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    ์ œํ•œ๋œ ํ‰๊ท  ์ƒ์กด์‹œ๊ฐ„ ์ถ”์ • ๋ฐฉ๋ฒ• ๊ณ ์ฐฐ ๋ฐ ์ƒˆ๋กœ์šด ๋ฏผ๊ฐ๋„ ๋ถ„์„ ๋ฐฉ๋ฒ• ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์‘์šฉ๋ฐ”์ด์˜ค๊ณตํ•™๊ณผ, 2023. 2. ๋ฐ•์ง€ํ›ˆ.The difference in restricted mean survival time (RMST) has been increasingly used as an alternative measure to hazard ratio in survival analysis. Unlike relative effect measure such as hazard ratio, RMST difference provides information about an intuitively interpretable absolute risk and is known to be robust regardless of the proportional hazards assumption. In experimental studies such as a randomized controlled trial, the RMST is calculated by integrating the area under the Kaplan-Meier curve up to a specific time point, and the difference in RMST between the two groups is used as a causal effect of exposure. However, in observational studies, the standard Kaplan-Meier estimator cannot be directly used for calculating the RMST because of confounding bias due to non-random exposure assignment. The difference in RMST adjusted for potential confounders can be estimated using methods such as direct RMST regression, inverse probability weighting, G-computation, etc. Through multiple simulation studies in which all the models were correctly specified, we confirmed that all the methods being considered provided the unbiased estimates with the percentile bootstrap confidence intervals achieving near nominal coverage probability. Although several methods have been developed for evaluating the difference in RMST adjusted for potential confounders in the observational study, there is no study on the sensitivity analysis of unmeasured confounding. Therefore, we propose a novel sensitivity analysis method that considers unmeasured confounding for evaluating the estimate of the difference in adjusted RMST. Given a user-specified sensitivity parameter, one can obtain the sensitivity range and confidence interval of bias-adjusted difference in RMST. It is necessary to solve a complex optimization problem to obtain the sensitivity range and confidence interval, but there is no analytic solution except in special cases. While the optimization problem can be directly solved by using an optimization algorithm such as L-BFGS-B (hereafter referred to this method as the direct optimization method), it takes considerable computational time. Therefore, we propose an approximate optimization method comparable to the direct optimization method in terms of bias, achieving substantial reduction in the computational time. Through intensive Monte Carlo simulation studies, we showed that the proposed approximate optimization method can be a practical alternative. When applying our sensitivity analysis method in practice, we recommend using the approximate optimization method in case that the censoring rate is less than 0.7. Otherwise, one may use the direct optimization method using an optimization algorithm.์ƒ์กด ๋ถ„์„์—์„œ ์ œํ•œ๋œ ํ‰๊ท  ์ƒ์กด ์‹œ๊ฐ„(restricted mean survival time; RMST)์˜ ์ฐจ์ด๋Š” ์œ„ํ—˜ ๋น„์œจ(hazard ratio)์— ๋Œ€ํ•œ ๋Œ€์•ˆ ์ฒ™๋„๋กœ ์ ์  ๋” ๋งŽ์ด ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์œ„ํ—˜ ๋น„์œจ๊ณผ ๊ฐ™์€ ์ƒ๋Œ€์  ํšจ๊ณผ ์ธก๋„(relative effect measure)์™€ ๋‹ฌ๋ฆฌ, RMST ์ฐจ์ด๋Š” ์ง๊ด€์ ์œผ๋กœ ํ•ด์„ ๊ฐ€๋Šฅํ•œ ์ ˆ๋Œ€ ์œ„ํ—˜(absolute risk)์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ ๋น„๋ก€ ์œ„ํ—˜ ๊ฐ€์ •์— ๊ด€๊ณ„์—†์ด ๋กœ๋ฒ„์ŠคํŠธํ•œ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ฌด์ž‘์œ„๋Œ€์กฐ์‹œํ—˜์—์„œ๋Š” Kaplan-Meier ๊ณก์„  ์•„๋ž˜์˜ ๋ฉด์ ์„ ํŠน์ • ์‹œ์ ๊นŒ์ง€ ์ ๋ถ„ํ•˜์—ฌ RMST๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ , ๋‘ ๊ทธ๋ฃน ๊ฐ„์˜ RMST ์ฐจ์ด๋ฅผ ๋…ธ์ถœ(exposure)์˜ ์ธ๊ณผํšจ๊ณผ๋กœ ์‚ฌ์šฉํ•œ๋‹ค. ์ด์— ๋ฐ˜ํ•ด, ๊ด€์ฐฐ ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„๋ฌด์ž‘์œ„ ๋…ธ์ถœ ํ• ๋‹น์œผ๋กœ ์ธํ•œ ๊ต๋ž€ ํŽธํ–ฅ ๋•Œ๋ฌธ์— ํ‘œ์ค€์ ์ธ Kaplan-Meier ์ถ”์ •๋Ÿ‰์„ RMST ๊ณ„์‚ฐ์— ์ง์ ‘ ์‚ฌ์šฉํ•  ์ˆ˜ ์—†๋‹ค. ์ด๋Ÿฌํ•œ ๊ต๋ž€ ํŽธํ–ฅ์„ ๋ณด์ •ํ•œ RMST์˜ ์ฐจ์ด๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ์ง์ ‘ RMST ํšŒ๊ท€, ์—ญ ํ™•๋ฅ  ๊ฐ€์ค‘์น˜ (inverse probability weighting), G-computation ๋“ฑ์ด ์žˆ๋‹ค. ๋ชจ๋“  ๋ชจ๋ธ์ด ์˜ฌ๋ฐ”๋ฅด๊ฒŒ ์ง€์ •๋œ ๋ณต์ˆ˜์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์šฐ๋ฆฌ๋Š” ๊ณ ๋ คํ•œ ๋ชจ๋“  ๋ฐฉ๋ฒ•์ด ๋น„ํŽธํ–ฅ์ถ”์ •๊ฐ’(unbiased estimate)์„ ์ œ๊ณตํ•˜๊ณ  ๋ฐฑ๋ถ„์œ„์ˆ˜ ๋ถ“์ŠคํŠธ๋žฉ (percentile bootstrap) ์‹ ๋ขฐ๊ตฌ๊ฐ„์ด ๋ช…๋ชฉํ‘œํ•จํ™•๋ฅ (nominal coverage probability)์„ ๋‹ฌ์„ฑํ•จ์„ ํ™•์ธํ–ˆ๋‹ค. ๊ด€์ฐฐ ์—ฐ๊ตฌ์—์„œ ๊ต๋ž€์— ๋Œ€ํ•ด ๋ณด์ •๋œ RMST์˜ ์ฐจ์ด๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ๋ช‡ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์ด ๊ฐœ๋ฐœ๋˜์—ˆ์ง€๋งŒ, ์ธก์ •๋˜์ง€ ์•Š์€ ๊ต๋ž€์˜ ๋ฏผ๊ฐ๋„ ๋ถ„์„(sensitivity analysis)์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์•„์ง๊นŒ์ง€ ์—†๋‹ค. ๋”ฐ๋ผ์„œ, ์šฐ๋ฆฌ๋Š” ๋ณด์ •๋œ RMST์˜ ์ฐจ์ด๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ์ธก์ •๋˜์ง€ ์•Š์€ ๊ต๋ž€์„ ๊ณ ๋ คํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฏผ๊ฐ๋„ ๋ถ„์„ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์‚ฌ์šฉ์ž ์ง€์ • ๋ฏผ๊ฐ๋„ ๋งค๊ฐœ๋ณ€์ˆ˜๊ฐ€ ์ฃผ์–ด์ง€๋ฉด, ํŽธํ–ฅ ์กฐ์ •๋œ RMST ์ฐจ์ด(bias-adjusted difference in RMST)์˜ ์ถ”์ •์น˜์— ๋Œ€ํ•œ ๋ฏผ๊ฐ๋„ ๋ฒ”์œ„(sensitivity range)์™€ ์‹ ๋ขฐ๊ตฌ๊ฐ„์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ๋ฏผ๊ฐ๋„ ๋ฒ”์œ„์™€ ์‹ ๋ขฐ๊ตฌ๊ฐ„์„ ์–ป๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋ณต์žกํ•œ ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ํ’€์–ด์•ผ ํ•˜์ง€๋งŒ, ํŠน๋ณ„ํ•œ ๊ฒฝ์šฐ๋ฅผ ์ œ์™ธํ•˜๊ณ ๋Š” ๋ถ„์„์  ํ•ด๊ฐ€ ์กด์žฌํ•˜์ง€ ์•Š๋Š”๋‹ค. ์ตœ์ ํ™” ๋ฌธ์ œ์˜ ํ•ด๋ฅผ L-BFGS-B์™€ ๊ฐ™์€ ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ตฌํ•  ์ˆ˜ ์žˆ์ง€๋งŒ (์ง์ ‘ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•), ์ด ๊ฒฝ์šฐ ํ•ด๋ฅผ ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด ์ƒ๋‹นํ•œ ๊ณ„์‚ฐ ์‹œ๊ฐ„์ด ์†Œ์š”๋œ๋‹ค. ๋”ฐ๋ผ์„œ, ์šฐ๋ฆฌ๋Š” ํŽธํ–ฅ๊ณผ ๊ณ„์‚ฐ์‹œ๊ฐ„ ๋ชจ๋‘์—์„œ ์ง์ ‘ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•๋ณด๋‹ค ์—ด๋“ฑํ•˜์ง€ ์•Š์€ ๊ทผ์‚ฌ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ–ˆ๊ณ , ์ง‘์•ฝ์ ์ธ Monte Carlo ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ œ์•ˆํ•œ ๊ทผ์‚ฌ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•์ด ์‹ค์šฉ์ ์ธ ๋Œ€์•ˆ์ฑ…์ด ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ๋ฏผ๊ฐ๋„ ๋ถ„์„์„ ์‹ค์ œ ๋ฌธ์ œ์— ์ ์šฉํ•  ๋•Œ, ์šฐ๋ฆฌ๋Š” ์ค‘๋„์ ˆ๋‹จ๋ฅ (censoring rate)์ด 0.7 ๋ฏธ๋งŒ์ธ ๊ฒฝ์šฐ ๊ทผ์‚ฌ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๊ณ , ์ค‘๋„์ ˆ๋‹จ๋ฅ ์ด 0.7 ์ด์ƒ์ธ ๊ฒฝ์šฐ ์ง์ ‘ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์„ ๊ถŒ๊ณ ํ•œ๋‹ค.1 Introduction 1 2 Restricted Mean Survival Time 6 2.1 Notation and assumptions 6 2.2 Difference in RMST 7 3 Methods for Estimation of Difference in RMST 9 3.1 Difference in RMST in randomized controlled trial 9 3.2 Difference in RMST in observational study 11 3.2.1 Direct regression 11 3.2.1.1 Pseudo-observation 11 3.2.1.2 ANCOVA-type model 13 3.2.2 Inverse probability weighting 14 3.2.2.1 IP weighted Cox model 14 3.2.2.2 Adjusted Kaplanโ€“Meier estimator 15 3.2.3 G-computation 17 3.3 Simulation study 1 18 3.3.1 Simulation settings 18 3.3.2 True value of difference in RMST 19 3.3.3 Simulation study 1 results 21 3.4 Real data analysis 1: Colon cancer data 25 4 Sensitivity Analysis 28 4.1 Background 28 4.2 Sensitivity model 29 4.3 Estimate of difference in bias-adjusted RMST 31 4.4 Sensitivity range 32 4.5 Analytic solution to bias-adjusted RMST in special case 35 4.6 Methods for solution of optimization problem in general case 38 4.7 Confidence interval for partially identified region 40 4.8 Simulation study 2 41 4.8.1 Simulation study 2.1: Bias and computational time 42 4.8.2 Simulation study 2.2: Sensitivity range and coverage rate 46 4.9 Real data analysis 2 47 4.9.1 Real data analysis 2.1: GBSG data 49 4.9.2 Real data analysis 2.2: NSCLC data 52 5 Discussion 56 Bibliography 61 Appendices 70 Appendix A Appendix for Chapter 3 70 A.1 Example R codes 70 A.1.1 R code for Kaplan-Meier estimator 72 A.1.2 R code for pseudo-observation 73 A.1.3 R code for ANCOVA-type model 74 A.1.4 R code for IP weighted Cox model 75 A.1.5 R code for adjusted Kaplan-Meier estimator 76 A.1.6 R code for G-computation 77 A.2 Proof of true value for difference in RMST 77 A.3 Simulation study 1 results for sample size 1,000 79 A.4 Pooled logistic regression model 83 Appendix B Appendix for Chapter 4 86 B.1 Proof of reducing (4.8) to linear fractional programming in special case 86 B.2 Proof of reducing (4.8) to linear fractional programming in alternative setting 89 B.3 Proof of non-convergence to boundary values 90 B.4 Details for simulation study 2.1 (ฮฒ0 = โˆ’1.9) 94 B.5 Details for simulation study 2.1 (ฮฒ0 = โˆ’0.425) 99 Appendix C Appendix for Chapter 5 105 C.1 Alternative sensitivity analysis method 105 C.2 Limitation of alternative method 108 Abstract (in Korean) 1๋ฐ•

