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: 205.41 PLN
Availability: InStock

Autor: Chuxiong Miao, Ming Zuo

Język:

Oprawa: Miękka

Wydawca: VDM Verlag

Dostępność: Na zamówienie

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205.41 zł

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

Informacje o książce

Autor

Chuxiong Miao, Ming Zuo

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2010

strony

EAN

9783639294057

ISBN

363929405X

Enbook ID

06836479

Wydawca

VDM Verlag

Waga

150

Wymiary

152 x 229 x 6

Pełny opis

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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