Affiliation:
1. School of Electronics and Information, Xi’an Polytechnic University, China
2. Branch of Shaanxi Artificial Intelligence Joint Laboratory, Xi’an Polytechnic University, China
3. School of Automation, Northwestern Polytechnical University, China
Abstract
During spinning, knitting and weaving processes, the yarn apparent evenness is an important factor, which determines the quality of subsequent spinning production and fabric performance. This paper presents a powerful method which is based on L0 norm smoothing and the expectation maximization method to detect the yarn apparent evenness. The L0 norm smoothing method is first applied to remove the noise and enhance the yarn apparent evenness diameter features. Then, the expectation maximization method and the morphological opening operation were used to obtain the yarn evenness. Finally, we calculated the yarn apparent evenness diameter and the coefficient of variation of the evenness of the yarn apparent diameter. Compared with the capacitive evenness testers, the Otsu detection method and the fuzzy C-means detection method, our method can accurately detect the yarn apparent evenness better than the selected state-of-the-art methods.
Funder
Shaanxi Provincial Key R&D Program Project
The Shaanxi Provincial College of Science and Technology Youth Talent Support Project
Shaanxi Provincial Department of Education Youth Innovation Team Project
Doctoral Scientific Research Foundation of Xi'an Polytechnic University
Innovation Capability Support Program of Shaanxi
National Natural Science Foundation of China
Subject
Polymers and Plastics,Chemical Engineering (miscellaneous)
Cited by
4 articles.
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