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    ๋ผ์ง€-์˜์žฅ๋ฅ˜ ์ „์ธต ์ด์ข…๊ฐ๋ง‰์ด์‹์—์„œ ํ˜•์งˆ์ „ํ™˜ ๋ฏธ๋‹ˆ๋ผ์ง€ ๊ฐ๋ง‰์˜ ์œ ํšจ์„ฑ๊ณผ ์ž„์ƒ์ ์šฉ ๊ฐ€๋Šฅํ•œ ๊ฑฐ๋ถ€๋ฐ˜์‘ ์˜ˆ์ธก ๋ฐ”์ด์˜ค๋งˆ์ปค์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ,2019. 8. ๊น€๋ฏธ๊ธˆ.Purpose: Corneal xenotransplantation using pig donors has been investigated as a substitute for human donor corneas. In this study, we investigated long-term survival of corneal grafts from ฮฑ1,3-galactosyltransferase gene-knockout miniature (GTKOm) pigs in nonhuman primates (NHPs). We also investigated the clinically applicable predictive biomarkers for corneal xenograft rejection including the results of our previous experiments. Methods: For GTKOm survival study, nine rhesus macaques undergoing full-thickness corneal xenotransplantation using GTKOm pigs were systemically administered steroid, basiliximab, intravenous immunoglobulin, and tacrolimus with (CD20 group; n = 4) or without (control group; n = 5) anti-CD20 antibody. The graft score (0-12) was calculated based on opacity, edema, and vascularization. Scores โ‰ฅ 6 were defined as rejection. Changes in T/B cell subsets, levels of anti-ฮฑGal IgG/M, donor-specific IgG/M from blood, and C3a from plasma and aqueous humor (AH) were evaluated. For biomarker study, 34 NHPs which had undergone full-thickness porcine corneal xenotransplantation were included. Five of them received GTKOm pig corneas, and 29 received SNU wild type miniature pig corneas. They were divided into two groups: (a) graft rejection within 6 months (rejection group); and (b) graft survival until 6 months (survival group). The entire rejection group included all NHPs whose graft was rejected within a 6-month period, while late rejection group included NHPs whose graft was rejected after more than 4 weeks up to 6 months. None of the NHPs showed rejection at postoperative week 2. In the evaluation of the 2-week biomarkers, entire rejection group (n = 16) or late rejection group (n = 12) was compared to survival group (n = 18). In the evaluation of 4-week biomarkers, four NHPs showing rejection within 4 weeks were excluded and late rejection group (n = 12) was compared to survival group (n = 18). Analysis of biomarker candidates included T/B cell subsets, levels of anti-ฮฑGal IgG/M, donor-specific IgG/M from blood, and C3a from plasma and aqueous humor (AH). Results: In GTKOm survival study, graft survival was significantly longer (P = 0.008) in the CD20 group (>375, >187, >187, >83 days) than control group (165, 91, 72, 55, 37 days). Activated B cells were lower in the CD20 group than control group (P = 0.026). Aqueous humor complement C3a was increased in the control group at last examination (P = 0.043), and was higher than that in the