Classifying Textile Faults with a Back-Propagation Neural Network Using Power Spectra-Reference-Cited by-同舟云学术

Classifying Textile Faults with a Back-Propagation Neural Network Using Power Spectra

Published:1998-02 Issue:2 Volume:68 Page:121-126
ISSN:0040-5175
Container-title:Textile Research Journal
language:en
Short-container-title:Textile Research Journal

Author:

Chen Pei-Wen¹,Liang Tsair-Chun²,Yau Hon-Fai¹,Sun Wan-Li¹,Wang Nai-Chueh¹,Horng Chi lin ³,Rong Cherng Lien ³

Affiliation:

1. Institute of Optical Science, National Central University, Chung-Li 320, Taiwan, Republic of China

2. Department of Telecommunication Engineering, National Kaohsiung Institute of Marine Technology Kaohsiung, Taiwan 811, Republic of China

3. Department of Fabric Formation, China Textile Institute, Tu-Chen City, Taiwan, Republic of China

Abstract

A real-time system designed to detect and classify textile defects is presented. The system starts with an analysis of the optical Fourier transform of sample textiles. We also use a back-propagation neural network to help detect and classify defects. Exper imental results show that the system is able to detect and classify nine out of the twelve kinds of defects in its data base.

Publisher

SAGE Publications

Subject

Polymers and Plastics,Chemical Engineering (miscellaneous)

Link

http://journals.sagepub.com/doi/pdf/10.1177/004051759806800207

Reference5 articles.

1. Freeman, James A., and Skapura, David M. "Neural Networks—Alogorithms, Applications, and Programming Techniques," Addison Wesley, NY, 1991, pp. 89-125.

2. Real-Time Fault Detection on Textiles Using Opto-electronic Processing

3. The Math Works, Inc., Neural Network Toolbox for use with MATLAB, pp. 5-1-5-38, 1995.

4. Applying Fourier and Associated Transforms to Pattern Characterization in Textiles

5. Carpet Texture Measurement Using Image Analysis

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