Optimal database combining with Multi Output Support Vector Machine for Eddy Current Testing

Abstract : This paper provides a new methodology for the characterization of defect size in a conductive nonmagnetic plate from the measurement of the impedance variations. The methodology is based on Finite Element Method (FEM) combined with the Multi Output Support Vector Machines (MO-SVM). The MO-SVM is a statistical learning method that has good generalization capability and learning performance. FEM is used to create the adaptive database required to train the MO-SVM and the Cross Validation (CV) is used to find the parameters of MO-SVM model. The results show the applicability of MO-SVM to solve eddy current inverse problems instead of using traditional iterative inversion methods which can be very time-consuming. With the experimental results we demonstrate the accuracy which can be provided by the MO-SVM technique.
Type de document :
Communication dans un congrès
EVER 2014, Mar 2014, Monaco, Monaco. Proceedings of the Ninth International Conference on Ecological Vehicles and Renewable Energies, pp.1 - 4, 〈10.1109/EVER.2014.6844142〉
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https://hal-supelec.archives-ouvertes.fr/hal-01099330
Contributeur : Thierry Leblanc <>
Soumis le : vendredi 2 janvier 2015 - 20:02:29
Dernière modification le : samedi 26 mai 2018 - 01:14:03

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M. Chelabi, Tarik Hacib, Z. Belli, Mohamed Mekideche, Yann Le Bihan. Optimal database combining with Multi Output Support Vector Machine for Eddy Current Testing. EVER 2014, Mar 2014, Monaco, Monaco. Proceedings of the Ninth International Conference on Ecological Vehicles and Renewable Energies, pp.1 - 4, 〈10.1109/EVER.2014.6844142〉. 〈hal-01099330〉

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