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    ์‹  ์‹ค์งˆ ์งˆํ™˜์—์„œ ์ •๋Ÿ‰์  ์ž๊ธฐ๊ณต๋ช…์˜์ƒ์˜ ์œ ์šฉ์„ฑ: ์ฅ ๋งŒ์„ฑ ์‹  ์งˆํ™˜ ๋ชจ๋ธ์„ ์ด์šฉํ•œ ์‹คํ—˜์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2021. 2. ์กฐ์ •์—ฐ.์—ฐ๊ตฌ ๋ชฉ์  ์•„๋ฐ๋‹Œ์„ ์ด์šฉํ•œ ๋งŒ์„ฑ ์‹ ์งˆํ™˜ ์‹คํ—˜๋™๋ฌผ ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ๋งŒ์„ฑ ์‹ ์งˆํ™˜์˜ ์‹ค์งˆ์„ฌ์œ ํ™”๋ฅผ ํ‰๊ฐ€ํ•จ์— ์žˆ์–ด ์ •๋Ÿ‰์  ์ž๊ธฐ๊ณต๋ช…์˜์ƒ์˜ ์œ ์šฉ์„ฑ์„ ๊ฒ€์ฆํ•˜๊ณ  ์ ์ ˆํ•œ ์ด๋ฏธ์ง€ ๋ฐ”์ด์˜ค ๋งˆ์ปค๋ฅผ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• ์ด 16๋งˆ๋ฆฌ์˜ ์ˆ˜์ปท ์œ„์Šคํƒ€ (Wistar) ์ฅ๋ฅผ ์„ธ ๊ฐœ์˜ ๊ทธ๋ฃน์œผ๋กœ ๋‚˜๋ˆ„์–ด โ€“ ๋Œ€์กฐ๊ตฐ (n=7), ๋งŒ์„ฑ์‹ ์งˆํ™˜ ์‹คํ—˜๊ตฐ 1 (n=5), ๋งŒ์„ฑ์‹ ์งˆํ™˜ ์‹คํ—˜๊ตฐ2 (n=4)- ์‹คํ—˜๊ตฐ์˜ ๊ฒฝ์šฐ, ๊ฐ๊ฐ 0.25% ์•„๋ฐ๋‹Œ์„ 3์ฃผ ๋˜๋Š” 6์ฃผ ๊ธฐ๊ฐ„ ๋™์•ˆ ๋…ธ์ถœ์‹œ์ผœ ๋งŒ์„ฑ ์‹ ์งˆํ™˜์„ ์œ ๋ฐœํ•œ ๋’ค, 9.4T ์†Œ๋™๋ฌผ MRI ๊ธฐ๊ธฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ™•์‚ฐ๊ฐ•์กฐ์˜์ƒ (DWI), T1ฯ (T1 rho), T2*๋งต (mapping), ์ƒ์ฒด ๋‚ด MR ๋ถ„๊ด‘๋ถ„์„(in vivo 1H-MRS) ๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์˜์ƒ ๊ฒ€์‚ฌ ํ›„, ์ ์ถœํ•œ ์‹ ์žฅ์˜ ๋ฐ˜์ •๋Ÿ‰์  ์กฐ์ง๋ณ‘๋ฆฌํ•™์  ๋ถ„์„์„ ์‹œํ–‰ํ•˜์—ฌ ๋งŒ์„ฑ ์‹ ์งˆํ™˜ ๋ณ‘๋ฆฌ ๋ถ„์„ ๊ฒฐ๊ณผ์™€ ์˜์ƒ ๋ถ„์„์„ ํ†ตํ•ด ์–ป์€ ์ •๋Ÿ‰์  ํŒŒ๋ผ ๋ฏธํ„ฐ ๊ฐ’ ์‚ฌ์ด์˜ ํ†ต๊ณ„์  ์œ ์˜์„ฑ์€ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ์ •์ƒ์‹ ์„ ๊ฐ€์ง„ ๋Œ€์กฐ๊ตฐ๊ณผ ๋น„๊ตํ•˜์˜€์„ ๋•Œ, ์•„๋ฐ๋‹Œ ๋…ธ์ถœ ๊ธฐ๊ฐ„์— ๋”ฐ๋ผ ๋งŒ์„ฑ ์‹ ์งˆํ™˜์˜ ์œ ์˜ํ•œ ์กฐ์ง๋ณ‘๋ฆฌํ•™์  ๋ณ€ํ™”๊ฐ€ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ํ™•์‚ฐ๊ณ„์ˆ˜(ADC), T1ฯ (T1 rho) ๊ฐ’์€ ๋งŒ์„ฑ์‹ ์งˆํ™˜ ์‹คํ—˜๊ตฐ์—์„œ ์œ ์˜ํ•œ ์ฆ๊ฐ€๋ฅผ ๋ณด์˜€๋‹ค. ์‹ ์žฅ ํ”ผ์งˆ(CO)๊ณผ ๋ฐ”๊นฅ ์ˆ˜์งˆ(OM)์—์„œ ์ธก์ •ํ•œ ํ™•์‚ฐ๊ณ„์ˆ˜, T1ฯ๊ฐ’์˜ ์œ ์˜ํ•œ ์ฆ๊ฐ€๋ฅผ ๋ณด์˜€๋‹ค. ํ™•์‚ฐ๊ณ„์ˆ˜ ๊ฐ’์€ ๋…ธ์ถœ ๊ธฐ๊ฐ„์— ๋”ฐ๋ผ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์—ˆ๊ณ , T1ฯ (T1 rho) ๊ฐ’์€ 3์ฃผ ๋™์•ˆ ๋…ธ์ถœ์‹œํ‚จ ๋งŒ์„ฑ์‹ ์งˆํ™˜ ์‹คํ—˜๊ตฐ 1์—์„œ ์ฆ๊ฐ€ํ•˜๋‹ค๊ฐ€ 6์ฃผ ์‹คํ—˜๊ตฐ์—์„œ๋Š” ๊ฐ์†Œํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. MR ๋ถ„๊ด‘๋ถ„์„ ๋Œ€์‚ฌ ๋ฌผ ๊ฐ€์šด๋ฐ, ์‹  ์ˆ˜์งˆ์—์„œ ์–ป์€ myo-inositol (Ins)-glycine (Gly) ๋Œ€๋น„ ์ฝœ๋ฆฐ (choline) ํ™”ํ•ฉ๋ฌผ (glycerophosphorylcholine (GPC)-choline (Cho)-phosphatidylcholine (PC)์˜ ๋น„์œจ์ด ๋งŒ์„ฑ์‹ ์งˆํ™˜ ์‹คํ—˜๊ตฐ์—์„œ ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์€ ์†Œ๊ฒฌ์„ ๋ณด์˜€๋‹ค. ๊ฒฐ๋ก  ์šฐ๋ฆฌ๋Š” ์ด ์‹คํ—˜์„ ํ†ตํ•ด ์ •๋Ÿ‰์  MR ์˜์ƒ์€ ๋น„ ์นจ์Šต์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ๋งŒ์„ฑ ์‹ ์งˆํ™˜์„ ์ง„๋‹จํ•˜๊ณ  ํ‰๊ฐ€ํ•˜๋Š” ๋„๊ตฌ๋กœ์„œ ์ž ์žฌ์  ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํŠนํžˆ T1ฯ์€ ์‹ ์‹ค์งˆ์˜ ์„ฌ์œ ํ™”๋ฅผ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๋Š”๋ฐ ์ ํ•ฉํ•œ ์ •๋Ÿ‰์  MR ์‹œํ€€์Šค ํŒŒ๋ผ ๋ฏธํ„ฐ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ƒ์ฒด ๋‚ด MR ๋ถ„๊ด‘๋ถ„์„์„ ์ด์šฉํ•œ ๋Œ€์‚ฌ๋ฌผ์˜ ๋ณ€ํ™” ์ถ”์ ์ด ๋งŒ์„ฑ ์‹ ์˜ osmolality๋ณ€ํ™”๋ฅผ ๋ฐ˜์˜ํ•˜๋Š” ๋น„์นจ์Šต์  ๋ฐฉ๋ฒ•์ด ๋  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.Objective This study aimed to validate the usefulness of quantitative multiparametric magnetic resonance imaging (MRI) sequence parameters and suggest the suitable spectroscopic metabolites in the evaluation of parenchymal fibrosis using an experimental animal model of chronic kidney disease (CKD) by long-term adenine intake. Materials and Methods Experimental adenine intake in rats induces renal dysfunction due to the deposition of 2,8-dihydroxyadenine crystals in the renal parenchyma. This pathophysiologic progression resembles that of human CKD. A total of 16 male Wistar rats were analyzed. They were divided into three groups: control (n = 7), CKD1 (n = 5), and CKD2 (n = 4). The CKD groups were kept under the 3- or 6-week term intake of 0.25% adenine. According to group assignment, quantitative MRI sequences, including diffusion-weighted image, T1ฯ (T1 rho), T2* mapping, and in vivo MR spectroscopy (1H-MRS), were performed using a 9.4T animal MR scanner. A semiquantitative histopathologic analysis for renal fibrosis was conducted. Comparative analyses of quantitative MR values measured from anatomic regions of kidneys between groups were performed. Results Compared to the control group, significant histopathologic changes were observed in CKD groups according to periods. The apparent diffusion coefficient (ADC) and T1 (T1 rho) values were significantly increased in all CKD groups compared with those in the control group. The differences in values measured from the cortex and outer medulla were significant between all CKD groups and control group. The total ADC values tended to increase according to periods. The T1ฯ (T1 rho) values were increased in the CKD1 group and decreased in the CKD2 group. Among MRS metabolites acquired from each region, the ratio of glycerophosphorylcholineโ€“cholineโ€“phosphatidylcholine signals to myo-inositolโ€“glycine signals collected from voxels located at medulla region was significantly lower in the CKD groups than in the control group (0.17 vs. 0.456, P = 0.0448). Conclusion Quantitative MRI sequences could be a noninvasive assessment modality in the diagnosis and evaluation of CKD. In particular, T1ฯ may be a suitable MR sequence parameter to assess renal parenchymal fibrosis in a quantitative manner. Moreover, monitoring the change in common metabolites using MRS may reflect the alteration of osmolality in the renal medulla in CKD.Abstract in English ----------------------------------------------- 1 Contents ------------------------------------------------------------ 4 List of tables and figures ---------------------------------------- 5 Introduction ------------------------------------------------------- 8 Material & Methods -------------------------------------------- 10 Results ------------------------------------------------------------- 16 Discussion --------------------------------------------------------- 25 References --------------------------------------------------------- 35 Abstract in Korean ---------------------------------------------- 42Docto

