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    ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •๊ณผ ์ ์ ˆ์„ฑ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ƒํ™œ๊ณผํ•™๋Œ€ํ•™ ์†Œ๋น„์žํ•™๊ณผ, 2018. 2. ์ตœํ˜„์ž.๊ฐ€๊ณ„๋Š” ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์žฌ๋ฌดํ–‰๋™์„ ์ˆ˜ํ–‰ํ•˜๊ณ  ๊ฐ๊ธฐ ๋‹ค๋ฅธ ํฌํŠธํด๋ฆฌ์˜ค๋ฅผ ๋ณด์œ ํ•˜๊ฒŒ ๋œ๋‹ค. ์ด์ฒ˜๋Ÿผ ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด์  ์˜์‚ฌ๊ฒฐ์ •์˜ ์‹œ์ž‘์€ ์žฌ๋ฌด๋ชฉํ‘œ์˜ ์„ค์ •์ด๋ผ๋Š” ์ ์—์„œ ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด๋ชฉํ‘œ์— ๋Œ€ํ•œ ์‹ฌ์ธต์ ์ธ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ€๊ณ„๊ฐ€ ์–ด๋– ํ•œ ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ์šฐ์„ ์ ์œผ๋กœ ์„ค์ •ํ•˜๊ณ  ์žˆ๋Š”์ง€๋ฅผ ์‚ดํŽด๋ณด๊ณ , ๊ฐ€๊ณ„๊ฐ€ ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ์ ์ ˆํ•˜๊ฒŒ ์„ค์ •ํ•˜์˜€๋Š”์ง€๋ฅผ ๊ทœ๋ช…ํ•จ์œผ๋กœ์จ ๊ฐ€๊ณ„๊ฐ€ ์žฌ๋ฌด์ƒํƒœ๋ฅผ ๊ฐœ์„ ํ•˜๋Š”๋ฐ ๋ฐ”๋žŒ์งํ•œ ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ์„ค์ •ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ์œ ์˜๋ฏธํ•œ ์‹œ์‚ฌ์ ์„ ์ œ๊ณตํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด์  ์•ˆ์ •๊ณผ ์„ฑ์žฅ์„ ์œ„ํ•œ ์žฌ๋ฌด์„ค๊ณ„ ๋ฐ ์žฌ๋ฌดํ–‰๋™์˜ ๊ธฐ์ค€์œผ๋กœ ์ •์˜ํ•˜๊ณ , ๊ฐ€๊ณ„๊ฐ€ ์žฌ๋ฌด์  ์•ˆ์ •๊ณผ ์„ฑ์žฅ์„ ์œ„ํ•ด ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ์–ด๋–ป๊ฒŒ ์„ค์ •ํ•˜๊ณ  ์žˆ๋Š”์ง€๋ฅผ ์‚ดํŽด๋ณด๊ณ  ์žฌ๋ฌด๋ชฉํ‘œ์˜ ์ ์ ˆ์„ฑ์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ํ‹€๋กœ์จ ๊ฐ€๊ณ„ ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์„ค์ •ํ•œ ์—ฐ๊ตฌ๋ฌธ์ œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. [์—ฐ๊ตฌ๋ฌธ์ œ 1]์—์„œ๋Š” ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด๋ชฉํ‘œ ์šฐ์„ ์ˆœ์œ„๋ฅผ ์–ด๋–ป๊ฒŒ ์„ค์ •ํ•˜๊ณ  ์žˆ๋Š”์ง€์™€ ์ตœ์šฐ์„  ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์— ๋”ฐ๋ฅธ ๊ฐ€๊ณ„์˜ ํŠน์„ฑ์„ ์‚ดํŽด๋ณด๊ณ , [์—ฐ๊ตฌ๋ฌธ์ œ 2]์—์„œ๋Š” ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์— ๊ธฐ์ดˆํ•˜์—ฌ ๊ฐ€๊ณ„ ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์˜ ์ ์ ˆ์„ฑ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. [์—ฐ๊ตฌ๋ฌธ์ œ 3]์—์„œ๋Š” ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ๋ถ€์ ์ ˆํ•˜๊ฒŒ ์„ค์ •ํ–ˆ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋Š” ๊ฐ€๊ณ„, ์ฆ‰ ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ์˜ ์ค€๊ฑฐ๊ธฐ์ค€์„ ์ถฉ์กฑํ•˜์ง€ ๋ชปํ–ˆ์Œ์—๋„ ๊ด€๋ จ ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ์„ค์ •ํ•˜์ง€ ์•Š์€ ๊ฐ€๊ณ„์˜ ํŠน์„ฑ์„ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ํ•œ๊ตญ ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด๊ฑด๊ฐ• ์—ฐ๊ตฌ ์กฐ์‚ฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์˜€์œผ๋ฉฐ, ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋„์ถœ๋œ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ฐ€๊ณ„ ์žฌ๋ฌด์ƒํƒœ๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ  ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐ€๊ณ„๊ฐ€ ์„ค์ •ํ•ด์•ผ ํ•  ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ๊ตฌ์ฒดํ™”ํ•˜๊ณ  ์žฌ๋ฌด๋ชฉํ‘œ์˜ ์ ์ ˆ์„ฑ์„ ํ‰๊ฐ€ํ•˜๋Š”๋ฐ ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ๊ฐ€ ์œ ์šฉํ•œ ์—ฐ๊ตฌ ํ‹€์ด ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘˜์งธ, ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ • ์šฐ์„ ์ˆœ์œ„๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ถฉ๋ถ„ํ•œ ๋…ธํ›„์ž๊ธˆ ์ค€๋น„๋ฅผ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์žฌ๋ฌด๋ชฉํ‘œ๋กœ ์„ค์ •ํ•˜๋Š” ๊ฐ€๊ณ„๊ฐ€ ๋งŽ์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ๊ฐ€๊ณ„๋Š” ์ฃผ๋กœ ๋‹จ๊ธฐ์ ์ด๊ณ  ์žฌ๋ฌด์  ์•ˆ์ •์„ ์œ„ํ•œ ์žฌ๋ฌด๋ชฉํ‘œ๋ณด๋‹ค ์žฌ๋ฌด์  ์„ฑ์žฅ์„ ์œ„ํ•œ ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ์šฐ์„ ์ ์œผ๋กœ ์„ค์ •ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์žฌ๋ฌด์  ์•ˆ์ •์ด ๊ฐ–์ถฐ์ ธ ์žˆ์ง€ ์•Š์€ ๊ฐ€๊ณ„์˜ ๊ฒฝ์šฐ ๋ฌด์กฐ๊ฑด ์žฌ๋ฌด์  ์„ฑ์žฅ์„ ์ถ”๊ตฌํ•˜๊ธฐ ๋ณด๋‹ค๋Š” ํ˜„์žฌ ์žฌ๋ฌด์ƒํƒœ๋ฅผ ์œ ์ง€ยท๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•œ ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ์šฐ์„ ์ ์œผ๋กœ ์„ค์ •ํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. ์…‹์งธ, ๊ฐ€๊ณ„ ํŠน์„ฑ๋ณ„๋กœ ์ตœ์šฐ์„  ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •๊ณผ ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ์˜ ์ถฉ์กฑ์ˆ˜์ค€์—์„œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ์ตœ์šฐ์„  ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์— ๋”ฐ๋ฅธ ๊ฐ€๊ณ„์˜ ํŠน์„ฑ์„ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ์—ฐ๋ น, ์„ฑ๋ณ„ ๋ฐ 18์„ธ ์ดํ•˜์˜ ์ž๋…€์œ ๋ฌด, ์ฃผ๊ฑฐํ˜•ํƒœ, ์†Œ๋“, ์ž์‚ฐ, ๋ถ€์ฑ„, ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ ๊ฐ’ ๋“ฑ์˜ ๊ฒฝ์ œ์  ํŠน์„ฑ๊ณผ ๊ฐ€๊ณ„์˜ ์žฌ๋ฌดํ–‰๋™ ๋ฐ ํ˜„์žฌ ์žฌ๋ฌด์ƒํƒœ์˜ ๋งŒ์กฑ์ˆ˜์ค€์ด ๊ฐ ์žฌ๋ฌด๋ชฉํ‘œ ๋ณ„๋กœ ์ƒ์ดํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ์˜ ์ถฉ์กฑ์ˆ˜์ค€์„ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ ์—ฐ๋ น๋ณ„๋กœ ์ถฉ์กฑ๋น„์ค‘์ด ๋‚ฎ์€ ์ง€ํ‘œ๊ฐ€ ์ƒ์ดํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋„ท์งธ, ๊ฐ€๊ณ„ ์žฌ๋ฌด๋ชฉํ‘œ์˜ ์ ์ ˆ์„ฑ์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ์˜ ์ถฉ์กฑ์—ฌ๋ถ€์™€ ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์—ฌ๋ถ€์— ๋”ฐ๋ผ ๊ฐ€๊ณ„๋ฅผ 4๊ฐ€์ง€ ์œ ํ˜•์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์˜€๊ณ , ์ถฉ๋ถ„ํ•œ ๋…ธํ›„์ž๊ธˆ ๋งˆ๋ จ๊ณผ ๊ณ„ํš์  ๋ถ€์ฑ„์ƒํ™˜์„ ์ œ์™ธํ•œ ๋‚˜๋จธ์ง€ ์žฌ๋ฌด๋ชฉํ‘œ์—์„œ๋Š” ์žฌ๋ฌด๋ชฉํ‘œ ๋ฏธ์„ค์ • ๊ฐ€๊ณ„์˜ ๋น„์ค‘์ด ์„ค์ • ๊ฐ€๊ณ„์˜ ๋น„์ค‘๋ณด๋‹ค ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ๊ฐ€๊ณ„ ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ์ ์ ˆํ•˜๊ฒŒ ์„ค์ •ํ•˜์ง€ ๋ชปํ•œ ๊ฐ€๊ณ„๊ฐ€ ์ƒ๋‹นํ•จ์„ ์˜๋ฏธํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ฒด๊ณ„์ ์ธ ์žฌ๋ฌด์„ค๊ณ„์™€ ์žฌ๋ฌดํ–‰๋™์„ ์œ ๋„ํ•˜๊ณ  ์žฌ๋ฌด์  ์•ˆ์ •๊ณผ ์„ฑ์žฅ์„ ๋‹ฌ์„ฑํ•˜๋Š” ์žฌ๋ฌด ํฌํŠธํด๋ฆฌ์˜ค๋ฅผ ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ฐ€๊ณ„์˜ ํŠน์„ฑ์„ ๊ณ ๋ คํ•œ ์ ์ ˆํ•œ ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์— ๋Œ€ํ•œ ์žฌ๋ฌด๊ต์œก์ด ํ•„์ˆ˜์ ์ด๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์— ์žˆ์–ด ์†Œ๋“ยท์ง€์ถœ ํ‰๊ฐ€์™€ ์ •๊ธฐ์  ์ €์ถ•์—ฌ๋ถ€ ๋“ฑ์˜ ์žฌ๋ฌดํ–‰๋™์ด ์œ ์˜ํ•œ ๋ณ€์ˆ˜๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•ด๋‚˜๊ฐ€๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ธฐ๋ณธ์ ์œผ๋กœ ๊ฐ€๊ณ„์ˆ˜์ง€๋ฅผ ์ถฉ์กฑํ•˜๊ณ  ๋ฏธ๋ž˜๋ฅผ ์œ„ํ•œ ์ค€๋น„๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ์ ์—์„œ ์ง€์ถœ๊ด€๋ฆฌํ–‰๋™๊ณผ ์ €์ถ•ํ–‰๋™์„ ๋ฐ”ํƒ•์œผ๋กœ ์žฌ๋ฌด์  ์•ˆ์ •๊ณผ ์„ฑ์žฅ์„ ๊ฐ–์ถฐ๋‚˜๊ฐ€๋Š” ๊ฐ€๊ณ„์˜ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋‹ค. ์ด์™€ ๊ฐ™์€ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์™€ ๊ฒฐ๋ก ์„ ๋ฐ”ํƒ•์„ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ œ์–ธ์„ ๋„์ถœํ•˜์˜€๋‹ค. ๋จผ์ €, ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด๋ชฉํ‘œ๋Š” ์žฌ๋ฌด์  ์•ˆ์ •๊ณผ ์„ฑ์žฅ์„ ๊ฐ–์ถ”๊ธฐ ์œ„ํ•œ ์žฌ๋ฌด์„ค๊ณ„์™€ ์žฌ๋ฌดํ–‰๋™์˜ ๋ฐฉํ–ฅ์„ ๊ฒฐ์ •ํ•˜๋Š” ๋งŒํผ ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ์žฌ๋ฌด๋ชฉํ‘œ์— ๋Œ€ํ•œ ์ธ์‹๊ณผ ์‹ค์ฒœ์ˆ˜์ค€ ๋“ฑ์„ ๊ณ ๋ คํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์งˆ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‘˜์งธ, ๊ฐ€๊ณ„ ์žฌ๋ฌด๋ชฉํ‘œ์— ๋Œ€ํ•œ ์‹ฌ์ธต์ ์ธ ๋…ผ์˜๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ฐ€๊ณ„ ์žฌ๋ฌด์˜ ์•ˆ์ •์„ฑ๊ณผ ์„ฑ์žฅ์„ ์ค‘์‹ฌ์œผ๋กœ ๊ฐ€๊ณ„ ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ์ •์˜ํ•˜๊ณ  ๊ทธ์— ๋งž๋Š” ์žฌ๋ฌด๋ชฉํ‘œ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€์œผ๋‚˜, ๊ฐ€๊ณ„์˜ ํŠน์„ฑ์— ๋”ฐ๋ผ ๋‹ค์–‘ํ•œ ์žฌ๋ฌด๋ชฉํ‘œ๊ฐ€ ์„ค์ •๋˜๊ณ  ๋น„์žฌ๋ฌด์  ์žฌ๋ฌด๋ชฉํ‘œ๋„ ์ค‘์š”ํ•œ ์š”์†Œ์ธ ๋งŒํผ ์žฌ๋ฌด๋ชฉํ‘œ์˜ ๊ฐœ๋…๊ณผ ๊ตฌ๋ถ„๋ฐฉ์‹์ด ๋ณด๋‹ค ์ •๊ตํ™” ๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ๋”๋ถˆ์–ด ๊ฐ€๊ณ„๊ฐ€ ๋ณด๋‹ค ์‰ฝ๊ณ  ์ •ํ™•ํ•œ ๊ฐ€๊ณ„ ์žฌ๋ฌด์ƒํƒœ ํ‰๊ฐ€๋ฅผ ํ•  ์ˆ˜ ์žˆ๊ณ  ๊ทธ์— ๋งž๋Š” ์ ์ ˆํ•œ ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ์„ค์ •ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋‹ค์–‘ํ•œ ์žฌ๋ฌด์ƒํƒœ ํ‰๊ฐ€๋ฐฉ์‹์ด ์ œ์•ˆ๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ๋˜ํ•œ ๊ฐ ์žฌ๋ฌด๋ชฉํ‘œ์˜ ํŠน์„ฑ๊ณผ ์ค‘์š”๋„๋ฅผ ํŒŒ์•…ํ•˜์—ฌ ๋ฐ”๋žŒ์งํ•œ ๊ฐ€๊ณ„ ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์˜ ๊ฐ€์ด๋“œ๋ผ์ธ์„ ์ œ์‹œํ•  ์ˆ˜ ์žˆ๋Š” ์—ฐ๊ตฌ๋„ ํ•„์š”ํ•˜๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๊ธˆ์œต๊ธฐ๊ด€์€ ๊ฐ€๊ณ„์˜ ๋‹ค์–‘ํ•œ ์žฌ๋ฌด๋ชฉํ‘œ๋ฅผ ๊ณ ๋ คํ•˜๊ณ  ์ด๋ฅผ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ๋„๋ก ๋•๋Š” ๊ธˆ์œต์ƒํ’ˆ์„ ๊ธฐํšํ•˜๊ณ  ์ถœ์‹œํ•  ํ•„์š”๊ฐ€ ์žˆ๊ณ , ํ•™๊ณ„ ๋ฐ ์‹ค๋ฌด์—์„œ ๊ฐ€๊ณ„๊ฐ€ ์„ค์ •ํ•œ ์žฌ๋ฌด๋ชฉํ‘œ์— ๋งž๋Š” ์žฌ๋ฌด๊ต์œก์„ ์‹œํ–‰ํ•œ๋‹ค๋ฉด ์žฌ๋ฌด๊ต์œก์˜ ํšจ๊ณผ๋ฅผ ์ฆ์ง„์‹œํ‚ฌ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.The households carry out a variety of financial activities and have different portfolios to achieve their financial goals. In this way, it is necessary to in-depth study of financial goals of the households in that the start of financial decision making in households is the setting of the financial goal. The purpose of this study is to investigate how households have set financial goals priorities, and to determine whether households has set the goals appropriately. This will provide meaningful implication to enable households to set desirable financial goals to improve their financial status. In this study, the financial goal is defined as the criteria for financial planning and financial behavior for household financial stability and growth. This study examines how households set financial goals for financial stability and growth, and uses financial status evaluation indicators as framework to assess the appropriateness of financial goals. The research questions in this study are as follows. [Research Question 1] is to examine how households set financial goal priorities and the characteristics of households according to their financial goal priority. [Research Question 2] is to assess the appropriateness of household financial goals setting based on whether the household is satisfied with financial status evaluation indicators. [Research Question 3] is to identify the characteristics of households that have set their financial goals improperly, that is, those who did not meet the criteria of the financial status evaluation indicators but did not set financial goals. In order to conduct the research, the analysis was carried out using Consumers Financial Health Research in Korea survey. The results of the analysis are as follows. Firstly, it is confirmed that financial status evaluation indicators could be a useful framework for evaluating household financial situation, specifying the financial goals that household should set based on the results of evaluation, and assessing the appropriateness of financial goals. Secondly, as the result of the analysis of financial goals setting priorities, many household set the most important financial goal to prepare sufficient retirement funds. In addition, the households were primarily setting financial goals for financial growth rather than short-term goals for financial stability. However, for households that are not financially stable, it is necessary to prioritize financial goals to maintain and improve their present financial situation first, rather than pursue financial growth. Thirdly, there was a difference in the level of satisfaction with the financial status evaluation indicator and the most important financial goals setting according to household characteristics. As a result of examining the characteristics of the household according to the top priority financial goal setting, socioeconomic characteristics such as age, gender, presence of children under age 18, house type, income assets, liabilities, financial management behavior and the level of satisfaction of the households financial situation have different effects on each financial goal respectively. As a result of examining the level of satisfaction of the financial status evaluation indicators, each age group has different indicator with a lower level of satisfaction. Fourthly, households are classified into four types, depending on their satisfaction with each financial status evaluation indicator and their financial goals to evaluate the appropriateness of household financial goals. With the exception of financial goals sufficient funds for retirement and planned debt payment, the proportion of households whose financial goals have not been set up is higher than the proportion of households whose financial goals are set. This means that households that fail to set appropriate financial goals are significant. Therefore, it is essential to provide financial education on proper financial goal setting considering the characteristics of the household so that systematic financial planning and financial management behavior can be achieved and financial portfolio that achieves financial stability and growth can be constructed. Lastly, financial behavior such as assessment of their incomeยทexpenditure and regular savings were significant variables in the household financial goal setting. In order to achieve financial goals, it is necessary to strive for financial stability and growth base on spending management and saving behavior in order to meet the household balance and prepare for the future. Based on the results and conclusion of this study, the following suggestions were drawn. Firstly, a variety of studies on financial goal setting are needed because financial goals of the household determine the direction of financial planning and financial behavior to achieve financial stability and growth. Therefor, it is necessary to conduct research considering the level of awareness and practice of financial goals. Secondly, in-depth discussion of household financial goals is needed. In this study, household financial goals were defined with a focus on stability and growth of household finance and corresponding financial goals were studied. However, as financial goals are set according to the characteristics of household and non-financial goals are also important factors, the concept of financial goals and the method of division should be refined. Thirdly, various financial status evaluation methods should be proposed to enable households to make easier and more accurate assessment of household financial situation and to set appropriate financial goals accordingly. In addition, research is also needed to identify the characteristics and importance of financial goals to provide guidelines for setting desirable household financial goals. Finally, financial institutions need to plan and launch financial products considering various financial goals of the household. If financial education that meets the financial goals set by the household is implemented in academic and business field, the effect of education will be enhanced.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ๋ฌธ์ œ์ œ๊ธฐ ๋ฐ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๋ชฉ์  ๋ฐ ์˜์˜ 3 ์ œ 2 ์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 5 ์ œ 1 ์ ˆ ์žฌ๋ฌด๋ชฉํ‘œ 5 1. ์žฌ๋ฌด๋ชฉํ‘œ์˜ ๊ฐœ๋… 5 2. ์žฌ๋ฌด๋ชฉํ‘œ ๊ด€๋ จ ์„ ํ–‰์—ฐ๊ตฌ 9 ์ œ 2 ์ ˆ ์žฌ๋ฌด๋ชฉํ‘œ์™€ ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€ 11 1. ๊ฐ€๊ณ„ ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ์˜ ๊ฐœ๋… 11 2. ๊ฐ€๊ณ„ ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€ ๊ด€๋ จ ์—ฐ๊ตฌ 12 2.1. ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ ๊ฐœ๋ฐœ๊ณผ ์ค€๊ฑฐ๊ธฐ์ค€ 12 2.2. ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ์˜ ์˜์˜์™€ ๊ตฌ๋ถ„๋ฐฉ์‹ 13 2.3. ์žฌ๋ฌด์ƒํƒœ ํ‰๊ฐ€์™€ ์˜ํ–ฅ์š”์ธ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 15 ์ œ 3 ์žฅ ์—ฐ๊ตฌ๋ฌธ์ œ ๋ฐ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 21 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋ฌธ์ œ 21 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 22 1. ๋ถ„์„์ž๋ฃŒ 22 2. ์กฐ์‚ฌ๋„๊ตฌ ๊ตฌ์„ฑ ๋ฐ ๋ณ€์ˆ˜ ์ธก์ • 24 2.1. ๊ฐ€๊ณ„ ์žฌ๋ฌด๋ชฉํ‘œ 24 2.2. ๊ฐ€๊ณ„ ์žฌ๋ฌด์ƒํƒœ ํ‰๊ฐ€ 24 2.3. ๊ฐ€๊ณ„์˜ ์žฌ๋ฌดํ–‰๋™๊ณผ ์žฌ๋ฌด์ƒํƒœ์˜ ์ฃผ๊ด€์  ํ‰๊ฐ€ 27 2.4. ๊ฐ€๊ณ„์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ 29 3. ๋ถ„์„๋ฐฉ๋ฒ• 30 4. ์กฐ์‚ฌ๋Œ€์ƒ์ž์˜ ํŠน์„ฑ 31 ์ œ 4 ์žฅ ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋ฐ ๋…ผ์˜ 35 ์ œ 1 ์ ˆ ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ • 35 1. ๊ฐ€๊ณ„ ์žฌ๋ฌด๋ชฉํ‘œ ์šฐ์„ ์ˆœ์œ„ 35 2. ์ตœ์šฐ์„  ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์— ๋”ฐ๋ฅธ ๊ฐ€๊ณ„์˜ ํŠน์„ฑ 37 ์ œ 2 ์ ˆ ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด์ƒํƒœ์™€ ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์˜ ์ ์ ˆ์„ฑ ํ‰๊ฐ€ 47 1. ๊ฐ€๊ณ„์˜ ์žฌ๋ฌด์ƒํƒœํ‰๊ฐ€์ง€ํ‘œ ์ค€๊ฑฐ๊ธฐ์ค€ ์ถฉ์กฑ์ˆ˜์ค€ 47 2. ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์˜ ์ ์ ˆ์„ฑ ํ‰๊ฐ€ 49 ์ œ 3 ์ ˆ ์žฌ๋ฌด๋ชฉํ‘œ ์„ค์ •์˜ ๋ถ€์ ์ ˆ์„ฑ ์˜ํ–ฅ์š”์ธ 54 ์ œ 5 ์žฅ ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  60 ์ œ 1 ์ ˆ ์š”์•ฝ 60 ์ œ 2 ์ ˆ ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 64 ์ฐธ๊ณ ๋ฌธํ—Œ 68 Abstract 75Maste

