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- W1973240135 abstract "This paper proposes a generalization for the solutions of an inversion method developed by the authors. The method is based upon an artificial neural network that simulates mapping between eddy current signals and crack profiles. One of the biggest advantages of the approach is that it can deal with conductive cracks, which is necessary to reconstruct natural cracks. However, it has one significant disadvantage: the reliability of reconstructed profiles was unknown. This paper also proposes a novel parameter that provides an index for assessment of the crack profile and overcomes this disadvantage. After the parameter is validated by reconstruction of simulated cracks, it is applied to reconstruction of natural cracks that occurred in steam generator tubes of a pressurized water reactor. It is shown that the parameter is applicable to not only simulated cracks but also real (natural) ones." @default.
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- W1973240135 date "2002-12-01" @default.
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- W1973240135 title "Generalized neural network approach to eddy current inversion for real cracks" @default.
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- W1973240135 doi "https://doi.org/10.1016/s0963-8695(02)00048-8" @default.
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