23 research outputs found

    ๋งˆ์ฐฐ๋„์ž… ๋น„์„ ํ˜• ๋‹ค์ค‘๋™์กฐ์งˆ๋Ÿ‰๊ฐ์‡ ๊ธฐ์˜ ์ตœ์ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์ถ•ํ•™๊ณผ, 2017. 8. ์ด์ฒ ํ˜ธ.Modern development of design techniques and material science in architectural engineering contributes to increase in demand for buildings with longer span and light weight structure. In spite of its advantageous aspects, such advances in technologies often leads to problems with undesired discomfort caused by excessive vibration. In order to help dampen the unwanted excessive vibration, a variety of relevant techniques have been investigated, among which tuned mass damper (TMD) is one of the most widely used techniques so as to control the problematic vibration. This study first investigates the optimal solution of linear multiple tuned mass dampers (linear MTMDs, LMTMDs) of various configurations. The configurations considered in this study include the cases where the frequency ratios are linearly distributed, the damping coefficients are uniformly distributed, the mass distributions are linearly distributed, or these constraints are combined in some ways. Two different optimization techniques are employed: Nominal Performance Optimization (NPO) and Robust Performance Optimization (RPO). The NPO searches a solution that minimizes the objective function deterministically, while the RPO minimizes the mean value of the objective function, assuming that the associated structural parameters are probabilistic rather than deterministic. Further, this study provides contour maps for the root-mean-square (RMS) displacement of main structure and the largest RMS displacement of LMTMDs, which can be useful in the design process. Next, this study seeks the optimal solution of frictional multiple tuned mass dampers (FMTMDs) in which the Coulomb-type frictional force is incorporated in either purposefully or unintentionally. In this study, four of the feasible FMTMD configurations are formulated and comparably analyzed. The investigated configurations involve: 1) no constraint on either the frequency ratios or the coefficient of friction (COF) is imposed2) the frequency ratios are linearly distributed and equally spaced3) the COFs are identically distributed4) the frequency ratios are equally spaced and the COFs are identical. In order to cope with the difficulties inherent in nonlinearity of the problem, this study adopts a statistical linearization technique, which enables the complicated nonlinear force terms to be linearized in a statistical sense. Some miscellaneous considerations such as stroke limitations and design procedure are also aptly included. This study mainly addresses RMS responses and extreme value distributions for the frictional multiple tuned mass dampers (FMTMDs). In designing of optimal FMTMD, the original nonlinear system arising from the frictional elements is replaced with an equivalent linear system by means of statistical linearization. In order to improve the accuracy for the estimation of peak distribution of MTMDs, this study exploits a statistical nonlinearization technique which replaces the nonlinear system at hand with a class of other nonlinear systems whose exact solution has been already explicitly derived. A correction factor that defines the ratio of RMS displacement between nonlinear and linear system is derived based on the results of statistical nonlinearization technique. This study also provides an explicit formula for evaluating a peak factor for frictional TMDs. The correction factor and the peak factor proposed are validated with Monte Carlo Simulation. Several application examples of MTMDs are included in this thesis. of multiple tuned mass dampers (MTMDs). In the first section, a mechanism-based frictional pendulum tuned mass damper (FPTMD) is proposed, which contributes to overcome some shortcomings of conventional translational TMDs with viscous damping. In the second section, a numerical study is carried out to provide a design procedure of MTMDs, which covered modal analysis based on finite element method, optimal design of tuned mass dampers, and evaluating their control performance and robustness under the frequency-perturbed states. The final section presents a project in an attempt to mitigate an excessive vibration of a problematic structure. The overall process of the project includes the vibration performance evaluation, modal analysis based on finite element method and optimal design and manufacturing of tuned mass dampers.Chapter 1 Introduction 1 1.1 Background 1 1.2 Scope and Objectives 4 1.3 Outline of Dissertation 5 Chapter 2 Literature Review 9 2.1 Optimization Criteria and Techniques 10 2.1.1 Hโˆž optimization 10 2.1.2 H2 optimization 12 2.1.3 Stability maximization 13 2.2 Multiple Tuned Mass Dampers 14 2.3 TMDs on Nonlinear Structures 23 2.4 Nonlinear Tuned Mass Dampers 24 2.5 Applications and Structural Implementations 29 2.5.1 Wind-induced vibration attenuation 29 2.5.2 Seismic response mitigation 30 2.5.3 Floor vibration control 35 2.6 Other Issues 38 2.6.1 Stroke limitations 38 2.6.2 Reliability-based optimization 39 Chapter 3 Linear Multiple Tuned Mass Dampers 43 3.1 Introduction 44 3.2 Model Formulation 46 3.2.1 Governing Equations of motion 46 3.2.2 LMTMD configurations 51 3.3 Optimization Strategies 58 3.3.1 Nominal performance optimization 58 3.3.2 Robust performance optimization 60 3.4 Results and Discussion 63 3.4.1 LMTMDs designed by NPO 63 3.4.2 LMTMDs designed by RPO 72 3.4.3 Approximate solution for LMTMDฮณฮถ 78 3.5 Stroke Consideration and Design Procedure 82 3.5.1 Stroke consideration 82 3.5.2 Design procedure 84 3.6 Concluding Remarks 85 Chapter 4 Frictional Multiple Tuned Mass Dampers 87 4.1 Introduction 88 4.2 Model Formulation 91 4.2.1 Governing equations of motion 91 4.2.2 FMTMD configurations 95 4.2.3 Statistical linearization 98 4.3 Optimization Strategies 101 4.3.1 Set 1: FMTMDo and FMTMDฮณ 103 4.3.2 Set 2: FMTMDฯ„ and FMTMDฮณฯ„ 104 4.4 Results and Discussion 105 4.4.1 Optimal parameters 105 4.4.2 Frequency responses with optimal parameters 