    ์œ ์ฒดํˆฌ๊ณผ์œจ ๋ถ„ํฌํŒจํ„ด ๊ฑฐ๋ฆฌ๊ธฐ๋ฐ˜์˜ ์•™์ƒ๋ธ” ์Šค๋ฌด๋”๋ฅผ ์ด์šฉํ•œ ์ €๋ฅ˜์ธต ํŠน์„ฑํ™”

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์—๋„ˆ์ง€์‹œ์Šคํ…œ๊ณตํ•™๋ถ€, 2016. 8. ์ตœ์ข…๊ทผ.A distance is the degree of model dissimilarity and it is important for effective model selection. This paper suggests a cross spatial pattern to find permeability distribution from an injector to a producer. The distance is defined as one minus correlation coefficient of permeability data obtained by the spatial pattern. Using multi-dimensional scaling, initial 400 reservoir models are projected on two dimensions based on the distance. By K-medoids clustering, they are classified into 10 groups. One representative medoid is chosen with the least difference in productions from the reference field. Then, 100 models are selected around the medoid for ensemble smoother(ES). The proposed distance can achieve improved reservoir characterization and history matching combined with ES. Also, this method helps to reduce uncertainty ranges of future oil and water productions, and decreases total simulation time by 75% with proper sampling of good 100 models.1. Introduction 1 2. Methodologies 7 2.1 Defined distance from spatial patterns 7 2.2 Multi-dimensional scaling 10 2.3 K-medoids clustering 14 2.4 Ensemble smoother 17 3. Results and discussions 18 3.1 Field with high permeability at the side corners 19 3.2 Field with high permeability in diagonal direction 43 4. Conclusions 62 References 65 ๊ตญ๋ฌธ ์ดˆ๋ก 68Maste

    Generation of pure lymphatic endothelial cells from human pluripotent stem cells and their therapeutic effects on wound repair

