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17th International Conference on intelligent robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Optimization design of welding parameters of key components of car body based on finite element 5

Optimization design of welding parameters of key components ...

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2024 5th International Conference on Advanced Materials and intelligent Manufacturing, ICAMIM 2024

作者： Li, Lina Fa, Ping Sun, Hongchang He, Chuan Qiao, Zhiliang Department of Automobile Engineering Tianjin Transportation Technical College Tianjin China Huaiyuan County Vocational and Technical School Anhui Province Bengbu City China School of Mechanical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Intelligent Robot Technology and Application Enterprises Tianjin China

The characteristics of the welding heat source, the morphology of the molten pool, the formation mechanism of the weld, and the amount of heat input have an important and direct influence on the welding quality and performance of the joint. At present, the empirical curve that depends on the experimental results or the calculation formula of statistics cannot comprehensively evaluate the impact of welding on the mechanical properties of the whole structure. Therefore, this research is based on the finite element analysis technique of Simufact Welding software and analyzes the evolution of the temperature field, stress field, and the deformation of the residual stress in the welding process, and provides a powerful tool and a lot of support for welding technology. The aim of the study is to develop an accurate numerical simulation method for the weld temperature field and a dynamic simulation analysis system for the weld stress field and deformation. 2025 Published under licence by IOP Publishing Ltd.

关键词： Residual stresses

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Point-voxel dual transformer for LiDAR 3D object detection

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Optoelectronics Letters 2025年

作者： Tong Jigang Yang Fanhang Yang Sen Du Shengzhi Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot La-boratory， Tianjin University of Technology， 391 Binshui West Road Department of Electrical Engineering， Tshwane University of Technology

Deep learning for point clouds faces the challenge of their inherent unordered nature， which makes traditional CNN-like methods not directly applicable. However， due to the inherent permutation invariance， transformers provide solutions to unordered points problems faced in LiDAR-based object detection. In this paper， a two-stage LiDAR 3D object detection framework is presented， namely Point-Voxel Dual Transformer (PV-DT3D)， which is a transformer-based method. In the proposed PV-DT3D， point-voxel fusion features are used for proposal refinement. Specifically， in the PV-DT3D， keypoints are sampled from entire point cloud scene and used to encode representative scene features via a proposal-aware voxel set abstraction module. Subsequently， following the generation of proposals by the region proposal networks (RPN)， the internal encoded keypoints are fed into dual transformer encoder-decoder architecture. In 3D object detection， the proposed PV-DT3D is the first to take advantage of both pointwise transformer and channel-wise architecture to capture contextual information from the perspective of spatial and channel dimensions. Experiments conducted on the highly competitive KITTI 3D car detection leaderboard show that， the PV-DT3D achieves superior detection accuracy among state-of-the-art point-voxel-based methods.

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Aerodynamic trajectory simulation system for composite sounding equipment 24

Aerodynamic trajectory simulation system for composite sound...

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Proceedings of the 2024 4th International Conference on Computational Modeling, Simulation and Data Analysis

作者： Chun Hu Jinyang Li Dezhi Zheng Peng Peng MIIT Key Laboratory of Complex-field Intelligent Exploration Beijing Institute of Technology Beijing China Chongqing Caeri Robot Technology Co Chongqing China

ISBN: (纸本)9798400711831

The initial exploration of aerodynamic models in sounding rocket flight dynamics is undertaken in this study. This includes a comprehensive modeling and analysis of the probe-carrying parachute system deployed during descent, as well as an investigation into the aerodynamic model of unmanned aerial vehicles carried by airships. Using OSGEarth and QT, a visualization simulation platform is established, integrating high-definition digital Earth imagery and constructing foundational backgrounds. This effort encompasses the creation of simulation animations and basic feature designs, along with the establishment of real-time communication interfaces between platforms. Additionally, a user-friendly human-machine interface is implemented, incorporating several practical functions to enhance usability and improve human-machine interaction experiences.

