit spike trains (MUST). Then, a linear window function was applied to track the motor units (MU) to obtain complete MUSTs. Three resistance levels were investigated during wrist flexion and extension: 20%, 40%, and 60% maximum voluntary contraction (MVC). Multiple linear regression (LR) and convolutional neural network (CNN) were used to estimate wrist angles within a range of ± 20° based on neural drives. Results showed the proposed framework could effectively identify MUs at these three resistance levels, with an average global pulse-to-noise ratio (PNR) above 20 dB. The determination coefficients of LR model were 0.92 ± 0.06, 0.91 ± 0.07, and 0.85 ± 0.13 at the three resistance levels, respectively, while those of CNN were 0.88 ± 0.10, 0.88 ± 0.11, and 0.81 ± 0.17. This study demonstrates it is feasible to estimate wrist angles based on decomposed neural drives at different resistance levels, and has significant implications for HMI development. © 1964-2012 IEEE.

关键词： Multiple linear regression

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Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency

Multi-Expert Adversarial Attack Detection in Person Re-ident...

引用

International Conference on Computer Vision (ICCV)

作者： Xueping Wang Shasha Li Min Liu Yaonan Wang Amit K. Roy-Chowdhury College of Electrical and Information Engineering Hunan University China National Engineering Laboratory for Robot Visual Perception and Control Technology China University of California

ISBN: (纸本)9781665428132

The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually in-conspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a fundamental requirement for robust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach to achieve this goal by checking context inconsistency, which is suitable for any DNN-based ReID systems. Specifically, three kinds of context inconsistencies caused by adversarial attacks are employed to learn a detector for distinguishing the perturbed examples, i.e., a) the embedding distances between a perturbed query person image and its top-K retrievals are generally larger than those between a benign query image and its top-K retrievals, b) the embedding distances among the top-K retrievals of a perturbed query image are larger than those of a benign query image, c) the top-K retrievals of a benign query image obtained with multiple expert ReID models tend to be consistent, which is not preserved when attacks are present. Extensive experiments on the Market1501 and DukeMTMC-ReID datasets show that, as the first adversarial attack detection approach for ReID, MEAAD effectively detects various adversarial attacks and achieves high ROC-AUC (over 97.5%).

关键词： Deep learning Computer vision Perturbation methods Neural networks Detectors Feature extraction Context modeling

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LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments

arXiv

引用

arXiv 2023年

作者： Wang, Ze Yang, Kailun Shi, Hao Zhang, Yufan Xu, Zhijie Gao, Fei Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

In this paper, we propose LF-PGVIO, a Visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV. To achieve this, we propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. The geodesic segment is sliced into multiple straight-line segments based on the radian and descriptors are extracted and recombined. Descriptor matching establishes the constraint relationship between 3D line segments in multiple frames. In our VIO system, line feature residual is also extended to support large-FoV cameras. Extensive evaluations on public datasets demonstrate the superior accuracy and robustness of LF-PGVIO compared to state-of-the-art methods. The source code will be made publicly available at https://***/flysoaryun/LF-PGVIO. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

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Nano-Micro Letters 2023年第12期15卷 161-187页

作者： Jin Li Naiteng Wu Jian Zhang Hong‑Hui Wu Kunming Pan Yingxue Wang Guilong Liu Xianming Liu Zhenpeng Yao Qiaobao Zhang College of Chemistry and Chemical Engineering and Henan Key Laboratory of Function‑Oriented Porous MaterialsLuoyang Normal UniversityLuoyang 471934People’s Republic of China New Energy Technology Engineering Lab of Jiangsu Province College of ScienceNanjing University of Posts and Telecommunications(NUPT)Nanjing 210023People’s Republic of China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083People’s Republic of China Department of Chemistry University of Nebraska-LincolnLincolnNE 8588USA Henan Key Laboratory of High‑Temperature Structural and Functional Materials National Joint Engineering Research Center for Abrasion Control and Molding of Metal MaterialsHenan University of Science and TechnologyLuoyang 471003People’s Republic of China National Engineering Laboratory for Risk Perception and Prevention Beijing 100041People’s Republic of China Center of Hydrogen Science Shanghai Jiao Tong UniversityShanghai 200000People’s Republic of China State Key Laboratory of Metal Matrix Composites School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200000People’s Republic of China State Key Laboratory of Physical Chemistry of Solid Surfaces College of MaterialsXiamen UniversityXiamen 361005People’s Republic of China

