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Design of Double-Loop Current Transformer Based on Magnetic Field Incomplete Cancellation

CSEE Journal of Power and Energy Systems 2025年第2期11卷 772-782页

作者： Wenbo Li Xiaojun Tang Youshui Lu Ling Yan Yi Wu Mingzhe Rong Department of Measurement and Control Technology&Instrumentation School of Electrical EngineeringXi'an Jiaotong UniversityXi'an 710000China State Key Laboratory of Electrical Insulation&Power Equipment School of Electrical EngineeringXi'an Jiaotong UniversityXi'an 710000China

Electromagnetic current transformer is one of the important pieces of equipment used in AC power system measurement. Due to continuous improvement of current level to be measured in the power system, ordinary single-loop magnetic core structure cannot meet industrial needs of wide measuring range with high accuracy, while it even has a magnetic saturation problem when measured current is reaching the limit. Most existing methods expand measuring range and increase antisaturation ability of the magnetic core by either opening an air gap in the magnetic core or increasing number of turns of primary and secondary windings, but it will bring additional problems such as volume and heat dissipation of the transformer. To solve such issues, in this paper, we proposed a magnetic core structure of double-loop current transformer based on incomplete magnetic field cancellation principle. Calculation formula of air gap size and theoretical error are analyzed. We provide a theo-retical analysis of working characteristics, amplitude error and phase error throughout a three-dimensional simulation model of transformer. In addition, we evaluate performance through a series of comparison experiments. Results demonstrate our double-loop current transformer can maintain good linear relationship between primary and secondary current while ensuring measurement accuracy at 0.2 level. With the same volume, it has a wider range of rated current measurements and a certain anti-saturation ability.

关键词： Current difference coefficient double-loop core incomplete magneticfield cancellation

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Optimized Algorithm for Steel Surface Defect Detection Based on YOLOv8n-CSDE

Optimized Algorithm for Steel Surface Defect Detection Based...

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Sun, Tong Wang, Wei Fu, Zhijia School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781510691773

Surface defect detection remains a significant challenge in the industrial sector due to frequent inspection errors, false positives, and low detection accuracy. Traditional object detection algorithms often face issues such as missed detections, false detections, insufficient feature extraction capabilities, and inadequate feature fusion when applied to steel surface defect detection tasks. To address these challenges, this study proposes an improved surface defect detection method for industrial components, named YOLOv8n-CSDE, based on the YOLOv8n object detection framework. Firstly, the CGAttention is incorporated into the C2f module to enhance the model's semantic understanding and feature generation capabilities. Secondly, the DySample method is employed to replace the nearest-neighbor algorithm, reducing computational complexity and improving upsampling efficiency. Thirdly, the original Spatial Pyramid Pooling Fast module is upgraded to the SimSPPF module to enable the network to better emphasize the target's features. Finally, the CIoU loss function in YOLOv8n has been replaced with the EIoU loss function to accelerate model convergence and improve localization performance. Extensive experiments conducted on the NEU-DET dataset demonstrate that the enhanced model achieves an accuracy of 76.6%, representing an improvement of 2.8% over traditional models. The experimental results indicate that the proposed method achieves more accurate detection of steel surface defects compared to state-of-the-art object detection algorithms. © 2025 SPIE.

关键词： Surface defects

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Hyperspectral Image Cross Scene Classification Combined Domain Adaptation with XGBoost

Hyperspectral Image Cross Scene Classification Combined Doma...

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Liu, Chengyang Wu, Haibin Li, Jiarong Liu, Xinyu Yan, Siqi Wang, Aili Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781510691773