CD20 group (P = 0.014). At last examination, anti-non-Gal IgG was increased in the control group alone (P = 0.013). In biomarker study, CD8+IFNฮณ+ cells at week 2 and AH C3a at week 4 were significantly elevated in the rejection group. Receiver operating characteristic areas under the curve was highest for AH C3a (0.847) followed by CD8+IFNฮณ+ cells (both the concentration and percentage: 0.715), indicating excellent or acceptable discrimination ability Conclusion: The GTKOm pig corneal graft achieved long-term survival when combined with anti-CD20 antibody treatment. Inhibition of activated B cells and complement is imperative even when using GTKO pig corneas. CD8+IFNฮณ+ cells at week 2 and AH C3a at week 4 are reliable biomarkers for predicting rejection in pig-to-NHP corneal xenotransplantation. Those biomarkers may be used as a standard of reference to predict rejection in clinical trials of corneal xenotransplantation.๋ชฉ์ : ์ด์ข…๊ฐ๋ง‰์ด์‹์€ ๋™์ข… ๊ณต์—ฌ๊ฐ๋ง‰์˜ ๋Œ€์ฒด์ œ๋กœ์„œ ์—ฐ๊ตฌ๋˜์–ด ์™”๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ฮฑ1,3-galactosyltransferase gene์„ knockout ์‹œํ‚จ ํ˜•์งˆ์ „ํ™˜ ๋ฏธ๋‹ˆ๋ผ์ง€ (GTKOm ๋ผ์ง€)-์˜์žฅ๋ฅ˜ ์ „์ธต ์ด์ข…๊ฐ๋ง‰์ด์‹์—์„œ ์ด์‹ํŽธ์˜ ์žฅ๊ธฐ ์œ ํšจ์„ฑ์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๋˜ํ•œ ์ด์ „์˜ ์•ผ์ƒํ˜• SNU ๋ฏธ๋‹ˆ๋ผ์ง€-์˜์žฅ๋ฅ˜ ์ „์ธต ์ด์ข…๊ฐ๋ง‰์ด์‹์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํฌํ•จํ•˜์—ฌ ์ž„์ƒ์ ์œผ๋กœ ์ ์šฉ์ด ๊ฐ€๋Šฅํ•œ ์ด์‹ํŽธ ๊ฑฐ๋ถ€ ๋ฐ˜์‘์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐ”์ด์˜ค ๋งˆ์ปค๋ฅผ ๋ฐœ๊ตดํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ฐฉ๋ฒ•: GTKOm ๋ผ์ง€ ๊ฐ๋ง‰์˜ ์žฅ๊ธฐ ์œ ํšจ์„ฑ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด์„œ ์ด 9๋งˆ๋ฆฌ ์˜์žฅ๋ฅ˜(Chinese rhesus macaques)์˜ ์šฐ์•ˆ์— GTKOm ๋ผ์ง€์˜ ๊ฐ๋ง‰์„ ์ „์ธต ์ด์‹ ์‹œํ–‰ํ–ˆ๋‹ค. 9๋งˆ๋ฆฌ์˜ ์˜์žฅ๋ฅ˜๋ฅผ ๋Œ€์กฐ๊ตฐ(n = 5)๊ณผ CD20๊ตฐ(n = 4)์œผ๋กœ ๋‚˜๋ˆด๋‹ค. ๋‘ ๊ตฐ ๋ชจ๋‘ ์ „์‹  tacrolimus, basiliximab, steroid๋ฅผ ํˆฌ์—ฌํ–ˆ์œผ๋ฉฐ, CD20๊ตฐ์€ ์ถ”๊ฐ€๋กœ ํ•ญ-CD20 ํ•ญ์ฒด๋ฅผ ํˆฌ์—ฌํ–ˆ๋‹ค. ์ด์‹ํŽธ์˜ ๋ถ€์ข…, ํ˜ผํƒ, ์‹ ์ƒํ˜ˆ๊ด€ํ˜•์„ฑ์„ ๊ฐ 0-4์ ์œผ๋กœ ํ‰๊ฐ€ํ•œ ๋’ค ํ•ฉ์‚ฐํ•˜์—ฌ ์ด์‹ํŽธ ์ ์ˆ˜(0-12) ๋ฅผ ์‚ฐ์ •ํ–ˆ๋‹ค. ์ด์‹ํŽธ ์ ์ˆ˜๊ฐ€ 6์  ์ด์ƒ์ผ ๊ฒฝ์šฐ ์ด์‹ํŽธ์˜ ๊ฑฐ๋ถ€๋ฐ˜์‘์œผ๋กœ ์ง„๋‹จํ–ˆ๋‹ค. ์ž‘๋™ ๋ฐ ๊ธฐ์–ต T ์„ธํฌ, ํ•ญ ฮฑGal ํ•ญ์ฒด, ํ•ญ non-ฮฑGal ํ•ญ์ฒด, ๊ณต์—ฌ์ž ํŠน์ด ํ•ญ์ฒด, ๋ณด์ฒด(C3a) ๋ณ€ํ™”๋ฅผ ๋น„๊ต ๋ถ„์„ํ–ˆ๋‹ค. ๋ฐ”์ด์˜ค๋งˆ์ปค ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด์„œ๋Š” ์šฐ์•ˆ์— ๋ผ์ง€ ๊ฐ๋ง‰์„ ์ „์ธต์ด์‹ ๋ฐ›์€ 34๋งˆ๋ฆฌ์˜ ์˜์žฅ๋ฅ˜๋ฅผ ๋ถ„์„ํ–ˆ๋‹ค. ์ด ์ค‘ 5๋งˆ๋ฆฌ๋Š” GTKOm ๋ผ์ง€ ๊ฐ๋ง‰์„ ์ด์‹ ๋ฐ›์•˜๊ณ , 29๋งˆ๋ฆฌ๋Š” ์•ผ์ƒํ˜• SNU ๋ฏธ๋‹ˆ๋ผ์ง€ ๊ฐ๋ง‰์„ ์ด์‹ ๋ฐ›์•˜๋‹ค. 34๋งˆ๋ฆฌ์˜ ์˜์žฅ๋ฅ˜๋ฅผ ๊ฑฐ๋ถ€๋ฐ˜์‘๊ตฐ(์ „์ฒด ๋˜๋Š” ๋Šฆ์€)๊ณผ ์ƒ์กด๊ตฐ ๋‘ ๊ทธ๋ฃน์œผ๋กœ ๋ถ„๋ฅ˜ํ–ˆ๋‹ค. ์ด์‹ํŽธ์ด 6 ๊ฐœ์›” ์ด๋‚ด์— ๊ฑฐ๋ถ€๋ฐ˜์‘์„ ๋ณด์ธ ๋ชจ๋“  ๊ฐœ์ฒด๋ฅผ ์ „์ฒด ๊ฑฐ๋ถ€๋ฐ˜์‘๊ตฐ์œผ๋กœ ์ •์˜ํ–ˆ๊ณ , ์ด์‹ํŽธ์˜ ๊ฑฐ๋ถ€๋ฐ˜์‘์ด 4์ฃผ์—์„œ 6๊ฐœ์›”์‚ฌ์ด์— ๋ฐœ์ƒํ•œ ๊ฐœ์ฒด๋ฅผ ๋Šฆ์€ ๊ฑฐ๋ถ€๋ฐ˜์‘๊ตฐ์œผ๋กœ ์ •์˜ํ–ˆ๋‹ค. ์ด์‹ ํ›„ 2์ฃผ์ด๋‚ด์— ๋ชจ๋“  ์˜์žฅ๋ฅ˜์˜ ์ด์ข… ์ด์‹ํŽธ์€ ๊ฑฐ๋ถ€ ๋ฐ˜์‘์„ ๋ณด์ด์ง€ ์•Š์•˜๊ณ , 2์ฃผ์งธ์˜ ๊ฑฐ๋ถ€๋ฐ˜์‘ ์˜ˆ์ธก ๋ฐ”์ด์˜ค๋งˆ์ปค ๋ถ„์„์„ ์œ„ํ•ด์„œ, ์ „์ฒด ๊ฑฐ๋ถ€๋ฐ˜์‘๊ตฐ(n = 16) ๋˜๋Š” ๋Šฆ์€ ๊ฑฐ๋ถ€๋ฐ˜์‘๊ตฐ(n = 12)์„ ์ƒ์กด๊ตฐ(n = 18)๊ณผ ๋น„๊ตํ–ˆ๋‹ค. 4์ฃผ์งธ์˜ ๋ฐ”์ด์˜ค๋งˆ์ปค ๋ถ„์„์—์„œ๋Š” 4์ฃผ ์ด๋‚ด์— ๊ฑฐ๋ถ€ ๋ฐ˜์‘์„ ๋ณด์ธ 4๋งˆ๋ฆฌ์˜ ์˜์žฅ๋ฅ˜๋Š” ์ œ์™ธํ•˜์˜€๊ณ , ๋Šฆ์€๊ฑฐ๋ถ€๋ฐ˜์‘๊ตฐ(n = 12)์„ ์ƒ์กด๊ตฐ(n = 18)๊ณผ ๋น„๊ตํ–ˆ๋‹ค. ์˜ˆ์ธก ๋ฐ”์ด์˜ค๋งˆ์ปค ๋ฐœ๊ตด์„ ์œ„ํ•ด์„œ ์ž‘๋™ ๋ฐ ๊ธฐ์–ต T ์„ธํฌ, ํ•ญ ฮฑGal ํ•ญ์ฒด, ํ•ญ non-ฮฑGal ํ•ญ์ฒด, ๊ณต์—ฌ์ž ํŠน์ด ํ•ญ์ฒด, ๋ณด์ฒด(C3a) ์ˆ˜์น˜๋ฅผ ๋ถ„์„ํ–ˆ๋‹ค. ๊ฒฐ๊ณผ: GTKOm ๋ผ์ง€ ๊ฐ๋ง‰์˜ ์žฅ๊ธฐ ์œ ํšจ์„ฑ ์—ฐ๊ตฌ์—์„œ CD20๊ตฐ์€ ์ด์‹ํŽธ์˜ ์žฅ๊ธฐ ์ƒ์กด์„ ๋ณด์˜€๊ณ (>375, >187, >187, >83์ผ), ์ด๋Š” ๋Œ€์กฐ๊ตฐ๋ณด๋‹ค(165, 91, 72, 55, 37์ผ)๋ณด๋‹ค ๊ธธ์—ˆ๋‹ค(P = 0.008). ๊ฑฐ๋ถ€๋ฐ˜์‘์ด ์˜จ ์‹œ์ ์— ํ™œ์„ฑ B์„ธํฌ๋Š” CD20๊ตฐ์ด ๋Œ€์กฐ๊ตฐ๋ณด๋‹ค ๋‚ฎ์•˜๋‹ค(P = 0.043). ๊ฑฐ๋ถ€๋ฐ˜์‘ ์‹œ์ ์— ๋Œ€์กฐ๊ตฐ์˜ ๋ฐฉ์ˆ˜ C3a ๋†๋„๊ฐ€ ์ˆ  ์ „๋ณด๋‹ค ์ฆ๊ฐ€ํ–ˆ๊ณ (P = 0.043), ๋น„์Šทํ•œ ์‹œ๊ธฐ์˜ CD20๊ตฐ๋ณด๋‹ค ๋†’์•˜๋‹ค(P = 0.014). 4์ฃผ์งธ์™€ ๊ฑฐ๋ถ€๋ฐ˜์‘์ด ์˜จ ์‹œ์ ์˜ ํ•ญ-non-ฮฑGal IgG๋„ ๋Œ€์กฐ๊ตฐ์—์„œ๋งŒ ์ˆ˜์ˆ  ์ „๋ณด๋‹ค ์ฆ๊ฐ€ํ–ˆ๋‹ค(P = 0.013). ์˜ˆ์ธก ๋ฐ”์ด์˜ค ๋งˆ์ปค ์—ฐ๊ตฌ์—์„œ 2 ์ฃผ์งธ์˜ CD8+IFNฮณ+ ์„ธํฌ์™€ 4 ์ฃผ์งธ์˜ ๋ฐฉ์ˆ˜C3a๋Š” ๊ฑฐ๋ถ€๋ฐ˜์‘๊ตฐ์—์„œ ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€ํ–ˆ๋‹ค. ์ˆ˜์‹ ์ž ์กฐ์ž‘ ํŠน์„ฑ ๊ณก์„  ํ•˜์˜ ๋„“์ด๋Š” 4์ฃผ์งธ ๋ฐฉ์ˆ˜์˜ C3a์˜ ๊ฐ’์€ 0.847, 2์ฃผ์งธ CD8+IFNฮณ+ ์„ธํฌ์˜ ๊ฐ’์€ 0.715 ์ด์—ˆ๋‹ค. ์ด๋Š” ๋ฐฉ์ˆ˜์˜ C3a๋Š” ์šฐ์ˆ˜ํ•œ, CD8+IFNฮณ+ ์„ธํฌ๋Š” ํ—ˆ์šฉ๊ฐ€๋Šฅํ•œ ํŒ๋ณ„๋ ฅ์„ ๊ฐ€์ง์„ ์˜๋ฏธํ•œ๋‹ค. ๊ฒฐ๋ก : ํ•ญ-CD20 ํ•ญ์ฒด๋ฅผ ํฌํ•จํ•œ ๋ฉด์—ญ์–ต์ œ์ œ ์กฐํ•ฉ์„ ์‚ฌ์šฉํ•˜์—ฌ์•ผ GTKOm ๋ผ์ง€ ๊ฐ๋ง‰์˜ ์ „์ธต ์ด์‹ํŽธ์˜ ์žฅ๊ธฐ ์ƒ์กด์ด ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ–ˆ๋‹ค. ์ด๋Š” ๋ผ์ง€-์˜์žฅ๋ฅ˜ ์ด์ข…๊ฐ๋ง‰์ด์‹์—์„œ ฮฑGal์„ ๋ฐœํ˜„ํ•˜์ง€ ์•Š๋Š” ๋ผ์ง€ ๊ฐ๋ง‰์„ ์‚ฌ์šฉํ•˜์—ฌ๋„ B ์„ธํฌ์™€ ๋ณด์ฒด ํ™œ์„ฑ์„ ์–ต์ œํ•˜๋Š” ๊ฒƒ์ด ์ด์‹ํŽธ์˜ ์žฅ๊ธฐ ์ƒ์กด์— ์ค‘์š”ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋˜ํ•œ 2 ์ฃผ์งธ์˜ CD8+IFNฮณ+ ์„ธํฌ์™€ 4 ์ฃผ์งธ ๋ฐฉ์ˆ˜์˜C3a๋Š” ๋ผ์ง€-์˜์žฅ๋ฅ˜ ์ „์ธต ๊ฐ๋ง‰ ์ด์‹์—์„œ ๊ฑฐ๋ถ€ ๋ฐ˜์‘์„ ์˜ˆ์ธกํ•˜๋Š” ์‹ ๋ขฐํ• ๋งŒํ•œ ๋ฐ”์ด์˜ค๋งˆ์ปค๋กœ ํ–ฅํ›„ ์ด์ข…๊ฐ๋ง‰ ์ž„์ƒ์‹œํ—˜์—์„œ ๊ฑฐ๋ถ€ ๋ฐ˜์‘์„ ์˜ˆ์ธกํ•˜๋Š” ๊ธฐ์ค€์œผ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.Chapter 1 ................................................................................................................. 1 Long-term survival of full thickness corneal xenografts from ฮฑ1,3-galactosyltransferase gene-knockout miniature pigs in nonhuman primates Introduction .................................................................................................. 