Renk reçetesi tahminlenmesinde yapay sinir ağlarının kullanımı

Renk reçete tahminlemesi; verilen bir referans renk ile aynı rengi üretmek için hangi boyarmaddelerin hangi konsantrasyonlarına ihtiyaç duyuldugunu tahmin etmektir. iyi bir tahminleme yapabilmek deneyimli boya uzmanları için bile zordur. Reçete tahmini; klasik reçete çıkarma sistemine göre veya bilgisayarlı reçete çıkarına sistemine göre yapılabilir. Klasik reçete çıkarma sisteminde bir boya uzmanı deneyimlerine dayanarak verilen renge yakın bir reçete tutturmaya çalışır, bilgisayarlı reçete çıkarına sistemleri ise yaygın olarak bilinen doğrusal modele (Kubelka-Munk modeli) veya doğrusal olmayan (yapay sinir ağları) modele dayanabilir bu makalede doğrusal olmayan sisteme dayanan yapay sinir ağlarının kullanımı ile reçete çıkarma sisteminin nasıl çalıştığı açıklanacaktır.

Neural networks model on color recipe prediction

Color recipe prediction was known to predict which colorants and their concentrations were required for a given reference color. Making a good color recipe prediction is even hard to expert co! o lists. Color recipe prediction e ail be made by classical and compute ring recipe prediction systems. The classical system is based on a colorist's prediction, while the latter is applied as linear model (Kubelka-Munk model) in common or nonlinear model {such as neural network model), in this paper, how color recipe prediction by neural network based on nonlinear model is working was reviewed.

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1.Jasper, W.J., Kovacs, E. T., Berkstresser, A.(1993) Using Neural Networks to Predict Dye Concentrations in Multiple-Dye Mixtures. Textile Research Journal. 63(9), pp. 545-551
2.Westland, S. (1998), Artificial Neural Networks and Colour Recipe Prediction. Proceedings of the International Conference and Exhibition Colour Science-98
3:Chung, B., Cho, C.-S., Park, M.-J. (2004) Color inspection of printed texture using scanner: Compensation of positional deviation via NN model, 8th International Conference on Control, Automation, Robotics and Vision, Vol.3, pp. 1826-1831
4.Balcı, O., Oğulata, R.T. (2009) Boyanmış Kumaşlarda Kimyasal Apre Uygulamaları Sonucunda Oluşabilecek Renk Değişiminin ve CIELab Değerlerinin Yapay Sinir Ağları Kullanılarak Tahmin Edilmesi, Tekstil ve Konfeksiyon, Vol. 1, pp. 61 -69
5.Golob, D., Osterman, D.P.,Zupan, J.(July/September 2008) Determination of Pigment Combinations for Textile Printing Using Artificial Neural Networks, Fibers and Textiles in Eastern Europe, Vol. 16, No. 3 (68), pp. 93-98
6.Furferi, R., Carfagni, M. (2010) Prediction of the color and of the color solidity of a jigger-dyed cellulose-based fabric: A cascade neural network approach, Textile Research Journal , Vol. 80, No. 16, pp. 1682-1696
7.Li, H.-T., Shi, A.-S., Zhang, B.-S. (2007)^4 dyeing color matching method combining RBF neural networks with genetic algorithms, Proceedings - SNPD 2007: Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, Vol. 2, pp. 701 -706
8.Vadood, M., Semnani, D., Morshed, M. (2011) Optimization of acrylic dry spinning production line by using artificial neural network and genetic algorithm, Journal of Applied Polymer Science, Vol. 120, No. 2, pp. 735-744
9.Naşiri, M., Shanbeh, M., Tavanai, H. (2005) Comparison of statistical regression, fuzzy regression and artificial neural network modeling methodologies in polyester dyeing, Proceedings -International Conference on Computational Intelligence for Modelling, Control and Automation, Vol. 1, pp. 505-510
