• Smart Media Journal
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• v.11 no.3
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• pp.74-89
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• 2022

Due to the unique recipes and processes of each company, not only differences among the results of dyeing textile materials exist but they are also difficult to predict. This study attempted to develop a color prediction model based on deep learning to optimize color realization in the dyeing process. For this purpose, deep learning-based models such as multilayer perceptron, CNN and LSTM models were selected. Three forecasting models were trained by collecting a total of 376 data sets. The three predictive models were compared and analyzed using the cross-validation method. The mean of the CMC (2:1) color difference for the prediction results of the LSTM model was found to be the best.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.33-48
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• 2022

This study was performed to compare the color emotion and preference of Koreans and Chinese for a two-color combination by dyeing cotton fabric with persimmon and indigo and to establish prediction models of color preference. Nine specimens prepared by combining two different colored fabrics (persimmon and indigo) were evaluated for color emotion and preference by Korean and Chinese groups of female college students. Koreans described most specimens as natural and traditional, whereas the Chinese described them as more pleasant and elegant as well as warmer and lighter than Koreans did. The contrast tone was the most preferred combination by both groups, whereas it was perceived as more modern and less warm by Koreans. Relationships between physical color variables and color emotions were quantified; these relationships were applied to establish a prediction model of color preference with tone combination types for each group. These results could help in making the design of fashion textiles more preference- and emotion-oriented for Korean and Chinese consumers.

• Fibers and Polymers
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• v.7 no.4
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• pp.344-351
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• 2006

This paper is intended to determine the optimal processing parameters applied to the dyeing procedure so that the desired color strength of a raw fabric can be achieved. Moreover, the processing parameters are also used for constructing a system to predict the fabric quality. The fabric selected is the nylon and Lycra blend. The dyestuff used for dyeing is acid dyestuff and the dyeing method is one-bath-two-section. The Taguchi quality method is applied for parameter design. The analysis of variance (ANOVA) is applied to arrange the optimal condition, significant factors and the percentage contributions. In the experiment, according to the target value, a confirmation experiment is conducted to evaluate the reliability. Furthermore, the genetic algorithm (GA) is combined with the back propagation neural network (BPNN) in order to establish the forecasting system for searching the best connecting weights of BPNN. It can be shown that this combination not only enhances the efficiency of the learning algorithm, but also decreases the dependency of the initial condition during the network training. Most of all, the robustness of the learning algorithm will be increased and the quality characteristic of fabric will be precisely predicted.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.10
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• pp.1028-1039
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• 2012

This study identified color emotion factors of naturally dyed two-color combination fabrics focused on yellowish and reddish shades to examine the relationship between color emotion factors and physical colorimetric variables (as well as tone combination groups) to provide prediction models for color emotion factors of naturally dyed fabrics with a two-color combination. Each of eight different stimuli were prepared by paring two pieces of silk fabrics colored in red and yellow by natural dyeing respectively; in addition, their color emotion descriptors were evaluated by human subjects using semantic deferential scales. 'Joyful', 'Natural', 'Classical', and 'Soft' were extracted as color emotion factors for the naturally dyed yellowish-reddish combination fabrics. They were found to be significantly affected by physical colorimetric variables such as CIE $C^*$ and $L^*$ and tone combination groups. Finally, prediction models for all color emotion factors were established using physical colorimetric variables and tone combination groups that led to the conclusion that they could be applicable to design a color combination for naturally dyed fashion fabrics.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.1
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• pp.180-199
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• 2021

This study investigated natural aroma fragrance on the fashion image of Galchon, a traditional natural dyeing textile made with immature persimmon from the Jeju area, Korea. Nine fabric pairs consisting of differently colored cotton and silk Galchon with various tones and fabric types were used for subjective evaluation. Thirty five female college students evaluated the specimens using a 7-point scale questionnaire for fashion image-related adjectives. A specimen with three different presentation types that included fabric without fragrance (FO), fabric with citrus fragrance, and fabric with chamaecyparis (FCP) were randomly provided to a subject. As a result, color variables of Galchon were found to be the primary influence on fashion images for both cotton and silk Galchon that showed interaction effects with presentation types. The citrus fragrance increased the feeling of 'Active' while chamaecyparis tended to contribute to a stronger perception of 'Elegance' for cotton Galchon. Finally, these results were used to develop prediction models for fashion images of Galchon that employed color variables and presentation types.

• Science of Emotion and Sensibility
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• v.19 no.3
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• pp.3-14
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• 2016

This study is aimed to measure the physical colorimetric property according to three conditions, natural dyestuffs (Gardenia, Sappan wood, Lac, Gardenia blue, Mugwort, and Indigo), fabric types (cotton, silk), and presence of mordant (without, with), and then to evaluate the psychological sensibility. Also, to perform analysis of variance (ANOVA) to find out the differences of physical properties according to the three natural dyeing conditions, and to analyze the relationship between physical property and psychological property by Pearson's correlation analysis and then suggest the prediction model by regression analysis using SPSS program (ver. 21.0). Finally, to propose a certain sensibility image map of naturally dyed fabrics, MDS (Multidimensional Scaling) was used, and as a result, Gardenia dyed fabrics having the color sensibilities such as 'hard' and 'heavy' were suggested to evoke masculine image, and to evoke feminine image, Sappan wood and Lac having 'bright', 'transparent', 'soft' and 'light' sensibilities were suggested. Natural image might be induced by using 'subdued' Mugwort dyed fabrics, and active image might be induced by using 'showy' Indigo dyed fabric.