8 research outputs found

    Case of Indonesia

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2023. 2. ํ™ฉ์ค€์„.The rapid development of digital technology and the use of information in productive processes cause structural changes in the economy in the current situation of Industry 4.0. (Neves et al., 2020) As a result of digital transformation, smart cities emerge as a type of interaction among technological, organizational, and political innovations. Innovation in mobility and transportation as an effect of smart city development, like ride-hailing, car-sharing, car-pooling, Mobility as a Service, electric vehicles, autonomous vehicles, and so on, seems to be a panacea for mobility issues (J. Lee et al., 2020a). Unfortunately, most innovation is not supported by policy and regulation. The public transport authorities frequently may take less time to regulate to enable the smart mobility concept, and like many other public authorities, transport authorities' bureaucracy may slow down the penetration of mobility innovation (Kamargianni & Matyas, 2017a) The overpopulated city will face difficulties in providing adequate transportation in implementing smart mobility agenda, mainly because the lack of public transportation cannot be solved only by expanding the road and building new transportation infrastructure. This study aims to understand the smart mobility characteristic to facilitate a strategic goal in creating public value based on citizen expectations. The study focuses on the case of Indonesia. Two essays were conducted through an in-depth literature review to achieve this objective. The first essay investigated smart mobility characteristics and factors, where expert judgment and opinion were used to categorize the most important criteria. The result is to help government design a strategy to implement smart urban mobility in Indonesia's new capital. At the same time, the second essay focused on the citizen satisfaction expectations for smart mobility. Both results will combine to fill the gap between government and citizens expectations for future urban mobility in the new capital of Indonesia.๋””์ง€ํ„ธ ํ…Œํฌ๋†€๋กœ์ง€์˜ ๊ธ‰์†ํ•œ ๋ฐœ์ „๊ณผ ์ƒ์‚ฐ์ ์ธ ํ”„๋กœ์„ธ์Šค์—์„œ์˜ ์ •๋ณด ์‚ฌ์šฉ์€ ์‚ฐ์—… 4.0์˜ ํ˜„์žฌ ์ƒํ™ฉ์—์„œ ๊ฒฝ์ œ์˜ ๊ตฌ์กฐ์  ๋ณ€ํ™”๋ฅผ ์•ผ๊ธฐํ•ฉ๋‹ˆ๋‹ค. (Neves ๋“ฑ, 2020) ๋””์ง€ํ„ธ ์ „ํ™˜์˜ ๊ฒฐ๊ณผ๋กœ, ์Šค๋งˆํŠธ ์‹œํ‹ฐ๋Š” ๊ธฐ์ˆ , ์กฐ์ง ๋ฐ ์ •์น˜์  ํ˜์‹  ์‚ฌ์ด์˜ ์ƒํ˜ธ์ž‘์šฉ์˜ ํ•œ ํ˜•ํƒœ๋กœ ๋‚˜ํƒ€๋‚ฉ๋‹ˆ๋‹ค. ์Šค๋งˆํŠธ ์‹œํ‹ฐ ๊ฐœ๋ฐœ์˜ ํšจ๊ณผ๋กœ์„œ ์Šน์ฐจ๊ฐ, ์นด์…ฐ์–ด๋ง, ์นดํ’€๋ง, ์„œ๋น„์Šค๋กœ์„œ์˜ ๋ชจ๋ฐ”์ผ์„ฑ, ์ „๊ธฐ์ฐจ, ์˜คํ† ๋…ธ๋งˆ์Šค ์ฐจ๋Ÿ‰ ๋“ฑ ์ด๋™์„ฑยท๊ตํ†ต์˜ ํ˜์‹ ์€ ์ด๋™์„ฑ ๋ฌธ์ œ์˜ ๋งŒ๋ณ‘ํ†ต์น˜์•ฝ์œผ๋กœ ๋ณด์ธ๋‹ค. (J. Lee ๋“ฑ, 2020a) ๋ถˆํ–‰ํžˆ๋„ ๋Œ€๋ถ€๋ถ„์˜ ํ˜์‹ ์€ ์ •์ฑ…๊ณผ ๊ทœ์ œ์— ์˜ํ•ด ๋’ท๋ฐ›์นจ๋˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ๋Œ€์ค‘๊ตํ†ต ๋‹น๊ตญ์€ ์Šค๋งˆํŠธ ์ด๋™์„ฑ ๊ฐœ๋…์„ ํ™œ์„ฑํ™”ํ•˜๊ธฐ ์œ„ํ•ด ๊ทœ์ œํ•˜๋Š” ๋ฐ ์‹œ๊ฐ„์ด ์ ๊ฒŒ ๊ฑธ๋ฆด ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋‹ค๋ฅธ ๋งŽ์€ ๊ณต๊ณต ๊ธฐ๊ด€๊ณผ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ๊ตํ†ต ๋‹น๊ตญ์˜ ๊ด€๋ฃŒ์ฃผ์˜๋Š” ์ด๋™์„ฑ ํ˜์‹ ์˜ ๋ณด๊ธ‰์„ ์ง€์—ฐ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. (์นด๋งˆ๋ฅด์ง€์•ˆ๋‹ˆ & ๋งˆํ‹ฐ์•„์Šค, 2017a) ์ธ๊ตฌ๊ณผ์ž‰ ๋„์‹œ๋Š” ์Šค๋งˆํŠธ ๋ชจ๋นŒ๋ฆฌํ‹ฐ ์–ด์  ๋‹ค๋ฅผ ์ดํ–‰ํ•˜๋Š” ๋ฐ ์žˆ์–ด ์ ์ ˆํ•œ ๊ตํ†ต์ˆ˜๋‹จ์„ ์ œ๊ณตํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์„ ๊ฒช์„ ๊ฒƒ์ด๋‹ค. ๊ทธ ์ฃผ๋œ ์ด์œ ๋Š” ๋„๋กœ๋ฅผ ํ™•์žฅํ•˜๊ณ  ์ƒˆ๋กœ์šด ๊ตํ†ต ์ธํ”„๋ผ๋ฅผ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ๋งŒ์œผ๋กœ ๋Œ€์ค‘๊ตํ†ต์˜ ๋ถ€์กฑ์„ ํ•ด๊ฒฐํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์Šค๋งˆํŠธ ๋ชจ๋นŒ๋ฆฌํ‹ฐ ํŠน์„ฑ์„ ํŒŒ์•…ํ•˜์—ฌ ์‹œ๋ฏผ์˜ ๊ธฐ๋Œ€์น˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ณต๊ณต ๊ฐ€์น˜๋ฅผ ์ฐฝ์ถœํ•˜๋Š” ์ „๋žต์  ๋ชฉํ‘œ๋ฅผ ์ด‰์ง„ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ธ๋„๋„ค์‹œ์•„์˜ ์‚ฌ๋ก€์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ์ด ๋ชฉ์ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ๋‘ ํŽธ์˜ ์—์„ธ์ด๊ฐ€ ์‹ฌ์ธต์ ์ธ ๋ฌธํ—Œ ๊ฒ€ํ† ๋ฅผ ํ†ตํ•ด ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—์„ธ์ด์—์„œ๋Š” ์Šค๋งˆํŠธ ๋ชจ๋นŒ๋ฆฌํ‹ฐ์˜ ํŠน์„ฑ๊ณผ ์š”์ธ์„ ์กฐ์‚ฌํ–ˆ์œผ๋ฉฐ, ์ „๋ฌธ๊ฐ€์˜ ํŒ๋‹จ๊ณผ ์˜๊ฒฌ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•œ ๊ธฐ์ค€์„ ๋ถ„๋ฅ˜ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ •๋ถ€๋Š” ์ธ๋„๋„ค์‹œ์•„์˜ ์ƒˆ๋กœ์šด ์ˆ˜๋„์—์„œ ์Šค๋งˆํŠธํ•œ ๋„์‹œ ์ด๋™์„ฑ์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•œ ์ „๋žต์„ ์„ค๊ณ„ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ๋™์‹œ์—, ๋‘ ๋ฒˆ์งธ ์—์„ธ์ด๋Š” ์Šค๋งˆํŠธ ๋ชจ๋นŒ๋ฆฌํ‹ฐ์— ๋Œ€ํ•œ ์‹œ๋ฏผ ๋งŒ์กฑ ๊ธฐ๋Œ€์— ์ดˆ์ ์„ ๋งž์ท„๋‹ค. ๋‘ ๊ฒฐ๊ณผ ๋ชจ๋‘ ์ƒˆ๋กœ์šด ์ˆ˜๋„ ์ธ๋„๋„ค์‹œ์•„์˜ ๋ฏธ๋ž˜ ๋„์‹œ ์ด๋™์— ๋Œ€ํ•œ ์ •๋ถ€์™€ ์‹œ๋ฏผ๋“ค์˜ ๊ธฐ๋Œ€ ์ฐจ์ด๋ฅผ ๋ฉ”์šฐ๊ธฐ ์œ„ํ•ด ๊ฒฐํ•ฉ๋  ๊ฒƒ์ด๋‹ค.Chapter 1. Introduction 10 1.1 Research Background 10 1.2 Indonesia New Capital Feasibility 12 1.3 Problem Description 16 1.4 Research Objectives 20 1.5 Research Questions 20 1.6 Research Outline 21 1.7 Contribution 22 Chapter 2. Smart City Initiatives Trends and Future Urban Mobility: A Literature Review 25 2.1 Smart City Development 25 2.2 Smart City Concept 26 2.2.1 Smart City Definition 28 2.2.2 Smart City Initiatives Trends 33 2.3 Future Urban Mobility Concept 34 2.3.1 Pedestrian and Walkability 37 2.3.2 Parking Management System 39 2.3.3 Innovative Mobility Services 40 2.3.3.1 Mobility as a Service (MaaS) 40 2.3.3.2 Automated Mobility on Demand (AmoD) 43 2.4 Public Value and Citizen Engagement 45 Chapter 3. Investigating Characteristics and Factors of Smart Mobility Project 48 3.1 Introduction 48 3.2 Literature Review 50 3.3 Research Methodology 59 3.3.1 Methodology Approach 59 3.3.2 Analytical Hierarchy Process (AHP) 60 3.4 Data Collection 62 3.5 Smart Mobility Characteristics 66 3.5.1 Accessibility 66 3.5.2 ICT/Technology 67 3.5.3 Infrastructure Availability 69 3.5.4 Delivery Channel 70 3.6 Smart Mobility Factors 71 3.6.1 Political & Regulatory 71 3.6.2 Socio-Economic 72 3.6.3 Digital Divide 73 3.7 Analysis Results 74 3.7.1 Characteristics Analysis Result 74 3.7.1.1 Characteristics Main Criteria Analysis 74 3.7.1.2 Characteristics Sub-Criteria Analysis 75 3.7.2 Factor Analysis Result 78 3.7.2.1 Factor Main Criteria Analysis 79 3.7.2.2 Factor Sub-Criteria Analysis 79 3.8 Analysis Result Summary and Discussion 81 3.8.1 Analysis Result Summary 81 3.8.2 Discussion 82 Chapter 4. Investigating Citizen Satisfaction Expectation on Future Mobility:Case of Indonesia 85 4.1 Introduction 85 4.2 Model Establishment and Hypothesis Development 89 4.3 Citizen Satisfaction Expectation 94 4.4 Safety and Security 95 4.4.1 Transport & Transit Safety 96 4.4.2 Transport & Transit Security 97 4.5 Comfort & Convenience 97 4.5.1 Public Transport and Density 98 4.5.2 Accessibility 99 4.5.3 Social Equity 99 4.5.4 Information 100 4.5.5 Comfort and Amenities 100 4.6 Government and Citizen Engagement 101 4.6.1 Vision & Strategy 102 4.6.2 Citizen Participation 103 4.6.3 Government Service & Transparency 103 4.7 Research Methodology 104 4.7.1 Structural Equation Model (SEM) 105 4.7.2 Covariance-based SEM (CB-SEM) and Partial Least Square SEM (PLS-SEM) 105 4.8 Survey and Data 107 4.9 Analysis Result 109 4.9.1 Measurement Model โ€“ Lower Order Construct 109 4.9.2 Indicator Reliability 110 4.9.3 Collinearity 112 4.9.4 Reliability Analysis 114 4.9.5 Convergent Validity 115 4.9.6 Discriminant Validity 116 4.9.7 Validating Higher Construct 124 4.9.8 Bootstrapping 124 4.9.9 Structural Model 125 4.10 Analysis Result Summary and Discussion 128 Chapter 5. Discussion and Policy Implication 131 5.1 Discussion 131 5.1.1 Availability, Accessibility, and Equity 134 5.1.2 Political and Regulatory Factors 135 5.1.3 The Digital Divide and Citizen Engagement 136 5.2 Policy Implication 137 5.3 Limitation & Future Research 139 Bibliography 141 Appendix 1: Smart Mobility Characteristics Questionnaire 167 Appendix 2: Smart Mobility Factors Questionnaire 177 Appendix 3: Citizen Satisfaction Expectation Questionnaire 184 Abstract (Korean) 191๋ฐ•

    Digital Identity Scheme

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ๊ธ€๋กœ๋ฒŒํ–‰์ •์ „๊ณต, 2023. 2. Junki Kim.๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ๋Š” ๋””์ง€ํ„ธ ์„œ๋น„์Šค์™€์˜ ์ƒํ˜ธ์ž‘์šฉ์—์„œ ๊ฐœ์ธ์„ ๊ณ ์œ ํ•˜๊ฒŒ ์ฐจ๋ณ„ํ™”ํ•˜๋Š” ์†์„ฑ์„ ์˜๋ฏธํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ „๋žต์€ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ๋ผ์ดํ”„์‚ฌ์ดํด์„ ๊ด€๋ฆฌํ•˜๋Š” ์ •์ฑ…, ๊ธฐ์ˆ , ์กฐ์ง ๋ฐ ํ”„๋กœ์„ธ์Šค์˜ ์ž˜ ์„ค๊ณ„๋œ ์ง‘ํ•ฉ์ฒด์ด๋‹ค. ์ด๋Š” ๋””์ง€ํ„ธ ๋ณ€ํ™˜์˜ ํ•„์ˆ˜ ์š”์†Œ์ด๋ฉฐ ๋””์ง€ํ„ธ ์‹ ๋ขฐ๋ฅผ ๊ฐ•ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ํ•ต์‹ฌ ์š”์†Œ์ด๋‹ค. ๊ทธ๋Ÿฐ ๋งฅ๋ฝ์—์„œ, ์ด ๋…ผ๋ฌธ์€ ๊ตญ๊ฐ€ ์ฐจ์›์—์„œ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ฒด๊ณ„๋ฅผ ๊ด€๋ฆฌํ•˜๋Š” ๋ฐ ์žˆ์–ด ์–ด๋ ค์›€์„ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์ •ํ™•์„ฑ, ํฌ๊ด„์„ฑ, ์•ˆ์ „์„ฑ, ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ๋””์ง€ํ„ธ ID์˜ ์ด์ ์€ ๊ณต๊ณต ๋ฐ ๋ฏผ๊ฐ„ ๋ถ€๋ฌธ, ์•„์นด๋ฐ๋ฏธ ๋ฐ ๊ตญ์ œ ์กฐ์ง์— ์˜ํ•ด ๋„๋ฆฌ ์ธ์‹๋˜๊ณ  ์žˆ๋‹ค. ์ด์™€ ๋”๋ถˆ์–ด COVID-19์˜ ์„ธ๊ณ„์ ์ธ ํ™•์‚ฐ์œผ๋กœ ์ธํ•ด ์‚ฌํšŒ์  ๊ฑฐ๋ฆฌ๋‘๊ธฐ ์กฐ์น˜์™€ ๋น„๋Œ€๋ฉด ๊ฑฐ๋ž˜๊ฐ€ ์ฆ๊ฐ€ํ•˜๋ฉด์„œ, ์šฐ๋ฆฌ๋Š” ์ •๋ถ€์™€ ๊ธฐ์—…์— ์˜ํ•ด ๊ฐœ๋ฐœ๋˜๋Š” ๋””์ง€ํ„ธ ์ธ์ฆ ํ”Œ๋žซํผ์ด ๋ฐœ์ „ํ•˜๋Š” ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ๋Œ€ํ•œ๋ฏผ๊ตญ(์ดํ•˜ ํ•œ๊ตญ)๊ณผ ํŽ˜๋ฃจ์™€ ๊ฐ™์€ ๋‚˜๋ผ๋“ค์€ ํ•ธ๋“œํฐ, ์ธ๊ณต์ง€๋Šฅ, ๋น…๋ฐ์ดํ„ฐ, ์ƒํ˜ธ์šด์šฉ์„ฑ, ๋ฐ์ดํ„ฐ์„ผํ„ฐ์™€ ๊ฐ™์€ ๋ถ€์ƒํ•œ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜์—ฌ ์‹๋ณ„ ๋ฐ ์ธ์ฆ ํ”„๋กœ์„ธ์Šค์˜ ํšจ์œจ์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ์„œ๋กœ ๋‹ค๋ฅธ ์ข…๋ฅ˜์˜ ์ด๋‹ˆ์…”ํ‹ฐ๋ธŒ์™€ ํ”Œ๋žซํผ์„ ๊ฐœ๋ฐœ, ์‹œํ–‰ํ•˜๊ณ  ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ํ˜„์žฌ๊นŒ์ง€ ์ •๋ถ€24๋ฅผ ์ „์ž์ •๋ถ€ ๊ณต์‹ํฌํ„ธ๋กœ, ๋””์ง€ํ„ธ์›ํŒจ์Šค(Digital ONEPASS)๋ฅผ ๋””์ง€ํ„ธ์ธ์ฆํ”Œ๋žซํผ์œผ๋กœ ๊ตฌํ˜„ํ•ด ์‹œ๋ฏผ ๋น„๋Œ€๋ฉด ์ธ์ฆ์ด ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ฃผ๋ฏผ๋“ฑ๋ก์ œ๋„(RRS)๋„ ํ•œ๊ตญ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ œ๋„์˜ ํ•ต์‹ฌ์š”์†Œ๋กœ ์ž๋ฆฌ๋งค๊น€ํ•˜๊ณ  ์žˆ๋‹ค. ์ด์™€ ๋น„์Šทํ•˜๊ฒŒ ํŽ˜๋ฃจ์˜ ๊ฒฝ์šฐ ๊ธฐ์กด์˜ ์ „์ž์ •๋ถ€ ์ ‘๊ทผ ๋ฐฉ์‹์ด ๋””์ง€ํ„ธ ์ •๋ถ€๋ผ๋Š” ์ƒˆ๋กœ์šด ํŒจ๋Ÿฌ๋‹ค์ž„์œผ๋กœ ๋ณ€๋ชจํ•˜์˜€๋‹ค๋Š” ๊ฒƒ๊ณผ, ๋””์ง€ํ„ธ ๊ธฐ์ˆ ์€ ๋” ์ด์ƒ ๊ธฐ์ˆ ์  ๋ฌธ์ œ๊ฐ€ ์•„๋‹ˆ๋ผ ์ •์น˜, ๋ฒ•๋ฅ , ํ˜‘๋ ฅ์  ๋ฌธ์ œ๋ผ๋Š” ์ดํ•ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ 2018๋…„ ๋””์ง€ํ„ธ ์ •๋ถ€๊ฐ€ ์ œ์ •๋˜์—ˆ๋‹ค. ๋””์ง€ํ„ธ ์ •์ฒด์„ฑ์„ ๊ฐ•ํ™”ํ•˜๊ธฐ ์œ„ํ•ด ๋‘ ๊ฐœ์˜ ๋””์ง€ํ„ธ ํ”Œ๋žซํผ์ด ์‹œํ–‰๋˜๊ณ  ์žˆ๋Š”๋ฐ, ํ•˜๋‚˜๋Š” ์‹œ๋ฏผ ์ง€ํ–ฅ์˜ ๋‹จ์ผ ๋””์ง€ํ„ธ ํ”Œ๋žซํผ(GOB.PE)์ด๋ฉฐ, ๋‹ค๋ฅธ ํ•˜๋‚˜๋Š” ๋””์ง€ํ„ธ ์‹ ์› ํ™•์ธ ๋ฐ ์ธ์ฆ์„ ์œ„ํ•œ ๊ตญ๊ฐ€ ํ”Œ๋žซํผ(ID)์ด๋‹ค. ๋‘ ํ”Œ๋žซํผ์€ ์ •๋ถ€์— ์˜ํ•ด ์œ ์ง€๋˜๊ณ  ๊ฐœ๋ฐœ๋œ๋‹ค. ์ด์ฒ˜๋Ÿผ ํ•œ๊ตญ๊ณผ ํŽ˜๋ฃจ์˜ ์ •์ฑ… ์‚ฌ์ด์— ์œ ์‚ฌ์ ์ด ์žˆ์ง€๋งŒ ๊ฒฐ๊ณผ๋Š” ๋‹ค๋ฅด๋‹ค. ์ „์ž์ •๋ถ€๊ฐœ๋ฐœ์ง€์ˆ˜(EDGI)์—์„œ ํ•œ๊ตญ์€ ์„ธ๊ณ„ 2์œ„, ํŽ˜๋ฃจ๋Š” 71์œ„, ํ•œ๊ตญ์€ ๋””์ง€ํ„ธ ์ธ์ฆ ํ”Œ๋žซํผ์ด ๊ตฌํ˜„๋˜์–ด ์žˆ๊ณ , ์ •๋ถ€24๋Š” ๋‹ค์–‘ํ•œ ์ธ์ฆ์„ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋‹ค. ONE PASS, KAKAO, ์‚ผ์„ฑ PASS ๋“ฑ ์‹œ๋ฏผ์„ ์œ„ํ•œ ๊ฐ„ํŽธํ•˜๊ณ  ํŽธ๋ฆฌํ•œ ์ธ์ฆ ๋ฐฉ๋ฒ•์ด ์‚ฌ์šฉ๋œ๋‹ค. ๋˜ํ•œ 2021๋…„๊นŒ์ง€ ์ •๋ถ€24๋ฅผ ํ†ตํ•ด ์˜จ๋ผ์ธ์œผ๋กœ ์ ‘์ˆ˜๋œ ์ฒญ์›์€ 13202๋งŒ 5035๊ฑด์— ๋‹ฌํ•˜๋ฉฐ, ์ฆ๋ช…์„œ์™€ ๋ฌธ์„œ๋Š” ์‹œ๋ฏผ์ด ์ง์ ‘ ํ”„๋ฆฐํ„ฐ๋ฅผ ํ†ตํ•ด ์ถœ๋ ฅํ–ˆ๋‹ค. ํŽ˜๋ฃจ์˜ ๊ฒฝ์šฐ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ „๋žต์€ ๋””์ง€ํ„ธ ์ •๋ถ€๋ฒ•์ด ๊ทœ์ œํ•˜๋Š” ๊ณต๊ณต๋ถ€๋ฌธ์˜ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ •๋ถ€๊ฐ€ ๊ธฐ๋ณธ์ ์œผ๋กœ ์ฃผ๋„ํ•˜๋Š” ์ง„ํ–‰ํ˜• ํ”„๋กœ์„ธ์Šค๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•œ๊ตญ์˜ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ „๋žต์ด ๊ฐœ์ธ์˜ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ์˜ ์ •ํ™•์„ฑ, ํฌ๊ด„์„ฑ, ๋ณด์•ˆ์„ฑ ๋ฐ ์‚ฌ์šฉ์„ฑ์„ ๊ฐ•ํ™”ํ•˜๊ธฐ ์œ„ํ•ด ์–ด๋–ค ์„ฑ๊ณผ๋ฅผ ๋‚ด๊ณ  ์žˆ๋Š”์ง€ ์ค‘์ ์ ์œผ๋กœ ์‚ดํŽด๋ณด๋ ค๊ณ  ํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์œ ์—”๊ณผ ๊ฒฝ์ œํ˜‘๋ ฅ๊ฐœ๋ฐœ๊ธฐ๊ตฌ(OECD)๊ฐ€ ์‚ฌ์šฉํ•˜๋Š” ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ ์šฉํ•œ ๋น„๊ต ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ํ™œ์šฉํ•ด ์œ ์‚ฌ์ ๊ณผ ์ฐจ์ด์ ์„ ๊ทœ๋ช…ํ•  ์˜ˆ์ •์ด๋‹ค. ํ•œ๊ตญ๊ณผ ํŽ˜๋ฃจ์˜ ๋น„๊ต ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ์‹œ์˜์ ์ ˆํ•˜๋‹ค. ์™œ๋ƒํ•˜๋ฉด ํŽ˜๋ฃจ๋Š” ํ•œ๊ตญ์˜ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ œ๋„์˜ ๋ชจ๋ฒ” ์‚ฌ๋ก€์™€ ์ข‹์€ ๊ตํ›ˆ์„ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๊ณ  ๋” ๋‚˜์€ ์ •์ฑ…๊ณผ ๊ฒฐ์ •์„ ์„ค๊ณ„ํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•œ๊ตญ๊ณผ ํŽ˜๋ฃจ์˜ ICT ์ „๋ฌธ๊ฐ€์™€ ์˜จ๋ผ์ธ ์ธํ„ฐ๋ทฐ๋ฅผ ํ†ตํ•ด ์–‘๊ตญ์˜ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ฒด๊ณ„์— ๋Œ€ํ•œ ์‹ฌ์ธต์ ์ธ ์ดํ•ด๋ฅผ ์ฐฝ์ถœํ•˜๋Š” ์ •์„ฑ์  ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ์ด 10๋ช…์˜ ์ „๋ฌธ๊ฐ€๋ฅผ ์ธํ„ฐ๋ทฐํ–ˆ๋Š”๋ฐ, ์ „๋ฌธ๊ฐ€์™€์˜ ์ธํ„ฐ๋ทฐ๋Š” ํ•œ๊ตญ๊ณผ ํŽ˜๋ฃจ์˜ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ง„ํ™”์— ๋Œ€ํ•œ ๊ฐœ์š”๋ฅผ ์ œ๊ณตํ•˜๊ณ  ํŽ˜๋ฃจ์˜ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ œ๋„ ๊ตฌํ˜„ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๊ณผ์ œ๋ฅผ ์‹๋ณ„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋””์ง€ํ„ธ ๊ณต๊ณต ์„œ๋น„์Šค์˜ ๊ฐœ๋ฐœ ๋ฐ ์ œ๊ณต์„ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•œ ๊ฐ•๋ ฅํ•˜๊ณ  ์ง€์†์ ์ธ ๋””์ง€ํ„ธ ๋ฆฌ๋”์‹ญ, ์‹œ์˜์ ์ ˆํ•œ ๋ฒ•์  ํ”„๋ ˆ์ž„์›Œํฌ, ํ˜„๋Œ€ ICT ๊ธฐ์ˆ ์ด๋ผ๋Š” ์„ธ ๊ฐ€์ง€ ์š”์†Œ์—์„œ ํฐ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚ฌ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์ด ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋˜ํ•œ ํŽ˜๋ฃจ์—์„œ ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ƒํƒœ๊ณ„๋ฅผ ์กฐ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ๋ชฉ์ ์œผ๋กœ ์ œ๋„์  ์ •๋น„๋ฅผ ํ•˜๊ณ , ๊ทœ์ œ๋ฅผ ๊ฐœ์„ ํ•˜๋ฉฐ, ์˜ˆ์‚ฐ์„ ์ตœ์ ํ™”ํ•œ๋‹ค๋ฉด ํฐ ์„ฑ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ฃผ์š” ํ‚ค์›Œ๋“œ: ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ, ๋””์ง€ํ„ธ ์ •๋ถ€, ๋””์ง€ํ„ธ ๋ณ€ํ™˜, ๋””์ง€ํ„ธ ์•„์ด๋ดํ‹ฐํ‹ฐ ์ „๋žตDigital identity is the collection of attributes that uniquely differentiates a person in his interaction with digital services. The literature and previous research suggest that it is an essential component to the digital transformation and a vital element for strengthening the digital trust. Currently, due to worldwide spread of COVID-19, which has accelerated the digital transition in the public and private sector, the non-face-to-face transactions have been increased, coupled with cybercrimes such as identity theft, private data leakage, fraud, among other cybercrimes. In this sense, governments should become aware of the importance of digital identity management, because it is increasingly embedded in everything we do in our digital and offline life (WEF, Identity in the Digital World a new chapter in the social contract, 2018, p. 9). To deal with those issues and leverage all the potential of digital identity at national level, many countries implement a Digital Identity Scheme, which is a well-designed and articulated collection of policies, business rules, technologies, organizations, and processes in charge of governing the digital identity lifecycle to promote a digital society. Hence, countries such as The Republic of Korea (hereinafter, Korea) and The Republic of Peru (hereinafter, Peru) have been developed and implemented different kind of policies, legal instruments, initiatives, and digital technologies to enhance accessibility, efficiency and security of the identification and authentication process, for instance, Korea has issued the Electronic Government Law and implemented cross-platforms such as Government24 (์ •๋ถ€24) as official electronic government portal, Digital ONEPASS (๋””์ง€ํ„ธ์›ํŒจ์Šค) as a digital authentication platform to enable a convenient no-face-to-face authentication of the citizens, Resident Registration System (RRS), as a fundamental national information system which manages and stores relevant personal information of Koreans, and Sharing Information System (ํ–‰์ •์ •๋ณด๊ณต๋™์ด์šฉ์‹œ์Šคํ…œ), as a interoperability platform to exchange information with governmental agencies. Moreover, Korea has a PKI Scheme which is divided into a National Public Key Infrastructure (NPKI), and a Government Public Key Infrastructure (GPKI). All these regulations, technologies and platforms are vital elements of the Korean Digital Identity Scheme. In the case of Peru, based on Law Nยฐ 26497 enacted in 1995, the government has been managing and maintaining the National Identification Registry of Peruvian. Moreover, since issuance of Digital Government Law in 2018, Peru has been implemented different kind of cross-platforms such as the Single Digital Platform for Citizen Orientation (GOB.PE), to offer one point of contact between government and citizens, National Interoperability Platform, to promote information exchange among public entities, the National Digital Government Platform, to provide cloud services to the public entities, and National Platform for Identification and Authentication of Digital Identity (ID.GOB.PE), to verify a persons identity. Although there are similarities, the outcomes are different, in the Electronic Government Development Index 2022, Korea is ranked 3rd in the world, while Peru is ranked 59th, from another side, in terms of digital identity, Korea has a digital identity ecosystem operating, for instance Government24 accepts several authentication methods which are easily and conveniently for the citizens such as ONEPASS, KAKAO, Samsung PASS, among others (MOIS, Status of Government 24, 2022). To 2021, almost 132,025,035 petitions were filed online through Government24 (MOIS, Status of Government 24, 2022). In the case of Peru, the digital identity scheme is an ongoing project, which is leading basically by the government, based on the Digital Government Law and its enforcement decree. In