    A Study on the Configuration and Control Strategies of the Heat Pump System with Thermal Storages for Simultaneous Heating and Cooling in Buildings

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์ถ•ํ•™๊ณผ, 2013. 2. ์—ฌ๋ช…์„.๊ฑด๋ฌผ์ด ๊ณ ์ธตํ™” ๋˜๊ณ  ์‹œ๊ณต๊ธฐ์ˆ ์ด ๊ณ ๋„ํ™” ๋ ์ˆ˜๋ก ์™ธํ”ผ๊ฐ€ ๊ธฐ๋ฐ€ํ•ด์ง€๊ณ  ์—ด์  ๊ตฌํš์ด ๋ช…ํ™•ํ•ด์ง€๋Š” ๊ณต๊ฐ„์ด ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ณต๊ฐ„์€ ๋‚ด๋ถ€๋ฐœ์—ด๋กœ ์ธํ•œ ์—ดํš๋“ ๋•Œ๋ฌธ์— ๊ณต๊ฐ„์˜ ๋ƒ‰๋ฐฉ๋ถ€ํ•˜๋ฅผ ์ง€์†์ ์œผ๋กœ ๋ฐœ์ƒ์‹œํ‚ค๋Š” ์›์ธ์ด ๋œ๋‹ค. ์ด์™€ ๊ฐ™์ด ์‚ฌ๊ณ„์ ˆ ๋‚ด๋‚ด ๋ƒ‰๋ฐฉ์ด ํ•„์š”ํ•œ ์ƒํ™ฉ์— ๋†“์ธ ๊ณต๊ฐ„์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๊ฑด๋ฌผ์€ ์ผ ๋…„ ๋‚ด๋‚ด ๋ƒ‰๊ฐํƒ‘์˜ ์šด์ „์„ ์š”๊ตฌํ•จ์œผ๋กœ์จ ์™ธ๊ธฐ๋งŒ์œผ๋กœ๋„ ๋ƒ‰๋ฐฉ์ด ์ถฉ๋ถ„ํžˆ ๊ฐ€๋Šฅํ•œ ์‹œ๊ธฐ๋‚˜ ํŠนํžˆ ๊ฒจ์šธ์ฒ ์— ๋ถˆํ•„์š”ํ•œ ์—๋„ˆ์ง€๋ฅผ ๋‚ญ๋น„ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ์•ˆ๊ณ  ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š”, ๋ณตํ•ฉ๋ถ€ํ•˜๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ๊ฑด๋ฌผ์„ ์œ„ํ•œ ์ถ•์—ด์‹ ํžˆํŠธํŽŒํ”„ ์‹œ์Šคํ…œ์˜ ๊ตฌ์„ฑ๊ณผ ์ œ์–ด ์ „๋žต์„ ๋„์ถœํ•˜์—ฌ, ์ œ์‹œํ•œ Energy Balancing System ๊ฐœ๋…์„ ๊ตฌํ˜„ํ•˜๊ณ  ์‹ค์ œ ๋Œ€์ƒ ๊ณต๊ฐ„์— ์ ์šฉํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์—์„œ ์ œ์‹œ๋œ ์‹œ์Šคํ…œ ์„ค๊ณ„ ๋ฐ ๊ตฌ์„ฑ ๊ณผ์ •, ์ œ์–ด ์ „๋žต์€ Energy Balancing System์„ ๊ตฌ์„ฑํ•˜๋Š” ์‹œ์Šคํ…œ์„ ์„ค๊ณ„ํ•˜๊ณ  ์ ์šฉํ•˜๋Š”๋ฐ ๊ธฐ์ดˆ ์ž๋ฃŒ๊ฐ€ ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. (1) ํžˆํŠธํŽŒํ”„ ์‹œ์Šคํ…œ๊ณผ ์ถ•์—ด ์‹œ์Šคํ…œ์€ ๋ƒ‰์—ด๊ณผ ์˜จ์—ด์„ ๋™์‹œ์— ์ƒ์‚ฐํ•˜๋Š” ํžˆํŠธํŽŒํ”„์™€ ์—ด์„ ์ €์žฅํ•˜๋Š” ์ถ•์—ด์กฐ์˜ ๊ฒฐํ•ฉ์„ ํ†ตํ•ด์„œ ๊ทธ ํšจ๊ณผ๊ฐ€ ์ฆ๋Œ€๋œ๋‹ค. ๋˜ํ•œ ๊ฑด๋ฌผ์˜ ์ฃผ๊ฑฐํ˜•ํƒœ, ์‚ฌ์šฉ์‹œ๊ฐ„, ์šฉ๋„ ๋“ฑ์— ์˜ํ•˜์—ฌ ์—๋„ˆ์ง€ ์ ˆ๊ฐ ํšจ๊ณผ๊ฐ€ ์ƒ์ดํ•˜๋ฏ€๋กœ ์‹œ์Šคํ…œ์„ ์ ์šฉํ•  ๋Œ€์ƒ์˜ ์„ ์ •์ด ์ค‘์š”ํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ๊ฑด๋ฌผ์˜ ๋‹ค์–‘ํ•œ ์šฉ๋„๋‚˜ ๊ณต๊ฐ„์˜ ๋ถ€ํ•˜๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๋™์‹œ์‚ฌ์šฉ์œจ์— ๋”ฐ๋ฅธ ๋ถ€ํ•˜ํŒจํ„ด์„ ํŒŒ์•…ํ•˜์—ฌ ๋ณตํ•ฉ๋ถ€ํ•˜์˜ profile์ด ์‹œ์Šคํ…œ์˜ ๊ตฌ์„ฑ์— ํ™œ์šฉ๋˜์–ด์•ผ ํ•œ๋‹ค. (2) ๋ณตํ•ฉ๋ถ€ํ•˜์— ๋”ฐ๋ฅธ ํžˆํŠธํŽŒํ”„์™€ ์ถ•์—ด์กฐ์˜ ์šฉ๋Ÿ‰์„ ์‚ฐ์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์—ด์› ์‹œ์Šคํ…œ ์šฉ๋Ÿ‰์„ค๊ณ„๋ฅผ ์ˆ˜ํ–‰ํ•  ๋•Œ์—๋Š” ๋ณตํ•ฉ๋ถ€ํ•˜์˜ profile ๋‹จ์œ„ ๊ธฐ๊ฐ„์„ ์ •ํ•˜์—ฌ ๊ฐ ๋ถ€ํ•˜์— ํ•ด๋‹นํ•˜๋Š” ์ถ•์—ด์กฐ ์šฉ๋Ÿ‰๊ณผ ํžˆํŠธํŽŒํ”„ ์šฉ๋Ÿ‰์„ ๊ณ„์‚ฐํ•˜๊ณ  ์ด๋ฅผ ์ตœ์ข…์ ์œผ๋กœ ์ œ์‹œ๋œ ์„ค๊ณ„ ๋ฐฉ๋ฒ•์„ ๊ฑฐ์ณ ์šฉ๋Ÿ‰์„ค๊ณ„๋ฅผ ์ง„ํ–‰ํ•ด์•ผ ํ•œ๋‹ค. ๊ทธ๊ฒƒ์€ ๋ƒ‰๋ฐฉ ๋˜๋Š” ๋‚œ๋ฐฉ์„ ์œ„ํ•ด ๊ณ„์‚ฐ๋œ ๊ฐ ์—ด์›์˜ ์šฉ๋Ÿ‰์ด ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๋Œ€์‘ ๋Šฅ๋ ฅ๊ณผ ๊ทธ ๋ฐ˜๋Œ€๋ถ€ํ•˜์˜ ์—ด์› ๋Šฅ๋ ฅ์„ ๋น„๊ตํ•˜๋Š” ๊ณผ์ •๊ณผ ๋ƒ‰๋ฐฉ๋ถ€ํ•˜๋ฅผ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์„ค์ •๋œ ์—ด์› ์šฉ๋Ÿ‰์ด ๋งŒ๋“œ๋Š” ์˜จ์—ด๋Šฅ๋ ฅ์ด ๊ธ‰ํƒ•๋ถ€ํ•˜์™€ ๊ฐ™์€ ์ถ”๊ฐ€์ ์ธ ์˜จ์—ด๋ถ€ํ•˜์— ์‚ฌ์šฉ๋˜๋Š” ์ง€๋ฅผ ํŒ๋‹จํ•˜๋Š” ๊ณผ์ •์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ๋‹ค. (3) ์‹œ์Šคํ…œ ์šด์ „์€ ์ œ์–ด ์ „๋žต์˜ ์ œ์–ด ๊ฒฝํ–ฅ์„ ํ†ตํ•˜์—ฌ ์‹œ์Šคํ…œ์˜ ํšจ๊ณผ๋ฅผ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•œ ๋ชฉ์ ์œผ๋กœ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ์‹œ์Šคํ…œ ์ฃผ์š”์ง€์ ์˜ ์ทจ๋“์—ด๋Ÿ‰๊ณผ ๋ฐฉ์ถœ์—ด๋Ÿ‰์„ ๊ณ„์‚ฐํ•˜์—ฌ ์ด๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ๊ทธ ์ค‘ Storage System์—์„œ๋Š” ์—ด์˜ ์ƒ์‚ฐ๋Ÿ‰ ๋ฐ ์ทจ๋“๋Ÿ‰์„ ๋น„๊ตํ•˜์—ฌ ํžˆํŠธํŽŒํ”„๊ฐ€ ์ƒ์‚ฐํ•˜๋Š” ๋ƒ‰์—ด๊ณผ ์˜จ์—ด์˜ ์—ด๋Ÿ‰ ์†์‹ค์˜ ์ฐจ์ด์™€ ์ถ•์—ด์กฐ์˜ ์ทจ๋“์—ด๋Ÿ‰๊ณผ ๋ฐฉ์ถœ์—ด๋Ÿ‰์˜ ์ฐจ์ด๋ฅผ ์‹ค์ œ ์šด์ „์„ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1.2 ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• ์ œ 2 ์žฅ ์ถ•์—ด์‹ ํžˆํŠธํŽŒํ”„ ์‹œ์Šคํ…œ์— ๊ด€ํ•œ ์˜ˆ๋น„์  ๊ณ ์ฐฐ 2.1 ๊ฐœ์š” 2.2 ๋ณตํ•ฉ๋ถ€ํ•˜ ์—ฐ๊ตฌ์‚ฌ๋ก€ 2.3 ๊ธฐ์กด ์ถ•์—ด์‹ ํžˆํŠธํŽŒํ”„ ์‹œ์Šคํ…œ 15 2.3.1 ํžˆํŠธํŽŒํ”„ ์‹œ์Šคํ…œ 2.3.2 ์ˆ˜์ถ•์—ด ์‹œ์Šคํ…œ 2.3.3 ์ถ•์—ด์‹ ํžˆํŠธํŽŒํ”„ ์‹œ์Šคํ…œ 2.4 ์ถ•์—ด์‹ ํžˆํŠธํŽŒํ”„ ์‹œ์Šคํ…œ ๊ตฌ์„ฑ 2.5 ์ถ•์—ด์‹ ํžˆํŠธํŽŒํ”„ ์‹œ์Šคํ…œ ์ œ์–ด ๋ฐฉ๋ฒ• 2.5.1 ์ œ์–ด์˜ ์ข…๋ฅ˜ ๋ฐ ๊ธฐ๋ณธ ๋™์ž‘ 2.5.2 ๋ฐฐ๊ด€ ๋ฐ ์žฅ๋น„ ์ œ์–ด 2.6 ์†Œ๊ฒฐ ์ œ 3 ์žฅ ์ถ•์—ด์‹ ํžˆํŠธํŽŒํ”„ ์‹œ์Šคํ…œ์˜ ์„ค๊ณ„ ๋ฐ ๊ตฌ์„ฑ 3.1 ๊ฐœ์š” 3.2 ์‹œ์Šคํ…œ ๊ตฌ์„ฑ์š”์†Œ 3.3 ์‹œ์Šคํ…œ ์ ์šฉ๋Œ€์ƒ ๋ถ„์„ 3.4 ์‹œ์Šคํ…œ ์„ค๊ณ„ 3.4.1 Plant์™€ Storage System 3.4.2 Distribution System 3.4.3 Terminal System 3.5 ์‹œ์Šคํ…œ ๊ตฌ์„ฑ๊ฒฐ๊ณผ 3.6 ์†Œ๊ฒฐ ์ œ 4 ์žฅ ์ถ•์—ด์‹ ํžˆํŠธํŽŒํ”„ ์‹œ์Šคํ…œ์˜ ์ œ์–ด ๋ฐ ํšจ๊ณผ ๋ถ„์„ 4.1 ๊ฐœ์š” 4.2 ์ œ์–ด ๋ชฉํ‘œ ์„ค์ • 4.3 ์ œ์–ด ์ „๋žต ์ˆ˜๋ฆฝ 4.3.1 ๊ด€๋ จ ์ œ์–ด ๋ณ€์ˆ˜ ๋„์ถœ 4.3.2 ์ตœ์ข… ์ œ์–ด ์ „๋žต ์ˆ˜๋ฆฝ 4.4 ์‹œ์Šคํ…œ ์šด์ „ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 4.4.1 ์šด์ „ ๊ฒฐ๊ณผ 4.4.2 ๊ฒฐ๊ณผ ๋ถ„์„ 4.5 ์†Œ๊ฒฐ ์ œ 5 ์žฅ ๊ฒฐ ๋ก  ์ฐธ๊ณ  ๋ฌธํ—Œ ABSTRACTMaste

    Optimal Design of High Precision Class Large Current Transformer Considering External Magnetic Field

