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    ๊ธ‰์„ฑ ์†Œ์Œ์„ฑ ๋‚œ์ฒญ์— ์˜ํ•œ ์™€์šฐํ•ต๊ณผ ํ•˜๊ตฌ์—์„œ์˜ ๋งˆ์ดํฌ๋กœRNA ๋ฐœํ˜„์˜ ๋ณ€ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋‡Œ๊ณผํ•™์ „๊ณต,2019. 8. ๋ฐ•๋ฌด๊ท .ํ˜„๋Œ€์‚ฌํšŒ์— ์žˆ์–ด์„œ ํ™˜๊ฒฝ์ŠคํŠธ๋ ˆ์Šค ์ค‘ ํ•˜๋‚˜์ธ ์†Œ์Œ๊ณผ ๊ฐœ์ธ ์ฒญ์ทจ ๊ธฐ๊ธฐ์˜ ์‚ฌ์šฉ์ด ๊ธ‰์ฆํ•˜๋ฉด์„œ ์†Œ์Œ์„ฑ ๋‚œ์ฒญ์€ ํ”ํ•˜๊ฒŒ ๋ฐœ์ƒํ•˜๋Š” ์งˆ๋ณ‘๋“ค ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๊ฐ๊ฐ์‹ ๊ฒฝ์„ฑ ๋‚œ์ฒญ์ธ ์†Œ์Œ์„ฑ ๋‚œ์ฒญ์€ ์ผ์ฐจ์ ์ธ ์™€์šฐ์˜ ์†์ƒ์— ์ด์–ด ์ด์ฐจ์ ์œผ๋กœ ์‹œ๋ƒ…์Šค์˜ ๊ฐ์†Œ, ์ฒญ๊ฐ์‹ ๊ฒฝ์„ฌ์œ ์˜ ํ‡ดํ™”์™€ ์ค‘์ถ” ์ฒญ๊ฐ ๊ฒฝ๋กœ์˜ ์‹ ๊ฒฝ ๊ฐ€์†Œ์„ฑ์„ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ๋Š” ์™€์šฐํ•ต๊ณผ ํ•˜๊ตฌ์˜ ๊ตฌ์กฐ์— ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ๋„ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ฒญ๋ ฅ์˜ ๋ณด์กด์„ ์œ„ํ•ด์„œ๋Š” ์œ ๋ชจ ์„ธํฌ์‚ฌ์˜ ์˜ˆ๋ฐฉ์ด๋‚˜ ์กฐ๊ธฐ์น˜๋ฃŒ๊ฐ€ ์ค‘์š”ํ•˜๋‹ค. MicroRNA (MiRNA)๋Š” ํŠน์ • mRNA๋ถ„์ž๋‚ด์˜ ์ƒ๋ณด์  ์„œ์—ด์„ ์นจ๋ฌต์‹œํ‚ด์œผ๋กœ์„œ ์„ธํฌ ๋ถ„ํ™”, ์„ธํฌ ํ™•์‚ฐ ๊ทธ๋ฆฌ๊ณ  ์„ธํฌ์˜ ์ƒ์กด์— ๊ด€์—ฌํ•˜๋Š” ์ƒ๋ฌผํ•™์  ๊ณผ์ •์— ์ค‘์š”ํ•œ ์กฐ์ ˆ ์žฅ์น˜์ด๋‹ค. ๋˜ํ•œ miRNA๋Š” ๋‹ค๋ฅธ small RNA์™€๋Š” ๋‹ฌ๋ฆฌ ์™„๋ฒฝํ•œ ์—ผ๊ธฐ ํŽ˜์–ด๋ง์„ ํ•„์š”๋กœ ํ•˜์ง€ ์•Š์•„์„œ ์ •ํ™•ํ•˜๋ฉด์„œ๋„ ์ข€ ๋” ๋„“์€ ๋ฒ”์œ„์˜ ๋„คํŠธ์›Œํฌ ์กฐ์ ˆ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ทธ๋ ‡๊ธฐ ๋•Œ๋ฌธ์— ์งˆ๋ณ‘์˜ ์‹œ์ž‘์ด๋‚˜ ์ง„ํ–‰์— ์žˆ์–ด์„œ miRNA์˜ ๊ด€์—ฌ๊ฐ€ ์น˜๋ฃŒ๋ฒ• ๊ฐœ๋ฐœ์— ํฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ์ด๋ฒˆ ์‹คํ—˜์—์„œ๋Š” ์ผํšŒ์„ฑ์˜ ์†Œ์Œ ๋…ธ์ถœ์—์„œ ์˜ค๋Š” ๊ธ‰์„ฑ ์†Œ์Œ์„ฑ ๋‚œ์ฒญ ๋ชจ๋ธ์„ ๋งŒ๋“ค๊ณ , ์ผ์‹œ์ ์ธ ์†Œ์Œ์ด ๋Œ์•„์˜ค๋Š” ๊ณผ์ •์—์„œ miRNA๊ฐ€ ์™€์šฐํ•ต๊ณผ ํ•˜๊ตฌ์—์„œ์˜ ์‹ ๊ฒฝ๊ฐ€์†Œ์„ฑ ๋ณ€ํ™”์— ๊ด€์—ฌํ•˜๋Š” ์—ญํ• ์„ ๊ทœ๋ช…ํ•˜๋ ค๊ณ  ํ•œ๋‹ค. ์ด 48๋งˆ๋ฆฌ์˜ SD ๋žซ๋“œ๋Š” 1์ผ์ฐจ ๋Œ€์กฐ๊ตฐ, ์†Œ์Œ ๋…ธ์ถœ ํ›„ 1์ผ์ฐจ, 3์ผ์ฐจ ๋Œ€์กฐ๊ตฐ, ์†Œ์Œ ๋…ธ์ถœ ํ›„ 3์ผ์ฐจ ๊ทธ๋ฃน์œผ๋กœ ๋‚˜๋ˆ„์–ด์ ธ ์‹คํ—˜๊ตฐ์€ 115 dB SPL์— 2์‹œ๊ฐ„ ๋™์•ˆ ๋…ธ์ถœ ๋˜์—ˆ๋‹ค. ๋Œ€์กฐ๊ตฐ ๋˜ํ•œ ๊ฐ™์€ ์‹œ๊ฐ„ ๋™์•ˆ ๋งˆ์ทจ๋˜์–ด ์†Œ์Œ์ด ์—†๋Š” ์กฐ๊ฑด์œผ๋กœ ๊ฐ™์€ ๊ณต๊ฐ„์— ๋†“์—ฌ ์žˆ์—ˆ๋‹ค. ์ฒญ์„ฑ๋‡Œ๊ฐ„๋ฐ˜์‘ ๊ฒ€์‚ฌ (Auditory Brain Stem Response Test, ABR)๋ฅผ ํ†ตํ•ด ์ฒญ๋ ฅ์˜ ๋ณ€ํ™”๋ฅผ ํ™•์ธํ•˜๊ณ , ๊ฐ ๊ทธ๋ฃน์—์„œ ์™€์šฐ ์กฐ์ง์„ ์ฑ„์ทจํ•˜์—ฌ ์™€์šฐ๋‚ด์˜ ์ฝ”๋ฅดํ‹ฐ๊ธฐ๊ด€์„ ํ™•์ธํ•˜๋Š” H&E ์—ผ์ƒ‰๊ณผ ์œ ๋ชจ์„ธํฌ์˜ ์ƒ์กด๋ฅ ์„ ํ™•์ธํ•˜๋Š” phalloidin ์—ผ์ƒ‰์„ ํ†ตํ•ด ๋‚œ์ฒญ์˜ ์œ ๋ฌด๋ฅผ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ค‘์ถ” ์ฒญ๊ฐ ๊ฒฝ๋กœ์—์„œ์˜ ์‹œ๋ฐœ์ ์ธ ์™€์šฐํ•ต, ์ฒ˜์Œ์œผ๋กœ ์–‘์ธก ์ฒญ๊ฐ ์‹ ํ˜ธ์˜ ํ†ตํ•ฉ์ด ์ผ์–ด๋‚˜๋Š” ํ•˜๊ตฌ์˜ microarray analysis์™€ qRT PCR ๊ฒ€์ฆ์„ ํ†ตํ•ด ํ›„๋ณด miRNA๋“ค์„ ๋ฐœ๊ฒฌํ•˜์˜€๊ณ , ๊ทธ์— ๋”ฐ๋ฅธ ์ถ”๊ฐ€์ ์ธ target pathway analysis์™€ KEGG analysis๋ฅผ ํ•˜์˜€๋‹ค. ๊ฐ ์‹คํ—˜๊ตฐ๋“ค์€ ๋Œ€์กฐ๊ตฐ๋“ค์— ๋น„ํ•ด ์ฒญ๋ ฅ ์—ญ์น˜๊ฐ€ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์„ ๋ณด์˜€๋‹ค. ์†Œ์Œ ๋…ธ์ถœ ํ›„ 1์ผ์ฐจ ๊ทธ๋ฃน์€ ์†Œ์Œ ๋…ธ์ถœ 4์‹œ๊ฐ„ ํ›„ ๋ณด๋‹ค ์ฒญ๋ ฅ ์—ญ์น˜๊ฐ€ ๊ฐœ์„  ๋˜๋Š” ๊ฒƒ์„ ๋ณด์˜€๊ณ , ์†Œ์Œ ๋…ธ์ถœ ํ›„ 3์ผ์ฐจ ๊ทธ๋ฃน์€ ์†Œ์Œ ๋…ธ์ถœ 1์ผ์ฐจ ๋ณด๋‹ค ์ฒญ๋ ฅ ์—ญ์น˜๊ฐ€ ๊ฐœ์„  ๋˜๋Š” ๊ฒƒ์„ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ์ œ 2 ํŒŒํ˜•์—์„œ ์†Œ์Œ ๋…ธ์ถœ ํ›„ 3์ผ์ฐจ ๊ทธ๋ฃน์˜ ์ง„ํญ์ด ์†Œ์Œ ๋…ธ์ถœ ํ›„ 1์ผ์ฐจ ๊ทธ๋ฃน์˜ ์ง„ํญ๋ณด๋‹ค ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณด์•„ ์™€์šฐํ•ต์—์„œ ๊ณผ์ž‰ ๋ฏผ๊ฐ์„ฑ ๋ฐ˜์‘์ด ์ผ์–ด๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ฐ ๊ทธ๋ฃน์—์„œ ์ฝ”๋ฅดํ‹ฐ ๊ธฐ๊ด€์„ ๊ตฌ์กฐ๋ฅผ ๊ด€์ฐฐ ํ•œ ๊ฒฐ๊ณผ ์‹คํ—˜๊ตฐ์€ ๊ธฐ์ €๋ถ€์—์„œ๋งŒ ์†์ƒ๋œ ๊ตฌ์กฐ๋ฅผ ๋ณด์˜€๊ณ , ์œ ๋ชจ์„ธํฌ ํ˜•๊ด‘ ๋ฉด์—ญ ์—ผ์ƒ‰์—์„œ๋Š” ๊ธฐ์ €๋ถ€์™€ ์ผ๋ถ€ ์ค‘๊ฐ„๋ถ€์—์„œ ์™ธ์œ ๋ชจ์„ธํฌ์˜ ์†์‹ค์„ ํ™•์ธํ•˜์˜€๋‹ค. ์™ธ์œ ๋ชจ์„ธํฌ์˜ ์†์‹ค์€ ์˜๊ตฌ์ ์ธ ์ฒญ๋ ฅ ์†์ƒ์„ ์˜๋ฏธํ•˜๋Š”๋ฐ, ์ด๋กœ ์ธํ•ด ์ฒญ๊ฐ์‹ ๊ฒฝ์„ฌ์œ  ๋˜๋Š” ์™€์šฐํ•ต, ํ•˜๊ตฌ์—์„œ๋„ ๋ณ€ํ™”๊ฐ€ ์žˆ์Œ์„ ์˜ˆ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ์™€์šฐํ•ต๊ณผ ํ•˜๊ตฌ๋ฅผ ์ฑ„์ทจํ•˜์—ฌ microarray๋ฅผ ์‹ค์‹œํ•˜์˜€์„๋•Œ, ๊ฐ 10๊ฐœ์™€ 13๊ฐœ์˜ ํ›„๋ณด miRNA๋ฅผ ์„ ์ •ํ•˜์˜€๋‹ค. ์ดํ›„ qRT-PCR ๊ฒ€์ฆ์„ ๊ฑฐ์ณ ์™€์šฐํ•ต์—์„œ๋Š” miR-200b-3p, miR-183-5p, miR-411-3p, miR-20b-5p, ๊ทธ๋ฆฌ๊ณ  miR-377-3p ์ด 5๊ฐœ์˜ ํ›„๋ณด๊ตฐ, ํ•˜๊ตฌ์—์„œ๋Š” miR-136-3p, miR-26b-5p, ๊ทธ๋ฆฌ๊ณ , 92a-1-5p ์ด 3๊ฐœ์˜ ์ตœ์ข… ํ›„๋ณด๊ตฐ์„ ์„ ์ •ํ•˜์˜€๋‹ค ์ œ์‹œ๋œ ๊ฒฐ๊ณผ๋“ค์„ ํ†ตํ•ด ๋‹จ๊ธฐ ์Œํ–ฅ ์ž๊ทน์— ์˜ํ•ด์„œ๋„ ์ถฉ๋ถ„ํžˆ ์ฒญ๋ ฅ ์ƒ์‹ค์„ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. Microarray analysis๋ฅผ ํ†ตํ•ด ์„ ๋ณ„๋œ miRNA๋“ค์ด ์ „๋ฐ˜์ ์ธ ์ค‘์ถ” ์ฒญ๊ฐ ๊ฒฝ๋กœ์˜ ์‹ ๊ฒฝ๊ฐ€์†Œ์„ฑ์˜ ๋ณ€ํ™”์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•จ์„ ์˜ˆ์ƒ ํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ์ตœ์ข… ์„ ์ •๋œ miRNA๋“ค์€ KEGG analysis๋ฅผ ํ†ตํ•ด MAP ์ธ์‚ฐํ™” ํšจ์†Œ ์‹ ํ˜ธ ๊ฒฝ๋กœ, ์ถ•์‚ญ ์ธ๋„, ๋ฒ ํƒ€ ์ข…์–‘ ์ฆ์‹ ์ธ์ž ์‹ ํ˜ธ ๊ฒฝ๋กœ ๋“ฑ์— ๊ด€์—ฌํ•จ์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํ˜„์žฌ๋กœ์„œ๋Š” ๊ณ ๋„๋‚œ์ฒญํ™˜์ž๋“ค์„ ์œ„ํ•œ ๋ณด์ฒญ๊ธฐ๋‚˜ ์ธ๊ณต์™€์šฐ์˜ ์‹œ์ˆ ์ด ์ž„์ƒ์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ ์ •์ƒ ์ฒญ๋ ฅ์œผ๋กœ์˜ ํšŒ๋ณต์ด ๋ถˆ๊ฐ€๋Šฅํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํšจ๊ณผ์ ์œผ๋กœ ์†Œ์Œ์„ฑ ๋‚œ์ฒญ์„ ์ง„๋‹จํ•˜๊ณ  ์น˜๋ฃŒํ•  ์ˆ˜ ์žˆ๋Š” ์‹ ๊ฐœ๋… ์น˜๋ฃŒ๋ฐฉ๋ฒ•์˜ ๊ฐœ๋ฐœ์ด ํ•„์š”ํ•˜๋‹ค. MiRNA๋Š” ๋‡Œ์กฐ์ง ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํ˜ˆ์•ก๋‚ด์—์„œ๋„ ๋งค์šฐ ์•ˆ์ •์ ์œผ๋กœ ๋ฐœํ˜„์ด ๋˜๋ฏ€๋กœ ๋น„์™ธ๊ณผ์ ์ธ ๊ฐ„๋‹จํ•œ ์ฑ„ํ˜ˆ์„ ํ†ตํ•ด์„œ๋„ ์งˆ๋ณ‘์˜ ์กฐ๊ธฐ๋ฐœ๊ฒฌ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๊ธฐ๋Œ€ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ํŠน์ • ๊ฒฝ๋กœ์— ๊ด€์—ฌํ•˜๋Š” miRNA๋ฅผ ๋ฐœ๊ฒฌํ•˜๊ณ  ์ด๋ฅผ viral vector๋‚˜ siRNA๋ฅผ ์ด์šฉํ•˜์—ฌ ๋Œ€์ƒ ์„ธํฌ์— ์ „๋‹ฌํ•˜๋Š” ์‹์˜ ์œ ์ „์ž ์น˜๋ฃŒ๋ฅผ ๋„์ž…ํ•œ๋‹ค๋ฉด ๋‚œ์ฒญ ๊ทน๋ณต์— ์œ ์šฉํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Noise-induced hearing loss (NIHL) is one of the most common auditory disorders and a major socioeconomic problem. NIHL can lead to secondary changes that induce neural plasticity in the central auditory pathway. These changes include decreases in the number of synapses, degeneration of auditory nerve fibers, and reorganization of the cochlear nucleus (CN) and inferior colliculus (IC). Either prevention of auditory hair cell (HC) death or treatment at an early stage is critical to preserve hearing. MicroRNAs (miRNAs) can silence complementary sequences within mRNA molecules and are important regulators of biological processes such as cell differentiation, proliferation, and survival. This study investigated the role of miRNAs in the neural plasticity of the central auditory pathway after acute NIHL. Four groups of 6-week-old Spragueโ€“Dawley rats (each group: n = 12; no. of ears = 24) were used in this study. One group was assayed 1 day after noise exposure (1N), another group was assayed 3 days after noise exposure (3N), and the other two groups were the 1-day and 3-day control groups. Anesthetized animals were exposed to 2 h of white-band noise (2โ€“20 kHz) at 115 dB in a sound-proofed chamber. Auditory brainstem response (ABR) thresholds were measured using a Smart EP system. The amplitude of waves II and IV and the latency of waves IVโ€“II were evaluated. Bilateral CN, IC and cochleae were harvested at Day 1 and Day 3 after noise exposure. Paraffin sections of the organ of Corti were stained using hematoxylin and eosin and evaluated for morphological changes. In addition, whole mount surface preparations were stained using phalloidin and HCs were counted. The Affymetrix miRNA 4.0 GeneChip was used for the microarray analysis of miRNAs from the CN and IC. Candidate miRNAs were validated using quantitative reverse transcription polymerase chain reaction (qRT-PCR), and putative miRNA target pathways were identified. Normal hearing levels were verified using the ABR thresholds of the 1-day (4 kHz, 20.6 ยฑ 2.2 dB; 8 kHz, 21.3 ยฑ 2.7 dB; 16 kHz, 25.4 ยฑ 3.6 dB) and 3-day (4 kHz, 20.8 ยฑ 2.4 dB; 8 kHz, 21.9 ยฑ 3.2 dB; 16 kHz, 25.6 ยฑ 4.3 dB) control groups. ABR thresholds increased significantly in both the 1N (4 kHz, 81.9 ยฑ 11.6 dB; 8 kHz, 87.1 ยฑ 3.3 dB; 16 kHz, 88.3 ยฑ 2.4 dB) and 3N (4 kHz, 78.8 ยฑ 11.8 dB; 8 kHz, 84.6 ยฑ 2.9 dB; 16 kHz, 86.7 ยฑ 3.8 dB) groups. At 3 days after noise exposure, animals exhibited a significant (p <0.001) decreased ABR threshold at all three frequencies (4 kHz, 42.7 ยฑ 17.1 dB; 8 kHz, 51.9 ยฑ 13.7 dB; 16 kHz, 63.5 ยฑ 12.6 dB) compared to animals exposed to 1 day of noise (4 kHz, 65.6 ยฑ 19.5 dB; 8 kHz, 73.7 ยฑ 10.7 dB; 16 kHz, 79.4 ยฑ 8.5 dB). The latencies of waves IVโ€“II were not differ significantly between 1N and 3N rats. However, wave II was significantly larger in the 3N group than in the 1N group at all frequencies (p <0.001). In addition, at 4 kHz, the amplitude of wave IV was slightly greater in the 3N group than in the 1N group (p <0.001). The middle and apical turn sections of the organs of Corti were intact, whereas in the basal turn sections, the outer HCs and other non-sensory cells were lost. The 1N and 3N rats had similar numbers of surviving HCs, most of the missing HCs were from the outer parts of the basal turn sections. There were significant differences in the basal and middle turn sections of the organs of Corti between the treatment and control groups. No significant differences were observed in the apical turn sections between the two treatment groups. Using a 1.5-fold change of normalized intensity values (p <0.1) as a criterion, we selected 10 candidate miRNAs from the CN and 13 candidate miRNAs from the IC microarray analysis. After validation by qRT-PCR, five miRNAs were retained from the CN candidates (miR-200b-3p, miR-183-5p, miR-411-3p, miR-20b-5p, and miR-377-3p) and three miRNAs were retained from the IC candidates (miR-92a-1-5p, miR-136-3p, and miR-26b-5p). These results, confirmed using ABR threshold data, show that even short-term acoustic stimulation can cause hearing loss. Changes in the ABR amplitude of wave II suggested that the CN may be particularly important. The microarray analysis and qRT-PCR results suggest that miR-200b-3p, miR-183-5p, miR-411-3p, miR-20b-5p, miR-377-3p, miR-92a-1-5p, miR-136-3p, and miR-26b-5p may play key roles in the neuroplasticity of the central auditory pathway. An analysis of the five candidate miRNAs from the CN using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database suggested that these miRNAs may be associated with the mitogen-activated protein kinase (MAPK) signaling pathway, axon guidance, and the neurotrophin signaling pathway. A similar analysis of the three candidate miRNAs from the IC also found a potential association with the MAPK signaling pathway. Further studies with miRNA oligomers are needed to validate these candidate miRNAs. Such miRNAs may be used in the early diagnosis and treatment of neural plasticity of the central auditory pathway after acute NIHL.Introduction ............................................................................................... 