112 4.4.3 Input-intensity sensitivity analysis 114 4.4.4 Approximate solution for FMTMDฮณฯ„ 117 4.5 Design Procedure 122 4.6 Concluding Remarks 123 Chapter 5 Extreme Value Analysis for Frictional MTMDs 125 5.1 Introduction 126 5.2 FMTMD Optimization 128 5.2.1 Governine equations of motion 128 5.2.2 Statistical linearization 132 5.2.3 Optimization strategy 135 5.3 Improved Estimation of Peak Distribution 137 5.3.1 Statistical nonlinearization 137 5.3.2 Correction factor 142 5.3.3 Peak factors 144 5.4 Model Evaluation 147 5.5 Concluding Remarks 148 Chapter 6 Applications of MTMDs 155 6.1 Frictional Pendulum Tuned Mass Dampers 156 6.1.1 Introduction 156 6.1.2 FPTMD proposed and equations of motion 158 6.1.3 Statistical linearization 165 6.1.4 Gradient-based optimization 167 6.1.5 Numerical example 171 6.1.6 Summary and conclusions 181 6.2 Vibration Attenuation of Hallway 183 6.2.1 Description of examined hallway 184 6.2.2 Design of multiple tuned mass dampers 187 6.2.3 Numerical investigation 187 6.2.4 Results and discussion 195 6.3 Project: Vibration Mitigation of Floating Cafรฉ 196 6.3.1 Introduction 196 6.3.2 Description of floating cafรฉ 197 6.3.3 Design of multiple tuned mass dampers 199 6.3.4 Vibration serviceability assessment 200 6.3.5 Results and discussion 202 Chapter 7 Summary and Conclusions 203 Appendices 209 Chapter A Point Estimation Method 211 Chapter B Statistical Linearization 217 B.1 Formulation 217 B.2 Solution Procedure 219 B.2.1 Error minimization 219 B.2.2 Response evaluation 221 B.3 Examples of Systems with Power-Law Damping 222 Chapter C Applying Pre-Filters 227 Bibliography 231 Abstract (in Korean) 247Docto

    A study on the Practical analysis of the Operational problems of Busan Port and Improvement Strategy

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    ๋ณธ ๋…ผ๋ฌธ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ๋กœ, ๋ฌธ์ œ์ ์š”์ธ์— ๋Œ€ํ•œ ์ˆœ์œ„๊ฒ€์ •๊ฒฐ๊ณผ์— ์˜ํ•˜๋ฉด, ๋น„ํšจ์œจ์ ์ธ ์ปจ๋ถ€๋‘ ๋…ธ๋ฌด๊ณต๊ธ‰์ฒด์ œ๊ฐ€ ๊ฐ€์žฅ ๋ฌธ์ œ๊ฐ€ ํฐ ์š”์ธ์œผ๋กœ ์ธ์‹๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ทธ ๋‹ค์Œ์œผ๋กœ ์‹ ํ•ญ๊ฐœ๋ฐœ๋กœ ์ธํ•œ ์š”์œจ๊ฒฝ์Ÿ์˜ ๊ฒฉํ™”, ์‹ ํ•ญ ๋ฐฐํ›„๋ฌผ๋ฅ˜๋‹จ์ง€ ์‹œ์„ค์˜ ๋ฏธํก์„ฑ, ๊ตญ๋‚ดํ•ญ๋งŒ๊ฐ„์˜ ๊ณผ๋‹น ๊ฒฝ์Ÿ, ๋ถํ•ญ๊ณผ ์‹ ํ•ญ๊ฐ„์˜ ์—ฐ๊ณ„์ˆ˜์†ก์ฒด๊ณ„์˜ ๋ฏธํก์„ฑ, ๊ณผ๋‹คํ•œ ๋‚ด๋ฅ™์šด์†ก๋น„์šฉ, ๋ถํ•ญ๋‚ด TOC ๋‚œ๋ฆฝ์œผ๋กœ ๊ฐ€๊ฒฉ๊ฒฝ์Ÿ๋ ฅ ์ƒ์‹ค, ๋ถํ•ญ๋‚ด TOC์˜ ๋ฌผ๋™๋Ÿ‰ ์œ ์น˜๋Šฅ๋ ฅ ์ทจ์•ฝ์„ฑ, ๋ถํ•ญ๋‚ด TOC์˜ ๋Œ€์„ ์‚ฌ ํ˜‘์ƒ๋ ฅ ์ทจ์•ฝ์„ฑ ๊ทธ๋ฆฌ๊ณ  ๋ถํ•ญ๋‚ด TOC์˜ ์žฌ๋ฌด๊ตฌ์กฐ ๋ฐ ๊ฒฝ์˜๋Šฅ๋ ฅ์˜ ์ทจ์•ฝ์„ฑ ์ˆœ์œผ๋กœ ์šฐ์„ ์ˆœ์œ„๋ฅผ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์„ ์‚ฌ๋“ค์€ ๋ถํ•ญ๊ณผ ์‹ ํ•ญ๊ฐ„์˜ ์—ฐ๊ณ„์ˆ˜์†ก์ฒด๊ณ„์˜ ๋ฏธํก์„ฑ์ด ๊ฐ€์žฅ ๋ฌธ์ œ๊ฐ€ ํฐ ์š”์ธ์œผ๋กœ ์ธ์‹๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ํ„ฐ๋ฏธ๋„์€ ์‹ ํ•ญ๊ฐœ๋ฐœ๋กœ ์ธํ•œ ์š”์œจ๊ฒฝ์Ÿ์˜ ๊ฒฉํ™”๊ฐ€ ๊ฐ€์žฅ ๋ฌธ์ œ๊ฐ€ ํฐ ์š”์ธ์œผ๋กœ ์ธ์‹๋˜๊ณ  ์žˆ์–ด ๋‹ค์†Œ๊ฐ„์˜ ์ธ์‹์ฐจ์ด๋ฅผ ๋ณด์ด๊ณ  ์žˆ๋‹ค. ๋‘˜์งธ๋กœ, ๋ถ€์‚ฐํ•ญ ํ„ฐ๋ฏธ๋„ ๊ฒฝ์Ÿ๋ ฅ ์š”์ธ์— ๋Œ€ํ•ด์„œ๋Š” ๋†’์€ ํ™˜์ ํ™”๋ฌผ๋น„์ค‘(b4), ์›ํ• ํ•œ ํ”ผ๋”๋„คํŠธ์›Œํฌ ๊ตฌ์ถ•(b5), ํ•ญ๋น„ ์ˆ˜์ค€(b9), ํ•˜์—ญ๊ณ„์•ฝ์กฐ๊ฑด๊ณผ ๋…ธ๋ฌด์กฐ๊ฑด(b12) ๊ทธ๋ฆฌ๊ณ  ๋น„ ์ง€์ • ์žฅ๊ธฐํ™”๋ฌผ ๊ทœ์ œ์™„ํ™”(b13)์ธ 5๊ฐœ ๋ณ€์ˆ˜๋Š” ์ค‘์š”๋„์™€ ํ˜„ํ™ฉ๊ฐ„์— ์œ ์˜์ ์ธ ์ฐจ์ด๊ฐ€ ์—†์œผ๋ฉฐ, ๋‚˜๋จธ์ง€ ๋ณ€์ˆ˜๋“ค์€ ์œ ์˜์ ์ธ ์ฐจ์ด๋ฅผ ๋ณด์ด๊ณ  ์žˆ๋‹ค. ์ €๋ ดํ•œ ํ•ญ๋น„(b1), ๊ณ ๊ฐ์„œ๋น„์Šค ์ œ๊ณต๋Šฅ๋ ฅ(b3), ํ•ญ๋งŒ๋Œ€๊ธฐ์‹œ๊ฐ„ ์ตœ์†Œํ™” ๋Šฅ๋ ฅ(b6), ํ•ญ๋งŒ ๋ฐฐํ›„๋ถ€์ง€ ์กฐ์„ฑ ํ™œ์„ฑํ™”(b7), ์œ„ํ—˜ํ™”๋ฌผ ์ฒ˜๋ฆฌ์‹œ์„ค ์„ค์น˜(b14), ์ž…์ถœํ•ญ ์„œ๋ฅ˜๊ฐ„์†Œํ™” ๋ฐ ์‹ ์†ํ™”(b15) ๋“ฑ 6๊ฐœ ๋ณ€์ˆ˜๋Š” ์ค‘์š”๋„์— ๋น„ํ•˜์—ฌ ๋ถ€์‚ฐํ•ญ์€ ์ƒ๋Œ€์ ์œผ๋กœ ์ž˜ ๋˜์–ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ์ธ์‹๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ๋†’์€ ํ•ญ๋งŒ์ƒ์‚ฐ์„ฑ(b2), ์ ๊ทน์ ์ธ ํ•ญ๋งŒ๋งˆ์ผ€ํŒ… ๋Šฅ๋ ฅ(b8), ํ™˜์ ํ™”๋ฌผ ์ธ์„ผํ‹ฐ๋ธŒ ๊ทœ๋ชจ(b10), ํ™˜์ ํ™”๋ฌผ ์ธ์„ผํ‹ฐ๋ธŒ ์‹คํšจ์„ฑ(b11)์€ ๊ทธ ์ค‘์š”๋„์— ๋น„ํ•˜์—ฌ ํ˜„ํ™ฉ ์ˆ˜์ค€์€ ๋‹ค์†Œ ๋ฏธํกํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค. ํ•œํŽธ, ๊ฒฝ์Ÿ๋ ฅ ์˜ํ–ฅ์š”์ธ์— ๋Œ€ํ•œ ์„ ์‚ฌ์™€ ํ„ฐ๋ฏธ๋„์‚ฌ๊ฐ€ ๋Š๋ผ๋Š” ์ฐจ์ด ์—ฌ๋ถ€๋ฅผ ๋ณด๋ฉด, ์ฒซ์งธ๋กœ, ์ €๋ ดํ•œ ํ•ญ๋น„ ์ˆ˜์ค€์— ๋Œ€ํ•ด์„œ๋Š” ์ค‘์š”๋„ ์ธ์‹์€ ์ฐจ์ด๊ฐ€ ์—†์ง€๋งŒ, ํ˜„ํ™ฉ ์ˆ˜์ค€์— ๋Œ€ํ•ด์„œ๋Š” ํ„ฐ๋ฏธ๋„์ด ์•ฝ๊ฐ„ ๋†’๊ฒŒ ์ธ์‹ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ํ•ญ๋งŒ ์ƒ์‚ฐ์„ฑ์— ๋Œ€ํ•ด์„œ๋Š” ์„ ์‚ฌ์— ๋น„ํ•˜์—ฌ ํ„ฐ๋ฏธ๋„์ด ๋ณด๋‹ค ์ค‘์š”ํ•˜๊ฒŒ ์ธ์‹ํ•˜๊ณ  ํ˜„ํ™ฉ์ˆ˜์ค€์— ๋Œ€ํ•ด์„œ๋Š” ์ฐจ์ด๊ฐ€ ์—†๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ ํ•ญ๋งŒ๋ฐฐํ›„๋ถ€์ง€๋Š” ํ•ญ๋น„ ์ˆ˜์ค€๊ณผ๋Š” ๋ฐ˜๋Œ€๋กœ ์ค‘์š”๋„ ์ธ์‹์€ ์ฐจ์ด๊ฐ€ ์—†์ง€๋งŒ, ํ˜„ํ™ฉ์ˆ˜์ค€์— ๋Œ€ํ•ด์„œ๋Š” ํ„ฐ๋ฏธ๋„์— ๋น„ํ•˜์—ฌ ์„ ์‚ฌ๊ฐ€ ๋ณด๋‹ค ์ข‹๊ฒŒ ์ธ์‹ํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ•ญ๋น„ ์ˆ˜์ค€์ด๋‚˜ ํ™˜์ ํ™”๋ฌผ ๋ณผ๋ฅจ์ธ์„ผํ‹ฐ๋ธŒ๊ทœ๋ชจ์— ๋Œ€ํ•ด์„œ๋Š” ์„ ์‚ฌ๋‚˜ ํ„ฐ๋ฏธ๋„ ๋ชจ๋‘ ์ค‘์š”๋„์™€ ํ˜„ํ™ฉ์ˆ˜์ค€์—์„œ ์„ ์‚ฌ์— ๋น„ํ•˜์—ฌ ํ„ฐ๋ฏธ๋„์ด ๋ณด๋‹ค ๋†’๊ฒŒ ์œ ์˜์ ์ธ ์ฐจ์ด๋ฅผ ๋ณด์ด๊ณ  ์žˆ์œผ๋ฉฐ, ํ™˜์ ํ™”๋ฌผ ์ธ์„ผํ‹ฐ๋ธŒ์˜ ์‹คํšจ์„ฑ์— ๋Œ€ํ•ด์„œ๋Š” ํ˜„ํ™ฉ์€ ๊ฑฐ์˜ ์ฐจ์ด๊ฐ€ ์—†๊ณ  ์ค‘์š”๋„ ์ธ์‹์— ์žˆ์–ด์„œ๋Š” ํ„ฐ๋ฏธ๋„์ด ๋ณด๋‹ค ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์œ„ํ—˜ํ™”๋ฌผ ์ฒ˜๋ฆฌ์‹œ์„ค์— ๋Œ€ํ•ด์„œ๋Š” ์ค‘์š”๋„๋‚˜ ํ˜„ํ™ฉ์ด ๋ชจ๋‘ ๋‚ฎ๊ฒŒ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋Š”๋ฐ, ํ˜„ํ™ฉ์ˆ˜์ค€์—์„œ ์„ ์‚ฌ์— ๋น„ํ•˜์—ฌ ํ„ฐ๋ฏธ๋„์ด ๋ณด๋‹ค ๋†’๊ฒŒ ์œ ์˜์ ์ธ ์ฐจ์ด๋ฅผ ๋ณด์ด๊ณ  ์žˆ๋‹ค. ์ƒ๊ธฐ ๋ณ€์ˆ˜์ด์™ธ์˜ ๋ณ€์ˆ˜๋“ค์€ ์„ ์‚ฌ์™€ ํ„ฐ๋ฏธ๋„ ๊ฐ„์— ์œ ์˜์ ์ธ ์ฐจ์ด๊ฐ€ ์—†๋Š” ๊ฒƒ์œผ๋กœ ํ‰๊ฐ€๋œ๋‹ค. ์…‹์งธ๋กœ, ๋ถ€์‚ฐํ•ญ ํ„ฐ๋ฏธ๋„ ๊ณผ์ž‰๊ฒฝ์Ÿ ๋ฐ ์ˆ˜์†ก์ฒด๊ณ„ ๋ฏธํก์„ฑ์— ๋Œ€ํ•œ ๋ฌธ์ œ์ ์€ ํ™”๋ฌผ์œ ์น˜๋Šฅ๋ ฅ๊ณผ ํ”ผ๋”๋„คํฌ์›Œํฌ ๋ฐ ํ•ญ๋งŒ๋ฐฐํ›„๋ฌผ๋ฅ˜๋ถ€์ง€ ๊ตฌ์ถ•์—๋Š” ์ค‘์š”ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€๋งŒ ๊ทœ์ œ์™„ํ™”์™€๋Š” ๋ฌด๊ด€ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ„ฐ๋ฏธ๋„ ์šด์˜๋Šฅ๋ ฅ์˜ ๋ฏธํก์„ฑ์€ ํ™”๋ฌผ์œ ์น˜๋Šฅ๋ ฅ์ด๋‚˜ ํ”ผ๋”๋„คํŠธ์›Œํฌ ๋ฐ ํ•ญ๋งŒ๋ฐฐํ›„๋ถ€์ง€ ๊ตฌ์ถ• ๊ทธ๋ฆฌ๊ณ  ๊ทœ์ œ์™„ํ™”์š”์ธ๊ณผ๋Š” ๋ฌด๊ด€ํ•œ ๊ฒƒ์œผ๋กœ ํ‰๊ฐ€๋œ๋‹ค. ๋„ท์งธ๋กœ, ํ™”๋ฌผ์œ ์น˜๋Šฅ๋ ฅ ์ œ๊ณ ์š”์ธ์ด๋‚˜ ํ™˜์ ํ™”๋ฌผ์œ ์น˜์š”์ธ์€ ๋ถ€์‚ฐํ•ญ ๋ฌผ๋™๋Ÿ‰ ์ฆ๋Œ€์™€ ๋™๋ถ์•„ ํ—ˆ๋ธŒํ•ญ๋งŒํ™” ํšจ๊ณผ์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€๋งŒ ๊ทœ์ œ์™„ํ™”์š”์ธ์€ ๋ถ€์‚ฐํ•ญ ๋ฌผ๋™๋Ÿ‰ ์ฆ๋Œ€์™€ ๋™๋ถ์•„ ํ—ˆ๋ธŒํ•ญ๋งŒํ™” ํšจ๊ณผ์™€๋Š” ๋ฌด๊ด€ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค.์ œ1์žฅ ์„œ ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ ํ•„์š”์„ฑ๊ณผ ์—ฐ๊ตฌ๋ชฉ์  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ๋ชฉ์  2 ์ œ2์ ˆ ์—ฐ๊ตฌ๋‚ด์šฉ๊ณผ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 4 ์ œ2์žฅ ๋ถ€์‚ฐํ•ญ์˜ ์ฃผ๋ณ€ ํ™˜๊ฒฝ๊ณผ ๋ถ€์‚ฐํ•ญ ํ˜„ํ™ฉ 5 ์ œ1์ ˆ ๋ถ€์‚ฐํ•ญ์˜ ์ฃผ๋ณ€ ํ™˜๊ฒฝ 5 1. ์ค‘๊ตญํ•ญ๋งŒ์˜ ๋Œ€๊ทœ๋ชจ ๊ฐœ๋ฐœ ๋ฐ ์„œ๋น„์Šค ํ™•์ถฉ 5 2. ์ผ๋ณธ์˜ ์ˆ˜ํผ ์ค‘์ถ”ํ•ญ๋งŒ ํ”„๋กœ์ ํŠธ ์ถ”์ง„ 8 3. ์ปจํ…Œ์ด๋„ˆ์„ ๋ฐ•์˜ ๋Œ€ํ˜•ํ™” 9 ์ œ2์ ˆ ๋ถ€์‚ฐํ•ญ์˜ ํ˜„ํ™ฉ 13 1. ๊ตญ๋‚ด ์ตœ๋Œ€์˜ ์ปจํ…Œ์ด๋„ˆ ์ฒ˜๋ฆฌํ•ญ๋งŒ 13 2. ์–‘ํ˜ธํ•œ ์ง€๋ฆฌ์  ์œ„์น˜ 14 3. ์šฐ์ˆ˜ํ•œ ํ”ผ๋” ๋„คํŠธ์› 15 4. ์–‘ํ˜ธํ•œ ๊ตญ์ œํ•ญ๋กœ ๊ธฐ๋ฐ˜ 16 5. ๋†’์€ ํ™˜์ ํ™”๋ฌผ ์˜์กด๋„ 18 6. ํ•ญ๋งŒ๋น„์šฉ 19 ์ œ3์ ˆ ๋ถ€์‚ฐํ•ญ์˜ ๊ฒฝ์Ÿ๋ ฅ ๋ถ„์„ 22 1. ๊ตญ๋‚ด ๊ฒฝ์Ÿ๋ ฅ ๋ถ„์„ 22 2. ๊ตญ์ œ ๊ฒฝ์Ÿ๋ ฅ ๋ถ„์„ 23 ์ œ3์žฅ ๋ถ€์‚ฐํ•ญ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„ ์šด์˜ํ˜„ํ™ฉ 28 ์ œ 1์ ˆ ๋ถ€์‚ฐํ•ญ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„ 28 1. ์‹œ์„คํ˜„ํ™ฉ 28 2, ์šด์˜ํ˜„ํ™ฉ 29 3. ์ผ๋ฐ˜๋ถ€๋‘ ์‹œ์„ค ํ˜„ํ™ฉ ๋ฐ ์ฒ˜๋ฆฌ์‹ค์  33 ์ œ 2์ ˆ ์„ธ๊ณ„์ฃผ์š”ํ•ญ๋งŒ์˜ ํ„ฐ๋ฏธ๋„ ์šด์˜ํ˜„ํ™ฉ 35 1. ์‹ฑ๊ฐ€ํฌ๋ฅดํ•ญ 35 2. ํ™์ฝฉํ•ญ 37 3. ์ƒํ•ดํ•ญ 38 4. ์„ ์ „ํ•ญ 41 ์ œ 3์ ˆ ๋ถ€์‚ฐํ•ญ ํ„ฐ๋ฏธ๋„์˜ ๊ฒฝ์Ÿ๋ ฅ ๋ถ„์„ 43 ์ œ4์žฅ ๋ถ€์‚ฐํ•ญ ํ„ฐ๋ฏธ๋„ ์šด์˜์˜ ๋ฌธ์ œ์ ๊ณผ ๊ฒฝ์Ÿ๋ ฅ ์˜ํ–ฅ์š”์ธ์— ๊ด€ํ•œ ์‹ค์ฆ๋ถ„์„ 51 ์ œ1์ ˆ ์—ฐ๊ตฌ๊ฐ€์„ค๊ณผ ์—ฐ๊ตฌ๋ชจํ˜• 51 1. ์—ฐ๊ตฌ๊ฐ€์„ค 51 2. ์—ฐ๊ตฌ๋ชจํ˜• 52 ์ œ2์ ˆ ๋ณ€์ˆ˜์˜ ์ •์˜์™€ ์ธก์ •๋ฐฉ๋ฒ• 53 1. ๋…๋ฆฝ๋ณ€์ˆ˜์˜ ์ •์˜์™€ ์ธก์ •๋ฐฉ๋ฒ• 53 2. ์ข…์†๋ณ€์ˆ˜์˜ ์ •์˜์™€ ์ธก์ •๋ฐฉ๋ฒ• 54 3. ์ž๋ฃŒ์ˆ˜์ง‘๊ณผ ์—ฐ๊ตฌ๋Œ€์ƒ 55 ์ œ3์ ˆ ๋ถ€์‚ฐํ•ญ ์ปจํ…Œ์ด๋„ˆํ„ฐ๋ฏธ๋„ ๋ฌธ์ œ์  ๋ฐ ๊ฒฝ์Ÿ๋ ฅ์š”์ธ ์‹คํƒœ๋ถ„์„ 58 1. ๋ถ€์‚ฐํ•ญ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ๋ฌธ์ œ์ ๋ถ„์„ 58 2. ๋ถ€์‚ฐํ•ญ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ๊ฒฝ์Ÿ๋ ฅ ์˜ํ–ฅ์š”์ธ ์‹คํƒœ๋ถ„์„ 69 ์ œ4์ ˆ ๋ถ€์‚ฐํ•ญ ์ปจํ…Œ์ด๋„ˆํ„ฐ๋ฏธ๋„ ์šด์˜๊ฐœ์„ ์ „๋žต์— ๊ด€ํ•œ ์‹ค์ฆ๋ถ„์„๊ฒฐ๊ณผ 93 1. ํƒ€๋‹น์„ฑ๋ถ„์„ 93 2. ์‹ ๋ขฐ์„ฑ๋ถ„์„ 98 3. ๊ฐ€์„ค๊ฒ€์ • 101 ์ œ5์žฅ ๊ฒฐ ๋ก  110 ์ œ1์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 110 ์ œ2์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์‹œ์‚ฌ์  112 ์ฐธ๊ณ ๋ฌธํ—Œ 113 ๋ถ€๋ก:์„ค๋ฌธ์ง€ 12

    Increasing Generality of Floorplan Analysis Process using Style Transfer

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2019. 2. ๊น€์šฉ์ผ.์ตœ๊ทผ ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ ์œ„์น˜ ์ถ”์ , ๋‚ด๋น„๊ฒŒ์ด์…˜ ๋“ฑ์˜ ์œ„์น˜ ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค๊ฐ€ ์‹ค๋‚ด๊นŒ์ง€ ํ™•์žฅ๋˜์–ด ๊ด€๋ จ ์‹œ์žฅ์˜ ๊ทœ๋ชจ๊ฐ€ ์ฆ๊ฐ€ํ•˜๋Š” ์ถ”์„ธ์ด๋‹ค. ์‹ค๋‚ด ๊ด€๋ จ ์„œ๋น„์Šค ๋ฐ ์—ฐ๊ตฌ์— ๋Œ€ํ•œ ์ˆ˜์š”์˜ ์ฆ๋Œ€๋กœ ์ธํ•ด ๊ทธ ๊ธฐ๋ฐ˜์ด ๋˜๋Š” ์‹ค๋‚ด๊ณต๊ฐ„์ •๋ณด๋ฅผ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ์ด ์—ฐ๊ตฌ์ž๋“ค์˜ ์ฃผ์š” ๊ด€์‹ฌ์‚ฌ๋กœ ๋ถ€์ƒํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ ์ค‘ ๋„๋ฉด์„ ํ™œ์šฉํ•œ ์ ‘๊ทผ์€ ๊ฐ„ํŽธํ•˜๊ฒŒ ํš๋“ํ•  ์ˆ˜ ์žˆ๊ณ  ์ ‘๊ทผ์„ฑ์ด ๋†’๋‹ค๋Š” ํŠน์ง•์œผ๋กœ ์ธํ•ด ์‹ค๋‚ด ์ •๋ณด์˜ ํšจ์œจ์ ์ธ ๊ตฌ์ถ• ๋ฐฉ๋ฒ•์œผ๋กœ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•œ ๊ธฐ์ˆ ์  ์ธก๋ฉด์˜ ์Ÿ์ ์€ ์ˆ˜์ง‘๋œ ๋‹ค์–‘ํ•œ ํฌ๋งท์˜ ๋„๋ฉด๋“ค์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๋ฒ”์šฉ์„ฑ์˜ ํ™•๋ณด์ด๋‹ค. ๋„๋ฉด ํ•ด์„ ๋ถ„์•ผ๋Š” ์ด๋Ÿฌํ•œ ํ๋ฆ„์— ๋ฐœ๋งž์ถฐ ํ•™์Šต ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๋„์ž…ํ•˜์—ฌ ์ƒˆ๋กœ์šด ํฌ๋งท์—๋„ ๊ธฐ์ˆ ์˜ ์ ์šฉ์ด ๊ฐ€๋Šฅํ•œ ๋ฐฉํ–ฅ์œผ๋กœ ๋ฐœ์ „ํ•ด์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ๋„๋ฉด ํ•ด์„ ํ”„๋กœ์„ธ์Šค๋Š” ํ•™์Šต ๋ฐ์ดํ„ฐ๋ฅผ ๊ตฌ์ถ•ํ•˜๋Š” ์ž‘์—…์ด ์–ด๋ ต๊ณ , ๋น„๊ต์  ๋‹จ์ˆœํ•œ ํฌ๋งท์˜ ๋„๋ฉด๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๊ธฐ ๋•Œ๋ฌธ์— ์‹ค์งˆ์ ์œผ๋กœ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜๋Š”๋ฐ ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋„๋ฉด ํ•ด์„ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜๊ธฐ ์œ„ํ•œ ๋ฒ”์šฉ์„ฑ์˜ ํ™•์žฅ์„ ๋ชฉ์ ์œผ๋กœ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์Šคํƒ€์ผ ํŠธ๋žœ์Šคํผ๋ฅผ ์ ์šฉํ•˜์˜€๋‹ค. ๋„๋ฉด์˜ ๋‹ค์–‘ํ•œ ํฌ๋งท์— ๋Œ€ํ•ด์„œ ์‹ค๋‚ด ๊ตฌ์กฐ์˜ ํ‘œํ˜„์„ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ์Šคํƒ€์ผ๋กœ ์ ‘๊ทผํ•˜์—ฌ, ์‹ค๋‚ด ๊ตฌ์กฐ๋ฅผ ์ž˜ ํ‘œํ˜„ํ•˜๋„๋ก ๋‹ค์–‘ํ•œ ๋„๋ฉด์„ ์ผ๊ด€๋œ ์Šคํƒ€์ผ๋กœ ๋ณ€ํ™˜์‹œํ‚ค๋ฉฐ ํ•„์š”ํ•œ ์ •๋ณด๋ฅผ ์ถœ๋ ฅํ•˜๋„๋ก ๋”ฅ๋Ÿฌ๋‹ ๋„คํŠธ์›Œํฌ๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•™์Šต ๋ฐ์ดํ„ฐ์…‹์„ ๊ตฌ์ถ•ํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•๊ณผ ์ด๋ฅผ ํ™œ์šฉํ•ด ์‹ค๋‚ด ๊ตฌ์กฐ๋ฅผ ๊ตฌํ˜„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์—ฌ ๋„๋ฉด ํ•ด์„ ํ”„๋กœ์„ธ์Šค๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ์œผ๋กœ๋Š” ๊ธฐ์กด ๊ณต์šฉ ๋„๋ฉด ๋ฐ์ดํ„ฐ์…‹๋“ค์— ๋น„ํ•ด ๋ณต์žกํ•˜๊ณ  ๋‹ค์–‘ํ•œ ๋„๋ฉด๋“ค๋กœ ๊ตฌ์„ฑ๋œ ์„ธ์›€ํ„ฐ ๋„๋ฉด ๋ฐ์ดํ„ฐ์…‹์„ ์ƒˆ๋กœ ๊ตฌ์ถ•ํ•˜์—ฌ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ๊ฐœ๋ฐœํ•œ ํ•ด์„ ํ”„๋กœ์„ธ์Šค๋ฅผ ํ†ตํ•ด ๊ตฌํ˜„ํ•œ ๋ฒกํ„ฐ ํฌ๋งท์˜ ์‹ค๋‚ด ์ •๋ณด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ฒ€์ฆ์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์™€์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ์Šคํƒ€์ผ ํŠธ๋žœ์Šคํผ์˜ ์ ์šฉ์ด ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ด์„ ๋ณด์˜€๊ณ , ๋ฐฉ์˜ ํƒ์ƒ‰๋ฅ ๊ณผ ์ธ์ง€ ์ •ํ™•๋„์—์„œ ๊ฐ 87%์™€ 85%์˜ ๊ฒฐ๊ณผ๋ฅผ ์–ป์–ด ๋‹จ์ˆœํ•œ ํฌ๋งท์— ๋Œ€ํ•œ ์ด์ „์˜ ์—ฐ๊ตฌ๋“ค์˜ ์„ฑ๋Šฅ๊ณผ ์œ ์‚ฌํ•œ ์ˆ˜์ค€์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ •๋ฆฌํ•˜๋ฉด, ๋ณธ ๋…ผ๋ฌธ์„ ํ†ตํ•ด ๋ณต์žกํ•˜๊ณ  ๋‹ค์–‘ํ•œ ํฌ๋งท์œผ๋กœ ๊ตฌ์„ฑ๋œ ๋„๋ฉด ๋ฐ์ดํ„ฐ์…‹์— ๋Œ€ํ•œ ํ•ด์„์ด ๊ฐ€๋Šฅํ•ด์กŒ๊ณ  ์ด๋Š” ๋„๋ฉด ํ•ด์„ ๊ธฐ์ˆ ์˜ ์ ์šฉ ๋ฒ”์œ„๋ฅผ ํ™•์žฅ์‹œํ‚จ๋‹ค๋Š” ์˜์˜๊ฐ€ ์žˆ๋‹ค.Owing to recent technological advances, location-based services such as tracking, navigation have expanded indoor and the size of the related markets has been increased. As the demand for indoor services and researches increases, constructing indoor information is emerging as a primary concern. Among them is the method using a floorplan, viewed as an effective way to construct information since a floorplan has a high accessibility. It is required to improve versatility of technology to apply to a wide range of floorplan formats. Floorplan analysis has been developed in this way by introducing learning algorithms. However there is a limitation