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ์ •ํ˜„๊ต.4์ฐจ ์‚ฐ์—…ํ˜๋ช…๊ณผ ์‚ฌ๋ฌผ ์ธํ„ฐ๋„ท(internet of things, IoT) ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์œผ๋กœ ์ „๋ ฅ๋ง ๋‚ด์˜ ๋ณด๋‹ค ํšจ์œจ์ ์ธ ์ •๋ณด ๊ตํ™˜์ด ๊ฐ€๋Šฅํ•ด์กŒ๊ณ  ์‚ฌ์šฉ ์ „๋ฅ˜์˜ ์ •ํ™•ํ•œ ์ •๋ณด ์ „๋‹ฌ์„ ์œ„ํ•ด ์ •๋ฐ€๋„ ๋†’์€ ๋ณ€๋ฅ˜๊ธฐ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์ด ๋†’์•„์กŒ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์™ธ๋ถ€์ž๊ณ„ ์˜ํ–ฅ์„ ๊ณ ๋ คํ•œ ๋Œ€์ „๋ฅ˜ ๋ณ€๋ฅ˜๊ธฐ์˜ ์ตœ์  ์„ค๊ณ„๋ฅผ ์œ„ํ•ด ๊ธฐ์กด์˜ ๋Œ€์ „๋ฅ˜ ๋ณ€๋ฅ˜๊ธฐ์˜ ๊ฐœ์„  ์„ค๊ณ„์™€ ์ธ์‡„ ํšŒ๋กœ ๊ธฐํŒ (printed circuit board, PCB)์„ ์ด์šฉํ•œ ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ์„ค๊ณ„๋ฅผ ์ œ์•ˆํ•˜์˜€๊ณ , ์„ค๊ณ„์•ˆ์„ ํ† ๋Œ€๋กœ ์ œ์ž‘ํ•œ ๋ณ€๋ฅ˜๊ธฐ์˜ ๋น„์˜ค์ฐจ ๋ฐ ์˜จ๋„ ์‹œํ—˜์„ ํ†ตํ•ด ์„ค๊ณ„์•ˆ์˜ ์œ ์šฉ์„ฑ๊ณผ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋จผ์ € ๋Œ€์ „๋ฅ˜ ์ธก์ •์„ ์œ„ํ•ด ๊ธฐ์กด์— ์‚ฌ์šฉํ•˜๋˜ ์ฒ ์‹ฌํ˜• ๋ณ€๋ฅ˜๊ธฐ์˜ ๊ฐœ์„  ์„ค๊ณ„์•ˆ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์™ธ๋ถ€ ์ž๊ณ„ ์ฐจ๋‹จ์„ ์œ„ํ•ด ์‰ด๋“œ ๊ถŒ์„ ์„ ์ ์šฉํ•œ ๊ธฐ์กด ๋ถ„ํ•  ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ์— ์†Œ์†์ด ๋ฐœ์ƒํ•˜๋Š” ๋ฌธ์ œ์ ์ด ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ํŒŒ์•…ํ•˜์˜€๊ณ  ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ณ ์•• ๋ณ€์••๊ธฐ์— ์‚ฌ์šฉํ•˜๋Š” transposition winding์„ ์ฐจ์šฉํ•œ ๊ต์ฐจ ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ์„ค๊ณ„์˜ ์œ ์šฉ์„ฑ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ์œ ํ•œ์š”์†Œ๋ฒ•์„ ์ด์šฉํ•œ ์ˆ˜์น˜ํ•ด์„์„ ์ง„ํ–‰ํ•˜์˜€๊ณ  ๋น„์˜ค์ฐจ ํŠน์„ฑ์„ ์ •๋ฐ€๊ธ‰ ๋ฒ”์œ„ ๋‚ด์—์„œ ์œ ์ง€ํ•˜๋ฉด์„œ ์˜จ๋„ ์ €๊ฐ ํšจ๊ณผ๊ฐ€ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ž์„ฑ ์žฌ๋ฃŒ์˜ ํ•œ๊ณ„๋กœ ์ธํ•ด ํฌ๊ธฐ์™€ ์•ˆ์ •์„ฑ, ๊ฒฝ์ œ์„ฑ ๋“ฑ์—์„œ ๋‹จ์ ์„ ๊ฐ–๋Š” ๊ธฐ์กด ๋ณ€๋ฅ˜๊ธฐ์˜ ๋Œ€์•ˆ์œผ๋กœ ์ฒ ์‹ฌ์„ ์‚ฌ์šฉํ•˜์ง€ ์•Š๋Š” ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ ๊ธฐ๋ฐ˜์˜ ๋ณ€๋ฅ˜๊ธฐ๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ๋‹จ์ ์ธ ์œ„์น˜์™€ ํ˜•์ƒ์˜ ๋ถˆ๊ท ์ผ์„ฑ์„ ํ•ด์†Œํ•˜๊ธฐ ์œ„ํ•ด PCB๋ฅผ ์ด์šฉํ•˜์˜€๊ณ  ์™ธ๋ถ€ ์ž๊ณ„๋กœ ์ธํ•œ ์˜ค์ฐจ๋ฅผ ์ €๊ฐํ•˜๊ธฐ ์œ„ํ•ด ๋ฆฌํ„ด ์ฝ”์ผ์„ ์ถ”๊ฐ€ํ•˜์˜€๋‹ค. ๊ธฐ์กด์˜ ๋ณ€๋ฅ˜๊ธฐ๋ฅผ ๋Œ€์ฒดํ•  ์ˆ˜ ์žˆ๋Š” ์‹ ๋ขฐ์„ฑ์„ ์–ป๊ธฐ ์œ„ํ•ด ์™ธ๋ถ€ ์ž๊ณ„ ์ฐจํ๊ฐ€ ๊ฐ€๋Šฅํ•œ ์ •๋ฐ€๊ธ‰ ์„ค๊ณ„๋ฅผ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๊ณ  ๋จผ์ € ํ•ด์„์  ๋ฐฉ๋ฒ•์œผ๋กœ ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์— ๋Œ€ํ•œ ์™ธ๋ถ€ ์ž๊ณ„์˜ ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ๋ฅผ ์ˆ˜์น˜ํ•ด์„์„ ํ†ตํ•ด ๋‹ค์‹œ ํ•œ ๋ฒˆ ๊ฒ€์ฆํ•˜๊ณ  ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ๊ธฐ์กด์˜ ๋Œ€์ „๋ฅ˜ ์‚ฌ์šฉ ํ™˜๊ฒฝ์˜ ์ œํ•œ ์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜๋Š” PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ์ตœ์  ์„ค๊ณ„์•ˆ์„ ๋„์ถœํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ๊ต์ฐจ ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ์™€ PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ์„ค๊ณ„์•ˆ์„ ํ† ๋Œ€๋กœ ์‹œ์ œํ’ˆ์„ ์ œ์ž‘ํ•˜์˜€๋‹ค. ๊ต์ฐจ ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ๋Š” ์˜จ๋„ ๋ฐ ๋Œ€์ „๋ฅ˜ ๋น„์˜ค์ฐจ ์‹œํ—˜์„ ํ†ตํ•ด ๊ธฐ์กด์˜ ๋ถ„ํ•  ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ์™€ ํŠน์„ฑ์„ ๋น„๊ตํ•˜์˜€๊ณ  PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์€ ๋น„์˜ค์ฐจ ์‹œํ—˜์„ ํ†ตํ•ด ๊ธฐ์กด ๋ณ€๋ฅ˜๊ธฐ์˜ ๋Œ€์ฒด ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ๊ต์ฐจ ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ๋Š” ์™ธ๋ถ€ ์ž๊ณ„ ์˜ํ–ฅ ํ•˜์—์„œ ์ •๋ฐ€๊ธ‰ ๋น„์˜ค์ฐจ๋ฅผ ๋งŒ์กฑํ•˜๋ฉด์„œ ๊ธฐ์กด์˜ ๋ถ„ํ•  ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ์— ๋น„ํ•ด ํ–ฅ์ƒ๋œ ์˜จ๋„ ํŠน์„ฑ์„ ๋ณด์—ฌ์คŒ์œผ๋กœ์จ ๋ณด๋‹ค ์•ˆ์ •์„ฑ ๋†’์€ ๋Œ€์ „๋ฅ˜ ๋ณ€๋ฅ˜๊ธฐ๋กœ์„œ ํ™œ์šฉ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์€ ์™ธ๋ถ€ ์ž๊ณ„ ์˜ํ–ฅ ํ•˜์—์„œ ์ •๋ฐ€๊ธ‰ ๋น„์˜ค์ฐจ๋ฅผ ๋งŒ์กฑํ•˜์˜€๋‹ค. ๊ธฐ์กด์˜ ๋ณ€๋ฅ˜๊ธฐ์™€ ๋น„๊ตํ–ˆ์„ ๋•Œ ๋Œ€์ฒด ๊ฐ€๋Šฅํ•œ ์ •๋ฐ€๊ธ‰ ๋น„์˜ค์ฐจ ํŠน์„ฑ๊ณผ ์†Œํ˜•ํ™”์˜ ์šฉ์ด์„ฑ, ์ธก์ •์˜ ๊ด‘๋ฒ”์œ„์„ฑ๊ณผ ์ œ์ž‘์˜ ๊ฒฝ์ œ์„ฑ์„ ๊ณ ๋ คํ•  ๋•Œ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์ด ๊ธฐ์ˆ ์ , ๊ฒฝ์ œ์ ์œผ๋กœ ์œ ์šฉํ•จ์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค.The 4th Industrial Revolution and the development of internet of things (IoT) technology have made communication between all parts of power system more efficient and the need for research on high precision class large current transformers has increased to deliver accurate information of working current. In this paper, for the optimal design of large current transformers considering external magnetic field, an improved design of the conventional current transformers and the design of a rogowski coil using printed circuit board (PCB) were proposed. Through temperature and ratio error tests, the usefulness and validity of proposed designs were verified. First, an improved design of conventional iron core type transformer measuring large current was proposed. It was identified that there is a problem of burnout in the existing split winding current transformer to which the shield winding is applied to block the external magnetic field. In order to solve the problem, a current transformer adopting the transposition winding used in a high-voltage transformer was proposed. Through numerical analyses using finite element method to verify the usefulness