@techreport{elediasc12712,
          author = {M. Salucci and N. Anselmi and G. Oliveri and A. Massa},
           title = {Fast Inversion of Eddy Current Testing Data Through a Learning-by-Examples Approach for Robust Crack Localization},
            type = {Technical Report},
       publisher = {University of Trento},
            year = {2016},
        keywords = {Eddy current testing, inverse scattering, nondestructive testing and evaluation, statistical learning, learning-by-examples, support vector regression},
             url = {http://www.eledia.org/students-reports/712/},
        abstract = {This document presents a new learning-by-example (LBE) technique for the computationally-efficient inversion of eddy current testing (ECT) data in non-destructive testing and evaluation (NDT-NDE) scenarios. More precisely, the developed approach exploits a uniform sampling strategy to build a training set of input/output (I/O) pairs and exploits such information to train a Support Vector Regressor (SVR). During the on-line testing phase, previously-unseen ECT data are given as input to the trained model in order to predict the position of a single narrow crack within a planar conductive structure. Some representative numerical results are shown, in order to preliminarily assess the capabilities of the developed approach when dealing with the presence of a non-negligible amount of noise on test data.}
}