10.Xu, B., Lin, S. (2002) Automatic color identification in printed fabrics by a neural-fuzzy system, AATCC Review, Vol. 2, No. 9, pp. 42-45
11.Thevenet, L., Dupont, D., Jolly-Desodt, A.M. (2003) Modeling color change after spinning process using feedforward neural networks. Color Research and Application. Vol. 28. No. 1, pp. 50-58
12.Trussell, H.J. (1998) Applications of digital signal processing, ızzy logic, and neural nets to computational color, Textile Chemist nd Colorist, Vol. 30, No. 2, pp. 28-32
13.Lin, J.J., Sun, P.T., Chen, J.J.-R, Wang, L.J., Kuo, H.C., Kuo, V.G. (2010) Applying gray model to predicting trend of textile ishion colors, Journal of the Textile Institute Vol. 101, No. 4, pp. 60-368
14.Westland, S., Bishop, J.M., Bushell, M.J., Ushert, A.L. (1991, uly/August). An intelligent approach to colour recipe prediction, SDC, Vol. 107
15.Wu, P., Fang, S.-C., Nuttle, H.L.W., King, R.E., Wilson, J.R. 1994) Decision surface modeling of textile spinning operations using eural network technology. Proceedings of the 1994 IEEE Annual extile, Fiber and Film Industry Technical Conference, p. 0-24
16.Yin, Y., Zhang, K., Lu, W. (2009) Textile flaw classification by wavelet reconstruction and BP neural network, 6th International Symposium on Neural Networks, Vol. 5552 LNCS, No: Part 2, pp. 94-701
17.Yean, Y., Wen, B.L., Ke, Z., Liang, J. (2009) Textile flaw 'etection and classification by wavelet reconstruction and BP eural network, Proceedings of the 2009 WRI Global Congress on ntelligent Systems, Vol. 4, pp 167-171
18.Liu, L., Yan, L., Xie, Y., Xia, G. (2010) Content measurement of zxtile mixture by near infrared spectroscopy based on BP neural etwork, Proceedings - 2010 3rd International Congress on Image nd Signal Processing, Vol. 7, pp. 3354-3358
19.Kumar, A., (2003) Neural network based detection of local extile defects, Pattern Recognition, Vol. 36, No: 7, pp. 1645-1659
20.Su, T.-L., Kung, F.-C., Kuo, Y.-L. (2008) Application ofback-ropagation Neural Network Fuzzy Clustering in textile texture utomatic recognition system, Proceedings of the 2008 nternational Conference on Wavelet Analysis and Pattern Lecognition, Vol. 1, pp. 46-49
21.Islam, M. A., Akhter, S.,Mursalin,T.E.,Ashraful,A.M. (2006) ,4 uitable neural network to detect Textile defects, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial ntelligence and Lecture Notes in Bioinformatics), Vol. 4233 LNCS II, pp. 430-438
22.Wu,P., Yang, W.-H., Wei, N.-C., (2004) An electromagnetism Igorithm of neural network analysis - An application to textile etail operation, Journal of the Chinese Institute of Industrial Engineers, Vol. 21,No. l,pp. 59-67
23.Bhattacharjee, D., Kothari, V.K. (2007) A neural network system ir prediction of thermal resistance of textile fabrics, Textile Leseprch Journal, Vol. 77, No. 1, pp. 4-12
24.Yuen, C.W.M., Wong, W.K., Qian, S.Q., Fan, D.D., Chan, L.K., ung, E.H.K. (2009) Fabric stitching inspection using segmented nndow technique and BP neural network, Textile Research ournal, Vol. 79, No. 1, pp. 24-35
25.Luo, X., Hou, W., Li, Y., Wang, Z., (2007) A fuzzy neural etwork modelfor predicting clothing thermal comfort, Computers nd Mathematics with Applications, Vol. 5 3, No. 12,pp. 1840-1846
26.Hadizadeh, M., Jeddi, A.A.A., Tehran, M.A. (2009) The rediction of initial load-extension behavior of woven fabrics using rtificial neural network, Textile Research Journal, Vol. 79, No. 17, pp. 1599-1609