    ๋ณด์™„์„ฑ ์œ ํ˜•์ด ํŒ€-๊ตฌ์„ฑ์›๊ฐ„ ์š”๊ตฌ-๋Šฅ๋ ฅ ์ ํ•ฉ์„ฑ๊ณผ ํŒ€์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜ํ•™๊ณผ, 2012. 8. ๋ฐ•์›์šฐ.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š”, ํŒ€ ๊ตฌ์„ฑ์›์˜ ๊ธฐ๋Šฅ, ์—ญํ• , ์ธ์‹ ๋‹ค์–‘์„ฑ์ด ์š”๊ตฌ-๋Šฅ๋ ฅ ์ ํ•ฉ์„ฑ์„ ๊ฑฐ์ณ ํŒ€ ํšจ๊ณผ์„ฑ์— ๋ณด์™„์ ์ด๊ณ  ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๊ณ  ๊ฐ€์ •ํ•œ ํ›„, ์ด๋ฅผ ์‹ค์ฆ์ ์œผ๋กœ ํ™•์ธํ•˜์—ฌ ๋ณธ๋‹ค. ๋˜ํ•œ ๋ณต์žก๊ณ„์  ์ ‘๊ทผ์„ ํ™œ์šฉํ•˜์—ฌ ๋‹ค์–‘์„ฑ ์—ฐ๊ตฌ์™€ ์ ํ•ฉ์„ฑ ์—ฐ๊ตฌ์˜ ์ ‘ํ•ฉ์ ์„ ์ฐพ์œผ๋ ค ์‹œ๋„ํ•œ๋‹ค. ์œ„์— ์–ธ๊ธ‰ํ•œ ๋‹ค์–‘์„ฑ์˜ ์ฃผ ํšจ๊ณผ์— ๋”ํ•˜์—ฌ ์ธ์ง€์š•๊ตฌ์™€ ๊ณผ์—…์ผ์ƒ์„ฑ์ด ๊ฐ€์ง€๋Š” ์กฐ์ ˆํšจ๊ณผ๋„ ํ•จ๊ป˜ ์‚ดํŽด๋ณธ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ๋Š”, ์ธ์ง€์š•๊ตฌ์™€ ๊ณผ์—…์ผ์ƒ์„ฑ์€ ๋‹ค์–‘์„ฑ์ด ํŒ€์˜ ์š”๊ตฌ ๋Šฅ๋ ฅ ์ ํ•ฉ์„ฑ๊ณผ ๊ฐ€์ง€๋Š” ์ •์  ๊ด€๊ณ„๋ฅผ ๊ฐ•ํ™”์‹œํ‚ฌ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒํ•˜์˜€๋‹ค. 44๊ฐœ ํŒ€์œผ๋กœ๋ถ€ํ„ฐ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ฒ€์ฆํ•œ ๊ฒฐ๊ณผ, ์—ญํ•  ๋‹ค์–‘์„ฑ์ด ์š”๊ตฌ๋Šฅ๋ ฅ์ ํ•ฉ์„ฑ์„ ๊ฑฐ์ณ ํŒ€ ์„ฑ๊ณผ์™€ ์ •์  ๊ด€๊ณ„๋ฅผ ๊ฐ€์ง์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ธ์‹ ๋‹ค์–‘์„ฑ์€ ์˜ˆ์ƒ๊ณผ ๋ฐ˜๋Œ€๋กœ ํŒ€ ์ ํ•ฉ์„ฑ, ํŒ€ ์„ฑ๊ณผ์™€ ๋ถ€์ ์ธ ๊ด€๊ณ„๋ฅผ ๋งบ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ธฐ๋Šฅ ๋‹ค์–‘์„ฑ์— ์žˆ์–ด์„œ๋Š”, ๋น„๋ก ์ฃผ ํšจ๊ณผ๋Š” ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜์œผ๋‚˜ ์ธ์ง€์š•๊ตฌ๊ฐ€ ํฐ ํŒ€์— ์žˆ์–ด์„œ ์š”๊ตฌ ๋Šฅ๋ ฅ ์ ํ•ฉ์„ฑ์„ ๊ฑฐ์ณ ํŒ€ ์„ฑ๊ณผ์™€ ์ •์  ๊ด€๊ณ„๋ฅผ ๋งบ๋Š” ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์ธ์ง€์š•๊ตฌ์™€ ๊ณผ์—…์ผ์ƒ์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ๋Š” ์ „๋ฐ˜์ ์œผ๋กœ ์ง€์ง€๋˜์—ˆ๋‹ค. ๊ธฐ๋Šฅ๋‹ค์–‘์„ฑ๊ณผ ์ธ์ง€๋‹ค์–‘์„ฑ์€ ์ธ์ง€์š•๊ตฌ๊ฐ€ ๊ฐ•ํ•œ ํŒ€์˜ ๊ฒฝ์šฐ์— ์š”๊ตฌ ๋Šฅ๋ ฅ ์ ํ•ฉ์„ฑ๊ณผ ์ •์  ๊ด€๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ณผ์—…์ผ์ƒ์„ฑ์ด ๋†’์„ ๋•Œ, ๊ธฐ๋Šฅ๋‹ค์–‘์„ฑ๊ณผ ์—ญํ• ๋‹ค์–‘์„ฑ์ด ํŒ€ ์ ํ•ฉ์„ฑ๊ณผ ํŒ€ ์„ฑ๊ณผ์— ์žˆ์–ด ์—ญ์‹œ ์ •์  ๊ด€๊ณ„๋ฅผ ์ง€๋‹ˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•˜์ง€๋งŒ ์˜ˆ์ƒ๊ณผ ๋ฐ˜๋Œ€๋กœ ์—ญํ•  ๋‹ค์–‘์„ฑ์˜ ๊ฒฝ์šฐ ์ธ์ง€๊ฒฝ์šฐ๊ฐ€ ๋†’์„ ๋•Œ ์˜คํžˆ๋ ค ์š”๊ตฌ-๋Šฅ๋ ฅ ์ ํ•ฉ์„ฑ๊ณผ ๋ถ€์ ๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค.I propose that member diversities in function, role, and cognitive style exert complementary positive influence on team effectiveness via Demands-Abilities fit (D-A fit). I apply complex system approach as the predominant theoretical lens to integrate diversity and fit research. In addition, I address the need to consider boundary conditions of team member diversity effects. Specifically, team need for cognition and