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Traditional patterns are widely used in the modern design due to their long history, rich connotation, and beautiful form. However, the current application of traditional patterns in the modern design is mostly based on the designer's subjective preferences, not from the perspective of consumers, to explore their feelings about traditional patterns, and which design factors have an impact on consumers, which is the main reason why modern applications of traditional patterns cannot meet the esthetic needs of modern consumers. Therefore, to make better inheritance of the traditional pattern and meet the needs of contemporary consumers, this article takes the caisson lotus pattern of Mogao Cave in the Tang dynasty as an example and first, using the theory of Kansei engineering to investigate the perceptual cognition of the young consumers aged 20–35 years old on the lotus pattern, then use SPSS 24.0 software to analyze the perceptual evaluation data, find the design element combination code corresponding to the perceptual vocabulary, and establish a mathematical model that can predict consumers’ emotional imagery of the lotus pattern of the caisson in the Tang dynasty. Through the verification of the model, the test results show that the model has a high degree of credibility; designers can use this model to quickly evaluate and redesign the lotus pattern to better meet the needs of modern consumers. At the same time, the method of this paper can also be applied to other design fields with user-centered concerns.

In order to solve the problem of mismatch between consumers' personalized needs and clothing pattern design, a method of clothing pattern image design was proposed based on Kansei engineering theory to obtain a perceptual consumer image. Then, a correlation model between clothing pattern design elements and perceptual images of young people was established through the quantitation theory type I, and the mapping relationship between the two and the degree of influence on consumer preference was presented by the diagram method. The paper-cut pattern of a T shirt is taken as an example to verify the feasibility of this research method. The results show that it not only provides designers with clear design indicators and references, but also makes the design process more objective and scientific. [ABSTRACT FROM AUTHOR]

A neural network structure of Long Short Term Memory (LSTM) is proposed which could be used to predict the temperature and humidity of other key parts from the temperature and humidity data of some parts of the human body when wearing tight sportswear, so as to realize the temperature and humidity data prediction of all key points of the human body. The temperature and humidity of different people wearing tights were collected by DHT sensors. The experimental results show that the LSTM neural network structure proposed has higher prediction accuracy than other algorithms, and the model evaluates the feasibility of temperature and humidity data of tights in a state of motion, which facilitates the study of dynamic thermal and humid comfort and reduces the time cost of analyzing the temperature and humidity distribution and changing the law during human movement. It will effectively promote the study of temperature and humidity changes when people wear sports tights, provide theoretical reference for the study of human skin temperature in the field of sports medicine, and provide practical guidance for the application of human skin temperature changes in sports clothing production, diagnosis and prevention of sports injuries. [ABSTRACT FROM AUTHOR]

In order to understand consumers' perceptual cognition of Zhangpu paper-cut patterns and grasp the innovative application direction. The four design elements of paper-cut patterns were extracted by morphological analysis, and representative perceptual vocabulary were selected using Kansei engineering theory and factor analysis, then the design elements and perceptual evaluation scores of representative words are used as the input and output data of the GWO-BP neural network, respectively, to establish an intelligent model that can predict consumers' perceptual cognition of paper-cut patterns. To verify the superiority of the model, the predicted result of BP and FA-BP are compared with GWO-BP neural network. The results show that although the convergence speed of the GWO-BP model is slightly lower than that of the FA-BP model, its prediction accuracy is significantly better than other algorithms. Designers can use the model to quickly redesign the paper-cut pattern to better meet the aesthetic needs of modern consumers. [ABSTRACT FROM AUTHOR]

In order to understand consumers' cognition of clothing style and design clothing products more in line with people's emotional needs, a garment style perceptual image prediction model based on PSO-BP neural network was constructed by taking professional dress as an example. Firstly, the professional dress samples were screened and the style design elements were deconstructed and coded. The Kansei engineering theory and factor analysis method were used to determine the representative adjectives, so as to reduce the cognitive dimension of the target users for the style characteristics and perceptual image of the dress. Then, using the sample style design element code as the input layer and the user's perceptual image evaluation score as the output layer, the PSO-BP neural network's perceptual image prediction model for professional dress styles is constructed. Finally, the sample data were input into the PSO-BP model, BP neural network and GA-BP model for simulation and calculation, and the error analysis of the results proved that the PSO-BP prediction model is effective and advanced. Designers can use this model to quickly transform customers' perceptual needs with dress style design elements, so as to improve the scientificity of design decision-making and better meet customer needs. [ABSTRACT FROM AUTHOR]

To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18–25 and 26–35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic Algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18–25 and 26–35 years. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure. [ABSTRACT FROM AUTHOR]

Aiming at the problem of digital inheritance and innovation of batik fabric patterns, a method for extracting batik fabric pattern and element was proposed. First, performing morphological preprocessing on the obtained digital image of batik fabric, including morphological gradient, reconstruction and local maximum image acquisition and adjustment, not only effectively remove the texture and noise in the image, but also get a binary image that can clearly reflect the overall characteristics of the pattern and elements. Then, for the complete elements in the image, according to the labeled number, the element is reserved, and other elements are set as the background to extract each element in the image, and generate an independent elements image. Finally, the canny algorithm was used to extract the overall contour of the batik fabric pattern and elements to obtain pattern contour and elements contour that can be read and edited independently. Experimental results for different batik fabric images and image segmentation methods proved the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In order to study the influence of underwear on microclimate heat transfer among different age groups, this study measured the temperature of the microclimate layer corresponding to the main parts of the human body or the key parts that affect average skin temperature. A computational fluid dynamics numerical model was then used to simulate the influence of underwear on heat transfer between the human body and the microenvironment and to explore the physical phenomenon. The results obtained show that underwear has a great influence on the average temperature of the microclimatic air layer, especially the air layer at the upper arm, forearm, and thigh. The findings of this study provide fundamental knowledge to improve the thermal comfort of underwear. [ABSTRACT FROM AUTHOR]

Seam slippage often occurs with some garments during the process of wearing or washing, which not only affects the appearance of the garment but also influences garment quality. The purpose of this study is to find out the factors that affect the seam slippage of garments. In order to make the test results closer to those of the garment itself, this study first proposed to make the fabric into a Japanese woman's prototype sample, in order to simulate the garments produced by a garment enterprise. Then, according to standards GB/T 13772.2-2008/ISO 13936-2: 2004 and GB/T 21294-2014, samples were made and seam slippage at the armhole and side seam was tested. Experimental results reveal that the factors that cause the seam slippage of garments are the fabric, seam type and sewing thread. According to the regression analysis, the seam type has a significant effect on the seam slippage of the armhole and side seam, with Pearson correlation coefficients of −0.715 and −0.650, respectively. Thickness, weight, weft density and weave type of the fabric are also important in terms of seam slippage. The weight of the fabric is more significant than other factors; the significant values at the armhole and side seam are 0.009 and 0.002, respectively. In the linear equation, it is shown that weft breaking strength of the fabric only impacts the seam slippage at the side seam. Sewing thread is another important factor for the seam slippage of garments, and its influence on the armhole is obvious; the larger the sewing thread linear density of polyester material is, the smaller the seam slippage is. [ABSTRACT FROM AUTHOR]