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Support Vector Machine Model for Pipe Crack Size Classification

Name: Support Vector Machine Model for Pipe Crack Size Classification
Brand: VDM Verlag
SKU: 06836479
Price: 17613 HUF
Availability: InStock
Author: Chuxiong Miao
ISBN: 9783639294057

Chuxiong Miao

Ming Zuo

Language

English

Book Paperback

Libristo code: 06836479

Publishers VDM Verlag, September 2010

The classification of pipe crack size from its pulse- echo ultrasonic signal is a difficult task but... Full description

Libristo code: 06836479

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The classification of pipe crack size from its pulse- echo ultrasonic signal is a difficult task but greatly significant for defect evaluation in pipe testing and the maintenance strategy making. In this book, we use Support Vector Machines (SVM) to classify the pipe crack into correct categories, large size or small size, with the ultrasonic signal data. In order to acquire an optimal input data set, we first select the features from the time and frequency domain on the ultrasonic data. Then a combined method, Sequential Backward Selection (SBS) and Sequential Forward Selection (SFS), is used for features reduction. These two steps are referred as data preprocessing in this book. To build SVM classifier, parameter selection is critical. In this book, a Kernel Fisher Discriminant Ratio (KFD Ratio) is proposed for speeding the parameter selection of the SVM classifier. As an indicator, KFD Ratio can greatly shorten computation time for finding the best parameters. To further improve the performance of the SVM classifier in terms of classification accuracy, a data dependent kernel is adopted for creating a more effective one.