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    Human pluripotent stem cells (hPSCs) have emerged as an important source for cell therapy. However, to date, no studies demonstrated generation of purified hPSC-derived lymphatic endothelial cells (LECs) and tested their therapeutic potential in disease models. Here we sought to differentiate hPSCs into the LEC lineage, purify them with LEC markers, and evaluate their therapeutic effects. We found that an OP9-assisted culture system reinforced by addition of VEGF-A, VEGF-C, and EGF most efficiently generated LECs, which were then isolated via FACS-sorting with LYVE-1 and PODOPLANIN. These hPSC-derived LYVE-1(+)PODOPLANIN(+)cells showed a pure committed LEC phenotype, formed new lymphatic vessels, and expressed lymphangiogenic factors at high levels. These hPSC-derived LECs enhanced wound healing through lymphangiogenesis and lymphvasculogenesis. Here we report, for the first time, that LECs can be selectively isolated from differentiating hPSCs, and that these cells are potent for lymphatic vessel formation in vivo and wound healing. This system and the purified hPSC-derived LECs can serve as a new platform for studying LEC development as well as for cell therapy.ope

    Generation of Red Blood Cells from Human Pluripotent Stem Cellsโ€”An Update

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    Red blood cell (RBC) transfusion is a lifesaving medical procedure that can treat patients with anemia and hemoglobin disorders. However, the shortage of blood supply and risks of transfusion-transmitted infection and immune incompatibility present a challenge for transfusion. The in vitro generation of RBCs or erythrocytes holds great promise for transfusion medicine and novel cell-based therapies. While hematopoietic stem cells and progenitors derived from peripheral blood, cord blood, and bone marrow can give rise to erythrocytes, the use of human pluripotent stem cells (hPSCs) has also provided an important opportunity to obtain erythrocytes. These hPSCs include both human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs). As hESCs carry ethical and political controversies, hiPSCs can be a more universal source for RBC generation. In this review, we first discuss the key concepts and mechanisms of erythropoiesis. Thereafter, we summarize different methodologies to differentiate hPSCs into erythrocytes with an emphasis on the key features of human definitive erythroid lineage cells. Finally, we address the current limitations and future directions of clinical applications using hiPSC-derived erythrocytes. ยฉ 2023 by the authors.ope

    Combination of clinical and laboratory characteristics may serve as a potential diagnostic marker for torsion on mature cystic teratomas

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    Objective: The objective of this study was to evaluate clinical and laboratory characteristics of torsion on mature cystic teratomas (MCTs). In addition, we examined whether these factors could be helpful in diagnosing MCT torsion. Methods: A retrospective medical record review was conducted for 384 patients who had undergone surgery and histologically verified ovarian MCTs at single university hospital between July 2006 and May 2017. Patients with or without torsion groups were compared with respect to clinical presentation, laboratory findings and surgical course. In addition, statistically significant indicators of the factors were additionally evaluated for diagnostic value. Results: White blood cell (WBC) count, neutrophil count, neutrophil to lymphocyte (N/L) ratio, and tumor size were higher in the torsion group (n=24) than in the control group (n=360; P</=0.005 for all). The age was younger in the torsion group than in the control (P=0.009). In the area under the curve (AUC) of the 5 factors obtained by univariate and multivariate logistic regression, the age was 0.657, the WBC count was 0.838, the neutrophil count was 0.806, the N/L ratio was 0.725, and the cyst size was 0.705. Receiver operating characteristic analysis indicated that the AUC for the combined use of age, WBC count, neutrophil count, N/L ratio, and tumor size was 0.898 (95% confidence interval, 0.833-0.962; P<0.001). Conclusion: The combined measurement of age, WBC count, neutrophil count, N/L ratio, and tumor size may be used as a potential diagnostic marker for the torsion on MCTs.ope

    Diabetic Mesenchymal Stem Cells Are Ineffective for Improving Limb Ischemia Due to Their Impaired Angiogenic Capability

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    The purpose of this study was to investigate the effects of diabetes on mesenchymal stem cells (MSCs) in terms of their angiogenic and therapeutic potential for repairing tissue ischemia. We culture-isolated MSCs from streptozotocin-induced diabetic rats (D-MSCs) and compared their proliferation, differentiation, and angiogenic effects with those from normal rats (N-MSCs). The angiogenic effects of MSCs were evaluated by real-time PCR, in vitro tube formation assay, and transplantation of the MSCs into a hindlimb ischemia model followed by laser Doppler perfusion imaging. The number of MSCs derived from diabetic rats was smaller, and their proliferation rate was slower than N-MSCs. Upon induction of differentiation, the osteogenic and angiogenic differentiation of D-MSCs were aberrant compared to N-MSCs. The expression of angiogenic factors was lower in D-MSCs than N-MSCs. D-MSCs cocultured with endothelial cells resulted in decreased tube formation compared to N-MSCs. D-MSCs were ineffective to improve hindlimb ischemia and showed lower capillary density and angiogenic gene expression in ischemic limbs than N-MSCs. D-MSCs have defective proliferation and angiogenic activities and are ineffective for repairing hindlimb ischemia. Newer measures are needed before MSCs can be employed as a source for autologous cell therapy.ope

    Sox9 ๋ฐœํ˜„์œ ๋„ ์ €๋ถ„์ž ํ™”ํ•ฉ๋ฌผ์— ์˜ํ•œ ์ง€๋ฐฉ์œ ๋ž˜ ์ค„๊ธฐ์„ธํฌ์˜ ๊ธฐ๋Šฅ์„ฑ ์—ฐ๊ณจ์„ธํฌ๋กœ์˜ ๋ถ„ํ™”