2 Materials and Methods ................................................................................. 4 Results .......................................................................................................... 12 Discussion .................................................................................................... 29 Chapter 2 ............................................................................................................... 33 Predictive biomarkers for graft rejection in pig-to-non-human primate corneal xenotransplantation Introduction ................................................................................................ 34 Materials and Methods ............................................................................... 36 Results .......................................................................................................... 46 Discussion .................................................................................................... 61 References ............................................................................................................. 64 Abstract in Korean ............................................................................................... 71โ€ƒ LIST OF TABLES AND FIGURES Chapter 1 Table 1.1 ............................................................................................................ 10 Table 1.2 ............................................................................................................ 16 Figure 1.1 .......................................................................................................... 11 Figure 1.2 .......................................................................................................... 18 Figure 1.3 .......................................................................................................... 19 Figure 1.4 .......................................................................................................... 20 Figure 1.5 .......................................................................................................... 21 Figure 1.6 .......................................................................................................... 22 Figure 1.7 .......................................................................................................... 23 Figure 1.8 .......................................................................................................... 25 Figure 1.9 .......................................................................................................... 26 Figure 1.10 ........................................................................................................ 27 Figure 1.11 ........................................................................................................ 28 Figure 1.12 ........................................................................................................ 32 Chapter 2 Table 2.1 ............................................................................................................ 41 Table 2.2 ............................................................................................................ 43 Table 2.3 ............................................................................................................ 49 Table 2.4 ............................................................................................................ 50 Table 2.5 ............................................................................................................ 52 Table 2.6 ............................................................................................................ 54 Table 2.7 ............................................................................................................ 56 Table 2.8 ............................................................................................................ 58 Figure 2.1 .......................................................................................................... 44 Figure 2.2 .......................................................................................................... 60Docto

    Evolution of contact area between a high-speed slider and ice surface

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2017. 8. ๊น€ํ˜ธ์˜.์–ผ์Œ์€ ๋…ํŠนํ•œ ๋งˆ์ฐฐํŠน์„ฑ์„ ๊ฐ–๋Š”๋ฐ, ๋งˆ์ฐฐ๋  ๋•Œ ์–ผ์Œ ํ‘œ๋ฉด์ด ๋งˆ์ฐฐ์—ด์— ์˜ํ•ด ๋…น์•„ ์–‡์€ ์ˆ˜๋ง‰์„ ํ˜•์„ฑํ•˜๊ณ , ํ˜•์„ฑ๋œ ์ˆ˜๋ง‰์— ์˜ํ•ด ์ž์ฒด์ ์œผ๋กœ ์œคํ™œ๋˜์–ด ๋‚ฎ์€ ๋งˆ์ฐฐ๋ ฅ์„ ๊ฐ–๋Š”๋‹ค. ์ด๋Ÿฌํ•œ ๋…ํŠนํ•œ ์–ผ์Œ์˜ ๋งˆ์ฐฐ ํŠน์„ฑ์€ ์†๋„๋ฅผ ๊ฒจ๋ฃจ๋Š” ๋™๊ณ„์Šคํฌ์ธ ๋‚˜ ํƒ€์ด์–ด ์‚ฐ์—…, ์‡„๋น™์„  ๋“ฑ์—์„œ ์–ผ์Œ ๋งˆ์ฐฐ๋ ฅ์„ ์ปจํŠธ๋กคํ•˜๊ธฐ ์œ„ํ•ด ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ์ž๋“ค์—๊ฒŒ ์—ฐ๊ตฌ๋˜์–ด์™”๋‹ค. ์•„๋ฌด๋ฆฌ ํ‰ํ‰ํ•œ ์–ผ์Œ์ด๋ผ๋„ ๊ทธ ํ‘œ๋ฉด์—๋Š” ์š”์ฒ ์ด ์กด์žฌํ•˜๊ณ , ์Šฌ๋ผ์ด๋”์™€ ๋ถ€๋ถ„์ ์œผ๋กœ ์ ‘์ด‰ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ํฌ๊ธฐ์™€ ํ˜•ํƒœ์˜ ์‹ค์ ‘์ด‰๋ฉด์ ์„ ํ˜•์„ฑํ•œ๋‹ค. ์ ‘์ด‰๋ฉด์— ํ˜•์„ฑ๋œ ์ˆ˜๋ง‰์˜ ๋„“์ด๋‚˜ ํ˜•ํƒœ๋Š” ์–ผ์Œ ๋งˆ์ฐฐ๋ ฅ์— ํ•„์—ฐ์ ์œผ๋กœ ์˜ํ–ฅ์„ ์ค„ ๊ฒƒ์ด๊ธฐ ๋•Œ๋ฌธ์— ์‹ค์ ‘์ด‰๋ฉด์ ์€ ์–ผ์Œ๋งˆ์ฐฐ์— ์ค‘๋Œ€ํ•œ ์˜ํ–ฅ์„ ์ฃผ๋Š” ์š”์†Œ์ด๋‹ค. ๋•Œ๋ฌธ์— ์–ผ์Œ ๋งˆ์ฐฐ๋ฉด์˜ ์‹ค์ ‘์ด‰๋ฉด์ ์€ ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ์ž๋“ค์— ์˜ํ•ด ๊ด€์ธก๋˜์–ด์™”์œผ๋‚˜ ๋งˆ์ฐฐ ํ›„ ์ •์ง€๋œ ์ƒํƒœ์—์„œ๋งŒ ๊ด€์ฐฐ๋˜์–ด์™”๊ณ , ๋™์ ์ธ ์ƒํ™ฉ์—์„œ ๊ด€์ฐฐ๋œ ๋ฐ” ์—†๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์–ผ์Œ ๋งˆ์ฐฐ๋ฉด์—์„œ ์ผ์–ด๋‚˜๋Š” ์ „๋ฐ˜์‚ฌ ํŠน์„ฑ์„ ์ด์šฉํ•œ ์‹ค์ ‘์ด‰๋ฉด์ ์„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๊ฐ€์‹œํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์„์˜ ํ”„๋ฆฌ์ฆ˜์„ ์–ผ์Œ ์œ„์—์„œ ๋งˆ์ฐฐ์‹œํ‚ค๋ฉฐ, ๋งˆ์ฐฐ๋ฉด์„ ํ”„๋ฆฌ์ฆ˜์„ ํ†ตํ•ด ๋น„์Šค๋“ฌํ•˜๊ฒŒ ๋ฐ”๋ผ๋ณด๋ฉด ์„์˜ ํ”„๋ฆฌ์ฆ˜๊ณผ ์–ผ์Œ์ด ์ง์ ‘ ๋งž๋‹ฟ์ง€ ์•Š๋Š” ๊ณณ์€ ์ „๋ฐ˜์‚ฌ๊ฐ€ ์ผ์–ด๋‚˜ ๋ฐ๊ฒŒ ๋ณด์ด์ง€๋งŒ, ์„์˜ ํ”„๋ฆฌ์ฆ˜๊ณผ ์–ผ์Œ์ด ๋งž๋‹ฟ๋Š” ๊ณณ์€ ๋น›์ด ํˆฌ๊ณผํ•˜์—ฌ ์–ด๋‘ก๊ฒŒ ๋ณด์—ฌ, ์–ผ์Œ์ด ์ง์ ‘ ๋งž๋‹ฟ๋Š” ์‹ค์ ‘์ด‰๋ฉด์ ์„ ๊ฐ€์‹œํ™”ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์œผ๋กœ ๊ด€์ฐฐ๋œ ์–ผ์Œ์˜ ์‹ค์ ‘์ด‰๋ฉด์ ์€ ๋‹ค์ˆ˜์˜ ์ ‘์ด‰์ ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ์œผ๋ฉฐ, ์ด๋“ค ์ ‘์ด‰์ ๋“ค์€ ์–ผ์Œ ํ‘œ๋ฉด์˜ ๋Œ๊ธฐ๋“ค์ด ํ”„๋ฆฌ์ฆ˜๊ณผ ๋‹ฟ์•„ ํ˜•์„ฑ๋œ ๊ฒƒ๋“ค์ด๋‹ค. ์ด๋“ค ์ ‘์ด‰์ ๋“ค์€ ํ”„๋ฆฌ์ฆ˜๊ณผ์˜ ์ ‘์ด‰ ์ดˆ๊ธฐ์—๋Š” ๋ชน์‹œ ์ž‘๋‹ค๊ฐ€๋„ ๋งˆ์ฐฐ์ด ์ผ์–ด๋‚˜๋Š” ์ค‘์— ๊ทธ ํฌ๊ธฐ๊ฐ€ ๋น ๋ฅด๊ฒŒ ์„ฑ์žฅํ•ด ๋‚˜๊ฐ€๋Š” ํ˜„์ƒ์ด ๋ฐœ๊ฒฌ๋˜์—ˆ๋‹ค. ์ด๋Š” ๋งˆ์ฐฐ์—ด์— ์˜ํ•ด ์–ผ์Œ ๋Œ๊ธฐ์˜ ์ƒ๋‹จ์ด ๋…น์•„๋‚˜๊ฐ€ ๋งˆ์ฐฐ๋ฉด์ ์ด ์ ์ฐจ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์ด๋Š” ๊ธฐ์กด์˜ ์–ผ์Œ ๋งˆ์ฐฐ์„ ์—ฐ๊ตฌํ•œ ์„ ํ–‰์—ฐ๊ตฌ์ž๋“ค์ด ๊ณ ๋ คํ•˜์ง€ ๋ชปํ–ˆ๋˜ ํ˜„์ƒ์ด๋‹ค. ์•ž์„œ ๊ด€์ฐฐํ•œ ์ ‘์ด‰์ ์˜ ์„ฑ์žฅ๊ณผ์ •์„ ๋ณด๋‹ค ํ†ต์ œ๋œ ํ™˜๊ฒฝ์—์„œ ๊ด€์ฐฐํ•˜๊ธฐ ์œ„ํ•ด, ๋ฐ˜๊ตฌํ˜•์œผ๋กœ ์ œ์ž‘๋œ ์–ผ์Œ ์‹œํŽธ์„ ์—ฐ๋งˆ๋œ ์„์˜๋””์Šคํฌ์— ์˜ฌ๋ฆฌ๊ณ , ์„์˜๋””์Šคํฌ๋ฅผ ํšŒ์ „์‹œ์ผœ ๊ณ ์ •๋œ ๋ฐ˜๊ตฌํ˜• ์–ผ์Œ ์‹œํŽธ๊ณผ ๋งˆ์ฐฐ์‹œ์ผœ ๊ทธ ๋งˆ์ฐฐ๋ ฅ์„ ์ธก์ •ํ•˜์˜€๋‹ค. ๋™์‹œ์— ์ดˆ๊ณ ์† ์นด๋ฉ”๋ผ๊ฐ€ ์„์˜๋””์Šคํฌ ์ธก๋ฉด์— ์„ค์น˜๋˜์–ด ์–ผ์Œ ์‹œํŽธ์™€ ์„์˜๋””์Šคํฌ์˜ ์‹ค์ œ์ ‘์ด‰๋ฉด์ ์„ ๋””์Šคํฌ ์˜†๋ฉด์„ ํ†ตํ•ด ์ดฌ์˜ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์‹ค์ ‘์ด‰๋ฉด์ ๊ณผ ๋งˆ์ฐฐ๋ ฅ์ด ์‹œ๊ฐ„์— ๋”ฐ๋ผ ์ ์ฐจ ์ฆ๊ฐ€ํ•˜๋Š” ํ˜„์ƒ์„ ํ†ต์ œ๋œ ํ™˜๊ฒฝ์—์„œ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์–ผ์Œ์˜ ๋ฐ˜์ง€๋ฆ„ , ์†๋„ , ๊ทธ๋ฆฌ๊ณ  ์ˆ˜์งํ•ญ๋ ฅ ์„ ๋ณ€ํ™”์‹œ์ผœ ๊ฐ€๋ฉฐ ์‹ค์ ‘์ด‰๋ฉด์ ๊ณผ ๋งˆ์ฐฐ๋ ฅ์„ ์ธก์ •ํ•˜๊ฒŒ ๋˜๋ฉด ๋„“๊ฒŒ ์‚ฐํฌ๋œ ๋ฐ์ดํ„ฐ ๋ผ์ธ๋“ค์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ ๊ตฌํ˜•์œผ๋กœ ๊ฐ€์ •๋œ ์–ผ์Œ ๋Œ๊ธฐ๊ฐ€ ๋งˆ์ฐฐ์—ด์— ์˜ํ•ด ๋…น์•„๊ฐ€๋Š” ๊ณผ์ •์„ ๋…น๋Š”์  ๊ทผ์ฒ˜์—์„œ ์Šค์ผ€์ผ๋ง ๋ถ„์„ํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ด€๊ณ„์‹์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. , . ์œ„์˜ ๊ด€๊ณ„์‹์œผ๋กœ ์–ผ์Œ์˜ ๋ฐ˜์ง€๋ฆ„ R, ์†๋„ U, ๊ทธ๋ฆฌ๊ณ  ์ˆ˜์งํ•ญ๋ ฅ ์„ ๋„์‹ํ•˜๊ฒŒ ๋˜๋ฉด ๋„“๊ฒŒ ์‚ฐํฌ๋˜์–ด์žˆ๋˜ ๋ฐ์ดํ„ฐ ๋ผ์ธ๋“ค์ด ํ•˜๋‚˜์˜ ๋งˆ์Šคํ„ฐ ์ปค๋ธŒ ์œ„์— ์ˆ˜๋ ดํ•˜๋Š” ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ œ์‹œ๋œ ๋ชจ๋ธ๊ณผ ์‹คํ—˜๊ฒฐ๊ณผ ๋ชจ๋‘์—์„œ ์‹ค์ ‘์ด‰๋ฉด์ ๊ณผ ๋งˆ์ฐฐ๋ ฅ์ด ์ˆ˜์งํ•ญ๋ ฅ์— ๋ฌด๊ด€ํ•˜๋‹ค๋Š” ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์–ผํ• ๋ณด๊ธฐ์— Amontons law of friction์— ์œ„๋ฐฐ๋˜์–ด ๋ณด์ด๋Š” ์ด๋Ÿฌํ•œ ํ˜„์ƒ์€ ์Šฌ๋ผ์ด๋”์— ์ž‘์šฉํ•˜๋Š” ์ˆ˜์ง๋ ฅ์ด ์–ผ์Œ๊ณผ ์Šฌ๋ผ์ด๋” ์‚ฌ์ด์— ํ˜•์„ฑ๋œ ๋ฌผ์ธต์˜ ์œ ๋™์— ์˜ํ–ฅ์„ ์ฃผ์ง€ ๋ชปํ•˜์—ฌ ๋‚˜ํƒ€๋‚˜๋Š” ํ˜„์ƒ์ด๋‹ค.1. ์„œ๋ก  1 2. ์‹คํ—˜์žฅ์น˜ ๋ฐ ์‹คํ—˜๋ฐฉ๋ฒ• 