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최지인(Choi, JeeIn),최종석(Choi, JongSuk),이현진(Lee, HyunJhin) 한국색채학회 2016 한국색채학회 논문집 Vol.30 No.3
This research is the study on color emotion by age groups of the elderly. In general, most countries have adopted the chronological age of 60 or 65 years as a definition of ‘elderly’. Globally, aging has been rapidly increased as well as itself aged. By 2050, the share of those aged 80 and over will grows more than three times compared to 2013.(UN, 2013) The rise of average life expectancy results in diversification of layers of age, which has occurred between the elderly to have different features physically, psychologically and socially. Therefore, the elderly group requires to be divided according to a narrow sense. This study was conducted to compare a middle-old group(aged 65-74) with an old-old group(aged 75-84) on color emotion for single color. Aging process has accelerated with age. Commonly, aging visual causes serious symptoms of visual problems, which is difficulty of cognition on cold color, and vision yellowing. As such, it will cause the differences in both color cognition and color emotion between two groups(middle-old, old-old). In this study, emotional adjectives on single color have been derived, and color emotion was examined. The result has been analyzed by SD(semantic differential) method. In conclusion, emotional differences have been found on single color between the two groups. This research indicates that blue had moderate emotional differences in comparison to warm color. However, white had the most distinctive differences. Furthermore, the emotional adjective of texture, soft-hard, frequently happened to show dissimilar results. As the rise of the elderly industries, the study of the elderly needs to have segmentation of the elderly group with age in order to find out accurate and valuable results. It is expected to be used in color marketing and accessible design for the elderly.
Jungeun Lim,Jeein Choi,원아영,Minji Kim,김성민,윤창상 한국의류학회 2022 Fashion and Textiles Vol.9 No.1
To prepare measures for washing synthetic fbers, which cause proliferation of microplastics in the marine ecosystem, a fundamental analysis is required. Therefore, this study established an efcient method for quantitatively analyzing microfbers using artifcial neural networks, comparing the amounts of microfbers generated in the manufacturing, wearing, and washing processes of clothing. The proportion of microfber emitted during the manufacturing process was the largest (49%), followed by that emitted during the washing (28%) and wearing (23%) processes. This suggests that minimizing the amount of microfber emitted during the manufacturing process is key to solving microfber issues in the fashion industry. Additionally, during the wearing process, the amount of waterborne microfber detected in washing was slightly larger than the amount of airborne microfber. In the washing process, the washing temperature did not signifcantly afect microfber emissions. However, when reducing the amount of water used or increasing the number of washings, microfber emissions increased noticeably due to the greater friction applied to clothes. A common result of all experiments was that the largest proportion of microfbers was released during the frst fve washing cycles. Therefore, before wearing new items, consumers can minimize microfber release by pre-washing using a laundry bag that flters microfbers. Furthermore, the most efective way to minimize microfbers is to eliminate them from the manufacturing process before they are distributed to consumers