151 research outputs found

    ์„œ๋ถ€ DMZ ์ผ์› ๊ฒฝ๊ด€ ์š”์†Œ์˜ ์‹œ๊ณต๊ฐ„์  ๋ณ€ํ™”์™€ ์ƒ๋ฌผ๋‹ค์–‘์„ฑ ๋ณด์ „ ์ „๋žต

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ƒ๋ช…๊ณผํ•™๋ถ€, 2021. 2. ์ด์€์ฃผ.์ƒ๋ฌผ ๋‹ค์–‘์„ฑ ๋ณด์ „์€ ์ƒํƒœ๊ณ„์„œ๋น„์Šค์˜ ์ œ๊ณต๊ณผ ๊ฐ™์ด ์ธ๋ฅ˜๊ฐ€ ์ง์ ‘์ ์œผ๋กœ ์ฒด๊ฐํ•˜๋Š” ์ธก๋ฉด์˜ ์ด์ต์„ ๋ณด์กดํ•˜๊ธฐ ์œ„ํ•œ ํ•„์š”์—์„œ๋ถ€ํ„ฐ ์ž์—ฐ์˜ ์ผ๋ถ€์ธ ์ธ๋ฅ˜๊ฐ€ ์กด์†ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋ถ„๋ฆฌํ•  ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด๋ผ๋Š” ์ธ์‹์— ์ด๋ฅด๊ธฐ๊นŒ์ง€, ๊ด€์ ์— ์ฐจ์ด๋Š” ์žˆ์ง€๋งŒ ์ค‘์š”ํ•˜๊ฒŒ ์ธ์‹๋˜๊ณ  ์žˆ๋‹ค. ์ƒ๋ฌผ ๋‹ค์–‘์„ฑ ๋ณด์ „์— ๊ด€ํ•œ ๊ฐœ๋… ์ค‘ ์ƒ๋ฌผ๋‹ค์–‘์„ฑ ์ค‘์ ์ง€์—ญ์€ ์ง€๊ตฌ ์ƒ 1.4%์˜ ๋ฉด์ ์— ๋ถˆ๊ณผํ•˜์ง€๋งŒ, ๋ถ„๋ฅ˜๊ตฐ์— ๋”ฐ๋ผ 28-58%๊ฐ€ ๊ทธ ์•ˆ์— ์„œ์‹ํ•  ์ •๋„๋กœ ์ค‘์š”ํ•œ ์ง€์—ญ์ด๋‹ค. ํ•œ๋ฐ˜๋„ ๋ถ„๋‹จ์˜ ๋ถ€์‚ฐ๋ฌผ์ธ ๋น„๋ฌด์žฅ์ง€๋Œ€ ์ผ์›์€ ํ•œ๋ฐ˜๋„ ๋ฉด์ ์˜ 2% ๋ฏธ๋งŒ์— ๋ถˆ๊ณผํ•˜์ง€๋งŒ ๋ถ„๋ฅ˜๊ตฐ์— ๋”ฐ๋ผ ํ•œ๋ฐ˜๋„ ์ƒ๋ฌผ ์ข…์˜ 30-60%๊ฐ€ ์„œ์‹ํ•˜๊ณ  ์žˆ๋Š”, ํ•œ๋ฐ˜๋„์˜ ์ƒ๋ฌผ๋‹ค์–‘์„ฑ ์ค‘์ ์ง€์—ญ์ด๋‹ค. ๋น„๋ฌด์žฅ์ง€๋Œ€๋Š” ํ•œ๋ฐ˜๋„๋ฅผ ๊ฐ€๋กœ์งˆ๋Ÿฌ ํ˜•์„ฑ๋˜์–ด, ํ•œ๋ฐ˜๋„์˜ ๋‹ค์–‘ํ•œ ์ง€ํ˜•๊ณผ ์ƒํƒœ๊ณ„๋“ค์„ ํฌํ•จํ•˜๊ณ  ์žˆ๊ณ  ์‚ฌ๋žŒ์˜ ์ถœ์ž…์ด ๊ทน๋„๋กœ ์ œํ•œ๋˜์–ด ์ˆ˜๋งŽ์€ ์ƒ๋ฌผ์ด ์„œ์‹ํ•˜๊ธฐ์— ์ข‹์€ ํŠน์„ฑ์„ ๊ฐ–์ถ”๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋น„๋ฌด์žฅ์ง€๋Œ€ ์ผ์›์˜ ๋ณด์ „ ๊ฐ€์น˜๋Š” ๋งค์šฐ ํฌ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ๋‚จํ•œ์˜ ๋†’์€ ๊ฐœ๋ฐœ ์••๋ ฅ์€ ๋น„๋ฌด์žฅ์ง€๋Œ€๋„ ๋น„๊ปด๊ฐ€์ง€ ์•Š์•„, ํŠนํžˆ ์ˆ˜๋„๊ถŒ์— ์œ„์น˜ํ•œ ๋น„๋ฌด์žฅ์ง€๋Œ€ ์„œ๋ถ€ ์ผ์›์€ ๋‚จ๋ถํ•œ์˜ ๋Œ€๋„์‹œ์ธ ์„œ์šธ, ๊ฐœ์„ฑ๊ณผ ์ธ์ ‘ํ•ด ์žˆ์–ด ๋น„๋ฌด์žฅ์ง€๋Œ€ ๊ถŒ์—ญ ์ค‘์—๋„ ํŠน๋ณ„ํžˆ ๊ฐœ๋ฐœ ์••๋ ฅ์ด ํฌ๋‹ค. ํ˜„์žฌ ๋น„๋ฌด์žฅ์ง€๋Œ€ ์„œ๋ถ€ ์ผ๋Œ€์—๋Š” ๋‚จ๋ถํ•œ์„ ์—ฐ๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๊ฐ์ข… ์‹œ์„ค์ด ๋งŽ์ด ๋“ค์–ด์„œ ์žˆ๊ณ  ์˜ˆ์ •๋œ ๊ฐœ๋ฐœ ๊ณ„ํš๋„ ๋งŽ๋‹ค. ํ•˜์ง€๋งŒ ๋น„๋ฌด์žฅ์ง€๋Œ€์˜ ๋†’์€ ์ƒํƒœ์  ๊ฐ€์น˜๋ฅผ ๊ณ ๋ คํ•  ๋•Œ ์ด ์ง€์—ญ์—๋Š” ์ƒํƒœ๊ณ„๋ฅผ ๋ณด์ „ํ•˜๊ธฐ ์œ„ํ•œ ๊ณ„ํš์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„๋ฌด์žฅ์ง€๋Œ€์˜ ๊ฒฝ๊ด€ ์š”์†Œ์˜ ์‹œ๊ณต๊ฐ„ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ƒํƒœ๊ณ„๋ฅผ ํƒ๊ตฌํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์žฅ๊ธฐ๊ฐ„์˜ ๋ชจ๋‹ˆํ„ฐ๋ง ์ž๋ฃŒ๋ฅผ ํฌํ•จํ•œ ๊ณผํ•™์  ๋ถ„์„์„ ๋ฐ”ํƒ•์œผ๋กœ ๋น„๋ฌด์žฅ์ง€๋Œ€ ์ผ์›์˜ ์ƒ๋ฌผ ๋‹ค์–‘์„ฑ์„ ๋ณด์ „ํ•˜๊ธฐ ์œ„ํ•œ ๊ณ„ํš์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋จผ์ €, ๋น„๋ฌด์žฅ์ง€๋Œ€๋ฅผ ๊ทœ์ •ํ•˜๊ธฐ ์ด์ „์ธ 1919๋…„๋ถ€ํ„ฐ 2010๋…„๋Œ€๊นŒ์ง€ ํ† ์ง€ ํ”ผ๋ณต์ด ์–ด๋–ป๊ฒŒ ๋ณ€ํ•˜์˜€๋Š”์ง€, ์ธ๊ฐ„ ํ™œ๋™์œผ๋กœ ์ธํ•œ ๊ฒฝ๊ด€ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ „์Ÿ์ด ๋ฉˆ์ถ”๊ณ  ์„ธ์›”์ด ํ๋ฅด๋ฉฐ, ์„œ๋ถ€ ๋น„๋ฌด์žฅ์ง€๋Œ€ ์ผ์›์˜ ์ˆฒ์€ ์นจ์—ฝ์ˆ˜๋ฆผ์—์„œ ํ™œ์—ฝ์ˆ˜๋ฆผ์œผ๋กœ ๋ฐ”๋€Œ์—ˆ๊ณ  ๋…ผ์€ ์ดˆ์ง€๊ฐ€ ๋˜์—ˆ๋‹ค. ๋ฏผ๊ฐ„์ธ์ถœ์ž…ํ†ต์ œ์ง€์—ญ์€ 1970๋…„๋Œ€์— ๋…ผ๋†์‚ฌ๋ฅผ ์žฌ๊ฐœํ•˜์—ฌ, 1990๋…„๋Œ€์—๋Š” ์ „์Ÿ ์ด์ „์— ๋…ผ์ด์—ˆ๋˜ ์ง€์—ญ์ด ๊ฑฐ์˜ ๋Œ€๋ถ€๋ถ„ ๋…ผ์œผ๋กœ ๋ฐ”๋€ ๋ฐ˜๋ฉด์—, ๋น„๋ฌด์žฅ์ง€๋Œ€์˜ ๋…ผ์€ ์ดˆ์ง€๋กœ ๋ณ€ํ•œ ์ดํ›„ 70๋…„ ๋™์•ˆ ๋ชฉ๋ณธ์ด ์œ ์ž…๋˜๋Š” ๋“ฑ์˜ ํ›„์† ์ฒœ์ด๊ฐ€ ๊ด€์ฐฐ๋˜์ง€ ์•Š์•˜๋‹ค. ์›์ธ์€ ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€๋กœ ๋ณด์ด๋Š”๋ฐ, ๋‚ด๋ฅ™์˜ ์ดˆ์ง€์—๋Š” ๊ตฐ์‚ฌ์  ์ด์œ ๋กœ ์‚ฐ๋ถˆ์ด ๋นˆ๋ฒˆํ•˜๊ฒŒ ๋ฐœ์ƒํ•œ๋‹ค๋Š” ์ ๊ณผ, ์ž„์ง„๊ฐ•๊ณผ ์†Œ๊ทœ๋ชจ ํ•˜์ฒœ์— ์ธ์ ‘ํ•œ ์ดˆ์ง€๋Š” ์ฃผ๊ธฐ์ ์œผ๋กœ ์นจ์ˆ˜๋ฅผ ๊ฒช๋Š” ์Šต์ดˆ์›์ง€์—ญ์ด๋ผ๋Š” ์ ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์˜จ๋Œ€์„ฑ ์Šต์ดˆ์›์ง€์—ญ์€ ์ „ ์„ธ๊ณ„์ ์œผ๋กœ๋„ ํฌ์†Œํ•˜๋ฉฐ, ํ•œ๋ฐ˜๋„์—์„œ๋„ ์ด์ฒ˜๋Ÿผ ๋„“์€ ๋ฉด์ ์ด ์œ ์ง€๋˜๋Š” ์ง€์—ญ์€ ๊ฑฐ์˜ ์—†์–ด ํ•™์ˆ ์  ๊ฐ€์น˜๊ฐ€ ๋งค์šฐ ํฌ๋‹ค. ๋น„๋ฌด์žฅ์ง€๋Œ€์˜ ์ดˆ์ง€๋Š” ๋‹ค์–‘ํ•œ ์ƒ๋ฌผ๋“ค์—๊ฒŒ ์ค‘์š”ํ•œ ์„œ์‹์ง€์ผ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํฌ์†Œํ•œ ์ƒํƒœ๊ณ„์—๋Š” ๊ทธ๊ณณ์—์„œ๋งŒ ์„œ์‹ํ•˜๋Š” ์ƒ๋ฌผ์ด ์žˆ์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์•„ ๋น„๋ฌด์žฅ์ง€๋Œ€ ๋‚ด์˜ ์ดˆ์ง€๋ฅผ ๋ณด์ „ํ•˜๊ณ  ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ๋น„๋ฌด์žฅ์ง€๋Œ€ ์„œ๋ถ€ ์ผ๋Œ€์˜ ๋†๊ฒฝ์ง€๋Š” ๊ฒฝ์ง€๋ฅผ ์ •๋ฆฌํ•˜์ง€ ์•Š์•˜๊ณ  ํ˜„๋Œ€์‹ ์ €์ˆ˜์ง€์™€ ๊ด€๊ฐœ์ˆ˜๋กœ๊ฐ€ ๋ฐœ๋‹ฌํ•ด ์žˆ์ง€ ์•Š์•„ ์ „ํ†ต ๋†์ดŒ ๊ฒฝ๊ด€์„ ์œ ์ง€ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„œ๋ถ€ ๋ฏผ๊ฐ„์ธํ†ต์ œ๊ตฌ์—ญ ๋‚ด ๋†์ดŒ ๊ฒฝ๊ด€์„ ์„ธ๋ถ€ ์š”์†Œ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๊ธฐํ›„๋ณ€ํ™”์™€ ํ•จ๊ป˜ ๋นˆ๋ฒˆํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฐ€๋ญ„์ด ์กฐ๋ฅ˜ ๊ตฐ์ง‘์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ตฐ์ง‘ ๋‚ด ์ข…์˜ ์ƒํƒœ์  ์ง€์œ„์˜ ๋ฒ”์œ„์™€ ํŠน์„ฑ์„ ๋ณด์—ฌ์ฃผ๋Š” ๊ธฐ๋Šฅ์  ๋‹ค์–‘์„ฑ์„ ์ง€ํ‘œ๋กœ ์ฑ„ํƒํ•˜์—ฌ ๊ฒฝ๊ด€ ๊ตฌ์„ฑ ๋˜๋Š” ํ™˜๊ฒฝ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๋ฐ˜์‘์„ ํƒ์ง€ํ•˜์˜€๋‹ค. ์„œ๋ถ€ DMZ ์ธ๊ทผ์˜ ๋†์ดŒ๊ฒฝ๊ด€์€ ๋…ผ์Šต์ง€๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ๋งŽ์ด ๋ถ„ํฌํ•˜๋Š” ๊ฒฝ๊ด€, ์ˆฒ์ด ์ƒ๋Œ€์ ์œผ๋กœ ๋งŽ์€ ๊ฒฝ๊ด€, ์ธ์‚ผ๋ฐญ, ๋‚˜์ง€๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ๋งŽ์ด ๋ถ„ํฌํ•ด ์žˆ๋Š” ๊ฒฝ๊ด€, ์„ธ ๊ฐ€์ง€๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ๋…ผ ์Šต์ง€๊ฐ€ ๋งŽ์€ ์ „ํ†ต ๋†์ดŒ ๊ฒฝ๊ด€ ๊ตฌ์กฐ์—์„œ๋Š” ์ข… ํ’๋ถ€๋„์™€ ๊ธฐ๋Šฅ์  ํ’๋ถ€๋„๊ฐ€ ๊ฐ€์žฅ ๋†’์•˜์ง€๋งŒ, ์ˆฒ์ด ๋งŽ์€ ๋…ผ์Šต์ง€ ํ™˜๊ฒฝ์—์„œ๋Š” ๊ฐ€๋ญ„์— ์˜ํ•œ ํƒ€๊ฒฉ์ด ์ ์–ด ๊ธฐ๋Šฅ์  ๋‹ค์–‘์„ฑ ์ง€์ˆ˜์™€ ์ข… ํ’๋ถ€๋„๊ฐ€ ์ผ์ •ํ•˜๊ฒŒ ์œ ์ง€๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋…ผ ์Šต์ง€๊ฐ€ ๋ฐœ๋‹ฌํ•œ ๋†์ดŒ ๊ฒฝ๊ด€์—์„œ ์ˆฒ์€ ๊ฐ€๋ญ„ ๋“ฑ์˜ ๊ฐ‘์ž‘์Šค๋Ÿฌ์šด ๋ณ€ํ™”์—๋„ ์ƒ๋ฌผ ๋‹ค์–‘์„ฑ์„ ์œ ์ง€ํ•˜๋Š” ๋ฐ ๋„์›€์„ ์ค€๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋น„๋ฌด์žฅ์ง€๋Œ€ ์„œ๋ถ€ ๊ถŒ์—ญ์˜ ์ˆฒ์€ ์‹์ƒํ‰๊ฐ€๊ธฐ์ค€์— ๋”ฐ๋ผ ํ‰๊ฐ€ํ•  ๋•Œ ๊ฐ€์น˜๊ฐ€ ๋†’์ง€ ์•Š๋‹ค๋Š” ํ•ด์„๋„ ์žˆ์ง€๋งŒ, ์กฐ๋ฅ˜์™€ ๋‹ค๋ฅธ ๋ถ„๋ฅ˜๊ตฐ์˜ ์„œ์‹์ง€๋กœ์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ธฐํ›„ ๋ณ€ํ™”๊ฐ€ ๊ฐ€์†ํ™”ํ•จ์— ๋”ฐ๋ผ ์ด์ƒ ๊ธฐ์ƒ ํ˜„์ƒ์ด ๋”์šฑ ๋นˆ๋ฒˆํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋˜๋Š” ๋งŒํผ ๋น„๋ฌด์žฅ์ง€๋Œ€ ์ผ๋Œ€์˜ ์ƒํƒœ๊ณ„ ๊ธฐ๋Šฅ์„ ์œ ์ง€ํ•˜๊ณ  ์ƒ๋ฌผ ๊ตฐ์ง‘์˜ ๋‹ค์–‘์„ฑ์„ ์•ˆ์ •์ ์œผ๋กœ ์œ ์ง€ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ˆฒ์„ ์œ ์ง€ํ•˜๊ณ  ๋ณต์›ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋น„๋ฌด์žฅ์ง€๋Œ€ ์„œ๋ถ€ ๊ถŒ์—ญ์˜ ๋‘๋“œ๋Ÿฌ์ง„ ํŠน์ง•์€ ํ•œ๊ฐ•-์ž„์ง„๊ฐ•์ด๋ผ๋Š” ๊ฑฐ๋Œ€ํ•œ ์ˆ˜๊ณ„์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„๋ฌด์žฅ์ง€๋Œ€ ์„œ๋ถ€ ์ผ์›์˜ ์ƒ๋ฌผ ๋‹ค์–‘์„ฑ์„ ๊ณ ๋ คํ•œ ๋ณด์ „ ๊ณ„ํš์„ ์ˆ˜๋ฆฝํ•˜๊ธฐ ์œ„ํ•ด ์ฒด๊ณ„์ ์ธ ๋ณด์ „ ๊ณ„ํš ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ๋ณด์ „ ๊ตฌ์—ญ ์„ค์ • ์‹œ์— ํ˜„์‹ค์ ์œผ๋กœ ๋งŽ์€ ์ดํ•ด ๊ด€๊ณ„๊ฐ€ ๊ฐœ์ž…ํ•˜๋Š” ์ ์„ ๊ณ ๋ คํ•˜์—ฌ, ์ฒด๊ณ„์ ์ธ ๋ณด์ „ ๊ณ„ํš ๋ฐฉ๋ฒ•์€ ๋น„์šฉ ํšจ์œจ์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ตฌ์—ญ์„ ํ‰๊ฐ€ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„œ๋ถ€ ๋น„๋ฌด์žฅ์ง€๋Œ€ ์ผ์›์„ ์ƒ์ง•ํ•˜๋Š” ๊นƒ๋Œ€์ข…์ธ ์žฌ๋‘๋ฃจ๋ฏธ์˜ ์„œ์‹์ง€๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๋ณด์ „ ๊ณ„ํš์„ ์ˆ˜๋ฆฝํ•˜์˜€๋‹ค. 2014-2019๋…„์— ๊ฑธ์ณ ์กฐ์‚ฌํ•œ ๋น„๋ฌด์žฅ์ง€๋Œ€ ์„œ๋ถ€ ์ผ์›์„ ์ฐพ๋Š” ๋ฉธ์ข… ์œ„๊ธฐ์˜ ๊ฒจ์šธ ์ฒ ์ƒˆ ์œ„์น˜ ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ข… ๋ถ„ํฌ ๋ชจํ˜•์„ ๊ตฌ์ถ•ํ•˜์˜€๊ณ (Maxent) ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ฒด๊ณ„์ ์ธ ๋ณด์ „ ๊ณ„ํš์•ˆ์„ ์ˆ˜๋ฆฝํ•˜์˜€๋‹ค(Marxan). ์กฐ๋ฅ˜ ๊ตฐ์ง‘์€ ์Šต์ง€ ๋ฐ ์ €์ง€๋Œ€๋ฅผ ์‚ฌ์šฉํ•˜๋ฉฐ ์‹๋ฌผ์ฒด ์œ„์ฃผ์˜ ์„ญ์‹์„ ํ•˜๋Š” ์ง‘๋‹จ(Group 1)๊ณผ ์œก์ƒ์—์„œ ์ง€๋‚ด๋ฉฐ ์œก์‹์„ ํ•˜๋Š” ์ง‘๋‹จ(Group 2)์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค. ์žฌ๋‘๋ฃจ๋ฏธ๋Š” Group 1์— ํฌํ•จ๋˜์—ˆ๊ณ  Group 2๋Š” ์žฌ๋‘๋ฃจ๋ฏธ์™€ ์ƒํƒœ ํŠน์„ฑ์ด ๋‹ค๋ฅธ ์ง‘๋‹จ์ด๋‹ค. ๋‘ ์ง‘๋‹จ๊ณผ ์ „์ฒด ๋ฉธ์ข…์œ„๊ธฐ์ข…์„ ๋Œ€์ƒ์œผ๋กœ ์ข… ๋ถ„ํฌ ๋ชจํ˜•์„ ์‚ฐ์ถœํ•˜์—ฌ ์žฌ๋‘๋ฃจ๋ฏธ์˜ ๋ถ„ํฌ ๋ชจํ˜•๊ณผ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, 90% ์ด์ƒ์˜ ๋ฉด์ ์ด ์žฌ๋‘๋ฃจ๋ฏธ์˜ ์„œ์‹์ง€์™€ ๊ฒน์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ ์ฒด๊ณ„์ ์ธ ๋ณด์ „ ๊ณ„ํš ๋ถ„์„์—์„œ ๋‘ ์กฐ๋ฅ˜ ์ง‘๋‹จ์˜ ์„œ์‹์ง€๋ฅผ ๊ฐ๊ฐ ๊ธฐ์ค€์œผ๋กœ ์˜ˆ์ธกํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค์™€ ์ „์ฒด ๋ฉธ์ข…์œ„๊ธฐ์ข…์˜ ์„œ์‹์ง€๋ฅผ ํฌํ•จํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค ๋ชจ๋‘ ์žฌ๋‘๋ฃจ๋ฏธ์˜ ์„œ์‹์ง€๋งŒ์„ ๋ณด์ „ ๋ชฉํ‘œ๋กœ ํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค์™€ ๋น„๊ตํ•  ๋•Œ 99% ์ด์ƒ์˜ ๋ฉด์ ์ด ์ผ์น˜ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ ์žฌ๋‘๋ฃจ๋ฏธ๋Š” ๊ทธ ์„œ์‹์ง€๋ฅผ ๋ณด์ „ํ•  ๊ฒฝ์šฐ ๋‹ค๋ฅธ ๋งŽ์€ ๋ฉธ์ข…์œ„๊ธฐ์ข…์„ ๋ณดํ˜ธํ•  ์ˆ˜ ์žˆ๋Š” ์šฐ์‚ฐ์ข…์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ์žฌ๋‘๋ฃจ๋ฏธ๋Š” ์ผ๋ฐ˜ ์‹œ๋ฏผ๋“ค๋„ ์‰ฝ๊ฒŒ ์‹๋ณ„ํ•  ์ˆ˜ ์žˆ๋Š” ์ข…์œผ๋กœ, ์žฌ๋‘๋ฃจ๋ฏธ ์œ„์ฃผ์˜ ์‹œ๋ฏผ ์ฐธ์—ฌ ๋ชจ๋‹ˆํ„ฐ๋ง์„ ์‹œํ–‰ํ•  ๊ฒฝ์šฐ ์‹œ๋ฏผ ์ธ์‹ ์ฆ์ง„ ํšจ๊ณผ๊ฐ€ ํฌ๊ณ  ๋ณธ ์ง€์—ญ์˜ ์ƒ๋ฌผ ๋‹ค์–‘์„ฑ์„ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ๋ชจ๋‹ˆํ„ฐ๋ง ๋„๊ตฌ๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ์ง€์†์ ์ธ ๋ชจ๋‹ˆํ„ฐ๋ง์€ ๋ณด์ „ ์ง€์—ญ์˜ ํ˜„ํ™ฉ ํŒŒ์•… ๋ฐ ์ง„๋‹จ์— ์ค‘์š”ํ•œ๋ฐ ์ตœ๊ทผ์—๋Š” ์‹œ๋ฏผ ์ฐธ์—ฌ ๋ชจ๋‹ˆํ„ฐ๋ง์ด ์ด๋Ÿฌํ•œ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณด์ „ ๊ตฌ์—ญ์„ ์„ค์ •ํ•œ ํ›„ ํšจ๊ณผ์ ์œผ๋กœ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ์•ˆ์œผ๋กœ ๊ทธ ์ง€์—ญ์— ํ™•์ธ๋œ ์šฐ์‚ฐ์ข… ์œ„์ฃผ์˜ ์‹œ๋ฏผ๊ณผํ•™์„ ํ™œ์šฉํ•  ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ ์ฒด๊ณ„์ ์ธ ๋ณด์ „ ๊ณ„ํš๋ฒ•์— ๋”ฐ๋ฅธ ๋ถ„์„ ๊ฒฐ๊ณผ, ์ตœ์„ ์˜ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ ์ œ์‹œํ•œ ๋ณด์ „ ๊ตฌ์—ญ์€ ์ž„์ง„๊ฐ•-ํ•œ๊ฐ•๊ณผ ์„œ๋ถ€ ๋น„๋ฌด์žฅ์ง€๋Œ€, ๋ฏผ๊ฐ„์ธ์ถœ์ž…ํ†ต์ œ๊ตฌ์—ญ, ๊ทธ๋ฆฌ๊ณ  ๋ถํ•œ ์ผ๋ถ€ ์ง€์—ญ์„ ํฌํ•จํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ์„œ๋ถ€ ๋น„๋ฌด์žฅ์ง€๋Œ€ ๋ณด์ „ ๊ณ„ํš ์ˆ˜๋ฆฝ ์‹œ, ๋‹ค์–‘ํ•œ ์š”์†Œ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์ตœ์†Œํ•œ์˜ ๋น„์šฉ์œผ๋กœ ์ด ์ง€์—ญ์˜ ์ƒ๋ฌผ ์„œ์‹์ง€๋ฅผ ๋ณด์ „ํ•˜๊ณ ์ž ํ•˜๋”๋ผ๋„, ์„œ๋ถ€ ๋น„๋ฌด์žฅ์ง€๋Œ€ ์ผ์›์˜ ์ „์ฒด ์ง€์—ญ์„ ๋ณด์ „ ์ง€์—ญ์œผ๋กœ ๊ณ„ํšํ•˜์—ฌ์•ผ ํ•จ์„ ์˜๋ฏธํ•œ๋‹ค. ๋น„๋ฌด์žฅ์ง€๋Œ€ ์„œ๋ถ€ ๊ถŒ์—ญ์€ ๋†’์€ ๊ฐœ๋ฐœ ์••๋ ฅ๊ณผ ๋‚จ๋ถํ•œ ๊ด€๊ณ„ ๋ณ€ํ™”์˜ ์ง์ ‘์ ์ธ ์˜ํ–ฅ ํ•˜์— ์žˆ๋Š” ์ง€์—ญ์ด์ง€๋งŒ ์ƒ๋ฌผ ์„œ์‹์ง€๋กœ์„œ์˜ ๊ฐ€์น˜๊ฐ€ ๋งค์šฐ ํฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ชฉ๋ณธ์ด ์ด์ž…๋˜์ง€ ์•Š๊ณ  ์ดˆ์ง€ ์ƒํƒœ๋ฅผ ์œ ์ง€ํ•˜๊ณ  ์žˆ๋Š” ๋น„๋ฌด์žฅ์ง€๋Œ€์˜ ์ƒํƒœ์ ยทํ•™์ˆ ์  ๊ฐ€์น˜๋ฅผ ์žฌํ™•์ธํ•˜์˜€๊ณ , ๋ฏผ๊ฐ„์ธํ†ต์ œ๊ตฌ์—ญ์˜ ์ˆฒ์˜ ๊ฐ€์น˜๋Š” ๊ธฐํ›„์œ„๊ธฐ ์‹œ๋Œ€๋ฅผ ๋งž์•„ ์žฌํ‰๊ฐ€๋˜์–ด์•ผ ํ•จ์„ ๊ฐ•์กฐํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ตœ์†Œํ•œ์˜ ๋น„์šฉ์œผ๋กœ ๋น„๋ฌด์žฅ์ง€๋Œ€์˜ ์ƒ๋ฌผ ๋‹ค์–‘์„ฑ์„ ๋ณด์ „ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์„œ๋ถ€ ๋ฏผ๊ฐ„์ธํ†ต์ œ๊ตฌ์—ญ, ํ•œ๊ฐ•-์ž„์ง„๊ฐ• ํ•˜๊ตฌ, ๋ถํ•œ ์ง€์—ญ ์ผ๋ถ€๋ฅผ ํฌํ•จํ•˜์—ฌ ๋ณด์ „๊ตฌ์—ญ์œผ๋กœ ์ง€์ •ํ•ด์•ผ ํ•จ์„ ๋ฐํ˜”๋‹ค. ํ•œ๋ฐ˜๋„ ํ‰ํ™”์˜ ์‹œ๋Œ€๋ฅผ ๊ตฌ์ƒํ•˜๋ฉฐ, ๋‚จ๋ถํ•œ ๊ด€๊ณ„ ๋ณ€ํ™”์— ๋”ฐ๋ผ ๋น„๋ฌด์žฅ์ง€๋Œ€์— ๋Œ€ํ•œ ๊ณ„ํš์„ ๋ณ€๊ฒฝํ•˜๊ธฐ ๋ณด๋‹ค๋Š”, ๊ธฐํ›„์œ„๊ธฐ ์‹œ๋Œ€์˜ ๋ฐ˜์„ฑ๊ณผ ํ•จ๊ป˜, ํ•œ๋ฐ˜๋„ ์ƒํƒœ๊ณ„์˜ ๋ณด๊ณ ์ธ ๋น„๋ฌด์žฅ์ง€๋Œ€์˜ ์ƒ๋ฌผ ๋‹ค์–‘์„ฑ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ์•ˆ์ •์ ์ธ ๋ณด์ „๊ณ„ํš์„ ์ˆ˜๋ฆฝํ•  ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค.The Korean Demilitarized Zone (DMZ), spanning 2 km to the south and 2 km to the north of the military demarcation line at around 38oN, was established by the Korean Armistice Agreement in 1953. Access to the DMZ has been strictly restricted for decades, with the South Korean military designating the Civilian Control Zone (CCZ), a concordant area 5-10 km to the south of the DMZ, as a military buffer zone. The Korean DMZ runs east to west across the Korean Peninsula, encompassing a wide range of topographic features. As such, wildlife in the DMZ has thrived without human intervention, resulting in exceptionally high biodiversity levels. However, the high development pressure in South Korea has influenced the DMZ and the CCZ despite their high conservation value. In particular, the western region of the DMZ, which is close to large metropolitan areas in South and North Korea, has faced very high development pressure. In this study, the ecological importance of the western DMZ was explored by analyzing various spatial and temporal dimensions of its landscape elements. In addition, the scientific basis for the conservation of the DMZ and the CCZ was outlined. To begin with, the land use and land cover change from 1919, before the DMZ was established, to the 2010s, by which time human activity had impacted the landscape, was examined. The study area covered the western DMZ and CCZ. Most trees in DMZ and CCZ forests were originally coniferous, while the plains were employed for rice paddies. After the war, the coniferous forests were replaced with broadleaf forests. The cultivation of rice was restarted on the plains of the CCZ in the early 1990s, while the plains of the DMZ remained as grassland without general succession, such as the invasion of woody species. There have been two main reasons for the lack of succession. First, fires, which act as a disturbance in the successional process, have frequently occurred due to military action in the DMZ, particularly around Yeoncheon. Second, the grassland adjacent to the Imjingang River and small streams has experienced periodic flooding, which has prevented successional species from becoming established. In general, temperate grassland areas have become scarcer worldwide and on the Korean Peninsula because they have been exploited for agricultural use. However, grassland remains an essential habitat for a variety of wildlife in the DMZ, meaning that it is worth monitoring and conserving. The agricultural landscape in the western CCZ consists of undeveloped land and traditional irrigation systems, such as the dumbeong system (the use of irrigation ponds), rather than modern reservoirs and irrigation canals. In this study, the traditional agricultural landscape (TAL) in the CCZ was explored. The agricultural landscape elements were classified, and the distribution of the bird community in response to drought in relation to these elements were analyzed. Using the functional diversity approach, which investigates the functional range and biological characteristics of a community, this study investigated the response of the avian community to landscape composition and drought events. As a result, three major elements representing the TAL in the CCZ were identified: rice paddies, forest, and fields. Under non-drought conditions, the area of the landscape with a large proportion of paddies (TAL1) had the highest species and functional richness. In areas where there was a relatively high ratio of forest (TAL2), the functional diversity index and species richness remained constant regardless of drought occurrence. In other words, forested areas are able to endure the stress of drought and act as a buffer zone for birds. Therefore, forests in TALs may help to maintain biodiversity when the regional environment faces natural disasters such as drought. Though western DMZ forests do not have high value based on standard vegetation evaluation criteria, they were shown in the present study to play an essential role in sustaining avian functional diversity by enhancing ecosystem resilience and resistance. As the signs of climate change indicate, rapid changes in weather conditions accelerate. Therefore, restoring and maintaining forests are necessary in order to support the ecosystem function of the DMZ and conserve species diversity. One of the distinctive geographical features of the western DMZ is the extensive river system that includes the Hangang River and Imjingang River and many smaller streams. The brackish water system of the Hangang River-Imjingang River, which flows into the West Sea, hosts an extraordinary biodiversity of waterbirds, fish, and mammals. This study suggested a conservation plan for the western DMZ ecosystem that adopts systematic conservation planning (SCP), in which an area is evaluated based on cost-effective criteria. This is an effective way to make decisions considering the many interests that are involved in setting up a conservation plan. This study presented a conservation plan focusing on the protection of the white-naped crane (WNC), a flagship species that represents the western DMZ ecosystem. In addition, because umbrella species are an excellent management tool for protected areas, the possibility of WNC acting as an umbrella species was examined. Thus, the extent to which protecting the habitat of the WNC can protect other endangered birds was also analyzed. A species distribution map was modeled based on the GPS tracking of endangered winter migratory birds surveyed over the 2014-2019 period (using Maxent software), and a systematic conservation plan was established based on this information (using Marxan software). The endangered winter migratory birds found in the western DMZ were divided into two groups. Group 1, which includes the WNC, uses wetlands and lowlands, feeding mainly on plants, while Group 2 lives on dryland and has a carnivorous diet. A species distribution model was then calculated for both groups and all endangered species. Under all conservation scenarios, more than 90% of the selected areas were identical to the distribution model for the WNC, meaning that it acted as an umbrella species that can be used to protect many other endangered species by preserving its habitat. The morphological characteristics of the WNC make it easy to identify, thus it is suitable for monitoring as a biological indicator for regional biodiversity management. The protected areas identified using an SCP approach included the Hangang-Imjingang River system, the western DMZ and CCZ, and a part of North Korea. The western DMZ and CCZ have been placed under high developmental pressure and are directly affected by inter-Korean relations, but they have high value as a habitat for wildlife. Reaffirming the ecological importance of the DMZ and the CCZ, this study argued that the conservation plan for the DMZ should not be contingent on inter-Korean relations. Instead, a conservation plan that protects and maintains the current biodiversity levels on the Korean Peninsula is required.Abstract i List of Figures x List of Tables xvi Chapter 1.General introduction 1 1.1 Biodiversity conservation 2 1.2 Conservation planning 4 1.3 The Korean DMZ and CCZ areas 6 1.3.1 Definition of the Korean Demilitarized Zone 6 1.3.2 Biodiversity of the DMZ 8 1.3.3 Focus on the western DMZ and CCZ 9 1.4 Objectives of the study 14 Chapter 2.Changes in Land Use and Vegetation Cover over 100 years in the Western DMZ and CCZ in South Korea 17 2.1 Introduction 18 2.2 Materials and Methods 19 2.2.1 Study Area 19 2.2.2 Data Collection, Preprocessing, and Analysis 22 2.3 Results 26 2.3.1 Land Use 26 2.3.2 Wildfire and NDVI and NDMI changes 33 2.4 Discussion 35 Chapter 3.Structural implications of traditional agricultural landscapes on the functional diversity of birds near the Korean DMZ 38 3.1 Introduction 39 3.2 Methods and Materials 41 3.2.1 Bird surveys and TAL units 41 3.2.2 Drought index 44 3.2.3 Taxonomic and Functional diversity 46 3.2.4 Statistical analysis 48 3.3 Results 49 3.3.1 Compositions of TALs and avian diversity 49 3.3.2 Surveyed avian population 56 3.3.3 Drought and diversity indices of TAL 61 3.4 Discussion 68 Chapter 4.Identifying high-priority conservation areas for endangered waterbirds using a flagship species in the Korean DMZ 71 4.1 Introduction 72 4.2 Materials and Methods 74 4.2.1 Target species 74 4.2.2 Study site 76 4.2.3 Bird surveys 78 4.2.4 Land use classification of the study site 78 4.2.5 Species trait-based clustering 79 4.2.6 Species distribution 81 4.2.7 Systematic conservation planning 82 4.3 Results 84 4.3.1 Threatened Species Characteristics 84 4.3.2 Species Distribution Models 89 4.3.3 Systematic Conservation Planning 91 4.4 Discussion 93 Chapter 5.General conclusion 96 References 102 Appendix 123 ๊ตญ๋ฌธ์ดˆ๋ก 126Docto

    ํ•ญ๋ณต์  ํ˜„์ƒ์œผ๋กœ ์ธํ•œ ๊ธˆ์† ๋ฐ•ํŒ ์žฌ๋ฃŒ์˜ ๊บพ์ž„ ๊ฒฐํ•จ๊ณผ ์†Œ๋ถ€ ๊ฒฝํ™”์— ๋Œ€ํ•œ ์‹คํ—˜ ๋ฐ ์ˆ˜์น˜ํ•ด์„ ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2020. 2. ๊น€๋„๋…„.The yield-point phenomenon of annealed or aged metals has duality in terms of avoiding it or utilizing it. Defects such as fluting in v-bending originated from the phenomenon can be avoided through roller-leveling process composed of multiple up-and-down bending operations. However, excessive leveling conditions can lead to superficies defects, and an adequate process condition remains still elusive. Utilizing the phenomenon, bake hardening characterized by the significant increase of yield stress after baking of pre-strained low carbon steel can be used for improving dent resistance in automotive sheet metal forming applications. However, many previous investigations about bake hardenability concentrate only on the bake hardening response of uniaxial tension with related influence factors, and numerous numerical studies for the dent resistance rarely consider bake hardening effect. To accurately predict the behavior of materials with this phenomenon, the constitutive model for computational elastoviscoplastic analysis should be able to depict the yield-point phenomenon, the Bauschinger effect, and bake hardenability, rendering it difficult to obtain a converged solution in implicit numerical analysis when the conventional one-point Newton method is used. In the present study, firstly, comprehensive experimental investigations are performed for the fluting defect in the v-bending process, its reduction by the roller-leveling process, and the dent resistance of an automotive bake hardenable steel. Systematic evaluation for the effect of roller-leveling condition on the fluting in v-bending is then carried out using pre-coated low carbon steel after examining the rate dependency and the cyclic characteristics of the phenomenon in uniaxial loads. The bake hardening behavior of a dual phase steel is observed in uniaxial load cases and static dent experiments conducted in pre-strained and bake hardened conditions. For numerical analysis to describe these experimental observation results, an implicit stress-integration procedure is formulated and implemented for a constitutive material model that can describe both the yield-point phenomenon and the Bauschinger effect. And we propose robust stress integration algorithms that can be used effectively in implicit finite element analysis employing the bisection method and the two-point Newton method. This material model is also integrated with a bake hardening model to illustrate bake hardening potentials. The validation results of the model with simple problems demonstrate that the model can be reliably used to calculate the solutions of the yield-point phenomenon problems that cannot be obtained using conventional iterative methods although these algorithms may require longer computational times. Numerical simulations corresponding to the experiments are carried out with material parameters determined to reproduce the uniaxial experiments. V-bending simulations at various roller-leveling conditions fairly demonstrate the fluting defect and its reduction experimentally observed. The bake hardening behaviors identified in the experiments are investigated in static dent simulations including a bake hardening step, and the bake hardening effect is overall described in numerical simulations. To conclude, the proposed analysis procedure is expected to be useful in estimating a proper leveling condition to prevent potential defects and dent resistance of automotive bake hardenable steels as well as investigating the effect of the yield-point phenomenon in various metal forming processes.์†Œ๋‘”์ด ๋˜์—ˆ๊ฑฐ๋‚˜ ์‹œํšจ๊ฐ€ ๋ฐœ์ƒํ•œ ๊ธˆ์†์˜ ํ•ญ๋ณต์  ํ˜„์ƒ์€ ์ด ํ˜„์ƒ์„ ํšŒํ”ผํ•˜๊ฑฐ๋‚˜ ํ™œ์šฉํ•˜๋Š” ๊ด€์ ์—์„œ ์–‘๋ฉด์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ด ํ˜„์ƒ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” V๊ตฝํž˜ ๊ณต์ •์—์„œ์˜ ๊บพ์ž„ ๊ฒฐํ•จ์€ ์†Œ์žฌ์— ์ƒํ•˜๋ฐฉํ–ฅ ๊ตฝํž˜์„ ๋ถ€๊ณผํ•˜๋Š” ๋กค๋Ÿฌ ๋ ˆ๋ฒจ๋ง ๊ณต์ •์˜ ์ ์šฉ์œผ๋กœ ๊ฐ์†Œ๋  ์ˆ˜ ์žˆ๋‹ค. ๋กค๋Ÿฌ ๋ ˆ๋ฒจ๋ง ์กฐ๊ฑด์ด ๊ณผํ•  ๊ฒฝ์šฐ ํ‘œ๋ฉด ๊ฒฐํ•จ์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๊ณ , ์ ์ ˆํ•œ ๊ณต์ • ์กฐ๊ฑด์„ ์ฐพ๋Š” ๊ฒƒ์ด ์—ฌ์ „ํžˆ ์–ด๋ ค์šด ์‹ค์ •์ด๋‹ค. ์˜ˆ๋ณ€ํ˜•๋œ ์ €ํƒ„์†Œ๊ฐ•์„ ๊ตฌ์› ์„ ๋•Œ ํ•ญ๋ณต์ ์ด ํ˜„์ €ํ•˜๊ฒŒ ๋†’์ด์ง€๋Š” ํŠน์ง•์„ ๋ณด์ด๋Š” ์†Œ๋ถ€ ๊ฒฝํ™” ๊ฑฐ๋™์€ ์ด ํ˜„์ƒ์„ ํ™œ์šฉํ•˜๋Š” ๊ฒฝ์šฐ์ด๋ฉฐ, ์ž๋™์ฐจ ๋ฐ•ํŒ ๊ธˆ์† ์„ฑํ˜• ์‘์šฉ ๋ถ„์•ผ์—์„œ ๋ดํŠธ ์ €ํ•ญ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์†Œ๋ถ€ ๊ฒฝํ™”๋Šฅ์— ๋Œ€ํ•œ ๋งŽ์€ ์—ฐ๊ตฌ๋Š” ์ผ์ถ• ์ธ์žฅ์—์„œ์˜ ์†Œ๋ถ€ ๊ฒฝํ™” ์‘๋‹ต ๋ฐ ๊ทธ ์˜ํ–ฅ์ธ์ž์—๋งŒ ์ง‘์ค‘ํ•˜๊ณ  ์žˆ๊ณ , ์ž๋™์ฐจ ๊ฐ•ํŒ์˜ ๋ดํŠธ ์ €ํ•ญ์„ฑ์„ ์œ„ํ•œ ์ˆ˜์น˜์  ์—ฐ๊ตฌ๋Š” ์†Œ๋ถ€ ๊ฒฝํ™” ํšจ๊ณผ๋ฅผ ๊ฑฐ์˜ ๊ณ ๋ คํ•˜์ง€ ์•Š๊ณ  ์žˆ๋‹ค. ํ•ญ๋ณต์  ํ˜„์ƒ์„ ๋ณด์ด๋Š” ์†Œ์žฌ์˜ ๊ฑฐ๋™์„ ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ ํƒ„์†Œ์„ฑ ์ˆ˜์น˜ํ•ด์„์„ ์œ„ํ•œ ๊ตฌ์„ฑ ๋ชจ๋ธ์ด ํ•ญ๋ณต์  ํ˜„์ƒ, ๋ฐ”์šฐ์‹ฑ๊ฑฐ ํšจ๊ณผ, ๊ทธ๋ฆฌ๊ณ  ์†Œ๋ถ€ ๊ฒฝํ™”๋Šฅ์„ ๋ฌ˜์‚ฌํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ์˜ˆ์ธก๊ณผ์ •์—์„œ ์ „ํ†ต์ ์ธ 1์  ๋‰ดํ„ด๋ฒ•์„ ์‚ฌ์šฉํ•  ๊ฒฝ์šฐ ๋‚ด์—ฐ์  ์ˆ˜์น˜ํ•ด์„์—์„œ ์ˆ˜๋ ด๋œ ํ•ด๋ฅผ ํš๋“ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋จผ์ € V๊ตฝํž˜ ๊ณต์ •์—์„œ์˜ ๊บพ์ž„, ๋กค๋Ÿฌ ๋ ˆ๋ฒจ๋ง ๊ณต์ •์„ ํ†ตํ•œ ๊บพ์ž„ ๊ฐ์†Œ, ๊ทธ๋ฆฌ๊ณ  ์ž๋™์ฐจ ์†Œ๋ถ€ ๊ฒฝํ™” ๊ฐ•ํŒ์˜ ๋ดํŠธ ์ €ํ•ญ์„ฑ์— ๋Œ€ํ•œ ํฌ๊ด„์ ์ธ ์‹คํ—˜์  ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ผ์ถ• ํ•˜์ค‘ ํ•˜์—์„œ ์ €ํƒ„์†Œ ๋„์žฅ ๊ฐ•ํŒ์˜ ์†๋„ ์˜์กด์„ฑ๊ณผ ์ฃผ๊ธฐ ๊ฑฐ๋™ ํŠน์„ฑ์„ ์กฐ์‚ฌํ•œ ๋’ค V๊ตฝํž˜์—์„œ์˜ ๊บพ์ž„์— ๋Œ€ํ•œ ๋กค๋Ÿฌ ๋ ˆ๋ฒจ๋ง ์กฐ๊ฑด์˜ ํšจ๊ณผ๋ฅผ ์ฒด๊ณ„์ ์œผ๋กœ ํ‰๊ฐ€ ํ•˜์˜€๋‹ค. 2์ƒ ๊ฐ•ํŒ์˜ ์†Œ๋ถ€ ๊ฒฝํ™” ๊ฑฐ๋™์€ ์˜ˆ๋ณ€ํ˜•๊ณผ ์†Œ๋ถ€ ๊ฒฝํ™” ์กฐ๊ฑด์˜ ์ผ์ถ• ํ•˜์ค‘ ๋ฐ ์ •์  ๋ดํŠธ ์‹คํ—˜์—์„œ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์‹คํ—˜์  ๊ด€์ฐฐ ๊ฒฐ๊ณผ๋ฅผ ์ˆ˜์น˜ํ•ด์„์œผ๋กœ ๋ฌ˜์‚ฌํ•˜๊ธฐ ์œ„ํ•ด, ํ•ญ๋ณต์  ํ˜„์ƒ๊ณผ ๋ฐ”์šฐ์‹ฑ๊ฑฐ ํšจ๊ณผ๋ฅผ ๋™์‹œ์— ๋ฌ˜์‚ฌํ•  ์ˆ˜ ์žˆ๋Š” ์žฌ๋ฃŒ ๊ตฌ์„ฑ ๋ชจ๋ธ์— ๋Œ€ํ•ด ๋‚ด์—ฐ์  ์‘๋ ฅ ์ ๋ถ„ ๊ณผ์ •์„ ์ˆ˜์‹ํ™”ํ•˜๊ณ  ์ด๋ฅผ ์œ ํ•œ ์š”์†Œ ํ•ด์„ ์ฝ”๋“œ๋กœ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์–‘๋ถ„๋ฒ•๊ณผ 2์  ๋‰ดํ„ด๋ฒ•์„ ์ฑ„ํƒํ•˜์—ฌ ๋‚ด์—ฐ์  ์œ ํ•œ ์š”์†Œ ํ•ด์„์—์„œ ํšจ์œจ์ ์œผ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ๊ฐ•๊ฑดํ•œ ์‘๋ ฅ ์ ๋ถ„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋˜ํ•œ ์†Œ๋ถ€ ๊ฒฝํ™”๋Šฅ์„ ๋ฌ˜์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ์†Œ๋ถ€ ๊ฒฝํ™” ๋ชจ๋ธ๊ณผ ๋ณธ ์žฌ๋ฃŒ๋ชจ๋ธ์„ ํ†ตํ•ฉํ•˜์˜€๋‹ค. ๋‹จ์ˆœํ•œ ๋ฌธ์ œ์— ๋ณธ ๋ชจ๋ธ์„ ์ ์šฉํ•˜์—ฌ ์ˆ˜ํ–‰ํ•œ ๊ฒ€์ฆ ํ•ด์„ ๊ฒฐ๊ณผ๋Š” ๊ณ ์ „์ ์ธ ๋ฐ˜๋ณต๋ฒ•์œผ๋กœ๋Š” ํ•ด๋ฅผ ์–ป์„ ์ˆ˜ ์—†๋Š” ํ•ญ๋ณต์  ํ˜„์ƒ ๋ฌธ์ œ์— ๋ณธ ๋ชจ๋ธ์ด ์‹ ๋ขฐํ•  ์ˆ˜์ค€์œผ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์ง€๋งŒ ๊ณ„์‚ฐ ์‹œ๊ฐ„์€ ์ฆ๊ฐ€ํ•  ์ˆ˜๋„ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์ผ์ถ• ์‹คํ—˜์„ ์žฌํ˜„ํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ฒฐ์ •๋œ ์žฌ๋ฃŒ ๋ณ€์ˆ˜๋“ค์„ ์ด์šฉํ•˜์—ฌ ์‹คํ—˜์— ๋Œ€์‘๋˜๋Š” ์ˆ˜์น˜ํ•ด์„์ด ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๋‹ค์–‘ํ•œ ๋กค๋Ÿฌ ๋ ˆ๋ฒจ๋ง ์กฐ๊ฑด์—์„œ์˜ V๊ตฝํž˜ ํ•ด์„์€ ์‹คํ—˜์ ์œผ๋กœ ๊ด€์ฐฐ๋œ ๊บพ์ž„ ๊ฒฐํ•จ๊ณผ ๊ทธ ๊ฐ์†Œ ํ˜„์ƒ์„ ์ž˜ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์‹คํ—˜์—์„œ ํ™•์ธ๋œ ์†Œ๋ถ€ ๊ฒฝํ™” ๊ฑฐ๋™์€ ์†Œ๋ถ€ ๊ฒฝํ™” ๋‹จ๊ณ„๋ฅผ ํฌํ•จํ•˜๋Š” ์ •์  ๋ดํŠธ ํ•ด์„์—์„œ ๋ถ„์„๋˜์—ˆ๊ณ , ์†Œ๋ถ€ ๊ฒฝํ™” ํšจ๊ณผ๊ฐ€ ์ˆ˜์น˜ ํ•ด์„์—์„œ ์ „๋ฐ˜์ ์œผ๋กœ ๋ฌ˜์‚ฌ๋˜์—ˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆ๋œ ํ•ด์„ ๋ฐฉ๋ฒ•์€ ๋‹ค์–‘ํ•œ ๊ธˆ์† ์„ฑํ˜• ๊ณต์ •์—์„œ ํ•ญ๋ณต์  ํ˜„์ƒ์˜ ํšจ๊ณผ๋ฅผ ์—ฐ๊ตฌํ•˜๋Š” ๊ฒƒ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊บพ์ž„ ๊ฒฐํ•จ์„ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•œ ๋กค๋Ÿฌ ๋ ˆ๋ฒจ๋ง ์กฐ๊ฑด์„ ์ถ”์ •ํ•˜๊ณ  ์ž๋™์ฐจ ์†Œ๋ถ€ ๊ฒฝํ™” ๊ฐ•ํŒ์˜ ๋ดํŠธ ์ €ํ•ญ์„ฑ์„ ์˜ˆ์ธกํ•˜๋Š”๋ฐ ์œ ์šฉํ•˜๊ฒŒ ์‚ฌ์šฉ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.1 Introduction 15 1.1 Yield-point phenomenon 15 1.2 Avoiding or utilizing the YPP 17 1.3 Literature review 20 1.3.1 YPP defect and its reduction method 20 1.3.2 Dent resistance considering BH behavior 21 1.3.3 Constitutive model and numerical analysis for YPP 21 1.3.4 Stress integration algorithms 23 1.3.5 Recent YPP studies 24 1.3.6 BH models 25 1.4 Objectives and outline 26 2 Experimental Investigations 29 2.1 Experimental observations for fluting defect and its reduction method with PLCS 30 2.1.1 Uniaxial tension test 30 2.1.2 Uniaxial cyclic test 32 2.1.3 Roller-leveling test 35 2.1.4 V-bending test 41 2.2 Experimental observations for static dent resistance considering BH behavior with 490DP 48 2.2.1 Uniaxial tension test with BH 48 2.2.2 Uniaxial tension-compression test with BH 55 2.2.3 Static dent test with BH 57 2.3 Summary 65 3 Material Modeling 67 3.1 Constitutive Model 68 3.1.1 YPP model 68 3.1.2 BH model 71 3.1.3 Integration of BH model into YPP model 73 3.2 Computational Implementation 74 3.2.1 BH calculation 74 3.2.2 Trial state assessment 77 3.2.3 Stress integration 78 3.2.4 Consistent tangent stiffness 87 3.2.5 Summary of the overall procedure 91 3.3 Summary 93 4 Validation of the Material Model 95 4.1 Single element analysis 96 4.2 Uniaxial tension and cyclic simulations 99 4.3 V-bending simulations 100 4.4 Cantilever bending simulation 103 4.5 Performance comparison 105 4.6 Single element BH simulations 107 4.7 Summary 110 5 Numerical Analysis 112 5.1 Numerical simulations of fluting defect and its reduction method for PLCS 113 5.1.1 Uniaxial tension and cyclic simulation 113 5.1.2 Roller-leveling and v-bending simulations 118 5.2 Numerical simulations of static dent resistance considering BH behavior for 490DP 130 5.2.1 Uniaxial tension and tension-compression simulations 130 5.2.2 Static dent simulations 138 5.3 Summary 146 6 Conclusion 151 A Appendix 155 A.1 Pseudocodes of UMAT subroutine for numerical simulations 155 A.2 Hertz contact problem for validating the parameters of the exponential pressure-overclosure relationship 159 References 162 Abstract (In Korean) 174Docto

