67 research outputs found

    ๋‹ค์ธต ํžˆ๋“  ๋งˆ์ฝ”๋ธŒ ๋ชจ๋ธ๊ณผ ULSTM ๋„คํŠธ์›Œํฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ˆ˜๋ฆฌ๊ณผํ•™๋ถ€, 2017. 8. ์ตœํ˜•์ธ.Financial data is a representative example of time series data. In analyzing time series data, unlike other data types, observations at other points of times act primarily to interpret the current observations.Time series data have been studied for a long time using traditional methodologies. This thesis present methods analyzing time series data especially the financial data. Several experiments presented in this thesis will show the effectiveness of the introduced machine learning models. This thesis cover not only a classical machine learning techniques but also recently active techniques. \\ Time series data is one of important subjects of machine learning. Compared to classical methods, Machine Learning has had an remarkable effect in analyzing time series. We will describe some time series analysis methods that are typical for machine learning. We will also present more advanced models. The first one of them is a model that uses the Markov chain. Chapter 2 provide the basic knowledges about the Markov chains. In Chapter 3, we present an existing model whose base is on the Markov chains. In consequent chapter, a new model that we created will be introduced. The experimental results are also contained in the chapter.\\ The second part of this thesis start from explaining the deep learning architecture. Chapter 5 contains explanations about basic notations in deep learning and specific type of models in deep learning architecture. The models introduced in this chapter are often used when dealing with time series data in deep learning. In Chapter 6 we present an extended version of the model based on the models introduced in Chapter 5. In this chapter, we conduct an experiment to compare our model with the existing model.1 Introduction 1 2 Markov Chains 4 2.1 Basics 4 2.2 Properties of Markov Chains 6 2.3 Conclusion 8 3 Hidden Markov Models 9 3.1 Construction of Models 9 3.1.1 De nitions 9 3.1.2 Main Problems 11 3.2 Learning HMM 11 3.2.1 Maximum-likelihood 12 3.2.2 Expectation-Maximizing Algorithm 13 3.2.3 Baum-Welch Algorithm 16 3.3 Conclusion 24 4 Multi-Level Hidden Markov Model 26 4.1 Model Construction 26 4.2 Estimation of MLHMM 29 4.2.1 Probability Evaluating Process . 30 4.2.2 Updating process 38 4.3 Application 48 4.3.1 Data Description 48 4.3.2 Model Construction 48 4.3.3 Result 49 4.4 Conclusion 52 5 Recurrent Neural Network 53 5.1 Neural Networks 53 5.2 Recurrent Neural Networks 56 5.3 Conclusion 58 6 Unity Long Short Term Memory 59 6.1 Construction of Network 59 6.2 Experiment 61 6.2.1 Data Description 61 6.2.2 Results 61 6.3 Conclusion 65 7 Conclusion 67 Abstract (in Korean) 73 Acknowledgement (in Korean) 74Docto

    Design optimization of aircraft by vortex generator and adjoint variable method

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    ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์œ ๋™ ๋ฐ•๋ฆฌ์™€ ์‹ค์† ํ˜„์ƒ์„ ์–ต์ œํ•˜๋Š” ์™€๋ฅ˜ ๋ฐœ์ƒ ์žฅ์น˜๋ฅผ ์ด์šฉํ•˜์—ฌ, ํ•ญ๊ณต๊ธฐ ๋™์ฒด-๋‚ ๊ฐœ ์—ฐ๊ฒฐ ๋ถ€๋ถ„์—์„œ ๋ฐœ์ƒํ•˜๋Š” junction vortex๋ฅผ ์ œ๊ฑฐํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ํ•ญ๊ณต๊ธฐ ๋‚ ๊ฐœ ์œ—๋ฉด๊ณผ ๋™์ฒด์— ์™€๋ฅ˜ ๋ฐœ์ƒ ์žฅ์น˜๋ฅผ ์„ค์น˜ํ•˜์˜€์œผ๋ฉฐ, ํŒŒ๋ผ๋ฉ”ํŠธ๋ฆญ ์Šคํ„ฐ๋””๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์™€๋ฅ˜ ๋ฐœ์ƒ ์žฅ์น˜์˜ ํ˜•์ƒ๊ณผ ์œ„์น˜์— ๋Œ€ํ•œ ์ตœ์  ์„ค๊ณ„๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ํŠนํžˆ ์™€๋ฅ˜ ๋ฐœ์ƒ ์žฅ์น˜๋ฅผ ๋…๋ฆฝ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ๋””์ž์ธ์„ ํ†ตํ•œ ์œ ๋™ ํŠน์„ฑ์˜ ํ–ฅ์ƒ ํšจ๊ณผ๋ฅผ ๊ทน๋Œ€ํ™”ํ•˜๊ณ ์ž ํ•˜์˜€์œผ๋ฉฐ, ๋งค๊ฐœ ๋ณ€์ˆ˜ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ๊ธฐ์šธ๊ธฐ ๊ธฐ๋ฐ˜์˜ ์ตœ์  ์„ค๊ณ„ ๊ธฐ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ 15๊ฐœ์˜ ๋งŽ์€ ๋ณ€์ˆ˜๋ฅผ ๋‹ค๋ฃฐ ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ์ตœ์  ์„ค๊ณ„ ๊ฒฐ๊ณผ, ํ•ญ๊ณต๊ธฐ์˜ ์–‘ํ•ญ๋น„๊ฐ€ 5% ์ด์ƒ ์ฆ๊ฐ€ํ•˜์˜€๊ณ  junction vortex์˜ ํฌ๊ธฐ ๋ฐ ์„ธ๊ธฐ ๋˜ํ•œ ๊ฐ์†Œํ•˜์˜€๋‹ค.To eliminate detrimental phenomena of junction vortex, this study dealt with an installation of the vortex generator on the wing surface and the body surface. A design optimization of vortex generator was also conducted by using results of the parametric study for the position of the vortex generator as a baseline. Because this design needed many design variables to consider each vortex generator individually, adjoint based sensitivity analysis for the gradient based design optimization was adopted. As a result, lift-to-drag ratio of the target aircraft was increased over 5%, and the junction vortex was also weakened.๋ณธ ๋…ผ๋ฌธ์€ ๊ตญํ† ํ•ด์–‘๋ถ€์˜ ใ€Œํ•˜๋Š˜ ํ”„๋กœ์ ํŠธใ€(Korea Ministry of Land, Transport and Maritime Affairs as ใ€ŒHaneul Projectใ€)์˜ ์ง€์›์„ ๋ฐ›์•„ ์ˆ˜ํ–‰๋˜์—ˆ์Œ.OAIID:oai:osos.snu.ac.kr:snu2013-01/104/0000004648/19SEQ:19PERF_CD:SNU2013-01EVAL_ITEM_CD:104USER_ID:0000004648ADJUST_YN:NEMP_ID:A001138DEPT_CD:446CITE_RATE:0FILENAME:2013 ํ•œ๊ตญํ•ญ๊ณต์šฐ์ฃผํ•™ํšŒ ์ถ”๊ณ„ํ•™์ˆ ๋Œ€ํšŒ_๊น€์€์‚ฌ.pdfDEPT_NM:๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€EMAIL:[email protected]:

    Design Optimization of Vortex Generator for Controlling Flows inside Subsonic Diffusing S-duct

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    ์•„์Œ์† ํ™•์‚ฐํ˜• S-duct ์˜ ์œ ๋™ ์™œ๊ณก ๋ฐ ์ „์••๋ ฅ ์†์‹ค์„ ์ตœ์†Œํ™”์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ, S-duct ๋‚ด๋ถ€์— ์„ค์น˜๋œ vortex generator ์˜ ํ˜•์ƒ์— ๋Œ€ํ•œ ์ตœ์ ์„ค๊ณ„๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ํŠนํžˆ, ์œ ๋™ ์งˆ ํ–ฅ์ƒ ํšจ๊ณผ๋ฅผ ๊ทน๋Œ€ํ™” ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์—ฌ๋Ÿฌ๊ฐœ์˜ vortex generator ๋ฅผ ๊ฐ๊ฐ์˜ ์ฃผ๋ณ€ ์œ ๋™ ํ˜„์ƒ์— ๋”ฐ๋ผ ๋…๋ฆฝ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์˜€๋‹ค. ์ˆ˜ํ•™์ ์ธ vortex generator ์†Œ์Šค ๋ชจ๋ธ์„ ์ ์šฉํ•˜์—ฌ ์ˆ˜์น˜ํ•ด์„ ์‹œ๊ฐ„์„ ๊ฐ์†Œ์‹œ์ผฐ์œผ๋ฉฐ, ์„ค๊ณ„ ํŒŒ๋ผ๋ฏธํ„ฐ๋กœ๋Š” ๊ฐ vortex generator ์˜ ๊ธธ์ด, ๋†’์ด, ์œ ๋™ํ๋ฆ„๊ณผ์˜ ๊ฐ๋„๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ด 11 ๊ฐœ vortex generator ์— ๋Œ€ํ•˜์—ฌ 33 ๊ฐœ ์„ค๊ณ„ ๋ณ€์ˆ˜๋ฅผ ์ ์šฉํ•˜์˜€์œผ๋ฉฐ, ์ƒ๋Œ€์ ์œผ๋กœ ๋งŽ์€ ์„ค๊ณ„๋ณ€์ˆ˜๋ฅผ ๋‹ค๋ฃจ๊ธฐ ์œ„ํ•ด์„œ adjoint ๊ธฐ๋ฐ˜์˜ ๋ฏผ๊ฐ๋„ ํ•ด์„ ๊ธฐ๋ฒ•์„ ์ ์šฉํ•œ ๊ธฐ์šธ๊ธฐ ๊ธฐ๋ฐ˜ ์„ค๊ณ„ ๊ธฐ๋ฒ•(Gradient Based Optimization Method)์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ์„ค๊ณ„๋œ vortex generator ๋Š” ์„ค๊ณ„์ „ S-duct ์˜ ์ „์••๋ ฅ ์†์‹ค๋Ÿ‰์„ ์œ ์ง€ํ•œ ์ฑ„๋กœ ์œ ๋™ ์™œ๊ณก์„ 70% ์ด์ƒ ๊ฐ์†Œ์‹œ์ผฐ๋‹ค.To minimize the flow distortion and the total pressure loss of subsonic diffusing S-duct, vortex generators installed in an S-duct are optimized. Especially, the influence of each vortex generator is independently considered by reflecting the local flow pattern to maximize the flow quality enhancement. To overcome the shortcomings of heavy computational costs in CFD analysis and design, a mathematical vortex generator source term model was employed. A total of 33 design variables for 11 vortex generators are treated with design parameters of chord length, height, and angle of incidence of each vortex generator. For a large number of design variables, the present design used the gradient based optimization method based on adjoint-based sensitivity analysis. Through this design, the distortion coefficient was decreased over 72% while maintaining the total pressure recovery ratio from the baseline of design.๋ณธ ์—ฐ๊ตฌ๋Š” 2011 ๋…„๋„ ์ •๋ถ€(๊ต์œก๊ณผํ•™๊ธฐ์ˆ ๋ถ€)์˜ ์žฌ์›์œผ๋กœ ํ•œ๊ตญ์—ฐ๊ตฌ์žฌ๋‹จ์˜ ์ง€์›(No. 2011-0027486) ๊ณผ 2011 ๋…„๋„ ์ •๋ถ€(๊ต์œก๊ณผํ•™๊ธฐ์ˆ ๋ถ€)์˜ ์žฌ์›์œผ๋กœ ํ•œ๊ตญ์—ฐ๊ตฌ์žฌ๋‹จ ์ฒจ๋‹จ์‚ฌ์ด์–ธ์Šค ๊ต์œกํ—ˆ๋ธŒ๊ฐœ๋ฐœ์‚ฌ์—…(No. 2011-0020559)์˜ ์ง€์›์„ ๋ฐ›์•„ ์ˆ˜ํ–‰๋˜์—ˆ์Œ.OAIID:oai:osos.snu.ac.kr:snu2011-01/104/0000004648/23SEQ:23PERF_CD:SNU2011-01EVAL_ITEM_CD:104USER_ID:0000004648ADJUST_YN:NEMP_ID:A001138DEPT_CD:446CITE_RATE:0FILENAME:์•„์Œ์†_ํ™•์‚ฐํ˜•_S-duct_๋‚ด๋ถ€_์œ ๋™์ œ์–ด๋ฅผ_์œ„ํ•œ_Vortex_Generator_์ตœ์ _์„ค๊ณ„.pdfDEPT_NM:๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€EMAIL:[email protected]:

    Objects in Yunchul Kim's Art and the Aspects of Fluid Art

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    2016๋…„ ์œ ๋Ÿฝ์ž…์ž๋ฌผ๋ฆฌ์—ฐ๊ตฌ์†Œ CERN์—์„œ ์ˆ˜์—ฌํ•˜๋Š” Collide์ƒ์„ ์ˆ˜์ƒํ•œ ํ•œ๊ตญ์ธ ๊น€์œค์ฒ (Yunchul Kim, 1970- ) ์ž‘๊ฐ€์˜ ์ž‘ํ’ˆ์„ ์‚ฌ๋ก€์—ฐ๊ตฌ๋กœ ์‚ผ์•„, ๋ณธ ๋…ผ๋ฌธ์€ ํ•˜์ด๋ฐ๊ฑฐ์˜ ๊ธฐ์ˆ ์  ๋„๊ตฌ๋ถ„์„๊ณผ ๊ทธ๋ ˆ์ด์—„ ํ•˜๋จผ(Graham Harman)์˜ ๊ฐ์ฒด์ง€ํ–ฅ์กด์žฌ๋ก (Object-Oriented Ontology)์„ ์ฐจ์šฉํ•˜์—ฌ ๋ถ„์„์„ ํ–‰ํ•œ๋‹ค. ๊ฐ์ฒด์ง€ํ–ฅ์กด์žฌ๋ก ์€ ์ตœ๊ทผ ๋“ฑ์žฅํ•˜๋Š” ์‹ ์œ ๋ฌผ๋ก (new materialism)์˜ ํ•œ ๋ถ„ํŒŒ๋กœ์„œ, ์ž์—ฐ๊ณผ ์‚ฌํšŒ๋ฅผ ๋ชจ๋‘ ๊ฐ์ฒด๋กœ ํ™˜์›ํ•˜๋ฉฐ ์ˆ˜ํ‰์  ์กด์žฌ๋ก (flat ontology)์˜ ๊ด€์ ์—์„œ ํ–‰์œ„์†Œ๋“ค์˜ ์•„์ƒ๋ธ”๋ผ์ฃผ ๊ตฌ์„ฑ์„ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์„ ์ง€ํ–ฅํ•œ๋‹ค. ํ…Œํฌ๋…ธ์‚ฌ์ด์–ธ์Šค๋ฅผ ๋งŽ์ด ํ™œ์šฉํ•˜๋Š” ๊น€์œค์ฒ  ์ž‘๊ฐ€์˜ ์ž‘ํ’ˆ๋“ค์€ ๋ฌผ์„ฑ์˜ ๋ณ€ํ™”๋ฅผ ํ†ตํ•ด ์šฉ์žฌ์„ฑ๊ณผ ์ „์žฌ์„ฑ์ด ๊ต์ฐจํ•˜๋Š” ์ธก๋ฉด์„ ๋ณด์—ฌ์ฃผ๋Š”๋ฐ, ์ด๋Š” ๊ฐ์ฒด์˜ ๊ฐ๊ฐ์†์„ฑ์ด ๋ถ€๋‹จํ•˜๊ฒŒ ๋ณ€ํ™”ํ•˜๋Š” ์ ์„ ์ž˜ ํฌ์ฐฉํ•œ๋‹ค. ๋ณธ๊ณ ์—์„œ๋Š” ์ด๋Ÿฐ ํŠน์„ฑ์„ ์ง€๋‹Œ ๊น€์œค์ฒ  ์ž‘๊ฐ€์˜ ์ž‘ํ’ˆ์„ โ€˜์œ ๋™์  ์˜ˆ์ˆ โ€™์ด๋ผ ์นญํ•˜์˜€๋‹ค.2

    Multi-layered Construction of Technoscientific Knowledge in a Neuroscience Research Lab: A Research on the Actor-Networks of Neuroscience Research Institute (NRI)

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ๊ณผํ•™์‚ฌ๋ฐ๊ณผํ•™์ฒ ํ•™ ์ „๊ณต, 2013. 8. ํ™์„ฑ์šฑ.1990๋…„ 7์›” 17์ผ, ๋ฏธ๊ตญ์˜ ์กฐ์ง€ ๋ถ€์‹œ ๋Œ€ํ†ต๋ น์€ ๋Œ€ํ†ต๋ น๋ น ์ œ 6158ํ˜ธ๋ฅผ ๊ณตํฌํ•˜์˜€๋‹ค. ์ด ์„ ์–ธ๋ฌธ์€ ํ•œ์ฐฝ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ๊ณผํ•™์  ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๋˜ ๋‡Œ๊ณผํ•™ ๋ถ„์•ผ์˜ ์„ฑ๊ณผ๋ฅผ ์ผ๋ฐ˜์ธ์—๊ฒŒ ๋„๋ฆฌ ์•Œ๋ฆฌ๊ธฐ ์œ„ํ•ด ๋งŒ๋“ค์–ด์ง„ ๊ฒƒ์œผ๋กœ์„œ, 1990๋…„๋ถ€ํ„ฐ 1999๋…„ ๊นŒ์ง€๋ฅผ ์ด๋ฅธ๋ฐ” ๋‡Œ๊ณผํ•™์˜ 10๋…„์œผ๋กœ ๊ณตํฌํ•˜๋Š” ๊ฒƒ์ด ๊ทธ ๊ณจ์ž์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ดํ›„ ๋‡Œ๊ณผํ•™์€ ์ฒจ๋‹จ ์œตํ•ฉ๊ณผํ•™์˜ ํ•œ ๋ถ„์•ผ๋กœ์„œ ํ•™๋ฌธ์  ์˜์—ญ๊ณผ ๋Œ€์ค‘๋ฌธํ™” ์˜์—ญ ๋ชจ๋‘์—์„œ ์ฃผ๋ชฉ์„ ๋ฐ›๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ๋ ‡๋‹ค๋ฉด ๋ช‡ ๊ฐœ ๋ถ„์•ผ๋ฅผ ์ œ์™ธํ•˜๋ฉด ์„ ์ง„๊ตญ์— ๋น„ํ•ด ๊ธฐ์ˆ ๊ณผํ•™์˜ ๋ฐœ์ „์ด ๋‹ค์†Œ ๋’ค์ง€๋Š” ํ•œ๊ตญ์—์„œ๋Š” ์ด๋“ค์„ ๋”ฐ๋ผ์žก๊ธฐ ์œ„ํ•ด ๋‡Œ๊ณผํ•™์„ ์–ด๋–ป๊ฒŒ ์—ฐ๊ตฌํ•˜๊ณ  ์žˆ์„๊นŒ? ๋˜ ํ•œ๊ตญ์˜ ๊ธฐ์ˆ ๊ณผํ•™์ž๋“ค์€ ์ตœ์ฒจ๋‹จ ํ•™๋ฌธ์ธ ๋‡Œ๊ณผํ•™์„ ์–ด๋–ค ๋ฐฉ์‹์œผ๋กœ ์ˆ˜์ž…ํ•ด์„œ ์ง€์‹์„ ์ฐฝ์ถœํ•˜๋Š” ์ž‘์—…์„ ํ•˜๊ณ  ์žˆ์„ ๊ฒƒ์ธ๊ฐ€? ๊ตญ๋‚ด ์œ„์น˜ํ•œ ์‹คํ—˜์‹ค์—์„œ๋Š” ๋‡Œ๊ณผํ•™ ์—ฐ๊ตฌ์˜ ์˜์ œ(์–ด์  ๋‹ค)๋“ค๊ณผ ์‹ค์ œ ์—ฐ๊ตฌ ์ˆ˜ํ–‰์˜ ํ”„๋กœํ† ์ฝœ์„ ์–ด๋–ป๊ฒŒ ๊ตฌ์„ฑํ•˜๋Š”๊ฐ€? ๋˜ ์ด๋“ค์€ ์—…์ ์„ ์ฐฝ์ถœํ•˜๊ณ  ๊ธ€๋กœ๋ฒŒ ๊ณผํ•™์ž ์ปค๋ฎค๋‹ˆํ‹ฐ์—์„œ ์ด๋ฅผ ์ธ์ •๋ฐ›๊ธฐ ์œ„ํ•ด ์–ด๋–ค ์ „๋žต์„ ์ทจํ•˜๋Š”๊ฐ€? ๋งˆ์ง€๋ง‰์œผ๋กœ ์‹คํ—˜์‹ค์—์„œ ์—ฐ๊ตฌ๋ฅผ ํ•˜๋Š” ๊ธฐ์ˆ ๊ณผํ•™์ž๋“ค์€ ์ธ๊ฐ„์˜ ๋‘๊ฐœ๊ณจ ์•ˆ์— ๋“ค์–ด์žˆ๋Š” ๋‡Œ๋ฅผ ์—ฐ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด ๋‡Œ๋ผ๋Š” ์‹ค์žฌ๋ฅผ ์–ด๋–ค ๋ฐฉ์‹์œผ๋กœ ๊ตฌ์„ฑํ•˜๋Š”๊ฐ€? ๊ทธ๋ฆฌ๊ณ  ๊ธฐ์ˆ ๊ณผํ•™์ž๋“ค์˜ ์ด๋Ÿฌํ•œ ์‹คํ–‰(practice)์€ ์‚ฌํšŒ์  ๋งฅ๋ฝ๊ณผ ์–ด๋–ป๊ฒŒ ์—ฐ๊ด€๋˜์–ด ์žˆ๋Š”๊ฐ€? ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด๋Ÿฌํ•œ ์งˆ๋ฌธ๋“ค์— ๋‹ตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ์ฆ‰, ์„ ์ง„๊ตญ์˜ ๊ธฐ์ˆ ๊ณผํ•™ ์ˆ˜์ค€์„ ์ถ”๊ฒฉํ•˜๊ธฐ ์œ„ํ•ด ๋…ธ๋ ฅํ•˜๋Š” ํ•œ๊ตญ์˜ ๊ธฐ์ˆ ๊ณผํ•™์ž๋“ค์ด ์‹คํ—˜์‹ค์—์„œ ์ฒจ๋‹จ ์œตํ•ฉ๊ณผํ•™์„ ์–ด๋–ค ๋ฐฉ์‹์œผ๋กœ ์—ฐ๊ตฌํ•˜๋Š”๊ฐ€๋ฅผ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์ด ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์ด๋‹ค. ์ด ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ํ•„๋“œ์‚ฌ์ดํŠธ๋กœ ์„ ํƒํ•œ ๋‡Œ๊ณผํ•™์—ฐ๊ตฌ์†Œ(NRI)๋Š” ๊ธธ์žฌ๋‹จ์˜ ์ด๊ธธ์—ฌ ์ด์‚ฌ์žฅ์ด 640์–ต์›์˜ ์‚ฌ์žฌ๋ฅผ ํˆฌ์žํ•ด ์„ค๋ฆฝํ•œ ์—ฐ๊ตฌ์†Œ๋กœ, ์•„์ง๊นŒ์ง€ ๊ธฐ์ˆ ๊ณผํ•™์˜ ์ฃผ๋ณ€๋ถ€๋ผ ํ•  ์ˆ˜ ์žˆ๋Š” ํ•œ๊ตญ์—์„œ ์ตœ์ดˆ๋กœ ๋…ธ๋ฒจ์ƒ ์ˆ˜์ƒ์ž๋ฅผ ๋ฐฐ์ถœํ•  ์ˆ˜์ค€์˜ ์—ฐ๊ตฌ์—…์ ์„ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๋งŒ๋“  ๊ณณ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด ๋ชฉํ‘œ์˜ ๋‹ฌ์„ฑ์„ ์œ„ํ•ด ์ปดํ“จํ„ฐ๋‹จ์ธต์ดฌ์˜(CT)๊ณผ ์ž๊ธฐ๊ณต๋ช…์˜์ƒ(MRI), ๊ทธ๋ฆฌ๊ณ  ์–‘์ „์ž๋ฐฉ์ถœ๋‹จ์ธต์ดฌ์˜(PET)์˜ ์‹œ๊ฐํ™” ๊ธฐ์ˆ ๋“ค์„ ๋ชจ๋‘ ์—ฐ๊ตฌํ•œ ๋ฐ” ์žˆ์œผ๋ฉด์„œ ํŠนํžˆ ์–‘์ „์ž๋ฐฉ์ถœ๋‹จ์ธต์ดฌ์˜ ๊ธฐ์ˆ ์˜ ์ตœ์ดˆ ๊ฐœ๋ฐœ์ž์˜ ํ•œ ๋ช…์œผ๋กœ ํ‰๊ฐ€๋ฐ›๋Š” ์žฌ๋ฏธ๊ณผํ•™์ž ์กฐ์žฅํฌ ๋ฐ•์‚ฌ๋ฅผ ์—ฐ๊ตฌ์†Œ ์†Œ์žฅ์œผ๋กœ ์˜์ž…ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ฑฐ์•ก์˜ ๊ธฐ๊ธˆ์ด ํˆฌ์ž๋˜์—ˆ๋‹ค๊ณ  ํ•ด์„œ ๊ทธ ์‹คํ—˜์‹ค์ด ๊ณง ์ˆ˜์›”์„ฑ์„ ํ™•๋ณดํ•œ๋‹ค๊ณ ๋Š” ๋ณผ ์ˆ˜ ์—†๋‹ค. ํ•˜๋‚˜์˜ ์‹คํ—˜์‹ค์„ ์„ค๋ฆฝํ•˜์—ฌ ๊ทธ ์‹คํ—˜์‹ค์˜ ์ˆ˜์ค€์„ ๋Œ์–ด์˜ฌ๋ฆฌ๋Š” ๊ฒƒ์€ ๊ฐ€์‹œ์ ์ธ ์„ค๋น„์˜ ํˆฌ์ž์™€ ์•ž์„œ๊ฐ€๋Š” ์ œ๋„์  ๊ตฌ๋น„, ํ˜น์€ ์šฐ์ˆ˜ ๊ณผํ•™์ž์˜ ์˜์ž… ๋“ฑ์œผ๋กœ๋งŒ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š๋Š”๋‹ค. ์œ ์˜๋ฏธํ•œ ๊ณผํ•™์  ์—…์ ์„ ์ฐฝ์ถœํ•˜๋Š” ์‹คํ—˜์‹ค์„ ๊ตฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ธฐ์ˆ ๊ณผํ•™์˜ ์‹ค์ฒœ๊ณผ ๋ฌธํ™” ์ˆ˜์ค€์—์„œ์˜ ์˜๋ฏธ์žˆ๋Š” ํ–‰์œ„๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ํ–‰์œ„์—๋Š” ์กฐ์ง ์ฐจ์›์—์„œ ์ƒˆ๋กœ์šด ํ˜•์‹์˜ ํ–‰์œ„์ž-์—ฐ๊ฒฐ๋ง์„ ๊ตฌ์„ฑํ•˜๋Š” ๊ฒƒ๊ณผ, ๋ฐฉ๋Œ€ํ•œ ๊ฒฝ์ œ์ž๋ณธ์˜ ์œ ์ž…์„ ์–ด๋–ป๊ฒŒ ์„ฑ๊ณต์ ์œผ๋กœ ์ง€์‹์ž๋ณธ๊ณผ ์ƒ์ง•์ž๋ณธ์œผ๋กœ ๋ณ€ํ™˜์‹œํ‚ค๋Š”๊ฐ€ ํ•˜๋Š” ๋“ฑ์˜ ๋ฌธ์ œ๊ฐ€ ํฌํ•จ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด ๋ฌธ์ œ์˜์‹์„ ์—ฐ์žฅํ•˜์—ฌ ๋‡Œ๊ณผํ•™์„ ์ „๋ฌธ์ ์œผ๋กœ ์—ฐ๊ตฌํ•˜๋Š” ์ด ์‹คํ—˜์‹ค์ด ์„ธ๊ณ„์  ์ธ์ •ํš๋“์„ ๋ชฉํ‘œ๋กœ ์„ฑ์žฅํ•˜๋ฉฐ ์–ด๋–ป๊ฒŒ ๊ณผํ•™์ง€์‹์„ ๊ตฌ์„ฑํ•ด ๊ฐ€๋Š”์ง€๋ฅผ ์•ฝ 1๋…„์—ฌ์˜ ์ฐธ์—ฌ๊ด€์ฐฐ์—์„œ ํš๋“ํ•œ ๋ฐ์ดํ„ฐ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ตœ๊ทผ ๊ณผํ•™๊ธฐ์ˆ ํ•™(STS)์€ ์‹คํ—˜์‹ค์ด ์œ„์น˜ํ•œ ์‚ฌํšŒ์™€ ๊ธฐ์ˆ ๊ณผํ•™์˜ ์ง€๋ฆฌ์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด๊ธฐ ์‹œ์ž‘ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ•œ๊ตญ๊ณผ ๊ฐ™์€ ๊ธฐ์ˆ ๊ณผํ•™์˜ ์ฃผ๋ณ€๋ถ€(periphery of technoscience)์—์„œ 7ํ…Œ์Šฌ๋ผ ์ž๊ธฐ๊ณต๋ช…์˜์ƒ๊ณผ ๊ณ ํ•ด์ƒ๋„์—ฐ๊ตฌ์šฉ ์–‘์ „์ž๋ฐฉ์ถœ๋‹จ์ธต์ดฌ์˜๊ฐ™์€ ์ฒจ๋‹จ ์‹œ๊ฐํ™” ๊ธฐ์ˆ ์„ ์šด์˜ํ•˜๋Š” ์‹คํ—˜์‹ค์˜ ๋ชจ์Šต์€ ์—ฐ๊ตฌ๋œ ๋ฐ”๊ฐ€ ์—†๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋“ค์ด ๋‹ค๋ฃจ์ง€ ๋ชปํ•œ, ๊ธฐ์ˆ ๊ณผํ•™์˜ ํ›„๋ฐœ์ฃผ์ž๋กœ์„œ ์„ธ๊ณ„์  ์ˆ˜์›”์„ฑ์„ ํ™•๋ณดํ•˜๊ณ ์ž ๋…ธ๋ ฅํ•˜๋Š” ํ•œ๊ตญ์ด๋ผ๋Š” ๋งฅ๋ฝ์— ์œ„์น˜์ง€์–ด์ง„ ์‹คํ—˜์‹ค์—์„œ์˜ ๊ณผํ•™์ง€์‹ ๊ตฌ์„ฑ ์ž‘์—…์„ ๋ถ„์„ํ•œ๋‹ค. ๋˜ ์„ ํ–‰ ์—ฐ๊ตฌ๋“ค๊ณผ๋Š” ๋‹ค๋ฅด๊ฒŒ ์ œ๋„์  ์ธต์œ„ใ†๊ธฐ์ˆ ๊ณผํ•™์  ์ธต์œ„(๋„๊ตฌ์  ์ธต์œ„ ๋ฐ ๊ฐœ๋…์  ์ธต์œ„)ใ†๋„คํŠธ์›Œํฌ ์ธต์œ„ใ†๋ฌธํ™”์  ์ธต์œ„์˜ ๋„ค ์ธต์œ„๋กœ ๋ถ„์„์„ ์‹œ๋„ํ•˜์˜€๊ณ , ๊ถ๊ทน์ ์œผ๋กœ ์ด ๋„ค ์ธต์œ„์˜ ์‹คํ–‰๋“ค์ด ๊ฐ๊ฐ์˜ ์‚ฌํšŒ์  ๋งฅ๋ฝ ์†์—์„œ ์ž‘๋™ํ•˜๋ฉฐ ์ค‘์ธต์ ใ†๋ณตํ•ฉ์ ์œผ๋กœ ๊ณผํ•™์ง€์‹์„ ์ƒ์‚ฐํ•œ๋‹ค๋Š” ์ ์„ ๋ณด์ด๊ณ ์ž ํ–ˆ๋‹ค. ๋…ธ๋ฒจ์ƒ ์ˆ˜์ƒ์ž๊ฐ€ ์ฆ๋น„ํ•œ ๊ณผํ•™์˜ ์ค‘์‹ฌ๋ถ€์™€ ๋‹ฌ๋ฆฌ ์•„์ง ํ•œ๊ตญ์—์„œ๋Š” ๋‹จ ํ•œ ๋ช…์˜ ์ˆ˜์ƒ์ž๋„ ๋ฐฐ์ถœํ•˜์ง€ ๋ชปํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ์‹คํ—˜์‹ค์˜ ๊ณผํ•™์  ์‹คํ–‰๊ณผ ๋ฌธํ™”์— ์ผ๋ จ์˜ ํŠน์ง•๋“ค์„ ๋ถ€์—ฌํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•œ๊ตญ์—์„œ ์ตœ๊ณ ์ˆ˜์ค€์˜ ์ƒ์ง•์ž๋ณธ์„ ํš๋“ํ•  ๋ชฉ์ ์œผ๋กœ ์‹คํ—˜์‹ค์ด ์„ค๋ฆฝ๋œ ๊ณผ์ •์„ ์‚ดํŽด๋ณด์•˜๊ณ (์ œ๋„์  ์ธต์œ„), ๊ทธ ๋’ค์—๋Š” ์žฌ๋‹จ ์ด์‚ฌ์žฅ ๊ฐœ์ธ์˜ ์˜์ง€์™€ ๊ตญ๋ฏผ์  ์—ด๋ง์ด๋ผ๋Š” ์‚ฌํšŒ๋ฌธํ™”์  ๋ฐฐ๊ฒฝ์ด ์žˆ์Œ์„ ์•Œ์•„๋ณด์•˜๋‹ค(๋ฌธํ™”์  ์ธต์œ„). ๋˜ ์—ฐ๊ตฌ์†Œ์—์„œ์˜ ์œตํ•ฉ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ๊ฐ์ข… ํ–‰์œ„์ž๋“ค์ด ๋ณต์žกํ•œ ์‹คํ—˜ ์•„์ƒ๋ธ”๋ผ์ฅฌ(assemblage)๋ฅผ ๋งŒ๋“œ๋Š” ๊ณผ์ •๋„ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ(๋„คํŠธ์›Œํฌ ์ธต์œ„), ์„ ๋„์ ์ธ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ๋‡Œ์ด๋ฏธ์ง• ๊ธฐ๊ธฐ(๋„๊ตฌ์  ์ธต์œ„)๋‚˜ ์ง€์‹์ž๋ณธใ†์ƒ์ง•์ž๋ณธ(๊ฐœ๋…์  ์ธต์œ„)์˜ ๊ธฐ์ˆ ๊ณผํ•™์  ์ธต์œ„๊ฐ€ ์ค‘์š”ํ•˜๊ฒŒ ์ž‘์šฉํ•จ์„ ์‚ดํŽด๋ณด์•˜๋‹ค. ์‹คํ—˜์‹ค์€ ๊ทธ ์‹คํ—˜์‹ค์ด ์œ„์น˜ํ•œ ๊ธฐ์ˆ ๊ณผํ•™์˜ ์ง€์ •ํ•™์— ์˜ํ–ฅ์„ ๋ฐ›๋Š”๋‹ค. ์ด ์‹คํ—˜์‹ค์€ ์„ค๋ฆฝ๊ณผ์ •๋ถ€ํ„ฐ ์šด์˜ ๋ฐฉ์‹, ๊ทธ ์•ˆ์— ๋‚ด์žฌํ•œ ๋ฌธํ™” ๋“ฑ์ด ๊ณผํ•™์˜ ์ฃผ๋ณ€๋ถ€์— ์„ค๋ฆฝ๋œ ์‹คํ—˜์‹ค์ด๋ผ๋Š” ์ง€์  ๋ฐฐ๊ฒฝ์— ๋Œ€ํ•ด ๋งฅ๋ฝ ์˜์กด์ (context-dependent)์ด์—ˆ๋‹ค. ๊ณผํ•™์  ์ง€์‹์˜ ์ƒ์‚ฐ๊ณต๊ฐ„์œผ๋กœ์„œ์˜ ์‹คํ—˜์‹ค์€ ๊ทธ ์ž์ฒด๋กœ ์ƒํ™ฉ์ ์ด๋ฉฐ(situated), ์ด๊ณณ์—์„œ ์ด๋ฃจ์–ด์ง€๋Š” ๊ณผํ•™์ง€์‹์˜ ๊ตฌ์„ฑ์ž‘์—…์€ ์ง€๊ธˆ๊นŒ์ง€ ์‚ดํŽด๋ณธ ์—ฌ๋Ÿฌ ์ธต์œ„์˜ ํ–‰์œ„์ž-์—ฐ๊ฒฐ๋ง๋“ค์ด ์ค‘์ธต์ ์œผ๋กœ ์ž‘๋™ํ•จ์œผ๋กœ์„œ ๋น„๋กœ์†Œ ์ด๋ฃจ์–ด์ง„๋‹ค. ๊ณผํ•™์ง€์‹์€ ์‚ฌํšŒ์  ์ง„๊ณต์ƒํƒœ์—์„œ ๊ตฌ์„ฑ๋˜์ง€ ์•Š๋Š”๋‹ค. ์ง€์‹์˜ ์ƒ์‚ฐ์€ ๋งฅ๋ฝ์„ฑ๊ณผ ์‚ฌํšŒ์„ฑ์ด ์ถฉ๋งŒํ•œ ์‹คํ—˜์‹ค์—์„œ ์ด๋ฃจ์–ด์ง€๋ฉฐ, ์ด๋Ÿฌํ•œ ์‹คํ—˜์‹ค์€ ๊ฐ์ข… ํ–‰์œ„์ž-์—ฐ๊ฒฐ๋ง์ด ๋‹ค์ธต์  ์ฐจ์›์—์„œ ๋ณตํ•ฉ์ ์œผ๋กœ ์ž‘๋™ํ•˜๋Š” ์‹œ๊ณต๊ฐ„ ์•„์ƒ๋ธ”๋ผ์ฅฌ์ด๋‹ค.On July 17, 1990, President G. Bush of the United States proclaimed the Presidential Proclamation 6158 which was intended to claim the Decade of the Brain. The Decade of the Brain designates the year between 1990 and 1999 as conduits to convey the results of the burgeoning neuroscience to the general public. After the Decade, neuroscience became a hot topic both in academia and mass media. Then how would technoscientists research neuroscience in Korea, where the level of technoscience is little behind the leaders of the world? How would they import this new field of science and strive to create new knowledge? How would the laboratories in Korea produce agendas of neuroscientific researches and construct protocols for the researches? What strategies would they use to produce