28 research outputs found

    An Ethnographic Investigation of an International Organization in Seoul

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์‚ฌํšŒํ•™๊ณผ, 2020. 8. ๊น€์„ํ˜ธ.This research draws upon an ethnographic investigation of Camarata Music, a multicultural music organization in Seoul, to analyze the role of interpretation in everyday multiculturalism. It explains the following: 1) what behavioral practices do participants of Camarata Music adopt in order to construct a multicultural place; 2) how do ethnic lines emerge in this multicultural place, and 3) how do the participants respond to the ethnic lines in Camarata Music. This research uses discourses on fractures in everyday multiculturalism as conceptual framework and the meaning maintenance model as an analytical framework. It introduces the concept of 'ethnic lines', defined as ethnically differentiated patterns of participation in a multicultural place. By examining the ways in which people try to make sense of 'ethnic lines', this research demonstrate the way interpretive practices complement behavioral practices in maintenance of a multicultural place. The data for this research were collected by participant observation and semi-structured in-depth interviews. The participant observation was conducted in two adult choirs of Camarata Music, Camarata Chorale and Camarata Chamber Singers, as well as the board meetings. From August 2019 to December 2019, 51 sessions of participant observation were conducted in rehearsals, meetings and social gatherings. Interviews were conducted from October 2019 to March 2020, with both Korean and non-Korean members, to the total of 17 interviews. Members have been chosen from various musical, ethnic, gender and age backgrounds to reflect diversity of the organization. The results have been presented in three sections. Firstly, Camarata Music acquired to meaning of as a multicultural place via behavioral practices of implementing policies of welcome, creating positive memories and experiencing diversity. Secondly, despite these behavioral practices of everyday multiculturalism, ethnic lines emerge as differences in participation coincide with ethnic differences. These lines form on three dimensions of conceptual, interactional and organizational. Thirdly, in-depth interviews revealed that participants used meaning maintenance mechanisms to make a positive sense of the ethnic lines so that these do not constitute discrimination. Some people focused more on non-social and non-ethnic aspects of Camarata Music, while others used non-ethnic categories such as age and musicality to explain the differences. Stereotypical understanding of ethnicities as well as previous experience with ethnicities were also used to diminish the severity of ethnic lines. Select few of the participants attempted to incorporate ethnic lines into their perception of the organization to create a new meaning for it. In conclusion, this research proposes that interpretive practices of everyday multiculturalism may be employed to make positive sense of seemingly negative behavioral practices in multicultural places. This suggests that just as repetitive behavioral practices are required to construct and maintain a multicultural place, so are interpretive practices in order to overcome real or potential fractures that may occur in a multicultural place.๋ณธ ์—ฐ๊ตฌ๋Š” ์ผ์ƒ์  ๋‹ค๋ฌธํ™”์ฃผ์˜์—์„œ ํ•ด์„์˜ ์—ญํ• ์„ ์ดํ•ดํ•˜๊ณ ์ž ์ง„ํ–‰๋œ ๋ฌธํ™”๊ธฐ์ˆ ์ง€ ์—ฐ๊ตฌ๋กœ, ์„œ์šธ์˜ ๋‹ค๋ฌธํ™” ์Œ์•… ๋‹จ์ฒด์ธ ์นด๋งˆ๋ผํƒ€ ๋ฎค์ง์—์„œ ์ง„ํ–‰๋œ ํ˜„์žฅ์กฐ์‚ฌ์— ๊ธฐ๋ฐ˜ํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋‹ค๋ฌธํ™” ๊ณต๊ฐ„์—์„œ ์ข…์กฑ์„ ๋”ฐ๋ผ ๋ถ„ํ™”๋œ ์ฐธ์—ฌ์–‘์ƒ์„ ์„ค๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ์ข…์กฑ์  ์„ ์ด๋ผ๋Š” ๊ฐœ๋…์„ ์†Œ๊ฐœํ•˜๋ฉฐ, ๋‹ค์Œ์˜ ์„ธ ๊ฐ€์ง€ ์—ฐ๊ตฌ ์งˆ๋ฌธ์— ๋Œ€ํ•œ ๋‹ต์„ ์ œ์‹œํ•œ๋‹ค. ์ฒซ์งธ, ์นด๋งˆ๋ผํƒ€ ๋ฎค์ง์˜ ์ฐธ์—ฌ์ž๋“ค์€ ์–ด๋–ค ํ–‰๋™์  ์‹ค์ฒœ์„ ํ†ตํ•ด ๋‹ค๋ฌธํ™” ๊ณต๊ฐ„์„ ๋งŒ๋“ค์–ด๋‚ด๋Š”๊ฐ€? ๋‘˜์งธ, ์ด์™€ ๊ฐ™์ด ๋งŒ๋“ค์–ด์ง„ ๋‹ค๋ฌธํ™” ๊ณต๊ฐ„์—์„œ ์–ด๋–ป๊ฒŒ ์ข…์กฑ์˜ ์„ ์ด ๋‚˜ํƒ€๋‚˜๋Š”๊ฐ€? ์…‹์งธ, ์ฐธ์—ฌ์ž๋“ค์€ ์นด๋งˆ๋ผํƒ€ ๋ฎค์ง์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์ข…์กฑ์— ์„ ์— ๋Œ€ํ•ด ์–ด๋–ป๊ฒŒ ๋ฐ˜์‘ํ•˜๋Š”๊ฐ€? ์ด์— ๋Œ€ํ•œ ๋‹ต์„ ์ œ์‹œํ•˜๊ธฐ ์œ„ํ•ด ๊ฐœ๋…์  ํ‹€๋กœ์„œ ์ผ์ƒ์  ๋‹ค๋ฌธํ™”์ฃผ์˜์˜ ๊ท ์—ด๊ณผ ๊ด€๋ จ๋œ ๋‹ด๋ก ๋“ค์„ ํ™œ์šฉํ•˜๊ณ , ๋ถ„์„์  ํ‹€๋กœ์„œ ์˜๋ฏธ์œ ์ง€๋ชจ๋ธ(meaning maintenance model)์„ ํ™œ์šฉํ•œ๋‹ค. ์ฐธ์—ฌ์ž๋“ค์ด ์ข…์กฑ์˜ ์„ ์„ ์ดํ•ดํ•˜๋Š” ๋ฐฉ๋ฒ•๋“ค์„ ๋ถ„์„ํ•˜๋ฉด์„œ ๋‹ค๋ฌธํ™” ๊ณต๊ฐ„์˜ ์œ ์ง€์— ์žˆ์–ด์„œ ํ•ด์„์  ์‹ค์ฒœ์ด ํ–‰๋™์  ์‹ค์ฒœ์„ ๋ณด์™„ํ•˜๋Š” ๋ชจ์Šต์„ ๋ณด์—ฌ์ฃผ๊ณ ์ž ํ–ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ์ฐธ์—ฌ๊ด€์ฐฐ๊ณผ ๋ฐ˜๊ตฌ์กฐํ™”๋œ ์‹ฌ์ธต๋ฉด๋‹ด์„ ํ†ตํ•ด ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ–ˆ๋‹ค. ์ฐธ์—ฌ๊ด€์ฐฐ์€ ์นด๋งˆ๋ผํƒ€ ๋ฎค์ง ์‚ฐํ•˜์˜ ๋‘ ์„ฑ์ธ ํ•ฉ์ฐฝ๋‹จ, ์นด๋งˆ๋ผํƒ€ ์ฝ”๋ž„๊ณผ ์นด๋งˆ๋ผํƒ€ ์ฑ”๋ฒ„ ์‹ฑ์–ด์ฆˆ, ๊ทธ๋ฆฌ๊ณ  ์ด์‚ฌํšŒ์—์„œ ์ง„ํ–‰๋์œผ๋ฉฐ, 2019๋…„ 8์›”๋ถ€ํ„ฐ 2019๋…„ 12์›”๊นŒ์ง€ ์—ฐ์Šต, ํšŒ์˜, ๋’คํ’€์ด ๋“ฑ ์ด 51ํšŒ์˜ ์ฐธ์—ฌ๊ด€์ฐฐ์„ ์‹ค์‹œํ–ˆ๋‹ค. ์‹ฌ์ธต๋ฉด๋‹ด์€ 2019๋…„ 10์›”๋ถ€ํ„ฐ 2020๋…„ 3์›”๊นŒ์ง€ ์ง„ํ–‰๋์œผ๋ฉฐ, ์ด 17์ธ์˜ ํ•œ๊ตญ์ธ๊ณผ ๋น„ํ•œ๊ตญ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์‹ค์‹œํ–ˆ๋‹ค. ์ธํ„ฐ๋ทฐ ์ฐธ์—ฌ์ž๋“ค์€ ๋‹จ์ฒด์˜ ๋‹ค์–‘์„ฑ์„ ๋ฐ˜์˜ํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์ข…์กฑ, ์  ๋”, ๋‚˜์ด ๋ฐ ์Œ์•…์  ๋ฐฐ๊ฒฝ์„ ๊ณ ๋ คํ•ด ์„ ์ •๋๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์šฐ์„  ์นด๋งˆ๋ผํƒ€ ๋ฎค์ง์ด ๋‹ค๋ฌธํ™” ๊ณต๊ฐ„์œผ๋กœ์„œ ์˜๋ฏธ๋ฅผ ํš๋“ํ•˜๋Š” ๊ณผ์ •์—์„œ ํ™˜๋Œ€์˜ ์ •์ฑ…, ๊ธ์ •์  ๊ธฐ์–ต ๋งŒ๋“ค๊ธฐ, ๊ทธ๋ฆฌ๊ณ  ๋‹ค์–‘์„ฑ์˜ ๊ฒฝํ—˜์ด๋ผ๋Š” ํ–‰๋™์  ์‹ค์ฒœ๋“ค์ด ์ค‘์š”ํ–ˆ๋‹ค. ํ•˜์ง€๋งŒ ์ด๋Ÿฐ ํ–‰๋™์  ์‹ค์ฒœ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ฐธ์—ฌ์˜ ๊ฒฉ์ฐจ๊ฐ€ ์ข…์กฑ ์ฐจ์ด์™€ ๊ฒน์น˜๋ฉด์„œ ๊ด€๋…์ , ์ƒํ˜ธ์ž‘์šฉ์ , ์กฐ์ง์  ์ฐจ์›์—์„œ ์ข…์กฑ์˜ ์„ ์ด ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ฐธ์—ฌ์ž๋“ค์€ ์˜๋ฏธ์œ ์ง€๊ธฐ์ œ(meaning maintenance mechanism)๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ข…์กฑ์˜ ์„ ์„ ๋ณด๋‹ค ๊ธ์ •์ ์œผ๋กœ ํ•ด์„ํ•˜๋ฉฐ ์ฐจ๋ณ„๋กœ์„œ ๋ฐ›์•„๋“ค์ด์ง€ ์•Š๋Š” ๋ชจ์Šต์„ ๋ณด์˜€๋‹ค. ์นด๋งˆ๋ผํƒ€ ๋ฎค์ง์—์„œ์˜ ์ƒํ™œ ์ค‘ ์‚ฌํšŒ์ ์ด๊ฑฐ๋‚˜ ์ข…์กฑ์ ์ด์ง€ ์•Š์€ ์š”์†Œ์— ์ง‘์ค‘ํ•˜๋ฉฐ ๊ด€์‹ฌ์„ ํ™˜๊ธฐํ•˜๊ฑฐ๋‚˜, ์ข…์กฑ๊ณผ ๊ด€๋ จ์ด ์—†๋Š” ๋‚˜์ด๋‚˜ ์Œ์•…์„ฑ์˜ ๋ฒ”์ฃผ๋กœ ์ฐธ์—ฌ์˜ ๊ฒฉ์ฐจ๋ฅผ ์„ค๋ช…ํ•˜๋ ค๋Š” ์‹œ๋„๋“ค์ด ์žˆ์—ˆ๋‹ค. ์ข…์กฑ์„ฑ์— ๋Œ€ํ•œ ์„ ํ—˜์ ์ธ ์ง€์‹, ํ˜น์€ ์˜ˆ์ „์˜ ์ข…์กฑ์  ์ฐจ๋ณ„์„ ๊ฒฝํ—˜ํ•œ ๊ธฐ์–ต์„ ํ™œ์šฉํ•˜์—ฌ ์นด๋งˆ๋ผํƒ€ ๋ฎค์ง ๋‚ด๋ถ€์˜ ์ข…์กฑ์˜ ์„ ์„ ์ƒ๋Œ€์  ๊ฒฝํ•œ ๊ฒƒ์œผ๋กœ ๋ณด๋ ค๋Š” ๋ชจ์Šต๋„ ์žˆ์—ˆ๋‹ค. ๊ทน์†Œ์ˆ˜์˜ ์ฐธ์—ฌ์ž๋“ค์€ ์ข…์กฑ์˜ ์„  ๊ทธ ์ž์ฒด๋ฅผ ๋‹จ์ฒด์— ๋ถ€์—ฌํ•œ ์˜๋ฏธ์— ํŽธ์ž…ํ•˜๋ฉด์„œ ์ƒˆ๋กœ์šด ์˜๋ฏธ๋ฅผ ์ฐฝ์ถœํ•ด๋‚ด๊ธฐ๋„ ํ–ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ผ์ƒ์  ๋‹ค๋ฌธํ™”์ฃผ์˜์— ์žˆ์–ด์„œ ์™ธ๊ฒฌ์ƒ์œผ๋กœ๋Š” ๋ถ€์ •์ ์œผ๋กœ ๋ณด์ผ ์ˆ˜ ์žˆ๋Š” ํ–‰๋™์  ์‹ค์ฒœ๋“ค๋„ ํ•ด์„์  ์‹ค์ฒœ์„ ํ†ตํ•ด ๊ธ์ •์ ์œผ๋กœ ์ดํ•ด๋  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•œ๋‹ค. ์œ„์™€ ๊ฐ™์€ ๊ฒฐ๊ณผ๋Š” ๋‹ค๋ฌธํ™” ๊ณต๊ฐ„์˜ ํ˜•์„ฑ์— ์žˆ์–ด์„œ ๋ฐ˜๋ณต์ ์ธ ํ–‰๋™์  ์‹ค์ฒœ์ด ํ•„์ˆ˜์ ์ธ ๋งŒํผ, ํ•ด์„์  ์‹ค์ฒœ์€ ๋‹ค๋ฌธํ™” ๊ณต๊ฐ„์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๊ท ์—ด์„ ๋ด‰ํ•ฉํ•˜๊ธฐ ์œ„ํ•œ ํ•˜๋‚˜์˜ ๊ฒฝ๋กœ๋กœ์„œ ๋‹ค๋ฌธํ™” ๊ณต๊ฐ„์˜ ์œ ์ง€์— ํ•„์ˆ˜์ ์ž„์„ ์•”์‹œํ•œ๋‹ค.Introduction 1 Conceptualizing Everyday Multiculturalism 4 Arrangements of Coexistence and Differences 4 The Role of Interpretation 10 Meaning Maintenance Model 12 Contextualizing Camarata Music 17 Everyday Multiculturalism in South Korea 17 Overview of Camarata Music 19 The Main Research Sites: Chorale and Chamber Singers 21 Research Method 25 Behavioral Practices of Everyday Multiculturalism 31 Policies of Welcome 31 Creating Positive Memories Non-Linguistically 38 Jokes, Comments and Laughters 42 Experiencing Diversity 44 Emergence of Ethnic Lines 48 Ethnicities as Categories 48 Mingling with Not Everyone 52 Not Quite An "International" Organization 55 Interpreting the Ethnic Lines 60 Ignoring the Lines 60 Alternative Explanations 63 Using Ethnic Stereotypes 66 Ethnic Lines in Context 70 Rethinking the Multicultural Place 74 Conclusion: Multiculturalism of Compromises 79 References 85Maste

