Abstract
In this paper we propose to use the “Multi-Frequency Excitation and Spectrogram Method” (MFES) and dynamic neural network models to reconstruct flaws profiles in planar conducting specimens. Two kinds of neural models are proposed: a forward model, which could be utilized as a fast solver in an inversion iterative algorithm, and an inverse model, which enable us to directly obtain a flaw profile within a few hundreds milliseconds. Dynamic feed-forward networks (with moving window) were applied in both cases.
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