38 research outputs found

    ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์—์„œ T-scan์„ ์ด์šฉํ•œ ํŽธ์‹ฌ์œ„ ๊ตํ•ฉ ์ ‘์ด‰์˜ ์žฌํ˜„์„ฑ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์น˜์˜ํ•™๋Œ€ํ•™์› ์น˜์˜๊ณผํ•™๊ณผ, 2020. 8. ๊ถŒํ˜ธ๋ฒ”.- ABSTRACT - Reproducibility of eccentric tooth contact on a semi-adjustable articulator using T-scan Min-Young Jeong, D.D.S., M.S.D Department of Prosthodontics, Graduate School, Seoul National University (Directed by Professor Ho-Beom Kwon, D.D.S., M.S.D., Ph.D.) Purpose: Semi-adjustable articulators have been used to simulate mandibular movements and occlusal relationships. However, it has reported that semi-adjustable articulator could not duplicate accurately human mandibular movement. Several previous studies have analyzed articulator movement, however, few have compared excursive tooth contact on articulator with the tooth contact during actual mandibular movement. The purpose of this study was to evaluate the concordance of semi-adjustable articulator contacts with intraoral contacts during eccentric movements using the T-scan. Materials and methods: Irreversible hydrocolloid impressions of upper and lower arches were taken from twenty-seven subjects to create dental stone casts. Before mounting, the maxillary casts of all subjects were scanned using a model scanner. Maxillary casts were mounted in a semi-adjustable articulator (PROTAR Evo 7) using the KaVo ARCUS facebow. Mandibular casts were mounted in maximum intercuspal position without any registration. The condylar guidance angle was set according to protrusive and lateral intraoral records taken using polyvinyl siloxane. Three recordings of right and left excursive mandibular movement and protrusive mandibular movement were taken using the T-scan v9.1 on supine position. The same procedure was performed for the articulator. The stereolithography (STL) files for the maxillary cast were aligned to the arch in the T-scan software. The interocclusal record from maximum intercuspation was used as a reference for positioning. The complete mandibular movement was divided into four time points for analysis, from T0 to T3. T0 represented the beginning of a jaw movement in one direction and T3 represented the point when all teeth on the non-working side for the right and left excursion and all posterior teeth for protrusion were completely separated. The time point halfway between T0 and T3 was defined as T1 and that three-quarters of the way between T0 and T3 as T2. The concordance of intraoral and articulator occlusal contacts were calculated at T0, T1, T2, and T3. The concordance of all teeth, and of the working and balancing sides (anterior and posterior teeth for protrusion), were calculated respectively. Intraclass correlation coefficient (ICC) analysis was used to evaluate the reproducibility of repeated tests. Repeated measures analysis of variance (RM-ANOVA) was used to analyze differences between concordances of intraoral and articulator contacts according to the direction of mandibular movement, time, and working and balancing sides. Bonferroni post hoc tests were used to examine the significant differences. All statistical analyses were conducted at the confidence level of 99%. Results: For all teeth, concordance between intraoral and articulator occlusal contacts during excursive mandibular movement was greatest at T0, with decreasing tendencies at T1 and T2, and was increased at T3. Concordances of all teeth between intraoral and articulator occlusal contacts at T3 were 85.2ยฑ10.4% on the right excursion, 85.0ยฑ9.4% on the left excursion, and 85.7ยฑ11.1% on the protrusive excursion. There were no significant differences among the concordances of right lateral, left lateral, and protrusive excursion. There were significant differences among the concordance between intraoral and articulator occlusal contacts during all excursive movements over time. When comparing concordances of the working sides during lateral excursion, concordance between intraoral occlusion and articular contacts of the working side at T0 was significantly lower than at T3. The rates of positive occlusal error on the working side at T3 were 18.10% on right excursion and 15.49% on left excursion, and the rate of the anterior side was 14.62% on protrusive excursion. The rates of positive occlusal error on the balancing side at T3 were 1.72% on right excursion and 2.12% on left excursion, and that of the posterior side was 2.63% on protrusive excursion. All ICC values of eccentric movements evaluated using the T-scan showed better than moderate reliability. Most ICC values for the mandible were higher than those for the articulator. Conclusions: As a result of assessment of the concordance between semi-adjustable articulator contact and intraoral contact during eccentric movement using T-scan, the concordance changed during excursive mandibular movements. When comparing intraoral and articulator contacts during lateral eccentric mandibular movement, concordance on the working side was significantly lower at T3 than at T0. Occlusal adjustment of the working side might be required after prosthesis delivery. When the balancing side (for lateral excursion) or posterior teeth (for protrusive excursion) were discluded, there were positive occlusal errors. Although these values are low, it is essential to consider the possibility that occlusal adjustment will be necessary on the balancing side after prosthesis delivery. Keywords : semi-adjustable articulator, T-scan, eccentric tooth contact, checkbite, occlusal contact Student Number : 2018-33666โ€“ ๊ตญ๋ฌธ์ดˆ๋ก โ€“ ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์—์„œ T-scan์„ ์ด์šฉํ•œ ํŽธ์‹ฌ์œ„ ๊ตํ•ฉ ์ ‘์ด‰์˜ ์žฌํ˜„์„ฑ ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์น˜์˜๊ณผํ•™๊ณผ ์น˜๊ณผ๋ณด์ฒ ํ•™ ์ „๊ณต (์ง€๋„๊ต์ˆ˜ ๊ถŒ ํ˜ธ ๋ฒ”) ์ • ๋ฏผ ์˜ ๋ชฉ ์  : ๊ตํ•ฉ๊ธฐ๋Š” ์ง„๋‹จ๊ณผ, ์ˆ˜๋ณต๊ณผ์ •์— ์žˆ์–ด์„œ ์„๊ณ  ๋ชจํ˜•์„ ์ด์šฉํ•˜์—ฌ ๊ตํ•ฉ์ ‘์ด‰์„ ์žฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋œ๋‹ค. ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์˜ ํ•˜์•… ์šด๋™ ๊ฒฝ๋กœ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋“ค์€ ์˜ค๋ž˜ ์ „๋ถ€ํ„ฐ ๋ณด๊ณ ๋˜์–ด ์™”์ง€๋งŒ, ๊ตฌ๊ฐ• ๋‚ด์—์„œ์˜ ํ•˜์•…์˜ ํŽธ์‹ฌ์œ„ ์šด๋™๊ณผ ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์—์„œ์˜ ํŽธ์‹ฌ์œ„ ์šด๋™์—์„œ ์น˜์•„ ์ ‘์ด‰์„ ๋น„๊ตํ•˜๋Š” ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋””์ง€ํ„ธ ๊ตํ•ฉ์ธก์ • ๊ธฐ๊ธฐ์ธ T-scan์„ ์ด์šฉํ•˜์—ฌ ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์™€ ๊ตฌ๊ฐ• ๋‚ด์—์„œ ํ•˜์•…์˜ ํŽธ์‹ฌ์œ„ ์šด๋™์‹œ์˜ ์น˜์•„ ์ ‘์ด‰์„ ๋น„๊ตํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋ฐฉ ๋ฒ• : ํ„ฑ๊ด€์ ˆ ์งˆํ™˜์ด ์—†๊ณ , ๊ต์ • ๋ฐœ์น˜ ๋ฐ ์ œ 3 ๋Œ€๊ตฌ์น˜๋ฅผ ์ œ์™ธํ•œ ์น˜์•„์˜ ์ƒ์‹ค์ด ์—†์œผ๋ฉฐ, ์‹ฌํ•œ ์ด์ƒ์ด ์—†๊ณ  ํ˜„์žฌ ๊ต์ •์น˜๋ฃŒ๋ฅผ ๋ฐ›๊ณ  ์žˆ์ง€ ์•Š๋Š”27๋ช…์˜ ํ”ผํ—˜์ž(๋‚จ์ž 11๋ช…, ์—ฌ์ž 16๋ช…)์—์„œ T-scan์„ ์ด์šฉํ•˜์—ฌ ๊ตฌ๊ฐ• ๋‚ด์—์„œ ํ•˜์•…์˜ ํŽธ์‹ฌ์œ„ ์šด๋™(์ „๋ฐฉ, ์šฐ์ธก๋ฐฉ, ์ขŒ์ธก๋ฐฉ)์„ 3ํšŒ ๋ฐ˜๋ณตํ–ˆ๋‹ค. ํ”ผํ—˜์ž์—์„œ ๋น„๊ฐ€์—ญ์„ฑ ์ˆ˜์„ฑ์ฝœ๋กœ์ด๋“œ์ธ์ƒ์ฑ„๋“์œผ๋กœ ์–ป์–ด์ง„ ์„๊ณ  ๋ชจํ˜•์„ ์•ˆ๊ถ์ด์ „์„ ํ†ตํ•ด ๋งˆ์šดํŒ…ํ•˜๊ณ , ๋ถ€๊ฐ€์ค‘ํ•ฉํ˜• ์‹ค๋ฆฌ์ฝ˜ ์ฒดํฌ๋ฐ”์ดํŠธ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์ธ Kavo 7 PROTARevo์˜ ๊ณผ๋กœ๊ฐ์„ ์„ค์ •ํ•œ ํ›„, T-scan์„ ์ด์šฉํ•˜์—ฌ ๊ตํ•ฉ๊ธฐ์—์„œ ํ•˜์•…์˜ ํŽธ์‹ฌ์œ„ ์šด๋™(์ „๋ฐฉ, ์šฐ์ธก๋ฐฉ, ์ขŒ์ธก๋ฐฉ)์„ 3ํšŒ ๋ฐ˜๋ณตํ–ˆ๋‹ค. ์ดํ›„, T-scan ์†Œํ”„ํŠธ์›จ์–ด์—์„œ ๊ตํ•ฉ์ ์„ ์ •ํ™•ํ•˜๊ฒŒ ์œ„์น˜์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ์ƒ์•… ์„๊ณ  ๋ชจํ˜•์„ ๋ชจ๋ธ์Šค์บ๋„ˆ์—์„œ ์Šค์บ”ํ•˜์—ฌ ์–ป์€ Stereolithography(STL) ํŒŒ์ผ์„ T-scan ์†Œํ”„ํŠธ์›จ์–ด์—์„œ ๊ตํ•ฉ์ ๊ณผ ์ค‘์ฒฉ์‹œํ‚จ ํ›„, ๊ฐ ์น˜์•„ ๋ณ„ ์ƒ๋Œ€์  ๊ตํ•ฉ๋ ฅ์„ ์ด์šฉํ•˜์—ฌ 3ํšŒ ๋ฐ˜๋ณต์˜ ์žฌํ˜„์„ฑ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. T-scan ์†Œํ”„ํŠธ์›จ์–ด์—์„œ ํ‘œ๊ธฐ๋œ ์ด๊ฐœ๊ฐ€ ์‹œ์ž‘๋˜๋Š” ์‹œ์ ์ธ C ์ง€์ ์„ T0, ์ด๊ฐœ๊ฐ€ ์™„๋ฃŒ๋œ ์‹œ์ ์ธ D๋ฅผ T3, ๊ทธ ์ค‘๊ฐ„์ธ 1/2 ์‹œ์ ๊ณผ 3/4 ์‹œ์ ์„ T1, T2๋กœ ํ•˜์—ฌ, ๊ตฌ๊ฐ• ๋‚ด์™€ ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์—์„œ ํ•˜์•…์˜ ํŽธ์‹ฌ์œ„ ์šด๋™์—์„œ์˜ ๊ตํ•ฉ์ ‘์ด‰์„ ์‹œ๊ฐ„์˜ ํ๋ฆ„์— ๋”ฐ๋ผ ๋ถ„์„ํ•˜์—ฌ ๊ทธ ์ฐจ์ด๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ์ผ์น˜๋„ ํ‰๊ฐ€ ์‹œ์—๋Š” 3๋ฐ˜๋ณตํ•œ ๋ฐ์ดํ„ฐ์˜ ํ‰๊ท ์„ ์ด์šฉํ•˜์—ฌ, ๊ตํ•ฉ์ ์˜ ์œ ๋ฌด๋กœ ์ผ์น˜๋„๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ขŒ, ์šฐ์ธก ์ธก๋ฐฉ ํŽธ์‹ฌ์œ„ ์ด๋™์˜ ๊ฒฝ์šฐ ์ž‘์—…์ธก๊ณผ ๋น„์ž‘์—…์ธก, ์ „๋ฐฉ ํŽธ์‹ฌ์œ„ ์ด๋™์˜ ๊ฒฝ์šฐ ์ „์น˜๋ถ€์™€ ๊ตฌ์น˜๋ถ€๋กœ ๋‚˜๋ˆ„์–ด ์‹œ๊ฐ„์— ๋”ฐ๋ฅธ ์ผ์น˜๋„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ฐ˜๋ณต ์žฌํ˜„์„ฑ์˜ ํ‰๊ฐ€๋Š” ๊ธ‰๋‚ด ์ƒ๊ด€ ๊ณ„์ˆ˜๋ฅผ ์ด์šฉํ•˜์˜€๊ณ , ํ‰๊ท ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ์ผ์น˜๋„์˜ ํ‰๊ฐ€๋Š” ์ด์› ๋ฐ˜๋ณต ๋ถ„์‚ฐ ๋ถ„์„์™€ ์‚ผ์› ๋ฐ˜๋ณต ๋ถ„์‚ฐ ๋ถ„์„์„ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ์ดํ›„ ๋ณธํŽ˜๋กœ๋‹ˆ ์‚ฌํ›„๊ฒ€์ •์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ฒฐ ๊ณผ : 27๋ช…์˜ ์‹œ์ƒ๊ณผ๋กœ๊ฐ์€ ์šฐ์ธก ํ‰๊ท  46.8ยฑ9.6๋„, ์ขŒ์ธก ํ‰๊ท  46.3ยฑ8.6๋„, ์ธก๋ฐฉ๊ณผ๋กœ๊ฐ์€ ์šฐ์ธก ํ‰๊ท  5.4ยฑ2.0๋„, ์ขŒ์ธก ํ‰๊ท  6.9ยฑ5.8๋„์˜ ๊ฐ’์„ ๋ณด์˜€๋‹ค. ๊ตฌ๊ฐ•๋‚ด์™€ ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์˜ ํ•˜์•… ์ธก๋ฐฉ ํŽธ์‹ฌ์œ„ ์šด๋™ ์‹œ ๊ตํ•ฉ์ ‘์ด‰์˜ ์ผ์น˜๋„๋ฅผ ๋น„๊ตํ–ˆ์„ ๋•Œ, ์šฐ์ธก, ์ขŒ์ธก์œผ๋กœ ์ด๊ฐœ๋˜๊ธฐ ์‹œ์ž‘ํ•˜๋Š” ์‹œ์ (T0)์—์„œ๋Š” 90% ์ด์ƒ์˜ ๋†’์€ ์ผ์น˜๋„๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ์™„์ „ํžˆ ํŽธ์‹ฌ์œ„๋กœ ์ด๋™ํ–ˆ์„ ๋•Œ(T3) ์ž‘์—…์ธก์—์„œ ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์€ ์ผ์น˜๋„๋ฅผ ๋ณด์˜€๋‹ค. ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์—์„œ ์šฐ์ธก, ์ขŒ์ธก, ์ „๋ฐฉ ์ด๋™์—์„œ ์™„์ „ํžˆ ํŽธ์‹ฌ์œ„๋กœ ์ด๋™ํ–ˆ์„ ๋•Œ, ์šฐ์ธก, ์ขŒ์ธก ์ด๋™ ์‹œ ๋น„์ž‘์—…์ธก์—์„œ ๊ฐ๊ฐ 2.58%, 2.65%, ์ „๋ฐฉ ์ด๋™์—์„œ๋Š” ๊ตฌ์น˜๋ถ€์—์„œ 5.27%์˜ ๋ถˆ์ผ์น˜๊ฐ€ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์ด ์ค‘ ์–‘ํ˜• ๊ตํ•ฉ ์˜ค๋ฅ˜๋Š” ์šฐ์ธก ํŽธ์‹ฌ์œ„ ์ด๋™์‹œ 1.72%, ์ขŒ์ธก ํŽธ์‹ฌํžˆ ์ด๋™์‹œ 2.12%, ์ „๋ฐฉ ํŽธ์‹ฌ์œ„ ์ด๋™์‹œ 2.63%๋กœ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. T-scan์„ ํ†ตํ•œ ๊ตฌ๊ฐ•๋‚ด์™€ ๊ตํ•ฉ๊ธฐ์˜ ๋ฐ˜๋ณต ์žฌํ˜„์„ฑ์„ ๋ถ„์„ํ–ˆ์„ ๋•Œ, ์ขŒ์ธก ํŽธ์‹ฌ์œ„์˜ T3์‹œ์ ์„ ์ œ์™ธํ•˜๊ณ ๋Š” ๊ตฌ๊ฐ•๋‚ด์—์„œ ์ข€ ๋” ๋†’์€ ์žฌํ˜„์„ฑ์„ ๋ณด์˜€๋‹ค. ๊ฒฐ ๋ก  : ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์™€ ๊ตฌ๊ฐ• ๋‚ด์˜ ๊ตํ•ฉ์  ์ผ์น˜๋„๋ฅผ ์‹œ๊ฐ„์— ๋”ฐ๋ผ, ํ•˜์•… ํŽธ์‹ฌ์œ„ ์šด๋™ ๋ฐฉํ–ฅ์— ๋”ฐ๋ผ ํ‰๊ฐ€ํ•œ ๊ฒฐ๊ณผ, ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๊ตํ•ฉ์  ์ผ์น˜๋„๊ฐ€ ๋‹ฌ๋ผ์ง์„ ๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํ•˜์•… ์ธก๋ฐฉ ํŽธ์‹ฌ์œ„ ์šด๋™ ์‹œ, ๊ตฌ๊ฐ• ๋‚ด์™€ ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์˜ ์ž‘์—…์ธก ๊ตํ•ฉ์  ์ผ์น˜๋„๋ฅผ ๋น„๊ตํ–ˆ์„ ๋•Œ, T0 ์‹œ์ ๋ณด๋‹ค T3 ์‹œ์ ์—์„œ ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์€ ๊ฐ’์„ ๋ณด์ด๋ฏ€๋กœ, ๋ณด์ฒ ๋ฌผ์„ ๊ตฌ๊ฐ• ๋‚ด์— ์ ํ•ฉ ์ดํ›„ ์ž‘์—…์ธก ๊ตํ•ฉ์กฐ์ •์„ ์š”ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์—์„œ ํ•˜์•… ํŽธ์‹ฌ์œ„ ์šด๋™ ์‹œ, ๊ตฌ๊ฐ• ๋‚ด์™€ ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ์˜ ๋น„์ž‘์—…์ธก ๊ตํ•ฉ์  ์ผ์น˜๋„๋ฅผ ๋น„๊ตํ–ˆ์„ ๋•Œ, T3 ์‹œ์ ์—์„œ 1.7~2.6%์˜ ์–‘ํ˜• ๊ตํ•ฉ ์˜ค๋ฅ˜๊ฐ€ ๊ด€์ฐฐ๋˜๋ฉฐ, ์ด๋Š” ์ ์€ ์–‘์ด์ง€๋งŒ ๊ตํ•ฉ๊ธฐ์˜ ๊ณผ๋ณด์ƒ์œผ๋กœ ํ•ด๊ฒฐ๋˜์ง€ ์•Š์œผ๋ฏ€๋กœ ๋ณด์ฒ ๋ฌผ ์ ํ•ฉ ์ดํ›„ ๊ตฌ๊ฐ• ๋‚ด์—์„œ ๋น„์ž‘์—…์ธก์˜ ์กฐ์ ˆ ๊ฐ€๋Šฅ์„ฑ์„ ์—ผ๋‘์— ๋‘์–ด์•ผ ํ•œ๋‹ค. ์ฃผ์š”์–ด : ๋ฐ˜์กฐ์ ˆ์„ฑ ๊ตํ•ฉ๊ธฐ, T-scan, ํŽธ์‹ฌ์œ„ ์น˜์•„ ์ ‘์ด‰, ์ฒดํฌ๋ฐ”์ดํŠธ, ๊ตํ•ฉ์ ‘์ด‰ ํ•™ ๋ฒˆ : 2018-33666โ… . INTRODUCTION 6 โ…ก. MATERIALS AND METHODS 10 โ…ข. RESULTS 20 โ…ฃ. DISCUSSION 34 โ…ค. CONCLUSIONS 40 REFERENCES 41 ABSTRACT IN KOREAN 48Docto

    ๋ณต๋ถ€ CT์—์„œ ๊ฐ„๊ณผ ํ˜ˆ๊ด€ ๋ถ„ํ•  ๊ธฐ๋ฒ•

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ์‹ ์˜๊ธธ.๋ณต๋ถ€ ์ „์‚ฐํ™” ๋‹จ์ธต ์ดฌ์˜ (CT) ์˜์ƒ์—์„œ ์ •ํ™•ํ•œ ๊ฐ„ ๋ฐ ํ˜ˆ๊ด€ ๋ถ„ํ• ์€ ์ฒด์  ์ธก์ •, ์น˜๋ฃŒ ๊ณ„ํš ์ˆ˜๋ฆฝ ๋ฐ ์ถ”๊ฐ€์ ์ธ ์ฆ๊ฐ• ํ˜„์‹ค ๊ธฐ๋ฐ˜ ์ˆ˜์ˆ  ๊ฐ€์ด๋“œ์™€ ๊ฐ™์€ ์ปดํ“จํ„ฐ ์ง„๋‹จ ๋ณด์กฐ ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜๋Š”๋ฐ ํ•„์ˆ˜์ ์ธ ์š”์†Œ์ด๋‹ค. ์ตœ๊ทผ ๋“ค์–ด ์ปจ๋ณผ๋ฃจ์…”๋„ ์ธ๊ณต ์‹ ๊ฒฝ๋ง (CNN) ํ˜•ํƒœ์˜ ๋”ฅ ๋Ÿฌ๋‹์ด ๋งŽ์ด ์ ์šฉ๋˜๋ฉด์„œ ์˜๋ฃŒ ์˜์ƒ ๋ถ„ํ• ์˜ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋˜๊ณ  ์žˆ์ง€๋งŒ, ์‹ค์ œ ์ž„์ƒ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋†’์€ ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์„ ์ œ๊ณตํ•˜๊ธฐ๋Š” ์—ฌ์ „ํžˆ ์–ด๋ ต๋‹ค. ๋˜ํ•œ ๋ฌผ์ฒด์˜ ๊ฒฝ๊ณ„๋Š” ์ „ํ†ต์ ์œผ๋กœ ์˜์ƒ ๋ถ„ํ• ์—์„œ ๋งค์šฐ ์ค‘์š”ํ•œ ์š”์†Œ๋กœ ์ด์šฉ๋˜์—ˆ์ง€๋งŒ, CT ์˜์ƒ์—์„œ ๊ฐ„์˜ ๋ถˆ๋ถ„๋ช…ํ•œ ๊ฒฝ๊ณ„๋ฅผ ์ถ”์ถœํ•˜๊ธฐ๊ฐ€ ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์— ํ˜„๋Œ€ CNN์—์„œ๋Š” ์ด๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ์žˆ๋‹ค. ๊ฐ„ ํ˜ˆ๊ด€ ๋ถ„ํ•  ์ž‘์—…์˜ ๊ฒฝ์šฐ, ๋ณต์žกํ•œ ํ˜ˆ๊ด€ ์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ ํ•™์Šต ๋ฐ์ดํ„ฐ๋ฅผ ๋งŒ๋“ค๊ธฐ ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์— ๋”ฅ ๋Ÿฌ๋‹์„ ์ ์šฉํ•˜๊ธฐ๊ฐ€ ์–ด๋ ต๋‹ค. ๋˜ํ•œ ์–‡์€ ํ˜ˆ๊ด€ ๋ถ€๋ถ„์˜ ์˜์ƒ ๋ฐ๊ธฐ ๋Œ€๋น„๊ฐ€ ์•ฝํ•˜์—ฌ ์›๋ณธ ์˜์ƒ์—์„œ ์‹๋ณ„ํ•˜๊ธฐ๊ฐ€ ๋งค์šฐ ์–ด๋ ต๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์œ„ ์–ธ๊ธ‰ํ•œ ๋ฌธ์ œ๋“ค์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋œ CNN๊ณผ ์–‡์€ ํ˜ˆ๊ด€์„ ํฌํ•จํ•˜๋Š” ๋ณต์žกํ•œ ๊ฐ„ ํ˜ˆ๊ด€์„ ์ •ํ™•ํ•˜๊ฒŒ ๋ถ„ํ• ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๊ฐ„ ๋ถ„ํ•  ์ž‘์—…์—์„œ ์šฐ์ˆ˜ํ•œ ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์„ ๊ฐ–๋Š” CNN์„ ๊ตฌ์ถ•ํ•˜๊ธฐ ์œ„ํ•ด, ๋‚ด๋ถ€์ ์œผ๋กœ ๊ฐ„ ๋ชจ์–‘์„ ์ถ”์ •ํ•˜๋Š” ๋ถ€๋ถ„์ด ํฌํ•จ๋œ ์ž๋™ ์ปจํ…์ŠคํŠธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, CNN์„ ์‚ฌ์šฉํ•œ ํ•™์Šต์— ๊ฒฝ๊ณ„์„ ์˜ ๊ฐœ๋…์ด ์ƒˆ๋กญ๊ฒŒ ์ œ์•ˆ๋œ๋‹ค. ๋ชจํ˜ธํ•œ ๊ฒฝ๊ณ„๋ถ€๊ฐ€ ํฌํ•จ๋˜์–ด ์žˆ์–ด ์ „์ฒด ๊ฒฝ๊ณ„ ์˜์—ญ์„ CNN์— ํ›ˆ๋ จํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์— ๋ฐ˜๋ณต๋˜๋Š” ํ•™์Šต ๊ณผ์ •์—์„œ ์ธ๊ณต ์‹ ๊ฒฝ๋ง์ด ์Šค์Šค๋กœ ์˜ˆ์ธกํ•œ ํ™•๋ฅ ์—์„œ ๋ถ€์ •ํ™•ํ•˜๊ฒŒ ์ถ”์ •๋œ ๋ถ€๋ถ„์  ๊ฒฝ๊ณ„๋งŒ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ธ๊ณต ์‹ ๊ฒฝ๋ง์„ ํ•™์Šตํ•œ๋‹ค. ์‹คํ—˜์  ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆ๋œ CNN์ด ๋‹ค๋ฅธ ์ตœ์‹  ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค ์ •ํ™•๋„๊ฐ€ ์šฐ์ˆ˜ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์ธ๋‹ค. ๋˜ํ•œ, ์ œ์•ˆ๋œ CNN์˜ ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ๊ฐ„ ํ˜ˆ๊ด€ ๋ถ„ํ• ์—์„œ๋Š” ๊ฐ„ ๋‚ด๋ถ€์˜ ๊ด€์‹ฌ ์˜์—ญ์„ ์ง€์ •ํ•˜๊ธฐ ์œ„ํ•ด ์•ž์„œ ํš๋“ํ•œ ๊ฐ„ ์˜์—ญ์„ ํ™œ์šฉํ•œ๋‹ค. ์ •ํ™•ํ•œ ๊ฐ„ ํ˜ˆ๊ด€ ๋ถ„ํ• ์„ ์œ„ํ•ด ํ˜ˆ๊ด€ ํ›„๋ณด ์ ๋“ค์„ ์ถ”์ถœํ•˜์—ฌ ์‚ฌ์šฉํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ํ™•์‹คํ•œ ํ›„๋ณด ์ ๋“ค์„ ์–ป๊ธฐ ์œ„ํ•ด, ์‚ผ์ฐจ์› ์˜์ƒ์˜ ์ฐจ์›์„ ๋จผ์ € ์ตœ๋Œ€ ๊ฐ•๋„ ํˆฌ์˜ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ์ด์ฐจ์›์œผ๋กœ ๋‚ฎ์ถ˜๋‹ค. ์ด์ฐจ์› ์˜์ƒ์—์„œ๋Š” ๋ณต์žกํ•œ ํ˜ˆ๊ด€์˜ ๊ตฌ์กฐ๊ฐ€ ๋ณด๋‹ค ๋‹จ์ˆœํ™”๋  ์ˆ˜ ์žˆ๋‹ค. ์ด์–ด์„œ, ์ด์ฐจ์› ์˜์ƒ์—์„œ ํ˜ˆ๊ด€ ๋ถ„ํ• ์„ ์ˆ˜ํ–‰ํ•˜๊ณ  ํ˜ˆ๊ด€ ํ”ฝ์…€๋“ค์€ ์›๋ž˜์˜ ์‚ผ์ฐจ์› ๊ณต๊ฐ„์ƒ์œผ๋กœ ์—ญ ํˆฌ์˜๋œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ „์ฒด ํ˜ˆ๊ด€์˜ ๋ถ„ํ• ์„ ์œ„ํ•ด ์›๋ณธ ์˜์ƒ๊ณผ ํ˜ˆ๊ด€ ํ›„๋ณด ์ ๋“ค์„ ๋ชจ๋‘ ์‚ฌ์šฉํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ ˆ๋ฒจ ์…‹ ๊ธฐ๋ฐ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋ณต์žกํ•œ ๊ตฌ์กฐ๊ฐ€ ๋‹จ์ˆœํ™”๋˜๊ณ  ์–‡์€ ํ˜ˆ๊ด€์ด ๋” ์ž˜ ๋ณด์ด๋Š” ์ด์ฐจ์› ์˜์ƒ์—์„œ ์–ป์€ ํ›„๋ณด ์ ๋“ค์„ ์‚ฌ์šฉํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์–‡์€ ํ˜ˆ๊ด€ ๋ถ„ํ• ์—์„œ ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋ณด์ธ๋‹ค. ์‹คํ—˜์  ๊ฒฐ๊ณผ์— ์˜ํ•˜๋ฉด ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ž˜๋ชป๋œ ์˜์—ญ์˜ ์ถ”์ถœ ์—†์ด ๋‹ค๋ฅธ ๋ ˆ๋ฒจ ์…‹ ๊ธฐ๋ฐ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค. ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ฐ„๊ณผ ํ˜ˆ๊ด€์„ ๋ถ„ํ• ํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ œ์•ˆ๋œ ์ž๋™ ์ปจํ…์ŠคํŠธ ๊ตฌ์กฐ๋Š” ์‚ฌ๋žŒ์ด ๋””์ž์ธํ•œ ํ•™์Šต ๊ณผ์ •์ด ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์„ ํฌ๊ฒŒ ํ–ฅ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์ธ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ œ์•ˆ๋œ ๊ฒฝ๊ณ„์„  ํ•™์Šต ๊ธฐ๋ฒ•์œผ๋กœ CNN์„ ์‚ฌ์šฉํ•œ ์˜์ƒ ๋ถ„ํ• ์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒํ•  ์ˆ˜ ์žˆ์Œ์„ ๋‚ดํฌํ•œ๋‹ค. ๊ฐ„ ํ˜ˆ๊ด€์˜ ๋ถ„ํ• ์€ ์ด์ฐจ์› ์ตœ๋Œ€ ๊ฐ•๋„ ํˆฌ์˜ ๊ธฐ๋ฐ˜ ์ด๋ฏธ์ง€๋กœ๋ถ€ํ„ฐ ํš๋“๋œ ํ˜ˆ๊ด€ ํ›„๋ณด ์ ๋“ค์„ ํ†ตํ•ด ์–‡์€ ํ˜ˆ๊ด€๋“ค์ด ์„ฑ๊ณต์ ์œผ๋กœ ๋ถ„ํ• ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ฐ„์˜ ํ•ด๋ถ€ํ•™์  ๋ถ„์„๊ณผ ์ž๋™ํ™”๋œ ์ปดํ“จํ„ฐ ์ง„๋‹จ ๋ณด์กฐ ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜๋Š” ๋ฐ ๋งค์šฐ ์ค‘์š”ํ•œ ๊ธฐ์ˆ ์ด๋‹ค.Accurate liver and its vessel segmentation on abdominal computed tomography (CT) images is one of the most important prerequisites for computer-aided diagnosis (CAD) systems such as volumetric measurement, treatment planning, and further augmented reality-based surgical guide. In recent years, the application of deep learning in the form of convolutional neural network (CNN) has improved the performance of medical image segmentation, but it is difficult to provide high generalization performance for the actual clinical practice. Furthermore, although the contour features are an important factor in the image segmentation problem, they are hard to be employed on CNN due to many unclear boundaries on the image. In case of a liver vessel segmentation, a deep learning approach is impractical because it is difficult to obtain training data from complex vessel images. Furthermore, thin vessels are hard to be identified in the original image due to weak intensity contrasts and noise. In this dissertation, a CNN with high generalization performance and a contour learning scheme is first proposed for liver segmentation. Secondly, a liver vessel segmentation algorithm is presented that accurately segments even thin vessels. To build a CNN with high generalization performance, the auto-context algorithm is employed. The auto-context algorithm goes through two pipelines: the first predicts the overall area of a liver and the second predicts the final liver using the first prediction as a prior. This process improves generalization performance because the network internally estimates shape-prior. In addition to the auto-context, a contour learning method is proposed that uses only sparse contours rather than the entire contour. Sparse contours are obtained and trained by using only the mispredicted part of the network's final prediction. Experimental studies show that the proposed network is superior in accuracy to other modern networks. Multiple N-fold tests are also performed to verify the generalization performance. An algorithm for accurate liver vessel segmentation is also proposed by introducing vessel candidate points. To obtain confident vessel candidates, the 3D image is first reduced to 2D through maximum intensity projection. Subsequently, vessel segmentation is performed from the 2D images and the segmented pixels are back-projected into the original 3D space. Finally, a new level set function is proposed that utilizes both the original image and vessel candidate points. The proposed algorithm can segment thin vessels with high accuracy by mainly using vessel candidate points. The reliability of the points can be higher through robust segmentation in the projected 2D images where complex structures are simplified and thin vessels are more visible. Experimental results show that the proposed algorithm is superior to other active contour models. The proposed algorithms present a new method of segmenting the liver and its vessels. The auto-context algorithm shows that a human-designed curriculum (i.e., shape-prior learning) can improve generalization performance. The proposed contour learning technique can increase the accuracy of a CNN for image segmentation by focusing on its failures, represented by sparse contours. The vessel segmentation shows that minor vessel branches can be successfully segmented through vessel candidate points obtained by reducing the image dimension. The algorithms presented in this dissertation can be employed for later analysis of liver anatomy that requires accurate segmentation techniques.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.2 Problem statement 3 1.3 Main contributions 6 1.4 Contents and organization 9 Chapter 2 Related Works 10 2.1 Overview 10 2.2 Convolutional neural networks 11 2.2.1 Architectures of convolutional neural networks 11 2.2.2 Convolutional neural networks in medical image segmentation 21 2.3 Liver and vessel segmentation 37 2.3.1 Classical methods for liver segmentation 37 2.3.2 Vascular image segmentation 40 2.3.3 Active contour models 46 2.3.4 Vessel topology-based active contour model 54 2.4 Motivation 60 Chapter 3 Liver Segmentation via Auto-Context Neural Network with Self-Supervised Contour Attention 62 3.1 Overview 62 3.2 Single-pass auto-context neural network 65 3.2.1 Skip-attention module 66 3.2.2 V-transition module 69 3.2.3 Liver-prior inference and auto-context 70 3.2.4 Understanding the network 74 3.3 Self-supervising contour attention 75 3.4 Learning the network 81 3.4.1 Overall loss function 81 3.4.2 Data augmentation 81 3.5 Experimental Results 83 3.5.1 Overview 83 3.5.2 Data configurations and target of comparison 84 3.5.3 Evaluation metric 85 3.5.4 Accuracy evaluation 87 3.5.5 Ablation study 93 3.5.6 Performance of generalization 110 3.5.7 Results from ground-truth variations 114 3.6 Discussion 116 Chapter 4 Liver Vessel Segmentation via Active Contour Model with Dense Vessel Candidates 119 4.1 Overview 119 4.2 Dense vessel candidates 124 4.2.1 Maximum intensity slab images 125 4.2.2 Segmentation of 2D vessel candidates and back-projection 130 4.3 Clustering of dense vessel candidates 135 4.3.1 Virtual gradient-assisted regional ACM 136 4.3.2 Localized regional ACM 142 4.4 Experimental results 145 4.4.1 Overview 145 4.4.2 Data configurations and environment 146 4.4.3 2D segmentation 146 4.4.4 ACM comparisons 149 4.4.5 Evaluation of bifurcation points 154 4.4.6 Computational performance 159 4.4.7 Ablation study 160 4.4.8 Parameter study 162 4.5 Application to portal vein analysis 164 4.6 Discussion 168 Chapter 5 Conclusion and Future Works 170 Bibliography 172 ์ดˆ๋ก 197Docto

    Automatic Extraction of Tall Buildings from Off-Nadir High Resolution Satellite Images Using Model-Based Approach

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2015. 2. ๊น€์šฉ์ผ.์ตœ๊ทผ ๋‹ค์–‘ํ•œ ๊ณ ํ•ด์ƒ๋„ ์ง€๊ตฌ๊ด€์ธก์œ„์„ฑ์ด ๋ฐœ์‚ฌ ๋˜๊ณ , ๊ณ ํ•ด์ƒ๋„ ์œ„์„ฑ์˜์ƒ์˜ ์ƒ์—…์ ์ธ ๋ณด๊ธ‰์ด ํ™œ๋ฐœํ•ด ์ง์— ๋”ฐ๋ผ ์ด๋ฅผ ์ด์šฉํ•œ ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ๋“ค์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ํŠนํžˆ 1m ์ดํ•˜์˜ ๋†’์€ ๊ณต๊ฐ„ํ•ด์ƒ๋„๋Š” ์ง€์ƒ์— ์œ„์น˜ํ•œ ๊ฑด๋ฌผ, ๋„๋กœ, ์ฐจ๋Ÿ‰ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ฌผ์ฒด์— ๊ด€ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ ๊ฑด๋ฌผ์˜ 2์ฐจ์› ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๋Š” ์—ฐ๊ตฌ๋Š” ๋„์‹œ ๋ชจ๋‹ˆํ„ฐ๋ง, ์žฌ๋‚œ๊ด€๋ฆฌ ๋“ฑ์˜ ๋ถ„์•ผ์— ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์–ด ํ•„์š”์„ฑ์ด ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ฑด๋ฌผ ์ถ”์ถœ ์ •ํ™•๋„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์†Œ๊ฐ€ ๋‹ค์–‘ํ•˜์—ฌ ๋Œ€๋‹ค์ˆ˜์˜ ๊ฑด๋ฌผ ์ถ”์ถœ ์—ฐ๊ตฌ๊ฐ€ ์—ฐ์ง์˜์ƒ์„ ์‚ฌ์šฉํ•œ ์ €์ธต ๊ฑด๋ฌผ ์ถ”์ถœ์— ์ œํ•œ๋˜์–ด ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋น„์—ฐ์ง ๋ฐฉํ–ฅ์œผ๋กœ ์ดฌ์˜๋œ ๊ณ ์ธต๊ฑด๋ฌผ์„ ์ถ”์ถœํ•˜๋Š” ๋ฐ๋Š” ํ•œ๊ณ„๊ฐ€ ๋”ฐ๋ฅด๋ฉฐ, ์ด๋Š” ๋‹ค์–‘ํ•œ ์ œ์›์˜ ์˜์ƒ์„ ์ด์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ๋†’์ด์˜ ๊ฑด๋ฌผ์„ ์ถ”์ถœํ•˜๋Š”๋ฐ ์–ด๋ ค์›€์ด ์กด์žฌํ•˜๊ฒŒ ๋งŒ๋“ ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋น„์—ฐ์ง ์˜์ƒ์—์„œ ๊ณ ์ธต๊ฑด๋ฌผ์˜ ์ƒ๋‹จ์„ ์ž๋™์œผ๋กœ ์ถ”์ถœํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์—ฌ ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ณ ์ธต๊ฑด๋ฌผ ์˜์—ญ ์ž๋™ ์ถ”์ถœ๊ณผ ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ ์ถ”์ถœ์˜ ๋‘ ๋‹จ๊ณ„๋กœ ๊ตฌ๋ถ„๋œ๋‹ค. ๊ฑด๋ฌผ์˜์—ญ ์ž๋™ ํƒ์ง€ ๊ณผ์ •์—์„œ๋Š” Otsu ๊ธฐ๋ฒ•๊ณผ ์˜์—ญํ™•์žฅ ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ทธ๋ฆผ์ž ์˜์ƒ๊ณผ ๊ฑด๋ฌผ ์˜์—ญ์„ ์ž๋™์œผ๋กœ ์ถ”์ถœํ•œ๋‹ค. ์ถ”์ถœ๋œ ๋‘ ์˜์—ญ๊ณผ ์˜์ƒ์˜ ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ, ์—์ง€ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ์˜ ์„ ์„ ์‹ค์ œ ๊ฑด๋ฌผ ์„ ์— ์ตœ์ ํ™”์‹œํ‚จ ํ›„, ๊ฑด๋ฌผ์˜ ๊ตฌ์กฐ์  ํŠน์ง•๊ณผ ์˜์—ญ์ ์ธ ํŠน์ง•์„ ๋ฐ˜์˜ํ•œ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ์˜์—ญ์„ ์ž๋™์œผ๋กœ ์™„์„ฑํ•˜์˜€๋‹ค. ์ œ์•ˆ ๋ฐฉ๋ฒ•์„ ์ฃผ๊ฑฐ์ง€๊ตฌ์™€ ์—…๋ฌด์ง€๊ตฌ์˜ IKONOS-2, QuickBird-2 ์˜์ƒ์— ์ ์šฉํ•˜์—ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์šฐ์ˆ˜์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ํ™”์†Œ ๋ฐ ๊ฐ์ฒด ๊ธฐ๋ฐ˜์˜ ์ •ํ™•๋„ ๋ถ„์„ ๊ฒฐ๊ณผ, ๋ชจ๋“  ๊ฒฝ์šฐ์— ๋Œ€ํ•˜์—ฌ ์‚ฌ์šฉ์ž ์ •ํ™•๋„๋Š” 0.87, ์ƒ์‚ฐ์ž ์ •ํ™•๋„๋Š” 0.79, ๊ทธ๋ฆฌ๊ณ  F ์ธก์ •์น˜๋Š” 0.83 ์ด์ƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜ ์˜์ƒ์˜ ์ข…๋ฅ˜์™€ ์‹คํ—˜ ์ง€์—ญ์˜ ์†์„ฑ๊ณผ ๋ฌด๊ด€ํ•˜๊ฒŒ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์œ ์šฉํ•จ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋˜ํ•œ ๊ฐ์ฒด ๊ธฐ๋ฐ˜์˜ ํ‰๊ท  F ์ธก์ •์น˜๋Š” 0.89๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ด๋Š” ๊ธฐ์กด ๊ฑด๋ฌผ ์ถ”์ถœ ์—ฐ๊ตฌ์™€ ๋น„๊ตํ•˜์—ฌ ๋น„์Šทํ•˜๊ฑฐ๋‚˜ ๋†’์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ‘๋ฐฑ์˜ ๋‹จ์˜์ƒ๋งŒ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋‹ค์ค‘ ๋ถ„๊ด‘ ์˜์ƒ์ด๋‚˜ ๋ถ€๊ฐ€ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์— ๋น„ํ•ด ๋น„์šฉ ํšจ์œจ์ ์ธ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋น„์—ฐ์ง ์˜์ƒ์—์„œ ๊ณ ์ธต๊ฑด๋ฌผ์˜ ์ƒ๋‹จ์„ ๋‹ค๋ฅธ ๋ฉด๊ณผ ๊ตฌ๋ถ„ํ•˜๋Š” ์ž๋™ํ™”๋œ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์—ฌ ๊ธฐ์กด ๊ฑด๋ฌผ ์ถ”์ถœ์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ  ๊ณ ํ•ด์ƒ๋„ ์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ ๊ณ ์ธต๊ฑด๋ฌผ์˜ ์ •๋ณด๋ฅผ ์ถ”์ถœํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๊ธฐ๋ฒ•์˜ ์šฐ์ˆ˜์„ฑ์„ ๋ฐ”ํƒ•์œผ๋กœ, ์ œ์•ˆ ๊ธฐ๋ฒ•์€ ๋‹ค์–‘ํ•œ ๋„์‹œ ์ง€์—ญ์˜ ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ์„ ์ถ”์ถœํ•˜๋Š” ์—ฐ๊ตฌ์— ์ ์šฉ๋  ์ˆ˜ ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ฑด๋ฌผ ์ƒ๋‹จ ๊ฐ„์˜ ๋งค์นญ์„ ํ†ตํ•œ 3์ฐจ์› ๊ฑด๋ฌผ ๋ชจ๋ธ ์ƒ์„ฑ, ๋„์‹œ๊ฑด๋ฌผ๋ณ€ํ™”ํƒ์ง€ ๋“ฑ์˜ ๋ถ„์•ผ์— ์ ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Š” ์ถ”์ถœ๋  ์ˆ˜ ์žˆ๋Š” ๊ฑด๋ฌผ ์ •๋ณด๋ฅผ ๋‹ค์–‘ํ™”ํ•˜์—ฌ ์˜์ƒ์„ ์ด์šฉํ•œ ๊ฑด๋ฌผ ์ถ”์ถœ ๋ถ„์•ผ๊ฐ€ ๋”์šฑ ๋ฐœ์ „ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฐ˜์„ ์ œ๊ณตํ•œ๋‹ค.1. ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ 1 1.2 ์—ฐ๊ตฌ๋™ํ–ฅ 2 1.3 ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๋ฐ ๋ฒ”์œ„ 7 2. ๊ณ ์ธต๊ฑด๋ฌผ ์˜์—ญ ์ž๋™ ์ถ”์ถœ 11 2.1 ์˜์ƒ ์ „์ฒ˜๋ฆฌ 12 2.2 Otsu ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ๊ฑด๋ฌผ ๊ทธ๋ฆผ์ž ์˜์—ญ ์ถ”์ถœ 15 2.3 ์˜์—ญํ™•์žฅ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ๊ฑด๋ฌผ ์˜์—ญ ์ถ”์ถœ 16 2.3.1 ์˜์—ญํ™•์žฅ ๊ธฐ๋ฒ•์„ ์œ„ํ•œ ์ดˆ๊ธฐ ์‹œ๋“œ ์ถ”์ถœ 16 2.3.2 ๊ณ ์ธต๊ฑด๋ฌผ ์˜์—ญ ์ค‘์ฒฉ ๋ฐ ์˜ค์ถ”์ถœ ์ œ๊ฑฐ 18 3. ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ ์ถ”์ถœ 21 3.1 ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ ์„  ์ถ”์ถœ 23 3.1.1 LSD๋ฅผ ์ด์šฉํ•œ ์˜์ƒ ๋‚ด ์ดˆ๊ธฐ ๊ฑด๋ฌผ ์˜์—ญ ์„  ์ถ”์ถœ 23 3.1.2 ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ ์˜์—ญ ์„  ์ถ”์ถœ 25 3.2 ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ ์„  ์ตœ์ ํ™” 32 3.3 ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ ์˜์—ญ ์ถ”์ถœ 36 3.3.1 ์ˆ˜์ง๊ด€๊ณ„๋ฅผ ์ด์šฉํ•œ ๊ฑด๋ฌผ ์ƒ๋‹จ ์˜์—ญ ์ถ”์ถœ 36 3.3.2 ํ‰ํ–‰๊ด€๊ณ„๋ฅผ ์ด์šฉํ•œ ๊ฑด๋ฌผ ์ƒ๋‹จ ์˜์—ญ ์ถ”์ถœ 39 3.3.3 ์ถ”์ถœ๋œ ๊ฑด๋ฌผ ์ƒ๋‹จ ์˜์—ญ ํ†ตํ•ฉ ๋ฐ ์ตœ์ ํ™” 43 4. ์‹คํ—˜ ๋ฐ ์ ์šฉ 47 4.1 ์‹คํ—˜ ์ง€์—ญ ๋ฐ ์ž๋ฃŒ 47 4.2 ์‹คํ—˜ ๊ฒฐ๊ณผ 48 4.2.1 ๊ณ ์ธต๊ฑด๋ฌผ ์˜์—ญ ์ž๋™ ์ถ”์ถœ ๊ฒฐ๊ณผ 48 4.2.2 ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ ์ถ”์ถœ ๊ฒฐ๊ณผ 53 4.2.2.1 ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ ์„  ์ถ”์ถœ ๋ฐ ์ตœ์ ํ™” ๊ฒฐ๊ณผ 53 4.2.2.2 ๊ณ ์ธต๊ฑด๋ฌผ ์ƒ๋‹จ ์˜์—ญ ์ถ”์ถœ ๊ฒฐ๊ณผ 59 5. ๊ฒฐ๋ก  71 6. ์ฐธ๊ณ ๋ฌธํ—Œ 74Maste

    ํ˜„์ง€์กฐ์‚ฌ๊ฐ€ ๋ถ€๋‹น์ฒญ๊ตฌ ๊ธฐ๊ด€์˜ ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌ ํ–‰ํƒœ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

    Get PDF
    ๋ณด๊ฑด์ •์ฑ…๊ด€๋ฆฌ์ „๊ณต/์„์‚ฌํ˜„์ง€์กฐ์‚ฌ๋Š” ์š”์–‘๊ธฐ๊ด€์˜ ๊ฑด์ „ํ•œ ์š”์–‘๊ธ‰์—ฌ๋น„์šฉ ์ฒญ๊ตฌ ํ’ํ† ๋ฅผ ์กฐ์„ฑํ•˜๊ณ  ์ ์ •์ง„๋ฃŒ๋ฅผ ์œ ๋„ํ•˜๊ณ ์ž ํ•˜๋ฉฐ, ๊ฑด๊ฐ•๋ณดํ—˜ ๊ฐ€์ž…์ž์˜ ์ˆ˜๊ธ‰๊ถŒ์„ ๋ณดํ˜ธํ•˜๊ณ  ๊ฑด์ „ํ•œ ์˜๋ฃŒ๊ณต๊ธ‰์ž๋ฅผ ๋ณดํ˜ธํ•˜๋ฉฐ ๋ถˆํ•„์š”ํ•œ ๊ฑด๊ฐ•๋ณดํ—˜์žฌ์ • ๋ˆ„์ˆ˜๋„ ๋ฐฉ์ง€ํ•˜๊ณ ์ž ์‹ค์‹œํ•˜๋Š” ๋ณด๊ฑด๋ณต์ง€๋ถ€ ์žฅ๊ด€์˜ ํ–‰์ •์กฐ์‚ฌ์ด๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” 2013๋…„ 1์›”๋ถ€ํ„ฐ 2013๋…„ 12์›”๊นŒ์ง€ ๋ณด๊ฑด๋ณต์ง€๋ถ€ ์ •๊ธฐ ํ˜„์ง€์กฐ์‚ฌ๋ฅผ ๋ฐ›์€ ์˜์›๊ธ‰ ์š”์–‘๊ธฐ๊ด€ 223๊ฐœ์†Œ ์ค‘ ๋Œ€ํ‘œ์ž 1์ธ ๊ธฐ๊ด€ 160๊ฐœ์†Œ์—์„œ ํ˜„์ง€์กฐ์‚ฌ๊ฐ€ ์š”์–‘๊ธฐ๊ด€์˜ ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌ ํ–‰ํƒœ ๋ณ€ํ™”์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ํ˜„์ง€์กฐ์‚ฌ ์ „๊ณผ ํ›„ ์ด ์ง„๋ฃŒ๋น„์™€ ์ฒญ๊ตฌ๊ฑด์ˆ˜, ๊ฑด๋‹น์ง„๋ฃŒ๋น„์˜ ๋ณ€ํ™”์™€ ๊ฐ ์ข…์†๋ณ€์ˆ˜์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์€ ๋ฌด์—‡์ธ์ง€ ์˜๋ฃŒ๊ธฐ๊ด€๊ณผ ์ˆ˜์ง„์ž ํŠน์„ฑ, ์ ๋ฐœ๋œ ๋ถ€๋‹น์ฒญ๊ตฌ ์œ ํ˜•์— ๋”ฐ๋ผ ๋ถ„์„ํ•˜๊ณ , ํ˜„์ง€์กฐ์‚ฌ ์‹ค์‹œ ์ „๊ณผ ํ›„ ์‹œ๊ฐ„์˜ ํ๋ฆ„์— ๋”ฐ๋ผ ํ˜„์ง€์กฐ์‚ฌ๋ฅผ ๋ฐ›์€ ์š”์–‘๊ธฐ๊ด€์˜ ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌ ํ–‰ํƒœ ๋ณ€ํ™”๊ฐ€ ์žˆ๋Š”์ง€ ์•Œ์•„๋ณด๊ณ  ์ด๋ฅผ ํ†ตํ•˜์—ฌ ํ˜„์ง€์กฐ์‚ฌ์˜ ํšจ๊ณผ์— ๋Œ€ํ•ด ํ‰๊ฐ€ํ•ด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์ž๋ฃŒ๋Š” ํ˜„์ง€์กฐ์‚ฌ๋ฅผ ๋ฐ›๊ธฐ ์ „์ธ 2011๋…„ 7์›”๋ถ€ํ„ฐ ์กฐ์‚ฌ๋ฅผ ๋ฐ›์€ ํ›„์ธ 2015๋…„ 12์›” ์‚ฌ์ด ์ด 54๊ฐœ์›”์˜ ์›” ๋‹จ์œ„ ๊ฑด๊ฐ•๋ณดํ—˜ ์ฒญ๊ตฌ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ ์š”์–‘๊ธฐ๊ด€๊ณผ ๊ทธ ์š”์–‘๊ธฐ๊ด€์„ ์ด์šฉํ•œ ์ˆ˜์ง„์ž์˜ ํ˜„์ง€์กฐ์‚ฌ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅธ ์ผ๋ฐ˜์  ํŠน์„ฑ์„ ์•Œ์•„๋ณด๊ณ ์ž ๋…๋ฆฝ๋œ ๋‘ ํ‘œ๋ณธ ์ง‘๋‹จ์˜ t-๊ฒ€์ •(two sample t-test) ๋ฐ ์นด์ด์Šคํ€˜์–ด๊ฒ€์ •์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋ชจ๋“  ์ข…์†๋ณ€์ˆ˜์˜ ๋ถ„ํฌ๋Š” ์ •๊ทœ๋ถ„ํฌ๊ฐ€ ์•„๋‹Œ ๊ฐ๋งˆ๋ถ„ํฌ๋กœ ๋ณด์•˜๋‹ค. ๋˜ํ•œ ํ˜„์ง€์กฐ์‚ฌ ๊ฒฐ๊ณผ, ์กฐ์‚ฌ๋ฅผ ๋ฐ›์€ ์š”์–‘๊ธฐ๊ด€์˜ ํŠน์„ฑ, ์˜๋ฃŒ๊ธฐ๊ด€์„ ์ด์šฉํ•œ ์ˆ˜์ง„์ž์˜ ํŠน์„ฑ๊ณผ ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌํ–‰ํƒœ ๋ณ€ํ™”๋ฅผ ์ธก์ •ํ•˜๋Š” ์ข…์†๋ณ€์ˆ˜(์ด ์ง„๋ฃŒ๋น„์™€ ๊ฑด๋‹น ์ง„๋ฃŒ๋น„, ์ฒญ๊ตฌ๊ฑด์ˆ˜)๊ฐ„์˜ ํ‰๊ท  ๋ณ€ํ™”๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ANOVA๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ํ˜„์ง€์กฐ์‚ฌ ๊ฒฐ๊ณผ์™€ ์กฐ์‚ฌ๋ฅผ ๋ฐ›์€ ์š”์–‘๊ธฐ๊ด€์˜ ํŠน์„ฑ, ์˜๋ฃŒ๊ธฐ๊ด€์„ ์ด์šฉํ•œ ์ˆ˜์ง„์ž์˜ ํŠน์„ฑ์ด ํ˜„์ง€์กฐ์‚ฌ์˜ ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌํ–‰ํƒœ ๋ณ€ํ™”๋ฅผ ์ธก์ •ํ•˜๋Š” ์ข…์†๋ณ€์ˆ˜์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ผ์น˜๋Š”์ง€ ๊ด€๋ จ ์š”์ธ์„ ์•Œ์•„๋ณด๊ณ  ํ˜„์ง€์กฐ์‚ฌ๊ฐ€ ์‹ค์‹œ๋œ ์‹œ์ ๊ณผ ๊ตฌ๊ฐ„์— ๋”ฐ๋ฅธ ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌํ–‰ํƒœ์˜ ๋ณ€ํ™”๋Ÿ‰์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋‹จ์ ˆ์  ์‹œ๊ณ„์—ด ์„ค๊ณ„(interrupted time-series)๋ถ„์„์œผ๋กœ ํ˜„์ง€์กฐ์‚ฌ ์‹œํ–‰ ์ „ใƒปํ›„๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ๋‹จ์ ˆ์  ์‹œ๊ณ„์—ด ๋ถ„์„์—์„œ ๊ตฌ๊ฐ„๋ณ„๋กœ ํ˜„์ง€์กฐ์‚ฌ์˜ ํšจ๊ณผ์— ๋Œ€ํ•œ ํฌ๊ธฐ๋ฅผ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ตฌ๊ฐ„๋ณ„ ํšŒ๊ท€๋ถ„์„(segmented regression analysis)์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ํ˜„์ง€์กฐ์‚ฌ ์งํ›„์™€ ์กฐ์‚ฌ ์ „ใƒปํ›„, 2011๋…„ 7์›”๋ถ€ํ„ฐ 2015๋…„๊นŒ์ง€์˜ ์ถ”์„ธ๋ฅผ ๋น„๊ตํ•˜์—ฌ ๋ณด์•˜์„ ๋•Œ ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌํ–‰ํƒœ์˜ ๋ณ€ํ™”๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์ง€ํ‘œ๋Š” ์‹œ์ ๋ณ„๋กœ ์ฐจ์ด๋Š” ์žˆ์œผ๋‚˜ ์กฐ์‚ฌ๋ฅผ ๋ฐ›์€ ๊ธฐ๊ด€ ๋ชจ๋‘์—์„œ ์ฐจ์ด๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜, ํ˜„์ง€์กฐ์‚ฌ๊ฐ€ ์š”์–‘๊ธฐ๊ด€์— ์˜ํ–ฅ์„ ๋ฏธ์นจ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์–‘ํ˜ธ๊ธฐ๊ด€์—์„œ๋Š” ํ˜„์ง€์กฐ์‚ฌ ์งํ›„๋‚˜ ํ˜„์ง€์กฐ์‚ฌ ์ „ใƒปํ›„ ๋น„๊ต์—์„œ ์ด ์ง„๋ฃŒ๋น„๋‚˜ ์ฒญ๊ตฌ๊ฑด์ˆ˜, ๊ฑด๋‹น ์ง„๋ฃŒ๋น„๊ฐ€ ์ผ์‹œ์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜์˜€์œผ๋‚˜ 2011๋…„ 7์›”๋ถ€ํ„ฐ 2015๋…„ 12์›”๊นŒ์ง€์˜ ์ถ”์„ธ์—์„œ๋Š” ๋ชจ๋‘ ๊ฐ์†Œํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๊ณ , ๋ถ€๋‹น์ด ์ ๋ฐœ๋œ ๊ธฐ๊ด€์—์„œ๋Š” ๋ฐ˜๋Œ€๋กœ ํ˜„์ง€์กฐ์‚ฌ ์งํ›„๋‚˜ ํ˜„์ง€์กฐ์‚ฌ ์ „ใƒปํ›„ ๋น„๊ต์—์„œ ์ด ์ง„๋ฃŒ๋น„๋‚˜ ๊ฑด๋‹น ์ง„๋ฃŒ๋น„๊ฐ€ ์ผ์‹œ์ ์œผ๋กœ ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ํ˜„์ง€์กฐ์‚ฌ ์ดํ›„ ์š”์–‘๊ธฐ๊ด€ ์Šค์Šค๋กœ ์ง„๋ฃŒ๋‚˜ ์ฒญ๊ตฌํ–‰ํƒœ๋ฅผ ์กฐ์ ˆํ•˜๋Š” ๋ถ€๋ถ„์ด ์žˆ์œผ๋‚˜ ์žฅ๊ธฐ์ ์œผ๋กœ๋Š” ํšจ๊ณผ๋ฅผ ๋ฏธ์น˜์ง€ ๋ชปํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ๋˜ํ•œ ์–‘ํ˜ธ๊ธฐ๊ด€์˜ ๊ฒฝ์šฐ 2011๋…„ 7์›”๋ถ€ํ„ฐ 2015๋…„ 12์›”๊นŒ์ง€์˜ ์ถ”์„ธ์—์„œ๋Š” ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌํ–‰ํƒœ๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์ง€ํ‘œ๊ฐ€ ๋ชจ๋‘ ๊ฐ์†Œํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€์œผ๋‚˜ ์กฐ์‚ฌ ์งํ›„์™€ ์กฐ์‚ฌ ์ „ใƒปํ›„ ์‹œ์ ์—์„œ๋Š” ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์ธ ๊ฒƒ์œผ๋กœ ๋ณด์•„, ์š”์–‘๊ธฐ๊ด€ ์ž์ฒด์ ์œผ๋กœ ์ง„๋ฃŒ๋‚˜ ์ฒญ๊ตฌํ–‰ํƒœ๋ฅผ ์กฐ์ ˆํ•˜๋ ค๋Š” ๋…ธ๋ ฅ์„ ํ•˜๊ณ  ์žˆ์—ˆ์œผ๋‚˜ ํ˜„์ง€์กฐ์‚ฌ๋กœ ์ธํ•œ ์—ญํšจ๊ณผ๋กœ ์˜คํžˆ๋ ค ์ผ์‹œ์ ์œผ๋กœ ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌํ–‰ํƒœ์˜ ์ง€ํ‘œ๊ฐ€ ์ฆ๊ฐ€ํ–ˆ์—ˆ๋‹ค๊ณ  ๋ณด์—ฌ์กŒ๋‹ค. ์ด๋Š” ๋ฏผ์›์ œ๋ณด๋‚˜ ์ง„๋ฃŒ์˜ ๊ฒฝํ–ฅ ๋“ฑ ๋ถ€๋‹น์ด ์˜์‹ฌ๋˜๋Š” ๊ฐ๊ด€์  ์ž๋ฃŒ๋“ค์„ ํ† ๋Œ€๋กœ ์กฐ์‚ฌ๋Œ€์ƒ๊ธฐ๊ด€์œผ๋กœ ์„ ์ •๋˜์–ด ํ˜„์ง€์กฐ์‚ฌ๋ฅผ ๋ฐ›์•˜์œผ๋‚˜ ์ด๋ฅผ ์ ๋ฐœํ•˜์ง€ ๋ชปํ•œ ๊ฒฝ์šฐ์—๋Š” ์˜คํžˆ๋ ค ๊ทธ ๊ธฐ๊ด€์˜ ์ง„๋ฃŒํ–‰ํƒœ์— ์ •๋‹น์„ฑ์„ ๋ถ€์—ฌํ•˜์—ฌ ์ง„๋ฃŒ์™€ ์ฒญ๊ตฌ๊ฐ€ ๋” ์ฆ๊ฐ€ํ•˜๊ฒŒ ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ๊ณ ๋ คํ•˜์—ฌ ์‹ ์ค‘ํ•œ ๊ธฐ๊ด€์„ ์ •๊ณผ ์กฐ์‚ฌ์‹ค์‹œ๊ฐ€ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•จ์„ ๋งํ•ด์ค€๋‹ค. ํ˜„ํ–‰ ํ˜„์ง€์กฐ์‚ฌ ์‹ค์‹œ ๊ธฐ๊ด€์ˆ˜๋Š” ์ „์ฒด ์˜๋ฃŒ๊ธฐ๊ด€์˜ 1% ๋‚ด์™ธ์— ๋ถˆ๊ณผํ•˜์—ฌ, ๊ฑฐ์ง“๋ถ€๋‹น์ฒญ๊ตฌ๋กœ ์ธํ•œ ์žฌ์ •๋ˆ„์ˆ˜๋ฅผ ๋ณด๋‹ค ์ ๊ทน์ ์œผ๋กœ ์ฐจ๋‹จํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ˜„์ง€์กฐ์‚ฌ ๋น„์œจ์„ ํ™•๋Œ€ํ•˜์—ฌ ํ˜„์ง€์กฐ์‚ฌ๋กœ ์ธํ•œ ๊ฒฝ์ฐฐํšจ๊ณผ๋ฅผ ์ข€ ๋” ๊ฐ•ํ™”ํ•ด์•ผ ํ•„์š”์„ฑ๋„ ๊ณ„์† ์ œ๊ธฐ๋˜๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ˜„์ง€์กฐ์‚ฌ ๋น„์œจ์„ ํ™•๋Œ€ํ•˜๋˜ ์ข…์ „๊นŒ์ง€์™€ ๊ฐ™์€ ์ •๊ธฐ ํ˜„์ง€์กฐ์‚ฌ ๊ธฐ๊ด€์ˆ˜์˜ ์–‘์  ์ฆ๊ฐ€์— ๊ทธ์น˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์ข€ ๋” ํŒŒ๊ธ‰๋ ฅ ์žˆ๊ณ  ์žฅ๊ธฐ์ ์ธ ์˜ํ–ฅ๋ ฅ์„ ๋ฏธ์น˜๋Š” ์งˆ์  ํ–ฅ์ƒ์ด ์ด๋ฃจ์–ด์ ธ์•ผ ํ•˜๊ฒ ๋‹ค. ํ˜„์ง€์กฐ์‚ฌ๊ฐ€ ์š”์–‘๊ธฐ๊ด€์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์ด๋กœ ์ธํ•ด ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌํ–‰ํƒœ์— ๋ณ€ํ™”๋ฅผ ๊ฐ€์ ธ์˜ค๋Š” ๋งŒํผ, ์กฐ์‚ฌ ๋Œ€์ƒ ๊ธฐ๊ด€์„ ์„ ์ •ํ•˜์—ฌ ์ค€๋น„ํ•˜๋Š” ๋‹จ๊ณ„๋ถ€ํ„ฐ ์กฐ์‚ฌ์‹ค์‹œ๊นŒ์ง€ ์ง„๋ฃŒ ๋ฐ ์ฒญ๊ตฌํ–‰ํƒœ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ ๋ถ„์„๊ณผ ๋ถ€๋‹น์œ ํ˜•๋ณ„ ์กฐ์‚ฌ๊ฒฐ๊ณผ ๋ถ„์„์„ ํ†ตํ•œ ์„ธ๋ถ„ํ™”๋œ ์ ‘๊ทผ์œผ๋กœ ๋ถ€๋‹น์˜ ์ ๋ฐœ๋ฅ ์„ ๋†’์ผ ์ˆ˜ ์žˆ๋Š” ๋‹ค๊ฐ์ ์ธ ๋ฐฉ์•ˆ ๋ชจ์ƒ‰์ด ํ•„์š”ํ•˜๋‹ค. The field investigation is an administrative investigation which is conducted to induce appropriate medical care, protect the rights of providers, or prevent health finance of unnecessary leakage. Even though government implemented field investigation to prevent further fault claim through administrative punishment, however there were such a few researches about field investigation to encourage the effect of system. Therefore, to evaluate the effect of field investigation on medical care and claim behavior of primary care as time goes by, 160 one representative institutions among 223 medical clinics who underwent the regular field investigation of the Ministry of Health and Welfare were included, and the investigation was performed from January 2013 to December 2013. To compare political intervention while time changes, research data was analyzed for monthly 54 months between July 2011, before receiving the field investigation and December 2015, after receiving the field investigation. In statistical analysis, multi-level analysis such as t-tests and chi-square tests were performed to investigate the general characteristics of primary care and patients using those institutions. The distribution of all dependent variables was considered to be a gamma distribution rather than a normal distribution. In addition, we conducted a segmented regression analysis for estimating the size of the effect of the field investigation by segment in the discrete time series analysis. In comparing each clinics, we matched institutions by political intervention whether or not, because the number of medical of institutions was different and to clarify polit...ope

    A Study on the Improvement of DPO Training Courses based on the Analysis of DP LOP Accidents

    Get PDF
    ์ตœ๊ทผ ์ €์œ ๊ฐ€๋กœ ์ธํ•˜์—ฌ ํ•ด์–‘ํ”Œ๋žœํŠธ ์‚ฐ์—…์ด ์ „๋ฐ˜์ ์œผ๋กœ ์นจ์ฒด๋˜์–ด ์žˆ์œผ๋‚˜, ํ•ด์–‘ํ”Œ๋žœํŠธ ์ž‘์—… ๋ฐ ๋ณด์ˆ˜ ์ž‘์—…์„ ์œ„ํ•œ DP์„ ๋ฐ•์˜ ์šด์˜์€ ์ง€์†์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋Š” ์‹ค์ •์ด๋‹ค. DP์„ ๋ฐ•์€ ์—…๋ฌด ๋ชฉ์  ์ƒ ์ฃผ๋กœ ํ•ด์–‘ํ”Œ๋žœํŠธ์™€ ๊ทผ์ ‘ํ•˜์—ฌ ์ž‘์—…์ด ์ด๋ฃจ์–ด์ง€๋ฉฐ, ์ด๋Š” ๊ณง ์ž ์žฌ์ ์ธ ์œ„ํ—˜์„ฑ์ด ํ•ญ์ƒ ๋’ค๋”ฐ๋ฅธ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ํ•ด์–‘ํ”Œ๋žœํŠธ ์‚ฌ๊ณ ๋Š” ๊ทธ ํŠน์„ฑ์ƒ ์งยท๊ฐ„์ ‘์ ์ธ ํ”ผํ•ด๊ฐ€ ๋ง‰๋Œ€ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ด๋ฅผ ์˜ˆ๋ฐฉํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์ด ๊ฐ•์กฐ๋˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์•„์‰ฝ๊ฒŒ๋„ ํ˜„์žฌ๊นŒ์ง€ ์•„์ฐจ์‚ฌ๊ณ ์— ํ•ด๋‹นํ•˜๋Š” DP์„ ๋ฐ•์˜ LOP์‚ฌ๊ณ ๋ฅผ ๋ถ„์„ํ•˜๊ณ  ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ˜์˜ํ•œ ์—ฐ๊ตฌ๋Š” ๋ฏธํกํ•œ ์‹ค์ •์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” DP์„ ๋ฐ•์˜ ์ž ์žฌ์ ์ธ ์‚ฌ๊ณ ์š”์ธ์œผ๋กœ ์ž‘์šฉํ•˜๋Š” LOP์‚ฌ๊ณ ์˜ ์›์ธ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ตœ๊ทผ 6๋…„(2011-2016)๊ฐ„ IMCA์— ๋ณด๊ณ ๋œ LOP์‚ฌ๊ณ ์‚ฌ๋ก€๋ฅผ ๊ฒ€ํ† ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, LOP์‚ฌ๊ณ ์˜ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์›์ธ ์ค‘์—์„œ ์“ฐ๋Ÿฌ์Šคํ„ฐ/์ถ”์ง„๊ธฐ๊ฐ€ 30.8%๋กœ ๊ฐ€์žฅ ๋†’์€ ๋น„์œจ์„ ์ฐจ์ง€ํ•˜์˜€๋‹ค. ์ธ์ ์˜ค๋ฅ˜์˜ ๊ฒฝ์šฐ, ์ง์ ‘์ ์ธ ์›์ธ์— ์˜ํ•œ LOP์‚ฌ๊ณ  ๋น„์œจ์€ 13.0%, ๊ฐ„์ ‘์ ์ธ ์›์ธ์€ 4.2%๋กœ ํ™•์ธ๋˜์—ˆ์œผ๋ฉฐ ์ด ์‚ฌ๊ณ ์˜ 17.2%๊ฐ€ ์ธ์ ์˜ค๋ฅ˜์™€ ์ง์ ‘ ๋˜๋Š” ๊ฐ„์ ‘์ ์œผ๋กœ ์—ฐ๊ด€๋˜์–ด ์žˆ์—ˆ๋‹ค. LOP์‚ฌ๊ณ ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ ์ธ์ ์˜ค๋ฅ˜์˜ ๊ฒฝํ–ฅ์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด ์ตœ๊ทผ 6๋…„๊ฐ„์˜ LOP์‚ฌ๊ณ  ์ค‘์—์„œ ์ธ์ ์˜ค๋ฅ˜์™€ ๊ด€๋ จ๋œ ์‚ฌ๊ณ ๋ฅผ HFACS ๋ถ„๋ฅ˜์ฒด๊ณ„๋กœ ๊ฒ€ํ† ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ๋ฒ ์ด์ง€์•ˆ ๋„คํŠธ์›Œํฌ ํ”„๋กœ๊ทธ๋žจ์ธ GeNIe๋ฅผ ํ™œ์šฉํ•ด ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ๊ธฐ์ˆ ๊ธฐ๋ฐ˜์— ์˜ํ•œ ์ธ์ ์˜ค๋ฅ˜์˜ ์กฐ๊ฑด๋ถ€ ํ™•๋ฅ ์€ Drift-off ๋ฐ Drive-off์—์„œ 60.8%๋กœ ๊ฐ€์žฅ ๋†’์•˜์œผ๋ฉฐ, Time loss ๋ฐ Operation abort์—์„œ๋Š” 48.2%๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” 10๋…„๊ฐ„(2001-2010)์˜ LOP ์‚ฌ๊ณ ๋ฅผ ๋™์ผํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๋ถ„์„ํ•œ ์„ ํ–‰์—ฐ๊ตฌ์™€ ๋น„๊ตยท๊ฒ€ํ† ํ•˜์˜€๊ณ  ์ด๋ฅผ ํ†ตํ•ด ํ™•์ธ๋œ ์ธ์ ์˜ค๋ฅ˜์˜ ๋ณ€ํ™”์ถ”์ด๋ฅผ ํ† ๋Œ€๋กœ ๊ธฐ์กด DPO ๋ฉดํ—ˆ ๋ฐ ๊ต์œกํ›ˆ๋ จ์˜ ๋ฌธ์ œ์ ์„ ํŒŒ์•…ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ ๊ธฐ๋ฐ˜ ์˜ค๋ฅ˜์˜ ๊ฐ์†Œ ๋ฐ DP์ž‘์—… ๊ด€๋ฆฌ์ž์˜ ํšจ๊ณผ์ ์ธ ๊ด€๋ฆฌยท๊ฐ๋…์„ ์œ„ํ•œ ๊ฐœ์„ ๋ฐฉ์•ˆ์„ ์ œ์•ˆํ•˜์˜€๋‹ค.|Recently the offshore plant industry has been in a slump overall due to low oil prices. However, the operation of the offshore plants and the maintenance work by DP vessels are ongoing. DP vessels work in close proximity to the offshore plants, which means that potential hazards always follow. If these potential hazards lead to an offshore plant accidents, there will be enormous direct and indirect damage. Therefore, it is important to study to prevent these accidents. Until recently, however, there was a lack of study on the LOP accidents of DP vessels caused by potential hazards and near-misses. In this study, the author reviewed the LOP accident cases reported to the IMCA over the last six years (2011-2016) to analyze the causes of LOP accidents. As a result, LOP accidents were caused by a variety of causes and among them, Thruster/Propulsion was the highest cause of the accident at 30.8%. Human error accounted for 17.2% of the total accidents. Among them, 13.0% were accidents caused by direct human error, and 4.2% were accidents caused by indirect causes. The author classified LOP accidents in the last six years through HFACS to identify the trends of human error that cause LOP accidents. The classified data were then analyzed using the Bayesian network program GeNIe. As a result, the conditional probability of human error due to skill-based was the highest with the ratio of drive-off and drift-off of 60.8%. Next, the ratio of Time loss and Operation abort was 48.3%. The author compared the above result with the previous study, which studied the LOP accidents in 2001-2010 in the same way. Through this, the author identified the trend of human error and confirmed the problems of the existing DPO license system and training courses. The author proposed improvement measures for the decrease of skill-based errors and effective management and supervision of DP project managers to address these problems.List of Tables โ…ณ List of Figures โ…ด Abstract โ…ต Glossary x ์ œ1์žฅ ์„œ ๋ก  1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๋ฐ ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 3 ์ œ2์žฅ DP์„ ๋ฐ• ๋ฐ DP์‹œ์Šคํ…œ์˜ ์ •์˜ 2.1 DP์„ ๋ฐ•์˜ ์ •์˜ 5 2.1.1 DP์„ ๋ฐ•์˜ ์ •์˜ 5 2.1.2 DP์‹œ์Šคํ…œ์˜ ์ •์˜ 6 2.1.3 DP์‹œ์Šคํ…œ์˜ ๋“ฑ๊ธ‰ 8 2.2 ์œ„์น˜์†์‹ค์˜ ์ •์˜ 9 ์ œ3์žฅ DP์„ ๋ฐ•์˜ LOP์‚ฌ๊ณ  ๋ถ„์„ 3.1 DP์„ ๋ฐ• LOP์‚ฌ๊ณ ์— ๋Œ€ํ•œ ์†Œ๊ฐœ 10 3.2 DP์„ ๋ฐ•์˜ LOP์‚ฌ๊ณ  ๊ฒ€ํ†  14 3.2.1 ์ผ๋ฐ˜์  ๋ถ„์„ 14 3.2.2 ์ธ์ ์˜ค๋ฅ˜ ์ƒ์„ธ๋ถ„์„ 18 3.3 DP์„ ๋ฐ•์˜ LOP์‚ฌ๊ณ  ๋ถ„์„ ๋„๊ตฌ 20 3.3.1 HFACS 20 3.3.2 ๋ฒ ์ด์ง€์•ˆ ๋„คํŠธ์›Œํฌ 29 3.4 HFACS ๋ฐ ๋ฒ ์ด์ง€์•ˆ ๋„คํŠธ์›Œํฌ ๊ธฐ๋ฐ˜ ์ธ์ ์˜ค๋ฅ˜ ๋ถ„์„ 35 3.4.1 HFACS ๋ถ„๋ฅ˜ 35 3.4.2 ๋ฒ ์ด์ง€์•ˆ ๋„คํŠธ์›Œํฌ ๋ถ„์„ 44 3.4.3 ์ธ์ ์˜ค๋ฅ˜ ์œ„ํ—˜๋ถ„์„ ์š”์•ฝ 52 ์ œ4์žฅ ์„ ํ–‰์—ฐ๊ตฌ์™€์˜ ๋น„๊ตยท๋ถ„์„์„ ํ†ตํ•œ DPO ๊ต์œกํ›ˆ๋ จ ๊ฐœ์„  ๋ฐฉ์•ˆ 4.1 ์„ ํ–‰์—ฐ๊ตฌ์˜ ์†Œ๊ฐœ 53 4.1.1 ์„ ํ–‰์—ฐ๊ตฌ์™€ ๋น„๊ตยท๋ถ„์„ 53 4.2 DPO ๊ต์œกํ›ˆ๋ จ์˜ ๊ฐœ์„ ๋ฐฉ์•ˆ ์ œ์•ˆ 59 4.2.1 ํ˜„ํ–‰ DPO ๊ต์œกํ›ˆ๋ จ ๊ณผ์ • 60 4.2.2 DPO ๊ต์œกํ›ˆ๋ จ์˜ ๊ฐœ์„ ๋ฐฉ์•ˆ ์ œ์•ˆ 66 ์ œ5์žฅ ๊ฒฐ๋ก  ์ฐธ๊ณ ๋ฌธํ—Œ 71 ๊ฐ์‚ฌ์˜ ๊ธ€ 74Maste

