期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2025-03-01 |
| 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), p.1236 |
| 摘要 | settingsOrder Article Reprints Open AccessArticle Application of Deep Dilated Convolutional Neural Network for Non-Flat Rough Surface by Chien-Ching Chiu 1,*ORCID,Yang-Han Lee 1,Wei Chien 2,Po-Hsiang Chen 1ORCID andEng Hock Lim 3 1 Department of Electrical and Computer Engineering, Tamkang University, Tamsui 251301, Taiwan 2 Department of Electrical Engineering, Tatung University, Zhongshan 104327, Taiwan 3 Department of Electrical and Electronic, University Tunku Abdul Rahman, Kajang 43200, Malaysia * Author to whom correspondence should be addressed. Electronics 2025, 14(6), 1236; https://doi.org/10.3390/electronics14061236 Submission received: 27 February 2025 / Revised: 18 March 2025 / Accepted: 19 March 2025 / Published: 20 March 2025 (This article belongs to the Special Issue Advanced Machine Learning Technologies and Their Applications in Intelligent Imaging and Image Processing) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract 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_US |
| ISSN | |
| 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 | USA |
| 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 ) |