To address the issues of domain discrepancies and limited labeled samples in hyperspectral image classification of cross scene, this paper proposes an ensemble learning framework that integrates eXtreme Gradient Boosting (XGBoost) with confidence domain adaptation strategies to improve classification performance. First, the Houston dataset from different scenes is input separately into a deep embedding model. The hyperspectral images are embedded into a low-dimensional space, ensuring that similar spectra are represented more closely in this space, thereby capturing the intrinsic structure and distribution of the hyperspectral image dataset. Subsequently, these samples are mapped into a discriminator model, where domain adaptation techniques are employed to reduce differences in feature distributions across different scenes. Additionally, confidence-based learning is used to identify and remove noisy samples in the Houston dataset, thereby enhancing the alignment of feature distributions among various scenes. Finally, through ensemble learning, a weighted voting strategy is employed to combine multiple base classifiers into a strong classification model. This approach better captures the complex features within the data, thereby reducing the bias of individual classifiers and enhancing the overall generalization ability and robustness of the model. Experimental results on the Houston dataset demonstrate that the proposed method outperforms popular domain adaptation learning models and traditional methods in terms of classification accuracy and robustness, showcasing its potential and advantages in practical applications. This result confirms the effectiveness of combining XGBoost and confidence domain adaptation strategies in handling hyperspectral image classification. © 2025 SPIE.

关键词： Photointerpretation

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A Low-Cost Robotic Assembly Method for Large Planar Workpieces Using Laser Displacement Sensors and Monocular Vision

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第17期49卷 155-172页

作者： Cao, Xiaobing Xu, Yicen Yao, Yonghong Chen, Sheng School of Control Technology Wuxi Institute of Technology Wuxi Jiangsu214121 China School of Intelligent Equipment and Automotive Engineering Wuxi Institute of Commerce Wuxi Jiangsu214153 China School of Communication and Electronic Engineering East China Normal University Shanghai200062 China

To address the challenges associated with large planar workpieces, such as the side window glass of a train, this paper proposes a practical, intelligent robotic assembly method that utilizes laser displacement sensors (LDSs) and monocular vision. The laser point cloud data is fitted to the equation of the plane of the side window optimally, allowing for the calculation of its plane coefficients and unit normal vector (UNV) using the Lagrange multiplier method. The robot's end flange is adjusted to ensure the camera imaging plane is along with the window plane at a specified distance. Monocular vision is employed to capture the pose features of the upper right corner of the window, facilitating a compensation method for precise assembly. The experimental results show that the robot achieved a positioning accuracy of less than 0.5mm and an orientation accuracy within 1 degree, confirming the effectiveness of the proposed method. In the experiment, the maximum displacement deviation in the X direction is about ± 0.4mm, the maximum offset in the Y and Z directions is about ± 0.3mm, the maximum rotation deviation in the X axis is about 0.6 °, and the minimum rotation deviation in the Y axis is about 0.8 °. All deviations meet the requirements of ± 1 ° attitude correction and ± 0.5mm displacement accuracy, and have good reliability. This method solves the practical problem of assembling large planar workpieces with integrated LDS and monocular vision. This technology involves using laser point cloud data to fit the precise plane equation of the window, determining alignment parameters through Lagrange multiplier method, and adjusting the posture of the end flange of the robot. Monocular vision further assists in extracting positional features, achieving precise real-time posture correction and alignment. This system proposes an effective and low-cost automation that reduces manual intervention and alleviates typical problems of remote processing of large workpieces. © 20

关键词： Lagrange multipliers

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Spatial Coordination of Multiple Nonholonomic Agents With Sensory Connectivity Maintenance

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3425-3435页

作者： Chen, Xi Chen, Meimin Zhu, Lijun Chai, Li Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Shandong Huayu University of Technology School of Electrical Engineering Shandong253034 China School of Artificial Intelligence and Automation Wuhan430074 China Zhejiang University College of Control Science and Engineering Hangzhou310027 China

This article aims to propose a general control strategy for coordination of multiple nonholonomic agents in three dimensional space. For real-world applications, since the field sensor equipped on the mobile agents for local information detection and estimation has some limited detecting range, it is necessary to guarantee that the nearby agents must stay within this range of the onboard sensor. This is called sensory connectivity maintenance. A function termed as coordination function with sensory connectivity maintenance (CFSCM) is defined to describe the performances of the coordination as well as the sensory connectivity status between the agents. Then, a general control strategy is designed based on the proposed CFSCM for spatial coordination of multiple nonholonomic agents with sensory connectivity maintenance. Moreover, the applications of the proposed control strategy for formation with omnidirectional sensors and flocking with directional sensors are shown, respectively. Finally, some numerical examples are conducted to validate the theoretical analysis. © 2013 IEEE.

