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Fast Characterization of Multiple Cracks in Conductive Media Based on Adaptive Feature Extraction and SVR

机译:基于自适应特征提取和SVR的导电介质中多裂缝的快速表征

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This paper describes real-time cracks characterization and localization inside a Structure Under Test (SUT) by exploiting Learning by Examples (LBE) strategy in the context of Eddy Current Testing (ECT). Within the framework of LBE, an optimal training set has been generated in offline phase by adopting Partial Least Squares (PLS) feature extraction combined with a customized version of output space filling (OSF). Support Vector Regression (SVR) algorithm is utilized for developing an accurate model based on the training set and subsequently real-time inversion (online phase) has been performed on unknown test set. The robustness of the proposed PLS-OSF/SVR approach is numerically assessed in presence of synthetic noisy test set.
机译:本文通过在涡流测试(ECT)的背景下,通过利用示例(LBE)策略来阐述所测试的结构(SUT)内的实时裂缝表征和定位。在LBE的框架内,通过采用部分最小二乘(PLS)特征提取与传输空间填充(OSF)的自定义版本组合,在离线相中生成了最佳训练集。支持向量回归(SVR)算法用于基于训练集开发准确模型,随后对未知测试集执行了实时反转(在线阶段)。在合成噪声测试集的存在下,在数值评估所提出的PLS-OSF / SVR方法的鲁棒性。

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