that vein, this research aims at understanding the components for governing and managing a Digital Identity Scheme in Korea and Peru and identifying the gap between them. Therefore, in this study we are going to focus on how the Digital Identity Scheme of Korea is performing to strengthen accuracy, inclusiveness, security, and usability of digital identity of persons. We are going to establish the similarities and differences by using a comparison framework which is an adaptation of the frameworks used by the United Nations (UN), International Telecommunication Union (UIT) and Organization for Economic Cooperation and Development (OECD). Additionally, in this moment, undertaking a comparison study between Korea and Peru is a relevant work, because Peru is implementing transversal digital government platforms based on the Digital Government Law, and based on that we are dealing with cybercrimes and digital threats, that is why we can learn of the best practices and good lessons of the Digital Identity Scheme in Korea and design better policies and decisions for Peruvian implementation. This research was carried out by using a qualitative research method which involved online interviews with ICT specialists from Korea and Peru to generate an in-depth understanding of the digital identity scheme of both countries. A total of ten specialists were interviewed. Interviews provide an overview of the digital identity evolution in Korea and allow me to identify challenges and policy recommendations in the implementation process of Digital Identity Scheme in Peru. Based on the results the big differences are integrated in three factors: strong and continuous digital leadership, timely legal framework, and modern ICT technology to support development and public services rendering. However, the results also suggest that it is possible to get big achievements on the Digital Identity Scheme in Peru, making institutional arrangements, enhancing digital regulation and optimizing the budget with the purpose to create a sustainable digital identity ecosystem.ABSTRACT 5 LIST OF ABBREVIATIONS 9 LIST OF TABLES 9 CHAPTER 1: INTRODUCTION 12 1.1 STUDY BACKGROUND 12 1.2 BACKGROUND OF THE COUNTRIES 20 1.3 THEORETICAL BACKGROUND 27 1.4 PURPOSE OF THE RESEARCH 39 CHAPTER 2. KEY CONCEPTS AND FRAMEWORK 43 CHAPTER 3: LITERATURE REVIEW 77 CHAPTER 4: DIGITAL IDENTITY IN KOREA AND PERU 86 4.1 LEGAL FRAMEWORK 86 4.2 TECHNOLOGY 100 4.3 GOVERNANCE AND LEADERSHIP 116 4.4 BUDGET 120 4.5 MARKET 122 4.6 FINDINGS 122 CHAPTER 5: CONCLUSIONS 132 5.1 SUMMARY OF THE THESIS 132 5.2 POLICY COMPARISON 143 5.3 POLICY RECOMMENDATIONS 145 5.4 LIMITATIONS OF THE RESEARCH 150 REFERENCES 152 APPENDICES 158 APPENDIX 1. QUESTIONNAIRE 158 APPENDIX 2. MATRIZ OF COMPARISON 167์„

    ๋™์˜์ƒ ์† ์‚ฌ๋žŒ ๋™์ž‘์˜ ๋ฌผ๋ฆฌ ๊ธฐ๋ฐ˜ ์žฌ๊ตฌ์„ฑ ๋ฐ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ์ด์ œํฌ.In computer graphics, simulating and analyzing human movement have been interesting research topics started since the 1960s. Still, simulating realistic human movements in a 3D virtual world is a challenging task in computer graphics. In general, motion capture techniques have been used. Although the motion capture data guarantees realistic result and high-quality data, there is lots of equipment required to capture motion, and the process is complicated. Recently, 3D human pose estimation techniques from the 2D video are remarkably developed. Researchers in computer graphics and computer vision have attempted to reconstruct the various human motions from video data. However, existing methods can not robustly estimate dynamic actions and not work on videos filmed with a moving camera. In this thesis, we propose methods to reconstruct dynamic human motions from in-the-wild videos and to control the motions. First, we developed a framework to reconstruct motion from videos using prior physics knowledge. For dynamic motions such as backspin, the poses estimated by a state-of-the-art method are incomplete and include unreliable root trajectory or lack intermediate poses. We designed a reward function using poses and hints extracted from videos in the deep reinforcement learning controller and learned a policy to simultaneously reconstruct motion and control a virtual character. Second, we simulated figure skating movements in video. Skating sequences consist of fast and dynamic movements on ice, hindering the acquisition of motion data. Thus, we