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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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SC-LSTM Based Human Action Recognition

SC-LSTM Based Human Action Recognition

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Naixin Kang Pengfei Yi Qingshu Geng Jing Dong Rui Liu Ling Wang Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Human Action Recognition is one of the most applied research directions in the field of Computer Vision, which is widely used in human-computer interaction, Augmented Reality (AR) technology, security monitoring, and other scenarios. However, due to the complexity of human action gestures, existing Human Action Recognition methods have certain deficiencies in dealing with variable human gestures and action information, and the accuracy needs to be improved. To improve the accuracy, We propose a multi-dimensional network model based on SC-LSTM(Skip-Connection + LSTM). First, a Temporal Feature Extraction Module is designed based on SC-LSTM, and a Spatial Feature Extraction Module is designed based on CNN and Multi-Attention Mechanism to extract potential human action features from both temporal and spatial dimensions, respectively. Then, a separate SC-LSTM classification network is utilized to process these spatio-temporal features to obtain the final HAR results. The experimental results show that compared to other algorithms, the present model can more fully utilize the information in the temporal dimension, and thus performs better in terms of HAR accuracy.

关键词： Human computer interaction Computer vision Accuracy Image recognition Feature extraction Complexity theory Human activity recognition

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An Active Visual Perception Methodology for the Safety of Human-Robot Collaborations

An Active Visual Perception Methodology for the Safety of Hu...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Siyuan Cheng Pengfei Yi Mengmeng Chen Jing Dong Rui Liu Ling Wang Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In industrial settings with multiple robotic arms, ensuring human-robot interaction safety through visual feedback is essential. However, existing methods use multiple pre-set cameras to avoid occlusion, making implementation challenging in compact spaces. We propose an adaptive active visual perception method for safety in human-robot collaboration by equipping a single RGBD camera on an idle robotic arm. This method eliminates the need to add additional brackets, is low cost, and is suitable for compact environments compared to current methods. Firstly, to capture comprehensive and accurate global information as much as possible, an active visual perception strategy integrating static visual perception and proprioceptive pose optimization is designed; then, an optimization-based robot position and pose decision method is proposed by using the real-time robots state and obstacles state, which enables the camera at the end of the robot to acquire more effective views; finally, a dual-robot following strategy is proposed to ensure that it can work in tandem. The validity was proved by experimentation. The video are available at https://***/03lwb.

关键词： Visualization Service robots Robot vision systems Human-robot interaction Streaming media Cameras Manipulators

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ADAPTIVE BOUNDARY CONTROL VIBRATION SUPPRESSION OF FLEXIBLE MANIPULATOR BASED ON IMPROVED RBF NEURAL NETWORK

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UPB Scientific Bulletin, Series D: Mechanical Engineering 2024年第2期86卷 3-18页

作者： Zheng, Qingchun Wei, Zhiyong Zhu, Peihao Ma, Wenpeng Deng, Jieyong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China China School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Jiangxi Technical College of Manufacturing Jiangxi Nanchang330095 China

With lighter weight, lower power consumption, and higher flexibility, flexible manipulators have been widely used in industrial fields. However, it is easy to produce elastic deformation, resulting in vibration. In this paper, an improved radial basis function (RBF) neural network (NN) for adaptive boundary control is proposed. The partial differential equation (PDE) model of the flexible manipulator is established, and the asymptotic stability of the system is proved by the first Lyapunov method. Finally, simulation results show that the proposed control method can eliminate the low-amplitude angular fluctuation and the high-amplitude angular velocity fluctuation. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Flexible manipulators

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GP-Net: Global Perceptual Network with Adaptive Gradient Clipping for Emotional Image Classification 8

GP-Net: Global Perceptual Network with Adaptive Gradient Cli...

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8th International Conference on intelligent computing and Signal Processing, ICSP 2023

作者： Wang, Xiaohua Yang, Jie Hu, Min Wu, Xia Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350302455

Traditional image emotion recognition focuses only on the emotion information embedded in the subject or part of the image, while ignoring the global emotion information. In this paper, we propose a new network based on Vision Transformer: a global perception network, containing a main module and a background module. The main module enhances the weighting of objects by showing the interdependencies between modelled channels. The background module further searches for emotional information in the background. In addition, we employ multi-task learning and Adaptive Gradient Clipping to improve the network. Extensive experiments show that our proposed method outperforms the state-of-the-art methods for image sentiment analysis. © 2023 IEEE.

关键词： Image classification

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The positive mass theorem for asymptotically flat manifolds with isolated conical singularities

引用

Science China Mathematics 2024年

作者： Xianzhe Dai Yukai Sun Changliang Wang Department of Mathematics University of California Santa Barbara Key Laboratory of Pure and Applied Mathematics School of Mathematical SciencesPeking University School of Mathematical Sciences and Institute for Advanced Study Key Laboratory of Intelligent Computing and Applications (Ministry of Education)Tongji University

We prove the positive mass theorem for asymptotically flat(AF for short) manifolds with finitely many isolated conical singularities. We do not impose the spin condition. Instead, we use the conformal blow-up techniqu...

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Acoustic Word Embedding Model with Transformer Encoder and Multivariate Joint Loss

Acoustic Word Embedding Model with Transformer Encoder and M...