27.Yu, R.-F., Chen, H.-W., Cheng, W.-P., Hsieh, P.-H. (2009) dosage control of the fenton process for color removal of textile wastewater applying ORP monitoring and artificial neural •etworks, Journal of Environmental Engineering, Vol. 135, No. 5, ip. 325-332
28.Chen, T., Zhang, C., Li, L., Chen, X. (2008) Simulating the Irawing of spunbonding nonwovenprocess using an artificial neural network technique, Journal of the Textile Institute, Vol. 99, No. 5, pp. 479-488
29.Chen, T., Zhang, C., Chen, X., Li, L. (2009) An input variable selection methodfor the artificial neural network of shear stiffness of worsted fabrics, Statistical Analysis and Data Mining, Vol. 1, No.5,pp. 287-295
30.Bo, Z. (2010) The prediction of warp breakage rate of weaving by considering sized yarn quality using artificial neural network theory, 2010 International Conference on Computer Design and Applications,, Vol. 2, pp. V2526- V2529
31.Nateri, A. S., Oroumei, A., Dadvar, S., Fallah-Shojaie, A., Khayati, Gh., Emamgholipur, O. (2010) Using fuzzy expert system to simulating the antibacterial activity of nanosilver coated cotton fabrics, Journal of Computational and Theoretical Nanoscience, Vol. 7,No. 8, pp. 1554-1558
32.Au, K.F., Wong, W.K., Zeng, X.H. (2006) Decision model for country site selection of overseas clothing plants, International Journal of Advanced Manufacturing Technology, Vol. 29, No. 3-4, pp.408-417
33.Chen, X., Huang, X.B. (2004) Evaluating fabric pilling with light-projected image analysis, Textile Research Journal, Vol 74, No. 11, pp. 977-981
34.Karras, D.A., Karkanis, S.A. (1998) Supervised and unsupervised neural network methods applied to textile quality control based on improved wavelet feature extraction techniques, International Journal of Computer Mathematics, Vol. 67, No. 1-2, pp. 169-181
35.Lui,S.-G., Qu, P.-G. (2008) Inspection of fabric defects based on wavelet analysis and BP neural network, Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, Vol. 1, pp. 232-236
36.Stojanovic, R, Mitropulos, P., Koulamas, C., Karayiannis, Y., Koubias, S., Papadopoulos, G. (2001) Real-time vision-based system for textile fabric inspection, Real-Time Imaging, Vol. 7, No.6,pp. 507-518
37.Tolba, A.S., Khan, H.A., Mutawa, A.M., Alsaleem, S.M. (2010) Decision fusion for visual inspection of textiles, Textile Research Journal, Vol. 80, No. 19, pp. 2094-2106
38.Zeng, Y.C., Wang, K.F., Yu, C.W. (2010) Predicting the tensile properties of air-jet spun yarns, Textile Research Journal, Vol. 74, No. 8, pp. 689-694
39.Ramesh, M.C., Rajamanickam, R., Jayaraman, S. (1995) Prediction of yarn tensile properties by using artificial neural networks, Journal of the Textile Institute, Vol. 86, No. 3, pp. 459-469
40.Chattopadhyay, R., Guha, A., Jayadeva (2004) Performance of neural networks for predicting yarn properties using principal component analysis, Journal of Applied Polymer Science, Vol. 91, No. 3, pp. 1746-1751
41.Shi, X.-J.„ Yu, W.-D. (2008) Fuzzy classification of animal fibers using neuro-fuzzy classifier, Proceedings - The 1st International Conference on Intelligent Networks and Intelligent Systems,pp.119-122
42.Shi, X.-J., Yu, W.-D. (2008) Identification for animalfibers with artificial neural network, Proceedings of the 2008 International Conference 'on Wavelet Analysis and Pattern Recognition, Vol. 1, pp. 227-231