    Dynamics of temperature sensitive wax dispersion in micro/macroscale over a wide range of volume fraction

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2019. 2. ์•ˆ๊ฒฝํ˜„.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์†Œ๋“ ๋„๋ฐ์‹ค ์„คํŽ˜์ดํŠธ(SDS)๋ฅผ ์ด์šฉํ•ด ์œ ํ™”์‹œํ‚จ 1-์—์ด์ฝ”์   ์™์Šค ํ˜„ํƒ์•ก์„ ์ฝœ๋กœ์ด๋“œ ๋ชจ๋ธ ์‹œ์Šคํ…œ์œผ๋กœ ์ด์šฉํ•˜์—ฌ ๋ธŒ๋ผ์šด ์—ด ์šด๋™ ๋ฐ ์‹œ์Šคํ…œ์˜ ์œ ๋ณ€ํ•™์  ๊ฑฐ๋™์„ ๋ถ„์„ํ•˜์˜€๋‹ค. 1-์—์ด์ฝ”์  ์ด 24.6 โ„ƒ์˜ ๋…น๋Š”์ ์„ ๊ฐ€์ง€๋ฏ€๋กœ 1-์—์ด์ฝ”์   ์™์Šค ํ˜„ํƒ์•ก์€ ๋…น๋Š”์  ์ดํ•˜์˜ ์˜จ๋„๋กœ ๋ƒ‰๊ฐ์‹œํ‚ฌ ๊ฒฝ์šฐ ๋งˆ์ดํฌ๋ก  ์‚ฌ์ด์ฆˆ์˜ ์ž…์ž๊ฐ€ ๊ณ ํ™”๋˜์–ด ๋ถ„์‚ฐ ๋ฏธ๋ฆฝ์ž ์‹œ์Šคํ…œ์œผ๋กœ ๊ฐ„์ฃผํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ ๋…น๋Š”์  ์ด์ƒ์—์„œ๋Š” ์™์Šค๊ฐ€ ๋…น์•„ ๋ถ€๋“œ๋Ÿฌ์šด ์ž…์ž๋กœ ์กด์žฌํ•˜๋ฏ€๋กœ ์ข‹์€ ๋ชจ๋ธ ์œ ์•ก์ด ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋‹จ์ˆœํžˆ ์˜จ๋„๋งŒ ์กฐ์ ˆํ•˜๋ฉด์„œ ๋ถ„์‚ฐ๋œ ์ž…์ž์˜ ํ‘œ๋ฉด ์„ฑ์งˆ์ด ๋ณ€ํ™”ํ•˜๊ณ , ์ด์— ๋”ฐ๋ผ ์ž…์ž ์‹œ์Šคํ…œ์˜ ์„ฑ์งˆ๋„ ๋‹ฌ๋ผ์ง€๊ฒŒ ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” T = 10/15 โ„ƒ์™€ T = 30 โ„ƒ์˜ ๋‘ ๊ฐ€์ง€ ์˜จ๋„์—์„œ ๋‹ค์–‘ํ•œ ๋†๋„์˜ 1-์—์ด์ฝ”์   ํ˜„ํƒ์•ก์˜ ๋™์  ํŠน์„ฑ๊ณผ ๊ด‘ํ•™ ํŠน์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์‹คํ—˜ ์˜จ๋„๋Š” ์™์Šค์˜ ๋…น๋Š”์ ๊ณผ ์ถฉ๋ถ„ํžˆ ๋จผ ์˜จ๋„๋ฅผ ํƒํ•˜์—ฌ ์‹œ์Šคํ…œ์˜ ์•ˆ์ •์„ฑ์„ ํ™•๋ณดํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์‹œ์Šคํ…œ ํ•˜์—์„œ ๋‘ ์ข…๋ฅ˜์˜ ์žฅ๋น„๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ž…์ž์˜ ํ‘œ๋ฉด์˜ ๊ฒฝ๋„๊ฐ€ ์˜จ๋„์— ๋”ฐ๋ผ ๋ณ€ํ™”ํ•จ์— ๋”ฐ๋ผ ์‹œ์Šคํ…œ์˜ ๋ฏธ์„ธ๊ตฌ์กฐ ๋ฐ ๊ฑฐ์‹œ ๊ตฌ์กฐ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋‹ค์–‘ํ•œ ์Šค์ผ€์ผ์—์„œ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋จผ์ € ๋ถ„์‚ฐ๋œ ์ฝœ๋กœ์ด๋“œ ์ž…์ž์˜ ์†Œ๊ทœ๋ชจ ์šด๋™ ๋ฐ ํ˜•ํƒœ ๋ณ€๋™์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด ํ™•์‚ฐ ๊ด‘ํŒŒ ๋ถ„๊ด‘๋ฒ• (DWS)์˜ ๋‹ค์ค‘ ๊ด‘ ์‚ฐ๋ž€ ๊ธฐ์ˆ ์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋…น๋Š”์ ๋ณด๋‹ค ๋†’์€ ์˜จ๋„์—์„œ๋Š” ์•ก์ ์˜ ๊ณ„๋ฉด์ด ๋ถ„์ž์˜ ์—ด์šด๋™ ์—๋„ˆ์ง€์— ์˜ํ•ด ํ‘œ๋ฉด ํŒŒ๋™์„ ๊ฐ€์ง€๊ฒŒ ๋˜๊ณ , ์ด์— ๋”ฐ๋ฅธ ์›€์ง์ž„์€ ๋งค์šฐ ์งง์€ ์‹œ๊ฐ„ ์Šค์ผ€์ผ์—์„œ์˜ ๊ฐ•๋„-๊ฐ•๋„ ์ƒ๊ด€ํ•จ์ˆ˜์— ๋ฐ˜์˜๋œ๋‹ค. ํ•œํŽธ ๋…น๋Š”์ ๋ณด๋‹ค ๋‚ฎ์€ ์˜จ๋„์˜ ๊ฒฝ์šฐ์—๋Š” ์งˆ๋Ÿ‰ ์ค‘์‹ฌ ์ด๋™์™ธ์— ํ‘œ๋ฉด ํŒŒ๋™์€ ์กด์žฌํ•˜์ง€ ์•Š์œผ๋ฏ€๋กœ ์•ž์˜ ๊ฒฝ์šฐ์™€ ๋‹ค๋ฅธ ๊ฐ•๋„-๊ฐ•๋„ ์ƒ๊ด€ํ•จ์ˆ˜๋ฅผ ๊ฐ€์ง€๊ฒŒ ๋œ๋‹ค. ๋˜ํ•œ ์ด๋Ÿฌํ•œ ๊ฐ•๋„-๊ฐ•๋„ ์ƒ๊ด€ํ•จ์ˆ˜๋Š” ์ž…์ž์˜ ํ‰๊ท ์ œ๊ณฑ๊ฑฐ๋ฆฌ์—์„œ์˜ ์ •์ฒด๊ธฐ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜๋ฉด ์ ํƒ„์„ฑ ์‹œ์Šคํ…œ์˜ ๊ธฐ๊ณ„์  ์„ฑ์งˆ์„ ๊ฒฐ์ •ํ•˜๋Š” ๋ฐ๋„ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ์ด ์‹œ์Šคํ…œ์—์„œ์˜ ์•ก์ ์˜ ์ƒ ๋ณ€ํ™”๋Š” ๊ฐ€์—ญ์ ์ด๋ฏ€๋กœ ๋ฏธ์„ธ ์ž…์ž์˜ ํ‘œ๋ฉด ์„ฑ์งˆ์ด ๋”์šฑ ์ค‘์š”ํ•œ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ณ ๋†๋„ ํ˜„ํƒ์•ก์—์„œ์˜ ์ƒ ๋ณ€ํ™”์— ํฅ๋ฏธ๋กœ์šด ๊ด€์ ์„ ์ œ์‹œํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ณ ๋†๋„๋ฅผ ํฌํ•จํ•˜์—ฌ ์‹œ์Šคํ…œ์˜ ์ฒด์ ๋ถ„์œจ ๋ฒ”์œ„๋Š” ํฌ๊ฒŒ ์„ธ ๊ฐ€์ง€ ๋ฒ”์œ„๋กœ ๋ถ„๋ฅ˜๋  ์ˆ˜ ์žˆ๋‹ค. ์ฒซ์งธ๋กœ ์ €์ฒด์ ๋ถ„์œจ ์˜์—ญ์„ 35 vol% ์ดํ•˜๋กœ ์ •์˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด ์˜์—ญ์—์„œ๋Š” ํ™•์‚ฐ ๊ณ„์ˆ˜๊ฐ€ ๋†๋„์— ๋”ฐ๋ผ ๊ฐ์†Œํ•˜๋Š” ์–‘์ƒ์„ ๋ณด์ด๋‚˜ ์ž…์ž์˜ ํ‘œ๋ฉด ์œ ์—ฐ์„ฑ์ด ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š”๋‹ค. ์ž…์ž์˜ ์œ ์—ฐ์„ฑ์€ ๊ฑฐ์‹œ์  ํŠน์„ฑ์ธ ์ ๋„์˜ ๋ณ€ํ™”์—์„œ ๊ด€์ฐฐ๋œ๋‹ค. ๋‘˜์งธ๋กœ ์ค‘๊ฐ„ ์ฒด์ ๋ถ„์œจ์€ 35 vol%์—์„œ ์ž„์˜์  ์กฐ๋ฐ€์Œ“์ž„ ์ฒด์ ๋ถ„์œจ๊นŒ์ง€๋กœ ์ •์˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋•Œ ํ™•์‚ฐ๊ณ„์ˆ˜๋Š” ์œ ์•ก๊ณผ ํ˜„ํƒ์•ก์—์„œ ํฐ ์ฐจ์ด๋ฅผ ๋ณด์ด๊ธฐ ์‹œ์ž‘ํ•˜๋ฉฐ, ๊ฑฐ์‹œ์  ๊ด€์ ์—์„œ๋Š” ํ˜„ํƒ์•ก์˜ ๊ฒฝ์šฐ ์ €์žฅ ํƒ„์„ฑ๋ฅ  ๋“ฑ์„ ํ†ตํ•ด ๊ณ ์ฒด์— ๊ฐ€๊นŒ์›Œ์ง„๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ž„์˜์  ์กฐ๋ฐ€ ์Œ“์ž„ ์ฒด์ ๋ถ„์œจ ์ด์ƒ์˜ ๋†๋„ ๋ฒ”์œ„๋Š” ๊ณ ์ฒด์ ๋ถ„์œจ ์˜์—ญ์œผ๋กœ ์ •์˜๋œ๋‹ค. ์ž„์˜์  ์กฐ๋ฐ€ ์Œ“์ž„ ์ฒด์ ๋ถ„์œจ ์ด์ƒ์˜ ๋†๋„๋ฅผ ๊ฐ€์งˆ ์ˆ˜ ์—†๋Š” ๊ธฐ์กด์˜ ํ˜„ํƒ์•ก๋“ค๊ณผ ๋‹ฌ๋ฆฌ, 1-์—์ด์ฝ”์   ํ˜„ํƒ์•ก์€ ๊ณ ๋†๋„ ์œ ์•ก์˜ ์˜จ๋„๋ฅผ ๋‚ฎํ˜€์„œ ๊ณ ์ฒด์ ๋ถ„์œจ์— ์‰ฝ๊ฒŒ ๋„๋‹ฌํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋•Œ ์‹œ์Šคํ…œ์€ ์ฒด์ ๋ถ„์œจ์˜ ์†์‹ค ํ˜น์€ ์•ก์  ํฌ๊ธฐ์˜ ๋ณ€ํ™” ์—†์ด ํ˜„ํƒ์•ก์œผ๋กœ ๋ณ€ํ•œ๋‹ค. ๊ด‘๋ฒ”์œ„ํ•œ ์ฒด์ ๋ถ„์œจ์—์„œ ์œ ๋ณ€ํ•™์  ๊ฑฐ๋™์„ ์กฐ์‚ฌํ•จ์œผ๋กœ์จ ์ž…์ž์˜ ํ‘œ๋ฉด ์„ฑ์งˆ์ด ๋ฏธ์„ธ/๊ฑฐ์‹œ ๊ตฌ์กฐ์—์„œ์˜ ๋™์—ญํ•™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ด์ „์—๋Š” ์—ฐ๊ตฌ๋œ ์  ์—†๋Š” ๋†’์€ ์ฒด์ ๋ถ„์œจ์—์„œ์˜ ํ˜„ํƒ์•ก ๋ฐ ์œ ์•ก์˜ ๊ฑฐ๋™์„ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๊ณ , ์œ ์•ก๊ณผ ํ˜„ํƒ์•ก์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ํ‘œ๋ฉด์˜ ์œ ์—ฐ์„ฑ์ด ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.We study the Brownian thermal motion of a colloidal model system made by emulsifying hot liquid ฮฑ-eicosene wax into an aqueous surfactant solution of sodium dodecyl sulfate (SDS). When this waxy oil-in-water emulsion is cooled below melting point of ฮฑ-eicosene, T_c ~ 24.6 โ„ƒ, the microscale emulsion droplets solidify, effectively yielding a dispersed particulate system. Meanwhile, above T_c, the system works as a good model emulsion. So, the wax droplets can be tuned from a viscous liquid to an elastic solid through very modest changes in absolute temperature. In the study, we analyze the dynamic and optical properties of dense ฮฑ-eicosene suspensions at two selected temperatures, T = 10/15 โ„ƒ and T = 30 โ„ƒ. These temperatures are chosen far away from T_c. We discuss the effect of changes in the particle deformability, from soft to hard, on the properties of these emulsions using two different apparatus. Using the multiple light scattering technique of diffusing wave spectroscopy (DWS), which is very sensitive to small-scale motion and shape fluctuations of dispersed colloidal objects, we show that the thermal fluctuations of the interfaces of these liquid droplets at higher temperature, seen in the DWS intensityโ€“intensity correlation function at early times, effectively disappear when these droplets solidify at lower temperature and the thermal motion can be attributed to the center of mass displacement alone. This transition is fully reversible and therefore particle softness that manifests itself via shape fluctuations of the droplets can be dialed in and out at will. Thus, this system could potentially provide an interesting playground for the concepts of the glass and the jamming transition in even denser suspensions that critically rely on the softness of constituent particles. Here, we show that the early-time behavior of this DWS correlation function can be used to probe mechanical properties of viscoelastic soft materials dispersed as droplets. The volume fraction regime is categorized into 3 parts in the discussion part. First is under 35 vol%, which is defined as the low volume fraction regime and microstructural changes is observed by diffusion coefficient, and the macroscopic properties are observed by viscosity. Second, the intermediate volume fraction regime ranging from 35 vol% to ฯ•_rcp shows the microstructural difference between hard/soft particles, which affects the bulk rheology of the system such as the viscosity or storage modulus of the system. Finally, the volume fraction regime above ฯ•_rcp is defined as high volume fraction regime. In this paper, the system could reach up to ฯ•_rcp using the melting and solidifying process of wax particles not only for the emulsion but also even for the suspension despite the non-deformability of hard particles themselves. High volume fraction emulsion system is solidified and the systems become suspension without loss of volume fraction or any change in droplet size. We suggest the effect of softness of particles in micro/macroscopic system dynamics through evaluating the bulk properties of both suspension and emulsion by applying shear in a wide range of volume fraction. From this work, one could understand the behavior of suspensions in high volume fraction which has never been studied before and clarify the influence of surface softness by the comparison between emulsion and suspension.Abstract i Contents iv List of Figures vi Chapter 1. Introduction 1 1.1. General introduction 2 1.2. Outline of the thesis 5 Chapter 2. Literature review 7 2.1. Beyond hard particle 8 2.1.1. common features of soft particles 8 2.1.2. Comparison between emulsion and suspension 12 2.2. Micro-scale analysis using DWS 15 2.2.1. Shape fluctuation 15 2.2.2. Elasticitiy index 18 2.2.3. Scattering transport mean free path 20 Chapter 3. Experimental section 22 3.1. Model system preparation 23 3.1.1. Materials 23 3.1.2. Preparation of crude emulsion 25 3.1.3. Refinement of crude emulsion 27 3.2. Model system characterization 29 3.2.1. System sizing 29 3.2.2. Visualization 31 3.2.3. System stability 34 3.2.4. Volume fraction of the emulsion/suspension 36 3.3. Apparatus 38 3.3.1. Diffusing wave spectroscopy (DWS) 38 3.3.2. Rotational rheometry 42 Chapter 4. Results and discussion 43 4.1. Microscale analysis 44 4.1.1. MSD for whole volume fraction regime 44 4.1.2 Diffusion coefficient under ฯ•_rcp 46 4.1.3. Very short time behavior under ฯ•_rcp 50 4.1.4. Elasticity index above ฯ•_rcp 53 4.1.5. Scattering transport mean free path l^* under ฯ•_rcp 56 4.2. Macroscale analysis 58 4.2.1. Viscosity for low/intermediate volume fraction regime 58 4.2.2. Viscoelastic behavior for high volume fraction regime 64 Chapter 5. Summary 74 Bibliography 80 ๊ตญ๋ฌธ ์ดˆ๋ก 86Docto

