Pattern Classification of Acoustic Emission Signals during Wood Drying by Principal Component Analysis and Artificial Neural Network

Ki Bok Kim; Ho Yang Kang; Dong-Jin Yoon; Man Yong Choi

doi:10.4028/www.scientific.net/KEM.297-300.1962

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 297-300Pattern Classification of Acoustic Emission...

Pattern Classification of Acoustic Emission Signals during Wood Drying by Principal Component Analysis and Artificial Neural Network

Abstract:

This study was performed to classify the acoustic emission (AE) signal due to surface check and water movement of the flat-sawn boards of oak (Quercus Variablilis) during drying using the principle component analysis (PCA) and artificial neural network (ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count, event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 % of the variance of AE parameters could be accounted for by the first and second principal components. An ANN was successfully used to classify the AE signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.

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Periodical:

Key Engineering Materials (Volumes 297-300)

Pages:

1962-1967

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.297-300.1962

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Online since:

November 2005

Authors:

Ki Bok Kim, Ho Yang Kang, Dong-Jin Yoon, Man Yong Choi

Keywords:

Acoustic Emission (AE), Artificial Neural Network (ANN), Principal Component Analysis (PCA), Wood Drying

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