task routineness are expected to strengthen the positive effects of diversities on team D-A fit. Using data from a total of 44 teams, I found role diversity had unique positive indirect effect on team performance via team D-A fit. In contrast to the expectation, cognitive diversity had negative indirect effect on team performance via team D-A fit. Although there was no significant main effect of functional diversity, it showed positive effect on team D-A fit and team performance when team members enjoy deliberating (high team need for cognition). Moderation effects of team need for cognition and task routineness were generally supported. Functional and cognitive diversities were positively related to team D-A fit when team need for cognition was high. And functional and role diversities had positive effect on team D-A fit when task routineness was high. As opposed to expectation, however, role diversity was negatively related to team D-A fit when team need for cognition was high.INTRODUCTION 1 Importance of Teams in the Organization 1 Human Resouces in Teams 2 Fit in Complex Teams 3 THEORETICAL BACKGROUND 5 Demands-Abilities Fit 5 Extension of D-A Fit to Team Level 6 Teams as Complex Adaptive System 9 Resilience of Teams 13 Exploring Variations 15 Overrepresentation of Exploitation over Exploration 19 Injection of Diversity 21 HYPOTHESIS DEVELOPMENT 23 Function 23 Role 24 Cognitive Style 25 Moderators: Need for Cognition and Task Routineness 27 Team Performance 30 METHODS 32 Participants and Procedures 32 Measures 33 RESULTS 40 Psychometric Characteristics of the Measure 40 Descriptive Statistics 42 Main Effects of Diversity Measures on Team D-A Fit 43 Moderating effects of team need for cognition and task routineness 46 Team D-A fit and team performance 53 Test of the integrative model: mediation of team D-A fit on diversityโ€”performance 53 DISCUSSION 57 Interpretation of Results 63 Theoretical Implications 42 Managerial Implications 67 Limitations and Directions for Future Research 69 Conclusion 72 REFERENCES 73 ABSTRACT IN KOREAN 94Maste

    ์˜ค๋ฆฌํ”ผ์Šค U ํŠœ๋ธŒ ๊ฐœ๋…์— ์˜ํ•œ ์†Œํ˜• ๋ถ€์œ ์‹

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    This study focuses on developing a method for analyzing a wave energy device that uses a cross-flow turbine and orifice U tube. To develop this kind of micro wave energy converter, various experimental and numerical methods are used to find and define the characteristics about wave energy converter and this performance study will be based on the baseline data of the floating wave energy converter. The results are summarized as follows. 1. Hydraulic model tests and numerical analysis have been conducted to verify the use of the orifice instead of a small scale cross-flow turbine. The difference in error is less than 10% which was determined to be acceptable. Using this method will decrease time and financial resources needed for experimental tests. 