基于改进神经网络的车体直线同步电机源重构估计

IF 1.9 3区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Magnetics Pub Date : 2025-04-16 DOI:10.1109/TMAG.2025.3561684

Jiahui Zhang;Dan Zhang;Yinghong Wen;Jinbao Zhang

{"title":"基于改进神经网络的车体直线同步电机源重构估计","authors":"Jiahui Zhang;Dan Zhang;Yinghong Wen;Jinbao Zhang","doi":"10.1109/TMAG.2025.3561684","DOIUrl":null,"url":null,"abstract":"To quickly and accurately characterize the effects of linear synchronous motors (LSMs) in the electromagnetic suspension (EMS) maglev system on the surrounding electromagnetic environment, the field-source equivalent model for electromagnetic emission with obstacles in the near zone was built using cascade neural network that integrates convolutional neural networks (CNNs) and backpropagation neural networks (BPNNs). The influence of the train body on the electromagnetic emissions of linear motors is equated with the magnetic field shielding effectiveness. Results by the proposed model were validated by measurement and full-wave simulation results.","PeriodicalId":13405,"journal":{"name":"IEEE Transactions on Magnetics","volume":"61 6","pages":"1-10"},"PeriodicalIF":1.9000,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved Neural-Network-Based Source Reconstruction for Estimating the Emission of Linear Synchronous Motors in Vehicle Bodies\",\"authors\":\"Jiahui Zhang;Dan Zhang;Yinghong Wen;Jinbao Zhang\",\"doi\":\"10.1109/TMAG.2025.3561684\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To quickly and accurately characterize the effects of linear synchronous motors (LSMs) in the electromagnetic suspension (EMS) maglev system on the surrounding electromagnetic environment, the field-source equivalent model for electromagnetic emission with obstacles in the near zone was built using cascade neural network that integrates convolutional neural networks (CNNs) and backpropagation neural networks (BPNNs). The influence of the train body on the electromagnetic emissions of linear motors is equated with the magnetic field shielding effectiveness. Results by the proposed model were validated by measurement and full-wave simulation results.\",\"PeriodicalId\":13405,\"journal\":{\"name\":\"IEEE Transactions on Magnetics\",\"volume\":\"61 6\",\"pages\":\"1-10\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Magnetics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10966504/\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Magnetics","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10966504/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

摘要

为了快速准确地表征电磁悬浮（EMS）磁悬浮系统中直线同步电机（lsm）对周围电磁环境的影响，采用融合卷积神经网络（cnn）和反向传播神经网络（bpnn）的级联神经网络，建立了近区有障碍物时电磁发射的场源等效模型。车体对直线电机电磁发射的影响等同于磁场屏蔽效能。实验结果和全波仿真结果验证了模型的正确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Improved Neural-Network-Based Source Reconstruction for Estimating the Emission of Linear Synchronous Motors in Vehicle Bodies

To quickly and accurately characterize the effects of linear synchronous motors (LSMs) in the electromagnetic suspension (EMS) maglev system on the surrounding electromagnetic environment, the field-source equivalent model for electromagnetic emission with obstacles in the near zone was built using cascade neural network that integrates convolutional neural networks (CNNs) and backpropagation neural networks (BPNNs). The influence of the train body on the electromagnetic emissions of linear motors is equated with the magnetic field shielding effectiveness. Results by the proposed model were validated by measurement and full-wave simulation results.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Transactions on Magnetics 工程技术-工程：电子与电气

CiteScore

4.00

自引率

14.30%

发文量

565

审稿时长

4.1 months

期刊介绍： Science and technology related to the basic physics and engineering of magnetism, magnetic materials, applied magnetics, magnetic devices, and magnetic data storage. The IEEE Transactions on Magnetics publishes scholarly articles of archival value as well as tutorial expositions and critical reviews of classical subjects and topics of current interest.