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    Department of Medical Scienceํ‡ดํ–‰์„ฑ ๊ด€์ ˆ์—ผ์€ ๊ด€์ ˆ ์—ฐ๊ณจ ๋ฐ ๊ด€์ ˆ ๋ฐ‘์˜ ๋ผˆ์˜ ๋ถ•๊ดด๋กœ ์ธํ•ด ๋ฐœ์ƒ๋˜๋Š” ์ผ๋ฐ˜์ ์ธ ๊ด€์ ˆ ์งˆํ™˜์ด๋‹ค. ๊ด€์ ˆ ์—ฐ๊ณจ์— ์†์ƒ์ด ๋ฐœ์ƒํ•  ๋•Œ, ์—ฐ๊ณจ์„ธํฌ๋Š” ๋น„๋Œ€ํ™” ์—ฐ๊ณจ์„ธํฌ๋กœ ํ˜•์งˆ๋ณ€ํ™”๊ฐ€ ์œ ๋„๋˜์–ด RUNX2 ๋ฐ X ํ˜• ์ฝœ๋ผ๊ฒ์˜ ๋ฐœํ˜„์„ ์ฆ๊ฐ€์‹œํ‚ค๊ฒŒ ๋œ๋‹ค. ๋น„๋Œ€ํ™” ์—ฐ๊ณจ์„ธํฌ๋Š” ์„ธํฌ ์™ธ ๊ธฐ์งˆ์„ ๋ถ„ํ•ดํ•˜๋Š” ํšจ์†Œ์ธ MMP13 ๋ฐ ADAMTS4 ์™€ ADMATS5๋ฅผ ๋ถ„๋น„ํ•˜์—ฌ ๊ด€์ ˆ์˜ ๋ถ„ํ•ด ๋ฐ ์„ํšŒํ™”๋ฅผ ์œ ๋„ํ•œ๋‹ค. ์„ฑ์ธ์˜ ์—ฐ๊ณจ๊ณผ ์—ฐ๊ณจ์„ธํฌ๋Š” ์ž์ฒด์ ์œผ๋กœ ์žฌ์ƒ ๋Šฅ๋ ฅ์ด ์—†๊ธฐ ๋•Œ๋ฌธ์— ํ‡ดํ–‰์„ฑ ๊ด€์ ˆ์—ผ์„ ์น˜๋ฃŒํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•๋“ค์ด ์‹œํ–‰๋˜์–ด ์™”๋‹ค. ์ตœ๊ทผ ์—ฐ๊ตฌ๋“ค์—์„œ ์ค„๊ธฐ์„ธํฌ๋ฅผ ์‚ฌ์šฉํ•œ ํ‡ดํ–‰์„ฑ ๊ด€์ ˆ์—ผ ์น˜๋ฃŒ๋ฒ•์„ ์—ฐ๊ตฌํ•ด์˜ค๊ณ  ์žˆ๋‹ค. ์ง€๊ธˆ๊นŒ์ง€ ์ค„๊ธฐ์„ธํฌ๋ฅผ ๊ธฐ๋Šฅ์„ฑ ์—ฐ๊ณจ์„ธํฌ๋กœ ๋ถ„ํ™” ์œ ๋„ํ•˜๊ธฐ ์œ„ํ•ด ํŠน์ • ์ธ์ž๋“ค์˜ ๋ฐœํ˜„์กฐ์ ˆ, microRNA, ์ €๋ถ„์ž ํ™”ํ•ฉ๋ฌผ์„ ์ด์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์„ ์‹œ๋„ํ•ด์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ํ˜„์žฌ๊นŒ์ง€ ์ค„๊ธฐ์„ธํฌ์—์„œ ๊ธฐ๋Šฅ์„ฑ ์—ฐ๊ณจ์„ธํฌ๋กœ์˜ ๋ถ„ํ™” ์œ ๋„ ์—ฐ๊ตฌ์— ์žˆ์–ด ๊ฐ€์žฅ ํฐ ๋ฌธ์ œ์ ์€ ๋ถ„ํ™”๋œ ์—ฐ๊ณจ์„ธํฌ์—์„œ ๋น„๋Œ€ํ™” ์—ฐ๊ณจ์„ธํฌ์˜ ํŠน์„ฑ์ด ๋‚˜ํƒ€๋‚˜๋Š” ์ ์ด๋‹ค. ๋”ฐ๋ผ์„œ, ์ค„๊ธฐ์„ธํฌ๋ฅผ ์ด์šฉํ•œ ๋น„๋Œ€ํ™” ์—ฐ๊ณจ์„ธํฌ์˜ ํŠน์„ฑ์„ ๊ฐ–์ง€ ์•Š๋Š” ๊ธฐ๋Šฅ์„ฑ ์—ฐ๊ณจ์„ธํฌ๋กœ ๋ถ„ํ™” ์œ ๋„ํ•˜๊ธฐ ์œ„ํ•œ ์ตœ์ ์˜ ๋ฐฉ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๊ด€์ ˆ ์—ฐ๊ณจ์˜ ๋ฐœ๋‹ฌ ๊ณผ์ • ์ดˆ๊ธฐ์— Sox9์ด ๋ฐœํ˜„๋˜์–ด ์—ฐ๊ณจ๊ณผ ์—ฐ๊ณจ์„ธํฌ๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ํ‘œ์ง€์ธ์ž๋“ค์˜ ๋ฐœํ˜„์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ์ด์ „์˜ ์—ฐ๊ตฌ์—์„œ Sox9์˜ ๋ฐœํ˜„์„ ์ œ๊ฑฐํ•œ ๋งˆ์šฐ์Šค ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์—ฐ๊ณจํ˜•์„ฑ์ด ๋ฐœ์ƒํ•˜์ง€ ์•Š์Œ์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋˜ํ•œ Sox9์˜ ๊ณผ๋ฐœํ˜„์„ ํ†ตํ•ด ์ค„๊ธฐ์„ธํฌ์—์„œ ์—ฐ๊ณจ์„ธํฌ๋กœ์˜ ๋ถ„ํ™”๋ฅผ ์œ ๋„ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ‡ดํ–‰์„ฑ ๊ด€์ ˆ์—ผ์˜ ์น˜๋ฃŒ๋ฅผ ์œ„ํ•ด Sox9์˜ ๋ฐœํ˜„์„ ์กฐ์ ˆ ํ•  ์ˆ˜ ์žˆ๋Š” ์ €๋ถ„์ž ํ™”ํ•ฉ๋ฌผ์„ ์ด์šฉํ•œ ์ค„๊ธฐ์„ธํฌ๋กœ๋ถ€ํ„ฐ ๊ธฐ๋Šฅ์„ฑ ์—ฐ๊ณจ์„ธํฌ๋กœ์˜ ๋ถ„ํ™” ์œ ๋„์— ์ดˆ์ ์„ ๋งž์ถ”์—ˆ๋‹ค. ๋จผ์ €, ์—ฐ๊ณจ์„ธํฌ์™€ ์ค„๊ธฐ ์„ธํฌ์—์„œ์˜ ๊ธฐ๋ณธ์ ์œผ๋กœ ๋ฐœํ˜„๋˜๋Š” Sox9์˜ ์–‘์  ๋น„๊ต๋ฅผ ํ†ตํ•ด Sox9์˜ ๋ฐœํ˜„ ์ฆ๊ฐ€์™€ ์—ฐ๊ณจ์„ธํฌ๋กœ์˜ ๋ถ„ํ™” ์œ ๋„์˜ ์—ฐ๊ด€์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. Sox9 ํ”„๋กœ๋ชจํ„ฐ๊ฐ€ ๋ถ™์€ GFP ๋ฒกํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ Sox9์˜ ๋ฐœํ˜„์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ์•ฝ๋ฌผ๋“ค์„ 1์ฐจ ์„ ๋ณ„ํ•˜์˜€๊ณ , ์„ ๋ณ„๋œ ์•ฝ๋ฌผ์„ ์ฒ˜๋ฆฌํ•˜์˜€์„ ๋•Œ ์—ฐ๊ณจ์„ธํฌ๋กœ์˜ ๋ถ„ํ™”์œ ๋„ ์—ฌ๋ถ€์™€ ๋น„๋Œ€ํ™” ์—ฐ๊ณจ์„ธํฌ์˜ ํŠน์„ฑ์„ ๋‚˜ํƒ€๋‚ด์ง€ ์•Š๋Š” Ellipticine์ด๋ผ๊ณ  ์•Œ๋ ค์ง„ 138๋ฒˆ์ด ์ตœ์ข… ์„ ๋ณ„๋˜์—ˆ๋‹ค. Ellipticine ์ฒ˜๋ฆฌ๋œ ์ค„๊ธฐ์„ธํฌ๋Š” ์„ฑ์ˆ™๋œ ์—ฐ๊ณจ์„ธํฌ์—์„œ ๋ฐœํ˜„๋˜๋Š” II ํ˜• ์ฝœ๋ผ๊ฒ๊ณผ Aggrecan์˜ ๋ฐœํ˜„์ด ์ฆ๊ฐ€๋˜์–ด์žˆ์—ˆ์œผ๋ฉฐ, ๋น„๋Œ€ํ™” ์—ฐ๊ณจ์„ธํฌ์—์„œ ๋ฐœํ˜„๋˜๋Š” RUNX2์™€ Xํ˜• ์ฝœ๋ผ๊ฒ, ์„ธํฌ ์™ธ ๊ธฐ์งˆ์€ ๋ถ„ํ•ดํ•˜๋Š” ํšจ์†Œ์ธ MMP13 ๋ฐ ADAMTS4์™€ ADAMTS5์˜ ๋ฐœํ˜„์ด ๊ฐ์†Œ๋˜์–ด ์žˆ๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ Ellipticine์€ p53์˜ ๋ฐœํ˜„์„ ์ฆ๊ฐ€์‹œํ‚ค๊ณ  ํ•ต์œผ๋กœ์˜ ์ „์ขŒ๋ฅผ ์ฆ๊ฐ€์‹œํ‚ด์œผ๋กœ์จ p53์˜ ํ™œ์„ฑ์„ ์กฐ์ ˆํ•˜์—ฌ Sox9์˜ ๋ฐœํ˜„์„ ์ฆ๊ฐ€์‹œํ‚จ ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. Collagenase์— ์˜ํ•ด ์œ ๋„๋œ ํ‡ดํ–‰์„ฑ ๊ด€์ ˆ์—ผ ๋™๋ฌผ ๋ชจ๋ธ์—์„œ, ์ค„๊ธฐ์„ธํฌ๋กœ๋ถ€ํ„ฐ ๋ถ„ํ™” ์œ ๋„๋œ ์—ฐ๊ณจ์„ธํฌ๋ฅผ ์ฒ˜๋ฆฌํ•œ ๊ทธ๋ฃน์ด ์ฒ˜๋ฆฌ๋˜์ง€ ์•Š์€ ์ค„๊ธฐ์„ธํฌ๋ฅผ ์ฃผ์ž…ํ•œ ๊ทธ๋ฃน๋ณด๋‹ค ํšจ๊ณผ์ ์œผ๋กœ ์†์ƒ๋œ ์—ฐ๊ณจ์„ ํšŒ๋ณต์‹œํ‚ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ƒˆ๋กœ์šด ์—ฐ๊ณจ์„ธํฌ๋กœ์˜ ๋ถ„ํ™” ๊ฐ€๋Šฅํ•œ ์ƒˆ๋กœ์šด ์ €๋ถ„์ž ํ™”ํ•ฉ๋ฌผ์˜ ํ™•์ธ์„ ํ†ตํ•ด ํ‡ดํ–‰์„ฑ ๊ด€์ ˆ์—ผ ์น˜๋ฃŒ์— ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ์ค„๊ธฐ์„ธํฌ๋กœ๋ถ€ํ„ฐ ๋ถ„ํ™”๋œ ๊ธฐ๋Šฅ์„ฑ ์—ฐ๊ณจ์„ธํฌ์˜ ์น˜๋ฃŒ์ œ๋กœ์จ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•˜์˜€๋‹ค.open๋ฐ•

