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    ํ—ฌ์Šค์ผ€์–ด ์„œ๋น„์Šค์—์„œ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์„ ์œ„ํ•œ ๋””์ž์ธ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(๋””์ง€ํ„ธ์ •๋ณด์œตํ•ฉ์ „๊ณต),2020. 2. ์ด์ค‘์‹.์Šค๋งˆํŠธํฐ๊ณผ ์›จ์–ด๋Ÿฌ๋ธ” ๊ธฐ๊ธฐ์˜ ๋ณด๊ธ‰์œผ๋กœ ์ธํ•ด ํ™˜์ž ์ƒ์„ฑ ๊ฑด๊ฐ• ๋ฐ์ดํ„ฐ(Patient-Generated Health Data; PGHD)๊ฐ€ ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ์ด๋Š” ์˜์‚ฌ-ํ™˜์ž ์˜์‚ฌ ์†Œํ†ต์„ ๊ฐœ์„ ํ•˜์—ฌ ๋ฐ์ดํ„ฐ ์ค‘์‹ฌ์œผ๋กœ ๋ฐœ์ „ ํ•  ์ˆ˜์žˆ๋Š” ์ƒˆ๋กœ์šด ๊ธฐํšŒ๋ฅผ ์ œ๊ณตํ–ˆ๋‹ค. PGHD๋ฅผ ์‚ฌ์šฉํ•œ ๋ฐ์ดํ„ฐ ์ค‘์‹ฌ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์„ ํ†ตํ•ด ํ™˜์ž์™€ ์˜์‚ฌ๋Š” ๊ธฐ์กด ์ž„์ƒ ๋ฐ์ดํ„ฐ๋ฅผ ๋ณด์™„ํ•˜์—ฌ ์ดํ•ด์˜ ์ฐจ์ด๋ฅผ ๋ฉ”์šธ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ํ™˜์ž ๊ฑด๊ฐ•์— ๋Œ€ํ•œ ํฌ๊ด„์ ์ธ ๊ด€์ ๋„ ํš๋“ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ด๋Ÿฌํ•œ ์ƒˆ๋กœ์šด ์œ ํ˜•์˜ ๋ฐ์ดํ„ฐ์™€ ๊ธฐ์ˆ ์„ ๊ธฐ์กด ์˜๋ฃŒ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์— ํ†ตํ•ฉํ•˜๋Š” ๋ฐ์—๋Š” ์—ฌ์ „ํžˆ ์–ด๋ ค์›€์ด ๋‚จ์•„ ์žˆ๋‹ค. ํ™˜์ž๋Š” ์ข…์ข… ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์— ๋Œ€ํ•œ ์ฐธ์—ฌ์™€ ๋™๊ธฐ๋ฅผ ์žƒ์–ด๋ฒ„๋ฆฌ๋ฉฐ, ์ด์— ๋”ฐ๋ผ ์ˆ˜์ง‘ํ•œ ๋ฐ์ดํ„ฐ๋Š” ๋ถˆ์™„์ „ํ•ด์ง€๋Š” ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ๋˜ํ•œ PGHD๊ฐ€ ์˜จ์ „ํ•˜๊ฒŒ ์ˆ˜์ง‘ ๋˜๋”๋ผ๋„ ์˜์‚ฌ์™€ ํ™˜์ž๋Š” ์˜๋ฃŒ ๊ด€ํ–‰์—์„œ ์ด๋Ÿฌํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์„ ๊ฒช๊ฒŒ ๋œ๋‹ค. ๋˜ํ•œ, ์‹œ๊ฐ„๊ณผ ์ •๋ณด์˜ ๋ถ€์กฑ์œผ๋กœ ์ธํ•ด ํ˜„์žฌ ์›Œํฌ ํ”Œ๋กœ์šฐ์—์„œ ํ™˜์ž์™€ ์˜์‚ฌ ๋ชจ๋‘๊ฐ€ PGHD๋ฅผ ํ†ตํ•ด ํ˜‘์—…ํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์–ด๋ ค์šด ์ผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. HCI ์—ฐ๊ตฌ ๊ด€์ ์—์„œ, PGHD๋ฅผ ํ™œ์šฉ ํ•œ ๋ฐ์ดํ„ฐ ์ค‘์‹ฌ ํ†ต์‹ ์„ ์ง€์›ํ•˜๋Š” ์‹œ์Šคํ…œ์„ ์„ค๊ณ„ํ•˜๋ฉด ์ด๋Ÿฌํ•œ ๊ณผ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ์ž ์žฌ๋ ฅ์ด ์žˆ์œผ๋ฉฐ, ์ด๋Š” ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘(collection), ํ‘œํ˜„(representation), ํ•ด์„(interpretation) ๋ฐ ํ˜‘์—…(collaboration)์˜ ๋„ค ๊ฐ€์ง€ ์„ค๊ณ„ ๊ณต๊ฐ„(design space)์—์„œ ์ถ”๊ฐ€์ ์ธ ํƒ์ƒ‰์„ ์š”๊ตฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ, ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์‹œ์Šคํ…œ ์„ค๊ณ„ ๋ฐ ํ˜„์žฅ ๋ฐฐํฌ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ, ๊ฐ ์„ค๊ณ„ ๊ณต๊ฐ„์—์„œ ํ•ด๊ฒฐ๋˜์ง€ ์•Š์€ ์งˆ๋ฌธ์„ ํƒ์ƒ‰ํ•˜๊ณ  ๊ฒฝํ—˜์  ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ๋ฐ ์„ค๊ณ„ ์ง€์นจ์„ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋จผ์ €, ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์— ๋Œ€ํ•œ ์„ค๊ณ„ ๊ณต๊ฐ„์˜ ์—ฐ๊ตฌ๋กœ์„œ, ์ ‘๊ทผ์„ฑ ๋†’์€ ๋ฐ์ดํ„ฐ ์ถ”์  ๋„๊ตฌ๊ฐ€ ํ™˜์ž๊ฐ€ ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ PGHD, ํŠนํžˆ ์‹์‚ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๋Š” ๋ฐ ์–ด๋–ค ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋Š”์ง€์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, ์ ‘๊ทผ์„ฑ ๋†’์€ ๋ฐ์ดํ„ฐ ์ถ”์  ๋„๊ตฌ์ธ mFood Logger์„ ๋””์ž์ธํ•œ ํ›„, 20 ๋ช…์˜ ํ™˜์ž์™€ 6 ๋ช…์˜ ์ž„์ƒ์˜๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์‹ค์ฆ์  ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ํ™˜์ž์™€ ์ž„์ƒ์˜๊ฐ€ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์„ ์œ„ํ•ด ์›ํ•˜๋Š” ๋ฐ์ดํ„ฐ ์œ ํ˜•์ด ๋ฌด์—‡์ธ์ง€ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ์ž„์ƒ์  ๋งฅ๋ฝ์—์„œ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ ํ•  ๋•Œ์˜ ๋‚œ์ ๊ณผ ๊ธฐํšŒ๋ฅผ ๋ฐœ๊ฒฌํ–ˆ๋‹ค. ๋‘˜์งธ, ์ž„์ƒ์˜๋ฅผ ์œ„ํ•œ ๋ฐ์ดํ„ฐ ํ‘œํ˜„์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด, 18๋ช…์˜ ๋‹ค์–‘ํ•œ ์ดํ•ด ๊ด€๊ณ„์ž(e.g., ์ž„์ƒ์˜, EMR ๊ฐœ๋ฐœ์ž)์™€ ์ฐธ์—ฌ์  ๋””์ž์ธ(participatory design) ํ”„๋กœ์„ธ์Šค๋ฅผ ํ†ตํ•ด PGHD๋ฅผ ํ‘œ์‹œํ•˜๋Š” DataMD๋ฅผ ์„ค๊ณ„ํ•˜๊ณ  ๊ตฌํ˜„ํ–ˆ๋‹ค. ์ฐธ์—ฌ์  ๋””์ž์ธ ์›Œํฌ์ƒต์„ ํ†ตํ•ด ์•Œ์•„๋‚ธ ๊ฒƒ์€, ์˜๋ฃŒ์  ์ƒํ™ฉ์˜ ์ œ์•ฝ ๋•Œ๋ฌธ์— ์ž„์ƒ์˜๊ฐ€ ์›ํ•˜๋Š” ๋ฐ์ดํ„ฐ ํ‘œํ˜„ ๋ฐฉ์‹์ด ํšจ์œจ์„ฑ๊ณผ ์นœ์ˆ™ํ•จ์œผ๋กœ ์ˆ˜๋ ด๋œ๋‹ค๋Š” ์ ์ด์—ˆ๋‹ค. ์ž„์ƒ์˜๋Š” ํ•™์Šต์— ๊ฑธ๋ฆฌ๋Š” ์‹œ๊ฐ„ ๋ฌธ์ œ๋กœ ์ธํ•ด ์ƒˆ๋กœ์šด ์‹œ๊ฐํ™” ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์ง€ ์•Š์•˜๊ณ , ํ•œ ๋ฒˆ์— ๋งŽ์€ ์–‘์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๋ณด๊ณ  ์‹ถ์–ดํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์š”๊ตฌ ์‚ฌํ•ญ์„ ๊ณ ๋ คํ•˜์—ฌ, ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ PGHD๊ฐ€ ํ•œ ๋ˆˆ์— ๋ณด์—ฌ์ง€๋ฉฐ, ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์ž„์ƒ ์ƒํ™ฉ์„ ๊ณ ๋ คํ•œ, DataMD๋ฅผ ์„ค๊ณ„ํ•˜๊ณ  ๊ตฌํ˜„ํ–ˆ๋‹ค. ์…‹์งธ, ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์˜ ์ค‘์š”ํ•œ ์ธก๋ฉด์œผ๋กœ์„œ, ํ™˜์ž๋ฅผ ์œ„ํ•œ ๋ฐ์ดํ„ฐ ํ•ด์„ ์ „๋žต์„ ์ œ์‹œํ•˜์—ฌ ํšจ๊ณผ์ ์ธ ๋ฐ์ดํ„ฐ ํ•ด์„์„ ๋•๋Š” ์„ค๊ณ„ ์ง€์นจ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. 20๋ช…์˜ ๋งŒ์„ฑ ์งˆํ™˜ ํ™˜์ž์™€์˜ ์ธํ„ฐ๋ทฐ๋ฅผ ํ†ตํ•ด, ํ™˜์ž๋“ค์ด PGHD๋ฅผ ํ•ด์„ํ•  ๋•Œ, ๋…ผ๋ฆฌ์  ์ฆ๊ฑฐ๊ฐ€ ์•„๋‹Œ ์ž์‹ ์˜ ๊ณผ๊ฑฐ ๊ฒฝํ—˜์— ๊ฐ•ํ•˜๊ฒŒ ์˜์กดํ•œ๋‹ค๋Š” ์ ์„ ๋ฐํ˜€๋ƒˆ๋‹ค. ํ™˜์ž๋“ค์€ ์ž์‹ ์˜ ์‹ ๋…๊ณผ ๊ฒฝํ—˜์— ๋”ฐ๋ผ ์—ฌ๋Ÿฌ ๋ฐ์ดํ„ฐ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๊ฐ€์ •ํ•˜๋ฉฐ, ์ด๋ฅผ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ๋„ค ๊ฐ€์ง€์˜ ๋ฐ์ดํ„ฐ ํ•ด์„ ์ „๋žต์„ ๊ตฌ์‚ฌํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ดํ•ด๋Š” ์„ค๊ณ„์ž์™€ ์—ฐ๊ตฌ์›์ด ๋ฐ์ดํ„ฐ ํ•ด์„์„ ์ง€์›ํ•˜๋Š” ์‹œ์Šคํ…œ ์„ค๊ณ„๋ฅผ ๋ฐœ์ „์‹œํ‚ค๋Š” ๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•œ ํ˜‘์—…์„ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•ด ์•ž์„  ์—ฐ๊ตฌ์—์„œ ๋””์ž์ธํ•œ ์‹œ์Šคํ…œ์„ ๊ธฐ๋ฐ˜์œผ๋กœ PGHD๋ฅผ ๊ณต์œ ํ•˜๊ณ  ํ™œ์šฉํ•จ์œผ๋กœ์จ, ์ž„์ƒ์˜์™€ ํ™˜์ž๊ฐ€ ์–ด๋–ป๊ฒŒ ํ˜‘์—…ํ•˜๋Š”์ง€๋ฅผ ์กฐ์‚ฌํ•˜๊ณ ์ž ํ–ˆ๋‹ค. ํ™˜์ž์˜ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋ฐ ํ•ด์„์„ ๋•๋Š” ์•ฑ์ธ MyHealthKeeper์™€ ์ž„์ƒ์˜๋ฅผ ์œ„ํ•œ ์ธํ„ฐํŽ˜์ด์Šค์ธ DataMD๋กœ ๊ตฌ์„ฑ๋œ ํ†ตํ•ฉ ์‹œ์Šคํ…œ์„ ์ž„์ƒ ํ˜„์žฅ์— ๋ฐฐํฌํ–ˆ๋‹ค. 80๋ช…์˜ ์™ธ๋ž˜ํ™˜์ž์™€์˜ ์ž„์ƒ์‹œํ—˜ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด PGHD๋ฅผ ํ†ตํ•œ ํ˜‘๋ ฅ์œผ๋กœ ํ™˜์ž๊ฐ€ ํ–‰๋™ ๋ณ€ํ™”์— ์„ฑ๊ณตํ•  ์ˆ˜์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ์•ฑ ์‚ฌ์šฉ ๋กœ๊ทธ์— ๋”ฐ๋ฅด๋ฉด ํ™˜์ž๋Š” ์ง์ ‘์ ์ธ ์ƒํ˜ธ ์ž‘์šฉ ์—†์ด๋„ ์ž„์ƒ์˜์™€ ์›๊ฒฉ์œผ๋กœ ํ˜‘์—… ํ•  ์ˆ˜๋„ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์ž„์ƒ์˜์™€ ํ™˜์ž ์‚ฌ์ด์˜ ํ˜‘๋ ฅ์„ ์ง€์›ํ•  ์ˆ˜์žˆ๋Š” ์ฃผ์š” ๊ธฐํšŒ๊ฐ€ ๊ธฐ์กด ์ž„์ƒ ์›Œํฌํ”Œ๋กœ์šฐ์— PGHD ์‚ฌ์šฉ์„ ํ†ตํ•ฉํ•˜๋Š” ๊ฒƒ์— ์žˆ์Œ์„ ์ œ์‹œํ•œ๋‹ค. ์•ž์„  ์—ฐ๊ตฌ๋“ค์„ ํ†ตํ•ด, ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์„ ์œ„ํ•œ ๋””์ž์ธ์ด ํ™˜์ž์™€ ์˜์‚ฌ๊ฐ€ PGHD๋ฅผ ํ†ตํ•ด ํ˜‘์—…ํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ–ˆ๋‹ค. PGHD๊ฐ€ ๋„ค ๊ฐœ์˜ ์„ค๊ณ„ ๊ณต๊ฐ„ ๋‚ด์—์„œ ๊ธฐ์กด ์˜์‚ฌ-ํ™˜์ž ํ†ต์‹ ์„ ๋ฐ์ดํ„ฐ ์ค‘์‹ฌ ํ†ต์‹ ์œผ๋กœ ๊ฐœ์„  ํ•  ์ˆ˜์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ๊ฐœ๋…ํ™”ํ•จ์œผ๋กœ์จ, ์ด ์—ฐ๊ตฌ๋Š” ํ™˜์ž์™€ ์˜์‚ฌ ๊ฐ„์˜ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์„ ์œ„ํ•œ ๋””์ž์ธ์ด ์–ด๋–ป๊ฒŒ ๋„์ถœ๋˜์–ด์•ผ ํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์‹œ๊ฐ์„ ์ œ๊ณตํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค. ์ด ์ž‘์—…์€ HCI, CSCW๊ณผ ๊ฑด๊ฐ• ์ •๋ณดํ•™ ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ๊ฒฝํ—˜์  ์ดํ•ด๋ฅผ ๋†’์ด๊ณ , ์‹ค์šฉ์ ์ธ ์„ค๊ณ„ ์ง€์นจ์„ ์ œ๊ณตํ•˜๋ฉฐ, ์ด๋ก ์  ํ™•์žฅ์— ๊ธฐ์—ฌํ•œ๋‹ค. ๋˜ํ•œ, ์ด ์—ฐ๊ตฌ๋Š” ํ–ฅํ›„ ๋‹ค๋ฅธ ๋ถ„์•ผ์—์„œ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์„ ์ง€์›ํ•˜๋Š” ์‹œ์Šคํ…œ์˜ ์„ค๊ณ„๊ฐ€ ์–ด๋–ป๊ฒŒ ์ด๋ค„์ ธ์•ผ ํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ๊ธฐ์ดˆ๋ฅผ ์ œ๊ณตํ•œ๋‹ค.The prevalence of smartphones and wearable devices has led to a dramatic increase in patient-generated health data (PGHD). The growing interest in PGHD has offered new opportunities to improve doctor-patient communication to become more data-driven. Data-driven communication using PGHD enables patients and physicians to fill in gaps between understandings by supplementing existing clinical data, as well as providing a more comprehensive picture of ongoing patient health. However, challenges in integrating such new types of data and technologies into existing healthcare communications remain. Patients often lose their engagement and motivation in data collection, resulting in incomplete data. Even if PGHD is wholly collected, physicians and patients encounter challenges in utilizing such data--representation and interpretation--in healthcare practices. Furthermore, it is challenging for both patients and physicians to collaborate through PGHD in the current workflow due to the lack of time and information overload. From the HCI research perspective, designing a system supporting data-driven communication utilizing PGHD has the potential to address such challenges, which calls for further exploration in four design spaces: data collection, representation, interpretation, and collaboration. Therefore, in this dissertation work, I aim to explore unsolved questions in each design space by conducting a series of design and deployment studies and provide empirical findings and design guidelines. In the design space of data collection, I investigated how the semi-automated tracking tool can support patients to track various types of PGHD, especially food journaling. With the design of mFood Logger, a semi-automated data tracking tool, I conducted an empirical study with 20 patients and 6 clinicians. I identified desired data types for data-driven communication from the patients' and clinicians' sides and uncovered the challenges and opportunities in collecting data within clinical contexts. I was able to understand the feasibility and acceptability of PGHD in clinical practices, as well as clinicians' presence--either remotely or in-person--as an enabler that encourages patients to keep tracking PGHD in the longer-term. Incorporating critical topics regarding data collection from the literature and findings from my work, I discuss the applicability of PGHD and data tracking modes. To support data representation for clinicians, I designed and implemented DataMD that displays PGHD, considering situational constraints through a participatory design process with 18 various stakeholders (e.g., clinicians, EMR developers). Through the participatory design workshop, I found that the ways of data representation that clinicians desired converged to efficiency and familiarity due to the situational constraints. Clinicians wanted to see a large amount of data at once, avoiding using novel visualization methods due to the issue of learnability. Considering those requirements, I designed and implemented DataMD, in which various types of PGHD are represented with considerations of clinical contexts. I discussed the role of data representation in data-driven communication. As the critical aspect of data-driven communication, I present different data-interpretation strategies from patients, providing design guidelines to help effective data-interpretation. By conducting interviews with 20 chronic disease patients, I found that they shaped their interests and assumptions by incorporating prior experiences rather than logical evidence. I also identified four data-interpretation strategies: finding evidence to confirm assumptions, discrediting data to preserve initial assumptions, discovering new insights, and deferring drawing hasty conclusions from data. These understandings help designers and researchers advance the design of systems to support data-interpretation. Lastly, to support collaboration via data, I demonstrate how clinicians and patients collaborate by sharing and utilizing PGHD based on the system I designed. I deployed the integrated system consisting of a patient app, MyHealthKeeper, and a clinician interface, DataMD. I investigated how the system could support collaboration via data. Clinical outcomes revealed that collaboration via PGHD led patients to succeed in behavior change. App usage log also showed that patients could even remotely collaborate with clinicians without direct interactions. Findings from these studies indicate that the key opportunities to facilitate collaboration between clinicians and patients are the integration of data prescriptions into the clinician's workflow and intervention based on natural language feedback generated within clinical contexts. Across these studies, I found that the design for data-driven communication can support patients and physicians to collaborate through PGHD. By conceptualizing how PGHD could improve the existing doctor-patient communication to data-driven communication within four design spaces, I expect that this work will shed new light on how the design should be derived for data-driven communication between patients and physicians in the real world. Taken together, I believe this work contributes to empirical understandings, design guidelines, theoretical extensions, and artifacts in human-computer interaction, computer-supported cooperative work, and health informatics communities. This work also provides a foundation for future researchers to study how the design of the system supporting data-driven communication can empower various users situated in different contexts to communicate through data in other domains, such as learning, beyond the context of healthcare services.1 Introduction 1 1.1 Background 1 1.2 Motivation 4 1.3 Topics of Interest 5 1.3.1 Design Spaces 5 1.3.2 Research Scope 11 1.4 Thesis Statements and Research Questions 13 1.5 Thesis Overview 15 1.6 Contribution 18 1.6.1 Empirical research