Paper Title:
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.

  Info
Periodical
Key Engineering Materials (Volumes 297-300)
Edited by
Young-Jin Kim, Dong-Ho Bae and Yun-Jae Kim
Pages
1962-1967
DOI
10.4028/www.scientific.net/KEM.297-300.1962
Citation
K. B. Kim, H. Y. Kang, D. J. Yoon, M. Y. Choi, "Pattern Classification of Acoustic Emission Signals during Wood Drying by Principal Component Analysis and Artificial Neural Network ", Key Engineering Materials, Vols. 297-300, pp. 1962-1967, 2005
Online since
November 2005
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Wen Tao Sui, Dan Zhang
Abstract:This paper presents a fault diagnosis method on roller bearings based on adaptive neuro-fuzzy inference system (ANFIS) in combination with...
886
Authors: Yan Wang, Xiu Xia Wang, Sheng Lai
Abstract:In ensemble learning, in order to improve the performance of individual classifiers and the diversity of classifiers, from the classifiers...
55
Authors: Dech Thammasiri, Phayung Meesad
Chapter 17: Metrology and Measurement
Abstract:In this research we propose an ensemble classification technique base on creating classification from a variety of techniques such as...
6572
Authors: Dai Yuan Zhang
Chapter 17: Computer Application, Mathematical Modeling and Analysis
Abstract:The statistical sensitivity of training neural networks by B-splines weight functions and its applications for digital modulation signal...
4962
Authors: Buket D. Barkana, Burak Uzkent, Inci Saricicek
Chapter 2: Mechanical Engineering, Construction and Manufacturing Technology
Abstract:Non-speech audio event detection and classification has become a very active subject of research, since it can be implemented in many...
200