期刊論文
| 學年 | 112 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2024-06-27 |
| 作品名稱 | Electromagnetic Imaging of Uniaxial Objects by Two-Step Neural Network |
| 作品名稱（其他語言） | |
| 著者 | Wei Chien;Chien-Ching Chiu;Po-Hsiang Chen;Hung-Yu Wu;Eng Hock Lim |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Applied Sciences 14(13)，p.5624 |
| 摘要 | The integration of electromagnetic imaging technology with the Internet of Things plays an important role in fields as diverse as healthcare, geophysics, and industrial diagnostics. This paper presents a novel two-step neural network architecture to solve the electromagnetic imaging for uniaxial objects which can be used in the Internet of Things. We incident TM and TE waves to unknown objects and receive the scattered fields. In order to reduce the training difficulty, we first input the gathered scattered field information into a deep convolutional neural network (DCNN) to obtain the preliminary guess. In the second step, we feed the guessed image into the convolutional neural network (CNN) to reconstruct high-resolution images. Our numerical results demonstrate the real-time imaging capability of our proposed two-step method in reconstructing high-contrast scatterers. |
| 關鍵字 | inverse scattering problems; two-step neural network method; deep convolutional neural network; convolutional neural network; electromagnetic imaging |
| 語言 | en_US |
| ISSN | |
| 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 | 否 |
| 國別 | TWN |
| 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/987654321/126942 ) |