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing ***,the conventional"trial and error"method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and ***,the advancement of machine learning brings new opportunities for electrocatalysts discovery and *** analyzing experimental and theoretical data,machine learning can effectively predict their hydrogen evolution reaction(HER)*** review summarizes recent developments in machine learning for low-dimensional electrocatalysts,including zero-dimension nanoparticles and nanoclusters,one-dimensional nanotubes and nanowires,two-dimensional nanosheets,as well as other *** particular,the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are ***,the future directions and perspectives for machine learning in electrocatalysis are discussed,emphasizing the potential for machine learning to accelerate electrocatalyst discovery,optimize their performance,and provide new insights into electrocatalytic ***,this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

关键词： Machine learning Hydrogen evolution reaction Low-dimensional electrocatalyst Descriptor Algorithm

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DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Clouds

arXiv

引用

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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Decentralized Trajectory Planning for Formation Flight in Unknown and Dense Environments

Decentralized Trajectory Planning for Formation Flight in Un...

引用

IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Jianxin Zeng Yaonan Wang Zhiqiang Miao Wei He Hesheng Wang Department of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Changsha China School of Intelligence Science and Technology University of Science and Technology Beiing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

For aerial swarms, formation flight has been applied in various scenes. However, most existing works do not consider balancing the conflicting requirements among keeping formation, keeping the smoothness of trajectories, and obstacle avoidance within the limited time. To address this issue, we propose a decentralized trajectory planning framework for formation flight in unknown and dense environments. To ensure that feasible trajectories can be found within the limited time, the formation optimization problem is decoupled into formation affine transformation and iterative trajectory generation. Firstly, the optimization problem based on affine transformation is designed to obtain the optimal affine transformation sequence, which provides the formation reference of trajectory optimization. Secondly, the iterative optimization framework of trajectory planning is designed, which balances the conflicting requirements of formation, smooth flight, and obstacle avoidance. Besides, to escape the local minima caused by non-convex dense environments, the method of topological path planning is designed to provide distinctive initial solutions for trajectory optimization. Finally, the proposed methods are proven to be effective through the simulations and real-world experiments.

关键词： Trajectory planning Planning Iterative methods Collision avoidance Trajectory optimization Intelligent robots

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Representing Domain-Mixing Optical Degradation for Real-World Computational Aberration Correction via Vector Quantization

arXiv

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arXiv 2024年

作者： Jiang, Qi Yi, Zhonghua Gao, Shaohua Gao, Yao Qian, Xiaolong Shi, Hao Sun, Lei Niu, JinXing Wang, Kaiwei Yang, Kailun Bai, Jian State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou310027 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Intelligent Optics & Photonics Research Center Jiaxing Research Institute Zhejiang University Jiaxing314031 China School of Mechanical Engineering North China University of Water Resources and Electric Power Zhenzhou450045 China

Relying on paired synthetic data, existing learning-based Computational Aberration Correction (CAC) methods are confronted with the intricate and multifaceted synthetic-to-real domain gap, which leads to suboptimal performance in real-world applications. In this paper, in contrast to improving the simulation pipeline, we deliver a novel insight into real-world CAC from the perspective of Unsupervised Domain Adaptation (UDA). By incorporating readily accessible unpaired real-world data into training, we formalize the Domain Adaptive CAC (DACAC) task, and then introduce a comprehensive Real-world aberrated images (Realab) dataset to benchmark it. The setup task presents a formidable challenge due to the intricacy of understanding the target optical degradation domain. To this intent, we propose a novel Quantized Domain-Mixing Representation (QDMR) framework as a potent solution to the issue. Centering around representing and quantizing the optical degradation which is consistent across different images, QDMR adapts the CAC model to the target domain from three key aspects: (1) reconstructing aberrated images of both domains by a VQGAN to learn a Domain-Mixing Codebook (DMC) characterizing the optical degradation;(2) modulating the deep features in CAC model with DMC to transfer the target domain knowledge;and (3) leveraging the trained VQGAN to generate pseudo target aberrated images from the source ones for convincing target domain supervision. Extensive experiments on both synthetic and real-world benchmarks reveal that the models with QDMR consistently surpass the competitive methods in mitigating the synthetic-to-real gap, which produces visually pleasant real-world CAC results with fewer artifacts. Codes and datasets are made publicly available at https://***/zju-jiangqi/QDMR. Copyright © 2024, The Authors. All rights reserved.

