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- W2115390035 abstract "In this paper the effectiveness of a procedure that allows the flaws characterization of pipes inspected by a long range guided waves is investigated. The method performs the extraction of correlation coefficients between the x, y, z components of the displacement of simulated guided waves reflected by defects on pipes. These features feed a neural network classifier which evaluates the dimensions of well defined geometry defects on the pipe under test. The results show lower error rates in the evaluation of both angular and axial extent of a defect." @default.
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- W2115390035 date "2007-10-01" @default.
- W2115390035 modified "2023-10-18" @default.
- W2115390035 title "3D-3 Classification of Defects for Guided Waves Inspected Pipes by a Neural Network Approach" @default.
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- W2115390035 doi "https://doi.org/10.1109/ultsym.2007.49" @default.
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