Research on Online Rapid Sorting Method of Waste Textiles Based on Near-Infrared Spectroscopy and Generative Adversity Network-Reference-Cited by-同舟云学术

Research on Online Rapid Sorting Method of Waste Textiles Based on Near-Infrared Spectroscopy and Generative Adversity Network

Published:2022-05-14 Issue: Volume:2022 Page:1-8
ISSN:1687-5273
Container-title:Computational Intelligence and Neuroscience
language:en
Short-container-title:Computational Intelligence and Neuroscience

Author:

Hu Jinquan¹²,Yang Huihua¹³^ORCID,Zhao Guoliang⁴,Zhou Ruizhi⁵

Affiliation:

1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing, China

2. School of Electronic Engineering and Automation Chemistry, Guilin University of Electronic Technology, No. 1 Jinji Road, Qixing District, Guilin, Guangxi, China

3. School of Computer Science and Information Security, Guilin University of Electronic Technology, No. 1 Jinji Road, Qixing District, Guilin, Guangxi, China

4. Beijing Institute of Fashion Technology, Beijing 100029, China

5. Shanghai Jicai Environmental Protection Technology Co., Ltd., Shanghai, China

Abstract

In this paper, aiming at the application of online rapid sorting of waste textiles, a large number of effective high-content blending data are generated by using generative adversity network to deeply mine the combination relationship of blending spectra, and A BEGAN-RBF-SVM classification model is constructed by compensating the imbalance of negative samples in the data set. Various experiments show that the model can effectively extract the spectrum of pure textile samples. The classification model has high robustness and high speed, reaches the performance of similar products in the world, and has a broad application market.

Funder

National Basic Research Program of China

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Link

http://downloads.hindawi.com/journals/cin/2022/6215101.pdf

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