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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 雷欽隆(Chin-Laung Lei) | |
| dc.contributor.author | Keng-Chang Huang | en |
| dc.contributor.author | 黃畊彰 | zh_TW |
| dc.date.accessioned | 2021-05-20T00:52:22Z | - |
| dc.date.available | 2020-08-25 | |
| dc.date.available | 2021-05-20T00:52:22Z | - |
| dc.date.copyright | 2020-08-25 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-04 | |
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[15] A Shams Nateri and Ehsan Ekrami. “Dye binary mixture formulation by means of derivative ratio spectra of the Kubelka–Munk function”. In: Color Research Application: Endorsed by Inter-Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Français de la Couleur 35.3 (2010), pp. 193–199. [16] Hongying Yang, Sukang Zhu, and Ning Pan. “On the Kubelka—Munk Single-Constant/Two-Constant Theories”. In: Textile research journal 80.3 (2010), pp. 263-270. [17] J Militkỳ. “Fundamentals of soft models in textiles”. In: Soft Computing in Textile Engineering. Elsevier, 2011, pp. 45–102. [18] Elham Sadat Yazdi Almodarresi, Javad Mokhtari, Seyed Mohammad Taghi Almodarresi, et al. “A scanner based neural network technique for color matching of dyed cotton with reactive dye”. In: Fibers and polymers 14.7 (2013), pp. 1196–1202. [19] Bahar Sennaroglu, Erhan Öner, and Ö Şenvar. “Colour recipe prediction in dyeing acrylic fabrics with fluorescent dyes using artificial neural network”. In: Industria textilă 65 (Jan. 2014), pp. 22–28. [20] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. “Deep learning”. In: nature 521.7553 (2015), pp. 436–444. [21] Alexander Mordvintsev, Christopher Olah, and Mike Tyka. Inceptionism: Going Deeper into Neural Networks. 2015. url: https://research.googleblog. com/2015/06/inceptionism-going-deeper-into-neural.html. [22] Michael A Nielsen. Neural networks and deep learning. Vol. 2018. Determination press San Francisco, CA, 2015. [23] Jürgen Schmidhuber. “Deep learning in neural networks: An overview”. In: Neural networks 61 (2015), pp. 85–117. [24] Tianqi Chen and Carlos Guestrin. “XGBoost: A Scalable Tree Boosting System”. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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[29] Steven HH Ding, Benjamin CM Fung, and Philippe Charland. “Asm2vec: Boosting static representation robustness for binary clone search against code obfuscation and compiler optimization”. In: 2019 IEEE Symposium on Security and Privacy (SP). IEEE. 2019, pp. 472–489. [30] Dmitry Tarasov and Oleg Milder. “The Inverse Problem of Spectral Reflection Prediction by Artificial Neural Networks: Neugebauers Primaries vs. Recipes”. In: 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). IEEE. 2019, pp. 0580–0583. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8339 | - |
| dc.description.abstract | 在紡織產業中，色彩再現 (color reproduction) 試誤的時間往往多達數日。若有準確的染料配方預測模型可以使用，則可降低配色師在配色時所需的時間。然而，找到一個具有良好擴充性的染料配方預測模型一直以來都是個令人困擾的問題。在之前的相關研究中提出的方法有許多限制，例如每個配方所使用的染料數量是固定的，或者可供挑選的染料數量只有三至四種。並且這些方法都沒辦法找到新穎配方，新穎配方是那些不曾出現在訓練集資料中的配方。
在本文中，我們提出了一個沒有上述限制的方法。首先，我們使用深層神經網路建立一個色彩預測模型，該模型之輸入為染料配方，輸出為布料的顏色表徵，如 CIEL∗a∗b∗ 或反射率光譜。當模型訓練完畢後，我們以目標布料的表徵為輸入，利用該模型來尋找對應的反函數以預測染料配方。最後，我們使用軟體驗證技術來檢視預測之染料配方是否能夠染出相同的顏色。 在本實驗中，我們使用了十重驗證 (ten-fold validation) 來檢視模型的成效。其中，資料總數為7604 筆，可使用的布料有 3 種，可使用的染料有 38 種。根據實驗的結果，本方法預測染料的效果良好，超過 87% 的驗證樣本 (test set) 的與實際資料 (ground truth) 的顏色差距 CIE∆E∗ab 小於2.3 (CIEL∗a∗b∗ 之恰辨差異，just noticeable difference [2])。 | zh_TW |
| dc.description.abstract | In the textile industry, a good recipe prediction model (also known as colorant prediction model) can help colorists to reduce the time needed in color reproduction, which may take days because of try-and-errors. However, finding a scalable recipe prediction model has been a problem for a long time. Although many attempts are proposed in previous studies, there are several restrictions among them. For example, the size of recipes is fixed or the number of candidate dyes is limited to 3 or 4 primary colorants. And neither of these methods can find novel recipes of which the combinations are not shown in the training sets.
