期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2024-11-27 |
| 作品名稱 | Machine learning-driven design of dual-band antennas using PGGAN and enhanced feature mapping |
| 作品名稱（其他語言） | |
| 著者 | Tuen, Lung-fai; Li, Ching-lieh; Chi, Yu-jen; Chiu, Chien-ching; Chen, Po-hsiang |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | IET Microwaves, Antennas & Propagation 18(12), p.1113-1138 |
| 摘要 | This paper presents a systematic antenna design methodology that integrates machine learning, leveraging the progressive growth technique of Progressive Growing of GANs (PGGAN) to generate images of various dual-band PIFA-like antenna structures. The process involves using data augmentation methods to generate 4180 antenna samples. In the latent space, the authors employ Latin Hypercube Sampling to maintain diversity and combine it with the Hough Transform to enhance the edge features of the antennas while providing labelling functionality. This labelling method strengthens the relationship between antenna frequency and wavelength characteristics. The paper clearly outlines the design process, starting from the simulation of two types of single-frequency PIFA-like antennas (2.45 and 5.2 GHz, respectively) to the completion of PGGAN's generation task, resulting in a novel dual-band Wi-Fi PIFA-like antenna structure. The measurement results of the dual-band antennas exhibit excellent consistency with the simulation results. |
| 關鍵字 | dual-band antenna;hough transform;Latin hypercube sampling;PGGAN;WGAN-GP |
| 語言 | en_US |
| ISSN | 1751-8733 |
| 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 | 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/987654321/126941 ) |