Application of Deep Dilated Convolutional Neural Network for Non-Flat Rough Surface
學年 113
學期 2
出版（發表）日期 2025-03-20
作品名稱 Application of Deep Dilated Convolutional Neural Network for Non-Flat Rough Surface
作品名稱（其他語言）
著者 Chien-Ching Chiu; Yang-Han Lee; Wei Chien; Po-Hsiang Chen; Eng Hock Lim
單位
出版者
著錄名稱、卷期、頁數 Electronics 14(6), 1236
摘要 In this paper, we propose a novel deep dilated convolutional neural network (DDCNN) architecture to reconstruct periodic rough surfaces, including their periodic length, dielectric constant, and shape. Historically, rough surface problems were addressed through optimization algorithms. However, these algorithms are computationally intensive, making the process very time-consuming. To resolve this issue, we provide measured scattered fields as training data for the DDCNN to reconstruct the periodic length, dielectric constant, and shape. The numerical results demonstrate that DDCNN can accurately reconstruct rough surface images under high noise levels. In addition, we also discuss the impacts of the periodic length and dielectric constant of the rough surface on the shape reconstruction. Notably, our method achieves excellent reconstruction results compared to DCNN even when the period and dielectric coefficient are unknown. Finally, it is worth mentioning that the trained network model completes the reconstruction process in less than one second, realizing efficient real-time imaging.
關鍵字 non-flat rough surface; deep dilated convolutional neural network; electromagnetic imaging; periodic surface; dilated convolution
語言 en
ISSN 2079-9292
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127753 )