关键词： Maintenance Robot sensing systems Kinematics Vectors Electronic mail Aerospace electronics Three-dimensional displays System dynamics Robot kinematics Multi-agent systems

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DOA Estimation Based on N-shape Array

DOA Estimation Based on N-shape Array

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Han, Chuang Gao, Mengran Heilongjiang Province Laboratory of Laser Spectroscopy Technology Application Harbin University of Science and Technology Harbin China School of Measurement-Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781510691773

This paper combines the L-shaped array and two parallel linear arrays to propose an N-shape array configuration. For the N-shape array, the multiple signal classification (MUSIC) algorithm is utilized for DOA estimation under far-field conditions. When the signal source is in the far-field region, the signal arriving at the array elements can typically be represented as a plane wave. By utilizing the array manifold matrices of the L-shaped array and two parallel linear arrays, the array manifold matrix for the N-shape array is derived, which is then used for DOA estimation via the MUSIC algorithm. Simulation results indicate that the N-shape array can effectively achieve DOA estimation of signals in far-field conditions using the MUSIC algorithm. © 2025 SPIE.

关键词： Matrix algebra

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Research on Identification Method of Toroidal Propeller

Research on Identification Method of Toroidal Propeller

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2024 International Conference on measurement, communication, and Virtual Reality, MCVR 2024

作者： Han, Chuang Hu, Aiai Wu, Haibin Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin China School of Measurement - Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781510691773

With the continuous development of marine science and technology, propeller shapes are developing in a more complex direction. Among them, the toroidal propeller represents the latest research progress in propeller technology. This design features a ring-shaped geometry for the blades, which are similar to the catenary curve of a hanging chain. This propeller offers the advantages of higher efficiency and lower noise. In this paper, starting from the geometric shape analysis study of toroidal propeller, we introduce the geometric parameters such as wheelbase, outboard angle, lateral inclination angle among others, derive the mathematical formula of the coordinates of toroidal propeller in detail, simulate the modulation waveform and spectrogram of the toroidal propeller, and compare with those of the ordinary propeller, and according to the experimental data, the geometric shape of toroidal propeller can be recognized by using the proposed method. This lays a firm foundation for further research on the physical properties and scientific problems of toroidal propellers. © 2025 SPIE.

关键词： Geometry

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Deformation Mechanism in Central China Revealed by Regional Earthquake Tomography

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Seismological Research Letters 2025年第3期96卷 1561-1572页

作者： Hua, Yujin Zhang, Shuangxi Liu, Yu Liu, Li Wu, Tengfei Wei, Yu Li, Bin Department of Earth Sciences and Engineering Taiyuan University of Technology Taiyuan China Department of Geophysics School of Geodesy and Geomatics Wuhan University Wuhan China School of Geophysics and Measurement-Control Technology East China University of Technology Nanchang China College of Civil Engineering Taiyuan University of Technology Taiyuan China School of Civil Engineering and Architecture Hubei Polytechnic University Huangshi China China Huadian Corporation Ltd. Beijing China

The central China region (CCN) has experienced significant deformation and complex structural arrangements. We conducted research on the deformation pattern of the CCN through the 3D velocity structure of the crust and uppermost mantle obtained via body-wave tomography. This velocity structure strongly correlates with the region’s topographic features. The relationship between velocity distributions and topography indicates that compression in the east–west direction, which can lead to crustal and mantle shortening, may play a crucial role in shaping topography. The various structures between the Qinling mountain range in the west and the Tongbai–Dabie mountain range in the east highlight distinct geological processes occurring along the Qinling–Dabie orogenic belt. In addition, we propose that reservoir water has infiltrated the crystalline basement rock layer up to 15–20 km beneath the Three Gorges Reservoir. Furthermore, the Three Gorges Dam serves as a solid foundation to prevent reservoir water from seeping downstream underground. © Seismological Society of America.

关键词： Crystalline rocks

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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