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    ์„œํ•ด์—ฐ์•ˆ ํ‡ด์ ๋ฌผ ๋‚ด ์ž”๋ฅ˜์„ฑ ์˜ค์—ผ๋ฌผ์งˆ ๋ฐ ์ž ์žฌ์  ๋…์„ฑ์˜ ์žฅ๊ธฐ๋ณ€ํ™” ํŠน์„ฑ ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€,2019. 8. ๊น€์ข…์„ฑ.The west coast of Korea, including various estuarine and coastal regions, has been experienced environmental deterioration from industrialization and urbanization for decades. This study focuses on โ…ฐ) long-term changes of persistent toxic substances (PTSs) in these areas, โ…ฑ) the sources of each pollutant by analyses of chemical compositions, โ…ฒ) evaluation of potential toxicities of sedimentary pollutants using cell lines and Vibrio fischeri bioassay, and โ…ณ) identification of the causative chemicals through the relationships between PTS concentrations and biological responses. Sediment samples have been collected from estuarine and coastal regions from year 2010 to 2018 annually. Target PTSs include 15 polycyclic aromatic hydrocarbons (PAHs), 10 styrene oligomers (SOs), 3 nonylphenols (NPs), and 8 heavy metals (HMs). The gas chromatograph coupled with a mass selective detector (GC/MSD) was used to determine the concentration of PAHs, SOs, and NPs, while inductively coupled plasma mass spectrometry (ICP/MS) and inductively coupled plasma optical emission spectrometry (ICP/OES) was used for HMs. This study conducted bioassays to determine AhR-mediated potency, ER-mediated potency, and bioluminescence inhibition rate using H4IIE-luc, MVLN cell lines, and V. fischeri, respectively. The concentration of PAHs, SOs, and NPs was reduced by 63.9%, 95.0%, and 44.5%, for the period between 2010 and 2018, respectively. The results suggested that the PAHs were majorly originated from pyrogenic sources including biomass or fuel combustions. After year 2015, the PAHs from vehicle emission were dominants in sediment samples. The fresh inputs of SOs were gradually decreased since 2010 and there was eventually no fresh input found in sediments collected in 2018. The NP concentrations also decreased from year 2010 to 2018 which can be resulted from strict regulations of these NPs by the Korean government. On the other hand, the PAH concentrations showed a positive relationship with AhR-mediated potencies (r = 0.53, p <0.01) and so did the NP concentrations with ER-mediated potencies (r = 0.31, p <0.01). Also, Cd concentrations showed significant correlation with bioluminescence inhibition rates of V. fischeri (r = 0.47, p <0.01). Nevertheless, the result of potency balance analyses between BaP-EQ and BEQ, E2-EQ and EEQ values indicated that the target pollutants analyzed in this study had little effect on total AhR and ER-mediated potencies in sediments. These findings reflected that target PTSs in this study could not be the major pollutants having potential biological toxicities. Therefore, it would be desirable to have more studies on identifying and monitoring of the causative chemicals in sediments.์šฐ๋ฆฌ๋‚˜๋ผ ์„œํ•ด ์—ฐ์•ˆ์€ ์ง€๋‚œ ์ˆ˜์‹ญ ๋…„ ๋™์•ˆ ๋„์‹œํ™” ๋ฐ ๊ณต์—…ํ™”๊ฐ€ ๊ธ‰์†ํžˆ ์ง„ํ–‰๋˜์—ˆ๊ณ , ์ด๋กœ ์ธํ•ด ์˜ค์—ผ๋ฌผ์งˆ์ด ํ•ด์–‘ํ™˜๊ฒฝ์œผ๋กœ ์œ ์ž…๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์„œํ•ด ์—ฐ์•ˆ์€ ๋ฐ˜ ํ์‡„์„ฑ ํ•ด์—ญ์ด๋ผ๋Š” ์ง€ํ˜•์ ์ธ ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ํ•ด์ˆ˜ ๊ตํ™˜์ด ์ƒ๋Œ€์ ์œผ๋กœ ๋Š๋ ค ์—ฐ์•ˆ ์ง€์—ญ์— ์œ„์น˜ํ•œ ์‚ฐ์—…๋‹จ์ง€๋กœ๋ถ€ํ„ฐ ๋ฐฐ์ถœ๋˜๊ฑฐ๋‚˜ ๋Œ€๊ธฐ๋ฅผ ํ†ตํ•ด ์œ ์ž…๋œ ์˜ค์—ผ๋ฌผ์งˆ์ด ์—ฐ์•ˆ ํ‡ด์ ๋ฌผ ๋‚ด์— ์ถ•์ ๋˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์ธ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„œํ•ด ์—ฐ์•ˆ ๋ฐ ์ฃผ์š” ํ•˜๊ตฌ์—ญ ํ‡ด์ ๋ฌผ์˜ ์œ ๊ธฐํ™”ํ•ฉ๋ฌผ์˜ ๋ถ„ํฌ ํŠน์„ฑ๊ณผ ์ž ์žฌ์  ์ƒ๋ฌผ ์˜ํ–ฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. 2010๋…„๋ถ€ํ„ฐ 2018๋…„ ๊นŒ์ง€ ๋งค๋…„ ์‹œํ™”ํ˜ธ, ์•„์‚ฐํ˜ธ, ์‚ฝ๊ตํ˜ธ, ๊ธˆ๊ฐ• ํ•˜๊ตฌ, ์˜์‚ฐ๊ฐ• ํ•˜๊ตฌ์˜ 10๊ฐœ ์ •์ ์—์„œ ๋ฐฉ์กฐ์ œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ๋‚ด, ์™ธ์ธก์œผ๋กœ ๋‚˜๋ˆ„์–ด์„œ ํ‘œ์ธต ํ‡ด์ ๋ฌผ์„ ์ฑ„์ทจํ•˜์˜€๋‹ค. ํ‡ด์ ๋ฌผ์„ ์œ ๊ธฐ ์šฉ๋งค๋กœ ์ถ”์ถœํ•œ ํ›„ ์ถ”์ถœ์•ก์„ ๊ทน์„ฑ ๋ณ„๋กœ ๋ถ„์•ก ํ•˜์—ฌ ๊ฐ€์Šคํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”ผ-์งˆ๋Ÿ‰๋ถ„์„๊ธฐ๋ฅผ ์ด์šฉํ•ด ์ž”๋ฅ˜์„ฑ์˜ค์—ผ๋ฌผ์งˆ์˜ ๋†๋„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. 15์ข…์˜ ๋‹คํ™˜๋ฐฉํ–ฅ์กฑํƒ„ํ™”์ˆ˜์†Œ, 10์ข…์˜ ์Šคํ‹ฐ๋ Œ์˜ฌ๋ฆฌ๊ณ ๋จธ์™€ 3์ข…์˜ ๋…ธ๋‹ํŽ˜๋†€์„ ์ •๋Ÿ‰ ๋ถ„์„ํ•˜์˜€๋‹ค. ์œ ๋„๊ฒฐํ•ฉํ”Œ๋ผ์ฆˆ๋งˆ-์งˆ๋Ÿ‰๋ถ„์„๊ธฐ๋ฅผ ์ด์šฉํ•˜์—ฌ 8์ข…์˜ ์ค‘๊ธˆ์†์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์œ ์ „์ž ์žฌ์กฐํ•ฉ ์„ธํฌ์ฃผ์ธ H4IIE-luc๊ณผ MVLN์„ ์ด์šฉํ•˜์—ฌ ๋‹ค์ด์˜ฅ์‹ ๋ฅ˜ ํ™œ์„ฑ ๋ฐ ์—์ŠคํŠธ๋กœ๊ฒ๋ฅ˜ ํ™œ์„ฑ์„ ํ‰๊ฐ€ํ•˜์˜€๊ณ , Vibrio fischeri ๋ฐœ๊ด‘๋ฐ•ํ…Œ๋ฆฌ์•„๋ฅผ ์ด์šฉํ•˜์—ฌ ํ‡ด์ ๋ฌผ ๋‚ด ์ž ์žฌ ๋…์„ฑ ์ •๋„๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์„œํ•ด ์—ฐ์•ˆ ํ‡ด์ ๋ฌผ ๋‚ด ๋‹คํ™˜๋ฐฉํ–ฅ์กฑํƒ„ํ™”์ˆ˜์†Œ ๋†๋„๋Š” 0.0 - 93.5 ng g-1 dry mass (dm)์˜€๊ณ , ์Šคํ‹ฐ๋ Œ์˜ฌ๋ฆฌ๊ณ ๋จธ ๋†๋„๋Š” 0.0 - 23.2 ng g-1 dm ์ด์—ˆ์œผ๋ฉฐ, ๋…ธ๋‹ํŽ˜๋†€๋†๋„๋Š” 0 - 68.3 ng g-1 dm ์ด์—ˆ๋‹ค. ์ค‘๊ธˆ์† ๋†๋„์˜ ๊ฒฝ์šฐ ํ•ด์–‘ํ™˜๊ฒฝ ์ฃผ์˜ ๊ธฐ์ค€๊ณผ ์„œํ•ด ์—ฐ์•ˆ ํ‡ด์ ๋ฌผ๋‚ด ๋†๋„ ๋น„์œจ์„ ํ†ตํ•œ ์œ„ํ—˜๋„ ์ง€์ˆ˜ (Hazard quotients)๋ฅผ ์ด์šฉํ•˜์—ฌ ์˜ค์—ผ ์ •๋„๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋ชจ๋“  ์ค‘๊ธˆ์†์˜ ์œ„ํ—˜๋„ ์ง€์ˆ˜๋ฅผ ํ•ฉํ•œ ๊ฒฐ๊ณผ 0 - 6 ์‚ฌ์ด ๋ถ„ํฌ๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ ์„œํ•ด ์—ฐ์•ˆ ํ‡ด์ ๋ฌผ ๋‚ด ์ž”๋ฅ˜ ์˜ค์—ผ ๋ฌผ์งˆ์˜ ๋†๋„๋Š” ์šฐ๋ฆฌ๋‚˜๋ผ ์—ฐ์•ˆ ๋ฐ ๋‹ค๋ฅธ ๋‚˜๋ผ์— ๋น„ํ•ด ์ƒ๋Œ€์ ์œผ๋กœ ๋‚ฎ์€ ์ˆ˜์ค€์ด์—ˆ๋‹ค. ์ž”๋ฅ˜ ์˜ค์—ผ๋ฌผ์งˆ์˜ ๊ฐœ๋ณ„ ํ™”ํ•ฉ ๋ฌผ์งˆ ๋†๋„ ๋น„์œจ์„ ํ†ตํ•œ ์˜ค์—ผ์› ์ถ”์  ๊ฒฐ๊ณผ ๋‹คํ™˜๋ฐฉํ–ฅ์กฑํƒ„ํ™”์ˆ˜์†Œ๋Š” ๋ฐœ์—ด์— ์˜ํ•œ ์ƒ์„ฑ์ด ๋Œ€๋ถ€๋ถ„์ด์—ˆ๊ณ , ํŠนํžˆ 2015๋…„ ์ดํ›„๋ถ€ํ„ฐ ์ž๋™์ฐจ ๋ฐฐ์ถœ๊ฐ€์Šค๋กœ๋ถ€ํ„ฐ์˜ ์˜ค์—ผ์ด ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์Šคํ‹ฐ๋ Œ์˜ฌ๋ฆฌ๊ณ ๋จธ์™€ ๋…ธ๋‹ํŽ˜๋†€์˜ ๋ถ„์„๊ฒฐ๊ณผ 2010๋…„๋„ ์ดํ›„ ์„œํ•ด ์—ฐ์•ˆ ํ‡ด์ ๋ฌผ๋‚ด ์ƒˆ๋กœ์šด ์œ ์ž…์ด ์„œ์„œํžˆ ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋…ธ๋‹ํŽ˜๋†€ ๋†๋„์˜ ๊ฐ์†Œ๋Š” ํ•œ๊ตญ ์ •๋ถ€์˜ ํšจ๊ณผ์ ์ธ ๋…ธ๋‹ํŽ˜๋†€ ๊ทœ์ œ์— ์˜ํ•œ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค. ๋‹ค์ด์˜ฅ์‹ ๋ฅ˜ ํ™œ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ ๋ชจ๋“  ์‹œ๋ฃŒ์—์„œ ๋†’์€ ๋†๋„์˜ ํ™œ์„ฑ์„ ๋ณด์˜€๊ณ , ์—์ŠคํŠธ๋กœ๊ฒ๋ฅ˜ ํ™œ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 2015๋…„ ์ดํ›„ ๊ฐ์†Œํ•˜๋Š” ๊ฒฝํ–ฅ์„ฑ์„ ๋ณด์˜€๋‹ค. ๋ฐœ๊ด‘๋ฐ•ํ…Œ๋ฆฌ์•„ ํ™œ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 2010๋…„๋ถ€ํ„ฐ 2013๋…„๊นŒ์ง€ ์ฆ๊ฐ€ ํ›„ ๋น„์Šทํ•œ ๋ฒ”์œ„์˜ ๋ฐœ๊ด‘์ €ํ•ด์œจ์„ ์œ ์ง€ํ•˜์˜€๋‹ค. ์„œํ•ด ์—ฐ์•ˆ ํ‡ด์ ๋ฌผ๋‚ด ์ž”๋ฅ˜ ์˜ค์—ผ๋ฌผ์งˆ์˜ ๋†๋„ ๋ถ„ํฌ์™€ ์ƒ๋ฌผ ์˜ํ–ฅ ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•˜์˜€์„ ๋•Œ ๋‹คํ™˜๋ฐฉํ–ฅ์กฑํƒ„ํ™”์ˆ˜์†Œ ๋†๋„์™€ ๋‹ค์ด์˜ฅ์‹ ๋ฅ˜ ํ™œ์„ฑ (r = 0.53, p <0.01) ๋…ธ๋‹ํŽ˜๋†€ ๋†๋„์™€ ์—์ŠคํŠธ๋กœ๊ฒ๋ฅ˜ ํ™œ์„ฑ (r = 0.31, p <0.01), ์นด๋“œ๋ฎด ๋†๋„์™€ ๋ฐœ๊ด‘๋ฐ•ํ…Œ๋ฆฌ์•„ ๋ฐœ๊ด‘์ €ํ•ด์œจ (r = 0.47, p <0.01)์—์„œ ๊ฐ๊ฐ ์œ ์˜ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค. ์ƒ๋ฌผ ์˜ํ–ฅ ๊ธฐ์—ฌ๋„ ํ‰๊ฐ€ ๊ฒฐ๊ณผ ์„œํ•ด ์—ฐ์•ˆ ํ‡ด์ ๋ฌผ ๋‚ด ๋‹คํ™˜๋ฐฉํ–ฅ์กฑํƒ„ํ™”์ˆ˜์†Œ์™€ ์Šคํ‹ฐ๋ Œ์˜ฌ๋ฆฌ๊ณ ๋จธ์˜ ๋†๋„๊ฐ€ ์ด ๋‹ค์ด์˜ฅ์‹ ๋ฅ˜ ํ™œ์„ฑ์˜ ์•ฝ 0.1%๋ฅผ ์„ค๋ช…ํ•˜์˜€๊ณ , ๋…ธ๋‹ํŽ˜๋†€์˜ ๋†๋„๋Š” ์—์ŠคํŠธ๋กœ๊ฒ๋ฅ˜ ํ™œ์„ฑ์„ ์„ค๋ช…ํ•˜์ง€ ๋ชปํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋ถ„์„ํ•œ ๋‹คํ™˜๋ฐฉํ–ฅ์กฑํƒ„ํ™”์ˆ˜์†Œ, ์Šคํ‹ฐ๋ Œ์˜ฌ๋ฆฌ๊ณ ๋จธ, ๋…ธ๋‹ํŽ˜๋†€ ์ด์™ธ์˜ ๋‹ค๋ฅธ ํ™”ํ•ฉ๋ฌผ๋“ค์ด ์„œํ•ด ์—ฐ์•ˆ ํ‡ด์ ๋ฌผ์— ์กด์žฌํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ž ์žฌ์ ์ธ ์ƒ๋ฌผ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ–ฅํ›„ ์ถ”๊ฐ€์ ์ธ ํ™”ํ•ฉ๋ฌผ์— ๋Œ€ํ•œ ์žฅ๊ธฐ๋ชจ๋‹ˆํ„ฐ๋ง ๋ฐ ์ง€์†์ ์ธ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค.Abstract โ…ฐ Table of Contents iii Abbreviations โ…ด List of Tables โ…ดiii List of Figures ix List of Appendices xi Chapter 1. Introduction 1 Chapter 2. Materials and methods 5 2.1 Sampling and sample preparation 5 2.2 Chemical analyses 10 2.3 Bioassays 12 2.4 Potency balance analyses 16 2.5 Data analyses 17 Chapter 3. Results and discussion 18 3.1 Spatio-temporal distributions of persistent toxic substances 18 3.1.1 Polycyclic aromatic hydrocarbons 18 3.1.2 Styrene oligomers 22 3.1.3 Nonylphenols 26 3.1.4 Heavy metals 30 3.2 Identification of the sources and compositions of PTSs 35 3.3 Long-term changes of potential biological activities 43 3.3.1 Dioxin-like and estrogenic activities 43 3.3.2 Bioluminescent inhibition assay 49 3.4 Relationship between chemical analyses and biological responses in sediments 52 3.5 Temporal trends of sedimentary pollution in the coastal area resulted from environmental regulations 57 Chapter 4. Conclusions 60 References 62 Appendix 71 Abstract in Korean 89Maste

    Development of Microstructural Segmentation and 3D Reconstruction Method Using Serial Section of Tissue: 3D Educational Model of Human Hypothalamus

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ,2019. 8. ํ™ฉ์˜์ผ์ตœํ˜•์ง„.INTRODUCTION: The 3D reconstruction technique of tissue staining images is very valuable in that it visualizes the microstructure information that Magnetic resonance imaging (MRI) and Computed tomography (CT) data cannot provide and is widely used for pathological diagnosis. Organizational 3D reconstruction needs the latest devices and software for each phase. However, in reality, it is not easy to equip all of the most brand-new equipment, and software, so the existing research has been done by only a limited number of people. For this reason, in this study, we tried to develop a 3D reconstruction method of the organization by using available laboratory equipment and highly accessible software. The human hypothalamus is relatively small compared to other brain structures, but it is the backbone of homeostasis regulation and an important structure directly linked to survival. It consists of more than 13 nuclei and microstructures, and many attempts have been made to identify them. However, most reported histology images were based on the 2D map, that cause researchers are experiencing difficulties in understanding spatial structure perception. In addition, most of the currently reported hypothalamic 3D maps are based on MRI data. This DICOM based medical image has the disadvantage that it is difficult to understand the detailed microstructure of the nucleus. In order to overcome these drawbacks, this study aims to develop a detailed 3D model of the human hypothalamus by using easily accessible devices and software. Since the 3D map of the human hypothalamus has not been reported so far, we have developed a method that allows a wider range of researchers to perform the 3D reconstruction of the tissue, which had previously been done by a limited number of people. We also tried to make the model created by the researchers easily accessible to the field. Methods: Nissl staining of human brain hypothalamus obtained by autopsy was converted to a digital image using a tissue slide scanner. The whole slide image was converted by using image processing software to adjust the resolution and extension. After that, segmentation of the hypothalamic microstructure was performed in the Adobe Photoshop software, and the missing slide images were prepared by manual interpolation. All the structure segmented images were transformed into black and white to produce a mask suitable for 3D reconstruction, and they were classified by structure. Then, the whole image was subjected to bit number correction and extension conversion suitable for 3D reconstruction using ImageJ software. Then 3D reconstruction software reconstructs the segmented structures into three dimensions and attempts 3D rendering. After transforming them into STL extensions, we tried to edit them using MeshMixer software. Through this process, 3D map was created with WebGL, and the 3D map education model of the human hypothesis was created. Results: A total of 100 staining images were obtained by Nissl staining using human brain hypothalamus. To make our results more clearly, hypothalamic 2D maps obtained in this study were compared with Allen atlas. A total of 23 segmentations were carried out including hypothalamic surrounding structures and nucleus distribution patterns. A total of 11 excluded slides were supplemented by manual interpolation. The hypothalamus 2D map was used to reconstruct the human hypothalamus as a 3D reconstructed volume model and a 3D reconstructed surface model. The 3D reconstruction surface model was obtained by using MeshMixer to complement the smoothing and the outlier point of each structure. Then, I created a hypothalamus 3D reconstruction education model using WebGL service to make possible for anyone to easily access and learn without the constraint of time and space. Discussion: In this study, I developed a method for producing 2D map and 3D reconstructed images of Nissl stained using hypothalamus tissue. This is the first 3D reconstruction model based on the hypothalamus, which is meant to help other researchers and medical personnel in education and research. Previous studies have shown that the spacing of the slices of the hypothalamus tissue was not constant, but this study succeeded in acquiring the results of the staining of the hypothalamus tissue at 100 