Abstract
This paper aims to predict fabric colours by analyzing the relationship between multiple process parameters and colours of dyed fabrics in pad dyeing. The task is approached as a multi-dimensional regression problem. Within the framework of machine learning designed for colour prediction, two models, back-propagation neural network (BPNN) and multi-dimensional support vector regressor (M-SVR) are implemented. The process parameters are fed to these multi-dimensional regression models to predict the fabric colours measured in CIELAB values. The raw data used in our study are directly provided by a dyeing and printing manufacturer. As our experiments show, BPNN outperforms M-SVR with a relatively large data set while M-SVR is more accurate than BPNN is with a relatively small data set.
This research has been financially supported by grants from the Young Scientists’ Sailing Project of Science and Technology Commission of Shanghai Municipal (No. 17YF1427400), the National Natural Science Foundation of China (No. 61702094), the Fundamental Research Funds for the Central Universities (No. 17D111206), the National Key R&D Program of China (No. 2017YFB0309800) and the Shanghai Municipal Natural Science Foundation (No. 18ZR1401200).
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Chen, Z., Zhou, C., Zhou, Y., Zhu, L., Lu, T., Liu, G. (2018). Multi-dimensional Regression for Colour Prediction in Pad Dyeing. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_61
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DOI: https://doi.org/10.1007/978-3-030-00006-6_61
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