6 2.1 ๋งˆ์ฐฐ์ธก์ •์žฅ๋น„ ๋ฐ ๋งˆ์ฐฐ์ธก์ •๋ฐฉ๋ฒ• 6 2.2 ์–ผ์Œ ๋งˆ์ฐฐ๋ฉด ๊ฐ€์‹œํ™” ์‹คํ—˜์žฅ์น˜ ๋ฐ ์‹คํ—˜๋ฐฉ๋ฒ• 12 2.3 ๋‹จ์ผ ์ ‘์ด‰์ ์˜ ์ ‘์ด‰๋ฉด ์„ฑ์žฅ ๊ฐ€์‹œํ™” ์‹คํ—˜์žฅ์น˜ ๋ฐ ์‹คํ—˜๋ฐฉ๋ฒ• 14 3. ๋‹จ์ผ์ ‘์ด‰์ ์˜ ์„ฑ์žฅ๊ณผ์ • ์ด๋ก ์  ๋ชจ๋ธ๋ง 18 4. ์‹คํ—˜๊ฒฐ๊ณผ ๋ฐ ๋…ผ์˜ 24 4.1 ์–ผ์Œ ๋งˆ์ฐฐ๋ฉด ๊ฐ€์‹œํ™” ์‹คํ—˜ ๊ฒฐ๊ณผ 24 4.2 ๋‹จ์ผ ์ ‘์ด‰๋ฉด์˜ ์„ฑ์žฅ ๊ฐ€์‹œํ™” ์‹คํ—˜ 31 5. ๊ฒฐ๋ก  40 ์ฐธ๊ณ ๋ฌธํ—Œ 41 Abstract (์˜๋ฌธ์ดˆ๋ก) 45Maste

    ์ž์œจํŒŒ์ข…์„ ์œ„ํ•œ ๋‘๋‘‘๊ฒ€์ถœ ๋ฐ ์ถ”์ข…์‹œ์Šคํ…œ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋ฐ”์ด์˜ค์‹œ์Šคํ…œยท์†Œ์žฌํ•™๋ถ€(๋ฐ”์ด์˜ค์‹œ์Šคํ…œ๊ณตํ•™),2020. 2. ๊น€ํ•™์ง„.This paper proposes a stereovision-based auto-guidance method for a riding cultivator. Ridge and furrow are corrugated field structures created before seeding operation for good water balance in a field. The stereovision provides the ability to aware these field structures and determine a navigation path. In developing an efficient ridge and furrow classification algorithm for the outdoor application, however, the stereovision would suffer from the erratic movement of a vehicle on uneven surface and interferences caused by strong sunlight. The developed algorithm adopts a combination of vdisparity representation, the Otsus thresholding and a roll angle compensation method proposed to overcome the problems. Feasibility tests were conducted using video data collected under outdoor conditions to analyze the image classification accuracy of the algorithm. The developed algorithm was able to classify the ridge and furrow with over 90% of accuracy in the rough outdoor conditions. Field testing with the automatic guided riding cultivator equipped with the stereo camera proved the developed ridge tracking algorithm would be applicable to a real-world agricultural application, showing the lateral deviations of the average RMSE of 2.5cm and 6.2cm in a flat field and a hilly field respectively.์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ์˜ ์ฐจ์„  ๊ฐ์ง€ ๊ธฐ์ˆ  ๋ฐ ์ž์œจ์ฃผํ–‰ ๋ฐฉ์ œ๊ธฐ์˜ ์ž‘๋ฌผ์—ด ๊ฐ์ง€ ๊ธฐ์ˆ ๊ณผ ๊ฐ™์ด ์ฐจ๋Ÿ‰์—์„œ ์‚ฌ์šฉ๋˜๋Š” ๋น„์ „ ๋ฐฉ์‹์˜ ์ž์œจ์ฃผํ–‰์€ ์‚ฌ๋žŒ์˜ ๋ˆˆ์„ ๋Œ€์‹ ํ•˜์—ฌ ์ฐจ๋Ÿ‰์˜ ๊ฒฝ๋กœ๋ฅผ ํŒ๋‹จํ•œ๋‹ค. ๋‘๋‘‘์€ ๊ณ ๋ž‘ ๊ด€๊ฐœ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋ฐญ์˜ ์ง€ํ˜•์  ํŠน์„ฑ์ด๋ฉฐ ์ž‘๋ฌผ์—ด๊ณผ ๊ฐ™์ด ํ‰ํ–‰ํ•œ ํ˜•ํƒœ๋กœ ๋‚˜์ง€๋งŒ ์ž‘๋ฌผ์—ด๊ณผ๋Š” ๋‹ค๋ฅด๊ฒŒ ์ƒ‰์˜ ํŠน์„ฑ์ด ๋ถ„๋ช…ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์ง€๋งŒ ๋‘๋‘‘๊ณผ ๊ณ ๋ž‘์˜ ๋†’๋‚ฎ์ด ์ฐจ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ๋†์ง€๋‚ด ์ฃผํ–‰ ๊ฒฝ๋กœ๋ฅผ ํŒ๋‹จ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์Šคํ…Œ๋ ˆ์˜ค ์นด๋ฉ”๋ผ๋ฅผ ํ†ตํ•ด ์–ป์€ 3์ฐจ์› ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ๋‘๋‘‘์˜ ๋†’๋‚ฎ์ด ํŠน์„ฑ์œผ๋กœ ๋‘๋‘‘๊ณผ ๊ณ ๋ž‘์„ ๊ตฌ๋ถ„ํ•˜๊ณ  ์ž์œจ์ฃผํ–‰ ํŠธ๋ž™ํ„ฐ์˜ ์ฃผํ–‰ ๊ธฐ์ค€์„ ์„ ์ถ”์ถœํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์›๋ฆฌ๋Š” ์Šคํ…Œ๋ ˆ์˜ค ์นด๋ฉ”๋ผ๋ฅผ ํ†ตํ•ด ์–ป์–ด์ง„ ๋‘๋‘‘ ํ‘œ๋ฉด์˜ ๊นŠ์ด ์ด๋ฏธ์ง€๊ฐ€ ๋ฐญ์˜ ๊ฑฐ์นœ ํ‘œ๋ฉด๊ณผ ํŠธ๋ž™ํ„ฐ์˜ ๊ฑฐ๋™์— ์˜ํ•ด ๋‚˜ํƒ€๋‚˜๋Š” ์นด๋ฉ”๋ผ์™€ ์ง€๋ฉด์˜ ์ƒ๋Œ€์ ์ธ ๋ณ€ํ™”๊ฐ’์„ Blurring ๊ธฐ๋ฒ•๊ณผ Contour ๋ผ์ธ์˜ ๊ธฐ์šธ๊ธฐ ์ •๋ณด๋ฅผ ํ†ตํ•ด ์˜ˆ์ธกํ•˜๊ณ , ์ด๋ฅผ ์–ป์–ด์ง„ ๊นŠ์ด ๋ฐ ์ดํ„ฐ์— ๋ฐ˜์˜ํ•œ๋‹ค. ๋ณด์ •๋œ ๊นŠ์ด ์ด๋ฏธ์ง€๋Š” v-disparity ๋ฐฉ๋ฒ•๊ณผ Otsus Thresholding์„ ํ†ตํ•ด ์ตœ์ข…์ ์œผ๋กœ ๋‘๋‘‘๊ณผ ๊ณ ๋ž‘์„ ๊ตฌ๋ถ„ํ•˜๊ณ  ๊ฐ Segmentation์— ๋Œ€ํ•œ ๋‘๋‘‘์˜ ์ค‘์•™๊ฐ’๋“ค์— ๋Œ€ํ•œ ์„ ํ˜• ํšŒ๊ท€ ๋ถ„์„์„ ํ†ตํ•ด ์ตœ์ข…์ ์ธ ์ฃผํ–‰ ๊ธฐ์ค€์„ ์„ ์ถ”์ถœํ•œ๋‹ค. ๊ฐœ๋ฐœ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์‹ค์ œ ๋‘๋‘‘ ํ™˜๊ฒฝ์—์„œ ์ทจ๋“ํ•œ ์˜์ƒ ๋ฐ์ดํ„ฐ๋ฅผ ํ† ๋Œ€๋กœ ์ด๋ฏธ์ง€ ๊ฒ€์ถœ๋ฅ  ๋ถ„์„๊ณผ ํšก๋ณ€์œ„ ์˜ค์ฐจ ๋ถ„์„์„ ์ˆ˜ํ–‰ ํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋‘๋‘‘ ๊ฒ€์ถœ์œจ ๋ถ„์„ ๊ฒฐ๊ณผ ํ‰๊ท  94.2%์˜ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€์œผ๋ฉฐ ์‹ค์ œ ์ž์œจ์ฃผํ–‰ ํ”Œ๋žซํผ์— ์ ์šฉ ๊ฒฐ๊ณผ 4.05cm์˜ ์ถ”์ข… ์˜ค์ฐจ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค.Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Purpose of Research 2 1.3. Review of Literature 3 Chapter 2. Meterials and Methods 6 2.1. Ridge and Furrow 6 2.2. Principle of Stereovision 8 2.3. Ridge and Furrow Detection and Tracking Algorithm 11 2.3.1 Ridge and Furrow Classification Algorithm using vdisparity and Otsus thresholding 12 2.3.2 Roll Angle Compensation 15 2.3.3 Sliding Window Technique 17 2.3.4 ROI Setting . 18 2.3.5 Path Tracking Model 19 2.4. Feasibility Tests 23 2.4.1 Data Collection 23 2.4.2 Image Calssification Accuracy Analysis 25 2.5. Field Testing 26 2.5.1 Ridge Tracking System 26 2.5.2 Test Fields and Performance Evaluation 28 Chapter 3. Results and Discussion 31 3.1. Effect of Sunlight on Detection Performance 31 3.2. Feasibility of Using Roll Angle Compensation Method for Uneven Surface Movement. 33 3.3. Field Test Result 35 Chapter 4. Conclusions 38 Bibliography 39 Abstract in Korean 43Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ํ•ด๋ถ€ํ•™ ์ „๊ณต,1999.Maste

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    Thesis(doctors) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ(์‹ ๊ฒฝ๊ณผํ•™์ „๊ณต),2010.2.Docto

    Identification of maximal noun phrases:using the head of base phrases

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    Maste

    ์‚ฌ๋žŒ๋‡Œ์˜ ๊ตฌ์กฐ์™€ ๊ธฐ๋Šฅ

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    ๋Œ€ํ‘œ์ €์ž: ์ •์ฒœ๊ธฐ์ €์ž: ์ง€์ œ๊ทผ, ํ™ฉ์˜์ผ, ๊น€์žฌํ˜•, ๊น€๋ช…์ˆ˜, ์ตœ์ฐฌ์˜, ํ™ฉ์ •๋ฏผ, ๊ตฌ์ž์›, ๋ฌธ์ œ์ผ, ๊น€์ง€์ˆ˜, ๊ฒฝ์ •์ˆ™, ์œค์ฐฝํ˜ธ, ๊ถŒ์ค€์ˆ˜, ๊น€์ œ์ค‘, ์‹ ๋ฏผ์„ญ, ๊น€๋ถ•๋…„, ํ•˜ํƒœํ˜„, ํ•˜๊ทœ์„ญ, ์ด์ค€์˜, ๋ฐ•์ˆ˜
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