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Electromagnetic Fields

Electromagnetic Fields

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.
However, the observation of...

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A Convolutional Neural Network with Multifrequency and Structural Similarity Loss Functions for Electromagnetic

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

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

Sensors (Basel, Switzerland)

|August 10, 2024

Summary

This summary is machine-generated.

Artificial intelligence enhances electromagnetic imaging of anisotropic objects using a novel convolutional neural network (CNN) approach. This method improves image accuracy and stability, outperforming single-frequency reconstructions.

Keywords:

anisotropic objects artificial intelligence back-propagation scheme convolutional neural network electromagnetic imaging loss function

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Area of Science:

Electromagnetic imaging
Artificial intelligence applications
Anisotropic object characterization

Background:

Electromagnetic imaging is crucial for detecting subsurface objects using magnetic anomaly sensing.
Current methods face challenges in accurately characterizing complex anisotropic materials.

Purpose of the Study:

To apply artificial intelligence (AI) to enhance electromagnetic imaging of anisotropic objects.
To improve the accuracy and stability of subsurface object reconstruction.

Main Methods:

Utilized multifrequency scattered fields and the backpropagation scheme (BPS) for initial dielectric constant calculation.
Employed a convolutional neural network (CNN) with adaptive moment estimation (ADAM) for refined image reconstruction.
Introduced an improved loss function combining structural similarity index measure (SSIM) and root mean square error (RMSE).

Main Results:

The enhanced CNN with the improved loss function significantly improved image quality.
Simulations considered transverse electric (TE) and transverse magnetic (TM) wave noise interference.
Multifrequency reconstructions demonstrated superior stability and precision compared to single-frequency methods.

Conclusions:

AI-driven electromagnetic imaging offers a powerful tool for characterizing anisotropic objects.
The proposed CNN approach with an optimized loss function enhances reconstruction fidelity.
Multifrequency analysis is key to achieving robust and precise subsurface imaging.