    ์‚ผ์ฐจ์› ํ”„๋ฆฐํ„ฐ๋ฅผ ์ด์šฉํ•œ ์ƒˆ๋กœ์šด ์ƒ๋ถ„ํ•ด์„ฑ ๋‹ด๋„ ์Šคํ…ํŠธ์˜ ๋ผ์ง€ ๋ชจ๋ธ์—์„œ์˜ ํƒ€๋‹น์„ฑ ๋ฐ ์•ˆ์ „์„ฑ ํ‰๊ฐ€: ์˜ˆ๋น„์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2023. 2. ํ•œ์ค€๊ตฌ.Objectives: To assess the feasibility and safety of a novel 3D-printed biodegradable biliary stent using polycaprolactone (PCL) in an in vivo porcine model Methods: In this animal study using domestic pigs, biodegradable radiopaque biliary stents made of polycaprolactone (PCL) and barium sulfate were produced using 3D printing and surgically inserted into the common bile duct (CBD) of pigs (stent group, n=12). Another five pigs were allocated to the control group that only underwent resection and anastomosis of the CBD without stent insertion. To check the position and status of the stents and stent-related complications, follow-up computed tomography (CT) was performed every month. The pigs were sacrificed 1 or 3 months after surgery, and their excised CBD specimens were examined at both the macroscopic and microscopic levels. Results: Three pigs (one in the stent group and two in the control group) died within one day after surgery and were excluded from further analysis; the remaining 11 in the stent group and 3 in the control group survived the scheduled follow-up period (1 month, 5 and 1; and 3 months, 6 and 2 in stent and control groups, respectively). In all pigs, no clinical symptoms or radiologic evidence of biliary complications was observed. In the stent group (n=11), stent migration (n=1 at 3 months; n=2 at 1 month) and stent fracture (n=3 at 2 months) were detected on CT scans. Macroscopic evaluation of the stent indicated no significant change at 1 month (n=3) or fragmentation with discoloration at 3 months (n=5). On microscopic examination of CBD specimens, the tissue inflammation score was significantly higher in the stent group than in the control group (meanยฑstandard deviation (SD), 5.63ยฑ2.07 vs. 2.00ยฑ1.73; P=0.039) and thickness of fibrosis of the CBD wall was significantly higher than that of the control group (0.46ยฑ0.12 mm vs. 0.21ยฑ0.05mm; P=0.012). Conclusion: Despite mild bile duct inflammation and fibrosis, 3D-printed biodegradable biliary stents showed good feasibility and safety in porcine bile ducts, suggesting their potential for use in the prevention of postoperative biliary strictures.์„œ๋ก : ์‚ผ์ฐจ์› ํ”„๋ฆฐํ„ฐ์™€ ํด๋ฆฌ์นดํ”„๋กœ๋ฝํ†ค (PCL)์„ ์ด์šฉํ•œ ์ƒˆ๋กœ์šด ์ƒ๋ถ„ํ•ด์„ฑ ๋‹ด๋„ ์Šคํ…ํŠธ์˜ ํƒ€๋‹น์„ฑ๊ณผ ์•ˆ์ •์„ฑ์„ ๋ผ์ง€ ๋ชจ๋ธ์—์„œ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ์ด๋‹ค. ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‚ผ์ฐจ์› ํ”„๋ฆฐํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ PLC๊ณผ ํ™ฉ์‚ฐ ๋ฐ”๋ฅจ์„ ์„ž์€ ์ƒ๋ถ„ํ•ด์„ฑ ๋ฐฉ์‚ฌ์„  ๋ถˆํˆฌ๊ณผ ๋‹ด๋„ ์Šคํ…ํŠธ๋ฅผ ์ œ์ž‘ํ•˜๊ณ , ๋ผ์ง€์˜ ์ด๋‹ด๊ด€์— ์‚ฝ์ž… ํ•˜์˜€๋‹ค (์Šคํ…ํŠธ ๊ตฐ, n =12). ๋˜ํ•œ, ๋‹ค๋ฅธ 5๋งˆ๋ฆฌ์˜ ๋ผ์ง€๋Š” ์Šคํ…ํŠธ ์‚ฝ์ž… ์—†์ด ์ด๋‹ด๊ด€์˜ ์ ˆ์ œ ๋ฐ ๋ฌธํ•ฉ๋งŒ์„ ์‹œํ–‰ํ•œ ๋Œ€์กฐ๊ตฐ์— ํ• ๋‹นํ•˜์˜€๋‹ค. ์Šคํ…ํŠธ์˜ ์œ„์น˜์™€ ์ƒํƒœ, ์Šคํ…ํŠธ ๊ด€๋ จ ํ•ฉ๋ณ‘์ฆ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ๋งค์›” ์ถ”์  ์ปดํ“จํ„ฐ ๋‹จ์ธต์ดฌ์˜(CT)์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋ผ์ง€๋Š” ์ˆ˜์ˆ  ํ›„ 1๊ฐœ์›” ํ˜น์€ 3๊ฐœ์›” ํ›„์— ํฌ์ƒ๋˜์—ˆ๊ณ , ์ ˆ์ œ๋œ ์ด๋‹ด๊ด€ ํ‘œ๋ณธ์€ ๋ณ‘๋ฆฌ๊ณผ ์ „๋ฌธ์˜๊ฐ€ ์œก์•ˆ์ , ํ˜„๋ฏธ๊ฒฝ์ ์œผ๋กœ ๊ด€์ฐฐ ๋ฐ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: 3๋งˆ๋ฆฌ์˜ ๋ผ์ง€(์Šคํ…ํŠธ ๊ตฐ์—์„œ 1๋งˆ๋ฆฌ, ๋Œ€์กฐ๊ตฐ 2๋งˆ๋ฆฌ)๋Š” ์ˆ˜์ˆ  ํ›„ ํ•˜๋ฃจ ์ด๋‚ด์— ์‚ฌ๋งํ–ˆ์œผ๋ฉฐ ์ดํ›„ ๋ถ„์„์—์„œ ์ œ์™ธ๋˜์—ˆ๋‹ค; ๋‚˜๋จธ์ง€ ์Šคํ…ํŠธ ๊ตฐ 11๋งˆ๋ฆฌ์™€ ๋Œ€์กฐ๊ตฐ 3๋งˆ๋ฆฌ๋Š” ์˜ˆ์ •๋œ ์ถ”์ ๊ด€์ฐฐ ๊ธฐ๊ฐ„ (์Šคํ…ํŠธ ๊ตฐ: 1๊ฐœ์›” 5๋งˆ๋ฆฌ, 3๊ฐœ์›” 6๋งˆ๋ฆฌ; ๋Œ€์กฐ๊ตฐ: 1๊ฐœ์›” 1๋งˆ๋ฆฌ, 3๊ฐœ์›” 2๋งˆ๋ฆฌ) ๋™์•ˆ ์ƒ์กดํ–ˆ๋‹ค. ๋ชจ๋“  ๋ผ์ง€์—์„œ ๋‹ด๋„ ํ•ฉ๋ณ‘์ฆ์˜ ์ž„์ƒ ์ฆ์ƒ์ด๋‚˜ ์˜์ƒ์˜ํ•™์  ์ฆ๊ฑฐ๋Š” ๊ด€์ฐฐ๋˜์ง€ ์•Š์•˜๋‹ค. ์Šคํ…ํŠธ ๊ตฐ (n = 11)์—์„œ๋Š” ์ถ”์ ๊ด€์ฐฐ CT์—์„œ ์Šคํ…ํŠธ ์ด๋™ (3๊ฐœ์›” ์งธ 1๋งˆ๋ฆฌ, 1๊ฐœ์›” ์งธ 2๋งˆ๋ฆฌ)๊ณผ ์Šคํ…ํŠธ ๊ณจ์ ˆ (2๊ฐœ์›” ์งธ 3๋งˆ๋ฆฌ)์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์Šคํ…ํŠธ์˜ ์œก์•ˆ ํ‰๊ฐ€ ๊ฒฐ๊ณผ 1๊ฐœ์›” ์งธ (n = 3)์—๋Š” ์œ ์˜ํ•œ ๋ณ€ํ™”๊ฐ€ ์—†์—ˆ๊ณ , 3๊ฐœ์›” ์งธ (n = 5)์— ๋ณ€์ƒ‰๊ณผ ๋‹จํŽธํ™”๊ฐ€ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์ด๋‹ด๊ด€ ํ‘œ๋ณธ์˜ ํ˜„๋ฏธ๊ฒฝ ๊ฒ€์‚ฌ์—์„œ ์กฐ์ง ์—ผ์ฆ ์ ์ˆ˜๋Š” ๋Œ€์กฐ๊ตฐ๋ณด๋‹ค ์Šคํ…ํŠธ ๊ตฐ์—์„œ ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜๊ณ  (meanยฑstandard deviation, 5.63ยฑ2.07 vs. 2.00ยฑ1.73; P=0.039), ์ด๋‹ด๊ด€ ๋ฒฝ์˜ ์„ฌ์œ ํ™” ๋‘๊ป˜๋„ ๋Œ€์กฐ๊ตฐ๋ณด๋‹ค ์Šคํ…ํŠธ ๊ตฐ์—์„œ ์œ ์˜ํ•˜๊ฒŒ ๋‘๊บผ์› ๋‹ค (0.46ยฑ0.12 mm vs. 0.21ยฑ0.05mm; P=0.012). ๊ฒฐ๋ก : ๊ฒฝ๋ฏธํ•œ ๋‹ด๊ด€ ์—ผ์ฆ ๋ฐ ์„ฌ์œ ํ™”์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์‚ผ์ฐจ์› ํ”„๋ฆฐํ„ฐ๋กœ ๋งŒ๋“  ์ƒ๋ถ„ํ•ด์„ฑ ๋‹ด๋„ ์Šคํ…ํŠธ๋Š” ๋ผ์ง€ ๋‹ด๊ด€์—์„œ ์ข‹์€ ํƒ€๋‹น์„ฑ๊ณผ ์•ˆ์ •์„ฑ์„ ๋ณด์—ฌ, ์ถ”ํ›„ ์ˆ˜์ˆ  ํ›„ ๋‹ด๋„ ํ˜‘์ฐฉ ์˜ˆ๋ฐฉ์— ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์˜€๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๊ฒ ๋‹ค.Introduction 1 Materials and Methods 4 Results 10 Discussion 23 References 30 ๊ตญ๋ฌธ์ดˆ๋ก 34๋ฐ•

    2,4,6 ์„ธ ์†Œ์•„์—์„œ ๋ถ€์‹  ์•ˆ๋“œ๋กœ๊ฒ๊ณผ ์Šคํ…Œ๋กœ์ด๋“œ ํ•ฉ์„ฑ ํšจ์†Œ ํ™œ์„ฑ๋„์˜ ๋ณ€ํ™”: ์ „ํ–ฅ์  ์ฝ”ํ˜ธํŠธ์—์„œ ์Šคํ…Œ๋กœ์ด๋“œ ํ˜ธ๋ฅด๋ชฌ ํ”„๋กœํŒŒ์ผ ๋ถ„์„

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2019. 2. ์–‘์„ธ์›.์„œ๋ก : ์„ฑ์ฆ๋ฐœ์ƒ(Adrenarche)์€ ๋ถ€์‹ ์—์„œ ์•ˆ๋“œ๋กœ๊ฒ์ด ์ƒ์„ฑ๋˜๋Š” ๊ฒƒ์„ ๋‚˜ํƒ€๋‚ด์ง€๋งŒ, ์•„์ง ์†Œ์•„์‹œ๊ธฐ์—์„œ์˜ ์ •ํ™•ํ•œ ์‹œ์ž‘ ๊ธฐ์ „์— ๋Œ€ํ•ด์„œ๋Š” ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ์—์„œ๋Š” ์ „ํ–ฅ์  ์ฝ”ํ˜ธํŠธ์—์„œ ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋กœ ํ•œ๊ตญ ์–ด๋ฆฐ์ด์—์„œ adrenarche์˜ ์‹œ์ž‘๊ณผ ๊ด€๋ จ๋œ ๋ถ€์‹  ์Šคํ…Œ๋กœ์ด๋“œ ํ˜ธ๋ฅด๋ชฌ ๋ฐ ๋ถ€์‹  ์Šคํ…Œ๋กœ์ด๋“œ ํ•ฉ์„ฑํšจ์†Œ์˜ ํ™œ์„ฑ๋„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ฐฉ๋ฒ•: ์–ด๋ฆฐ์ด ํ™˜๊ฒฝ๋ฐœ๋‹ฌ ์ฝ”ํ˜ธํŠธ์—์„œ 2, 4, 6์„ธ ๋•Œ ์ถ”์ ๊ด€์ฐฐ์„ ์‹ค์‹œํ•œ 229๋ช…(๋‚จ์ž 124๋ช…, 52.4%)์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ์‹ ์ฒด๊ณ„์ธก ๊ธฐ๋ก ๋ฐ ์ถœ์ƒ ์ •๋ณด๊ฐ€ ์ˆ˜์ง‘๋˜์—ˆ๋‹ค. LC-MS/MS ๋ฐฉ๋ฒ•์œผ๋กœ ์Šคํ…Œ๋กœ์ด๋“œ ํ”„๋กœํŒŒ์ผ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์ธก์ •๋œ ๋ถ€์‹  ํ˜ธ๋ฅด๋ชฌ์€ dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEA-S), 17-hydroxyprogesterone, androstenedione, testosterone, pregnenolone sulfate, cholesterol sulfate, testosterone, progesterone, 17-hydroxypregnenolone, pregnenolone์ด๋‹ค. ์ „๊ตฌ๋ฌผ์งˆ๊ณผ ์ƒ์„ฑ๋ฌผ์งˆ์˜ ๋น„๋ฅผ ํ†ตํ•˜์—ฌ 17ฮฑ-hydroxylase, 17,20-lyase, 3ฮฒ- hydroxysteroid dehydrogenase (HSD), 17ฮฒ-HSD, DHEA Sulfotransferase์˜ ํ™œ์„ฑ๋„๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๊ณ  ์ด๋ฅผ ์„ฑ๋ณ„, ์—ฐ๋ น๋ณ„๋กœ ๋น„๊ตํ•˜์˜€๋‹ค. DHEA-S ๋†๋„ ์ฆ๊ฐ€์™€ ๊ด€๋ จ์ด ์žˆ๋Š” ์š”์ธ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: 2,4,6์„ธ ๋•Œ์˜ ์Šคํ…Œ๋กœ์ด๋“œ ํ”„๋กœํŒŒ์ผ ๊ฒฐ๊ณผ๊ฐ€ ๋ชจ๋‘ ์žˆ๋Š” 200๋ช… (๋‚จ์ž 114๋ช…, 57%)๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. DHEA, DHEA-S, androstenedione๋Š” ๋‚จ๋…€ ๋ชจ๋‘์—์„œ 2-4์„ธ ์‚ฌ์ด์— ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. DHEA์™€ androstenedione์€ 6์„ธ ๋•Œ ์—ฌ์•„์—์„œ ๋†’์•˜๋‹ค. DHEA sulfotransferase ํ™œ์„ฑ๋„๋Š” ๋‚จ๋…€ ๋ชจ๋‘ 2-4์„ธ ์‚ฌ์ด์— ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. 4-6์„ธ ์‚ฌ์ด์—๋Š” 17ฮฑ-hydroxylase, 17,20-lyase์˜ ํ™œ์„ฑ๋„๋Š” ์ฆ๊ฐ€ํ•˜๊ณ , 3ฮฒ-HSD์™€ 17ฮฒ-HSD ํ™œ์„ฑ๋„๋Š” ๊ฐ์†Œํ•˜์˜€๋‹ค. ์—ฌ์•„์—์„œ ๋‚จ์•„๋ณด๋‹ค 17,20-lyase์˜ ํ™œ์„ฑ๋„๋Š” ๋†’์•˜๊ณ , 3ฮฒ-HSD์™€ 17ฮฒ-HSD์˜ ํ™œ์„ฑ๋„๋Š” ๋‚ฎ์•˜๋‹ค. DHEA-S ๋†๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜๋Š” ์ถ”์„ธ๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ๊ณผ ๊ด€๋ จ๋œ ์ธ์ž๋Š” ์—ฐ๋ น๊ณผ ์ฒด์งˆ๋Ÿ‰์ง€์ˆ˜์˜€๋‹ค. 6์„ธ ๋•Œ์˜ DHEA-S ๋†๋„์™€ ๊ด€๋ จ์ด ์žˆ๋Š” ์ธ์ž๋Š” ๋ถ€๋‹น๊ฒฝ๋Ÿ‰์•„ ์—ฌ๋ถ€์™€ ๊ณจ์—ฐ๋ น์ด์—ˆ๋‹ค. ์ƒํ™”ํ•™์  adrenarche๋Š” 6์„ธ ๋•Œ ์ด 27๋ช… (13.5%) ์—์„œ ๋ฐœ๊ฒฌ๋˜์—ˆ์œผ๋ฉฐ, ๋‚จ๋…€ ์ฐจ์ด๋Š” ์—†์—ˆ๋‹ค. ๊ฒฐ๋ก : 2-6์„ธ ํ•œ๊ตญ ์–ด๋ฆฐ์ด์—์„œ ๋ถ€์‹  ์•ˆ๋“œ๋กœ๊ฒ์€ 2-4์„ธ์— ์ฆ๊ฐ€ํ•˜๊ธฐ ์‹œ์ž‘ํ•˜์—ฌ 4-6์„ธ ์‚ฌ์ด์— ๋งŽ์ด ์ฆ๊ฐ€ํ•œ๋‹ค. ๋ถ€์‹  ์Šคํ…Œ๋กœ์ด๋“œ ํ•ฉ์„ฑํšจ์†Œ๋Š” 2-4์„ธ์— DHEA sulfotransferase ํ™œ์„ฑ๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ , 4-6์„ธ ์‚ฌ์ด์— 17,20-lyase์˜ ์ฆ๊ฐ€, 3ฮฒ-HSD์˜ ๊ฐ์†Œ๋ฅผ ๋ณด์ธ๋‹ค. ์ถ”ํ›„ ์žฅ๊ธฐ์ ์ธ ์ถ”์ ๊ด€์ฐฐ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค.Introduction: Adrenarche refers to the increase in adrenal androgen synthesis. However, process of adrenal androgen production in early childhood remains to be elucidated. The aim of this study was to evaluate changes in adrenal androgen levels and steroidogenic enzyme activities associated with adrenarche using a prospective cohort. Methods: A total of 229 children (124 boys, 52.4%), who had participated in the Environment and Development of Children (EDC) cohort at age 2, 4, and 6 years old were enrolled. Anthropometric data at each visit and birth data were collected. Steroid profiles were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 10 adrenal hormones were measured including dehydroepiandrosterone (DHEA), DHEA sulfate (DHEA-S), 17-hydroxyprogesterone, androstenedione, testosterone, pregnenolone sulfate, cholesterol sulfate, testosterone, progesterone, 17-hydroxypregnenolone, and pregnenolone. Steroidogenic enzyme activities were calculated using precursor/product ratios, such as 17ฮฑ-hydroxylase, 17,20-lyase, 3ฮฒ-hydroxysteroid dehydrogenase (HSD), 17ฮฒ-HSD, and DHEA sulfotransferase. Steroid levels and enzyme activities were compared according to age and sex. Factors affecting increasing levels of DHEA-S were analyzed. Results: Data for 200 subjects (114 boys, 57.0%) with all steroid profiling results at 2, 4, and 6 years were analyzed. DHEA, DHEA-S, and androstenedione increased between 2 and 4 years in both sexes. DHEA and androstenedione were higher in girls than in boys at the age of 6 years. DHEA sulfotransferase activity increased between 2 and 4 years in both sexes. Between 4 and 6 years, activities of 17ฮฑ-hydroxylase and 17,20-lyase increased, although 3ฮฒ-HSD and 17ฮฒ-HSD activities decreased. In girls, 17,20-lyase activity was higher and 3ฮฒ-HSD and 17ฮฒ-HSD activities were lower than in boys. Factors associated with increasing DHEA-S concentration over visits were age and body mass index. DHEA-S levels at the age of 6 years were significantly associated with being born small for gestational age and bone age on the third visit. Biochemical adrenarche was observed in 27 children (13.5%) with no sex difference. Conclusions: Adrenal androgens began to increase between the ages 2 to 4 years. Increased activity of DHEA sulfotransferase began between 2 and 4 years. Changes in steroidogenic enzyme activity to increase DHEA-S concentrations started between 4 and 6 years with increased 17,20-lyase and decreased 3ฮฒ-HSD activity. A longitudinal study with samples at the age of 8 years would be needed.Abstract i Contents iv List of tables v List of figures vii List of abbreviations ix Introduction 1 Materials and Methods 4 Results 14 Discussion 44 Conclusions 49 References 50 Acknowledgments 57 Abstract in Korean 58Docto

