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    The Relationship between Self-Directed Learning Ability, Learner-Instructor Interaction, Learning, and Learning Transfer of Participants in Real-Time Online Job Training at Large Corporations

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์‚ฐ์—…๊ต์œก๊ณผ, 2022. 8. ๊น€์ง„๋ชจ.The purpose of this study is to explore the relationship between self-directed learning ability, learner-instructor interaction, learning, and learning transfer of participants in real-time online job training at large companies. The study objectives are as follows. First, the influence relationship between self-directed learning ability, learner-instructor interaction, learning, and learning transfer of participants in real-time online job training at large companies is explored. Second, the mediating effect of learning in the relationship between self-directed learning ability, learner-instructor interaction, and learning transfer of real-time online job training participants of large corporations is explored. The target population of the study is workers currently employed by companies who have experienced in-house real-time online job training at least 1 to 6 months ago. Accordingly, large companies that are likely to operate real-time online training, were selected as research targets, and the scope of large companies was set as companies with assets of more than 500 billion South Korean won among holding companies and affiliates in 71 public disclosure companies announced by the Korea Fair Trade Commission in April 2021. In addition, considering the period from the end of training to the occurrence of learning transfer, the scope was limited to workers who experienced real-time online training within at least 1 to 6 months, and job training in which the occurrence of learning transfer is important. Since it is practically impossible to measure the population size of the study, significance sampling was used among non-probability sampling methods. A total of 384 questionnaire responses were collected using an online survey, and 318 questionnaire responses were used for analysis, excluding inappropriate responses, unfaithful responses, and outliers. SPSS Statistics 23.0 was used for statistical analysis. The main results of this study are as follows. First, self-directed learning ability and learning of participants in real-time online job training for large corporations had a statistically significant positive effect on learning transfer. However, learners-instructor interaction did not have a significant effect. Second, self-directed learning ability and learner-instructor interaction had a statistically significant positive effect on learning. Third, learning had a partial mediating effect in the relationship between self-directed learning ability and learning transfer. Fourth, learning had a partial mediating effect in the relationship between learner-instructor interaction and learning transfer. The conclusions of this study are as follows. First, self-directed learning ability and learning of participants in real-time online job training at large companies have a significant positive effect on learning transfer. Companies should provide support such as training so that learners can learn self-directed learning abilities, and provide appropriate instructional strategies for each level of learners. In addition, various support is needed to improve the knowledge, skills, and attitudes that learners aimed at through participation in training. On the other hand, learner-instructor interaction has no direct influence on learning transfer. However, even if learner-instructor interaction is actively conducted, learning transfer is difficult to occur if the learner's learning level is low. Therefore, active interaction between learners and instructors in real-time online training is very important. If various online functions and tools that can be used in real-time online training, are used in the right place according to the learner's characteristics and purpose and content of training, active participation of learners can be led and learning performance can be improved through active interaction. Second, learners' self-directed learning ability and learner-instructor interaction have a significant positive effect on learning. In order to improve the level of learning achieved through training, various methods are required to improve learners' self-directed learning ability, and various measures need to be prepared so that learners can actively participate in and communicate with training in real-time online training where physical restrictions exist. Third, learning has a mediating effect in the relationship between learners' self-directed learning ability, learner-instructor interaction, and learning transfer. In order to improve the level of learner's learning transfer, efforts are required to improve the level of acquisition of knowledge, skills, and attitudes aimed at training. Suggestions for follow-up research are as follows. First, it is necessary to consider the various types of interactions that can occur in real-time online training. Second, it is necessary to explore variables that affect learning and learning transfer in a real-time online training environment. Third, it is necessary to conduct a study that compares the variables affecting learning transfer in the existing training environment, such as offline or e-learning type non-real-time online training, with the variables affecting learning transfer in the real-time online training environment. Fourth, it is necessary to investigate the relationship between learning transfer and preceding variables in a real-time online training environment for various organizations and training. Fourth, it is necessary to investigate the relationship between learning transfer and antecedent variables in a real-time online training environment targeting various organizations and training.