Wood Structure Nondestructive Detection Based on Singular Spectrum Analysis and SVM

Authors

Guoxiong Zhou, 1. School of Electricity & information Engineering, Central South University of Forestry & Technology, Changsha 410004, China;;
Aibin Chen, 1. School of Electricity & information Engineering, Central South University of Forestry & Technology, Changsha 410004, China;;
Xianyan Zhou, 2. College of Civil Engineering and Mechanics, Central South University of Forestry and Technology, Changsha 410004, China;

Abstract

Abstract: In view of unknown defect of wood component defect, a method of wood structure nondestructive recognition based on Singular spectrum analysis and SVM was proposed. The wood specimen was tested to obtain the test signal by ultrasonic testing instrument. In order to eliminate the testing effect of the tester gain control and defect size, angle variation on the test defect echo amplitude, the abnormal fluctuation was to filter and characteristic of the original signal was extracted by singular spectrum analysis, and SVM could train the parameters and distinguish the wood defects. Simulation results show that the proposed method can distinguish standard samples and glue joint with accuracy of specimen for 97.5%, and recognition of knots specimens also reaches 95% with high accuracy.

Recommended Citation

Zhou, Guoxiong; Chen, Aibin; and Zhou, Xianyan (2020) "Wood Structure Nondestructive Detection Based on Singular Spectrum Analysis and SVM," Journal of System Simulation: Vol. 28: Iss. 8, Article 22.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss8/22

First Page

1863

Revised Date

2015-08-27

DOI Link

https://doi.org/

Last Page

1869

CLC

TP229

Recommended Citation

Zhou Guoxiong, Chen Aibin, Zhou Xianyan. Wood Structure Nondestructive Detection Based on Singular Spectrum Analysis and SVM[J]. Journal of System Simulation, 2016, 28(8): 1863-1869.