【摘 要】
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Background, Motivation and Objective Ultrasonic flaw detection in NDE applications is a challenging problem due to suppression of flaw echoes within the high scattering noise (clutter) echoes.Advanced
【机 构】
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ECE Department, Illinois Institute of Technology, Chicago, Illinois, USA
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Background, Motivation and Objective Ultrasonic flaw detection in NDE applications is a challenging problem due to suppression of flaw echoes within the high scattering noise (clutter) echoes.Advanced signal processing techniques are necessary in order to enhance the visibility of the flaws and increase the flaw-to-clutter echo ratio.In this work, a Support Vector Machine (SVM) classifier is introduced for flaw detection based on features extracted from the output of the subband decomposition filters, (i.e.split-spectrum processing).SVM is a machine learning method used for classification and regression analysis of complex real-world problems that may be difficult to analyze theoretically.Typically, SVM has been used for pattern recognition but recently it has been applied to financial market analysis, electric utility forecasting and also ultrasonic NDE.Previous ultrasound SVM applications used A-scan data itself or ultrasonic signal parameters such as time-of-arrival, center frequency and bandwidth as feature vectors.In contrast, we use subband filter outputs as feature vectors for the SVM in order to exploit the frequency diversity among the flaw and clutter echoes.
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