关键词： Aerodynamic model

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Multi Scale SAR Image Target Detection Algorithm Based on RT-DETR

Multi Scale SAR Image Target Detection Algorithm Based on RT...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Wenhao Zhao Gengsheng Zheng School of Computer Science and Engineering School of Artificial Intelligence Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

To address low detection accuracy in SAR images due to complex backgrounds, large scale variations, and small targets, an improved algorithm SRA-DETR based on RT-DETR is proposed. It uses repBlock on four backbone branches to reduce parameters and enhance speed, incorporates a self-attention vision transformer for feature similarity extraction, and employs SE-CBAM for small target detection to suppress background interference. Adaptive path downsampling improves feature retention and background filtering compared to nearest neighbor methods. SRA-DETR decreases parameters by 40% and computational complexity by 20%, achieving aP50 of 96.6% and increasing mAP from 77.9% to 80.1% over the original RT-DETR.

关键词： Accuracy Soft sensors Object detection Interference Nearest neighbor methods Feature extraction Transformers Radar polarimetry Remote sensing Testing

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Super-resolution reconstruction of remote sensing images based on multi-scale reparameterized pyramids

Super-resolution reconstruction of remote sensing images bas...

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IEEE Information technology and Mechatronics Engineering Conference (ITOEC)

作者： RuiNan Huang GengSheng Zheng Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In order to solve the problems of loss of structural details and insufficient attention to edge information in the existing lightweight remote sensing super-resolution reconstruction methods, a multi-scale reparameterized pyramid network (MRPN) method for remote sensing image super-resolution reconstruction based on multi-scale reparameterized pyramids was proposed. The model is based on the deep residual network structure, and multiple cascading multi-scale reparameterized pyramid modules are designed in the network for feature extraction. In the module, a multi-scale separation convolution and fusion module is designed to help the network refine image information, and the unglobal and global feature expressions are enhanced, and the link-enhanced spatial attention module is used to improve the attention to high-frequency feature information. Finally, the jumping connection method is used to fuse multi-level features to achieve effective spatial information fusion. The model is verified by 2x, 3x and 4x super-resolution reconstruction on the NWPU-RESISC45 dataset and the AID dataset, and the experimental results show that the peak signal-to-noise ratio and structural similarity are improved, and the method has good performance in improving image quality and preserving details.

关键词： PSNR Convolution Superresolution Semantics Reconstruction algorithms Feature extraction Visual effects Remote sensing Image reconstruction Residual neural networks

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Two-Stage Hand-Eye Calibration based on Variance Minimization Principle

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Ding, Tao Tian, Dazhuang Wu, Hao Zhu, Dahu Li, Wenlog Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components School of Automotive Engineering Wuhan430070 China Wuhan University of Technology Hubei Digital Manufacturing Key Laboratory School of Mechanical and Electronic Engineering Wuhan430070 China Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Provincial Engineering Research Center of Robot and Intelligent Manufacturing Wuhan430070 China Huazhong University of Science and Technology State Key Laboratory of Intelligent Manufacturing Equipment and Technology Wuhan430074 China

Hand-eye calibration in industrial scenarios greatly determines the efficiency and effectiveness of 3D vision-guided robotic tasks. Most of the existing hand-eye calibration methods, however, rely on collecting large-scale point cloud samples to guarantee high measurement accuracy, which is time-consuming and particularly influenced by measurement defects such as local missing, noise, and uneven density. To overcome this challenging problem, we present a novel two-stage hand-eye calibration method based on the variance minimization principle. In the first stage, a robust function weighted variance minimization (RFWVM) registration algorithm is proposed for fine registration of defective point clouds, aiming at solving the coarse hand-eye calibration matrix with few-shot point clouds. In the second stage, an objective function of the calibration error correction algorithm is established via the point-to-plane distance variance minimization, which is intended to suppress the influence of measurement defects. Through these two stages, the globally optimal hand-eye matrix is solved by iteratively correcting the coarse hand-eye calibration matrix. A series of experiments validate the universality, accuracy, and robustness of the proposed method. © 1963-2012 IEEE.

关键词： Error correction

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GDPS: A general distillation architecture for end-to-end person search

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Journal of Visual Communication and Image Representation 2025年 110卷

作者： Fu, Shichang Lu, Tao Wang, Jiaming Gu, Yu Cai, Jiayi Jiang, Kui Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China Faculty of Computing Harbin Institute of Technology Harbin China

