Li Junfeng, Xie Dehong, Li Miaoxin, Liu Shiwei, Wei Chun'Ao
School of Packaging and Printing Engineering, Henan University of Animal Husbandry and Economy, Zhengzhou, China.
College of Information Science and Technology, Nanjing Forestry University, Nanjing, China.
Front Neurosci. 2022 Jul 1;16:945454. doi: 10.3389/fnins.2022.945454. eCollection 2022.
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.
由于染色过程，一旦纤维批次发生变化，用于预染色纤维混合物颜色预测的学习样本就需要重新制备。样本的制备耗时且会导致人力和物力的浪费。本文选择双常数库贝尔卡-蒙克理论来研究将该理论的学习样本最小化和优化的可行性，因为在所有颜色预测模型中，它具有最高的预测精度和适中的学习样本量要求。结果表明，两个样本，即由100%预染色纤维获得的主色样本和由40%预染色纤维与60%白色纤维混合而成的浅色样本，就足以确定预染色纤维的吸收系数和散射系数。此外，还探索了单常数库贝尔卡-蒙克理论的最佳样本。