์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋Œ€๊ธฐ์—… ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ์ง๋ฌด ๊ต์œก ์ฐธ์—ฌ์ž์˜ ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ, ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ, ํ•™์Šต ๋ฐ ํ•™์Šต์ „์ด์˜ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ ๋ชฉํ‘œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋Œ€๊ธฐ์—… ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ์ง๋ฌด ๊ต์œก ์ฐธ์—ฌ์ž์˜ ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ, ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ, ํ•™์Šต ๋ฐ ํ•™์Šต์ „์ด ๊ฐ„์˜ ์˜ํ–ฅ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ๋‘˜์งธ, ๋Œ€๊ธฐ์—… ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ์ง๋ฌด ๊ต์œก ์ฐธ์—ฌ์ž์˜ ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ ๋ฐ ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ๊ณผ ํ•™์Šต์ „์ด์˜ ๊ด€๊ณ„์—์„œ ํ•™์Šต์˜ ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ตฌ๋ช…ํ•œ๋‹ค. ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œ ๋ชจ์ง‘๋‹จ์€ ํ˜„์žฌ ๊ธฐ์—…์— ์žฌ์ง ์ค‘์ธ ๊ทผ๋กœ์ž ์ค‘ ์ตœ์†Œ 1~6๊ฐœ์›” ์ „์— ์‚ฌ๋‚ด ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ์ง๋ฌด ๊ต์œก์„ ๊ฒฝํ—˜ํ•œ ๊ทผ๋กœ์ž์ด๋‹ค. ์ด์— ์ž์ฒด์ ์œผ๋กœ ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ๊ต์œก์„ ์šด์˜ํ•˜๊ณ  ์žˆ์„ ๊ฐ€๋Šฅ์„ฑ์ด ํฐ ๋Œ€๊ธฐ์—…์„ ์—ฐ๊ตฌ ๋Œ€์ƒ์œผ๋กœ ์„ ์ •ํ•˜์˜€์œผ๋ฉฐ, ๋Œ€๊ธฐ์—…์˜ ๋ฒ”์œ„๋Š” ๊ณต์ •๊ฑฐ๋ž˜์œ„์›ํšŒ๊ฐ€ 2021๋…„ 4์›” ๋ฐœํ‘œํ•œ 71๊ฐœ์˜ ๊ณต์‹œ๋Œ€์ƒ๊ธฐ์—…์ง‘๋‹จ ๋‚ด ์ง€์ฃผํšŒ์‚ฌ ๋ฐ ๊ณ„์—ด์‚ฌ ์ค‘ ์ค‘์†Œ๊ธฐ์—…๊ธฐ๋ณธ๋ฒ•์— ๋”ฐ๋ผ ์ž์‚ฐ์ด์•ก 5,000์–ต ์› ์ด์ƒ์˜ ๊ธฐ์—…์œผ๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ต์œก ์ข…๋ฃŒ ํ›„ ํ•™์Šต์ „์ด๊ฐ€ ๋ฐœ์ƒํ•˜๊ธฐ๊นŒ์ง€์˜ ๊ธฐ๊ฐ„์„ ๊ณ ๋ คํ•˜์—ฌ ์ตœ์†Œ 1~6๊ฐœ์›” ์ด๋‚ด์— ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ๊ต์œก์„ ๊ฒฝํ—˜ํ•œ ๊ทผ๋กœ์ž, ํ•™์Šต์ „์ด์˜ ๋ฐœ์ƒ์ด ์ค‘์š”ํ•œ ์ง๋ฌด ๊ต์œก์„ ๋Œ€์ƒ์œผ๋กœ ๋ฒ”์œ„๋ฅผ ํ•œ์ •ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์˜ ๋ชจ์ง‘๋‹จ ํฌ๊ธฐ๋ฅผ ์ธก์ •ํ•˜๋Š” ๊ฒƒ์€ ํ˜„์‹ค์ ์œผ๋กœ ๋ถˆ๊ฐ€๋Šฅํ•˜๊ธฐ์— ๋น„ํ™•๋ฅ ์  ํ‘œ์ง‘ ๋ฐฉ๋ฒ• ์ค‘ ์œ ์˜ํ‘œ์ง‘์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ž๋ฃŒ์ˆ˜์ง‘์€ ์˜จ๋ผ์ธ ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ด 384๋ถ€๋ฅผ ํšŒ์ˆ˜ํ•˜์˜€์œผ๋ฉฐ, ์‘๋‹ต ๋Œ€์ƒ์— ๋ถ€์ ํ•ฉํ•œ ์‘๋‹ต, ๋ถˆ์„ฑ์‹ค ์‘๋‹ต ๋ฐ ์ด์ƒ์น˜๋ฅผ ์ œ์™ธํ•œ 318๋ถ€๋ฅผ ๋ถ„์„์— ํ™œ์šฉํ•˜์˜€๋‹ค. ์กฐ์‚ฌ๋„๊ตฌ๋Š” ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ 21๋ฌธํ•ญ, ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ 5๋ฌธํ•ญ, ํ•™์Šต 3๋ฌธํ•ญ, ํ•™์Šต์ „์ด 5๋ฌธํ•ญ, ์‘๋‹ต์ž์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ์„ ์กฐ์‚ฌํ•˜๋Š” 10๋ฌธํ•ญ, ์ด 44๊ฐœ ๋ฌธํ•ญ์œผ๋กœ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์ผ๋ฐ˜์  ํŠน์„ฑ์„ ์ œ์™ธํ•œ ๋ชจ๋“  ๋ณ€์ธ์€ 5์  Likert ์ฒ™๋„๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ์˜ˆ๋น„์กฐ์‚ฌ์™€ ๋ณธ์กฐ์‚ฌ ๋ชจ๋‘ ๋ถ„์„์— ์ ํ•ฉํ•œ ์‹ ๋ขฐ๋„๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์ž๋ฃŒ ๋ถ„์„์€ SPSS Statistics 23.0 ํ†ต๊ณ„ ํ”„๋กœ๊ทธ๋žจ์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ธฐ์ˆ ํ†ต๊ณ„๋ถ„์„, ์ง‘๋‹จ ๊ฐ„ ์ฐจ์ด๋ถ„์„, ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„, PROCESS macro model 4๋ฅผ ํ™œ์šฉํ•œ ๋งค๊ฐœํšจ๊ณผ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋ชจ๋“  ๋ถ„์„์— ์žˆ์–ด์„œ ํ†ต๊ณ„์  ์œ ์˜์ˆ˜์ค€์€ .05๋กœ ์„ค์ •ํ•˜์˜€์œผ๋ฉฐ, ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•œ ๋ถ€ํŠธ์ŠคํŠธ๋ž˜ํ•‘ ํ‘œ๋ณธ ์ˆ˜๋Š” 10,000๊ฐœ๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋Œ€๊ธฐ์—… ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ์ง๋ฌด ๊ต์œก ์ฐธ์—ฌ์ž์˜ ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ(ฮฒ= .138, p<0.01), ํ•™์Šต(ฮฒ= .472, p<0.001)์€ ํ•™์Šต์ „์ด์— ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ •์  ์˜ํ–ฅ์„ ๋ฏธ์ณค์œผ๋‚˜, ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ์€ ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ(ฮฒ= .391, p<0.001)๊ณผ ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ(ฮฒ= .326, p<0.001)์€ ํ•™์Šต์— ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ •์  ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ๊ณผ ํ•™์Šต์ „์ด์˜ ๊ด€๊ณ„์—์„œ ํ•™์Šต์€ ๋ถ€๋ถ„๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฐ–๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋„ท์งธ, ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ๊ณผ ํ•™์Šต์ „์ด์˜ ๊ด€๊ณ„์—์„œ ํ•™์Šต์€ ๋ถ€๋ถ„๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฐ–๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์–ป์€ ๊ฒฐ๋ก ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋Œ€๊ธฐ์—… ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ์ง๋ฌด ๊ต์œก ์ฐธ์—ฌ์ž์˜ ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ๊ณผ ํ•™์Šต์€ ํ•™์Šต์ „์ด์— ์œ ์˜ํ•œ ์ •์  ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๊ธฐ์—…์€ ํ•™์Šต์ž๊ฐ€ ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ์„ ํ•™์Šตํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ต์œกํ›ˆ๋ จ ๋“ฑ์˜ ์ง€์›์„ ์ œ๊ณตํ•ด์•ผ ํ•˜๋ฉฐ, ํ•™์Šต์ž๋“ค์˜ ๊ฐ ์ˆ˜์ค€์— ๋งž์ถ”์–ด ์ ์ ˆํ•œ ์ˆ˜์—…์ „๋žต์„ ์ œ๊ณตํ•ด์•ผ ํ•œ๋‹ค. ๋˜ํ•œ ํ•™์Šต์ž๋“ค์ด ๊ต์œก ํ”„๋กœ๊ทธ๋žจ ์ฐธ์—ฌ๋ฅผ ํ†ตํ•ด ๋ชฉํ‘œ๋กœ ํ•˜์˜€๋˜ ์ง€์‹, ๊ธฐ์ˆ , ํƒœ๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋„๋ก ๋‹ค์–‘ํ•œ ์ง€์›์ด ํ•„์š”ํ•˜๋‹ค. ํ•œํŽธ, ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ์€ ํ•™์Šต์ „์ด์™€ ์ง์ ‘์ ์ธ ์˜ํ–ฅ๊ด€๊ณ„๊ฐ€ ์—†๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์ง€๋งŒ, ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ์ด ํ™œ๋ฐœํ•˜๊ฒŒ ์ด๋ฃจ์–ด์งˆ์ง€๋ผ๋„ ํ•™์Šต์ž์˜ ํ•™์Šต ์ˆ˜์ค€์ด ๋‚ฎ๋‹ค๋ฉด, ํ•™์Šต์ „์ด๊ฐ€ ๋ฐœ์ƒํ•˜๊ธฐ ์–ด๋ ค์šฐ๋ฏ€๋กœ ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ๊ต์œก์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํ•™์Šต์ž์™€ ๊ต์ˆ˜์ž์˜ ํ™œ๋ฐœํ•œ ์ƒํ˜ธ์ž‘์šฉ ๋˜ํ•œ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ๊ต์œก์—์„œ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์˜จ๋ผ์ธ ๊ธฐ๋Šฅ ๋ฐ ๋„๊ตฌ๋ฅผ ํ•™์Šต์ž์˜ ํŠน์„ฑ๊ณผ ๊ต์œก์˜ ๋ชฉ์ , ๋‚ด์šฉ์— ๋งž๊ฒŒ ์ ์žฌ์ ์†Œ์— ํ™œ์šฉํ•œ๋‹ค๋ฉด ํ•™์Šต์ž์˜ ์ ๊ทน์ ์ธ ์ฐธ์—ฌ๋ฅผ ์ด๋Œ๊ณ  ํ™œ๋ฐœํ•œ ์ƒํ˜ธ์ž‘์šฉ์„ ํ†ตํ•ด ํ•™์Šต์„ฑ๊ณผ๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋‘˜์งธ, ํ•™์Šต์ž์˜ ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ๊ณผ ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ์€ ํ•™์Šต์— ์œ ์˜ํ•œ ์ •์  ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๊ต์œก์„ ํ†ตํ•ด ์„ฑ์ทจํ•œ ํ•™์Šต ์ˆ˜์ค€์˜ ํ–ฅ์ƒ์„ ์œ„ํ•ด์„œ๋Š” ํ•™์Šต์ž์˜ ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์ด ์š”๊ตฌ๋˜๋ฉฐ, ๋ฌผ๋ฆฌ์ ์ธ ์ œ์•ฝ์ด ์กด์žฌํ•˜๋Š” ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ๊ต์œก์—์„œ ํ•™์Šต์ž๊ฐ€ ๊ต์œก์— ์ ๊ทน์ ์œผ๋กœ ์ฐธ์—ฌํ•˜๊ณ  ์˜์‚ฌ์†Œํ†ตํ•  ์ˆ˜ ์žˆ๋„๋ก ๋‹ค์–‘ํ•œ ๋ฐฉ์•ˆ์„ ๋งˆ๋ จํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์…‹์งธ, ํ•™์Šต์ž์˜ ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ ๋ฐ ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ๊ณผ ํ•™์Šต์ „์ด์˜ ๊ด€๊ณ„์—์„œ ํ•™์Šต์€ ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฐ–๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•™์Šต์ž์˜ ํ•™์Šต์ „์ด ์ˆ˜์ค€์„ ์ œ๊ณ ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ต์œก ํ”„๋กœ๊ทธ๋žจ์ด ๋ชฉํ‘œํ•œ ์ง€์‹, ๊ธฐ์ˆ , ํƒœ๋„์˜ ์Šต๋“ ์ˆ˜์ค€์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์ด ์š”๊ตฌ๋œ๋‹ค. ํ›„์† ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์ œ์–ธ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ๊ต์œก์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์ƒํ˜ธ์ž‘์šฉ์˜ ์œ ํ˜•์„ ์ถ”๊ฐ€์ ์œผ๋กœ ๊ณ ๋ คํ•ด์•ผํ•œ๋‹ค. ๋‘˜์งธ, ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ๊ต์œก ํ™˜๊ฒฝ์—์„œ ํ•™์Šต ๋ฐ ํ•™์Šต์ „์ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋ณ€์ธ๋“ค์„ ์ถ”๊ฐ€์ ์œผ๋กœ ํƒ์ƒ‰ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์…‹์งธ, ์˜คํ”„๋ผ์ธ ๋˜๋Š” e-Learning ํ˜•ํƒœ์˜ ๋น„์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ๊ต์œก ๋“ฑ ๊ธฐ์กด ๊ต์œก ํ™˜๊ฒฝ์—์„œ ํ•™์Šต์ „์ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋ณ€์ธ๊ณผ ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ๊ต์œก ํ™˜๊ฒฝ์—์„œ ํ•™์Šต์ „์ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋ณ€์ธ๋“ค์„ ๋น„๊ตํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋„ท์งธ, ๋‹ค์–‘ํ•œ ์กฐ์ง ๋ฐ ๊ต์œก์„ ๋Œ€์ƒ์œผ๋กœ ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ๊ต์œก ํ™˜๊ฒฝ์—์„œ ํ•™์Šต์ „์ด์™€ ์„ ํ–‰๋ณ€์ธ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  5 3. ์—ฐ๊ตฌ ๊ฐ€์„ค 5 4. ์šฉ์–ด์˜ ์ •์˜ 6 5. ์—ฐ๊ตฌ์˜ ์ œํ•œ 8 II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 9 1. ๋Œ€๊ธฐ์—… ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ์ง๋ฌด ๊ต์œก 9 2. ํ•™์Šต์ „์ด 14 3. ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ, ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ ๋ฐ ํ•™์Šต 32 4. ํ•™์Šต์ „์ด์™€ ๊ด€๋ จ ๋ณ€์ธ์˜ ๊ด€๊ณ„ 45 III. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 59 1. ์—ฐ๊ตฌ ๋ชจํ˜• 59 2. ์—ฐ๊ตฌ ๋Œ€์ƒ 60 3. ์กฐ์‚ฌ ๋„๊ตฌ 62 4. ์ž๋ฃŒ ์ˆ˜์ง‘ 65 5. ์ž๋ฃŒ ๋ถ„์„ 69 โ…ฃ. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 72 1. ์—ฐ๊ตฌ๋ณ€์ธ์˜ ์ผ๋ฐ˜ํ†ต๊ณ„๋Ÿ‰ 72 2. ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ, ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ, ํ•™์Šต์ด ํ•™์Šต์ „์ด์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 78 3. ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ, ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ์ด ํ•™์Šต์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 82 4. ์ž๊ธฐ์ฃผ๋„ํ•™์Šต๋Šฅ๋ ฅ ๋ฐ ํ•™์Šต์ž-๊ต์ˆ˜์ž ์ƒํ˜ธ์ž‘์šฉ๊ณผ ํ•™์Šต์ „์ด์˜ ๊ด€๊ณ„์—์„œ ํ•™์Šต์˜ ๋งค๊ฐœํšจ๊ณผ 85 5. ์—ฐ๊ตฌ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋…ผ์˜ 90 V. ์š”์•ฝ, ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 95 1. ์š”์•ฝ 95 2. ๊ฒฐ๋ก  96 3. ์ œ์–ธ 97 ์ฐธ๊ณ ๋ฌธํ—Œ 101 [๋ถ€ ๋ก] ์„ค๋ฌธ์ง€ 122 Abstract 127์„

    A study for development of model and HILS system for PEMFC performance analysis

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    ํ™˜๊ฒฝ๋ณดํ˜ธ์˜ ์ค‘์š”์„ฑ์ด ๊ฐ•์กฐ๋จ์— ๋”ฐ๋ผ ์„ธ๊ณ„์ ์œผ๋กœ ํ™˜๊ฒฝ์˜ค์—ผ์— ๋Œ€ํ•œ ๊ทœ์ œ๋ฅผ ๊ฐ•ํ™”ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ ์˜ค์—ผ๋ฌผ์งˆ ๋ฐฐ์ถœ์„ ์ „ํ˜€ ๋ฐœ์ƒ์‹œํ‚ค์ง€ ์•Š๋Š” ์—ฐ๋ฃŒ์ „์ง€๊ฐ€ ์œ ๋ ฅํ•œ ์ฐจ์„ธ๋Œ€ ๋™๋ ฅ์žฅ์น˜๋กœ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ๋‹ค์–‘ํ•œ ์—ฐ๋ฃŒ์ „์ง€ ์ค‘ ์ €์˜จํ˜• ์—ฐ๋ฃŒ์ „์ง€์ธ ๊ณ ๋ถ„์ž์ „ํ•ด์งˆ๋ง‰ ์—ฐ๋ฃŒ์ „์ง€(Proton Exchange Membrane Fuel Cell, PEMFC)๋Š” ์ž‘๋™์˜จ๋„๊ฐ€ 80~100โ„ƒ๋กœ ์‹œ๋™ ๋ฐ ์šด์ „์— ์šฉ์ดํ•˜๋ฉฐ, ๊ณ ์˜จํ˜• ์—ฐ๋ฃŒ์ „์ง€์— ํ•„์š”ํ•œ ๋‚ด์—ด ์žฌ์งˆ์ด ๋ถˆํ•„์š”ํ•˜๊ณ  ์œ ์ง€/๋ณด์ˆ˜ ์ธก๋ฉด์—์„œ ์œ ๋ฆฌํ•˜๋‹ค. ๋˜ํ•œ ๋ถ€ํ•˜ ๋ณ€๋™์— ์‹ ์†ํ•œ ๋Œ€์‘์ด ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์ด ์žˆ์–ด ํœด๋Œ€์šฉ ๊ธฐ๊ธฐ๋ฟ ์•„๋‹ˆ๋ผ ์„ ๋ฐ•๊ณผ ๊ฐ™์€ ๋Œ€์šฉ๋Ÿ‰์˜ ๋™๋ ฅ์žฅ์น˜๋กœ์จ ์ ํ•ฉํ•œ ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ํ˜„์žฌ๊นŒ์ง€ ์ƒ์šฉํ™”๋œ ๊ธฐ์ˆ ๋กœ๋Š” ์ž๋™์ฐจ ๋˜๋Š” ์†Œํ˜•์œ ๋žŒ์„ ์— ์ ์šฉํ•˜๋Š” ๊ฒƒ ์ด์ƒ์œผ๋กœ ๋Œ€ํ˜•ํ™”ํ•˜๊ธฐ๋Š” ์–ด๋ ค์šฐ๋ฉฐ ์‹ค์ œ ์šด์ „ ๋ฐ์ดํ„ฐ๊ฐ€ ๋ถ€์กฑํ•œ ์‹ค์ •์ด๋‹ค. ๋”ฐ๋ผ์„œ PEMFC์˜ ๋Œ€ํ˜•ํ™”์™€ ๊ทธ ์ ์šฉ์„ ์œ„ํ•ด์„œ๋Š” ์ถฉ๋ถ„ํ•œ ์šด์ „ ๊ฒฐ๊ณผ, ์‹คํ—˜ ๋ฐ์ดํ„ฐ๊ฐ€ ํ•„์š”ํ•œ๋ฐ ์‹œ๊ฐ„์ , ๋น„์šฉ์  ๋ฌธ์ œ ๋•Œ๋ฌธ์— ์‹ค์ œ PEMFC ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜์—ฌ ์šด์ „ ๊ฒฐ๊ณผ๋ฅผ ์–ป๊ธฐ ์–ด๋ ต๋‹ค. ๊ทธ๋ ‡๊ธฐ ๋•Œ๋ฌธ์— PEMFC ์‹œ์Šคํ…œ ๋ชจ๋ธ๋ง ๋ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•œ ์šด์ „ ๋ฐ์ดํ„ฐ ์ถ•์  ๋ฐ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ผ๋ฐ˜์ ์ธ ๋ชจ๋ธ๋ง ๋ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ํ†ตํ•ด์„œ ๋ชจ๋ธ์˜ ์šด์ „ ๋ฐ์ดํ„ฐ๋ฅผ ์–ป๋Š” ๋ฐฉ๋ฒ•์ด๋ฉฐ, ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ HILS (Hardware In Loop Simulation) ์‹œ์Šคํ…œ์„ ์‚ฌ์šฉํ•˜๋ฉด ์‹ค์‹œ๊ฐ„์œผ๋กœ ์šด์ „๋˜๋Š” ์†Œํ”„ํŠธ์›จ์–ด์™€ ์‹ค์ œ ํ”Œ๋žœํŠธ์—์„œ ์‚ฌ์šฉ๋  ํ•˜๋“œ์›จ์–ด์ธ ์ œ์–ด๊ธฐ ๋ฐ ์ฃผ๋ณ€๊ธฐ๊ธฐ์˜ ์—ฐ๋™์„ ํ†ตํ•ด ํ”Œ๋žœํŠธ๋ฅผ ๊ตฌ์ถ•ํ•˜๊ธฐ ์ „ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋ฌธ์ œ์ ์„ ๋ฏธ๋ฆฌ ํŒŒ์•…ํ•˜์—ฌ ๊ทธ ์†์‹ค์„ ์ตœ์†Œํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์†Œํ”„ํŠธ์›จ์–ด์™€ ํ•˜๋“œ์›จ์–ด์˜ ์—ฐ๋™๋œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ์ดํ„ฐ ์ถ•์ ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ์—ฐ๋ฃŒ์ „์ง€ ์‚ฐ์—…๊ณผ ๊ฐ™์€ ๊ณ ๋น„์šฉ์˜ ์‚ฐ์—…์—์„œ๋Š” HILS ์‹œ์Šคํ…œ์„ ์ด์šฉํ•˜์—ฌ ์—ฐ๊ตฌ์— ํ•„์š”ํ•œ ์‹œ๊ฐ„๊ณผ ๋น„์šฉ ์ ˆ๊ฐ์„ ํ†ตํ•œ ํšจ์œจ์ ์ธ ๊ฐœ๋ฐœ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋Œ€ํ˜•์„ ๋ฐ•์— PEMFC ์‹œ์Šคํ…œ ์ ์šฉ์„ ์œ„ํ•œ ์—ฐ๋ฃŒ์ „์ง€ ํŠน์„ฑ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ์‹ค์ œ ์šด์ „ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ 120kW๊ธ‰ PEMFC ์‹œ์Šคํ…œ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜๊ณ  HILS ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ๋ชจ๋ธ์€ Matlab/Simulink ๋ฐ Thermolib์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์‹ค์ œ ์šด์ „์—์„œ ์‚ฌ์šฉ๋œ ๊ฒƒ๊ณผ ๊ฐ™์€ ์‚ฐ์†Œ์™€ ์ˆ˜์†Œ๋ฅผ ์ด์šฉํ•˜๋Š” ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ์šด์ „ ๋ฐ์ดํ„ฐ์™€์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ๊ทธ ์‹ ๋ขฐ์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์‹ ๋ขฐ์„ฑ์„ ๋ฐ”ํƒ•์œผ๋กœ ๋ชจ๋ธ์„ ๋‹ค์–‘ํ•œ ์กฐ๊ฑด์—์„œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜์—ฌ ์šด์ „ ํŠน์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋จผ์ € ์‚ฐ์†Œ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ณต๊ธฐ๋ฅผ ์‚ฌ์šฉํ–ˆ์„ ๋•Œ์˜ ์„ฑ๋Šฅ ํŠน์„ฑ์„ ๋น„๊ตํ•˜์˜€์œผ๋ฉฐ ์Šคํƒ์˜ ์šด์ „์˜จ๋„, ์—ฐ๋ฃŒ/๊ฐ€์Šค ๊ณต๊ธ‰์••๋ ฅ, ์ „ํ•ด์งˆ๋ง‰์˜ ๋ฐ˜์‘๋ฉด์ , ์ „ํ•ด์งˆ๋ง‰์˜ ๋‘๊ป˜์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๊ฐ๊ฐ์˜ ์š”์ธ์ด ์šด์ „ ํŠน์„ฑ์— ์ฃผ๋Š” ์˜ํ–ฅ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์Šคํƒ์˜ ์—ด์šฉ๋Ÿ‰๊ณผ ์Šคํƒ์˜ ๋ƒ‰๊ฐ๋งค์ฒด์™€์˜ ์—ด์ „๋‹ฌ๊ณ„์ˆ˜์˜ ๋ณ€ํ™”๊ฐ€ ์šด์ „ ํŠน์„ฑ์— ์ฃผ๋Š” ์˜ํ–ฅ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ฐœ๋ฐœํ•œ Simulink ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ HILS ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜์—ฌ ์‹ค์‹œ๊ฐ„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์‹คํ–‰ํ•˜์˜€๋‹ค. ์‹คํ—˜ ๋ฐ์ดํ„ฐ์™€ HILS ์‹œ์Šคํ…œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์˜ ๊ฒฐ๊ณผ ๋น„๊ต๋ฅผ ํ†ตํ•ด์„œ HILS ์‹œ์Šคํ…œ์˜ ์‹ค์šฉ์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์‹คํ—˜ ๋ฐ์ดํ„ฐ์™€ ๊ฐœ๋ฐœํ•œ ๋ชจ๋ธ์˜ ์šด์ „ ๊ฒฐ๊ณผ ์Šคํƒ ์ถœ๋ ฅ ์ „์••์˜ ์ฐจ์ด๊ฐ€ ์ตœ๋Œ€ 1.9%์˜€์œผ๋ฉฐ ์œ ์‚ฌํ•˜๊ฒŒ ์šด์ „๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ชจ๋ธ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ, Cathode ๊ณต๊ธ‰ ๊ธฐ์ฒด๋ฅผ ์‚ฐ์†Œ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๊ฒฝ์šฐ ์Šคํƒ ์ „์••์ด ๋†’์•˜์œผ๋ฉฐ ์ž‘๋™์˜จ๋„๊ฐ€ ๋†’์„์ˆ˜๋ก, ์—ฐ๋ฃŒ/๊ฐ€์Šค ๊ณต๊ธ‰์••๋ ฅ์ด ๋†’์„์ˆ˜๋ก, ์ „ํ•ด์งˆ๋ง‰์˜ ๋ฐ˜์‘๋ฉด์ ์ด ๋„“์„์ˆ˜๋ก, ์ „ํ•ด์งˆ๋ง‰์˜ ๋‘๊ป˜๊ฐ€ ์–‡์„์ˆ˜๋ก ์ „์•• ๋ฐ ์ถœ๋ ฅ์ด ๋” ๋†’์•„์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ ์Šคํƒ์˜ ์—ด์šฉ๋Ÿ‰ ๋ฐ ๋ƒ‰๊ฐ๋งค์ฒด์™€์˜ ์—ด์ „๋‹ฌ๊ณ„์ˆ˜์˜ ๊ฒฝ์šฐ ์Šคํƒ์˜ ์ถœ๋ ฅ์— ์˜ํ–ฅ์„ ํฌ๊ฒŒ ๋ฏธ์น˜์ง€๋Š” ์•Š๋Š”๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ HILS ์‹œ์Šคํ…œ์„ ์ด์šฉํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์˜ ๊ฒฐ๊ณผ๊ฐ€ ์‹คํ—˜ ๋ฐ์ดํ„ฐ์™€ ํก์‚ฌํ•˜๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์—ฌ, ์‹ค์ œ 120kW PEMFC๋ฅผ ๋Œ€์ฒดํ•˜๋Š” ์‹ค์‹œ๊ฐ„ Simulator๋กœ์จ ๊ตฌ์ถ•ํ•œ ์‹œ์Šคํ…œ์˜ ์‹ค์šฉ์„ฑ์„ ์ž…์ฆํ•˜์˜€๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ๋ฐฉ๋ฒ• 3 ์ œ 2 ์žฅ ๊ณ ๋ถ„์ž ์ „ํ•ด์งˆ๋ง‰ ์—ฐ๋ฃŒ์ „์ง€(PEMFC) ๋ฐ HILS ์‹œ์Šคํ…œ 2.1 ๊ฐœ์š” 4 2.2 PEMFC์˜ ํŠน์„ฑ 5 2.3 HILS ์‹œ์Šคํ…œ์˜ ํŠน์„ฑ 6 ์ œ 3 ์žฅ ๊ณ ๋ถ„์ž ์ „ํ•ด์งˆ๋ง‰ ์—ฐ๋ฃŒ์ „์ง€(PEMFC) ์‹œ์Šคํ…œ ๋ชจ๋ธ๋ง 3.1 ์ „์ฒด ์‹œ์Šคํ…œ ๊ตฌ์„ฑ 7 3.2 ์‹œ์Šคํ…œ ๊ตฌ์„ฑ ์š”์†Œ ์†Œ๊ฐœ 13 3.2.1 PEMFC ์Šคํƒ 13 3.2.2 ํŽŒํ”„ 19 3.2.3 ์—ด๊ตํ™˜๊ธฐ 20 3.2.4 ํƒฑํฌ 21 3.2.5 PI ์ œ์–ด๊ธฐ 21 ์ œ 4 ์žฅ HILS ์‹œ์Šคํ…œ 4.1 HILS ์‹œ์Šคํ…œ ๊ตฌ์„ฑ 22 4.2 HILS ์‹œ์Šคํ…œ ๊ตฌ์„ฑ ์š”์†Œ ์†Œ๊ฐœ 23 4.3 HILS ์‹œ์Šคํ…œ ๊ตฌ์ถ• ๊ณผ์ • 28 ์ œ 5 ์žฅ ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๊ณ ์ฐฐ ๋ฐ ๊ฒ€ํ†  5.1 ์—ฐ๋ฃŒ์ „์ง€ ๋ชจ๋ธ ๊ฒ€์ฆ 58 5.2 ์—ฐ๋ฃŒ์ „์ง€ ๋ชจ๋ธ์˜ ์กฐ๊ฑด๋ณ„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ 61 5.2.1 ์˜จ๋„์— ๋”ฐ๋ฅธ ์˜ํ–ฅ 61 5.2.2 ์••๋ ฅ์— ๋”ฐ๋ฅธ ์˜ํ–ฅ 66 5.2.3 ๋ฐ˜์‘๋ฉด์ ์— ๋”ฐ๋ฅธ ์˜ํ–ฅ 71 5.2.4 ์ „ํ•ด์งˆ๋ง‰ ๋‘๊ป˜์— ๋”ฐ๋ฅธ ์˜ํ–ฅ 76 5.2.5 ์Šคํƒ์˜ ์—ด์šฉ๋Ÿ‰์— ๋”ฐ๋ฅธ ์˜ํ–ฅ 81 5.2.6 ์Šคํƒ๊ณผ ๋ƒ‰๊ฐ๋งค์ฒด ์‚ฌ์ด์˜ ์—ด์ „๋‹ฌ๊ณ„์ˆ˜์— ๋”ฐ๋ฅธ ์˜ํ–ฅ 85 5.3 ์—ฐ๋ฃŒ์ „์ง€ ๋ชจ๋ธ ๋ฐ HILS ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ๋น„๊ต 89 ์ œ 6 ์žฅ ๊ฒฐ ๋ก  91 ๊ฐ์‚ฌ์˜ ๊ธ€ 93 ์ฐธ๊ณ  ๋ฌธํ—Œ 94Maste

    Flow separation and nozzle side load simulations for TOP nozzle