    ๋ฏธ๊ตญ ์‹œํŠธ์ฝค ์†์—์„œ์˜ ์˜์–ด ํ™•์žฅ์‚ฌ: Friends์™€ How I Met Your Mother์— ๊ด€ํ•œ ์ฝ”ํผ์Šค ๊ธฐ๋ฐ˜ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜์–ด์˜๋ฌธํ•™๊ณผ ์˜์–ดํ•™์ „๊ณต, 2015. 8. ๊ถŒํ˜์Šน.Amplifiers are considered one of the most interesting grammatical features to study for their versatility and tendency to change quickly. Amplifiers are also often linked with colloquial usage and female speakers. The purpose of this study is to investigate the competition of different English amplifiers in American television sitcoms in the past decade, the current standings of the selected amplifiers, and whether the television sitcoms reflect the actual amplifier use in contemporary American English. This study also aims to explore the types of adjectives and verbs collocated with the selected amplifiers, and the sociolinguistic correlation between the amplifier use and gender. The transcripts from the two world-popular American television sitcoms were collected to create an original corpus, Friends-HIMYM Corpus, and it was compared with the spoken portion of Corpus of Contemporary American English (COCA), Corpus of Historical American English (COHA), and Corpus of American Soap Operas (CASO). The data was analyzed for chronological distribution, collocation and gender/age-preferential usage of the three selected amplifiers: very, really and totally. The results indicated the amplifier use in the sitcoms partially reflected contemporary American English. Very was the oldest amplifier, followed by really and totally, in all corpora used in the studyhowever, really was the most popular amplifier for Friends-HIMYM Corpus and CASO while very was most frequently used in COCA and COHA. The results also showed that very and really are collocated with common, scalar adjectives and totally with more complex, prefixed adjectives. Very was never collocated with verbs, really was collocated with auxiliary and cognitive verbs, and totally was collocated with suffixed verbs. All three amplifiers were used more often by female characters than male characters. However, a comparative analysis on MICASE and BNCweb indicated that amplifier preferences varied depending on different gender and age groups in American and British English samples.ABSTRACT I CHAPTER 1: INTRODUCTION 1 1.1 Motivation of the study 1 1.2 Background: English amplifiers 4 1.3 Organization of the study 7 CHAPTER 2: LITERATURE REVIEW 9 2.1 History and characteristics of English amplifiers 9 2.1.1 Historical trajectory of English amplifiers 9 2.1.2 Characteristics of English amplifiers 13 2.2 Gender and age differences in amplifier use 16 2.3 Media-related studies on amplifiers 20 2.4 Research questions 23 CHAPTER 3: DATA AND METHODOLOGY 25 3.1 Data 25 3.1.1 Friends 25 3.1.2 How I Met Your Mother 27 3.2.3 Friends-HIMYM Corpus 29 3.2 Comparative corpora 31 3.2.1 Corpus of Contemporary American English (COCA) 31 3.2.2. Corpus of Historical American English (COHA) 32 3.2.3. Corpus of American Soap Operas (CASO) 32 3.2.4. Michigan Corpus of Academic Spoken English (MICASE) 34 3.2.5. British National Corpus web CQP Edition (BNCweb) 35 3.3 Methodology 36 3.3.1 Tool 36 3.3.2 Amplifier selection 37 3.3.3 Collocates 38 CHAPTER 4: ANALYSIS AND DISCUSSION 40 4.1 Chronological Analysis 40 4.1.1 Friends-HIMYM Corpus 40 4.1.2 Comparison with other corpora 43 4.2 Collocational Analysis 51 4.2.1 Amplifying 54 4.2.2 Non-amplifying 62 4.2.3 Independent Utterances 63 4.3 Gender/age-based analysis 66 4.3.1 Friends-HIMYM Corpus 66 4.3.2 Comparison with other corpora 69 CHAPTER 5 CONCLUSION 77 5.1 Summary 77 5.2 Limitations of the study 80 5.3 Directions for future research 81 References 82 Appendix A 88 Appendix B 89 Appendix C 92 Appendix D 94 Appendix E 95 Appendix F 97 Appendix G 100 Appendix H 101Maste