    ์—๋„ˆ์ง€ ํšจ์œจ์  ์ธ๊ณต์‹ ๊ฒฝ๋ง ์„ค๊ณ„

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2019. 2. ์ตœ๊ธฐ์˜.์ตœ๊ทผ ์‹ฌ์ธต ํ•™์Šต์€ ์ด๋ฏธ์ง€ ๋ถ„๋ฅ˜, ์Œ์„ฑ ์ธ์‹ ๋ฐ ๊ฐ•ํ™” ํ•™์Šต๊ณผ ๊ฐ™์€ ์˜์—ญ์—์„œ ๋†€๋ผ์šด ์„ฑ๊ณผ๋ฅผ ๊ฑฐ๋‘๊ณ  ์žˆ๋‹ค. ์ตœ์ฒจ๋‹จ ์‹ฌ์ธต ์ธ๊ณต์‹ ๊ฒฝ๋ง ์ค‘ ์ผ๋ถ€๋Š” ์ด๋ฏธ ์ธ๊ฐ„์˜ ๋Šฅ๋ ฅ์„ ๋„˜์–ด์„  ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ธ๊ณต์‹ ๊ฒฝ๋ง์€ ์—„์ฒญ๋‚œ ์ˆ˜์˜ ๊ณ ์ •๋ฐ€ ๊ณ„์‚ฐ๊ณผ ์ˆ˜๋ฐฑ๋งŒ๊ฐœ์˜ ๋งค๊ฐœ ๋ณ€์ˆ˜๋ฅผ ์ด์šฉํ•˜๊ธฐ ์œ„ํ•œ ๋นˆ๋ฒˆํ•œ ๋ฉ”๋ชจ๋ฆฌ ์•ก์„ธ์Šค๋ฅผ ์ˆ˜๋ฐ˜ํ•œ๋‹ค. ์ด๋Š” ์—„์ฒญ๋‚œ ์นฉ ๊ณต๊ฐ„๊ณผ ์—๋„ˆ์ง€ ์†Œ๋ชจ ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•˜์—ฌ ์ž„๋ฒ ๋””๋“œ ์‹œ์Šคํ…œ์—์„œ ์ธ๊ณต์‹ ๊ฒฝ๋ง์ด ์‚ฌ์šฉ๋˜๋Š” ๊ฒƒ์„ ์ œํ•œํ•˜๊ฒŒ ๋œ๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ธ๊ณต์‹ ๊ฒฝ๋ง์„ ๋†’์€ ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ์„ ๊ฐ–๋„๋ก ์„ค๊ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ๋ฒˆ์งธ ํŒŒํŠธ์—์„œ๋Š” ๊ฐ€์ค‘ ์ŠคํŒŒ์ดํฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ์งง์€ ์ถ”๋ก  ์‹œ๊ฐ„๊ณผ ์ ์€ ์—๋„ˆ์ง€ ์†Œ๋ชจ์˜ ์žฅ์ ์„ ๊ฐ–๋Š” ์ŠคํŒŒ์ดํ‚น ์ธ๊ณต์‹ ๊ฒฝ๋ง ์„ค๊ณ„ ๋ฐฉ๋ฒ•์„ ๋‹ค๋ฃฌ๋‹ค. ์ŠคํŒŒ์ดํ‚น ์ธ๊ณต์‹ ๊ฒฝ๋ง์€ ์ธ๊ณต์‹ ๊ฒฝ๋ง์˜ ๋†’์€ ์—๋„ˆ์ง€ ์†Œ๋น„ ๋ฌธ์ œ๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ์œ ๋งํ•œ ๋Œ€์•ˆ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ์‹ฌ์ธต ์ธ๊ณต์‹ ๊ฒฝ๋ง์„ ์ •ํ™•๋„ ์†์‹ค์—†์ด ์ŠคํŒŒ์ดํ‚น ์ธ๊ณต์‹ ๊ฒฝ๋ง์œผ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๋ฐœํ‘œ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋“ค์€ rate coding์„ ์‚ฌ์šฉํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ธด ์ถ”๋ก  ์‹œ๊ฐ„์„ ๊ฐ–๊ฒŒ ๋˜๊ณ  ์ด๊ฒƒ์ด ๋งŽ์€ ์—๋„ˆ์ง€ ์†Œ๋ชจ๋ฅผ ์•ผ๊ธฐํ•˜๊ฒŒ ๋˜๋Š” ๋‹จ์ ์ด ์žˆ๋‹ค. ์ด ํŒŒํŠธ์—์„œ๋Š” ํŽ˜์ด์ฆˆ์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ์ŠคํŒŒ์ดํฌ ๊ฐ€์ค‘์น˜๋ฅผ ๋ถ€์—ฌํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ์ถ”๋ก  ์‹œ๊ฐ„์„ ํฌ๊ฒŒ ์ค„์ด๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. MNIST, SVHN, CIFAR-10, CIFAR-100 ๋ฐ์ดํ„ฐ์…‹์—์„œ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•œ ์ŠคํŒŒ์ดํ‚น ์ธ๊ณต์‹ ๊ฒฝ๋ง์ด ๊ธฐ์กด ๋ฐฉ๋ฒ•์— ๋น„ํ•ด ํฐ ํญ์œผ๋กœ ์ถ”๋ก  ์‹œ๊ฐ„๊ณผ ์ŠคํŒŒ์ดํฌ ๋ฐœ์ƒ ๋นˆ๋„๋ฅผ ์ค„์—ฌ์„œ ๋ณด๋‹ค ์—๋„ˆ์ง€ ํšจ์œจ์ ์œผ๋กœ ๋™์ž‘ํ•จ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋‘๋ฒˆ์งธ ํŒŒํŠธ์—์„œ๋Š” ๊ณต์ • ๋ณ€์ด๊ฐ€ ์žˆ๋Š” ์ƒํ™ฉ์—์„œ ๋™์ž‘ํ•˜๋Š” ๊ณ ์—๋„ˆ์ง€ํšจ์œจ ์•„๋‚ ๋กœ๊ทธ ์ธ๊ณต์‹ ๊ฒฝ๋ง ์„ค๊ณ„ ๋ฐฉ๋ฒ•์„ ๋‹ค๋ฃจ๊ณ  ์žˆ๋‹ค. ์ธ๊ณต์‹ ๊ฒฝ๋ง์„ ์•„๋‚ ๋กœ๊ทธ ํšŒ๋กœ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ตฌํ˜„ํ•˜๋ฉด ๋†’์€ ๋ณ‘๋ ฌ์„ฑ๊ณผ ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ์„ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์•„๋‚ ๋กœ๊ทธ ์‹œ์Šคํ…œ์€ ๋…ธ์ด์ฆˆ์— ์ทจ์•ฝํ•œ ์ค‘๋Œ€ํ•œ ๊ฒฐ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋…ธ์ด์ฆˆ ์ค‘ ํ•˜๋‚˜๋กœ ๊ณต์ • ๋ณ€์ด๋ฅผ ๋“ค ์ˆ˜ ์žˆ๋Š”๋ฐ, ์ด๋Š” ์•„๋‚ ๋กœ๊ทธ ํšŒ๋กœ์˜ ์ ์ • ๋™์ž‘ ์ง€์ ์„ ๋ณ€ํ™”์‹œ์ผœ ์‹ฌ๊ฐํ•œ ์„ฑ๋Šฅ ์ €ํ•˜ ๋˜๋Š” ์˜ค๋™์ž‘์„ ์œ ๋ฐœํ•˜๋Š” ์›์ธ์ด๋‹ค. ์ด ํŒŒํŠธ์—์„œ๋Š” ReRAM์— ๊ธฐ๋ฐ˜ํ•œ ๊ณ ์—๋„ˆ์ง€ ํšจ์œจ ์•„๋‚ ๋กœ๊ทธ ์ด์ง„ ์ธ๊ณต์‹ ๊ฒฝ๋ง์„ ๊ตฌํ˜„ํ•˜๊ณ , ๊ณต์ • ๋ณ€์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ํ™œ์„ฑ๋„ ์ผ์น˜ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•œ ๊ณต์ • ๋ณ€์ด ๋ณด์ƒ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์ธ๊ณต์‹ ๊ฒฝ๋ง์€ 1T1R ๊ตฌ์กฐ์˜ ReRAM ๋ฐฐ์—ด๊ณผ ์ฐจ๋™์ฆํญ๊ธฐ๋ฅผ ์ด์šฉํ•œ ๋‰ด๋Ÿฐ์„ ์ด์šฉํ•˜์—ฌ ๊ณ ๋ฐ€๋„ ์ง‘์ ๊ณผ ๊ณ ์—๋„ˆ์ง€ ํšจ์œจ ๋™์ž‘์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ์•„๋‚ ๋กœ๊ทธ ๋‰ด๋Ÿฐ ํšŒ๋กœ์˜ ๊ณต์ • ๋ณ€์ด ์ทจ์•ฝ์„ฑ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ด์ƒ์ ์ธ ๋‰ด๋Ÿฐ์˜ ํ™œ์„ฑ๋„์™€ ๋™์ผํ•œ ํ™œ์„ฑ๋„๋ฅผ ๊ฐ–๋„๋ก ๋‰ด๋Ÿฐ์˜ ๋ฐ”์ด์–ด์Šค๋ฅผ ์กฐ์ ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์†Œ๊ฐœํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ 32nm ๊ณต์ •์—์„œ ๊ตฌํ˜„๋œ ์ธ๊ณต์‹ ๊ฒฝ๋ง์€ 3-sigma ์ง€์ ์—์„œ 50% ๋ฌธํ„ฑ ์ „์•• ๋ณ€์ด์™€ 15%์˜ ์ €ํ•ญ๊ฐ’ ๋ณ€์ด๊ฐ€ ์žˆ๋Š” ์ƒํ™ฉ์—์„œ๋„ MNIST์—์„œ 98.55%, CIFAR-10์—์„œ 89.63%์˜ ์ •ํ™•๋„๋ฅผ ๋‹ฌ์„ฑํ•˜์˜€์œผ๋ฉฐ, 970 TOPS/W์— ๋‹ฌํ•˜๋Š” ๋งค์šฐ ๋†’์€ ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ์„ ๋‹ฌ์„ฑํ•˜์˜€๋‹ค.Recently, deep learning has shown astounding performances on specific tasks such as image classification, speech recognition, and reinforcement learning. Some of the state-of-the-art deep neural networks have already gone over humans ability. However, neural networks involve tremendous number of high precision computations and frequent off-chip memory accesses with millions of parameters. It incurs problems of large area and exploding energy consumption, which hinder neural networks from being exploited in embedded systems. To cope with the problem, techniques for designing energy efficient neural networks are proposed. The first part of this dissertation addresses the design of spiking neural networks with weighted spikes which has advantages of shorter inference latency and smaller energy consumption compared to the conventional spiking neural networks. Spiking neural networks are being regarded as one of the promising alternative techniques to overcome the high energy costs of artificial neural networks. It is supported by many researches showing that a deep convolutional neural network can be converted into a spiking neural network with near zero accuracy loss. However, the advantage on energy consumption of spiking neural networks comes at a cost of long classification latency due to the use of Poisson-distributed spike trains (rate coding), especially in deep networks. We propose to use weighted spikes, which can greatly reduce the latency by assigning a different weight to a spike depending on which time phase it belongs. Experimental results on MNIST, SVHN, CIFAR-10, and CIFAR-100 show that the proposed spiking neural networks with weighted spikes achieve significant reduction in classification latency and number of spikes, which leads to faster and more energy-efficient spiking neural networks than the conventional spiking neural networks with rate coding. We also show that one of the state-of-the-art networks the deep residual network can be converted into spiking neural network without accuracy loss. The second part of this dissertation focuses on the design of highly energy-efficient analog neural networks in the presence of variations. Analog hardware accelerators for deep neural networks have taken center stage in the aspect of high parallelism and energy efficiency. However, a critical weakness of the analog hardware systems is vulnerability to noise. One of the biggest noise sources is a process variation. It is a big obstacle to using analog circuits since the variation shifts various parameters of analog circuits from the correct operating points, which causes severe performance degradation or even malfunction. To achieve high energy efficiency with analog neural networks, we propose resistive random access memory (ReRAM) based analog implementation of binarized neural networks (BNNs) with a novel variation compensation technique through activation matching (VCAM). The proposed architecture consists of 1-transistor-1-resistor (1T1R) structured ReRAM synaptic arrays and differential amplifier based neurons, which leads to high-density integration and energy efficiency. To cope with the vulnerability of analog neurons due to process variation, the biases of all neurons are adjusted in the direction that matches average output activation of ideal neurons without variation. The technique effectively restores the classification accuracy degraded by the variation. Experimental results on 32nm technology show that the proposed architecture achieves the classification accuracy of 98.55% on MNIST and 89.63% on CIFAR-10 in the presence of 50% threshold voltage variation and 15% resistance variation at 3-sigma point. It also achieves 970 TOPS/W energy efficiency with MLP on MNIST.1 Introduction 1 1.1 Deep Neural Networks with Weighted Spikes . . . . . . . . . . . . . 2 1.2 VCAM: Variation Compensation through Activation Matching for Analog Binarized Neural Networks . . . . . . . . . . . . . . . . . . . . . 5 2 Background 8 2.1 Spiking neural network . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Spiking neuron model . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Rate coding in SNNs . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 Binarized neural networks . . . . . . . . . . . . . . . . . . . . . . . 13 2.5 Resistive random access memory . . . . . . . . . . . . . . . . . . . . 18 3 RelatedWork 22 3.1 Training SNNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2 SNNs with various spike coding schemes . . . . . . . . . . . . . . . 25 3.3 BNN implementations . . . . . . . . . . . . . . . . . . . . . . . . . 28 4 Deep Neural Networks withWeighted Spikes 33 4.1 SNN with weighted spikes . . . . . . . . . . . . . . . . . . . . . . . 34 4.1.1 Weighted spikes . . . . . . . . . . . . . . . . . . . . . . . . 34 4.1.2 Spiking neuron model for weighted spikes . . . . . . . . . . . 35 4.1.3 Noise spike . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1.4 Approximation of the ReLU activation . . . . . . . . . . . . 39 4.1.5 ANN-to-SNN conversion . . . . . . . . . . . . . . . . . . . . 41 4.2 Optimization techniques . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2.1 Skipping initial input currents in the output layer . . . . . . . 45 4.2.2 The number of phases in a period . . . . . . . . . . . . . . . 47 4.2.3 Accuracy-energy trade-off by early decision . . . . . . . . . . 50 4.2.4 Consideration on hardware implementation . . . . . . . . . . 52 4.3 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.4.1 Comparison between SNN-RC and SNN-WS . . . . . . . . . 56 4.4.2 Trade-off by early decision . . . . . . . . . . . . . . . . . . . 64 4.4.3 Comparison with other algorithms . . . . . . . . . . . . . . . 67 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5 VCAM: Variation Compensation through Activation Matching for Analog Binarized Neural Networks 71 5.1 Modification of Binarized Neural Network . . . . . . . . . . . . . . . 72 5.1.1 Binarized Neural Network . . . . . . . . . . . . . . . . . . . 72 5.1.2 Use of 0 and 1 Activations . . . . . . . . . . . . . . . . . . . 72 5.1.3 Removal of Batch Normalization Layer . . . . . . . . . . . . 73 5.2 Hardware Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 75 5.2.1 ReRAM Synaptic Array . . . . . . . . . . . . . . . . . . . . 75 5.2.2 Neuron Circuit . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2.3 Issues with Neuron Circuit . . . . . . . . . . . . . . . . . . . 82 5.3 Variation Compensation . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.1 Variation Modeling . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.2 Impact of VT Variation . . . . . . . . . . . . . . . . . . . . . 87 5.3.3 Variation Compensation Techniques . . . . . . . . . . . . . . 88 5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5.4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . 93 5.4.2 Accuracy of the Modified BNN Algorithm . . . . . . . . . . 94 5.4.3 Variation Compensation . . . . . . . . . . . . . . . . . . . . 95 5.4.4 Performance Comparison . . . . . . . . . . . . . . . . . . . . 99 5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 6 Conclusion 102Docto