meaningful results and get world-wide recognition? Lastly, how would they construct the real of the brain which is located inside human skulls? And how would their technoscientific practices be connected with social contexts? This research was done trying to answer some of these questions. In other words, to observe and analyze the practices of technoscientists in Korea where scientists try to catch up with the leaders of the world is the key goal of this research. I chose Neuroscience Research Institute (NRI) of Gachon Medical School as the field site. NRI was built in 2006 with personally donated 6.4 million won from Dr. Gil-Ya Lee, the chairwoman of Gil Foundation. It was built to produce the Nobel prize winner in Korea that still belongs to the technoscientific periphery. In order to achieve this goal, Dr. Zang-Hee Cho, a Korean-American scientist who has experiences of researching CT, MRI and PET was invited as the director of NRI. Especially he was considered as one of first inventors of PET. But inputting grand sum of monetary resources and inviting outstanding scientists would not guarantee excellency of the lab. To found a laboratory and make it as a world-class laboratory requires changes in technoscientific practices and culture. Recent STS literatures analyze how geographical locations of laboratories in developing nations affect practices and cultures of the lab. But none analyzes East-Asian cases. This research uses four categories to analyze the neuroscience laboratory: institutional foundation, technoscientific foundation (this again categorized into instrumental foundation and conceptual foundation), network foundation, and cultural foundation. Using this newly suggested categories and data obtained from participatory observation that was conducted over a year, this research shows how these four foundations, or four-layers upon which new scientific facts are made, interacts with society. Laboratories are context-driven. They are situated among specific context, and it heavily affects the fact-making practices and cultures of the lab.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ๋ชฉ์ ๊ณผ ๋ฐฐ๊ฒฝ 1 2. ์ด๋ก ์  ์ •ํ–ฅ 7 (1) ์‹คํ—˜์‹ค ์—ฐ๊ตฌ(laboratory studies) 7 (2) ํ–‰์œ„์ž-์—ฐ๊ฒฐ๋ง ์ด๋ก (ANT) 11 (3) ์‹œ๊ฐํ™” ์—ฐ๊ตฌ(visualization studies) 15 3. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๋ก  20 (1) ํ•„๋“œ ์‚ฌ์ดํŠธ 20 (2) ๋ฏผ์†์ง€(ethnography) 26 4. ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 30 II. ์‹คํ—˜์‹ค ๊ตฌ์ถ•๊ณผ ์—ฐ๊ตฌ๋‹จ ๊ตฌ์„ฑ 33 1. ๋‡Œ๊ณผํ•™์—ฐ๊ตฌ์†Œ(NRI, Neuroscience Research Institute)์˜ ํ˜•์„ฑ 33 (1) ์„ค๋ฆฝ ๋ฐฐ๊ฒฝ ๋ฐ ๊ณผ์ • 33 (2) ํ•œ๊ตญ์  ๋งฅ๋ฝ์—์„œ์˜ ์˜์˜ 44 2. ์ค‘์‹ฌ ๊ณผํ•™์ž์˜ ๋“ฑ์žฅ: ์กฐ์žฅํฌ ๋ฐ•์‚ฌ 52 (1) ๊ต์œก๋ฐฐ๊ฒฝ 52 (2) ์–‘์ „์ž๋ฐฉ์ถœ๋‹จ์ธต์ดฌ์˜(PET) ์—ฐ๊ตฌ 58 (3) ์ž๊ธฐ๊ณต๋ช…์˜์ƒ(MRI) ์—ฐ๊ตฌ 67 (4) ํ•ต์‹ฌ ํ…Œ๋งˆ ์„ ์ •: PET/MRI 75 III. ๊ณผํ•™์ง€์‹ ๊ตฌ์„ฑ์˜ ๊ฐœ๋…์ ใ†๋„๊ตฌ์  ์ธต์œ„์—์„œ ๋“œ๋Ÿฌ๋‚˜๋Š” ํ˜ผ์ข…์„ฑ 85 1. ์—ฐ๊ตฌํŒ€์˜ ๊ตฌ์„ฑ 86 (1) PET-MRI Fusion ํŒ€ 95 (2) RF Coil ํŒ€ 119 (3) Angio ํŒ€ 129 (4) fMRI ํŒ€ 136 (5) Micro Imaging ํŒ€ 146 (6) Advanced MRI ํŒ€ 157 (7) ํ™•์‚ฐํ…์„œ์ด๋ฏธ์ง•(DTI) ํŒ€ 161 (8) ์‚ฌ์ดํด๋กœํŠธ๋ก  ํŒ€ 164 2. ๊ธ€๋กœ์ปฌ(glocal) ๋„คํŠธ์›Œํ‚น๊ณผ ๋น„๊ฐ€์‹œ์  ํ˜‘๋ ฅ 167 3. ๋ฌผ์งˆ์„ฑ(materiality)์˜ ์ €ํ•ญ๊ณผ ์ ์‘ 201 IV. ์ƒ์‚ฐ๋œ ์ง€์‹์˜ ํ™•์‚ฐ๊ณผ ๊ธ€๋กœ์ปฌ ๋„คํŠธ์›Œํฌ 216 1. ์ถœํŒ ํ™œ๋™ 216 (1) SCI ๋…ผ๋ฌธ ์ถœํŒ 216 (2) 7T ๋‡Œ์ง€๋„(brain atlas) ์ถœํŒ 227 2. ๊ตญ์ œ ์‹ฌํฌ์ง€์—„ 236 (1) UHF์™€ EHF 236 (2) ์ œ 1ํšŒ ๊ตญ์ œ EHF ์‹ฌํฌ์ง€์—„ 237 V. ์ง€์‹๊ตฌ์„ฑ์˜ ๋กœ๊ณ ์Šคใ†ํŒŒํ† ์Šคใ†์—ํ† ์Šค 258 1. ๋ฌธํ™” ์†์˜ ์‹คํ—˜์‹ค: ๊ฑฐ์‹œ ๋ฌธํ™” 258 (1) ์„ธ๊ณ„์ผ๋ฅ˜์ฃผ์˜์™€ ๋…ธ๋ฒจ์ƒ์ฃผ์˜ 258 (2) ์„ธ๊ณ„ํ™”์˜ ๊ฒฝํ–ฅ 264 2. ์‹คํ—˜์‹ค ์†์˜ ๋ฌธํ™”: ๋ฏธ์‹œ ๋ฌธํ™” 268 (1) ํ•ด์™ธ ์—ฐ๊ตฌ๋ฌธํ™” ๋„์ž… 268 (2) ์—ฐ๊ตฌ์œค๋ฆฌ ๋ฐ ๊ทœ๋ฒ” 274 3. ๋‹ค์„ฏ ๊ฐ€์ง€ ํŠน์ง• 279 VI. ๊ฒฐ๋ก  301 ๋ถ€๋ก 311 ์ฐธ๊ณ ๋ฌธํ—Œ 331 ABSTRACT 358Docto

    ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์‹œ์Šคํ…œ ๋ชจ๋ธ์„ ์ด์šฉํ•œ ์ €์ „๋ ฅ ์‹œ์Šคํ…œ์˜ ์ „๋ ฅ ์†Œ๋ชจ ์ตœ์†Œํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ „๊ธฐ.์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2001.Maste

    Factors on prevalence of musculoskeletal disorders among dental technicians in Korea.

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    ์—ญํ•™ ๋ฐ ๊ฑด๊ฐ•์ฆ์ง„ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] ๋ณธ ์—ฐ๊ตฌ๋Š” ์„œ์šธ์‹œ ์น˜๊ณผ๊ธฐ๊ณต์‚ฌ์˜ ๋ชฉ, ์–ด๊นจ, ๊ทธ๋ฆฌ๊ณ  ํ—ˆ๋ฆฌ ๋ถ€์œ„์˜ ๊ทผ๊ณจ๊ฒฉ ๊ณ„ ์งˆํ™˜์˜ ์œ ๋ณ‘์ƒํƒœ๋ฅผ ์กฐ์‚ฌํ•˜๊ณ  ์ด์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ์ž‘์—…์  ํŠน์„ฑ๊ณผ ์ •์‹ ยท์‚ฌํšŒ์  ์š”์ธ๋“ค์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ์‹ค์‹œ๋˜์—ˆ๋‹ค. ๊ทผ๊ณจ๊ฒฉ๊ณ„ ์งˆํ™˜์˜ ์œ ๋ณ‘์ƒํƒœ๋ฅผ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด ํ‘œ์ค€ํ™”๋œ Nordic ์„ค๋ฌธ์ง€๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ 2001๋…„ 3์›” ํ˜„์žฌ ์„œ์šธ์‹œ์—์„œ ๊ทผ๋ฌดํ•˜๋Š” ์น˜๊ณผ๊ธฐ๊ณต์‚ฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ž๊ธฐ๊ธฐ์ž…์‹ ์„ค๋ฌธ์ง€๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. 2์ฐจ์— ๊ฑธ์นœ ์šฐํŽธ ์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์˜€์œผ๋ฉฐ, ์ตœ์ข… ๋ถ„์„ ๋Œ€์ƒ์€ ๋‚จ์ž 184๋ช…, ์—ฌ์ž 35๋ช…์ด์—ˆ๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ์˜ ์‹ ์ฒด ๋ถ€์œ„๋ณ„ ์œ ๋ณ‘์œจ์€ ์ง€๋‚œ 12๊ฐœ์›” ๋™์•ˆ ํ†ต์ฆ์„ ๋Š๋‚€ ๊ฒฝ์šฐ๋Š” ๋ชฉ ๋ถ€์œ„๊ฐ€ 61.7%, ์–ด๊นจ ๋ถ€์œ„๊ฐ€ 65.1%, ํ—ˆ๋ฆฌ ๋ถ€์œ„๊ฐ€ 56.7%์ด์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ง€๋‚œ 12๊ฐœ์›” ๋™์•ˆ 8์ผ ์ด์ƒ ํ†ต์ฆ์ด๋‚˜ ๋ถˆํŽธ์„ ๋Š๋‚€ ๊ฒƒ์„ ์ฆ์ƒ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๊ฐ„์ฃผํ•  ๋•Œ, ๋ชฉ ๋ถ€์œ„๋Š” 33.8%, ์–ด๊นจ ๋ถ€์œ„๋Š” 39.7%, ํ—ˆ๋ฆฌ ๋ถ€์œ„๋Š” 35.6%์ด์—ˆ๋‹ค. ์ด๋“ค ์—ฐ๊ตฌ ๋Œ€์ƒ ์ค‘์—์„œ 59.3%๋Š” ๋ชฉ, ์–ด๊นจ, ํ—ˆ๋ฆฌ ๋ถ€์œ„ ์ค‘ ํ•œ ๋ถ€์œ„๋ผ๋„ ์ฆ์ƒ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ์˜ ์ง์—…์  ํŠน์„ฑ๊ณผ ๊ด€๋ จํ•˜์—ฌ ์—ฐ๊ตฌ๋Œ€์ƒ์˜ 76.3%๊ฐ€ ์ฃผ๋‹น 48์‹œ๊ฐ„ ์ด์ƒ์„ ๊ทผ๋ฌดํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ํœด์‹๊ฐ„๊ฒฉ๋„ 1์‹œ๊ฐ„์ด ๋„˜๋Š” ์‚ฌ๋žŒ์ด 80.5%๋ฅผ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ์ฃผ์—…๋ฌด๊ฐ€ 1๊ฐ€์ง€์ธ ๊ทธ๋ฃน๋ณด๋‹ค 2๊ฐ€์ง€ ์ด์ƒ์ธ ๊ทธ๋ฃน์ด ํ—ˆ๋ฆฌ๋ถ€์œ„์—์„œ ์œ ๋ณ‘์œจ์ด ๋†’์•˜๋‹ค. ์ž‘์—…์˜ ์ข…๋ฅ˜์— ๋”ฐ๋ผ์„œ๋Š” ํ—ˆ๋ฆฌ ๋ถ€์œ„์—์„œ wax ์กฐ๊ฐ ์ž‘์—…, ์„๊ณ (๋ชจํ˜•) ์ž‘์—…์ด ์œ ๋ณ‘์œจ์„ ๋†’์˜€๋‹ค. ์ •์‹ ยท์‚ฌํšŒ์  ์š”์ธ๊ณผ ๊ด€๋ จํ•˜์—ฌ ์ž‘์—… ์ œ์–ด ์ง€์ˆ˜์— ๋”ฐ๋ผ์„œ๋Š” ๋ชฉ, ํ—ˆ๋ฆฌ ๋ถ€์œ„์—์„œ ์ž‘์—…์ œ์–ด ์ง€์ˆ˜๊ฐ€ ๋‚ฎ์„์ˆ˜๋ก ์œ ๋ณ‘์œจ์ด ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ณผ๋„ํ•œ ์ŠคํŠธ๋ ˆ์Šค ์ง€์ˆ˜์— ๋”ฐ๋ผ์„œ๋Š” ์ŠคํŠธ๋ ˆ์Šค๊ฐ€ ๋†’์„์ˆ˜๋ก ํ—ˆ๋ฆฌ๋ถ€์œ„์—์„œ ์œ ๋ณ‘์œจ์ด ๋†’์•˜๋‹ค. ๋‹ค๋ณ€๋Ÿ‰ ๋ถ„์„ ๊ฒฐ๊ณผ ๋ชฉ ๋ถ€์œ„์—์„œ๋Š” ์ธ์ง€ํ•œ ๊ฑด๊ฐ• ์ƒํƒœ๊ฐ€ ๋‚˜์ ์ˆ˜๋ก, ์ž‘์—… ์ œ์–ด ์ง€์ˆ˜๊ฐ€ ๋‚ฎ์„์ˆ˜๋ก ์œ ๋ณ‘์œจ์ด ๋†’์•˜๋‹ค. ์–ด๊นจ ๋ถ€์œ„์—์„œ๋Š” ์ธ์ง€ํ•œ ๊ฑด๊ฐ•์ƒํƒœ๊ฐ€ ๋‚˜์ ์ˆ˜๋ก ์œ ๋ณ‘์œจ์ด ๋†’์•˜์œผ๋ฉฐ, ์ž‘์—… ๊ฒฝ๋ ฅ 5๋…„ ๋ฏธ๋งŒ์ธ ๊ตฐ์— ๋น„ํ•ด ์ž‘์—… ๊ฒฝ๋ ฅ(6โˆผ10๋…„), ์ž‘์—… ๊ฒฝ๋ ฅ(11โˆผ15๋…„)์ผ์ˆ˜๋ก ์œ ๋ณ‘์œจ์ด ๋‚ฎ์•˜๋‹ค. ํ—ˆ๋ฆฌ ๋ถ€์œ„์—์„œ๋Š” ์ฃผ 1ํšŒ ์ด์ƒ ์Œ์ฃผ๋ฅผ ํ• ์ˆ˜๋ก, ์ธ์ง€ํ•œ ๊ฑด๊ฐ• ์ƒํƒœ๊ฐ€ ๋‚˜์ ์ˆ˜๋ก,์ž‘์—… ์ œ์–ด ์ง€์ˆ˜๊ฐ€ ๋‚ฎ์„์ˆ˜๋ก, ์›ฉ์Šค(wax) ์กฐ๊ฐ ์ž‘์—…์„ ํ•  ์ˆ˜๋ก ๋†’์•˜๋‹ค. 5๋…„ ๋ฏธ๋งŒ ๊ฒฝ๋ ฅ์ž์—๋น„ํ•ด ์ž‘์—… ๊ฒฝ๋ ฅ(6โˆผ10๋…„), ์ž‘์—… ๊ฒฝ๋ ฅ(11โˆผ15)๋…„์˜ ์ž‘์—… ๊ฒฝ๋ ฅ์„ ๊ฐ€์งˆ์ˆ˜๋ก ์œ ๋ณ‘์œจ์ด ๋‚ฎ์•˜๋‹ค ํ–ฅํ›„์—๋Š” ์น˜๊ณผ๊ธฐ๊ณต ์ž‘์—…์— ๋Œ€ํ•œ ์ธ๊ฐ„๊ณตํ•™์  ๋ถ„์„์„ ๋™๋ฐ˜ํ•œ ์ž‘์—…์˜ ๋ณตํ•ฉ์  ์š”์ธ๋“ค์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ถ”๊ฐ€๋กœ ์žˆ์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. [์˜๋ฌธ] This study was conducted to determine the prevalence of musculoskeletal complaints of the neck, shoulders, and low back among dental laboratory technicians in Seoul Korea, and to investigate the relation between these complaints and work related and psychosocial variables. The study was based on the Nordic Musculoskeletal Questionnaire (NMQ), a self-administrated questionnaire, which deals with musculoskeletal symptoms, psychosocial factors, and job factors and which was supplemented with additional questions regarding psychosocial and job factors. A questionnaire was mailed to randomly selected 500 dental laboratory technicians in Seoul, Korea. Among them, 219 dental laboratory technician filled out the questionnaires and returned them. A questionnaire was completed by 184 male and 35 female. The results were as follows; Of the 219 technicians who answered the questionnaire, 61.7% felt subjective musculoskeletal symptoms in neck, 65.1% in shoulders, 56.7% in low back during the preceding 12 months. Neck pain lasting for more than 8 days within the previous 12 months was reported by 74 technicians(33.8%). Shoulders pain lasting for more than 8 days within the previous 12 months was reported by 83 technicians(39.7%). Low back pain lasting for more than 8 days within the previous 12 months was reported by 78 technicians(35.6%). 124 technicians(59.3%) had experienced trouble some time during the preceding 12 months in neck, shoulders, and low back. In bivariate analyses, of the complexity of work, the low back symptom rates were higher in the group carring out simple work than the group carring out complex work. For the type of work, the low back symptom rates were higher in the group doing wax work and plaster work. For the psychosocial factors, there is no significant difference in work content and social relationships. Higher work control were associated with the symptom rates in neck and low back. Higher overstrain were associated with the symptom rates in low back. In multivariate analyses, perceived poor health status, high work control were associated with neck symptoms. perceived poor health status were related to shoulders symptoms. In comparing years on the job, under 5 years, 6โˆผ10 years or 11โˆผ15 years, the under 5 years reported a high prevalence of shoulders symptoms than the other groups. Alcohol consumption(over 1 times per week), perceived poor health status, high work control, and doing wax work were related to low back symptoms. In comparing years on the job, under 5 years, 6โˆผ10 years or 11โˆผ15 years, the under 5 years reported a high prevalence of low back symptoms than the other groups. From these results it may be concluded that future research of health risks of dental laboratory work should have a wider focus than the relation between physical and psychosocial factors and musculoskeletal complaints with ergonomic health effect analysis.ope

    ๋ถ€ ๋Œ€๋ฆฝ ์œ ์ „์ž ๋นˆ๋„ ์™€ ์˜ˆ์ธก ๋„๊ตฌ๋ฅผ ์ด์šฉํ•œ ๋‚œ์ฒญ ๊ด€๋ จ ๋ณ€์ด๋“ค์˜ ์ฒด๊ณ„์ ์ธ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•

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    ์˜๊ณผํ•™Non-syndromic hearing loss (NSHL) is extremely genetically heterogeneous, and to date, more than 96 genes have been linked to NSHL and explain about half of the clinical cases. Although high throughput DNA sequencing technology facilitates the identification of causative mutations in many human diseases, hundreds or thousands of variants identified by this method require interpretation to assess their likelihood of causing a disease. Here, we aim to systemically evaluate variants in 96 genes, which have been identified in NSHL patients, using minor allele frequency (MAF) and predictive tools. The MAF thresholds were determined considering allele frequency of the most common pathogenic variant of GJB2, and the prevalence of NSHL. For the 96 NSHL known genes, 3,082 variants reported in HGMD and 1,210 reported as pathogenic or likely pathogenic in ClinVar were classified according to the MAF threshold and then according to the pLI scores of corresponding genes into three categories (pLI0.9). The number of missense variants reported in recessive (rec), dominant (dom) and dom/rec genes was 1,040, 244, and 668 respectively. The prediction scores of the missense variants were obtained using PolyPhen-2, SIFT, and Condel. As a result of analysis, the variants above the MAF threshold were 61, 23 and 14 in recessive, dominant and dom/rec genes, respectively. Using Korean control dataset, three variants that would be found more frequently in Koreans than in any other population were identified suggesting that several variants having MAF levels which are implausible for highly penetrance Mendelian disease could be found through other certain population control datasets. Additionally, there were statistical differences in prediction scores between the variants below and above the MAF threshold in recessive genes. Although prediction scores were not different between the variants below and above the MAF threshold for dominant genes, the scores were significantly different for dominant genes with > 0.9 pLI score. These data showed that prediction tools could be more useful for predicting variants in recessive genes and dominant genes with > 0.9 pLI score. Based on this study, we can prioritize novel candidate variants that have a causal relationship with the disease by using the MAF threshold and the prediction tool to evaluate variants in NSHL.open์„

    Development of High Speed Programmable Controller

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    Maste

    Factors on Dynamics in Domestic Politics of Post-Soviet Countries after the Color Revolution