on utilizing technology since previous studies were performed with comparatively simple floorplans and it is hard to construct training data set. The main aims of this research are improving performance of analyzing complex floorplans by applying style transfer, and developing floorplan analysis process which can be generally utilized by suggesting simple criteria to construct labelling data. More specifically, this research constructs new dataset which is more various and intricate than the previous one, and performs vectorization of building factors using deep learning based style transfer. Regarding a format of floorplan as a style, deep learning network outputs information on indoor structure in the process of transferring a style of floorplan. Analysis performance on complex floorplans has been improved by using style transfer, and in terms of constructing indoor structure, it shows 87% of detection rate and 85% of recognition accuracy of a room, which is a similar level as the previous researches. To sum up, style transfer lets us do floorplan analysis on complex and diverse formats, easily constructing labelling data.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œ 5 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ ํ๋ฆ„๋„ 8 ์ œ 2 ์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ 10 ์ œ 1 ์ ˆ ๋„๋ฉด ๋ฐ์ดํ„ฐ์…‹ 10 ์ œ 2 ์ ˆ ๋„๋ฉด ํ•ด์„์˜ ์—ฐ๊ตฌ ๋™ํ–ฅ 14 ์ œ 3 ์ ˆ ํ•™์Šต ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ™•์žฅ 18 ์ œ 3 ์žฅ ๋„๋ฉด ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์ถ• 20 ์ œ 1 ์ ˆ ์„ธ์›€ํ„ฐ ๋„๋ฉด 20 ์ œ 2 ์ ˆ ์‹ค๋‚ด ๊ตฌ์กฐ์˜ ๋ผ๋ฒจ๋ง 22 ์ œ 4 ์žฅ ๋„๋ฉด์˜ ๋ฒกํ„ฐํ™” ํ”„๋กœ์„ธ์Šค 26 ์ œ 1 ์ ˆ ์Šคํƒ€์ผ ํŠธ๋žœ์Šคํผ์˜ ์ ์šฉ 27 ์ œ 2 ์ ˆ ์ •์ˆ˜ ๊ณ„ํš๋ฒ•์„ ํ†ตํ•œ ๋ณด์™„ 32 ์ œ 5 ์žฅ ๊ฒฐ๊ณผ ๋ฐ ์ •ํ™•๋„ ๊ฒ€์ฆ 36 ์ œ 1 ์ ˆ ์ •ํ™•๋„ ๊ฒ€์ฆ ๋ฐฉ๋ฒ•๋ก  36 ์ œ 2 ์ ˆ ๊ฒฐ๊ณผ ๋ฐ ๊ฒ€์ฆ 39 ์ œ 3 ์ ˆ ๋„๋ฉด ํ•ด์„ ํ”„๋กœ์„ธ์Šค์˜ ๋”ฅ๋Ÿฌ๋‹ ํ™œ์šฉ 43 ์ œ 6 ๊ฒฐ๋ก  45 ์ฐธ๊ณ ๋ฌธํ—Œ 47Maste

    The Change of Practice Patterns of the Hereditary Breast Cancer Management in Korea after the Korean Hereditary Breast Cancer Study

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    ๋ณธ ๋…ผ๋ฌธ์€ 2010 ํ•œ๊ตญ์œ ๋ฐฉ์•”ํ•™ํšŒ ์ถ˜๊ณ„ํ•™์ˆ ๋Œ€ํšŒ์—์„œ ๊ตฌ์—ฐ ๋ฐœํ‘œ๋˜์—ˆ์Œ.Purpose: The objective of this study was to evaluate the change in the practice patterns for managing hereditary breast and ovarian cancer (HBOC) among Korean physicians after the Korean Hereditary Breast Cancer (KOHBRA) study. Methods: The first survey was performed from July to August 2007, at the initiation of the KOHBRA study, and the follow-up survey was conducted from July to December 2009. Members of the Korean Breast Cancer Society were invited to participate in the study by e-mail. The 2009 survey was conducted with a self-administered questionnaire concerning HBOC management and was identical to the previous questionnaire. Results: According to the 2009 survey, most physicians (60.0%) tended to draw a pedigree (48.0% in 2007 survey). The rate of genetic test recommendations for patients at risk for HBOC was higher in the 2009 survey (84.0%) than that in the 2007 survey (64.0%). Physicians tended to select a BRCA genetic testing candidate more appropriately than in the previous survey (42.4% answered right in 2007 survey; 74.4% in 2009 survey). Fifteen of 25 participants (60.0%) provided genetic counseling before their patients underwent a genetic test, which was higher than that (40.0%) in the 2007 survey. According to the 2009 survey, half of the genetic counseling was being conducted by KOHBRA study research nurses; whereas most of the genetic counseling was conducted by physicians in 2007. Conclusion: The KOHBRA study has played an important role in the appropriate selection of candidates for genetic testing. However, more effort should be placed on improving the pre-test genetic counseling rate.๋ณธ ์—ฐ๊ตฌ๋Š” ๋ณด๊ฑด๋ณต์ง€๋ถ€ ์•”์ •๋ณต์—ฐ๊ตฌ๋น„์˜ ์ง€์›์„ ๋ฐ›์•„ ์‹œํ–‰๋˜์—ˆ์Œ(๊ณผ์ œ๋ฒˆํ˜ธ 0720450).Robson ME, 2010, J CLIN ONCOL, V28, P893, DOI 10.1200/JCO.2009.27.0660Han SA, 2009, J BREAST CANCER, V12, P92, DOI 10.4048/jbc.2009.12.2.92Ko SS, 2008, J SURG ONCOL, V98, P318, DOI 10.1002/jso.21110Kim KS, 2008, J BREAST CANCER, V11, P95Kim EK, 2007, J BREAST CANCER, V10, P241Chenevix-Trench G, 2007, BREAST CANCER RES, V9, DOI 10.1186/bcr1670Fisher B, 2005, J NATL CANCER I, V97, P1652, DOI 10.1093/jnci/dji372Eisen A, 2005, J CLIN ONCOL, V23, P7491, DOI 10.1200/JCO.2004.00.7138Nelson HD, 2005, ANN INTERN MED, V143, P362Green MJ, 2004, JAMA-J AM MED ASSOC, V292, P442Choi DH, 2004, J CLIN ONCOL, V22, P1638, DOI 10.1200/JCO.2004.04.179Ahn SH, 2004, J KOREAN MED SCI, V19, P269Rebbeck TR, 2004, J CLIN ONCOL, V22, P1055, DOI 10.1200/JCO.2004.04.188Antoniou A, 2003, AM J HUM GENET, V72, P1117Rebbeck TR, 2002, NEW ENGL J MED, V346, P1616KANG HC, 2002, HUM MUTAT, V20, P235Malone KE, 2000, CANCER, V88, P1393Hartmann LC, 1999, NEW ENGL J MED, V340, P77Fisher B, 1998, J NATL CANCER I, V90, P1371Ford D, 1998, AM J HUM GENET, V62, P676Parmigiani G, 1998, AM J HUM GENET, V62, P145ShattuckEidens D, 1997, JAMA-J AM MED ASSOC, V278, P1242Dinkel MK, 1997, J CLIN ONCOL, V15, P2157Claus EB, 1996, CANCER, V77, P2318WOOSTER R, 1995, NATURE, V378, P789OH JH, 1995, J KOREAN CANC ASS, V27, P1061FRIEDMAN LS, 1994, NAT GENET, V8, P399*NAT COMPR CANC NE, NCCN CLIN PRACT GUID

    A Constrained Self-Calibration Technique

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    Maste

