Yang, J;
Tong, KF;
(2021)
Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network.
Presented at: 2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC), Honolulu, HI, USA.
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Abstract
This work presents a surface wave antenna metallic pattern prediction from electric field in near-field by applying Bidirectional Gated Recurrent Unit neural network prediction model. The metallic pattern of the proposed antenna has been predicted by using Bi-GRU neural network model with prediction accuracy 100% at 34.5GHz. Different uniform mark-space-ratios (MSR) of the metallic pattern do not affect the metallic pattern prediction accuracy.
| Type: | Conference item (Presentation) |
|---|---|
| Title: | Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network |
| Event: | 2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC) |
| Location: | Honolulu, HI, USA |
| Dates: | 9-13 Aug 2021 |
| ISBN-13: | 9781665413886 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/APWC52648.2021.9539634 |
| Publisher version: | http://dx.doi.org/10.1109/APWC52648.2021.9539634 |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10139114 |
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