Textile Science and Engineering (한국섬유공학회지)

The Korean Fiber Society (한국섬유공학회)
권호 표지
DOI QR코드

DOI QR Code

A New Color Recipe Prediction Method in the Cold Pad Batch Dyeing of Cotton Knit Using NNMF Algorithm

면니트의 CPB 염색에서 NNMF 알고리즘을 이용한 새로운 컬러처방 예측 기법

  • Lee, Jung Ho (Korea Hightech Textile Research Institute) ;
  • Lee, In Yeol (Korea Hightech Textile Research Institute) ;
  • Cho, Seong Hun (Department of Organic Materials & Fiber Engineering, Soongsil University) ;
  • Cho, Hyeon Tae (Department of Organic Materials & Fiber Engineering, Soongsil University)
  • 이정호 (한국섬유소재연구소) ;
  • 이인열 (한국섬유소재연구소) ;
  • 조성훈 (숭실대학교 유기신소재.파이버공학과) ;
  • 조현태 (숭실대학교 유기신소재.파이버공학과)
  • Received : 2012.12.08
  • Accepted : 2013.02.04
  • Published : 2013.02.28
⟨ Previous Next ⟩

Abstract

To ensure the reproducibility of the dyeing of cotton knit by cold pad batch(CPB), the solubility, alkali stability and substantivity of multi functional type reactive dyes were tested. The color difference of the cotton knit CPB dyeing products between laboratory and dye house was measured. The results showed that the equipments, dyes and fabrics, all of them influenced the color differences of the final samples between the laboratory and dye house. These factors could not be controlled to reduce the color difference between the laboratory and dye house in the conventional computer color matching algorithm. A new color match prediction method based on the non-negative matrix factorization(NNMF) algorithm was introduced and the applicability was studied by comparing with the conventional color match prediction method based on the Newton-Lapson algorithm. The color match prediction method based on the NNMF showed more accurate recipe prediction results than the conventional color matching method for the CPB dyeing process of cotton knit fabric.

Keywords

References

  1. U. Denter and E. Schollmeyer, "Bestimmung der Farbstoffaufnahme beim trichromen Faerben von Baumwolle, Teil 4: Untersuchungen zur Substantivitaet von Reaktiv farbstoffen unter den Bedingungen des KKV-Verfahrens", Textilveredlung, 1995, 30, 158-168.
  2. Z. Khatri, M. Memon, and K. Brohi, "Energy, Environment and Sustainable Development", Springer, Verlag, Wien, 2012, pp.299-305.
  3. K. Ansari, S. H. Amirshahi, and S. Moradian, "Recovery of Reflectance Spectra from CIE Tristimulus Values Using a Progressive Database Selection Technique", Coloration Tech, 2006, 122, 128-134. https://doi.org/10.1111/j.1478-4408.2006.00019.x
  4. L. I. Rong and G. U. Feng, "Tristimulus Algorithm of Colour Matching for Precoloured Fibre Blends Based on the Stearns-Noechel Model", Coloration Tech, 2006, 122, 74-81. https://doi.org/10.1111/j.1478-4408.2006.00003.x
  5. M. EI Sherif, O. A. Bayoumi, and T. Z. N. Sokkar, "Prediction of Absorbance from Reflectance for an Absorbing-Scattering Fabric", Color Res Appl, 1997, 22, 32-39. https://doi.org/10.1002/(SICI)1520-6378(199702)22:1<32::AID-COL6>3.0.CO;2-5
  6. J. M. Bishop, M. J. Bushnell, and S. Westland, "Application. of Neural Networks to Computer Recipe Prediction", Color Res Appl, 1991, 16, 3-9. https://doi.org/10.1002/col.5080160104
  7. S. Westland, L. Lovine, and J. M. Bishop, "Kubelka-Munk or Neural Networks for Computer Colorant Formulation", 9th Congress of the International Color Association, Proceedings of SPI, 2002, 442, 745-748.
  8. E. Mizutani, J. R. Jang, K. Nishio, H. Takagi, and D. Auslaner, "Coactive Neuro-Fuzzy Modeling for Color Recipe Prediction", IEEE Int'l Conf. on Neural Networks(ICNN' 95), pp.2252-2257, 1995.
  9. D. D. Lee and H. S. Seung, "Learning the Parts of Objects by Non-Negative Matrix Factorization", Nature, 1999, 401, 788-791. https://doi.org/10.1038/44565
  10. D. D. Lee and H. S. Seung, "Algorithms for Non-negative Matrix Factorization", Proceedings of the Advances in Neural Information Processing Systems, 2001, 13, pp.43-56.
  11. M. D. Plumbley, "Algorithms for Non-negative Independent Component Analysis", IEEE Transactions on Neural Network, 2003, 14, 534-543. https://doi.org/10.1109/TNN.2003.810616
  12. P. O. Hoyer, "Non-negative Matrix Factorization with Sparseness Constraints", J Machine Learning Res, 2004, 5, 145-149.
  13. C. H. Zhen, D. S. Huang, Z. L. Sun, M. R. Lyu, and T. M. Lok, "Non-negative Independent Component Analysis Based on Minimizing Mutual Information Techniques", Neuro Computing, 2006, 69, 878-883.
  14. C. Kuo, "Blind Primary Colorant Spectral Separation Combining ICA and POCS Non-negative Matrix Factorization", ICASSP, 2004, 489-492.
  15. Suss-Leonhardt, "New Developments in Cold Pad Batch Dyeing", Colourage, 2006, 53, 47-51.
  16. H. H. Kim, C. K. Park, and H. T. Cho, "Analysis of Dye Aggregation Using NNMF Algorithm (I)", Text Sci Eng, 2012, 49, 75-81. https://doi.org/10.12772/TSE.2012.49.2.075