contributions 18 1.6.2 Artifacts contributions 18 1.6.3 Theoretical contributions 19 2 Conceptual Background & Related Work 20 2.1 Data-driven Communication in Healthcare Services 20 2.1.1 Concept of Doctor-Patient Communication 21 2.1.2 Brief History of Patient-Centered Approach 25 2.1.3 Emergence of Patient-Generated Health Data 27 2.2 Four Design Spaces for Data-Driven Communication 30 2.2.1 Data collection 34 2.2.2 Data Representation 41 2.2.3 Data Interpretation 47 2.2.4 Collaboration via Data 50 3 Data Collection: Study of mFood Logger 54 3.1 Motivation 55 3.2 Preliminary Work & Tool Design 57 3.2.1 Clinical Requirements for Data Collection 57 3.2.2 Design of Data Collection Tool: mFood Logger 60 3.3 Study Design 63 3.3.1 Participants 63 3.3.2 StudyProcedure 64 3.4 Results 69 3.4.1 PatientSide 69 3.4.2 ClinicianSide 76 3.5 Limitations & Conclusion 80 3.6 Chapter 3 Summary 81 4 Data Representation: Design of DataMD 83 4.1 Motivation 84 4.2 Preliminary Work 86 4.2.1 Workflow Journey Maps 87 4.2.2 DesignGoals 89 4.3 Study Design 90 4.3.1 Participants 91 4.3.2 ParticipatoryDesignworkshop 91 4.4 Results 92 4.4.1 DesignRequirements 92 4.4.2 Implementation: DataMD 98 4.5 Limitations & Conclusion 102 4.6 Summary of Chapter4 102 5 Data Interpretation: Data-Interpretation Strategies 103 5.1 Motivation 103 5.2 Study Design 106 5.2.1 Participants 106 5.2.2 Study Procedure 108 5.2.3 Data Analysis 110 5.3 Results 111 5.3.1 Change of Interest in Data 111 5.3.2 Assumptions on Relationships between Data Types 113 5.3.3 Data-InterpretationStrategy 117 5.4 Limitations & Conclusion 124 5.5 Summary of Chapter5 125 6 Collaboration via Data: Deployment Study 126 6.1 Motivation 127 6.2 System Design 128 6.2.1 MyHealthKeeper: Patient App 128 6.2.2 DataMD: Clinician Interface 132 6.3 Study Design 133 6.3.1 Participants 134 6.3.2 Procedure 135 6.4 Data Analysis 138 6.4.1 Statistical Analysis of Clinical Outcomes 139 6.4.2 App Usage Log 139 6.4.3 Observation Data Analysis 139 6.5 Results 140 6.5.1 Behavior Change 140 6.5.2 Data-Collection & Journaling Rate 144 6.5.3 Workflow Integration & Communication Support 146 6.6 Limitations & Conclusion 150 6.7 Summary of Chapter6 151 7 Discussion 152 7.1 Towards a Design for Data-Driven Communication 152 7.1.1 Improve Data Quality for Clinical Applicability 153 7.1.2 Support Accessibility of Data Collection 154 7.1.3 Understand Clinicians Preference for Familiar Data Representation. 157 7.1.4 Embrace Lived Experience for Rich Data Interpretation 158 7.1.5 Prioritize Workflow Integration for Successful Data-Driven Communication 163 7.1.6 Consider Risks of Using Patient-Generated Health Data in Clinical Settings 165 7.2 Opportunities for Future Work 166 7.2.1 Leverage Ubiquitous Technology to Design Data CollectionTools 166 7.2.2 Provide Data-Interpretation Guidelines for People with Different Levels of Literacy and Goals 169 7.2.3 Consider Cultural Differences in Data-Driven Communication 170 8 Conclusion 173 8.1 Summary of Contributions 173 8.2 Future Directions 175 8.3 Final Remarks 176Docto

    BaSnO3 ๊ธฐ๋ฐ˜์˜ 2์ฐจ์› ์‹œ์Šคํ…œ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌํ•™๊ณผ, 2021.8. ์ฐจ๊ตญ๋ฆฐ.Oxide semiconductors have been widely studied because of their optical transparency and great electrical properties. In particular, oxides with a perovskite structure showed additional novel characteristics such as ferromagnetism, ferroelectricity, multiferroicity, and superconductivity. However, the oxygen instability at high temperature and low mobility at room temperature of oxides have been problems for device applications. BaSnO3 is a perovskite oxide with the highest electron mobility of 320 cm2/Vs among oxides at a carrier density of about 1020 cm-3, and it has high oxygen stability that makes the p-n junction possible. BaSnO3 has been and can be applied to various fields such as power electronics, high frequency device, solar cell, etc. This dissertation focuses on the study of BaSnO3-based two-dimensional systems to investigate the electrical characteristics of quantum wells made by the structures of BaSnO3/(Ba,La)SnO3/BaSnO3 (delta-doped BaSnO3) and LaInO3/BaSnO3. Delta-doped BaSnO3 has quantum well at the La-doped BaSnO3 layer, which is made by conduction band bending at the BaSnO3/(Ba,La)SnO3 interface. LaInO3/BaSnO3 has quantum well on the BaSnO3 side due to the large conduction band offset between the two materials and polarization of LaInO3. In the delta-doped BaSnO3, two-dimensional carrier densities were measured at various thicknesses and doping levels, and exhibited two unpredictable electrical properties; too low conductance in thin (Ba,La)SnO3 sample and the conductance increase as the undoped BaSnO3 capping layer thickens. Analysis using Poisson-Schrรถdinger simulation shows that these macroscopic properties are physically well explained by continuous band bending and changing surface boundary conditions. Temperature dependent resistance has also been investigated in delta-doped BaSnO3 films and will be the basis for quantum phenomenon measurements. LaInO3/BaSnO3 showed conductance enhancement at the interface even though both LaInO3 and BaSnO3 have insulating properties. The interface has been thought of as a two-dimensional electron gas, and I measured electrical properties of the interface with varing doping level of BaSnO3 and LaInO3 thicknesses using epitaxially well grown films confirmed by XRD and STEM. And the field effect transistor was fabricated using a two-dimensional electron gas as a channel layer and LaInO3 as the high dielectric oxide, and it operated well. The temperature dependent resistance has also been investigated at the LaInO3/BaSnO3 interface, and still requires lower dislocation density than now to see the quantum phenomena. Experimental results of the LaInO3/BaSnO3 interface were analyzed using Poisson-Schrรถdinger simulation to understand how quantum well with high two-dimensional carrier density is formed. 