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    ์„ ํƒ์  ๋ ˆ์ด์ € ์šฉ์œต ์ ์ธต ์ œ์กฐ ๊ณต์ •์˜ ์šฉ์œตํ’€ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ ๊ธฐ๊ณต ๊ฐ์ง€

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ•ญ๊ณต์šฐ์ฃผ๊ณตํ•™๊ณผ, 2022.2. ์œค๊ตฐ์ง„.๋ณธ ํ•™์œ„๋…ผ๋ฌธ์€ ์„ ํƒ์  ์†Œ๊ฒฐ ๋ฐฉ์‹์˜ 3D ํ”„๋ฆฐํŒ… ์ ์ธต๊ณต์ •์œผ๋กœ ์ถœ๋ ฅ๋œ ๋Œ€์ƒ์˜ ๋‚ด๋ถ€ ๊ธฐ๊ณต์„ ํƒ์ง€ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๊ด€ํ•œ ๋…ผ๋ฌธ์ด๋‹ค. ๊ธˆ์† ์ ์ธต์ œ์กฐ๊ณต๋ฒ•์€ ํ˜•์ƒ์ด ๋ณต์žกํ•œ ๋ถ€ํ’ˆ์„ ์ „ํ†ต์ ์ธ ์ œ์กฐ๋ฐฉ์‹ (์ ˆ์‚ญ, ์ฃผ์กฐ ๋“ฑ) ๋ณด๋‹ค ๋น„๊ต์  ์‰ฝ๊ณ  ๋น ๋ฅด๊ฒŒ ์ œ์ž‘ํ•  ์ˆ˜ ์žˆ๋Š” ์žฅ์ ์ด ์žˆ์ง€๋งŒ, ์—ฐ์†์ ์œผ๋กœ ๋ถ„๋ง์„ ์šฉ์œต-์†Œ๊ฒฐ์‹œ์ผœ ์ œ์ž‘ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ทธ ๊ณผ์ •์—์„œ ์ผ์–ด๋‚˜๋Š” ๋‹ค์–‘ํ•œ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ์˜ํ•ด ๊ฒฐํ•จ์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ ๋ถ€ํ’ˆ์˜ ํ’ˆ์งˆ์„ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•ด X-ray๋ฅผ ํ™œ์šฉํ•œ ๋น„ํŒŒ๊ดด์ ์ธ ๊ฒ€์‚ฌ ๋ฐฉ๋ฒ•์ด ์ฃผ๋กœ ์‚ฌ์šฉ๋˜์–ด์™”์ง€๋งŒ, ๋น„์šฉ๊ณผ ์‹œ๊ฐ„์ด ๋งŽ์ด ์†Œ์š”๋œ๋‹ค๋Š” ๋‹จ์ ์ด ์žˆ๋‹ค. ์ด๋ฅผ ๊ทน๋ณตํ•˜๊ณ ์ž ๊ณต์ • ์ค‘์˜ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ (์ด๋ฏธ์ง€, ์ŒํŒŒ์‹ ํ˜ธ ๋“ฑ)๋ฅผ ์ธ๊ณต์ง€๋Šฅ๊ณผ ๊ฒฐํ•ฉํ•œ ๋ฐฉ๋ฒ•๋“ค์ด ์‹œ๋„๋˜์—ˆ๊ณ  ๋Š์ž„์—†์ด ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ถ€๊ฐ€์ ์ธ ๋ฐ์ดํ„ฐ ํš๋“ ์žฅ์น˜ ์—†์ด ๊ณต์ • ์ค‘ ์šฉ์œตํ’€์—์„œ ๋ฐ˜์‚ฌ๋œ ๊ด‘๋Ÿ‰ ์‹ ํ˜ธ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ 3์ฐจ์› ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง (3D-CNN) ํ•™์Šต์„ ํ†ตํ•ด ๊ฒฐํ•จ (lack-of-fusion ๋ฐ keyhole ๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ๊ธฐ๊ณต)์„ ์˜ˆ์ธกํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ํ›ˆ๋ จ ๋ฐ ๊ฒ€์ฆ์„ ์œ„ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด ๊ณต์ • ๋งค๊ฐœ๋ณ€์ˆ˜์ธ ์—๋„ˆ์ง€๋ฐ€๋„๋ฅผ 19.84 J/mm^3 ์—์„œ 110.12 J/mm^3 ๊นŒ์ง€ ์ž„์˜๋กœ ์„ค์ •ํ•˜์—ฌ ์ธ๊ณต์ ์œผ๋กœ ๊ธฐ๊ณต์ด ํ˜•์„ฑ๋˜๋Š” ์‹œํŽธ์„ ์ œ์ž‘ํ•œ๋‹ค. ์ œ์•ˆ๋œ ์‹ ๊ฒฝ๋ง์€ ๊ณต์ • ์ค‘ ์ˆ˜์ง‘๋œ 3์ฐจ์›ํ™”๋œ ๊ด‘๋Ÿ‰ ์‹ ํ˜ธ๋ฅผ ์ž…๋ ฅ์œผ๋กœ ๋ฐ›์•„ ์ž‘์€ ํฌ๊ธฐ์˜ 3D moving window๋กœ ์Šค์บ”ํ•˜์—ฌ ๊ตญ๋ถ€์  ๊ฒ€์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋ฉฐ, micro-CT ๊ฒฐ๊ณผ๋กœ ๋ผ๋ฒจ๋ง ๋œ ์ถœ๋ ฅ๊ฐ’๊ณผ ๋งค์นญ๋˜์–ด ํ•™์Šต๋œ๋‹ค. ์ถœ๋ ฅ๊ฐ’์œผ๋กœ ๊ธฐ๊ณต์˜ ์ข…๋ฅ˜์™€ ๊ตญ์†Œ๋ถ€ํ”ผ๋ถ„์œจ์„ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด ๋ถ„๋ฅ˜ (classification) ์™€ ํšŒ๊ท€ (regression) ์ด ๋™์‹œ์— ๊ณ„์‚ฐ๋˜๋Š” ๋ชจ๋ธ์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ํ›ˆ๋ จ๋œ ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ๊ธฐ๊ณต์ด ์ž„์˜๋กœ ๋ฐฐ์น˜๋œ ํ…Œ์ŠคํŠธ์šฉ ์‹œํŽธ์ด ์ œ์ž‘๋˜์—ˆ์œผ๋ฉฐ, ๊ทธ ๊ฒฐ๊ณผ ์ œ์•ˆ๋œ ๋ชจ๋ธ์€ lack-of-fusion ๋ฐ keyhole ๋‘๊ฐ€์ง€ ๊ฒฝ์šฐ ๋ชจ๋‘์—์„œ ์ง๊ฒฝ์ด 80 ฮผm ์ด์ƒ์ธ ๊ธฐ๊ณต์„ ์ตœ๋Œ€ 78.37%์˜ ์ง„์–‘์„ฑ๋ฅ  (true positive ratio) ๋กœ ๊ฒ€์ถœํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.This thesis is about a method for detecting the internal pores in the additive manufacturing (AM) process, especially selective laser melting (SLM). Metal additive manufacturing has the advantage of producing parts with complex shapes more easily and quickly than traditional manufacturing methods (cutting, casting, etc.). As this gradually expanded, non-destructive inspection methods using X-rays have been mainly used to ensure the quality of parts, but they have the disadvantage of being costly and time-consuming. To overcome such limitations, several methods using various data (images, acoustic signals, etc.) in artificial intelligence have been attempted. In this thesis, defects caused by lack-of-fusion and keyholes pores are predicted through a three-dimensional convolutional neural network (3D-CNN) based on photodiode light intensity data reflected from the melt pool during the process. Specimens with artificial defects are manufactured by arbitrarily setting the energy density from 19.84 J/ใ€–mmใ€—^3 to 110.12 J/ใ€–mmใ€—^3, as a process parameter. The proposed network takes the three-dimensional light intensity data collected during the process as an input, scans it with a small 3D moving window performing local inspection, and is trained by matching the output value labeled with the micro-CT results. In order to predict the type of pores and the local volume fraction as output values, a joint model is used which classification and regression are calculated simultaneously. Furthermore, test specimens with random pores are fabricated to evaluate the performance. As a result, the proposed model can detect pores with a diameter over 80 ฮผm with a true positive ratio of up to 78.37% in both lack-of-fusion and keyhole cases.1. Introduction 7 1.1. Motivation 7 2. Backgrounds and related research 9 2.1. Theoretical background 9 2.1.1. Metal additive manufacturing 9 2.1.2. Melt pool monitoring system 11 2.1.3. Computed tomography (CT) analysis 14 2.1.4. Convolutional neural network 16 2.2. Related research 17 2.2.1. Acoustic signal based defect detection 17 2.2.2. Image-based defect detection 18 3. Experimental Setup 21 3.1. Material and Equipment 21 3.1.1. Material 21 3.1.2. Equipment 22 3.2. Design of specimen with pores 23 3.3. Porosity analysis with X-ray microscopes 27 3.4. Melt pool monitoring data preparation 30 3.4.1. Preprocessing of MPM data 31 3.4.2. Training and validation dataset labeling 35 4. Pore Detection Method 38 4.1. 3D-CNN model for pore detection 38 4.2. The decision of hyperparameters for 3D-CNN 42 5. Results and discussion 44 5.1. The pore distribution of specimen 44 5.2. Pore prediction results 50 5.2.1. Test Specimen configuration 50 5.2.2. Evaluation with test dataset of test specimens 53 6. Conclusion 60์„

    A Pattern of Commercial Gentrification on the Periphery of the Cultural Quarter near Hongik University

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€ ๋„์‹œ์„ค๊ณ„ ์ „๊ณต, 2016. 2. ๊ถŒ์˜์ƒ.์ตœ๊ทผ๋“ค์–ด ์ด๊ตญ์ ์ด๊ณ  ๋‹ค์–‘ํ•œ ๋ฌธํ™”์— ๋Œ€ํ•œ ์†Œ๋น„์š•๊ตฌ์˜ ์ฆ๊ฐ€์™€ ์—ฌ๊ฐ€ ๋ฐ ์‚ฌํšŒ์  ํ™œ๋™์„ ๋ชฉ์ ์œผ๋กœ ํ•˜๋Š” ์ƒ์—…์ง€์—ญ ๋ฐฉ๋ฌธ์˜ ์ฆ๊ฐ€๋กœ ์ธํ•˜์—ฌ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์ด๋ผ ๋ถˆ๋ฆฌ์šฐ๋Š” ์ƒˆ๋กœ์šด ์œ ํ˜•์˜ ์ƒ์—…๊ณต๊ฐ„์ด ์ฃผ๋ชฉ ๋ฐ›๊ณ  ์žˆ๋‹ค. ์ง€์†์ ์ธ ๋ฐฉ๋ฌธ๊ฐ์˜ ์ฆ๊ฐ€์™€ ํ™œ์„ฑํ™”๋กœ ์‚ผ์ฒญ๋™, ์ดํƒœ์›, ํ™๋Œ€์™€ ๊ฐ™์€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„๋“ค์€ ์„œ์šธ์˜ ๋Œ€ํ‘œ์ ์ธ ์ƒ์—…๊ณต๊ฐ„์œผ๋กœ ์ž๋ฆฌ๋งค๊น€ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ํ™œ์„ฑํ™”์— ๋”ฐ๋ฅธ ์ƒ์Šน๋œ ์ž„๋Œ€๋ฃŒ์™€ ๋ณด๋‹ค ํฐ ์ž๋ณธ์„ ๊ฐ–๋Š” ์ƒ์ ๋“ค์˜ ์นจํˆฌ๋กœ ๊ธฐ์กด์˜ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ๋…ํŠนํ•œ ์žฅ์†Œ์„ฑ์„ ํ˜•์„ฑํ•˜์˜€๋˜ ์†Œ๊ทœ๋ชจ์˜ ๊ฐœ์„ฑ์  ์ƒ์ ๋“ค์€ ์—†์–ด์ง€๊ฑฐ๋‚˜ ๋ณด๋‹ค ์ €๋ ดํ•œ ์ž„๋Œ€๊ณต๊ฐ„์„ ์ฐพ์•„ ๋‹ค๋ฅธ์ง€์—ญ์œผ๋กœ ์ด๋™ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ณผ์ • ์†์—์„œ ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ๊ฒฝ์šฐ ์ƒ๊ถŒ์˜ ์˜์—ญ์ด ์ง€์†์ ์œผ๋กœ ํ™•์žฅ๋˜๊ณ  ์žˆ๋‹ค. ๊ธฐ์กด ์ค‘์‹ฌ์ƒ๊ถŒ ์ฃผ๋ณ€๋ถ€, ์ฆ‰ ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„ ๊ฒฝ๊ณ„๋ถ€์— ์œ„์น˜ํ•œ ์ฃผ๊ฑฐ์ง€์—ญ์œผ๋กœ ์ƒ์—…์šฉ๋„๊ฐ€ ํ™•์‚ฐ๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ๋…ํŠนํ•œ ๊ฐœ์„ฑ์„ ๊ฐ–๋Š” ์ƒ์ ๋“ค์ด ์ž…์ง€ํ•˜๋ฉด์„œ ์ƒˆ๋กœ์šด ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์„ ํ˜•์„ฑํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์ผ๋ จ์˜ ๊ณผ์ •์„ ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ™•์žฅ๊ณผ์ •์œผ๋กœ ๋ณด๊ณ , ์ „์ฒด ํ™๋Œ€๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ‰๋ฉด์  ํ™•์žฅ๊ณผ์ •์˜ ์–‘์ƒ๊ณผ ํ™•์žฅ๊ณผ์ • ์†์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒฝ๊ณ„๋ถ€ ์ฃผ๊ฑฐ์ง€์—ญ์— ์œ„์น˜ํ•œ ๊ฐœ๋ณ„๊ฑด์ถ•๋ฌผ๋“ค์˜ ๋ณ€ํ™” ํŠน์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋ฐœ๋‹ฌํ•˜๋Š” ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ๊ฒฝ๊ณ„๋ถ€์— ์œ„์น˜ํ•œ ์ฃผ๊ฑฐ์ง€์—ญ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์šฉ๋„ ๋ฐ ๋ฌผ๋ฆฌ์ ์ธ ๋ณ€ํ™”์˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ดํ•ดํ•˜๊ณ , ํ–ฅํ›„ ์ด๋Ÿฌํ•œ ์ง€์—ญ์˜ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•œ ์ œ๋„์  ์žฅ์น˜ ๋งˆ๋ จ์— ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ดˆ ์ž๋ฃŒ๋ฅผ ๋งŒ๋“œ๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋Š” ํฌ๊ฒŒ ์ƒ์—…์šฉ๋„์˜ ํ™•์žฅ๊ณผ์ •์˜ ์–‘์ƒ์„ ๋ถ„์„ํ•˜๋Š” ๋ถ€๋ถ„๊ณผ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์„ ๊ตฌ์„ฑํ•˜๋Š” ์„ธ๋ถ€์šฉ๋„์‹œ์„ค๋“ค์˜ ์šฉ๋„๋ณ€ํ™” ๋ฐ ๊ฑด์ถ•๋ฌผ ์™ธ๊ด€๋ณ€ํ™”์˜ ํŠน์„ฑ์„ ๋ถ„์„ํ•˜๋Š” ๋ถ€๋ถ„์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. 4์žฅ์—์„œ๋Š” ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ™•์žฅ์˜ ์–‘์ƒ์„ ๊ด‘์—ญ์  ์ฐจ์›๊ณผ ๋ฏธ์‹œ์ ์ฐจ์›์—์„œ์˜ ์ƒ์—…์šฉ๋„์˜ ํ™•์žฅ๊ณผ์ •์„ ๋ถ„์„ํ•˜์˜€๊ณ , 5์žฅ์—์„œ๋Š” ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์„ ๊ตฌ์„ฑํ•˜๋Š” ์‹œ์„ค์˜ ์šฉ๋„๋ฅผ ๋ณด๋‹ค ์„ธ๋ถ€์ ์œผ๋กœ ๋‚˜๋ˆ ์„œ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„ ๊ฒฝ๊ณ„๋ถ€์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์šฉ๋„ ๋ฐ ๊ฑด์ถ•๋ฌผ์˜ ์™ธ๊ด€ ๋ณ€ํ™”์˜ ํŠน์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ํ™•์žฅ ๋ฐ ๋ณ€ํ™”๊ณผ์ •์˜ ํ•ด์„์„ ์œ„ํ•˜์—ฌ 3์žฅ์—์„œ ์ง€์—ญ์˜ ๊ฐ€๋กœ๊ณต๊ฐ„๊ตฌ์กฐ ๋ฐ ํ•„์ง€ ํŠน์„ฑ์„ ๋ถ„์„ํ•˜์˜€๊ณ , ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ™๋Œ€๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ™•์žฅ๊ณผ ๊ฒฝ๊ณ„๋ถ€์˜ ๋ณ€ํ™”๋ฅผ ํ•ด์„ํ•˜์—ฌ ๊ทธ ํŠน์„ฑ์„ ๋„์ถœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ด‘์—ญ์  ์ฐจ์›์—์„œ ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ์ƒ์—…์šฉ๋„ ํ™•์žฅ๊ณผ์ •์„ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ๊ธฐ์กด ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ๊ฒฝ๊ณ„๋ถ€์˜ ๋ชจ๋“  ์ง€์—ญ์—์„œ ๋™์ผํ•˜๊ฒŒ ์ƒ์—…์šฉ๋„๋กœ์˜ ๋ณ€ํ™”์™€ ํ™•์žฅ์ด ์ผ์–ด๋‚˜์ง€๋Š” ์•Š์œผ๋ฉฐ, ์„ ํƒ์ ์œผ๋กœ ์ƒ์—…์šฉ๋„๋กœ ๋ณ€ํ™”ํ•˜๋Š” ๊ฑด์ถ•๋ฌผ์˜ ๋ถ„ํฌ๊ฐ€ ์ง‘์ค‘๋จ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ƒ์—…์šฉ๋„๋กœ์˜ ๋ณ€ํ™”๊ฐ€ ์ง‘์ค‘์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ์ง€์—ญ์€ ํ†ตํ•ฉ๋„์™€ ๊ฐ™์ด ๊ฐ€๋กœ๊ณต๊ฐ„๊ตฌ์กฐ์ƒ์—์„œ ์œ ๋ฆฌํ•œ ์œ„์น˜์— ์žˆ๋Š” ๊ฐ€๋กœ์™€ ๋งž๋‹ฟ์•„ ์žˆ๋Š” ์ง€์—ญ์ด์—ˆ์œผ๋ฉฐ, ํŠนํžˆ ๋ณดํ–‰ํ๋ฆ„์˜ ์ค‘์‹ฌ์ด ๋˜๋Š” ๊ฐ€๋กœ์— ์ธ์ ‘ํ•œ ๊ฒฝ์šฐ์— ๋จผ์ € ์ƒ์—…ํ™”๊ฐ€ ๋จ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ์ƒ์—…์šฉ๋„์˜ ํ™•์žฅ๊ณผ์ •์—์„œ ์ฐจ๋Ÿ‰ ์ค‘์‹ฌ์˜ ๊ฐ„์„ ๊ฐ€๋กœ๋Š” ํ™•์žฅ์— ์žฅ์• ์ ์ธ ์š”์†Œ๋กœ ์ž‘๋™ํ•˜๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ๋ณดํ–‰ํ๋ฆ„์˜ ์ค‘์‹ฌ์ด ๋˜๋Š” ๋„๋กœ๊ฐ€ ์ƒ์—…์šฉ๋„์˜ ํ™•์žฅ๊ณผ์ •์—์„œ ์ค‘์‹ฌ์ ์ธ ์—ญํ• ์„ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„ ๊ฒฝ๊ณ„๋ถ€์— ์œ„์น˜ํ•œ ๊ฐ€๊ตฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ง„ํ–‰ํ•œ ๋ฏธ์‹œ์  ์ฐจ์›์—์„œ์˜ ์ƒ์—…์šฉ๋„๋ณ€ํ™”๊ณผ์ •์˜ ๋ถ„์„์—์„œ๋„ ๋ณดํ–‰ํ๋ฆ„์˜ ์ค‘์‹ฌ์ด ๋˜๋Š” ๊ฐ€๋กœ์™€ ์ธ์ ‘ํ•œ ๊ฐ€๋กœ๋กœ๋ถ€ํ„ฐ ๊ฐ€๊ตฌ์˜ ๋‚ด๋ถ€๋กœ ์ƒ์—…์šฉ๋„์˜ ํ™•์žฅ์ด ์ง„ํ–‰๋˜๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ฐ€๊ตฌ ๋‚ด๋ถ€๋กœ์˜ ์ƒ์—…์šฉ๋„ ํ™•์žฅ๊ณผ์ •์—์„œ๋Š” ๊ตญ๋ถ€ํ†ตํ•ฉ๋„๋‚˜ ํ‰๊ท ์‹ฌ๋„์™€ ๊ฐ™์€ ๊ฐ’๋ณด๋‹ค๋Š” ๋ณดํ–‰ํ๋ฆ„์˜ ์ค‘์‹ฌ์ด ๋˜๋Š” ํŠน์ • ๊ฐ€๋กœ๋กœ๋ถ€ํ„ฐ์˜ ์‹ฌ๋„, ์ง€ํ•˜์ฒ ์—ญ๊ณผ ๊ฐ™์€ ๋ณดํ–‰ํ™œ๋™ ์œ ๋ฐœ์‹œ์„ค๋กœ๋ถ€ํ„ฐ์˜ ์ ‘๊ทผ์„ฑ์ด ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜์˜€๋‹ค. ๊ฐ€๋กœ๊ณต๊ฐ„๊ตฌ์กฐ ์™ธ์—๋„ ๊ฑด์ถ•๋ฌผ์˜ ํ•„์ง€๋ฉด์ , ์—ฐ๋ฉด์ , ๊ฑด์ถ•๋ฉด์ , ์šฉ์ ๋ฅ ๊ณผ ๊ฐ™์€ ๊ทœ๋ชจ์™€ ๊ด€๋ จ๋œ ํŠน์„ฑ๊ณผ ๋…ธํ›„๋„, ๊ฑด์ถ•๋ฌผ์˜ ์œ ํ˜•์ด ์ƒ์—…ํ™”์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ์œผ๋ฉฐ, ํŠนํžˆ ์กฐ์ ์กฐ์˜ ์†Œ๊ทœ๋ชจ ๋‹จ๋…, ๋‹ค๊ฐ€๊ตฌ ์ฃผํƒ์œผ๋กœ ์ƒ์—…์šฉ๋„์‹œ์„ค์ด ๋งŽ์ด ์ž…์ง€ํ•˜๋Š” ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ์—ˆ๊ณ , ํ•„๋กœํ‹ฐ ์ฃผ์ฐจ์žฅ์„ ํฌํ•จํ•˜๋Š” ๋‹ค์„ธ๋Œ€ ์ฃผํƒ์€ ์ƒ์—…ํ™”๊ฐ€ ์ž˜ ๋˜์ง€ ์•Š๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋ก ์  ๊ณ ์ฐฐ์„ ํ†ตํ•ด ๋ณด๋ฉด ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„ ํ˜•์„ฑ์˜ ๋ฐฐ๊ฒฝ์ด ๋˜๋Š” ์‹œ์„ค์€ ์˜ˆ์ˆ ๋ฌธํ™”์™€ ๊ด€๋ จํ•œ ์‹œ์„ค์ด์—ˆ์œผ๋ฉฐ, ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ์‹ค์ œ ์žฅ์†Œ์„ฑ์„ ํ˜•์„ฑํ•˜๋Š” ์ฃผ์š” ์‹œ์„ค์€ ๋…ํŠนํ•œ ์นดํŽ˜/๋ ˆ์Šคํ† ๋ž‘/ํŽ๊ณผ ์†Œ๊ทœ๋ชจ์˜ ๋…๋ฆฝ ํŒ๋งค์‹œ์„ค์ด์—ˆ๋‹ค. ์  ํŠธ๋ฆฌํ”ผ์ผ€์ด์…˜์˜ ๊ณผ์ •์—์„œ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ๊ฐœ์„ฑ์„ ์—†์• ๋Š” ์‹œ์„ค์€ ์ผ๋ฐ˜์ ์ธ ์ƒ์—…์‹œ์„ค๊ณผ ํ”„๋žœ์ฐจ์ด์ฆˆ ์ƒ์—…์‹œ์„ค๋กœ ์ด๋“ค ์‹œ์„ค๋“ค์˜ ๋ถ„ํฌํŠน์„ฑ๊ณผ ์ƒ์—…ํ™” ๊ณผ์ •์—์„œ ์šฉ๋„์˜ ๋ณ€ํ™”๊ณผ์ •์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋Œ€์ƒ์ง€์˜ ์ฃผ์š” ์˜ˆ์ˆ ๋ฌธํ™”์™€ ๊ด€๋ จ๋œ ์‹œ์„ค์€ ์Œ์•… ๋ฐ ๊ณต์—ฐ๊ด€๋ จ์‹œ์„ค๋“ค์ด์—ˆ์œผ๋ฉฐ, ๊ฐ™์€ ๊ณ„์—ด์˜ ์˜ˆ์ˆ ํ™œ๋™๊ณผ ๊ด€๊ณ„๋œ ๋ฌธํ™”์˜ˆ์ˆ ์‹œ์„ค๊ณผ ๋ฌธํ™”๊ด€๋ จ์ƒ์—…์‹œ์„ค์€ ๋น„์Šทํ•œ ์ž…์ง€ํŠน์„ฑ์„ ๋ณด์ด๋ฉฐ, ์„œ๋กœ ๊ทผ์ ‘ํ•˜์—ฌ ์ž…์ง€ํ•˜์—ฌ ์žˆ๋Š” ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ™•์žฅ์„ ์ฃผ๋„ํ•˜๋Š” ์‹œ์„ค์€ ๋…ํŠนํ•œ ์นดํŽ˜/๋ ˆ์Šคํ† ๋ž‘/ํŽ๊ณผ ๊ฐ™์€ ์‹œ์„ค๋กœ ๊ธฐ์กด์˜ ์ฃผ๊ฑฐ์šฉ๋„์‹œ์„ค์ด๋‚˜ ์—…๋ฌด์šฉ๋„์‹œ์„ค์„ ๋Œ€์ฒดํ•˜์—ฌ ์ž…์ง€ํ•˜๋ฉฐ, ๋‹ค๋ฅธ ์œ ํ˜•์˜ ์ƒ์—…์‹œ์„ค๋ณด๋‹ค ๋จผ์ € ๋‚ด๋ถ€๊ฐ€๋กœ์— ์ž…์ง€ํ•˜๋Š” ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋Œ€์ƒ์ง€์˜ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ™•์žฅ๊ณผ์ •์—์„œ์˜ ์šฉ๋„๋ณ€ํ™”๋Š” 2012๋…„์„ ๊ธฐ์ ์œผ๋กœ ๊ทธ ํŠน์„ฑ์ด ๋ณ€ํ™”ํ•œ๋‹ค, 2012๋…„๊นŒ์ง€๋Š” ์ฃผ๋กœ ๋น„์ƒ์—…์šฉ๋„์‹œ์„ค์ด ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„ ๊ด€๋ จ์‹œ์„ค์ธ ๋…ํŠนํ•œ ์นดํŽ˜/๋ ˆ์Šคํ† ๋ž‘/ํŽ๊ณผ ์†Œ๊ทœ๋ชจ ๋…๋ฆฝ ํŒ๋งค์‹œ์„ค๋กœ ๋ฐ”๋€Œ๊ณ , 2012๋…„ ์ดํ›„์—๋Š” ๋…ํŠนํ•œ ์นดํŽ˜/๋ ˆ์Šคํ† ๋ž‘/ํŽ ๋“ฑ์˜ ์‹œ์„ค์ด ํ”„๋žœ์ฐจ์ด์ฆˆ ์ƒ์—…์‹œ์„ค๋กœ ๋ฐ”๋€Œ๋ฉด์„œ ์‚ฌ๋ก€ ๋Œ€์ƒ ๊ฐ€๋กœ๋‚ด์˜ ํ”„๋žœ์ฐจ์ด์ฆˆ ์ƒ์—…์‹œ์„ค์ด ๋งค์šฐ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์ƒ์—…ํ™”๊ฐ€ ์ง„ํ–‰๋˜๋ฉด์„œ ์ ํฌ๊ณต๊ฐ„์„ ํ™•์žฅํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ์žˆ๋Š”๋ฐ, ์ผ๋ฐ˜์ ์œผ๋กœ ๋น„์ƒ์—…์šฉ๋„์ธ ๋‹ค๋ฅธ ์ธต์˜ ์ ํฌ๊ณต๊ฐ„์„ ์ƒ์—…์šฉ ์ ํฌ๋กœ ๋ณ€ํ™”ํ•˜๋Š” ์ˆ˜์ง์  ํ™•์žฅ์˜ ๋ฐฉ์‹์ด ๊ฐ€์žฅ ๋งŽ์ด ์ด์šฉ๋œ๋‹ค. ์ด ์™ธ์— ์ธก๋ฉด๊ณต์ง€, ์ „๋ฉด๊ณต์ง€, ํ•„๋กœํ‹ฐ๊ณต๊ฐ„, ์ธก๋ฒฝ๋ถ€, ์ฐจ๊ณ  ๋“ฑ์˜ ์žํˆฌ๋ฆฌ๊ณต๊ฐ„์„ ์ ํฌํ™”ํ•˜๋Š” ๋ฐฉ์‹์ด ์žˆ๋Š”๋ฐ, ์†Œ๊ทœ๋ชจ์˜ ์ ํฌ๊ณต๊ฐ„์ด ๋งˆ๋ จ๋˜๊ธฐ ๋•Œ๋ฌธ์— ์ผ๋ฐ˜์ ์ธ ์ƒ์—…์‹œ์„ค์ด๋‚˜ ํ”„๋žœ์ฐจ์ด์ฆˆ ์ƒ์—…์‹œ์„ค์€ ์ด๋Ÿฌํ•œ ์ ํฌ๊ณต๊ฐ„์— ์ž…์ง€ํ•˜์ง€ ๋ชปํ•˜๋ฉฐ, ์†Œ๊ทœ๋ชจ ๋…๋ฆฝํŒ๋งค์‹œ์„ค์ด๋‚˜ ๋…ํŠนํ•œ ์นดํŽ˜/๋ ˆ์Šคํ† ๋ž‘/ํŽ๊ณผ ๊ฐ™์€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„๊ด€๋ จ์‹œ์„ค์ด ์ฃผ๋กœ ์ž…์ง€ํ•˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋‹ค๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์Œ์„ ์ฐพ์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ฌผ๋ฆฌ์ ์ธ ์™ธ๊ด€ ๋ณ€ํ™”์˜ ๋ถ„์„์„ ํ†ตํ•ด์„œ ์ƒ์—…ํ™” ๊ณผ์ •์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฑด์ถ•๋ฌผ ์œ ํ˜•๋ณ„ ์™ธ๊ด€๋ณ€ํ™”๋ฅผ ์œ ํ˜•ํ™”์˜ ๊ฒฐ๊ณผ, ๋น„์ƒ์—…์šฉ๋„ ๊ฑด์ถ•๋ฌผ์ด ๋‹ค๋…์ฃผํƒ, ๋‹ค๊ฐ€๊ตฌ์ฃผํƒ, ๋‹ค์„ธ๋Œ€์ฃผํƒ, ์˜คํ”ผ์Šค ๊ฑด์ถ•๋ฌผ์ด ์„œ๋กœ ๋‹ค๋ฅธ ํ˜•ํƒœ๋กœ ์ƒ์—…ํ™”๋˜๋Š” ํŠน์„ฑ์ด ์žˆ๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด ๊ณผ์ •์—์„œ ๋‹จ๋…์ฃผํƒ์˜ ๊ฒฝ์šฐ, ๋ฒฝ์˜ ์ œ๊ฑฐ ๋ฐ ๊ธฐ์กด์— ๋งˆ๋‹น์œผ๋กœ ์ด์šฉ๋˜์—ˆ๋˜ ์ „๋ฉด๊ณต์ง€๋กœ ์ ํฌ๋ฅผ ํ™•์žฅํ•˜์—ฌ ๋‚˜๊ฐ€๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์—ˆ์œผ๋ฉฐ, ๋‹ค๊ฐ€๊ตฌ์ฃผํƒ์˜ ๊ฒฝ์šฐ๋Š” ์ฃผ๊ฑฐ๊ณต๊ฐ„์œผ๋กœ์จ๋Š” ๋น„์„ ํ˜ธ๋˜๋Š” ๋ฐ˜์ง€ํ•˜์ธต์„ ์ ํฌ๋กœ ๋ณ€ํ™”์‹œํ‚ค๋Š” ํŠน์ง•์ด ์žˆ์—ˆ๋‹ค. ์ด์ƒ๊ณผ ๊ฐ™์€ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ธฐ์กด ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„ ์ฃผ๋ณ€๋ถ€์— ์œ„์น˜ํ•œ ๋‹จ๋… ๋ฐ ๋‹ค์„ธ๋Œ€ ์ฃผํƒ์ด ๋ฐ€์ง‘ํ•˜๊ณ  ์žˆ๋Š” ์ฃผ๊ฑฐ์ง€์—ญ๋“ค์— ๋Œ€ํ•ด์„œ ๊ธฐ์กด ์ƒ๊ถŒ๊ณผ์˜ ์ ‘๊ทผ์„ฑ, ๊ฐ€๋กœ๊ณต๊ฐ„๊ตฌ์กฐ ํŠน์„ฑ, ๋…ธํ›„๋„, ๊ฑด์ถ•๋ฌผ์˜ ๊ทœ๋ชจ ๋“ฑ์˜ ํŠน์„ฑ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ƒ์—…ํ™”๋ฅผ ๋Œ€๋น„ํ•œ ์ง€๊ตฌ์˜ ์ง€์ • ๋ฐ ๊ด€๋ฆฌ์™€ ์ง€์—ญ์˜ ์—ญํ• ๊ณผ ์„ฑ๊ฒฉ์— ๋Œ€ํ•œ ์žฌ๊ณ ์˜ ํ•„์š”์„ฑ์„ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ ์ƒ์—…์šฉ๋„์˜ ์ฃผ๊ฑฐ์ง€์—ญ์œผ๋กœ์˜ ์นจํˆฌ์— ์˜ํ•ด์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฐˆ๋“ฑ์˜ ๊ฐ์†Œ๋ฅผ ์œ„ํ•œ ๊ฐ€๋กœ๋‹จ์œ„์˜ ๋ณด๋‹ค ์ƒ์„ธํ•œ ๊ฑด์ถ•๋ฌผ ์œ ํ˜• ๋ฐ ๋ฐ€๋„์˜ ๊ด€๋ฆฌ๊ฐ€ ํ•„์š”ํ•จ์„ ์ œ์‹œํ•˜์˜€๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  13 1.1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  13 1.1.1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 13 1.1.2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  16 1.2. ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• 17 1.2.1. ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ ๋ฐ ๋ฒ”์œ„ 17 1.2.2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 21 ์ œ 2 ์žฅ ์ด๋ก ์  ๊ณ ์ฐฐ 25 2.1. ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํŠน์„ฑ 25 2.1.1. ๋„์‹œ ์ƒ์—…๊ณต๊ฐ„์˜ ์—ญํ• ๊ณผ ๋„์‹œ์„ค๊ณ„์  ๊ณ ์ฐฐ 25 2.1.2. ์†Œ๋น„์žํ–‰ํƒœ ๋ณ€ํ™”์™€ ์ƒ์—…๊ณต๊ฐ„์˜ ๋ณ€ํ™”์–‘์ƒ 27 2.1.3. ์  ํŠธ๋ฆฌํ”ผ์ผ€์ด์…˜๊ณผ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ˜•์„ฑ 30 2.1.4. ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํŠน์„ฑ 32 2.2. ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ™•์žฅ๊ณผ ๋ณ€ํ™” 35 2.2.1. ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์— ๊ด€ํ•œ ์—ฐ๊ตฌ 35 2.2.2. ์ฃผ๊ฑฐ์ง€์—ญ์—์„œ ๋น„์ฃผ๊ฑฐ ์šฉ๋„์˜ ํ™•์žฅ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 37 2.3. ์„ ํ–‰์—ฐ๊ตฌ ์ •๋ฆฌ ๋ฐ ๋ถ„์„์˜ ํ‹€ ์„ค์ • 37 ์ œ 3 ์žฅ ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ˜•์„ฑ๊ณผ ํŠน์„ฑ 40 3.1. ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ˜•์„ฑ๊ณผ์ • 40 3.2. ํ™๋Œ€๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ๋„์‹œ๊ณต๊ฐ„๊ตฌ์กฐ ํŠน์„ฑ 43 3.2.1. ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ๋„์‹œ๊ณต๊ฐ„๊ตฌ์กฐ 43 3.2.2. ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„ ๊ฒฝ๊ณ„๋ถ€ ๋Œ€์ƒ์ง€์˜ ๋„์‹œ๊ณต๊ฐ„๊ตฌ์กฐ 60 3.3. ์†Œ๊ฒฐ 58 ์ œ 4 ์žฅ ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ™•์žฅ ์–‘์ƒ 72 4.1. ๊ด‘์—ญ์  ์ฐจ์›์—์„œ ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ™•์žฅ 72 4.1.1. ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„์˜ ํ™•์žฅ ๊ณผ์ • 72 4.1.2. ๊ด‘์—ญ์  ์ฐจ์›์—์„œ ์ƒ์—…์šฉ๋„ ํ™•์žฅ ํŠน์„ฑ 78 4.2. ๊ฒฝ๊ณ„๋ถ€ ์ฃผ๊ฑฐ์ง€์—ญ์—์„œ ์ƒ์—…์šฉ๋„์˜ ํ™•์žฅ ์–‘์ƒ 80 4.2.1. ๊ฒฝ๊ณ„๋ถ€ ์ฃผ๊ฑฐ์ง€์—ญ์œผ๋กœ์˜ ์ƒ์—…์šฉ๋„์˜ ํ™•์žฅ ๊ณผ์ • 80 4.2.2. ์ฃผ๊ฑฐ์ง€์—ญ์œผ๋กœ์˜ ์ƒ์—…์šฉ๋„ ํ™•์žฅ ํŠน์„ฑ 87 4.2.3. ์†Œ๊ฒฐ : ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„ ๊ฒฝ๊ณ„๋ถ€ ์ƒ์—…์šฉ๋„์˜ ํ‰๋ฉด์  ํ™•์‚ฐ์˜ ์–‘์ƒ 103 4.3. ์†Œ๊ฒฐ 105 ์ œ 5 ์žฅ ํ™๋Œ€ ๋ฌธํ™”์†Œ๋น„๊ณต๊ฐ„ ๊ฒฝ๊ณ„๋ถ€์˜ ๋ณ€ํ™” ํŠน์„ฑ 107 5.1. ๊ฒฝ๊ณ„๋ถ€ ๊ฑด์ถ•๋ฌผ์˜ ์šฉ๋„ ๋ณ€ํ™” 107 5.1.1. ๊ฑด์ถ•๋ฌผ ์šฉ๋„๋ณ„ ๋ถ„ํฌ ํŠน์„ฑ 107 5.1.2. ๊ฑด์ถ•๋ฌผ์˜ ์šฉ๋„๋ณ€ํ™” ํŠน์„ฑ 125 5.2. ๊ฑด์ถ•๋ฌผ์˜ ๋ฌผ๋ฆฌ์ ์ธ ๋ณ€ํ™” 141 5.2.1. ๊ฑด์ถ•๋ฌผ์˜ ์œ ํ˜•๊ณผ ๋ถ„ํฌ ํŠน์„ฑ 141 5.2.2. ๊ฑด์ถ•๋ฌผ ์™ธ๊ด€๋ณ€ํ™”์˜ ํŠน์„ฑ 145 5.3. ๊ฑด์ถ•๋ฌผ ์šฉ๋„๋ณ€ํ™”์™€ ์™ธ๊ด€๋ณ€ํ™”์˜ ๊ด€๊ณ„ 152 5.3.1. ๊ฑด์ถ•๋ฌผ์˜ ์œ ํ˜•๊ณผ ์šฉ๋„๋ณ„ ์ž…์ง€ ํŠน์„ฑ 152 5.3.2. ์ƒ์—…ํ™”์— ๋”ฐ๋ฅธ ์ฃผ๊ฑฐ์šฉ๋„์˜ ๋ณ€ํ™”์™€ ์™ธ๊ด€๋ณ€ํ™” ํŠน์„ฑ 156 5.4. ์†Œ๊ฒฐ 159 ์ œ 6 ์žฅ ๊ฒฐ๋ก  163 6.1. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋ฐ ์š”์•ฝ 163 6.2. ์—ฐ๊ตฌ์˜ ์‹œ์‚ฌ์  166 6.3. ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์  167 ์ฐธ๊ณ ๋ฌธํ—Œ 170 Abstract 179Docto

    Magnetic monopoles in abelian and non-abelian gauge theories

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€(๋ฌผ๋ฆฌํ•™์ „๊ณต), 2011.2. ๊น€์„.Maste
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