Creativity and Sustainable Design of Wickerwork Handicraft Patterns Based on Artificial Intelligence

Abstract

Protecting and inheriting local traditional handicrafts and developing them into characteristic handicraft industries plays a certain role in maintaining social harmony and stability. This study proposes an innovative design method for wickerwork patterns to achieve the sustainable development of wickerwork handicraft culture. In order to accurately grasp the emotional perception law of wickerwork handicraft patterns and creatively generate wickerwork pattern design schemes in accordance with the user’s emotional preference, a wickerwork pattern design method based on deep learning is proposed. Firstly, the image recognition model of the Funan wickerwork patterns is established by using the ResNet. The experimental results show that the best recognition rate of ResNet34 for the whole pattern design image dataset is 94.36%, the recognition rate of modern patterns is 95.92%, and the recognition rate of traditional wickerwork patterns is 93.45%. Secondly, based on deep convolution generative adversarial network (DCGAN), a design scheme generation model of Funan wickerwork patterns is built. DCGAN can automatically and creatively generate pattern design schemes that can effectively stimulate consumers’ emotional feelings. Finally, the designer uses creative pictures as a source of inspiration, innovates the design of the generated images, and designs wickerwork patterns with exquisite personality. This proposed method will increase the diversity of patterns and promote the sustainable development of traditional wickerwork techniques. Moreover, this proposed method can help design companies identify customers’ psychological needs and support designers in innovatively and efficiently creating new cultural innovation design solutions.

An Elderly-Oriented Design of HMI in Autonomous Driving Cars Based on Rough Set Theory and Backpropagation Neural Network

“…Firstly, the data in the table are divided into equivalence classes according to conditional attributes (design characteristics) and decision attributes (elderly-oriented satisfaction). (1,3,13,15,19,21,23,24,31,33,39,43,45,49,53,54,55,58,59), (2,4,5,6,8,10,14,17,20,22,26,28,29,30,32,34,35,36,37,38,42,46,50,51,52,56,57), (7,9,11,12,16,…”

“…The linear statistical methods used in the past, such as quantitative theory type I (QT-I) and principal component analysis (PCA), cannot directly measure and quantify the subjective and uncertain information of users' emotions. With the development of artificial intelligence technology, such as Support Vector Machine (SVM) [56], Genetic Algorithm (GA) [57], Deep Convolutional Neural Network (DCNN) [58] and others have been applied to construct the mapping relationship between products' design features and users' emotions [59]. To compare, the computational complexity of SVM is high and the processing ability of high-dimensional data is weak; while DCNN has high requirements on image size and input data is relatively weak in non-image data processing.…”