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- W2912136996 abstract "In this work, the real-time non-destructive testing and evaluation (NDT/NDE) of faulty conductive tubes from eddy current (EC) measurements is addressed and solved in a computationally efficient way by means of an innovative learning-by-examples (LBE) methodology. More specifically, the estimation of the descriptors of a defect embedded within the cylindrical structure under test (SUT) is yielded by combining a non-linear feature extraction technique with an adaptive sampling strategy able to uniformly explore the arising feature space. Predictions are then performed during the on-line testing phase by means of a support vector regression (SVR). Representative results from a numerical/experimental validation are reported to assess the effectiveness of the proposed approach also in comparison with competitive state-of-the-art approaches." @default.
- W2912136996 created "2019-02-21" @default.
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- W2912136996 date "2019-02-02" @default.
- W2912136996 modified "2023-10-13" @default.
- W2912136996 title "A nonlinear Kernel-based adaptive learning-by-examples method for robust NDT/NDE of conductive tubes" @default.
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- W2912136996 doi "https://doi.org/10.1080/09205071.2019.1572546" @default.
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