    Investigation of Drug-Drug Interactions between Tacrolimus and Mycophenolate Mofetil with Pharmacometrics Approach

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๋Œ€ํ•™ ์•ฝํ•™๊ณผ, 2018. 2. ์˜ค์ •๋ฏธ.P<0.05), MPA์™€ ๋Œ€์‚ฌ์ฒด์˜ ์•ฝ๋™๋ ฅํ•™ ํŒŒ๋ผ๋ฏธํ„ฐ๋Š” ์œ ์˜๋ฏธํ•œ ๋ณ€ํ™”๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. TAC์˜ ์ฒญ์†Œ์œจ์€ MMF์™€ ๋ณ‘์šฉํ•˜์ง€ ์•Š์•˜์„ ๋•Œ์—๋Š” 17.8 L/h (์ƒ๋Œ€ํ‘œ์ค€์˜ค์ฐจ 11%)์˜€๊ณ , MMF์™€ ๋ณ‘์šฉํ•˜์˜€์„ ๋•Œ์—๋Š” 13.8 L/h (์ƒ๋Œ€ํ‘œ์ค€์˜ค์ฐจ 11%)์˜€๋‹ค. ์ƒํ˜ธ์ž‘์šฉ์€ ์ง€์ˆ˜ ๋ชจํ˜•์œผ๋กœ ์„ค๋ช…๋˜์—ˆ๋‹ค. CYP3A5 ์œ ์ „์žํ˜•์ด ์œ ์˜๋ฏธํ•œ ๊ณต๋ณ€๋Ÿ‰์œผ๋กœ ๋„์ถœ๋˜์—ˆ๋‹ค. TAC์˜ ์ฒญ์†Œ์œจ์˜ ๋Œ€ํ‘ฏ๊ฐ’์€ CYP3A5 expresser์—์„œ 1.48๋ฐฐ(์ƒ๋Œ€ํ‘œ์ค€์˜ค์ฐจ 16%) ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. TAC์˜ ์ฒญ์†Œ์œจ์€ MMF์™€ ๋ณ‘์šฉํ•˜์˜€์„ ๋•Œ ๊ฐ์†Œํ•˜์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ณ ๋ คํ•œ ํ†ตํ•ฉ๋œ ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ์—์„œ TAC๊ณผ MMF์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ํ™•์ธํ•˜์˜€๋‹ค. TAC์˜ ํ˜ˆ์ค‘ ๋†๋„๋Š” MMF์˜ ๋ณ‘์šฉ์œผ๋กœ ์ธํ•˜์—ฌ ์ƒ์Šนํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ์ตœ๊ทผ์˜ ์ž„์ƒ์‹œํ—˜์ด TAC๊ณผ MMF์˜ ์—ฌ๋Ÿฌ๊ฐ€์ง€ ์šฉ๋Ÿ‰ ์กฐํ•ฉ์„ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์„ ๊ณ ๋ คํ•˜๋ฉด, ๋‘ ์•ฝ๋ฌผ ์‚ฌ์ด์˜ ์ƒํ˜ธ์ž‘์šฉ ์–‘์ƒ์„ ํ™•์ธํ•˜๋Š” ๊ฒƒ์€ ์ค‘์š”ํ•˜๋‹ค. ๊ฐœ๋ฐœ๋œ ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ TAC๊ณผ MMF์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ณ ๋ คํ•˜๋ฉด์„œ ๋‘ ์•ฝ๋ฌผ์˜ ํ˜ˆ์ค‘๋†๋„๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์•ฝ๋ฌผ ์ƒํ˜ธ์ž‘์šฉ์˜ ์˜ํ–ฅ์ด ์•ฝ๋ฌผ์ด ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ํ‘œ์  ํ™˜์ž ์ง‘๋‹จ์—์„œ๋„ ์œ ์ง€๋˜๋Š”์ง€ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด์„œ ์ถ”๊ฐ€ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. 2. ์‹ ์žฅ์ด์‹ ํ™˜์ž์—์„œ ๊ฑด๊ฐ•์ธ ๋Œ€์ƒ์œผ๋กœ ๊ฐœ๋ฐœ๋œ ์ง‘๋‹จ ์•ฝ๋™ํ•™ ๋ชจ๋ธ์˜ ๊ฒ€์ฆ ์—ฐ๊ตฌ TAC๊ณผ MMF๋Š” ์‹ ์žฅ์ด์‹ ํ™˜์ž์—์„œ ๊ฑฐ๋ถ€๋ฐ˜์‘์„ ์˜ˆ๋ฐฉํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ€์žฅ ํ”ํ•˜๊ฒŒ ๋ณ‘์šฉ๋˜๋Š” ๋ฉด์—ญ์–ต์ œ์ œ์ด๋‹ค. ์ผ๋ถ€ in vitro ์—ฐ๊ตฌ์—์„œ TAC๊ณผ MMF์˜ ์ƒํ˜ธ์ž‘์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ๊ธฐํ•˜์˜€๋‹ค. Section I์˜ ๊ฑด๊ฐ•ํ•œ ์„ฑ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์—ฐ๊ตฌ์—์„œ TAC๊ณผ MMF์˜ ์ƒํ˜ธ์ž‘์šฉ์˜ ํฌ๊ธฐ์™€ ์–‘์ƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์‹ ์žฅ์ด์‹ ํ™˜์ž์—๊ฒŒ ๊ฑด๊ฐ•ํ•œ ์„ฑ์ธ ๋Œ€์ƒ์œผ๋กœ ๊ฐœ๋ฐœ๋˜์—ˆ๋˜ ๋ชจ๋ธ์„ ์ ์šฉํ•˜์—ฌ ์ƒํ˜ธ์ž‘์šฉ์˜ ์˜ํ–ฅ์ด ํ™˜์ž์—์„œ๋„ ๋‚˜ํƒ€๋‚˜๋Š”์ง€ ํ™•์ธํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. 32๋ช…์˜ ์‹ ์žฅ์ด์‹ ํ™˜์ž๊ฐ€ ์—ฐ๊ตฌ์— ์ฐธ์—ฌํ•˜์˜€๋‹ค. TAC๊ณผ MMF๋ฅผ ๋ณต์šฉํ•œ ํ›„ ์ตœ๋Œ€ 4์‹œ๊ฐ„๊นŒ์ง€ ์ฑ„ํ˜ˆํ•˜์˜€๋‹ค. TAC, MPA, MPAG, ๊ทธ๋ฆฌ๊ณ  AcMPAG์˜ ํ˜ˆ์ค‘๋†๋„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. CYP3A4, CYP3A5, SLCO1B1, SLCO1B3, ABCC2, UGT1A9, ๊ทธ๋ฆฌ๊ณ  UGT2B7์˜ ์œ ์ „์žํ˜•์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฑด๊ฐ•ํ•œ ์„ฑ์ธ ๋Œ€์ƒ์œผ๋กœ ๊ฐœ๋ฐœํ•˜์˜€๋˜ ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ์„ ์‹ ์žฅ์ด์‹ ํ™˜์ž์—์„œ์˜ ๊ธฐ๋ณธ ์•ฝ๋™ํ•™ ๊ตฌ์กฐ๋ชจํ˜•์œผ๋กœ ํ™œ์šฉํ•˜์˜€๋‹ค. ์ž„์ƒ์  ๋ณ€์ˆ˜์™€ ์œ ์ „์žํ˜•์„ ๋ชจ๋ธ์˜ ๊ณต๋ณ€๋Ÿ‰์œผ๋กœ ๊ณ ๋ คํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ๋œ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์ž„์ƒ์  ์ƒํ™ฉ์—์„œ TAC์˜ ์ตœ์ € ํ˜ˆ์ค‘๋†๋„๋ฅผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ•˜์˜€๋‹ค. ์ด 562๊ฐœ์˜ ์•ฝ๋ฌผ ๋†๋„๊ฐ€ ๋ถ„์„๋˜์—ˆ๊ณ  ๋ชจ๋ธ ๊ฐœ๋ฐœ์— ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. TAC๊ณผ MMF์˜ ์ตœ์ข… ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ์— ์—ญ-์ง€์ˆ˜(inverse exponential) ๊ด€๊ณ„๋กœ ์ƒํ˜ธ์ž‘์šฉ์ด ๋ฐ˜์˜๋˜์—ˆ๋‹ค. ์ตœ์ข… ๋ชจ๋ธ์— ์œ ์˜๋ฏธํ•œ ๊ณต๋ณ€๋Ÿ‰์œผ๋กœ CYP3A5, SLCO1B3, UGT2B7์˜ ์œ ์ „์žํ˜•์ด ๋„์ถœ๋˜์—ˆ๋‹ค. TAC์˜ ์ฒญ์†Œ์œจ์˜ ์ง‘๋‹จ ๋Œ€ํ‘ฏ๊ฐ’์€ 21.9 L/h์˜€๊ณ  CYP3A5 expresser์—์„œ ์ฒญ์†Œ์œจ์ด 1.49๋ฐฐ๋กœ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. SLCO1B3 T carrier์—์„œ MPAG์˜ ๋ถ„ํฌ ์šฉ์ ์ด 1.2๋ฐฐ์ธ ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. AcMPAG์˜ ์ƒ์„ฑ์†๋„์ƒ์ˆ˜๋Š” UGT2B7 T carrier์—์„œ 0.8๋ฐฐ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. ๋ฉด์—ญ์–ต์ œ์ œ์˜ ์šฉ๋Ÿ‰๊ณผ ์œ ์ „์žํ˜•์„ ์กฐํ•ฉํ•œ ๋‹ค์–‘ํ•œ ์ž„์ƒ์  ์ƒํ™ฉ์—์„œ TAC์˜ ํ˜ˆ์ค‘๋†๋„๊ฐ€ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋˜์—ˆ๋‹ค. CYP3A5 nonexpresser์ด๋ฉด์„œ ๋ณ‘์šฉํ•˜๋Š” MMF์˜ ์šฉ๋Ÿ‰์ด ํด์ˆ˜๋ก TAC์˜ ์ตœ์ €ํ˜ˆ์ค‘๋†๋„๊ฐ€ ๋†’๊ฒŒ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. SLCO1B3์™€ UGT2B7์˜ ์œ ์ „์žํ˜•์€ TAC์˜ ์ตœ์ €ํ˜ˆ์ค‘๋†๋„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฑด๊ฐ•ํ•œ ์„ฑ์ธ์—์„œ ๊ฐœ๋ฐœํ•œ ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ ์‹ ์žฅ์ด์‹ ํ™˜์ž์—์„œ TAC๊ณผ MMF์˜ ์ƒํ˜ธ์ž‘์šฉ์˜ ์˜ํ–ฅ์„ ํ™•์ธํ•œ ์ตœ์ดˆ์˜ ์—ฐ๊ตฌ์ด๋‹ค. TAC์˜ ์ตœ์ €ํ˜ˆ์ค‘๋†๋„๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ์ฃผ์š”ํ•œ ์š”์ธ์€ MMF์™€์˜ ์•ฝ๋ฌผ์ƒํ˜ธ์ž‘์šฉ๊ณผ CYP3A5 ์œ ์ „์žํ˜•์ด์—ˆ๋‹ค. MMF๊ฐ€ ๋ณ‘์šฉ๋˜๊ฑฐ๋‚˜ MMF์˜ ์šฉ๋Ÿ‰์ด ๋ณ€๊ฒฝ๋˜๋Š” ์ž„์ƒ์  ์ƒํ™ฉ์—์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ฐœ๋ฐœํ•œ ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ TAC์˜ ์šฉ๋Ÿ‰์„ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ๋‹ค.1. ๊ฑด๊ฐ•ํ•œ ์„ฑ์ธ์—์„œ ํƒ€ํฌ๋กœ๋ฆฌ๋ฌด์Šค์™€ ๋ฏธ์ฝ”ํŽ˜๋†€๋ ˆ์ดํŠธ ์ƒํ˜ธ์ž‘์šฉ ๊ทœ๋ช… ์—ฐ๊ตฌ ํƒ€ํฌ๋กœ๋ฆฌ๋ฌด์Šค(Tacrolimus, TAC)์™€ ๋ฏธ์ฝ”ํŽ˜๋†€๋ ˆ์ดํŠธ ๋ชจํŽ˜ํ‹ธ(Mycophenolate mofetil, MMF)์€ ์žฅ๊ธฐ์ด์‹ ๊ฑฐ๋ถ€๋ฐ˜์‘์„ ์˜ˆ๋ฐฉํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ€์žฅ ํ”ํžˆ ๋ณ‘์šฉ๋˜๋Š” ๋ฉด์—ญ์–ต์ œ์ œ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ TAC๊ณผ MMF์˜ ์•ฝ๋™ํ•™์  ์•ฝ๋ฌผ์ƒํ˜ธ์ž‘์šฉ์„ ๊ฑด๊ฐ•ํ•œ ํ•œ๊ตญ์ธ ์„ฑ์ธ ์ž์›์ž์—์„œ ๊ทœ๋ช…ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. 17๋ช…์˜ ์ฐธ์—ฌ์ž๊ฐ€ 3๊ธฐ, ๋‹จํšŒ ํˆฌ์•ฝ, ๊ณ ์ • ์ˆœ์„œ์˜ ์ž„์ƒ์‹œํ—˜์— ์ฐธ์—ฌํ•˜์˜€๋‹ค. ์ž„์ƒ์‹œํ—˜ ์ฐธ์—ฌ์ž๋“ค์€ ์ˆœ์„œ๋Œ€๋กœ ๊ฐ๊ฐ MMF, TAC, ๊ทธ๋ฆฌ๊ณ  MMF์™€ TAC์„ ๋ณต์šฉํ•˜์˜€๋‹ค. TAC๊ณผ mycophenolic acid (MPA), ๊ทธ๋ฆฌ๊ณ  MPA์˜ ๋Œ€์‚ฌ์ฒด์ธ MPA 7-O-glucuronide (MPAG)์™€ MPA acyl glucuronide (AcMPAG)์˜ ๋†๋„๋ฅผ ์ธก์ •ํ•˜์˜€๋‹ค. CYP3A4, CYP3A5, SLCO1B1, SLCO1B3, ABCC2, UGT1A9, ๊ทธ๋ฆฌ๊ณ  UGT2B7์˜ ์œ ์ „์žํ˜•์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋น„๊ตฌํš๋ถ„์„์œผ๋กœ ์ƒํ˜ธ์ž‘์šฉ์„ ํ‰๊ฐ€ํ•˜๊ณ  ๊ทธ ์˜ํ–ฅ์„ ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ๋กœ ์ •๋Ÿ‰ํ•˜์˜€๋‹ค. ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ ๊ฐœ๋ฐœ ์‹œ ๊ตฌ์กฐ๋ชจํ˜•์œผ๋กœ ๋‹จ์ผ๊ตฌํš, ๋‹ค๊ตฌํš ๋ชจํ˜• ๋“ฑ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ƒํ˜ธ์ž‘์šฉ์˜ ์˜ํ–ฅ์„ ๋ฐ˜์˜ํ•˜๊ธฐ ์œ„ํ•ด ์„ ํ˜•, ์‹œ๊ทธ๋ชจ์ด๋“œ, ๊ทธ๋ฆฌ๊ณ  ์ง€์ˆ˜ ๋ชจํ˜•์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ณต๋ณ€๋Ÿ‰์˜ ์˜ํ–ฅ์€ ๋‹จ๊ณ„์  ๊ณต๋ณ€๋Ÿ‰ ๋ชจ๋ธ๋ง ๋ฐฉ๋ฒ•์œผ๋กœ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋ชจ๋ธ ๊ฐœ๋ฐœ ๊ณผ์ •์—์„œ ๋ชจ๋ธ์˜ ๊ฐœ์„  ์ •๋„ ํ‰๊ฐ€๋Š” ๋ชฉ์  ํ•จ์ˆ˜ ๊ฐ’, ์ ํ•ฉ๋„, ์‹œ๊ฐ์  ์˜ˆ์ธก ์ ๊ฒ€์œผ๋กœ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ด 1,082๊ฐœ์˜ ์•ฝ๋ฌผ ๋†๋„๊ฐ€ ๋ถ„์„๋˜์—ˆ๋‹ค. TAC์˜ AUC0-inf(Area Under the time-concentration Curve from time 0 to infinity)๋Š” MMF์™€ ๋ณ‘์šฉํ•˜์˜€์„ ๋•Œ 22.1% ์ฆ๊ฐ€ํ•˜์˜€์œผ๋‚˜ (322.4ยฑ174.1์—์„œ 393.6ยฑ121.7 ngยทh/mLSection I. Identification of Drug-Drug Interaction between Tacrolimus and Mycophenolate Mofetil in Healthy Volunteers 1 Chapter 1. Introduction 2 1.1. ๊ณ„๋Ÿ‰์•ฝ๋ฆฌํ•™ 2 1.2. ์‹ ์žฅ์ด์‹๊ณผ ์œ ์ง€ ๋ฉด์—ญ์–ต์ œ์ œ 4 1.3. ์œ ์ง€ ๋ฉด์—ญ์–ต์ œ์ œ์˜ ์•ฝ๋ฌผ ์ƒํ˜ธ์ž‘์šฉ 8 1.4. ์—ฐ๊ตฌ์˜ ๋ชฉ์  11 Chapter 2. Methods 12 2.1. ์—ฐ๊ตฌ ์„ค๊ณ„ ๋ฐ ์ž„์ƒ์‹œํ—˜ ๋Œ€์ƒ์ž 12 2.2. ์‹œํ—˜ ์•ฝ๋ฌผ ํˆฌ์—ฌ 15 2.3. ์•ฝ๋ฌผ ํ˜ˆ์ค‘ ๋†๋„ ๋ถ„์„ 15 2.4. ์œ ์ „ํ˜• ๋ถ„์„ ๋ฐ ์ž๋ฃŒ ์ˆ˜์ง‘ 16 2.5. ๋น„๊ตฌํš ๋ถ„์„ 17 2.6. ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ ๊ฐœ๋ฐœ 18 Chapter 3. Results 21 3.1. ์ธ๊ตฌํ•™์  ํŠน์„ฑ 21 3.2. ๋น„๊ตฌํš ๋ถ„์„ 27 3.3. ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ ๊ฐœ๋ฐœ 31 3.4. ๋ชจ๋ธ์˜ ํ‰๊ฐ€ 36 Chapter 4. Discussion 42 Section II. Validation of a Population Pharmacokinetic Model Derived from Healthy Volunteers with Kidney Transplant Recipients 46 Chapter 1. Introduction 47 1.1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 47 1.2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  48 Chapter 2. Methods 49 2.1. ์—ฐ๊ตฌ ์„ค๊ณ„ ๋ฐ ์ž„์ƒ์‹œํ—˜ ๋Œ€์ƒ์ž 49 2.2. ์‹œํ—˜ ์•ฝ๋ฌผ ํˆฌ์—ฌ 50 2.3. ์•ฝ๋ฌผ ํ˜ˆ์ค‘ ๋†๋„ ๋ถ„์„ 50 2.4. ์œ ์ „ํ˜• ๋ถ„์„ ๋ฐ ์ž๋ฃŒ ์ˆ˜์ง‘ 51 2.5. ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ ๊ฐœ๋ฐœ 52 Chapter 3. Results 55 3.1. ์ธ๊ตฌํ•™์  ํŠน์„ฑ 55 3.2. ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ ๊ฐœ๋ฐœ 57 3.3. ๋ชจ๋ธ์˜ ํ‰๊ฐ€ 60 3.4. ์ง‘๋‹จ์•ฝ๋™ํ•™ ๋ชจ๋ธ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 66 Chapter 4. Discussion 69 References 75Docto