    ๊ณต๊ธ‰์ž ์š”์ธ์„ ๊ณ ๋ คํ•œ ์•ค๋”์Šจ ๋ชจํ˜•์˜ ์ ์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์‚ฌํšŒ๋ณต์ง€ํ•™๊ณผ, 2019. 2. ๊ฐ•์ƒ๊ฒฝ.In order for individuals with mental illness (hereafter IMIs) to recover and successfully return to their community, diverse community welfare services should be provided for IMIs, together with health and medical services. Until recently, however, the delivery system of mental health service in Korea has been primarily focused on the provision of health and medical services and thus the use of welfare services by IMIs has been restricted. This is because mental health services has been limitedly developed under the Mental Health Act enacted in 1995. As a result, IMIs have been excluded from society by being hospitalized or admitted to a mental hospital or a long-term care mental institution instead of independently living in the community, thereby causing a structural vicious circle of repeated hospitalization. Under these circumstances, in 2016 the revision of the Mental Health Act to the Mental Health Welfare Act (the Act on Promotion of Mental Health and Support for Welfare Services for Individuals with Mental Illness, hereafter MHWA) provided the legal ground for providing welfare services for IMIs, which is a major turning point for IMIs to be de-institutionalized and to ensure a independent living in their community. However, given the delivery system of mental health care oriented on health and medical services due to the lack of welfare service infrastructure, it is unclear whether MHWA, just stipulating the basis for support of welfare services, can provide sufficient welfare services with IMIs. Therefore, at the time of the revision of the MHWA, it is necessary to examine to what extent IMIs use the various welfare services prescribed by the MHWA and what factors influence the use of the welfare services for IMIs. However, research on the use of services by IMIs is sparse, and the existent research deals with rehabilitation and social services rather than focusing on welfare service use of IMIs. In particular, the Andersen model applied to the use of services by IMIs has its limitations in that the characteristics of IMIs and service providers have not been fully considered. Given these limitations, this study figures out the current state of use of welfare services for IMIs and explores the factors affecting the use of welfare service of IMIs through applying the Andersen model with a focus on the influential provider-related factors. This study aims to address two research questions as follows. First, what factors influence the level of use of welfare services for IMIs? This question is to identify the factors affecting the level of use of the five types of welfare services prescribed by the MHWA. Second, what factors influence the use of each five sub-types of welfare services? The second question is to examine the factors that affect the use of each five sub-types of welfare services. This study utilizes data from the Mental Health Welfare Center(hereafter MHWC) conducted by the Ministry of Health and Welfare in 2016. Data of 420 IMIs from 83 basic mental welfare centers are analyzed. In order to answer the research questions, it uses hierarchical linear model and logistic hierarchical linear models. For the analysis of Research Question 1, five types of welfare service uses defined in the MHWA are combined and utilized as a dependent variable. For the hierarchical linear model, the characteristics of IMIs are reflected in the model at the individual level, while the institutional characteristics of the MHWC are reflected in the model at the organizational level. In terms of the analysis of Research Question 2, with each use of five individual welfare services as dependent variables, the logistic hierarchical linear model is applied to the individual characteristics of IMIs and the organizational characteristics of the MHWC in the model. Results of the first analysis are as follows. Age and education as predisposing factors at the individual level have a significant impact on the level of use of welfare services. Marital status, religious status, self-esteem, income as enabling factors, symptoms and diagnosis as needs factors also have a significant effect on the level of use of welfare services. These results mean that how IMIs use various welfare services is explained more by the enabling factors than by the predisposing factors or needs factors. The size of the per capita mental health budget and workers perceived stigma also have a significant impact on use of welfare services for IMIs at the organizational level. This means that there is a more complex and diverse welfare services for IMIs in areas where there are relatively large mental health budgets and staff, and where workers are more sensitive to the public stigma. In addition, the organizational variables associated with referral and case management do not significantly affect the use of welfare services for IMIs, which reflects the reality of heavy workload and perfunctory case management. Next, the second analysis relying on the logistic hierarchical linear model of the five welfare services shows that the results are overall similar to the results on the level of the use of welfare services in the first analysis, but there are different results depending on what welfare service is considered. The results drive the following implications: From a theoretical perspective, this study confirms that the use of welfare services by IMIs in community can be explained through the Andersen model, and in particular the provider-related factors need to be considered. In other words, this provides a theoretical implication that the Andersen model, which has been mostly applied to the use of medical services, can be applied to the use of welfare services by IMIs, and that the Andersen model should be considered not only in terms of service demand but also in terms of service supply. Second, it is confirmed that the explanatory factors of the Andersen model may be different depending on the contents and characteristics of the welfare service. This has a theoretical meaning to ensure that the Andersen model's explanatory factors do not apply consistently, but may have different contexts depending on the content or the use of the service. Third, in contrast to traditional theory dealing with labeling effect which suggests that the perceived stigma of IMIs as well as worker has a negative impact on the use of welfare services for IMIs, the results of this study show that the use of welfare services does not have a negative effect, and rather, despite the perceived stigma, the use of welfare services has increased. This