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Using Deep Principal Components Analysis-Based Neural Networks for Fabric Pilling Classification

1
Department of Industrial Education and Technology, National Changhua University of Education, Changhua County 50007, Taiwan
2
Department of Computer Science & Information Engineering, National Chin-Yi University of Technology, Taichung City 41170, Taiwan
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 474; https://doi.org/10.3390/electronics8050474
Received: 16 March 2019 / Revised: 21 April 2019 / Accepted: 25 April 2019 / Published: 28 April 2019
(This article belongs to the Section Artificial Intelligence)
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PDF [2479 KB, uploaded 16 May 2019]
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Abstract

A manufacturer’s fabric first undergoes an abrasion test and manual visual inspection to grade the fabric prior to shipment to ensure that there are no defects present. Manual visual classification consumes a considerable amount of human resources. Furthermore, extended use of the eyes during visual inspection often causes occupational injuries, resulting in a decrease in the efficiency of the entire operation. In order to overcome and avoid such situations, this study proposed the use of deep principal components analysis-based neural networks (DPCANNs) for fabric pilling identification. In the proposed DPCANN, the characteristics of the hairball were automatically captured using deep principal components analysis (DPCA), and the hairball class was identified using the neural network and the support vector machine (SVM). The experimental results showed that the proposed DPCANN has an average accuracy of 99.7% at the hairball level, which is in line with the needs of the industry. The results also confirmed that the proposed hairball classification method is superior to other methods. View Full-Text
Keywords: fabric pilling; principal component analysis; support vector machine; neural network; classification fabric pilling; principal component analysis; support vector machine; neural network; classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yang, C.-S.; Lin, C.-J.; Chen, W.-J. Using Deep Principal Components Analysis-Based Neural Networks for Fabric Pilling Classification. Electronics 2019, 8, 474.

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