    ์ˆ˜์ค‘ ์Œ์ด์˜จ ์˜ค์—ผ๋ฌผ์งˆ ์ œ๊ฑฐ๋ฅผ ์œ„ํ•œ ๊ธฐ๋Šฅ์„ฑ ๋ฌด๊ธฐ์†Œ์žฌ ๋ฐ ๊ณ ๋ถ„์ž ๋ณตํ•ฉ์ฒด์˜ ํ•ฉ์„ฑ๊ณผ ์ ์šฉ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ƒํƒœ์กฐ๊ฒฝยท์ง€์—ญ์‹œ์Šคํ…œ๊ณตํ•™๋ถ€, 2017. 2. ๊น€์„ฑ๋ฐฐ.This thesis deals with the synthesis, characterization, and application of an inorganic functional materials as adsorbents for phosphorous, chromium, and fluorine removal from aqueous solutions. The inorganic functional materials presented in this study include iron oxide nanoparticle-chitosan composite, triamine-functionalized mesoporous silica-polymer composite, calcined Mg-Fe layered double hydroxide-PVDF/PVA composite, and quintinite. Iron oxide nanoparticle(ION)-chitosan composites were prepared using acidified chitosan, an environmentally friendly polymer, suspension to blend iron oxide nanoparticles by a cross-linking method. The removal of phosphate by ION-chitosan composites was verified by batch experiments, column experiments and pilot-scale adsorption tower experiment. The adsorption properties were analyzed and quantified using kinetic and equilibrium models and thermodynamic analysis. ION-chitosan composites successfully removed phosphate from aqueous solution and showed good reversibility, multicycle stability. It is a good candidate for environmentally friendly inorganic composites as adsorbents. The characterization of triamine-functionalized mesoporous silica-polymer composites for Cr(VI) removal was also studied. The mesoporous material with various amounts of functional group had high surface area. The kinetics of the functionalized mesoporous silica were found to be sufficiently fast and it was observed that maximum sorption capacity was 330.88 mg/g. The composites showed good performance of chromate removal from real industrial wastewater. The calcined Mg-Fe layered double hydroxide(LDH) was prepared through a co-precipitation and calcination at 300 oC. The calcined LDH could be used repeatedly for phosphate removal through desorption with 0.1 M NaOH solution. MgFe calcined LDH-PVDF/PVA composites also could be used for phosphate removal from aqueous solutions with regeneration and repeated use. The phosphate removal was relatively constant at an acidic and alkaline pHs. Quintinite was applied as adsorbents for removal of phosphate and fluoride. The maximum phosphate adsorption capacity was 4.77 mgP/g. The phosphate adsorption to quintinite was not varied at pH 3.0 โ€“ 7.1 (1.50 โ€“1.55 mgP/g) but decreased considerably at a highly alkaline solution (0.70 mgP/g at pH 11.0). Experimental results showed that the maximum adsorption capacity of fluoride to quintinite was 7.71 mg/g. The adsorption of fluoride to quintinite was not changed at pH 5 โ€“ 9 but decreased considerably at the highly acidic (pH 11) solution conditions. Therefore, this study elucidated that the inorganic functional materials removed phosphorous, chromium, and fluorine from aqueous solutions, effectively. These results also demonstrate that the functional polymer composites developed in this study can be applied to water treatment system.Chapter 1 Introduction 1 1.1. Background 2 1.2. Objective 6 Chapter 2 Literature Review 9 2.1. Inorganic adsorbents for anionic contaminants removal 10 2.2. Composite adsorbents 15 2.3. Polymer composites 16 2.4. Data analyses 19 Chapter 3 Phosphate removal from aqueous solution by using iron oxide nanoparticle-chitosan composites 26 3.1. Materials and Methods 27 3.1.1. Synthesis of ION-chitosan composites 27 3.1.2. Characterization of ION-chitosan composites 28 3.1.3. Stream water samples 29 3.1.4. Batch experiments 31 3.1.5. Fixed-bed column experiment 34 3.1.6. Long-term pilot-scale experiment 36 3.2. Results and Discussion 41 3.2.1. Characteristics of ION-chitosan composites 41 3.2.2. Batch adsorption of phosphate 46 3.2.3. Kinetic, isotherm and thermodynamic model analyses 55 3.2.4. Fixed-bed adsorption of phosphate 67 3.2.5. Pilot test 71 3.2.6. Conclusions 74 Chapter 4 Preparation and characterization of triamine-functionalized mesoporous silica-polymer composites for Cr(VI) removal from industrial plating wastewater 75 4.1. Materials and Methods 76 4.1.1. Synthesis of DAEAPTS-SBA-15 PVA/alginate composites 76 4.1.2. Characterization of DAEAPTS-SBA-15 PVA/alginate composites 78 4.1.3. Industrial plating wastewaters 79 4.1.4. Batch experiments 83 4.1.5. Fixed-bed column experiments 86 4.2. Results and Discussion 88 4.2.1. Characteristics of DAEAPTS-SBA-15 88 4.2.2. Batch experiments 101 4.2.3. Kinetic, isotherm and thermodynamic model analyses 107 4.2.4. Fixed-bed adsorption of chromate 116 4.2.5. Conclusions 119 Chapter 5 Characterization of calcined Mg-Fe layered double hydroxide for phosphate removal from aqueous solutions 120 5.1. Materials and Methods 121 5.1.1. Synthesis of calcined Mg-Fe layered double hydroxide 121 5.1.2. Characterization of Mg-Fe CLDH 122 5.1.3. Phosphate sorption experiments 123 5.2. Results and Discussion 128 5.2.1. Characterization of Mg-Fe CLDH 128 5.2.2. Characterization of phosphate removal 133 5.2.3. Kinetic, isotherm and thermodynamic analyses 143 5.2.4. Conclusions 152 Chapter 6 Preparation and characterization of calcined Mg-Fe layered double hydroxide PVDF/PVA composites for phosphate removal from aqueous solutions 153 6.1. Materials and Methods 154 6.1.1. Synthesis of MgFe CLDH-PVDF/PVA composites 154 6.1.2. Characterization of MgFe CLDH-PVDF/PVA composites 155 6.1.3. Batch experiments 156 6.1.4. Fixed-bed experiments 160 6.2. Results and Discussion 162 6.2.1. Characteristics of MgFe CLDH-PVDF/PVA composites 162 6.2.2. Batch adsorption of phosphate 167 6.2.3. Kinetic, isotherm and thermodynamic model analyses 176 6.2.4. Fixed-bed adsorption of phosphate 186 6.2.5. Conclusions 189 Chapter 7 Removal of phosphate and fluoride from aqueous solution by quintinite particles 190 7.1. Materials and Methods 191 7.1.1. Synthesis of quintinite 191 7.1.2. Characterization of quintinite 192 7.1.3. Stream water samples 193 7.1.4. Batch experiments 195 7.2. Results and Discussion 200 7.2.1. Characteristics of quintinite 200 7.2.2. Batch adsorption of phosphate 206 7.2.3. Kinetic, isotherm and thermodynamic model analyses 214 7.2.4. Batch adsorption of fluoride 222 7.2.5. Kinetic, isotherm and thermodynamic model analyses 230 7.2.6. Conclusions 237 Chapter 8 General Conclusions and Recommendations 238 8.1 General conclusions 239 8.2. Recommendations 242 REFERENCES 243 ๊ตญ๋ฌธ ์ดˆ๋ก 259Docto

    Part A. ๋ถ„์ž ๋น„๋Œ€์นญ์„ฑ ๊ธฐ์–ต ํ˜„์ƒ๊ณผ ๋™์  ์†๋„๋ก ์  ๋ถ„ํ•  ํ˜„์ƒ์„ ์ด์šฉํ•œ (โ€“)-penibruguieramien A์˜ ๋น„๋Œ€์นญ ์ „ํ•ฉ์„ฑ. Part B. ์ƒ ์ „์ด ์ด‰์ง„์ œ๋ฅผ ์ด์šฉํ•œ 2์ƒ CuAAC ๋ฐ˜์‘.

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๊ณผ, 2017. 2. ๊น€์ƒํฌ.Part A. Asymmetric Total Synthesis of (โ€“)-Penibruguieramine A Using Memory of Chirality and Dynamic Kinetic Resolution. (โ€“)-Penibruguieramine A is a novel marine pyrrolizidine alkaloid, which was recently identified by Guo from the endophytic fungus Penicillium sp. GD6 associated with Chinese mangroves. This natural product has an unprecedented 1-hydroxyl-2-methyl pyrrolizidin-3-one skeleton. We have achieved the first total synthesis of this marine alkaloid using the principles of memory of chirality (MOC) and dynamic kinetic resolution (DKR) for the asymmetric synthesis from proline as the only chiral source. MOC and DKR are attractive strategies for asymmetric synthesis. However, although there have been reports of DKR being utilized, there are only few reports of MOC being applied in total synthesis of natural products. Moreover, the combination of these two concepts has not been previously reported for asymmetric synthesis. Our synthesis follows the proposed biosynthetic pathway and features an asymmetric construction of stereocenters with essentially complete diastereo- and enantioselectivity in the absence of external chiral sources. Noteworthy is the excellent level of memory of chirality in a protic solvent environment. To understand this, we performed some experiments and provided a mechanistic rationale. Part B. Biphasic CuAAC Reaction Using a Phase Transfer Agent. A phase transfer agent assisted biphasic Cu(I) catalyzed azide-alkyne 1,3-dipolar cycloaddition (CuAAC) reaction system was developed. A biphasic reaction media consisting of water and an organic solvent ensures a dissolution of reagents and substrates. Tris(triazolylmethyl)amine ligands with an appropriate hydrophilic-lipophilic balance are able to extract copper from the aqueous phase to the organic phase, accelerating the CuAAC reaction rate. The present system is widely applicable to substrates with various functionalities, including a free amino group and especially to lipophilic substrates.Part A. Asymmetric Total Synthesis of ()-Penibruguieramine A Using Memory of Chirality and Dynamic Kinetic Resolution 1 I. Introduction 2 II. Results and Discussion 4 III. Conclusions 14 IV. Experimental 15 V. References 50 VI. Acknowledgements 54 Part B. Biphasic CuAAC Reaction Using a Phase Transfer Agent 55 I. Introduction 56 II. Results and Discussion 60 III. Conclusions 69 IV. Experimental 70 V. References 83 VI. Acknowledgements 88 Appendix I 89 Appendix II 104 Abstract in Korean 127Docto

    A Study on Structural Vulnerability Countermeasure Design and Analysis Techniques for Survivability Enhancement of Naval Ships