ใŽ› intervals as the basic data for 3D reconstruction. Many other types of missing images were found due to the lack of consideration of various variables that occurred during the reconstruction process. The anatomical structure and various parameters were considered and corrected for more satisfactory results. In addition, existing image-based software provides automatic segmentation function considering only the distinctive features of shaded images, so it is very inappropriate to classify subtle clustering patterns such as nucleus and structures in the human hypothalamus. It is significant that the progress process is segmented, and the separate software suitable for each process is applied, and the process of working with them is compatible with each other. Most software is free, low cost and easy to learn and use, so it provides a way to easily create an organization 3D image without expensive software or equipment. The existing hypothalamus training data were mostly 2D illustrations, but the 3D reconstructed images produced in this study are easy to grasp the positional relationship of structures more space. In particular, since the hypothalamus does not contain data showing nuclear reconstruction as a 3D reconstructed image, the educational model of this study will be of great help to many hypothalamus researchers. And the 3D WebGL education model has pedagogical value because it enables free access and access through users personal device, enabling ubiquitous learning that is not restricted by time and space. Conclusions: Through this study, I have established a method for producing 2D map and 3D reconstruction using human hypothalamus. Through the 3D reconstruction image and the education model, the positional relation of the human hypothalamus can be recognized by spatial perception. This result is pedagogically worthy because it can be used as U-learning material to help researchers self-directed learning by opening it to open source WebGL for easy use by anyone.์„œ๋ก : ์กฐ์ง ์—ผ์ƒ‰ ์˜์ƒ์˜ 3์ฐจ์› ์žฌ๊ตฌ์„ฑ ๊ธฐ์ˆ ์€ MRI์™€ CT๊ฐ€ ์ œ๊ณตํ•˜์ง€ ๋ชปํ•˜๋Š” ๋ฏธ์„ธ๊ตฌ์กฐ๋ฅผ ์‹œ๊ฐํ™” ํ•˜์—ฌ 3์ฐจ์› ์กฐ์งํ•™์— ํ™œ์šฉ๋œ๋‹ค๋Š” ์ ์—์„œ ๊ฐ€์น˜๊ฐ€ ์žˆ๋‹ค. ์กฐ์ง 3์ฐจ์› ์žฌ๊ตฌ์„ฑ์—๋Š” ๊ฐ ๋‹จ๊ณ„์— ์ ํ•ฉํ•œ ๊ธฐ๊ธฐ์™€ ์†Œํ”„ํŠธ์›จ์–ด๊ฐ€ ์‚ฌ์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ณ ๊ฐ€์˜ ๊ธฐ๊ธฐ์™€ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ์ „๋ถ€ ๊ฐ–์ถ”๊ธฐ๋ž€ ์‰ฝ์ง€ ์•Š์œผ๋ฏ€๋กœ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋Š” ์ œํ•œ์ ์ธ ์†Œ์ˆ˜์— ์˜ํ•ด ์ด๋ฃจ์–ด์ ธ ์™”๋‹ค. ์‚ฌ๋žŒ ์‹œ์ƒํ•˜๋ถ€๋Š” ๋‹ค๋ฅธ ๋‡Œ ๊ตฌ์กฐ๋ฌผ์— ๋น„ํ•ด ์ƒ๋Œ€์ ์œผ๋กœ ํฌ๊ธฐ๋Š” ์ž‘์ง€๋งŒ ํ•ญ์ƒ์„ฑ ์กฐ์ ˆ์˜ ์ค‘์ถ”์ด๋ฉฐ ์ƒ์กด๊ณผ ์ง๊ฒฐ๋œ ์‹ ์ฒด ํ™œ๋™์„ ์กฐ์ ˆํ•˜๋Š” ์ค‘์š”ํ•œ ๊ธฐ๊ด€์ด๋‹ค. ๊ธฐ์กด ์‹œ์ƒํ•˜๋ถ€์— ๋Œ€ํ•œ ์‹œ๊ฐ์  ์—ฐ๊ตฌ๋Š” ์กฐ์งํ•™ ์˜์ƒ ๊ธฐ๋ฐ˜์˜ 2์ฐจ์› ์ง€๋„ ์ค‘์‹ฌ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ๊ตฌ์กฐ๋ฅผ ๊ณต๊ฐ„์ง€๊ฐ์ ์œผ๋กœ ์ดํ•ดํ•˜๋Š” ๋ฐ ๋งŽ์€ ์–ด๋ ค์›€์ด ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ํ˜„์žฌ ๋ฐœํ‘œ๋œ ๋Œ€๋ถ€๋ถ„์˜ ์‚ฌ๋žŒ ์‹œ์ƒํ•˜๋ถ€ 3์ฐจ์› ์ง€๋„๋Š” MRI ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ž‘์„ฑ๋˜์–ด ์žˆ์–ด ์‹ ๊ฒฝํ•ต ๋‹จ์œ„์˜ ๋ฏธ์„ธ๊ตฌ์กฐ ์ •๋ณด๋ฅผ ์ถฉ๋ถ„ํžˆ ์ „๋‹ฌํ•˜์ง€ ๋ชปํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘๊ฐ€์ง€ ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ฒซ์งธ๋กœ, ์ ‘๊ทผ์„ฑ์ด ์ข‹์€ ์žฅ๋น„์™€ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ณด๋‹ค ๋„“์€ ๋ฒ”์œ„์˜ ์—ฐ๊ตฌ์ž๋“ค์ด ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ์กฐ์ง์˜ 3์ฐจ์› ์žฌ๊ตฌ์„ฑ ๋ฐฉ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋‘˜์งธ๋กœ, ์œ„์˜ ๊ณผ์ •์„ ํ†ตํ•ด ํ™•๋ฆฝํ•œ ๋ฐฉ๋ฒ•์„ ์‹œ์ƒํ•˜๋ถ€์— ์ ์šฉํ•˜์—ฌ ํ•ด๋‹น ๋ถ„์•ผ ์—ฐ๊ตฌ์ž๋“ค์ด ํ•™์Šต ๋ฐ ๊ต์œก์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” 3์ฐจ์› ๋ชจ๋ธ์„ ์ œ์ž‘ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ•: ์‚ฌ๋žŒ ์‹œ์ƒํ•˜๋ถ€ ์ „์ฒด์™€ ์‹œ๊ฐ๋กœ๊ฐ€ ํฌํ•จ๋œ ์กฐ์ง์„ ๋Œ€์ƒ์œผ๋กœ ์กฐ์ง 3์ฐจ์› ์žฌ๊ตฌ์„ฑ์„ ์‹œ๋„ํ•˜์˜€๋‹ค. ์—ผ์ƒ‰๋œ ๊ฐ ์กฐ์ง์ ˆํŽธ์„ ์Šฌ๋ผ์ด๋“œ ์Šค์บ๋„ˆ๋ฅผ ์ด์šฉํ•ด ๋””์ง€ํ„ธ ์˜์ƒ์œผ๋กœ ๋ณ€ํ™˜ํ•˜์˜€๊ณ  ZEN์„ ์‚ฌ์šฉํ•˜์—ฌ ํ•ด์ƒ๋„ ์กฐ์ ˆ๊ณผ ํ™•์žฅ์ž ๋ณ€ํ™˜์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์ „์ฒด ์˜์ƒ์„ Adobe Photoshop์„ ์ด์šฉํ•˜์—ฌ ์‹œ๊ฐ๋กœ์™€ ๋ฏธ์„ธํ˜ˆ๊ด€, ์•ˆ์ชฝํ›„๊ฐ๊ฒ‰์งˆ์˜ ์™ธ๊ณฝ์„ ์„ ๊ธฐ์ค€์œผ๋กœ ์ •ํ•ฉ ํ•˜์˜€๋‹ค. ์ด ํ›„ ๋ฏธ์„ธ์กฐ์ง ๊ตฌ์—ญํ™”, ์†Œ์‹ค๋œ ์Šฌ๋ผ์ด๋“œ ์˜์ƒ์˜ ์ˆ˜๋™ ๋ณด๊ฐ„๋ฒ• ์ ์šฉ, ์ „์ฒด ๊ตฌ์—ญํ™” ์˜์ƒ์˜ ํ‘๋ฐฑ ๋ณ€ํ™˜, ๋งˆ์Šคํฌ ์ œ์ž‘, ๊ตฌ์กฐ๋ฌผ ๋ณ„ ๋ถ„๋ฅ˜๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ „์ฒด ์˜์ƒ์„ ImageJ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ bit์ˆ˜ ๊ต์ • ๋ฐ ํ™•์žฅ์ž ๋ณ€ํ™˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ด ํ›„ MEDIP์—์„œ ๊ตฌ์—ญํ™” ํ•œ ๊ตฌ์กฐ๋ฌผ ์˜์ƒ์˜ 3์ฐจ์› ์žฌ๊ตฌ์„ฑ, STL ํ™•์žฅ์ž ๋ณ€ํ™˜ ๋ฐ ๋‚ด๋ณด๋‚ด๊ธฐ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์ด ํ›„ MeshMixer๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ‘œ๋ฉด์˜ ์š”์ฒ ๊ณผ ์ด์ƒ์ ์„ ๊ต์ •ํ•œ ๋’ค webGL ๊ต์œก๋ชจ๋ธ๋กœ ์ œ์ž‘ํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ์ˆ˜๋ฆฝ๋œ protocol์„ ์‚ฌ๋žŒ ์‹œ์ƒํ•˜๋ถ€์— ์ ์šฉํ•˜์—ฌ ๋‚ด๋ถ€ ์‹ ๊ฒฝํ•ต๊ณผ ๋ฏธ์„ธ๊ตฌ์กฐ๋ฅผ 3์ฐจ์›์œผ๋กœ ์žฌ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: Zen, Adobe Photoshop, ImageJ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์กฐ์ง ์—ผ์ƒ‰ ์˜์ƒ, ์‹œ๊ฐ๋กœ 2์ฐจ์› ์ง€๋„, ์ƒ‰๋ฉด ๋ ˆ์ด์–ด, ํŒจ์Šค์˜์—ญ ๋ ˆ์ด์–ด, ํ‘๋ฐฑ๋ณ€ํ™˜ ์˜์ƒ, ํ‘๋ฐฑ ๋ฐ˜์ „ ์˜์ƒ, Raw data mask๋ฅผ ์ƒ์„ฑํ•˜์˜€๋‹ค. ์ด ํ›„ MEDIP๊ณผ Meshmixer ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ „์ฒด์กฐ์ง๊ณผ ์‹œ๊ฐ๋กœ ๋ถ€ํ”ผ๋ชจ๋ธ, ํ‘œ๋ฉด๋ชจ๋ธ, ๊ต์œก๋ชจ๋ธ์„ ์ƒ์„ฑํ•˜์˜€๋‹ค. ์ด๋ฅผ ์‚ฌ๋žŒ ์‹œ์ƒํ•˜๋ถ€ ๋ฏธ์„ธ๊ตฌ์กฐ์˜ ์‹œ๊ฐํ™”์— ์ ์šฉํ•˜์—ฌ ์กฐ์ง ์—ผ์ƒ‰ ์˜์ƒ, 2์ฐจ์› ์ง€๋„, ๋ฏธ์„ธ๊ตฌ์กฐ์™€ ์‹ ๊ฒฝํ•ต ๊ตฌ์—ญํ™” ์ƒ‰๋ฉด ๋ ˆ์ด์–ด, ํŒจ์Šค์˜์—ญ ๋ ˆ์ด์–ด, ํ‘๋ฐฑ ๋ณ€ํ™˜ ์˜์ƒ, ํ‘๋ฐฑ ๋ฐ˜์ „ ์˜์ƒ, 3์ฐจ์› Raw data mask, 3์ฐจ์› ์žฌ๊ตฌ์„ฑ ๋ถ€ํ”ผ ๋ชจ๋ธ, ํ‘œ๋ฉด ๋ชจ๋ธ, 3์ฐจ์› ๊ต์œก๋ชจ๋ธ์„ ์ƒ์„ฑํ•˜์˜€๋‹ค. ๊ณ ์ฐฐ: ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” Zen, Adobe Photoshop, ImageJ, MEDIP, Meshmixer๋กœ ์ด์–ด์ง€๋Š” ์กฐ์ง์˜ 3์ฐจ์› ์žฌ๊ตฌ์„ฑ ์ œ์ž‘ protocol์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์˜ 3์ฐจ์› ์žฌ๊ตฌ์„ฑ ๋ฐฉ๋ฒ•๋ก ๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ์™ธ๊ณฝ์„ ์ด ๋šœ๋ ทํ•˜์ง€ ์•Š์€ ๊ตฌ์กฐ๋ฌผ์˜ ์˜์ƒ ์œ„์— ์ˆ˜๋™์œผ๋กœ ๊ตฌ์—ญํ™”๋ฅผ ์ˆ˜ํ–‰ํ–ˆ๋‹ค๋Š” ์ ์—์„œ ์ฐจ๋ณ„์„ฑ์ด ์žˆ๋‹ค. ๋˜ํ•œ ๊ธฐ์กด์˜ ์กฐ์ง 3์ฐจ์› ์žฌ๊ตฌ์„ฑ ๋ฐฉ๋ฒ•๋ก ๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ๊ฐ ๋‹จ๊ณ„์— ์‚ฌ์šฉ๋˜๋Š” ๊ณ ๊ฐ€์˜ ์†Œํ”„ํŠธ์›จ์–ด์™€ ๊ธฐ๊ธฐ๋ฅผ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ์ ‘๊ทผ์„ฑ์ด ๋†’์€ ์†Œํ”„ํŠธ์›จ์–ด๋กœ ๋ถ„ํ•  ๋ฐ ์ ์šฉํ–ˆ๋‹ค๋Š” ์ ์—์„œ ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ๋‹ค๋ฅด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ 2์ฐจ์› ์ง€๋„๋Š” Allen atlas๊ฐ€ ์ œ๊ณตํ•˜๋Š” ์‚ฌ๋žŒ ๋‡Œ 2์ฐจ์› ์ง€๋„์™€ ๋น„๊ตํ–ˆ์„ ๋•Œ ๋ณด๋‹ค ์ด˜์ด˜ํ•œ 100 ใŽ›์˜ ์˜์ƒ์„ ์ผ์ •ํ•œ ๊ฐ„๊ฒฉ์œผ๋กœ ํš๋“ํ–ˆ๋‹ค๋Š” ์ ์—์„œ ์ฐจ๋ณ„์„ฑ์ด ์žˆ๋‹ค. ๋˜ํ•œ MRI ๊ธฐ๋ฐ˜ 3์ฐจ์› ์žฌ๊ตฌ์„ฑ ๋ชจ๋ธ์ด ์ œ๊ณตํ•˜์ง€ ๋ชปํ•˜๋Š” ์—ฌ๋Ÿฌ ๋ฏธ์„ธ๊ตฌ์กฐ์™€ ์‹ ๊ฒฝํ•ต์„ ์‹œ๊ฐํ™” ํ–ˆ๋‹ค๋Š” ์ ์—์„œ ๊ฐ€์น˜๊ฐ€ ์žˆ๋‹ค. ๊ธฐ์กด์˜ 2์ฐจ์› ์‹ ๊ฒฝํ•ด๋ถ€ํ•™ ๊ต์œก์ž๋ฃŒ์™€ ๋น„๊ตํ–ˆ์„ ๋•Œ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์ž‘ํ•œ 3์ฐจ์› ๊ต์œก๋ชจ๋ธ์€ ๊ตฌ์กฐ๋ฌผ๋“ค์˜ ์œ„์น˜๊ด€๊ณ„๋ฅผ ๊ณต๊ฐ„์ง€๊ฐ์ ์œผ๋กœ ๋ณด๋‹ค ์‰ฝ๊ฒŒ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฌผ์€ ๊ธฐ์กด์˜ 3์ฐจ์› ์‹ ๊ฒฝํ•ด๋ถ€ํ•™ ๊ต์œก์ž๋ฃŒ์™€ ๋‹ฌ๋ฆฌ ์‹คํ—˜์„ ํ†ตํ•ด ์–ป์€ ์‹ค๋ฌผ ์ž๋ฃŒ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ œ์ž‘๋˜์—ˆ๋‹ค๋Š” ์ ์—์„œ ๊ฐ€์น˜๊ฐ€ ์žˆ๋‹ค. ๊ฒฐ๋ก : ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‚ฌ๋žŒ ์‹œ์ƒํ•˜๋ถ€ ์กฐ์ง์„ ๋งค๊ฐœ๋กœ ์กฐ์ง์˜ 3์ฐจ์› ์žฌ๊ตฌ์„ฑ protocol์„ ํ™•๋ฆฝํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์‚ฌ๋žŒ ์‹œ์ƒํ•˜๋ถ€ ๋‚ด๋ถ€์˜ ๋ฏธ์„ธ๊ตฌ์กฐ์™€ ์‹ ๊ฒฝํ•ต์˜ ์œ„์น˜๊ด€๊ณ„๋ฅผ ๊ณต๊ฐ„์ง€๊ฐ์ ์œผ๋กœ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๋Š” ๋ถ€ํ”ผ๋ชจ๋ธ๊ณผ ํ‘œ๋ฉด๋ชจ๋ธ์„ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด ๊ฒฐ๊ณผ๋ฌผ๋“ค์€ ์˜๋ฃŒ์ธ ๊ต์œก์— ์ ํ•ฉํ•œ ๊ต์œก๋ชจ๋ธ๋กœ ๋ณ€ํ™˜ํ•˜์—ฌ ๊ณต๊ฐœ ์ž๋ฃŒ๋กœ ์ œ๊ณต๋˜์—ˆ๋‹ค.์ดˆ ๋ก โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ…ฐ ๋ชฉ ์ฐจ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ โ…ณ ํ‘œ ๋ชฉ๋ก โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ v ๊ทธ๋ฆผ ๋ชฉ๋กโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ vi ์„œ ๋ก โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 9 ๋ณธ ๋ก  โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 15 Chapter 1. ์กฐ์ง์˜ 3์ฐจ์› ์žฌ๊ตฌ์„ฑ ์ œ์ž‘ protocol ๊ฐœ๋ฐœ...... 15 Chapter 2. ์‚ฌ๋žŒ ์‹œ์ƒํ•˜๋ถ€ ์กฐ์ง์˜ 3์ฐจ์› ์žฌ๊ตฌ์„ฑ.............. 52 ๊ฒฐ ๋ก โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 95 ์ฐธ๊ณ ๋ฌธํ—Œโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ96 Abstractโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ101Maste

    M-Protein genotyping and pulsed-field gel electrophoresis analysis of group a streptococci isolated from blood

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ์ž„์ƒ๋ณ‘๋ฆฌํ•™์ „๊ณต,1999.Docto

    A study on the ontology of image in Henri Bergson`s Matter and Memory

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ฏธํ•™๊ณผ,2009.2.Maste

    <๋ณต์„ ํ™”์Œ๊ฐ€>์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ ์ดํ•ด ๊ต์œก ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ตญ์–ด๊ต์œก๊ณผ, 2014. 8. ๊ณ ์ •ํฌ.์ด ์—ฐ๊ตฌ๋Š” ์— ๋‚˜ํƒ€๋‚œ ๋„๋•์  ์‚ถ์„ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์˜ ๊ฐœ๋…์„ ํ†ตํ•ด ์‚ดํ”ผ๊ณ , ์ด๋ฅผ ํ† ๋Œ€๋กœ ํ•™์Šต์ž๋“ค์ด ํ…์ŠคํŠธ๋ฅผ ์ฃผ์ฒด์ ์ธ ๊ด€์ ์—์„œ ์ดํ•ดํ•˜๋ฉฐ, ๊ฐ€์‚ฌ ๊ฐ์ƒ ๋Šฅ๋ ฅ์„ ๊ธฐ๋ฅผ ์ˆ˜ ์žˆ๋Š” ํ•™์Šต ๋ฐฉ์•ˆ์„ ๋ชจ์ƒ‰ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ๋„๋•์  ๋ฏผ๊ฐ์„ฑ(moral sensitivity)์€ ๋ ˆ์ŠคํŠธ(J.R.Rest)๊ฐ€ ์ œ์•ˆํ•œ ๊ฐœ๋…์ด๋‹ค. ์ด๊ฒƒ์€ ์ƒํ™ฉ์„ ๋„๋•์ ์œผ๋กœ ํ•ด์„ํ•˜๊ณ  ์ƒํ™ฉ๊ณผ ๊ด€๋ จ๋œ ํƒ€์ž์˜ ๊ฐ์ •, ์š”๊ตฌ๋ฅผ ๊ณ ๋ คํ•˜๋ฉฐ ์ž์‹ ์ด ํ•  ์ˆ˜ ์žˆ๋Š” ํ–‰๋™์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๊ฐ€๋Š ํ•˜๋Š” ๊ฒƒ์„ ๋œปํ•œ๋‹ค. ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์˜ ์‹ค์ฒœ์—๋Š” ๋„๋• ๊ทœ๋ฒ”์— ๋Œ€ํ•œ ์ง€์‹, ๊ฐ€์น˜ ์ง€ํ–ฅ์ ์ธ ํƒœ๋„, ๊ด€๊ณ„๋œ ํƒ€์ž๋ฅผ ์ˆ˜์šฉํ•˜๋Š” ํƒœ๋„ ์ด๋ ‡๊ฒŒ ์„ธ ๊ฐ€์ง€ ๊ตฌ์„ฑ ์š”์†Œ๊ฐ€ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ด€์—ฌํ•œ๋‹ค. ๋Š” ์กฐ์„  ํ›„๊ธฐ ํ–ฅ์ดŒ ์‚ฌ์กฑ์ธต ์—ฌ์„ฑ๋“ค ์‚ฌ์ด์—์„œ ํ–ฅ์œ  ๋˜์—ˆ๋˜ ๊ฐ€์‚ฌ์ด๋‹ค. ์—๋Š” ๋‘ ์ธ๋ฌผ์ด ๋“ฑ์žฅํ•œ๋‹ค. ํ™”์ž์˜ ์‚ถ์€ ๋„๋•์ ์ธ ๋ชจ๋ฒ” ์‚ฌ๋ก€์ธ๋ฐ ๋ฐ˜ํ•ด ๊ดด๋˜ฅ์–ด๋ฏธ๋Š” ๊ทœ๋ฒ”๊ณผ ๊ฐ€์น˜, ํƒ€์ž๋ฅผ ์ „ํ˜€ ์‹ ๊ฒฝ ์“ฐ์ง€ ์•Š๋Š”๋‹ค. ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์˜ ๊ฐœ๋…์„ ๋นŒ๋ฆฌ๋ฉด, ํ™”์ž์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์€ ๋ฐ”๋žŒ์งํ•˜์ง€๋งŒ, ๊ดด๋˜ฅ์–ด๋ฏธ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์€ ๋ฐ›์•„๋“ค์ด๊ธฐ ํž˜๋“ค๊ณ , ๋ถ€์ ์ ˆํ•˜๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋Š” ํ™”์ž์™€ ๊ดด๋˜ฅ์–ด๋ฏธ์˜ ์‚ถ์„ ๋Œ€์กฐํ•˜๋ฉด์„œ ์œ ๊ต์  ๊ฐ€์น˜๊ด€์— ๋”ฐ๋ฅด๋Š” ์‚ถ์„ ์‚ด์•„๊ฐ€๋ผ๊ณ  ๊ถŒ๊ณ ํ•œ๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ๊ทธ ์ด๋ฉด์—๋Š” ๋„๋•์œผ๋กœ ํฌ์„ญ๋˜์ง€ ์•Š๋Š” ์ธ๊ฐ„์˜ ๊ณ ๋‡Œ์™€ ์š•๋ง์ด ๊ทธ๋Œ€๋กœ ๋…ธ์ถœ๋œ๋‹ค. ํ™”์ž๋Š” ๋„๋•์  ์‹ค์ฒœ ๊ฐ€์šด๋ฐ ์ƒ๊ธฐ๋Š” ๋‚ด์  ๊ฐˆ๋“ฑ๊ณผ ๊ณ ๋œ ์ •์„œ๋ฅผ ์ˆจ๊ธฐ์ง€ ์•Š๊ณ , ๊ดด๋˜ฅ์–ด๋ฏธ๋Š” ๊ฐœ์ธ์˜ ์š•๋ง์„ ์ถ”๊ตฌํ•˜๋ฉด์„œ๋„ ๋Š์ž„์—†์ด ํƒ€์ž๋กœ๋ถ€ํ„ฐ ์ธ์ •์„ ๊ฐˆ๊ตฌํ•œ๋‹ค. ํ…์ŠคํŠธ๊ฐ€ ์ „๋ฉด์— ๋‚ด์„ธ์šฐ๋Š” ๋„๋•์  ์ฃผ์ œ์™€ ์ด๋ฉด์— ํ˜•์ƒํ™” ๋œ ์‚ถ์˜ ์–‘์ƒ์€ ์— ๋„๋•์  ๊ธด์žฅ์„ ํ˜•์„ฑํ•˜๋ฉด์„œ ์ฒญ์ž๋กœ ํ•˜์—ฌ๊ธˆ ๋„๋•์  ์‚ถ์ด๋ž€ ๋ฌด์—‡์ธ์ง€ ์ˆ™๊ณ ํ•˜๊ฒŒ ํ•œ๋‹ค. ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์„ ์ดํ•ดํ•œ๋‹ค๋Š” ๊ฒƒ์€ ํ…์ŠคํŠธ๊ฐ€ ์ œ์‹œํ•˜๋Š” ๋„๋•์  ๊ณผ์ œ๊ฐ€ ๊ธด์žฅ์„ ๋‚ดํฌํ•˜๊ณ  ์žˆ์Œ์„ ์ดํ•ดํ•˜๊ณ  ์ž์‹ ์˜ ๋ฌธ์ œ๋กœ์„œ ๋ฐ›์•„๋“ค์ด๋Š” ์ผ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ํ•™์Šต์ž๊ฐ€ ์˜ ์ฒญ์ž๋กœ์„œ ํ…์ŠคํŠธ๊ฐ€ ๊ฑด๋„ค๋Š” ๋„๋•์  ์‚ฌ์œ ์— ์ ๊ทน์ ์œผ๋กœ ์ฐธ์—ฌํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ์ดํ•ด๋Š” ํ…์ŠคํŠธ์™€ ๋…์ž ์‚ฌ์ด์— ์ผ์–ด๋‚˜๋Š” ์ƒํ˜ธ ๋ณ€ํ™”์˜ ๊ณผ์ •์ด๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ํ•™์Šต์ž์˜ ์ ๊ทน์  ์ฐธ์—ฌ๋ฅผ ๋„๋ชจํ•˜๋Š” ๋ฐฉ์•ˆ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํƒ๊ตฌ์˜ ๊ฐœ๋…์— ์ฃผ๋ชฉํ•˜์˜€๋‹ค. ํƒ๊ตฌ๋Š” ํ•™์Šต์ž๊ฐ€ ์ž์‹ ์˜ ์‚ถ์œผ๋กœ๋ถ€ํ„ฐ ๋ถˆ๊ท ํ˜•์„ ์ธ์ง€ํ•˜์—ฌ ๋ฌธ์ œ๋ฅผ ๋ฐœ๊ฒฌํ•˜๊ณ , ์Šค์Šค๋กœ ์ง€์‹์„ ๊ตฌ์„ฑํ•ด ๋‚˜๊ฐ€๋Š” ๊ณผ์ •์ด๋‹ค. ๋Š” ํ…์ŠคํŠธ ๋‚ด์ ์œผ๋กœ ๋„๋•์  ๊ธด์žฅ์„ ๋‚ดํฌํ•˜๊ณ  ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์™€ ํ•™์Šต์ž ์‚ฌ์ด์—์„œ๋„ ์‹œ๊ณต๊ฐ„์  ๊ฑฐ๋ฆฌ๋กœ ์ธํ•œ ๋ถˆ๊ท ํ˜•์ด ๊ฐ์ง€๋  ์ˆ˜ ์žˆ์–ด ํ•™์Šต์ž์˜ ํƒ๊ตฌ๋ฅผ ์ด‰๋ฐœํ•˜๊ธฐ์— ์ ํ•ฉํ•˜๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์€ ์ฒ ํ•™์  ์‚ฌ์œ ๋ฅผ ํ•„์š”๋กœ ํ•˜๊ณ , ์–ธ์ œ๋‚˜ ๊ณต๋™์ฒด๋ฅผ ์ „์ œํ•˜๋Š” ๊ฒƒ์ด๋ฏ€๋กœ, ํƒ๊ตฌ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ํ•™์Šต ๋ชจํ˜• ๊ฐ€์šด๋ฐ ํ•™์Šต์ž ์ƒํ˜ธ๊ฐ„์˜ ๋Œ€ํ™”๋ฅผ ์ค‘์‹œํ•œ ๋ฆฝ๋งจ(M.Lipman)์˜ ํƒ๊ตฌ ๊ณต๋™์ฒด ํ•™์Šต ๋ชจํ˜•์„ ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ด๋ฅผ ๊ฐ€์‚ฌ ๊ต์œก์— ํ™œ์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์„ธ ๋ฒˆ์˜ ๋ชจ์˜ ์ˆ˜์—…์„ ์‹ค์‹œํ•˜๊ณ , ํ•™์Šต์ž๊ฐ€ ๋„๋•์  ๊ธด์žฅ์„ ํฌ์ฐฉํ•˜๋Š” ๋ถ€๋ถ„์€ ์–ด๋””์ด๋ฉฐ ๊ทธ์— ๋Œ€ํ•œ ํƒ๊ตฌ๊ฐ€ ์–ด๋–ป๊ฒŒ ์ด๋ฃจ์–ด์ง€๋Š” ์ง€ ์‚ดํŽด๋ณด์•˜๋‹ค. ์˜ ๋„๋•์  ๊ธด์žฅ์€ ํ™”์ž์˜ ์‚ถ, ๊ดด๋˜ฅ์–ด๋ฏธ์˜ ์‚ถ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํ…์ŠคํŠธ ์ „์ฒด๋ฅผ ์กฐ์งํ•˜๊ณ  ๊ตฌ์„ฑํ•˜๋Š” ์„œ์ˆ ์ž์— ์˜ํ•ด์„œ๋„ ํ˜•์„ฑ๋˜๊ธฐ ๋•Œ๋ฌธ์— ๊ฐ๊ฐ์˜ ์ฃผ์ฒด๊ฐ€ ๋ณด์—ฌ์ฃผ๋Š” ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์— ๋Œ€ํ•ด ํƒ๊ตฌํ•˜๋Š” ๊ฒƒ์ด ๊ณผ์ œ์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ํ•™์Šต์ž๋“ค์ด ์—์„œ ๋„๋•์  ๊ธด์žฅ์˜ ์š”์†Œ๋ฅผ ๋ฐœ๊ฒฌํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ํ™”์ž์™€ ๊ดด๋˜ฅ์–ด๋ฏธ์˜ ์‚ถ์„ ๋„๋•์  ํŒ๋‹จ์˜ ๋Œ€์ƒ์ด ์•„๋‹ˆ๋ผ ํ•œ ๊ฐœ์ธ์˜ ์‚ถ์œผ๋กœ์„œ ๋ฐ›์•„๋“ค์ด๋ฉด์„œ ๋„๋•์  ์‚ฌ์œ ์— ์ฐธ์—ฌํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋ชจ์˜ ์ˆ˜์—…์€ ํ•™์Šต์ž๋“ค์ด ์ฃผ์ฒด์ ์œผ๋กœ ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์„ ์ดํ•ดํ•˜๋Š” ๊ณผ์ •๊ณผ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค๋Š” ์ ์—์„œ ์œ ์˜๋ฏธํ–ˆ๋‹ค. ํŠนํžˆ ๊ฐœ์ธ์  ํƒ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ† ์˜๋ฅผ ์ง„ํ–‰ ํ–ˆ์„ ๋•Œ, ํ•™์Šต์ž๋“ค์ด ์ž์‹ ์˜ ์ผ์ƒ์  ๊ฒฝํ—˜์„ ๊ณต์œ ํ•˜๋ฉฐ ์˜ ์˜๋ฏธ๋ฅผ ์ƒˆ๋กญ๊ฒŒ ๊ตฌ์„ฑํ•˜๋ ค๋Š” ๋ชจ์Šต์ด ์ž˜ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹ค๋งŒ, ํ•™์Šต์ž์˜ ์ดํ•ด๊ฐ€ ๋‹จํŽธ์  ๊ฐ์ƒ์— ๊ทธ์น˜๊ฑฐ๋‚˜, ๋ฐฐ๊ฒฝ์ง€์‹์˜ ๋ถ€์กฑ ๋“ฑ์œผ๋กœ ๋„๋•์  ๊ธด์žฅ์„ ๋ฐœ๊ฒฌํ•˜๊ณ ๋„ ์ถฉ๋ถ„ํžˆ ์˜๋ฏธํ™”ํ•˜์ง€ ๋ชปํ•œ ๋ถ€๋ถ„๋“ค์ด ์žˆ์–ด ์ด๋ฅผ ๋ณด์™„ํ•  ํ•„์š”๊ฐ€ ์žˆ์—ˆ๋‹ค. ๊ทธ๋ž˜์„œ ๊ธฐ๋ณธ์ ์œผ๋กœ๋Š” ํƒ๊ตฌ ๊ณต๋™์ฒด์˜ ํ•™์Šต ์ ˆ์ฐจ๋ฅผ ๋”ฐ๋ฅด๋˜, ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์„ ๊ทธ๋ ค๋‚ด๋Š” ์–ธ์–ด์ ยท์‹ฌ๋ฏธ์  ์ž์งˆ์— ๋Œ€ํ•ด์„œ๋„ ๊ณ ๋ คํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ต์œก ๋‚ด์šฉ์„ ๋งˆ๋ จํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋งŽ์€ ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์ง€๋งŒ ์™€ ๊ฐ™์ด ๊ณผ๊ฑฐ์˜ ๋„๋•์— ๋Œ€ํ•ด ๋‹ค๋ฃจ๋Š” ๊ฐ€์‚ฌ๋ฅผ ํ˜„๋Œ€์  ์†Œํ†ต ๋งฅ๋ฝ์—์„œ ์žฌํ˜„ํ•˜๊ณ , ํ•™์Šต์ž๊ฐ€ ์ฃผ์ฒด์  ์ฒญ์ž๋กœ์„œ ์ด๋ฅผ ์ดํ•ดํ•˜๋Š” ๋ฐฉ์•ˆ์„ ๋ชจ์ƒ‰ํ•˜๊ณ ์ž ํ–ˆ๋‹ค๋Š” ์ ์—์„œ ๋ฏธ์•ฝํ•˜๋‚˜๋งˆ ๋‹ค์†Œ์˜ ์˜์˜๊ฐ€ ์žˆ๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•œ๋‹ค.๊ตญ๋ฌธ ์ดˆ๋ก i I. ์„œ ๋ก  1 1. ๋ฌธ์ œ์ œ๊ธฐ 1 2. ์—ฐ๊ตฌ์‚ฌ 3 3. ์—ฐ๊ตฌ ๋Œ€์ƒ 8 4. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 10 II. ์— ๋‚˜ํƒ€๋‚œ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ ์ดํ•ด์˜ ๊ต์œก์  ์˜์˜ 13 1. ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์˜ ๊ฐœ๋…๊ณผ ๊ตฌ์„ฑ์š”์†Œ 13 1.1. ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์˜ ๊ฐœ๋… 13 1.2. ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์˜ ๊ตฌ์„ฑ ์š”์†Œ 16 (1) ๋„๋• ๊ทœ๋ฒ”์— ๋Œ€ํ•œ ์ง€์‹ 16 (2) ๊ฐ€์น˜ ์ง€ํ–ฅ์  ํƒœ๋„ 18 (3) ๊ด€๊ณ„๋œ ํƒ€์ž์˜ ์ˆ˜์šฉ 19 2. ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ ์ดํ•ด์˜ ์˜๋ฏธ์™€ ๊ต์œก ๋ฐฉ์•ˆ 21 2.1. ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ ์ดํ•ด์˜ ์˜๋ฏธ 21 2.2. ๋„๋•์  ๋ฏผ๊ฐ์„ฑ ์ดํ•ด๋ฅผ ์œ„ํ•œ ํƒ๊ตฌ ๊ณต๋™์ฒด ๋ชจํ˜•์˜ ๊ฐœ๋…๊ณผ ์˜์˜ 25 3. ์— ๋‚˜ํƒ€๋‚œ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ ์ดํ•ด์˜ ๊ต์œก์  ์˜์˜ 31 3.1. ๋„๋•์  ์„ฑ์žฅ์˜ ๋น„๊ณ„ 31 3.2. ๊ฐ€์‚ฌ์˜ ๋Œ€ํ™”์„ฑ ๊ฒฝํ—˜ 32 3.3. ๊ฐ€์‚ฌ ์ „ํ†ต์—์˜ ์ฐธ์—ฌ 35 III. ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์— ๋Œ€ํ•œ ํ•™์Šต์ž์˜ ํƒ๊ตฌ ์–‘์ƒ 38 1. ํ™”์ž์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์— ๋Œ€ํ•œ ๋น„ํŒ์  ์„ฑ์ฐฐ 41 1.1. ๊ทœ๋ฒ” ์‹ค์ฒœ ๋งฅ๋ฝ์—์„œ ๊ทœ๋ฒ” ์ง€์‹์˜ ํ˜„์‹ค์„ฑ ๊ฒ€ํ†  42 1.2. ๊ณต๋™์ฒด์  ๊ฐ€์น˜ ์ถ”๊ตฌ์— ๋Œ€ํ•œ ๊ฐœ์ธ์„ ์˜ ์ค‘์š”์„ฑ ๊ฐ•์กฐ 48 1.3. ๋ชจ์ˆœ์  ํƒ€์ž ์ˆ˜์šฉ ํƒœ๋„์— ๋Œ€ํ•œ ์ฃผ์ฒด ์ข…์†์˜ ์šฐ๋ ค 55 2. ๋“ฑ์žฅ์ธ๋ฌผ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์— ๋Œ€ํ•œ ๊ณต๊ฐ์  ๊ฐ์ƒ 60 2.1. ์ผํƒˆ์  ํ–‰์‹ค์— ๋Œ€ํ•œ ์ด์œ ์˜ ์ถ”์ธก 61 2.2. ๊ฐœ์ธ์  ๊ฐ€์น˜ ์ถ”๊ตฌ์— ๋Œ€ํ•œ ๊ธ์ •์  ํ‰๊ฐ€ 66 2.3. ๋™์งˆ์  ํƒ€์ž ์ˆ˜์šฉ ํƒœ๋„์— ๋Œ€ํ•œ ๊ฐ์ •์ด์ž…์  ์ ‘๊ทผ 72 3. ์„œ์ˆ ์ž์˜ ๋„๋•์  ํƒœ๋„์— ๋Œ€ํ•œ ๋Œ€ํ™”์  ํƒ๊ตฌ 76 3.1. ๊ตฌ์ฒด์  ๊ทœ๋ฒ” ์„œ์ˆ ์— ๋Œ€ํ•œ ํšจ์šฉ์  ๊ฐ€์น˜ ๋ถ€์—ฌ 77 3.2. ํ‘œ๋ช…๋œ ์ธ๊ณผ์  ๊ฐ€์น˜์— ๋Œ€ํ•œ ์˜๋ฌธ ์ œ๊ธฐ 84 3.3. ํ˜ธ๋ช…๋œ ์ฒญ์ž๋กœ์„œ์˜ ์ ๊ทน์  ์‘๋‹ต ํ•„์š” 89 IV. ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ ์ดํ•ด ๊ต์œก์˜ ์‹ค์ œ 95 1. ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ ์ดํ•ด ๊ต์œก์˜ ๋ชฉํ‘œ 95 1.1. ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์— ๋Œ€ํ•œ ์ฃผ์ฒด์  ๊ด€์  ์ •๋ฆฝ 95 1.2. ์˜ ๋Œ€ํ™”์  ์†Œํ†ต ๋ฐฉ์‹ ์ดํ•ด 97 1.3. ์˜ ์—ญํ• ๊ณผ ํ˜„๋Œ€์  ํšจ์šฉ ๊ฒ€ํ†  99 2. ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ ์ดํ•ด ๊ต์œก์˜ ๋‚ด์šฉ 100 2.1. ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ์— ๋Œ€ํ•œ ์ฃผ์ฒด์  ๊ด€์  ํ˜•์„ฑ 102 (1) ๊ทœ๋ฒ” ์ง€์‹์˜ ํ˜„์‹ค ์ ํ•ฉ์„ฑ ํŒ๋‹จ 102 (2) ๊ณต๋™์ฒด์  ๊ฐ€์น˜์™€ ๊ฐœ์ธ์  ๊ฐ€์น˜์˜ ์กฐ์œจ 105 (3) ์ฃผ์ฒด์™€ ํƒ€์ž์˜ ๋„๋•์  ๊ด€๊ณ„ ๋ชจ์ƒ‰ 107 2.2. ์— ๋‚˜ํƒ€๋‚œ ์†Œํ†ต ๋ฐฉ์‹ ํŒŒ์•… 110 (1) ์šด์œจ์„ ํ†ตํ•œ ์†Œํ†ต ๋ฐฉ์‹ ํŒŒ์•… 111 (2) ์ˆ˜์‚ฌ์  ํ‘œํ˜„์„ ํ†ตํ•œ ์†Œํ†ต ๋ฐฉ์‹ ํŒŒ์•… 113 2.3. ์˜ ํ˜„๋Œ€์  ์˜๋ฏธ ์„ฑ์ฐฐ 115 (1) ๊ฐ€์‚ฌ ํ–ฅ์œ ์ž๋กœ์„œ์˜ ์—ญํ•  ์ˆ˜ํ–‰ 116 (2) ์˜ ํ˜„๋Œ€์  ์˜๋ฏธ์— ๋Œ€ํ•œ ํ† ์˜ 118 3. ์˜ ๋„๋•์  ๋ฏผ๊ฐ์„ฑ ์ดํ•ด ๊ต์œก ๋ฐฉ๋ฒ• 122 3.1. ํƒ๊ตฌ ๊ณต๋™์ฒด ๋ชจํ˜•์„ ํ™œ์šฉํ•œ ํ•™์Šต ์ ˆ์ฐจ 123 3.2. ํƒ๊ตฌ ๊ณต๋™์ฒด ๋ชจํ˜•์„ ํ™œ์šฉํ•œ ํ•™์Šต์˜ ํšจ๊ณผ 131 V. ๊ฒฐ ๋ก  138 ์ฐธ๊ณ ๋ฌธํ—Œ 143 Abstract 157Maste

    ๊ธ‰์„ฑ ์ผ์‚ฐํ™”ํƒ„์†Œ ์ค‘๋… ํ™˜์ž๊ตฐ์—์„œ ๋ฐœ์ƒํ•œ ๊ฐ„ ์†์ƒ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    Objective : Carbon monoxide (CO) competes with oxygen for hemoglobin binding. The affinity of hemoglobin for CO is 250 times higher than that for oxygen. Therefore, exposure to CO leads to a reduction in oxygen delivery to tissues, resulting in cellular hypoxia. Hepatic dysfunction in critically ill patients is related to poor outcome, but few studies have been conducted on this subject that occurs after carbon monoxide poisoning. This study aims to conduct a study of hepatic injury in carbon monoxide poisoned patients in emergency department (ED). Methods : This retrospective observational study collected data from patients who were diagnosed with acute CO poisoning at the ED between June 2011 and May 2018 in local tertiary-care hospital (Wonju, Republic of Korea). The primary endpoint of this study was to describe the prevalence of hepatic injury in acute COpoisoned patients. The secondary goals were to investigate the recovery trends of hepatic injury caused by acute CO poisoning and the relation to neurologic outcome and mortality. Results : 894 patients were enrolled in the final analysis, 128 cases (14.3%) had subclinical hepatic injury and 15 (1.6%) cases had hepatic injury. the relationship with mortality was not statistically significant. However, in comparison to patients in the normal group, the admission rate to the intensive care unit and the occurrence of other complications in the hepatic injury patient group. Patients in the hepatic injury group recovered through conservative management within one week of being admitted to the ED. Conclusions : While CO-induced hepatic injury is relatively