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    ์ ์‘ํ˜• ํŒŒํ‹ฐํด ํ•„ํ„ฐ์™€ ์•™์ƒ๋ธ” ํ•™์Šต ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ์ง€์ง„๋™ ์‘๋‹ต ๊ธฐ๋ฐ˜ ๊ตฌ์กฐ ์‹œ์Šคํ…œ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2019. 2. ์†ก์ค€ํ˜ธ.๊ฒฝ์ฃผยทํฌํ•ญ ์ง€์ง„ ๋ฐœ์ƒ ์ดํ›„ ์‚ฌํšŒ ๊ธฐ๋ฐ˜ ์‹œ์„ค์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ์‚ฌํ›„ ํ‰๊ฐ€์™€ ๋ชจ๋‹ˆํ„ฐ๋ง์— ๋Œ€ํ•œ ์‚ฌํšŒ์  ์š”๊ตฌ๊ฐ€ ์ ์ฐจ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ •ํ™•๋„๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•ด์„  ์‹œ์Šคํ…œ ๋ฐฉ์ •์‹, ์ฆ‰ ์‹œ์Šคํ…œ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์ •ํ™•ํ•œ ์ถ”์ •์„ ํ†ตํ•œ ์‹œ์Šคํ…œ ์‹๋ณ„์ด ํ•„์ˆ˜์ ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์‹œ์Šคํ…œ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์ง์ ‘ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ๋งŽ์€ ์‹œ๊ฐ„๊ณผ ๋น„์šฉ์ด ์†Œ์š”๋˜์–ด, ์žฌ๋‚œ ์žฌํ•ด ์‹œ ๋น ๋ฅธ ๋Œ€์ฒ˜๊ฐ€ ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ๋”ฐ๋ผ์„œ, ์ œํ•œ๋œ ๋ฐ์ดํ„ฐ๋กœ ์‹œ์Šคํ…œ์„ ์ถ”์ •ํ•˜๋Š” ๊ฐ„์ ‘ ์ถ”์ • ๋ฐฉ๋ฒ•์ด ๊ฐœ๋ฐœ๋˜์–ด์™”๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋งŽ์€ ์—ฐ๊ตฌ์—์„œ ์ž๋ฃŒ ๋™ํ™”์— ๊ธฐ๋ฐ˜ํ•œ ๊ธฐ๊ณ„ ํ•™์Šต ๋ฐฉ๋ฒ•, ๊ทธ ์ค‘์—์„œ๋„ ๋น„์„ ํ˜•์„ฑ์ด ๊ฐ•ํ•œ ์‹œ์Šคํ…œ์„ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด ๊ฐœ๋ฐœ๋œ ํŒŒํ‹ฐํด ํ•„ํ„ฐ๋ฅผ ์‚ฌ์šฉํ–ˆ๋‹ค. ํŒŒํ‹ฐํด ํ•„ํ„ฐ๋Š” ์ƒ˜ํ”Œ๋ง์— ๊ธฐ๋ฐ˜ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์‹œ์Šคํ…œ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •์—์„œ ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋‹ฌ์„ฑํ–ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ง€์ง„๊ณผ ๊ฐ™์€ ๊ทนํ•œ ์ƒํ™ฉ ์ค‘์— ๋ฐœ์ƒํ•˜๋Š” ๊ฐ•์„ฑ ์—ดํ™”์™€ ๊ฐ™์€ ๊ตฌ์กฐ๋ฌผ์˜ ์†์ƒ์€ ์‹œ์Šคํ…œ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ๊ฐ‘์ž‘์Šค๋Ÿฐ ๋ณ€ํ™”๋ฅผ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ๊ธฐ์กด์˜ ํŒŒํ‹ฐํด ํ•„ํ„ฐ ๋ฐฉ๋ฒ•์€ ์‹œ์Šคํ…œ ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ์‹œ๊ฐ„์— ๋”ฐ๋ผ ์ผ์ •ํ•˜๋‹ค๊ณ  ๊ฐ€์ •ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์ถ”์ • ์„ฑ๋Šฅ์ด ๋–จ์–ด์ง„๋‹ค๋Š” ๋‹จ์ ์ด ์žˆ๋‹ค. ์„ ํ–‰ ์—ฐ๊ตฌ์—์„œ ๊ธ‰๊ฒฉํžˆ ๋ณ€ํ™”ํ•˜๋Š” ์‹œ์Šคํ…œ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๊ธฐ์กด ํŒŒํ‹ฐํด ํ•„ํ„ฐ์— ๋น„ํ•ด ์ •ํ™•ํ•˜๊ฒŒ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด ์ ์‘ํ˜• ํŒŒํ‹ฐํด ํ•„ํ„ฐ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ ์‘ํ˜• ํŒŒํ‹ฐํด ํ•„ํ„ฐ๋Š” ๊ฐ•์„ฑ ์—ดํ™”์™€ ๊ฐ™์ด ์‹œ๊ฐ„์ด ๋”ฐ๋ผ ๋ณ€ํ•˜๋Š” ์‹œ์Šคํ…œ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด, ์ƒํ™ฉ์— ๋”ฐ๋ผ ์ธ์œ„์ ์œผ๋กœ ํŒŒํ‹ฐํด ํ•„ํ„ฐ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ • ๋…ธ์ด์ฆˆ๋ฅผ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ์ƒ์ˆ˜๋ฅผ ๋„์ž…ํ•˜์—ฌ ํŒŒํ‹ฐํด ํ•„ํ„ฐ์˜ ์ˆ˜๋ ด ์†๋„๋ฅผ ์ฆ๊ฐ€์‹œํ‚จ ์ถ”์ • ๋ฐฉ๋ฒ•์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„ , ์„ ํ–‰ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ๋ฐœ์ „์‹œ์ผœ, ๊ฐ ์ž์œ ๋„์—์„œ ์–ป์€ ์ธก์ •์น˜๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ฐ๊ฐ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ • ๋…ธ์ด์ฆˆ์— ๋‹ค๋ฅธ ์ƒ์ˆ˜๊ฐ’์„ ํ• ๋‹นํ•˜๋Š” ์ˆ˜์ • ์ ์‘ํ˜• ํŒŒํ‹ฐํด ํ•„ํ„ฐ๋ฅผ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ ์‘ํ˜• ํŒŒํ‹ฐํด ํ•„ํ„ฐ๋Š” ์ถ”์ •์˜ ํŽธํ–ฅ์€ ๊ฐ์†Œํ•˜์ง€๋งŒ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ • ๋…ธ์ด์ฆˆ์˜ ์ฆ๊ฐ€๋กœ ์ธํ•ด, ์ถ”์ •์˜ ๋ถ„์‚ฐ์ด ์ฆ๊ฐ€ํ•˜๋Š” ๋ฌธ์ œ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ๋ณธ ์—ฐ๊ตฌ๋Š” ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ๊ฐ๊ฐ์˜ ๋ณ‘๋ ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ ๊ฐ๊ฐ ์–ป์€ ์ถ”์ •์น˜๋ฅผ ์กฐํ•ฉํ•˜์—ฌ ์ตœ์ข… ์ถ”์ •์น˜๋ฅผ ๊ตฌํ•˜๋Š” ์•™์ƒ๋ธ” ํ•™์Šต๋ฒ•์„ ๋„์ž…ํ–ˆ๋‹ค. ๊ทธ ์ค‘์—์„œ, ๋ณ‘๋ ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ ๋™์ผํ•œ ๊ฐ€์ค‘์น˜๋กœ ์ถ”์ •์น˜๋ฅผ ์กฐํ•ฉํ•˜์—ฌ ์ตœ์ข… ์ถ”์ •์น˜๋ฅผ ์–ป๋Š” Bootstrap Aggregating ๋˜๋Š” Bagging ๋ฐฉ๋ฒ•์„ ๋„์ž…ํ•˜์—ฌ ์ถ”์ •์˜ ๋ถ„์‚ฐ์„ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด, ๋ณด๋‹ค ์ •ํ™•ํ•˜๊ณ  ํšจ๊ณผ์ ์ธ ์‚ฌํ›„ ํ‰๊ฐ€ ๋ฐ ๋ชจ๋‹ˆํ„ฐ๋ง์ด ์ˆ˜ํ–‰๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ฉฐ, ๊ตฌ์กฐ๋ฌผ์˜ ์†์ƒ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ์ง„๋‹จ์„ ํ†ตํ•ด ๊ตฌ์กฐ๋ฌผ์˜ ์‘๋‹ต๊ณผ ๊ฐ™์€ ์ œํ•œ๋œ ์ •๋ณด๋งŒ์œผ๋กœ ํšจ๊ณผ์ ์ธ ์œ ์ง€๊ด€๋ฆฌ ๋ฐ ๋ณด์ˆ˜๊ฐ€ ๊ฐ€๋Šฅํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Social demand for accurate post-evaluation and monitoring of infrastructure has been increasing since the earthquake in Gyeongju in 2016 and Pohang in 2017. To increase the accuracy of post-evaluation and monitoring, an accurate estimation of system equation, (i.e. system parameters) is required. Among other machine learning methods based on the data assimilation, a sampling-based particle filter was used to estimate systems with strong nonlinearity, which achieved high accuracy in estimation of system parameters. However, damage, such as stiffness degradation, that occurs during extreme events can cause sudden changes in the system parameters. The existing methods have shown