supports the recent findings of a study that there might be a variety of responses and coping to stigma. Fourth, this paper shows, methodologically, that the Andersen model becomes more effective by employing a hierarchical linear model that is more appropriate to the characteristics of the population. In practical terms, the implications of this study are as follows: First, it is important to have an individual approach when we expand individual welfare services for IMIs. It is also important to note that different types of services may differ in the characteristics of IMIs, in their needs and in their service delivery characteristics. Second, in order to increase the use of welfare services for IMI, we need to improve the level of self-esteem of IMIs at the individual level. To this end, practitioners should make an effort to ensure that IMIs express their needs and opinions appropriately in the process of using welfare services. Third, practitioners should understand that the effects of stigma on the use of welfare services by IMIs may not always be negative, rather depending on the content and nature of the service. Fourth, workers perceived stigma is not negatively effective, which means that the positive role identification of the worker can mitigate public stigma and have a positive impact on the use of services by IMIs. From a policy standpoint, the implications of this study are as follows: First, in order to expand welfare services for IMIs, approach toward supply side of the service is necessary. This study shows that the use of welfare services for IMIs are influenced not only by the individual characteristics of IMIs, but also by the organizational characteristics of the MHWC. Thus, gradual increase in the budget and manpower of the delivery system for mental health services in the long term in order to increase the use of welfare services. Second, the revision of the MHWA contributed to providing the basis for the support of welfare services, but stopped short of rapidly implementing detailed plans on how to operate the actual welfare service support system for IMIs. Third, in order that the MHWC can focus more on case management and referral, clear differentiation of roles and coordination among mental health promotion institutions are imperative. To this end, it proposes the need to develop optimum level of case management and establish an information sharing system in community. Fifth, because effective sharing roles among mental health promotion institutions calls for more mental health resources such as mental rehabilitation facilities and welfare facilities, this paper suggests that government establish at least one mental rehabilitation facilities in each community and loosen the restrictions on Article 15 of the Welfare of Disabled Persons Act in the long run.์ •์‹ ์žฅ์• ์ธ์˜ ํšŒ๋ณต ๋ฐ ์„ฑ๊ณต์ ์ธ ์‚ฌํšŒ๋ณต๊ท€๋ฅผ ์œ„ํ•ด์„œ๋Š” ๋ณด๊ฑดยท์˜๋ฃŒ์„œ๋น„์Šค ๋ฟ ์•„๋‹ˆ๋ผ ์ง€์—ญ์‚ฌํšŒ์—์„œ ๋‹ค์–‘ํ•œ ๋ณต์ง€์„œ๋น„์Šค๋ฅผ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ด์•ผ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ๊นŒ์ง€ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์ •์‹ ๊ฑด๊ฐ•์„œ๋น„์Šค ์ „๋‹ฌ์ฒด๊ณ„๋Š” ๋ณด๊ฑดยท์˜๋ฃŒ ์ค‘์‹ฌ์œผ๋กœ ๋ฐœ์ „ํ•ด์™”์œผ๋ฉฐ, ์ •์‹ ๋ณด๊ฑด๋ฒ•์˜ ํŠน๋ณ„๋ฒ•์  ์ง€์œ„๋กœ ์ธํ•ด ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์€ ์ œ๋„์ ์œผ๋กœ ์ œํ•œ๋˜์—ˆ๋‹ค. ์ด์— ์ •์‹ ์žฅ์• ์ธ์˜ ๊ฐœ์ธ์  ์‚ถ์€ ์ง€์—ญ์‚ฌํšŒ์—์„œ ๋ถ€์กฑํ•œ ๋ณต์ง€์„œ๋น„์Šค๋ฅผ ์ด์šฉํ•˜๋ฉด์„œ ์ž๋ฆฝ์ƒํ™œ์„ ํ•˜์ง€ ๋ชปํ•˜๊ณ  ์–ด์ฉ” ์ˆ˜ ์—†์ด ์ •์‹ ๋ณ‘์›์ด๋‚˜ ์ •์‹ ์š”์–‘์‹œ์„ค์— ์ž…์›ยท์ž…์†Œํ•˜์—ฌ ์‚ฌํšŒ๋กœ๋ถ€ํ„ฐ ๋ฐฐ์ œ๋˜๋Š” ๊ตฌ์กฐ์ ์ธ ์•…์ˆœํ™˜์ด ์ง€์†๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ 2016๋…„ ์ •์‹ ๋ณด๊ฑด๋ฒ•์ด ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€๋ฒ•(์ •์‹ ๊ฑด๊ฐ•์ฆ์ง„ ๋ฐ ์ •์‹ ์งˆํ™˜์ž ๋ณต์ง€์„œ๋น„์Šค ์ง€์›์— ๊ด€ํ•œ ๋ฒ•๋ฅ )์œผ๋กœ ๊ฐœ์ •๋˜๋ฉด์„œ ์ •์‹ ์žฅ์• ์ธ์— ๋Œ€ํ•œ ๋ณต์ง€์„œ๋น„์Šค ์ง€์› ๊ทผ๊ฑฐ๋ฅผ ๋งˆ๋ จํ•œ ๊ฒƒ์€ ์ •์‹ ์žฅ์• ์ธ์˜ ํƒˆ์‹œ์„คํ™” ๋ฐ ์ง€์—ญ์‚ฌํšŒ์—์„œ์˜ ์ž๋ฆฝ์ ์ธ ์‚ถ์„ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•œ ํฐ ์ „ํ™˜์ ์ด๋ผ๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณด๊ฑดยท์˜๋ฃŒ ์ค‘์‹ฌ์˜ ์ •์‹ ๊ฑด๊ฐ•์„œ๋น„์Šค ์ „๋‹ฌ์ฒด๊ณ„์™€ ๋ถ€์กฑํ•œ ์„œ๋น„์Šค ๊ธฐ๋ฐ˜์„ ๊ณ ๋ คํ•˜๋ฉด, ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€๋ฒ•์—์„œ ๋ณต์ง€์„œ๋น„์Šค ์ง€์› ๊ทผ๊ฑฐ๋ฅผ ๋ช…๋ฌธํ™”ํ•œ ๊ฒƒ๋งŒ์œผ๋กœ ์ •์‹ ์žฅ์• ์ธ์— ๋Œ€ํ•œ ๋ณต์ง€์„œ๋น„์Šค๋ฅผ ์ถฉ๋ถ„ํžˆ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์„์ง€๋Š” ๋ถˆ๋ช…ํ™•ํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋ฒ• ๊ฐœ์ •์˜ ์‹œ์ ์—์„œ ์ •์‹ ์žฅ์• ์ธ๋“ค์€ ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€๋ฒ•์—์„œ ๊ทœ์ •ํ•˜๋Š” ๋ณต์ง€์„œ๋น„์Šค๋ฅผ ์–ผ๋งˆ๋‚˜ ๋‹ค์–‘ํ•˜๊ฒŒ ์ด์šฉํ•˜๊ณ  ์žˆ๋Š”์ง€, ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์—๋Š” ์–ด๋–ค ์š”์ธ์ด ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ์‚ดํŽด๋ณผ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ทธ๋™์•ˆ ์ •์‹ ์žฅ์• ์ธ์˜ ์„œ๋น„์Šค ์ด์šฉ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์–‘์ ์œผ๋กœ ๋“œ๋ฌผ๊ณ , ๋Œ€์ฒด๋กœ ๋ณต์ง€์„œ๋น„์Šค๊ฐ€ ์•„๋‹Œ ์žฌํ™œ ๋ฐ ์‚ฌํšŒ์„œ๋น„์Šค์˜ ์ด์šฉ์— ๋Œ€ํ•ด ๋‹ค๋ฃจ์–ด์ง„ ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ํŠนํžˆ, ์ •์‹ ์žฅ์• ์ธ์˜ ์„œ๋น„์Šค ์ด์šฉ์— ๋Œ€ํ•ด ์•ค๋”์Šจ ๋ชจํ˜•์„ ์ ์šฉํ•œ ์—ฐ๊ตฌ์—์„œ๋Š” ์ •์‹ ์žฅ์• ์ธ์˜ ํŠน์„ฑ๊ณผ ์„œ๋น„์Šค ๊ณต๊ธ‰์ž์˜ ํŠน์„ฑ์„ ์ถฉ๋ถ„ํžˆ ๊ณ ๋ คํ•˜์ง€ ๋ชปํ•œ ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์šฐ๋ฆฌ๋‚˜๋ผ ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์˜ ํ˜„ํ™ฉ์„ ์ „๋ฐ˜์ ์œผ๋กœ ํŒŒ์•…ํ•˜๊ณ , ์ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์ด ๋ฌด์—‡์ธ์ง€ ๊ณต๊ธ‰์ž ์š”์ธ์„ ๊ณ ๋ คํ•œ ์•ค๋”์Šจ ๋ชจํ˜•์„ ์ ์šฉํ•˜์—ฌ ํƒ์ƒ‰์ ์œผ๋กœ ์‚ดํŽด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์„ค์ •ํ•œ ์—ฐ๊ตฌ๋ฌธ์ œ๋Š” ์ฒซ์งธ, ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์ˆ˜์ค€์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์€ ๋ฌด์—‡์ธ๊ฐ€?์ด๋ฉฐ ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€๋ฒ•์—์„œ ๊ทœ์ •ํ•œ 5๊ฐ€์ง€ ๋ณต์ง€์„œ๋น„์Šค๋ฅผ ์–ผ๋งˆ๋‚˜ ๋‹ค์–‘ํ•˜๊ฒŒ ์ด์šฉํ•˜๊ณ  ์žˆ๋Š”์ง€์˜ ์ด์šฉ์ˆ˜์ค€์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋‘˜์งธ, ์ •์‹ ์žฅ์• ์ธ์˜ ๊ฐœ๋ณ„ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์—ฌ๋ถ€์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์€ ๋ฌด์—‡์ธ๊ฐ€?