13 kinds of material parameters of LaInO3 and BaSnO3 (polarization, concentration and activation energy of donor, deep donor, acceptor, and deep acceptor, effective mass, dielectric constant, band gap, and conduction band offset between two materials) were analyzed to understand their effect on quantum well. High polarization of LaInO3, appropriate concentrations and activation energies of carriers, not too small effective mass, not too high dielectric constant, and large conduction band offset make quantum well with high two-dimensional carrier density compared to conventional two-dimensional electron gases. Based on these calculational analysis, I suggest methods for improvement of LaInO3/BaSnO3 two-dimensional electron gas and predict another BaSnO3-based two-dimensional electron gas interface. These studies of delta-doped BaSnO3 and two-dimensional electron gas at LaInO3/BaSnO3 have led to a physical understanding of the macroscopic electrical characteristics in the two-dimensional system, and the analysis results predict another advanced BaSnO3-based two-dimensional systems. Furthermore, it will develop into the observation of quantum phenomena by solving the current problem of dislocation density.1. Introduction 1 1.1. Oxide semiconductors 1 1.2. Perovskite oxide BaSnO3 1 1.3. Electrical properties of BaSnO3 4 References 6 2. delta-doped BaSnO3 7 2.1. Introduction 7 2.2. Experiment and calculation details 8 2.3. Band bending of BaSnO3/(Ba,La)SnO3 interface 12 2.4. Fermi level pinning of (Ba,La)SnO3 surface 14 2.5. Temperature dependent electrical property 21 2.6. Conclusion 24 References 25 3. Two-dimensional electron gas at LaInO3/BaSnO3 interface 27 3.1. Introduction 27 3.2. Structural properties of LaInO3/BaSnO3 interface 29 3.3. Electrical properties of LaInO3/BaSnO3 interface 33 3.4. Field effect device based on LaInO3/BaSnO3 interface 36 3.5. Temperature dependent electrical property 38 3.6. Conclusion 40 References 41 4. Analysis of LaInO3/BaSnO3 interface by Poisson-Schrรถdinger equation 43 4.1. Introduction 43 4.2. Poisson-Schrรถdinger simulations of conventional two-dimensional electron gases 46 4.2.1. GaAs two-dimensional electron gas 47 4.2.2. GaN two-dimensional electron gas 49 4.2.3. ZnO two-dimensional electron gas 51 4.2.4. Comparison of three conventional two-dimensional electron gases 53 4.3. Experimental results of LaInO3/BaSnO3 interface 54 4.4. Poisson-Schrรถdinger simulations and analysis of LaInO3/BaSnO3 interfce 56 4.4.1. Polarization and deep donor density of LaInO3 59 4.4.2. Deep acceptor and shallow donor density of BaSnO3 62 4.4.3. Deep carrier activation energy 65 4.4.4. Effective mass, dielectric constant, and conduction band offset 69 4.5. Comparison of two-dimensional electron gases 75 4.6. Conclusion 81 References 82 5. Future direction of LaInO3/BaSnO3 and possibility of other BaSnO3 based interface 87 5.1. Properties of LaInO3/BaSnO3 interface 87 5.2. Reduction of dislocation density 89 5.3. Additional conductance enhancement of LaInO3/BaSnO3 interface 91 5.4. Other two-dimensional electron gas based on BaSnO3 94 5.5. Conclusion 98 References 99 6. Summary 101 Abstract in Korean 103๋ฐ•

    ์ธ๊ณต์ง€๋Šฅ ๊ด€๋ จ ๋‰ด์Šค ๊ธฐ์‚ฌ์˜ ํ”„๋ ˆ์ž„, ๊ฐ์ • ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์–ธ๋ก ์ •๋ณดํ•™๊ณผ, 2022. 8. ์ด์ฒ ์ฃผ .This study examines how artificial intelligence (AI) is presented in the news media by examining the frames and emotions expressed in news coverage about AI. For analysis, I used computational text analysis techniques -structural topic model (STM) to extract frames and NRC Emotion Lexicon and Linguistic Inquiry and Word Count (LIWC) to detect emotions. Then I examined their correlations with the political ideology of media outlets (conservative vs. liberal) and media type (newspapers vs TV news). By identifying the frames and the emotions embedded in the news media, it would be possible to predict how they influence the formation of public opinions and attitudes towards AI.๋ณธ ์—ฐ๊ตฌ๋Š” ์ปดํ“จํ„ฐ ํ…์ŠคํŠธ ๋ถ„์„ ๊ธฐ์ˆ ์„ ํ†ตํ•ด ์ธ๊ณต์ง€๋Šฅ (AI)์— ๋Œ€ํ•œ ๋‰ด์Šค ๋ณด๋„์— ๋“œ๋Ÿฌ๋‚œ ํ”„๋ ˆ์ž„๊ณผ ๊ฐ์ •์„ ๋ถ„์„ํ•˜์—ฌ ์ธ๊ณต์ง€๋Šฅ์ด ๋‰ด์Šค ๋ฏธ๋””์–ด์—์„œ ์–ด๋–ป๊ฒŒ ํ‘œํ˜„๋˜๋Š”์ง€๋ฅผ ์‚ดํŽด๋ณด๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ํ”„๋ ˆ์ž„ ์ถ”์ถœ์„ ์œ„ํ•ด Structural Topic Model (STM) ๊ธฐ๋ฒ•์„, ๊ฐ์ • ์ถ”์ถœ์„ ์œ„ํ•ด NRC Emotion Lexicon๊ณผ Linguistic Inquiry and Word Count (LIWC) ํ”„๋กœ๊ทธ๋žจ์„ ํ™œ์šฉํ–ˆ๋‹ค. ์–ธ๋ก ์‚ฌ์˜ ์ •์น˜ ์„ฑํ–ฅ(๋ณด์ˆ˜ โ€“ ์ง„๋ณด)๊ณผ ๋ฏธ๋””์–ด ์œ ํ˜•(์‹ ๋ฌธ โ€“ ๋ฐฉ์†ก)์„ ๋ณ€์ˆ˜๋กœ ์„ค์ •ํ•ด, ์ถ”์ถœ๋œ ๊ฒฐ๊ณผ์™€ ๊ฐ ๋ณ€์ˆ˜์™€์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ–ˆ๋‹ค. ๋‰ด์Šค ๋ฏธ๋””์–ด์— ๋‚ด์žฌ๋œ ํ”„๋ ˆ์ž„๊ณผ ๊ฐ์ •์„ ํŒŒ์•…ํ•จ์œผ๋กœ์จ, ๊ทธ๊ฒƒ์ด AI์— ๋Œ€ํ•œ ์—ฌ๋ก  ๋ฐ ํƒœ๋„ ํ˜•์„ฑ์— ์–ด๋–ค ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.Chapter 1. Introduction 1 Chapter 2. Literature Review and Research Aim 2 Chapter 3. Conceptual Framework 8 Chapter 4. Methods 21 Chapter 5. Results 27 Chapter 6. Discussion 45 Appendix. 49 Bibliography. 51 Abstract in Korean. 57์„

    ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ์˜ ์‹ค์ฒœ์  ์ง€์‹(PPK) ํ•จ์–‘์„ ์œ„ํ•œ ์‚ฌ๋ก€ ์—ฐ๊ตฌ - ์‹คํ–‰๊ณต๋™์ฒด(CoP) ํ™œ๋™์˜ ๋ฐ˜์„ฑ์  ๋…ผ์˜๋ฅผ ์ค‘์‹ฌ์œผ๋กœ -

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ๊ณผํ•™๊ต์œก๊ณผ, 2018. 2. ํ™ํ›ˆ๊ธฐ.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ค‘๋“ฑ ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ๊ฐ€ ์‚ฌ๋ฒ”๋Œ€ํ•™์˜ ์‚ถ๊ณผ ๊ต์œก์„ ํ†ตํ•ด ํ•จ์–‘ํ•ด๊ฐ€๋Š” ๊ต์‚ฌ ์ „๋ฌธ์„ฑ์„ ์‹ค์ฒœ์  ์ง€์‹(PPK)์˜ ๊ด€์ ์—์„œ ๋ถ„์„ํ•จ์œผ๋กœ์จ ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ ๊ต์œก์„ ์œ„ํ•œ ์‹œ์‚ฌ์ ์„ ๋„์ถœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์„œ์šธ ์†Œ์žฌ ์‚ฌ๋ฒ”๋Œ€ํ•™์— ์žฌํ•™ ์ค‘์ธ 5๋ช…์˜ ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ๊ฐ€ ์‹คํ–‰๊ณต๋™์ฒด(CoP) ํ™œ๋™์„ ํ†ตํ•˜์—ฌ ์‚ฌ๋ฒ”๋Œ€ํ•™ ์‚ถ๊ณผ ๊ต์œก์— ๋Œ€ํ•œ ๋ฐ˜์„ฑ์  ๋…ผ์˜์— ์ฐธ์—ฌํ•˜์˜€์œผ๋ฉฐ, ๋…ผ์˜์˜ ๋‚ด์šฉ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ์˜ ์‹ค์ฒœ์  ์ง€์‹ 5๊ฐ€์ง€ ์˜์—ญ ์ค‘ ๊ต๊ณผ๋‚ด์šฉ ์ง€์‹์€ ๋ฐฉ๋Œ€ํ•œ ํ™”ํ•™ ์ง€์‹์œผ๋กœ ํŽธ๊ทนํ™”๋œ ๊ฒฝํ–ฅ์ด ์žˆ๊ณ , ๊ต์œก๊ณผ์ • ์ง€์‹์€ ๊ต์œก๊ณผ์ • ์ž๋ฃŒ๋“ค์„ ๋น„ํŒ์ ์œผ๋กœ ์„ ํƒยท๋ถ„์„ยท์‘์šฉํ•˜๋Š” ์ˆ˜์ค€์œผ๋กœ ์ด์–ด์ง€๋Š” ๋ฐ ํ•œ๊ณ„๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ต์ˆ˜ยทํ•™์Šต ์ง€์‹์—์„œ๋Š” ํŠนํžˆ ํƒ๊ตฌ ์‹คํ—˜ ์ง€๋„๋ ฅ์ด ๋ถ€์กฑํ•˜๋ฉฐ, ๊ต์ˆ˜ยทํ•™์Šตํ™˜๊ฒฝ ์ง€์‹์€ ์‚ฌ๋ฒ”๋Œ€ํ•™-์ง€์—ญ์‚ฌํšŒ ๊ฐ„ ์—ฐ๊ณ„๋ถ€์กฑ, ๊ต์œก ๊ด€๋ จ์ž๋“ค๊ณผ์˜ ์†Œํ†ต์˜ ์žฅ ๋ถ€์กฑ์œผ๋กœ ์ธํ•ด ๋ฐœ๋‹ฌ์— ์ œํ•œ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•œํŽธ ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ๋“ค์€ ๊ต์ง์— ๋ชฉ์ ์„ ๋‘์ง€ ์•Š๋Š” ์ฃผ๋ณ€์ธ๋“ค๋กœ๋ถ€ํ„ฐ ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฐ›๊ณ  ์žˆ์—ˆ๊ณ , ๊ต์‚ฌ๋กœ์„œ์˜ ์‹ ๋…์„ ์ถฉ๋ถ„ํžˆ ์ˆ™๊ณ ํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€ํ™”์˜ ์žฅ์ด ํ•™๊ณผ ๋‚ด ๋ถ€์กฑํ•˜์—ฌ ๊ต์‚ฌ์ž์‹ ๊ด€๋ จ ์ง€์‹์„ ์ถฉ๋ถ„ํžˆ ํ•จ์–‘ํ•˜์ง€ ๋ชปํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ์˜ ์‹ค์ฒœ์  ์ง€์‹ ํ–ฅ์ƒ์„ ์œ„ํ•ด์„œ๋Š” ๋ฌด์—‡๋ณด๋‹ค ๊ต์‚ฌ์ž์‹ ๊ด€๋ จ ์ง€์‹์„ ๊ฐ•ํ™”์‹œํ‚ฌ ํ•„์š”๊ฐ€ ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ์˜ˆ๋น„๊ต์‚ฌยทํ˜„์ง๊ต์‚ฌยท๊ต์ˆ˜์žยท์ง€์—ญ์‚ฌํšŒ ๋‹ด๋‹น์žยท๊ต์œกํ–‰์ •๊ฐ€ยท๊ต์œก ์—ฐ๊ตฌ์ž ๋“ฑ์ด