2. Through hydraulic model test with a 6 axis acceleration sensor, the high performance operating ranges have been defined. In the high performance ranges, the reasons of high performance were defined by pitch and heave motion of floating wave energy converter. 3. From previous experiments, maximum shaft power and torque are simulated by a commercial CFD code, ANSYS CFX ver. 14. Between the pitch angles from 7 to 15 degrees, 4.9W is the maximum shaft power. in the real scale model, 15.5W of shaft power is expected by using the Froude Scale at a scale of 5.3:1. 4. This kind of the floating wave energy converter is limited because of the restricted dimension of the nozzle and turbine size. But in the chapter 4, the enlarged model shows the possibility of producing around two times of the shaft power and torque. 5. The floating wave energy converter uses only pitch motion to generate mechanical energy and high performance range are restricted. In case of the wave energy converter with heave controller, high performance range can be extended. The high rotational speed of turbine range is in 3.0 m, 4.6 m, 5.2 m of wave length.๋ชฉ ์ฐจ List of Tables โ…ฒ List of Figures โ…ณ Abstract โ…ต Nomenclature โ…ท 1. ์„œ ๋ก  1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ๋™ํ–ฅ 6 1.3 ์—ฐ๊ตฌ๋ชฉ์  9 1.4 ๊ตฌ๋™์›๋ฆฌ 10 2. ๋ถ€์œ ์‹ ํŒŒ๋ ฅ๋ฐœ์ „ ์žฅ์น˜ ์„ค๊ณ„ 2.1 ๊ทœ์น™ํŒŒ ์ด๋ก ๊ณผ ํ•ด์„ 12 2.2 Floude number๋ฅผ ์ด์šฉํ•œ ์„ค๊ณ„ ํŒŒ๋ผ๋ฏธํ„ฐ ์ง€์ • 15 2.3 ์ˆ˜์น˜ํ•ด์„ ๊ธฐ๋ฒ• 16 2.3.1 ์ง€๋ฐฐ ๋ฐฉ์ •์‹ 17 2.3.2 ์ด์‚ฐํ™”๋ฐฉ๋ฒ• 18 2.3.3 ๋‚œ๋ฅ˜๋ชจ๋ธ 22 3. ์–‘๋ฐฉํ–ฅ ํšก๋ฅ˜ํ„ฐ๋นˆ์˜ ํŠน์„ฑ์— ๊ด€ํ•œ ์‹คํ—˜ ๋ฐ ์ˆ˜์น˜ํ•ด์„ 3.1 6์ž์œ ๋„ ํŒŒ๋ ฅ๋ฐœ์ „ ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅํ•ด์„ 25 3.1.1 ๋ชจ์…˜ ํ”Œ๋žซํผ 26 3.1.2 ์–‘๋ฐฉํ–ฅ ํšก๋ฅ˜ํ„ฐ๋นˆ์„ ์ด์šฉํ•œ ํŒŒ๋ ฅ๋ฐœ์ „ ๋ชจ๋ธ 27 3.1.3 ํ„ฐ๋นˆ์„ฑ๋Šฅํ•ด์„ ๋ฐ ์‹œํ—˜ 30 3.2 U-shaped tube ์ด์‚ฐํ™” ๋ชจ๋ธ 31 3.2.1 U-shaped tube ํŒŒ๋ ฅ๋ฐœ์ „ ๋ชจ๋ธ 31 3.2.2 MATLAB CODE์— ์˜ํ•œ ๋น„๊ต ๊ฒฐ๊ณผ 35 3.3 CFD์— ์˜ํ•œ ์–‘๋ฐฉํ–ฅ ํšก๋ฅ˜ํ„ฐ๋นˆ๊ณผ ์˜ค๋ฆฌํ”ผ์Šค ๋ชจ๋ธ ํ•ด์„ 37 3.3.1 ํ˜•์ƒ ๋ชจ๋ธ๋ง ๋ฐ ๊ณ„์‚ฐ ๊ฒฉ์ž 37 3.3.2 ๊ฒฝ๊ณ„ ์กฐ๊ฑด 41 3.4 ์š”์•ฝ ๋ฐ ๊ฒ€ํ†  43 4. ๋ถ€์œ ์‹ ํŒŒ๋ ฅ๋ฐœ์ „์žฅ์น˜ ์ˆ˜๋ฆฌ ๋ชจํ˜• ์‹คํ—˜ ๋ฐ ๊ฒ€์ฆ 4.1 ์‹คํ—˜ ๋ชฉ์  47 4.2 ์‹คํ—˜ ์„ค๊ณ„ 48 4.2.1 ์ˆ˜์กฐ ์‹คํ—˜ ๊ตฌ์„ฑ 48 4.2.2 ํŒŒ๋ ฅ๋ฐœ์ „์žฅ์น˜ ์„ค๊ณ„ 49 4.3 6์ถ• ๊ฐ€์† ์„ผ์„œ์— ์˜ํ•œ ์žฅ์น˜ ์„ฑ๋Šฅ ์‹คํ—˜ 50 4.3.1 6์ถ• ๊ฐ€์† ์„ผ์„œ ๋ฐ ์‹คํ—˜ ๊ตฌ์„ฑ 50 4.3.2 ์‹คํ—˜ ์กฐ๊ฑด ์„ ์ • 51 4.3.3 ์‹คํ—˜ ๊ฒฐ๊ณผ 52 4.4 CFD์— ์˜ํ•œ ๋ถ€์œ ์‹ ํŒŒ๋ ฅ๋ฐœ์ „์žฅ์น˜ ์„ฑ๋Šฅ ๊ฒ€์ฆ 56 4.4.1 ํ˜•์ƒ ๋ชจ๋ธ๋ง ๋ฐ ๊ฒฉ์ž 56 4.4.2 ๊ฒฝ๊ณ„ ์กฐ๊ฑด ๋ฐ ๊ณ„์‚ฐ ์กฐ๊ฑด 58 4.4.3 ์œ ๋™ ํ•ด์„ ๊ฒฐ๊ณผ 61 4.5 CFD์— ์˜ํ•œ ํŒŒ๋ ฅ๋ฐœ์ „ ์žฅ์น˜ ๊ฐœ์„  63 4.5.1 CFD ํ•ด์„ ๋ชฉ์  63 4.5.2 ํ˜•์ƒ ๋ชจ๋ธ๋ง ๋ฐ ๊ฒฉ์ž 63 4.5.3 ๊ฒฝ๊ณ„ ์กฐ๊ฑด ๋ฐ ๊ณ„์‚ฐ ์กฐ๊ฑด 67 4.5.4 ์œ ๋™ ํ•ด์„ ๊ฒฐ๊ณผ 68 4.6 ์‹ค ์Šค์ผ€์ผ ์‹คํ—˜ ์กฐ๊ฑด ์„ ์ • 71 4.7 ์š”์•ฝ ๋ฐ ๊ฒ€ํ†  74 5. ๋ชจ์…˜ ์ œ์–ด์— ์˜ํ•œ ๋ถ€์œ ์‹ ํŒŒ๋ ฅ๋ฐœ์ „์žฅ์น˜์˜ ์ˆ˜๋ฆฌ ๋ชจํ˜• ์‹คํ—˜ 5.1 ์‹คํ—˜๋ชฉ์  75 5.2 ๋ชจ์…˜ ์ œ์–ด์— ์˜ํ•œ ๋ถ€์œ ์‹ ํŒŒ๋ ฅ๋ฐœ์ „์žฅ์น˜ ์„ฑ๋Šฅ ํ•ด์„ 77 5.2.1 ๋ชจ์…˜ ์ œ์–ด์žฅ์น˜ ๋ฐ ์‹คํ—˜ ๊ตฌ์„ฑ 77 5.2.2 ์‹คํ—˜ ์กฐ๊ฑด ์„ ์ • 79 5.2.3 ์‹คํ—˜ ๊ฒฐ๊ณผ 80 5.3 ์š”์•ฝ ๋ฐ ๊ฒ€ํ†  83 6. ๊ฒฐ ๋ก  84 ์ฐธ๊ณ ๋ฌธํ—Œ 8

