Paper
7 December 2023 Research progress on detection technology for warp sizing rate
Haojie Lu, Enqi Jin, Jiu Zhou, Manli Li
Author Affiliations +
Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129414Q (2023) https://doi.org/10.1117/12.3011792
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China
Abstract
To scrutinize the uniformity and quality of the sizing rate during the warp sizing process, this research elucidates the underlying principles of various sizing rate detection methods and their respective roles and advancements in detection technology. The paper embarks on a comprehensive exploration and analysis of the models, applicability, and evolution of a myriad of sizing rate detection technologies, namely, the de-sizing method, near-infrared spectroscopy method, image processing method, quantitative liquid level method, sizing yarn moisture content measurement method, and artificial intelligence model prediction method. Further, the unique attributes and potential constraints of each method are critically evaluated.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Haojie Lu, Enqi Jin, Jiu Zhou, and Manli Li "Research progress on detection technology for warp sizing rate", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129414Q (7 December 2023); https://doi.org/10.1117/12.3011792
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KEYWORDS
Moisture

Liquids

Data modeling

Microwave radiation

Sampling rates

Sensors

Artificial intelligence

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