1 Materials and Methods................................................................................ 9 Results ...................................................................................................... 27 Discussion ................................................................................................. 56 Conclusions ............................................................................................... 67 References ................................................................................................. 68 Abstract in Korean ..................................................................................... 73Maste

    ์ •์‹ ๊ฑด๊ฐ•์—์„œ ์‚ฌ์šฉ์ž ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ์™€ ์ž์•„์„ฑ์ฐฐ์„ ์ง€์›ํ•˜๋Š” ๋Œ€ํ™”ํ˜• ์—์ด์ „ํŠธ ๋””์ž์ธ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(๋””์ง€ํ„ธ์ •๋ณด์œตํ•ฉ์ „๊ณต), 2020. 8. ์„œ๋ด‰์›.In the advent of artificial intelligence (AI), we are surrounded by technological gadgets, devices and intelligent personal assistant (IPAs) that voluntarily take care of our home, work and social networks. They help us manage our life for the better, or at least that is what they are designed for. As a matter of fact, few are, however, designed to help us grapple with the thoughts and feelings that often construct our living. In other words, technologies hardly help us think. How can they be designed to help us reflect on ourselves for the better? In the simplest terms, self-reflection refers to thinking deeply about oneself. When we think deeply about ourselves, there can be both positive and negative consequences. On the one hand, reflecting on ourselves can lead to a better self-understanding, helping us achieve life goals. On the other hand, we may fall into brooding and depression. The sad news is that the two are usually intertwined. The problem, then, is the irony that reflecting on oneself by oneself is not easy. To tackle this problem, this work aims to design technology in the form of a conversational agent, or a chatbot, to encourage a positive self-reflection. Chatbots are natural language interfaces that interact with users in text. They work at the tip of our hands as if SMS or instant messaging, from flight reservation and online shopping to news service and healthcare. There are even chatbot therapists offering psychotherapy on mobile. That machines can now talk to us creates an opportunity for designing a natural interaction that used to be humans own. This work constructs a two-dimensional design space for translating self-reflection into a human-chatbot interaction, with user self-disclosure and chatbot guidance. Users confess their thoughts and feelings to the bot, and the bot is to guide them in the scaffolding process. Previous work has established an extensive line of research on the therapeutic effect of emotional disclosure. In HCI, reflection design has posited the need for guidance, e.g. scaffolding users thoughts, rather than assuming their ability to reflect in a constructive manner. The design space illustrates different reflection processes depending on the levels of user disclosure and bot guidance. Existing reflection technologies have most commonly provided minimal levels of disclosure and guidance, and healthcare technologies the opposite. It is the aim of this work to investigate the less explored space by designing chatbots called Bonobot and Diarybot. Bonobot differentiates itself from other bot interventions in that it only motivates the idea of change rather than direct engagement. Diarybot is designed in two chat versions, Basic and Responsive, which create novel interactions for reflecting on a difficult life experience by explaining it to and exploring it with a chatbot. These chatbots are set up for a user study with 30 participants, to investigate the user experiences of and responses to design strategies. Based on the findings, challenges and opportunities from designing for chatbot-guided reflection are explored. The findings of this study are as follows. First, participants preferred Bonobots questions that prompted the idea of change. Its responses were also appreciated, but only when they conveyed accurate empathy. Thus questions, coupled with empathetic responses, could serve as a catalyst for disclosure and even a possible change of behavior, a motivational boost. Yet the chatbot-led interaction led to surged user expectations for the bot. Participants demanded more than just the guidance, such as solutions and even superhuman intelligence. Potential tradeoff between user engagement and autonomy in designing human-AI partnership is discussed. Unlike Bonobot, Diarybot was designed with less guidance to encourage users own narrative making. In both Diarybot chats, the presence of a bot could make it easier for participants to share the most difficult life experiences, compared to a no-chatbot writing condition. Yet an increased interaction with the bot in Responsive chat could lead to a better user engagement. On the contrary, more emotional expressiveness and ease of writing were observed with little interaction in Basic chat. Coupled with qualitative findings that reveal user preference for varied interactions and tendency to adapt to bot patterns, predictability and transparency of designing chatbot interaction are discussed in terms of managing user expectations in human-AI interaction. In sum, the findings of this study shed light on designing human-AI interaction. Chatbots can be a potential means of supporting guided disclosure on lifes most difficult experiences. Yet the interaction between a machine algorithm and an innate human cognition bears interesting questions for the HCI community, especially in terms of user autonomy, interface predictability, and design transparency. Discussing the notion of algorithmic affordances in AI agents, this work proposes meaning-making as novel interaction design metaphor: In the symbolic interaction via language, AI nudges users, which inspires and engages users in their pursuit of making sense of lifes agony. Not only does this metaphor respect user autonomy but also it maintains the veiled workings of AI from users for continued engagement. This work makes the following contributions. First, it designed and implemented chatbots that can provide guidance to encourage user narratives in self-reflection. Next, it offers empirical evidence on chatbot-guided disclosure and discusses implications for tensions and challenges in design. Finally, this work proposes meaning-making as a novel design metaphor. It calls for the responsible design of intelligent interfaces for positive reflection in pursuit of psychological wellbeing, highlighting algorithmic affordances and interpretive process of human-AI interaction.์ตœ๊ทผ ์ธ๊ณต์ง€๋Šฅ(Artificial Intelligence; AI) ๊ธฐ์ˆ ์€ ์šฐ๋ฆฌ ์‚ถ์˜ ๋ฉด๋ฉด์„ ๋งค์šฐ ๋น ๋ฅด๊ฒŒ ๋ฐ”๊ฟ”๋†“๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์• ํ”Œ์˜ ์‹œ๋ฆฌ(Siri)์™€ ๊ตฌ๊ธ€ ์–ด์‹œ์Šคํ„ดํŠธ (Google Assistant) ๋“ฑ ์ž์—ฐ์–ด ์ธํ„ฐํŽ˜์ด์Šค(natural language interfaces)์˜ ํ™•์žฅ์€ ๊ณง ์ธ๊ณต์ง€๋Šฅ ์—์ด์ „ํŠธ์™€์˜ ๋Œ€ํ™”๊ฐ€ ์ธํ„ฐ๋ž™์…˜์˜ ์ฃผ์š” ์ˆ˜๋‹จ์ด ๋  ๊ฒƒ์ž„์„ ๋Šฅํžˆ ์ง์ž‘์ผ€ ํ•œ๋‹ค. ์‹ค์ƒ ์ธ๊ณต์ง€๋Šฅ ์—์ด์ „ํŠธ๋Š” ์‹ค์ƒํ™œ์—์„œ ์ฝ˜ํ…์ธ  ์ถ”์ฒœ๊ณผ ์˜จ๋ผ์ธ ์‡ผํ•‘ ๋“ฑ ๋‹ค์–‘ํ•œ ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๊ณ  ์žˆ์ง€๋งŒ, ์ด๋“ค์˜ ๋Œ€๋ถ€๋ถ„์€ ๊ณผ์—…-์ง€ํ–ฅ์ ์ด๋‹ค. ์ฆ‰ ์ธ๊ณต์ง€๋Šฅ์€ ์šฐ๋ฆฌ์˜ ์‚ถ์„ ํŽธ๋ฆฌํ•˜๊ฒŒ ํ•˜์ง€๋งŒ, ๊ณผ์—ฐ ํŽธ์•ˆํ•˜๊ฒŒ ํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€? ๋ณธ ์—ฐ๊ตฌ๋Š” ํŽธํ•˜์ง€๋งŒ ํŽธํ•˜์ง€ ์•Š์€ ํ˜„๋Œ€์ธ์„ ์œ„ํ•œ ๊ธฐ์ˆ ์˜ ์—ญํ• ์„ ๊ณ ๋ฏผํ•˜๋Š” ๋ฐ์—์„œ ์ถœ๋ฐœํ•œ๋‹ค. ์ž์•„์„ฑ์ฐฐ(self-reflection), ์ฆ‰ ์ž์‹ ์— ๋Œ€ํ•ด ๊นŠ์ด ์ƒ๊ฐํ•ด ๋ณด๋Š” ํ™œ๋™์€ ์ž๊ธฐ์ธ์‹๊ณผ ์ž๊ธฐ์ดํ•ด๋ฅผ ๋„๋ชจํ•˜๊ณ  ๋ฐฐ์›€๊ณผ ๋ชฉํ‘œ์˜์‹์„ ๊ณ ์ทจํ•˜๋Š” ๋“ฑ ๋ถ„์•ผ๋ฅผ ๋ง‰๋ก ํ•˜๊ณ  ๋„๋ฆฌ ์—ฐ๊ตฌ ๋ฐ ์ ์šฉ๋˜์–ด ์™”๋‹ค. ํ•˜์ง€๋งŒ ์ž์•„์„ฑ์ฐฐ์˜ ๊ฐ€์žฅ ํฐ ์–ด๋ ค์›€์€ ์Šค์Šค๋กœ ๊ฑด์„ค์ ์ธ ์„ฑ์ฐฐ์„ ๋„๋ชจํ•˜๊ธฐ ํž˜๋“ค๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ํŠนํžˆ, ๋ถ€์ •์ ์ธ ๊ฐ์ •์  ๊ฒฝํ—˜์— ๋Œ€ํ•œ ์ž์•„์„ฑ์ฐฐ์€ ์ข…์ข… ์šฐ์šธ๊ฐ๊ณผ ๋ถˆ์•ˆ์„ ๋™๋ฐ˜ํ•œ๋‹ค. ๊ทน๋ณต์ด ํž˜๋“  ๊ฒฝ์šฐ ์ƒ๋‹ด ๋˜๋Š” ์น˜๋ฃŒ๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ์ง€๋งŒ, ์‚ฌํšŒ์  ๋‚™์ธ๊ณผ ์žฃ๋Œ€์˜ ๋ถ€๋‹ด๊ฐ์œผ๋กœ ๊บผ๋ ค์ง€๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋‹ค์ˆ˜์ด๋‹ค. ์„ฑ์ฐฐ ๋””์ž์ธ(Reflection Design)์€ ์ธ๊ฐ„-์ปดํ“จํ„ฐ์ƒํ˜ธ์ž‘์šฉ(HCI)์˜ ์˜ค๋žœ ํ™”๋‘๋กœ, ๊ทธ๋™์•ˆ ํšจ๊ณผ์ ์ธ ์„ฑ์ฐฐ์„ ๋„์šธ ์ˆ˜ ์žˆ๋Š” ๋””์ž์ธ ์ „๋žต๋“ค์ด ๋‹ค์ˆ˜ ์—ฐ๊ตฌ๋˜์–ด ์™”์ง€๋งŒ ๋Œ€๋ถ€๋ถ„ ๋‹ค์–‘ํ•œ ์‚ฌ์šฉ์ž ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ์ „๋žต์„ ํ†ตํ•ด ๊ณผ๊ฑฐ ํšŒ์ƒ ๋ฐ ํ•ด์„์„ ๋•๋Š” ๋ฐ ๊ทธ์ณค๋‹ค. ์ตœ๊ทผ ์†Œ์œ„ ์ฑ—๋ด‡ ์ƒ๋‹ด์‚ฌ๊ฐ€ ๋“ฑ์žฅํ•˜์—ฌ ์‹ฌ๋ฆฌ์ƒ๋‹ด๊ณผ ์น˜๋ฃŒ ๋ถ„์•ผ์— ์ ์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, ์ด ๋˜ํ•œ ์„ฑ์ฐฐ์„ ๋•๊ธฐ๋ณด๋‹ค๋Š” ํšจ์œจ์ ์ธ ์ฒ˜์น˜ ๋„๊ตฌ์— ๋จธ๋ฌด๋ฅด๊ณ  ์žˆ์„ ๋ฟ์ด๋‹ค. ์ฆ‰ ๊ธฐ์ˆ ์€ ์น˜๋ฃŒ ์ˆ˜๋‹จ์ด๊ฑฐ๋‚˜ ์„ฑ์ฐฐ์˜ ๋Œ€์ƒ์ด ๋˜์ง€๋งŒ, ๊ทธ ๊ณผ์ •์— ๊ฐœ์ž…ํ•˜๋Š” ๊ฒฝ์šฐ๋Š” ์ œํ•œ์ ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ์„ฑ์ฐฐ ๋™๋ฐ˜์ž๋กœ์„œ ๋Œ€ํ™”ํ˜• ์—์ด์ „ํŠธ์ธ ์ฑ—๋ด‡์„ ๋””์ž์ธํ•  ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ์ฑ—๋ด‡์˜ ์—ญํ• ์€ ์‚ฌ์šฉ์ž์˜ ๋ถ€์ •์ ์ธ ๊ฐ์ •์  ๊ฒฝํ—˜ ๋˜๋Š” ํŠธ๋ผ์šฐ๋งˆ์— ๋Œ€ํ•ด ์ด์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋„๋ก ๋„์šธ ๋ฟ ์•„๋‹ˆ๋ผ, ๊ทธ ๊ณผ์ •์—์„œ ๋ฐ˜์ถ”๋ฅผ ํ†ต์ œํ•˜์—ฌ ๊ฑด์„ค์ ์ธ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ๋ฅผ ์ด๋Œ์–ด ๋‚ด๋Š” ๊ฐ€์ด๋“œ๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ฑ—๋ด‡์„ ์„ค๊ณ„ํ•˜๊ธฐ ์œ„ํ•ด, ์„ ํ–‰ ์—ฐ๊ตฌ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์‚ฌ์šฉ์ž์˜ ์ž๊ธฐ๋…ธ์ถœ(user self-disclosure)๊ณผ ์ฑ—๋ด‡ ๊ฐ€์ด๋“œ(guidance)๋ฅผ ๋‘ ์ถ•์œผ๋กœ ํ•œ ๋””์ž์ธ ๊ณต๊ฐ„(design space)์„ ์ •์˜ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ž๊ธฐ๋…ธ์ถœ๊ณผ ๊ฐ€์ด๋“œ์˜ ์ •๋„์— ๋”ฐ๋ฅธ ๋„ค ๊ฐ€์ง€ ์ž์•„์„ฑ์ฐฐ ๊ฒฝํ—˜์„ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค: ์ž๊ธฐ๋…ธ์ถœ๊ณผ ๊ฐ€์ด๋“œ๊ฐ€ ์ตœ์†Œํ™”๋œ ํšŒ์ƒ ๊ณต๊ฐ„, ์ž๊ธฐ๋…ธ์ถœ์ด ์œ„์ฃผ์ด๊ณ  ๊ฐ€์ด๋“œ๊ฐ€ ์ตœ์†Œํ™”๋œ ์„ค๋ช… ๊ณต๊ฐ„, ์ž๊ธฐ๋…ธ์ถœ๊ณผ ์ฑ—๋ด‡์ด ์ด๋„๋Š” ๊ฐ€์ด๋“œ๊ฐ€ ํ˜ผํ•ฉ๋œ ํƒ์ƒ‰ ๊ณต๊ฐ„, ๊ฐ€์ด๋“œ๋ฅผ ์ ๊ทน ๊ฐœ์ž…์‹œ์ผœ ์ž๊ธฐ๋…ธ์ถœ์„ ๋†’์ด๋Š” ๋ณ€ํ™” ๊ณต๊ฐ„์ด ๊ทธ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œ๋Š” ์ƒ์ˆ ๋œ ๋””์ž์ธ ๊ณต๊ฐ„์—์„œ์˜ ์„ฑ์ฐฐ ๊ฒฝํ—˜๊ณผ ๊ณผ์ •์„ ๋•๋Š” ์ฑ—๋ด‡์„ ๊ตฌํ˜„ํ•˜๊ณ , ์‚ฌ์šฉ์ž ์‹คํ—˜์„ ํ†ตํ•ด ์„ฑ์ฐฐ ๊ฒฝํ—˜๊ณผ ๋””์ž์ธ ์ „๋žต์— ๋Œ€ํ•œ ๋ฐ˜์‘์„ ์ˆ˜์ง‘ ๋ฐ ๋ถ„์„ํ•จ์œผ๋กœ์จ ์ฑ—๋ด‡ ๊ธฐ๋ฐ˜์˜ ์ž์•„ ์„ฑ์ฐฐ ์ธํ„ฐ๋ž™์…˜์„ ์ƒˆ๋กญ๊ฒŒ ์ œ์‹œํ•˜๊ณ  ์ด์— ๋Œ€ํ•œ ์‹ค์ฆ์  ๊ทผ๊ฑฐ๋ฅผ ๋งˆ๋ จํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ํ˜„์žฌ๊นŒ์ง€ ๋งŽ์€ ์„ฑ์ฐฐ ๊ธฐ์ˆ ์€ ํšŒ์ƒ์— ์ง‘์ค‘๋˜์–ด ์žˆ๊ธฐ์—, ๋‚˜๋จธ์ง€ ์„ธ ๊ณต๊ฐ„์—์„œ์˜ ์„ฑ์ฐฐ์„ ์ง€์›ํ•˜๋Š” ๋ณด๋…ธ๋ด‡๊ณผ ๊ธฐ๋ณธํ˜•๋ฐ˜์‘ํ˜• ์ผ๊ธฐ๋ด‡์„ ๋””์ž์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์‚ฌ์šฉ์ž ํ‰๊ฐ€๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋„์ถœํ•œ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๋„๋ž˜ํ•œ ์ธ๊ฐ„-์ธ๊ณต์ง€๋Šฅ ์ƒํ˜ธ์ž‘์šฉ(human-AI interaction)์˜ ๋งฅ๋ฝ์—์„œ ์„ฑ์ฐฐ ๋™๋ฐ˜์ž๋กœ์„œ์˜ ์ฑ—๋ด‡ ๊ธฐ์ˆ ์ด ๊ฐ–๋Š” ์˜๋ฏธ์™€ ์—ญํ• ์„ ํƒ๊ตฌํ•œ๋‹ค. ๋ณด๋…ธ๋ด‡๊ณผ ์ผ๊ธฐ๋ด‡์€ ์ธ๊ฐ„์ค‘์‹ฌ์ƒ๋‹ด๊ณผ ๋Œ€ํ™”๋ถ„์„์˜ ์ด๋ก ์  ๊ทผ๊ฑฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ ์ •์„œ์ง€๋Šฅ(emotional intelligence)๊ณผ ์ ˆ์ฐจ์ง€๋Šฅ(proecedural intelligence)์„ ํ•ต์‹ฌ ์ถ•์œผ๋กœ, ๋Œ€ํ™” ํ๋ฆ„ ์ œ์–ด(flow manager)์™€ ๋ฐœํ™” ์ƒ์„ฑ(response generator)์„ ํ•ต์‹ฌ ๋ชจ๋“ˆ๋กœ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ๋จผ์ €, ๋ณด๋…ธ๋ด‡์€ ๋™๊ธฐ๊ฐ•ํ™”์ƒ๋‹ด(motivational interviewing)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ณ ๋ฏผ๊ณผ ์ŠคํŠธ๋ ˆ์Šค์— ๋Œ€ํ•œ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ๋ฅผ ์ด๋Œ์–ด๋‚ด์–ด, ์ด์— ๋Œ€ํ•œ ํ•ด๊ฒฐ์„ ์œ„ํ•œ ๊ฐ€์ด๋“œ ์งˆ๋ฌธ์„ ํ†ตํ•ด ๋ณ€ํ™”๋ฅผ ์œ„ํ•œ ์„ฑ์ฐฐ์„ ๋•๋Š”๋‹ค. ์ฑ—๋ด‡์˜ ๊ตฌํ˜„์„ ์œ„ํ•ด, ๋™๊ธฐ๊ฐ•ํ™”์ƒ๋‹ด์˜ ๋„ค ๋‹จ๊ณ„ ๋Œ€ํ™”๋ฅผ ์„ค์ •ํ•˜๊ณ  ๊ฐ ๋‹จ๊ณ„๋ฅผ ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์ƒ๋‹ด์‚ฌ ๋ฐœํ™” ํ–‰๋™์„ ๊ด€๋ จ๋ฌธํ—Œ์—์„œ ์ˆ˜์ง‘ ๋ฐ ์ „์ฒ˜๋ฆฌ ๊ณผ์ •์„ ๊ฑฐ์ณ ์Šคํฌ๋ฆฝํŠธํ™”ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์‚ฌ์ „ ์ „์ฒ˜๋ฆฌ๋œ ๋ฌธ์žฅ์ด ๋งฅ๋ฝ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€ํ™”์— ์“ฐ์ผ ์ˆ˜ ์žˆ๋„๋ก, ๋Œ€ํ™”์˜ ์ฃผ์ œ๋Š” ๋Œ€ํ•™์›์ƒ์˜ ์–ด๋ ค์›€์œผ๋กœ ํ•œ์ •ํ•˜์˜€๋‹ค. ๋ณด๋…ธ๋ด‡๊ณผ์˜ ๋Œ€ํ™”๊ฐ€ ์‚ฌ์šฉ์ž์˜ ์„ฑ์ฐฐ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ์ด์— ๋Œ€ํ•œ ์ธ์‹์„ ํƒ์ƒ‰ํ•˜๊ธฐ ์œ„ํ•ด ์งˆ์  ์—ฐ๊ตฌ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ 30๋ช…์˜ ๋Œ€ํ•™์›์ƒ๊ณผ ์‚ฌ์šฉ์ž ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์‹คํ—˜๊ฒฐ๊ณผ, ์‚ฌ์šฉ์ž๋Š” ๋ณ€ํ™” ๋Œ€ํ™”๋ฅผ ์œ ๋„ํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ํƒ์ƒ‰ ์งˆ๋ฌธ์„ ์„ ํ˜ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์‚ฌ์šฉ์ž์˜ ๋งฅ๋ฝ์— ์ •ํ™•ํžˆ ๋“ค์–ด๋งž๋Š” ์งˆ๋ฌธ๊ณผ ํ”ผ๋“œ๋ฐฑ์€ ์‚ฌ์šฉ์ž๋ฅผ ๋”์šฑ ์ ๊ทน์ ์ธ ์ž๊ธฐ ๋…ธ์ถœ๋กœ ์ด๋Œ๊ฒŒ ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ฑ—๋ด‡์ด ๋งˆ์น˜ ์ƒ๋‹ด์‚ฌ์ฒ˜๋Ÿผ ๋Œ€ํ™”๋ฅผ ์ด๋Œ์–ด๊ฐˆ ๊ฒฝ์šฐ, ๋†’์•„์ง„ ์‚ฌ์šฉ์ž์˜ ๊ธฐ๋Œ€ ์ˆ˜์ค€์œผ๋กœ ์ธํ•ด ์ผ๋ถ€ ์‚ฌ์šฉ์ž๊ฐ€ ๋ณ€ํ™”์— ๋Œ€ํ•œ ๋™๊ธฐ๋ฅผ ํ‘œ์ถœํ•˜์˜€์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋ณ€ํ™”์— ๋Œ€ํ•œ ์ž์œจ์„ฑ์„ ์ฑ—๋ด‡์— ์–‘๋„ํ•˜๋ ค๋Š” ๋ชจ์Šต ๋˜ํ•œ ๋‚˜ํƒ€๋‚จ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณด๋…ธ๋ด‡ ์—ฐ๊ตฌ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ผ๊ธฐ๋ด‡์€ ์ฑ—๋ด‡ ๋Œ€์‹  ์‚ฌ์šฉ์ž๊ฐ€ ๋ณด๋‹ค ์ ๊ทน์ ์œผ๋กœ ์„ฑ์ฐฐ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ๋ฅผ ์ „๊ฐœํ•  ์ˆ˜ ์žˆ๋„๋ก ๋””์ž์ธํ•˜์˜€๋‹ค. ์ผ๊ธฐ๋ด‡์€ ํŠธ๋ผ์šฐ๋งˆ์— ๋Œ€ํ•œ ํ‘œํ˜„์  ๊ธ€์“ฐ๊ธฐ๋ฅผ ์ง€์›ํ•˜๋Š” ์ฑ—๋ด‡์œผ๋กœ, ๊ธฐ๋ณธํ˜• ๋˜๋Š” ๋ฐ˜์‘ํ˜• ๋Œ€ํ™”๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๊ธฐ๋ณธํ˜• ๋Œ€ํ™”๋Š” ํŠธ๋ผ์šฐ๋งˆ์— ๋Œ€ํ•ด ์ž์œ ๋กญ๊ฒŒ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€ํ™” ํ™˜๊ฒฝ์„ ์ œ๊ณตํ•˜๊ณ , ๋ฐ˜์‘ํ˜• ๋Œ€ํ™”๋Š” ์‚ฌ์šฉ์ž๊ฐ€ ์ž‘์„ฑํ•œ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ์— ๋Œ€ํ•œ ํ›„์† ์ธํ„ฐ๋ž™์…˜์„ ํ†ตํ•ด ๊ณผ๊ฑฐ์˜ ๊ฒฝํ—˜์„ ์žฌํƒ์ƒ‰ํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ํ›„์† ์ธํ„ฐ๋ž™์…˜์˜ ๋ฐœํ™” ํ–‰๋™์€ ๋‹ค์–‘ํ•œ ์ƒ๋‹ด์น˜๋ฃŒ์—์„œ ๋ฐœ์ทŒํ•˜๋˜ ์œ ์ €์˜ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ์—์„œ ์ถ”์ถœํ•œ ๊ฐ์ •์–ด ๋ฐ ์ธ๊ฐ„๊ด€๊ณ„ ํ‚ค์›Œ๋“œ๋ฅผ ํ™œ์šฉํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๊ฐ ์ผ๊ธฐ๋ด‡์— ๋Œ€ํ•œ ๋ฐ˜์‘์„ ๋น„๊ต ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด, ์ฑ—๋ด‡ ์—†์ด ๋„ํ๋จผํŠธ์— ํ‘œํ˜„์  ๊ธ€์“ฐ๊ธฐ ํ™œ๋™๋งŒ์„ ํ•˜๋Š” ๋Œ€์กฐ๊ตฐ์„ ์„ค์ •ํ•˜๊ณ  30๋ช…์˜ ์‚ฌ์šฉ์ž๋ฅผ ๋ชจ์ง‘ํ•˜์—ฌ ๊ฐ ์กฐ๊ฑด์— ๋žœ๋ค์œผ๋กœ ๋ฐฐ์ •, ์„ค๋ฌธ๊ณผ ๋ฉด๋‹ด์„ ๋™๋ฐ˜ํ•œ 4์ผ๊ฐ„์˜ ๊ธ€์“ฐ๊ธฐ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์‹คํ—˜๊ฒฐ๊ณผ, ์‚ฌ์šฉ์ž๋Š” ์ผ๊ธฐ๋ด‡๊ณผ์˜ ์ธํ„ฐ๋ž™์…˜์„ ํ†ตํ•ด ๋ณด์ด์ง€ ์•Š๋Š” ๊ฐ€์ƒ์˜ ์ฒญ์ž๋ฅผ ์ƒ์ƒํ•จ์œผ๋กœ์จ ๊ธ€์“ฐ๊ธฐ๋ฅผ ๋Œ€ํ™” ํ™œ๋™์œผ๋กœ ์ธ์ง€ํ•˜๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํŠนํžˆ, ๋ฐ˜์‘ํ˜• ๋Œ€ํ™”์˜ ํ›„์† ์งˆ๋ฌธ๋“ค์€ ์‚ฌ์šฉ์ž๋กœ ํ•˜์—ฌ๊ธˆ ์ƒํ™ฉ์„ ๊ฐ๊ด€ํ™”ํ•˜๊ณ  ์ƒˆ๋กœ์šด ๊ด€์ ์œผ๋กœ ์ƒ๊ฐํ•ด ๋ณผ ์ˆ˜ ์žˆ๋Š” ํšจ๊ณผ๋ฅผ ๊ฑฐ๋‘์—ˆ๋‹ค. ๋ฐ˜์‘ํ˜• ๋Œ€ํ™”์—์„œ ํ›„์† ์ธํ„ฐ๋ž™์…˜์„ ๊ฒฝํ—˜ํ•œ ์‚ฌ์šฉ์ž๋Š” ์ผ๊ธฐ๋ด‡์˜ ์ธ์ง€๋œ ์ฆ๊ฑฐ์›€๊ณผ ์‚ฌํšŒ์„ฑ, ์‹ ๋ขฐ๋„์™€ ์žฌ์‚ฌ์šฉ ์˜ํ–ฅ์— ๋Œ€ํ•œ ํ‰๊ฐ€๊ฐ€ ๋‹ค๋ฅธ ๋‘ ์กฐ๊ฑด์—์„œ๋ณด๋‹ค ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜๋‹ค. ๋ฐ˜๋ฉด, ๊ธฐ๋ณธํ˜• ๋Œ€ํ™” ์ฐธ์—ฌ์ž๋Š” ๋‹ค๋ฅธ ๋‘ ์กฐ๊ฑด์—์„œ๋ณด๋‹ค ๊ฐ์ •์  ํ‘œํ˜„์˜ ์šฉ์ด์„ฑ๊ณผ ๊ธ€์“ฐ๊ธฐ์˜ ์–ด๋ ค์›€์„ ๊ฐ๊ฐ ์œ ์˜ํ•˜๊ฒŒ ๋†’๊ฒŒ, ๊ทธ๋ฆฌ๊ณ  ๋‚ฎ๊ฒŒ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฆ‰, ์ฑ—๋ด‡์€ ๋งŽ์€ ์ธํ„ฐ๋ž™์…˜ ์—†์ด๋„ ์ฒญ์ž์˜ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์—ˆ์ง€๋งŒ, ํ›„์† ์งˆ๋ฌธ์„ ํ†ตํ•œ ์ธํ„ฐ๋ž™์…˜์ด ๊ฐ€๋Šฅํ–ˆ๋˜ ๋ฐ˜์‘ํ˜• ๋Œ€ํ™”๋Š” ๋”์šฑ ์ ๊ทน์ ์ธ ์œ ์ € ์ฐธ์—ฌ(engagement)๋ฅผ ์ด๋Œ์–ด๋‚ผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ์‹คํ—˜์ด ์ง„ํ–‰๋จ์— ๋”ฐ๋ผ, ์‚ฌ์šฉ์ž๊ฐ€ ๋ฐ˜์‘ํ˜• ์ผ๊ธฐ๋ด‡์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์ž์‹ ์˜ ๊ธ€์“ฐ๊ธฐ ์ฃผ์ œ์™€ ๋‹จ์–ด ์„ ํƒ ๋“ฑ์„ ๋งž๊ฒŒ ๋ฐ”๊พธ์–ด ๊ฐ€๋Š” ์ ์‘์ (adaptive) ํ–‰๋™์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์•ž์„  ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด, ๋‹ค์–‘ํ•œ ์ฑ—๋ด‡ ๋””์ž์ธ ์ „๋žต์„ ๋ฐ”ํƒ•์œผ๋กœ ์‚ฌ์šฉ์ž์˜ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ๊ฐ€ ๋‹ค๋ฅด๊ฒŒ ์œ ๋„๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋”ฐ๋ผ์„œ ์„œ๋กœ ๋‹ค๋ฅธ ์œ ํ˜•์˜ ์„ฑ์ฐฐ ๊ฒฝํ—˜์„ ์ด๋Œ์–ด๋‚ผ ์ˆ˜ ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ž์œจ์ ์ธ ํ–‰์œ„์ธ ์ž์•„์„ฑ์ฐฐ์ด ๊ธฐ์ˆ ๊ณผ์˜ ์ƒํ˜ธ์ž‘์šฉ์œผ๋กœ ํ˜ธํ˜œ์  ์„ฑ์งˆ์„ ๊ฐ–๊ฒŒ ๋  ๋•Œ ์‚ฌ์šฉ์ž์˜ ์ž์œจ์„ฑ, ์ƒํ˜ธ์ž‘์šฉ์˜ ์˜ˆ์ธก๊ฐ€๋Šฅ์„ฑ๊ณผ ๋””์ž์ธ ํˆฌ๋ช…์„ฑ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐˆ๋“ฑ๊ด€๊ณ„(tensions)๋ฅผ ํƒ์ƒ‰ํ•˜๊ณ  ์ธ๊ณต์ง€๋Šฅ ์—์ด์ „ํŠธ์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์–ดํฌ๋˜์Šค(algorithmic affordances)๋ฅผ ๋…ผ์˜ํ•˜์˜€๋‹ค. ๋ณด์ด์ง€ ์•Š๋Š” ์ฑ—๋ด‡ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์˜ํ•ด ์‚ฌ์šฉ์ž์˜ ์„ฑ์ฐฐ์ด ์œ ๋„๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์€ ๊ธฐ์กด์˜ ์ธ๊ฐ„-์ปดํ“จํ„ฐ ์ƒํ˜ธ์ž‘์šฉ์—์„œ ๊ฐ•์กฐ๋˜๋Š” ์‚ฌ์šฉ์ž ์ œ์–ด์™€ ๋””์ž์ธ ํˆฌ๋ช…์„ฑ์—์„œ ์ „๋ณต์„ ์ดˆ๋ž˜ํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ ๋ณด์ผ ์ˆ˜ ์žˆ์œผ๋‚˜, ์ƒ์ง•์  ์ƒํ˜ธ์ž‘์šฉ(symbolic interaction)์˜ ๋งฅ๋ฝ์—์„œ ์˜คํžˆ๋ ค ์‚ฌ์šฉ์ž๊ฐ€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์˜ํ•ด ์ง€๋‚˜๊ฐ„ ๊ณผ๊ฑฐ์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์˜๋ฏธ๋ฅผ ์ ๊ทน ํƒ์ƒ‰ํ•ด๋‚˜๊ฐ€๋Š” ๊ณผ์ •์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด๊ฒƒ์„ ์ƒˆ๋กœ์šด ๋””์ž์ธ ๋ฉ”ํƒ€ํฌ, ์ฆ‰ ์˜๋ฏธ-๋งŒ๋“ค๊ธฐ(meaning-making)๋กœ ์ œ์•ˆํ•˜๊ณ  ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋„›์ง€(nudge)์— ์˜ํ•œ ์‚ฌ์šฉ์ž์˜ ์ฃผ๊ด€์  ํ•ด์„ ๊ฒฝํ—˜(interpretive process)์„ ๊ฐ•์กฐํ•œ๋‹ค. ์ด๊ฒƒ์€ ํ•˜๋‚˜์˜ ์ฑ—๋ด‡ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด๋ผ ํ• ์ง€๋ผ๋„ ์„œ๋กœ ๋‹ค๋ฅธ ์‚ฌ์šฉ์ž์˜ ๋‹ค์–‘ํ•œ ์„ฑ์ฐฐ ๊ฒฝํ—˜์„ ์œ ๋„ํ•ด๋‚ผ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•˜๋ฉฐ, ์ด๋Ÿฌํ•œ ๋งฅ๋ฝ์—์„œ ์ธ๊ณต์ง€๋Šฅ์€ ๊ธฐ์กด์˜ ๋ธ”๋ž™ ๋ฐ•์Šค๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ๋„ ์‚ฌ์šฉ์ž์˜ ์ž์œจ์„ฑ์„ ๋ณด์žฅํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์šฐ๋ฆฌ์™€ ํ˜‘์—…ํ•˜๋Š” ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡ ๊ธฐ์ˆ ์˜ ๋””์ž์ธ์— ๋Œ€ํ•œ ๊ฒฝํ—˜์  ์ดํ•ด๋ฅผ ๋†’์ด๊ณ , ์ด๋ก ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ฑ—๋ด‡์„ ๊ตฌํ˜„ํ•จ์œผ๋กœ์จ ๋””์ž์ธ ์ „๋žต์— ๋Œ€ํ•œ ์‹ค์ฆ์  ๊ทผ๊ฑฐ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ๋˜ํ•œ ์ž์•„ ์„ฑ์ฐฐ ๊ณผ์ •์— ๋™ํ–‰ํ•˜๋Š” ๋™๋ฐ˜์ž(companion)๋กœ์„œ์˜ ๊ธฐ์ˆ ๋กœ ์ƒˆ๋กœ์šด ๋””์ž์ธ ๋ฉ”ํƒ€ํฌ๋ฅผ ์ œ์‹œํ•จ์œผ๋กœ์จ ์ธ๊ฐ„์ปดํ“จํ„ฐ์ƒํ˜ธ์ž‘์šฉ(HCI)์˜ ์ด๋ก ์  ํ™•์žฅ์— ๊ธฐ์—ฌํ•˜๊ณ , ์‚ฌ์šฉ์ž์˜ ๋ถ€์ •์  ๊ฒฝํ—˜์— ๋Œ€ํ•œ ์˜๋ฏธ ์ถ”๊ตฌ๋ฅผ ๋•๋Š” ๊ด€๊ณ„์ง€ํ–ฅ์  ์ธ๊ณต์ง€๋Šฅ์œผ๋กœ์„œ ํ–ฅํ›„ ํ˜„๋Œ€์ธ์˜ ์ •์‹ ๊ฑด๊ฐ•์— ์ด๋ฐ”์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ์‚ฌํšŒ์ , ์‚ฐ์—…์  ์˜์˜๋ฅผ ๊ฐ–๋Š”๋‹ค.CHAPTER 1. INTRODUCTION ๏ผ‘ 1.1. BACKGROUND AND MOTIVATION ๏ผ‘ 1.2. RESEARCH GOAL AND QUESTIONS ๏ผ• 1.2.1. Research Goal ๏ผ• 1.2.2. Research Questions ๏ผ• 1.3. MAJOR CONTRIBUTIONS ๏ผ˜ 1.4. THESIS OVERVIEW ๏ผ™ CHAPTER 2. LITERATURE REVIEW ๏ผ‘๏ผ‘ 2.1. THE REFLECTING SELF ๏ผ‘๏ผ‘ 2.1.1. Self-Reflection and Mental Wellbeing ๏ผ‘๏ผ‘ 2.1.2. The Self in Reflective Practice ๏ผ‘๏ผ• 2.1.3. Design Space ๏ผ’๏ผ’ 2.2. SELF-REFLECTION IN HCI ๏ผ’๏ผ– 2.2.1. Reflection Design in HCI ๏ผ’๏ผ– 2.2.2. HCI for Mental Wellbeing ๏ผ“๏ผ– 2.2.3. Design Opportunities ๏ผ”๏ผ 2.3. CONVERSATIONAL AGENT DESIGN ๏ผ”๏ผ’ 2.3.1. Theoretical Background ๏ผ”๏ผ’ 2.3.2. Technical Background ๏ผ”๏ผ— 2.3.3. Design Strategies ๏ผ”๏ผ™ 2.4. SUMMARY ๏ผ–๏ผ™ CHAPTER 3. DESIGNING CHATBOT FOR TRANSFORMATIVE REFLECTION ๏ผ—๏ผ‘ 3.1. DESIGN GOAL AND DECISIONS ๏ผ—๏ผ‘ 3.2. CHATBOT IMPLEMENTATION ๏ผ—๏ผ– 3.2.1. Emotional Intelligence ๏ผ—๏ผ– 3.2.2. Procedural Intelligence ๏ผ—๏ผ— 3.3. EXPERIMENTAL USER STUDY ๏ผ—๏ผ™ 3.3.1. Participants ๏ผ—๏ผ™ 3.3.2. Task ๏ผ˜๏ผ 3.3.3. Procedure ๏ผ˜๏ผ 3.3.4. Ethics Approval ๏ผ˜๏ผ 3.3.5. Surveys and Interview ๏ผ˜๏ผ‘ 3.4. RESULTS ๏ผ˜๏ผ’ 3.4.1. Survey Findings ๏ผ˜๏ผ’ 3.4.2. Qualitative Findings ๏ผ˜๏ผ“ 3.5. IMPLICATIONS ๏ผ˜๏ผ˜ 3.5.1. Articulating Hopes and Fears ๏ผ˜๏ผ™ 3.5.2. Designing for Guidance ๏ผ™๏ผ‘ 3.5.3. Rethinking Autonomy ๏ผ™๏ผ’ 3.6. SUMMARY ๏ผ™๏ผ” CHAPTER 4. DESIGNING CHATBOTS FOR EXPLAINING AND EXPLORING REFLECTIONS ๏ผ™๏ผ– 4.1. DESIGN GOAL AND DECISIONS ๏ผ™๏ผ– 4.1.1. Design Decisions for Basic Chat ๏ผ™๏ผ˜ 4.1.2. Design Decisions for Responsive Chat ๏ผ™๏ผ˜ 4.2. CHATBOT IMPLEMENTATION ๏ผ‘๏ผ๏ผ’ 4.2.1. Emotional Intelligence ๏ผ‘๏ผ๏ผ“ 4.2.2. Procedural Intelligence ๏ผ‘๏ผ๏ผ• 4.3. EXPERIMENTAL USER STUDY ๏ผ‘๏ผ๏ผ– 4.3.1. Participants ๏ผ‘๏ผ๏ผ– 4.3.2. Task ๏ผ‘๏ผ๏ผ— 4.3.3. Procedure ๏ผ‘๏ผ๏ผ— 4.3.4. Safeguarding of Study Participants and Ethics Approval ๏ผ‘๏ผ๏ผ˜ 4.3.5. Surveys and Interviews ๏ผ‘๏ผ๏ผ˜ 4.4. RESULTS ๏ผ‘๏ผ‘๏ผ‘ 4.4.1. Quantitative Findings ๏ผ‘๏ผ‘๏ผ‘ 4.4.2. Qualitative Findings ๏ผ‘๏ผ‘๏ผ˜ 4.5. IMPLICATIONS ๏ผ‘๏ผ’๏ผ— 4.5.1. Telling Stories to a Chatbot ๏ผ‘๏ผ’๏ผ˜ 4.5.2. Designing for Disclosure ๏ผ‘๏ผ“๏ผ 4.5.3. Rethinking Predictability and Transparency ๏ผ‘๏ผ“๏ผ’ 4.6. SUMMARY ๏ผ‘๏ผ“๏ผ“ CHAPTER 5. DESIGNING CHATBOTS FOR SELF-REFLECTION: SUPPORTING GUIDED DISCLOSURE ๏ผ‘๏ผ“๏ผ• 5.1. DESIGNING FOR GUIDED DISCLOSURE ๏ผ‘๏ผ“๏ผ™ 5.1.1. Chatbots as Virtual Confidante ๏ผ‘๏ผ“๏ผ™ 5.1.2. Routine and Variety in Interaction ๏ผ‘๏ผ”๏ผ‘ 5.1.3. Reflection as Continued Experience ๏ผ‘๏ผ”๏ผ” 5.2. TENSIONS IN DESIGN ๏ผ‘๏ผ”๏ผ• 5.2.1. Adaptivity ๏ผ‘๏ผ”๏ผ• 5.2.2. Autonomy ๏ผ‘๏ผ”๏ผ— 5.2.3. Algorithmic Affordance ๏ผ‘๏ผ”๏ผ˜ 5.3. MEANING-MAKING AS DESIGN METAPHOR ๏ผ‘๏ผ•๏ผ 5.3.1. Meaning in Reflection ๏ผ‘๏ผ•๏ผ‘ 5.3.2. Meaning-Making as Interaction ๏ผ‘๏ผ•๏ผ“ 5.3.3. Making Meanings with AI ๏ผ‘๏ผ•๏ผ• CHAPTER 6. CONCLUSION ๏ผ‘๏ผ•๏ผ˜ 6.1. RESEARCH SUMMARY ๏ผ‘๏ผ•๏ผ˜ 6.2. LIMITATIONS AND FUTURE WORK ๏ผ‘๏ผ–๏ผ‘ 6.3. FINAL REMARKS ๏ผ‘๏ผ–๏ผ“ BIBLIOGRAPHY ๏ผ‘๏ผ–๏ผ• ABSTRACT IN KOREAN ๏ผ‘๏ผ™๏ผ’Docto

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ง€๋ฆฌํ•™๊ณผ, 2015. 2. ๊ตฌ์–‘๋ฏธ.๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—…์ด ์„ฑ์ˆ™ํ•œ ๊ธฐ๊ฐ„์‚ฐ์—…์ด๋ผ๋Š” ์„ธ๊ฐ„์˜ ์ธ์‹๊ณผ๋Š” ๋‹ฌ๋ฆฌ ๊ธ‰๊ฒฉํ•œ ๋ณ€ํ™”๋ฅผ ๊ฒฝํ—˜ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ ์ง€์†์ ์ด๊ณ  ํ™œ๋ฐœํ•œ ๊ธฐ์ˆ ์ง€์‹ ํ™œ๋™์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์—์„œ ์‹œ์ž‘๋˜์—ˆ๋‹ค. ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—…์˜ ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ์˜ ํ˜•์„ฑ์„ ์‚ฐ์—…์˜ ๊ตฌ์กฐ์  ์ธก๋ฉด๊ณผ ๊ธฐ์—… ๋“ฑ ํ–‰์œ„์ž ๊ฐ„ ๊ด€๊ณ„ ์•ˆ์—์„œ ๊ณ ์ฐฐํ•จ์œผ๋กœ์จ ๊ธฐ์กด ํŠน์ • ์ง€์—ญ ํ˜น์€ ๊ธฐ์—… ์ค‘์‹ฌ์˜ ๋ถ„์„์—์„œ ๋ณด๊ธฐ ํž˜๋“ค์—ˆ๋˜ ๋ถ€๋ถ„์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ–ˆ๋‹ค. 1988๋…„๋ถ€ํ„ฐ 2013๋…„๊นŒ์ง€์˜ ํŠนํ—ˆ ๊ณต๋™ ์ถœ์› ์ž๋ฃŒ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—…์˜ ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ๋ฅผ ๊ตฌ์ถ•ํ–ˆ๊ณ , ๊ทผ์ ‘์„ฑ(proximity) ๊ฐœ๋…์„ ๋ฐ”ํƒ•์œผ๋กœ ๋„คํŠธ์›Œํฌ ํ˜•์„ฑ์˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๋ถ„์„ํ–ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—…์€ ๊ธฐ์ˆ ์  ๋‹ค์–‘ํ™” ๋ฐ ๊ณ ๋„ํ™”, ์กฐ์ง์  ํ†ตํ•ฉํ™” ๋ฐ ์œ ์—ฐํ™”, ์ง€๋ฆฌ์  ๋ถ„์‚ฐํ™” ๋ฐ ๋‹ค์ค‘์ž…์ง€์˜ ์ธก๋ฉด์—์„œ, ์ด์ „๊ณผ๋Š” ๋‹ค๋ฅธ ๊ฒฝํ–ฅ์„ ๋ณด์ธ๋‹ค. ์ฒ ๊ฐ•์‚ฐ์—…์€ 2000๋…„๋Œ€ ์ „ํ›„ ์ผ๋ จ์˜ ์ œ๋„ ๋ฐ ๊ตญ๋‚ด์™ธ์  ์‚ฐ์—… ํ™˜๊ฒฝ์˜ ๋ณ€ํ™” ๋“ฑ์„ ๊ฒฝํ—˜ํ•˜๋ฉด์„œ ๊ฒฝ์Ÿ์ ์ธ ์‹œ์žฅ ํ™˜๊ฒฝ์„ ๋ ๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ๊ธฐ์ˆ ์ ์œผ๋กœ ์ž๋™์ฐจ์‚ฐ์—… ๋ฐ ๋น„์ฒ ์‚ฐ์—… ๋“ฑ์œผ๋กœ ๋ฒ”์œ„๊ฐ€ ๋„“์–ด์กŒ์œผ๋ฉฐ ๊ณ ๊ฐ•๋„โ€ค๊ณ ๊ธฐ๋Šฅ ์ œํ’ˆ ๊ฐœ๋ฐœ์— ๋Œ€ํ•œ ์ˆ˜์š”๊ฐ€ ์ฆ๋Œ€๋˜์—ˆ๋‹ค. ์กฐ์ง์  ์ธก๋ฉด์—์„œ๋Š” ํฌ์Šค์ฝ”, ํ˜„๋Œ€, ๋™๊ตญ ๋“ฑ ๊ธฐ์—…์ง‘๋‹จ์„ ์ค‘์‹ฌ์œผ๋กœ ์ˆ˜์ง์  ํ†ตํ•ฉ์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ด์™€ ๋”๋ถˆ์–ด ์œ ์—ฐํ™”๊ฐ€ ์ง„ํ–‰๋˜๋ฉด์„œ ๋„คํŠธ์›Œํฌ ํ˜•์‹์˜ ๊ด€๊ณ„๋ฅผ ํ†ตํ•ด ์™ธ๋ถ€ ์ž์›์„ ํ™œ์šฉํ•˜๋Š” ๊ฒฝ์˜ ๊ธฐ๋ฒ•์˜ ์‚ฌ์šฉ์ด ์ฆ๊ฐ€ํ–ˆ๋‹ค. ์ง€๋ฆฌ์ ์œผ๋กœ๋Š” ๊ธฐ์กด ํฌํ•ญ์˜ ์ค‘์‹ฌ์„ฑ์ด ์™„ํ™”๋˜๋ฉด์„œ ์ˆ˜๋„๊ถŒ ๋ฐ ์ถฉ์ฒญ๊ถŒ์œผ๋กœ ๋ถ„์‚ฐ๋˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์ธ๋‹ค. ํŠนํžˆ ์ฒ ๊ฐ•์‚ฐ์—… ๋‚ด ์ฃผ์š” ํ–‰์œ„์ž์ธ ๋Œ€๊ธฐ์—…๋“ค์€ ๋‹ค์–‘ํ•œ ํˆฌ์ž์™€ ์ธ์ˆ˜โ€คํ•ฉ๋ณ‘, ๊ณต์žฅ ํ์‡„ ๋“ฑ์˜ ๊ณต๊ฐ„์ ์œผ๋กœ ์ƒ์‚ฐ ์„ค๋น„๋ฅผ ์กฐ์งํ™”ํ–ˆ์œผ๋ฉฐ, ์ด๋กœ ์ธํ•ด ๊ฐ ๊ธฐ์—…์€ ๊ฐ ๊ฑฐ์ ์— ์„ค๋น„๋ฅผ ๋‹ค์ค‘์ž…์ง€ํ•˜๋Š” ์–‘์ƒ์„ ๋ณด์ธ๋‹ค. ๋‘˜์งธ, ์ปค๋ฎค๋‹ˆํ‹ฐ ๋ถ„์„์„ ํ†ตํ•ด ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—…์˜ ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ๋Š” ์‚ฐ์—… ๋‚ด ์œ„๊ณ„, ๊ฒฝ์Ÿ ๊ด€๊ณ„, ์ œํœด ๊ด€๊ณ„ ๋“ฑ์„ ๋ฐ˜์˜ํ•˜๊ณ  ์žˆ์Œ์„ ๋ฐํ˜”๋‹ค. ์ดˆ๊ธฐ์—๋Š” ํฌ์Šค์ฝ”๊ฐ€ ์ „์ฒด ๋„คํŠธ์›Œํฌ์˜ ์ค‘์‹ฌ ํ–‰์œ„์ž๋กœ ์ค‘๊ฐœ์ž์˜ ์ค‘๊ฐœ์ž(broker of brokers) ์ง€์œ„๋ฅผ ์ ํ•˜๋Š” ๊ตฌ์กฐ์˜€์œผ๋‚˜, 2004๋…„ ์ดํ›„ ๋„คํŠธ์›Œํฌ ๋‚ด ํ–‰์œ„์ž๊ฐ€ ๋‹ค์–‘ํ•ด์ง€๋ฉด์„œ ๊ธฐ์ˆ ์ , ์ง€๋ฆฌ์  ์ €๋ณ€์ด ํ™•๋Œ€๋˜์—ˆ๋‹ค. ์ด์™€ ๋™์‹œ์— ๊ฐ ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ์ค‘์‹ฌํ–‰์œ„์ž๋ฅผ ํ—ˆ๋ธŒ๋กœ ํ•œ ์—ฐ๊ณ„์˜ ๋‚ด๋ถ€์„ฑ์ด ๊ฐ•ํ™”๋˜๋ฉด์„œ ๊ฐ•ํ•œ ํ—ˆ๋ธŒ์•ค์Šคํฌํฌ ๊ตฌ์กฐ๋ฅผ ํ˜•์„ฑํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ํŠนํžˆ ๋Œ€๊ธฐ์—… ๋ฐ ์—ฐ๊ตฌ์›์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ๊ฐ ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ์ค‘์‹ฌ ํ–‰์œ„์ž๋“ค์ด ์ปค๋ฎค๋‹ˆํ‹ฐ ๋‚ด๋ถ€์—์„œ ๋ฐฐํƒ€์  ์—ฐ๊ณ„๋ฅผ ํ˜•์„ฑํ•˜๊ณ , ๋™์‹œ์— ํƒ€ ์ปค๋ฎค๋‹ˆํ‹ฐ์™€ ์ค‘๊ฐœ ํ–‰์œ„๋ฅผ ํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ๋„คํŠธ์›Œํฌ๊ฐ€ ์„ฑ์žฅํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋„คํŠธ์›Œํฌ ์—ฐ๊ณ„ ํ˜•์„ฑ์˜ ์—ญ๋™์„ฑ์— ์‚ฐ์—… ๋‚ด ์œ„๊ณ„, ๊ฒฝ์Ÿ, ์ œํœด ๋“ฑ ์กฐ์ง์  ๋ฐฐ์น˜ ๋…ผ๋ฆฌ๊ฐ€ ๋‚ด์žฌํ•ด ์žˆ์Œ์„ ๊ณ ์ฐฐํ–ˆ๋‹ค. ํ•œํŽธ ์˜ˆ์™ธ์ ์œผ๋กœ ๋น„-์˜๋ฆฌ์กฐ์ง๊ณผ ์ค‘ํ•ต๊ธฐ์—…์˜ ์ค‘๊ฐœ์ž ์—ญํ• ์ด ๊ด€์ฐฐ๋˜์—ˆ๋Š”๋ฐ, ํŠนํžˆ 2004๋…„ ์ดํ›„ ์ผ๊ด€์ œ์ฒ ์—…์— ์ง„์ถœํ•œ ํ˜„๋Œ€์ œ์ฒ ๊ณผ ๊ธฐ์กด ์ œ์ฒ  ๊ธฐ์ˆ  ์—ญ๋Ÿ‰์„ ๋ณด์œ ํ•œ ํฌ์Šค์ฝ” ๊ฐ„์˜ ์ง€์‹์„ ์ค‘๊ฐœํ•˜๋Š” ๋ฐ์— ์ค‘ํ•ต๊ธฐ์—…์ด ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ๋„คํŠธ์›Œํฌ ์ถ”๋ก ํ†ต๊ณ„ ๊ธฐ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ LR-QAP ๋ชจํ˜•์„ ๊ตฌ์ถ•ํ•˜์—ฌ ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—… ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ์˜ ํ˜•์„ฑ์— ํ–‰์œ„์ž ๊ฐ„ ๊ธฐ์ˆ ์ , ์กฐ์ง์ , ์ง€๋ฆฌ์  ๊ทผ์ ‘์„ฑ์ด ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ๋ ฅ์„ ๋ฏธ์นจ์„ ๊ทœ๋ช…ํ–ˆ๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ์ „๋ฐ˜์ ์œผ๋กœ ๊ธฐ์ˆ ์ , ์กฐ์ง์ , ์ง€๋ฆฌ์  ๊ทผ์ ‘์„ฑ์ด ๋†’์„์ˆ˜๋ก ์ƒํ˜ธ ํ•™์Šต ๋ฐ ๊ธฐ์ˆ  ์ฐฝ์ถœ ํ™•๋ฅ ์ด ๋†’์•„์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‚˜์•„๊ฐ€ ๊ฐ ์ฐจ์›์˜ ๊ทผ์ ‘์„ฑ์— ๋Œ€ํ•œ ์—ญ-U ๊ด€๊ณ„ ์กด์žฌ ์—ฌ๋ถ€๋ฅผ ๊ฒ€์ฆํ•œ ๊ฒฐ๊ณผ, ๊ธฐ์ˆ ์ โ€ค์ง€๋ฆฌ์  ์ฐจ์›์—๋Š” ์„ ํ˜• ๊ด€๊ณ„๊ฐ€, ์กฐ์ง์  ์ฐจ์›์—๋Š” ๋ฌผ๊ฒฐ ํ˜•ํƒœ์˜ ๊ด€๊ณ„๊ฐ€ ์กด์žฌํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ฆ‰, ๋ณธ ๋ถ„์„์€ ์„ ํ–‰์—ฐ๊ตฌ์—์„œ ์ด๋ก ์ , ์‹ค์ฆ์ ์œผ๋กœ ์ œ์‹œํ–ˆ๋˜ ์—ญ-U ๊ด€๊ณ„์™€๋Š” ๋‹ค์†Œ ์ƒ์ดํ•œ ์–‘์ƒ์„ ๋ณด์ธ๋‹ค. ๋Œ€๊ธฐ์—… ๋ฐ ์—ฐ๊ตฌ์›์˜ ์ค‘์‹ฌ์  ์—ญํ• ๊ณผ ๋ฐฐํƒ€์  ์—ฐ๊ณ„ ํ˜•์„ฑ์ด๋ผ๋Š” ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—… ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ์˜ ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ๋‚˜ํƒ€๋‚œ ๊ฒฐ๊ณผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ์ด๋Š” ๊ธฐ์กด ํ˜์‹ ์ง€๋ฆฌํ•™์—์„œ ์ฃผ๋กœ ๋‹ค์ˆ˜์˜ ์ค‘์†Œ๊ธฐ์—… ๋ฐ ์—ฐ๊ตฌ๊ธฐ๊ด€์œผ๋กœ ๊ตฌ์„ฑ๋œ ์ฒจ๋‹จ ์‚ฐ์—…์„ ๋‹ค๋ฃจ์—ˆ๋˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์™€๋Š” ๋‹ค์†Œ ์ƒ์ดํ•œ ๊ฒฐ๊ณผ๋กœ, ์‚ฐ์—… ๋‚ด ์œ„๊ณ„์„ฑ์ด ์กด์žฌํ•˜๋Š” ๋Œ€๋‹ค์ˆ˜์˜ ์„ฑ์ˆ™์‚ฐ์—… ๋ฐ ์žฅ์น˜์‚ฐ์—…์—์„œ ๋ณธ ์—ฐ๊ตฌ์™€ ์œ ์‚ฌํ•œ ํŠน์„ฑ์„ ๋ณด์ผ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—…์˜ ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ๊ฐ€ ์‚ฐ์—… ์ „๋ฐ˜์˜ ๊ธฐ์ˆ ์ โ€ค์กฐ์ง์ โ€ค์ง€๋ฆฌ์  ๋ณ€ํ™” ์•ˆ์—์„œ, ๋Œ€๊ธฐ์—…์„ ์ค‘์‹ฌ์œผ๋กœ ๊ทธ๋“ค์˜ ๊ธฐ์ˆ ์  ํ•„์š”์„ฑ๊ณผ ์ง€๋ฆฌ์  ์šฉ์ดํ•จ์— ๋”ฐ๋ผ ํ์‡„์ ์œผ๋กœ ์กฐ์งํ™”๋˜๋Š” ๋ฐฉ์‹์œผ๋กœ ํ˜•์„ฑ๋˜์—ˆ์Œ์„ ๊ณ ์ฐฐํ–ˆ๋‹ค. ์ด๋Š” ์ค‘์‹ฌ ํ–‰์œ„์ž ๋ฐ ๊ธฐ์—…์ง‘๋‹จ์˜ ์ฃผ๋„์„ฑ์ด ๋งค์šฐ ํฌ๋‹ค๋Š” ์‚ฐ์—… ๋‚ด ์œ„๊ณ„์  ์„ฑ๊ฒฉ์— ๊ธฐ์ธํ•œ ๊ฒƒ์ด๋‹ค. ๋™์‹œ์— ์ด๋Ÿฌํ•œ ๋„คํŠธ์›Œํฌ์— ๋‚ด์žฌ๋œ ๋น„๋Œ€์นญ์  ๊ถŒ๋ ฅ๊ณผ ํ์‡„์„ฑ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๋น„์˜๋ฆฌ์กฐ์ง๊ณผ ์ค‘ํ•ต๊ธฐ์—…์˜ ์ค‘๊ฐœ์ž ์—ญํ• ์ด ์ƒํ˜ธ ํ•™์Šต ๋ฐ ํ˜์‹ ์—, ๋‚˜์•„๊ฐ€ ์‚ฐ์—… ๋ฐœ์ „์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค ๋˜ํ•œ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•™์ˆ ์  ์ธก๋ฉด์—์„œ ์„ฑ์ˆ™์‚ฐ์—…์„ ๋‹ค๋ฃธ์œผ๋กœ์จ ํ˜์‹  ์—ฐ๊ตฌ ๋‚ด ๋…ผ์˜ ๋‹ค์–‘ํ™”์— ๊ธฐ์—ฌํ–ˆ์œผ๋ฉฐ, ๋ถ„์„์˜ ๊ฒฝ๊ณ„๋ฅผ ๋„คํŠธ์›Œํฌ ํ–‰์œ„์ž ์ฐจ์›์—์„œ ๊ฒฐ์ •ํ•จ์œผ๋กœ์จ ๊ตญ๊ฐ€ ์Šค์ผ€์ผ์—์„œ์˜ ์ง€๋ฆฌ์  ๋ณ€ํ™”๋ฅผ ๋‹ค๋ฃจ์—ˆ๋‹ค๋Š” ์˜์˜๋ฅผ ๊ฐ–๋Š”๋‹ค. ๋ฐฉ๋ฒ•๋ก ์  ์ธก๋ฉด์—์„œ๋Š” ๊ธฐ์กด ๊ฒฝ์ œ์ง€๋ฆฌํ•™ ๋‚ด QAP ๋ชจํ˜•์—์„œ ๋„คํŠธ์›Œํฌ ๋‚ด์ƒ ๋ณ€์ˆ˜๋ฅผ ์ถ”๊ฐ€ํ–ˆ๋‹ค๋Š” ํ•œ๊ณ„๋ฅผ ์กฐ์ง์  ๊ทผ์ ‘์„ฑ ๋ณ€์ˆ˜์˜ ๊ตฌ์ถ•์„ ํ†ตํ•ด ๋ณด์™„ํ–ˆ๋‹ค. ํŠนํžˆ ๊ธฐ์กด ๊ทผ์ ‘์„ฑ ์—ฐ๊ตฌ์—์„œ ์ถฉ๋ถ„ํžˆ ๋‹ค๋ฃจ์ง€ ๋ชปํ–ˆ๋˜ ์กฐ์ง์  ๊ทผ์ ‘์„ฑ์„ ์„ธ๋ถ„ํ™”ํ•˜์—ฌ ์ธก์ •ํ–ˆ์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ์—ญ-U ๊ด€๊ณ„ ์กด์žฌ ์—ฌ๋ถ€๋ฅผ ์‹ค์ฆ์ ์œผ๋กœ ๊ฒ€์ฆํ–ˆ๋‹ค. ์ œโ… ์žฅ ์„œ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์  1 ์ œ2์ ˆ ์—ฐ๊ตฌ ๋Œ€์ƒ 4 1. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 4 2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 5 ์ œ3์ ˆ ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 10 ์ œโ…ก์žฅ ๋ฌธํ—Œ ์—ฐ๊ตฌ ๋ฐ ๋ถ„์„ํ‹€ 12 ์ œ1์ ˆ ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ์™€ ๊ทผ์ ‘์„ฑ ์—ฐ๊ตฌ 12 1. ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ ์—ฐ๊ตฌ 12 2. ๊ทผ์ ‘์„ฑ ์—ฐ๊ตฌ 15 ์ œ2์ ˆ ๋„คํŠธ์›Œํฌ ๋ถ„์„ ๋ฐฉ๋ฒ•๋ก  24 1. ๋„คํŠธ์›Œํฌ ๋ถ„์„์˜ ๋ชฉ์  ๋ฐ ๋ฒ”์ฃผ 24 2. ๋„คํŠธ์›Œํฌ ๋ถ„์„ ๊ธฐ๋ฒ• 25 ์ œ3์ ˆ ๋ถ„์„ํ‹€ 29 ์ œโ…ข์žฅ ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—… ํ˜„ํ™ฉ 31 ์ œ1์ ˆ ์ฒ ๊ฐ•์‚ฐ์—…์˜ ์ฃผ์š” ํŠน์„ฑ 31 1. ์ฒ ๊ฐ•์‚ฐ์—… ๊ฐœ์š” 31 2. ์ฒ ๊ฐ• ์ œ์กฐ๊ณต์ • ๋ฐ ์ œํ’ˆ 32 3. ์ฒ ๊ฐ•์‚ฐ์—…์˜ ํŠน์ง• 37 ์ œ2์ ˆ ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—…์˜ ๋ณ€ํ™” 40 1. ๊ธฐ์ˆ ์  ๋‹ค์–‘ํ™” ๋ฐ ๊ณ ๋„ํ™” 40 2. ์กฐ์ง์  ํ†ตํ•ฉํ™” ๋ฐ ์œ ์—ฐํ™” 46 3. ์ง€๋ฆฌ์  ๋ถ„์‚ฐํ™” ๋ฐ ๋‹ค์ค‘์ž…์ง€ 51 ์ œ3์ ˆ ์†Œ๊ฒฐ 56 ์ œโ…ฃ์žฅ ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—… ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ์˜ ํ˜•์„ฑ ๊ณผ์ • 58 ์ œ1์ ˆ ํŠนํ—ˆ ๊ณต๋™ ์ถœ์›๊ณผ ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ 58 1. ํŠนํ—ˆ ์ž๋ฃŒ์™€ ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ 58 2. ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—… ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ์˜ ๋ฐœ์ „ ์–‘์ƒ 59 ์ œ2์ ˆ ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—… ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ์˜ ํŠน์„ฑ 64 1. ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—… ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ์˜ ์ „์—ญ์  ํŠน์„ฑ 64 2. ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—… ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ์˜ ๊ตญ์ง€์  ํŠน์„ฑ 72 ์ œ3์ ˆ ์†Œ๊ฒฐ 82 ์ œโ…ค์žฅ ํ•œ๊ตญ ์ฒ ๊ฐ•์‚ฐ์—… ํ–‰์œ„์ž ๊ฐ„ ๊ทผ์ ‘์„ฑ๊ณผ ๊ธฐ์ˆ ์ง€์‹ ๋„คํŠธ์›Œํฌ 86 ์ œ1์ ˆ ๋ชจํ˜• ๊ตฌ์ถ• 86 1. ๊ธฐ์ˆ ์  ๊ทผ์ ‘์„ฑ 88 2. ์กฐ์ง์  ๊ทผ์ ‘์„ฑ 89 3. ์ง€๋ฆฌ์  ๊ทผ์ ‘์„ฑ 95 4. ๋ณ€์ˆ˜ ๊ธฐ์ˆ ํ†ต๊ณ„ 97 ์ œ2์ ˆ ๋ถ„์„ ๊ฒฐ๊ณผ 99 1. ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ 99 2. LR-QAP ๋ชจํ˜• ๊ตฌ์ถ• ๊ฒฐ๊ณผ 100 ์ œ3์ ˆ ์†Œ๊ฒฐ 104 ์ œโ…ฅ์žฅ ๊ฒฐ๋ก  108 ์ฐธ๊ณ  ๋ฌธํ—Œ 113 IPC ์ฝ”๋“œ ์ •๋ณด 123 LR-QAP ๋ชจํ˜• ๋ณ€์ˆ˜ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ํ‘œ 124Maste

    An Association Rule Mining-Based Framework for Understanding Lifestyle Risk Behaviors

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋ณด๊ฑด๋Œ€ํ•™์› : ๋ณด๊ฑดํ•™๊ณผ, 2014. 8. ๊น€ํ˜ธ.๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ : ๊ฑด๊ฐ•ํ•œ ์ƒํ™œ์Šต๊ด€์€ ์งˆ๋ณ‘ ์˜ˆ๋ฐฉ๊ณผ ์กฐ๊ธฐ์‚ฌ๋ง์˜ ์˜ˆ๋ฐฉ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํ‰์ƒ์‹œ์˜ ์‹ ์ฒด๊ธฐ๋Šฅ์„ ์ ์ ˆํžˆ ์œ ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ์ค‘์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ง€๊ธˆ๊นŒ์ง€์˜ ์—ฐ๊ตฌ์— ์˜ํ•˜๋ฉด ์ด์— ๋ฐ˜ํ•˜๋Š” ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋Š” ์ค„์–ด๋“ค์ง€ ์•Š๊ณ , ๊ทธ๋กœ ์ธํ•œ ์งˆ๋ณ‘ ๋ถ€๋‹ด์€ ์ ์  ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋Š” ์‹ค์ •์ด๋‹ค. ๊ฑด๊ฐ•ํ–‰์œ„ ๋ฐ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋Š” ๊ฐœ๋ณ„์ , ๋…๋ฆฝ์ ์ด์ง€ ์•Š๊ณ  ์ƒํ˜ธ ์—ฐ๊ด€๋˜์–ด ํ–‰ํ•ด์ง„๋‹ค. ์ด๋ฅผ ๊ตฐ์ง‘ํ˜„์ƒ์ด๋ผ๊ณ  ํ•˜๋Š”๋ฐ, ์ง€๊ธˆ๊นŒ์ง€์˜ ๊ฑด๊ฐ•ํ–‰์œ„ ๊ตฐ์ง‘ ์—ฐ๊ตฌ๋Š” ํก์—ฐ, ์Œ์ฃผ, ์‹ ์ฒดํ™œ๋™ ๋ถ€์กฑ ๋“ฑ ์ผ๋ถ€ ํ–‰์œ„๋งŒ์„ ๋ถ„์„ํ•˜๊ฑฐ๋‚˜, ๊ฑด๊ฐ•ํ–‰์œ„ ๊ฐœ์ˆ˜์˜ ๋‹จ์ˆœํ•œ ํ•ฉ์„ ์ด์šฉํ•œ ๋ถ„์„์— ๊ทธ์ณ ์ƒํ˜ธ์—ฐ๊ด€์„ฑ์ด ์žˆ๋Š” ๊ตฌ์ฒด์ ์ธ ์œ„ํ—˜ํ–‰์œ„๋ฅผ ์•Œ ์ˆ˜ ์—†์—ˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์—ฐ๊ด€์„ฑ๋ถ„์„(Association rule mining: ARM)์„ ์ด์šฉํ•˜์—ฌ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์˜ ์œ ํ˜•์„ ๋ถ„์„ํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘ ์œ ํ˜• ๋ถ„์„ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์œ„ํ—˜ํ–‰์œ„๋ฅผ ํ•จ๊ป˜ ํ•˜๋Š” ๋Œ€์ƒ์ž๋“ค์„ ์œ„ํ•œ ๊ฑด๊ฐ•ํ–‰์œ„๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ํŠน์ • ์œ„ํ—˜ํ–‰์œ„๋ฅผ ํ•˜๋Š” ๋Œ€์ƒ์ž์˜ ๋˜ ๋‹ค๋ฅธ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„(co-occurring behavior)์˜ ์˜ˆ์ธก์— ์œ ์šฉํ•˜๊ฒŒ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ถ„์„์„ ์œ„ํ•˜์—ฌ ์ œ4๊ธฐ ๊ตญ๋ฏผ๊ฑด๊ฐ•์˜์–‘์กฐ์‚ฌ ์ž๋ฃŒ(2007~2008) ์ค‘ ๊ฑด๊ฐ•ํ–‰์œ„ ์„ค๋ฌธ์— ์‘๋‹ตํ•œ 20์„ธ ์ด์ƒ์˜ ์„ฑ์ธ 14,833๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ฑด๊ฐ•ํ–‰์œ„์˜ ์ง€์นจ์„ ์ง€ํ‚ค์ง€ ์•Š๋Š” ๊ฒฝ์šฐ๋ฅผ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋กœ ์ •์˜ํ•˜์˜€์œผ๋ฉฐ, ์—ฌ๋Ÿฌ ๋ฌธํ—Œ๊ณผ ์—ฐ๊ตฌ์—์„œ ์ค‘์š”ํ•˜๊ฒŒ ์ œ์‹œํ•˜๊ณ  ์žˆ๋Š” ํก์—ฐ, ๊ณผ๋„ํ•œ ์Œ์ฃผ, ์‹ ์ฒด์  ๋น„ํ™œ๋™, ๋ถ€์ ์ ˆํ•œ ์ฒด์ค‘, ๋ถ€์ ์ ˆํ•œ ์ˆ˜๋ฉด์‹œ๊ฐ„, ์•„์นจ ๊ฒฐ์‹, ์žฆ์€ ๊ฐ„์‹ ์„ญ์ทจ ๋“ฑ 7๊ฐ€์ง€ ์œ„ํ—˜ํ–‰์œ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ฒซ์งธ, ์ „์ฒด ๋Œ€์ƒ์ž๋“ค์˜ ์ผ๋ฐ˜์  ํŠน์ง•๊ณผ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„์— ๋Œ€ํ•œ ๋ถ„ํฌ๋ฅผ ๋‚จ๋…€๋กœ ๋‚˜๋ˆ„์–ด ์‚ดํŽด๋ณด์•˜๋‹ค. ๋Œ€์ƒ์ž๋“ค์ด ํ–‰ํ•˜๊ณ  ์žˆ๋Š” ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ฐœ์ˆ˜์˜ ํ•ฉ์„ ์ด์šฉํ•˜์—ฌ, ๊ทธ๋ฆฌ๊ณ  ์ „์ฒด ๋Œ€์ƒ์ž๋“ค์˜ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„์˜ ํ™•๋ฅ ์„ ๊ทผ๊ฑฐ๋กœ ํ•œ ๊ด€์ฐฐ์น˜ ์ˆ˜์™€ ๊ธฐ๋Œ€์น˜ ์ˆ˜์˜ ๋น„(O/E ratio)๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ตฐ์ง‘ํ˜„์ƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘˜์งธ, ARM์„ ์ด์šฉํ•˜์—ฌ 7๊ฐ€์ง€ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๊ฐ€ ํ•จ๊ป˜ ์ผ์–ด๋‚˜๋Š” ์œ ํ˜•์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ARM ์—ฐ๊ด€๊ทœ์น™์˜ ํ†ต๊ณ„์  ์œ ์˜์„ฑ ํ‰๊ฐ€๋Š” chi-square test๋ฅผ ์ด์šฉํ•˜์˜€๋‹ค. ์—ฐ๊ด€๊ทœ์น™์— ํฌํ•จ๋˜๋Š” ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋“ค์„ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์ด๋ผ๊ณ  ์ •์˜ํ•˜๊ณ  ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์˜ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ํ˜•ํƒœ๋ฅผ ์œ ํ˜•ํ™”ํ•˜์˜€๋‹ค. ๋Œ€ํ‘œ์ ์ธ ๊ตฐ์ง‘์„ ์ฐพ์•„ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์˜ ์ธ๊ตฌ์‚ฌํšŒํ•™์  ํŠน์„ฑ, ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ํŠน์„ฑ, ๊ฑด๊ฐ• ์ˆ˜์ค€ ํŠน์„ฑ ๋“ฑ์„ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋‹ค์ค‘ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ชจํ˜•์„ ์ด์šฉํ•˜์—ฌ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์„ ์˜ˆ์ธกํ•˜๋Š” ๋ชจ๋ธ๋ง์„ ํ•˜์˜€๋‹ค. ์…‹์งธ, ๋ณธ ์—ฐ๊ตฌ์˜ ARM ๊ฒฐ๊ณผ๋ฅผ ๋‹ค๋ฅธ ๊ฑด๊ฐ•์กฐ์‚ฌ ์ž๋ฃŒ(์ง€์—ญ์‚ฌํšŒ ๊ฑด๊ฐ•์กฐ์‚ฌ)์— ์ ์šฉํ•˜์—ฌ ๊ฒฐ๊ณผ๊ฐ’์ด ์œ ์˜ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š”์ง€ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: ํ•œ๊ตญ์ธ์—๊ฒŒ ๊ฐ€์žฅ ๋งŽ์€ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋Š” ์‹ ์ฒดํ™œ๋™ ๋ถ€์กฑ ๋ฐ ๋ถ€์ ์ ˆํ•œ ์ˆ˜๋ฉด์‹œ๊ฐ„์ด์—ˆ๋‹ค. ๋˜ํ•œ ์ „์ฒด ๋Œ€์ƒ์ž์˜ 69.8%๋Š” 2๊ฐ€์ง€ ์ด์ƒ์˜ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋ฅผ ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 7๊ฐ€์ง€๋ฅผ ๋ชจ๋‘ ํ•˜๋Š” ๋Œ€์ƒ์ž๋Š” ์—†์—ˆ์œผ๋ฉฐ, ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋ฅผ ํ•˜๋‚˜๋„ ํ•˜์ง€ ์•Š๋Š” ๊ฐ€์žฅ ์ด์ƒ์ ์ธ ๋Œ€์ƒ์ž๋Š” 5.0%์˜€๋‹ค. ๋‚จ์ž ๋Œ€์ƒ์ž๋“ค์˜ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๋น„์œจ์ด ๋” ๋†’์•˜๋‹ค. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ 2๊ฐœ, 3๊ฐœ, 4๊ฐœ, 5๊ฐœ, 6๊ฐœ ๋“ฑ์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ๊ตฐ์ง‘ํ˜„์ƒ์ด ๊ด€์ฐฐ๋˜์—ˆ์œผ๋ฉฐ, ์œ„์™€ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ๋‚จ์ž ๋Œ€์ƒ์ž์—์„œ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘ํ˜„์ƒ์ด ๋” ๋นˆ๋ฒˆํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์—์„œ ๊ฐ€์žฅ ๋งŽ์ด ๋‚˜ํƒ€๋‚˜๋Š” ํ–‰์œ„๋Š” ๋ถ€์ ์ ˆํ•œ ์ˆ˜๋ฉด, ์‹ ์ฒด์  ๋น„ํ™œ๋™, ํ˜„์žฌ ํก์—ฐ ๋“ฑ์ด์—ˆ๋‹ค. ๊ตฐ์ง‘๋งˆ๋‹ค ํŠน์ • ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋“ค์ด ์žˆ์—ˆ์œผ๋ฉฐ, ๊ทธ๊ฒƒ๋“ค์€ 2~3๊ฐœ์˜ ๋‹ค๋ฅธ ์œ„ํ—˜ํ–‰์œ„์™€ ํ•จ๊ป˜ ๊ตฐ์ง‘์„ ์ด๋ฃจ๊ณ  ์žˆ์—ˆ๋‹ค. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ์—ฐ๊ด€๊ทœ์น™์œผ๋กœ ์‚ดํŽด๋ณธ ๋Œ€ํ‘œ์ ์ธ ๊ตฐ์ง‘์€ ์‹ ์ฒด์  ๋น„ํ™œ๋™, ํ˜„์žฌ ํก์—ฐ, ๋ถ€์ ์ ˆํ•œ ์ˆ˜๋ฉด์‹œ๊ฐ„์˜ ๊ตฐ์ง‘์œผ๋กœ ์ด๋Š” ๋‚จ๋…€์—์„œ ๊ณตํ†ต์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค(์—ฌ: Lift 1.06, OR 1.29, 95% CI 1.05-1.58๋‚จ: Lift 1.01, OR 1.26, 95% CI 1.09-1.45). ๋‚จ์ž ๋Œ€์ƒ์ž๋“ค์—์„œ๋Š” ์‹ ์ฒด์  ๋น„ํ™œ๋™, ๋ถ€์ ์ ˆํ•œ ์ฒด์ค‘, ์•„์นจ๊ฒฐ์‹, ํก์—ฐ์˜ ๊ตฐ์ง‘(Lift 1.52, OR 2.87, 95% CI 2.23-3.70)์ด ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„์— ๋Œ€ํ•œ ์—ฐ๊ด€๊ทœ์น™์€ ์ง€์—ญ์‚ฌํšŒ ๊ฑด๊ฐ•์กฐ์‚ฌ ์ž๋ฃŒ์—์„œ๋„ ์œ ์˜ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์˜ ๋ถ„ํฌ๋„ ๋น„์Šทํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฒฐ๋ก : ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋Š” ๋…๋ฆฝ์ ์ด์ง€ ์•Š๊ณ  ์ƒํ˜ธ ์—ฐ๊ด€์„ฑ์„ ๊ฐ–๊ณ  ํ•จ๊ป˜ ๋‚˜ํƒ€๋‚œ๋‹ค. ARM์„ ์ด์šฉํ•˜์—ฌ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์˜ ์œ ํ˜•์„ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๊ณ , ARM์„ ๋‹ค๋ฅธ ๊ฑด๊ฐ•์กฐ์‚ฌ ์ž๋ฃŒ ๋ถ„์„์—๋„ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฑด๊ฐ•ํ–‰์œ„ ๋Œ€์ƒ์ž๋“ค์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋†’์ด๊ณ , ๊ทธ๋“ค์—๊ฒŒ ํ•„์š”ํ•œ ์œ„ํ—˜ํ–‰์œ„ ์˜ˆ๋ฐฉ์— ๋Œ€ํ•œ ์ค‘์žฌ๋ฅผ ๊ณ„ํšํ•  ์ˆ˜ ์žˆ๋‹ค.