    Systemic approach for Etiology,Diagnosis and Treatment of Snoring and Sleep Apnea

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ์น˜์˜ํ•™๋Œ€ํ•™์› :์น˜์˜ํ•™๊ณผ,2010.2.Maste

    ์ผ๊ฐœ ์š”์–‘๋ณ‘์› ๋ฐฉ๋ฌธ๊ฐ์˜ ์†์œ„์ƒ ์ˆ˜ํ–‰ ์‹คํƒœ ์กฐ์‚ฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฐ„ํ˜ธ๋Œ€ํ•™ ๊ฐ„ํ˜ธํ•™๊ณผ, 2018. 2. ๊ฐ•์žํ˜„.๋ฐฐ๊ฒฝ: ๋ณธ ์—ฐ๊ตฌ๋Š” ์š”์–‘๋ณ‘์› ๋ฐฉ๋ฌธ๊ฐ์˜ ์†์œ„์ƒ ์ˆ˜ํ–‰ ์‹คํƒœ๋ฅผ ํŒŒ์•…ํ•˜๊ณ  ์„ธ๋ถ€์ ์œผ๋กœ๋Š” ์—ฐ๋ น ๊ทธ๋ฃน๋ณ„, ์„ฑ๋ณ„, ํ–‰์œ„ ์ข…๋ฅ˜๋ณ„ ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ ์˜ ์ฐจ์ด์™€ ์‚ฌ์šฉํ•˜๋Š” ์†์œ„์ƒ ๋ฐฉ๋ฒ•์˜ ์ฐจ์ด๋ฅผ ํ™•์ธํ•˜์—ฌ ์š”์–‘๋ณ‘์› ๋ฐฉ๋ฌธ๊ฐ์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋Š” ๊ฐ์—ผ๊ด€๋ฆฌ์ง€์นจ ๊ฐœ๋ฐœ ๋ฐ ์†์œ„์ƒ ๊ต์œก ํ”„๋กœ๊ทธ๋žจ ๊ฐœ๋ฐœ์— ๋„์›€์ด ๋˜๋Š” ๊ธฐ์ดˆ ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•ด ์‹œํ–‰๋˜์—ˆ๋‹ค. ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ๋Š” ํšก๋‹จ์  ์„œ์ˆ ์  ์กฐ์‚ฌ ์—ฐ๊ตฌ๋กœ, ๊ฒฝ๊ธฐ๋„ ์†Œ์žฌ 502๋ณ‘์ƒ์˜ ์ผ๊ฐœ ์š”์–‘๋ณ‘์›์—์„œ ์‹œํ–‰๋˜์—ˆ๋‹ค. ๋ฐฉ๋ฌธ๊ฐ์˜ ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ ์„ ์ง์ ‘์ ์œผ๋กœ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ์€๋‹‰๊ด€์ฐฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ 2017๋…„ 7์›” 1์ผ๋ถ€ํ„ฐ 8์›” 15์ผ๊นŒ์ง€, ์ด 13ํšŒ(ํ† ์š”์ผ 6ํšŒ, ์ผ์š”์ผ 6ํšŒ, ์ฃผ์ค‘ ๊ณตํœด์ผ 1ํšŒ)์— ๊ฑธ์ณ 1์ธ์˜ ์—ฐ๊ตฌ์ž๊ฐ€ ๋ฐฉ๋ฌธ๊ฐ์˜ ์†์œ„์ƒ ๊ด€์ฐฐ์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋ณธ๊ด€ 5๊ฐœ ๋ณ‘๋™์—์„œ ๊ฐ ๋ณ‘๋™๋‹น ์†์œ„์ƒ ๊ธฐํšŒ์ˆœ๊ฐ„ 200๊ฑด์„ ๊ด€์ฐฐํ•˜์˜€์œผ๋ฉฐ, ์†์œ„์ƒ ๊ด€์ฐฐ์„ ์œ„ํ•ด WHO์˜ ์†์œ„์ƒ ๊ด€์ฐฐ ๋„๊ตฌ๋ฅผ ๋ฐฉ๋ฌธ๊ฐ์˜ ํŠน์„ฑ์— ๋งž์ถฐ ์ผ๋ถ€ ์ˆ˜์ • ๋ฐ ๋ณด์™„ํ•œ ๋„๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ด€์ฐฐ ํ–‰์œ„ ์ข…๋ฅ˜ ์ค‘ ๋ฐฉ๋ฌธ๊ฐ๊ณผ ๊ด€๋ จ ์—†๋Š” ์น˜๋ฃŒ์  ํ–‰์œ„ ์‹œํ–‰ ์ „์€ ์ œ์™ธ์‹œ์ผฐ์œผ๋ฉฐ ๋Œ€์ƒ์ž์˜ ์ง์ข…์„ ์ ๋Š” ๋Œ€์‹  ๋Œ€์ƒ์ž๊ฐ€ ์†ํ•  ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜๋Š” ์—ฐ๋ น ๊ทธ๋ฃน(์•„๋™, ์ฒญ์†Œ๋…„, ์„ฑ์ธ, ๋…ธ์ธ) ๋ฐ ์„ฑ๋ณ„์„ ๊ธฐ๋กํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: ์ด 766๋ช…์˜ ๋ฐฉ๋ฌธ๊ฐ์œผ๋กœ๋ถ€ํ„ฐ 1,000๊ฑด์˜ ์†์œ„์ƒ ๊ธฐํšŒ์ˆœ๊ฐ„์„ ๊ด€์ฐฐํ•œ ๊ฒฐ๊ณผ, ์ „์ฒด ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ ์€ 20.3%๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์—ฐ๋ น ๊ทธ๋ฃน๋ณ„ ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ ์€ ์„ฑ์ธ 23.4%, ์ฒญ์†Œ๋…„ 15.8%, ์•„๋™ 9.4%, ๋…ธ์ธ 8.2%๋กœ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค(p<0.001). ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ ์˜ ์ฐจ์ด๋Š” ์œ ์˜ํ•˜์ง€ ์•Š์•˜๋‹ค. ํ–‰์œ„ ์ข…๋ฅ˜๋ณ„ ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ  ๋˜ํ•œ ์ฒด์•ก์— ๋…ธ์ถœ ๊ฐ€๋Šฅํ•œ ํ–‰์œ„ ์‹œํ–‰ ํ›„ 83.5%, ํ™˜์ž์™€ ์ ‘์ด‰ ์ „ 16.1%, ํ™˜์ž์™€ ์ ‘์ด‰ ํ›„ 14.0%, ํ™˜์ž์˜ ์ฃผ๋ณ€ ํ™˜๊ฒฝ ์ ‘์ด‰ ํ›„ 7.5%๋กœ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค(p<0.001). ์•Œ์ฝ”์˜ฌ ์ ค์„ ์ด์šฉํ•œ ์†์œ„์ƒ์€ 94๊ฑด, ๋ฌผ๊ณผ ๋น„๋ˆ„๋ฅผ ์ด์šฉํ•œ ์†์œ„์ƒ์€ 109๊ฑด ๊ด€์ฐฐ๋˜์—ˆ์œผ๋ฉฐ, ์—ฐ๋ น ๊ทธ๋ฃน๋ณ„ ์†์œ„์ƒ ๋ฐฉ๋ฒ• ์‚ฌ์šฉ ๋นˆ๋„์˜ ์ฐจ์ด๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค(p=0.020). ์ฒญ์†Œ๋…„์— ํ•ด๋‹นํ•˜๋Š” ์—ฐ๋ น ๊ทธ๋ฃน์—์„œ๋งŒ ๋ฌผ๊ณผ ๋น„๋ˆ„๋ฅผ ์ด์šฉํ•œ ์†์œ„์ƒ ๋ฐฉ๋ฒ•๋ณด๋‹ค ์•Œ์ฝ”์˜ฌ ์ ค์„ ์ด์šฉํ•œ ์†์œ„์ƒ ๋ฐฉ๋ฒ•์„ ๋” ๋งŽ์ด ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ด€์ฐฐ ํ–‰์œ„ ์ข…๋ฅ˜๋ณ„ ์†์œ„์ƒ ๋ฐฉ๋ฒ• ์‚ฌ์šฉ ๋นˆ๋„์˜ ์ฐจ์ด ๋˜ํ•œ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ–ˆ์œผ๋ฉฐ(p<0.001), ํ™˜์ž์™€ ์ ‘์ด‰ ์ „์— ํ•ด๋‹นํ•˜๋Š” ๊ฒฝ์šฐ์—์„œ๋งŒ ๋ฌผ๊ณผ ๋น„๋ˆ„๋ฅผ ์ด์šฉํ•œ ์†์œ„์ƒ ๋ฐฉ๋ฒ•๋ณด๋‹ค ์•Œ์ฝ”์˜ฌ ์ ค์„ ์ด์šฉํ•œ ์†์œ„์ƒ ๋ฐฉ๋ฒ•์„ ๋” ๋งŽ์ด ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ์†์œ„์ƒ ๋ฐฉ๋ฒ• ์‚ฌ์šฉ ๋นˆ๋„์˜ ์ฐจ์ด๋Š” ์œ ์˜ํ•˜์ง€ ์•Š์•˜๋‹ค. ์ฒด์•ก์— ๋…ธ์ถœ ๊ฐ€๋Šฅํ•œ ํ–‰์œ„ ์‹œํ–‰ ํ›„์— ํ•ด๋‹นํ•˜๋Š” 79๊ฑด ์ค‘, ๋ฐฉ๋ฌธ๊ฐ์ด ๋…ธ์ถœ๋œ ์ฒด์•ก ์ข…๋ฅ˜๋กœ๋Š” ํƒ€์•ก(48.1%)์ด ๊ฐ€์žฅ ๋งŽ์•˜๋‹ค. ๊ฒฐ๋ก : ์ ‘์ด‰์„ ํ†ตํ•œ ๊ฐ์—ผ์„ฑ ๊ท ์ฃผ ์ „ํŒŒ์˜ ์œ„ํ—˜์„ฑ์„ ๊ณ ๋ คํ•ด๋ณผ ๋•Œ, ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋ฐฉ๋ฌธ๊ฐ์ด ํ™˜์ž์˜ ์ฒด์•ก์—๋„ ๋…ธ์ถœ๋  ๊ฐ€๋Šฅ์„ฑ์ด ์ถฉ๋ถ„ํ•จ์„ ํ™•์ธํ•˜์˜€์œผ๋ฏ€๋กœ ๋ฐฉ๋ฌธ๊ฐ์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋Š” ๊ฐ์—ผ๊ด€๋ฆฌ ๊ต์œก์€ ๋งค์šฐ ํ•„์š”ํ•˜๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ์ด๋ฒˆ ์—ฐ๊ตฌ์—์„œ ๋‚˜ํƒ€๋‚œ 20.3%์˜ ๋ฐฉ๋ฌธ๊ฐ ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ ์€ ๋งค์šฐ ๋‚ฎ์€ ์ˆ˜์น˜์ด๋ฉฐ, ์ฒด์•ก์— ๋…ธ์ถœ ๊ฐ€๋Šฅํ•œ ํ–‰์œ„ ์‹œํ–‰ ํ›„๋ฅผ ์ œ์™ธํ•œ ๋‚˜๋จธ์ง€ ์†์œ„์ƒ์ด ํ•„์š”ํ•œ ์ˆœ๊ฐ„์—์„œ์˜ ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ ์ด ๋งค์šฐ ์ €์กฐํ•œ ๊ฒƒ์€ ์†์œ„์ƒ์ด ํ•„์š”ํ•œ ์ˆœ๊ฐ„์— ๋Œ€ํ•œ ๋ฐฉ๋ฌธ๊ฐ๋“ค์˜ ์ธ์‹ ์ „ํ™˜์„ ์œ„ํ•œ ๊ต์œก์ด ํ•„์š”ํ•จ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋‹ค๋ฅธ ๋ณ‘์› ๋ฐฉ๋ฌธ๊ฐ์˜ ์†์œ„์ƒ ์‹คํƒœ๋ฅผ ํ™•๋Œ€ ์กฐ์‚ฌํ•  ํ•„์š”๊ฐ€ ์žˆ์œผ๋ฉฐ, ์ด๋ฒˆ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋ฐฉ๋ฌธ๊ฐ์˜ ๋‚ฎ์€ ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ ์„ ์ฆ์ง„์‹œํ‚ฌ ๋ฐฉ์•ˆ๊ณผ ๋ฐฉ๋ฌธ๊ฐ์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋Š” ๊ฐ์—ผ๊ด€๋ฆฌ์ง€์นจ ๋ฐ ์†์œ„์ƒ ๊ต์œก ํ”„๋กœ๊ทธ๋žจ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ์ถ”ํ›„ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค.โ… . ์„œ ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  5 3. ์šฉ์–ด ์ •์˜ 6 โ…ก. ๋ฌธ ํ—Œ ๊ณ  ์ฐฐ 7 1. ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 7 2. ์†์œ„์ƒ ์ˆ˜ํ–‰์ด ์˜๋ฃŒ ๊ด€๋ จ ๊ฐ์—ผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 10 3. ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ  ์กฐ์‚ฌ ๋ฐฉ๋ฒ• ๋ฐ ๋„๊ตฌ 12 โ…ข. ์—ฐ๊ตฌ์˜ ๊ฐœ๋… ํ‹€ 15 โ…ฃ. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 18 1. ์—ฐ๊ตฌ ์„ค๊ณ„ 18 2. ์—ฐ๊ตฌ ์‹œํ–‰ ๊ธฐ๊ด€ 18 3. ์—ฐ๊ตฌ ๋Œ€์ƒ 19 4. ์—ฐ๊ตฌ ๋„๊ตฌ 19 5. ์ž๋ฃŒ์ˆ˜์ง‘ ๋ฐฉ๋ฒ• ๋ฐ ์ ˆ์ฐจ 21 6. ์ž๋ฃŒ ๋ถ„์„ ๋ฐฉ๋ฒ• 23 โ…ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 24 1. ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฃฐ 24 2. ์†์œ„์ƒ ์ˆ˜ํ–‰ ๋ฐฉ๋ฒ• 28 3. ๋…ธ์ถœ๋œ ์ฒด์•ก์— ๋”ฐ๋ฅธ ์†์œ„์ƒ ์ˆ˜ํ–‰๋ฅ  ๋ฐ ๋ฐฉ๋ฒ• 29 โ…ฅ. ๋…ผ ์˜ 32 โ…ฆ. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 39 ์ฐธ๊ณ ๋ฌธํ—Œ 42 ๋ถ€ ๋ก 51 Abstract 56Maste

    ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„์™€ ๊ตฌ์„ฑ์›์˜ ์ง๋ฌด์—ญํ• ์„ฑ๊ณผ ์‚ฌ์ด์— ์กด์žฌํ•˜๋Š” ์–‘๋ฉด์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๊ด€ํ•œ ์—ฐ๊ตฌ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜ํ•™๊ณผ, 2012. 8. ์œค์„ํ™”.๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„๋กœ ์ธํ•˜์—ฌ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๊ตฌ์„ฑ์›์˜ ์–‘๋ฉด์ ์ธ ์‹ฌ๋ฆฌ๊ณผ์ •์— ๋Œ€ํ•˜์—ฌ ํƒ๊ตฌํ•œ๋‹ค. ๊ทผ๋ž˜์˜ ์œ ์—ฐํ•œ ์กฐ์ง ์„ค๊ณ„ ๋ฐ ์กฐ์ง ๋‚ด์˜ ํ™œ๋ฐœํ•œ ์ž„ํŒŒ์›Œ๋จผํŠธ ์›€์ง์ž„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„์— ๋Œ€ํ•œ ํฌ๊ด„์ ์ธ ๊ณ ์ฐฐ์€ ํ•œ์ •๋˜์—ˆ๋‹ค. ๋ช‡ ๋ช‡ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋“ค์€ ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„์™€ ์ง๋ฌด๊ด€๋ จ ๊ฒฐ๊ณผ ๋ณ€์ˆ˜๋“ค ๊ฐ„์˜ ๊ธ์ •์ ์ธ ๊ด€๊ณ„์—๋งŒ ์ง‘์ค‘ํ•˜์—ฌ, ํ•™์ž๋“ค๊ณผ ์‹ค๋ฌด์ž๋“ค๋กœ ํ•˜์—ฌ๊ธˆ ๊ทธ๋ ‡๋‹ค๋ฉด ํ•œ ๋‹จ๊ณ„ ๋†’์€ ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„๋Š”, ํ•ญ์ƒ ํ•œ ๋‹จ๊ณ„ ๋ฐ”๋žŒ์งํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ถˆ๋Ÿฌ ์ผ์œผํ‚ค๋Š” ๊ฒƒ์ธ๊ฐ€ ๋ผ๊ณ  ํ•˜๋Š” ๊ทœ๋ฒ”์ ์ธ ์˜๋ฌธ์„ ์ œ์‹œํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์˜์‹์—์„œ ์ถœ๋ฐœํ•˜์˜€์œผ๋ฉฐ, ์ €์ž๋Š” ๋ณธ ๋…ผ๋ฌธ์—์„œ ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„๋Š” ๊ตฌ์„ฑ์›๋“ค๋กœ ํ•˜์—ฌ๊ธˆ ์„œ๋กœ ๋‹ค๋ฅธ, ์ฆ‰ ์–‘๋ฉด์ ์ธ ์‹ฌ๋ฆฌ์  ๋ฐ˜์‘ (๊ธ์ •์ ์ธ ์ธ์ง€์  ์ฐจ์›: ์ž์•„ํšจ๋Šฅ๊ฐ๋ถ€์ •์ ์ธ ๊ฐ์ •์  ์ฐจ์›: ์ง๋ฌด๊ธด์žฅ)์„ ์ผ์œผํ‚ฌ ์ˆ˜ ์žˆ๋Š” ํŠน์ง•์„ ๊ฐ€์ง€๊ณ  ์žˆ์Œ์„ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ•œ๋‹ค. ๊ตฌ์„ฑ์›์œผ๋กœ ํ•˜์—ฌ๊ธˆ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š”(enabling) ๋ฉ”์ปค๋‹ˆ์ฆ˜์œผ๋กœ์„œ์˜ ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„๋Š”, ๊ตฌ์„ฑ์›์˜ ์ž์•„ํšจ๋Šฅ๊ฐ๊ณผ ๊ธ์ •์ ์ธ ๊ด€๊ณ„๋ฅผ ๊ฐ€์งˆ ๊ฒƒ์œผ๋กœ ์˜ˆ์ธก๋˜๋ฉฐ, ์ด๋กœ ์ธํ•ด ์ฆ๊ฐ€๋œ ๊ตฌ์„ฑ์›์˜ ์ž์•„ํšจ๋Šฅ๊ฐ์€ ๊ฒฐ๊ณผ์ ์œผ๋กœ ๊ตฌ์„ฑ์›์˜ ์ง๋ฌด์—ญํ• ์„ฑ๊ณผ์™€ ๊ธ์ •์ ์ธ ๊ด€๊ณ„๋ฅผ ๊ฐ€์งˆ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ํ•œํŽธ, ์œ„์ž„ ๊ทธ๋ฆฌ๊ณ  ๋ถ€๊ฐ€์ ์ธ ์ฑ…์ž„๋ถ€์—ฌ์™€ ๊ฐ™์€ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„์˜ ์ผ๋ถ€ ํŠน์ง•์€, ๋ณธ ๋…ผ๋ฌธ์—์„œ ๊ตฌ์„ฑ์›๋“ค์—๊ฒŒ ๋ถ€๋‹ด์„ ์ฃผ๋Š”(burdening)๋ฉ”์ปค๋‹ˆ์ฆ˜์œผ๋กœ ๋ช…๋ช…๋˜์—ˆ์œผ๋ฉฐ, ์ด๋Š” ๊ตฌ์„ฑ์›์˜ ์ง๋ฌด ๊ธด์žฅ๊ณผ ๊ธ์ •์ ์ธ ๊ด€๊ณ„๋ฅผ ๊ฐ€์งˆ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์ด๋Ÿฌํ•œ ์ง๋ฌด๊ธด์žฅ์€ ๋‹ค์‹œ๊ธˆ ๊ตฌ์„ฑ์›์˜ ์ง๋ฌด ์—ญํ• ์„ฑ๊ณผ์™€ ๋ถ€์ •์ ์ธ ๊ด€๊ณ„๋ฅผ ๊ฐ€์ง€๋Š” ๊ฒƒ์œผ๋กœ ์˜ˆ์ธก๋œ๋‹ค. ๋”์šฑ์ด ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฆฌ๋”์‹ญ์˜ ์ƒํ˜ธ์ž‘์šฉ์  ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ, ์ง๋ฌดํŠน์„ฑ(์ฆ‰, ์ง๋ฌด์ž์œจ์„ฑ)๋ฐ ๊ตฌ์„ฑ์›์˜ ํŠน์„ฑ(์ฆ‰, ๋ชฉํ‘œ์„ฑํ–ฅ)์ด ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„์™€ ๊ตฌ์„ฑ์›์˜ ์‹ฌ๋ฆฌ์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์ ˆํ•œ๋‹ค๋Š” ์˜ˆ์ธก ๋˜ํ•œ ๊ฐ€์„คํ™”๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฐ€์„ค๋“ค์€ ํ•œ๊ตญ์˜ 11 ๊ฐœ ์กฐ์ง๊ณผ 6 ๊ฐœ ์—ฐ๊ตฌ๊ธฐ๊ด€๋“ค๋กœ๋ถ€ํ„ฐ ์ˆ˜์ง‘๋œ 226๊ฐœ์˜ ๋ฆฌ๋”-๊ตฌ์„ฑ์› ๋ฐ์ดํ„ฐ์Œ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ฒ€์ฆ ๋˜์—ˆ๋‹ค. ์˜ˆ์ธกํ•œ ๋ฐ”์™€ ๊ฐ™์ด, ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„๋Š” ๊ตฌ์„ฑ์›์˜ ์ž์•„ํšจ๋Šฅ๊ฐ๊ณผ ๊ธ์ •์ ์ธ ๊ด€๊ณ„๋ฅผ ๊ฐ€์งˆ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๋™์‹œ์— ๊ตฌ์„ฑ์›์˜ ์ง๋ฌด๊ธด์žฅ๊ณผ๋„ ๊ธ์ •์ ์ธ ๊ด€๊ณ„๋ฅผ ๊ฐ–๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‚˜์•„๊ฐ€, ๊ตฌ์„ฑ์›์˜ ์ž์•„ํšจ๋Šฅ๊ฐ์€ ์ง๋ฌด ์—ญํ• ์„ฑ๊ณผ์— ๊ธ์ •์ ์ธ ๊ด€๊ณ„๋ฅผ (์ฆ‰, ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜), ์ง๋ฌด๊ธด์žฅ์€ ์ง๋ฌด์—ญํ• ์„ฑ๊ณผ์— ๋ถ€์ •์ ์ธ ๊ด€๊ณ„๋ฅผ ๊ฐ–๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค (์ฆ‰, ๋ถ€๋‹ด์„ ์ฃผ๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜). ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„์™€ ๊ตฌ์„ฑ์›์˜ ์‹ฌ๋ฆฌ์  ๋ฐ˜์‘์‚ฌ์ด๋ฅผ ์กฐ์ ˆํ•˜๋Š” ์ง๋ฌดํŠน์„ฑ์˜ ์กฐ์ ˆํšจ๊ณผ์— ๋Œ€ํ•œ ๊ฐ€์„ค์€ ์ง€์ง€๋˜์ง€ ์•Š์•˜์œผ๋‚˜, ์ด ๊ด€๊ณ„ ์‚ฌ์ด์—์„œ ๊ตฌ์„ฑ์›์˜ ์„ฑ๊ณผํšŒํ”ผ ๋ชฉํ‘œ์„ฑํ–ฅ์€ ํ•œ๊ณ„์ ์œผ๋กœ๋‚˜๋งˆ ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„์™€ ์ง๋ฌด๊ธด์žฅ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋“ค๋กœ ๋ณผ ๋•Œ, ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„์™€ ๊ตฌ์„ฑ์›์˜ ์ง๋ฌด์—ญํ• ์„ฑ๊ณผ์‚ฌ์ด์—๋Š” ๊ตฌ์„ฑ์›์˜ ์–‘๋ฉด์  ์‹ฌ๋ฆฌ์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ์กด์žฌํ•œ๋‹ค๋Š” ์‚ฌ์‹ค์ด ์ฆ๋ช…๋œ๋‹ค. ๋”์šฑ์ด, ๋ถ€๋ถ„์ ์ด๊ธด ํ•˜๋‚˜ ๊ตฌ์„ฑ์›์˜ ํŠน์„ฑ(์ฆ‰, ๋ชฉํ‘œ์„ฑํ–ฅ)์ด, ๋‘˜ ๊ฐ„์˜ ๊ด€๊ณ„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ํŒŒ์•…๋˜๋ฏ€๋กœ, ์•ž์œผ๋กœ ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„๋ฅผ ํ†ตํ•œ ๋ฐ”๋žŒ์งํ•œ ๊ฒฐ๊ณผ์˜ ์ตœ๋Œ€ํ™”๋ฅผ ์œ„ํ•œ ์ƒํ™ฉ์ ์š”์†Œ๋“ค์— ๋” ๋งŽ์€ ๊ด€์‹ฌ์„ ๊ฐ€์งˆ ํ•„์š”๊ฐ€ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ์กฐ์ง์‹ฌ๋ฆฌ ๋ฐ ์กฐ์งํ–‰๋™์˜ ์—ฐ๊ตฌํ๋ฆ„์— ์žˆ์–ด์„œ ์ค‘์š”ํ•˜๊ณ ๋„ ์˜๋ฏธ์žˆ๋Š” ์ฃผ์ œ๋กœ์จ ์—ฐ๊ตฌ๋˜๋Š” ์ž„ํŒŒ์›Œ๋จผํŠธ, ํŠนํžˆ ๋ฆฌ๋”์˜ ์ž„ํŒŒ์›Œ๋ง ํ–‰์œ„๋Š”, ์—ฌ์ „ํžˆ ๋”์šฑ ๋งŽ์€ ๋…ผ์˜์™€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค๊ณ  ํ•  ๊ฒƒ์ด๋ฉฐ, ์ด๋Ÿฌํ•œ ๊ณผ์ •์— ์žˆ์–ด ๋ณธ ๋…ผ๋ฌธ์ด ํ•œ ๋‹จ๊ณ„ ๋ฐœ์ „๋œ ์ž„ํŒŒ์›Œ๋จผํŠธ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ๋…ธ๋ ฅ์˜ ์ผํ™˜์œผ๋กœ ์—ฌ๊ฒจ์ง€๊ธฐ๋ฅผ ๋ฐ”๋ž€๋‹ค.This study examines two different intervening mechanisms of empowering behaviors of leader on followers work role performance. Despite the current movement toward empowering and flexible organizational designs, the comprehensive role of leader on employee empowerment has been somewhat overlooked. While several studies have found positive outcomes of empowering behaviors of leader at work, there remain some points in question regarding the notion of whether more empowering behaviors of leader actually lead to more desirable outcomes. The present study starts from this notion and I suggest that empowering behaviors of leader may have two separate effects on followers psychological reactionscognitively positive aspect (i.e. self-efficacy), and emotionally negative aspect (i.e. job induced tension). In turn, each different psychological reaction of employees will have different relationships with their work role performance. On the one hand, one mechanism of empowering behaviors of leader as an enabling process is hypothesized to be positively related to followers self-efficacy, and this may increase followers work role performance. On the other hand, some features of empowering behaviors of leader such as delegation, and assuming responsibility to the followers which are called burdening process is hypothesized to be positively related to followers job induced tension. Then, this negative psychological reaction would prevent followers to achieve optimal work role performance. In addition, drawing on the interactional framework of leadership, moderating effects of job characteristics (i.e. job autonomy) and followers individual difference (i.e. goal orientation) are also hypothesized on the relationship between empowering behaviors of leader and two different psychological reactions of employees. These hypotheses were tested with data collected from 226 leader-follower dyads in 11 firms and 6 research centers located in Republic of Korea. The results demonstrated that, as expected, empowering behaviors of leader was both positively related to followers self efficacy and job induced tension. In turn, followers self efficacy was positively related to work role performance (i.e. enabling process), while followers job induced tension was negatively related to work role performance (i.e. burdening process). Unfortunately, moderating effects of job autonomy within the relationship between empowering behaviors of leader and different two psychological reactions were not significant. In addition, among the moderating effects of followers goal orientations within these relationships, only the moderating effects of performance avoidance goal orientation within the relationship between empowering behaviors of leader and followers job induced tension was statistically significant at marginal significance level. Based on the current empirical research, it is discussed that there are two contradictory mechanisms existed within the relation between empowering behaviors of leader and followers work role performance. Moreover, followers individual characteristics appeared to shape an important boundary condition within these mechanisms. These results indicate that a comprehensive understanding of empowering behaviors of leader is required to maximize the effectiveness of empowering behaviors of leader. As one of the most crucial and significantly researched topics in organizational studies, empowerment, especially empowering behaviors of leader toward their followers, still has much more issues to be explored and investigated. I hope this study can be highly conducive for studies on empowering leadership at the next level.TABLE OF CONTENTS I.INTROODUCTIONโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™1 II. THEORETICALBACKGROUNDโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™6 1. Leadershipโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™6 2. Empowering leadershipโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™8 3. Paradoxical mechanisms of empowering behaviors of leaderโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™18 3.1. Enabling process of empowering behaviors of leader through self efficacyโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™18 3.2. Burdening process of empowering behaviors of leader through job induced tensionโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™21 4. Work role performanceโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™25 5. Interactional framework of leadershipโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™28 