Multi-Source Data-Driven Framework for Work State Classification in Fabric Pilling and Linting Performance Assessment-Reference-Cited by-同舟云学术

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Multi-Source Data-Driven Framework for Work State Classification in Fabric Pilling and Linting Performance Assessment

Published:2024 Issue: Volume:12 Page:101089-101105
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mao Yuanqing¹,Jiao Qingchun²^ORCID,Qian Zifan²^ORCID,Wang Chuncong³,Sun Tingting⁴

Affiliation:

1. Zhejiang Market Regulation Digital Media Center, Hangzhou, China

2. School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, China

3. Zhejiang Jinhui Digital Technology Company Ltd., Hangzhou, China

4. Zhejiang Light Industrial Products Inspection and Research Institute, Hangzhou, China

Funder

National Science and Technology Plan Project of the State Administration for Market Regulation

“Chu Ying” Core Project of Zhejiang Administration for Market Supervision

Natural Science Foundation of Zhejiang Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10604863.pdf?arnumber=10604863

Reference31 articles.

1. Evaluation method of fabric pilling grades based on saliency-based deep convolutional network

2. Assessment of pilling severity for color fabrics based on homomorphic filtering;Huang;Prog. Textile Sci. Technol.,2021

3. Improving the Anti-Pilling Performance of Cellulose Fiber Blended Knitted Fabrics with 2,4,6-Trichloropyrimidine Treatment

4. Wool knitted fabric pilling objective evaluation based on double-branch convolutional neural network

5. Objective rating method for fabric pilling based on LSNet network

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