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    ์ง€๋ฐฉ์„ธํฌ ํ˜•ํƒœ ๋ณ€ํ™”์— ์˜ํ•œ ์ธ์Š๋ฆฐ ์˜์กด์  ํฌ๋„๋‹น ํก์ˆ˜๋Šฅ ์กฐ์ ˆ ๊ธฐ์ „ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ƒ๋ช…๊ณผํ•™๋ถ€, 2019. 2. ๊น€์žฌ๋ฒ”.์ง€๋ฐฉ์กฐ์ง์€ ์ฒด๋‚ด ์—๋„ˆ์ง€ ํ•ญ์ƒ์„ฑ์„ ์กฐ์ ˆํ•˜๋Š” ์ฃผ์š” ์žฅ๊ธฐ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ์ง€๋ฐฉ์กฐ์ง์€ ํ•ด๋ถ€ํ•™์  ์œ„์น˜์™€ ๊ธฐ๋Šฅ์„ ๊ธฐ์ค€์œผ๋กœ ํฌ๊ฒŒ ๋ฐฑ์ƒ‰๋‚ด์žฅ์ง€๋ฐฉ, ๋ฐฑ์ƒ‰ํ”ผํ•˜์ง€๋ฐฉ, ๊ทธ๋ฆฌ๊ณ  ๊ฐˆ์ƒ‰์ง€๋ฐฉ์œผ๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ง€๋ฐฉ์กฐ์ง์˜ ์ฃผ์š” ๊ธฐ๋Šฅ ์ค‘ ํ•˜๋‚˜๋Š” ์ธ์Š๋ฆฐ์— ๋ฐ˜์‘ํ•˜์—ฌ ์ฒด๋‚ด ์ž‰์—ฌ ์—๋„ˆ์ง€๋ฅผ ํก์ˆ˜ํ•˜์—ฌ ์ค‘์„ฑ์ง€๋ฐฉ๋Œ€์‚ฌ๋ฌผ์„ ์ง€๋ฐฉ์†Œ์ฒด์— ์ €์žฅํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ธ์Š๋ฆฐ ์ž๊ทน์— ๋Œ€ํ•œ ์„ธํฌ์˜ ์ ์ ˆํ•œ ๋ฐ˜์‘์„ฑ์„ ์ธ์Š๋ฆฐ ๋ฏผ๊ฐ๋„๋ผ ํ•œ๋‹ค. ๋ฐ˜๋Œ€๋กœ ์ธ์Š๋ฆฐ ์ž๊ทน์ด ์กด์žฌํ•จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์„ธํฌ๊ฐ€ ์ด์— ์ ์ ˆํ•˜๊ฒŒ ๋ฐ˜์‘ํ•˜์ง€ ๋ชปํ•˜๋Š” ๊ฒฝ์šฐ๋ฅผ ์ธ์Š๋ฆฐ ์ €ํ•ญ์„ฑ์ด๋ผ ์ •์˜ํ•œ๋‹ค. ์ธ์Š๋ฆฐ ์ €ํ•ญ์„ฑ์€ ๋‹ค์–‘ํ•œ ๋Œ€์‚ฌ ์งˆํ™˜์˜ ์ฃผ์š” ๋ฐœ๋ณ‘ ์›์ธ์œผ๋กœ ์ธ์‹๋œ๋‹ค. ๋‹ค์–‘ํ•œ ์ƒ๋ฆฌ์  ํ˜น์€ ๋ณ‘๋ฆฌ์  ์ƒํ™ฉ์—์„œ ์ง€๋ฐฉ์„ธํฌ๋Š” ์ธ์Š๋ฆฐ์— ๋Œ€ํ•œ ๋ฐ˜์‘์„ฑ๊ณผ ํ˜•ํƒœํ•™์  ๋ณ€ํ™”๋ฅผ ์ˆ˜๋ฐ˜ํ•œ๋‹ค. ์ง€๋ฐฉ์กฐ์ง์€ ์ง€๋ฐฉ์„ธํฌ์™€ ๋ฉด์—ญ์„ธํฌ๋“ค์„ ํฌํ•จํ•˜๋Š” ๋งฅ๊ด€๊ณ„ ๊ฐ„์„ธํฌ๋“ค(stromal vascular cells)๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ์œผ๋ฉฐ, ์ด๋“ค ์‚ฌ์ด์—๋Š” ๋ณต์žกํ•œ ์„ธํฌ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์ด ์กด์žฌํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ ๋Œ€์‚ฌ์งˆํ™˜, ํŠนํžˆ, ์ธ์Š๋ฆฐ์ €ํ•ญ์„ฑ ์œ ๋ฐœ ์ธก๋ฉด์—์„œ ์ง€๋ฐฉ์กฐ์ง์ด ์ค‘์š”ํ•จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์ง€๋ฐฉ์„ธํฌ ๋‚ด์žฌ์  ์›์ธ์— ์˜ํ•œ ์ธ์Š๋ฆฐ ๋ฏผ๊ฐ๋„ ์กฐ์ ˆ๊ธฐ์ „์— ๋Œ€ํ•ด์„œ๋Š” ์—ฐ๊ตฌ๊ฐ€ ๋ถ€์กฑํ•œ ์ƒํ™ฉ์ด๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ํ•™์œ„๋…ผ๋ฌธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ง€๋ฐฉ์„ธํฌ์˜ ํ˜•ํƒœ์™€ ์ธ์Š๋ฆฐ ๋ฐ˜์‘์„ฑ์˜ ์ƒํ˜ธ๊ด€๊ณ„์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์ง€๋ฐฉ์„ธํฌ ํ˜•ํƒœ์  ๋ณ€ํ™”๊ฐ€ ๊ธฐ๋Šฅ์  ๋ณ€ํ™”๋ฅผ ์ˆ˜๋ฐ˜ํ•˜๋Š” ๊ธฐ์ „์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ 1์žฅ์—์„œ๋Š” ๋น„๋งŒ์ด ์œ ๋„๋œ ๊ฐœ์ฒด์—์„œ ๋‹จ์ผ์ง€๋ฐฉ์†Œ์ฒด ํฌ๊ธฐ ์ฆ๊ฐ€์— ์˜ํ•œ ์ง€๋ฐฉ์„ธํฌ ๊ฑฐ๋Œ€ํ™” ํ˜„์ƒ๊ณผ ์ธ์Š๋ฆฐ ์ €ํ•ญ์„ฑ์ด ์œ ๋ฐœ๋˜๋Š” ๊ธฐ์ „์— ๋Œ€ํ•ด ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด, ๋‹ค์–‘ํ•œ ์ž์œ ์ง€๋ฐฉ์‚ฐ์„ ๋ถ„ํ™”๋œ 3T3-L1์ง€๋ฐฉ์„ธํฌ์— ์ฒ˜๋ฆฌํ•จ์œผ๋กœ์จ ๊ฑฐ๋Œ€ ์ง€๋ฐฉ์„ธํฌ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ์˜ฌ๋ ˆ์‚ฐ (Oleic acid)์„ ์ฒ˜๋ฆฌํ•˜์—ฌ ์œ ๋„ํ•œ ๊ฑฐ๋Œ€์ง€๋ฐฉ์„ธํฌ์˜ ๊ฒฝ์šฐ, ์—ผ์ฆ๋ฐ˜์‘ ๋ฐ ์ธ์Š๋ฆฐ ํ•˜์œ„ ์‹ ํ˜ธ์ „๋‹ฌ๊ณผ์ •๊ณผ ๋…๋ฆฝ์ ์œผ๋กœ ์ธ์Š๋ฆฐ ์ €ํ•ญ์„ฑ์ด ๋ฐœ์ƒํ•˜์˜€๋‹ค. ๋˜ํ•œ, ํ˜„๋ฏธ๊ฒฝ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•œ ๋‹จ์ผ์„ธํฌ์ˆ˜์ค€์—์„œ์˜ ๊ด€์ฐฐ์„ ํ†ตํ•ด, ๊ฑฐ๋Œ€ ์ง€๋ฐฉ์„ธํฌ ํŠน์ด์ ์œผ๋กœ ์ธ์Š๋ฆฐ์˜์กด์  ํฌ๋„๋‹น ์ˆ˜์†ก์ฒด4์˜ ์ด๋™์ด ์ €ํ•ด๋˜์—ˆ์œผ๋ฉฐ, ์ด ๋•Œ ์„ธํฌ๋ง‰์— ์ธ์ ‘ํ•œ ์•กํ‹ด์„ฌ์œ ๊ตฌ์กฐ๊ฐ€ ์†์ƒ๋˜์–ด ์žˆ์Œ์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ 2์žฅ์—์„œ๋Š” ์ง€๋ฐฉ์†Œ์ฒด ์ˆ˜์  ๋ณ€ํ™”์— ์˜ํ•œ ์ธ์Š๋ฆฐ ์˜์กด์  ํฌ๋„๋‹น ํก์ˆ˜๋Šฅ์— ๋Œ€ํ•œ ๊ธฐ์ „์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ์˜ฌ๋ ˆ์‚ฐ ์ฒ˜๋ฆฌ๋ฅผ ํ†ตํ•ด 1) ์ค‘์„ฑ์ง€๋ฐฉ ์ถ•์ ์— ์˜ํ•œ ์ง€๋ฐฉ์†Œ์ฒด ๋‹จ์ผํ™”์™€ ๊ฑฐ๋Œ€ํ™”๋ฅผ ์œ ๋„ํ•œ ๊ฒฝ์šฐ ๊ทธ๋ฆฌ๊ณ  2) ๊ฑฐ๋Œ€ํ™” ์œ ๋„ ํ›„ ์ถ•์ ๋œ ์ค‘์„ฑ์ง€๋ฐฉ์˜ ๋ถ„ํ•ด๋ฅผ ํ†ตํ•ด ์ง€๋ฐฉ์†Œ์ฒด ์ˆ˜๋ฅผ ๋‹ค์‹œ ๋Š˜๋ ค์ค€ ๊ฒฝ์šฐ, ๊ฐ๊ฐ์—์„œ ์ธ์Š๋ฆฐ์˜์กด์  ํฌ๋„๋‹น ํก์ˆ˜๋Šฅ ๋ฐ ํฌ๋„๋‹น ์ˆ˜์†ก์ฒด4์˜ ์ด๋™๋Šฅ๋ ฅ์„ ์ธก์ •ํ•˜์˜€๋‹ค. ์ง€๋ฐฉ์†Œ์ฒด์˜ ์ˆ˜์  ์ฆ๊ฐ€์™€ ํฌ๊ธฐ ๊ฐ์†Œ๊ฐ€ ์œ ๋„๋˜๋Š” ์ƒ๋ฆฌ์  ์กฐ๊ฑด๋“ค (์ถ”์œ„์ž๊ทน/๋ฒ ํƒ€์•„๋“œ๋ ˆ๋‚ ๋ฆฐ ์ž๊ทน)์—์„œ๋Š” ์ธ์Š๋ฆฐ ์˜์กด์  ํฌ๋„๋‹น ํก์ˆ˜ ๋ฐ ํฌ๋„๋‹น ์ˆ˜์†ก์ฒด4์˜ ์ด๋™์ด ์ฆ๊ฐ€ํ•œ ๋ฐ˜๋ฉด ์ง€๋ฐฉ์†Œ์ฒด ๋‹จ์ผํ™” ํ˜น์€ ๊ฑฐ๋Œ€ํ™”๊ฐ€ ์œ ๋„๋˜๋Š” ์ƒ๋ฆฌ์  (thermoneutral condition) ๋˜๋Š” ๋ณ‘๋ฆฌ์  (๋น„๋งŒ) ์กฐ๊ฑด์—์„œ๋Š” ์ธ์Š๋ฆฐ ์˜์กด์  ํฌ๋„๋‹น ํก์ˆ˜ ๋ฐ ํฌ๋„๋‹น ์ˆ˜์†ก์ฒด4์˜ ์ด๋™์ด ์ €ํ•ด๋˜์—ˆ๋‹ค. ๊ฐ ์กฐ๊ฑด์—์„œ ์ง€๋ฐฉ์„ธํฌ ๋‚ด F์•กํ‹ด๊ณผ G์•กํ‹ด๊ฐ„์˜ ๋น„์œจ์ด ์—ญ๋™์ ์œผ๋กœ ๋ณ€ํ™”๋จ์„ ๊ด€์ฐฐํ•˜์˜€๊ณ  ์ด๋ฅผ ํ†ตํ•ด F/G์•กํ‹ด ๋น„์œจ ๋ณ€ํ™”์— ์˜ํ•œ ์ธ์Š๋ฆฐ ์˜์กด์  ํฌ๋„๋‹น ์ˆ˜์†ก์ฒด4 ์ด๋™ ์กฐ์ ˆ์ด ํ˜•ํƒœ์  ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ง€๋ฐฉ์„ธํฌ ์ธ์Š๋ฆฐ ์˜์กด์  ํฌ๋„๋‹น ํก์ˆ˜ ๊ณผ์ •์„ ๋งค๊ฐœํ•˜๋Š” ์ฃผ์š” ๊ธฐ์ „ ์ค‘ ํ•˜๋‚˜์ž„์„ ์ƒˆ๋กœ์ด ์ œ์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ 1) ์ง€๋ฐฉ์†Œ์ฒด์˜ ํฌ๊ธฐ ๋ฐ ์ˆ˜์  ๋ณ€ํ™”๋ฅผ ๋™๋ฐ˜ํ•˜๋Š” ํ˜•ํƒœ ๋ณ€ํ™”๊ฐ€ ์ง€๋ฐฉ์„ธํฌ ํ”ผ์งˆ ๋ถ€์œ„์˜ ์•กํ‹ด ์„ธํฌ๊ณจ๊ฒฉ ๊ตฌ์กฐ๋ฅผ ์กฐ์ ˆํ•จ๊ณผ 2) F์•กํ‹ด/G์•กํ‹ด๊ฐ„์˜ ์—ญ๋™์  ๋ณ€ํ™”๊ฐ€ ์ธ์Š๋ฆฐ ์˜์กด์  ํฌ๋„๋‹น ์ˆ˜์†ก์ฒด4 ์ด๋™์„ ์กฐ์ ˆํ•จ์„ ๊ทœ๋ช…ํ•˜์˜€๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์ง€๋ฐฉ์„ธํฌ ํ˜•ํƒœ ๋ณ€ํ™”๊ฐ€ ์ง€๋ฐฉ์„ธํฌ ์ธ์Š๋ฆฐ ๋ฏผ๊ฐ๋„๋ฅผ ์ง์ ‘ ์กฐ์ ˆํ•˜๋Š” ๊ธฐ์ „์ž„์„ ์ƒˆ๋กœ์ด ์ œ์•ˆํ•˜์˜€๋‹ค. ํŠนํžˆ, ์ด๋Ÿฌํ•œ ์กฐ์ ˆ ๊ธฐ์ „์€ ์„ธํฌ์ˆ˜์ค€์—์„œ์˜ ์„ธํฌ ๋ณธ์—ฐ์˜ ํ˜•ํƒœ ๋ณ€ํ™”๊ฐ€ ์กฐ์ง ๋ฐ ๊ฐœ์ฒด์˜ ๋Œ€์‚ฌ๊ณผ์ •์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ์Œ์„ ์ œ์‹œํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ํฐ ์˜๋ฏธ๋ฅผ ์ง€๋‹ˆ๋ฉฐ, ๋น„๋งŒ ํ˜น์€ ์šด๋™ ๋“ฑ ๋‹ค์–‘ํ•œ ์ƒ๋ฆฌ์  ๋ณ‘๋ฆฌ์  ์กฐ๊ฑด์— ๋”ฐ๋ฅธ ์ง€๋ฐฉ์„ธํฌ ๊ธฐ๋Šฅ ์กฐ์ ˆ์— ์žˆ์–ด ์ง€๋ฐฉ์„ธํฌ์˜ ํ˜•ํƒœ ์กฐ์ ˆ์ด ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ์Œ์„ ์•”์‹œํ•œ๋‹ค.Adipose tissue (AT) is a major metabolic organ that regulates energy homeostasis. AT can be largely categorized into visceral white adipose tissue, subcutaneous white adipose tissue, and brown adipose tissue based on anatomical location and functions. The major role of AT is to reserve excess energy sources in lipid droplets (LDs) in form of triglycerides upon nutritional balance. Cellular responsiveness to insulin is defined as insulin sensitivity, on the contrary, insufficient response to insulin provokes insulin resistance, and insulin resistance is considered as a key etiology of metabolic diseases. Under various physiological or pathological conditions, adipocytes accompany dramatic changes