Parameter optimization of fiber detection equipment based on complex cubic fuzzy aggregation operator

Many factors affect the detection rate of foreign fibers; apart from the algorithms of foreign fiber detection, there are the system parameters corresponding to the fiber detection equipment, such as light source parameters, camera parameters, cotton flow speed, and so on. For different types of foreign fiber, the parameters need to be adjusted according to the actual working conditions to achieve the ideal detection effect. The problem of meeting the detection requirements of a variety of fibers as closely as possible and improving the detection rate under the premise of ensuring the detection effect is an important one that the fiber detection equipment needs to solve. In this work, a multi-objective decision-making method is adopted, considering the influence of the single value of the influencing parameter and the change in the floating range on the detection effect, and a multi-data fusion model for foreign fiber detection parameters, based on a complex cubic fuzzy aggregation operator, is established. Then, the theory of a complex cubic q -rung orthopair fuzzy aggregation operator is derived, the corresponding fuzzy decision method is constructed, and the parameter combination of detection equipment is optimized to improve the detection rate. Further, the effectiveness and practicability of the proposed method are verified through qualitative and quantitative methods. In summary, the research described in this paper is of certain guiding significance for the multi-parameter adjustment and expansion of decision methods.

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Publication Details

Journal
Textile Research Journal
Published
2025-04-29
DOI
https://doi.org/10.1177/00405175251326238
Citations
1
Primary Topic
Advanced Measurement and Detection Methods
Type
article
Field-Weighted Citation Impact
0.73

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article

Parameter optimization of fiber detection equipment based on complex cubic fuzzy aggregation operator

Ming Xu, Weijia Ren, Wanxiang Li
1 citations
Textile Research Journal
Advanced Measurement and Detection Methods
0.73
article

Parameter optimization of fiber detection equipment based on complex cubic fuzzy aggregation operator

Ming Xu, Weijia Ren, Wanxiang Li
article en
1 citations

Abstract

Many factors affect the detection rate of foreign fibers; apart from the algorithms of foreign fiber detection, there are the system parameters corresponding to the fiber detection equipment, such as light source parameters, camera parameters, cotton flow speed, and so on. For different types of foreign fiber, the parameters need to be adjusted according to the actual working conditions to achieve the ideal detection effect. The problem of meeting the detection requirements of a variety of fibers as closely as possible and improving the detection rate under the premise of ensuring the detection effect is an important one that the fiber detection equipment needs to solve. In this work, a multi-objective decision-making method is adopted, considering the influence of the single value of the influencing parameter and the change in the floating range on the detection effect, and a multi-data fusion model for foreign fiber detection parameters, based on a complex cubic fuzzy aggregation operator, is established. Then, the theory of a complex cubic q -rung orthopair fuzzy aggregation operator is derived, the corresponding fuzzy decision method is constructed, and the parameter combination of detection equipment is optimized to improve the detection rate. Further, the effectiveness and practicability of the proposed method are verified through qualitative and quantitative methods. In summary, the research described in this paper is of certain guiding significance for the multi-parameter adjustment and expansion of decision methods.

Textile Research JournalVol. 96(3-4)
Tianjin University of Science and Technology (CN), Tiangong University (CN), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China, Tianjin Research Innovation Project for Postgraduate Students
Openalex Percentile: Top 29%
Advanced Measurement and Detection Methods
0.73
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