    ๊ธฐ์˜จ์ด ๋ฒผ์˜ ๋‹จ์ˆ˜์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†๊ฒฝ์ œ์‚ฌํšŒํ•™๋ถ€, 2018. 2. ๊ถŒ์˜ค์ƒ.21์„ธ๊ธฐ ๋“ค์–ด ๊ธฐํ›„๋ณ€ํ™”๊ฐ€ ๊ตญ์ œ์ ์ธ ๋ฌธ์ œ๋กœ ๋ถ€์ƒํ•˜๋ฉด์„œ ๊ธฐํ›„๋ณ€ํ™”๊ฐ€ ๋†์—… ๋ถ„์•ผ์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น ์ง€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜์–ด ์™”๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ๋Š” ๋ฒผ๋ฅผ ์ฃผ์‹์œผ๋กœ ์‚ผ๊ณ  ์žˆ๊ณ  ์žฌ๋ฐฐ๊ทœ๋ชจ ๋˜ํ•œ ๊ฐ€์žฅ ํฌ๊ธฐ ๋•Œ๋ฌธ์— ๊ธฐํ›„๋ณ€ํ™”๊ฐ€ ๋ฒผ ์žฌ๋ฐฐ์— ๋ฏธ์น  ์˜ํ–ฅ์€ ์†Œ๋น„์ž ๋ฐ ์ƒ์‚ฐ์ž ํ›„์ƒ, ๋‚˜์•„๊ฐ€ ์‹๋Ÿ‰์ฃผ๊ถŒ๊ณผ๋„ ์ง๊ฒฐ๋œ ์ค‘์š”ํ•œ ๋ฌธ์ œ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฒผ๊ฐ€ ๊ฐœํ™”๊ธฐ ๊ณ ์˜จ์— ์ทจ์•ฝํ•˜๋‹ค๋Š” ์‚ฌ์‹ค์€ ์ž‘๋ฌผํ•™ ๋ถ„์•ผ์—์„œ ์ฃผ์ง€๋˜๊ณ  ์žˆ๋Š” ์‚ฌ์‹ค์ด๊ธฐ ๋•Œ๋ฌธ์— ๊ธฐํ›„๋ณ€ํ™”์™€ ๊ทธ๋กœ ์ธํ•œ ํ‰๊ท ๊ธฐ์˜จ ์ƒ์Šน์ด ๋‹จ์ˆ˜ ๊ฐ์†Œ๋ฅผ ์ดˆ๋ž˜ํ•  ๊ฒƒ์ด๋ผ๋Š” ์šฐ๋ ค๋ฅผ ๋‚ณ๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋ฒผ์˜ ์ƒ์žฅ ๋‹จ๊ณ„๋ฅผ ๊ฐœํ™”๊ธฐ์™€ ๋น„๊ฐœํ™”๊ธฐ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๊ธฐ์˜จ์ด ๋ฒผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ์ด ๋‘ ์‹œ๊ธฐ์— ์–ด๋– ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ๋Š”์ง€ ๋ถ„์„ํ•˜๊ณ , ๋‚˜์•„๊ฐ€ ์ด๋Ÿฌํ•œ ์ฐจ์ด๊ฐ€ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๋‹จ์ˆ˜ ๋ณ€ํ™” ์˜ˆ์ธก์น˜์—๋Š” ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ์‚ดํŽด๋ณด๋Š” ๋ฐ ๋ชฉ์ ์ด ์žˆ๋‹ค. ๋ถ„์„์„ ์œ„ํ•ด ํŒจ๋„ ๊ณ ์ •ํšจ๊ณผ ๋ชจํ˜•์„ 2000-2017๋…„ ๋‹จ์ˆ˜ ๋ฐ ๊ธฐ์ƒ ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•ด ์ถ”์ •ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ธฐ์˜จ๊ณผ ๋‹จ์ˆ˜ ๊ฐ„์˜ ํ•จ์ˆ˜ ๊ด€๊ณ„๋ฅผ ์‹ ์ถ•์ ์œผ๋กœ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด ํŠน์ •ํ•œ ํ•จ์ˆ˜ ํ˜•ํƒœ๋ฅผ ๊ฐ€์ •ํ•˜์ง€ ์•Š๋Š” ์ค€๋ชจ์ˆ˜ ํ•จ์ˆ˜๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. ์ถ”์ • ๊ฒฐ๊ณผ 30ยฐC ์ด์ƒ์˜ ๊ณ ์˜จ ๊ตฌ๊ฐ„์—์„œ ๊ฐœํ™”๊ธฐ์™€ ๋น„๊ฐœํ™”๊ธฐ ๊ฐ„์˜ ์ฐจ์ด๊ฐ€ ๋šœ๋ ทํ–ˆ๋‹ค. ๊ฐœํ™”๊ธฐ์—๋Š” 30ยฐC ์ด์ƒ์˜ ๊ธฐ์˜จ์ด ๋ฐœ์ƒํ•œ ์‹œ๊ฐ„์ด ํ•˜๋ฃจ ์ฆ๊ฐ€ํ•  ๊ฒฝ์šฐ 0.9%์˜ ๋‹จ์ˆ˜๊ฐ€ ๊ฐ์†Œํ•˜์ง€๋งŒ ๋น„๊ฐœํ™”๊ธฐ์—๋Š” 30-33ยฐC์ด 0.4%, 33ยฐC ์ด์ƒ์ด 0.9%์˜ ๋‹จ์ˆ˜๋ฅผ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. ์ฆ‰ ๋‹จ์ˆ˜๊ฐ€ ๊ณ ์˜จ์— ๋Œ€ํ•ด ๊ฐœํ™”๊ธฐ์™€ ๋น„๊ฐœํ™”๊ธฐ์— ์„œ๋กœ ๋‹ค๋ฅธ ๋ฐฉํ–ฅ์œผ๋กœ ๋ฐ˜์‘ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. ์œ„์™€ ๊ฐ™์€ ์ถ”์ • ๊ฒฐ๊ณผ๋Š” ๊ณ ์˜จ์žฅํ•ด๋กœ ์ธํ•œ ๋‹จ์ˆ˜ ๊ฐ์†Œ์˜ ๋Œ€๋ถ€๋ถ„์ด ๊ฐœํ™”๊ธฐ์— ์ง‘์ค‘๋˜์–ด ์žˆ์Œ์„ ์•Œ๋ ค์ค€๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ๋“ฑ์ˆ™๊ธฐ ๊ณ ์˜จ ์—ญ์‹œ ๋“ฑ์ˆ™๋ฅ ์„ ๋‚ฎ์ถฐ ๋‹จ์ˆ˜๋ฅผ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ์œผ๋‚˜, ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ํ˜„์žฌ ํ•œ๊ตญ์˜ ๊ธฐํ›„ํ•˜์—์„œ๋Š” 30ยฐC ์ด์ƒ์˜ ๊ธฐ์˜จ์— ์˜ํ•œ ๊ณ ์˜จ์žฅํ•ด๊ฐ€ ๊ฐœํ™”๊ธฐ ์ด์™ธ์˜ ์ƒ์žฅ ๋‹จ๊ณ„์—์„œ๋Š” ๋šœ๋ ทํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋”ฐ๋ผ์„œ ํ–ฅํ›„ ๊ธฐํ›„๋ณ€ํ™” ๋Œ€์‘์ฑ…์˜ ์ดˆ์ ์„ ๊ฐœํ™”๊ธฐ ๊ณ ์˜จ์žฅํ•ด์— ๋งž์ถœ ํ•„์š”๊ฐ€ ์žˆ์–ด ๋ณด์ธ๋‹ค. ๋˜ํ•œ ์œ„์™€ ๊ฐ™์€ ์ƒ์žฅ์‹œ๊ธฐ๋ณ„ ์ฐจ์ด๊ฐ€ ๋‹จ์ˆ˜ ์˜ˆ์ธก ๋ชจํ˜•์— ๋ฐ˜์˜๋  ๊ฒฝ์šฐ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๋‹จ์ˆ˜ ๊ฐ์†Œํญ์ด ํฌ๊ฒŒ ๋‚ฎ์•„์ง€๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ ๊ฐœํ™”๊ธฐ๋ฅผ ๊ตฌ๋ถ„ํ•œ ๋ชจํ˜•์€ ๊ธฐ์˜จ์ด ๊ท ์ผํ•˜๊ฒŒ 5ยฐC ์ƒ์Šนํ•œ๋‹ค๊ณ  ๊ฐ€์ •ํ•  ๋•Œ 3.5%์˜ ๋‹จ์ˆ˜ ๊ฐ์†Œ๋ฅผ ์˜ˆ์ธกํ–ˆ์ง€๋งŒ, ๊ตฌ๋ถ„ํ•˜์ง€ ์•Š์€ ๋ชจํ˜•์€ 19.3%๋กœ ๋‹จ์ˆ˜ ๊ฐ์†Œํญ์ด ํ˜„์ €ํ•˜๊ฒŒ ๋Š˜์–ด๋‚ฌ๋‹ค. ๋˜ํ•œ ๊ธฐ์ƒ์ฒญ์˜ ๊ธฐํ›„๋ณ€ํ™” ์‹œ๋‚˜๋ฆฌ์˜ค(RCP 8.5)๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋‹จ์ˆ˜๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๊ฒฝ์šฐ์—๋„ ๋‘ ์‹œ๊ธฐ๋ฅผ ๊ตฌ๋ถ„ํ•œ ๋ชจํ˜•์€ 2080-2100๋…„๊นŒ์ง€ 2020-2040๋…„ ๋Œ€๋น„ 1%, ๊ตฌ๋ถ„ํ•˜์ง€ ์•Š์€ ๋ชจํ˜•์€ 8.3%๊ฐ€ ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ์ƒ์žฅ ๋‹จ๊ณ„์— ๋”ฐ๋ผ ๊ธฐ์˜จ์˜ ์˜ํ–ฅ์ด ๋‹ฌ๋ผ์ง„๋‹ค๋Š” ์‚ฌ์‹ค์„ ์ ์ ˆํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•  ๊ฒฝ์šฐ ๊ธฐ์˜จ ์ƒ์Šน์œผ๋กœ ์ธํ•œ ๋‹จ์ˆ˜ ํ”ผํ•ด๋ฅผ ๊ณผ๋Œ€ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•ด์ค€๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ์™€์˜ ์ฐจ๋ณ„์„ฑ 3 ์ œ 3 ์ ˆ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  5 ์ œ 4 ์ ˆ ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 8 ์ œ 2 ์žฅ ๋ถ„์„ ์ž๋ฃŒ 9 ์ œ 1 ์ ˆ ๋‹จ์ˆ˜ ๋ฐ ๊ธฐ์ƒ์ž๋ฃŒ 9 ์ œ 2 ์ ˆ ์ž๋ฃŒ ๋ณ€ํ™˜ 10 ์ œ 3 ์žฅ ๋ถ„์„ ๋ชจํ˜• 13 ์ œ 4 ์žฅ ๋ถ„์„ ๊ฒฐ๊ณผ 16 ์ œ 1 ์ ˆ ๋ชจํ˜•๋ณ„ ์ถ”์ •๊ฒฐ๊ณผ 16 ์ œ 2 ์ ˆ ๋ชจํ˜• ๊ฐ„ ์˜ˆ์ธก๋ ฅ ๋น„๊ต 23 ์ œ 3 ์ ˆ ๊ธฐํ›„๋ณ€ํ™” ์˜ํ–ฅ ํ‰๊ฐ€ 24 ์ œ 5 ์žฅ ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  28 ์ฐธ๊ณ ๋ฌธํ—Œ 30 Abstract 34Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์–ธ์–ดํ•™๊ณผ ์–ธ์–ดํ•™์ „๊ณต,2004.Maste