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ •์น˜์™ธ๊ตํ•™๋ถ€(์™ธ๊ตํ•™์ „๊ณต), 2013. 8. ์‹ ๋ฒ”์‹.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํƒˆ ์†Œ๋น„์—ํŠธ ์ง€์—ญ์˜ ์กฐ์ง€์•„, ์šฐํฌ๋ผ์ด๋‚˜, ๊ทธ๋ฆฌ๊ณ  ํ‚ค๋ฅด๊ธฐ์ฆˆ์Šคํƒ„์„ ๋Œ€์ƒ์œผ๋กœ ์ƒ‰๊น” ํ˜๋ช…์„ ๊ฒฝํ—˜ํ•œ ์„ธ ๋‚˜๋ผ์—์„œ ๊ทธ ์ดํ›„ ๊ด€์ฐฐ๋˜๋Š” ์ƒ์ดํ•œ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™”์˜ ์ด์œ ์— ๋Œ€ํ•œ ๋ถ„์„์„ ์‹œ๋„ํ•˜์˜€๋‹ค. ํƒˆ ์†Œ๋น„์—ํŠธ ์ง€์—ญ ๋ฏผ์ฃผํ™”์˜ ์ƒ์ง•์œผ๋กœ ์ธ์‹๋˜์—ˆ๋˜ 2003-2005๋…„์˜ ์ƒ‰๊น” ํ˜๋ช…์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ํ˜๋ช…์„ ๊ฒฝํ—˜ํ•˜์˜€๋˜ ์กฐ์ง€์•„, ์šฐํฌ๋ผ์ด๋‚˜, ๊ทธ๋ฆฌ๊ณ  ํ‚ค๋ฅด๊ธฐ์ฆˆ์Šคํƒ„์˜ 3๊ฐœ๊ตญ์—์„œ ๋ฏผ์ฃผ์ฃผ์˜ ์ดํ–‰์˜ ํ›„ํ‡ด ๋˜๋Š” ์ •์ฒด์˜ ํ˜„์ƒ์ด ๊ด€์ฐฐ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด์™€ ๊ฐ™์€ 3๊ฐœ๊ตญ์˜ ํ˜๋ช… ์ดํ›„ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™”๋ฅผ ๋ฐ”๋ผ๋ด„์— ์žˆ์–ด ๊ธฐ์กด์˜ ๋ฏผ์ฃผ์ฃผ์˜ ์ดํ–‰๋ก ์  ์‹œ๊ฐ์˜ ์•ฝ์ ์„ ์ง€์ ํ•˜๋ฉฐ ๊ทธ ๋Œ€์•ˆ์„ ์ œ์‹œํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ํƒˆ ์†Œ๋น„์—ํŠธ ์ง€์—ญ์˜ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™”๊ฐ€ ์ฒด์ œ ์ „ํ™˜๊ณผ ๊ตญ๊ฐ€ ๊ฑด์„ค์˜ ๋ณตํ•ฉ์ ์ธ ๊ฒฐ๊ณผ๋ฌผ์ž„์„ ๊ณ ๋ ค, ์ด๋กœ๋ถ€ํ„ฐ ์ƒ‰๊น” ํ˜๋ช… ์ดํ›„ 3๊ฐœ๊ตญ์˜ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™”๋ฅผ ์ƒˆ๋กญ๊ฒŒ ์กฐ๋งํ•˜์˜€๋‹ค. ๋ณธ๊ฒฉ์ ์ธ ๋…ผ์˜์— ์•ž์„œ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์กฐ์ง€์•„, ์šฐํฌ๋ผ์ด๋‚˜, ๊ทธ๋ฆฌ๊ณ  ํ‚ค๋ฅด๊ธฐ์ฆˆ์Šคํƒ„์ด ๋™์ผํ•œ ์ •์น˜์  ์‚ฌ๊ฑด์„ ๊ฒช์—ˆ์œผ๋ฉฐ, ์‚ฌ๊ฑด ์งํ›„ ์œ ์‚ฌํ•œ ๊ตญ๋‚ด์ •์น˜์  ํ™˜๊ฒฝ์— ์ฒ˜ํ•ด ์žˆ์—ˆ์Œ์„ ๋…ผ์ฆํ•˜์˜€๋‹ค. ์ƒ‰๊น” ํ˜๋ช…์€ ๊ทธ ์ง„ํ–‰ ๊ณผ์ •์ƒ์—์„œ ํฌ๊ฒŒ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์„ธ ๊ฐ€์ง€์˜ ๊ณตํ†ต์ ์„ ๋ณด์˜€๋‹ค. ์šฐ์„  ์ „๊ตญ ๋‹จ์œ„์˜ ๋ถ€์ • ์„ ๊ฑฐ๊ฐ€ ํ˜๋ช…์˜ ์ง์ ‘์ ์ธ ๋„ํ™”์„ ์ด ๋˜์—ˆ์œผ๋ฉฐ, ๋‹ค์Œ์œผ๋กœ ๋ถ€์ • ์„ ๊ฑฐ์— ๋ฐ˜๋Œ€ํ•˜๋Š” ์‹œ๋ฏผ ์ค‘์‹ฌ์˜ ๋Œ€๊ทœ๋ชจ ๋ฐ˜์ •๋ถ€ ์‹œ์œ„๊ฐ€ ์ „๊ฐœ๋˜์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋‹น์‹œ ์ง‘๊ถŒ ์—˜๋ฆฌํŠธ์˜ ํ‡ด์ง„ ํ›„ ์กฐ๊ธฐ ๋Œ€์„ ์„ ํ†ตํ•ด ์ •๊ถŒ ๊ต์ฒด๊ฐ€ ์ด๋ฃจ์–ด์ง์œผ๋กœ์„œ ํ˜๋ช…์€ ์ข…๊ฒฐ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ํ˜๋ช… ์ดํ›„ ๊ณตํ†ต์ ์œผ๋กœ ์ด๋ฅผ ์ฃผ๋„ํ•˜์˜€๋˜ ์ €ํ•ญ ์—˜๋ฆฌํŠธ ์ง‘๋‹จ์— ์˜ํ•ด ์ผ์ข…์˜ ์ง‘๋‹จ์ง€๋„์ฒด์ œ๊ฐ€ ํ˜•์„ฑ๋˜์—ˆ๋‹ค๋Š” ์ ๋„ 3๊ฐœ๊ตญ์ด ํ˜๋ช…์ด๋ผ๋Š” ๋™์ผํ•œ ์‚ฌ๊ฑด์„ ๊ฒช์—ˆ์Œ์„ ์ฆ๋ช…ํ•˜๋Š” ํ•˜๋‚˜์˜ ๋‹จ๋ฉด์ด๋‹ค. ๊ทธ๋ ‡๋‹ค๋ฉด ๋™์ผํ•œ ์ •์น˜์  ์‚ฌ๊ฑด์„ ๊ฒฝํ—˜ํ•œ 3๊ฐœ๊ตญ์˜ ์ดํ›„ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™”์˜ ์ฐจ์ด๋Š” ๊ณผ์—ฐ ๋ฏผ์ฃผ์ฃผ์˜ ์ดํ–‰์˜ ์‹œ๊ฐ์œผ๋กœ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€? ๊ธฐ์กด์˜ ์ด๋ก ์  ๋…ผ์˜์— ๋น„์ถ”์–ด ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฏผ์ฃผ์ฃผ์˜ ์ดํ–‰์˜ ์–‘์ƒ๊ณผ ๊ทธ ์ •๋„๋ฅผ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด ๋ณดํ†ต ์„ ๊ฑฐ์˜ ์‹ค์‹œ์™€ ์„ ๊ฑฐ๊ถŒ, ์–ธ๋ก  โ€ค ์ง‘ํšŒ โ€ค ๊ฒฐ์‚ฌ์˜ ์ž์œ , ๊ทธ๋ฆฌ๊ณ  ๋ฒ•์น˜๋ฅผ ์„ ์ •ํ•˜์˜€๋‹ค. ์„ธ ๊ฐ€์ง€ ํ•˜์œ„ ๊ฐœ๋…์„ ์ค‘์‹ฌ์œผ๋กœ ํ˜๋ช… ์ดํ›„ ์กฐ์ง€์•„, ์šฐํฌ๋ผ์ด๋‚˜, ๊ทธ๋ฆฌ๊ณ  ํ‚ค๋ฅด๊ธฐ์ฆˆ์Šคํƒ„์˜ ๋ฏผ์ฃผ์ฃผ์˜ ์ดํ–‰ ์ •๋„๋ฅผ ์ธก์ •ํ•œ ๊ฒฐ๊ณผ, ์ด๋“ค 3๊ฐœ๊ตญ์€ ์ƒํ˜ธ๊ฐ„์— ํฐ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. ๋ฐ˜๋ฉด, 3๊ฐœ๊ตญ์˜ ํ˜๋ช… ์ „ํ›„ ์ž…๋ฒ•๋ถ€๋ฅผ ํ†ตํ•œ ์ œ๋„์  ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”๊ฐ€ ์ด๋“ค ๊ตญ๊ฐ€์˜ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™”์— ์žˆ์–ด ๋ณด๋‹ค ๋” ์ ํ•ฉํ•œ ์„ค๋ช…์˜ ํ‹€์„ ์ œ๊ณตํ•ด ์ค„ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. 3๊ฐœ๊ตญ์—์„œ๋Š” ๊ฐ๊ฐ ํ˜๋ช…์„ ์ „ํ›„ํ•˜์—ฌ ๊ฐœํ—Œ์„ ํ†ตํ•œ ๋Œ€ํ†ต๋ น - ์ž…๋ฒ•๋ถ€ ๊ฐ„ ๊ด€๊ณ„ ๋ณ€ํ™”๊ฐ€ ๊ด€์ฐฐ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋Š” ์ž…๋ฒ•๋ถ€ ๋‚ด ์—˜๋ฆฌํŠธ ์„ธ๋ ฅ ๋ถ„ํฌ์™€ ๋งž๋ฌผ๋ ค ๊ทธ ํ˜๋ช… ์ดํ›„ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™”์— ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ๋ณต์ˆ˜์˜ ์ •ํŒŒ ๊ฐ„ ์„ธ๋ ฅ ๊ท ํ˜•์ด ์กด์žฌํ•˜๋Š” ๊ตญ๊ฐ€์—์„œ๋Š” ๋Œ€ํ†ต๋ น - ์ž…๋ฒ•๋ถ€ ๊ฐ„ ๊ด€๊ณ„์— ๋”ฐ๋ผ ์นด๋ฅดํ…”์ด ํ˜•์„ฑ๋˜๊ฑฐ๋‚˜ (๋น„)๊ณต์‹์  ์ œ๋„์— ์˜ํ•œ ๊ฒฝ์Ÿ์ด ์ด๋ฃจ์–ด์ง€๋Š” ๋“ฑ ๋ฏผ์ฃผ์ฃผ์˜ ์ดํ–‰ ์ •์ฒด์˜ ๋ชจ์Šต์ด ๋‚˜ํƒ€๋‚œ๋‹ค(์šฐํฌ๋ผ์ด๋‚˜, ํ‚ค๋ฅด๊ธฐ์ฆˆ์Šคํƒ„). ๋ฐ˜๋ฉด, ์ง‘๊ถŒ ์ •ํŒŒ๋กœ์˜ ๊ถŒ๋ ฅ์ด ์ง‘์ค‘๋œ ๊ฒฝ์šฐ ๋Œ€ํ†ต๋ น - ์ž…๋ฒ•๋ถ€ ๊ฐ„ ๊ด€๊ณ„์— ๋”ฐ๋ผ ๋Œ€์ฒด๋กœ ๊ถŒ์œ„์ฃผ์˜๋กœ ํ‡ดํ–‰ํ•˜๋Š” ์–‘์ƒ์ด ๋ฐœ๊ฒฌ๋˜์—ˆ๋‹ค(์กฐ์ง€์•„). ๊ฒฐ๋ก ์ ์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋Š” ํƒˆ ์†Œ๋น„์—ํŠธ ์ง€์—ญ์— ํ•œ์ •๋œ ๊ทธ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋“ฑ์— ์žˆ์–ด ๋…ผ์˜์˜ ํ•œ๊ณ„๋ฅผ ๋…ธ์ •ํ•˜๊ณ  ์žˆ์œผ๋‚˜, ์ƒ‰๊น” ํ˜๋ช…์˜ ์‚ฌํ›„ ๊ฒฝ๊ณผ์— ์ฃผ๋ชฉํ•˜์˜€์œผ๋ฉฐ ๊ทธ ๊ณผ์ •์—์„œ ์ œ๋„์  ํ™˜๊ฒฝ์ด๋ผ๋Š” ์ถ”๊ฐ€์ ์ธ ๋ณ€์ˆ˜์˜ ์˜ํ–ฅ๋ ฅ์— ์ฃผ๋ชฉํ•˜์˜€๋‹ค๋Š” ์—ฐ๊ตฌ ์˜์˜๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค.This research is aiming at analyzing dynamics in domestic politics of Post-Soviet countries after the Color Revolution(2003-2005). Despite successful regime change by mass demonstrations against electoral fraud, stagnation or retreat in democratic transition have been observed in Georgia, Ukraine, and Kyrgyzstan. The main research question comes out of the observation: Why do Georgia, Ukraine, and Kyrgyzstan show different dynamics in domestic politics although they experienced the same radical political change? Pointing out that pre-existing theory of democratic transition is not sufficient to provide proper explanations on the dynamics, this dissertation suggests that the alternate model based on the influence of institutional conditions in those three countries. Before the main argument, concrete analysis on three Color Revolutions have been carried out to prove that Georgia, Ukraine and Kyrgyzstan underwent similar political change. Each revolution shares mainly three common steps in chronological order from its breakout. First, it was nationwide electoral fraud that directly triggered the revolutionnext, rapid proliferation of mass movement led by opposition elites was observed throughout the entire countryfinally, regime change has been completed via early presidential (re)election while incumbent president resigned in responsible for unfair elections. Additionally, the new government established by the opposition elites