ํ•จ๊ป˜ ํ•˜๋Š” ์ „๋ฌธ์  ํ•™์Šต๊ณต๋™์ฒด์˜ ์šด์˜์ด ํšจ๊ณผ์ ์ผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๊ต๊ณผ๋‚ด์šฉ ์ง€์‹์˜ ๊นŠ์ด์™€ ๋ฐฉํ–ฅ์„ฑ์— ๋Œ€ํ•œ ์ œ๊ณ , ๊ต์œก๊ณผ์ •์„ ๋น„ํŒ์ ์œผ๋กœ ๊ฒ€ํ† ํ•˜๊ณ  ์žฌ๊ตฌ์„ฑํ•ด๋ณผ ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ์˜ ์ œ๊ณต, ํƒ๊ตฌ์‹คํ—˜ ์ง€๋„๋ ฅ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๊ต๊ณผ๋‚ด์šฉ์‹คํ—˜ ๊ณผ๋ชฉ์˜ ๊ฐœ์„ , ๊ต์œก๋ด‰์‚ฌ ํ™œ๋™์˜ ๊ฐœ์„ ์ด ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ์˜ ์‹ค์ฒœ์  ์ง€์‹ ํ•จ์–‘์— ๋„์›€์ด ๋  ์ˆ˜ ์žˆ์Œ์„ ์ œ์–ธํ•œ๋‹ค.I. ์„œ๋ก  1.1 ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 1.2 ์—ฐ๊ตฌ ๋ฌธ์ œ 4 1.3 ์—ฐ๊ตฌ์˜ ์ œํ•œ์  5 1.4 ์šฉ์–ด์˜ ์ •์˜ 7 II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 2.1 ๊ณผํ•™๊ต์‚ฌ์™€ ์‹ค์ฒœ์  ์ง€์‹ 10 2.1.1 ์‹ค์ฒœ์  ์ง€์‹์˜ ๋“ฑ์žฅ ๋ฐฐ๊ฒฝ 10 2.1.2 ์‹ค์ฒœ์  ์ง€์‹์˜ ๊ฐœ๋…๊ณผ ํŠน์ง• 12 2.1.3 ๊ณผํ•™๊ต์‚ฌ์˜ ์ „๋ฌธ์„ฑ๊ณผ ์‹ค์ฒœ์  ์ง€์‹ 14 2.1.4 ์‹ค์ฒœ์  ์ง€์‹ ๊ด€๋ จ ์„ ํ–‰์—ฐ๊ตฌ 16 2.2 ์‹คํ–‰๊ณต๋™์ฒด 19 2.2.1 ์‹คํ–‰๊ณต๋™์ฒด์˜ ๊ฐœ๋…๊ณผ ํŠน์ง• 19 2.2.2 ์‹คํ–‰๊ณต๋™์ฒด ๊ด€๋ จ ์„ ํ–‰์—ฐ๊ตฌ 21 III. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• ๋ฐ ์ ˆ์ฐจ 3.1 ์—ฐ๊ตฌ ์ฐธ์—ฌ์ž 24 3.2 ์—ฐ๊ตฌ ์ ˆ์ฐจ 25 3.3 ๊ฒฐ๊ณผ ๋ถ„์„ ๋ฐฉ๋ฒ• 26 3.4 ์—ฐ๊ตฌ ์œค๋ฆฌ 28 IV. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 29 4.1 ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ์˜ ๊ต์‚ฌ์ž์‹ ๊ด€๋ จ ์ง€์‹ 29 4.2 ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ์˜ ๊ต๊ณผ๋‚ด์šฉ ์ง€์‹ 34 4.2.1 ํ™”ํ•™ ์ง€์‹์œผ๋กœ์˜ ํŽธ๊ทนํ™” 34 4.2.2 ๊ต์–‘ ์ง€์‹์˜ ๋ถ€์กฑ 38 4.3 ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ์˜ ๊ต์œก๊ณผ์ • ์ง€์‹ 40 4.4 ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ์˜ ๊ต์ˆ˜ํ•™์Šต ์ง€์‹ 41 4.4.1 ๊ต์ˆ˜ ๋ฐ ๊ต์ˆ˜๋ฒ• ์ง€์‹์˜ ํ™œ์šฉ ๋ถ€์กฑ 41 4.4.2 ํƒ๊ตฌ์‹คํ—˜ ๊ต์ˆ˜ ์ง€์‹์˜ ๋ถ€์กฑ 43 4.5 ์˜ˆ๋น„ ํ™”ํ•™๊ต์‚ฌ์˜ ๊ต์ˆ˜ํ•™์Šตํ™˜๊ฒฝ ์ง€์‹ 44 4.5.1 ๊ต์‹ค์ƒํ™ฉ ์ง€์‹์˜ ๋ถ€์กฑ 44 4.5.2 ๊ต์‹ค์„ ๋‘˜๋Ÿฌ์‹ผ ํ™˜๊ฒฝ ์ง€์‹์˜ ๋ถ€์กฑ 45 โ…ค. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 48 ์ฐธ๊ณ  ๋ฌธํ—Œ 52 ์ถœํŒ๋ฌผ 59 Abstract 60Maste

    ์ธ์‚ผ ๋ฐ ์‚ฐ์–‘์‚ผ ์”จ์•—์˜ ์ดํ™”ํ•™์  ํŠน์„ฑ ๋ฐ ํ•ญ์‚ฐํ™”๋Šฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์ƒํ™œ๊ณผํ•™๋Œ€ํ•™ ์‹ํ’ˆ์˜์–‘ํ•™๊ณผ, 2017. 8. ํ™ฉ๊ธˆํƒ.์ธ์‚ผ(Panax ginseng Meyer)์€ ํ•ญ๋…ธํ™”, ํ•ญ์—ผ์ฆ, ํ•ญ์‚ฐํ™”, ํ•ญ์ข…์–‘๊ณผ ๊ฐ™์€ ์ƒ๋ฆฌ ํ™œ์„ฑ์„ ์ง€๋‹Œ ์•ฝ์šฉ ์‹๋ฌผ๋กœ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์–ด ์™”๋‹ค. ์žฌ๋ฐฐ ์กฐ๊ฑด์ด ์ธ์‚ผ์˜ ์”จ์•—, ์”จ์•— ๊ธฐ๋ฆ„, ์žŽ, ๋ฟŒ๋ฆฌ์˜ ํ™”ํ•™์  ํŠน์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋“ค์ด ๋ณด๊ณ  ๋˜์—ˆ์œผ๋‚˜, ์ธ์‚ผ ๋ฐ ์‚ฐ์–‘์‚ผ์˜ ์”จ์•— ๊ธฐ๋ฆ„๊ณผ ์”จ์•— ํƒˆ์ง€๋ฐ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ธ์‚ผ ๋ฐ ์‚ฐ์–‘์‚ผ ์”จ์•— ๊ธฐ๋ฆ„์˜ ์ดํ™”ํ•™์  ํŠน์„ฑ์„ ๋ถ„์„ํ•˜๊ณ  ํƒˆ์ง€ํ•œ ์”จ๋ฐ•์˜ ํ•ญ์‚ฐํ™” ๋ฌผ์งˆ๊ณผ ํ•ญ์‚ฐํ™”๋Šฅ์„ ์ธก์ •ํ•˜์˜€๋‹ค. ์ธ์‚ผ ๋ฐ ์‚ฐ์–‘์‚ผ ์”จ์•—์˜ ์กฐ์ง€๋ฐฉ, ์กฐ๋‹จ๋ฐฑ์งˆ, ์กฐํšŒ๋ถ„์˜ ํ•จ๋Ÿ‰์€ ๊ฐ๊ฐ 17.9-22.1, 11.5-15.2, 1.4-1.7%(dry basis)์ด์—ˆ๋‹ค. ์ธ์‚ผ ๋ฐ ์‚ฐ์–‘์‚ผ ์”จ์•— ๊ธฐ๋ฆ„์˜ 95% ์ด์ƒ์ด ๋ถˆํฌํ™” ์ง€๋ฐฉ์‚ฐ์ด์—ˆ๊ณ , ์ฃผ์š” ์ง€๋ฐฉ์‚ฐ์€ ์˜ฌ๋ ˆ์‚ฐ(77.9-78.5%)๊ณผ ๋ฆฌ๋†€๋ ˆ์‚ฐ(16.6-17.4%)์ด์—ˆ๋‹ค. ์‚ฐ์–‘์‚ผ ์”จ์•— ๊ธฐ๋ฆ„์€ ์ธ์‚ผ ์”จ์•— ๊ธฐ๋ฆ„๋ณด๋‹ค Hunter b ๊ฐ’์ด ์œ ์˜์ ์œผ๋กœ ๋†’์•˜๊ณ , carotenoid (4.3 ฮผg ฮฒ-carotene equivalent/g oil), ฮฒ/ฮณ-tocotrienol (3.7 mg/100 g oil), ฮด-tocotrienol (0.2 mg/100 g oil), squalene (405.3 mg/100 g oil) ํ•จ๋Ÿ‰์ด ์œ ์˜์ ์œผ๋กœ ๋” ๋งŽ์•˜๋‹ค(p<0.05). ์”จ์•— ๊ธฐ๋ฆ„์˜ ์ฃผ์š” phytosterol์€ ฮฒ-sitosterol (48.1-83.6 mg/100 g oil)์ด์—ˆ๊ณ  ์‚ฐ์–‘์‚ผ ์”จ์•— ๊ธฐ๋ฆ„์— ๊ฐ€์žฅ ๋งŽ์•˜๋‹ค. ์ธ์‚ผ ๋ฐ ์‚ฐ์–‘์‚ผ ํƒˆ์ง€ ์”จ๋ฐ•์˜ ์กฐ์‚ฌํฌ๋‹Œ ํ•จ๋Ÿ‰์€ 7.9-8.5 mg/g dried meal์ด์—ˆ๋‹ค. ์‚ฐ์–‘์‚ผ์˜ ํƒˆ์ง€ํ•œ ์”จ๋ฐ• ์ถ”์ถœ๋ฌผ์€ ์ธ์‚ผ์˜ ํƒˆ์ง€ํ•œ ์”จ๋ฐ• ์ถ”์ถœ๋ฌผ๋ณด๋‹ค ์ด ํด๋ฆฌํŽ˜๋†€(28.1 mg gallic acid equivalent/g dried extracts)๊ณผ ํ”Œ๋ผ๋ณด๋…ธ์ด๋“œ(5.5 mg quercetin equivalent/g dried extracts)๊ฐ€ ์œ ์˜์ ์œผ๋กœ ๋งŽ์•˜๋‹ค(p<0.05). ๋˜ํ•œ, ์‚ฐ์–‘์‚ผ์˜ ํƒˆ์ง€ํ•œ ์”จ๋ฐ• ์ถ”์ถœ๋ฌผ์˜ DPPH์™€ ABTS ๋ผ๋””์นผ ์†Œ๊ฑฐ๋Šฅ, FRAP ๊ฐ’์ด ๊ฐ€์žฅ ๋†’์•˜๋‹ค (p<0.05). ๊ฒฐ๋ก ์ ์œผ๋กœ ์žฌ๋ฐฐ ํ™˜๊ฒฝ์€ ์ธ์‚ผ ๋ฐ ์‚ฐ์–‘์‚ผ ์”จ์•— ๊ธฐ๋ฆ„์˜ ์ดํ™”ํ•™์  ํŠน์„ฑ ๋ฐ ํƒˆ์ง€ํ•œ ์”จ๋ฐ•์˜ ํ•ญ์‚ฐํ™”๋ฌผ์งˆ๊ณผ ํ•ญ์‚ฐํ™”๋Šฅ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ํŒ๋‹จํ•œ๋‹ค.Background: Ginseng (Panax ginseng Meyer) has been well known for a medicinal plant. Effect of cultivations on chemical characteristics of ginseng plants has been studied. However, seed oils and defatted seed meals from farm-cultivated (FG) and mountain-cultivated ginsengs (MG) have not been studied. Methods: Fatty acid compositions, carotenoids, tocols, squalene, and phytosterols of seed oils from two FG and one MG were analyzed. Crude saponin contents, phenolic and flavonoid contents, and anti-oxidant activities of defatted seed meals from the FG and MG were also determined. Results: Crude lipids, crude proteins, and ash in the seeds were 17.9-22.1% (dry basis), 11.5-15.2%, and 1.4-1.7%, respectively. The major fatty acid in the seed oils was oleic acid (77.9-78.5%), followed by linoleic acid (16.6-17.4%). The seed oils from MG had higher Hunter b value, carotenoids (4.3 ฮผg ฮฒ-carotene equivalent/g oil), ฮฒ/ฮณ-tocotrienol (3.7 mg/100 g oil), and ฮด-tocotrienol (0.2 mg/100 g oil), and lower ฮฑ-tocotrienol (20.8 mg/100 g oil) than the others. The MG seed oils had also more squalene (405.3 mg/100 g oil) than the others. The major phytosterol in the oils was ฮฒ-sitosterol (48.1-83.6 mg/100 g oil) with MG the highest. Crude saponins in the defatted ginseng seed meals were 7.9-8.5 mg/g dried meal. Defatted seed meal extracts from MG had more total phenolics (28.1 mg gallic acid equivalent/g dried extracts) and flavonoids (5.5 mg quercetin equivalent/g dried extracts), and higher DPPH or ABTS radical scavenging activities and ferric reducing anti-oxidant power than the others. Conclusion: Physicochemical characteristics of ginseng seed oil and anti-oxidant activities of defatted ginseng seed meal might be affected by cultivation environment.ABSTRACT I CONTENTS III LIST OF TABLES V LIST OF FIGURES VI INTRODUCTION 1 MATERIALS AND METHODS 3 1. Materials and reagent 3 2. Preparation of samples 4 3. Analysis of proximate composition of ginseng seeds 4 4. Analysis of fatty acid composition of ginseng seed oils 4 5. Analysis of color of ginseng seed oils 5 6. Analysis of carotenoids in ginseng seed oils 5 7. Analysis of tocols (tocopherols and tocotrienols) in ginseng seed oils 5 8. Preparation of unsaponifiable fraction of ginseng seed oils 6 9. Analysis of squalene and phytosterols in ginseng seed oils 7 10. Determination of crude saponin in defatted ginseng seed meals 8 11. Determination of total phenolics and flavonoids in defatted ginseng seed meal extracts 8 12. Anti-oxidant activities of defatted ginseng seed meal extracts 9 13. Statistical analysis 10 RESULTS AND DISCUSSION 11 1. Proximate composition of ginseng seeds 11 2. Yields of oils and defatted meal extracts from ginseng seeds 11 3. Fatty acid composition of ginseng seed oils 11 4. Color and carotenoid contents of ginseng seed oils 14 5. Tocols in ginseng seed oils 14 6. Squalene and phytosterols in ginseng seed oils 17 7. Crude saponin in defatted ginseng seed meals 22 8. Total phenolics and flavonoids in defatted ginseng seed meal extracts 22 9. Anti-oxidant activities of defatted ginseng seed meal extracts 24 CONCLUSION 26 REFERENCES 27 ๊ตญ๋ฌธ์ดˆ๋ก 33Maste