of the proposed design, the effect of temperature rise reduction was confirmed while maintaining the ratio error characteristics within the high precision class. Also, rogowski coil-based current transformer that does not use an iron core was designed as an alternative to conventional current transformers that have disadvantages in size, stability, and price due to the limitations of magnetic materials. PCB was used to solve the non-uniformity of position and shape, disadvantages of rogowski coil, and return coil was added to reduce errors due to external magnetic fields. In order to obtain the reliability that enables replacement of the existing current transformer, high precision class ratio error was required. The effect of external magnetic field on the rogowski coil was analyzed by an analytical method. Then, the analysis results are verified through numerical analyses. Using genetic algorithm, an optimal design of the PCB rogowski coil that satisfies the limitations of the large current environment was derived. Prototypes were fabricated based on the proposed transposition winding current transformer and PCB rogowski coil designs. The transposition winding current transformer was compared with the existing split winding current transformers through temperature and large current ratio error tests. The PCB rogowski coil was verified for the possibility of replacing the conventional current transformers through ratio error tests. It was confirmed that proposed transposition winding current transformer can be used as a more stable current transformer by satisfying high precision class ratio error under the influence of external magnetic field and showing improved temperature characteristics compared to a conventional split winding current transformer. Proposed PCB rogowski coil also satisfies the high precision class ratio error under the influence of external magnetic field. Compared to conventional current transformers, considering the replaceable ratio error characteristics, ease of downsizing, wide range of measurement, and economic feasibility in manufacture, proposed PCB rogowski coil in this paper is technically and economically useful.์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉํ‘œ 1 1.2 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 6 ์ œ 2 ์žฅ ๋Œ€์ „๋ฅ˜ ๋ณ€๋ฅ˜๊ธฐ์˜ ์™ธ๋ถ€ ์ž๊ณ„ ์ฐจํ ์„ฑ๋Šฅ ํ–ฅ์ƒ ์„ค๊ณ„ 7 2.1 ์ •๋ฐ€๊ธ‰ ๋Œ€์ „๋ฅ˜ ๋ณ€๋ฅ˜๊ธฐ 7 2.1.1 ๋ณ€๋ฅ˜๊ธฐ 7 2.1.2 ๋น„์˜ค์ฐจ 9 2.1.3 ๋ณ€๋ฅ˜๊ธฐ์˜ ๋ถ„๋ฅ˜์™€ ๊ณ„๊ธ‰ 10 2.2 ๋Œ€์ „๋ฅ˜ ๋ณ€๋ฅ˜๊ธฐ์—์„œ์˜ ์™ธ๋ถ€ ์ž๊ณ„ ์˜ํ–ฅ 14 2.3 ์‰ด๋“œ ๊ถŒ์„  19 2.3.1 Flux equalizing winding 19 2.3.2 ๋ถ„ํ•  ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ์˜ ํ•ด์„ 21 2.4 ๊ต์ฐจ ๊ถŒ์„ ์„ ์ฐจ์šฉํ•œ ๋Œ€์ „๋ฅ˜ ๋ณ€๋ฅ˜๊ธฐ 28 2.4.1 Tranposition winding 28 2.4.2 ๋ณ€๋ฅ˜๊ธฐ์—์„œ์˜ ๊ต์ฐจ ๊ถŒ์„  29 ์ œ 3 ์žฅ ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ์™ธ๋ถ€ ์ž๊ณ„ ์ฐจํŽ˜ ์„ฑ๋Šฅ ํ–ฅ์ƒ ์„ค๊ณ„ 38 3.1 ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ 38 3.1.1 ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ๊ธฐ๋ณธ ์›๋ฆฌ 38 3.1.2 ์ ๋ถ„๊ธฐ 40 3.1.3 ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ํŠน์ง• 42 3.2 ๋น„์˜ค์ฐจ์˜ ์›์ธ 43 3.3 Printed circuit board ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ 46 3.4 PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ์™ธ๋ถ€ ์ž๊ณ„ ์ฐจํ ์„ฑ๋Šฅ 48 3.4.1 Excursion 50 3.4.2 Eccentricity 53 3.4.3 1์ฐจ ๋„์ฒด์™€ ํ‰ํ–‰ํ•œ ์™ธ๋ถ€์˜ ๋„์ฒด 55 3.4.4 1์ฐจ ๋„์ฒด์™€ ์ˆ˜์ง์ธ ์™ธ๋ถ€์˜ ๋„์ฒด 57 3.4.5 ๋ฆฌํ„ด ์ฝ”์ผ๊ณผ ์™ธ๋ถ€ ์ž๊ณ„ ์˜ํ–ฅ์˜ ์ƒ๊ด€ ๊ด€๊ณ„ 61 ์ œ 4 ์žฅ PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ์ตœ์ ์„ค๊ณ„ 66 4.1 PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ๊ฐœ๋…์„ค๊ณ„ 66 4.1.1 PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ์˜ ์ถœ๋ ฅ 66 4.1.2 ๋ฆฌํ„ด ์ฝ”์ผ๊ณผ ์œ„์น˜ 67 4.1.3 PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ ์„ค๊ณ„์•ˆ 71 4.2 ์ œํ•œ ์กฐ๊ฑด๊ณผ ์„ค๊ณ„ ๋ณ€์ˆ˜ 74 4.2.1 ์ œํ•œ ์กฐ๊ฑด 74 4.2.2 ์„ค๊ณ„ ๋ณ€์ˆ˜ 75 4.3 ๋ชฉ์  ํ•จ์ˆ˜ 78 4.3.1 ์ถœ๋ ฅ ์ „์•• 78 4.3.2 ๋น„์˜ค์ฐจ 79 4.4 ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ์ˆœ์„œ๋„ 80 4.4.1 ์œ ์ „ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 80 4.4.2 ์„ค๊ณ„ ๋ณ€์ˆ˜ ๋ฒ”์œ„ 81 4.4.3 ์ตœ์  ์„ค๊ณ„ ์ˆœ์„œ๋„ 82 4.5 ์ตœ์  ์„ค๊ณ„ ๊ฒฐ๊ณผ 83 ์ œ 5 ์žฅ ๊ต์ฐจ ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ ๋ฐ PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ ์ œ์ž‘๊ณผ ์‹œํ—˜ 85 5.1 ๊ต์ฐจ ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ ์ œ์ž‘๊ณผ ์‹œํ—˜ 85 5.1.1 ์‹œํ—˜์šฉ ๊ต์ฐจ ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ ์ œ์ž‘ 85 5.1.2 ๊ต์ฐจ ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ ๋น„์˜ค์ฐจ ์‹œํ—˜ 87 5.1.3 ๊ต์ฐจ ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ ์˜จ๋„ ์‹œํ—˜ 89 5.1.4 ๊ต์ฐจ ๊ถŒ์„  ๋ณ€๋ฅ˜๊ธฐ ์‹œํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 91 5.2 PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ ์ œ์ž‘๊ณผ ์‹œํ—˜ 94 5.2.1 ์‹œํ—˜์šฉ PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ ์ œ์ž‘ 94 5.2.2 ๋น„์˜ค์ฐจ ์‹œํ—˜ 95 5.2.3 PCB ๋กœ๊ณ ์Šคํ‚ค ์ฝ”์ผ ์‹œํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 97 ์ œ 6 ์žฅ ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ ๊ณ„ํš 98 6.1 ๊ฒฐ๋ก  98 6.2 ํ–ฅํ›„ ์—ฐ๊ตฌ ๊ณ„ํš 100 ์ฐธ๊ณ ๋ฌธํ—Œ 101 Abstract 115Docto