turned out to be polyarchical, where the political power is distributed among several people, including the leader of the revolution as its main post. Previous analysis on the Color Revolution suggests that Georgia, Ukraine, and Kyrgyzstan seemed to be under the similar political circumstances during and just after the revolution. Then, is it plausible to explain such differences in dynamics of domestic politics by the perspective of democratic transition? Free elections and electoral rights, freedom of speech, assembly, and association, and rule of law based on previous theories on democracy and its transition are chosen to estimate whether there are any changes or improvements in democracy of three countries after the revolution. As a result, no significant sign of democratic development is observed among three countries. Defective explanation model of democratic transition, therefore, suggests that the institutional conditions should be regarded as a main influential factor on understanding the political dynamics in Georgia, Ukraine, and Kyrgyzstan after the Color Revolution. Institutional conditions in each country defined as the distribution of power among elites in parliament and president-parliament relations, which also reflects that the legislative body is the core of institutions established in Post-Soviet region since its independence. As a result, where one-sided distribution of power in parliament combines with superior presidential power upon the legislative branch, its regime tends to be authoritarian(Georgia). Otherwise, where power is distributed equally in parliament, cartel-like regime type(Kyrgyzstan) or competition by (in)formal rules(Ukraine and partially in Kyrgyzstan) likely to emerge due to the relation between president and parliament. In conclusion, the main contribution of this research is twofold. First, this research may provide an explanatory model particularly focused on the period less examined, after the Color Revolution. Second, this research may suggest that the institutional conditions as an important factor should be considered in further studies on Post-Soviet regime transition.I. ์„œ๋ก โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 1 1. ๋ฌธ์ œ์˜ ์ œ๊ธฐ์™€ ์—ฐ๊ตฌ ์งˆ๋ฌธโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 1 1) ์ƒ‰๊น” ํ˜๋ช… ์ดํ›„ ํƒˆ ์†Œ๋น„์—ํŠธ ์ง€์—ญ ๋ฏผ์ฃผํ™” ๋ถ€์ง„โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ1 2) ํ˜๋ช…์ ์ด์ง€ ์•Š์•˜๋˜ ํ˜๋ช…โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ4 2. ์„ ํ–‰ ์—ฐ๊ตฌ ๊ฒ€ํ† โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 6 1) ์ƒ‰๊น” ํ˜๋ช…๊ณผ ๊ทธ ์ดํ›„โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ6 2) ํƒˆ ์†Œ๋น„์—ํŠธ ์ง€์—ญ์˜ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™”โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ10 3. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• ๋ฐ ์ฃผ์š” ๋…ผ์˜โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 17 1) ์ด๋ก ์  ๋ฐฐ๊ฒฝโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 17 2) ์ฃผ์š” ๋…ผ์˜ ๋ฐ ์—ฐ๊ตฌ์˜ ๋ฐฉํ–ฅ โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 22 3) ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๊ณผ ์ž๋ฃŒ์˜ ์ด์šฉโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 26 II. ์ƒ‰๊น” ํ˜๋ช… ์ดํ›„ ์กฐ์ง€์•„, ์šฐํฌ๋ผ์ด๋‚˜, ํ‚ค๋ฅด๊ธฐ์ฆˆ์Šคํƒ„์˜ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™”โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 30 1. ์ƒ‰๊น” ํ˜๋ช…์˜ ์‹œ๊ฐ„์  ์ „๊ฐœ โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 30 1) ์ „๊ตญ ๋‹จ์œ„์˜ ๋ถ€์ • ์„ ๊ฑฐโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 31 2) ์‹œ๋ฏผ์— ์˜ํ•œ ๋Œ€๊ทœ๋ชจ ๋ฐ˜์ •๋ถ€ ์‹œ์œ„ ๋ฐœ์ƒโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 36 3) ์ง‘๊ถŒ ์—˜๋ฆฌํŠธ์˜ ํŒจ๋ฐฐ์™€ ์ •๊ถŒ๊ต์ฒดโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ38 2. ์ƒ‰๊น” ํ˜๋ช… ์ดํ›„์˜ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™” โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 40 1) 2003๋…„ ์ดํ›„์˜ ์กฐ์ง€์•„โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 41 2) 2004๋…„ ์ดํ›„์˜ ์šฐํฌ๋ผ์ด๋‚˜โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 43 3) 2005๋…„ ์ดํ›„์˜ ํ‚ค๋ฅด๊ธฐ์ฆˆ์Šคํƒ„โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 48 3. ๊ฐ™์€ ์‚ฌ๊ฑด, ๊ทธ๋Ÿฌ๋‚˜ ๋‹ค๋ฅธ ๊ฒฐ๊ณผโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 50 III. ์ƒ‰๊น” ํ˜๋ช… ์ดํ›„์˜ ์ฒด์ œ ์ „ํ™˜ โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 53 1. ํ˜๋ช… ์ดํ›„์˜ ์ฒด์ œ ์ „ํ™˜ โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 53 1) ๋ณดํ†ต ์„ ๊ฑฐ์˜ ์‹ค์‹œ์™€ ์„ ๊ฑฐ๊ถŒ โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 53 2) ์–ธ๋ก โ€ค์ง‘ํšŒโ€ค๊ฒฐ์‚ฌ์˜ ์ž์œ  โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 58 3) ๋ฒ•์น˜โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ62 2. ์†Œ๊ฒฐ: ๊ฐ๊ตญ ์ฒด์ œ ์ „ํ™˜ ์ •๋„์˜ ํ‰๊ฐ€โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ63 IV. ์ฒด์ œ ์ „ํ™˜๊ณผ ์ œ๋„์  ํ™˜๊ฒฝ โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 66 1. ํƒˆ ์†Œ๋น„์—ํŠธ ์ง€์—ญ์— ๋Œ€ํ•œ ๊ธฐ์กด ๋…ผ์˜โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 66 2. ๋ถ„์„ํ‹€์˜ ๋ชจ์ƒ‰: ์ž…๋ฒ•๋ถ€๋ฅผ ์ค‘์‹ฌ์œผ๋กœโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ70 1) ์—˜๋ฆฌํŠธ ๊ตฌ์กฐ: ์—˜๋ฆฌํŠธ์˜ ์„ฑ๊ฒฉ๊ณผ ๊ทธ ์„ธ๋ ฅ ๋ถ„ํฌโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 70 2) ํ—Œ์ •(ๆ†ฒๆ”ฟ) ๊ตฌ์กฐ: ๋Œ€ํ†ต๋ น - ์ž…๋ฒ•๋ถ€ ๊ฐ„ ๊ด€๊ณ„โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 80 3. ์†Œ๊ฒฐ: ์ƒ‰๊น” ํ˜๋ช… ์ดํ›„ ๊ตญ๋‚ด์ •์น˜์  ๋ณ€ํ™”โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ86 V. ๊ฒฐ๋ก  โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ89 1. ์š”์•ฝ ๋ฐ ์ •๋ฆฌ โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ90 2. ๋ณธ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ•จ์˜ โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 91 ์ฐธ๊ณ ๋ฌธํ—Œ โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ 92 Abstractโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ105Maste
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