Support Vector Machine Model for Pipe Crack Size Classification

Name: Support Vector Machine Model for Pipe Crack Size Classification
Brand: VDM Verlag
SKU: 9783639294057
Price: 50.87 EUR
Availability: InStock

Автор: Chuxiong Miao, Ming Zuo

Език:

Корици: С меки корици

Издател: VDM Verlag

Наличност: Налично при издателя, по поръчка

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50.87 € 99.50 лв

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

Информация за книгата

Автор

Chuxiong Miao, Ming Zuo

Език

Английски език

Корици

Книга - С меки корици

Издадена

2010

страници

EAN

9783639294057

ISBN

363929405X

Enbook ID

06836479

Издател

VDM Verlag

Теглоt

150

Размери

152 x 229 x 6

Пълно описание

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.

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Support Vector Machine Model for Pipe Crack Size Classification

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