    Features of Intellectual Resources for Innovation Performance on Related Diversifying Firms

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2014. 2. ๊ฐ•์ง„์•„.๊ธฐ์—…์˜ ๊ด€๋ จ๋‹ค๊ฐํ™”๋Š” ๋น„ ๊ด€๋ จ ๋‹ค๊ฐํ™”์— ๋น„ํ•ด ๊ธฐ์—…์„ฑ๊ณผ ์ฆ์ง„์— ์œ ๋ฆฌํ•œ ๋ฐ˜๋ฉด, ํ•œ ์‚ฐ์—…์— ๋Œ€ํ•œ ์ง‘์ค‘๋„๊ฐ€ ๋†’์•„์ ธ ์ƒˆ๋กœ์šด ์ง€์‹์ž์› ํš๋“๊ณผ ํ™œ์šฉ์— ํ•œ๊ณ„๋ฅผ ๋ณด์ผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์‹  ์‚ฐ์—… ์ง„์ถœ์ด ์–ด๋ ค์›Œ์ง์— ๋”ฐ๋ผ ๊ธฐ์—… ์กด์†์— ์œ„ํ˜‘์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ๊ด€๋ จ๋‹ค๊ฐํ™”๋ฅผ ํ†ตํ•ด ๋‹จ๊ธฐ์  ์„ฑ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ๋ฐ˜๋ฉด, ๊ธฐ์—…์˜ ์„ฑ์žฅ๊ณผ ์ง€์†๊ฐ€๋Šฅ์„ฑ์„ ์œ„ํ•œ ํ˜์‹ ์„ฑ๊ณผ์—๋Š” ๋ถ€์ •์ ์œผ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ด€๋ จ๋‹ค๊ฐํ™” ๋‚ด์—์„œ์˜ ํ˜์‹ ์„ฑ๊ณผ ์ถ”๊ตฌ๋ฐฉ์‹์„ ๊ณ ์ฐฐํ•ด๋ณด๊ธฐ ์œ„ํ•ด, M&Aํ™œ๋™์œผ๋กœ ๊ด€๋ จ ๋‹ค๊ฐํ™”ํ•˜๋Š” ๊ธฐ์—…์„ ๋ถ„์„ํ•˜์—ฌ, ํ”ผ ์ธ์ˆ˜๊ธฐ์—…์—์„œ ์œ ์ž…๋˜๋Š” ์ง€์‹์žฌ์‚ฐ๊ณผ ํ˜์‹ ์„ฑ๊ณผ์˜ ์ƒ๊ด€๊ด€๊ณ„์— ๋Œ€ํ•ด ์•Œ์•„๋ณด์•˜๋‹ค. ํŠนํžˆ ํ”ผ ์ธ์ˆ˜๊ธฐ์—…์˜ ํŠนํ—ˆ ์ธ์šฉ๋„คํŠธ์›Œํฌ๋ฅผ ๋ถ„์„ํ•ด ๋‹ค๊ฐํ™”๋ฅผ ์œ„ํ•œ ๊ธฐ์—…์ธ์ˆ˜๊ฐ€ ์ƒˆ๋กœ์šด ๋ถ„์•ผ(Class)์˜ ํŠนํ—ˆ์ถœ์› ๊ฐ€๋Šฅ์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ™•์ธํ•ด๋ณด์•˜๋‹ค. ํŠนํ—ˆ์˜ ์ธ์šฉ๋„คํŠธ์›Œํฌ๋Š” ํ”ผ ์ธ์šฉ(Cited)๊ณผ ์ธ์šฉ(Citing)์œผ๋กœ ๋‚˜๋ˆˆ ํ›„ ๊ฐ ํŠนํ—ˆ๊ฐ€ ์–ผ๋งˆ๋‚˜ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์˜ ํŠนํ—ˆ๋“ค์— ๊ท ๋“ฑํ•˜๊ฒŒ ์ธ์šฉ๋˜๋Š”๊ฐ€๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” Generality์™€ ๊ธฐ์—…์˜ ํŠนํ—ˆ๊ฐ€ ์–ผ๋งˆ๋‚˜ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์˜ ํŠนํ—ˆ๋“ค์„ ๊ท ์ผํ•˜๊ฒŒ ์ธ์šฉํ•˜๋Š”์ง€๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” Originality๋ฅผ ์ธก์ •ํ•˜์˜€์œผ๋ฉฐ, ๊ทธ ๊ฒฐ๊ณผ ๊ด€๋ จ์‚ฐ์—… ๋‚ด ๊ธฐ์—…์„ ์ธ์ˆ˜ํ•˜๋Š” ๊ฒƒ๋„ ํ˜์‹ ์„ฑ๊ณผ ์ถ”๊ตฌ์— ๊ธ์ •์ ์ธ ์ „๋žต์ธ ๊ฒƒ์œผ๋กœ ๊ฒฐ๋ก ์ง€์„ ์ˆ˜ ์žˆ๋‹ค.1. ์„œ๋ก  1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1.2 ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ๊ณผ ๋ชฉ์  1.3 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 2. ๋ฌธํ—Œ ๊ณ ์ฐฐ 2.1 ๋‹ค๊ฐํ™” ์ „๋žต 2.1.1 ๊ด€๋ จ ๋‹ค๊ฐํ™” 2.1.2 ๋น„ ๊ด€๋ จ ๋‹ค๊ฐํ™” 2.1.3 ์„ ํ–‰ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์  2.2 ์ง€์  ์ž์›์˜ ํŠน์„ฑ: ์ธ์šฉ ๊ด€๊ณ„(CITATION) 2.2.1 ๋ฒ”์šฉ์„ฑ(Generality) 2.2.2 ๋…์ฐฝ์„ฑ(Originality) 2.2.3 ๊ด€๋ จ ๋‹ค๊ฐํ™”์™€ ์ง€์  ์ž์›์˜ ํŠน์„ฑ๊ณผ ๊ธฐ์ˆ  ์ฐฝ์ถœ์˜ ๊ฐ€๋Šฅ์„ฑ 3. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 3.1 ์—ฐ๊ตฌ ๋ชจํ˜• 3.2 ์—ฐ๊ตฌ ๊ฐ€์„ค 3.3 ๋ฐ์ดํ„ฐ 3.3.1 ์—ฐ๊ตฌ ๋Œ€์ƒ ๊ธฐ์—…๊ณผ ๋Œ€์ƒ ๊ธฐ๊ฐ„ ์„ ์ • 3.3.2 ์—ฐ๊ตฌ ํ‘œ๋ณธ์˜ ๊ตฌ์„ฑ 3.4 ๋ณ€์ˆ˜์ •์˜์™€ ์ธก์ •๋ฐฉ๋ฒ• 3.4.1 ์ข…์†๋ณ€์ˆ˜ 3.4.2 ๋…๋ฆฝ๋ณ€์ˆ˜ 3.4.3 ํ†ต์ œ๋ณ€์ˆ˜ 3.5 ์—ฐ๊ตฌ ๋ชจ๋ธ์˜ ์„ ์ • 4. ์‹ค์ฆ๋ถ„์„ ๊ฒฐ๊ณผ 4.1 ํ†ต๊ณ„ ๋ถ„์„ ๋ฐ ๊ฒฐ๊ณผํ‘œ 4.2 ๋ถ„์„๊ฒฐ๊ณผ ๋ฐ ๊ฐ€์„ค ๊ฒ€์ฆ 5. ๊ฒฐ๋ก  5.1 ์—ฐ๊ตฌ์˜์˜ 5.2 ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ ์ œ์•ˆ 6. AbstractMaste