    ํ•œ๊ตญ์–ด ๋ช…๋ช…๊ตฌ๋ฌธ์˜ ํ†ต์‹œ์  ๋ณ€ํ™”

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    ์ด ์—ฐ๊ตฌ๋Š” 2007๋…„๋„ ์„œ์šธ๋Œ€ํ•™๊ต ์ธ๋ฌธํ•™์—ฐ๊ตฌ์› ์ž์œ ๊ณผ์ œ ์ง€์›์— ์˜ํ•˜์—ฌ ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค

    Tissue Factor Pathway Inhibitor Levels and Thrombin Generation in Hemorrhagic Disease

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    Background: Tissue factor pathway inhibitor (TFPI) regulates blood coagulation. To treat hemorrhagic disease, studies have been conducted to achieve a hemostatic effect by inhibiting TFPI. In this study, blood TFPI levels of healthy and hemorrhagic disease groups were compared, and the effects of blood TFPI level and its activity on coagulation were investigated. Methods: The blood TFPI levels of the healthy, chronic liver disease (CLD), chronic kidney disease (CKD), and hemophilia groups were measured and compared. Thrombin generation assay (TGA) was performed to investigate the effects of TFPI level and its activity on coagulation. Results: The mean blood TFPI levels of the healthy, CLD, CKD, and hemophilia groups were 16.47 ng/mL, 24.90 ng/mL, 49.47 ng/mL, and 17.35 ng/mL, respectively. The healthy and CLD groups showed correlations between their blood TFPI levels and TGA parameters (P<0.05). The TGA parameters of the CLD and hemophilia groups showed significant differences compared with those of the healthy group (P<0.05). In FVIII-deficient plasma with anti-TFPI antibodies, the TGA parameters tended to be normalized according to anti-TFPI antibody levels. However, the differences in TGA parameters caused by FVIII concentration were unclear. Conclusions: Although the TFPI level was related to bleeding in the disease groups, it is difficult to interpret its effects independently because of the various factors that affect the coagulation process. However, TFPI is worth studying to facilitate the development of a treatment strategy for bleeding due to lack of FVIII.ope