Existing knowledge distillation methods for person search tasks handle detection and re-identification (re-id) tasks separately, which may lead to feature conflicts between the two subtasks. On the one hand, by distilling only the detection task, the network will focus more on the common features of pedestrians, which may affect the performance of re-id. On the other hand, by distilling only the re-id task, the network will be more inclined to focus on the personality characteristics of pedestrians, which may harm the detection performance. To solve this problem, we propose a novel distillation method for person search tasks, treating person search as a single task and distilling different tasks in a unified framework, which is called General Distillation for Person Search (GDPS). Specifically, we optimize the general features of detection and re-id by distilling feature-based knowledge, aiming for accurate localization of individuals. In addition, we focus on the re-id task and perform relationship-based and response-based knowledge distillation to obtain more discriminative person features. Finally, we integrate feature-based, relation-based and response-based knowledge into a general framework to achieve simultaneous distillation of two sub-tasks, which can be readily applied to various end-to-end person search methods. Extensive experiments demonstrate the effectiveness of GDPS across different one-step person search methods. Specifically, AlginPS with ResNet-50 achieves 94.1% in mAP with GDPS on the CUHK-SYSU dataset, which surpasses the baseline 93.1% by 1.0%, and is even better than the ResNet-50 DCN-based teacher model with 94.0% mAP. © 2025 Elsevier Inc.

关键词： Knowledge acquisition

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MorNet: Asymmetric UNet-like Network with Morphological Opening and Closing for Image Segmentation 17th

MorNet: Asymmetric UNet-like Network with Morphological Open...

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17th International Conference on intelligent robotics and Applications, ICIRA 2024

作者： Li, Meng Yun, Juntong Jiang, Du Tao, Bo Li, Gongfa Liu, Rong Liu, Ying Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China School of Medicine Wuhan University of Science and Technology No. 1 Huangjia Lake University Town Wuhan430065 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

Image segmentation plays a crucial role in target recognition as it provides an essential prerequisite for identifying targets. However, UNet cannot acquire comprehensive information about the image, which limits its ability to effectively segment small and discrete objects. A new network structure, called MorNet, is proposed. It applies dilation and erosion operations in morphological opening and closing for connected and discrete region segmentation. MorNet effectively integrates global segmentation images, emphasizing small and discrete area segmentation and enhancing the segmentation of large continuous areas. This method enhances the understanding of the location of organs and diseased sites, enabling accurate segmentation. A side-out (SO) monitoring mechanism is introduced into the decoder to provide monitoring information for the original image and the predicted results. Compared with current UNet-like networks, our proposed method achieved the highest Dice scores (96.4635%, 94.0514%, 94.3444%) and PPV scores (94.2226%, 90.2361%, 91.5582%) for liver tumor, brain tumor, and liver segmentation tasks, respectively. The experimental results show that MorNet successfully combines the segmentation of connected and discrete regions. MorNet locates organs and lesions more accurately than current state-of-the-art medical image segmentation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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GaitFormer: Two-Stream Transformer Gait Recognition Using Wearable IMU Sensors in the Context of Industry 5.0

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IEEE Sensors Journal 2025年第11期25卷 19947-19956页

作者： Lu, Zhangli Zhou, Huiying Wang, Longqiang Kong, Depeng Lyu, Honghao Wu, Haiteng Chen, Bing Chen, Fuguo Dong, Na Yang, Geng Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Hangzhou310058 China Innovation Institute Co. Ltd China Hangzhou Shenhao Technology Key Laboratory of Intelligent Robot for Operation and Maintenance of Zhejiang province Hangzhou China

The rising attention to human-centricity, a core value of Industry 5.0, makes researchers propose various methods to place humans in the center of production, such as the Human Digital Twin (HDT) and gait identity recognition. However, previous HDT applications in the Industry 5.0 era have less consideration of identity recognition, the vital way to link physical signals to personal archives in cyber systems. Herein GaitFormer, a Two-Stream Transformer gait recognition method is proposed for application with the wearable inertial measurement unit (IMU) motion capture system. GaitFormer consists of Temporal and Channel Modules enclosing Gaussian Range Encoding, multi-head Auto-Correlation mechanism, and multi-scale as convolutional neural network (CNN) layers. It outperforms state-of-the-art architectures with extensive experiments over publicly available IMU-based datasets respectively: whuGait and OU-ISIR. Then the study intends to combine the motion capture system and gait identity recognition algorithm to propose an application of personal monitoring in the Human-Cyber-Physical-System (HCPS), which uses GaitFormer to recognize the identity of the user with motion capture system and show their corresponding customized HDT. This work may contribute to making a connection between gait identity recognition and HDT, constructing personal working profiles in the environment of Industry 5.0. © 2001-2012 IEEE.

关键词： Convolutional neural networks

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