poor performance in this case because they assume that the system parameters are constant over the time. In this study, an adaptive particle filter is introduced to accurately estimate system parameters that suddenly change in extreme events. The adaptive particle filter is intended to artificially increase the parameter estimation noise of the particle filter according to the situation in order to estimate the system parameters that change over time as damage occurs. Furthermore, we propose modified adaptive particle filter that allocates different parameter estimation noises to each degree of freedom based on measurements. However, the adaptive particle filter has the problem of increasing the variance of estimation. Therefore, this study introduces an ensemble learning method that obtains the final estimate by aggregating estimates from usable parallel algorithms. In this study, Bootstrap Aggregating or Bagging is used, which aggregates estimates with the same weight from parallel algorithms to obtain the final estimate. We expect that a more accurate and effective post-evaluation and monitoring of infrastructures can be carried out, and effective maintenance can be possible through accurate information about the damaged element obtained from the proposed method.1. Introduction 1 1.1. Research Background 1 1.2. Research Objectives and Scope 6 1.3. Outline 8 2. Theoretical Background 9 2.1. Estimation in the State Space 9 2.2 Particle Filter 13 2.2 Adaptive Particle Filter 21 3. Proposed Modified Adaptive Particle Filter with Ensemble Learning Method 26 3.1. Modified Adaptive Particle Filter 26 3.2 Ensemble Learning Method 31 4. Verification of Proposed Method 36 4.1.Numerical Example 36 4.1.1 Target Structural System 36 4.1.2 Ground Acceleration 38 4.1.3 Stiffness Degradation 39 4.2. Verification Results and Discussion 42 4.2.1 Original Particle Filter 42 4.2.2 Modified Adaptive Particle Filter 44 4.2.3 Modified Adaptive Particle Filter with Bagging 45 5. Conclusion 53 REFERENCE 55 ๊ตญ๋ฌธ ์ดˆ๋ก 58Maste

    ๋ด‰ํ•ฉ๋งค๊ฐœ ์ง€ํ˜ˆ๊ธฐ๊ตฌ ์‚ฌ์šฉ ํ›„ ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ ๊ด€๋ จ์š”์ธ

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    ๊ฑด๊ฐ•์ฆ์ง„๊ต์œก์ „๊ณต/์„์‚ฌ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ๋ด‰ํ•ฉ๋งค๊ฐœ ์ง€ํ˜ˆ๊ธฐ๊ตฌ๋Š” ํ˜ˆ๊ด€ ์กฐ์˜ ๊ฒ€์‚ฌ๋‚˜ ์ธํ„ฐ๋ฒค์…˜ ์‹œ์ˆ  ํ›„ ํ™˜์ž์˜ ์กฐ๊ธฐ ๋ณดํ–‰๊ณผ ํ•ญ์‘๊ณ ์š”๋ฒ• ์น˜๋ฃŒ ์ค‘์ธ ํ™˜์ž์˜ ํ•ฉ๋ณ‘์ฆ์„ ์ค„์ด๋Š”๋ฐ ์œ ์šฉํ•˜๋ฉฐ, ์žฌ์ฒœ์ž์˜ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋Š” ํ™˜์ž์—๊ฒŒ ์ฃผ๋กœ ์‚ฌ์šฉํ•œ๋‹ค. ๋ด‰ํ•ฉ์— ์‚ฌ์šฉ๋˜๋Š” ์žฌ๋ฃŒ๊ฐ€ ๋น„ํก์ˆ˜์„ฑ ๋ด‰ํ•ฉ์‚ฌ๋ฅผ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ์–ด, ์ด ๋ด‰ํ•ฉ์‚ฌ์˜ ์˜ค์—ผ์ด๋‚˜ ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ์˜ ๋ฐœ์ƒ์€ ๊ฐ์—ผ ๋ฐœ์ƒ์˜ ์›์ธ์ด ๋œ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ด‰ํ•ฉ๋งค๊ฐœ ์ง€ํ˜ˆ๊ธฐ๊ตฌ ์‚ฌ์šฉ์ด ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ•œ๋‹ค.๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ•์ผ๊ฐœ ๋Œ€ํ•™๋ณ‘์›์—์„œ 2013๋…„ 3์›” 1์ผ์—์„œ 2014๋…„ 8์›” 31์ผ ์‚ฌ์ด์— ํ˜ˆ๊ด€ ์กฐ์˜ ๊ฒ€์‚ฌ ๋˜๋Š” ์ธํ„ฐ๋ฒค์…˜ ์‹œ์ˆ ์„ ๋ฐ›์€ ํ™˜์ž ์ค‘ ๋ด‰ํ•ฉ๋งค๊ฐœ ์ง€ํ˜ˆ ๊ธฐ๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜๊ณ  ๊ฐ ํŠน์„ฑ์˜ ๋ณ€์ˆ˜ ๊ฐ’์ด ์ธก์ • ๊ฐ€๋Šฅํ•œ 460๋ช…์„ ๋Œ€์ƒ์ž๋กœ ์„ ์ •ํ•˜์˜€๋‹ค. ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ์˜ ๊ด€๋ จ์š”์ธ์„ ์ฐพ๊ธฐ ์œ„ํ•ด ๊ฐ ํ™˜์ž๊ฐ€ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์ผ๋ฐ˜์  ํŠน์„ฑ(์„ฑ๋ณ„, ๋‚˜์ด, ํก์—ฐ์—ฌ๋ถ€, ์Œ์ฃผ์—ฌ๋ถ€), ์ž„์ƒ์  ํŠน์„ฑ(๊ณ ํ˜ˆ์•• ์ง„๋‹จ์—ฌ๋ถ€, ๋‹น๋‡จ๋ณ‘ ์ง„๋‹จ์—ฌ๋ถ€, ๊ณ ์ง€ํ˜ˆ์ฆ ์ง„๋‹จ์—ฌ๋ถ€, ๋™๋งฅ๊ฒฝํ™” ์ง„๋‹จ์—ฌ๋ถ€, ์•” ์ง„๋‹จ์—ฌ๋ถ€), ํ•ด๋ถ€ํ•™์  ํŠน์„ฑ(ํ”ผํ•˜ ์กฐ์ง ๊นŠ์ด, ์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜), ๊ธฐ์ˆ ์  ํŠน์„ฑ(์ฒœ์ž๋ถ€์œ„ ํ”ผ๋ถ€ ์ ˆ๊ฐœ ๊ธธ์ด, ์‹œ์ˆ ์ž์˜ ๊ฒฝ๋ ฅ) ๋ณ€์ˆ˜๋ฅผ ์ •ํ•˜์—ฌ ํ›„ํ–ฅ์  ์กฐ์‚ฌ ํ›„ ๋ถ„์„ํ•˜์˜€๋‹ค.์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋Œ€์ƒ์ž์˜ ์„ฑ๋ณ„์€ ๋‚จ์„ฑ 336๋ช…, ์—ฌ์„ฑ 124๋ช…์ด์—ˆ์œผ๋ฉฐ, ํ‰๊ท  ์—ฐ๋ น์€ ๊ฐ๊ฐ 62์„ธ, 64์„ธ์˜€๋‹ค.๋‹จ๋ณ€์ˆ˜ ๋ถ„์„์—์„œ ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ๊ณผ ์œ ์˜ํ•œ ๊ด€๋ จ์„ฑ์„ ๋ณด์ธ ๋ณ€์ˆ˜๋Š” ํ”ผ๋ถ€์—์„œ ์ฒœ์ž ๋™๋งฅ๊นŒ์ง€์˜ ๊นŠ์ด๊ฐ€ ์–•์€ ๊ฒฝ์šฐ, ์ €์ฒด์ค‘(18.5ใŽ/ใŽก ๋ฏธ๋งŒ)์ธ ๊ฒฝ์šฐ(p<0.001), ํ”ผ๋ถ€ ์ ˆ๊ฐœ ๊ธธ์ด๊ฐ€ ์งง์€ ๊ฒฝ์šฐ์˜€์œผ๋ฉฐ(p=0.003), ์‹œ์ˆ ์ž์˜ ๊ฒฝ๋ ฅ์— ๋”ฐ๋ฅธ ์ฐจ์ด๋Š” ์—†์—ˆ๋‹ค. ์„ฑ๋ณ„๊ณผ ๋‚˜์ด๋ฅผ ๋ณด์ •ํ•œ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ์‹œํ–‰ํ•œ ๊ฒฐ๊ณผ, ํ”ผ๋ถ€์—์„œ ์ฒœ์ž ๋™๋งฅ๊นŒ์ง€์˜ ๊นŠ์ด(OR 0.27, 95% CI 0.13-0.56), ์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜(OR 0.62, 95% CI 0.51-0.75) ๋˜๋Š” ํ”ผ๋ถ€ ์ ˆ๊ฐœ ๊ธธ์ด(OR 0.42, 95% CI 0.23-0.76)๋Š” ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ ๊ฐ์†Œ์™€ ์œ ์˜ํ•œ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค.๊ฒฐ๋ก ๋ด‰ํ•ฉ๋งค๊ฐœ ์ง€ํ˜ˆ๊ธฐ๊ตฌ ์‚ฌ์šฉ ์‹œ ํ™˜์ž์˜ ํ”ผ๋ถ€โˆผ์ฒœ์ž ๋™๋งฅ ๊นŠ์ด๊ฐ€ ์–•๊ฑฐ๋‚˜, ์ €์ฒด์ค‘, ๋˜๋Š” ํ”ผ๋ถ€ ์ ˆ๊ฐœ ๊ธธ์ด๊ฐ€ ์งง์€ ํ™˜์ž์—์„œ๋Š” ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ ์œ„ํ—˜์ด ๋†’๋‹ค. ์ž„์ƒ์—์„œ ์ด๋Ÿฌํ•œ ๊ด€๋ จ์š”์ธ์„ ๊ณ ๋ คํ•จ์œผ๋กœ์จ ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ๊ณผ ๊ทธ์— ๋”ฐ๋ฅธ ๊ฐ์—ผ ๋“ฑ ๋ถ€์ž‘์šฉ์„ ์˜ˆ๋ฐฉํ•˜๊ธฐ ์œ„ํ•œ ์ธ์‹ ๊ฐœ์„ ๊ณผ ์กฐ์น˜๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์ด๋‹ค.ope