extracted 3D key poses from a video to then successfully replicate several figure skating movements using trajectory optimization and a deep reinforcement learning controller. Third, we devised an algorithm for gait analysis through video of patients with movement disorders. After acquiring the patients joint positions from 2D video processed by a deep learning network, the 3D absolute coordinates were estimated, and gait parameters such as gait velocity, cadence, and step length were calculated. Additionally, we analyzed the optimization criteria of human walking by using a 3D musculoskeletal humanoid model and physics-based simulation. For two criteria, namely, the minimization of muscle activation and joint torque, we compared simulation data with real human data for analysis. To demonstrate the effectiveness of the first two research topics, we verified the reconstruction of dynamic human motions from 2D videos using physics-based simulations. For the last two research topics, we evaluated our results with real human data.์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์Šค์—์„œ ์ธ๊ฐ„์˜ ์›€์ง์ž„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ๋ถ„์„์€ 1960 ๋…„๋Œ€๋ถ€ํ„ฐ ๋‹ค๋ฃจ์–ด์ง„ ํฅ๋ฏธ๋กœ์šด ์—ฐ๊ตฌ ์ฃผ์ œ์ด๋‹ค. ๋ช‡ ์‹ญ๋…„ ๋™์•ˆ ํ™œ๋ฐœํ•˜๊ฒŒ ์—ฐ๊ตฌ๋˜์–ด ์™”์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , 3์ฐจ์› ๊ฐ€์ƒ ๊ณต๊ฐ„ ์ƒ์—์„œ ์‚ฌ์‹ค์ ์ธ ์ธ๊ฐ„์˜ ์›€์ง์ž„์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜๋Š” ์—ฐ๊ตฌ๋Š” ์—ฌ์ „ํžˆ ์–ด๋ ต๊ณ  ๋„์ „์ ์ธ ์ฃผ์ œ์ด๋‹ค. ๊ทธ๋™์•ˆ ์‚ฌ๋žŒ์˜ ์›€์ง์ž„ ๋ฐ์ดํ„ฐ๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด์„œ ๋ชจ์…˜ ์บก์ณ ๊ธฐ์ˆ ์ด ์‚ฌ์šฉ๋˜์–ด ์™”๋‹ค. ๋ชจ์…˜ ์บก์ฒ˜ ๋ฐ์ดํ„ฐ๋Š” ์‚ฌ์‹ค์ ์ธ ๊ฒฐ๊ณผ์™€ ๊ณ ํ’ˆ์งˆ ๋ฐ์ดํ„ฐ๋ฅผ ๋ณด์žฅํ•˜์ง€๋งŒ ๋ชจ์…˜ ์บก์ณ๋ฅผ ํ•˜๊ธฐ ์œ„ํ•ด์„œ ํ•„์š”ํ•œ ์žฅ๋น„๋“ค์ด ๋งŽ๊ณ , ๊ทธ ๊ณผ์ •์ด ๋ณต์žกํ•˜๋‹ค. ์ตœ๊ทผ์— 2์ฐจ์› ์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ ์‚ฌ๋žŒ์˜ 3์ฐจ์› ์ž์„ธ๋ฅผ ์ถ”์ •ํ•˜๋Š” ์—ฐ๊ตฌ๋“ค์ด ๊ด„๋ชฉํ•  ๋งŒํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ปดํ“จํ„ฐ ๊ทธ๋ž˜ํ”ฝ์Šค์™€ ์ปดํ“จํ„ฐ ๋น„์ ผ ๋ถ„์•ผ์˜ ์—ฐ๊ตฌ์ž๋“ค์€ ๋น„๋””์˜ค ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ๋‹ค์–‘ํ•œ ์ธ๊ฐ„ ๋™์ž‘์„ ์žฌ๊ตฌ์„ฑํ•˜๋ ค๋Š” ์‹œ๋„๋ฅผ ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋“ค์€ ๋น ๋ฅด๊ณ  ๋‹ค์ด๋‚˜๋ฏนํ•œ ๋™์ž‘๋“ค์€ ์•ˆ์ •์ ์œผ๋กœ ์ถ”์ •ํ•˜์ง€ ๋ชปํ•˜๋ฉฐ ์›€์ง์ด๋Š” ์นด๋ฉ”๋ผ๋กœ ์ดฌ์˜ํ•œ ๋น„๋””์˜ค์— ๋Œ€ํ•ด์„œ๋Š” ์ž‘๋™ํ•˜์ง€ ์•Š๋Š”๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„๋””์˜ค๋กœ๋ถ€ํ„ฐ ์—ญ๋™์ ์ธ ์ธ๊ฐ„ ๋™์ž‘์„ ์žฌ๊ตฌ์„ฑํ•˜๊ณ  ๋™์ž‘์„ ์ œ์–ดํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ € ์‚ฌ์ „ ๋ฌผ๋ฆฌํ•™ ์ง€์‹์„ ์‚ฌ์šฉํ•˜์—ฌ ๋น„๋””์˜ค์—์„œ ๋ชจ์…˜์„ ์žฌ๊ตฌ์„ฑํ•˜๋Š” ํ”„๋ ˆ์ž„ ์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๊ณต์ค‘์ œ๋น„์™€ ๊ฐ™์€ ์—ญ๋™์ ์ธ ๋™์ž‘๋“ค์— ๋Œ€ํ•ด์„œ ์ตœ์‹  ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ๋™์›ํ•˜์—ฌ ์ถ”์ •๋œ ์ž์„ธ๋“ค์€ ์บ๋ฆญํ„ฐ์˜ ๊ถค์ ์„ ์‹ ๋ขฐํ•  ์ˆ˜ ์—†๊ฑฐ๋‚˜ ์ค‘๊ฐ„์— ์ž์„ธ ์ถ”์ •์— ์‹คํŒจํ•˜๋Š” ๋“ฑ ๋ถˆ์™„์ „ํ•˜๋‹ค. ์šฐ๋ฆฌ๋Š” ์‹ฌ์ธต๊ฐ•ํ™”ํ•™์Šต ์ œ์–ด๊ธฐ์—์„œ ์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ ์ถ”์ถœํ•œ ํฌ์ฆˆ์™€ ํžŒํŠธ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ณด์ƒ ํ•จ์ˆ˜๋ฅผ ์„ค๊ณ„ํ•˜๊ณ  ๋ชจ์…˜ ์žฌ๊ตฌ์„ฑ๊ณผ ์บ๋ฆญํ„ฐ ์ œ์–ด๋ฅผ ๋™์‹œ์— ํ•˜๋Š” ์ •์ฑ…์„ ํ•™์Šตํ•˜์˜€๋‹ค. ๋‘˜ ์งธ, ๋น„๋””์˜ค์—์„œ ํ”ผ๊ฒจ ์Šค์ผ€์ดํŒ… ๊ธฐ์ˆ ์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•œ๋‹ค. ํ”ผ๊ฒจ ์Šค์ผ€์ดํŒ… ๊ธฐ์ˆ ๋“ค์€ ๋น™์ƒ์—์„œ ๋น ๋ฅด๊ณ  ์—ญ๋™์ ์ธ ์›€์ง์ž„์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์–ด ๋ชจ์…˜ ๋ฐ์ดํ„ฐ๋ฅผ ์–ป๊ธฐ๊ฐ€ ๊นŒ๋‹ค๋กญ๋‹ค. ๋น„๋””์˜ค์—์„œ 3์ฐจ์› ํ‚ค ํฌ์ฆˆ๋ฅผ ์ถ”์ถœํ•˜๊ณ  ๊ถค์  ์ตœ์ ํ™” ๋ฐ ์‹ฌ์ธต๊ฐ•ํ™”ํ•™์Šต ์ œ์–ด๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์—ฌ๋Ÿฌ ํ”ผ๊ฒจ ์Šค์ผ€์ดํŒ… ๊ธฐ์ˆ ์„ ์„ฑ๊ณต์ ์œผ๋กœ ์‹œ์—ฐํ•œ๋‹ค. ์…‹ ์งธ, ํŒŒํ‚จ์Šจ ๋ณ‘์ด๋‚˜ ๋‡Œ์„ฑ๋งˆ๋น„์™€ ๊ฐ™์€ ์งˆ๋ณ‘์œผ๋กœ ์ธํ•˜์—ฌ ์›€์ง์ž„ ์žฅ์• ๊ฐ€ ์žˆ๋Š” ํ™˜์ž์˜ ๋ณดํ–‰์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. 