Support vector machine classification of ultrasonic shaft inspection data using discrete wavelet transform

Lee, Kyungmi , and Estivill-Castro, Vladimir (2004) Support vector machine classification of ultrasonic shaft inspection data using discrete wavelet transform. In: Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications - MLMTA'04 (2). From: International Conference on Machine Learning; Models, Technologies and Applications - MLMTA'04 , 21-24 June 2004, Nevada, USA.

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Abstract

While many non-destructive ultrasonic test scenarios involve shallow surfaces, but when signals for testing come from long shafts a major problem of mode-converted reflection emerges. These reflections are echoes that do not correspond to cracks in the material, neither to characteristics in the shaft. Moreover, the length of the signals demands the application of efficient feature extraction mechanisms to reduce the dimension of pattern vectors for feasible automated classification. Experimental evidence has shown that the Discrete Wavelet Transform (DWT) provides faster and more reliable extraction for Artificial Neural Network (ANN) in these long signals. This paper demonstrates that DWT is not only highlighting more adequate vectors for ANN but it is more general and in particular it also improves the performance of Support Vector Machines (SVM).

Item ID: 10124
Item Type: Conference Item (Refereed Research Paper - E1)
Keywords: feature selection and classification; support vector machines; signal processing applications in engineering; discrete wavelet transforms
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ISBN: 1-932415-32-7
Date Deposited: 26 Jul 2010 05:29
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 70%
08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems > 080699 Information Systems not elsewhere classified @ 20%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 10%
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