    A comparative study of next-generation sequencing and fragment analysis for the detection and allelic ratio determination of FLT3 internal tandem duplication

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    Background: Currently, FLT3 internal tandem duplication (ITD) is tested by fragment analysis. With next-generation sequencing (NGS), however, not only FLT3 ITD but also other mutations can be detected, which can provide more genetic information on disease. Methods: We retrospectively reviewed the results of two tests-fragment analysis and a custom-designed, hybridization capture-based, targeted NGS panel-performed simultaneously. We used the Pindel algorithm to detect FLT3 ITD mutations. Results: Among 277 bone marrow aspirate samples tested by NGS and fragment analysis, the results revealed 99.6% concordance in FLT3 ITD detection. Overall, the allele frequency (AF) attained by NGS positively correlated with the standard allelic ratio (AR) attained by fragment analysis, with a Spearman correlation coefficient (r) of 0.757 (95% confidence interval: 0.627-0.846; p < 0.001). It was concluded that an AF of 0.11 attained by NGS is the most appropriate cutoff value (with 85.3% sensitivity and 86.7% specificity) for high mutation burden criterion presented by guidelines. Conclusion: Sensitive FLT3 ITD detection with comprehensive information of other mutation offered by NGS could be a useful tool in clinical laboratories. Future studies will be needed to evaluate and standardize NGS AF cutoff to predict actual clinical outcomes.ope

