eprintid: 714
rev_number: 8
eprint_status: archive
userid: 4
dir: disk0/00/00/07/14
datestamp: 2016-12-29 08:50:00
lastmod: 2018-02-26 15:59:17
status_changed: 2016-12-29 08:50:00
type: monograph
metadata_visibility: show
creators_name: Salucci, M.
creators_name: Anselmi, N.
creators_name: Oliveri, G.
creators_name: Massa, A.
title: NDT-NDE Crack Characterization Through a Learning-by-Examples Approach
ispublished: pub
subjects: MLBE
full_text_status: public
monograph_type: technical_report
keywords: Eddy current testing, inverse scattering, nondestructive testing and evaluation, statistical learning, learning-by-examples, support vector regression
abstract: This document deals with the characterization of a single narrow crack in a planar conductive structure starting from eddy current testing (ECT) measurements. More precisely, the inversion problem at hand is formulated within the so-called learning-by-examples (LBE) paradigm, by considering the problem of estimating the dimensions of the defect as a regression one. Accordingly, a set of known input-output pairs is generated during an off-line phase and is given as input to a Support Vector Regressor (SVR) prediction model in order to train it on the relationship between defect and corresponding ECT data. Some numerical results are shown in order to verify the effectiveness, as well as the limits, of the proposed LBE technique when dealing with the presence of noise on testing data during the on-line inversion phase.
date: 2016
publisher: University of Trento
referencetext: [1] M. Salucci, N. Anselmi, G. Oliveri, P. Calmon, R. Miorelli, C. Reboud, and A. Massa, "Real-time NDT-NDE through an innovative adaptive partial least squares SVR inversion approach," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 11, pp. 6818-6832, Nov. 2016. 
[2] M. Salucci, G. Oliveri, F. Viani, R. Miorelli, C. Reboud, P. Calmon, and A. Massa, "A learning-by-examples approach for non-destructive localization and characterization of defects through eddy current testing measurements," in 2015 IEEE International Symposium on Antennas and Propagation, Vancouver, 2015, pp. 900-901.
[3] M. Salucci, S. Ahmed and A. Massa, "An adaptive Learning-by-Examples strategy for efficient Eddy Current Testing of conductive structures," in 2016 European Conference on Antennas and Propagation, Davos, 2016, pp. 1-4.
[4] P. Rocca, M. Benedetti, M. Donelli, D. Franceschini, and A. Massa, "Evolutionary optimization as applied to inverse problems," Inverse Probl., vol. 25, pp. 1-41, Dec. 2009.
[5] A. Massa, P. Rocca, and G. Oliveri, "Compressive sensing in electromagnetics - A review," IEEE Antennas Propag. Mag., pp. 224-238, vol. 57, no. 1, Feb. 2015.
[6] N. Anselmi, G. Oliveri, M. Salucci, and A. Massa, "Wavelet-based compressive imaging of sparse targets," IEEE Trans. Antennas Propag., vol. 63, no. 11, pp. 4889-4900, Nov. 2015. 
[7] M. Salucci, G. Oliveri, and A. Massa, "GPR prospecting through an inverse-scattering frequency-hopping multifocusing approach," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 12, pp. 6573-6592, Dec. 2015.
[8] T. Moriyama, G. Oliveri, M. Salucci, and T. Takenaka, "A multi-scaling forward-backward time-stepping method for microwave imaging," IEICE Electron. Express, vol. 11, no. 16, pp. 1-12, Aug. 2014.
[9] T. Moriyama, M. Salucci, M. Tanaka, and T. Takenaka, "Image reconstruction from total electric field data with no information on the incident field," J. Electromagnet. Wave., vol. 30, no. 9, pp. 1162-1170, 2016. 
[10] M. Salucci, L. Poli, and A. Massa, "Advanced multi-frequency GPR data processing for non-linear deterministic imaging," Signal Processing - Special Issue on "Advanced Ground-Penetrating Radar Signal-Processing Techniques," vol. 132, pp. 306-318, Mar. 2017. 
citation:   Salucci, M. and Anselmi, N. and Oliveri, G. and Massa, A.  (2016) NDT-NDE Crack Characterization Through a Learning-by-Examples Approach.  Technical Report. University of Trento.     
document_url: http://www.eledia.org/students-reports/714/1/NDT-NDE%20Crack%20Characterization%20Through%20a%20Learning-by-Examples%20Approach.pdf