โ… . ์„œ๋ก  ๏ผ‘ 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ ๏ผ‘ 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๏ผ” โ…ก. ๋ฌธํ—Œ๊ณ ์ฐฐ ๏ผ– 1. ๊ฑด๊ฐ•ํ–‰์œ„ ๊ตฐ์ง‘ํ˜„์ƒ ๏ผ– 2. ๊ฑด๊ฐ•ํ–‰์œ„ ๊ตฐ์ง‘ํ˜„์ƒ์˜ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ๏ผ— 2.1 Correlation(์ƒ๊ด€๊ด€๊ณ„) ๏ผ— 2.2 Accumulation(ํ–‰์œ„์˜ ๊ฐœ์ˆ˜ ํ•ฉ์„ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•) ๏ผ— 2.3 O/E ratio(๊ด€์ฐฐ์น˜์™€ ๊ธฐ๋Œ€๋นˆ๋„์˜ ๋น„) ๏ผ˜ 2.4 K-means cluster analysis ๏ผ™ 2.5 ์™ธ๊ตญ์˜ ๊ฑด๊ฐ•ํ–‰์œ„ ๊ตฐ์ง‘ ์—ฐ๊ตฌ ๏ผ‘๏ผ 2.6 ์šฐ๋ฆฌ๋‚˜๋ผ์—์„œ ๊ฑด๊ฐ•ํ–‰์œ„ ๊ตฐ์ง‘์„ ์ด์šฉํ•œ ์—ฐ๊ตฌ ๏ผ‘๏ผ“ 3. ์—ฐ๊ด€์„ฑ ๋ถ„์„(ARM: Association Rule Mining) ๏ผ‘๏ผ• 3.1 ARM(Apriori algorithm & minimum support) ๏ผ‘๏ผ– 3.2 Support, Confidence, and Lift ๏ผ‘๏ผ— 3.3 Lift์™€ OR, RR์˜ ๋น„๊ต ๏ผ‘๏ผ˜ 3.4 ARM์˜ ํ™œ์šฉ ๏ผ’๏ผ 3.5 ARM์˜ ์žฅ์  ๋ฐ ๋‹จ์  ๏ผ’๏ผ 4. ๊ฑด๊ฐ•๊ฒฐ์ • ์š”์ธ ๏ผ’๏ผ‘ 5. ๊ฑด๊ฐ• ์ˆ˜์ค€ ๏ผ’๏ผ’ 5.1 ์ฃผ๊ด€์  ๊ฑด๊ฐ• ๏ผ’๏ผ“ 5.2 ๋งŒ์„ฑ์งˆ๋ณ‘ ๏ผ’๏ผ” 6. ์ŠคํŠธ๋ ˆ์Šค ๏ผ’๏ผ– 7. ๊ฑด๊ฐ•ํ–‰์œ„์™€ ๊ฑด๊ฐ• ๏ผ’๏ผ— 7.1 ํก์—ฐ ๏ผ’๏ผ™ 7.2 ๊ณผ๋„ํ•œ ์Œ์ฃผ ๏ผ“๏ผ 7.3 ์‹ ์ฒด์  ๋น„ํ™œ๋™ ๏ผ“๏ผ‘ 7.4 ๋ถ€์ ์ ˆํ•œ ์ฒด์ค‘(์ €์ฒด์ค‘ ๋˜๋Š” ๋น„๋งŒ) ๏ผ“๏ผ’ 7.5 ๋ถ€์ ์ ˆํ•œ ์ˆ˜๋ฉด ์‹œ๊ฐ„ ๏ผ“๏ผ“ 7.6 ์•„์นจ ๊ฒฐ์‹ ๏ผ“๏ผ“ 7.7 ์žฆ์€ ๊ฐ„์‹ ์„ญ์ทจ ๏ผ“๏ผ” 8. ์‚ฌํšŒ์ธ๊ตฌํ•™์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ๊ฑด๊ฐ•ํ–‰์œ„ ๏ผ“๏ผ• 8.1 ์„ฑ๋ณ„ ๏ผ“๏ผ• 8.2 ์—ฐ๋ น ๏ผ“๏ผ• 8.3 ๊ฒฐํ˜ผ ๏ผ“๏ผ– 9. ์‚ฌํšŒ๊ฒฝ์ œ์  ์š”์ธ๊ณผ ๊ฑด๊ฐ•ํ–‰์œ„ ๏ผ“๏ผ— 9.1 ๊ต์œก ๏ผ“๏ผ™ 9.2 ์ง์—… ๏ผ“๏ผ™ 9.3 ์†Œ๋“ ๏ผ”๏ผ โ…ข. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ๏ผ”๏ผ‘ 1. ์ž๋ฃŒ ๋ฐ ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ ๏ผ”๏ผ‘ 2. ๋ณ€์ˆ˜ ์ •์˜ ๏ผ”๏ผ’ 2.1 ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๋ณ€์ˆ˜ ๏ผ”๏ผ’ 2.2 ๊ฑด๊ฐ• ์ˆ˜์ค€ ๋ณ€์ˆ˜ ๏ผ”๏ผ“ 2.3 ๊ฐœ์ธ์˜ ํŠน์„ฑ ๋ณ€์ˆ˜ ๏ผ”๏ผ• 3. ์—ฐ๊ตฌ์˜ ๋ชจํ˜• ๏ผ”๏ผ– 4. ๋ถ„์„๋ฐฉ๋ฒ• ๏ผ”๏ผ— 4.1 ๋Œ€์ƒ์ž๋“ค์˜ ํŠน์ง• ๏ผ”๏ผ˜ 4.2 ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘ํ˜„์ƒ ๏ผ”๏ผ˜ 4.3 ARM ๊ฒฐ๊ณผ์˜ ํ‰๊ฐ€ ๋ฐ ๋‹ค๋ฅธ ๋ฐ์ดํ„ฐ์— ์ ์šฉ ๏ผ•๏ผ“ 4.4 ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘ ์˜ˆ์ธก ์š”์ธ ๏ผ•๏ผ“ 4.5 ๋ถ„์„์— ์‚ฌ์šฉํ•œ ํ†ต๊ณ„ ํŒจํ‚ค์ง€ ๏ผ•๏ผ“ โ…ฃ. ๊ฒฐ๊ณผ ๏ผ•๏ผ” 1. ๋Œ€์ƒ์ž๋“ค์˜ ํŠน์ง• ๏ผ•๏ผ” 1.1 ๋Œ€์ƒ์ž๋“ค์˜ ์ผ๋ฐ˜์  ํŠน์ง• ๏ผ•๏ผ” 1.2 ๋Œ€์ƒ์ž๋“ค์˜ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ํŠน์ง• ๏ผ•๏ผ– 1.3 ๋Œ€์ƒ์ž๋“ค์˜ ๊ฑด๊ฐ• ์ˆ˜์ค€ ํŠน์ง• ๏ผ•๏ผ˜ 2. O/E ratio๋กœ ๋ณธ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘ํ˜„์ƒ ๏ผ–๏ผ 3. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ARM ๊ฒฐ๊ณผ ๏ผ–๏ผ“ 4. ARM ๊ฒฐ๊ณผ์˜ ํ‰๊ฐ€ ๏ผ–๏ผ— 4.1 ์—ฌ์ž ๋Œ€์ƒ์ž์—์„œ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๏ผ–๏ผ— 4.2 ๋‚จ์ž ๋Œ€์ƒ์ž์—์„œ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๏ผ–๏ผ˜ 5. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์ด ๋‚˜ํƒ€๋‚˜๋Š” ๋Œ€์ƒ์ž๋“ค์˜ ํŠน์ง• ๏ผ—๏ผ 5.1 ์—ฌ์ž ๋Œ€์ƒ์ž์—์„œ ์„ธ ๊ฐ€์ง€ ํ–‰์œ„ ๊ตฐ์ง‘์˜ ํŠน์„ฑ ๏ผ—๏ผ 5.2 ๋‚จ์ž ๋Œ€์ƒ์ž์—์„œ ์„ธ ๊ฐ€์ง€ ํ–‰์œ„ ๊ตฐ์ง‘์˜ ํŠน์„ฑ ๏ผ—๏ผ” 5.3 ๋‚จ์ž ๋Œ€์ƒ์ž์—์„œ ๋„ค ๊ฐ€์ง€ ํ–‰์œ„ ๊ตฐ์ง‘์˜ ํŠน์„ฑ ๏ผ—๏ผ˜ 6. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์„ ์˜ˆ์ธกํ•˜๋Š” ์š”์ธ ๏ผ˜๏ผ” 6.1 ์—ฌ์ž ๋Œ€์ƒ์ž์—์„œ PICSIS, PIIWIS ๊ตฐ์ง‘์˜ ์˜ˆ์ธก ์š”์ธ ๏ผ˜๏ผ” 6.2 ๋‚จ์ž ๋Œ€์ƒ์ž์—์„œ PICSIS, PICSBS ๊ตฐ์ง‘์˜ ์˜ˆ์ธก ์š”์ธ ๏ผ˜๏ผ– 6.3 ๋‚จ์ž ๋Œ€์ƒ์ž์—์„œ PIIWBSCS, PIISBSCS ๊ตฐ์ง‘์˜ ์˜ˆ์ธก ์š”์ธ ๏ผ˜๏ผ˜ 7. ๋‹จ์ผ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„์™€ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„์˜ ๊ตฐ์ง‘ ๏ผ™๏ผ 7.1 ์—ฌ์ž ๋Œ€์ƒ์ž ๏ผ™๏ผ 7.2 ๋‚จ์ž ๋Œ€์ƒ์ž ๏ผ™๏ผ‘ 8. ๋‹ค๋ฅธ ๋ฐ์ดํ„ฐ์— ์ ์šฉ ๏ผ™๏ผ” 8.1 ๋ถ„์„๋ฐ์ดํ„ฐ ๏ผ™๏ผ” 8.2 ๋ณ€์ˆ˜ ๏ผ™๏ผ” 8.3 ๋ถ„์„๋ฐฉ๋ฒ• ๏ผ™๏ผ— 8.4 ๋Œ€์ƒ์ž๋“ค์˜ ํŠน์ง• ๏ผ™๏ผ— 8.5 KNHANES โ…ฃ์˜ ์—ฐ๊ด€๊ทœ์น™์„ CHS 2010์— ์ ์šฉ ๏ผ‘๏ผ๏ผ‘ โ…ค. ๋…ผ์˜ ๏ผ‘๏ผ๏ผ” 1. ๊ฐ€์žฅ ๋นˆ๋„๊ฐ€ ๋†’์€ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๏ผ‘๏ผ๏ผ” 2. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘ ๏ผ‘๏ผ๏ผ• 3. ํก์—ฐ ๏ผ‘๏ผ๏ผ– 4. ์‹ ์ฒด์  ๋น„ํ™œ๋™ ๏ผ‘๏ผ๏ผ– 5. ๋ถ€์ ์ ˆํ•œ ์ˆ˜๋ฉด ๏ผ‘๏ผ๏ผ— 6. ์•„์นจ ๊ฒฐ์‹ ๏ผ‘๏ผ๏ผ˜ 7. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์˜ ํŠน์„ฑ ๋ฐ ๊ตฐ์ง‘์„ ๋ณด์ด๋Š” ์˜ˆ์ธก ์š”์ธ ๏ผ‘๏ผ๏ผ˜ 8. ๋‹จ์ผ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„์™€ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘์—์„œ์˜ ์˜ˆ์ธก ์š”์ธ ๏ผ‘๏ผ‘๏ผ 9. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฐ•์  ๏ผ‘๏ผ‘๏ผ‘ 10. ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘ ์—ฐ๊ตฌ์˜ ๋ณด๊ฑดํ•™์  ์˜์˜ ๏ผ‘๏ผ‘๏ผ“ โ…ฅ. ์—ฐ๊ตฌ์˜ ์ œํ•œ์  ๏ผ‘๏ผ‘๏ผ– โ…ฆ. ๊ฒฐ๋ก  ๏ผ‘๏ผ‘๏ผ— ์ฐธ๊ณ ๋ฌธํ—Œ ๏ผ‘๏ผ‘๏ผ™ ๋ถ€๋ก ๏ผ‘๏ผ“๏ผ• 1. O/E ratio๋กœ ๋ณธ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๊ตฐ์ง‘ํ˜„์ƒ ๏ผ‘๏ผ“๏ผ• 1.1 ์—ฌ์ž ๋Œ€์ƒ์ž์—์„œ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๏ผ‘๏ผ“๏ผ• 1.2 ๋‚จ์ž ๋Œ€์ƒ์ž์—์„œ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ๏ผ‘๏ผ“๏ผ™ 2. ARM(์—ฐ๊ด€์„ฑ ๋ถ„์„)์˜ ๊ณผ์ • ๏ผ‘๏ผ”๏ผ“ 2.1 ๋ฐ์ดํ„ฐ ์„ธํŠธ๋ถ€ํ„ฐ Link analysis ๏ผ‘๏ผ”๏ผ“ 3. ๋‹จ์ผ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„ ์˜ˆ์ธก ์š”์ธ ๏ผ‘๏ผ”๏ผ– 3. 1 ์—ฌ์ž ๋Œ€์ƒ์ž์—์„œ ๋‹จ์ผ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ์š”์ธ ๏ผ‘๏ผ”๏ผ– 3. 2 ๋‚จ์ž ๋Œ€์ƒ์ž์—์„œ ๋‹จ์ผ ๊ฑด๊ฐ•์œ„ํ—˜ํ–‰์œ„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ์š”์ธ ๏ผ‘๏ผ”๏ผ— Abstract ๏ผ‘๏ผ”๏ผ˜Docto

    Evaluation of Contrast-detail Characteristics of an A-Se Based Digital X-ray Imaging System

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    In this study, we have performed contrast-detail analysis for an amorphous selenium(a-Se) based digital X-ray imaging system by using a contrast-detail phantom(CDRAD 2.0) to test its low contrast performance. The X-ray imaging system utilizes an 500-mm-thick a-Se semiconductor X-ray absorber coated over an amorphous silicon(a-Si) TFT(thin-film transistor) detector matrix with a 139mmร—139mm139mm{\times}139mm pixel size and a 46.7cmร—46.7cm46.7cm{\times}46.7cm active area. In the measurement of contrast-detail curves we first acquired X-ray images of the CDRAD 2.0 phantom at given test conditions(i.e., 40, 50, 60, 70, 80 kVp, and 16 mA.s), and then evaluated the contrast-detail characteristics of the imaging system from each phantom image by using an image quality factor called the image-quality-figure-inverse(IQFinv). The IQFinv values for the imaging system gradually improved with the photon fluence, indicating the improvement of image visibility: 24.4, 35.3, 39.2, 41.5, and 43.4 at photon fluences of 1.8ร—1051.8{\times}105, 5.9ร—1055.9{\times}105, 11.3ร—10511.3{\times}105, 19.4ร—10519.4{\times}105, and 29.4ร—10529.4{\times}105 photons/mm2mm^2, respectively.ope

    Study of Mongolian Traditional Children's Song

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    This is a survey of Mongolia's traditional children's songs. It is based on data survey and collection over a period of six months as well as an 8-day spot survey in Mongolia. The original spot survey in Mongolia was scheduled to be carried out in Ulaanbaatar, the capital of Mongolia, for l0 days from May 14-24, 2009. However, due to the airplane schedule, the actually survey period was 8 days. The origins of Mongolia's traditional children's songs coincides with the appearance of the term called xuxdiin duu, which means children's song, during the beginnings of socialism in the 1940s. Traditionally, Mongolia's children's songs had been included in the category of adults songs without distinction as traditional children's song or children's song. Children's songs were created as a separate category as part of educational developments in schools. Before this educational provision, distinct traditional children's song or children's song in Mongolia did not exist primarily due to the nomatic lifestyles of the people. Mongolian people make a living while moving the family unit from place to place. Thus, there are songs that adults sing for children or that became children's songs naturally due to exposure to the adults' songs. However, those are not officially categorized as chi1dren's songs. This examination of the birth of Mongolia's traditional children's songs has led to a concwsion that the songs are based on a combination of folk songs and epic song styles in Mongolia. Mongolia's traditional children's songs, which were collected through the spot survey, totaled approximately 36 pieces. Musical analysis of the 36 pieces reveals a mainstream rhythm consisting of two-four time, three-four time, and four-four time. Two of the 36 pieces are in three-eight time and six-eight time. The musical scale is a pentatonic scale. It is typical for the musical range not to exceed an octave and a half. Mongolia's traditional children's songs can be characterized as usually short, neat, fluent, formal, easy to know, and easy to memorize. Given the musical characteristics of Mongolia's traditional children's songs, we can see that they are not greatly different from Korea's traditional children's songs. These songs are, as well, most likely quite similar to traditional children's songs in other parts of the world. In terms of methodology, some doubt the validity of the spot survey in researching foreign music. But, such surveys of 'traditional children's songs' have helped us gain a general view of their characteristics. Therefore, these types of surveys are helpful in learning about musical classifications, structures, and concepts and comparing these with the characteristics of traditional children's songs elsewhere. ๋ณธ ์—ฐ๊ตฌ๋Š” ์„œ์šธ๋Œ€ํ•™๊ต ๋™์–‘์Œ์•…์—ฐ๊ตฌ์†Œ์—์„œ ์š”์ฒญ๋ฐ›์•„ 2009๋…„ 1์›”๋ถ€ํ„ฐ ์ˆ˜ํ–‰๋œ ๊ฒƒ์ด๋‹ค. ์—ฐ๊ตฌ์†Œ์—์„œ๋Š” ๋ชฝ๊ณจ์˜ ์ „๋ž˜๋™์š”์— ๋Œ€ํ•œ ๊ฐœํ™ฉ๊ณผ ๋ถ„๋ฅ˜, ์Œ์›, ์•…๋ณด์™€ ํ•จ๊ป˜ ๋ชฝ๊ณจ์˜ ๋Œ€ํ‘œ๋ฏผ์š”๊นŒ์ง€ ์กฐ์‚ฌํ•  ๊ฒƒ์„ ๋‹น๋ถ€ํ–ˆ๋‹ค. ๋ชฝ๊ณจ์—๋Š” ์ „๋ž˜๋™์š”๊ฐ€ ์—†๋‹ค. ์—ฌ๊ธฐ์„œ ๋ชฝ๊ณจ์— ์ „๋ž˜๋™์š”๊ฐ€ ์—†๋‹ค๋Š” ์˜๋ฏธ๋Š” ๋ชฝ๊ณจ ์ „ํ†ต ์„ฑ์•…๊ณก์—์„œ ์ „๋ž˜๋™์š”๋ฅผ ๊ตฌ๋ถ„ํ•˜์ง€ ์•Š๋Š”๋‹ค๋Š” ์˜๋ฏธ์ด๋‹ค. 10์—ฌ ๋…„๊ฐ„ ๋ชฝ๊ณจ์Œ์•…์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋ฅผ ํ•ด์™”์ง€๋งŒ ๋ชฝ๊ณจ์ „๋ž˜๋™์š”๋ž€ ๋‹จ์–ด๋Š” ๋ชฝ๊ณจ ํ•™์ž๋“ค ํ˜น์€ ํ˜„์ง€์กฐ์‚ฌ ์ค‘์— ๋งŒ๋‚˜ ๋ชฝ๊ณจ์ธ๋“ค ์‚ฌ์ด์—์„œ ํ•œ ๋ฒˆ๋„ ์–ธ๊ธ‰๋˜์ง€ ์•Š์•˜๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ์กฐ์‚ฌ๋ฅผ ์œ„ํ•˜์—ฌ ๋จผ์ € ๋ชฝ๊ณจ์ „๋ž˜๋™์š”์— ๋Œ€ํ•œ ๊ฐœ๋… ์ •๋ฆฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๊ทธ ๋™์•ˆ ์ˆ˜์ง‘ํ•œ ๋ชฝ๊ณจ์Œ์•…๊ด€๋ จ ๊ฐœ์„ค์„œ์™€ ์‚ฌ์ „ ๊ทธ๋ฆฌ๊ณ  ใ€Ž๋ชฝ๊ณจ ๊ตฌ๋น„๋ฌธํ•™ใ€์—์„œ ๋ชฝ๊ณจ์ „๋ž˜๋™์š”์— ๋Œ€ํ•œ ๋‚ด์šฉ์„ ์ฐพ์•˜์œผ๋‚˜ ์ฐพ์„ ์ˆ˜ ์—†์—ˆ๋‹ค. ๋‹ค๋งŒ ใ€Ž๋ชฝ๊ณจ ๊ตฌ๋น„๋ฌธํ•™ใ€์˜ ๋‚ด์šฉ ์ค‘ ์•„๋™๊ตฌ๋น„๋ฌธํ•™(Xyyx)์ด๋ž€ ๊ตฌ๋ถ„์— ์ž์žฅ๊ฐ€(๋ถ€์›ฝ ๋„: Byy)๋งŒ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ตญ๋‚ด์—์„œ ์ž์žฅ๊ฐ€๋Š” ๋™์š”๋กœ ๊ตฌ๋ถ„ํ•˜๋Š”๋ฐ ์ด๊ฒฌ(็•ฐ่ฆ‹)๋“ค์ด ์žˆ๋‹ค