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intelligent computing and Wireless Optical Communications (ICWOC), International Conference on

作者： Yunyun Gao Qiang Zhang Lasheng Zhao Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

As a representation of speech information, acoustic word embedding can enable query-by-example keyword search with low-resource speech data. An acoustic word embedding model with Transformer encoder and multivariate joint loss is presented in this article. First of all, on the basis of BLSTM, the model can extract richer information by adding a Transformer encoder, which improves representation ability of the embedding vector. Then, the contrast loss is improved according to the multiple negative samples, and combined with the hinge loss with distance characteristics, the model generates a more compact vector representation and improves the identification of features. Finally, considering the unbalanced characteristics of samples, the anti-focal loss is introduced to form a multivariate joint loss together with the above two losses, so as to make the model performance better. The model has achieved certain results in word recognition tasks and has certain competitiveness compared with other acoustic word models.

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IABC: An iteratively automatic machine learning boosted hand-eye calibration method for laser displacement sensor

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Chinese Journal of Aeronautics 2023年第8期36卷 422-453页

作者： Helin LI Bin LIN Tianyi SUI Liang XU Chen ZHANG Shuai YAN Yang WANG Xinquan HAO Deyu LOU School of Mechanical Engineering Ministry of EducationTianjin UniversityTianjin 300350China International Institute for Innovative Design and Intelligent Manufacturing of Tianjin University Shaoxing 312000China Science and Technology of Advanced Functional Composite Laboratory Aerospace Research Institute of Materials and Processing TechnologyBeijing 100076China Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education Tianjin UniversityTianjin 300072China

An on-machine measuring(OMM)system with a laser displacement sensor(LDS)is designed for measuring free-form surfaces of hypersonic aircraft’s *** improve the measurement accuracy of the OMM system,a novel Iteratively Automatic machine learning Boosted hand-eye Calibration(IABC)method is *** the hand-eye relationship and LDS measurement errors can be calibrated in one calibration process without any hardware changes via ***,a new objective function is derived,containing analytical parameters of the handeye relationship and LDS ***,a hybrid calibration model composed of two kernels is proposed to solve the objective *** kernel is the analytical kernel designed for solving analytical *** kernel is the automatic machine learning(AutoML)kernel designed to model LDS *** two kernels are connected with stepwise iterations to find the best calibration *** with traditional methods,hand-eye experiments show that IABC reduces the calibration RMSE by about 50%.Verification experiments show that IABC reduces the measurement deviations by about 25%-50%and RMSEs within 40%.Even when the training data are obviously less than the test data,IABC performs *** demonstrate that IABC is more accurate than traditional hand-eye methods.

关键词： Hand-Eye Calibration Laser Displacement Sensor Machine learning Measurement errors On-Machine Measurement

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Lateral and Longitudinal Coordinated Control of intelligent Vehicle Based on High-Precision Dynamics Model under High-Speed Limit Condition

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on intelligent Vehicles 2024年 1-18页

作者： He, Zhicheng Gong, Liangke Zhou, Enlin Wei, Baolv Li, Eric Huang, Jin State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle the College of Mechanical and Transportation Engineering Hunan University Changsha China School of Computing Engineering & Digital Technologies Teesside University Middlesbrough U.K School of Vehicle and Mobility Tsinghua University Beijing China

This study focuses on improving the trajectory tracking control for intelligent vehicles in high-speed and large curvature limit conditions. To this end, a high-precision five-degree-of-freedom (5-DOF) dynamics model (HPM) that incorporates suspension characteristics is introduced. Furthermore, a coordinated lateral and longitudinal control system is developed. The lateral model predictive control (MPC) involves two crucial stages: initially, a desired trajectory with associated speed data is generated based on path curvature. Subsequently, using the high-precision 5-DOF dynamics model, an objective function is formulated to minimize the difference between the vehicle's current state and the desired state. This process determines the optimal front wheel steering angle, taking into account vehicle positional constraints and steering limitations. Additionally, a double proportional-integral-derivative (PID) controller for longitudinal control adjusts the throttle and brake pressure based on real-time position and speed data, ensuring integrated control over both lateral and longitudinal movements. The effectiveness of this approach is confirmed through real vehicle testing and simulation. Results show that the high-precision 5-DOF dynamics model markedly enhances the accuracy of vehicle response modeling, and the coordinated control system successfully executes precise trajectory tracking. In extreme scenarios of high-speed and large curvature, the enhanced model substantially improves trajectory accuracy and driving stability, thus promoting safe vehicle operation. IEEE

关键词： Trajectories

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Humanoid Robot Collaborative Lifting Integrating Executable Judgment

Humanoid Robot Collaborative Lifting Integrating Executable ...

引用

2022 IEEE International Symposium on Product Compliance Engineering - Asia, ISPCE-ASIA 2022

作者： Chang, Hua Yi, Pengfei Liu, Rui Dong, Jing Hou, Yaqing Zhou, Dongsheng The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

Humanoid robot collaborative lifting can be used in a variety of scenarios that require repetitive lifting tasks. Most existing studies of humanoid robot collaboration often assume that objects can always be lifted, which may result in damage to both the robot and the object if objects are too heavy to lift. To avoid such situations as much as possible, a collaborative lifting approach integrating executable judgment is proposed. First, a target search and localization method is constructed using monocular vision and marker points to identify the task object. Then, an executable judgment strategy is designed to determine whether the object is overweight or not according to robot force analysis. Finally, a multi-robot joint control model is proposed based on collaborative communication to perform collaborative tasks with different loads based on the judgment results. Experiments on two humanoid robots for different types and weights of targets show the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Anthropomorphic robots

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