    Knowledge Distillation Method for CTC-based Speech Recognition via Adjustment Training

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€,2020. 2. ๊น€๋‚จ์ˆ˜.DNN-HMM (Deep Neural Network-Hidden Markov Model) ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์Œํ–ฅ๋ชจ๋ธ์„ ๋Œ€์ฒดํ•œ end-to-end ์Œ์„ฑ์ธ์‹ ๋ชจ๋ธ์€ ์—ฌ๋Ÿฌ ์Œ์„ฑ์ธ์‹์˜ ๋‹จ๊ณ„๋ฅผ ํ•˜๋‚˜์˜ ์‹œ์Šคํ…œ์œผ๋กœ ํ†ตํ•ฉํ•˜๊ณ  ๊ธฐ์กด์˜ DNN-HMM ์Œ์„ฑ์ธ์‹์„ ๋›ฐ์–ด๋„˜๋Š” ์„ฑ๋Šฅ์„ ๋ณด์ž„์œผ๋กœ์จ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋Œ€๋ถ€๋ถ„์˜ end-to-end ์Œ์„ฑ์ธ์‹ ๋ชจ๋ธ์˜ ๊ฒฝ์šฐ, ๋†’์€ ์„ฑ๋Šฅ์„ ๋‚ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊นŠ์€ ์‹ ๊ฒฝ๋ง๊ณผ ํฐ ๊ณ„์‚ฐ๋Ÿ‰์ด ์š”๊ตฌ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ํ•œ์ •๋œ ๋ฉ”๋ชจ๋ฆฌ์™€ ๊ณ„์‚ฐ ๋‚ด์—์„œ ๋†’์€ ์„ฑ๋Šฅ์„ ๋‚ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ด์— ์ตœ์ ํ™”๋œ ์‹ ๊ฒฝ๋ง์„ ์„ค๊ณ„ํ•ด์•ผ ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” CTC (Connectionist Temporal Classification) ๊ธฐ๋ฐ˜์˜ ์Œ์„ฑ์ธ์‹ ๋ชจ๋ธ์„ ์œ„ํ•œ ๋‘ ๊ฐ€์ง€์˜ ์ง€์‹ ์ฆ๋ฅ˜ ๊ธฐ๋ฒ•(knowledge distillation)์„ ์ œ์‹œํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ๊ต์‚ฌ ๋ชจ๋ธ(teacher model)๊ณผ ํ•™์ƒ ๋ชจ๋ธ(student model)์˜ ๊ตฌ์กฐ๊ฐ€ CNN (Convolutional Neural Network) ๊ธฐ๋ฐ˜ ๋ชจ๋ธ, RNN (Recurrent Neural Network) ๊ธฐ๋ฐ˜ ๋ชจ๋ธ๋กœ ๊ฐ๊ฐ ๋‹ค๋ฅธ ์ƒํ™ฉ์—์„œ ๊ต์‚ฌ ๋ชจ๋ธ์˜ ์ •๋ณด๋ฅผ ํ•™์ƒ ๋ชจ๋ธ์— ์ „์ดํ•ด์ฃผ๋Š” ์ ์‘ ํ•™์Šต์ด๋ฉฐ, ๋‘ ๋ฒˆ์งธ๋Š” ๊ต์‚ฌ ๋ชจ๋ธ์˜ ํ”„๋ ˆ์ž„ ๋‹จ์œ„์˜ ์†Œํ”„ํŠธ๋งฅ์Šค(softmax) ๊ฐ’์„ ํ•™์ƒ ๋ชจ๋ธ์ด ์ž˜ ๋”ฐ๋ผ๊ฐˆ ์ˆ˜ ์žˆ๊ฒŒ ํ•™์Šตํ•˜๋Š” ์†Œํ”„ํŠธ๋งฅ์Šค ์ง€์‹ ์ฆ๋ฅ˜ ๊ธฐ๋ฒ•์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์„ ํ‰๊ฐ€ํ•˜์˜€์„ ๋•Œ, ๋ฒ ์ด์Šค๋ผ์ธ๊ณผ ๋‹ค๋ฅธ ๊ธฐ์กด์˜ ์ง€์‹ ์ฆ๋ฅ˜ ๊ธฐ๋ฒ•์— ๋น„ํ•ด ๋†’์€ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ๋ณด์˜€์œผ๋ฉฐ, ํŠนํžˆ ์ ์‘ ํ•™์Šต๊ณผ ์†Œํ”„ํŠธ๋งฅ์Šค ์ง€์‹ ์ฆ๋ฅ˜ ๊ธฐ๋ฒ•์„ ๋ชจ๋‘ ์ ์šฉํ•œ ๋ชจ๋ธ์ด ๊ฐ€์žฅ ํฐ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ๋ณด์˜€๋‹ค.Recently, there has been much research for end-to-end speech recognition that replaces DNN-HMM (Deep Neural Network-Hidden Markov Model) hybrid system with an integrated system. In addition, it shows better results compared to the conventional hybrid system. However, most end-to-end speech recognition models require heavy computation and large model size to produce better predictions. So to reach competitive performance within the constraints on the storage and computational resources, it is required to design the lightweight model. In this paper, we propose two knowledge distillation methods for CTC (Connectionist Temporal Classification)-based speech recognition model. The first method is adjustment training. Even if the student model is based on the different types of neural networks of the teacher model, the teacher model can transfer knowledge to the student model. The second is the softmax knowledge distillation that the frame-level softmax value of the student model has the same as that of the teacher model. Through the experiments using LibriSpeech dataset, we achieved better WERs in comparison with conventional methods.1. Introduction 1 2. Background 4 2.1. End-to-End Speech Recognition using CTC 4 2.1.1. Connectionist Temporal Classification (CTC) 4 2.1.2. Feature Extraction 6 2.1.3. Word Error Rate (WER) 6 2.1.4. Relative WER Reduction 7 2.2. Knowledge Distillation 7 2.2.1. Conventional Knowledge Distillation 7 2.2.2. Sequence-Level Knowledge Distillation 10 3. Proposed Method 12 3.1. Adjustment Training 13 3.2. Softmax Knowledge Distillation 14 3.3. Learning Procedure 17 4. Experimental Results 18 4.1. Dataset 18 4.2. Experiment Setting 19 4.2.1. Teacher Model 19 4.2.2. Student Model 22 4.2.3. Adjustment Training 23 4.2.4. Softmax Knowledge Distillation 23 4.3. Results 24 5. Conclusion and Future Work 26 Abstract (In Korean) 29Maste

    The Disappearance of Collective Defense Initiative to Northeast Asia of the U.S. in Post-war era: Considering U.S.-China Relation