in insulin sensitivity and morphology. Given that, ATs are largely divided into adipocytes and stromal vascular cells including various immune cells, and exhibit complex cell-to-cell and tissue-to-tissue interactions, it has been difficult to study intrinsic roles of adipocytes in the regulation of insulin sensitivity in ATs. Thus, this dissertation study has been focused on the relationship between the morphology/shape and insulin responsiveness of adipocytes, and investigated the mechanism by which alteration of adipocyte morphology could affect its functions, particularly, in insulin sensitivity. In the first chapter, in order to investigate the mechanism of adipocyte hypertrophy-induced insulin resistance in obesity, I developed in vitro hypertrophic adipocyte model system by challenging with various free fatty acids to differentiated 3T3-L1 adipocytes. Unlike long chain saturated- fatty acids-induced hypertrophic adipocytes, mono unsaturated-fatty acid, oleic acid, -induced adipocytes hypertrophy potentially provoked insulin resistance without any influences of inflammatory responses and insulin signaling cascades. By adopting microscopic approaches, I could determine the insulin sensitivity of adipocytes with several morphologies at the single cell level. Notably, insulin-dependent glucose transporter 4 (GLUT4) translocation to plasma membrane was preferentially impaired in a unilocular hypertrophic adipocyte-specific manner. In addition, I found that cortical filamentous (F)-actin structure was disrupted compared to small and/or multilocular adipocytes. In the second chapter, to unveil the roles of adipocyte LDs locularity on insulin sensitivity control, I investigated insulin-dependent GLUT4 trafficking and glucose uptake ability in 1) oleic acids-induced hypertrophic adipocytes with unilocular and enlarged LDs, and 2) reversibly multilocularized adipocytes from oleic acids-induced hypertrophy. The extents of insulin-dependent GLUT4 trafficking and glucose uptake were improved in most physiological conditions when LDs were multilocularized and reduced in their size. On the contrary, physiological or pathological conditions that exhibited LDs unilocularization and LD enlargement decreased insulin-dependent GLUT4 translocation and glucose uptake of adipocytes. From these observations, I propose novel roles of F/G-actin dynamics as a modulator of insulin-dependent glucose uptake upon different morphologies of adipocytes. In this study, I discovered followings that 1) adipocyte hypertrophy per se provoked insulin resistance via impaired GLUT4 trafficking, independent of inflammatory responses, 2) LDs multilocularization and decrease in their sizes lead to increase the ratio of F/G-actin dynamics, accompanied with improved insulin sensitivity, while 3) LD unilocularization and increase in LD size downregulated the ratio of F/G-actin dynamics, concurrently with impaired insulin sensitivity. Collectively, these data clearly suggest that morphological changes of LDs could affect insulin sensitivity of adipocytes in a cell autonomous manner. I believe that this research provides a new insight that adipocytes morphology and their dynamic regulation plays important roles in the regulation of insulin sensitivity upon nutritional status.ABSTRACTโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.i TABLE OF CONTENTSโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.iv LIST OF FIGURESโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆvi LIST OF TABLESโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆviii BACKGROUNDSโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.1 1. Obesity, adipose tissue remodeling, and insulin resistance...โ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.1 1) Obesity and insulin resistance โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..1 2) Adipose tissue remodelingโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..2 2. Adipocyte and glucose homeostasisโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.3 1) White and brown adipocytes.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ3 2) Insulin action in adipocytesโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.5 3. Purpose of this studyโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆ.7 CHAPTER ONE: Lipid-overloaded enlarged adipocytes provoke insulin resistance independent of inflammationโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.10 1. Abstractโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ11 2. Introductionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.12 3. Materials and methodsโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ15 4. Resultsโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..21 5. Discussionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ41 CHAPTER TWO: Adipocyte lipid droplets play a key role in regulating insulin-dependent glucose uptake via control of F/G-actin dynamicsโ€ฆ49 1. Abstractโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ........50 2. Introductionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..51 3. Materials and methodsโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ55 4. Resultsโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ....59 5. Discussionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ81 CONLUSION & PERSPECTIVESโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..89 1. Adipocyte hypertrophy per se provoke insulin resistanceโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...90 2. In early obesity, inflammation is dissociated from insulin resistanceโ€ฆโ€ฆโ€ฆโ€ฆ91 3. Intrinsic reversibility of lipid droplet and size affect adipocyte insulin sensitivityโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.....91 4. F/G-actin dynamics mediate insulin sensitivity control upon LD locularity changesโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.92 REFERENCESโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ....โ€ฆ94 ABSTRACT IN KOREANโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ106Docto