uncommon, it can be associated with complications and poor neurologic outcome. However, CO-induced hepatic injury was not found to have a statistically significant effect on mortality rate ๋ชฉ์  : ์ผ์‚ฐํ™”ํƒ„์†Œ๋Š” ํ—ค๋ชจ๊ธ€๋กœ๋นˆ ๊ฒฐํ•ฉ์— ์žˆ์–ด ์‚ฐ์†Œ์™€ ๊ฒฝ์Ÿ์ ์œผ๋กœ ๊ฒฐํ•ฉํ•˜๋ฉฐ, ๊ทธ ์นœํ™”๋„๋Š” 250๋ฐฐ ์ฐจ์ด๊ฐ€ ๋‚œ๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋•Œ๋ฌธ์— ์ผ์‚ฐํ™”ํƒ„์†Œ๊ฐ€ ์ธ์ฒด ๋‚ด๋ถ€์— ๋“ค์–ด์˜ค๊ฒŒ ๋  ๊ฒฝ์šฐ, ์กฐ์ง์œผ๋กœ ์ „๋‹ฌ๋˜๋Š” ์‚ฐ์†Œ ๋น„์œจ์ด ๊ฐ์†Œํ•˜๊ฒŒ ๋˜๋ฉฐ, ์ด๋Š” ์กฐ์ง-์„ธํฌ ๋‹จ์œ„์˜ ์ € ์‚ฐ์†Œ์ฆ์„ ์œ ๋ฐœ ์‹œํ‚ฌ ๊ฒƒ์ด๊ณ , ๊ฐ„ ์—ญ์‹œ ํ•ด๋‹น ์†์ƒ์„ ๋ฐ›์„ ์ˆ˜ ๋ฐ–์— ์—†๋‹ค. ์ค‘ํ™˜์ž ์˜์—ญ์—์„œ์˜ ๊ฐ„ ๊ธฐ๋Šฅ ๋ถ€์ „์€ ์ข‹์ง€ ์•Š์€ ์˜ˆํ›„์™€ ๊ด€๋ จ์ด ๋˜์–ด ์žˆ์Œ์ด ๋งŽ์ด ์—ฐ๊ตฌ๊ฐ€ ๋˜์–ด์žˆ์œผ๋‚˜,์ผ์‚ฐํ™”ํƒ„์†Œ ์ค‘๋… ์ดํ›„ ๋ฐœ์ƒํ•˜๋Š” ๊ฐ„ ๊ธฐ๋Šฅ ๋ถ€์ „์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ง€๊ธˆ๊นŒ์ง€ ์•Œ๋ ค์ง„ ๋ฐ” ์—†์–ด ๋ณธ ์—ฐ๊ตฌ๋ฅผ ์‹œํ–‰ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ• : 2011๋…„ 6์›”๋ถ€ํ„ฐ 2018๋…„ 5์›”๊นŒ์ง€ ์›์ฃผ์„ธ๋ธŒ๋ž€์Šค๊ธฐ๋…๋ณ‘์›์—์„œ ๊ธ‰์„ฑ ์ผ์‚ฐํ™”ํƒ„์†Œ ์ค‘๋…์œผ๋กœ ๋‚ด์›ํ•œ ํ™˜์ž๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๊ฐ„ ์†์ƒ์˜ ์ •์˜๋Š” ํ˜ˆ์ค‘ ALT ๊ฐ’์ด 3๋ฐฐ์ด์ƒ์œผ๋กœ ์ •์˜ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ผ์ฐจ ๋ชฉํ‘œ๋Š” ์ผ์‚ฐํ™”ํƒ„์†Œ ์ค‘๋… ์ดํ›„์˜ ๊ฐ„ ์†์ƒ์˜ ์œ ๋ณ‘๋ฅ ์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด๋ฉฐ, ์ด์ฐจ ๋ชฉํ‘œ๋Š” ๊ฐ„ ์†์ƒ์˜ ํšŒ๋ณต์˜ ํ๋ฆ„๊ณผ ์‹ ๊ฒฝํ•™์  ๊ฒฐ๊ณผ ๋ฐ ์‚ฌ ๋ง๋ฅ ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ฒฐ๊ณผ : ์ด 894๋ช…์ด ์ตœ์ข… ๋ถ„์„์— ํฌํ•จ๋˜์—ˆ์œผ๋ฉฐ, 128๋ช…(14.3%) ์ด ์ž ์žฌ์„ฑ์˜ ๊ฐ„์†์ƒ์„ ๋ณด์˜€๊ณ , 15๋ช…(1.6%)์ด ๊ฐ„์†์ƒ์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ๊ฐ„ ๊ธฐ๋Šฅ ๋ถ€์ „๊ณผ์˜ ์‚ฌ๋ง๋ฅ  ๊ณผ์˜ ๊ด€๊ณ„๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ๋šœ๋ ทํ•˜์ง€ ์•Š์•˜์œผ๋‚˜, ์ค‘ํ™˜์ž์‹ค ์ž…์›๋ฅ ๊ณผ ๊ธฐํƒ€ ๋‹ค๋ฅธ ํ•ฉ๋ณ‘์ฆ๊ณผ์˜ ๊ด€๊ณ„๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ๊ฐ„ ๊ธฐ๋Šฅ ๋ถ€์ „ ๊ทธ๋ฃน์—์„œ๋Š” ๋ณด์กด์  ์น˜๋ฃŒ๋ฅผ ํ†ตํ•ด ์ผ์ฃผ์ผ ์ด๋‚ด ์ •์ƒ์œผ๋กœ ๋Œ์•„๊ฐ์„ ํ™•์ธํ•˜์˜€๋‹ค.open๋ฐ•

    ๋Œ€๋‘(Glycine max)์žŽ์—์„œ์˜ ornithine carbamoyltransferase์˜ ์ •์ œ์™€ ํŠน์„ฑ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ƒ๋ช…๊ณผํ•™๋ถ€,2001.Maste

    ํ•œ๊ตญ์–ด ๋ฆฌ๋“ฌํŒจํ„ด ๋ณ€ํ™”์— ๊ด€ํ•œ ์‹คํ—˜์Œ์„ฑํ•™์  ์—ฐ๊ตฌ : 2์Œ์ ˆ ๋‚ฑ๋ง์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์–ธ์–ดํ•™๊ณผ ์–ธ์–ดํ•™์ „๊ณต,2000.Maste

    ์ž‘์—…์žฅ์˜ ๋‚˜๋…ธ์ž…์ž์ƒ ๋ฌผ์งˆ์— ๋Œ€ํ•œ ์‹œ๊ฐ„๋ˆ„์  ์‹œ๋ฃŒ์ฑ„์ทจ์™€ ์‹ค์‹œ๊ฐ„ ์‹œ๋ฃŒ์ฑ„์ทจ ๋ฐฉ๋ฒ• ๋น„๊ต

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋ณด๊ฑด๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋ณด๊ฑดํ•™๊ณผ, 2015. 2. ์œค์ถฉ์‹.Objective: Nanoparticles are generated by engineered and unintended in a variety of workplaces and process. Nanoparticles generation in unintended nanoparticle emitting workplace may originate from hot process, welding process. Number and surface area concentration of nanoparticles is generally assessed by a variety of direct reading instruments unlike traditional method. The purposes of this study were to compare time integrated sampling and direct reading instrument sampling method at nanoparticles generation workplaces, and to compare statistically full time sampling and time interval sampling categorized by time integrated sampling method. As characteristics of a variety of workplaces measured in this study were investigated, this study suggests appropriate sampling method by workplace. Methods: Sampling was used two methodsthe way to use a filter which sampled the air of workplace as time integrated sapling method and the way to sample by direct reading instrument such as SMPS, DustTrak, AeroTrak. Each filter was measured the direct reading instrument simultaneously, and for full time sampling compared with time interval, time interval sampling is measured for working time while replacing a filter by predetermined time interval. Analysis is performed for mass, metals, and TEM. Statistical analysis is performed to compare associations between metrics. Results: Concentrations measured at unintended nanoparticle emitting workplaces were higher than those at engineered nanoparticle manufacturing workplaces. CV of concentration is larger as shorter time interval. Full time samples were significantly higher coefficient than time interval samples in spearmans rank test. Conclusions: As PM2.5 concentration out of total mass concentration measured at welding workplaces was above 90%, mass concentration is recommended by gravimetric method. Although concentration measured at engineered nanoparticle manufacturing workplaces was relatively low, nanoparticles generation ratio is high. Therefore, the best way of sampling methods are recommended by using together direct reading instrument and gravimetric sampling method.Contents Abstract i Contents iii List of Tables iv List of Figures v 1 Introduction 1 2 Methods and materials 4 2.1 Definition of expression 4 2.2 Study design 5 2.3 Sampling methods 11 2.3.1 Time integrated sampling 11 2.3.2 Direct reading sampling method 13 2.4 Analysis methods 15 2.5 Statistical analysis 16 3 Results 17 3.1 Characteristics of workplaces 17 3.2 Comparison of time integrated sampling and direct reading sampling method 24 3.3 Comparison of full time and time interval sampling methods Comparison of the full shift and time-interval sampling methods 28 4 Discussions 33 5 Conclusions 38 6 References 39 ๊ตญ๋ฌธ์ดˆ๋ก 42Maste

    A study On the Education Method of Cohesive Devices In the Writing of Indonesian Korean learners

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ๊ตญ์–ด๊ต์œก๊ณผ(ํ•œ๊ตญ์–ด๊ต์œก์ „๊ณต), 2019. 