    Get PDF
    The naval ship has to ensure the safety from the enemy attack and damages so that it must be able to accomplish a given task. Like this ability of the naval ship is expressed in ability of the security for the survivability, and the survivability especially can be expressed the relationship of susceptibility, vulnerability and recoverability. Surface naval ship is easily noticeable by the enemy and moreover there are many attack weapons to surface naval ship. Therefore, it is important for modern naval ships, especially combat naval ships, to establish countermeasure of vulnerability for survivability. In this study, The author reviewed the developing procedure for the technique of the naval ships structures considering survivability and described the basic design concept, analysis method and so on. It is known that the countermeasure of vulnerability is to establish the double hull structure, box girder, blast hardened bulkhead, protection wall against fragments and so on, and it must be decided within possible design conditions. Among these methods, it is known that the installation of box girder and blast hardened bulkhead is the one of the most economical and efficient method. In order to design naval ship considering survivability, it is demanded that designers should establish the reasonable attack scenarios, which generally are divided into external and internal explosions to the ship. Explosion may induce local damage as well as global collapse to the ship. Therefore possible damaged should be realistically estimated in the design stage. The general underwater explosion problem begins with an explosive charge of a certain size and material located at a depth below the free surface of the water surrounding it. The water is assumed to behave as a compressible fluid that is incapable of supporting significant tension. The ship is on the free surface of the water, and depending on the location of the explosive relative to the ship, The important result of the explosion are the ship's early and late time response, however it is important to first understand the phenomena of a generic underwater explosion. In this thesis, the author studied the method of the naval ship design and analysis against underwater explosion. The numerical simulation of collision and explosion etc. have achieved collision analysis and complex analysis of structure-fluid interaction problem was begun to achieve in code of LS-DYNA, MSC/DYTRAN etc.. that is explicit hydrocode. There is ALE (Arbitrary Lagrangian-Eulerian) technique by representative analysis technique of structure-fluid interaction problem, and it can be applied that explosive and air can be modeled with fluid element and hull structures can be modeled with structure element. In this thesis, the author used ALE technique to simulate explosion analysis and investigated survival capability of damaged naval ships. Fragment protection concepts are discussed covering materials, concepts and objectives. The purpose of fragment protection material is to stop or slow down a threat projectile such that any resulting system deactivation is acceptable. Typical ship hull and deckhouse external plating is insufficient to stop most threat projectiles, thus requiring additional armor. In this thesis, the author recommend that once threat weapon characteristics are defined, an iterative analysis approach as outlined be conducted until a satisfactory design is achieved.๋ชฉ์ฐจ = โ…ฐ ๊ทธ๋ฆผ๋ชฉ๋ก = โ…ต ํ‘œ๋ชฉ๋ก = xi ๊ธฐํ˜ธ์„ค๋ช… = xiii ์ œ 1 ์žฅ ์„œ๋ก  = 1 1.1 ๊ฐœ์š” = 1 1.2 ๋ฏผ๊ฐ์„ฑ(susceptibility) = 2 1.3 ์ทจ์•ฝ์„ฑ(vulnerability) = 2 1.4 ํšŒ๋ณต์„ฑ(recoverability) = 3 1.5 ์ƒ์กด์„ฑ ์—ฐ๊ตฌ๋™ํ–ฅ = 3 1.6 ์—ฐ๊ตฌ๋ฒ”์œ„ ๋ฐ ๋ชฉํ‘œ = 4 ์ œ 2 ์žฅ ์ƒ์กด์„ฑ ํ–ฅ์ƒ์„ ์œ„ํ•œ ์„ ์ฒด ๊ตฌ์กฐ์„ค๊ณ„ ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๊ณผ์ • = 6 2.1 19์„ธ๊ธฐ ์ค‘๋ฐ˜ = 6 2.2 ์„ธ๊ณ„ ์ œ1์ฐจ ๋ฐ ์ œ2์ฐจ๋Œ€์ „ = 6 2.3 1960๋…„๋Œ€ ์ดํ›„ = 7 2.4 ํ˜„๋Œ€์˜ ์ „ํˆฌ ํ™˜๊ฒฝ ๋ฐ ์„ค๊ณ„ ๊ฐœ๋… = 7 ์ œ 3 ์žฅ ์œ„ํ˜‘์˜ ์ข…๋ฅ˜, ํ‰๊ฐ€ ๋ฐ ์‹œ๋‚˜๋ฆฌ์˜ค ์„ค์ • = 10 3.1 ์œ„ํ˜‘์˜ ํ‰๊ฐ€๊ณผ์ • = 10 3.2 ์œ„ํ˜‘์˜ ์ข…๋ฅ˜ = 10 3.2.1 ๊ณต์ค‘๋ฌด๊ธฐ(Air Delivered Weapons) = 11 3.2.2 ์ˆ˜์ค‘๋ฌด๊ธฐ(Water Delivered Weapons) = 12 3.3 ์œ„ํ˜‘ ์‹œ๋‚˜๋ฆฌ์˜ค = 13 3.3.1 ๋‚ด๋ถ€ ํญ๋ฐœ(Internal Blast) = 13 3.3.2 ์™ธ๋ถ€ ํญ๋ฐœ(External Blast) = 14 3.3.3 ํŒŒํŽธ(Fragment) = 15 3.3.4 ์‹œ๋‚˜๋ฆฌ์˜ค ์„ค์ • = 15 ์ œ 4 ์žฅ ์ƒ์กด์„ฑ ํ–ฅ์ƒ์„ ์œ„ํ•œ ์ทจ์•ฝ์„ฑ ๊ฐ์†Œ๋Œ€์ฑ… = 16 4.1 ์ด์ค‘์„ ์ฒด(Double Hull)์˜ ๊ตฌ์กฐ = 16 4.2 ๋ฐ•์Šค ๊ฑฐ์–ด๋”(Box Girder)์˜ ์ ์šฉ = 17 4.2.1 ๋ฐ•์Šค ๊ฑฐ์–ด๋”์˜ ์—ญํ• ๊ณผ ํ•„์š”์„ฑ = 17 4.2.2 ๋ฐ•์Šค ๊ฑฐ์–ด๋”์˜ ์œ ์šฉ์„ฑ = 18 4.2.3 ๋ฐ•์Šค ๊ฑฐ์–ด๋”์˜ ํ˜•ํƒœ = 19 4.2.4 ๋ฐ•์Šค ๊ฑฐ์–ด๋”์˜ ์„ค๊ณ„ ์ ˆ์ฐจ = 20 4.3 ์ด์ค‘ ํšก ๊ฒฉ๋ฒฝ(Double Transverse Bulkhead) ๊ตฌ์กฐ = 22 4.4 ํญ๋ฐœ ๊ฐ•ํ™” ๊ฒฉ๋ฒฝ(BHBBlast Hardened Bulkhead)์˜ ์ ์šฉ = 22 4.4.1 ํญ๋ฐœ ๊ฐ•ํ™” ๊ฒฉ๋ฒฝ์˜ ์—ญํ• ๊ณผ ํ•„์š”์„ฑ = 22 4.4.2 ํญ๋ฐœ ๊ฐ•ํ™” ๊ฒฉ๋ฒฝ์˜ ๊ธฐ๋ณธ ๊ฐœ๋… = 23 4.4.3 ํญ๋ฐœ ๊ฐ•ํ™” ๊ฒฉ๋ฒฝ์˜ ์œ ์šฉ์„ฑ = 24 4.4.4 ํญ๋ฐœ ๊ฐ•ํ™” ๊ฒฉ๋ฒฝ์˜ ์„ค๊ณ„ ์ ˆ์ฐจ = 24 4.5 ํ”ผํƒ„ ๋ณดํ˜ธ(Fragmentation Protection) = 26 ์ œ 5 ์žฅ ์ˆ˜์ค‘ํญํŒŒ ํ•ด์„์— ์˜ํ•œ ํ•จ์ • ์„ค๊ณ„ = 27 5.1 ์ˆ˜์ค‘ํญํŒŒ ๊ธฐ์ดˆ ์ด๋ก  = 27 5.1.1 ์ถฉ๊ฒฉํŒŒ์™€ ์ถฉ๊ฒฉ๊ณ„์ˆ˜ = 27 5.1.2 ๊ฐ€์Šค๊ตฌ์ฒด์˜ ๊ฑฐ๋™ = 32 5.1.3 ๊ด‘์—ญ ์บ๋น„ํ…Œ์ด์…˜ = 33 5.2 ์ˆ˜์ค‘ํญ๋ฐœ ์‹ค์„ ์‹œํ—˜ = 38 5.3 ์„ ์ฒด ๊ฑฐ์–ด๋”, ์žฅ๋น„ ๊ธฐ๊ธฐ ๋ฐ ๋ฐ›์นจ๋Œ€ ๋‚ด์ถฉ๊ฒฉ ์„ค๊ณ„ ์ ˆ์ฐจ = 46 5.3.1 ์„ ์ฒด ๊ฑฐ์–ด๋” ๋‚ด์ถฉ๊ฒฉ ์„ค๊ณ„ = 46 5.3.2 ์žฅ๋น„ ๋ฐ ๊ธฐ๊ธฐ ๊ณ„ํ†ต์˜ ๋‚ด์ถฉ๊ฒฉ ์„ค๊ณ„ = 47 5.3.3 ๋ฐ›์นจ๋Œ€ ๋ฐ ์ง€์ง€๊ตฌ์กฐ ๋‚ด์ถฉ๊ฒฉ ์„ค๊ณ„ = 49 ์ œ 6 ์žฅ ์„ ์ฒด ๊ฑฐ์–ด๋” ํœ˜ํ•‘์‘๋‹ต ํ•ด์„ = 50 6.1 ๊ฐœ์š” = 50 6.2 ์ˆ˜์ค‘ํญ๋ฐœ ์กฐ๊ฑด = 50 6.3 ํ•ด์„ ๋ฐฉ๋ฒ• ๋ฐ ๋‚ด์šฉ = 53 6.3.1 1์ฐจ์› ๋ณด ์œ ์ถ” ํ•ด์„ = 53 6.3.2 3์ฐจ์› ์œ ํ•œ์š”์†Œ ํ•ด์„ = 54 6.4 ํ•ด์„ ๊ฒฐ๊ณผ ๊ณ ์ฐฐ = 56 6.4.1 ๊ฐ€์Šค๊ตฌ์ฒด ์••๋ ฅํŒŒ์— ์˜ํ•œ ์œ ์ฒด ๊ฐ€์†๋„ ๋ฐ ์œ ์ฒด ์••๋ ฅ ๊ณ„์‚ฐ = 56 6.4.2 ํœ˜ํ•‘์‘๋‹ต ๋ณ€์œ„ ๊ณ„์‚ฐ ๋ฐ ์ข…๊ฐ•๋„ ๊ฒ€ํ†  = 57 ์ œ 7 ์žฅ ๊ณต๊ธฐ์ค‘ ํญํŒŒ ํ•ด์„์— ์˜ํ•œ ํ•จ์ • ์„ค๊ณ„ = 60 7.1 ๊ฐœ์š” = 60 7.2 ALE ๊ธฐ๋ฒ• ์•Œ๊ณ ๋ฆฌ์ฆ˜ = 61 7.3 ์œ ์ฒด ์˜์—ญ์˜ ์ƒํƒœ๋ฐฉ์ •์‹ = 61 7.3.1 ํญ์•ฝ(explosion)์˜ ์ƒํƒœ๋ฐฉ์ •์‹ = 62 7.3.2 ๊ณต๊ธฐ(air)์˜ ์ƒํƒœ๋ฐฉ์ •์‹ = 64 7.3.3 ํ•ด์ˆ˜(sea water)์˜ ์ƒํƒœ๋ฐฉ์ •์‹ = 65 7.4 ์ˆ˜์น˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์˜ ๋น„๊ต = 65 7.4.1 ํญ๋ฐœํ•˜์ค‘ ๊ฐ„๋žต ๊ณ„์‚ฐ ๋ฐฉ๋ฒ• = 65 7.4.2 ์ˆ˜์น˜ํ•ด์„ ๊ฒฐ๊ณผ ๋น„๊ต = 69 7.4.3 ํ•ด์ˆ˜(sea water)์˜ ์ƒํƒœ ๋ฐฉ์ •์‹ = 65 7.5 ํ”ผ๊ฒฉ ํ›„ ์ƒ์กด์‹œ๊ฐ„ ๊ณ„์‚ฐ = 70 7.5.1 ํ•ด์ƒ์ƒํƒœ์— ์˜ํ•œ ์ƒ์กด์‹œ๊ฐ„ = 70 7.5.2 ๊ตฌ์กฐ ๋ถ•๊ดดํŒŒ๊ณ  ๋ฐ ์ƒ์กด์‹œ๊ฐ„ ๊ณ„์‚ฐ = 71 ์ œ 8 ์žฅ ALE ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ์ˆ˜์ค‘ํญํŒŒ ํ•ด์„ = 73 8.1 ๊ฐœ์š” = 73 8.2 ์ˆ˜์ค‘ํญ๋ฐœ ์‹œ๋‚˜๋ฆฌ์˜ค ๋ฐ ํ•˜์ค‘ ์‚ฐ์ • = 74 8.3 ๋ชจ๋ธ๋ง = 75 8.4 ์•กํ™”์‚ฐ์†Œ ํƒฑํฌ์˜ ์ถฉ๊ฒฉ ๊ฐ•๋„ ํ‰๊ฐ€ = 79 ์ œ 9 ์žฅ ํ•จ์ •์˜ ํ”ผํƒ„ ๋ณดํ˜ธ๊ตฌ ์„ค๊ณ„ = 85 9.1 ๊ฐœ์š” = 85 9.2 ํŒŒํŽธ์˜ ์ข…๋ฅ˜ ๋ฐ ์ •์˜ = 85 9.2.1 Gurney method = 86 9.2.2 THOR ๋ฐฉ์ •์‹ = 89 9.2.3 TM5-1300 ๋ฐฉ๋ฒ• = 90 9.3 ํ”ผํƒ„ ๋ณดํ˜ธ๊ตฌ ์„ค๊ณ„ ๊ธฐ์ค€ = 91 9.3.1 ๋ฏธ๊ตฐ์˜ ๊ตฐ์ˆ˜ํ’ˆ ์ถฉ๋Œ ์‹คํ—˜ ๊ธฐ์ค€ = 92 9.3.2 ์˜๊ตญ์„ ๊ธ‰(LR) ํŒŒํŽธ ๋ณดํ˜ธ ์„ค๊ณ„ ๊ธฐ์ค€ = 93 9.3.3 ๋‚˜ํ†  ํ”ผํƒ„ ๋ณดํ˜ธ ์„ค๊ณ„ ๊ธฐ์ค€ = 96 9.3.4 ์œ ์‚ฌ ์‹ค์  ์ž๋ฃŒ ๋น„๊ต = 96 9.4 ํ”ผํƒ„ ๋ณดํ˜ธ๊ตฌ ์„ค๊ณ„ ๊ฐœ๋… = 98 9.5 ํ”ผํƒ„ ๋ณดํ˜ธ๊ตฌ ์„ค๊ณ„ ๋ฐฉ๋ฒ• = 100 9.5.1 ํŒŒํŽธ ๊ด€ํ†ต ๋ฐฉ์ •์‹ = 100 9.5.2 ์žฌ์งˆ ๋ฐฉํƒ„ ์„ฑ๋Šฅ ๊ณ„์ˆ˜(MSF) = 101 9.5.3 ๋‹ค์ค‘ํŒ ๊ด€ํ†ต ๋ฐฉํƒ„ ์„ฑ๋Šฅ ๊ณ„์ˆ˜(MPPF) = 102 9.5.4 ์ž…์‚ฌ๊ฐ์— ๋”ฐ๋ฅธ ๋ฐฉํƒ„ ์„ฑ๋Šฅ ๊ณ„์ˆ˜(OAF) = 103 9.5.5 ํŒŒํŽธ ์ถฉ๋Œ์— ๋Œ€ํ•œ ์žฅ๋น„ ๋‚ด๊ตฌ๋ ฅ = 104 9.6 ํŒŒํŽธ ์†์ƒ ์ˆ˜์น˜ ํ•ด์„ = 105 9.6.1 ACE 1.0 ํ”„๋กœ๊ทธ๋žจ = 105 9.6.2 LS-DYNA ํ”„๋กœ๊ทธ๋žจ ์ˆ˜ํ–‰ ๊ฒฐ๊ณผ = 108 ์ œ 10 ์žฅ ํ•จ์ •์˜ ์ทจ์•ฝ์„ฑ ํ•ด์„ ๋ฐ ์„ค๊ณ„ ์˜ˆ = 110 10.1 ์ƒ์กด์„ฑ ํ•ด์„์—์„œ ์ƒ์„ธ ์ทจ์•ฝ์„ฑ ํ•ด์„ ๋ฐ ์„ค๊ณ„ ๊ณผ์ • = 110 10.2 ์ˆ˜์ค‘ํญํŒŒ ํ•ด์„ ๋ฐ ์„ค๊ณ„ ์ ์šฉ ์˜ˆ = 112 10.2.1 ์ˆ˜์ค‘ํญํŒŒ ์กฐ๊ฑด = 112 10.2.2 ํ•ด์„ ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• = 113 10.2.3 ํ•ด์„ ๋ชจ๋ธ = 114 10.2.4 ํ•ด์„ ๋ชจ๋ธ์— ๋”ฐ๋ฅธ ๊ฒฐ๊ณผ ๋น„๊ต = 115 10.2.5 ์ข…๊ฐ•๋„ ์•ˆ์ „์„ฑ ๊ฒ€ํ†  = 117 10.3 ๊ณต๊ธฐ์ค‘ ํญํŒŒ ํ•ด์„ ๋ฐ ์„ค๊ณ„ ์ ์šฉ ์˜ˆ = 120 10.3.1 ํ•ด์„ ์‹œ๋‚˜๋ฆฌ์˜ค = 121 10.3.2 ํ•ด์„๊ฒฐ๊ณผ ๋น„๊ต = 121 10.3.3 ์˜ˆ์ œ๋ฅผ ํ†ตํ•œ ์ƒ์กด ์‹œ๊ฐ„ ๊ณ„์‚ฐ = 131 10.4 ํ”ผํƒ„ ๋ณดํ˜ธ๊ตฌ ์„ค๊ณ„ ์ ์šฉ ์˜ˆ = 134 ์ œ 11 ์žฅ ๊ฒฐ๋ก  = 137 ์ฐธ๊ณ ๋ฌธํ—Œ = 139 ์ดˆ๋ก(Abstract) = 146 ๋ฐœํ‘œ ๋…ผ๋ฌธ = 14

    Numerical Investigation of Aeroacoustic Characteristics of a Flatback Blade for Large-Scale Wind Turbines

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2016. 2. ์ด์ˆ˜๊ฐ‘.ํ”Œ๋žซ๋ฐฑ ์ตํ˜•์„ ์ ์šฉํ•œ ์ดˆ๋Œ€ํ˜• ํ’๋ ฅ๋ฐœ์ „๊ธฐ ๋กœํ„ฐ์˜ ๊ณต๋ ฅ ์†Œ์Œ ํŠน์„ฑ์— ๋Œ€ํ•œ ํ•ด์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ตํ˜•์ž์ฒด์†Œ์Œ์—๋Š” ๊ธฐ์กด Brooks, Pope, Marcolini์˜ ๋ฐ˜๊ฒฝํ—˜์‹๊ณผ ํ”Œ๋žซ๋ฐฑ ์ตํ˜• ์†Œ์Œํ•ด์„์„ ์œ„ํ•ด ๊ณ ์•ˆ๋œ ์‹์„ ํ™œ์šฉํ•˜์˜€์œผ๋ฉฐ, ๋‚œ๋ฅ˜ ์œ ์ž… ์†Œ์Œ์—๋Š” Lowson์˜ ๋ฐ˜๊ฒฝํ—˜์‹์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ์ „์‚ฐ์œ ์ฒด์—ญํ•™์„ ์ด์šฉํ•œ ์œ ๋™ํ•ด์„๊ณผ ์™€๋ฅ˜ ๊ฒฉ์ž ๊ธฐ๋ฒ•(Vortex Lattice Method)์„ ์ด์šฉํ•˜์—ฌ ์†Œ์Œ ๊ณ„์‚ฐ์— ํ•„์š”ํ•œ ์ •๋ณด๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ็พŽ Sandia ๊ตญ๋ฆฝ ์—ฐ๊ตฌ์†Œ์˜ BSDS(Blade System Design Study) ๋ธ”๋ ˆ์ด๋“œ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋กœํ„ฐ ๊ณต๋ ฅ ์†Œ์Œ ํ•ด์„ ๊ธฐ๋ฒ•์— ๋Œ€ํ•œ ๊ฒ€์ฆ์„ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ  ๋น„๊ต์  ํƒ€๋‹นํ•œ ๊ฒฐ๊ณผ๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ดˆ๋Œ€ํ˜• ํ’๋ ฅ๋ฐœ์ „๊ธฐ์— ํ”Œ๋žซ๋ฐฑ ์ตํ˜•์„ ์ ์šฉํ•  ๊ฒฝ์šฐ ๋ธ”๋ŸฐํŠธ ๋’ท์ „ ์™€๋ฅ˜ ํ˜๋ฆผ ์†Œ์Œ๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€์ง€๋งŒ, ์ €์ฃผํŒŒ ๋Œ€์—ญ์—์„œ ๋ฐœ์ƒํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์‚ฌ๋žŒ์˜ ์ฒญ๊ฐ ํŠน์„ฑ(A-weighting)์„ ๊ณ ๋ คํ•  ๊ฒฝ์šฐ ๋ฌธ์ œ๊ฐ€ ๋  ๋งŒํ•œ ์†Œ์Œ๋„ ๋ณ€ํ™”๋Š” ์—†๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ, ํ’๋ ฅ๋ฐœ์ „๊ธฐ ์†Œ์Œ์˜ Amplitude Modulation ํŠน์„ฑ์„ ๊ณ ๋ คํ•œ ์ฒญ๊ฐ์‹คํ—˜์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค.1. ์„œ ๋ก  1 1.1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 2. ์ˆ˜์น˜ํ•ด์„ ๊ธฐ๋ฒ• 4 2.1. ํ•ด์„ ์ ˆ์ฐจ 4 2.2. ์ตํ˜• ์œ ๋™ ํ•ด์„ 5 2.3. ๋กœํ„ฐ ๊ณต๋ ฅ ํ•ด์„ 6 2.4. ๋กœํ„ฐ ์†Œ์Œ ํ•ด์„ 7 2.4.1. ๋‚œ๋ฅ˜ ๊ฒฝ๊ณ„์ธต ๋’ท์ „ ์†Œ์Œ 8 2.4.2. ๋ธ”๋ŸฐํŠธ ๋’ท์ „ ์™€๋ฅ˜ ํ˜๋ฆผ ์†Œ์Œ 9 2.4.3. ๋‚œ๋ฅ˜ ์œ ์ž… ์†Œ์Œ 10 2.5. ํ•ด์„ ๊ธฐ๋ฒ• ๊ฒ€์ฆ 11 2.5.1. ํ’๋ ฅ๋ฐœ์ „๊ธฐ ๋ชจ๋ธ 11 2.5.2. ๋กœํ„ฐ ๊ณต๋ ฅ ์†Œ์Œ ํ•ด์„ 15 3. ๊ฒฐ๊ณผ ๋ฐ ๋…ผ์˜ 23 3.1. ํ’๋ ฅ๋ฐœ์ „๊ธฐ ๋ชจ๋ธ 23 3.2. ๋กœํ„ฐ ๊ณต๋ ฅ ์†Œ์Œ ์˜ˆ์ธก 24 4. ๊ฒฐ ๋ก  38 ์ฐธ๊ณ ๋ฌธํ—Œ 39 Abstract 42Maste
    • โ€ฆ
    corecore