    Automatic classification of classical music based on musical contents

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ธ๋ฌธ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์ธ์ง€๊ณผํ•™์ „๊ณต, 2018. 2. ์ •๋ฏผํ™”.์Œ์•…์€ ์ƒ๋ถ€ ์ธต์œ„์˜ ์ง€์‹, ์ค‘๊ฐ„ ์ธต์œ„์˜ ์ฝ˜ํ…์ธ , ๊ทธ๋ฆฌ๊ณ  ํ•˜๋ถ€ ์ธต์œ„์˜ ์‹ ํ˜ธ ํŠน์ง• ์œผ๋กœ ๊ณ„์ธตํ™”๋˜์–ด ๋ถ„๋ฅ˜๋  ์ˆ˜ ์žˆ๋‹ค. ์ค‘๊ฐ„ ์ธต์œ„๋Š” ์Œ์•…์„ ์ธ๊ฐ„๊ณผ ์†Œํ†ตํ•˜๋Š” ๋„๊ตฌ๋กœ์„œ ์Œ๊ณ , ๋ฉœ๋กœ๋””, ๋ฆฌ๋“ฌ, ์Œ๋Ÿ‰, ์Œ์ƒ‰, ์Œ๊ฐ€, ํ…์Šค์ณ, ํ™”์„ฑ ๋“ฑ์œผ๋กœ ํ‘œํ˜„๋˜๋ฉฐ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์Œ๊ณ , ์Œ๋Ÿ‰, ์Œ๊ฐ€, ์Œ์ƒ‰, ํ™”์„ฑ์˜ ๋‹ค์„ฏ ๊ฐ€์ง€ ๊ธฐ๋ณธ ์š”์†Œ๋ฅผ ์‚ฌ์šฉํ•ด์„œ ์Œ์•… ์ž๋™๋ถ„๋ฅ˜์— ์ ์šฉํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ƒ๋ถ€ ์ธต์œ„์˜ ์ง€์‹์€ ์Œ์•…์— ๋Œ€ํ•œ ์‹ฌ๋ฆฌ์ , ๊ฐ์„ฑ์  ๊ฒฐ๊ณผ๋ฌผ๋กœ์„œ ์Œ์•…์— ๋Œ€ํ•œ ๊ฐ์ •, ๊ธฐ์–ต, ๊ธฐ๋Œ€ ๋“ฑ์œผ๋กœ ์Œ์•…์ด ์ถ”๊ตฌํ•˜๋Š” ๋ชฉํ‘œ์ด๋‹ค. ์ค‘๊ฐ„ ์ธต์œ„์˜ ํŠน ์„ฑ์€ ์Œ์˜ ํฌ๊ธฐ, ์—๋„ˆ์ง€, ์ฃผํŒŒ์ˆ˜ ํŠน์„ฑ, ์ŠคํŽ™ํŠธ๋Ÿผ ๋“ฑ์˜ ํ•˜๋ถ€ ์ธต์œ„์˜ ๋””์ง€ํ„ธ ์‹ ํ˜ธ๋กœ ๋ณ€์กฐ๋˜์–ด ์ธ๊ฐ„์—๊ฒŒ ์ง€๊ฐ๋˜๊ณ  ์Œ์•…์˜ ์‹ค์ฒด๋ฅผ ์ง€๊ฐํ•˜๊ณ  ๋ถ„๋ฅ˜ํ•˜๋Š”๋ฐ ํ™œ์šฉ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋”ฅ๋Ÿฌ๋‹์œผ๋กœ ์Œ์•…์„ ๋ถ„๋ฅ˜ํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ ์ค‘๊ฐ„ ์ธต์œ„์˜ ์ฝ˜ํ…์ธ ์˜ ํŠน์„ฑ๊ณผ ํ•˜๋ถ€ ์ธต์œ„์˜ ์‹ ํ˜ธ์˜ ํŠน์ง•์˜ ๊ด€๊ณ„๋ฅผ ๋ณ€์ˆ˜๋กœ ๋„์ž…ํ•˜์—ฌ ๊ทธ๋“ค์˜ ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜ ๊ณ ์ž ํ•œ๋‹ค. ๋˜ํ•œ ์ฝ”๋“œ ์ „ํ™˜ ํ–‰๋ ฌ(chord transition matrix)๋ฅผ ์ด์šฉํ•˜์—ฌ, ํ™”์„ฑ์ ์ธ ์ธก๋ฉด๋„ ๊ณ ๋ คํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ค‘๊ฐ„ ์ธต์œ„์˜ ํŠน์„ฑ๊ณผ ํ•˜๋ถ€ ์ธต์œ„์˜ ๋””์ง€ํ„ธ ์‹ ํ˜ธ์˜ ํŠน์„ฑ๊ณผ์˜ ์ƒํ˜ธ๊ด€๊ณ„๋ฅผ ๋„์ถœํ•˜๊ณ  ์ค‘๊ฐ„ ์ธต์œ„์˜ ํŠน์„ฑ์„ ๋…๋ฆฝ์ ์œผ๋กœ ์ ์šฉํ•˜๋Š” ๊ฒƒ ๋ณด๋‹ค ์ด๋“ค์„ ํ†ตํ•ฉ์ ์œผ๋กœ ํ•จ๊ป˜ ์ ์šฉํ•˜๋Š” ๊ฒƒ์ด ์Œ์•…์„ ์ž๋™์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฐ ๋” ์ •ํ™•ํ•จ ์„ ๋ณด์—ฌ์ฃผ๊ณ ์ž ํ•œ๋‹ค. ์žฅ๋ฅด๋Š” ํด๋ž˜์‹ ์Œ์•…์œผ๋กœ ์ œํ•œํ•˜๋ฉฐ 2016๋…„ ๊ณต๊ฐœ๋œ Musicnet ํด๋ž˜์‹ ๋ฐ์ดํ„ฐ ์…‹์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ๋‹ค. ์ด๋ฅผ ์ข€ ๋” ๊ตฌ์ฒด์ ์œผ๋กœ ๊ธฐ์ˆ ํ•˜๋ฉด (1) ์ค‘๊ฐ„ ์ธต์œ„์˜ ์ฝ˜ํ…์ธ ์˜ ํŠน์ง•๊ณผ ํ•˜๋ถ€ ์ธต์œ„ ์˜ ์‹ ํ˜ธ์˜ ํŠน์ง•๊ณผ์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฒ€ํ† ํ•˜๊ณ , (2) ์ค‘๊ฐ„ ์ธต์œ„์˜ ์ฝ˜ํ…์ธ ์˜ ํŠน์ง•์„ ๊ฐ๊ฐ ๋…๋ฆฝ์ ์œผ๋กœ ์ ์šฉํ•˜๊ณ  (3) ์ค‘๊ฐ„ ์ธต์œ„์˜ ์ฝ˜ํ…์ธ ์˜ ํŠน์ง•์„ ํ†ตํ•ฉ์ ์œผ๋กœ ๋ชจ๋‘ ์ ์šฉํ•˜์—ฌ (4) 2์™€ 3์˜ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•˜๊ณ  ์ด๋ฅผ ์ž‘๊ณก์ž๊ณผ ์•™์ƒ๋ธ”์„ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ํ”ผ๋“œ ํฌ์›Œ๋“œ ๋”ฅ ๋‰ด๋Ÿด ๋„ท์„ ์ด์šฉํ•˜์—ฌ ์ž๋™ ๋ถ„๋ฅ˜ํ•˜๋Š”๋ฐ ์ ์šฉํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ์—์„œ ์ž‘๊ณก๊ฐ€ ๋ถ„ ๋ฅ˜ ์ •ํ™•๋„์—์„œ๋Š” ์Œ๊ณ +์Œ๋Ÿ‰+์Œ์ƒ‰+์Œ๊ฐ€์˜ ํ†ตํ•ฉ์  ํŠน์ง•์ด 57.27%๋กœ ๊ฐ€์žฅ ๋†’์•˜๋‹ค. ๋˜ํ•œ ๊ฐœ๋ณ„ํŠน์ง• ์ •ํ™•๋„์—์„œ๋Š” ์Œ์ƒ‰๊ณผ ๊ด€๋ จ๋œ ํŠน์ง•์ด 44.24%๋กœ ๊ฐ€์žฅ ๋†’์•˜๋‹ค. ์•™์ƒ ๋ธ” ๋ถ„๋ฅ˜ ์ •ํ™•๋„์—์„œ๋„ ์Œ๊ณ +์Œ๋Ÿ‰+์Œ์ƒ‰+์Œ๊ฐ€์˜ ํ†ตํ•ฉ์  ํŠน์ง•์ด 59.09% ๋กœ ๊ฐ€์žฅ ๋†’์•˜๋‹ค. ๋˜ํ•œ ๊ฐœ๋ณ„ ํŠน์ง• ์ •ํ™•๋„์—์„œ๋Š” ์Œ๋Ÿ‰์ด 40.75%๋กœ ๊ฐ€์žฅ ๋†’์•˜๋‹ค. ๋ณธ ์‹คํ—˜์„ ํ†ตํ•ด ์ค‘๊ฐ„ ์ธต์œ„์˜ ํŠน์„ฑ์„ ๋…๋ฆฝ์ ์œผ๋กœ ์ ์šฉํ•˜๋Š” ๊ฒƒ ๋ณด๋‹ค ์ด๋“ค์„ ํ†ตํ•ฉ์ ์œผ๋กœ ํ•จ๊ป˜ ์ ์šฉํ•˜๋Š” ๊ฒƒ์ด ์Œ์•…์„ ์ž๋™์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฐ ๋” ์ •ํ™•ํ•จ์„ ๋ณด์ผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋„์ถœ๋œ ์ค‘๊ฐ„ ์ธต์œ„์™€ ํ•˜๋ถ€ ์ธต์œ„์˜ ์ƒ๊ด€๊ด€๊ณ„๋Š” ๋จธ์‹ ๋Ÿฌ๋‹์—์„œ ํ•˜๋ถ€ ์ธต์œ„์˜ ์‹ ํ˜ธ์— ๋Œ€ํ•œ ๋” ๋งŽ์€ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์Œ์•…์˜ ์ค‘๊ฐ„ ์ธต์œ„์˜ ํŠน์„ฑ์ธ ์Œ๊ณ , ์Œ๋Ÿ‰, ์Œ์ƒ‰, ์Œ๊ฐ€ ๋ฐ ํ™”์„ฑ์„ ๋‹ค๊ฐ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ์„ค๊ณ„ํ•˜ ๋Š” ๋ฐฉํ–ฅ์„ฑ์„ ์ œ์‹œํ•ด ์ค„ ๊ฒƒ์ด๋‹ค. ๋˜ํ•œ ์Œ๊ณ , ์Œ๋Ÿ‰, ์Œ์ƒ‰, ์Œ๊ฐ€ ๋ฐ ํ™”์„ฑ์— ์˜ํ–ฅ์„ ๋ฐ›๋Š” ํ™”์ž ์ธ์‹์ด๋‚˜ ํ™”์ž ํ”„๋กœํŒŒ์ผ๋ง ๊ฐ™์€ ๋ถ„์•ผ์— ์žˆ์–ด์„œ๋„ ์˜๋ฏธ ์žˆ๋Š” ๊ฒฐ๊ณผ๋ฅผ ์ œ๊ณตํ•  ๊ฒƒ์ด๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ชฉ์  1 1.2 ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋ก  1 ์ œ 2 ์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ 5 2.1 Music Information Plane 5 2.2 ์‹ฌ๋ฆฌ ์Œํ–ฅํ•™์  ๊ด€์ ์—์„œ ์ค‘๊ฐ„ ์ธต์œ„ ์ฝ˜ํ…์ธ  ํŠน์ง• 6 2.3 ์ค‘๊ฐ„ ์ธต์œ„์˜ ์ฝ˜ํ…์ธ ์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 7 2.3.1 ์Œ๊ณ (Pitch) 7 2.3.2 ์Œ๋Ÿ‰(Dynamics) 8 2.3.3 ์Œ์ƒ‰(Timbre) 9 2.3.4 ์Œ๊ฐ€(Tempo) 10 2.3.5 ํ™”์„ฑ(harmony) 11 2.4 ํ•˜๋ถ€ ์ธต์œ„ ์‹ ํ˜ธ ํŠน์ง• ์ถ”์ถœ 13 2.4.1 Essentia 13 2.4.2 Sonic Visualiser์™€ Vamp Plugin 13 2.4.3 Librosa 14 2.5 ๋จธ์‹ ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์Œ์•…ํ•™ ์—ฐ๊ตฌ 15 2.5.1 ๋”ฅ๋Ÿฌ๋‹ ์ผ๋ฐ˜ 15 2.5.2 ๋จธ์‹  ๋Ÿฌ๋‹์„ ํ™œ์šฉํ•œ ์ž‘๊ณก๊ฐ€ ๋ถ„๋ฅ˜ 15 2.5.3 ๋จธ์‹  ๋Ÿฌ๋‹์„ ์ด์šฉํ•œ ์ฝ”๋“œ ์ธ์‹ 16 ์ œ 3 ์žฅ ์ฝ˜ํ…์ธ  ๊ธฐ๋ฐ˜์˜ ํด๋ž˜์‹ ์Œ์•… ์ž๋™ ๋ถ„๋ฅ˜ 19 3.1 ๋ชฉํ‘œ 19 3.2 ์‹คํ—˜ ์š”์•ฝ 21 3.3 ์ค‘๊ฐ„ ์ธต์œ„์˜ ์ฝ˜ํ…์ธ  ํŠน์ง•๊ณผ ํ•˜๋ถ€ ์ธต์œ„์˜ ์‹ ํ˜ธ ํŠน์ง•์˜ ์ƒ๊ด€ ๊ด€๊ณ„ 22 3.3.1 MIDI์˜ ํ•„์š”์„ฑ 22 3.3.2 Sonic Visualiser์˜ Vamp Plugin์„ ์ด์šฉํ•œ ๋ถ„์„ 23 3.3.3 ์‹คํ—˜ ๋ฐฉ๋ฒ•: ์˜ค๋””์˜ค์—์„œ ์Œ๊ณ , ์Œ๋Ÿ‰, ์Œ์ƒ‰, ์Œ๊ฐ€ ์ถ”์ถœ 23 3.4 ์‹คํ—˜1: ๊ฒฐ๊ณผ 25 3.4.1 Sequential data์˜ ๊ฒฐ๊ณผ ํ•ด์„ 25 3.4.2 ์‹คํ—˜ ๊ฒฐ๊ณผ 25 3.5 ์ฝ˜ํ…์ธ  ํŠน์ง• ๊ธฐ๋ฐ˜์˜ ํด๋ž˜์‹ ์Œ์•… ์ž๋™ ๋ถ„๋ฅ˜ 34 3.5.1 ์Œ๊ณ , ์Œ๋Ÿ‰, ์Œ์ƒ‰, ์Œ๊ฐ€ ๊ตฌ์„ฑ 35 3.5.2 ํ™”์„ฑ ๋ถ„์„ 35 3.5.3 ์ฝ”๋“œ vs ์Œ๊ณ , ์Œ๋Ÿ‰, ์Œ์ƒ‰, ์Œ๊ฐ€ 37 3.5.4 ๋ฐ์ดํ„ฐ์…‹ : Musicnet 38 3.5.5 10-fold Cross Validation 40 3.5.6 Task 1. ํด๋ž˜์‹ ์Œ์•… ์ž‘๊ณก๊ฐ€ ์ž๋™ ๋ถ„๋ฅ˜ 41 3.5.7 Task 2. ํด๋ž˜์‹ ์Œ์•… ์•™์ƒ๋ธ” ์ž๋™ ๋ถ„๋ฅ˜ 43 3.5.8 Deep neural network ๋ชจ๋ธ ๊ตฌ์„ฑ 44 3.5.9 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    Thesis(master`s)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์›์žํ•ต๊ณตํ•™๊ณผ,2006.Maste