    [์ด์Šˆ๋ถ„์„] 4์ฐจ ์‚ฐ์—…ํ˜๋ช…๊ณผ ์†Œํ”„ํŠธํŒŒ์›Œ

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    โ… . ์ธ๋ฅ˜์˜ ์ƒ์‚ฐ์„ฑ ํ–ฅ์ƒ๊ณผ 4์ฐจ ์‚ฐ์—…ํ˜๋ช…์˜ ์˜๋ฏธ โ…ก. 4์ฐจ ์‚ฐ์—…ํ˜๋ช…์œผ๋กœ ์ธํ•œ ์ธ๊ฐ„์˜ ์—ญํ•  ๋ณ€ํ™” โ…ข. 4์ฐจ ์‚ฐ์—…ํ˜๋ช… '์†Œํ”„ํŠธํŒŒ์›Œ'์˜ ์ •์˜ 1. ์†Œํ”„ํŠธํŒŒ์›Œ์˜ ์ •์˜ 2. ์—ฐ๊ฒฐ์„ฑ(Connectivity) 3. ์ฐฝ์˜์„ฑ(Creativity) โ…ฃ. 4์ฐจ ์‚ฐ์—…ํ˜๋ช…์—์„œ ์†Œํ”„ํŠธํŒŒ์›Œ์˜ ๊ฐ•ํ™” ์ „๋žต 1. 'Copenhagen Connecting Project'์˜ ๊ตํ›ˆ 2. ์†Œํ”„ํŠธํŒŒ์›Œ์˜ ์ค‘์š”

    ๊ตฌ๋ฉด์›ํ˜•์˜ ์—ฐ๊ตฌ

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    ํŽŒํ”„์žฅ ํก์ˆ˜์ •์˜ ์™€๋ฅ˜ ๊ฐ•๋„๋ฅผ ๊ณ ๋ คํ•œ ์ตœ์  ์„ค๊ณ„์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    The vortex which generated around suction region of a pump sump causes problems such as damage to the pump, increase in maintenance costs, and failure to supply coolant smoothly. Therefore, it is essential to analyze the vortex for proper pump sump design. The common method to investigate the vortex behavior around the sump pump is by Computational Fluid Dynamics (CFD) simulations. Although the CFD simulations provide high efficiency in analysis time and resources, it is insufficient to predict accurate the vortex behavior especially in scaled model. Thus, the vortex analysis by CFD simulations and experiments was conducted in this research to obtain high accuracy and validation in the results. There are 2 types of the vortices in the pump sump, type 2 of sub-surface vortex and type 3 of free-surface vortex, are used for the pump sump design based on ANSI HI 9.8 2018 standard. This study conducted the model test to validate a suitable CFD simulation method by identifying the vortex. The dye test and PIV(Particle Image Velocimetry) technology were used to visualize the 2 types of the vortex, whereas the PIV vorticity results were compared to the CFD results. Velocity analysis was conducted to find the influence of the flow entering the bell. To apply the finalized result from the PIV and CFD analysis, there were CFD simulations for vortices by real scale pump sump and modified models regarding the mainly applied in industry diameter of bell. Finally, related equations could be found for 2 kinds of vortices of the behavior regarding the results.1. ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2 ๊ตญ๋‚ด์™ธ ํŽŒํ”„์žฅ ํก์ˆ˜์ • ์—ฐ๊ตฌ ๋™ํ–ฅ 3 1.3 ์—ฐ๊ตฌ ๋ชฉ์  4 2. ํŽŒํ”„์žฅ ํก์ˆ˜์ • 6 2.1 ํŽŒํ”„์žฅ ํก์ˆ˜์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์™€๋ฅ˜์˜ ๋ถ„๋ฅ˜ 6 2.2 ํŽŒํ”„์žฅ ํก์ˆ˜์ •์˜ ์ผ๋ฐ˜์  ์น˜์ˆ˜ ์ •์˜ 10 2.3 ํŽŒํ”„์žฅ ํก์ˆ˜์ •์˜ ์ƒ์‚ฌ ๋ชจ๋ธ 11 2.4 ํŽŒํ”„์žฅ ํก์ˆ˜์ •์˜ ์ƒ์‚ฌ ๋ชจ๋ธ ํŒ์ • ๊ธฐ์ค€ 13 2.4.1 ์ž์œ ํ‘œ๋ฉด ๋ณดํ…์Šค์™€ ์ˆ˜์ค‘ ๋ณดํ…์Šค 13 2.4.2 ์Šค์›” ๊ฐ๋„ 13 2.4.3 ์†๋„ ์ธก์ • 15 3. ์ž…์ž์˜์ƒ์œ ์†๊ณ„ ์‹คํ—˜์˜ ์ด๋ก  17 3.1 PIV ๊ฐœ์š” 17 3.2 PIV ์‹คํ—˜ ์žฅ์น˜์˜ ๊ตฌ์„ฑ 19 3.3 ์กฐ๋ช… ๋ฐ ์ถ”์  ์ž…์ž 20 3.4 ์˜์ƒ์ž…๋ ฅ ๋ฐ ์ €์žฅ ์žฅ์น˜ 21 3.5 ๋™์ผ์ž…์ž ์ถ”์  22 4. ์ „์‚ฐ์œ ์ฒด์—ญํ•™์˜ ์ด๋ก  23 4.1 ๊ฐœ์š” 23 4.2 ์ง€๋ฐฐ ๋ฐฉ์ •์‹ 23 4.3 ์ด์‚ฐํ™” ๋ฐฉ๋ฒ• 27 4.4 ๋‚œ๋ฅ˜ ๋ชจ๋ธ : RANS 30 4.5 ๋‚œ๋ฅ˜ ๋ชจ๋ธ : LES 34 4.5.1 ํ•„ํ„ฐ 34 4.5.2 LES ๋ชจ๋ธ 36 5. ํก์ˆ˜์ • ์™€๋ฅ˜ ํŠน์„ฑ ์—ฐ๊ตฌ 37 5.1 ์ธก๋ฉด ์ˆ˜์ค‘ ์™€๋ฅ˜ 37 5.1.1 ๋ชจ๋ธ ์‹œํ—˜ ๋ฐฉ๋ฒ• 37 5.1.2 ์—ผ๋ฃŒ ์‹œํ—˜ ๋ฐ PIV ํ›„์ฒ˜๋ฆฌ ๋ฐฉ์•ˆ 41 5.1.3 ์ธก๋ฉด ์ˆ˜์ค‘ ์™€๋ฅ˜ ๋ถ„์„์„ ์œ„ํ•œ CFD ์„ค์ • 46 5.1.4 ๊ฒฐ๊ณผ ๋ฐ ํ† ์˜ 50 5.1.5 ๊ฒฐ๋ก  55 5.2 ํŽŒํ”„์žฅ ํก์ˆ˜์ •์˜ ์™€๋ฅ˜ 56 5.2.1 ๋ชจ๋ธ ์‹œํ—˜ ๋ฐฉ๋ฒ• 56 5.2.2 Dye test ๋ฐ PIV ํ›„์ฒ˜๋ฆฌ ๋ฐฉ์•ˆ 63 5.2.3 Bell์—์„œ์˜ ์†๋„์žฅ ๋ถ„์„ 69 5.2.4 ํŽŒํ”„์žฅ ํก์ˆ˜์ •์˜ ์™€๋ฅ˜ ๋ถ„์„์„ ์œ„ํ•œ CFD ์„ค์ • 71 5.2.5 ๊ฒฐ๊ณผ ๋ฐ ํ† ์˜ 74 5.3 ํŽŒํ”„ ์„ค๊ณ„ 96 5.3.1 Case ์„ ์ • 97 5.3.2 Bell์˜ ์น˜์ˆ˜์— ๋”ฐ๋ฅธ ์™€๋„ ๊ด€๊ณ„๋ฅผ ์ฐพ๊ธฐ ์œ„ํ•œ CFD ์„ค์ • 98 5.3.3 ๊ฒฐ๊ณผ ๋ฐ ํ† ์˜ 98 6. ๊ฒฐ๋ก  110 ์ฐธ๊ณ ๋ฌธํ—Œ 112Docto

    ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•œ ์žฅ๊ฑฐ๋ฆฌ ์ธก์ •์˜ ์ˆ˜ํ‰๋ฐฉํ–ฅ ๋ถ„ํ•ด๋Šฅ ํ–ฅ์ƒ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2018. 8. ๋ฐ•ํฌ์žฌ.The research in this dissertation aims at improvement of spatial resolution using convolution neural network in critical dimension (CD) measurement. CD measurement is widely used to get characteristic features of inspection targets in manufacturing process. This metrology step examines whether or not patterns are fabricated as designed. The results of such implementation are important for manufacturing process to produce high-quality products reliably. In recent years, organic light-emitting diodes (OLEDs) display continues to be a major trend, improving high pixel density. Because most OLED display are made using fully automated encapsulation system in order to prevent organic materials from oxidation, metrology equipment locates out of system, for example, vacuum chamber. This prevalence have demanded long working distance (W.D) measurement of small features, satisfying the measurement performance of industryโ€“level at the same time. Long W.D measurement result in reducing numerical aperture (NA) of optical system. It is desirable to use a larger lens in order to increase the NA, however, telephoto lens are expensive and heavier. Imaging from large stand-off distance typically suffer from low spatial resolution. Edge detection senses the intensity change of image and separates the object from the background by finding a boundary line. Therefore, blurry image drops the edge detection