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์ •์น˜์™ธ๊ตํ•™๋ถ€(์™ธ๊ตํ•™์ „๊ณต), 2020. 8. ์ „์žฌ์„ฑ.2์ฐจ ๋Œ€์ „์ด ๋๋‚˜๊ณ  ๋ƒ‰์ „์ด ์‹œ์ž‘๋œ ์ดํ›„, ๋ฏธ๊ตญ์€ ๊ณต์‚ฐ๊ถŒ์„ ๊ฒฌ์ œํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ง€์—ญ์  ์ง‘๋‹จ๋ฐฉ์œ„์ฒด์ œ๋ฅผ ๊ณ ์•ˆํ•˜์˜€๋‹ค. ์œ ๋Ÿฝ์˜ NATO๊ฐ€ ๊ทธ ๋Œ€ํ‘œ์ ์ธ ์˜ˆ๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋Š”๋ฐ, ์ด๋Š” ๋น„๋‹จ ์œ ๋Ÿฝ์—๋งŒ ๊ตญํ•œ๋œ ์ „๋žต์€ ์•„๋‹ˆ์—ˆ๋‹ค. ์•„์‹œ์•„ํƒœํ‰์–‘์—์„œ๋„ ๋ฏธ๊ตญ์€ ์ง€์—ญ์„ ์ „์ฒด์ ์œผ๋กœ ํฌ๊ด„ํ•˜๋Š” ๋„“์€ ๋ฒ”์œ„์˜ ์ง‘๋‹จ๋ฐฉ์œ„์ฒด์ œ๋ฅผ ๊ตฌ์ƒํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Š” ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ํ˜„์‹ค์ ์ธ ๋ฌธ์ œ๋“ค๋กœ ์‹คํ˜„์ด ์–ด๋ ค์› ๊ณ , ์ด์— ๋ฏธ๊ตญ์€ ๋ณด๋‹ค ์ข์€ ๋‹จ์œ„์˜ ์ง€์—ญ์„ ๋‹จ์œ„๋กœ ํ•˜์—ฌ ๋ณธ๋ž˜์˜ ๊ตฌ์ƒ์„ ๋ถ„ํ• ํ•˜์˜€๋‹ค. ์ด๋•Œ ๋ฏธ๊ตญ์€ ํ˜ธ์ฃผ์™€ ๋‰ด์งˆ๋žœ๋“œ์— ANZUS, ๋™๋‚จ์•„์— SEATO๋ฅผ ์„ฑ๋ฆฝ์‹œํ‚ค๋ฉฐ ๊ทธ ์ด์›ƒ ์ง€์—ญ์ธ ๋™๋ถ์•„์‹œ์•„์—๋„ ์ง€์—ญ์  ์ง‘๋‹จ๋ฐฉ์œ„์ฒด์ œ๋ฅผ ๋งŒ๋“ค ๊ฒƒ์„ ๊ตฌ์ƒํ•˜์˜€๋‹ค. ์ด๋Š” 1954๋…„, ํ•œ๊ตญ, ์ผ๋ณธ, ๋Œ€๋งŒ, ํ•„๋ฆฌํ•€์„ ์ž‡๋Š” ์„œํƒœํ‰์–‘ ์ง‘๋‹จ๋ฐฉ์œ„(Western Pacific Collective Defense)๋กœ ๊ทน๋™ ์ •์ฑ…์— ์ˆ˜๋ก๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ถˆ๊ณผ 5๋…„ ํ›„์ธ 1959๋…„, ์‚ฌ์‹ค์ƒ ๋™๋ถ์•„์‹œ์•„ 3๊ฐœ๊ตญ์˜ ์ง‘๋‹จ๋ฐฉ์œ„๋ผ๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋Š” ์ด ๊ตฌ์ƒ์€ ๊ทน๋™ ์ •์ฑ…์—์„œ ์‚ญ์ œ๋˜๋ฉฐ ๊ทธ ์ž์ทจ๋ฅผ ๊ฐ์ถ”๊ฒŒ ๋œ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฏธ๊ตญ์˜ ๋™๋ถ์•„ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์ด ์™œ, ์–ด๋–ป๊ฒŒ ์‚ฌ๋ผ์กŒ๋Š”์ง€์— ๋Œ€ํ•˜์—ฌ ์˜๋ฌธ์„ ๋Š๊ปด, ๊ทธ ์›์ธ์— ๋Œ€ํ•˜์—ฌ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๊ณ ์˜ ํ•ต์‹ฌ ์งˆ๋ฌธ์€ 1954๋…„์— ์‹œ์ž‘๋œ ๋ฏธ๊ตญ์˜ ๋™๋ถ์•„์‹œ์•„ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์ด ์™œ 5๋…„์—ฌ ๋งŒ์— ์‚ฌ๋ผ์กŒ๋Š”๊ฐ€๋กœ ์ •๋ฆฌ๋  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์‹ค 1954๋…„ ์ฆˆ์Œ์—๋Š” ๋™๋ถ์•„์˜ 3๊ฐœ๊ตญ ํ•œ, ์ผ, ๋Œ€๋งŒ๊ณผ ๋ฏธ๊ตญ ๊ฐ„ ์ด๋ฏธ ์–‘์ž๋™๋งน์ด ์ฒด๊ฒฐ๋˜์–ด ์žˆ์—ˆ๊ณ , ์ดํ›„์˜ ์„œํƒœํ‰์–‘ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์€ ๊ทธ ์ „๋ถ€ํ„ฐ ์ด์–ด์ ธ ์˜ค๋˜ ์—ฌ๋Ÿฌ ์กฐ๊ฑด ๋•Œ๋ฌธ์— ์‚ฌ์‹ค์ƒ ์‹คํ˜„์ด ์–ด๋ ค์› ๋‹ค๊ณ  ๋ณด๋Š” ๊ฒƒ์ด ํƒ€๋‹นํ•˜๋‹ค. ๋ฏธ๊ตญ์ด ๋™๋ถ์•„ ์ง‘๋‹จ๋ฐฉ์œ„์˜ ์ค‘์‹ฌ์œผ๋กœ ๊ณ ๋ คํ–ˆ๋˜ ์ผ๋ณธ์€ ์ค„๊ณง ์—ฌ๊ธฐ์— ์†Œ๊ทน์ ์ด์—ˆ์œผ๋ฉฐ, ํ•œ-์ผ์˜ ์‚ฌ์ด๋Š” ๋งค์šฐ ์ข‹์ง€ ์•Š์•˜๋˜ ๋“ฑ, ๊ทน๋ณต์ด ์–ด๋ ค์šด ์ƒํ™ฉ์  ์กฐ๊ฑด์ด ๊ณผํžˆ ๋งŽ์•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋‹น์‹œ ๊ตญ๋ฌด์žฅ๊ด€์ด๋˜ ๋œ๋ ˆ์Šค๋ฅผ ๋น„๋กฏํ•˜์—ฌ ๋งŽ์€ ์ธ์‚ฌ๋“ค์ด ์„œํƒœํ‰์–‘ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์— ๋™์˜ํ•˜์˜€๊ณ , ๊ทธ์— ํž˜์ž…์–ด ์ด๋Š” ๊ทน๋™ ์ •์ฑ…์— ํฌํ•จ๋˜์—ˆ๋‹ค. ์–ด์ฉŒ๋ฉด ํ•ด๋‹น ๊ตฌ์ƒ์˜ ์‹คํ˜„์„ ๋‹น์œ„์ ์ธ ๊ฒƒ์œผ๋กœ ์—ฌ๊ฒผ๋Š”์ง€๋„ ๋ชจ๋ฅผ ์ผ์ด๋‹ค. ๋ณธ ๊ณ ์—์„œ ๋ณด๋‹ค ๊นŠ์ด ์•Œ๊ณ ์ž ํ•˜์˜€๋˜ ๊ฒƒ์€, ์‹คํ˜„์ด ์–ด๋ ต๋‹ค๋Š” ๊ฒƒ์„ ์ธ์ง€ํ•œ ํ›„์—๋„ ๊ทน๋™ ์ •์ฑ…์— ๋ฐ˜์˜๋˜์—ˆ๋˜ ์„œํƒœํ‰์–‘ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์ด 5๋…„ ์‚ฌ์ด ์™„์ „ํžˆ ์ž์ทจ๋ฅผ ๊ฐ์ถ”๊ฒŒ ๋œ ๊ฒƒ์ด ์–ด๋– ํ•œ ์ด์œ  ๋•Œ๋ฌธ์ด์—ˆ๋Š”๊ฐ€์— ๋Œ€ํ•œ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ทธ ์›์ธ์„ ๋ฏธ์ค‘๊ด€๊ณ„์—์„œ ์ฐพ๋Š”๋‹ค. ๋ณธ ๊ณ ๋Š” ์„œํƒœํ‰์–‘ ์ง‘๋‹จ์•ˆ๋ณด๊ตฌ์ƒ์ด ๊ทน๋™ ์ •์ฑ…์— ์‹ค๋ ค ์žˆ์—ˆ๋˜ 1954-1959๋…„์˜ 5๋…„ ๋™์•ˆ, ๋ฏธ๊ตญ์˜ ๋Œ€์ค‘ ํƒœ๋„๊ฐ€ ๋ˆ„๊ทธ๋Ÿฌ์ง์— ๋”ฐ๋ผ์„œ ๋™๋ถ์•„ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์— ๋Œ€ํ•œ ์ง€์ง€ ์—ญ์‹œ ์‡ ํ‡ดํ•˜์˜€์Œ์— ์ฃผ๋ชฉํ•˜์˜€๋‹ค. 1950๋…„๋Œ€ ์ดˆ์ค‘๋ฐ˜์—๋Š” ๋ฐ˜๊ณต์ฃผ์˜๊ฐ€ ๋Œ€์„ธ๋ฅผ ์ด๋ฃจ๊ณ  ์žˆ์–ด ๋ฏธ๊ตญ์ด ์ค‘๊ตญ์— ๊ฐ•๊ฒฝํ•œ ํƒœ๋„๋ฅผ ๋ณด์˜€์ง€๋งŒ, 50๋…„๋Œ€ ์ค‘ํ›„๋ฐ˜๊ธฐ๋กœ ์ ‘์–ด๋“ค๋ฉด์„œ ๋ฏธ๊ตญ์€ ์ด ๊ฐ™์€ ๋Œ€์ค‘ ์ •์ฑ…์„ ์™„ํ™”ํ•ด์•ผ๋งŒ ํ•˜๋Š” ์ƒํ™ฉ์— ๋†“์ด๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ ์†์—์„œ ๋Œ€๋งŒ์„ ํฌํ•จํ•œ ์ง‘๋‹จ๋ฐฉ์œ„๊ฐ€ ๊ทน๋™ ์ •์ฑ…์—์„œ ์‚ฌ๋ผ์ง€๊ฒŒ ๋˜์—ˆ๋‹ค๋Š” ๊ฒƒ์ด ๋ณธ ๊ณ ๊ฐ€ ์ด์•ผ๊ธฐํ•˜๊ณ ์ž ํ•˜๋Š” ํ•ต์‹ฌ ๋‚ด์šฉ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๊ณ ์˜ ๋…ผ์˜๋Š” ์ค‘๊ตญ์— ๋Œ€ํ•œ ๋ฏธ๊ตญ์˜ ์ธ์‹๊ณผ ์ •์ฑ…๋ณ€ํ™”๊ฐ€ ๊ฒฐ๊ตญ ๋™๋ถ์•„์‹œ์•„์˜ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์—๊นŒ์ง€ ์˜ํ–ฅ์„ ๋ฏธ์ณค์œผ๋ฉฐ, ๊ฒฐ๊ตญ์€ ๊ตฌ์ƒ์ด ๊ทน๋™ ์ •์ฑ…์—์„œ ์‚ญ์ œ๋˜๊ณ  ๋ฌด์‚ฐ๋œ ํ•˜๋‚˜์˜ ์š”์ธ์ด ๋˜์—ˆ๋‹ค๋Š” ์ฃผ์žฅ์œผ๋กœ ์ •๋ฆฌ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋™๋ถ์•„์‹œ์•„๋ผ๋Š” ํ•œ์ •๋œ ๊ณต๊ฐ„์— ๋Œ€ํ•œ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์ด ์‚ฌ๋ผ์ง€๊ฒŒ ๋œ ๊ฒฝ์œ„์— ์žˆ์–ด, ๊ทธ๊ฐ„ ์ฃผ๋ชฉ๋ฐ›์ง€ ๋ชปํ–ˆ๋˜ ๋ฏธ์ค‘๊ด€๊ณ„๋ฅผ ์กฐ๋งํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง„๋‹ค. ์ค‘๊ตญ์ด ๋ถ€์ƒํ•˜์—ฌ G2๋ผ๊ณ ๊นŒ์ง€ ๋ถˆ๋ฆฌ๊ณ  ์žˆ๋Š” ํ˜„์žฌ์—๋Š” ๋‘ ๊ฐ•๋Œ€๊ตญ์˜ ๊ด€๊ณ„๊ฐ€ ์—ฌ๋Ÿฌ๋ชจ๋กœ ๋” ์ฒจ์˜ˆํ•˜๊ณ  ์ค‘์š”ํ•ด์กŒ๋‹ค๊ณ  ์ƒ๊ฐ๋˜๋Š”๋ฐ”, ๋ณธ ๊ณ ์˜ ๋…ผ์˜๊ฐ€ ์•ž์œผ๋กœ์˜ ๊ด€๋ จ ์—ฐ๊ตฌ์—๋„ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•œ๋‹ค.Since the end of World War II and the beginning of the Cold War, The United States devised regional collective defenses to contain the Communist bloc. NATO is an great example of this plan which was applied to Europe and Asia universally. The U.S. devised a wide regional collective defense in Asia-Pacific. However, it was difficult to realize this idea because there were too many problems that were hard to solved. Thus the U.S. divided the original idea into smaller areas. At that time the U.S. considered a regional collective defense in Northeast Asia, with establishment of ANZUS and SEATO. This consideration named Western Pacific Collective Defense was consisted of South Korea, Japan, Taiwan and Philippines, but the actual members were three countries except Philippines because she had already joined SEATO. The idea of Western Pacific Collective Defense was listed in Far East policy of the U.S. in 1954. However this clause was deleted in 1959. This dissertation pursue to find out why and how this clause was disappeared. Therefore the main topic of this dissertation is Why did the idea of Western Pacific Collective Defense in the U.S., which began in 1954, disappeared in 1959? In fact around 1954, ROK, Japan and Taiwan already signed in bilateral security treaty with the U.S. and Western Pacific Collective Defense was almost impossible idea to realize, because too many troublesome problems existed. First of all, The relationship of ROK and Japan, the tentative member of the idea, was worst at that time. Additionally Japan, that was expected by the U.S. to play a leading role in collective defense, took passive stance continually. Though many people, including John Foster Dulles, agreed Western Pacific Collective Defense and this idea was listed in Far East Policy. What this dissertation wants to find is why Western Pacific Collective Defense was erased just five years later, although the U.S. had already known its hard conditions for establishment from the beginning. This dissertation found a clue from relation of the U.S.-China at that time. While 5 years, from 1954 to 1959, Western Pacific Collective Defense had gradually declined as the U.S. had softened its policy toward China. In the early and mid-1950s, anti-Communism had strong power in the U.S. society but as time went by, it gradually weakened. Moreover China had expanded its power in international society. Accordingly the U.S., had taken firm stand to China, should change its own policy to China because of changed conditions. Under these circumstances, it would have been hard to include Taiwan in collective defense, since Taiwan issue was sensitive to China. Therefore it can be summed up that the change in the U.S. perception and policy toward China eventually affected delete of Western Pacific Collective Defense from Far East policy at that time. This study is meaningful in that it viewed the U.S.-China relations in the context of the disappearance of the collective defense initiative in Northeast Asia. Now that China became more powerful, it is important to know about history of relationship between the two powers, the U.S. and China. Hopefully, this dissertation helps further related research.โ… . ์„œ๋ก  1 1. ๋ฌธ์ œ์ œ๊ธฐ 1 2. ์„ ํ–‰์—ฐ๊ตฌ๊ฒ€ํ†  6 3. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๊ณผ ๊ฐ€์„ค์„ค์ • 10 4. ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 12 โ…ก. ์ „ํ›„ ๋ฏธ๊ตญ์˜ ์ง€์—ญ์  ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ 14 1. ์œ ๋Ÿฝ๊ณผ ์•„์‹œ์•„ 14 2. ๋™๋‚จ์•„์‹œ์•„์™€ ๋™๋ถ์•„์‹œ์•„ 17 โ…ข. ๋™๋ถ์•„์‹œ์•„ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์˜ ์—ญ์‚ฌ 19 1. ๊ตฌ์ƒ์˜ ์‹œ์ž‘ 19 1) ์•„์‹œ์•„ ๋ฐœ(็™ผ) ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ 19 2) ๋ฏธ๊ตญ์˜ ๊ณ„ํš 21 2. ๊ตฌ์ƒ์˜ ๊ตฌ์ฒดํ™” ๋ฐ ์ „๊ฐœ๊ณผ์ • 24 โ…ฃ. ๋™๋ถ์•„์‹œ์•„ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์˜ ๋ฌด์‚ฐ ์š”์ธ 28 1. ํ•œ์ผ๊ด€๊ณ„์˜ ๊ฐ„๊ทน 29 2. ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์— ๋Œ€ํ•œ ์ผ๋ณธ์˜ ๋น„ํ˜‘์กฐ์  ํƒœ๋„ 32 3. ๋Œ€๋งŒํ•ดํ˜‘ ์œ„๊ธฐ์™€ ๋ฏธ์ค‘๊ด€๊ณ„ 35 โ…ค. ๋ฌด์‚ฐ๋œ ์›์ธ์˜ ์กฐ๋ง: ๋ฏธ-๋Œ€๋งŒ์˜ ๊ด€๊ณ„์™€ ๋Œ€์ค‘์ „๋žต 37 1. 1954-1955๋…„ 38 1) 1์ฐจ ๋Œ€๋งŒํ•ดํ˜‘ ์œ„๊ธฐ์™€ ๋ฏธ-๋Œ€๋งŒ ์ƒํ˜ธ๋ฐฉ์œ„์กฐ์•ฝ์˜ ์ฒด๊ฒฐ 38 2) ๋™๋ถ์•„์‹œ์•„ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์˜ ๋“ฑ์žฅ๊ณผ ๋Œ€๋งŒ ๋ณ€์ˆ˜ 41 2. 1956-1957๋…„ 45 1) ๊ตญ์ œ์‚ฌํšŒ์˜ ๋Œ€์ค‘ ์ธ์‹ ๋ณ€ํ™”์™€ ๋ฐ˜๊ณต์ฃผ์˜์˜ ํ›„ํ‡ด 45 2) ๋™๋ถ์•„์‹œ์•„ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์˜ ์ ์ง„์  ์‡ ํ‡ด 46 3. 1958-1960๋…„ 51 1) 2์ฐจ ๋Œ€๋งŒํ•ดํ˜‘ ์œ„๊ธฐ์™€ ๋ฏธ๊ตญ์˜ ๋Œ€์ค‘ ํƒœ๋„ ์œ ์—ฐํ™” 51 2) ์–‘์•ˆ๊ด€๊ณ„์™€ ๋™๋ถ์•„์‹œ์•„ ์ง‘๋‹จ๋ฐฉ์œ„๊ตฌ์ƒ์˜ ์‹ค์งˆ์  ์ข…๊ฒฐ 54 3) ์ผ€๋„ค๋”” ํ–‰์ •๋ถ€์˜ ์ถœ๋ฒ”๊ณผ ๋™๋ถ์•„์‹œ์•„ 59 โ…ฅ. ๊ฒฐ๋ก  61 ์ฐธ๊ณ ๋ฌธํ—Œ 64 Abstract 80Maste