In the thesis, we propose a method in predicting dye recipes of fabric without the restrictions mentioned above. First, we leverage a deep neural network to build a color prediction model that takes dye recipes as input and output color representation of fabrics, such as CIEL∗a∗b∗ or reflectance spectra. After the model is trained, we use it to predict dye recipes by finding the inverse value of the model with CIEL∗a∗b∗ or reflectance spectra of a given fabric. Last, we use soft proofing techniques to validate if the predicted recipe could produce the same color or not. We use 10-fold validation on 7604 samples in total where 38 different dyes and 3 different fabrics are involved. The result is promising, showing that more than 87% of the samples in the test set that result in CIE∆E∗ab < 2.3, (just noticeable difference [2]). | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T00:52:22Z (GMT). No. of bitstreams: 1 U0001-0308202010375800.pdf: 2237683 bytes, checksum: ea74052f58b10bc80b388d365aa847aa (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 Structure of a Perceptron . . . . . . . . . . . . . . . . . . . . 5 2.1.2 Structure of a Neural Network . . . . . . . . . . . . . . . . . . 5 2.1.3 Training of Neural Networks . . . . . . . . . . . . . . . . . . . 6 2.1.4 Overfitting . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 CIE Color Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 CIERGB color Space . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2 CIEXYZ color space . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.3 CIEL∗a∗b∗ Color Space and Just Noticeable Difference . . . . . . 11 3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1 Color Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.1 Kubelka Munk Theory . . . . . . . . . . . . . . . . . . . . . . 14 3.1.2 Color Prediction Using Neural Networks . . . . . . . . . . . . . 15 3.2 Colorant Prediction . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.1 Colorant Prediction Leveraging K-M theory . . . . . . . . . . . 16 3.2.2 Colorant Prediction Leveraging Neural Networks . . . . . . . . . 16 3.2.3 Colorant Prediction by Inverse of CPM . . . . . . . . . . . . . 17 3.2.4 Restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.1 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2 Training Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3 Prediction Phase . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.4 Finding Inverse Value . . . . . . . . . . . . . . . . . . . . . . . 23 5 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.1 Prediction of Novel Recipes . . . . . . . . . . . . . . . . . . . . 27 5.2 Soft-Proofing Model . . . . . . . . . . . . . . . . . . . . . . . . 28 5.3 Physical Validation . . . . . . . . . . . . . . . . . . . . . . . . 29 5.4 Prediction Error . . . . . . . . . . . . . . . . . . . . . . . . . . 31 6 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . 35 Reference . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . 37 | |
| dc.language.iso | en | |
| dc.title | 浸染布料之配方預測－使用深層神經網路 | zh_TW |
| dc.title | Recipe Prediction of Dyed Textile Using Deep Neural Network | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭斯彥(Sy-Yen Kuo),顏嗣鈞(Hsu-chun Yen) | |
| dc.subject.keyword | 色彩配對,色彩再現,色彩管理,染料配方預測,配方預測,色彩預測,深層神經網路, | zh_TW |
| dc.subject.keyword | Color Matching,Color Reproduction,Color Management,Recipe Prediction,Color Prediction,Colorant Prediction,Deep Neural Network, | en |
| dc.relation.page | 40 | |
| dc.identifier.doi | 10.6342/NTU202002250 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2020-08-05 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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| U0001-0308202010375800.pdf | 2.19 MB | Adobe PDF | 檢視/開啟 |
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