关键词： Vector quantization

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FocusFlow: Boosting Key-Points Optical Flow Estimation for Autonomous Driving

arXiv

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arXiv 2023年

作者： Yi, Zhonghua Shi, Hao Yang, Kailun Jiang, Qi Ye, Yaozu Wang, Ze Ni, Huajian Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Company Ltd. Shanghai201210 China

Key-point-based scene understanding is fundamental for autonomous driving applications. At the same time, optical flow plays an important role in many vision tasks. However, due to the implicit bias of equal attention on all points, classic data-driven optical flow estimation methods yield less satisfactory performance on key points, limiting their implementations in key-point-critical safety-relevant scenarios. To address these issues, we introduce a points-based modeling method that requires the model to learn key-point-related priors explicitly. Based on the modeling method, we present FocusFlow, a framework consisting of 1) a mix loss function combined with a classic photometric loss function and our proposed Conditional Point control Loss (CPCL) function for diverse point-wise supervision;2) a conditioned controlling model which substitutes the conventional feature encoder by our proposed Condition control Encoder (CCE). CCE incorporates a Frame Feature Encoder (FFE) that extracts features from frames, a Condition Feature Encoder (CFE) that learns to control the feature extraction behavior of FFE from input masks containing information of key points, and fusion modules that transfer the controlling information between FFE and CFE. Our FocusFlow framework shows outstanding performance with up to +44.5% precision improvement on various key points such as ORB, SIFT, and even learning-based SiLK, along with exceptional scalability for most existing data-driven optical flow methods like PWC-Net, RAFT, and FlowFormer. Notably, FocusFlow yields competitive or superior performances rivaling the original models on the whole frame. The source code will be available at https://***/ZhonghuaYi/FocusFlow_official. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Exploring Event-based Human Pose Estimation with 3D Event Representations

arXiv

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arXiv 2023年

作者： Yin, Xiaoting Shi, Hao Chen, Jiaan Wang, Ze Ye, Yaozu Yang, Kailun Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics Hunan University Changsha410012 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Company Ltd. Shanghai201210 China

Human pose estimation is a fundamental and appealing task in computer vision. Although traditional cameras are commonly applied, their reliability decreases in scenarios under high dynamic range or heavy motion blur, where event cameras offer a robust solution. Predominant event-based methods accumulate events into frames, ignoring the asynchronous and high temporal resolution that is crucial for distinguishing distinct actions. To address this issue and to unlock the 3D potential of event information, we introduce two 3D event representations: the Rasterized Event Point Cloud (RasEPC) and the Decoupled Event Voxel (DEV). The RasEPC aggregates events within concise temporal slices at identical positions, preserving their 3D attributes along with statistical information, thereby significantly reducing memory and computational demands. Meanwhile, the DEV representation discretizes events into voxels and projects them across three orthogonal planes, utilizing decoupled event attention to retrieve 3D cues from the 2D planes. Furthermore, we develop and release EV-3DPW, a synthetic event-based dataset crafted to facilitate training and quantitative analysis in outdoor scenes. Our methods are tested on the DHP19 public dataset, MMHPSD dataset, and our EV-3DPW dataset, with further qualitative validation via a derived driving scene dataset EV–JAAD and an outdoor collection vehicle. Our code and dataset have been made publicly available at https://***/MasterHow/EventPointPose. Copyright © 2023, The Authors. All rights reserved.

关键词： 3D modeling

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Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency

arXiv

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arXiv 2021年

作者： Wang, Xueping Li, Shasha Liu, Min Wang, Yaonan Roy-Chowdhury, Amit K. College of Electrical and Information Engineering Hunan University China National Engineering Laboratory for Robot Visual Perception and Control Technology China University of California Riverside United States

The success of deep neural networks (DNNs) has promoted the widespread applications of person reidentification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually inconspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a fundamental requirement for robust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach to achieve this goal by checking context inconsistency, which is suitable for any DNN-based ReID systems. Specifically, three kinds of context inconsistencies caused by adversarial attacks are employed to learn a detector for distinguishing the perturbed examples, i.e., a) the embedding distances between a perturbed query person image and its top-K retrievals are generally larger than those between a benign query image and its top-K retrievals, b) the embedding distances among the top-K retrievals of a perturbed query image are larger than those of a benign query image, c) the top-K retrievals of a benign query image obtained with multiple expert ReID models tend to be consistent, which is not preserved when attacks are present. Extensive experiments on the Market1501 and DukeMTMC-ReID datasets show that, as the first adversarial attack detection approach for ReID, MEAAD effectively detects various adversarial attacks and achieves high ROC-AUC (over 97.5%). Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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