Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets

Xiao Min Pan; Bo Yue Song; Si Lu Huang; Xin Qing Sheng

doi:10.1109/COMPEM.2019.8778911

Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets

Xiao Min Pan, Bo Yue Song, Si Lu Huang, Xin Qing Sheng

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

A general data sparse representation of electromagnetic characteristics of an arbitrarily-shaped target is developed by using the machine learning model. The data sparse representation of the electromagnetic response is firstly figured out by the skeletonization technique. The machine learning approach is then employed to construct a general and flexible model which can capture the electromagnetic characteristics of the target of interest. Numerical experiments are conducted to validate the performance of the model.

源语言	英语
主期刊名	2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings
出版商	Institute of Electrical and Electronics Engineers Inc.
ISBN（电子版）	9781538671115
DOI	https://doi.org/10.1109/COMPEM.2019.8778911
出版状态	已出版 - 3月 2019
活动	5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Shanghai, 中国期限: 20 3月 2019 → 22 3月 2019

出版系列

姓名	2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings

会议

会议	5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019
国家/地区	中国
市	Shanghai
时期	20/03/19 → 22/03/19

访问文件

10.1109/COMPEM.2019.8778911

其它文件与链接

链接到 Scopus 的出版物

引用此

Pan, X. M., Song, B. Y., Huang, S. L., & Sheng, X. Q. (2019). Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets. 在 2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings 文章 8778911 (2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/COMPEM.2019.8778911

Pan, Xiao Min ; Song, Bo Yue ; Huang, Si Lu 等. / Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets. 2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings).

@inproceedings{373eebf087bb4efbbc62ae1d812e90ed,

title = "Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets",

abstract = "A general data sparse representation of electromagnetic characteristics of an arbitrarily-shaped target is developed by using the machine learning model. The data sparse representation of the electromagnetic response is firstly figured out by the skeletonization technique. The machine learning approach is then employed to construct a general and flexible model which can capture the electromagnetic characteristics of the target of interest. Numerical experiments are conducted to validate the performance of the model.",

keywords = "Machine Learning, Method of Moments(MoM), artificial neural network(ANN), data sparse representation",

author = "Pan, {Xiao Min} and Song, {Bo Yue} and Huang, {Si Lu} and Sheng, {Xin Qing}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019 ; Conference date: 20-03-2019 Through 22-03-2019",

year = "2019",

month = mar,

doi = "10.1109/COMPEM.2019.8778911",

language = "English",

series = "2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings",

address = "United States",

}

Pan, XM, Song, BY, Huang, SL & Sheng, XQ 2019, Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets. 在 2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings., 8778911, 2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019, Shanghai, 中国, 20/03/19. https://doi.org/10.1109/COMPEM.2019.8778911

Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets. / Pan, Xiao Min; Song, Bo Yue; Huang, Si Lu 等.
2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8778911 (2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets

AU - Pan, Xiao Min

AU - Song, Bo Yue

AU - Huang, Si Lu

AU - Sheng, Xin Qing

PY - 2019/3

Y1 - 2019/3

N2 - A general data sparse representation of electromagnetic characteristics of an arbitrarily-shaped target is developed by using the machine learning model. The data sparse representation of the electromagnetic response is firstly figured out by the skeletonization technique. The machine learning approach is then employed to construct a general and flexible model which can capture the electromagnetic characteristics of the target of interest. Numerical experiments are conducted to validate the performance of the model.

AB - A general data sparse representation of electromagnetic characteristics of an arbitrarily-shaped target is developed by using the machine learning model. The data sparse representation of the electromagnetic response is firstly figured out by the skeletonization technique. The machine learning approach is then employed to construct a general and flexible model which can capture the electromagnetic characteristics of the target of interest. Numerical experiments are conducted to validate the performance of the model.

KW - Machine Learning

KW - Method of Moments(MoM)

KW - artificial neural network(ANN)

KW - data sparse representation

UR - http://www.scopus.com/inward/record.url?scp=85070534142&partnerID=8YFLogxK

U2 - 10.1109/COMPEM.2019.8778911

DO - 10.1109/COMPEM.2019.8778911

M3 - Conference contribution

AN - SCOPUS:85070534142

T3 - 2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings

BT - 2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019

Y2 - 20 March 2019 through 22 March 2019

ER -

Pan XM, Song BY, Huang SL, Sheng XQ. Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets. 在 2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8778911. (2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings). doi: 10.1109/COMPEM.2019.8778911

Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets

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