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2018. 8. ๊น€์ข…์•”.The inside of the solid-propellant rocket motor has many difficulties for the numerical analysis because of the combustion of the propellant, the high temperature and high pressure flow and the structural deformation of the propellant. It involves complex physical phenomena which are influenced by each domain. The analytical region of the solid-propellant rocket motor is divided into the fluid region, the structure region and the combustion region. The deformation occurs in each region over time. Therefore, it is necessary to generate a suitable grid for the analytical domain that deforms over time. In addition, it is necessary to generate suitable grids for the boundary layer for efficient and accurate viscous flow analysis. When the grid deformation is periodical such as the aero-elastic analysis, the grid-moving method and the grid deformation method are used. On the other hand, because the solid-propellant rocket motor is a variation of the area due to the continuous combustion, unlike the above-mentioned problem, it is difficult to generate the grid by the two techniques. Therefore, it is necessary to regenerate the entire grid automatically during the analysis, which is called the grid regeneration technique. It is important that the grid regeneration technique usually has a stable and efficient grid generation process without user intervention. In the case of internal flow geometries such as solid propulsion rocket engines, unlike ordinary external flow geometries, overlap between neighborhood grids occurs during the automatic grid generation process, and inevitably invalid grids can be generated. In this study, it has purpose to develop an automatic grid regeneration program that automatically generates suitable grid for two-dimensional and three-dimensional solid-propellant rocket motors. To generate grids automatically, methods for automatically detecting poor grids and solving these problems without user intervention are needed. These methods are applied to the grid regeneration program. The several methods for improving the quality of the grid are introduced in this paper. In addition, the application of the developed grid regeneration program is conducted for several complex configurations and solid-propellant rocket motors. When the quality of the grid is checked in terms of skewness, better result can be obtained compared to the conventional methods. Since poor meshes are unexpectedly generated at the complex region, it can be applied to generate the grid of complex configurations.Chapter 1 Introduction 1 1.1 The phenomenon of Solid-propellant Rocket Motor (SRM) 1 1.2. Simulation on Solid-propellant Rocket Motors 3 Chapter 2 Numerical methods for grid generation 9 2.1. Delaunay triangulation [10,12] 9 2.2. Bowyer/Watson Algorithm 10 2.3. Quad-Tree data structure and Oc-tree data structure 12 2.4. Grid untangling method 14 2.5. Advancing-Layers Method (ALM) 15 2.5.1 Advancing-Layers Method for two-dimension 16 2.5.2. Advancing-Layers Method for three-dimension 20 Chapter 3 Grid generation methods for internal flows 26 3.1 Grid generation method for internal flow for two-dimension 26 3.2 Three dimensional grid generation methods for internal flows 34 Chapter 4 Grid generation program 41 4.1. Linking process with FSI solver 41 4.2. Overall procedure of grid regeneration module 42 Chapter 5 Aplications 47 5.1. Applications on complex geometries 47 5.2. Applications on solid-propellant rocket motors. 49 5.2.1 Two-dimensional Solid-propellant rocket motor 49 5.2.2 Three-dimensional Solid-propellant rocket motor 56 Chapter 6 Conclusion 59 Appendix A. Advancing-Layers Method by Pirzadeh [19,21] 61 Appendix B. Skewness and Area/Volume ratio [24] 63 References 64 ๊ตญ ๋ฌธ ์ดˆ ๋ก 67Maste

    (The) effects of exchange rate misalignment on producer support estimates : a case study of South Korea

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋†๊ฒฝ์ œ์‚ฌํšŒํ•™๋ถ€(๋†๊ฒฝ์ œํ•™์ „๊ณต),2010.2.Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธๅคงๅญธๆ ก ่กŒๆ”ฟๅคงๅญธ้™ข :่กŒๆ”ฟๅญธ็ง‘ ่กŒๆ”ฟๅญธๅฐˆๆ”ป,1996.Maste

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    ๅ…ƒๆ›‰(617-686)๊ฐ€ ํ•œ๊ตญ ์ฒ ํ•™์‚ฌ์—์„œ ์ฐจ์ง€ํ•˜๋Š” ๋น„์ค‘๊ณผ ๋‚จ๊ธด ์ €์ˆ ์˜ ์งˆ์ โ€ค์–‘์  ์ˆ˜์ค€์œผ๋กœ ์ธํ•˜์—ฌ, ๊ทธ์˜ ์ €์ˆ  ๋ฒˆ์—ญ ํ˜„ํ™ฉ์€ ํ˜„ ๋‹จ๊ณ„ ํ•œ๊ตญ๊ณ ์ „ ๋ฒˆ์—ญ์˜ ํ˜„ํ™ฉ๊ณผ ์ˆ˜์ค€์„ ๋ณด์—ฌ์ค€๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์›ํšจ๋Š” ํ•œ๊ตญ ์ฒ ํ•™์‚ฌ์—์„œ ๊ฐ€์žฅ ๋งŽ์ด ๊ตญ๋‚ด์™ธ์ ์œผ๋กœ ์•Œ๋ ค์ง„ ์ธ๋ฌผ์ด๋‹ค. ๊ทธ๋Š” ์ƒ์กด ๋‹น์‹œ์— ์ด๋ฏธ ์‹ ๋ผ ์‚ฌํšŒ์—์„œ ์œ ๋ช…ํ•œ ํ•™์Šน์œผ๋กœ ๋ช…์„ฑ์„ ๋‚ ๋ ธ์œผ๋ฉฐ, ์‚ฌํ›„ ๋ช‡์„ธ๊ธฐ ๋‚ด์— ๋™์•„์‹œ์•„๋Š” ๋ฌผ๋ก  ์ธ๋„์—๊นŒ์ง€ ๊ทธ ํ•™๋ฌธ์  ๋ช…์Šน์„ ๋‚ ๋ ธ๋‹ค. ๊ทผ๋Œ€์— ์™€์„œ๋„ ๊ทธ์˜ ํ•™๋ฌธ์— ๋Œ€ํ•œ ์ˆ˜๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ๊ตญ๋‚ด์™ธ์—์„œ ์ด๋ฃจ์–ด์กŒ๋‹ค. [ํ•œ๊ตญ๋ถˆ๊ต๊ด€๊ณ„๋…ผ์ €์ข…ํ•ฉ๋ชฉ๋ก](์ด์ฒ ๊ตโ€ค์ด๋™๊ทœ ํŽธ์ฐฌ, ๊ณ ๋ ค๋Œ€์žฅ๊ฒฝ์—ฐ๊ตฌ์†Œ, 2002)์— ๋”ฐ๋ฅด๋ฉด 1892๋…„๋ถ€ํ„ฐ 2002๋…„ 4์›”๊นŒ์ง€ 110๋…„๊ฐ„ 1301ํŽธ์˜ ์›ํšจ๊ด€๋ จ ๋…ผ์ €๊ฐ€ ๊ตญ๋‚ด ๋ฐ ์™ธ๊ตญ์—์„œ ์ถœํŒ, ๋ฐœํ‘œ๋˜์—ˆ๋‹ค
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