    ์ค‘์†Œ๊ธฐ์—… ๊ทผ๋กœ์ž๊ฐ€ ์ธ์‹ํ•œ ์ง๋ฌด์ˆ˜ํ–‰๊ณผ ํ•™์Šต์กฐ์งํ™” ๋ฐ ๊ฐœ์ธ์—ญ๋Ÿ‰์˜ ๊ด€๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์‚ฐ์—…๊ต์œก๊ณผ, 2014. 2. ์ด์ฐฌ.์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์šฐ๋ฆฌ๋‚˜๋ผ ์ค‘์†Œ๊ธฐ์—…์˜ ํ•™์Šต์กฐ์งํ™” ์ˆ˜์ค€๊ณผ ์ง๋ฌด์ˆ˜ํ–‰์˜ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š”๋ฐ ์žˆ์—ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•œ ๊ตฌ์ฒด์ ์ธ ์—ฐ๊ตฌ๋ชฉํ‘œ๋Š” ์šฐ๋ฆฌ๋‚˜๋ผ ์ค‘์†Œ๊ธฐ์—…์˜ ํ•™์Šต์กฐ์งํ™”์™€ ์ง๋ฌด์ˆ˜ํ–‰ ์ˆ˜์ค€์„ ๊ตฌ๋ช…ํ•˜๊ณ , ํ•™์Šต์กฐ์งํ™”์™€ ์ง๋ฌด์ˆ˜ํ–‰์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๊ณ , ํ•™์Šต์กฐ์งํ™”์˜ ์ง๋ฌด์ˆ˜ํ–‰์— ๋Œ€ํ•œ ์˜ํ–ฅ๋ ฅ์„ ๊ตฌ๋ช…ํ•˜๋Š”๋ฐ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ์ค‘์†Œ๊ธฐ์—…๊ทผ๋กœ์ž์˜ ์ง๋ฌด์ˆ˜ํ–‰๊ณผ ํ•™์Šต์กฐ์งํ™”์˜ ๊ด€๊ณ„์—์„œ ๊ฐœ์ธ์—ญ๋Ÿ‰์˜ ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š”๋ฐ ์žˆ์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๋ชจ์ง‘๋‹จ์€ ๊ตญ๋‚ด ์ค‘์†Œ๊ธฐ์—… ๊ทผ๋กœ์ž ์ „์ฒด์ด๋‚˜ ๊ธฐ์—… ๊ทผ๋กœ์ž ๊ฐœ์ธ์— ๋Œ€ํ•œ ์ •๋ณด๊ฐ€ ๊ณต๊ฐœ๋˜์ง€ ์•Š๊ณ , ๋ชจ๋“  ์ค‘์†Œ๊ธฐ์—…์ด ํ•™์Šต์กฐ์งํ™”๋ฅผ ์šด์˜ํ•˜๊ณ  ์žˆ์ง€ ์•Š๋‹ค๊ณ  ํŒ๋‹จ๋˜์—ˆ๊ธฐ ๋•Œ๋ฌธ์—, ๊ณ ์šฉ๋…ธ๋™๋ถ€์™€ ํ•œ๊ตญ์‚ฐ์—…์ธ๋ ฅ๊ณต๋‹จ์ด ์‹ค์‹œํ•˜๊ณ  ์žˆ๋Š” ์ค‘์†Œ๊ธฐ์—… ํ•™์Šต์กฐ์งํ™” ์‚ฌ์—…์— ์ฐธ์—ฌํ•˜๊ณ  ์žˆ๋Š” ๊ธฐ์—… 239๊ฐœ๋ฅผ ๋ชฉํ‘œ๋ชจ์ง‘๋‹จ์œผ๋กœ ์„ ์ •ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์กฐ์‚ฌ๋Œ€์ƒ์— ๋Œ€ํ•œ ์ ‘๊ทผ์˜ ํ•œ๊ณ„๋กœ ์ธํ•ด ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„ํ™•๋ฅ ํ‘œ์ง‘๋ฐฉ๋ฒ• ์ค‘ ์œ ์˜ํ‘œ์ง‘๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์„ค๋ฌธ์— ์ฐธ์—ฌํ•˜๊ณ ์ž ํ•˜๋Š” 40๊ฐœ์˜ ๊ธฐ์—…์„ ์„ ์ •ํ•˜์˜€๊ณ , ๊ธฐ์—…๋‹น 10๋ช…์˜ ์‘๋‹ต์ž๋ฅผ ํ‘œ์ง‘ํ•˜์˜€๋‹ค. ์ž๋ฃŒ์ˆ˜์ง‘์„ ์œ„ํ•œ ์กฐ์‚ฌ๋„๊ตฌ๋กœ๋Š” ์งˆ๋ฌธ์ง€๊ฐ€ ์‚ฌ์šฉ๋˜์—ˆ์œผ๋ฉฐ, ์งˆ๋ฌธ์ง€๋Š” ํ•™์Šต์กฐ์งํ™” ์ฒ™๋„, ์ง๋ฌด์ˆ˜ํ–‰์ฒ™๋„, ๊ฐœ์ธ์—ญ๋Ÿ‰ ์ฒ™๋„๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ํ•™์Šต์กฐ์งํ™” ์ฒ™๋„์—๋Š” ์ง€์†์  ํ•™์Šต, ๋Œ€ํ™”์™€ ์งˆ์˜, ํŒ€ํ•™์Šต, ์ฒด์ œ๊ตฌ์ถ•, ๊ถŒํ•œ๋ถ€์—ฌ, ์ฒด์ œ์—ฐ๊ณ„, ๋ฆฌ๋”์‹ญ์ด, ์ง๋ฌด์ˆ˜ํ–‰ ์ฒ™๋„์—๋Š” ๊ณผ์—…์ˆ˜ํ–‰๊ณผ ๋งฅ๋ฝ์ˆ˜ํ–‰์ด ํฌํ•จ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ๊ฐœ์ธ์—ญ๋Ÿ‰์—๋Š” ์ง€์‹, ๊ธฐ์ˆ ์—ญ๋Ÿ‰์ด ํฌํ•จ๋˜์—ˆ๋‹ค. ํ•™์Šต์กฐ์งํ™” ์ฒ™๋„๋Š” Watkins์™€ Marsick(1993)์ด ๊ฐœ๋ฐœํ•œ DLOQ(Dimensions of the Learning Organization Questionnaire)๊ฐ€ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์ด DLOQ๋Š” ์ง€์†์ ํ•™์Šต, ๋Œ€ํ™”์™€ ์งˆ์˜, ํŒ€ํ•™์Šต, ์ฒด์ œ๊ตฌ์ถ•, ๊ถŒํ•œ๋ถ€์—ฌ, ์ฒด์ œ์—ฐ๊ณ„, ๋ฆฌ๋”์‹ญ์š”์ธ์œผ๋กœ ๊ตฌ์„ฑ๋˜๋Š” ํ•™์Šต์กฐ์งํ™” ์ˆ˜์ค€์€ 7๊ฐ€์ง€ ์š”์ธ์— ๋Œ€ํ•œ ํ•ฉ์˜ ํ‰๊ท ์œผ๋กœ ์ธก์ •๋˜์—ˆ๋‹ค. ์ง๋ฌด์ˆ˜ํ–‰ ์ˆ˜์ค€์€ Williams์™€ Stella(1991)์˜ ๊ณผ์—…์ˆ˜ํ–‰ ์ธก์ •๋„๊ตฌ์™€ Borman๊ณผ Motowidlo(1994)์˜ ๋งฅ๋ฝ์ˆ˜ํ–‰ ์ธก์ •๋„๊ตฌ๋ฅผ ์—ฐ๊ตฌ์ž๊ฐ€ ์šฐ๋ฆฌ๋‚˜๋ผ ๊ทผ๋กœ์ž์—๊ฒŒ ์ ํ•ฉ๋„๋ก ์ˆ˜์ •โ€ค๋ณด์™„ํ•œ ์žฅํ˜„์ง„(2012)์˜ ์ง๋ฌด์ˆ˜ํ–‰ ์ธก์ •๋„๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ง๋ฌด์ˆ˜ํ–‰์€ ํฌ๊ฒŒ ๊ณผ์—…์ˆ˜ํ–‰๊ณผ ๋งฅ๋ฝ์ˆ˜ํ–‰์œผ๋กœ ๋‚˜๋ˆ„์–ด์ง„๋‹ค. ๊ฐœ์ธ์—ญ๋Ÿ‰์€ ๊ณตํƒ(2004)์ด ๊ฐœ๋ฐœํ•œ ๋„๊ตฌ๋ฅผ ์ˆ˜์ •ยท๋ณด์™„ํ•˜์—ฌ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ์ง€์‹๊ณผ ๊ธฐ์ˆ ์—ญ๋Ÿ‰์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ์ž๋ฃŒ ์ˆ˜์ง‘์€ ์งˆ๋ฌธ์ง€๋ฅผ ์ด์šฉํ•˜์—ฌ ์šฐํŽธ์กฐ์‚ฌ ๋ฐ e-mail, ๋ฐฉ๋ฌธ์„ค๋ฌธ์„ ํ†ตํ•ด 40๊ฐœ์˜ ์ค‘์†Œ๊ธฐ์—…์ฒด ์ด 504๋ถ€๊ฐ€ ๋ฐฐํฌ๋˜์—ˆ๊ณ , ์ด๋กœ์จ 29๊ฐœ์˜ ์ค‘์†Œ๊ธฐ์—…์œผ๋กœ๋ถ€ํ„ฐ 324๋ถ€๊ฐ€ ํšŒ์ˆ˜๋˜์—ˆ๋‹ค. ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋Š” Window SPSS 19.0 ํ†ต๊ณ„ํŒจํ‚ค์ง€ ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•˜์—ฌ ๋ถ„์„๋˜์—ˆ๊ณ , ํ†ต๊ณ„์  ์œ ์˜ ์ˆ˜์ค€์€ 5%๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ๋กœ๋ถ€ํ„ฐ ์–ป๋Š” ๊ฒฐ๋ก ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์š”์•ฝ๋  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ฒซ์งธ, ์šฐ๋ฆฌ๋‚˜๋ผ ์ค‘์†Œ๊ธฐ์—… ์ง๋ฌด์ˆ˜ํ–‰์€ 5์  ๋งŒ์ ์— 3.77๋กœ ๋ณดํ†ต์ด์ƒ์ด๋‹ค. ๋˜ํ•œ, ๊ณผ์—…์ˆ˜ํ–‰๋ณด๋‹ค๋Š” ๋งฅ๋ฝ์ˆ˜ํ–‰์ด ๋” ๋†’์•˜๋‹ค. ์ด๋Š” ์ค‘์†Œ๊ธฐ์—…์ด ๊ทœ๋ชจ๊ฐ€ ์ž‘์€ ๋งŒํผ ๊ทผ๋กœ์ž๊ฐ„์˜ ์ง€์‹์˜ ๊ณต์œ ๋‚˜ ํ™œ์šฉ์ด ํ™œ๋ฐœํ•˜๊ฒŒ ์ด๋ฃจ์–ด์ง€๊ณ  ์ฒด๊ณ„์ ์ธ ์„ฑ๊ณผํ‰๊ฐ€์ฒด๊ณ„ ์•ˆ์—์„œ ํ‰๊ฐ€๋˜๊ธฐ๋ณด๋‹ค๋Š” ์กฐ์ง ๋งฅ๋ฝ์ ์ธ ๊ด€์ ์—์„œ ์„ฑ๊ณผ๊ฐ€ ํ‰๊ฐ€๋˜์–ด ์ด๋ฃจ์–ด์ง€๊ธฐ ๋•Œ๋ฌธ์ด๋ผ๊ณ  ์‚ฌ๋ฃŒ๋œ๋‹ค. ๋‘˜์งธ, ์šฐ๋ฆฌ๋‚˜๋ผ ์ค‘์†Œ๊ธฐ์—… ํ•™์Šต์กฐ์งํ™” ์ˆ˜์ค€์€ 6์  ๋งŒ์ ์— 3.86์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์š”์ธ๋ณ„๋กœ๋Š” ๋Œ€ํ™”์™€ ์งˆ์˜, ํŒ€ํ•™์Šต, ์ง€์†์  ํ•™์Šต, ์ฒด์ œ์—ฐ๊ณ„, ๋ฆฌ๋”์‹ญ, ๊ถŒํ•œ๋ถ€์—ฌ, ์ฒด์ œ๊ตฌ์ถ• ์ˆœ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ทœ๋ชจ๋ณ„๋กœ๋Š” ๊ทœ๋ชจ๊ฐ€ ํด์ˆ˜๋ก ํ•™์Šต์กฐ์งํ™”์ˆ˜์ค€์ด ๋†’์€ ํŽธ์ด๋ฉฐ, ์ œ์กฐ์—… ๋ฐ ๊ฑด์„ค์—…์˜ ํ•™์Šต์กฐ์งํ™” ์ˆ˜์ค€์ด ๊ฐ€์žฅ ๋†’์•˜๋‹ค. ์…‹์งธ, ์ค‘์†Œ๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ฐœ์ธ์—ญ๋Ÿ‰์€ 5์  ๋งŒ์ ์— 3.58๋กœ ํ‰๊ท ์ด์ƒ์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์š”์ธ๋ณ„๋กœ๋Š” ๊ธฐ์ˆ ์—ญ๋Ÿ‰๋ณด๋‹ค๋Š” ์ง€์‹์—ญ๋Ÿ‰์ด ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ์ค‘์†Œ๊ธฐ์—…๊ทผ๋กœ์ž๋“ค์ด ์ƒ๋Œ€์ ์œผ๋กœ ํ•ต์‹ฌ๊ธฐ์ˆ ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ˆ˜ํ–‰ํ•˜๋Š” ์ง€์‹๋ณด๋‹ค๋Š” ์—…๋ฌด์— ์ง€์†์ ์ธ ๋…ธํ•˜์šฐ์ถ•์ ์„ ํ†ตํ•œ ์ง€์‹์กฐ์ง ๋‚ด ๊ทœ๋ชจ๊ฐ€ ์ž‘๊ณ , ๊ตฌ์„ฑ์› ๊ฐ„ ์นœ๋ฐ€๋„๊ฐ€ ๋†’๊ณ , ์ฒด๊ณ„์ ์ธ ์‹œ์Šคํ…œ๋ณด๋‹ค๋Š” ๊ตฌ์„ฑ์›๊ฐ„์˜ ๊ต๋ฅ˜์— ์˜ํ•ด ์ง๋ฌด์ˆ˜ํ–‰์ด ๋งŽ์€ ๊ฒฝํ–ฅ์ด ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ๊ทธ๋Ÿฌ๋‚˜ ์ค‘์†Œ๊ธฐ์—…๋งˆ๋‹ค ํ•™์Šต์กฐ์งํ™” ์ˆ˜์ค€ ํŽธ์ฐจ๊ฐ€ ๋„ท์งธ, ์ค‘์†Œ๊ธฐ์—… ํ•™์Šต์กฐ์งํ™”๋Š” ์ง๋ฌด์ˆ˜ํ–‰๊ณผ ์ •์ ์ธ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ํ•™์Šต์กฐ์งํ™”์— ๋Œ€ํ•œ ์ง๋ฌด์ˆ˜ํ–‰ ์„ค๋ช…๋ ฅ์€ 25.8%๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ์ค‘์†Œ๊ธฐ์—… ํ•™์Šต์กฐ์งํ™”์˜ ์ˆ˜์ค€์ด ๋†’์„์ˆ˜๋ก ๊ทผ๋กœ์ž๋“ค์˜ ์ง๋ฌด์ˆ˜ํ–‰ ์ˆ˜์ค€์ด ๋†’์•„์ง€๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์กฐ์ง ๋‚ด์—์„œ๋Š” ํ•™์Šต์กฐ์งํ™”๋ฅผ ํ†ตํ•ด ๊ตฌ์„ฑ์›๊ฐ„์˜ ์ง€์†์ ์ธ ํ•™์Šต์„ ์œ ๋„ํ•˜๊ณ , ๋Œ€ํ™”์™€ ์งˆ์˜, ํŒ€ํ•™์Šต, ์ง€์†์ ํ•™์Šต, ์ฒด์ œ์—ฐ๊ณ„, ๋ฆฌ๋”์‹ญ, ๊ถŒํ•œ๋ถ€์—ฌ, ์ฒด์ œ๊ตฌ์ถ•์„ ์ฆ์ง„์‹œ์ผœ ์—…๋ฌด์— ํ•„์š”ํ•œ ์ง€์‹์ด ๊ณต์œ ๋˜๊ณ  ์ด๋ฅผ ํ†ตํ•ด ์ง๋ฌด์ˆ˜ํ–‰์ˆ˜์ค€์„ ๋†’์ผ ์ˆ˜ ์žˆ๋Š” ๊ฐœ์ž…์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๋‹ค์„ฏ์งธ, ์ค‘์†Œ๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ฐœ์ธ์—ญ๋Ÿ‰์€ ์ค‘์†Œ๊ธฐ์—… ํ•™์Šต์กฐ์งํ™”์™€ ์ง๋ฌด์ˆ˜ํ–‰์˜ ๊ด€๊ณ„์—์„œ ๋ถˆ์™„์ „ ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฐ€์ง„๋‹ค. ์ฆ‰, ํ•™์Šต์ž์˜ ๊ฐœ์ธ์—ญ๋Ÿ‰์€ ํ•™์Šต์กฐ์งํ™”์™€ ์ง๋ฌด์ˆ˜ํ–‰๊ฐ„์˜ ๊ด€๊ณ„์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ๋ผ์น˜์ง„ ์•Š์ง€๋งŒ ๊ฐ„์ ‘์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋”ฐ๋ผ์„œ ์กฐ์ง ๋‚ด์—์„œ ๊ฐœ์ธ์—ญ๋Ÿ‰ ๊ฐ•ํ™”ํ”„๋กœ๊ทธ๋žจ์„ ํ†ตํ•˜์—ฌ ์ง๋ฌด์ˆ˜ํ–‰์„ ๋†’์ผ ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ๊ฐœ์ž…์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์ด์ƒ๊ณผ ๊ฐ™์€ ์—ฐ๊ตฌ ๊ฒฐ๋ก ์„ ํ† ๋Œ€๋กœ, โ‘  ์ •๋ถ€์˜ ํ•™์Šต์กฐ์งํ™” ์ง€์›์„ ์œ„ํ•œ ์ฒด๊ณ„์ ์ธ ์ฒด์ œ๊ตฌ์ถ•์˜ ํ•„์š”, โ‘ก ๋‹ค์–‘ํ•œ ์ธก๋ฉด์—์„œ ์ง๋ฌด์ˆ˜ํ–‰์„ ์ธก์ •๋„๊ตฌ ๊ฐœ๋ฐœ, โ‘ข ํ•™์Šต์„ ์ฆ์ง„์‹œํ‚ค๋Š” ๊ฐœ์ธ์—ญ๋Ÿ‰ ๊ฐ•ํ™”, โ‘ฃ ์ •๋ถ€์˜ ์ค‘์†Œ๊ธฐ์—… ํ•™์Šต์กฐ์งํ™” ์ง€์› ๋ฐฉ์•ˆ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋ณด๋‹ค ๋‹ค์–‘ํ•œ ๋ณ€์ธ๋“ค์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ํ›„์†์—ฐ๊ตฌ๋กœ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค.๋ชฉ ์ฐจ โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  4 3. ์—ฐ๊ตฌ ๋ฌธ์ œ 4 4. ์šฉ์–ด์˜ ์ •์˜ 6 5. ์—ฐ๊ตฌ์˜ ์ œํ•œ 7 โ…ก. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 8 1. ์ค‘์†Œ๊ธฐ์—…๊ณผ ํ•™์Šต 8 2. ์ง๋ฌด์ˆ˜ํ–‰ 13 3. ํ•™์Šต์กฐ์งํ™” 16 4. ๊ฐœ์ธ์—ญ๋Ÿ‰ 27 5. ์ง๋ฌด์ˆ˜ํ–‰๊ณผ ํ•™์Šต์กฐ์งํ™” ๋ฐ ๊ฐœ์ธ์—ญ๋Ÿ‰์˜ ๊ด€๊ณ„ 32 โ…ข. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 34 1. ์—ฐ๊ตฌ ๋ชจํ˜• 34 2. ์—ฐ๊ตฌ ๋Œ€์ƒ 35 3. ์กฐ์‚ฌ ๋„๊ตฌ 37 4. ์ž๋ฃŒ ์ˆ˜์ง‘ 42 5. ์ž๋ฃŒ ๋ถ„์„ 45 โ…ฅ. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 48 1. ์ง๋ฌด์ˆ˜ํ–‰๊ณผ ํ•™์Šต์กฐ์งํ™” ๋ฐ ๊ฐœ์ธ์—ญ๋Ÿ‰์˜ ์ˆ˜์ค€ 48 2. ๊ธฐ์—…ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์ง๋ฌด์ˆ˜ํ–‰๊ณผ ํ•™์Šต์กฐ์งํ™” ์ˆ˜์ค€์˜ ์ฐจ์ด 50 3. ์ง๋ฌด์ˆ˜ํ–‰๊ณผ ํ•™์Šต์กฐ์งํ™” ๋ฐ ๊ฐœ์ธ์—ญ๋Ÿ‰์˜ ์ƒ๊ด€๊ด€๊ณ„ 57 4. ์ง๋ฌด์ˆ˜ํ–‰์— ๋Œ€ํ•œ ํ•™์Šต์กฐ์งํ™”์˜ ์˜ํ–ฅ 60 5. ํ•™์Šต์กฐ์งํ™”์™€ ์ง๋ฌด์ˆ˜ํ–‰์˜ ๊ด€๊ณ„์—์„œ ๊ฐœ์ธ์—ญ๋Ÿ‰์˜ ๋งค๊ฐœํšจ๊ณผ 63 6. ์—ฐ๊ตฌ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋…ผ์˜ 64 โ…ค. ์š”์•ฝ, ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 67 1. ์š”์•ฝ 67 2. ๊ฒฐ๋ก  69 3. ์ œ์–ธ 70 ์ฐธ๊ณ ๋ฌธํ—Œ 72 ๋ถ€๋ก 77Maste

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    Thesis(masters) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์น˜์˜ํ•™๊ณผ,2008.2.Maste
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