eprintid: 713
rev_number: 11
eprint_status: archive
userid: 4
dir: disk0/00/00/07/13
datestamp: 2016-12-23 10:40:53
lastmod: 2024-10-24 12:28:31
status_changed: 2016-12-23 10:40:53
type: monograph
metadata_visibility: show
creators_name: Salucci, M.
creators_name: Anselmi, N.
creators_name: Oliveri, G.
creators_name: Massa, A.
title: An Innovative Learning-by-Examples Approach for Crack Localization Based on Partial Least Squares and Adaptive Sampling
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, output space filling, partial least squares, adaptive sampling
abstract: This document presents an innovative adaptive learning-by-examples (LBE) strategy for accurate crack localization in planar conductive specimens. The developed approach exploits a Partial Least Squares (PLS) feature extraction technique and an adaptive sampling strategy in order to build optimal training sets of input/output (I/O) pairs. Such information is then used to train, during a preliminary off-line phase, a Support Vector Regressor (SVR) in order to build a fast surrogate model of the inverse operator linking ECT data and crack position. Finally, during the on-line test phase previously-unseen ECT measurements are given as input to the trained SVR in order to retrieve an estimation of the defect coordinates. Some numerical results are shown, in order to verify the effectiveness of the proposed LBE inversion methodology.
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) An Innovative Learning-by-Examples Approach for Crack Localization Based on Partial Least Squares and Adaptive Sampling.  Technical Report. University of Trento.     
document_url: http://www.eledia.org/students-reports/713/1/An_Innovative_Learning-by-Examples_Approach_for_Crack_Localization_Based_on_Partial_Least_Squares_and_Adaptive_Sampling.v2.pdf