2์ฐจ์› ๋น„๋””์˜ค๋กœ๋ถ€ํ„ฐ ๋”ฅ๋Ÿฌ๋‹์„ ์‚ฌ์šฉํ•œ ์ž์„ธ ์ถ”์ •๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ํ™˜์ž์˜ ๊ด€์ ˆ ์œ„์น˜๋ฅผ ์–ป์–ด๋‚ธ ๋‹ค์Œ, 3์ฐจ์› ์ ˆ๋Œ€ ์ขŒํ‘œ๋ฅผ ์–ป์–ด๋‚ด์–ด ์ด๋กœ๋ถ€ํ„ฐ ๋ณดํญ, ๋ณดํ–‰ ์†๋„์™€ ๊ฐ™์€ ๋ณดํ–‰ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๊ณ„์‚ฐํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๊ทผ๊ณจ๊ฒฉ ์ธ์ฒด ๋ชจ๋ธ๊ณผ ๋ฌผ๋ฆฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ด์šฉํ•˜์—ฌ ์ธ๊ฐ„ ๋ณดํ–‰์˜ ์ตœ์ ํ™” ๊ธฐ์ค€์— ๋Œ€ํ•ด ํƒ๊ตฌํ•œ๋‹ค. ๊ทผ์œก ํ™œ์„ฑ๋„ ์ตœ์†Œํ™”์™€ ๊ด€์ ˆ ๋Œ๋ฆผํž˜ ์ตœ์†Œํ™”, ๋‘ ๊ฐ€์ง€ ๊ธฐ์ค€์— ๋Œ€ํ•ด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•œ ํ›„, ์‹ค์ œ ์‚ฌ๋žŒ ๋ฐ์ดํ„ฐ์™€ ๋น„๊ตํ•˜์—ฌ ๊ฒฐ๊ณผ๋ฅผ ๋ถ„์„ํ•œ๋‹ค. ์ฒ˜์Œ ๋‘ ๊ฐœ์˜ ์—ฐ๊ตฌ ์ฃผ์ œ์˜ ํšจ๊ณผ๋ฅผ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•ด, ๋ฌผ๋ฆฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์‚ฌ์šฉํ•˜์—ฌ ์ด์ฐจ์› ๋น„๋””์˜ค๋กœ๋ถ€ํ„ฐ ์žฌ๊ตฌ์„ฑํ•œ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์—ญ๋™์ ์ธ ์‚ฌ๋žŒ์˜ ๋™์ž‘๋“ค์„ ์žฌํ˜„ํ•œ๋‹ค. ๋‚˜์ค‘ ๋‘ ๊ฐœ์˜ ์—ฐ๊ตฌ ์ฃผ์ œ๋Š” ์‚ฌ๋žŒ ๋ฐ์ดํ„ฐ์™€์˜ ๋น„๊ต ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ํ‰๊ฐ€ํ•œ๋‹ค.1 Introduction 1 2 Background 9 2.1 Pose Estimation from 2D Video . . . . . . . . . . . . . . . . . . . . 9 2.2 Motion Reconstruction from Monocular Video . . . . . . . . . . . . 10 2.3 Physics-Based Character Simulation and Control . . . . . . . . . . . 12 2.4 Motion Reconstruction Leveraging Physics . . . . . . . . . . . . . . 13 2.5 Human Motion Control . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.5.1 Figure Skating Simulation . . . . . . . . . . . . . . . . . . . 16 2.6 Objective Gait Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.7 Optimization for Human Movement Simulation . . . . . . . . . . . . 17 2.7.1 Stability Criteria . . . . . . . . . . . . . . . . . . . . . . . . 18 3 Human Dynamics from Monocular Video with Dynamic Camera Movements 19 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.3 Pose and Contact Estimation . . . . . . . . . . . . . . . . . . . . . . 21 3.4 Learning Human Dynamics . . . . . . . . . . . . . . . . . . . . . . . 24 3.4.1 Policy Learning . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4.2 Network Training . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4.3 Scene Estimator . . . . . . . . . . . . . . . . . . . . . . . . 29 3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.1 Video Clips . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.2 Comparison of Contact Estimators . . . . . . . . . . . . . . . 33 3.5.3 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.5.4 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 Figure Skating Simulation from Video 42 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.3 Skating Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3.1 Non-holonomic Constraints . . . . . . . . . . . . . . . . . . 46 4.3.2 Relaxation of Non-holonomic Constraints . . . . . . . . . . . 47 4.4 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.5 Trajectory Optimization and Control . . . . . . . . . . . . . . . . . . 50 4.5.1 Trajectory Optimization . . . . . . . . . . . . . . . . . . . . 50 4.5.2 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5 Gait Analysis Using Pose Estimation Algorithm with 2D-video of Patients 61 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.2.1 Patients and video recording . . . . . . . . . . . . . . . . . . 63 5.2.2 Standard protocol approvals, registrations, and patient consents 66 5.2.3 3D Pose estimation from 2D video . . . . . . . . . . . . . . . 66 5.2.4 Gait parameter estimation . . . . . . . . . . . . . . . . . . . 67 5.2.5 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . 68 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.3.1 Validation of video-based analysis of the gait . . . . . . . . . 68 5.3.2 gait analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.4.1 Validation with the conventional sensor-based method . . . . 75 5.4.2 Analysis of gait and turning in TUG . . . . . . . . . . . . . . 75 5.4.3 Correlation with clinical parameters . . . . . . . . . . . . . . 76 5.4.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.5 Supplementary Material . . . . . . . . . . . . . . . . . . . . . . . . . 77 6 Control Optimization of Human Walking 80 6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.2.1 Musculoskeletal model . . . . . . . . . . . . . . . . . . . . . 82 6.2.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.2.3 Control co-activation level . . . . . . . . . . . . . . . . . . . 83 6.2.4 Push-recovery experiment . . . . . . . . . . . . . . . . . . . 84 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 7 Conclusion 90 7.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Docto

