文献类型：期刊文献

英文题名：Textile pattern reconstruction and innovation using artificial neural network

作者：Tao, Chen[1];Duan, Yafeng[1];Hong, Xinghua[2]

机构：[1]Shaoxing Univ, Coll Text & Garment, Shaoxing, Peoples R China;[2]Zhejiang Sci Tech Univ, Coll Mat & Text, Hangzhou, Zhejiang, Peoples R China

年份：2019

卷号：31

期号：1

起止页码：103

外文期刊名：INTERNATIONAL JOURNAL OF CLOTHING SCIENCE AND TECHNOLOGY

收录：SCI-EXPANDED(收录号：WOS:000458915100008)、、EI(收录号：20190806530596)、Scopus(收录号：2-s2.0-85061722834)、WOS

基金：The authors would like to thank the Philosophy and Social Science Project of Zhejiang Province (19NDJC128YB) and the National Natural Science Foundation of China (51803185).

语种：英文

外文关键词：Innovation; Artificial neural network; Reconstruction; Textile; Pattern

外文摘要：Purpose The purpose of this paper is to advance a digital technology that is intended to bring about innovations on the existing textile patterns. Design/methodology/approach The pattern is deemed as a relation function between colors and positions which can be learnt by the artificial neural network (ANN). The outputs of the ANN are used for the reconstruction of the pattern and the innovation is performed by interceptors in the input/output layer. The ANN is carried out with one input layer, one output layer and several hidden layers, and the capacity of the architecture is adjusted by the scale of hidden layers to accommodate different function relations of the patterns. The training is conducted repeatedly on a sample set extracted from the pixels of the pattern image to minimize the error, and the chromatic outputs of the architecture are replaced to their origins so as to rebuild the pattern. Then, the interceptors are installed into the input and output layers to modulate the positions and the colors, and consequently the innovations are achieved on the geometric formation and color distribution of the pattern. Findings It has turned out that the precision of reconstruction is concerned with network scale, training epochs and color mode of the sample set. Four primary innovative effects including stripes, twisters, sandification and overprints have been qualified in terms of interceptors. Originality/value This study introduces ANN into textile pattern generation and provides a novel way to perform digital innovation of textile patterns.