NDLI: Neural networks for ultrasonic NDE signal classification using time-frequency analysis

Content Provider	IEEE Xplore Digital Library
Author	Chen, C.H. Lee, G.G.
Copyright Year	1993
Description	Author affiliation: Electr. & Comput. Eng. Dept., Massachusetts Dartmouth Univ., N. Dartmouth, MA, USA (Chen, C.H.; Lee, G.G.)
Abstract	Ultrasonic nondestructive evaluation (NDE) of material defects typically involves signals which are nonstationary in nature. Whether deconvolution or signal classification is carried out, time-frequency analysis, instead of frequency or time domain analysis alone, is required. The authors examine features derived from the Wigner distribution and its derivatives, and features derived from subband coding wavelet decomposition. Both the traditional nearest neighbor decision rule and the neural network classifiers, the backpropagation trained network and the Nestor's RCE network, are considered to classify the ultrasonic pulse echoes into one of three hidden geometrical defect classes. Neural network classifiers using features properly derived from the time-frequency analysis are shown to provide the best classification results. Although the data set employed is small, the conclusion is fairly consistent with experiments in other large data sets.<>
Starting Page	493
Ending Page	496
File Size	276501
Page Count	4
File Format	PDF
ISBN	0780374029
ISSN	15206149
DOI	10.1109/ICASSP.1993.319163
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-04-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Pattern classification Time frequency analysis Time domain analysis Signal resolution Feature extraction Deconvolution Frequency domain analysis Nearest neighbor searches Backpropagation
Content Type	Text
Resource Type	Article

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