    ํด๋ฆฌ์•„๋‹๋ฆฐ/์œ ๊ธฐ๋ฌผ โ€ข ๋ฌด๊ธฐ๋ฌผ ๋ณตํ•ฉ์ฒด ์ œ์กฐ์™€ ์ด์˜ ์Šˆํผ์ปคํŒจ์‹œํ„ฐ ์ „๊ทน์œผ๋กœ์˜ ์‘์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2016. 2. ์žฅ์ •์‹.A supercapacitor has widely been utilized in diverse vehicles needing rapid energy delivery because of its high power density: within automobile, trams, light-rails, and cranes. As smaller forms, they have also been utilized as memory backup for static random-access memory. Materials used for supercapacitor electrode possess their pros and cons. For example, carbon materials show a good cycling stability, but specific capacitance is relatively low. In the case of conducting polymers (CPs), in contrast to carbon materials, they possess high specific capacitance, but the cycling stability is relatively poor due to their poor mechanical strength. Thus, large efforts have been made to fabricate supercapacitor with high capacitance and good cycling stability. In this dissertation, three different polyaniline (PANI)/organic โ€ข inorganic composites were prepared using self-stabilized dispersion polymerization (SSDP) method to achieve supercapacitors with high capacitance and good electrochemical stability. First, PANI/silicon dioxide (SiO2) nanocomposite was fabricated by SSDP method. Produced PANI/SiO2 nanocomposite exhibited improved electrochemical performances (specific capacitance: ca. 305 F g-1, cycling stability: maintaining 72 % of initial gravimetric capacitance after 500 cycles) compared with PANI/SiO2 nanocomposite synthesized by conventional polymerization method and other previously reported PANI nanomaterials owing to high electrical conductivity (ca. 25.6 S cmโ€“1), large specific surface area (ca. 170 m2 gโ€“1), and improved crystallinity. Second, PANI/ molybdenum disulfide (MoS2) nanocomposite, synthesized by SSDP method, showed enhanced specific capacitance (ca. 400 F g-1) in comparison with both PANI (ca. 232 F g-1) and MoS2 nanosheet (ca. 3 F g-1) due to high electrical conductivity (ca. 28.6 S cm-1) and pseudo capacitive characteristics of PANI and MoS2. Additionally, PANI/MoS2 nanosheet exhibited good cycling stability (84 % after 500 cycles) due to honeycomb-like structured PANI on MoS2 nanosheet and incorporation of MoS2 nanosheet possessing good mechanical properties. Lastly, PANI/reduced graphene oxide (RGO) film was fabricated through solution processing for highly scalable and flexible supercapacitor electrodes. Produced PANI/RGO film exhibited extremely high electrical conductivity of ca. 906 S cm-1 due to improved crystallinity. In the electrochemical tests, PANI/RGO film exhibited enhanced capacitance (ca. 431 F g-1) and cycling stability (74 % after after 500 cycles) in comparison with pure PANI film (specific capacitance: ca. 256 F g-1, cycling stability: 60 % after 500 cycles). The PANI/RGO film also demonstrated excellent performance ability as a scalable and flexible electrode material. The stratigies and specific synthetic methods described here can be useful tool for fabricating supercapacitor electrodes with high capacitance and good elctrochemical stability.1. INTRODUCTION 1 1.1. Background 1 1.1.1. Supercapacitors 1 1.1.2. PANI 5 1.1.2.1. Synthetic methods of PANI 5 1.2. Objectives and Outlines 10 1.2.1. Objectives 10 1.2.2. Outlines 10 2. EXPERIMENTAL DETAILS 14 2.1. PANI/SiO2 nanocomposite for supercapacitor electrodes 14 2.1.1. Fabrication of PANI/SiO2 nanocomposite 14 2.1.2. Supercapacitors based on PANI/SiO2 nanocomposites 17 2.2. PANI/MoS2 nanocomposite for supercapacitor electrodes 19 2.2.1. Fabrication of PANI/MoS2 nanocomposite 19 2.2.2. Supercapacitor based on PANI/MoS2 nanocomposite 20 2.3. PANI/graphene film for flexible supercapacitor electrodes 22 2.3.1. Fabication of PANI/RGO film 22 2.3.2. Flexible supercapacitor based on PANI/RGO film 26 3. RESULTS AND DISCUSSION 29 3.1. PANI/SiO2 nanocomposite for supercapacitor electrodes 29 3.1.1. Fabrication of PANI/SiO2 nanocomposite 29 3.1.2. Supercapacitors based on PANI/SiO2 nanocomposite 44 3.2. PANI/MoS2 nanocomposite for supercapacitor electrodes 50 3.2.1. Fabrication of PANI/MoS2 nanocomposite 50 3.2.2. Supercapacitor based on PANI/MoS2 nanocomposite 57 3.3. PANI/graphene film for flexible supercapacitor electrodes 65 3.3.1. Fabication of PANI/RGO film 65 3.3.2. Flexible supercapacitor based on PANI/RGO film 88 4. CONCLUSIONS 99 REFERENCES 103Docto

    Enhancing graphics quality and optimizing power consumption considering the human visual system in mobile devices