    determination of the local Hubble constant and satellite luminosity functions of low-mass galaxy groups

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€(์ฒœ๋ฌธํ•™์ „๊ณต), 2021.8. ์ด๋ช…๊ท .์€ํ•˜๋“ค์„ ์ด์šฉํ•ด ์šฐ์ฃผ ๋ชจํ˜•์˜ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๊ฒฐ์ •ํ•˜๊ณ  ๊ฐœ๋ณ„ ์€ํ•˜์˜ ํŠน์„ฑ์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์€ํ•˜๊นŒ์ง€์˜ ์ •ํ™•ํ•œ ๊ฑฐ๋ฆฌ๋ฅผ ์•„๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๊ทธ ์ค‘์—์„œ ์ ์ƒ‰๊ฑฐ์„ฑ๊ฐ€์ง€ ์ตœ๋Œ€๋ฐ๊ธฐ(tip of the red giant branch) ๋ฐฉ๋ฒ•๊ณผ ํ‘œ๋ฉด๋ฐ๊ธฐ์š”๋™ (surface rightness fluctuation) ๋ฐฉ๋ฒ•์€ ๋‚˜์ด ๋“  ํ•ญ์„ฑ์ข…์กฑ์„ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•๋“ค์ด๋ฉฐ, ์ด๋Š” ๋ชจ๋“  ์€ํ•˜์— ์กด์žฌํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ฐ€๊นŒ์šด ์šฐ์ฃผ์— ์žˆ๋Š” ์€ํ•˜๊นŒ์ง€์˜ ๊ฑฐ๋ฆฌ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๋ฐ์— ์œ ์šฉํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ฒ˜๋…€์ž๋ฆฌ ์€ํ•˜๋‹จ์•ž์— ์œ„์น˜ํ•œ ๋‘ ์™œ์†Œ ๋‚˜์„ ์€ํ•˜ NGC 4437๊ณผ NGC 4592๊นŒ์ง€์˜ ๊ฑฐ๋ฆฌ๋ฅผ ์ ์ƒ‰๊ฑฐ์„ฑ๊ฐ€์ง€ ์ตœ๋Œ€๋ฐ๊ธฐ ๋ฐฉ๋ฒ•์œผ๋กœ ์ธก์ •ํ•˜์˜€๊ณ , ๊ทธ ์ฃผ์œ„์—์„œ ์ฐพ์€ 10๊ฐœ์˜ ์™œ์†Œ์€ํ•˜๋“ค์˜ ๊ฑฐ๋ฆฌ๋ฅผ ํ‘œ๋ฉด๋ฐ๊ธฐ์š”๋™ ๋ฐฉ๋ฒ•์œผ๋กœ ์ธก์ •ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์™œ์†Œ์€ํ•˜๋“ค์— ๋Œ€ํ•œ ํ‘œ๋ฉด๋ฐ๊ธฐ์š”๋™ ์ธก์ •์„ ๋ณด์™„ํ•˜๋Š” ์—ฐ๊ตฌ์™€ NGC 4437 ์€ํ•˜๊ตฐ์„ ์ด์šฉํ•œ 2๊ฐ€์ง€ ์ฃผ์ œ์˜ ๊ทผ๊ฑฐ๋ฆฌ ์šฐ์ฃผ๋ก  ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ์™œ์†Œ์€ํ•˜๋“ค (0.2 < (g โˆ’i)_0 < 0.8)์— ์ ์šฉ ๊ฐ€๋Šฅํ•œ ํ‘œ๋ฉด๋ฐ๊ธฐ์š”๋™ ๋ฐฉ๋ฒ•๊ณผ ํ‘œ๋ฉด๋ฐ๊ธฐ์š”๋™์˜ ์ ˆ๋Œ€๋“ฑ๊ธ‰์„ ์•Œ์•„๋‚ด๋Š” ๋ฐ์— ์‚ฌ์šฉ๋˜๋Š” ์ ˆ๋Œ€๋“ฑ๊ธ‰ - ์€ํ•˜ ์ƒ‰์ง€์ˆ˜ ๊ฒฝํ—˜์‹์„ ์ œ์‹œํ•œ๋‹ค. ๊ฒฝํ—˜์‹์„ ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ์™œ์†Œ์€ํ•˜ 12๊ฐœ์— ๋Œ€ํ•˜์—ฌ Hyper Suprime-Cam (HSC) ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•ด ํ‘œ๋ฉด๋ฐ๊ธฐ์š”๋™ ๋“ฑ๊ธ‰์„ ์ธก์ •ํ•˜์˜€๊ณ , ์•Œ๋ ค์ง„ ์ ์ƒ‰๊ฑฐ์„ฑ๊ฐ€์ง€ ์ตœ๋Œ€๋“ฑ๊ธ‰ ๊ฑฐ๋ฆฌ๋ฅผ ์ด์šฉํ•˜์˜€๋‹ค. ์ Š์€ ํ•ญ์„ฑ์ข…์กฑ์— ์˜ํ•œ ์š”๋™์„ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•ด์„œ gโˆ’๋ฐด๋“œ ์ž„๊ณ„๋“ฑ๊ธ‰๋ณด๋‹ค ๋ฐ์€ ๊ด‘์›๋“ค์„ ์ œ๊ฑฐํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, 5๊ฐœ์˜ ์ž„๊ณ„๋“ฑ๊ธ‰ (M_g,thres = โˆ’3.5, โˆ’4.0, โˆ’4.5, โˆ’5.0, โˆ’5.5 ๋“ฑ๊ธ‰) ์œผ๋กœ ์‹คํ—˜ํ•ด ๋ณด์•˜๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ œ๊ณฑํ‰๊ท ์ œ๊ณฑ๊ทผ(rms) ํŽธ์ฐจ๊ฐ€ M_g,thres = โˆ’4.0 ๋“ฑ๊ธ‰์ผ ๋•Œ 0.16 ๋“ฑ๊ธ‰์œผ๋กœ ๊ฐ€์žฅ ์ž‘์•˜์œผ๋ฉฐ, ์ ˆ๋Œ€๋“ฑ๊ธ‰ - ์€ํ•˜ ์ƒ‰์ง€์ˆ˜ ๊ฒฝํ—˜์‹์€ M_i = (โˆ’2.65ยฑ0.13)+ (1.28ยฑ0.24)ร—(g โˆ’i)_0 ์™€ ๊ฐ™์•˜๋‹ค. ์ด ํŽธ์ฐจ๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ๊ฐ’์ธ 0.26 ๋“ฑ๊ธ‰๋ณด๋‹ค ์ž‘๊ณ , ๋ฌด๊ฑฐ์šด ์€ํ•˜๋“ค์—์„œ์˜ ๊ฒฝ์šฐ์ธ 0.12 ๋“ฑ๊ธ‰์— ๊ฐ€๊น๋‹ค. ์ด ๊ฒฝํ—˜์‹์€ ์ค‘์›์†Œํ•จ๋Ÿ‰์ด ์ž‘์€ ๋‹จ์ผ ํ•ญ์„ฑ์ข…์กฑ ๋ชจํ˜•์—์„œ์˜ ์˜ˆ์ธก๊ณผ๋„ ์ž˜ ์ผ์น˜ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ตญ๋ถ€ ์€ํ•˜๊ตฐ๊ณผ ์ฒ˜๋…€์ž๋ฆฌ ์€ํ•˜๋‹จ ์‚ฌ์ด์— ์œ„์น˜ํ•œ 33๊ฐœ ์€ํ•˜๋“ค์˜ ์†๋„์™€ ์ ์ƒ‰๊ฑฐ์„ฑ๊ฐ€์ง€ ์ตœ๋Œ€๋“ฑ๊ธ‰ ๊ฑฐ๋ฆฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธก์ •ํ•œ ํ—ˆ๋ธ” ์ƒ์ˆ˜ ๊ฐ’์„ ์ œ์‹œํ•œ๋‹ค. ๊ทผ๋ฐฉ์˜ ์€ํ•˜๋“ค์˜ ์†๋„๋Š” ํ—ˆ๋ธ” ํŒฝ์ฐฝ๋ฟ ์•„๋‹ˆ๋ผ ์ฒ˜๋…€์ž๋ฆฌ ์€ํ•˜๋‹จ์˜ ์งˆ๋Ÿ‰์— ์˜ํ•ด ์€ํ•˜๋‹จ ๋ฐฉํ–ฅ์œผ๋กœ ์œ ์ž…๋˜๋Š” ์†๋„๋ฅผ ๊ฐ–๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ํ—ˆ๋ธ” ์ƒ์ˆ˜, ์•”ํ‘์—๋„ˆ์ง€ ๋ฐ€๋„, ์ฒ˜๋…€์ž๋ฆฌ ์€ํ•˜๋‹จ์˜ ์งˆ๋Ÿ‰ ๋ฐ ๊ณ ์œ  ์†๋„๋ถ„์‚ฐ์˜ ํ•จ์ˆ˜๋กœ ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์žˆ๋‹ค. ์ด ๋ชจํ˜•์„ 33๊ฐœ ์€ํ•˜๋“ค์˜ ์†๋„์™€ ๊ฑฐ๋ฆฌ์— ์ ์šฉํ•˜์—ฌ 65.8 ยฑ 3.5(stat) ยฑ 2.4(sys) km sโˆ’1 Mpcโˆ’1 ์˜ ํ—ˆ๋ธ” ์ƒ์ˆ˜ ๊ฐ’์„ ์–ป์—ˆ๋‹ค. ์ด๋Š” ์šฐ์ฃผ๋ฐฐ๊ฒฝ๋ณต์‚ฌ๋ฅผ ์ด์šฉํ•ด ๊ตฌํ•œ ํ—ˆ๋ธ” ์ƒ์ˆ˜ ๊ฐ’๊ณผ ์˜ค์ฐจ ๋ฒ”์œ„ ๋‚ด์—์„œ ์ผ์น˜ํ•˜๋Š” ๊ฐ’์ด๋‹ค. ์„ธ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š”, ์šฐ๋ฆฌ ์€ํ•˜์™€ ๋น„์Šทํ•˜๊ฑฐ๋‚˜ ๋” ์ž‘์€ ์งˆ๋Ÿ‰์„ ๊ฐ–๋Š” ์€ํ•˜๋“ค์˜ ์œ„์„ฑ ์€ํ•˜ ๊ฐœ์ˆ˜์™€ ์€ํ•˜์˜ ์งˆ๋Ÿ‰ ํ˜•์„ฑ ์—ญ์‚ฌ์™€์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์‚ฌํ•œ๋‹ค. ์—ฌ๊ธฐ์—์„œ, ์€ํ•˜๊ตฐ์—์„œ ๊ฐ€์žฅ ๋ฐ์€ ์€ํ•˜์™€ ๋‘ ๋ฒˆ์งธ ๋ฐ์€ ์€ํ•˜์˜ rโˆ’๋ฐด๋“œ ๋“ฑ๊ธ‰ ์ฐจ์ด (ฮ”m12)๋ฅผ ์งˆ๋Ÿ‰ ํ˜•์„ฑ ์—ญ์‚ฌ์˜ ์ง€ํ‘œ๋กœ ์‚ฌ์šฉํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ ์ œ์‹œํ•œ ํ‘œ๋ฉด๋ฐ๊ธฐ์š”๋™์˜ ์ ˆ๋Œ€๋“ฑ๊ธ‰ - ์€ํ•˜ ์ƒ‰์ง€์ˆ˜ ๊ฒฝํ—˜์‹์„ ์ด์šฉํ•ด NGC 4437 ์ฃผ์œ„์— ์žˆ๋Š” ์œ„์„ฑ ์€ํ•˜ ํ›„๋ณด 10๊ฐœ์˜ ๊ฑฐ๋ฆฌ๋ฅผ ์ธก์ •ํ•˜๊ณ , ์ด ์ค‘ 5๊ฐœ๊ฐ€ ์œ„์„ฑ ์€ํ•˜์ž„์„ ๋ฐํ˜”๋‹ค. ๋”ฐ๋ผ์„œ NGC 4437 ์€ํ•˜๊ตฐ์—๋Š” rโˆ’๋ฐด๋“œ ์ ˆ๋Œ€ ๋“ฑ๊ธ‰์ด -11๋“ฑ๊ธ‰๋ณด๋‹ค ๋ฐ์€ ์€ํ•˜๋“ค์ด 7๊ฐœ๊ฐ€ ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด ์ •๋ณด์™€ ๋ฌธํ—Œ๋“ค์— ์žˆ๋Š” ๊ด€์ธก๋œ ์€ํ•˜๊ตฐ๋“ค์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜์—ฌ, ์ค‘์‹ฌ์€ํ•˜์˜ ํ•ญ์„ฑ์งˆ๋Ÿ‰์ด ์ผ์ •ํ•  ๋•Œ ฮ”m12์ด ์ž‘์„์ˆ˜๋ก ์œ„์„ฑ์€ํ•˜ ๊ฐœ์ˆ˜๊ฐ€ ๋งŽ์Œ์„ ๋ณด์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฝํ–ฅ์€ IllustrisTNG50 ์‹œ๋ฎฌ๋ ˆ์ด์…˜์—์„œ๋„ ์ž˜ ๋ณด์ธ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์—์„œ NGC 4437๊ณผ ๊ฐ™์ด ฮ”m12๊ฐ€ ์ž‘์€ ์€ํ•˜๊ตฐ์˜ ์ค‘์‹ฌ ์€ํ•˜๋Š” ๋” ์ตœ๊ทผ์— ํ—ค์ผ๋กœ ์งˆ๋Ÿ‰์ด ํ˜•์„ฑ๋˜์—ˆ์œผ๋ฉฐ ํ•ญ์„ฑ-ํ—ค์ผ๋กœ ์งˆ๋Ÿ‰๋น„๊ฐ€ ๋” ์ž‘๋‹ค. ์ฆ‰, ์šฐ๋ฆฌ์€ํ•˜๋‚˜ ๊ทธ๋ณด๋‹ค ๊ฐ€๋ฒผ์šด ์€ํ•˜๊ตฐ์˜ ์œ„์„ฑ์€ํ•˜ ๊ฐœ์ˆ˜์— ํฐ ๋ถ„์‚ฐ์ด ์žˆ๋Š” ๊ฒƒ์€ ๊ด€์ธก๋œ ฮ”m12 ์˜ ๋ฒ”์œ„๊ฐ€ ๋„“์€ ๊ฒƒ๊ณผ ๊ด€๋ จ์ด ์žˆ๊ณ , ์€ํ•˜์˜ ์งˆ๋Ÿ‰ ํ˜•์„ฑ ์—ญ์‚ฌ๊ฐ€ ๋‹ค์–‘ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ๋Š” ๊ฒƒ์ด๋‹ค. ๋”ฐ๋ผ์„œ ฮ”m12๋Š” ์€ํ•˜์˜ ์งˆ๋Ÿ‰ ํ˜•์„ฑ ์—ญ์‚ฌ๋ฅผ ๋ณด์—ฌ์ฃผ๋Š” ์ข‹์€ ์ง€ํ‘œ๊ฐ€ ๋œ๋‹ค.Knowledge of precise distances to galaxies is crucial, both for constraining cosmological parameters and for obtaining true galaxy properties. Among other methods, the tip of the red giant branch (TRGB) and the surface brightness fluctuation (SBF) techniques use bright and old stellar populations, which are ubiquitous in every nearby galaxy. In this work, we measure TRGB distances to two spiral galaxies in front of the Virgo cluster, NGC 4437 (D = 9.28 ยฑ 0.39 Mpc) and NGC 4592 (D = 9.07 ยฑ 0.27 Mpc), and suggest that they are physically close to each other (0.29 Mpc). In addition, we find ten dwarf satellite candidates near the two galaxies