2. ๋ฏผํ˜„์‹.With an aim of improving the writing ability of Indonesian learners of Korean as a second language, this study analyzes the usage of Cohesive Devices in Korean writing and reveals the cause of error in the text using the written writing data of Indonesian Korean learners. By identifying the characteristics of their usage of Cohesive Devices and comparatively analyzing the results with those of Korean native writers, the study also aims to suggest a teaching method of Cohesive Devices for Indonesian learners of Korean as a second language. ย With this purpose, 162 1st, 2nd, and 3rd year Indonesian students majoring in Korean Education were to select one of two topics (hobbies and travel) to write texts. Of this group, 121 subjects who were competent in writing in Korean were selected to be analyzed. The texts of Korean native writers were also collected to compare the usage patterns of the writing of both groups. Based on the collected texts, the cause of the errors of Cohesive Device usage are analyzed, and an education method of the Cohesive Devices is suggested. In Chapter 2, the existing discussion on Korean Cohesive Devices is reviewed. This becomes the basis of the analysis framework of Korean Cohesive Devices to be used for analysis of the Korean learners writing data. First, the concept of text and textuality are summarized and the relationship between cohesion and coherence are discussed. Cohesion and coherence are distinguished, and cohesion is defined as the word(s) that connect clauses to clauses and sentences to key propositions of sentences to provide a unified meaning, and the language device used in text is referred to as a Cohesive Device'. To understand the characteristics of the errors of Cohesive Device usage in Indonesian Korean learners writing, a comparative analysis of the Cohesive Device framework is made between Korean and Indonesian. The purpose is to predict the errors that may appear in the learner 's writing data. ย In Chapter 3, a framework for analyzing the Korean learners writing is suggested as the method of analysis. The analytical main framework is divided into three different parts: Referential Cohesion, Conjunctional Cohesion, and Lexical Cohesion Devices, and the frequency and pattern of Cohesive Devices in composition data is analyzed. The study finds that the connective ending devices that do not exist in Indonesian shows the greatest difference between Indonesian and Korean. Due to the influence of the mother tongue language, the expansion and contraction phenomenon of the register in the place directive pronoun and the connective ending appears, and there is an error due to the intralingual transfer in the connective ending devices due to the complexity of the target language. Also, because of lack of textbooks and teaching content, Indonesian learners do not correctly recognize the use of Cohesive devices. ย In Chapter 4, an education method for cohesive devices for Indonesians is developed based on the analysis of the use of cohesive devices analyzed in Chapter 3. The purpose and the goal of the cohesive devices is set separately, and a method of education is suggested after the education content of cohesive devices is selected. The OHE model is used to recognize the Cohesive Devices role and function of Indonesian and Korean language. The ultimate goal of the training of Cohesive Devices is to improve the writing skill of Indonesian Korean learners. Therefore, process approach principle is set as teaching principle. Finally, a practical teaching model is presented to apply the cohesive devices to actual education field. ย Cohesive Devices connect the core proposition that writers want to convey between clauses and clauses, and sentences and sentences, to deliver a clear meaning. Therefore, it is necessary to understand the usage of Cohesive Devices, which are grammatical mechanisms used in text, and to be able to apply it properly in the text. The purpose of this study is to, for the first time, analyze the writing of Indonesian learners to improve the writing ability of Indonesian Korean learners.๋ณธ ์—ฐ๊ตฌ๋Š” ์ธ๋„๋„ค์‹œ์•„์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ๊ธ€์“ฐ๊ธฐ ๋Šฅ๋ ฅ ํ–ฅ์ƒ์„ ๋ชฉํ‘œ๋กœ ํ•™๋…„๋ณ„ ์“ฐ๊ธฐ ์ž‘๋ฌธ ์ž๋ฃŒ๋ฅผ ๋ถ„์„ ๋Œ€์ƒ์œผ๋กœ ์‚ผ์•„ ํ…์ŠคํŠธ์—์„œ ๋‚˜ํƒ€๋‚œ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ์„ ๋ถ„์„ํ•˜๊ณ  ๊ทธ ์‚ฌ์šฉ ์–‘์ƒ ์ค‘ ๋‚˜ํƒ€๋‚œ ์˜ค๋ฅ˜ ์›์ธ์„ ๋ฐํ˜€๋‚ด๊ณ ์ž ํ–ˆ๋‹ค. ๋˜ํ•œ ํ•œ๊ตญ์ธ ๋ชจ์–ด ํ•„์ž์™€ ๋น„๊ตํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž์˜ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ์˜ ํŠน์„ฑ์„ ๋ฐํžŒ ํ›„ ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ธ๋„๋„ค์‹œ์•„์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž ์“ฐ๊ธฐ ๊ต์œก์˜ ์‘๊ฒฐ์žฅ์น˜ ๊ต์ˆ˜ํ•™์Šต ๋ฐฉ์•ˆ์„ ๋ชจ์ƒ‰ํ•˜๋Š” ๋ฐ ๋ชฉ์ ์„ ๋‘์—ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ ์ธ๋„๋„ค์‹œ์•„ ์‚ฌ๋ฒ”๋Œ€ํ•™๊ต ํ•œ๊ตญ์–ด๊ต์œก๊ณผ์— ์žฌํ•™ ์ค‘์ธ 1, 2, 3ํ•™๋…„ ์ด 162๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ๋‘ ๊ฐ€์ง€ ์ฃผ์ œ์ธ ์ทจ๋ฏธ์™€ ์—ฌํ–‰ ์ค‘ ํ•˜๋‚˜๋ฅผ ๊ณจ๋ผ ํ…์ŠคํŠธ๋ฅผ ์ž‘์„ฑํ•˜๊ฒŒ ํ•˜์˜€๊ณ  ๊ทธ์ค‘ ํ•œ๊ตญ์–ด ์ž‘๋ฌธ ๋Šฅ๋ ฅ์„ ๊ฐ–์ถ˜ ์ธ๋„๋„ค์‹œ์•„์ธ 121๋ช…์˜ ์ž‘๋ฌธ ์ž๋ฃŒ๋ฅผ ๋ถ„์„ ๋Œ€์ƒ์œผ๋กœ ์‚ผ์•˜๋‹ค. ๋˜ํ•œ ํ•œ๊ตญ์ธ ๋ชจ์–ด ํ•„์ž์˜ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ๊ณผ ๋น„๊ตํ•˜๊ธฐ ์œ„ํ•ด ํ•œ๊ตญ์ธ ๋ชจ์–ด ํ•„์ž์—๊ฒŒ ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž์™€ ๋™์ผํ•œ ์ฃผ์ œ๋กœ ๊ธ€์„ ์“ฐ๊ฒŒ ํ•œ ํ›„ ํ…์ŠคํŠธ๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž์˜ ์ž‘๋ฌธ ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ๊ณผ ์˜ค๋ฅ˜์˜ ์›์ธ์„ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ์ฐธ์กฐํ•˜์—ฌ ์‘๊ฒฐ์žฅ์น˜ ๊ต์ˆ˜ํ•™์Šต ๋ฐฉ์•ˆ์„ ๋ชจ์ƒ‰ํ•˜์˜€๋‹ค. 2์žฅ์—์„œ๋Š” ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜์— ๊ด€ํ•œ ๊ธฐ์กด ๋…ผ์˜๋ฅผ ๊ฒ€ํ† ํ•˜๊ณ  ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™์Šต์ž์˜ ์ž‘๋ฌธ ์ž๋ฃŒ ๋ถ„์„์— ์‚ฌ์šฉ๋  ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜ ๋ถ„์„ ํ‹€์„ ์ œ์‹œํ•˜์˜€๊ณ , ์ธ๋„๋„ค์‹œ์•„์–ด ์‘๊ฒฐ์žฅ์น˜์˜ ํŠน์„ฑ์„ ๋ฐํ˜€๋‚ธ ํ›„ ํ•œ๊ตญ์–ด์˜ ์„ธ๋ถ€ ์‘๊ฒฐ์žฅ์น˜์™€ ๋Œ€์กฐ๋ถ„์„ํ•˜์˜€๋‹ค. ๋จผ์ € ํ…์ŠคํŠธ์˜ ๊ฐœ๋…๊ณผ ํ…์ŠคํŠธ์„ฑ์„ ์ •๋ฆฌํ•˜์˜€๊ณ  ํ…์ŠคํŠธ์„ฑ ์ค‘์—์„œ ์ฃผ์š”ํ•œ ๋‘ ๊ฐ€์ง€ ํŠน์ง•์ธ ์‘๊ฒฐ์„ฑ๊ณผ ์‘์ง‘์„ฑ์˜ ๊ด€๊ณ„๋ฅผ ๋…ผํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์‘๊ฒฐ์„ฑ๊ณผ ์‘์ง‘์„ฑ์„ ๊ตฌ๋ถ„ํ•˜๊ณ  ์‘๊ฒฐ์„ฑ์„ ์ ˆ๊ณผ ์ ˆ, ๋ฌธ์žฅ๊ณผ ๋ฌธ์žฅ์˜ ํ•ต์‹ฌ ๋ช…์ œ๋ฅผ ์—ฐ๊ฒฐํ•ด ์ฃผ๊ณ  ํ†ต์ผ๋œ ์˜๋ฏธ๋ฅผ ๋‹ด๊ฒŒ ํ•ด ์ฃผ๋Š” ์—ญํ• ์„ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณด๋ฉด์„œ ์‘๊ฒฐ์„ฑ์ด ํ…์ŠคํŠธ ๋‚ด์—์„œ ํ™œ์šฉ๋˜์–ด ํ‘œํ˜„๋œ ์–ธ์–ด ์žฅ์น˜๋ฅผ ์‘๊ฒฐ์žฅ์น˜(cohesive device)๋ผ๊ณ  ๋ณด๋Š” ์ •์˜๋ฅผ ๋”ฐ๋ž๋‹ค. ์ดํ›„ ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž์˜ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ์˜ ํŠน์„ฑ๊ณผ ๋ฐœํ˜„๋˜๋Š” ์˜ค๋ฅ˜์˜ ์›์ธ์„ ๋ฐํ˜€๋‚ด๊ธฐ ์œ„ํ•ด ๋จผ์ € ํ•œ๊ตญ์–ด์˜ ์‘๊ฒฐ์žฅ์น˜ ์ด๋ก ์„ ๊ฒ€ํ† ํ•˜์—ฌ ์ •๋ฆฌํ•˜์˜€๊ณ  ์ธ๋„๋„ค์‹œ์•„์–ด ์‘๊ฒฐ์žฅ์น˜์˜ ํŠน์ง•์„ ๋ถ„์„ํ•œ ๋’ค ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ธ๋„๋„ค์‹œ์•„์–ด์™€ ํ•œ๊ตญ์–ด์˜ ์„ธ๋ถ€ ์‘๊ฒฐ์žฅ์น˜๋ฅผ ๋Œ€์กฐ๋ถ„์„ํ•˜์˜€๋‹ค. ๋Œ€์กฐ๋ถ„์„์„ ํ†ตํ•ด ํ•™์Šต์ž ์ž‘๋ฌธ ์ž๋ฃŒ์—์„œ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ์˜ค๋ฅ˜๋ฅผ ์˜ˆ์ธกํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. 