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Electromagnetic Fields01:30

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Electric fields generated by static charges, often referred to as electrostatic fields, are characteristically different from electric fields created by time-varying magnetic fields. While the former is a conservative field, implying that no net work is done on a test charge if it goes around in a complete loop in the field, the latter is, by definition, not a conservative field; net work is done, and it is proportional to the rate of change of magnetic flux.
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一个具有多频率和结构相似性损失函数的卷积神经网络用于电磁成像.

Chien-Ching Chiu1, Che-Yu Lin1, Yu-Jen Chi1

  • 1Department of Electrical and Computer and Engineering, Tamkang University, New Taipei City 251301, Taiwan.

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概括

人工智能增强了使用新型卷积神经网络 (CNN) 方法对异构物体的电磁成像. 这种方法提高了图像的准确性和稳定性,优于单频重建.

关键词:
异型物体是异型物体的物体.人工智能的人工智能是人工智能.反向繁殖计划可以实现.卷积神经网络是一种卷积神经网络.电磁成像技术的使用功能损失功能损失的功能.

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科学领域:

  • 电磁成像技术 电磁成像技术
  • 人工智能应用的人工智能应用.
  • 不同类型的物体的特征描述.

背景情况:

  • 电磁成像对于使用磁异常传感检测地下物体至关重要.
  • 当前的方法在准确地描述复杂的异性质材料方面面临着挑战.

研究的目的:

  • 应用人工智能 (AI) 来增强异构物体的电磁成像.
  • 为了提高地下物体重建的准确性和稳定性.

主要方法:

  • 使用多频散射场和反向传播方案 (BPS) 进行初始介电常数计算.
  • 采用一个卷积神经网络 (CNN) 与自适应时刻估计 (ADAM) 进行精细的图像重建.
  • 引入了一个改进的损失函数,将结构相似度指数 (SSIM) 和根平均平方误差 (RMSE) 结合起来.

主要成果:

  • 增强的CNN与改进的损失功能显著改善了图像质量.
  • 模拟被认为是横向电 (TE) 和横向磁 (TM) 波噪声干扰.
  • 与单频方法相比,多频重建显示出更高的稳定性和精度.

结论:

  • 人工智能驱动的电磁成像为特征异性物体提供了强大的工具.
  • 建议的CNN方法与优化的损失函数增强了重建保真度.
  • 多频分析是实现强大的和精确的地下成像的关键.