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Man-algorithm Cooperation Intelligent Design of Clothing Products in Multi Links

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The changes in technology have led to a synchronous change in the clothing design method, as well as media and artistic aesthetics in the same period. The intelligence algorithm is constantly increasing its participation in development and production in the clothing industry. In this study, a variety of intelligent algorithms, including the parameterised numer state algorithm, Generative Adversarial Networks, and style transfer were introduced into the multi-links of clothing product design and development, such as clothing shape, print pattern, texture craft, product vision, and so on. Then, an innovative clothing design method based on the cooperation of the intelligent algorithm and various human functional roles was constructed. The method improves the efficiency of the multiple links of clothing design, such as generating 10000 printing patterns every 72.12 seconds, and completing the migration of 92.7 frames of the garment process style every second. To a certain extent, this study realizes the scale economy of clothing design and reduces its marginal cost through the unlimited computing power brought about by Moore’s law of digital technology, which provides a reference for the exploration of clothing design in the era of industry 4.0.
Rocznik
Strony
59--66
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
autor
  • Jiangnan University, School of Design, 214122, Wuxi, China
autor
  • Jiangnan University, School of Design, 214122, Wuxi, China
  • Jiangnan University, School of Design, 214122, Wuxi, China
  • Fudan University, School of Computer Science and Technology, 200125, Shanghai, China
autor
  • Politecnico di Milano, School of Design, 20158, Milan, Italy
Bibliografia
  • 1. Gao F, Jiao Y. Artificial Intelligence Aided Creative Design (in Chinese). Zhuang Shi. [J] 2019 (11):34-37. DOI:10.16272/j.cnki.cn11-1392/j.2019.11.010.
  • 2. Qin JY, Jia R. Innovative Design of Artificial Intelligence in Intangible Cultural Heritage: Take Cloisonné as an Example. Packaging Engineering 2020; 41(06): 1-6.
  • 3. Isola P, Zhu J Y, Zhou T, et al. Image-to-Image Translation with Conditional Adversarial Networks. IEEE Conference on Computer Vision & Pattern Recognition, 2016.
  • 4. Kim T, Cha M, Kim H, et al. Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. In: International Conference on Machine Learning. PMLR, 2017: 1857-1865.
  • 5. Yoo D, Kim N, Park S, et al. Pixel-Level Domain Transfer. In: European Conference on Computer Vision. Springer, Cham, 2016: 517-532.
  • 6. Monedero J. Parametric Design: A Review and Some Experiences. Urban Environment Design 2010; 9(4): 369-377.
  • 7. Zhang WH, Beckers P, Fleury C. A Unified Parametric Design Approach to Structural Shape Optimization. International Journal for Numerical Methods in Engineering 2010; 38(13): 2283-2292.
  • 8. Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Networks. arXiv preprint arXiv: 1406.2661, 2014.
  • 9. Zhang H, Dana K. Multi-Style Generative Network for Real-Time Transfer. Proceedings of the European Conference on Computer Vision (ECCV) Workshops. 2018.
  • 10. Gatys LA, Ecker AS, Bethge M. Image Style Transfer Using Convolutional Neural Networks//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016.
  • 11. Johnson J, Alahi A, Fei-Fei L. Perceptual Losses for Real-Time Style Transfer and Super-Resolution//European Conference on Computer Vision. Springer, Cham, 2016.
  • 12. Dumoulin V, Shlens J, Kudlur M. A Learned Representation For Artistic Style. 2016.
  • 13. Chen T Q, Schmidt M. Fast Patch-Based Style Transfer of Arbitrary Style. arXiv preprint arXiv:1612.04337, 2016.
  • 14. Huang X, Belongie S. Arbitrary Style Transfer In Real-Time With Adaptive Instance Normalization//Proceedings of the IEEE International Conference on Computer Vision 2017: 1501-1510.
  • 15. Ritter FE, Baxter GD, Churchill EF. Introducing User-Centered Systems Design. Springer London. 2014.10.1007/978-1-4471-5134-0 (Chapter 1): 3-31.
  • 16. Meng Y, Mok P Y, Jin X. Computer Aided Clothing Pattern Design with 3D Editing and Pattern Alteration. Computer-Aided Design 2012; 44(8): 721-734.
  • 17. Wang LC, Zeng XY, Koehl L, et al. Intelligent Fashion Recommender System: Fuzzy Logic in Personalized Garment Design. IEEE Transactions on Human-Machine Systems 2015; 45(1): 95-109.
  • 18. Cao H, Ji X. Prediction of Garment Production Cycle Time Based on a Neural Network. FIBRES & TEXTILES in Eastern Europe 2021; 29, 1(145): 8-12.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8dee9860-04da-46be-a4c0-906eb0b3a969
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