期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2024-08-01 |
| 作品名稱 | A Convolutional Neural Network with Multifrequency and Structural Similarity Loss Functions for Electromagnetic Imaging |
| 作品名稱（其他語言） | |
| 著者 | Chiu, Chien-ching; Lin, Che-yu; Chi, Yu-jen; Hsu, Hsiu-hui; Chen, Po-hsiang |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Sensors 24(15), 4994 |
| 摘要 | In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions. |
| 關鍵字 | electromagnetic imaging;artificial intelligence;anisotropic objects;back-propagation scheme;loss function;convolutional neural network |
| 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 | |
| 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/987654321/126940 ) |