and apply the SBF techniques to confirm their membership. In this thesis, we present the results of three research works, one project improving the application of SBF techniques to dwarf galaxies, and two projects in near-field cosmology. In the first work, we study the use of SBF methods to dwarf galaxies and provide an empirical calibration for the Hyper Suprime-Cam (HSC) gi system, valid in a blue regime, 0.2 < (g โˆ’i)_0 < 0.8. We measure SBF magnitudes for 12 nearby dwarf galaxies of various morphological types with archival HSC imaging data, and use their TRGB distances to derive fluctuation - color relations. In order to subtract contributions of fluctuations due to young stellar populations, we use five different gโˆ’band magnitude masking thresholds, M_g,thres = โˆ’3.5, โˆ’4.0, โˆ’4.5, โˆ’5.0, and โˆ’5.5 mag. We find that the rms scatter of the linear fit to the relation is the smallest (rms = 0.16 mag) in the case of M_g,thres = โˆ’4.0 mag, M_i = (โˆ’2.65 ยฑ 0.13) + (1.28 ยฑ 0.24) ร— (g โˆ’ i)_0. This scatter is much smaller than those in the previous studies (rms=0.26 mag), and is closer to the value for bright red galaxies (rms=0.12 mag). This calibration is consistent with predictions from metal-poor simple stellar population models. In the second work, we present a new determination of H0 using velocities and TRGB distances to 33 galaxies located between the Local Group and the Virgo cluster. We use a model of the infall pattern of the local Hubble flow modified by the Virgo mass, which is given as a function of the cosmological constants (H0, ฮฉ_ฮ›), the radius of the zero-velocity surface R0, and the intrinsic velocity dispersion, ฯƒv. Fitting velocities and TRGB distances of 33 galaxies to the model, we obtain H0 = 65.8 ยฑ 3.5(stat) ยฑ 2.4(sys) km sโˆ’1 Mpcโˆ’1 and R0 = 6.76 ยฑ 0.35 Mpc. Our local H0 is consistent with the global H0 determined from CMB radiation, showing no tension. In the third work, given that there is a large scatter in the satellite number of Milky Way-like galaxies, we study the correlation between the satellite number and galaxy group assembly history. Here, we use a rโˆ’band magnitude gap (ฮ”m12) between the first and the second brightest galaxies as an indicator. Using the SBF methods and the calibration described in the first work, we confirm five of the dwarf satellite candidates as members of the NGC 4437 group, resulting in a total of seven members. Combining this with other groups in the literature, we find a stratification of the number of satellites by ฮ”m12 for given host stellar mass. The number of satellites for given host stellar mass decreases as ฮ”m12 increases. The same trends are found from simulated galaxy groups in IllustrisTNG50 cosmological simulations. From these simulated galaxy groups we find also that the host galaxies in the groups with a smaller ฮ”m12 (like NGC 4437) have assembled their halo mass more recently than those in the larger gap groups (like M94) and that their stellar-to-halo mass ratios (SHMRs) increase as ฮ”m12 increases. These results show that the large scatter in the number of satellites is consistent with a large range of ฮ”m12 indicating diverse group assembly histories. Thus ฮ”m12 is an efficient indicator to trace galaxy group assembly history.1 Introduction 1 1.1 Old Stellar Populations as Standard Candles 1 1.2 Outline of This Thesis 4 1.2.1 SBF Calibration for Dwarf Galaxies 4 1.2.2 Hubble Tension 5 1.2.3 Low-mass Galaxy Group Assembly History 5 2 Calibration of Surface Brightness Fluctuations for Dwarf Galaxies in the Hyper Suprime-Cam gi System 7 2.1 Introduction 7 2.2 Data and Galaxy Sample 10 2.3 SBF Measurement 14 2.3.1 SBF Method 14 2.3.2 Galaxy Modelling 18 2.3.3 Masking Contaminating Sources 19 2.3.4 Power Spectrum Fitting 24 2.4 SBF Calibration 24 2.4.1 Dependence on Masking Thresholds 25 2.4.2 Fitting SBF Magnitudeโ€“Color Relations 27 2.5 Comparison with Previous Calibrations and Stellar Population Models 28 2.6 Summary 31 3 Determination of the Local Hubble Constant from Virgo Infall Using TRGB Distances 33 3.1 Introduction 33 3.2 Data and Data Reduction 37 3.3 TRGB Distances to NGC 4437 and NGC 4592 37 3.3.1 Selection of RGB Star Candidates 37 3.3.2 Edge Detection Method 43 3.3.3 Maximum Likelihood TRGB Detection Method 45 3.3.4 Summary of TRGB Distance Estimation 46 3.3.5 Comparison with Previous TRGB Magnitude for NGC 4437 47 3.4 Virgo Infall 50 3.4.1 Virgo Infall Galaxy Sample 50 3.4.2 Theoretical Description of the Virgo Infall Model 54 3.4.3 Fitting the Virgo Infall Pattern 56 3.4.4 Fitting Results 61 3.5 Discussion 64 3.5.1 The NGC 4437 Group 64 3.5.2 Systematic Uncertainties of H0 65 3.5.3 The Local H0 69 3.6 Summary 72 4 A Rich Satellite Population of the NGC 4437 Group and Implications of a Magnitude Gap for Galaxy Group Assembly History 75 4.1 Introduction 75 4.2 Data and Dwarf Satellite Galaxy Survey 80 4.3 Group membership Confirmation Using SBF Distances 86 4.3.1 SBF Measurement 86 4.3.2 Group Membership Confirmation 92 4.4 Discussion 96 4.4.1 Comparison with Previous Surveys on Low-mass Galaxy Groups 96 4.4.2 Satellite Luminosity Functions 97 4.4.3 Number of Satellites and Host Stellar Mass 102 4.4.4 Number of Satellites and Magnitude Gap 104 4.4.5 Magnitude Gap as an Indicator for Galaxy Assembly History 108 4.5 Summary 113 Bibliography 116 ์š”์•ฝ 129์„

    (A)Cost-effectiveness analysis of age-specific screening for type 2 diabetes

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋ณด๊ฑด๋Œ€ํ•™์› :๋ณด๊ฑดํ•™๊ณผ ๋ณด๊ฑดํ†ต๊ณ„ํ•™์ „๊ณต,1999.Maste

    A Study on the Production of Artwork Using Metal Structures and the Curing Characteristics of Epoxy Resin