    Finding the relationship between functional brain connectivity and behavior scores in ADHD

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ธ๋ฌธ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์ธ์ง€๊ณผํ•™์ „๊ณต,2019. 8. ์ด๋™์ˆ˜.Background: Studies have persistently investigated brain regions in ADHD patients that show abnormalities compared to normal controls. However, due to diverse cognitive dysfunctions and related neural correlates in ADHD, results are non-converging showing lack of reliability. To resolve this problem models have been developed incorporating related brain regions and investigating relationships with relevant behaviors or symptoms with increased robustness. Yet, few models predict each subjects primary symptoms of ADHD which takes individuality into account. Methods: Using resting state brain scans of 299 children with and without ADHD, we constructed a model by using CPM(Connectome based Predictive Modeling) to predict individual differences in inattentive and hyperactive symptom severity. Results: Using 5-fold cross validation CPM, inattentive model showed significant predictive power in all 5 models. Likewise, hyperactive models showed significant predictive power in 5 models except one model constructed from positively correlated functional connections showed insignificant predictive power. Conclusions: Functional connections of an individual contain information about inattentive symptom or phenotype in ADHD. Selected functional connections indicate that inattentiveness is not contributed to a single region but from a coordinated activity of relevant edges.์ฃผ์˜๋ ฅ๊ฒฐํ• ๊ณผ์ž‰ํ–‰๋™์žฅ์• (attention-deficit/hyperactivity disorder, ADHD) ํ™˜์ž๊ตฐ์—์„œ ์ •์ƒ๊ตฐ๊ณผ ๋‹ค๋ฅด๊ฒŒ ๋ณด์—ฌ์ง€๋Š” ๋‡Œ ์˜์—ญ๋“ค์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ง€์†์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ์œผ๋‚˜ ํ™˜์ž๊ตฐ ๋‚ด ๊ฐ ๊ฐœ์ธ๋งˆ๋‹ค ์ฆ์ƒ์˜ ์‹ฌ๊ฐ๋„๋‚˜ ์ธ์ง€์  ๊ฒฐ์†์ด ๋‹ค์–‘ํ•˜์—ฌ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋“ค์€ ๋น„์ผ๊ด€์ ์ธ ๊ฒฝํ–ฅ์„ ๋ณด์ธ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด์„œ ์ฆ์ƒ๊ณผ ๊ด€๋ จ๋œ ์—ฌ๋Ÿฌ ๋‡Œ์˜์—ญ๋“ค๊ณผ ์ธ์ง€์  ๊ธฐ๋Šฅ์˜ ๊ด€๊ณ„๋ฅผ ์ˆ˜ํ•™์  ๋ชจ๋ธ๋ง์ด๋‚˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ๋ณด์—ฌ์ฃผ๋Š” ์—ฐ๊ตฌ๋“ค์ด ๋ฐœ์ „ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ADHD์˜ ์ฃผ์š” ์ฆ์ƒ์ธ ๋ถ€์ฃผ์˜์™€ ์ถฉ๋™์„ฑ ์ •๋„๋ฅผ ๊ฐ ํ™˜์ž๋“ค์˜ ๊ธฐ๋Šฅ์  ์—ฐ๊ฒฐ์„ฑ์œผ๋กœ ์˜ˆ์ธกํ•˜๋Š” ๋ชจ๋ธ์„ ๋งŒ๋“ค๊ธฐ ์œ„ํ•ด์„œ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ์ปค๋„ฅํ†ฐ ๊ธฐ๋ฐ˜ ์˜ˆ์ธก ๋ชจ๋ธ๋ง(connectome based predictive modeling, CPM)์€ ๋‡Œ ์—ฐ๊ฒฐ๋ง๊ณผ ํ–‰๋™์ ์ˆ˜๊ฐ„ ๊ด€๊ณ„๋ฅผ ๋ชจ๋ธ๋กœ ๋งŒ๋“ค์–ด์„œ ๊ฐœ์ธ์˜ ํ–‰๋™์„ ์˜ˆ์ธกํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—๋Š” 299๋ช…์˜ ADHD ์•„๋™๋“ค๊ณผ ์ •์ƒ์•„๋™๋“ค์ด ์ฐธ์—ฌํ•˜์˜€๋‹ค. ๋Œ€์ƒ์ž๋“ค์€ ํŠน์ •ํ•œ ์ธ์ง€๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•˜์ง€ ์•Š๊ณ  ๊นจ์–ด์žˆ๋Š” ์ƒํƒœ๋ฅผ ์ดฌ์˜ํ•œ ํœด์ง€๊ธฐ fMRI ์ด๋ฏธ์ง€์™€ Korean ADHD Rating Scales(K-ARS) ๊ฒ€์‚ฌ๋ฅผ ํ†ตํ•ด ์ธก์ •ํ•œ ๋ถ€์ฃผ์˜ ์ ์ˆ˜์™€ ์ถฉ๋™์„ฑ ์ ์ˆ˜๋ฅผ CPM ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์ ์šฉํ•˜์—ฌ ๊ธฐ๋Šฅ์  ์—ฐ๊ฒฐ์„ฑ์œผ๋กœ ๋ถ€์ฃผ์˜์™€ ์ถฉ๋™์„ฑ ์ •๋„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ชจ๋ธ์„ ๋งŒ๋“ค์—ˆ๋‹ค. CPM ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ๋งŒ๋“ค์–ด์ง„ ๋ชจ๋ธ์„ 5-๋ฌถ์Œ ๊ต์ฐจ๊ฒ€์ฆ๋ฒ•(5-fold cross validation)์„ ์ด์šฉํ•˜์—ฌ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ํ›ˆ๋ จ ๋ฐ์ดํ„ฐ๋กœ ๋งŒ๋“ค์–ด์ง„ ๋ชจ๋ธ์— ์‹œํ—˜ ๋ฐ์ดํ„ฐ์˜ ๊ธฐ๋Šฅ์  ์—ฐ๊ฒฐ์„ฑ ๋ฐ์ดํ„ฐ๋ฅผ ๋„ฃ์—ˆ์„ ๋•Œ ๋‚˜์˜ค๋Š” ๊ฐ’์ธ ์˜ˆ์ธก๋œ ์ž„์ƒ์ ์ˆ˜์™€ ์‹ค์ œ ์ž„์ƒ์ ์ˆ˜์™€์˜ ์ƒ๊ด€์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ์˜ˆ์ธก๋œ 5 ๋ฌถ์Œ์˜ ์‹œํ—˜ ๋ฐ์ดํ„ฐ์˜ ๋ถ€์ฃผ์˜ ์ ์ˆ˜๋Š” ์‹ค์ œ ๋ถ€์ฃผ์˜ ์ ์ˆ˜์™€ ์œ ์˜๋ฏธํ•œ ์ƒ๊ด€์„ ๋ณด์˜€๊ณ  ์ถฉ๋™์„ฑ ์ ์ˆ˜๋Š” ํ•˜๋‚˜์˜ ๋ฌถ์Œ์„ ์ œ์™ธํ•˜๊ณ  ๋ชจ๋‘ ๊ฒ€์ฆ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” CPM์„ ํ†ตํ•ด ADHD์˜ ์ฃผ์š”ํ•œ ์ฆ์ƒ์ธ ๋ถ€์ฃผ์˜๋ฅผ 5๋ฒˆ์˜ ๊ต์ฐจ๊ฒ€์ฆ์œผ๋กœ ๋ชจ๋“  ์‹œํ—˜ ๋ฐ์ดํ„ฐ๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด ๊ฒฐ๊ณผ๋Š” ๊ฐœ์ธ์˜ ํœด์ง€๊ธฐ ๊ธฐ๋Šฅ์  ์—ฐ๊ฒฐ์„ฑ์ด ADHD์˜ ์ฃผ์š”ํ•œ ์ž„์ƒ ์ฆ์ƒ ์ค‘ ํ•˜๋‚˜์ธ ๋ถ€์ฃผ์˜์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ๋‹ด๊ณ  ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ 2 ์ œ 2 ์žฅ ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ• 4 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ 4 ์ œ 2 ์ ˆ ํ‰๊ฐ€๋„๊ตฌ 4 ์ œ 3 ์ ˆ ๋‡Œ ๋„คํŠธ์›Œํฌ ๋งคํŠธ๋ฆญ์Šค 5 ์ œ 4 ์ ˆ ์ปค๋„ฅํ†ฐ ๊ธฐ๋ฐ˜ ์˜ˆ์ธก ๋ชจ๋ธ๋ง(Connectome based Predictive modeling) 7 ์ œ 3 ์žฅ ๊ฒฐ ๊ณผ 9 ์ œ 1 ์ ˆ ๋‚ด์  ๊ฒ€์ฆ(Internal Validation) 9 ์ œ 4 ์žฅ ๋…ผ ์˜ 11 ์ฐธ๊ณ ๋ฌธํ—Œ 15 Abstract 32 ํ‘œ ๋ชฉ์ฐจ [ํ‘œ 1] ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ 21 [ํ‘œ 2] AAL ํ…œํ”Œ๋ฆฟ 22 ๊ทธ๋ฆผ ๋ชฉ์ฐจ [๊ทธ๋ฆผ 1]CPM ์•Œ๊ณ ๋ฆฌ์ฆ˜ 25 [๊ทธ๋ฆผ 2]๋ถ€์ฃผ์˜, ์ถฉ๋™์„ฑ ๋ชจ๋ธ์˜ ์˜ˆ์ธก๋ ฅ 26 [๊ทธ๋ฆผ 3]๋†’์€ ๋ถ€์ฃผ์˜ ์ ์ˆ˜ ์˜ˆ์ธก ๊ธฐ๋Šฅ์  ์—ฐ๊ฒฐ์„ฑ ํŒจํ„ด 30 [๊ทธ๋ฆผ 4]๋‚ฎ์€ ๋ถ€์ฃผ์˜ ์ ์ˆ˜ ์˜ˆ์ธก ๊ธฐ๋Šฅ์  ์—ฐ๊ฒฐ์„ฑ ํŒจํ„ด 31Maste

    ์˜ค๋ณด์—์˜ ๊ตฌ์กฐ์  ๋ฐœ๋‹ฌ๊ณผ์ •์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์Œ์•…๊ณผ ์˜ค๋ณด์—์ „๊ณต,1997.Maste

    2016๋…„ WHO ์ง„๋‹จ๊ธฐ์ค€์— ๋”ฐ๋ผ ์žฌ๋ถ„๋ฅ˜ํ•œ ํ•œ๊ตญ์ธ ํ•„๋ผ๋ธํ”ผ์•„ ์—ผ์ƒ‰์ฒด ์Œ์„ฑ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘ ํ™˜์ž์˜ ๋ถ„์ž ๋ฐ ์„ธํฌ ์œ ์ „ํ•™์  ํŠน์„ฑ๊ณผ ์ด์— ๋”ฐ๋ฅธ ์ž„์ƒ์  ์˜์˜

