Feature extraction and learning decision rules from ultrasonic signals-applicability in non-destructive testing

The author presents a supervised multiple-concept learning method for generating decision rules from a set of ultrasonic data for defect characterization purposes in nondestructive testing. The first step towards flaw discrimination is to extract relevant information from the collected defect signat...

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Bibliographic Details
Published inIEEE 1988 Ultrasonics Symposium Proceedings pp. 533 - 536 vol.1
Main Author Perron, M.-C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1988
Subjects
Data mining
Density estimation robust algorithm
Feature extraction
Frequency domain analysis
Frequency estimation
Learning systems
Nondestructive testing
Signal processing
Transducers
Transfer functions
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Summary:The author presents a supervised multiple-concept learning method for generating decision rules from a set of ultrasonic data for defect characterization purposes in nondestructive testing. The first step towards flaw discrimination is to extract relevant information from the collected defect signatures. The large-dimension signal space is mapped into a smaller feature space. The learning set consists of preclassified examples described by a set of continuous attributes measuring the selected features. A decision-tree based algorithm is used to build classification rules able to classify any object from its values of attributes.< >
DOI:10.1109/ULTSYM.1988.49434