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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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This study intended to evaluate the visual sensibility of textiles of knitting simulation through the painting function of SDS-ONE, a computer knitting system, with the use of various fancy yarns for hand knitting. For this study, six kinds of fancy yarns with difference manufacturing style and form were selected: loop yarn, tamtam yarn, fur1 yarn, fur2 yarn, ladder yarn, and tape yarn. In order to create a computer knitting simulation, paint function of SDS-ONE was employed to scan the fancy yarns for hand knitting with a scanner. Then, after the registration of the yarns as data, the yarn was brought in for knitting simulation in plain stitch with 3 gauge. After that a survey was conducted involving 60 female experts in their twenties. Using SPSS 12.0, factor analysis, reliability, Pearson correlation coefficient, ANOVA, Duncan test, and regression analysis were performed to analyze the collected data. First, according to the result of analyzing the visual sense factor, three factors of the 'inflexibleness/ruggedness sense', 'lightweight sense', and 'smoothness sense' was extracted. According to the result of analyzing the visual sensibility factor, three factors of the 'activity', 'grace', and 'purity' was extracted. Second, With regard to preference and purchase intention, the knitted fabric with tamtam yarn(H2) was preferred than the other samples, indicating that the respondents had intention to purchase it. As for word-of-mouth intention, H2 ranked the first place, implying the respondents had intention to recommend it to other people. Third, the examination of the use of products each knitting simulation knit found that sweater and muffler are the most appropriate knit products. And H2 was the most suitable sample for sweater and muffler products. This study tried to prove that knit textile can be visually evaluated through a computer knitting simulation without the actual knitting and then to provide accurate data to related companies or the academic circle.
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