ArticleFrontiers in neuroscience2022
Optimal Learning Samples for Two-Constant Kubelka-Munk Theory to Match the Color of Pre-colored Fiber Blends.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A novel ZnO-MgO-Gd₂O₃ nanocomposite synthesized with Ocimum basilicum seed extract for enhanced photocatalysis.Scientific reports · 2025Article
- Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing.Sensors (Basel, Switzerland) · 2024Article
- Photocatalytic activity and anticancer properties of green synthesized ZnO-MgO-MnScientific reports · 2024Article
- Estimation of dye concentration by using Kubelka-Munk and Allen-Goldfinger reflective models: comparing the performance.Scientific reports · 2023Article
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5 authors.
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Abstract
Due to the dyeing process, learning samples used for color prediction of pre-colored fiber blends should be re-prepared once the batches of the fiber change. The preparation of the sample is time-consuming and leads to manpower and material waste. The two-constant Kubelka-Munk theory is selected in this article to investigate the feasibility to minimize and optimize the learning samples for the theory since it has the highest prediction accuracy and moderate learning sample size requirement among all the color prediction models. Results show that two samples, namely, a masstone obtained by 100% pre-colored fiber and a tint mixed by 40% pre-colored fiber and 60% white fiber, are enough to determine the absorption and scattering coefficients of a pre-colored fiber. In addition, the optimal sample for the single-constant Kubelka-Munk theory is also explored.
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