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2016. 2. ์‹ ํ˜„์‹.์ตœ๊ทผ๊นŒ์ง€ GPU์˜ ํ•˜๋“œ์›จ์–ด๊ฐ€ ๋ˆˆ์— ๋„๊ฒŒ ๋ฐœ์ „ํ•˜๊ณ  ์žˆ์ง€๋งŒ, ์•„์ง๋„ 60fps๋ฅผ ๋งŒ์กฑํ•˜๋ฉด์„œ ๋†’์€ ํ’ˆ์งˆ์˜ ๊ทธ๋ž˜ํ”ฝ ์š”๊ตฌ์‚ฌํ•ญ์„ ๋งŒ์กฑํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋˜ํ•œ ์ตœ๊ทผ ๋†’์€ ํ•ด์ƒ๋„์˜ ์š”๊ตฌ์‚ฌํ•ญ์€ ์ „๋ ฅ ์†Œ๋ชจ์™€ ์˜จ๋„ ๋ฌธ์ œ ๊ด€์ ์—์„œ๋„ ๋งค์šฐ ์–ด๋ ค์šด ๋ฌธ์ œ์ด๋‹ค. GPU์˜ ์ „๋ ฅ ์†Œ๋ชจ๋Š” GPU์˜ ์—ฐ์‚ฐ๋Ÿ‰๊ณผ ์ •๋น„๋ก€ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์‚ฌ๋žŒ์˜ ์ธ์ง€ ๋Šฅ๋ ฅ ๊ด€์ ์—์„œ ์ด๋“์ด ์—†์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๊ณ ์ •๋œ ๋†’์€ ํ•ด์ƒ๋„์™€ ๋†’์€ ํ”„๋ ˆ์ž„ ์†๋„๋กœ ์ธํ•œ GPU ๋†’์€ ์—ฐ์‚ฐ๋Ÿ‰์€ ์˜๋ฏธ๊ฐ€ ์—†๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‚ฌ๋žŒ์˜ ์ธ์ง€ ๋Šฅ๋ ฅ์„ ๊ณ ๋ คํ•œ GPU ์—ฐ์‚ฐ๋Ÿ‰์„ ์ค„์ด๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•๋“ค์„ ์ œ์•ˆํ•œ๋‹ค. ์‚ฌ๋žŒ์˜ ์ธ์ง€ ๋Šฅ๋ ฅ์„ ๊ณ ๋ คํ•œ GPU ์—ฐ์‚ฐ๋Ÿ‰์„ ์ค„์ด๋Š” ์‹œ์ž‘ ๋‹จ๊ณ„๋กœ, ์ „๋ ฅ ์†Œ๋ชจ์˜ ์ฃผ์š” ์š”์ธ๋“ค์„ ์ƒ์šฉํ™”๋œ LG G3 ๋ชจ๋ฐ”์ผ ๊ธฐ๊ธฐ๋กœ ๋ถ„์„ํ•œ๋‹ค. ์ด ๊ณผ์ •์„ ํ†ตํ•ด ๋ชจ๋ฐ”์ผ GPU์˜ ์ „๋ ฅ ์†Œ๋ชจ์˜ 3 ๊ฐ€์ง€ ์ฃผ์š” ์š”์ธ์ธ ํ•ด์ƒ๋„, ํ”„๋ ˆ์ž„ ์†๋„ ๊ทธ๋ฆฌ๊ณ  ๋ฐ์ดํ„ฐ ์ค‘๋ณต์„ฑ์— ๋Œ€ํ•ด ๋ถ„์„ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ฃผ์š” ์š”์ธ๋“ค์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์‚ฌ๋žŒ์˜ ์ธ์ง€ ๋Šฅ๋ ฅ์„ ๊ณ ๋ คํ•œ ์ƒˆ๋กœ์šด ๋ Œ๋”๋ง ๊ธฐ๋ฒ•๋“ค์„ ํ†ตํ•ด ์—ฐ์‚ฐ๋Ÿ‰์„ ํšจ๊ณผ์ ์œผ๋กœ ์ ˆ๊ฐํ•˜๋Š” ๊ธฐ๋ฒ•๋“ค์„ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ์งธ๋กœ ํ•ด์ƒ๋„ ๊ด€์ ์—์„œ GPU์—์„œ์˜ ํ•ด์ƒ๋„ ๋ณ€๊ฒฝ ๊ธฐ๋ฐ˜ ์—ฐ์‚ฐ๋Ÿ‰ ๊ฐ์†Œ ๊ธฐ๋ฒ•๋“ค์— ๋Œ€ํ•ด ์ œ์•ˆํ•œ๋‹ค. ์ตœ๊ทผ์˜ ์—ฐ๊ตฌ๋“ค์€ ์‚ฌ๋žŒ์˜ ์ธ์ง€๋Šฅ๋ ฅ๊ณผ ์ฝ˜ํ…์ธ ์˜ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•˜์—ฌ, ๊ทธ๋ž˜ํ”ฝ ๊ฒฐ์ ์ด ์ง€์†์ ์œผ๋กœ ๊ด€์ฐฐ๋œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค๊ณผ๋Š” ๋‹ค๋ฅด๊ฒŒ, ์ œ์•ˆํ•˜๋Š” ๋™์  ๋ Œ๋”๋ง ํ™”์งˆ ๊ฐœ์„  ์Šค์ผ€์ผ๋ง (Dynamic Rendering Quality Scaling: DRQS)์€ ์ตœ์†Œํ•œ์˜ ์ถ”๊ฐ€๋น„์šฉ์œผ๋กœ ๋ณ€ํ™˜ ํ–‰๋ ฌ์„ ํ™œ์šฉํ•œ ํ”„๋ ˆ์ž„ ๊ฐ„ ๋ณ€ํ™”๋Ÿ‰์„ ์ด์šฉํ•˜์—ฌ ํ•ด์ƒ๋„ ์กฐ์ ˆ ๋ฐ ํ’ˆ์งˆ ๊ฐœ์„  ์Šค์ผ€์ผ๋ง์„ ํ†ตํ•ด ์„ฑ๋Šฅ์„ ์ตœ๋Œ€ 38%๊นŒ์ง€ ๊ฐœ์„ ํ•œ๋‹ค. ๋˜ํ•œ ์ € ์‚ฌ์–‘ ๊ทธ๋ž˜ํ”ฝ์Šค ์‘์šฉํ”„๋กœ๊ทธ๋žจ์˜ ๊ฒฝ์šฐ์—์„œ๋Š” ์‚ฌ๋žŒ์˜ ์ธ์ง€ ๋Šฅ๋ ฅ๊ด€์ ์—์„œ ๊ทธ๋ž˜ํ”ฝ ํ’ˆ์งˆ์˜ ๊ฐ์†Œ ์—†์ด GPU์˜ ์—ฐ์‚ฐ๋Ÿ‰์„ 24%๊นŒ์ง€ ์ค„์ธ๋‹ค. ๋‘˜์งธ๋กœ ํ”„๋ ˆ์ž„ ๋ณด๊ฐ„ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•œ ๊ทธ๋ž˜ํ”ฝ ํ’ˆ์งˆ ํ–ฅ์ƒ ๊ธฐ๋ฒ•๋“ค์— ๋Œ€ํ•ด์„œ ์ œ์•ˆํ•œ๋‹ค. ์ตœ๊ทผ์˜ ํ”„๋ ˆ์ž„ ๋ณด๊ฐ„ ๋ฐฉ์‹์€ ๋ชจ์…˜ ๋ณด์ƒ ๊ธฐ๋ฐ˜์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ธฐ๋ฐ˜์œผ๋กœ ์ค‘๊ฐ„ํ”„๋ ˆ์ž„์„ ์ƒ์„ฑํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์š”๊ตฌ๋˜๋Š” ๋†’์€ ๋น„์šฉ์€ ๋ชจ๋ฐ”์ผ์—์„œ ์ ์šฉํ•  ์ˆ˜ ์—†๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด, ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์ธ GPU์˜ ํƒ€์ผ ๋ Œ๋”๋ง์„ ์ด์šฉํ•œ ์ค‘๊ฐ„ ํ”„๋ ˆ์ž„ ์ „๋‹ฌ ๋ฐฉ์‹์˜ ํ”„๋ ˆ์ž„ ๋ณด๊ฐ„ ๊ธฐ๋ฒ•์€ ์ง€์—ฐ๊ณผ ์ถ”๊ฐ€์ ์ธ ๋†’์€ ๋น„์šฉ ์—†์ด ์ค‘๊ฐ„ ํ”„๋ ˆ์ž„์„ ์ƒ์„ฑํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋“ค ๋Œ€๋น„ ์‹œ์Šคํ…œ ๊ด€์ ์—์„œ ์ ˆ๋ฐ˜์˜ ์—ฐ์‚ฐ ๋น„์šฉ์œผ๋กœ ์‚ฌ๋žŒ์˜ ์ธ์ง€ ๋Šฅ๋ ฅ ๊ด€์ ์—์„œ ๋™๋“ฑํ•œ ๊ทธ๋ž˜ํ”ฝ ํ’ˆ์งˆ์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๊ฐ€์žฅ ์ตœ๊ทผ์— ๋ฐœํ‘œ๋œ OpenGL ES 3.0์—์„œ ์ œ์•ˆ๋œ ๊ธฐ์ˆ ์ธ Multi render target(MRT) ๊ธฐ์ˆ ์„ ์žฌ์‚ฌ์šฉ ๊ด€์ ์—์„œ ์ตœ์ ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. MRT ๋Š” ์ง€์—ฐ ์‰์ด๋”ฉ์„ ํ†ตํ•œ ๋ณต์žกํ•œ ๋ผ์ดํŒ… ์—ฐ์‚ฐ์„ ํšจ์œจ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” ๊ธฐ์ˆ ์ด๋‹ค. ํ•˜์ง€๋งŒ, ํ•œ๊บผ๋ฒˆ์— ๋ Œ๋” ํƒ€๊นƒ์— ๋ Œ๋”๋ง์„ ํ•ด์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํฐ ๋ฉ”๋ชจ๋ฆฌ ๋Œ€์—ญํญ์„ ์š”๊ตฌํ•œ๋‹ค, ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋Š” ์ œํ•œ๋œ ๋ชจ๋ฐ”์ผ ํ™˜๊ฒฝ์—์„œ๋Š” ํฐ ์žฅ์• ์ด๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ์‹œ๊ฐ„์  ์ค‘๋ณต์„ฑ์„ ์ด์šฉํ•œ ๋ฐ์ดํ„ฐ ์žฌ์‚ฌ์šฉ์„ ํ†ตํ•ด ์ด๋ฏธ ์“ฐ์ธ ๋ Œ๋” ํƒ€๊นƒ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์„ ํƒ์ ์œผ๋กœ ์žฌ์‚ฌ์šฉํ•˜์—ฌ GPU์˜ ์—ฐ์‚ฐ๋Ÿ‰ ๋ฐ ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ์„ ๊ฐ์†Œ์‹œํ‚จ๋‹ค. ์‹คํ—˜์„ ํ†ตํ•ด ์‚ฌ๋žŒ์˜ ์ธ์ง€ ๋Šฅ๋ ฅ ๊ณผ์ ์—์„œ ๊ทธ๋ž˜ํ”ฝ ํ’ˆ์งˆ์„ ์œ ์ง€ํ•˜๋ฉด์„œ 18%์˜ ์‹œ์Šคํ…œ ๋ ˆ๋ฒจ์˜ ์ „๋ ฅ ์†Œ๋ชจ ๊ฐ์†Œ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ชฉ์  1 1.2 ์—ฐ๊ตฌ ๊ณตํ—Œ 3 1.3 ๋…ผ๋ฌธ ๊ตฌ์„ฑ 7 ์ œ 2 ์žฅ ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 10 2.1 ๋ชจ๋ฐ”์ผ ๊ทธ๋ž˜ํ”ฝ์Šค์˜ ๋ฐœ์ „ 10 2.1.1 ๋ชจ๋ฐ”์ผ ๊ทธ๋ž˜ํ”ฝ์Šค ํ•˜๋“œ์›จ์–ด์˜ ์ง„ํ™” 10 2.1.2 ๋ชจ๋ฐ”์ผ ๊ทธ๋ž˜ํ”ฝ์Šค ์†Œํ”„ํŠธ์›จ์–ด ์ง„ํ™” 14 2.2 ๋ชจ๋ฐ”์ผ ํ™˜๊ฒฝ์˜ ์†Œ๋ชจ ์ „๋ ฅ ๋ถ„์„ 19 2.3 ํ•ด์ƒ๋„ 23 2.4 ํ”„๋ ˆ์ž„ ์†๋„ 25 2.5 ๋ฐ์ดํ„ฐ ์ค‘๋ณต 26 ์ œ 3 ์žฅ ๊ฐ€๋ณ€ ํ•ด์ƒ๋„ ๊ธฐ๋ฐ˜ ์ตœ์ ํ™” 29 3.1 ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ˜ ๊ฐ€๋ณ€ ํ•ด์ƒ๋„ ๋ณ€ํ™˜ ๊ธฐ๋ฒ• 29 3.2 ์‘์šฉ ํ”„๋กœ๊ทธ๋žจ ํŠน์„ฑ ๊ธฐ๋ฐ˜ ํ•ด์ƒ๋„ ๋ณ€ํ™˜ ๊ธฐ๋ฒ• 32 3.3 ๋™์  ๋ Œ๋”๋ง ๊ธฐ๋ฐ˜ ์ „๋ ฅ ์†Œ๋ชจ ์ตœ์ ํ™” ๋ฐ ํ’ˆ์งˆ ๊ฐœ์„  33 3.3.1 ์ธ๊ฐ„ ์‹œ๊ฐ ์‹œ์Šคํ…œ ๊ธฐ๋ฐ˜ ๋™์  ๋ Œ๋”๋ง 35 3.3.2 ๋ณ€ํ™˜ ํ–‰๋ ฌ์„ ํ†ตํ•œ ๋ณ€ํ™”๋Ÿ‰ ๊ณ„์‚ฐ 38 3.3.3 ๊ทธ๋ž˜ํ”ฝ ํ’ˆ์งˆ ๊ฐœ์„  ์Šค์ผ€์ผ๋ง 44 ์ œ 4 ์žฅ ํ”„๋ ˆ์ž„ ์†๋„ ๊ธฐ๋ฐ˜ ์ตœ์ ํ™” 47 4.1 ํ”„๋ ˆ์ž„ ๋ณด๊ฐ„ 47 4.2 ์ •๋ฐฉํ–ฅ ์žฌ ํˆฌ์˜ ๊ธฐ๋ฒ• 49 4.3 ์—ญ๋ฐฉํ–ฅ ์žฌ ํˆฌ์˜ ๊ธฐ๋ฒ• 52 4.4 ํ์ƒ‰ ์˜์—ญ ์ฒ˜๋ฆฌ ๋ฐ ํ•œ๊ณ„ 54 4.5 ์ธ๊ฐ„ ์‹œ๊ฐ ์‹œ์Šคํ…œ ๊ธฐ๋ฐ˜ ํ™€๋“œ-ํƒ€์ž… ๋ญ‰๊ฐœ์ง 55 4.6 ํƒ€์ผ ๊ธฐ๋ฐ˜ GPU์˜ ์ „๋ ฅ ์†Œ๋ชจ ์ตœ์ ํ™” ๋ฐ ํ’ˆ์งˆ ๊ฐœ์„  58 4.6.1 ํƒ€์ผ ๊ธฐ๋ฐ˜ ๋ Œ๋”๋ง 60 4.6.2 ์ค‘๊ฐ„ ํ”„๋ ˆ์ž„ ์ „๋‹ฌ ๊ธฐ๋ฒ• ๊ธฐ๋ฐ˜ ํ”„๋ ˆ์ž„ ์†๋„ ์ฆ๊ฐ€ 63 4.6.3 ์ธ๊ฐ„ ์‹œ๊ฐ ์‹œ์Šคํ…œ ๊ธฐ๋ฐ˜ ํ”„๋ ˆ์ž„ ๋ถ„์„ 69 4.6.4 ๋ Œ๋”๋ง ์šฐ์„ ์ˆœ์œ„ ๊ณ„์‚ฐ ๋ฐ ํ•ฉ์„ฑ 72 ์ œ 5 ์žฅ ๋ฐ์ดํ„ฐ ์žฌ์‚ฌ์šฉ์„ ํ†ตํ•œ ์ตœ์ ํ™” 77 5.1 ๋ฐ์ดํ„ฐ ์žฌ์‚ฌ์šฉ 77 5.2 ๋ฉ€ํ‹ฐ ๋ Œ๋” ํƒ€๊นƒ์˜ ๋ฐ์ดํ„ฐ ์žฌ์‚ฌ์šฉ์„ ํ†ตํ•œ ์ตœ์ ํ™” 78 5.2.1 ๋ฉ€ํ‹ฐ ๋ Œ๋” ํƒ€๊นƒ 80 5.2.2 ์‹œ๊ฐ„์  ์ผ๊ด€์„ฑ ๊ธฐ๋ฐ˜ ๋ฐ์ดํ„ฐ ์žฌ์‚ฌ์šฉ 83 5.2.3 ๋ Œ๋” ํƒ€๊นƒ ์ €์žฅ 87 5.2.4 ์ธ๊ฐ„ ์‹œ๊ฐ ์‹œ์Šคํ…œ ๊ธฐ๋ฐ˜ ๋ Œํ„ฐ ํƒ€๊นƒ ์žฌ์‚ฌ์šฉ 88 ์ œ 6 ์žฅ ์„ฑ๋Šฅ ๋ถ„์„ 92 6.1 ์‹คํ—˜ ํ™˜๊ฒฝ 93 6.1.1 ๊ตฌํ˜„ ๋ฐ ํ™˜๊ฒฝ 93 6.1.2 ์‹คํ—˜ ๋ฒกํ„ฐ 95 6.1.3 ์‹œ๊ฐ ์‹œ์Šคํ…œ ๊ธฐ๋ฐ˜ ํ™”์งˆ ํ‰๊ฐ€ ๊ธฐ์ค€ 96 6.2 ์„ฑ๋Šฅ ๋ฐ ์†Œ๋ชจ ์ „๋ ฅ ํ‰๊ฐ€ 99 6.2.1 ํ”„๋ ˆ์ž„ ๊ฐ„ ๋ณ€ํ™”๋Ÿ‰์„ ์ด์šฉํ•œ ๋™์  ๋ Œ๋”๋ง ๊ธฐ๋ฒ• 99 6.2.2 ํƒ€์ผ ๊ธฐ๋ฐ˜ GPU ๋ฅผ ์œ„ํ•œ ํ”„๋ ˆ์ž„ ์†๋„ ์ฆ๊ฐ€ ๊ธฐ๋ฒ• 104 6.2.3 ๋ฉ€ํ‹ฐ ๋ Œ๋” ํƒ€๊นƒ์„ ์œ„ํ•œ ๋ฐ์ดํ„ฐ ์žฌ์‚ฌ์šฉ ๊ธฐ๋ฒ• 108 ์ œ 7 ์žฅ ๊ฒฐ๋ก  115 ์ฐธ๊ณ  ๋ฌธํ—Œ 118 Abstract 126Docto

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ–‰์ •๋Œ€ํ•™์› ํ–‰์ •ํ•™๊ณผ(ํ–‰์ •ํ•™์ „๊ณต),2019. 8. ๊น€๋™์šฑ.๊ณต๊ณต ์„ฑ๊ณผ๊ด€๋ฆฌ ์ด๋ก ์€ ์ •๋ถ€ ๊ด€๋ฆฌ์ž์˜ ์—ญํ• ์„ ์ค‘์‹œํ•ด์™”๋‹ค. ๊ณต๊ณต ์„ฑ๊ณผ๋ผ๋Š” ๊ฒƒ์ด ๊ฒฐ๊ตญ ๊ด€๋ฆฌ ํ–‰์œ„๋ฅผ ํ†ตํ•ด ๋‹ฌ์„ฑ๋˜๋Š” ๊ฒƒ์ธ๋ฐ, ๊ด€๋ฆฌ์˜ ์ฃผ์ฒด์ธ ์ •๋ถ€ ๊ด€๋ฆฌ์ž์— ๋Œ€ํ•ด์„œ๋Š” ๋ณด๋‹ค ์‹ฌ์ธต์ ์œผ๋กœ ๋ถ„์„๋  ํ•„์š”๊ฐ€ ์žˆ์—ˆ๋˜ ๊ฒƒ์ด๋‹ค. ์ „๋žต์  ์ธ์ ์ž์› ๊ด€๋ฆฌ๋ฅผ ๋น„๋กฏํ•œ ์—ฌ๋Ÿฌ ์ด๋ก ์  ์„ฑ์ทจ๋ฅผ ํ†ตํ•ด ์ •๋ถ€ ๊ด€๋ฆฌ์ž๋กœ ํ•˜์—ฌ๊ธˆ ์—ญ๋Ÿ‰์„ ํ–ฅ์ƒํ•จ์œผ๋กœ์„œ ์กฐ์ง ์„ฑ๊ณผ์™€ ์—ฐ๊ณ„ํ•˜๋„๋ก ์žฅ๋ คํ•˜๋Š” ๊ฒƒ์ด ๋ฐ”๋žŒ์งํ•˜๋‹ค๋Š” ์ ์— ๋Œ€ํ•ด์„œ๋Š” ๋Œ€์ฒด๋กœ ๋ฐ›์•„๋“ค์—ฌ์ง€๊ณ  ์žˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ ์ค‘์š”ํ•œ ๋ฌธ์ œ๋Š” ์ฒซ์งธ, ์ •๋ถ€ ๊ด€๋ฆฌ์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ๊ฒฝ๋ ฅ๊ฐœ๋ฐœ์€ ์–ด๋– ํ•œ ํŠน์ง•์„ ๋‚˜ํƒ€๋‚ผ๊นŒ, ๋‘˜์งธ, ๊ทธ๋“ค์ด ํ˜•์„ฑํ•œ ์—ญ๋Ÿ‰์€ ์‹ค์ œ ์„ฑ๊ณผ์™€ ๊ด€๋ จ์„ฑ์ด ์žˆ๋Š”๊ฐ€, ์…‹์งธ, ๊ทธ๋ ‡๋‹ค๋ฉด ์–ด๋–ป๊ฒŒ ๊ทธ๋“ค์˜ ์—ญ๋Ÿ‰์„ ํ˜•์„ฑํ•˜๋Š” ๊ฒƒ์ด ๋ฐ”๋žŒ์งํ•œ๊ฐ€์— ๋Œ€ํ•œ ๋…ผ์˜๊ฐ€ ์ œ๊ธฐ๋  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•œ๊ตญ์˜ ํ˜„์ƒ๊ณผ ์ž๋ฃŒ์— ๊ทผ๊ฑฐํ•ด ์ •๋ถ€ ๊ด€๋ฆฌ์ž์˜ ์—ญ๋Ÿ‰๊ณผ ์„ฑ๊ณผ์™€์˜ ๊ด€๊ณ„ ๊ทœ๋ช…์„ ์œ„ํ•ด ๋…ธ๋ ฅํ–ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„  ๋ฌด์—‡๋ณด๋‹ค๋„ ํ•œ๊ตญ ํ˜„์‹ค์— ์ ํ•ฉํ•œ ์ •๋ถ€ ๊ด€๋ฆฌ์ž์˜ ์—ญ๋Ÿ‰ ๋ณ€์ˆ˜์˜ ๋ฐœ๊ตด๊ณผ ์ด๋ฅผ ๋’ท๋ฐ›์นจํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐ์ดํ„ฐ ํ™•๋ณด๊ฐ€ ํ•„์š”ํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ƒˆ๋กœ์ด ์ œ์‹œํ•œ ๋…๋ฆฝ๋ณ€์ˆ˜๋“ค์€ ํ•™ยท์„์‚ฌ๊ฐ„ ์ „๊ณต ์ด๋™, ์ •๋ถ€ ๊ด€๋ฆฌ์ž์˜ ๋ถ€์ฒ˜์ด๋™, ํ•™์—ฐ, ๋Œ€ํ†ต๋ น(๋น„์„œ)์‹ค ๊ทผ๋ฌด ์—ฌ๋ถ€์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ƒˆ๋กœ์šด ๋…๋ฆฝ๋ณ€์ˆ˜๋“ค์€ ๊ธฐ์กด ๊ณต๊ณต ์„ฑ๊ณผ๊ด€๋ฆฌ ์—ฐ๊ตฌ์—์„œ ์ด๋ฏธ ์กฐ์ง ๋ฐ ์‚ฌ์—… ํŠน์„ฑ ๋“ฑ ์˜ํ–ฅ๋ ฅ์ด ํ™•์ธ๋œ ์š”์ธ๋“ค๊ณผ์˜ ๊ด€๊ณ„์—์„œ ์–ด๋Š ์ •๋„ ์„ค๋ช…๋ ฅ์„ ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์žˆ์„์ง€์— ๋Œ€ํ•œ ์—ผ๋ ค๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋˜ ๋ณธ ๋…ผ๋ฌธ์€ ๊ฐ๊ด€์  ์ž๋ฃŒ์™€ ๋Œ€ํ‘œ๋ณธ์„ ์‚ฌ์šฉํ•ด ์ •๋ถ€ ๊ด€๋ฆฌ์ž ์—ญ๋Ÿ‰๊ณผ ์„ฑ๊ณผ์™€์˜ ๊ด€๊ณ„๋ฅผ ๊ฒฝํ—˜์ ์œผ๋กœ ๊ทœ๋ช…ํ•˜๋ ค๋Š” ์‹œ๋„๋ฅผ ํ•˜์˜€๋‹ค. ์ •๋ถ€ ๊ด€๋ฆฌ์ž๊ฐ€ ํ˜•์„ฑํ•œ ๊ฐ ์—ญ๋Ÿ‰ ๋ณ€์ˆ˜๊ฐ€ ์ค‘์•™ํ–‰์ •๊ธฐ๊ด€์˜ ์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ†ต๊ณ„ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ๊ตฌ์ฒด์ ์œผ๋กœ ์„ฑ๊ณผ๋Š” ์žฌ์ •์‚ฌ์—… ํ‰๊ฐ€์ ์ˆ˜, ํ‰๊ฐ€๋“ฑ๊ธ‰๊ณผ ์˜ˆ์‚ฐ์ฆ๊ฐ€์œจ๋กœ์„œ ์ธก์ •ํ–ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, 40์—ฌ๊ฐœ ์ค‘์•™ํ–‰์ •๊ธฐ๊ด€์˜ 2,456๊ฐœ ๋‹จ์œ„์‚ฌ์—…(์ค‘๋ณต ํฌํ•จ)์„ ๋Œ€์ƒ(2008๋…„~2015๋…„)์œผ๋กœ ํ–ˆ์œผ๋ฉฐ, ๋ฐ์ดํ„ฐ๋Š” ์ฃผ๋กœ ์ •๋ถ€๊ธฐ๊ด€, ์–ธ๋ก ์‚ฌ ๋“ฑ์—์„œ ๊ณต๊ฐœ์ ์œผ๋กœ ์ œ๊ณตํ•˜๋Š” ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ ๋ถ„์„ํ–ˆ๋‹ค. ์ •๋ถ€ ๊ด€๋ฆฌ์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ๊ธฐ์กด์˜ ์„ฑ๊ณผ ์—ฐ๊ตฌ์˜ ๋Œ€๋ถ€๋ถ„์ด ์„ค๋ฌธ์— ๊ธฐ๋ฐ˜์„ ๋‘” ๋‹ค์†Œ ์ถ”์ƒ์ ์ด๊ณ  ๋ชจํ˜ธํ•œ ๊ฐœ๋…์„ ํ† ๋Œ€๋กœ ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ธก์ •ํ•˜์˜€์œผ๋‚˜, ๋ณธ ๋…ผ๋ฌธ์€ ์žฌ์ •์‚ฌ์—…์ž์œจํ‰๊ฐ€์™€ ๊ฐ™์€ ์ƒ๋Œ€์ ์œผ๋กœ ๊ฐ๊ด€์ ์ธ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ˜•ํƒœ์˜ ์ •๋ถ€ ๊ด€๋ฆฌ์ž์˜ ์„ฑ๊ณผ์— ๋Œ€ํ•œ ์‹ค์ฆ ์—ฐ๊ตฌ๋Š” ์ผ๋ถ€ ๋ฏธ๊ตญ ๋“ฑ ํ•ด์™ธ์‚ฌ๋ก€๋ฅผ ์ œ์™ธํ•˜๋ฉด ์•„์ง ๊ตญ๋‚ด์—์„œ๋Š” ์‹œ๋„๋˜์ง€ ์•Š์•˜๋‹ค๋Š” ์ ์—์„œ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์žฌ์ •์‚ฌ์—… ํ‰๊ฐ€์ ์ˆ˜์™€ ํ‰๊ฐ€๋“ฑ๊ธ‰์„ ์ข…์†๋ณ€์ˆ˜๋กœ ํ•œ ์ •๋ถ€ ๊ด€๋ฆฌ์ž์˜ ์—ญ๋Ÿ‰๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ „๋ถ€ ๋˜๋Š” ์ผ๋ถ€์—์„œ ๊ฐ€์„ค์ด ์˜ˆ์ธกํ•œ ๋ฐฉํ–ฅ๋Œ€๋กœ ๊ฒฐ๊ณผ๊ฐ€ ๋„์ถœ๋˜์—ˆ๋‹ค. ์ฆ‰, ์‚ฌ๊ณ  ์—ญ๋Ÿ‰์—์„œ๋Š” ์ „๊ณต์ด๋™์ด, ์—…๋ฌด ์—ญ๋Ÿ‰์—์„œ๋Š” ๋ถ€์ฒ˜์ด๋™์ด, ๊ด€๊ณ„ ์—ญ๋Ÿ‰์—์„œ๋Š” ๋Œ€ํ†ต๋ น(๋น„์„œ)์‹ค ๊ทผ๋ฌด์—ฌ๋ถ€ ๋ณ€์ˆ˜๊ฐ€ ๊ฐ€์„ค์˜ ์˜ˆ์ธก๊ณผ ๋™์ผํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค์Œ ์˜ˆ์‚ฐ ์ฆ๊ฐ€๋ฅผ ์ข…์†๋ณ€์ˆ˜๋กœ ํ•œ ๋ถ„์„๊ฒฐ๊ณผ ์‚ฌ๊ณ  ์—ญ๋Ÿ‰ ์ค‘ ์ „๊ณต์ด๋™์—์„œ, ๊ด€๊ณ„์—ญ๋Ÿ‰ ์ค‘ ๋Œ€ํ†ต๋ น(๋น„์„œ)์‹ค ๊ทผ๋ฌด์—์„œ ๊ฐ€์„ค๊ณผ ์ผ์น˜ํ•˜๋Š” ๋ฐฉํ–ฅ์˜ ๊ฒฐ๊ณผ๊ฐ€ ๋„์ถœ๋˜์—ˆ๋‹ค. ๋‹ค๋งŒ ์˜ˆ์‚ฐ ์ฆ๊ฐ€์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์—…๋ฌด ์—ญ๋Ÿ‰์€ ๋ณธ ๋…ผ๋ฌธ์—์„œ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด์ง€ ์•Š์•˜๋‹ค. ์ด๋“ค ๋ณ€์ˆ˜๋Š” ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ƒˆ๋กœ์ด ์ œ์‹œํ•œ ์ˆ˜ํ‰์  ์ฐจ์›์˜ ๊ฒฝ๋ ฅ๊ฐœ๋ฐœ ๋ณ€์ˆ˜๋“ค๋กœ์„œ ํ•™๋ ฅ์ˆ˜์ค€๊ณผ ์žฌ์ง๊ธฐ๊ฐ„๊ณผ ๋Œ€ํ†ต๋ น(๋น„์„œ)์‹ค ๊ทผ๋ฌด์™€ ๊ฐ™์€ ๋ณ€์ˆ˜๋Š” ๊ธฐ์กด ์„ ํ–‰์—ฐ๊ตฌ์—์„œ ์ฃผ๋ชฉํ•ด์˜จ ๋ณ€์ˆ˜์˜ ํšจ๊ณผ๊ฐ€ ํฌ์ง€ ์•Š๊ฑฐ๋‚˜ ํ˜น์€ ๋“œ๋Ÿฌ๋‚˜์ง€ ์•Š๋Š” ๊ฐ€์šด๋ฐ ํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ธ ๊ฒƒ์ด์–ด์„œ ๋”์šฑ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค. ์‚ฌ๊ณ  ๋˜๋Š” ์—…๋ฌด ์—ญ๋Ÿ‰์˜ ์ผ๋ถ€ ์ˆ˜์ง์  ์ฐจ์›์— ํ•ด๋‹นํ•˜๋Š” ๋ณ€์ˆ˜๋“ค์€ ํ•œ๊ตญ๊ณผ ๊ฐ™์ด ๊ฒฝ์ œ ๋ฐ ์‚ฌํšŒ๊ฐ€ ๋ฐœ์ „ํ•œ ์ƒํ™ฉ์—์„œ ์—ญํšจ๊ณผ๋ฅผ ๊ฐ–๊ฑฐ๋‚˜ ์œ ์˜๋ฏธํ•˜์ง€ ์•Š์„ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ์—ˆ๋Š”๋ฐ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ด๋Ÿฌํ•œ ์˜ˆ์ƒ์— ๊ธฐ๋ฐ˜์„ ๋‘” ํšจ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚จ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค.The topic of government managers competence is related the theory of public performance management where the role of management in government has been taken as central issues. Most of previous studies did not fully focused on government managers role itself and they relatively neglected on how government managers traits are effect on governmental performance. Although some studies tried to include them in their research model just as control variable, these are not sufficient to explain performance due to limited usage such as demographic traits. Also these variable is not effective because they are failed to explain performance based results or reflect changing current policy environment. This thesis focused on the government managers competence within Korean governmental context and using three competence factors which is thinking, work, and relation competence. These factors leads to explain two important governmental performance, program and budget. In this thesis, these new factors of competence are vital one to explain the governmental outcomes and these variable would be distinguishable compared to other variables already proven in the previous studies. For this, this thesis prepared data for 2,456 units of programs from 2008 to 2015. Especially data for senior executive members is gathered diverse sources including governmental archive, news agencies, and private data bases. Some hypothesis in this thesis are proven as expected. Specifically, major mobility in thinking competence, organization mobility in work competence, and presidential office experience in relate competence were shown as effective independent variables to explain governmental performances. In addition, these results not only applicable to expand coverage of public performance management theory but also to helpful to understand how to manage and nuture governmental managers as valuable assets in the context of Korean Central government.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ๋ชฉ์ ๊ณผ ํ•„์š”์„ฑ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๊ตฌ์„ฑ 5 1. ์—ฐ๊ตฌ ์„ค๊ณ„ 5 2. ๋…ผ๋ฌธ ๊ตฌ์„ฑ ๊ฐœ์š” 6 ์ œ 2 ์žฅ ์ด๋ก  ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ 7 ์ œ 1 ์ ˆ ๊ณต๊ณต ์„ฑ๊ณผ๊ด€๋ฆฌ์— ๋Œ€ํ•œ ์ด๋ก  7 1. ๊ณต๊ณต ์„ฑ๊ณผ๊ด€๋ฆฌ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 7 2. ๊ณต๊ณต์„ฑ๊ณผ์— ๋Œ€ํ•œ ์ •๋ถ€ ๊ด€๋ฆฌ์ž์˜ ์—ญํ•  11 ์ œ 2 ์ ˆ ์ •๋ถ€ ๊ด€๋ฆฌ์ž์™€ ์„ฑ๊ณผ์— ๋Œ€ํ•œ ์ด๋ก ์  ๋…ผ์˜ 14 1. ์ •๋ถ€ ๊ด€๋ฆฌ์ž์˜ ์„ฑ๊ณผ์— ๋Œ€ํ•œ ์„ ํ–‰ ์—ฐ๊ตฌ 14 1) ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ์„ฑ๊ณผ์™€ ํ•œ๊ณ„ 14 2) ์ˆ˜ํ‰์  ์ธก๋ฉด์— ๋Œ€ํ•œ ์—ฐ๊ตฌ 18 2. ์ •๋ถ€ ๊ด€๋ฆฌ์ž์˜ ์„ฑ๊ณผ ์ธก์ • 21 1) ์„ฑ๊ณผ ์ธก์ • ๋ฐ์ดํ„ฐ์˜ ์ข…๋ฅ˜์™€ ํŠน์„ฑ 21 2) ํ–‰์ •๋ฐ์ดํ„ฐ ํ™œ์šฉ ๊ด€๋ จ ์„ฑ๊ณผ ์ธก์ •์˜ ํ•œ๊ณ„ 23 ์ œ 3 ์ ˆ ์ •๋ถ€ ๊ด€๋ฆฌ์ž ์—ญ๋Ÿ‰์— ๊ด€ํ•œ ์ด๋ก  29 1. ์—ญ๋Ÿ‰ ์ด๋ก ์˜ ๋ฐฐ๊ฒฝ 29 2. ์—ญ๋Ÿ‰ ๊ฐœ๋… ์—ฐ๊ตฌ 32 3. ์—ญ๋Ÿ‰ ๋ถ„๋ฅ˜์— ๊ด€ํ•œ ์„ ํ–‰ ์—ฐ๊ตฌ 37 1) ์—ญ๋Ÿ‰ ๊ตฌ๋ถ„์— ๊ด€ํ•œ ์ผ๋ฐ˜์  ๋…ผ์˜ 37 2) ์ •๋ถ€ ๊ด€๋ฆฌ์ž ์—ญ๋Ÿ‰ ๊ตฌ๋ถ„ ๋ฐฉ๋ฒ• 39 4. ์„ ํ–‰ ์—ญ๋Ÿ‰ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ 43 1) ์ˆœ์ˆ˜ ํŠน์„ฑ ๋ฆฌ๋”์‹ญ ์—ฐ๊ตฌ 43 2) ํ–‰ํƒœ๋ก ์  ์‹œ๊ฐ์˜ ์—ญ๋Ÿ‰ ์—ฐ๊ตฌ 44 3) ๋ณธ ๋…ผ๋ฌธ์˜ ์—ญ๋Ÿ‰ ์—ฐ๊ตฌ์— ๋Œ€ํ•œ ์ง€ํ–ฅ์  45 5. ์—ญ๋Ÿ‰์— ๋Œ€ํ•œ ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฐœ๋…์  ์ •์˜ 47 1) ์‚ฌ๊ณ  ์—ญ๋Ÿ‰ 47 2) ์—…๋ฌด ์—ญ๋Ÿ‰ 48 3) ๊ด€๊ณ„ ์—ญ๋Ÿ‰ 49 ์ œ 4 ์ ˆ ์ฃผ์š” ์—ฐ๊ตฌ์™€ ๋ณธ ๋…ผ๋ฌธ์˜ ์ฐจ์ด์  51 ์ œ 5 ์ ˆ ์‚ฌ๋ก€ ์„ ์ • : ์ค‘์•™ํ–‰์ •๊ธฐ๊ด€ ๊ณ ์œ„๊ณต๋ฌด์› 54 1. ์ค‘์•™ํ–‰์ •๊ธฐ๊ด€ ๊ณ ์œ„๊ณต๋ฌด์› ๊ตฌ์„ฑ๊ณผ ํ˜„ํ™ฉ 54 2. ์ค‘์•™ํ–‰์ •๊ธฐ๊ด€ ๊ณ ์œ„๊ณต๋ฌด์› ์ฃผ์š” ์—ญ๋Ÿ‰ ํ˜„ํ™ฉ 62 ์ œ 3 ์žฅ ์—ฐ๊ตฌ์„ค๊ณ„ 72 ์ œ 1 ์ ˆ ๋ถ„์„๋ชจํ˜•์˜ ์„ค์ • 72 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๊ฐ€์„ค์˜ ์„ค์ • 75 1. ์‚ฌ๊ณ  ์—ญ๋Ÿ‰ 75 1) ํ•™๋ ฅ ์ˆ˜์ค€ 75 2) ์ „๊ณต ์ด๋™ 