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ,2020. 2. ์ด๋™์ˆœ.Introduction: The 2016 World Health Organization (WHO) myeloproliferative neoplasm (MPN) diagnostic criteria announced updated criteria of prefibrotic primary myelofibrosis (prefibrotic PMF) whose diagnosis is difficult in differentiating with essential thrombocythemia (ET). Myeloproliferative neoplasm, unclassifiable (MPN-U) shows findings that do not meet all the criteria for any specific disorder. Herein, the first goal is to reclassify the diagnosis of Philadelphia chromosome-negative MPN by from 2008 to 2016 classification in focus on MPN-U. The second one is to identify useful markers to discriminate between ET and prefibrotic PMF. The last one is to broaden the understanding of characteristics of Korean MPN patients using the clinical, cytogenetic, telomere lengths, molecular, and BM histologic features. Methods: In 53 MPN and 6 AML evolved from MPN patients diagnosed in Seoul National University Hospital between 2005 and 2014, G-banding, fluorescence in situ hybridization (FISH), targeted capture sequencing for 88 hematopoiesis-related genes, and telomere length (TL) measurement were performed. Survival analysis was performed including the assessment of progression. Additionally, 99 MPN cases diagnosed in the hospital between 2017 and 2019 were analyzed to find out CD34 positive megakaryocytes. Results: By applying 2016 WHO criteria, 38.5% and 15.4% of MPN-U were reclassified to prefibrotic PMF and overt PMF, respectively. In survival analysis, the updated criteria showed a better stratification of MPN diagnosis. In our study, the mutated genes found in prefibrotic PMF but not in ET were CSF3R, DNMT3A, SF3B1, and SRSF2, which are potential candidate markers for the differential diagnosis between ET and prefibrotic PMF. The genomic profile of Korean MPN was similar to that of the previous study. We found novel MPL mutations (MPL D128N, D261Y) in a PMF patient. Although telomere length of ET was not shorter than that of normal control, that of prefibrotic PMF was (P = 0.0635), suggesting telomere length as a potential marker for differentiating two diseases. 44.4% of MPN cases showed CD34-positive megakaryocytes but CD34 positive percentages in total megakaryocytes are not different between ET and prefibrotic PMF. Additional ASXL1 mutation was related to lower hemoglobin concentration in PMF patients. Conclusion: By revealing the reallocation of MPN-U into prefibrotic PMF and overt PMF according to 2016 WHO criteria, we found that the updated criteria provide a precise diagnosis of MPN. Genomic study and telomere length analysis can help the discrimination of ET and prefibrotic PMF. Our overall analysis provides a wider understanding of the clinical, cytogenetic, telomere lengths, molecular, and BM histologic findings of Korean MPN patients.์„œ๋ก : 2016๋…„ WHO ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘ ์ง„๋‹จ ๊ธฐ์ค€์€ ์—…๋ฐ์ดํŠธ๋œ ์ „์„ฌ์œ ํ™” ๋‹จ๊ณ„ ์›๋ฐœ์„ฑ๊ณจ์ˆ˜์„ฌ์œ ์ฆ ์ง„๋‹จ ๊ธฐ์ค€์„ ์ œ์‹œํ•˜์˜€๊ณ , ์ด ์งˆํ™˜์˜ ์ง„๋‹จ์€ ๋ณธํƒœ์„ฑํ˜ˆ์†ŒํŒ ์ฆ๊ฐ€์ฆ๊ณผ ๊ฐ๋ณ„ํ•˜๊ธฐ๊ฐ€ ์–ด๋ ต๋‹ค. ๋ฏธ๋ถ„๋ฅ˜ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘์€ ์–ด๋Š ํ•œ ๊ฐ€์ง€์˜ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘ ์„ธ๋ถ€์งˆํ™˜์˜ ๋ชจ๋“  ์ง„๋‹จ๊ธฐ์ค€์„ ์ถฉ์กฑํ•˜์ง€ ์•Š๋Š” ํŠน์„ฑ์„ ๋ณด์ธ๋‹ค. ์—ฌ๊ธฐ์„œ ์šฐ๋ฆฌ์˜ ์ฒซ๋ฒˆ์งธ ๋ชฉํ‘œ๋Š” WHO 2008๋…„์—์„œ 2016๋…„ ์ง„๋‹จ๊ธฐ์ค€์œผ๋กœ ๊ฐ€๋ฉด์„œ ํ•„๋ผ๋ธํ”ผ์•„ ์—ผ์ƒ‰์ฒด ์Œ์„ฑ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘์„ ์žฌ๋ถ„๋ฅ˜ํ•˜๋˜, ๋ฏธ๋ถ„๋ฅ˜ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ๋‹ค. ๋‘๋ฒˆ์งธ ๋ชฉํ‘œ๋Š” ๋ณธํƒœ์„ฑํ˜ˆ์†ŒํŒ ์ฆ๊ฐ€์ฆ๊ณผ ์ „์„ฌ์œ ํ™” ๋‹จ๊ณ„ ์›๋ฐœ์„ฑ๊ณจ์ˆ˜์„ฌ์œ ์ฆ์„ ๊ตฌ๋ณ„ํ•  ์ˆ˜ ์žˆ๋Š” ์œ ์šฉํ•œ ๋งˆ์ปค๋ฅผ ์ฐพ๋Š” ๊ฒƒ์ด๋‹ค. ๋งˆ์ง€๋ง‰ ๋ชฉํ‘œ๋Š” ์ž„์ƒ์ , ์„ธํฌ์œ ์ „ํ•™์ , ํ…”๋กœ๋ฏธ์–ด ๊ธธ์ด, ๋ถ„์ž์œ ์ „ํ•™์ , ๊ณจ์ˆ˜ ์กฐ์งํ•™์  ํŠน์„ฑ์„ ํ†ตํ•ด ํ•œ๊ตญ์ธ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘ ํ™˜์ž์˜ ํŠน์ง•์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋„“ํžˆ๋Š” ๊ฒƒ์ด๋‹ค. ๋ฐฉ๋ฒ•: 2005๋…„๋ถ€ํ„ฐ 2014๋…„๊นŒ์ง€ ์„œ์šธ๋Œ€ํ•™๊ต ๋ณ‘์›์—์„œ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘์œผ๋กœ ์ง„๋‹จ๋œ 53๋ช…์˜ ํ™˜์ž ๋ฐ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘์—์„œ ๊ธ‰์„ฑ๊ณจ์ˆ˜์„ฑ๋ฐฑํ˜ˆ๋ณ‘์œผ๋กœ ์ดํ™˜๋œ 6๋ช…์˜ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ, ํ•ตํ˜• ๊ฒ€์‚ฌ(G-banding), ํ˜•๊ด‘์ œ์ž๋ฆฌ๋ถ€ํ•ฉ๋ฒ•(FISH), 88๊ฐœ์˜ ์กฐํ˜ˆ ๊ด€๋ จ ์œ ์ „์ž ํŒจ๋„๋กœ ๊ตฌ์„ฑ๋œ ํƒ€๊ฒŸ ์‹œํ€€์‹ฑ, ํ…”๋กœ๋ฏธ์–ด ๊ธธ์ด ๋ถ„์„์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์งˆ๋ณ‘ ์ง„ํ–‰ ํ‰๊ฐ€๊ฐ€ ํฌํ•จ๋œ ์ƒ์กด ๋ถ„์„๋„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, CD34 ์–‘์„ฑ ๊ฑฐ๋Œ€ํ•ต์„ธํฌ๋ฅผ ๊ด€์ฐฐํ•˜๊ธฐ ์œ„ํ•ด ์ด ๋ณ‘์›์—์„œ 2017๋…„๋ถ€ํ„ฐ 2019๋…„๊นŒ์ง€ ์ง„๋‹จ๋œ 99๊ฐœ์˜ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘ ์ผ€์ด์Šค๋„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: 2016๋…„ WHO ์ง„๋‹จ๊ธฐ์ค€์„ ์ ์šฉํ–ˆ์„ ๋•Œ, ๋ฏธ๋ถ„๋ฅ˜ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘์˜ 38.5%์™€ 15.4%๋Š” ๊ฐ๊ฐ ์ „์„ฌ์œ ํ™” ๋‹จ๊ณ„ ๋ฐ ์„ฌ์œ ํ™” ๋‹จ๊ณ„ ์›๋ฐœ์„ฑ ๊ณจ์ˆ˜์„ฌ์œ ์ฆ์œผ๋กœ ์žฌ๋ถ„๋ฅ˜๋˜์—ˆ๋‹ค. ์ƒ์กด ๋ถ„์„์—์„œ๋Š” ์—…๋ฐ์ดํŠธ๋œ ์ง„๋‹จ๊ธฐ์ค€์ด ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘ ์ง„๋‹จ์„ ์ข€ ๋” ์ž˜ ๊ณ„์ธตํ™”ํ•จ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์šฐ๋ฆฌ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์—์„œ ์ „์„ฌ์œ ํ™” ๋‹จ๊ณ„ ๊ณจ์ˆ˜์„ฌ์œ ์ฆ์—์„œ ๋ฐœ๊ฒฌ๋˜๋‚˜ ๋ณธํƒœ์„ฑ ํ˜ˆ์†ŒํŒ์ฆ๊ฐ€์ฆ์—์„œ ๋ฐœ๊ฒฌ๋˜์ง€ ์•Š๋Š” ๋Œ์—ฐ๋ณ€์ด๋ฅผ ๊ฐ€์ง„ ์œ ์ „์ž๋Š” CSF3R, DNMT3A, SF3B1, SRSF2์ด๋ฉฐ, ์ด๋“ค์€ ๋‘ ์งˆํ™˜์„ ๊ฐ๋ณ„ํ•˜๊ธฐ ์œ„ํ•œ ์ž ์žฌ์  ํ›„๋ณด ๋งˆ์ปค๋กœ ์ƒ๊ฐ๋œ๋‹ค. ํ•œ๊ตญ์ธ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘์˜ ์œ ์ „์  ํ”„๋กœํŒŒ์ผ์€ ์ด์ „ ์—ฐ๊ตฌ์—์„œ์™€ ๋น„์Šทํ–ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ƒˆ๋กœ์šด MPL ๋Œ์—ฐ๋ณ€์ด (MPL D128N, D261Y)๋ฅผ ํ•œ ๋ช…์˜ ๊ณจ์ˆ˜์„ฌ์œ ์ฆ ํ™˜์ž์—์„œ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋ณธํƒœ์„ฑ ํ˜ˆ์†ŒํŒ์˜ ํ…”๋กœ๋ฏธ์–ด ๊ธธ์ด๋Š” ์ •์ƒ์— ๋น„ํ•ด ์งง์•„์ ธ ์žˆ์ง€ ์•Š์•˜์œผ๋‚˜, ์ „์„ฌ์œ ํ™” ๋‹จ๊ณ„ ๊ณจ์ˆ˜์„ฌ์œ ์ฆ์˜ ํ…”๋กœ๋ฏธ์–ด ๊ธธ์ด๋Š” ์ •์ƒ์— ๋น„ํ•ด ์งง์•˜๋‹ค (P=0.0635). ์ด๋Š” ํ…”๋กœ๋ฏธ์–ด ๊ธธ์ด๊ฐ€ ๋‘ ์งˆํ™˜์„ ๊ฐ๋ณ„ํ•˜๋Š”๋ฐ ์ž ์žฌ์  ๋งˆ์ปค๋กœ ์ด์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘์˜ 44.4%์—์„œ CD34 ์–‘์„ฑ ๊ฑฐ๋Œ€ํ•ต์„ธํฌ๊ฐ€ ๊ด€์ฐฐ๋˜์—ˆ์œผ๋‚˜ ๋ณธํƒœ์„ฑ ํ˜ˆ์†ŒํŒ์ฆ๊ฐ€์ฆ๊ณผ ์ „์„ฌ์œ ํ™” ๋‹จ๊ณ„ ๊ณจ์ˆ˜์„ฌ์œ ์ฆ์—์„œ์˜ CD34 ์–‘์„ฑ ๊ฑฐ๋Œ€ํ•ต์„ธํฌ ๋ถ„์œจ์€ ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. ์ถ”๊ฐ€์ ์ธ ASXL1 ๋Œ์—ฐ๋ณ€์ด๋Š” ๊ณจ์ˆ˜์„ฌ์œ ์ฆ ํ™˜์ž์—์„œ ๋‚ฎ์€ ํ—ค๋ชจ๊ธ€๋กœ๋นˆ ๋†๋„์™€ ์—ฐ๊ด€์ด ์žˆ์—ˆ๋‹ค. ๊ฒฐ๋ก : 2016๋…„ WHO ์ง„๋‹จ๊ธฐ์ค€์— ๋”ฐ๋ผ ๋ฏธ๋ถ„๋ฅ˜ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘์ด ์ „์„ฌ์œ ํ™” ๋‹จ๊ณ„ ๋ฐ ์„ฌ์œ ํ™” ๋‹จ๊ณ„ ๊ณจ์ˆ˜์„ฌ์œ ์ฆ์œผ๋กœ ์žฌ๋ถ„๋ฅ˜๋˜๋Š” ๊ฒƒ์„ ๋ฐํžˆ๋ฉด์„œ, ์šฐ๋ฆฌ๋Š” ์—…๋ฐ์ดํŠธ๋œ ์ง„๋‹จ๊ธฐ์ค€์ด ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘์˜ ์ •ํ™•ํ•œ ์ง„๋‹จ์„ ์ œ๊ณตํ•จ์„ ์•Œ๊ฒŒ ๋˜์—ˆ๋‹ค. ๋ถ„์ž ์œ ์ „ํ•™์  ๊ฒ€์‚ฌ์™€ ํ…”๋กœ๋ฏธ์–ด ๊ธธ์ด ๋ถ„์„์€ ์ „์„ฌ์œ ํ™” ๋‹จ๊ณ„ ๊ณจ์ˆ˜์„ฌ์œ ์ฆ๊ณผ ๋ณธํƒœ์„ฑ ํ˜ˆ์†ŒํŒ์ฆ๊ฐ€์ฆ์˜ ๊ฐ๋ณ„์— ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ์šฐ๋ฆฌ์˜ ์ข…ํ•ฉ์  ๋ถ„์„์€ ํ•œ๊ตญ์ธ ๊ณจ์ˆ˜์ฆ์‹์ข…์–‘ ํ™˜์ž์˜ ์ž„์ƒ์ , ์„ธํฌ์œ ์ „ํ•™์ , ํ…”๋กœ๋ฏธ์–ด ๊ธธ์ด, ๋ถ„์ž์œ ์ „ํ•™์ , ๊ณจ์ˆ˜ ์กฐ์งํ•™์  ํŠน์„ฑ์— ๋Œ€ํ•œ ํญ๋„“์€ ์ดํ•ด๋ฅผ ์ œ๊ณตํ•œ๋‹ค.Introduction 1 Material and Methods 6 Results 18 Discussion 54 References 59 Abstract in Korean 61Maste
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