Application of Self-Attention Generative Adversarial Network for Electromagnetic Imaging in Half-Space
學年 112
學期 2
出版（發表）日期 2024-04-05
作品名稱 Application of Self-Attention Generative Adversarial Network for Electromagnetic Imaging in Half-Space
作品名稱（其他語言）
著者 Chien-Ching Chiu; Yang-Han Lee; Po-Hsiang Chen; Ying-Chen Shih; Jiang Hao
單位
出版者
著錄名稱、卷期、頁數 Sensors 24(7), 2322
摘要 In this paper, we introduce a novel artificial intelligence technique with an attention mechanism for half-space electromagnetic imaging. A dielectric object in half-space is illuminated by TM (transverse magnetic) waves. Since measurements can only be made in the upper space, the measurement angle will be limited. As a result, we apply a back-propagation scheme (BPS) to generate an initial guessed image from the measured scattered fields for scatterer buried in the lower half-space. This process can effectively reduce the high nonlinearity of the inverse scattering problem. We further input the guessed images into the generative adversarial network (GAN) and the self-attention generative adversarial network (SAGAN), respectively, to compare the reconstruction performance. Numerical results prove that both SAGAN and GAN can reconstruct dielectric objects and the MNIST dataset under same measurement conditions. Our analysis also reveals that SAGAN is able to reconstruct electromagnetic images more accurately and efficiently than GAN.
關鍵字 inverse scattering problem; self-attention; generative adversarial network; real-time imaging; back-propagation scheme
語言 en
ISSN 1424-8220
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者 Chien-Ching Chiu
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125884 )