    A Study on the Causes of the Restoration Direction and the Feature of the Restoration Practice of the Geunjeong Hall in the Gyeongbok Palace

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    This study explored the causes of restoration direction and the feature of the restoration practice of the Geunjeong Hall in the Gyeongbok Palace by analyzing the process and the result of the restoration of the heritage. The result of the study shows that the causes of restoration direction are restoration guidelines for the project, restoration principles for the architectural heritage of Korea, advisory committee of the restoration project, standard specification for the restoration of architectural heritage, and traditional craftsmanship. This study also reveals that the restoration of the Geunjeong Hall pursued enhancement of architectural functions, ideal prototype of traditional building, adoption of the contemporary scientific technology, retention of the old members, and recovery of the deformed elements

    CTL Jump Start: IT ํ™œ์šฉ ์—ญ๋Ÿ‰ ํ–ฅ์ƒ ์›Œํฌ์ˆ

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    1910๋…„๋ถ€ํ„ฐ 1987๋…„๊นŒ์ง€์˜ ๋ฒˆ์•ˆ๊ฐ€์š”๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์Œ์•…๋Œ€ํ•™ ์Œ์•…๊ณผ, 2021. 2. ๋ฏผ์€๊ธฐ.The purpose of this thesis is to analyze Popular Music-Translation using political background of Korean society. Definition of Popular Music-Translation is a popular music that changed only lyrics into its own language based on songs that are already created in other countries. Lyrics are changed in order to adapt into different countriesโ€™ society. This music also help a foreign society or foreign listeners to easily accept them. Popular Music โ€“ Translation has been produced in Korean Society from 1910 to 1987, which was before Koreaโ€™s participation in the WTO TRIPS agreement, and this music have influenced Korean popular music market in many aspects, for example, diversifying popular music genre and played as a bridge that connected Korean society and Foreign popular music. Interestingly, history of Popular Music โ€“ Translation was longer. This music had existed at the very beginning of Korean popular music market. In 1926, was released, which was based on Invanoviciโ€™s . was the first Korean popular music and also the first Popular Music โ€“ Translation in Korean society. This piece vitalized Korean Popular music market, settling standard form of popular music. As this example illustrates, Popular music โ€“ Translation was not only the indicator of inflow of foreign popular music but also a large body of Korean popular music history from the beginning. In spite of this importance, Popular music โ€“ translationโ€™s empirical researches were not done vigorously because it was โ€˜remake of existing songโ€™ which is not an โ€˜original songโ€™. However, recently there has been increase in research. Some are focused on lyrical or rhythmic changes comparing Popular music โ€“ translation and an original song, and others have demonstrated โ€˜Modernityโ€™ through examining popular music โ€“ translation due to their origination - mostly from Western countries โ€“ which have led into dichotomous discussion. These recent studies might have been able to dig up the fact that had not been covered by former Korean popular music studies but still they are not effective enough to make comprehensive perspective of Popular music โ€“ translation. As known by the explanations from recent studies, Popular music โ€“ translation was a root for variety of genres in Korean popular music market. Analysis on this kind of music equals to analysis on every aspect of Western music that has played a major role in the popular music industry from 1910 to 1987. More importantly, in order to play a major role in the industry their existence had to be in the same position as the original works. What were the key component that made them possible to remain in the same position as the original work? Although Korean copyright law has existed since 1957, this law was not strictly applied to remake songs at the time. This means that more tremendous power had been applied in the production process of Popular music โ€“ translation. And the โ€˜more tremendous powerโ€™ are assumed to be closely related to governmentsโ€™ institutional devices, which had changed based on political topographical changes. Based on the main question, this thesis will use political background in order to analyze Popular music โ€“ translations in Korea. According to Korean political topography, the duration from 1910 to 1987 could be divided into 3 separate time lines, Japanese Colonial Period (1910-1945), USAMGIK and Presidency of Syngman Rhee (1945-1961), and Presidency of Junghee Park and Duhwan Jeong (1961-1987). For more detailed analysis, each duration is also divided based on the institutional changes made by each government. The samples for this study were based on different sources for each political era due to difference of social aspect and technical background - โ€˜Korean Sp Archiveโ€™ for the first part, News Archive for the middle, and โ€˜KBS Music Archiveโ€™ for the last part of this study. The list of Popular music โ€“ translations for each designated time were organized according to the timeline. Yearly released rate and genre proportion based on the list were suggested and analysis were done at the point where unusual rates or the outliers were found on the graph. These analysis were done in order to find the relation of political background and Popular music โ€“ translation. As a result, production of Korean popular music โ€“ translation was either increased or decreased based on the political aim of each time line, which means that the production process of the music was controlled by government. In Japanese Colonial Era, from 1910 to 1938 government led society to synchronize with Western music which resulted in increase of production rate. However after 1938 dramatical decrease was found due to prohibition of Western music by government. From 1945 to 1961, AFKN and US 8th army show led popular society to get familiar with the American culture and also, the anti-communism - which was main ideology of the government at the time- had provoked society to accept the Western liberal countiesโ€™ culture. This era functioned as a base for the adaptation of Western music afterwards. From 1961 to 1987, Park and Jeonโ€™s presidencyโ€™s graph also looks similar to Japanese colonial era, production rate increased until 1975 and dramatical decrease after 1975. At first, Parkโ€™s regime was positive about the translated popular music but after 1966 there were several attempts made by government to control the popular music which is reflected on the decrease of production rate. and After 1975, when the highest level of inspection was applied to popular culture, production rate has more decreased due to the continuing censorship done by both regime. The yearly production graphs had proved that the political aim and production rate of popular music โ€“ translation have a strong correlation, also meaning that the popular music industry was effected by the institutional device. This institutional devices were based on political ideology which had shown changes in governmentsโ€™ attitudes toward Western popular music over the period. Because of these changes made by the government, Popular music โ€“ translation had been constantly repositioned in music industry time to time, from alternative to main and from main to minor stream. By looking at the proportion of Genre, the changes had occured whenever government changed their attitude toward Western music. In Japanese colonial era, government prohibited Jazz genre music after 1938 where the proportion of Western popular music genre decreased compared to the former time. Also, after 1975, when government framed Folk and Rock genre as โ€˜anti-governmentโ€™ music in order to control the protest movement, Folk and Rock genresโ€™ propotion had decreased compared to 1960s. This demonstrates that Popular music - translation genre and political aim were strongly connected. Analysis on Popular music โ€“ translation has revealed several important facts and also captured significant moments in Korean popular music history. And as this study has proved through Popular music โ€“ translation, popular music is not only art but also a political and social product due to the relationship of society and popular music, which exhibited strong bond between popular music and society. And most importantly, by the method using political background and statistical information of Popular music โ€“ translation, this thesis suggests that the history of Korean popular music should be described based on the changes of political topography as Popular music โ€“ translation represents large part of Korean popular music history.