应用自我注意力生成对抗网络用于半空间中的电磁成像
Chien-Ching Chiu1, Yang-Han Lee1, Po-Hsiang Chen1
1Department of Electrical and Computer Engineering, Tamkang University, New Taipei City 251301, Taiwan.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究引入了一种用于半空间电磁成像的新型人工智能方法. 与传统的生成对抗网络 (GAN) 相比,自我注意力生成对抗网络 (SAGAN) 在重建介电物体方面表现出更高的准确性和效率.
科学领域:
- 电磁成像技术 电磁成像技术
- 人工智能的人工智能是人工智能.
- 信号处理 信号处理
背景情况:
- 电磁学的反射散射问题非常非线性,具有挑战性.
- 在半空间成像中,有限的测量角度加剧了重建困难.
- 现有的方法在埋藏物体的准确性和效率方面扎.
研究的目的:
- 开发和评估一种新的AI技术,用于改进半空间电磁成像.
- 为了比较自我注意力生成对抗网络 (SAGAN) 与生成对抗网络 (GAN) 的性能,用于图像重建.
- 解决逆分散问题的非线性和有限数据的挑战.
主要方法:
- 应用反向传播方案 (BPS) 来从分散的领域生成初始图像.
- 使用生成对抗网络 (GAN) 和自我注意力生成对抗网络 (SAGAN) 来进行图像重建.
- 在相同的测量条件下测试介电物体和MNIST数据集的重建性能.
主要成果:
- 萨甘和GAN都成功地重建了介电物体和MNIST数据集.
- 与GAN相比,SAGAN在重建电磁图像方面表现出更高的准确性.
- 萨甘实现了比GAN更高效的重建性能.
结论:
- 拟议的AI技术,特别是SAGAN,在半空间电磁成像方面取得了重大进展.
- 萨甘有效地减轻了与反向散射问题固有的非线性.
- 这种方法为绘制埋藏物体的图像提供了更准确,更有效的解决方案.
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