    ๋„“์€ ๊นŠ์ด ๋ฒ”์œ„์˜ ์ดˆ์  ์กฐ์ ˆ์ด ๊ฐ€๋Šฅํ•œ ๊ณ„์‚ฐ ์ฒด์  ๊ทผ์•ˆ ๋””์Šคํ”Œ๋ ˆ์ด

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ์ด๋ณ‘ํ˜ธ.๋””์Šคํ”Œ๋ ˆ์ด ํ”Œ๋žซํผ์„ ํ†ตํ•ด ๊ฐ€์ƒ ์ด๋ฏธ์ง€๋ฅผ ์ ‘ํ•˜๊ธฐ ์‹œ์ž‘ํ•œ ์ด๋ž˜, ์‚ฌ๋žŒ์€ ๋” ๋ชฐ์ž…๊ฐ๊ณผ ํ˜„์‹ค๊ฐ ์žˆ๋Š” ๊ฐ€์ƒ ์„ธ๊ณ„ ๊ฒฝํ—˜์„ ์ถ”๊ตฌํ•ด์™”๋‹ค. ์ตœ๊ทผ ๋””์Šคํ”Œ๋ ˆ์ด ์‚ฐ์—…์€ ๊ทผ์•ˆ ๋””์Šคํ”Œ๋ ˆ์ด ํ”Œ๋žซํผ์ด ๊ทธ ์ˆ˜์š”๋ฅผ ์ถฉ์กฑํ•˜๋Š” ์ฐจ์„ธ๋Œ€ ๊ธฐ์ˆ ์ด ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทผ์•ˆ ๋””์Šคํ”Œ๋ ˆ์ด๋Š” ์‚ฌ์šฉ์ž๊ฐ€ ๊ฐ€์ƒ ๋ฐ ์‹ค์ œ ์„ธ๊ณ„ ์† ์ปดํ“จํ„ฐ ์ƒ์„ฑ ์ด๋ฏธ์ง€์™€ ์ƒํ˜ธ์ž‘์šฉํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜๋ฉฐ, ๊นŠ์ด ์ธ์‹์˜ ์ƒ๋ฆฌ์  ์ž๊ทน์„ ์ฃผ์–ด ๋” ํฐ ๋ชฐ์ž…๊ฐ๊ณผ ํ˜„์‹ค๊ฐ์„ ๋Š๋ผ๊ฒŒ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์˜ค๋Š˜๋‚ ์˜ ๊ทผ์•ˆ ๋””์Šคํ”Œ๋ ˆ์ด๋Š” ์ดˆ์  ์กฐ์ ˆ ๊ธฐ๋Šฅ์ด ์—†์–ด, ์‹ค์ œ์™€ ๊ฐ™์€ ๊ฒฝํ—˜์„ ์ œ๊ณตํ•˜๋Š” ๊ถ๊ทน์˜ ๋””์Šคํ”Œ๋ ˆ์ด์— ๋ฏธ์น˜์ง€ ๋ชปํ•œ๋‹ค. ์ดˆ์  ์กฐ์ ˆ ๊ธฐ๋Šฅ์ด ์—†๋Š” ๊ฒฝ์šฐ ์‚ฌ์šฉ์ž๋Š” ์ปดํ“จํ„ฐ ์ƒ์„ฑ ์ด๋ฏธ์ง€์—์„œ ์‹œ๊ฐ์  ํ”ผ๋กœ๋„๋ฅผ ๋Š๋ผ๊ฑฐ๋‚˜ ๋ถ€์ž์—ฐ์Šค๋Ÿฌ์›€์„ ์ธ์ง€ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ดˆ์  ์กฐ์ ˆ์€ ์ž์—ฐ์Šค๋Ÿฌ์šด 3์ฐจ์› ๊ฒฝํ—˜์„ ์œ„ํ•ด ์ค‘์š”ํ•œ ๊ธฐ๋Šฅ์ด๋‚˜, ๋ช‡ ๊ฐ€์ง€ ๊ธฐ์ˆ ์  ๋ฌธ์ œ๋กœ ๊ทธ๋™์•ˆ์˜ ์ดˆ์  ์กฐ์ ˆ ๊ตฌํ˜„์€ ์‹ค์šฉ์ ์ด์ง€ ์•Š์•˜๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ์ดˆ์  ์กฐ์ ˆ ๊ตฌํ˜„์—๋Š” ํ•ด์ƒ๋„, ํ”„๋ ˆ์ž„ ์ˆ˜, ์‹œ์ฒญ ์˜์—ญ, ํ˜น์€ ์‹ ํ˜ธ ๋Œ€ ์žก์Œ๋น„๊ฐ€ ํฌ์ƒ๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ดˆ์  ์กฐ์ ˆ์ด ๊ฐ€๋Šฅํ•˜๋ฉด์„œ๋„ ๋””์Šคํ”Œ๋ ˆ์ด ์„ฑ๋Šฅ์˜ ํฌ์ƒ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ํ˜์‹ ์  ๊ธฐ์ˆ ๋“ค์„ ํƒ์ƒ‰ํ•œ๋‹ค. ๋˜ํ•œ ์ดˆ์  ์กฐ์ ˆ์ด ๊ฐ€๋Šฅํ•œ 3์ฐจ์› ๋””์Šคํ”Œ๋ ˆ์ด์— ๋‚ด์žฌ๋œ ํŠธ๋ ˆ์ด๋“œ ๊ด€๊ณ„๋“ค์„ ์™„ํ™”ํ•˜๋Š” ๊ณ„์‚ฐ์  ๋ฐฉ๋ฒ•๋ก ๋“ค์„ ๊ณ ์•ˆ ๋ฐ ์ ์šฉํ•œ๋‹ค. ๊ณ„์‚ฐ์  ๋ฐฉ๋ฒ•๋ก ๋“ค์„ ํ†ตํ•ด ์ œํ•œ๋œ ์ž์›์„ ํ™œ์šฉํ•˜์—ฌ ์ตœ์ ์˜ ๋””์Šคํ”Œ๋ ˆ์ด ์„ฑ๋Šฅ์„ ๋‹ฌ์„ฑํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ดˆ์  ์กฐ์ ˆ์ด ๊ฐ€๋Šฅํ•œ ์ƒˆ๋กœ์šด ๋ฐฉ์‹์˜ ์ฒด์  ๊ทผ์•ˆ ๋””์Šคํ”Œ๋ ˆ์ด๋ฅผ ์ œ์•ˆํ•˜๋ฉฐ, ์ด๋ฅผ ํ† ๋ชจ๊ทธ๋ž˜ํ”ฝ ๊ทผ์•ˆ ๋””์Šคํ”Œ๋ ˆ์ด๋ผ ํ•œ๋‹ค. ํ† ๋ชจ๊ทธ๋ž˜ํ”ฝ ๊ทผ์•ˆ ๋””์Šคํ”Œ๋ ˆ์ด๋Š” ๋ฐฑ๋ผ์ดํŠธ์™€ ๊ฐ€๋ณ€ ์ดˆ์  ๋ Œ์ฆˆ์˜ ๋น ๋ฅธ ๋™๊ธฐํ™”๋ฅผ ํ†ตํ•ด ์ดˆ์  ์กฐ์ ˆ ๊ทผ์•ˆ ๋””์Šคํ”Œ๋ ˆ์ด์˜ ๊ณ ์งˆ์  ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•œ๋‹ค. ๋‘˜์งธ๋กœ, ํ† ๋ชจ๊ทธ๋ž˜ํ”ฝ ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์‹œ์ฒญ ๊ฒฝํ—˜ ์ตœ์ ํ™”๋ฅผ ์œ„ํ•œ ํšจ์œจ์  ๊ณ„์‚ฐ ๋ฐฉ๋ฒ•๋ก ๋“ค์„ ์†Œ๊ฐœํ•œ๋‹ค. ํฌ๋น„์—ํ‹ฐ๋“œ ๋ง๋ง‰ ์ตœ์ ํ™”๋Š” ๋ง๋ง‰์˜ ์‹œ์‹ ๊ฒฝ ๋ถ„ํฌ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์ค‘์‹ฌ ํ•ด์ƒ๋„ ์†์‹ค ์—†์ด ์‹œ์ฒญ ์˜์—ญ์„ ํ™•์žฅํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์†Œํ˜• ํ† ๋ชจ๊ทธ๋ž˜ํ”ฝ ๊ทผ์•ˆ ๋””์Šคํ”Œ๋ ˆ์ด ๊ตฌํ˜„์„ ์œ„ํ•ด ์‹ค์šฉ์  ์ ‘๊ทผ๋ฒ•๋“ค์— ๋Œ€ํ•ด ๋…ผ์˜ํ•œ๋‹ค. ์†Œํ˜• ๋ฐฑ๋ผ์ดํŠธ ๋ชจ๋“ˆ๋กœ ๋ฐœ๊ด‘ ๋‹ค์ด์˜ค๋“œ ์–ด๋ ˆ์ด๋‚˜ ๋ฏธ์„ธ์ „์ž๊ธฐ๊ณ„์‹œ์Šคํ…œ ์Šค์บ๋‹ ๋ฏธ๋Ÿฌ๊ฐ€ ์‚ฌ์šฉ๋œ๋‹ค. ์†Œํ˜• ๋ฐฑ๋ผ์ดํŠธ ๋ชจ๋“ˆ์— ์ˆ˜๋ฐ˜๋˜๋Š” ํ•œ๊ณ„ ๊ทน๋ณต์„ ์œ„ํ•œ ๋„์ „์  ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ, ์ŠคํŽ˜ํด ์ €๊ฐ ํ™€๋กœ๊ทธ๋ž˜ํ”ฝ ๋””์Šคํ”Œ๋ ˆ์ด์™€ ํ† ๋ชจ๊ทธ๋ž˜ํ”ฝ ๊ธฐ๋ฒ•์„ ๊ฒฐํ•ฉํ•œ ์ƒˆ๋กœ์šด ๋””์Šคํ”Œ๋ ˆ์ด๋„ ํ•จ๊ป˜ ์†Œ๊ฐœํ•œ๋‹ค. ๊ฒฐ๋ก ์œผ๋กœ ์ดˆ์  ์กฐ์ ˆ 3์ฐจ์› ๋””์Šคํ”Œ๋ ˆ์ด ๊ณ ์œ ์˜ ๊ธฐ์ˆ ์  ๊ณผ์ œ์— ๋Œ€ํ•œ ์‹ค์งˆ์ ์ธ ํ•ด๊ฒฐ๋ฒ•์„ ํƒ์ƒ‰ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ œ์•ˆํ•œ ์ ‘๊ทผ ๋ฐฉ์‹๋“ค์ด ๊ถ๊ทน์˜ ๋””์Šคํ”Œ๋ ˆ์ด๋ฅผ ํ–ฅํ•œ ๋ณด๋‹ค ํ˜์‹ ์ ์ธ ๋ฐฉ๋ฒ•์— ์˜๊ฐ์„ ์ค„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.Since people started to see virtual imagery through display platforms, there has been a desire to experience a more immersive and realistic virtual world. The desire has drawn significant efforts to realize the ultimate display system that delivers the same experience from the real one. These days, the display industry believes near-eye display platforms are next-generation technologies to realize the dream. Through these platforms, users can interact with the computer-generated imagery surrounded by virtual or real worlds. It allows users to feel more immersion and realism with the physiological stimulation of depth perception. However, state-of-the-art near-eye displays are still far from the ultimate display system because of the absence of accommodation (focus cue). Without accommodation, users might feel visual fatigues or recognize artificiality from the computer-generated imagery. It makes providing accommodation important for the ultimate 3D experience. However, the focus cue reproduction has not been practical because of several technical challenges. The focus cue reproduction usually involves sacrifices in the resolution, frame rate, eye box, or signal-to-noise ratio. This dissertation aims to investigate break-through technologies that minimize the sacrifice of display performance while providing accommodation. The dissertation also conceives and applies various computational approaches to alleviate the trade-off relationship inherent in 3D displays with focus cues. The computational approaches allow us to achieve optimal display performance utilizing the restricted resources. This dissertation presents a new family of volumetric near-eye displays providing focus cues, which is called tomographic near-eye displays. With the fast synchronization of a backlight and a focus-tunable lens, tomographic near-eye displays resolve the troublesome trade-off in near-eye displays providing accommodation. Second, the dissertation introduces efficient computational approaches to optimize display performance of tomographic near-eye displays. Considering the foveated acuity of human vision, a foveated retinal optimization extends the eye-box without sacrificing foveal resolution. Lastly, feasible approaches are discussed to implement compact tomographic near-eye displays. A light-emitting diode array or micro electro mechanical systems scanning mirror is employed for a compact backlight module. a venturesome method is also introduced combining tomographic synthesis with speckle reduced holographic displays to compensate limitations of the compact backlight module. In conclusion, the dissertation endeavors to investigate practical solutions for the technical challenges inherent in 3D displays providing accommodation. The author believes the proposed approaches would inspire more innovative methods towards the ultimate displays.1 Introduction 1 1.1 Towards Immersive and Realistic Displays 1 1.2 Human Visual System 2 1.3 Display Approaches for Ultimate Experience 5 1.4 Dissertation Overview 7 2 3D Displays Providing Accommodation 9 2.1 Necessity of Accommodation 9 2.1.1 Vergence and Accommodation 9 2.1.2 Accommodation-Invariant Displays 12 2.1.3 Dynamic Focus Displays 13 2.2 Previous Approaches to Provide Focus Cues 15 2.2.1 Light Field Displays 15 2.2.2 Holographic Displays 16 2.3 Technical Challenges towards Ultimate Displays 17 3 Light Field Parameterization 21 3.1 4D Light Field 21 3.1.1 Parameterization 21 3.1.2 Light Field in Multi-Layer Displays 22 3.2 Light Field Optimization 23 3.2.1 Non-Negative Least Squares Problem 23 3.2.2 Additive Light Field Displays 24 3.3 Computational Near-Eye Displays 27 4 Tomographic Near-Eye Displays 29 4.1 Introduction 29 4.2 Tomographic Near-Eye Displays for Virtual Reality 32 4.2.1 Technical Background 32 4.2.2 Experiments 34 4.2.3 Implementation 36 4.3 Occlusion Blending for Accurate Viewing Effects 40 4.3.1 Depth Discontinuity Artifact 40 4.3.2 Least Squares Problem for Occlusion Blending 42 4.3.3 Algorithm to Solve Binary Least Squares Problem 43 4.4 Evaluation and Analysis 48 4.4.1 Determination of the Number of Focal Planes 48 4.4.2 Display Capability 50 4.4.3 Comparison with 80-Plane Displays 53 4.4.4 Black Frame 55 4.5 Discussion 56 4.5.1 Illumination Strategy for Real-time Operation 56 4.5.2 Advanced Applications of Tomographic Displays 63 4.6 Conclusion 65 5 Foveated Retinal Optimization 67 5.1 Introduction 67 5.2 Related Work 70 5.2.1 Computational Multi-Layer Displays 70 5.2.2 Foveated Rendering 72 5.3 Foveated Retinal Optimization 72 5.3.1 Focus Cue Reconstruction via Multi-Layer Displays 73 5.3.2 Pupil Movement Effect 73 5.3.3 Formulation of Foveated Retinal Optimization 76 5.4 Implementation 82 5.4.1 Specifications of Rendering and Experiment 82 5.4.2 See-through Near-Eye Displays with Focus Cues 83 5.4.3 Experiments 85 5.5 Evaluation and Analysis 87 5.6 Discussion 91 5.6.1 Contribution of Foveated Retinal Optimization 91 5.6.2 Limitations 92 5.6.3 Application to Tomographic Near-Eye Displays 93 5.7 Conclusion 93 6 Towards Compact Tomographic Near-Eye Displays 95 6.1 Compact Backlight Module 95 6.1.1 LED Array 97 6.1.2 MEMS 100 6.1.3 Limitation of Compact Tomographic Displays 101 6.2 Tomographic Synthesis of Local Holograms 103 6.2.1 Introduction 104 6.2.2 Implementation 106 6.2.3 Experiment 109 6.3 Conclusion 115 7 Conclusion 117 Appendix 133 Abstract (In Korean) 135Docto

    New design of a quasi-monolithic detector module with DOI capability for small animal PET