43.She, F.H., Chow, S., Wang, B., Kong, L.X. (2001)Identification and classification of animal fibres using artificial neural networks, Seni Kikai Gakkai Shi/Journal of the Textile Machinery Society of Japan, Vol. 54, No. 9, pp. 60-63
44.Chen, T., Li, L.,Koehl, L.,Vroman, P., Zeng, X. (2007) A soft computing approach to model the structure-property relations of nonwoven fabrics, Journal of Applied Polymer Science, Vol. 103, No. 1, pp. 442-450
45.Kim, S., Vachtsevanos, G.J. (2000) Intelligent approach to integration and control of textile processes, Information sciences, Vol. 123,No. 3, pp. 181-199
46.Kim, S., Lee, M.H., Woo, K.-B. (1999) Intelligent approach to integration of textile processes, IEEE International Symposium on Industrial Electronics, Vol. 3, pp. 1454-1457
47.Kim, S., Lee, M.H. (1999) Intelligent approach to integrated optimization and control of textile processes, IEEE International Conference on Intelligent Robots and Systems, Vol. 3, pp. 1908-1913
48.Mori, T., Komiyama, J. (2002) Evaluating wrinkledfabrics with image analysis and neural networks, Textile Research Journal, Vol. 72, No. 5, pp. 417-422
49.Vroman, P., Koehl, L., Zeng, X., Chen, T. (2008) Designing structural parameters of nonwovens using fuzzy logic and neural networks, International Journal of Computational Intelligence Systems, Vol. 1, No. 4, pp. 329-339
50.Au, K.-F., Choi, T.-M., Yu, Y. (2008) Fashion retail forecasting by evolutionary neural Networks, International Journal of Production Economics, Vol. 114,No.2,p. 615-630
51.Lin, J.-J. (2007) Prediction of yarn shrinkage using neural nets, Textile Research Journal, Vol. 77, No. 5, p. 336-342
52.Pynckels, F., Kiekens, P., Sette, S., Van Langenhove, L., Impe, K. (1995) Use of neural nets for determining the spinnability of fibres, Journal of the Textile Institute, Vol. 86, No. 3, pp. 425-437
53.Chattopadyay, R., Guha, A., Artifical Neural Networks: Applications to Textiles, The Textile Institute, Textile Progress, Vol. 35,No:l,p.5,p40
54.Bezerra, C.M., Hawkyard, C.J. (2000). Match prediction for fluorescent dyes by neural networks. J. Society of Dyers and Colourist, 116, pp. 163-16.9
55.Mizutani, E., Takagi, H., Auslander, D.M., Jang, J-S., R. (2000, November). Evolving Color Recipes. IEE Transactions on Systems, Man, And Cybernetics, Part- C: Applications and Reviews, Vol.30, No: 4, pp. 537-550
56.Limin, F. (1994). ISBN: 0-07-113319-4. Neural networks in computer intelligence. McGraw-Hill International Editions, Computer Science Series. The MIT Press & McGraw Hill, Inc., Singapore, p. 460.
57.Bishop, J.M., Bushnell, M.J., Westland, S. (1991). Application of neural network to computer recipe prediction. Color Research and Application. 16(1), pp.3-8
58.Vangheluwe, L. (1994), Comments on "Using Neural Networks to Predict Dye Concentrations in Multiple-Dye Mixtures" TRJ,64(3), pp. 182-183
59.Jasper, W.J. (1994). Reply to "Using Neural Networks to Predict Dye Concentrations in Multiple-Dye Mixtures". TRJ, 64(3), pp. 184
60.Marjoniemi, M.,& Maentysalo, E. (1997). Neuro-fuzzy modelling of spectroscopic data. Part B- Dye concentration prediction.J. Society of Dyers and Colourist, 113,pp. 13-17
61.Westland, S. (2002). Kubelka- Munk or Neural Networks for Computer Colorant Formulation. 9th Congress of the International Color Association, Proceedings of SPI Vol. 4421
62.Mizutani, E., Jang, J-S., R., Nishio, K., Takagi, H., Auslander, D.M. (1995, November). Coactive Neuro-Fuzzy Modeling for Color Recipe Prediction. Proc. IEEE Int. Conf. Neural Networks, pp. 2252-2257