Effective approaches to extract features and classify echoes in long ultrasound signals for metal shafts

Lee, Kyungmi (2008) Effective approaches to extract features and classify echoes in long ultrasound signals for metal shafts. In: Proceedings of 2008 International Symposium on Intelligent Information Technology Application (1), pp. 728-733. From: 2008 International Workshop on Geoscience and Remote Sensing, 21-22 December, 2008, Shanghai, China.

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Abstract

A-scans from ultrasonic testing of long shafts are complex signals, thus the discrimination of different types of echoes is of importance for non-destructive testing and equipment maintenance. Research has focused on selecting features of physical significance or exploring classifier like Artificial Neural Networks and Support Vector Machines. This paper summarizes and reports on our comprehensive exploration on efficient feature extraction schemes and classifiers for shaft testing system and further on the diverse possibilities of heterogeneous and homogeneous ensembles.

Item ID: 7640
Item Type: Conference Item (Refereed Research Paper - E1)
Keywords: signal classification; feature extraction; non-destructive testing
ISBN: 978-0-7695-3563-0
Date Deposited: 14 Jul 2010 23:45
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890202 Application Tools and System Utilities @ 50%
89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 50%
Citation Count from Web of Science Web of Science 2
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