A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICA Mixture Modeling for the Classification of Materials in Impact-Echo Testing
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Image similarity based on hierarchies of ICA mixtures
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Application of independent component analysis for evaluation of ashlar masonry walls
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
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This paper presents an application of neural networks in pattern recognition of defects in sonic signals from non-destructive evaluation by multichannel impact-echo. The problem approached consists in allocating parallelepiped-shape materials in four levels of classifications defining material condition (homogeneous or defective), kind of defects (holes and cracks), defect orientation, and defect dimension. Various signal features as centroid frequency, attenuation and amplitude of the principal frequency are estimated per channel and processed by PCA and feature selection methods to reduce dimensionality. Results for simulations and experiments applying Radial Basis Function, Multilayer Perceptron and Linear Vector Quantization neural networks are presented. Neural networks obtain good performance in classifying several 3D finite element models and specimens of aluminum alloy.