Abstract
This paper proposes a new source reconstruction method (SRM) based on deep learning. The conventional SRM usually requires oversampled measurements data to ensure higher accuracy. Thus, conventional SRM numerical system is usually highly singular. A deep convolutional neural network (ConvNet) is proposed to reconstruct the equivalent sources of the target to overcome difficulty. The deep ConvNet allows us to employ less data samples. Besides, the ill-conditioned numerical system can be effectively avoided. Numerical examples are presented to demonstrate the feasibility and accuracy of the proposed method. Its performance is also compared with the traditional neural network and interpolation method. Moreover, we further expand the proposed method to measure the permittivity of dielectric scatterers.
Recommended Citation
H. M. Yao et al., "Source Reconstruction Method Based On Machine Learning Algorithms," 2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility, EMC Sapporo/APEMC 2019, pp. 774 - 777, article no. 8893747, The Institute of Engineering and Technology, Jun 2019.
The definitive version is available at https://doi.org/10.23919/EMCTokyo.2019.8893747
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Convolutional neural network; Machine learning; Source reconstruction method
International Standard Book Number (ISBN)
978-488552322-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 The Institute of Engineering and Technology, All rights reserved.
Publication Date
01 Jun 2019
Comments
National Natural Science Foundation of China, Grant FA2386-17- 1-0010