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    ๊ฐ„ํ˜ธ๋Œ€ํ•™/์„์‚ฌ๋ณธ ์—ฐ๊ตฌ๋Š” ์„œ์šธ์‹œ ์ผ๋ถ€ ์ง€์—ญ์— ๊ฑฐ์ฃผํ•˜๊ณ  ์žˆ๋Š” 65์„ธ ์ด์ƒ ๋…ธ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ์‹œํ–‰ ๊ด€๋ จ ์š”์ธ์„ ๊ทœ๋ช…ํ•˜๊ธฐ ์œ„ํ•œ ํšก๋‹จ์  ์กฐ์‚ฌ์—ฐ๊ตฌ๋กœ์„œ, ๊ฑด๊ฐ•์‹ ๋…๋ชจ๋ธ์„ ์ด๋ก ์  ๊ธฐํ‹€๋กœ ์ ์šฉํ•˜์˜€๋‹ค. ์ž๋ฃŒ ์ˆ˜์ง‘์€ ์ผ ์ง€์—ญ์— ๊ฑฐ์ฃผํ•˜๋Š” 65์„ธ ๋…ธ์ธ ์ธ๊ตฌ๋ฅผ ์ž„์˜ ํ‘œ์ถœํ•˜์—ฌ ๊ตฌ์กฐํ™”๋œ ์„ค๋ฌธ์ง€๋ฅผ ํ†ตํ•œ ์ผ๋Œ€์ผ ๋ฉด๋‹ด ๋ฐฉ์‹์œผ๋กœ ์‹œํ–‰ํ•˜์˜€๊ณ , ์ด 121๋ถ€๊ฐ€ ํ†ต๊ณ„๋ถ„์„์— ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์กฐ์‚ฌํ•œ ์ž๋ฃŒ์—๋Š” ์ผ๋ฐ˜์  ํŠน์„ฑ, ์น˜๋งค ๊ด€๋ จ ์ง€์‹, ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ๊ฑด๊ฐ•์‹ ๋… ๋ฐ ์ž๊ธฐํšจ๋Šฅ๊ฐ, ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ์‹œํ–‰ ๊ด€๋ จ ํ–‰๋™์˜ ๊ณ„๊ธฐ๊ฐ€ ํฌํ•จ๋˜์—ˆ๋‹ค. ์น˜๋งค ๊ด€๋ จ ์ง€์‹์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด ์กฐํ˜„์˜ค(1999)๊ฐ€ ๊ฐœ๋ฐœํ•œ ์น˜๋งค ์ง€์‹ ๋„๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ๊ฑด๊ฐ•์‹ ๋… ๋ฐ ์ž๊ธฐํšจ๋Šฅ๊ฐ์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด Galvin ๋“ฑ(2006)์ด ๊ฐœ๋ฐœํ•œ ์„ค๋ฌธ์ง€๋ฅผ ํ•œ๊ตญ์–ด๋กœ ๋ฒˆ์•ˆํ•˜์—ฌ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ž๋ฃŒ๋ถ„์„์€ SPSS statistics 23.0์„ ์ด์šฉํ•˜์—ฌ ๊ธฐ์ˆ ํ†ต๊ณ„, ๋นˆ๋„๋ถ„์„, ์นด์ด์ œ๊ณฑ๊ฒ€์ •, t ๊ฒ€์ •, ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. 1. ๋Œ€์ƒ์ž์˜ ์„ฑ๋ณ„์€ ์—ฌ์„ฑ์ด 61.2%, ํ‰๊ท  ์—ฐ๋ น 77.26(ยฑ6.30)์„ธ ์˜€๊ณ , ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ์‹œํ–‰๊ตฐ 58.7%, ๋ฏธ์‹œํ–‰๊ตฐ 41.3%์ด์—ˆ๋‹ค. ์„ฑ๋ณ„์€ ์‹œํ–‰๊ตฐ์˜ ๊ฒฝ์šฐ ์—ฌ์ž๊ฐ€ 74.6%๋กœ ๋†’์€ ๋นˆ๋„๋ฅผ ์ฐจ์ง€ํ•˜์˜€๊ณ  ๋ฏธ์‹œํ–‰๊ตฐ์˜ ๊ฒฝ์šฐ ๋‚จ์ž๊ฐ€ 58.0%๋กœ ์—ฌ์„ฑ๋ณด๋‹ค ๋น„์œจ์ด ๋†’์•˜๋‹ค(p<.001). ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ๋ฏธ์‹œํ–‰๊ตฐ์˜ ์Œ์ฃผ์œจ์ด ์‹œํ–‰๊ตฐ๋ณด๋‹ค ๋†’์•˜์œผ๋ฉฐ(p<.05), ํก์—ฐ์œจ ๋˜ํ•œ ๋ฏธ์‹œํ–‰๊ตฐ์—์„œ ๋” ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค(p<.01). 2. ๋Œ€์ƒ์ž์˜ ์น˜๋งค ๊ด€๋ จ ์ง€์‹ ์ •๋„๋Š” ์ „์ฒด ํ‰๊ท  9.29ยฑ2.81์ (์ ์ˆ˜ ๋ฒ”์œ„ 0-16์ )์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ์‹œํ–‰๊ตฐ 9.77ยฑ2.76์ , ๋ฏธ์‹œํ–‰๊ตฐ 8.60ยฑ2.75์ ์œผ๋กœ ์‹œํ–‰๊ตฐ์˜ ์ ์ˆ˜๊ฐ€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜๋‹ค(p<.05). 3. ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ๊ฑด๊ฐ•์‹ ๋…์€ ์ง€๊ฐ๋œ ๋ฏผ๊ฐ์„ฑ, ์ง€๊ฐ๋œ ์‹ฌ๊ฐ์„ฑ, ์ง€๊ฐ๋œ ์œ ์ต์„ฑ, ์ง€๊ฐ๋œ ์žฅ์• ์„ฑ์˜ ๊ฒฝ์šฐ ์‹œํ–‰๊ตฐ๊ณผ ๋ฏธ์‹œํ–‰๊ตฐ์—์„œ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋‚ด์ง€ ์•Š์•˜๋‹ค. ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ์ž๊ธฐํšจ๋Šฅ๊ฐ์€ ์‹œํ–‰๊ตฐ์—์„œ 27.97ยฑ4.14์ , ๋ฏธ์‹œํ–‰๊ตฐ์—์„œ๋Š” 24.64ยฑ5.51์ ์œผ๋กœ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค(p<.001). 4. ํ–‰๋™์˜ ๊ณ„๊ธฐ๋Š” ์ฃผ๋ณ€์— ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•œ ๊ฐ€์กฑ ๋˜๋Š” ์นœ๊ตฌ์˜ ์œ ๋ฌด์—์„œ ์‹œํ–‰๊ตฐ์˜ 42.3%, ๋ฏธ์‹œํ–‰๊ตฐ์˜ 18.0%๊ฐ€ ์žˆ๋‹ค๊ณ  ์‘๋‹ตํ•˜์˜€์œผ๋ฉฐ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๋‹ค(p<.01). 5. ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ์‹œํ–‰์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์€ ์„ฑ๋ณ„, ์ฃผ๋ณ€์— ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•œ ๊ฐ€์กฑ์ด๋‚˜ ์นœ๊ตฌ์˜ ์œ ๋ฌด, ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ์ž๊ธฐํšจ๋Šฅ๊ฐ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์—ฌ์„ฑ์ด ๋‚จ์„ฑ๋ณด๋‹ค ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•  ํ™•๋ฅ ์ด ๋†’์•˜๊ณ (OR=4.922, p<.01), ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•œ ๊ฐ€์กฑ์ด๋‚˜ ์นœ๊ตฌ๊ฐ€ ์žˆ๋Š” ๊ฒฝ์šฐ์— ๋†’์•˜์œผ๋ฉฐ(OR=4.599, p<.01), ์ž๊ธฐํšจ๋Šฅ๊ฐ์ด ๋†’์„์ˆ˜๋ก ๊ฒ€์‚ฌ ์‹œํ–‰ ํ™•๋ฅ ์ด ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค(OR=2.850, p<.01). ์ด์ƒ์˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋…ธ์ธ์˜ ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ์‹œํ–‰์œจ์„ ๋†’์ด๊ธฐ ์œ„ํ•˜์—ฌ ๊ฐ€์กฑ ๋˜๋Š” ์นœ๊ตฌ์˜ ๋„คํŠธ์›Œํฌ๋ฅผ ํ™œ์šฉํ•œ ์ ‘๊ทผ๊ณผ ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ์— ๋Œ€ํ•œ ์ž๊ธฐํšจ๋Šฅ๊ฐ์„ ๋†’์—ฌ์ฃผ๋Š” ๊ต์œก ๋ฐ ํ”„๋กœ๊ทธ๋žจ์ด ํ•„์š”ํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ์‹œํ–‰ ์˜ํ–ฅ ์š”์ธ์„ ํŒŒ์•…ํ•œ ์ฃผ์š” ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด, ํ–ฅํ›„ ์ง€์—ญ์‚ฌํšŒ ๋…ธ์ธ์˜ ์น˜๋งค ์„ ๋ณ„๊ฒ€์‚ฌ ์‹œํ–‰์œจ์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ํšจ์œจ์ ์ธ ํ”„๋กœ๊ทธ๋žจ ๊ตฌ์ถ•๊ณผ ๊ต์œก์„ ์œ„ํ•œ ๊ธฐ์ดˆ ์ž๋ฃŒ๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.ope

    ์‹œ๋ฉ˜ํŠธ ํ†ต ์†์˜ ํŽธ์ง€

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    ่‘‰ๅฑฑๅ˜‰ๆจนใ€Žใ‚ปใƒกใƒณใƒˆๆจฝใฎไธญใฎๆ‰‹็ด™ใ€Translations Korean ํ•œ๊ตญ

    ๋ฌผ์„ ์†Œ์žฌ๋กœ ํ•œ ์ธ์ƒ์ฃผ์˜ ํ”ผ์•„๋…ธ์Œ์•… ์—ฐ๊ตฌ : Liszt, Ravel, Debussy ์ž‘ํ’ˆ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์Œ์•…ํ•™๊ณผ ํ”ผ์•„๋…ธ์ „๊ณต,1998.Maste
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