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Permanent link (DOI): https://doi.org/10.7939/R3N62K

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A support vector machine model for pipe crack size classification Open Access

Descriptions

Other title
Subject/Keyword
KFD
support vector machines
data dependent kernel
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Miao, Chuxiong
Supervisor and department
Ming J. Zuo, Mechanical Engineering
Examining committee member and department
Ming J. Zuo, Mechanical Engineering
Xiaodong Wang, Mechanical Engineering
Marinal Mandal, Electrical and Computer Engineering
Department
Department of Mechanical Engineering
Specialization

Date accepted
2009-05-11T15:46:41Z
Graduation date
2009-11
Degree
Master of Science
Degree level
Master's
Abstract
Classifying pipe cracks by size from their pulse-echo ultrasonic signal is difficult but highly significant for the defect evaluation required in pipe testing and maintenance decision making. For this thesis, a binary Support Vector Machine (SVM) classifier, which divides pipe cracks into two categories: large and small, was developed using collected ultrasonic signals. To improve the performance of this SVM classifier in terms of reducing test errors, we first combined the Sequential Backward Selection and Sequential Forward Selection schemes for input feature reduction. Secondly, we used the data dependent kernel instead of the Gaussian kernel as the kernel function in the SVM classifier. Thirdly, as it is time-consuming to use the classic grid-search method for parameter selection of SVM, this work proposes a Kernel Fisher Discriminant Ratio (KFD Ratio) which makes it possible to more quickly select parameters for the SVM classifier.
Language
English
DOI
doi:10.7939/R3N62K
Rights
License granted by Chuxiong Miao (chuxiong@ualberta.ca) on 2009-05-08T20:26:02Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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