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Proceedings Paper

Classification of mixed acoustic emission signals via neural networks
Author(s): Jian Yang; Guy D. Dumont
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Paper Abstract

In order to offer operating information to the monitoring system of a chip refiner, a neural network classifier is proposed for identifying the acoustic emission signals of wood species. In addition to classifying single wood species, the system is required to be able to recognize mixed species. The classification task is accomplished by a multilayer feedforward neural network in which both supervised and unsupervised learning are included. The simulations are run on the testing data, mixing two single species to represent mutually mixed wood species of five categories. If a signal is identified as a mixture, the network will indicate the corresponding component species according to a lookup table. Some expected classification accuracy is obtained on both single and mixed species identification and performance of classification is discussed based on the simulation results.

Paper Details

Date Published: 16 September 1992
PDF: 6 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140070
Show Author Affiliations
Jian Yang, Univ. of British Columbia (Canada)
Guy D. Dumont, Univ. of British Columbia (Canada)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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