3์žฅ์—์„œ๋Š” ๋จผ์ €, ํ•™์Šต์ž์˜ ์“ฐ๊ธฐ ์ž‘๋ฌธ ์ž๋ฃŒ ๋ถ„์„ ๋ฐฉ๋ฒ•์„ ๊ธฐ์ˆ ํ•˜๊ณ  ์‘๊ฒฐ์žฅ์น˜ ๋ถ„์„ ํ‹€์„ ๊ธฐ์ค€์œผ๋กœ ํ•™์Šต์ž์˜ ์ž‘๋ฌธ ์ž๋ฃŒ์—์„œ ๋Œ€์šฉ ์‘๊ฒฐ์žฅ์น˜, ์ ‘์† ์‘๊ฒฐ์žฅ์น˜, ์–ดํœ˜์  ์‘๊ฒฐ์žฅ์น˜๊ฐ€ ๋‚˜ํƒ€๋‚˜๋Š” ์‚ฌ์šฉ ๋นˆ๋„์™€ ๊ทธ ์–‘์ƒ์„ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž์˜ ์ž‘๋ฌธ ์ž๋ฃŒ์—์„œ ๋‚˜ํƒ€๋‚œ ์˜ค๋ฅ˜์™€ ์˜ค๋ฅ˜์˜ ์›์ธ์„ ์‚ดํŽด๋ณด์•˜๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ธ๋„๋„ค์‹œ์•„์–ด์™€ ํ•œ๊ตญ์–ด์—์„œ ๊ฐ€์žฅ ํฐ ์ฐจ์ด๋ฅผ ๋ณด์ด๋Š” ์‘๊ฒฐ์žฅ์น˜๋Š” ์ ‘์† ์‘๊ฒฐ์žฅ์น˜๋กœ ๊ทธ์ค‘์—์„œ๋„ ์ธ๋„๋„ค์‹œ์•„์–ด์—๋Š” ์กด์žฌํ•˜์ง€ ์•Š๋Š” ์—ฐ๊ฒฐ์–ด๋ฏธ์˜ ์‚ฌ์šฉ ์–‘์ƒ์ด ๊ฐ€์žฅ ๋‘๋“œ๋Ÿฌ์ง€๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋จผ์ € ๋Œ€์šฉ ์‘๊ฒฐ์žฅ์น˜์˜ ๊ฒฝ์šฐ ๋Œ€๋ช…์‚ฌ ์ด/๊ทธ์˜ ์‚ฌ์šฉ ์–‘์ƒ์ด ๊ฑฐ์˜ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๊ณ  ์žฅ์†Œ ์ง€์‹œ๋Œ€๋ช…์‚ฌ ์—ฌ๊ธฐ/์ €๊ธฐ, ์ด๊ณณ/์ €๊ณณ์˜ ์‚ฌ์šฉ์—ญ์— ๋”ฐ๋ฅธ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ ‘์† ์‘๊ฒฐ์žฅ์น˜์—์„œ๋Š” ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž๊ฐ€ ๋ชจ๊ตญ์–ด์˜ ์˜ํ–ฅ์œผ๋กœ ํ•˜๋‚˜์˜ ์˜๋ฏธ๊ธฐ๋Šฅ ๋‚ด์—์„œ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ํ˜•ํƒœ๋กœ ๋‚˜ํƒ€๋‚œ ์‘๊ฒฐ์žฅ์น˜ ๊ฐ„ ํ˜ผ๋™ ์–‘์ƒ์„ ๋ณด์˜€๊ณ , ์„œ๋กœ ๋‹ค๋ฅธ ์˜๋ฏธ๊ธฐ๋Šฅ ๊ฐ„์—์„œ๋„ ํ˜ผ๋™ ์–‘์ƒ์ด ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์–ธ์–ด ๊ฐ„ ์ „์ด์— ๋”ฐ๋ฅธ ์˜ค๋ฅ˜๊ฐ€ ๋Œ€๋ถ€๋ถ„์ด์—ˆ์œผ๋ฉฐ ํ•™์Šต ํ™˜๊ฒฝ์˜ ์›์ธ์— ๋”ฐ๋ฅธ ๊ต์ˆ˜ ๋‚ด์šฉ ๋ถ€์กฑ์œผ๋กœ ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž๊ฐ€ ์‘๊ฒฐ์žฅ์น˜์˜ ์šฉ๋ฒ•์„ ๋ฐ”๋ฅด๊ฒŒ ์ธ์‹ํ•˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. 4์žฅ์—์„œ๋Š” 3์žฅ์—์„œ ๋ถ„์„ํ•œ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ ๋ฐ ์˜ค๋ฅ˜์™€ ์˜ค๋ฅ˜ ์›์ธ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ธ๋„๋„ค์‹œ์•„์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž ์“ฐ๊ธฐ ๊ต์œก์˜ ์‘๊ฒฐ์žฅ์น˜ ๊ต์ˆ˜ํ•™์Šต ๋ฐฉ์•ˆ์„ ๋ชจ์ƒ‰ํ•˜์˜€๋‹ค. ์‘๊ฒฐ์žฅ์น˜ ๊ต์œก ๋ชฉ์ ๊ณผ ๋ชฉํ‘œ๋ฅผ ๊ตฌ๋ถ„ํ•˜์—ฌ ์„ค์ •ํ•˜๊ณ  ์‘๊ฒฐ์žฅ์น˜ ๊ต์œก ๋‚ด์šฉ์„ ์„ ์ •ํ•œ ํ›„ ์‘๊ฒฐ์žฅ์น˜ ๊ต์œก ๋ฐฉ๋ฒ•์„ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž๊ฐ€ ํ•œ๊ตญ์–ด์™€ ์ธ๋„๋„ค์‹œ์•„์–ด ์‘๊ฒฐ์žฅ์น˜์˜ ์—ญํ• ๊ณผ ๊ธฐ๋Šฅ์„ ์ธ์‹ํ•˜๋„๋ก OHE ๋ชจํ˜•์„ ํ™œ์šฉํ•˜์˜€๊ณ , ์‘๊ฒฐ์žฅ์น˜ ๊ต์œก์˜ ๊ถ๊ทน์ ์ธ ๋ชฉ์ ์€ ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž์˜ ๊ธ€์“ฐ๊ธฐ ์‹ค๋ ฅ ํ–ฅ์ƒ์ด๋ฏ€๋กœ ์“ฐ๊ธฐ ๊ต์œก์˜ ์ง€๋„ ์›๋ฆฌ ์ค‘ ํ•˜๋‚˜์ธ ๊ณผ์ • ์ค‘์‹ฌ ์ ‘๊ทผ๋ฒ•์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ธ๋„๋„ค์‹œ์•„์ธ ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜์˜ ๊ต์ˆ˜ํ•™์Šต ๋ฐฉ์•ˆ์„ ์„ค์ •ํ•˜์˜€๊ณ , ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‹ค์ œ ๊ต์œก ํ˜„์žฅ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜ ์ˆ˜์—… ๋ชจํ˜•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์‘๊ฒฐ์žฅ์น˜๋Š” ํ•„์ž๊ฐ€ ์ „๋‹ฌํ•˜๊ณ ์ž ํ•˜๋Š” ํ•ต์‹ฌ ๋ช…์ œ๋ฅผ ์ ˆ๊ณผ ์ ˆ, ๋ฌธ์žฅ๊ณผ ๋ฌธ์žฅ ์‚ฌ์ด์—์„œ ์—ฐ๊ฒฐํ•˜๊ณ  ํ†ต์ผ๋œ ์˜๋ฏธ๋ฅผ ๋‹ด๋Š” ์—ญํ• ์„ ํ•œ๋‹ค. ์ด์— ๋”ฐ๋ผ ํ•™์Šต์ž๋Š” ํ…์ŠคํŠธ ๋‚ด์—์„œ ํ™œ์šฉ๋˜์–ด ํ‘œํ˜„๋œ ๋ฌธ๋ฒ•์  ๊ธฐ์ œ์ธ ์‘๊ฒฐ์žฅ์น˜์˜ ์šฉ๋ฒ•์„ ์ •ํ™•ํžˆ ์ดํ•ดํ•˜๊ณ  ์ด๋ฅผ ํ…์ŠคํŠธ ๋‚ด์—์„œ ์ ์ ˆํ•˜๊ฒŒ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ธ๋„๋„ค์‹œ์•„์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ํ•œ๊ตญ์–ด ๊ธ€์“ฐ๊ธฐ ๋Šฅ๋ ฅ ํ–ฅ์ƒ์„ ์œ„ํ•ด ์ตœ์ดˆ๋กœ ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž์˜ ์“ฐ๊ธฐ ์ž‘๋ฌธ ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜์—ฌ ๊ทธ ์‚ฌ์šฉ ์–‘์ƒ๊ณผ ์˜ค๋ฅ˜ ์›์ธ์„ ๋ฐํ˜€๋ƒˆ๊ณ  ์ธ๋„๋„ค์‹œ์•„์ธ ํ•™์Šต์ž์—๊ฒŒ ์ ํ•ฉํ•œ ๊ต์ˆ˜ํ•™์Šต ๋ฐฉ์•ˆ์„ ๋งˆ๋ จํ–ˆ๋‹ค๋Š” ์ ์—์„œ ์˜์˜๊ฐ€ ์žˆ๋‹ค.๋ชฉ ์ฐจ โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ํ•„์š”์„ฑ 1 2. ์„ ํ–‰ ์—ฐ๊ตฌ 3 3. ์—ฐ๊ตฌ ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ• 7 3.1. ์—ฐ๊ตฌ ๋Œ€์ƒ 7 3.2. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 8 โ…ก. ํ…์ŠคํŠธ์„ฑ๊ณผ ์‘๊ฒฐ์žฅ์น˜์˜ ๋Œ€์กฐ๋ถ„์„ 11 1. ํ…์ŠคํŠธ ์–ธ์–ดํ•™์˜ ๊ตฌ์„ฑ ์š”์†Œ 11 1.1. ํ…์ŠคํŠธ์™€ ํ…์ŠคํŠธ์„ฑ 11 1.2. ์‘๊ฒฐ์„ฑ์™€ ์‘์ง‘์„ฑ 13 2. ์ธ๋„๋„ค์‹œ์•„์–ด์˜ ์‘๊ฒฐ์žฅ์น˜ ๋ฐ ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜์™€์˜ ๋Œ€์กฐ๋ถ„์„ 16 2.1. ๋Œ€์กฐ๋ถ„์„ ์ด๋ก  16 2.2. ํ•œ๊ตญ์–ด์˜ ์‘๊ฒฐ์žฅ์น˜ 18 2.2.1. ๋Œ€์šฉ ์‘๊ฒฐ์žฅ์น˜ 21 2.2.2. ์ ‘์† ์‘๊ฒฐ์žฅ์น˜ 25 2.2.3. ์ƒ๋žต ์‘๊ฒฐ์žฅ์น˜ 31 2.2.4. ์–ดํœ˜์  ์‘๊ฒฐ์žฅ์น˜ 34 2.3. ์ธ๋„๋„ค์‹œ์•„์–ด์˜ ์‘๊ฒฐ์žฅ์น˜ 37 2.3.1. ๋Œ€๋ช…์‚ฌ ์‘๊ฒฐ์žฅ์น˜ 38 2.3.2. ๋Œ€์šฉ ์‘๊ฒฐ์žฅ์น˜ 41 2.3.3. ์ ‘์† ์‘๊ฒฐ์žฅ์น˜ 44 2.3.4. ์ƒ๋žต ์‘๊ฒฐ์žฅ์น˜ 46 2.3.5. ์–ดํœ˜์  ์‘๊ฒฐ์žฅ์น˜ 50 2.4. ํ•œ๊ตญ์–ด์™€ ์ธ๋„๋„ค์‹œ์•„์–ด์˜ ์‘๊ฒฐ์žฅ์น˜ ๋Œ€์กฐ๋ถ„์„ 51 2.4.1. ๋Œ€์šฉ ์‘๊ฒฐ์žฅ์น˜ 51 2.4.2. ์ ‘์† ์‘๊ฒฐ์žฅ์น˜ 56 โ…ข. ํ•™์Šต์ž ์ž‘๋ฌธ ์ž๋ฃŒ์— ๋‚˜ํƒ€๋‚œ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 66 1. ์‘๊ฒฐ์žฅ์น˜ ๋ถ„์„ ๋ฐฉ๋ฒ• 66 2. ์ธ๋„๋„ค์‹œ์•„์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 70 2.1. ๋Œ€์šฉ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 71 2.1.1. ๋Œ€๋ช…์‚ฌํ˜• ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 73 2.1.2. ๊ด€ํ˜•์‚ฌํ˜• ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 80 2.1.3. ๋ถ€์‚ฌ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 81 2.1.4. ์ˆ˜์‚ฌ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 83 2.2. ์ ‘์† ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 85 2.2.1. ์—ฐ๊ฒฐ์–ด๋ฏธ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 86 2.2.2. ์ ‘์†๋ถ€์‚ฌ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 101 2.2.3. ๊ด€์šฉ์  ์—ฐ๊ฒฐ์–ด ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 107 2.3. ์–ดํœ˜์  ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์–‘์ƒ 113 2.3.1. ๋™์ผ์–ด ๋ฐ˜๋ณต ์‚ฌ์šฉ ์–‘์ƒ 114 2.3.2. ์œ ์˜์–ด ์‚ฌ์šฉ ์–‘์ƒ 115 2.3.3. ๋ฐ˜์˜์–ด ์‚ฌ์šฉ ์–‘์ƒ 118 2.3.4. ์ƒยทํ•˜์œ„์–ด ์‚ฌ์šฉ ์–‘์ƒ 120 3. ์ธ๋„๋„ค์‹œ์•„์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ์‘๊ฒฐ์žฅ์น˜ ์‚ฌ์šฉ ์›์ธ ๋ถ„์„ 122 3.1. ์–ธ์–ด ๊ฐ„ ์ „์ด 122 3.2. ํ•™์Šต์˜ ์žฅ 126 โ…ฃ. ์ธ๋„๋„ค์‹œ์•„์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž ์“ฐ๊ธฐ ๊ต์œก์˜ ์‘๊ฒฐ์žฅ์น˜ ๊ต์ˆ˜ํ•™์Šต ๋ฐฉ์•ˆ ์—ฐ๊ตฌ 130 1. ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜ ๊ต์œก ๋ชฉํ‘œ 130 2. ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜ ๊ต์œก ๋‚ด์šฉ 131 2.1. ์ธ๋„๋„ค์‹œ์•„์–ด์™€ ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜์˜ ์—ญํ• ๊ณผ ๊ธฐ๋Šฅ ์ฐจ์ด 131 2.1.1. ๋Œ€์šฉ ์‘๊ฒฐ์žฅ์น˜ 132 2.1.2. ์ ‘์† ์‘๊ฒฐ์žฅ์น˜ 133 2.1.3. ์–ดํœ˜์  ์‘๊ฒฐ์žฅ์น˜ 134 2.2. ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜ ๊ต์œก ๋‚ด์šฉ ์„ ์ • 134 2.2.1. ๋Œ€์šฉ ์‘๊ฒฐ์žฅ์น˜ 134 2.2.2. ์ ‘์† ์‘๊ฒฐ์žฅ์น˜ 135 2.2.3. ์–ดํœ˜์  ์‘๊ฒฐ์žฅ์น˜ 137 3. ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜ ๊ต์ˆ˜ํ•™์Šต ๋ฐฉ์•ˆ 138 3.1. OHE ๋ชจํ˜•์„ ํ™œ์šฉํ•œ ์‘๊ฒฐ์žฅ์น˜ ์ธ์‹ ๊ต์ˆ˜ํ•™์Šต ๋ฐฉ์•ˆ 138 3.2. ๊ณผ์ • ์ค‘์‹ฌ ์“ฐ๊ธฐ๋ฅผ ํ†ตํ•œ ์‘๊ฒฐ์žฅ์น˜ ๊ต์ˆ˜ํ•™์Šต ๋ฐฉ์•ˆ 141 4. ํ•œ๊ตญ์–ด ์‘๊ฒฐ์žฅ์น˜ ๊ต์œก์˜ ์‹ค์ œ 143 4.1. ์ˆ˜์—… ๋ชจํ˜• ์„ค๊ณ„ 143 4.2. ์ˆ˜์—…์˜ ์‹ค์ œ 145 โ…ค. ๊ฒฐ๋ก  152 ์ฐธ๊ณ  ๋ฌธํ—Œ 155 ๋ถ€๋ก 163 Abstract 171Maste
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