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ฏธ์ˆ ๋Œ€ํ•™ ๋””์ž์ธํ•™๋ถ€ ๊ณต์˜ˆ์ „๊ณต, 2018. 8. ๋ฏผ๋ณต๊ธฐ.๋ณธ ๋…ผ๋ฌธ์€ ์—ํญ์‹œ ์ˆ˜์ง€(epoxy resin)์˜ ๊ฒฝํ™”๊ณผ์ •์—์„œ ๋ฐœ๊ฒฌ๋˜๋Š” ๋‹ค์–‘ํ•œ ํŠน์„ฑ์„ ๊ธˆ์†๊ตฌ์กฐ์™€ ํ•จ๊ป˜ ํ™œ์šฉํ•˜์—ฌ ์ž‘ํ’ˆ์— ์ ์šฉํ•œ ์—ฐ๊ตฌ์ด๋‹ค. ์—ํญ์‹œ ์ˆ˜์ง€๋Š” ์—ด๊ฒฝํ™”์„ฑ ์ˆ˜์ง€์˜ ํ•œ ์ข…๋ฅ˜๋กœ, ๊ณ ์ฒด์™€์˜ ์ ‘์ฐฉ์„ฑ์ด ๋›ฐ์–ด๋‚˜๊ณ  ๋‹ค๋Ÿ‰์˜ ์ถฉ์ „์žฌ๋ฅผ ํ•จ์œ ์‹œํ‚ฌ ์ˆ˜ ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๊ฒฝํ™” ํ›„์—๋„ ๊ฐ€๊ณต์ด ์šฉ์ดํ•˜๋‹ค๋Š” ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ๊ณต์—…๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์˜ˆ์ˆ ๋ถ„์•ผ์—์„œ๋„ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š”, ์—ํญ์‹œ ์ˆ˜์ง€๊ฐ€ ๊ฐ€์ง„ ๋‹ค์–‘ํ•œ ํŠน์„ฑ ์ค‘์—์„œ๋„ ์™„์ „ํžˆ ๊ฒฝํ™”๋˜๊ธฐ ์ „์˜ ๋ฌผ์„ฑ์— ๋Œ€ํ•œ ๊ด€์‹ฌ์œผ๋กœ๋ถ€ํ„ฐ ์ถœ๋ฐœํ•˜์˜€๋‹ค. ์šฐ์—ฐํžˆ ๊ฒฝํ—˜ํ•˜๊ฒŒ ๋œ ์™„์ „ํžˆ ๊ฒฝํ™”๋˜์ง€ ์•Š์•„ ๋ง๋ž‘๋ง๋ž‘ํ•œ ์—ํญ์‹œ ์ˆ˜์ง€๋Š” ์ž์œ ๋กญ๊ฒŒ ๋Š˜์ด๊ฑฐ๋‚˜ ํœ˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ–ˆ๊ณ , ์ด๋ฅผ ์‘์šฉํ•œ๋‹ค๋ฉด ์ƒˆ๋กœ์šด ์กฐํ˜•์  ํŠน์ง•์„ ๊ฐ€์ง„ ์ž‘ํ’ˆ์„ ์ œ์ž‘ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ผ ํŒ๋‹จํ–ˆ๋‹ค. ์ž‘ํ’ˆ์„ ์ œ์ž‘ํ•˜๊ธฐ์— ์•ž์„œ, ์—ํญ์‹œ ์ˆ˜์ง€์˜ ์œ ์—ฐํ•œ ์ƒํƒœ๋ฅผ ์•ˆ์ •์ ์œผ๋กœ ์–ป๊ธฐ ์œ„ํ•œ ์žฌ๋ฃŒ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ, ์ด๋Š” ์ถ”๊ฐ€์ ์ธ ๋ฐœ๊ฒฌ๊ณผ ์‹คํ—˜์„ ๋‚ณ์•„ ์ด ์„ธ ๊ฐ€์ง€ ๋ถ„๋ฅ˜์˜ ์—ฐ๊ตฌ๋กœ ์ด์–ด์กŒ๋‹ค. ์ฒซ ์‹œ์ž‘์œผ๋กœ๋Š”, ์•ก์ฒด์ƒํƒœ์ธ ์—ํญ์‹œ ์ˆ˜์ง€ ํ˜ผํ•ฉ๋ฌผ์ด ๊ฒฝํ™”ํ•˜์—ฌ ๋น„ํ™œ์„ฑ ๊ณ ์ฒด๊ฐ€ ๋˜๋Š” ๊ณผ์ •์„ ์‚ดํŽด๋ณธ ํ›„, 2์ฐจ ์„ฑํ˜• ๊ฐ€๋Šฅ ๋‹จ๊ณ„๋ผ ํ•  ์ˆ˜ ์žˆ๋Š” ๊ณ ์ฒด์— ๊ฐ€๊นŒ์šด ๊ฒ”์ƒํƒœ์˜ ์กด์žฌ๋ฅผ ํ™•์ธํ•˜๊ณ  ๋ช…๋ช…ํ–ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ๊ณ ์ฒด์— ๊ฐ€๊นŒ์šด ๊ฒ”์ƒํƒœ์˜ ์ˆ˜์ง€๋ฉด์„ ์•ˆ์ •์ ์œผ๋กœ ์–ป๊ธฐ ์œ„ํ•ด ์ˆ˜์ง€์™€ ๋ถ„๋ฆฌ ๊ฐ€๋Šฅํ•œ ํ”Œ๋ผ์Šคํ‹ฑ ํ•„๋ฆ„์„ ์‹คํ—˜์„ ํ†ตํ•ด ๋ถ„๋ฅ˜ํ•˜๊ณ , ์„ธ ๋ฒˆ์งธ๋กœ, ํ”ผ์ฐฉ์žฌ๋กœ ์“ฐ์ด๋Š” ํ”Œ๋ผ์Šคํ‹ฑ ํ•„๋ฆ„์˜ ํ…์Šค์ณ๋ฅผ ํ™œ์šฉํ•ด ์—ํญ์‹œ ์ˆ˜์ง€๋ฉด์— ๋ฌธ์–‘์„ ๋‚ผ ์ˆ˜ ์žˆ์Œ์„ ์„œ์ˆ ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ˆ˜์ง€๋ฉด์„ ์›ํ•˜๋Š” ํ˜•ํƒœ๋กœ ์œ ๋„ํ•˜๊ณ  ์›€์ง์ž„์„ ์„ธ๋ฐ€ํ•˜๊ฒŒ ์กฐ์ ˆํ•˜๊ธฐ ์œ„ํ•ด ๊ธˆ์†๊ตฌ์กฐ๋ฅผ ๋„์ž…ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ํ˜•ํƒœ๋ณ€ํ™”๋ฅผ ์‹œ๋„ํ•˜์˜€๋‹ค. ์ด์–ด์ง„ ์ž‘ํ’ˆ์—ฐ๊ตฌ์—์„œ๋Š” ์žฌ๋ฃŒ์—ฐ๊ตฌ์—์„œ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋˜ ์—ํญ์‹œ ์ˆ˜์ง€์˜ ํŠน์„ฑ์„ ์‘์šฉํ•˜์—ฌ ์ž‘ํ’ˆ์— ์ ์šฉ์‹œํ‚ค๊ณ ์ž ํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ๋‘ ๊ฐ€์ง€๋กœ ๋ถ„๋ฅ˜๋œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ๋Š”, ๊ณ ์ฒด์— ๊ฐ€๊นŒ์šด ๊ฒ”์ƒํƒœ์˜ ์—ํญ์‹œ ์ˆ˜์ง€์™€ ๊ธˆ์†๊ตฌ์กฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํž˜์— ๋ฐ˜์‘ํ•œ ์›€์ง์ž„์„ ํ‘œํ˜„ํ•˜์˜€๋‹ค. ์ž‘์€ ์›€์ง์ž„๋ถ€ํ„ฐ ์ œ์–ดํ•˜๊ธฐ ์‹œ์ž‘ํ•˜์—ฌ ์ ์ฐจ ์ƒˆ๋กœ์šด ์กฐํ˜•์„ ์‹œ๋„ํ•˜๋Š” ๊ณผ์ •์„ ๋‹ด์•˜๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ๋Š”, ํ…์Šค์ณ ์ „์‚ฌ๊ธฐ๋ฒ•์„ ์ ์šฉํ•œ ์ˆ˜์ง€๋ฉด์„ ์ค‘์ฒฉ์‹œ์ผœ ํ‘œํ˜„ํ•œ ์ž‘ํ’ˆ๋“ค๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ํˆฌ๋ช…๋„์™€ ์ƒ‰๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋ฌธ์–‘ ํ‘œํ˜„๋„ ๊ฐ€๋Šฅํ•ด์ง„ ์ˆ˜์ง€๋ฉด์„ ํ™œ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์‹œ์ง€๊ฐ์  ํšจ๊ณผ๋ฅผ ์ง€๋‹Œ ์žฅ์‹ ๊ตฌ๋ฅผ ์ œ์ž‘ํ•˜์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ๋Š”, ์„ ํ–‰์—ฐ๊ตฌ์™€ ๊ธฐ์ดˆ์‹คํ—˜์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ ์žฌ๋ฃŒ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์—ํญ์‹œ ์ˆ˜์ง€์˜ ํฅ๋ฏธ๋กœ์šด ํŠน์„ฑ์„ ๋ถ„์„ํ•˜์—ฌ ์ด๋ฅผ ์•ˆ์ •์ ์œผ๋กœ ๋„์ถœํ•ด๋‚ธ ํ›„, ์ž‘ํ’ˆ์— ์กฐํ˜•์ ์œผ๋กœ ์‘์šฉํ•œ ๊ณผ์ •์„ ๋‹ด์€ ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋ณดํŽธ์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๋˜ ๊ธฐ๋ฒ•์—์„œ ๋ฒ—์–ด๋‚˜ ๋˜ ๋‹ค๋ฅธ ๋ฐฉ์‹์œผ๋กœ ์—ํญ์‹œ ์ˆ˜์ง€๋ฅผ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•˜๋ฉฐ, ๋”๋ถˆ์–ด ์—ํญ์‹œ ์ˆ˜์ง€๋ฅผ ํ”Œ๋ผ์Šคํ‹ฑ ๋ฉ์–ด๋ฆฌ๊ฐ€ ์•„๋‹Œ ๋ฉด(้ข)์œผ๋กœ ๋งˆ์ฃผํ•˜์˜€๊ธฐ์— ํ‘œํ˜„ ๊ฐ€๋Šฅํ–ˆ๋˜ ๋‹ค์–‘ํ•œ ์ž‘ํ’ˆ๋“ค์„ ๋ณด์—ฌ์ฃผ๊ณ ์ž ํ•œ๋‹ค.This thesis is a study about how diverse characteristics found in the curing process of epoxy resin can be used along with metal structures for artwork. Epoxy resin is a kind of thermosetting resin that has an excellent adhesive property, can contain a great amount of fillers, and is easy to process even after hardening, so it is widely used not only in industry but also in art. This study started from the researchers interest in the state of the material before complete curing among the diverse characteristics of epoxy resin. The researcher accidentally discovered the soft state of epoxy resin before complete curing and how it can be freely extended or bent, and thus had an idea to use it to make artwork with a new visual characteristic. Before creating an artwork the researcher conducted experiments in order to attain stable flexibility of the epoxy resin panel and this resulted in additional discoveries and further studies of three different types. First examined was how the mix of liquid epoxy resin hardens and becomes an inactive solid in order to identify and designate the state of the gel closer to a solid which could be also seen as a second available state of figuration. Second, in order to attain the stable state of the gel closer to a solid a plastic film that can be removed from the epoxy resin was understood through experimentation. Third, the researcher studied how patterns can be made on the surface of the epoxy resin using the texture of a plastic film. Furthermore, metal structures were used to help in making a form and control details resulting in various experiments with form. Next conducted was the research for making an artwork. The characteristics of epoxy resin panel that were found in the previous experiments were applied in two different methods. First, metal structures and the resin panel in the state of a gel closer to a solid were used to represent movement responding to a force. From the small control of movements the work was extended to a new visual form. Second, artwork was made with overlaps of the resin panel applying a texture technique. This resin panel was both clear and colorful but also patterned so that accessories with diverse visual effects could be made. To sum up, this study analyzes the interesting characteristics of epoxy resin based on the precedent research and fundamental experiments with the material that discovered its stable state and describes how it is applied to an artwork. By doing so it suggests a new possibility of using epoxy resin beyond the existing method of using epoxy resin more as a plastic lump.โ… . ๋“ค์–ด๊ฐ€๋Š” ๋ง 1 1. ์—ฐ๊ตฌ์˜ ๋™๊ธฐ ๋ฐ ๋ชฉ์  1 2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• ๋ฐ ๋ฒ”์œ„ 2 โ…ก. ์žฌ๋ฃŒ ์—ฐ๊ตฌ 3 1. ์—ํญ์‹œ ์ˆ˜์ง€์˜ ์ผ๋ฐ˜์  ๊ณ ์ฐฐ 3 1.1. ์—ํญ์‹œ ์ˆ˜์ง€๋ž€ 3 1.2. ์—ํญ์‹œ ์ˆ˜์ง€๋ฅผ ํ™œ์šฉํ•œ ์ž‘ํ’ˆ ์‚ฌ๋ก€ 4 2. ์ž‘ํ’ˆ์— ํ™œ์šฉํ•œ ์—ํญ์‹œ ์ˆ˜์ง€์˜ ํŠน์„ฑ ์—ฐ๊ตฌ 11 2.1. ์—ํญ์‹œ ์ˆ˜์ง€์˜ ๊ฒฝํ™”๋‹จ๊ณ„ 11 2.2. ์—ํญ์‹œ ์ˆ˜์ง€์™€ ๋ถ„๋ฆฌ ๊ฐ€๋Šฅํ•œ ํ”Œ๋ผ์Šคํ‹ฑ์˜ ๋ถ„๋ฅ˜ 13 2.3. ์—ํญ์‹œ ์ˆ˜์ง€๋ฉด์— ์ ์šฉํ•œ ํ…์Šค์ณ ์ „์‚ฌ๊ธฐ๋ฒ• 18 3. ๊ธˆ์†๊ตฌ์กฐ์™€ ์—ํญ์‹œ ์ˆ˜์ง€๋ฉด์˜ ๊ฒฐํ•ฉ 20 โ…ข. ์ž‘ํ’ˆ ์—ฐ๊ตฌ 24 1. ํž˜์— ์˜ํ•œ ์ˆ˜์ง€๋ฉด์˜ ์›€์ง์ž„ ํ‘œํ˜„ 25 ์ž‘ํ’ˆ 1. Small Movements 28 ์ž‘ํ’ˆ 2. Lining 31 ์ž‘ํ’ˆ 3. Capturing Traces of Gravity 35 ์ž‘ํ’ˆ 4. Extending 38 ์ž‘ํ’ˆ 5. Intended or Unintended 42 2. ํˆฌ๋ช…ํ•œ ์ˆ˜์ง€๋ฉด์˜ ์ค‘์ฒฉ์„ ํ†ตํ•œ ์‹œ์ง€๊ฐ์  ํ‘œํ˜„ 46 ์ž‘ํ’ˆ 6. Line, Point and Surface 47 ์ž‘ํ’ˆ 7. Hidden 49 ์ž‘ํ’ˆ 8. Viewpoint 52 ์ž‘ํ’ˆ 9. ๋ฏธ๋ฌผ(ๅพฎ็‰ฉ) 55 ์ž‘ํ’ˆ 10. Pac-Man and Ghost 59 โ…ฃ. ๋งบ์Œ๋ง 63 ์ฐธ๊ณ ๋ฌธํ—Œ 65 Abstract 67Maste
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