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    ๋ฐฉ์‚ฌ์„ ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€]์–‘์ „์ž๋ฐฉ์ถœ๋‹จ์ธต์ดฌ์˜๊ธฐ๊ธฐ(Positron Emission Tomography, PET)๋Š” ์ƒ์ฒด ๋‚ด์— ์–‘์ „์ž๋ฅผ ๋ฐฉ์ถœํ•˜๋Š” ๋ฐฉ์‚ฌ์„ฑ์˜์•ฝํ’ˆ์„ ์ฃผ์ž…ํ•˜์—ฌ ์ƒ์ฒด๋ฅผ ๋‘˜๋Ÿฌ์‹ธ๊ณ  ์žˆ๋Š” ๊ฒ€์ถœ๊ธฐ๋กœ ์ธก์ •ํ•˜์—ฌ ์–‘์ „์ž ๋ฐฉ์ถœํ•ต์ข…์˜ ์ฒด๋‚ด๋ถ„ํฌ๋ฅผ ์˜์ƒ์œผ๋กœ ์žฌ๊ตฌ์„ฑํ•˜๋Š” ๊ธฐ๊ธฐ์ด๋‹ค. ์ด๋Ÿฌํ•œ PET์€ ์ธ์ฒด์˜ ์ƒ์ฒด๊ธฐ๋Šฅ์˜์ƒ์„ ์ œ๊ณตํ•  ๋ฟ ์•„๋‹ˆ๋ผ, ์ƒˆ๋กœ์šด ์˜์•ฝํ’ˆ ๋ฐ ๋ฐฑ์‹  ๊ฐœ๋ฐœ, ์œ ์ „์ž ๋ฐœํ˜„ ๋“ฑ์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ธ์ฒด๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ƒˆ๋กœ์šด ์˜์•ฝํ’ˆ ๊ฐœ๋ฐœ ๋ฐ ์œ ์ „์ž ๋ฐœํ˜„ ๋“ฑ์˜ ์‹คํ—˜์„ ์‹ค์‹œํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ์†Œ๋™๋ฌผ์„ ๋Œ€์ฒด ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋‹ค. ์†Œ๋™๋ฌผ์„ ์‚ฌ์šฉํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์ธ์ฒด์šฉ PET์„ ๋Œ€์‹ ํ•  ๋‚ฎ์€ ๋ถ„ํ•ด๋Šฅ๊ณผ ๋ฏผ๊ฐ๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚จ ์†Œ๋™๋ฌผ ์ „์šฉ์˜ PET์˜ ๊ฐœ๋ฐœ์— ๋Œ€ํ•œ ํ•„์š”์„ฑ์ด ๋Œ€๋‘๋˜์—ˆ๋‹ค. ๋งŽ์€ ์—ฐ๊ตฌ ๊ธฐ๊ด€์—์„œ ์†Œ๋™๋ฌผ ์ „์šฉ์˜ PET์„ ๊ฐœ๋ฐœํ•˜์˜€๊ณ , ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ์†Œ๋™๋ฌผ PET์—์„œ ์ž‘์€ FOV(Field of View)์™€ ๊ธธ๊ณ  ์–‡์€ ์„ฌ๊ด‘์ฒด ์‚ฌ์šฉ์œผ๋กœ ์ธํ•œ ๊ฒ€์ถœ๊ธฐ FOV ์™ธ๊ณฝ์—์„œ์˜ ๋ถ„ํ•ด๋Šฅ ๊ฐ์†Œ์™€ ํ”ฝ์…€ํ˜• ์„ฌ๊ด‘์ฒด๋ฅผ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ๋‚˜ํƒ€๋‚˜๋Š” ๋‚ฎ์€ ๋ฏผ๊ฐ๋„์— ๋Œ€ํ•œ ๋ฌธ์ œ์ ์ด ์ œ๊ธฐ๋˜์—ˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋งŽ์€ ์—ฐ๊ตฌ ๊ธฐ๊ด€์—์„œ ์ƒˆ๋กœ์šด ๊ฒ€์ถœ๊ธฐ ๊ฐœ๋ฐœ์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ํ˜„์žฌ๊นŒ์ง€ ๊ฐœ๋ฐœ๋œ ๊ฒ€์ถœ๊ธฐ์˜ ๋Œ€๋ถ€๋ถ„์€ ํ”ฝ์…€ํ˜• ์„ฌ๊ด‘์ฒด๋ฅผ ์‚ฌ์šฉํ•˜๊ณ , ๋ฐ˜์‘ ๊นŠ์ด๋ฅผ ์ธก์ •ํ•จ์œผ๋กœ์จ FOV ์™ธ๊ณฝ์—์„œ์˜ ๋ถ„ํ•ด๋Šฅ ์ €ํ•˜๋ฅผ ํ•ด๊ฒฐํ•˜์˜€์ง€๋งŒ ๋ฏผ๊ฐ๋„์— ๋Œ€ํ•œ ํ•ด๊ฒฐ๋ฐฉ์•ˆ์€ ์ œ์‹œํ•˜์ง€ ๋ชปํ•˜์˜€๋‹ค. ๋ฏผ๊ฐ๋„๋Š” ํ˜„์žฌ ์ผ๋ฐ˜์ ์œผ๋กœ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋Š” ํ”ฝ์…€ํ˜• ์„ฌ๊ด‘์ฒด๋ฅผ ๋Œ€์‹ ํ•˜์—ฌ ๋ธ”๋กํ˜• ์„ฌ๊ด‘์ฒด๋ฅผ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์กด์˜ ํ”ฝ์…€ํ˜• ์„ฌ๊ด‘์ฒด์—์„œ๋Š” ์„ฌ๊ด‘์ฒด ์‚ฌ์ด์‚ฌ์ด์˜ ๋ฐ˜์‚ฌ์ฒด ๋ฌผ์งˆ๋กœ ์ธํ•œ ๋‚ฎ์€ ๊ฒ€์ถœ ์˜์—ญ์œผ๋กœ ์ธํ•ด ๋ฏผ๊ฐ๋„๊ฐ€ ๋‚ฎ์•˜์ง€๋งŒ ๋ธ”๋กํ˜• ์„ฌ๊ด‘์ฒด์—์„œ๋Š” ํ•˜๋‚˜์˜ ํ˜•ํƒœ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ฒ€์ถœ ์˜์—ญ์ด ์ฆ๊ฐ€ํ•˜์—ฌ ๋ฏผ๊ฐ๋„๋ฅผ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฐ˜์‘ ๊นŠ์ด๋ฅผ ์ธก์ •ํ•จ์œผ๋กœ์จ ๋ถ„ํ•ด๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ณ , ์ค€ ๋ธ”๋กํ˜• ์„ฌ๊ด‘์ฒด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ฏผ๊ฐ๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๊ฒ€์ถœ๊ธฐ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋น›์˜ ์ด๋™์„ ๋ชจ์‚ฌํ•˜๋Š” DETECT2000 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ฝ”๋“œ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐœ๋ฐœํ•  ๊ฒ€์ถœ๊ธฐ๋ฅผ ๋ชจ์‚ฌํ•˜์—ฌ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. 20.0 ร— 2.0 ร— 10.0 mm3 ํฌ๊ธฐ์˜ ์ค€ ๋ธ”๋กํ˜• Lutetium Yttrium Orthosilicate(LYSO) ์„ฌ๊ด‘์ฒด์™€ H7546B ์œ„์น˜๋ฏผ๊ฐํ˜• ๊ด‘์ „์ž์ฆ๋ฐฐ๊ด€(Position Sensitive Photo Multiplier Tube, PSPMT), ์•ต๊ฑฐ ํšŒ๋กœ, ์‹ ํ˜ธ ์ฆํญ๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ฒ€์ถœ๊ธฐ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๊ณ , ์ตœ๋Œ€์šฐ๋„ ํ•จ์ˆ˜๋ฅผ ํ†ตํ•ด ์œ„์น˜๋ฅผ ํŒ๋ณ„ํ•˜์˜€๋‹ค. ๊ฐ ์œ„์น˜์— ๋Œ€ํ•œ X์ถ•๊ณผ ๊นŠ์ด ๋ฐฉํ–ฅ์˜ ์œ„์น˜ ํŒ๋ณ„ ๋น„์œจ์€ ํ‰๊ท ์ ์œผ๋กœ ๊ฐ๊ฐ 96.8%, 55%์˜ ์ •ํ™•๋„๋ฅผ ๋‚˜ํƒ€๋‚ด์–ด ๊ฐœ๋ฐœํ•œ ๊ฒ€์ถœ๊ธฐ๊ฐ€ ๋ชจ๋“  ๋ฐฉํ–ฅ์— ๋Œ€ํ•œ ์œ„์น˜ ํŒ๋ณ„ ์„ฑ๋Šฅ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ฐœ๋ฐœํ•œ ๊ฒ€์ถœ๊ธฐ๋Š” ๊ณ  ๋ฏผ๊ฐ๋„์™€ ๊ณ  ๋ถ„ํ•ด๋Šฅ์„ ์ง€๋‹Œ ์†Œ๋™๋ฌผ์šฉ PET ์‹œ์Šคํ…œ์— ์‚ฌ์šฉ์ด ๊ธฐ๋Œ€๋œ๋‹ค. [์˜๋ฌธ]restrictio

    The role of HIF-1 and AMPK in the regulation of lifespan by mitochondrial respiration in C. elegans

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