๋ฒˆ์•ˆ๊ฐ€์š”๋ž€ ํƒ€๊ตญ์—์„œ ์ด๋ฏธ ์ฐฝ์ž‘๋œ ๊ณก์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐ€์‚ฌ๋งŒ์„ ์ž๊ตญ ์–ธ์–ด๋กœ ๋ณ€ํ™˜ํ•œ ๊ฐ€์š”๋ฅผ ์˜๋ฏธํ•œ๋‹ค. ํƒ€๊ตญ์˜ ์Œ์•…์€ ๋Œ€๊ฒŒ ์ž๊ตญ์˜ ์–ธ์–ด๋กœ ๋ฒˆ์•ˆ๋œ ๋ฒˆ์•ˆ๊ฐ€์š”๋ฅผ ํ†ตํ•ด ์ •์ฐฉ๋œ๋‹ค. ๊ฐ€์‚ฌ๊ฐ€ ์ž๊ตญ์˜ ์–ธ์–ด๋กœ ๋ณ€ํ™˜๋จ์œผ๋กœ์จ ์–ธ์–ด์˜ ์žฅ๋ฒฝ์ด ํ—ˆ๋ฌผ์–ด์ง€๊ณ  ๋Œ€์ค‘๋“ค์ด ์ˆ˜์šฉํ•˜๊ธฐ ์šฉ์ดํ•ด์ง€๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ํ˜„๋Œ€ ์‚ฌํšŒ์™€ ๊ฐ™์ด ๋งค์Šค๋ฏธ๋””์–ด๋ฅผ ํ†ตํ•ด ์ ๊ทน์ ์œผ๋กœ ์ˆ˜์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํ™˜๊ฒฝ์ด ๋งˆ๋ จ๋˜๊ธฐ ์ด์ „์— ๊ตญ๊ฐ€๋“ค ๊ฐ„์˜ ์Œ์•…์  ๊ต๋ฅ˜๋Š” ๋ฒˆ์•ˆ๊ฐ€์š”๋ฅผ ํ†ตํ•ด์„œ ์ด๋ฃจ์–ด์กŒ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ํ•œ๊ตญ์˜ ๊ฒฝ์šฐ๋ฅผ ์‚ดํŽด๋ณด๋ฉด, 1987๋…„ ์ €์ž‘๊ถŒ ํ˜‘ํšŒ ๊ฐ€์ž… ์ด์ „๊นŒ์ง€ ๋ฒˆ์•ˆ๊ฐ€์š”๊ฐ€ ๋‹ค๋Ÿ‰์œผ๋กœ ๋ฐœ๋งค๋˜์—ˆ๋‹ค. ํ•œ๊ตญ ์ƒ์—…์Œ์•… ์‹œ์žฅ ์† ๋ฒˆ์•ˆ๊ฐ€์š”๋Š” ์„œ๊ตฌ๊ถŒ์„ ๋น„๋กฏํ•œ ๋‹ค๋ฅธ ๊ตญ๊ฐ€๋“ค์˜ ๋Œ€์ค‘์Œ์•…์„ ํ•œ๊ตญ์œผ๋กœ ์ •์ฐฉ์‹œํ‚ค๋Š” ๋™์‹œ์— ํ•œ๊ตญ ๋Œ€์ค‘์Œ์•… ์žฅ๋ฅด์˜ ๋‹ค์–‘ํ™”๋ฅผ ์ด‰์ง„์‹œ์ผฐ๋‹ค. ๋Œ€ํ‘œ์ ์œผ๋กœ ๊ฑฐ๋ก ๋˜๋Š” 1960๋…„๋Œ€ ํฌํฌ์™€ ๋ฝ ์žฅ๋ฅด์˜ ์‹œ์ž‘๋„ ๋ฒˆ์•ˆ๊ฐ€์š”์— ์˜ํ•œ ๊ฒƒ์ด์—ˆ๋‹ค. ๋Œ€์ค‘์ ์œผ๋กœ ์ž˜ ์•Œ๋ ค์ง„ ๋ฐ”์™€ ๋‹ฌ๋ฆฌ ๋ฒˆ์•ˆ๊ฐ€์š”์˜ ์—ญ์‚ฌ๋Š” ํ•œ๊ตญ ๋Œ€์ค‘๊ฐ€์š”์˜ ์‹œ์ž‘์ ์œผ๋กœ ๊ฑฐ์Šฌ๋Ÿฌ ์˜ฌ๋ผ๊ฐ„๋‹ค. 1926๋…„ ๋ฐœ๋งค๋œ ์ฒซ ๋Œ€์ค‘๊ฐ€์š”์ธ ๋Š” ์„ ์›๊ณก์œผ๋กœ ํ•˜์—ฌ ๋ฒˆ์•ˆํ•œ ๊ฒƒ์ด๋‹ค. ์ด ์ž‘ํ’ˆ์€ ๋ ˆ์ฝ”๋“œ์— ๋Œ€ํ•œ ๋Œ€์ค‘๋“ค์˜ ๊ด€์‹ฌ์„ ์ด๋Œ์—ˆ๋˜ ๋™์‹œ์— ํ•œ๊ตญ ์Œ์•…๊ณ„์— ๋Œ€์ค‘๊ฐ€์š”์˜ ํ˜•์‹์˜ ๋„์ž…์„ ๊ฐ€๋Šฅ์ผ€ ํ–ˆ๋‹ค. ์ด์ฒ˜๋Ÿผ ๋ฒˆ์•ˆ๊ฐ€์š”๋Š” ํ•œ๊ตญ ๋Œ€์ค‘๊ฐ€์š”์˜ ์‹œ์ดˆ์˜€์œผ๋ฉฐ, ์ดํ›„ ์„œ์–‘ ๋Œ€์ค‘๊ฐ€์š” ์œ ์ž…์˜ ์ง€ํ‘œ๋กœ์„œ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ–ˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋ฒˆ์•ˆ๊ฐ€์š”๋Š” 1์ฐจ ์ €์ž‘๋ฌผ์ด ์•„๋‹Œ 2์ฐจ ์ €์ž‘๋ฌผ์ด๋ผ๋Š” ์ ์— ์žˆ์–ด์„œ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์ด ๊ฐ„๊ณผ๋˜์–ด ์™”๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ์ฐฝ์ž‘๊ฐ€์š”๊ฐ€ ๋ฒˆ์•ˆ๊ฐ€์š”๋ฅผ ํ†ตํ•ด ์œ ์ž…๋œ ๋‹ค๋ฅธ ๊ตญ๊ฐ€์˜ ๋Œ€์ค‘๊ฐ€์š”๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฐœ์ „๋˜์—ˆ์Œ์—๋„ ์•„์ง๊นŒ์ง€ ํ•œ๊ตญ ๋Œ€์ค‘์Œ์•…์‚ฌ๋Š” ์ฐฝ์ž‘๊ฐ€์š”๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ธฐ์ˆ ๋˜๊ณ  ์žˆ๋‹ค. ์ตœ๊ทผ 10๋…„๊ฐ„ ํ•™๊ณ„์—์„œ ๋ฒˆ์•ˆ๊ฐ€์š”์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ๋˜๊ธฐ ์‹œ์ž‘ํ–ˆ์œผ๋‚˜, ์Œ์•…๋‚ด์  ์š”์†Œ์— ๋Œ€ํ•œ ๋ถ„์„ ํ˜น์€ ํ˜ผ์ข…์  ์ •์ฒด์„ฑ์— ๋Œ€ํ•œ ๋ถ„์„์—์„œ ๊ทธ์ณค๋‹ค. ํ•œ๊ตญ ๋Œ€์ค‘์Œ์•…์‚ฌ ์† ๋ฒˆ์•ˆ๊ฐ€์š”์˜ ์ค‘์š”์„ฑ์€ ๋ฒˆ์•ˆ๊ฐ€์š”๊ฐ€ ์ƒ์‚ฐ๋  ์ˆ˜ ์žˆ์—ˆ๋˜ ์š”์ธ์— ๋Œ€ํ•œ ๋ถ„์„์„ ํ†ตํ•ด ๊ทธ๋Ÿฌํ•œ ์š”์ธ์„ ๋ฐํ˜€๋‚ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ทธ ๋‹น์‹œ์˜ ์ œ๋„์  ์žฅ์น˜์— ์ ‘๊ทผํ•ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ํ”ํžˆ ๋Œ€์ค‘์Œ์•…์˜ ์ œ๋„์  ์žฅ์น˜๋ผ ํ•จ์€ ์ €์ž‘๊ถŒ๋ฒ•์„ ๊ฐ€๋ฆฌํ‚ค๋Š”๋ฐ, ํ•œ๊ตญ ์‚ฌํšŒ ์†์—์„œ ์ €์ž‘๊ถŒ๋ฒ•์ด 1957๋…„์— ์ •์‹์œผ๋กœ ์ œ์ •๋˜์—ˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋ฒˆ์•ˆ๊ฐ€์š”๊ฐ€ ์ง€์†์ ์œผ๋กœ ์ƒ์‚ฐ๋˜์—ˆ๋˜ ๊ฒƒ์€ ๊ทธ์™€ ๋ฐ€์ ‘ํ•œ ์—ฐ๊ด€์„ฑ์„ ๋งบ๊ณ  ์žˆ์ง€ ์•Š์•˜๋‹ค๋Š” ๊ฒƒ์œผ๋กœ ํ•ด์„๋œ๋‹ค. ๋‹ค์‹œ ๋งํ•ด, ๋ฒˆ์•ˆ๊ฐ€์š”์˜ ์ƒ์‚ฐ ๊ณผ์ •์—๋Š” ์ €์ž‘๊ถŒ๋ฒ•์ด ์•„๋‹Œ ๋” ํฐ ํž˜์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ œ๋„์  ์žฅ์น˜๊ฐ€ ์ž‘์šฉ๋˜์—ˆ๋˜ ๊ฒƒ์ด๋‹ค. ๊ทธ๋ ‡๊ธฐ์— ๋ฒˆ์•ˆ๊ฐ€์š”๋Š” ์ •์น˜์  ๋งฅ๋ฝ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ถ„์„๋˜์–ด์•ผ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ถ„์„์„ ํ†ตํ•ด์„œ ๋ฒˆ์•ˆ๊ฐ€์š”์˜ ์ค‘์š”์„ฑ์— ๋Œ€ํ•œ ์ •๋‹นํ•œ ์ด์œ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํ•œ๊ตญ ๋Œ€์ค‘์Œ์•…์˜ ์ •์ฐฉ๋ถ€ํ„ฐ ์—ฌ๋Ÿฌ ์ „ํ™˜์ ๊นŒ์ง€ ํ•œ๊ตญ๋Œ€์ค‘์Œ์•…์‚ฌ์˜ ์ „์ฒด์  ๋งฅ๋ฝ์— ๋Œ€ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๊ทธ๋ฆฌํ•˜์—ฌ ๋ณธ๋ฌธ์—์„œ๋Š” 1910๋…„๋ถ€ํ„ฐ 1987๋…„๊นŒ์ง€ 3 ๊ฐœ์˜ ์‹œ๊ธฐ๋กœ ๋ถ„๋ฅ˜ํ•˜์—ฌ ์ •์น˜์  ๋งฅ๋ฝ์—์„œ ๋ฒˆ์•ˆ๊ฐ€์š”๋ฅผ ํ•ด์„ํ•˜์˜€๋‹ค. ์ผ์ œ๊ฐ•์ ๊ธฐ(1910-1945), ๋ฏธ๊ตฐ์ •๊ธฐ์™€ ์ด์Šน๋งŒ ์ •๋ถ€ ์‹œ๊ธฐ (1945-1961), ๋ฐ•์ •ํฌ ์ •๋ถ€์™€ ์ „๋‘ํ™˜ ์ •๋ถ€ ์‹œ๊ธฐ (1961-1987)๋กœ ๋‚˜๋ˆ„์—ˆ๊ณ , ๊ฐ๊ฐ์˜ ๊ธฐ๊ฐ„์€ ์ •์ฑ…๊ธฐ๊ด€์˜ ๋ณ€ํ™”์— ๋”ฐ๋ผ ์„ธ๋ถ„ํ™”ํ–ˆ๋‹ค. ์ผ์ œ๊ฐ•์ ๊ธฐ์˜ ๊ฒฝ์šฐ ์กฐ์„ ์ด๋…๋ถ€๊ฐ€ ์‹ค์‹œํ•œ ์กฐ์„  ๊ต์œก๋ น์— ๋”ฐ๋ผ 4 ๊ฐœ์˜ ์‹œ๊ธฐ๋กœ, ๋ฏธ๊ตฐ์ •๊ธฐ์™€ ์ด์Šน๋งŒ ์ •๋ถ€์‹œ๊ธฐ๋Š” ์ •์ฑ…์— ๋”ฐ๋ผ 3 ๊ฐœ์˜ ์‹œ๊ธฐ๋กœ, ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ฐ•์ •ํฌ ์ •๋ถ€์™€ ์ „๋‘ํ™˜ ์ •๋ถ€ ์‹œ๊ธฐ๋Š” ์ •์ฑ…๊ธฐ๊ด€์— ๋”ฐ๋ฅธ 3 ๊ฐœ์˜ ์‹œ๊ธฐ๋กœ ๋ถ„ํ™”ํ–ˆ๋‹ค. ๋ถ„์„์— ์‚ฌ์šฉ๋œ 1์ฐจ ์ž๋ฃŒ๋Š” ๊ฐ ๊ธฐ๊ฐ„๋ณ„ ๋ฐœ๋งค๋œ ๋ฒˆ์•ˆ๊ฐ€์š”์˜€์œผ๋ฉฐ, 1์ฐจ ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋ฒˆ์•ˆ๊ฐ€์š”๋ฅผ ํ†ตํ•ด ์œ ์ž…๋œ ์„œ์–‘๋Œ€์ค‘๊ฐ€์š”์˜ ํ˜•ํƒœ๋ฅผ ๋ถ„์„ํ–ˆ๋‹ค. ๊ฐ๊ฐ์˜ ๋ชฉ๋ก์— ๊ธฐ์ดˆํ•˜์—ฌ ์—ฐ๋„๋ณ„ ์ƒ์‚ฐ ์ˆ˜์น˜ ๊ทธ๋ž˜ํ”„์™€ ์žฅ๋ฅด๋ณ„ ๋ถ„ํฌ ๋น„์œจ ๊ทธ๋ž˜ํ”„๋ฅผ ์ž‘์„ฑํ•˜์˜€๊ณ  ์ด๋Ÿฌํ•œ ํ†ต๊ณ„์ž๋ฃŒ์— ๋Œ€ํ•œ ๋ถ„์„์„ ์ƒ์„ธํžˆ ์„œ์ˆ ํ–ˆ๋‹ค. ์ด ์ž๋ฃŒ๋“ค์„ ํ†ตํ•ด ์ •์ฑ…๊ณผ ๋ฒˆ์•ˆ๊ฐ€์š”์˜ ๊ธด๋ฐ€ํ•œ ์—ฐ๊ด€์„ฑ์„ ๋ฐํžˆ๊ณ ์ž ํ–ˆ๋‹ค. 1910๋…„๋ถ€ํ„ฐ 1987๋…„๊นŒ์ง€ ๋ถ„์„์„ ํ–‰ํ•œ ๊ฒฐ๊ณผ ๋ฒˆ์•ˆ๊ฐ€์š”๋ฅผ ํ†ตํ•œ ์„œ์–‘ ๋Œ€์ค‘๊ฐ€์š” ์œ ์ž…์˜ ํ˜•ํƒœ๋Š” ์ •์น˜์  ์ƒํ™ฉ์— ๋”ฐ๋ผ ๋ณ€ํ™”ํ–ˆ๋‹ค. ์šฐ์„ , ๋ฒˆ์•ˆ๊ฐ€์š”์˜ ์—ฐ๋„๋ณ„ ํ†ต๊ณ„ ์ˆ˜์น˜ ๋ถ„์„์„ ํ†ตํ•ด ์ •๊ถŒ์˜ ์ •์ฑ…์˜ ๋ฐฉํ–ฅ์— ๋”ฐ๋ฅธ ์„œ์–‘๋Œ€์ค‘๊ฐ€์š” ์œ ์ž…๋Ÿ‰์˜ ๋ณ€ํ™”๊ฐ€ ์žˆ์—ˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๊ฐ๊ธฐ ๋‹ค๋ฅธ ์ •๊ถŒ์ด์—ˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ณตํ†ต์ ์œผ๋กœ ๋ฒˆ์•ˆ๊ฐ€์š” ์ƒ์‚ฐ์— ๋Œ€ํ•œ ์žฅ๋ ค์ •์ฑ…๊ณผ ๊ฒ€์—ด์ •์ฑ…์ด ์žˆ์—ˆ์œผ๋ฉฐ, ์ด์— ๋”ฐ๋ฅธ ์œ ์ž…๋Ÿ‰์˜ ๋ณ€ํ™”๊ฐ€ ๋ฐœ์ƒํ–ˆ๋˜ ๊ฒƒ์ด๋‹ค. ํŠนํžˆ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์žฅ๋ ค์ •์ฑ…์— ๋Œ€ํ•œ ๋ถ„์„์„ ํ•จ์œผ๋กœ์จ ๋ฒˆ์•ˆ๊ฐ€์š”๊ฐ€ ํ•˜๋‚˜์˜ ์ €์ž‘๋ฌผ๋กœ ์ธ์ •๋  ์ˆ˜ ์žˆ์—ˆ๋˜ ์š”์ธ์ด ์žˆ์—ˆ์Œ์„ ๋ช…ํ™•ํžˆ ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ ์›๊ณก์˜ ์žฅ๋ฅด๋ณ„ ๋ถ„ํฌ ๋น„์œจ์€ ์„œ์–‘ ๋Œ€์ค‘๊ฐ€์š”์— ๋Œ€ํ•œ ์ •๊ถŒ์˜ ์ ‘๊ทผ ๋ฐฉ์‹ ํ˜น์€ ์ž‘์šฉ๋ฐฉ์‹์˜ ๋ณ€ํ™” ์–‘์ƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ผ์ œ๊ฐ•์ ๊ธฐ ์ดˆ๊ธฐ๋ถ€ํ„ฐ ์„œ๊ตฌ๊ถŒ ์ œ๊ตญ์˜ ์Œ์•… ์ˆ˜์šฉ์— ๋Œ€ํ•œ ๊ธ์ •์ ์ธ ํƒœ๋„๊ฐ€ ์žˆ์—ˆ๋˜ ๋ฐ˜๋ฉด, ํ›„๋ฐ˜๊ธฐ์—๋Š” ์ „์Ÿ์„ ๊ฑฐ์น˜๋ฉด์„œ โ€˜์žฌ์ฆˆโ€™๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ์„œ๊ตฌ๊ถŒ ์Œ์•… ๊ธˆ์ง€ ์ •์ฑ…์ด ํŽผ์ณ์ง์— ๋”ฐ๋ผ ์œ ์ž…๋œ ์žฅ๋ฅด์˜ ๋น„์œจ์— ๋ณ€ํ™”๊ฐ€ ์ผ์–ด๋‚ฌ๋‹ค. ํ•ด๋ฐฉ ์ดํ›„ ๋ฐ˜๊ณต์ด๋ฐ์˜ฌ๋กœ๊ธฐ์™€ ๋ฏธ๊ตฐ์˜ ์„ ์ „์ •์ฑ…์€ ๋Œ€์ค‘๋“ค๋กœ ํ•˜์—ฌ๊ธˆ ์„œ์–‘๋Œ€์ค‘๊ฐ€์š”๋ฅผ ์ˆ˜์šฉํ•ด์•ผ ํ•˜๋Š” ์Œ์•…์œผ๋กœ ์ธ์‹ํ•˜๊ฒŒ ํ•˜์˜€์œผ๋ฉฐ, 1960๋…„๋Œ€ ์ดํ›„ ์„œ์–‘๋Œ€์ค‘๊ฐ€์š” ์žฅ๋ ค์ •์ฑ…์˜ ๊ธฐ๋ฐ˜์„ ๋งˆ๋ จํ–ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 1975๋…„ ํฌํฌ, ๋ฝ ๊ณผ ๊ฐ™์€ ํŠน์ •์žฅ๋ฅด๋ฅผ โ€˜์—ญ๋ฌธํ™”โ€™, โ€˜๋ฐ˜์ •๋ถ€์ โ€™์ธ ์Œ์•…์œผ๋กœ ํ•ด์„ํ•จ์— ๋”ฐ๋ผ ๋ฐœ๋งค๋œ ๋ฒˆ์•ˆ๊ฐ€์š”์˜ ์›๊ณก ์žฅ๋ฅด ๋น„์œจ ๋ถ„ํฌ์—์„œ ํฐ ๋ณ€ํ™”๊ฐ€ ๋ฐœ๊ฒฌ๋œ๋‹ค. ์ด์ฒ˜๋Ÿผ ๋ฒˆ์•ˆ๊ฐ€์š”๋ฅผ ํ†ตํ•ด ์œ ์ž…๋œ ์„œ์–‘ ๋Œ€์ค‘๊ฐ€์š”์˜ ํŠน์ •์žฅ๋ฅด๋“ค์€ ์ •๊ถŒ์˜ ์ ‘๊ทผ๋ฐฉ์‹์— ๋”ฐ๋ผ ์žฅ๋ ค ํ˜น์€ ๊ฒ€์—ด๋˜๋ฉด์„œ ์ •์น˜์  ์ง€ํ˜•์— ๋”ฐ๋ฅธ ์žฅ๋ฅด๋ณ„ ๋ถ„ํฌ๊ฐ€ ๋‹ค๋ฅธ ์–‘์ƒ์„ ๋„์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ณผ์ • ์†์—์„œ ๋ฐœ๋งค๋œ ๋ฒˆ์•ˆ๊ฐ€์š”๋Š” ํ•œ๊ตญ ๋Œ€์ค‘์Œ์•…์‚ฌ์— ์—ฌ๋Ÿฌ ๋ณ€ํ™”๋ฅผ ์•ผ๊ธฐํ•˜์˜€๋‹ค. ์ผ์ œ ๊ฐ•์ ๊ธฐ์—๋Š” ๋‹น์‹œ ๋Œ€์ค‘๊ฐ€์š” ์žฅ๋ฅด์ธ ์žฌ์ฆˆ์†ก์ด, 1950๋…„๋Œ€ ์ดํ›„์—๋Š” ์Šคํƒ ๋‹ค๋“œ ํŒ์ด, 1960๋…„๋Œ€ ์ดํ›„์—๋Š” ํฌํฌ์†ก, ๋ฝ, ๋Œ„์Šค ๋“ฑ์ด ๋ฒˆ์•ˆ๊ฐ€์š”๋ฅผ ํ†ตํ•ด ํ•œ๊ตญ์‚ฌํšŒ๋กœ ํ˜๋Ÿฌ๋“ค์–ด์˜ค๋ฉด์„œ ์ƒˆ๋กœ์šด ์žฅ๋ฅด์˜ ์ •์ฐฉ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ๋ฒˆ์•ˆ๊ฐ€์š”๋Š” ํ•œ๊ตญ ๋Œ€์ค‘๊ฐ€์š”์‚ฌ ์† ํƒ€๊ตญ ์Œ์•…์˜ ์ •์ฐฉ๊ณผ ๋™์‹œ์— ์ „ํ™˜์ ์„ ์ผ์œผํ‚ค๋Š”๋ฐ ํฐ ์˜ํ–ฅ๋ ฅ์„ ๋ฏธ์ณค๋˜ ๊ฒƒ์ด๋‹ค. ๋ฒˆ์•ˆ๊ฐ€์š”์— ๋Œ€ํ•œ ๋ถ„์„์„ ํ†ตํ•ด ๋ฐํ˜”๋“ฏ์ด, ๋Œ€์ค‘๊ฐ€์š”๋Š” ์ •์น˜์  ์‚ฌํšŒ์  ์‚ฐ๋ฌผ์ด๋ฉฐ ์ง€๋ฐฐ ๊ณ„์ธต์— ์˜ํ•ด ์ •์น˜์  ์ˆ˜๋‹จ์œผ๋กœ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๋Œ€์ค‘๊ฐ€์š”๋Š” ์Œ์•…์ด๊ธฐ ์ „์— ํ•˜๋‚˜์˜ ๋ฌธํ™”์  ์ •์ฒด์„ฑ์„ ํ˜•์„ฑํ•˜๋Š” ํฐ ์˜ํ–ฅ๋ ฅ์„ ๊ฐ€์ง„๋‹ค๋Š” ์ ์— ์žˆ์–ด์„œ ์‚ฌํšŒ์˜ ์งˆ์„œ๋ฅผ ๋งˆ๋ จํ•˜๊ณ  ์‚ฌํšŒ๋ฅผ ํ†ต์ œํ•˜๋Š” ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋ฒˆ์•ˆ๊ฐ€์š”๊ฐ€ ํ•œ๊ตญ ๋Œ€์ค‘๊ฐ€์š”์‚ฌ์—์„œ ํฐ ํ๋ฆ„์„ ํ˜•์„ฑํ•œ๋‹ค๋Š” ์ ์— ์žˆ์–ด์„œ, ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ํ•œ๊ตญ ๋Œ€์ค‘์Œ์•…์˜ ์—ญ์‚ฌ์  ํ๋ฆ„ ๋˜ํ•œ ์ •์น˜์  ์›€์ง์ž„์— ๋”ฐ๋ผ ํ˜•์„ฑ๋˜์—ˆ์Œ์„ ์ž…์ฆํ•˜๋Š” ๋™์‹œ์— ๋Œ€์ค‘๊ฐ€์š”๊ฐ€ ์ˆœ์ˆ˜ํ•œ ๊ทธ ์ž์ฒด์˜ ์Œ์•…์œผ๋กœ๋งŒ ํ•ด์„๋˜๊ธฐ๋ณด๋‹ค๋„ ์ •์น˜์  ๋งฅ๋ฝ์„ ํ†ตํ•ด์„œ ๋ถ„์„๋˜์–ด์•ผ ํ•˜๋Š” ํ˜„์ƒ์ž„์„ ์ฃผ์žฅํ•˜๋Š” ๋ฐ”์ด๋‹ค.๋ชฉ ์ฐจ 1. ์„œ๋ก  1 1.1. ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 4 1.3. ์„ ํ–‰์—ฐ๊ตฌ 6 1.4. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 9 2.์ผ์ œ๊ฐ•์ ๊ธฐ(1910-1945) 12 2.1. 1910๋…„-1922๋…„ 12 2.2. 1922๋…„-1938๋…„ 18 2.3. 1938๋…„-1943๋…„ 35 2.4. 1943๋…„-1945๋…„ 42 2.5. ์ข…ํ•ฉ์  ๋ถ„์„ 44 3.๋ฏธ๊ตฐ์ •๊ธฐ์™€ ์ด์Šน๋งŒ ์ •๋ถ€ ์‹œ๊ธฐ(1945-1961) 50 3.1. 1945๋…„-1948๋…„ 50 3.2. 1948๋…„-1953๋…„ 53 3.3. 1953๋…„-1961๋…„ 56 3.4. ์ข…ํ•ฉ์  ๋ถ„์„ 61 4.๋ฐ•์ •ํฌ ์ •๋ถ€์™€ ์ „๋‘ํ™˜ ์ •๋ถ€ ์‹œ๊ธฐ(1961-1987) 63 4.1. 1961๋…„-1966๋…„ 63 4.2. 1967๋…„-1974๋…„ 74 4.3. 1975๋…„-1987๋…„ 86 4.4. ์ข…ํ•ฉ์  ๋ถ„์„ 99 5.๊ฒฐ๋ก 105 ์ฐธ๊ณ ๋ฌธํ—Œ108 ๋ถ€๋ก116 Abstract158Maste
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