75 2. ์—…๋ฌด ์—ญ๋Ÿ‰ 79 1) ์žฌ์ง ๊ธฐ๊ฐ„ 80 2) ๋ถ€์ฒ˜ ์ด๋™ 81 3. ๊ด€๊ณ„ ์—ญ๋Ÿ‰ 83 1) ํ•™์—ฐ 84 2) ๋Œ€ํ†ต๋ น(๋น„์„œ)์‹ค ๊ทผ๋ฌด 85 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 87 1. ์ž๋ฃŒ์˜ ์ˆ˜์ง‘๊ณผ ํ‘œ๋ณธ 87 1) ์ข…์†๋ณ€์ˆ˜ ์ž๋ฃŒ ์›์ฒœ 87 2) ๋…๋ฆฝ๋ณ€์ˆ˜ ์ž๋ฃŒ ์›์ฒœ 88 3) ๋‘ ์ž๋ฃŒ์›์˜ ๊ฒฐํ•ฉ ๋ฐฉ๋ฒ• 90 2. ์ฃผ์š”๋ณ€์ˆ˜์˜ ์ธก์ • 92 1) ์ข…์†๋ณ€์ˆ˜ 92 2) ๋…๋ฆฝ๋ณ€์ˆ˜ 94 3) ํ†ต์ œ๋ณ€์ˆ˜ 98 3. ๋ถ„์„๋ฐฉ๋ฒ• 103 ์ œ 4 ์žฅ ํ†ต๊ณ„๋ถ„์„ 105 ์ œ 1 ์ ˆ ๊ธฐ์ˆ ํ†ต๊ณ„ 105 ์ œ 2 ์ ˆ ํšŒ๊ท€๋ถ„์„ 117 1. ์‚ฌ์—…์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ๋ถ„์„ 117 1) ์‚ฌ์—…์„ฑ๊ณผ ์ ์ˆ˜ ํšŒ๊ท€๋ถ„์„ 117 2) ์‚ฌ์—…์„ฑ๊ณผ ๋“ฑ๊ธ‰ ๋กœ์ง€์Šคํ‹ฑ๋ถ„์„ 123 2. ์˜ˆ์‚ฐ์ฆ๊ฐ€์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ๋ถ„์„ 128 ์ œ 3 ์ ˆ ํ•ด์„ 132 1. ์ผ๋ฐ˜์  ๋…ผ์˜ 132 2. ์‚ฌ์—…์„ฑ๊ณผ์— ๋Œ€ํ•œ ํ•ด์„ 133 1) ์‚ฌ๊ณ ์—ญ๋Ÿ‰์ด ์‚ฌ์—…์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 134 2) ์—…๋ฌด์—ญ๋Ÿ‰์ด ์‚ฌ์—…์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 135 3) ๊ด€๊ณ„์—ญ๋Ÿ‰์ด ์‚ฌ์—…์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 136 3. ์˜ˆ์‚ฐ์ฆ๊ฐ€์— ๋Œ€ํ•œ ํ•ด์„ 137 1) ์‚ฌ๊ณ ์—ญ๋Ÿ‰์ด ์˜ˆ์‚ฐ์ฆ๊ฐ€์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 138 2) ์—…๋ฌด์—ญ๋Ÿ‰์ด ์˜ˆ์‚ฐ์ฆ๊ฐ€์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 139 3) ๊ด€๊ณ„์—ญ๋Ÿ‰์ด ์˜ˆ์‚ฐ์ฆ๊ฐ€์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 139 ์ œ 5 ์žฅ ๊ฒฐ๋ก  141 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 141 ์ œ 2 ์ ˆ ์ด๋ก ์  ํ•จ์˜์™€ ์ •์ฑ…์  ์‹œ์‚ฌ์  143 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ 148Docto

    ๋ด‰ํ•ฉ๋งค๊ฐœ ์ง€ํ˜ˆ๊ธฐ๊ตฌ ์‚ฌ์šฉ ํ›„ ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ ๊ด€๋ จ์š”์ธ

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    ๊ฑด๊ฐ•์ฆ์ง„๊ต์œก์ „๊ณต/์„์‚ฌ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ๋ด‰ํ•ฉ๋งค๊ฐœ ์ง€ํ˜ˆ๊ธฐ๊ตฌ๋Š” ํ˜ˆ๊ด€ ์กฐ์˜ ๊ฒ€์‚ฌ๋‚˜ ์ธํ„ฐ๋ฒค์…˜ ์‹œ์ˆ  ํ›„ ํ™˜์ž์˜ ์กฐ๊ธฐ ๋ณดํ–‰๊ณผ ํ•ญ์‘๊ณ ์š”๋ฒ• ์น˜๋ฃŒ ์ค‘์ธ ํ™˜์ž์˜ ํ•ฉ๋ณ‘์ฆ์„ ์ค„์ด๋Š”๋ฐ ์œ ์šฉํ•˜๋ฉฐ, ์žฌ์ฒœ์ž์˜ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋Š” ํ™˜์ž์—๊ฒŒ ์ฃผ๋กœ ์‚ฌ์šฉํ•œ๋‹ค. ๋ด‰ํ•ฉ์— ์‚ฌ์šฉ๋˜๋Š” ์žฌ๋ฃŒ๊ฐ€ ๋น„ํก์ˆ˜์„ฑ ๋ด‰ํ•ฉ์‚ฌ๋ฅผ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ์–ด, ์ด ๋ด‰ํ•ฉ์‚ฌ์˜ ์˜ค์—ผ์ด๋‚˜ ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ์˜ ๋ฐœ์ƒ์€ ๊ฐ์—ผ ๋ฐœ์ƒ์˜ ์›์ธ์ด ๋œ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ด‰ํ•ฉ๋งค๊ฐœ ์ง€ํ˜ˆ๊ธฐ๊ตฌ ์‚ฌ์šฉ์ด ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ•œ๋‹ค.๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ•์ผ๊ฐœ ๋Œ€ํ•™๋ณ‘์›์—์„œ 2013๋…„ 3์›” 1์ผ์—์„œ 2014๋…„ 8์›” 31์ผ ์‚ฌ์ด์— ํ˜ˆ๊ด€ ์กฐ์˜ ๊ฒ€์‚ฌ ๋˜๋Š” ์ธํ„ฐ๋ฒค์…˜ ์‹œ์ˆ ์„ ๋ฐ›์€ ํ™˜์ž ์ค‘ ๋ด‰ํ•ฉ๋งค๊ฐœ ์ง€ํ˜ˆ ๊ธฐ๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜๊ณ  ๊ฐ ํŠน์„ฑ์˜ ๋ณ€์ˆ˜ ๊ฐ’์ด ์ธก์ • ๊ฐ€๋Šฅํ•œ 460๋ช…์„ ๋Œ€์ƒ์ž๋กœ ์„ ์ •ํ•˜์˜€๋‹ค. ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ์˜ ๊ด€๋ จ์š”์ธ์„ ์ฐพ๊ธฐ ์œ„ํ•ด ๊ฐ ํ™˜์ž๊ฐ€ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์ผ๋ฐ˜์  ํŠน์„ฑ(์„ฑ๋ณ„, ๋‚˜์ด, ํก์—ฐ์—ฌ๋ถ€, ์Œ์ฃผ์—ฌ๋ถ€), ์ž„์ƒ์  ํŠน์„ฑ(๊ณ ํ˜ˆ์•• ์ง„๋‹จ์—ฌ๋ถ€, ๋‹น๋‡จ๋ณ‘ ์ง„๋‹จ์—ฌ๋ถ€, ๊ณ ์ง€ํ˜ˆ์ฆ ์ง„๋‹จ์—ฌ๋ถ€, ๋™๋งฅ๊ฒฝํ™” ์ง„๋‹จ์—ฌ๋ถ€, ์•” ์ง„๋‹จ์—ฌ๋ถ€), ํ•ด๋ถ€ํ•™์  ํŠน์„ฑ(ํ”ผํ•˜ ์กฐ์ง ๊นŠ์ด, ์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜), ๊ธฐ์ˆ ์  ํŠน์„ฑ(์ฒœ์ž๋ถ€์œ„ ํ”ผ๋ถ€ ์ ˆ๊ฐœ ๊ธธ์ด, ์‹œ์ˆ ์ž์˜ ๊ฒฝ๋ ฅ) ๋ณ€์ˆ˜๋ฅผ ์ •ํ•˜์—ฌ ํ›„ํ–ฅ์  ์กฐ์‚ฌ ํ›„ ๋ถ„์„ํ•˜์˜€๋‹ค.์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋Œ€์ƒ์ž์˜ ์„ฑ๋ณ„์€ ๋‚จ์„ฑ 336๋ช…, ์—ฌ์„ฑ 124๋ช…์ด์—ˆ์œผ๋ฉฐ, ํ‰๊ท  ์—ฐ๋ น์€ ๊ฐ๊ฐ 62์„ธ, 64์„ธ์˜€๋‹ค.๋‹จ๋ณ€์ˆ˜ ๋ถ„์„์—์„œ ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ๊ณผ ์œ ์˜ํ•œ ๊ด€๋ จ์„ฑ์„ ๋ณด์ธ ๋ณ€์ˆ˜๋Š” ํ”ผ๋ถ€์—์„œ ์ฒœ์ž ๋™๋งฅ๊นŒ์ง€์˜ ๊นŠ์ด๊ฐ€ ์–•์€ ๊ฒฝ์šฐ, ์ €์ฒด์ค‘(18.5ใŽ/ใŽก ๋ฏธ๋งŒ)์ธ ๊ฒฝ์šฐ(p<0.001), ํ”ผ๋ถ€ ์ ˆ๊ฐœ ๊ธธ์ด๊ฐ€ ์งง์€ ๊ฒฝ์šฐ์˜€์œผ๋ฉฐ(p=0.003), ์‹œ์ˆ ์ž์˜ ๊ฒฝ๋ ฅ์— ๋”ฐ๋ฅธ ์ฐจ์ด๋Š” ์—†์—ˆ๋‹ค. ์„ฑ๋ณ„๊ณผ ๋‚˜์ด๋ฅผ ๋ณด์ •ํ•œ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ์‹œํ–‰ํ•œ ๊ฒฐ๊ณผ, ํ”ผ๋ถ€์—์„œ ์ฒœ์ž ๋™๋งฅ๊นŒ์ง€์˜ ๊นŠ์ด(OR 0.27, 95% CI 0.13-0.56), ์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜(OR 0.62, 95% CI 0.51-0.75) ๋˜๋Š” ํ”ผ๋ถ€ ์ ˆ๊ฐœ ๊ธธ์ด(OR 0.42, 95% CI 0.23-0.76)๋Š” ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ ๊ฐ์†Œ์™€ ์œ ์˜ํ•œ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค.๊ฒฐ๋ก ๋ด‰ํ•ฉ๋งค๊ฐœ ์ง€ํ˜ˆ๊ธฐ๊ตฌ ์‚ฌ์šฉ ์‹œ ํ™˜์ž์˜ ํ”ผ๋ถ€โˆผ์ฒœ์ž ๋™๋งฅ ๊นŠ์ด๊ฐ€ ์–•๊ฑฐ๋‚˜, ์ €์ฒด์ค‘, ๋˜๋Š” ํ”ผ๋ถ€ ์ ˆ๊ฐœ ๊ธธ์ด๊ฐ€ ์งง์€ ํ™˜์ž์—์„œ๋Š” ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ ๋ฐœ์ƒ ์œ„ํ—˜์ด ๋†’๋‹ค. ์ž„์ƒ์—์„œ ์ด๋Ÿฌํ•œ ๊ด€๋ จ์š”์ธ์„ ๊ณ ๋ คํ•จ์œผ๋กœ์จ ๊ฐœ๋ฐฉ์„ฑ ์ฐฝ์ƒ๊ณผ ๊ทธ์— ๋”ฐ๋ฅธ ๊ฐ์—ผ ๋“ฑ ๋ถ€์ž‘์šฉ์„ ์˜ˆ๋ฐฉํ•˜๊ธฐ ์œ„ํ•œ ์ธ์‹ ๊ฐœ์„ ๊ณผ ์กฐ์น˜๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์ด๋‹ค.ope

    ์ง์—…๊ณ„๊ณ  ์ง€์—ญ ๋ถ„ํฌ์™€ ์ง์—…๊ต์œก ๊ธฐํšŒ

