Convolutional Neural Network-Based Electromagnetic Imaging of Uniaxial Objects in a Half-Space
學年 113
學期 2
出版（發表）日期 2025-03-01
作品名稱 Convolutional Neural Network-Based Electromagnetic Imaging of Uniaxial Objects in a Half-Space
作品名稱（其他語言）
著者 [2] Chien-Ching Chiu, Jen-Shiun Chiang, Po-Hsiang Chen, Hao Jiang
單位
出版者
著錄名稱、卷期、頁數 MDPI Sensors, 25(6)
摘要 In this paper, we adopt artificial intelligence (AI) technology for the electromagnetic imaging of uniaxial objects buried in a half-space environment. The limited measurement angle inherent to half-space configurations significantly increases the difficulty of data collection. This paper discusses the simultaneous emission of Transverse Magnetic (TM) and Transverse Electric (TE) electromagnetic waves to illuminate a uniaxial object embedded in a half-space. The dominant current scheme (DCS) and the backpropagation scheme (BPS) are subsequently employed to compute the initial permittivity distribution, which is then used as a dataset for training Convolutional Neural Networks (CNNs). The numerical results compare the reconstruction capabilities of both methods under identical conditions, demonstrating that the DCS exhibits superior generalization and noise immunity compared to the BPS. These findings confirm the effectiveness of both schemes in reconstructing the dielectric constant distribution of uniaxial objects buried in a half-space.
關鍵字 artificial intelligence, electromagnetic imaging, uniaxial objects, half-space, convolutional neural network, dominant current scheme, backpropagation scheme
語言 en
ISSN
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Chien-Ching Chiu
審稿制度 0
國別 GBR
公開徵稿
出版型式 ,電子版
SDGS 優質教育