performance and it is hard to attain high-accuracy and high-repeatability. In this thesis, convolutional neural network (CNN) was suggested to improve spatial resolution without any changes in optics. The proposed convolutional neural network use a single image acquired from long working distance measurement system as input, and rapidly outputs an image that having an improved spatial resolution. The convolution neural network was used to connect two different optical systems. We had learned the network so that the long-distance measured image was transformed like an image measured at a very close distance. In order to improve the performance and speed of learning, neurons, layers, and input-output data were newly constructed. In addition, it was configured to learn and correctly recognize dead neurons that were not learning properly in the network Under 195 mm W.D condition, the measurement accuracy and 3ฯƒ repeatability is 3.0%, 60 nm (20 repeat measurements), respectively. The measurable minimum size is 0.5 ใŽ› and the measurement time is only under 0.2 s. The proposed method not only showed sub-micro level resolution, but also successfully industry-level measurement accuracy and repeatability in real time by implementing CNN. These results highlight the promise of the proposed method as a long-working distance measurement system of industry field. Chapter 1. Introduction 1 1.1. Research motivation 1 1.2. Trends of research 6 1.3. Research objectives and coverage 8 Chapter 2. Background Theory 9 2.1. Neural Network 9 2.2. Edge Detection 12 2.3. Working Distance and Image Resolution 15 Chapter 3. Convolutional Neural Network 16 3.1. Data preparation 16 3.1.1. Optics mapping 16 3.1.2. Wavelet transformation 20 3.2. Network Architecture 22 3.2.1. Neuron 23 3.2.2. Layer 24 Chapter 4. Dead Neuron 26 4.1. Neuron State 26 4.2. Reduction of dead neuron 30 4.3. Effect of dead neuron ratio 32 Chapter 5. Experimental Setup 34 5.1. Configuration of optics 34 5.1.1. Lens 34 5.1.2. Light source 36 5.2. Training process 39 5.2.1. Data preparation 39 5.2.2. Implementation 40 5.2.3. Evaluation metrics 41 Chapter 6. Experimental Result 42 6.1. Training Result 42 6.1.1. Training loss 42 6.1.2. Sharpness and noise 43 6.1.3. Accuracy and repeatability 44 6.1.4. Data compressing 46 6.2. Test Result 47 6.2.1. Standard specimen 47 6.2.2. Test sample result 50 6.2.3. Defect Sample 57 6.2.4. Computation time 58 Chapter 7. Conclusion 59 REFERENCES 61 ABSTRACT IN KOREAN 64Docto

    ์‚ผ๊ฐํ˜•์˜ ์˜ค์‹ฌ์˜ ์ขŒํ‘œ

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    microRNA์— ์˜ํ•œ ์ง€๋ฐฉ์„ธํฌ ๋ถ„ํ™” ๊ธฐ์ž‘์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    Thesis(masters) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ˜‘๋™๊ณผ์ • ์œ ์ „๊ณตํ•™์ „๊ณต,2010.2.Maste
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