Pattern Classification of Acoustic Emission Signals During Wood Drying by Artificial Neural Network
Kim, K.B.;Yoon, D.J.;Choi, M.Y.(Korea Research Institute of Standards and Science (KRISS), Daejeon, Republic of Korea)E-mail:kimkibok@kriss.re.kr | Kang, H.Y.(Chungnam National University, Daejeon, Republic of Korea)
This study was Performed to classify the acoustic emission(AE) signal due to surface cracking and moisture movement in the flat-sawn boards of oak(Quercus Variablilis) during drying using the principal 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.
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