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    ๊ฐ•์›, ์ „๋‚จ ๋“ฑ ์‹œยท๊ตฐยท๊ตฌ ๋‹จ์œ„์—์„œ ์ง์—…๊ณ„๊ณ ๊ฐ€ ์„ค์น˜๋˜์ง€ ์•Š์€ ์ผ๋ถ€ ์‹œยท๋„์˜ ๊ฒฝ์šฐ, ๊ธธ์–ด์ง„ ํ†ตํ•™๊ฑฐ๋ฆฌ๋กœ ์ธํ•ด ํ•™์ƒ๊ณผ ํ•™๋ถ€๋ชจ์˜ ๋ถ€๋‹ด์ด ๋ฐœ์ƒํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ง€์—ญ ์‚ฐ์—…์ฒด ๋˜ํ•œ ์ธ๊ทผ ์ง€์—ญ ๋‚ด ์ˆ™๋ จ ๊ธฐ์ˆ  ์ธ์žฌ ํ™•๋ณด์— ์–ด๋ ค์›€์„ ๊ฒช๊ณ  ์žˆ์Œ. ๊ฒฝ๊ธฐ ๋“ฑ ์ผ๋ถ€ ์ˆ˜๋„๊ถŒ ์ง€์—ญ์˜ ๊ฒฝ์šฐ ํ•™์ƒ ์ž์›์€ ๋น„๊ต์  ํ’๋ถ€ํ•œ ํŽธ์ด๋‚˜ ์ผ๋ฐ˜๊ณ  ์„ ํ˜ธ ํ˜„์ƒ์œผ๋กœ ์‹ ๋„์‹œ์— ์ง์—…๊ณ„๊ณ ๋ฅผ ์„ค์น˜ํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์ด ์žˆ์Œ. ๊ฐ•์› ๋“ฑ ์ผ๋ถ€ ์‹œยท๋„ ์ง€์—ญ์—์„œ๋Š” ์ง์—…๊ณ„๊ณ  ์ง€์—ญ ํŽธ์ค‘ ํ˜„์ƒ์œผ๋กœ ์ธํ•ด ICT๋‚˜ ๋””์ž์ธ๊ณผ ๊ฐ™์ด ์ธ๊ธฐ ์žˆ๋Š” ํ•™๊ณผ์˜ ์ง์—…๊ต์œก์„ ๋ฐ›์„ ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๊ฐ€ ์ œํ•œ๋˜์–ด ์žˆ์Œ.In some provinces, such as Gangwon-do and Jeollanam-do, where vocational high school has not been established at the district or city level, the students and parents are burdened due to the long distance to schools, and the local industries also have difficulties in securing skilled workers. In some metropolitan areas, such as Gyeonggi-do, although the number of students is relatively high, it is difficult to set up vocational high schools in new cities due to the preference for general high schools. In some cities and provinces, including Gangwon-do, there are limited opportunities for students to be enrolled in popular vocational courses, such as ICT and design, due to the regional bias of vocational high schools. *The full-text is available in Korean only

    ์ง€๋‚œ 10๋…„๊ฐ„ OECD ๊ตญ๊ฐ€์˜ ์ค‘๋“ฑ๋‹จ๊ณ„ ์ง์—…๊ณ„๊ณ  ํ•™์ƒ ๋น„์ค‘ ๋ณ€ํ™” ๋ถ„์„๊ณผ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๋Œ€์‘ ๋ฐฉ์•ˆ

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    2015๋…„ ์ง์—…๊ณ„๊ณ  ์ž…ํ•™์ •์›์€ 11๋งŒ 3์ฒœ ๋ช…์œผ๋กœ, ์ „์ฒด ํ•™์ƒ์˜ 19.0%๋ฅผ ์ฐจ์ง€ํ•จ. ์šฐ๋ฆฌ๋‚˜๋ผ ์ค‘๋“ฑ๋‹จ๊ณ„ ์ง์—…๊ณ„๊ณ  ํ•™์ƒ ๋น„์ค‘์€ OECD ํ‰๊ท (49.1%)๋ณด๋‹ค ๋‚ฎ์€ 16.7%๋กœ ์ตœํ•˜์œ„ ์ˆ˜์ค€์ด๋ฉฐ, ๋‹ค๋ฅธ OECD ๊ตญ๊ฐ€์— ๋น„ํ•ด ์ง€๋‚œ 10๋…„ ๊ฐ„ ์ค‘๋“ฑ๋‹จ๊ณ„ ์ง์—…๊ณ„๊ณ  ํ•™์ƒ ๋น„์ค‘์˜ ํ•˜๋ฝํญ์ด ํผ. 2015๋…„ ์ง์—…๊ณ„๊ณ  ์ž…ํ•™ ์ˆ˜์š”๋Š” 14๋งŒ 7์ฒœ ๋ช…(24%)์œผ๋กœ ์•ฝ 3๋งŒ 1์ฒœ ๋ช…์˜ ์ดˆ๊ณผ ์ˆ˜์š”๊ฐ€ ์กด์žฌํ•จ. ์ค‘์žฅ๊ธฐ ์ธ๋ ฅ์ˆ˜๊ธ‰์ „๋ง์— ๋”ฐ๋ฅด๋ฉด ๊ณ ์กธ ์ˆ˜์ค€์˜ ๊ธฐ์ˆ ยท๊ธฐ๋Šฅ ์ธ๋ ฅ ํ™•๋ณด์— ์–ด๋ ค์›€์ด ์žˆ์œผ๋‚˜ ์ง์—…๊ณ„๊ณ  ํ•™์ƒ ์ˆ˜๋Š” ์ง€์†์ ์œผ๋กœ ๊ฐ์†Œํ•˜๊ณ  ์žˆ์Œ.In Korea, the total number of students who enrolled in vocational high schools in 2015 was 113,000, accounting for 19.0% of whole students. The proportion of vocational high school students in Korea is 16.7% which is lower than the OECD average of 49.1%, and its declining rate is higher than other OECD countries. In 2015, 147,000(24%) places were in demand for vocational high school admissions, suggesting that there was an excess demand of about 31,000 places. According to the mid to long-term skills supply/demand forecast, although there are difficulties in securing skilled workers and technicians at high school graduate level, the number of vocational high school students has been decreasing continuously. *The full-text is available in Korean only

    ์ •๊ทœ ์‚ผ๋ณ€์ˆ˜ ์‚ผ๊ฐํ˜•์‹์˜ ๋ถ„๋ฅ˜

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ˆ˜๋ฆฌ๊ณผํ•™๋ถ€,2019. 8. ์˜ค๋ณ‘๊ถŒ.Let T_x=x(x+1)/2. For positive integers a_1,a_2,...,a_k, a polynomial of the form a_1T_{x_1}+a_2T_{x_2}+...+a_kT_{x_k} is called a triangular form. In this thesis, we study various properties of representations of integers by ternary and quaternary triangular forms. A triangular form is called regular if it represents all positive integers that are locally represented. We classify the regular ternary triangular forms. We also prove several conjectures of Sun regarding the number of representations of integers by ternary and quaternary triangular forms.๋‹คํ•ญ์‹ T_x ๋ฅผ x(x+1)/2 ๋ผ ์ •์˜ํ•˜์ž. ์ž์—ฐ์ˆ˜ a_1,a_2,...,a_k ์— ๋Œ€ํ•ด a_1T_{x_1}+a_2T_{x_2}+...+a_kT_{x_k} ํ˜•ํƒœ์˜ ๋‹คํ•ญ์‹์„ ์‚ผ๊ฐํ˜•์‹์ด๋ผ ๋ถ€๋ฅธ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์‚ผ๋ณ€์ˆ˜ ์‚ผ๊ฐํ˜•์‹๊ณผ ์‚ฌ๋ณ€์ˆ˜ ์‚ผ๊ฐํ˜•์‹์˜ ํ‘œํ˜„์— ๊ด€ํ•œ ๋‹ค์–‘ํ•œ ์„ฑ์งˆ์— ๊ด€ํ•ด ์—ฐ๊ตฌํ•œ๋‹ค. ์–ด๋– ํ•œ ์‚ผ๊ฐํ˜•์‹์ด ๊ตญ์†Œ์ ์œผ๋กœ ํ‘œํ˜„ํ•˜๋Š” ๋ชจ๋“  ์ž์—ฐ์ˆ˜๋ฅผ ๋Œ€์—ญ์ ์œผ๋กœ ํ‘œํ˜„ํ•˜๋Š” ๊ฒฝ์šฐ ์ด๋ฅผ ์ •๊ทœ ์‚ผ๊ฐํ˜•์‹์ด๋ผ ๋ถ€๋ฅธ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ •๊ทœ ์‚ผ๋ณ€์ˆ˜ ์‚ผ๊ฐํ˜•์‹์„ ๋ถ„๋ฅ˜ํ•œ๋‹ค. ๋˜ํ•œ ์‚ผ๋ณ€์ˆ˜ ํ˜น์€ ์‚ฌ๋ณ€์ˆ˜ ์‚ผ๊ฐํ˜•์‹์— ๊ด€ํ•œ Sun์˜ ๋‹ค์–‘ํ•œ ์ถ”์ธก๋“ค์„ ์ฆ๋ช…ํ•œ๋‹ค.1. Introduction 1 2. Preliminaries 6 2.1. Triangular numbers and triangular forms 6 2.2. Quadratic spaces and lattices 8 2.3. Watson transformations 12 3. Regular ternary triangular forms 14 3.1. The descending trick 14 3.2. Stable regular ternary triangular forms 16 3.3. Classifications of regular ternary triangular forms 33 4. The number of representations of ternary triangular forms 49 4.1. The number of representations of ternary triangular forms 49 4.2. Triangular forms and diagonal quadratic forms 51 5. The number of representations of quaternary triangular forms 65 Abstract (in Korean) 79Docto
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