์ด๋ฉฐ, 5๊ฐ€์ง€ ๊ฐœ๋ณ„ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์—ฌ๋ถ€์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์ด ๋ฌด์—‡์ธ์ง€ ๊ฐ๊ฐ ์‚ดํŽด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” 2016๋…„ ๋ณด๊ฑด๋ณต์ง€๋ถ€๋กœ๋ถ€ํ„ฐ ์—ฐ๊ตฌ์šฉ์—ญ ๊ณผ์ œ๋ฅผ ์ˆ˜ํƒํ•˜์—ฌ ์ˆ˜ํ–‰๋œ ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€์„ผํ„ฐ ํ˜„ํ™ฉ์กฐ์‚ฌ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. ์ด 83๊ฐœ ๊ธฐ์ดˆ ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€์„ผํ„ฐ๋ฅผ ์ด์šฉํ•˜๋Š” ์ •์‹ ์žฅ์• ์ธ 420๋ช…์˜ ๊ฐœ์ธ ์ˆ˜์ค€ ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ์œ„๊ณ„์„ ํ˜•๋ชจํ˜•๊ณผ ๋กœ์ง€์Šคํ‹ฑ ์œ„๊ณ„์„ ํ˜•๋ชจํ˜•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์šฐ๋ฆฌ๋‚˜๋ผ ์ •์‹ ๊ฑด๊ฐ• ์ •์ฑ… ํ™˜๊ฒฝ ๋ณ€ํ™” ๋ฐ ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€๋ฒ•์—์„œ์˜ ๋ณต์ง€์„œ๋น„์Šค ์˜๋ฏธ์™€ ๋‚ด์šฉ์„ ๊ด€๋ จ ๋ฌธํ—Œ์„ ํ†ตํ•ด ์‚ดํŽด๋ณด๊ณ , ์ •์‹ ์žฅ์• ์ธ์˜ ํŠน์„ฑ ๋ฐ ๊ณต๊ธ‰์ž ์š”์ธ์„ ๊ณ ๋ คํ•œ ์•ค๋”์Šจ ๋ชจํ˜•์— ๋Œ€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ๋ฅผ ๊ฒ€ํ† ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฌธ์ œ 1์˜ ๋ถ„์„์„ ์œ„ํ•ด์„œ๋Š” ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€๋ฒ•์—์„œ ๊ทœ์ •ํ•œ 5๊ฐ€์ง€ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ๊ฒฝํ—˜ ์—ฌ๋ถ€๋ฅผ ํ•ฉ์‚ฐํ•˜์—ฌ ์ข…์†๋ณ€์ˆ˜๋กœ ํ™œ์šฉํ•˜์˜€๊ณ , ์ •์‹ ์žฅ์• ์ธ์˜ ํŠน์„ฑ์„ ๊ฐœ์ธ ์ˆ˜์ค€์œผ๋กœ ๋ชจํ˜•์— ๋ฐ˜์˜ํ•˜๊ณ  ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€์„ผํ„ฐ์˜ ๊ธฐ๊ด€๋ณ„ ํŠน์„ฑ์„ ์กฐ์ง ์ˆ˜์ค€์œผ๋กœ ๋ชจํ˜•์— ๋ฐ˜์˜ํ•˜์—ฌ ์œ„๊ณ„์„ ํ˜•๋ชจํ˜•์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฌธ์ œ 2์˜ ๋ถ„์„์„ ์œ„ํ•ด์„œ๋Š” 5๊ฐ€์ง€ ๊ฐœ๋ณ„ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์—ฌ๋ถ€๋ฅผ ์ข…์†๋ณ€์ˆ˜๋กœ ํ•˜์—ฌ, ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์ •์‹ ์žฅ์• ์ธ์˜ ๊ฐœ์ธ ํŠน์„ฑ ๋ฐ ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€์„ผํ„ฐ์˜ ์กฐ์ง ํŠน์„ฑ์„ ๋ชจํ˜•์— ๋ฐ˜์˜ํ•˜์—ฌ ๋กœ์ง€์Šคํ‹ฑ ์œ„๊ณ„์„ ํ˜•๋ชจํ˜•์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์ˆ˜์ค€์— ๋Œ€ํ•ด ์œ„๊ณ„์„ ํ˜•๋ชจํ˜•์„ ์ ์šฉํ•˜์—ฌ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ๊ฐœ์ธ ์ˆ˜์ค€์˜ ๋ณ€์ˆ˜์—์„œ๋Š” ์„ ํ–‰์š”์ธ์œผ๋กœ ์—ฐ๋ น, ๊ต์œก์ˆ˜์ค€์ด, ๊ฐ€๋Šฅ์š”์ธ์œผ๋กœ๋Š” ๊ฒฐํ˜ผ์ƒํƒœ, ์ข…๊ต์œ ๋ฌด, ์ž์•„์กด์ค‘๊ฐ ์ˆ˜์ค€, ์†Œ๋“ ์ˆ˜์ค€์ด, ์š•๊ตฌ์š”์ธ์œผ๋กœ๋Š” ์ฆ์ƒ ์ˆ˜์ค€๊ณผ ์ง„๋‹จ๋ช…์ด ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์ •์‹ ์žฅ์• ์ธ์ด ์–ผ๋งˆ๋‚˜ ๋‹ค์–‘ํ•œ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉํ•˜๋Š”์ง€์— ๋Œ€ํ•ด์„œ๋Š” ์„ ํ–‰์š”์ธ์ด๋‚˜ ์š•๊ตฌ์š”์ธ์— ๋น„ํ•ด ๊ฐ€๋Šฅ์š”์ธ์— ์˜ํ•ด์„œ ๋” ๋งŽ์ด ์„ค๋ช…๋˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ๋˜ํ•œ ์š•๊ตฌ์— ์˜ํ•ด์„œ ๋ณต์ง€์„œ๋น„์Šค๋ฅผ ๋‹ค์–‘ํ•˜๊ฒŒ ์ด์šฉํ•˜๊ธฐ ๋ณด๋‹ค๋Š” ์ •์‹ ์žฅ์• ์ธ์— ๋Œ€ํ•œ ๋ณต์ง€์„œ๋น„์Šค ๊ณต๊ธ‰ ์ˆ˜์ค€์ด ์ ์€ ํ˜„์‹ค์„ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์กฐ์ง์ˆ˜์ค€์˜ ๋ณ€์ˆ˜๋กœ๋Š” 1์ธ๋‹น ์ •์‹ ๊ฑด๊ฐ• ์˜ˆ์‚ฐ๊ทœ๋ชจ์™€ ์ข…์‚ฌ์ž ๋‚™์ธ ์ธ์‹ ์ˆ˜์ค€์ด ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ์ด๋Š” ์ธ๊ตฌ๋Œ€๋น„ ์ •์‹ ๊ฑด๊ฐ• ์˜ˆ์‚ฐ ๋ฐ ์ธ๋ ฅ์ด ์ƒ๋Œ€์ ์œผ๋กœ ๋งŽ๊ณ , ์ข…์‚ฌ์ž๊ฐ€ ๋Œ€์ค‘์˜ ๋ถ€์ •์ ์ธ ๋‚™์ธ์„ ๋”์šฑ ๋ฏผ๊ฐํ•˜๊ฒŒ ์ธ์‹ํ•˜๋Š” ๊ณณ์—์„œ ์ •์‹ ์žฅ์• ์ธ์€ ๋” ๋ณตํ•ฉ์ ์ด๊ณ  ๋‹ค์–‘ํ•œ ๋ณต์ง€์„œ๋น„์Šค๋ฅผ ์ด์šฉํ•œ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์—ฐ๊ณ„ ๋ฐ ์‚ฌ๋ก€๊ด€๋ฆฌ์™€ ๊ด€๋ จํ•œ ์กฐ์ง ํŠน์„ฑ ๋ณ€์ˆ˜๋Š” ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ–ˆ๋Š”๋ฐ, ์ด๋Š” ๊ณผ์ค‘ํ•œ ์—…๋ฌด ๋ถ€๋‹ด๊ณผ ํ˜•์‹์ ์ธ ์‚ฌ๋ก€๊ด€๋ฆฌ๊ฐ€ ์ด๋ฃจ์–ด์ง€๋Š” ํ˜„์‹ค์ด ๋ฐ˜์˜๋œ ๊ฒƒ์œผ๋กœ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ 5๊ฐ€์ง€ ๊ฐœ๋ณ„ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์—ฌ๋ถ€์— ๋Œ€ํ•œ ๋กœ์ง€์Šคํ‹ฑ ์œ„๊ณ„์„ ํ˜•๋ชจํ˜•์„ ์ ์šฉํ•˜์—ฌ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์ˆ˜์ค€์— ๋Œ€ํ•œ ๋ถ„์„๊ฒฐ๊ณผ์™€ ๋Œ€์ฒด๋กœ ์œ ์‚ฌํ•œ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚ฌ์œผ๋‚˜, ๋ณต์ง€์„œ๋น„์Šค์— ๋”ฐ๋ผ ์„œ๋กœ ์ƒ์ดํ•œ ๊ฒฐ๊ณผ๋„ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด์™€ ๊ฐ™์€ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๋‹ค์Œ๊ณผ ๊ฐ™์€ ํ•จ์˜๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ์ด๋ก ์  ์ธก๋ฉด์—์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฒซ์งธ, ์ง€์—ญ์‚ฌํšŒ ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์„ ์•ค๋”์Šจ ๋ชจํ˜•์„ ํ†ตํ•ด ์ ์šฉ๊ฐ€๋Šฅํ•˜๋ฉฐ, ํŠนํžˆ ๊ณต๊ธ‰์ž ์š”์ธ์„ ๊ณ ๋ คํ•  ํ•„์š”๊ฐ€ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Š” ๊ทธ๋™์•ˆ ์˜๋ฃŒ์„œ๋น„์Šค ์ด์šฉ์— ์ฃผ๋กœ ์ ์šฉ๋˜์–ด์˜จ ์•ค๋”์Šจ ๋ชจํ˜•์ด ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์—๋„ ํ™•์žฅ๋˜์–ด ์ ์šฉ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์•ค๋”์Šจ ๋ชจํ˜•์„ ์ ์šฉํ•  ๋•Œ ์„œ๋น„์Šค ์ˆ˜์š” ์ธก๋ฉด ์™ธ์—๋„ ์„œ๋น„์Šค ๊ณต๊ธ‰ ์ธก๋ฉด๊นŒ์ง€ ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค๋Š” ์ด๋ก ์  ํ•จ์˜๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๋‘˜์งธ, ๋ณต์ง€์„œ๋น„์Šค์˜ ๋‚ด์šฉ ๋ฐ ํŠน์„ฑ์— ๋”ฐ๋ผ ์•ค๋”์Šจ ๋ชจํ˜•์˜ ์„ค๋ช…์š”์ธ์ด ์„œ๋กœ ๋‹ค๋ฅธ ์š”์ธ์œผ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Š” ์•ค๋”์Šจ ๋ชจํ˜•์˜ ์„ค๋ช… ์š”์ธ์ด ์ผ์ •ํ•˜๊ฒŒ ์ ์šฉ๋˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์„œ๋น„์Šค ๋‚ด์šฉ์ด๋‚˜ ์ด์šฉ ์ƒํ™ฉ์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ๋งฅ๋ฝ์„ ๊ฐ€์งˆ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜๋Š” ์ด๋ก ์  ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์…‹์งธ, ์ „ํ†ต ๋‚™์ธ์ด๋ก ์— ๋”ฐ๋ฅด๋ฉด ์ •์‹ ์žฅ์• ์ธ ๋‹น์‚ฌ์ž ๋ฐ ์ข…์‚ฌ์ž์˜ ๋‚™์ธ ์ธ์‹์€ ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์— ๋ถ€์ •์  ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ์œผ๋‚˜, ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ์—์„œ๋Š” ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์ด ๋‚˜ํƒ€๋‚˜์ง€ ์•Š๊ฑฐ๋‚˜ ์˜คํžˆ๋ ค ๋‚™์ธ ์ธ์‹์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์ด ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ๋‚™์ธ์— ๋Œ€ํ•ด ๋‹ค์–‘ํ•œ ๋Œ€์ฒ˜ํ–‰๋™๊ณผ ๋ฐ˜์‘์ด ๋‚˜ํƒ€๋‚  ๊ฒƒ์ด๋ผ๊ณ  ๋ณด๋Š” ์ตœ๊ทผ์˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ง€์ง€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋„ท์งธ, ๋ชจ์ง‘๋‹จ์˜ ํŠน์„ฑ์— ๋ณด๋‹ค ์ ํ•ฉํ•œ ์œ„๊ณ„์„ ํ˜•๋ชจํ˜•์„ ์ ์šฉํ•˜์—ฌ, ์•ค๋”์Šจ ๋ชจํ˜•์„ ๋”์šฑ ํšจ๊ณผ์ ์œผ๋กœ ์ ์šฉํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ๋ฐฉ๋ฒ•๋ก ์  ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์‹ค์ฒœ์  ์ธก๋ฉด์—์„œ ๋ณธ ์—ฐ๊ตฌ์˜ ํ•จ์˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ํ™•๋Œ€๋ฅผ ์œ„ํ•ด์„œ๋Š” ๊ฐ ๊ฐœ๋ณ„ ๋ณต์ง€์„œ๋น„์Šค๋ฅผ ๊ฐ๊ฐ ํ™•๋Œ€ํ•˜๋Š” ๊ฐœ๋ณ„์  ์ ‘๊ทผ์ด ์ค‘์š”ํ•˜๋‹ค. ์„œ๋น„์Šค ์ข…๋ฅ˜์— ๋”ฐ๋ผ ์ •์‹ ์žฅ์• ์ธ์˜ ํŠน์„ฑ๊ณผ ์š•๊ตฌ, ์„œ๋น„์Šค ๊ณต๊ธ‰ ํŠน์„ฑ์ด ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ์Œ์„ ์‹ค์ฒœ๊ฐ€๋“ค์ด ์œ ๋…ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‘˜์งธ, ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์„ ํ™•๋Œ€์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ฐœ์ธ ์ˆ˜์ค€์—์„œ๋Š” ๊ณตํ†ต์ ์œผ๋กœ ์ •์‹ ์žฅ์• ์ธ์˜ ์ž์•„์กด์ค‘๊ฐ ์ˆ˜์ค€์„ ํ–ฅ์ƒ์‹œํ‚ฌ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ๊ณผ์ •์—์„œ ์š•๊ตฌ ๋ฐ ์˜์‚ฌ๊ฐ€ ์ž˜ ํ‘œํ˜„๋  ์ˆ˜ ์žˆ๋„๋ก ์‹ค์ฒœ๊ฐ€์˜ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋‹ค. ์…‹์งธ, ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์— ์žˆ์–ด ๋‚™์ธ์˜ ํšจ๊ณผ๊ฐ€ ํ•ญ์ƒ ๋ถ€์ •์ ์œผ๋กœ๋งŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์„œ๋น„์Šค์˜ ๋‚ด์šฉ๊ณผ ์„ฑ๊ฒฉ์— ๋”ฐ๋ผ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ์Œ์„ ์‹ค์ฒœ๊ฐ€๋“ค์ด ์ดํ•ดํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋„ท์งธ, ์ข…์‚ฌ์ž ๋‚™์ธ ์ธ์‹์ด ๋ถ€์ •์ ์œผ๋กœ ์ž‘๋™๋˜๋Š” ๊ฒƒ์€ ์•„๋‹ˆ๋ฉฐ, ์ข…์‚ฌ์ž์˜ ๊ธ์ •์ ์ธ ์—ญํ• ์ •์ฒด์„ฑ์€ ๋Œ€์ค‘์˜ ๋‚™์ธ์„ ์™„์ถฉํ•˜๊ณ  ์ •์‹ ์žฅ์• ์ธ์˜ ์„œ๋น„์Šค ์ด์šฉ์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋‹ค. ์ •์ฑ…์ ์ธ ์ธก๋ฉด์—์„œ ๋ณธ ์—ฐ๊ตฌ์˜ ํ•จ์˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ํ™•๋Œ€๋ฅผ ์œ„ํ•ด์„œ๋Š” ์„œ๋น„์Šค ๊ณต๊ธ‰ ์ธก๋ฉด์˜ ์ ‘๊ทผ์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ, ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์€ ์ •์‹ ์žฅ์• ์ธ์˜ ๊ฐœ์ธ ํŠน์„ฑ ๋ฟ ์•„๋‹ˆ๋ผ ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€์„ผํ„ฐ์˜ ์กฐ์ง ํŠน์„ฑ์—๋„ ์˜ํ–ฅ์„ ๋ฐ›๊ณ  ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ ํ™•๋Œ€๋ฅผ ์œ„ํ•ด์„œ๋Š” ์žฅ๊ธฐ์ ์œผ๋กœ ์ •์‹ ๊ฑด๊ฐ•์„œ๋น„์Šค ์ „๋‹ฌ์ฒด๊ณ„์˜ ์˜ˆ์‚ฐ ๋ฐ ์ธ๋ ฅ์„ ์ ์ง„์ ์œผ๋กœ ํ™•๋Œ€ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค๋Š” ํ•จ์˜๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ๋‘˜์งธ, ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€๋ฒ• ๊ฐœ์ •์œผ๋กœ ๋ณต์ง€์„œ๋น„์Šค ์ง€์›์˜ ๊ทผ๊ฑฐ๋Š” ๋งˆ๋ จํ•˜์˜€์œผ๋ฏ€๋กœ ์ •์‹ ์žฅ์• ์ธ์— ๋Œ€ํ•œ ์‹ค์งˆ์ ์ธ ๋ณต์ง€์„œ๋น„์Šค ์ง€์›์ฒด๊ณ„๋ฅผ ์–ด๋–ป๊ฒŒ ์ž‘๋™์‹œํ‚ฌ์ง€์— ๋Œ€ํ•œ ์„ธ๋ถ€์ ์ธ ๊ณ„ํš์„ ์กฐ์†ํžˆ ์ œ๋„ํ™”ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค๋Š” ํ•จ์˜๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ์…‹์งธ, ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€์„ผํ„ฐ๊ฐ€ ์‚ฌ๋ก€๊ด€๋ฆฌ ๋ฐ ์—ฐ๊ณ„ ์—…๋ฌด์— ์ดˆ์ ์„ ๋งž์ถœ ์ˆ˜ ์žˆ๋„๋ก ์ •์‹ ๊ฑด๊ฐ•์ฆ์ง„๊ธฐ๊ด€ ๊ฐ„ ์—ญํ• ๋ถ„๋‹ด ๋ฐ ์กฐ์ •์ด ํ•„์š”ํ•˜๋ฉฐ, ์ •์ƒ์ ์ธ ์—ญํ•  ์ˆ˜ํ–‰์ด ๊ฐ€๋Šฅํ•˜๋„๋ก ์—ฌ๊ฑด ๋งˆ๋ จ์ด ํ•„์š”ํ•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ ์ • ์‚ฌ๋ก€๊ด€๋ฆฌ ๊ธฐ์ค€์„ ๋งˆ๋ จํ•˜๊ณ  ์ง€์—ญ ๋‚ด ์ •๋ณด๊ณต์œ  ์ฒด๊ณ„๋ฅผ ๊ตฌ์ถ•ํ•  ํ•„์š”์„ฑ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋‹ค์„ฏ์งธ, ์ •์‹ ๊ฑด๊ฐ•์ฆ์ง„๊ธฐ๊ด€ ๊ฐ„์˜ ํšจ๊ณผ์ ์ธ ์—ญํ•  ๋ถ„๋‹ด์„ ์œ„ํ•ด์„œ๋Š” ์ •์‹ ์žฌํ™œ์‹œ์„ค ๋ฐ ๋ณต์ง€์‹œ์„ค๊ณผ ๊ฐ™์€ ์ •์‹ ๊ฑด๊ฐ•์ž์›์˜ ํ™•์ถฉ์ด ํ•„์š”ํ•˜๋ฏ€๋กœ, ์žฅ๊ธฐ์ ์œผ๋กœ๋Š” ์ง€์—ญ๋งˆ๋‹ค ์ตœ์†Œํ•œ์˜ ์ •์‹ ์žฌํ™œ์‹œ์„ค์„ ํ™•๋ณดํ•˜๊ณ  ์žฅ์• ์ธ๋ณต์ง€๋ฒ• 15์กฐ์˜ ์ œํ•œ ๊ทœ์ •์„ ๊ฐœ์ •ํ•˜๋Š” ๋“ฑ์˜ ์ œ๋„์  ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ํ•จ์˜๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค.์ œ1์žฅ ์„œ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 ์ œ2์ ˆ ์—ฐ๊ตฌ ๋ฌธ์ œ 7 ์ œ2์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 10 ์ œ1์ ˆ ์ •์‹ ๊ฑด๊ฐ• ์ •์ฑ… ํ™˜๊ฒฝ ๋ณ€ํ™”์™€ ์ •์‹ ์žฅ์• ์ธ์„ ์œ„ํ•œ ๋ณต์ง€์„œ๋น„์Šค 10 1. ์ •์‹ ๊ฑด๊ฐ• ์ •์ฑ… ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”์™€ ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€๋ฒ• ๊ฐœ์ • 10 2. ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€๋ฒ•์— ์˜๊ฑฐํ•œ ์ •์‹ ์žฅ์• ์ธ ๋ณต์ง€์„œ๋น„์Šค 15 3. ์šฐ๋ฆฌ๋‚˜๋ผ ์ •์‹ ๊ฑด๊ฐ•์„œ๋น„์Šค ์ „๋‹ฌ์ฒด๊ณ„ ํ˜„ํ™ฉ๊ณผ ์ •์‹ ๊ฑด๊ฐ•๋ณต์ง€์„ผํ„ฐ์˜ ์—ญํ•  21 ์ œ2์ ˆ ์•ค๋”์Šจ์˜ ์„œ๋น„์Šค ์ด์šฉํ–‰๋™๋ชจํ˜• 26 1. ์•ค๋”์Šจ ๋ชจํ˜•์˜ ํ™•์žฅ 26 2. ์ •์‹ ์žฅ์• ์ธ์˜ ์„œ๋น„์Šค ์ด์šฉ๊ณผ ํ™•์žฅ ์•ค๋”์Šจ ๋ชจํ˜• 34 ์ œ3์ ˆ ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ ๊ด€๋ จ ์š”์ธ 45 1. ๊ฐœ์ธ ์ˆ˜์ค€ ์š”์ธ 45 2. ์กฐ์ง ์ˆ˜์ค€ ์š”์ธ 49 ์ œ3์žฅ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 54 ์ œ1์ ˆ ์—ฐ๊ตฌ ๋ฒ”์œ„ ๋ฐ ์—ฐ๊ตฌ ๋ชจํ˜• 54 ์ œ2์ ˆ ์—ฐ๊ตฌ ๊ฐ€์„ค 59 ์ œ3์ ˆ ๋ถ„์„์ž๋ฃŒ ๋ฐ ๋ถ„์„๋Œ€์ƒ 66 ์ œ4์ ˆ ์—ฐ๊ตฌ์œค๋ฆฌ์— ๋Œ€ํ•œ ๊ณ ๋ ค 67 ์ œ5์ ˆ ๋ณ€์ˆ˜์˜ ์ •์˜ ๋ฐ ์ธก์ • 68 ์ œ6์ ˆ ์ž๋ฃŒ ๋ถ„์„ ๋ฐฉ๋ฒ• 73 1. ์ž๋ฃŒ์˜ ์ ๊ฒ€ 73 2. ๊ธฐ์ดˆ์ž๋ฃŒ ๋ถ„์„ 73 3. ์œ„๊ณ„์„ ํ˜•๋ชจํ˜•์˜ ๊ฐœ์š” 73 4. ์œ„๊ณ„์„ ํ˜•๋ชจํ˜•์˜ ์ ์šฉ 75 (1) ๊ธฐ์ดˆ๋ชจํ˜• 75 (2) ์กฐ๊ฑด๋ชจํ˜• 1: ์ •์‹ ์žฅ์• ์ธ์˜ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์ˆ˜์ค€ ์˜ํ–ฅ ์š”์ธ 78 (3) ์กฐ๊ฑด๋ชจํ˜• 2: ์ •์‹ ์žฅ์• ์ธ์˜ ๊ฐœ๋ณ„ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์—ฌ๋ถ€์— ๋Œ€ํ•œ ์˜ํ–ฅ ์š”์ธ 81 ์ œ4์žฅ ๋ถ„์„๊ฒฐ๊ณผ 83 ์ œ1์ ˆ ์ž๋ฃŒ์˜ ์ ๊ฒ€ 83 1. ๊ฒฐ์ธก์น˜ ๊ฒ€ํ† ์™€ ์ฒ˜๋ฆฌ 83 2. ๋‹ค์ค‘๊ณต์„ ์„ฑ ์ง„๋‹จ 84 ์ œ2์ ˆ ๊ธฐ์ดˆํ†ต๊ณ„ ๋ถ„์„ 88 1. ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์ˆ˜์ค€์˜ ๊ธฐ์ดˆ ํ†ต๊ณ„์น˜ ๋ฐ ์ •๊ทœ์„ฑ ๊ฒ€ํ†  88 2. ๊ฐœ์ธ ํŠน์„ฑ ๋ณ€์ˆ˜์˜ ๊ธฐ์ดˆ ํ†ต๊ณ„์น˜ ๋ฐ ์ •๊ทœ์„ฑ ๊ฒ€ํ†  90 3. ์กฐ์ง ํŠน์„ฑ ๋ณ€์ˆ˜์˜ ๊ธฐ์ดˆ ํ†ต๊ณ„์น˜ ๋ฐ ์ •๊ทœ์„ฑ ๊ฒ€ํ†  92 ์ œ3์ ˆ ์—ฐ๊ตฌ๋ชจํ˜• ๋ถ„์„๊ฒฐ๊ณผ 94 1. ๊ธฐ์ดˆ๋ชจํ˜• ๋ถ„์„๊ฒฐ๊ณผ 94 2. ์—ฐ๊ตฌ๋ชจํ˜• ๋ถ„์„๊ฒฐ๊ณผ 97 (1) ์—ฐ๊ตฌ๋ชจํ˜• 1: ์ •์‹ ์žฅ์• ์ธ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์ˆ˜์ค€ ์˜ํ–ฅ ์š”์ธ 97 (2) ์—ฐ๊ตฌ๋ชจํ˜• 2: ์ •์‹ ์žฅ์• ์ธ์˜ ๊ฐœ๋ณ„ ๋ณต์ง€์„œ๋น„์Šค ์ด์šฉ์—ฌ๋ถ€์— ๋Œ€ํ•œ ์˜ํ–ฅ ์š”์ธ 100 3. ๋ถ„์„๊ฒฐ๊ณผ์˜ ๋น„๊ต 110 ์ œ5์žฅ ๊ฒฐ๋ก  116 ์ œ1์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋ฐ ๋…ผ์˜ 116 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ํ•จ์˜ 128 1. ์—ฐ๊ตฌ์˜ ์ด๋ก ์  ํ•จ์˜ 128 2. ์—ฐ๊ตฌ์˜ ์‹ค์ฒœ์  ํ•จ์˜ 131 3. ์—ฐ๊ตฌ์˜ ์ •์ฑ…์  ํ•จ์˜ 133 ์ œ3์ ˆ ์—ฐ๊ตฌํ•œ๊ณ„์™€ ํ›„์†์—ฐ๊ตฌ ์ œ์–ธ 136 ์ฐธ๊ณ ๋ฌธํ—Œ 139 ๋ถ€๋ก(์„ค๋ฌธ์ง€) 151 Abstract 156Docto

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