Application of Deep Dilated Convolutional Neural Network for Non-Flat Rough Surface
Abstract
:1. Introduction
- We present a DDCNN architecture designed for the reconstruction of periodic non-flattened surfaces. Notably, this approach is able to reconstruct the period, permittivity and shape of the rough surface simultaneously.
- Our numerical simulations demonstrate the robustness and precision of the proposed DDCNN architecture across varying noise conditions.
- The findings of this study highlight the pivotal role of AI in the reconstruction of surface coefficients. We delineate a hierarchy of reconstruction priorities during the training process: the primary focus is on achieving a surface period, followed by the secondary goal of estimating the dielectric constant, with the shape factor representing the final target. This prioritization arises from the varying sensitivities of each coefficient to the scattered field data, indicating an intricate interplay between these parameters in the reconstruction process.
2. Formula and Theory
3. Deep Dilated Convolutional Neural Network
4. Numerical Results
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Period | 0.04 | 0.06 | 0.083 | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Noise | DP | DEPS | DF | DP | DEPS | DF | DP | DEPS | DF | |
5% | 0.09% | 0.01% | 10.45% | 0.12% | 0.1% | 10.65% | 0.1% | 0.01% | 7.32% | |
10% | 0.1% | 0.01% | 10.94% | 0.26% | 0.13% | 13.09% | 0.1% | 0.02% | 7.90% | |
15% | 0.12% | 0.02% | 12.06% | 0.33% | 0.19% | 16.58% | 0.13% | 0.02% | 9.45% | |
20% | 0.14% | 0.03% | 15.93% | 0.41% | 0.26% | 23.28% | 0.15% | 0.03% | 10.34% |
Period | DCNN | DDCNN | |||||
---|---|---|---|---|---|---|---|
Noise | DP | DEPS | DF | DP | DEPS | DF | |
5% | 0.18% | 0.32% | 13.9% | 0.1% | 0.01% | 7.32% |
Period | 0.083 | |||
---|---|---|---|---|
Noise | DP | DEPS | DF | |
10% | 0.1% | 0.02% | 8.80% | |
15% | 0.15% | 0.03% | 10.45% | |
20% | 0.21% | 0.04% | 11.34% |
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Chiu, C.-C.; Lee, Y.-H.; Chien, W.; Chen, P.-H.; Lim, E.H. Application of Deep Dilated Convolutional Neural Network for Non-Flat Rough Surface. Electronics 2025, 14, 1236. https://doi.org/10.3390/electronics14061236
Chiu C-C, Lee Y-H, Chien W, Chen P-H, Lim EH. Application of Deep Dilated Convolutional Neural Network for Non-Flat Rough Surface. Electronics. 2025; 14(6):1236. https://doi.org/10.3390/electronics14061236
Chicago/Turabian StyleChiu, Chien-Ching, Yang-Han Lee, Wei Chien, Po-Hsiang Chen, and Eng Hock Lim. 2025. "Application of Deep Dilated Convolutional Neural Network for Non-Flat Rough Surface" Electronics 14, no. 6: 1236. https://doi.org/10.3390/electronics14061236
APA StyleChiu, C.-C., Lee, Y.-H., Chien, W., Chen, P.-H., & Lim, E. H. (2025). Application of Deep Dilated Convolutional Neural Network for Non-Flat Rough Surface. Electronics, 14(6), 1236. https://doi.org/10.3390/electronics14061236