Paper Info
Title
Methodologies for characterizing ultrasonic transducers using neural network and pattern recognition techniques
Abstract
System hardware for characterizing ultrasonic transducers and the associated data acquisition software and characterizing algorithms are considered. The hardware consists mainly of a workstation computer, a receiver/pulser with gated peak detector, various monitoring devices, a microcomputer-based 3D positioning controller, and an A/D converter. The characterization algorithms are based on neural network and pattern recognition techniques. It is found that artificial neural network techniques provide far better classification results than the pattern recognition techniques. A multilayer backpropagation neural network which provides a classification accuracy of 94% is developed. Two other multilayer neural networks-sum-of-products and a newly devised neural network called hybrid sum-of-products-have a classification accuracy of 90% and 93%, respectively. The most successful pattern recognition technique for this application is found to be the perceptron, which provides a classification accuracy of 77%.
Year
DOI
Venue
1992
10.1109/41.170972
Industrial Electronics, IEEE Transactions
Keywords
Field
DocType
neural nets,ultrasonic transducers,A/D converter,characterizing algorithms,data acquisition software,gated peak detector,microcomputer-based 3D positioning controller,neural network,pattern recognition,receiver/pulser,ultrasonic transducers,workstation computer
Ultrasonic sensor,Control theory,Pattern recognition,Computer science,Workstation,Time delay neural network,Artificial intelligence,Artificial neural network,Backpropagation,Microcomputer,Perceptron
Journal
Volume
Issue
ISSN
39
6
0278-0046
Citations 
PageRank 
References 
6
2.06
8
Authors
2
Name
Order
Citations
PageRank
Obaidat, M.S.129039.97
Abu-Saymeh, D.S.262.06