Empirical validation of the performance of a class of transient detector

P. J. Jacob, A. D. Ball

Research output: Contribution to journalArticle

Abstract

Transient detection in the presence of noise is a problem which occurs in many areas of engineering. A desription is given of a classifier system suitable for the identification of high frequency waveforms. It uses the Wavelet Transform for signal pre-processing and a forward feature selection algorithm. A Radial Basis Function neural network is employed to model the class conditional probability density function. A short review of statistical pattern recognition is presented. The classifier is applied to the identification of a number of difficult to classify, high frequency Acoustic Emission signals.

LanguageEnglish
JournalIEE Colloquium (Digest)
Volume1996
Issue number261
DOIs
Publication statusPublished - 1 Dec 1996
Externally publishedYes
EventIEE Colloquium on Intelligent Sensors - Leicester, United Kingdom
Duration: 19 Sep 199619 Sep 1996

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Classifiers
Detectors
Acoustic emissions
Wavelet transforms
Probability density function
Pattern recognition
Feature extraction
Neural networks
Processing

Cite this

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Empirical validation of the performance of a class of transient detector. / Jacob, P. J.; Ball, A. D.

In: IEE Colloquium (Digest), Vol. 1996, No. 261, 01.12.1996.

Research output: Contribution to journalArticle

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