5.1. Job characteristics as situational factorโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™28 5.2. Followers goal orientation as follower factorโˆ™โˆ™โˆ™30 III. HYPOTHESES DEVELOPMENTโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™33 1.1. Empowering behaviors of leader and followers self efficacyโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™33 1.2. Followers self efficacy and work role performanceโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™34 1.3. Mediating effects of followers self efficacyโˆ™โˆ™โˆ™โˆ™35 2.1. Empowering behaviors of leader and followers job induced tensionโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™36 2.2. Followers job induced tension and work role performanceโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™37 2.3. Mediating effects of followers job induced tensionโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™38 3.1. Moderating effect of job autonomyโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™45 3.2. Moderating effect of followers goal orientationsโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™49 IV. METHODโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™57 1. Sample and data collectionโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™57 2. Measuresโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™59 3. Analytical strategyโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™70 V. RESULTSโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™72 1. Descriptive statisticsโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™72 2. Hypotheses testingโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™74 3. Summary of the resultsโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™81 VI. DISCUSSIONโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™84 1. Summary of the findingsโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™84 2. Theoretical and practical implicationโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™87 3. Limitations and future researchโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™90 4. Conclusionโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™94 REFERENCESโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™96 APPENDIXโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™118 ABSTRACT IN KOREANโˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™โˆ™124Maste

    ๊ทผ๊ด€์„ธ์ฒ™์ œ์˜ ํ˜„ํ™ฉ๊ณผ ๋ณ‘์šฉ์ฒ˜๋ฆฌ์— ๊ด€ํ•œ ๊ณ ์ฐฐ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ์น˜์˜ํ•™๋Œ€ํ•™์› : ์น˜์˜ํ•™๊ณผ, 2013. 2. ํ•œ์Šนํ˜„.๊ทผ๊ด€ ์น˜๋ฃŒ์˜ ๋ชฉํ‘œ๋Š” ๊ทผ๊ด€ ๋‚ด ๊ดด์‚ฌ๋œ ์น˜์ˆ˜์กฐ์ง, ์„ธ๊ท , ๊ฐ์—ผ๋œ ์ƒ์•„์งˆ ๋“ฑ์„ ์ œ๊ฑฐํ•˜๊ณ  ์žฌ๊ฐ์—ผ์„ ๋ง‰๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ทผ๊ด€๊ณ„๋Š” ํ•ด๋ถ€ํ•™์ ์œผ๋กœ ๋ณต์žกํ•œ ํ˜•ํƒœ๋ฅผ ์ง€๋…€ ๊ธฐ๊ณ„์ ์ธ ๊ทผ๊ด€ ํ™•๋Œ€๋งŒ์œผ๋กœ ๊ทผ๊ด€ ๋‚ด ๊ฐ์—ผ์›์„ ์™„์ „ํžˆ ์ œ๊ฑฐํ•  ์ˆ˜ ์—†๋‹ค. ๋”ฐ๋ผ์„œ ์œ ๊ธฐ ์กฐ์ง์„ ์šฉํ•ด์‹œํ‚ค๊ณ  ํ•ญ๊ท  ์ž‘์šฉ์„ ํ•˜๋Š” ๊ทผ๊ด€์„ธ์ฒ™์ œ๋ฅผ ์ด์šฉํ•˜๋Š” ํ™”ํ•™์  ์„ธ์ • ๋ฐฉ๋ฒ•์ด ํ•„์ˆ˜์ ์ธ ๊ณผ์ •์œผ๋กœ, ๋‹ค์–‘ํ•œ ๊ทผ๊ด€์„ธ์ฒ™์ œ๋“ค์ด ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด์ƒ์ ์ธ ๊ทผ๊ด€์„ธ์ฒ™์ œ๋Š” ๋„“์€ ํ•ญ๊ท  ๋ฒ”์œ„๋ฅผ ๊ฐ€์ง€๊ณ , ๊ดด์‚ฌ๋œ ์น˜์ˆ˜์กฐ์ง ์ž”์‚ฌ๋ฅผ ์šฉํ•ด์‹œํ‚ฌ ์ˆ˜ ์žˆ์–ด์•ผ ํ•˜๋ฉฐ, ์„ธ๊ท ์˜ ๋‚ด๋…์†Œ๋ฅผ ๋น„ํ™œ์„ฑํ™” ์‹œํ‚ฌ ์ˆ˜ ์žˆ์–ด์•ผ ํ•˜๊ณ , ๋„๋ง์ธต์ด ์ƒ๊ธฐ๋Š” ๊ฒƒ์„ ๋ฐฉ์ง€ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด์ƒ์ ์ธ ๊ทผ๊ด€์„ธ์ฒ™์ œ๊ฐ€ ์กด์žฌํ•˜์ง€ ์•Š์•„ ๊ฐ๊ฐ์˜ ๊ทผ๊ด€์„ธ์ฒ™์ œ๋“ค์˜ ์žฅ๋‹จ์ ๊ณผ ๋ณ‘์šฉ์ฒ˜๋ฆฌ์— ๋Œ€ํ•ด ์•Œ์•„๋ณด๊ณ ์ž ํ•œ๋‹ค. Sodium hypochlorite (NaOCl)์€ ๊ทผ๊ด€์„ธ์ฒ™์ œ์‹œ ๊ฐ€์žฅ ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” ๊ทผ๊ด€์„ธ์ฒ™์ œ๋กœ ๊ด‘๋ฒ”์œ„ํ•œ ํ•ญ๊ท ๋ฒ”์œ„, ๊ดด์‚ฌ ์กฐ์ง ์šฉํ•ด ๋“ฑ์ด ๊ฐ€๋Šฅํ•˜๋‚˜ ๋…์„ฑ์ด ๊ฐ•ํ•˜๊ณ , ๋„๋ง์ธต์„ ํ™•์‹คํžˆ ์ œ๊ฑฐํ•˜์ง€ ๋ชปํ•œ๋‹ค. Chlorhexidine (CHX)์€ ๊ด‘๋ฒ”์œ„ํ•œ ํ•ญ๊ท  ํšจ๊ณผ๋ฅผ ๊ฐ–๊ณ , ํŠนํžˆ Enterococcus faecalis๋กœ ์ธํ•œ ์—ผ์ฆ๋ฐ˜์‘์„ ๊ฐ์†Œ์‹œํ‚ค์ง€๋งŒ, ์กฐ์ง ์šฉํ•ด ๋Šฅ๋ ฅ์ด ์—†๊ณ , ๋„๋ง์ธต ์ œ๊ฑฐ ๋Šฅ๋ ฅ๋„ ๋–จ์–ด์ง„๋‹ค. Ethylenediaminetetraacetic acid (EDTA)์˜ ๊ฒฝ์šฐ ๋„๋ง์ธต ์ œ๊ฑฐ ๋Šฅ๋ ฅ์ด ๋›ฐ์–ด๋‚˜์ง€๋งŒ, ํ•ญ๊ท  ์ž‘์šฉ์ด ๋‹ค๋ฅธ ๊ทผ๊ด€์„ธ์ฒ™์ œ์— ๋น„ํ•ด ๋–จ์–ด์ง€๋ฉฐ ์œ ๊ธฐ๋ฌผ ์šฉํ•ด ๋Šฅ๋ ฅ์ด ์—†๋‹ค. ๋‹ค์–‘ํ•œ ๊ทผ๊ด€์„ธ์ฒ™์ œ์˜ ์กด์žฌ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ด์ƒ์ ์ธ ๊ทผ๊ด€์„ธ์ฒ™์ œ๋Š” ์กด์žฌํ•˜์ง€ ์•Š์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๊ณ , ๊ทธ์— ๋”ฐ๋ผ ๋ณ‘์šฉ์ฒ˜๋ฆฌ๊ฐ€ ๊ณ ๋ คํ•ด ๋ณผ ์ˆ˜ ์žˆ๋‹ค. NaOCl๊ณผ CHX์„ ๋ณ‘์šฉ์ฒ˜๋ฆฌ ํ•  ๊ฒฝ์šฐ ๋‹จ๋… ์‚ฌ์šฉ์‹œ ๋ณด๋‹ค ๋” ํฐ ํ•ญ๊ท ํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์žˆ์ง€๋งŒ, ๋ณ‘์šฉ์ฒ˜๋ฆฌ ์‹œ ๋‘˜์˜ ํ™”ํ•™๋ฐ˜์‘ ๊ฒฐ๊ณผ๋กœ ํŒŒ๋ผํด๋กœ๋กœ์•„๋‹๋ฆฐ (parachloroaniline, PCA)์ด๋ผ๋Š” ์ ๊ฐˆ์ƒ‰์˜ ์นจ์ „๋ฌผ์ด ์ƒ์„ฑ๋œ๋‹ค. PCA๋Š” ์น˜์•„์˜ ์ ๊ฐˆ์ƒ‰ ๋ณ€์ƒ‰์„ ์œ ๋ฐœํ•˜๋ฉฐ, ๊ทผ๊ด€์ถฉ์ „์‹œ ๊ทผ๊ด€ํ์‡„์— ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋ฐœ์•”์„ฑ์ด ์žˆ๋‹ค. NaOCl์€ ์ƒ์•„์งˆ์ด๋‚˜ ๋„๋ง์ธต์„ ๊ฑฐ์˜ ์ œ๊ฑฐํ•˜์ง€ ๋ชปํ•˜๋Š”๋ฐ, ์ด๋ฅผ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด ํƒˆํšŒ์ž‘์šฉ์ด ๊ฐ€๋Šฅํ•œ EDTA๋ฅผ ๋ณ‘์šฉ์ฒ˜๋ฆฌ์‹œ ๋„๋ง์ธต์ œ๊ฑฐ์™€ ํƒˆํšŒ๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ EDTA์™€ NaOCl์„ ํ˜ผํ•ฉํ•˜์—ฌ ์‚ฌ์šฉ์‹œ EDTA์˜ ํ‚ฌ๋ ˆ์ด์…˜ ๋Šฅ๋ ฅ์€ ์œ ์ง€๋˜์ง€๋งŒ, NaOCl์˜ ์กฐ์ง ์šฉํ•ด๋Šฅ๋ ฅ์ด ๋–จ์–ด์ง€๊ฒŒ ๋œ๋‹ค. ์ด์ƒ์ ์ธ ๊ทผ๊ด€์„ธ์ฒ™์„ ์œ„ํ•œ ๋ณ‘์šฉ์ฒ˜๋ฆฌ์— ๋Œ€ํ•ด์„œ ์•„์ง๋„ ์•Œ๋ ค์ง€์ง€ ์•Š์€ ์ž‘์šฉ๊ธฐ์ž‘๊ณผ ๋ฌธ์ œ์ ์ด ๋งŽ์•„ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋ฉฐ ๊ทธ์— ๋”ฐ๋ฅธ ๋Œ€์•ˆ์— ๋Œ€ํ•ด์„œ๋„ ์—ฐ๊ตฌ๊ฐ€ ๋” ์‹œํ–‰๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค.The primary goal of endodontic treatment is to disinfect the root canals with infection and further to prevent re-infection. Although mechanical instrumentation could substantially reduce the number of bacteria existing in the infected root canals, it is difficult to obtain complete disinfection because root canal system is anatomically complicated. Thus, chemical irrigation is to be accompanied for dissolving necrotic tissues and eliminating the bacteria. An ideal irrigant should have a broad antibacterial spectrum, dissolve necrotic pulp tissue remnants, and prevent the formation of a smear layer during instrumentation. Commonly-used root canal irrigants are sodium hypochlorite (NaOCl), chlorhexidine (CHX), and ethylenediaminetetraacetic acid (EDTA). NaOCl has the capacity of dissolving necrotic tissues and potent antimicrobial activity, but it often cause tissue damages and it cannot dissolve inorganic components of a smear layer. CHX has a broad antibacterial spectrum and residual antimicrobial activity, and is effective in eradicating Enterococcus faecalis biofilm. However, it is not effective in dissolving necrotic tissues and a smear layer. EDTA has a potent capacity of dissolving a smear layer, but not necrotic tissues, with weak antimicrobial activity. Although approximately seven intracanal irrigants have been introduced so far, none of them is ideal calling for the co-treatment to complement each other. Co-treatment with NaOCl and CHX has more potent antimicrobial activity, but parachloroaniline (PCA), a brownish-red precipitation, is frequently formed. PCA results in discoloration of teeth and obliteration of root canal system, and it is carcinogenic. Co-treatment with NaOCl and EDTA removes both organic and inorganic smear layers, however, it results in the loss of tissue dissolving activity. Further studies are required to improve the current co-treatments, to investigate the clinical effectiveness and to elucidate the action mechanisms.๋ชฉ์ฐจ 1. ์„œ๋ก  1.1. ๊ทผ๊ด€์น˜๋ฃŒ์—์„œ ๊ทผ๊ด€์„ธ์ฒ™์ œ์˜ ํ•„์š”์„ฑ 1.2. ํ˜„์žฌ ์ผ๋ฐ˜์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ๊ทผ๊ด€์„ธ์ฒ™์ œ์˜ ์ข…๋ฅ˜ 2. ๋ณธ๋ก : ๊ทผ๊ด€์„ธ์ฒ™์ œ์˜ ์ž‘์šฉ๊ธฐ์ž‘, ์žฅ์  ๋ฐ ๋ฌธ์ œ์  2.1. Sodium hypochlorite (NaOCl) 2.2. Chlorhexidine gluconate (CHX) 2.3. Mixture of tetracycline, acid, detergent (MTAD) 2.4. Ethylenediaminetetraacetic acid (EDTA) 2.5. Maleic acid 2.6. Hydrogen peroxide 2.7. Iodine Potassium Iodide 3. ๊ฒฐ๋ก  3.1. ๊ทผ๊ด€์„ธ์ฒ™์ œ์˜ ๋‹ค์–‘์„ฑ 3.2. ์ž„์ƒ์—์„œ ์‚ฌ์šฉ๋˜๋Š” ๊ทผ๊ด€์„ธ์ฒ™์ œ์˜ ํ•œ๊ณ„ 4. ๊ณ ์ฐฐ 4.1. ๊ทผ๊ด€์„ธ์ฒ™์ œ ๋ณ‘์šฉ์ฒ˜๋ฆฌ์— ๋Œ€ํ•œ ์˜์˜ 4.2. NaOCl๊ณผ CHX์˜ ๋ณ‘์šฉ์ฒ˜๋ฆฌ 4.3. Maleic acid์™€ CHX์˜ ๋ณ‘์šฉ์ฒ˜๋ฆฌ 4.4. CHX๊ณผ EDTA์˜ ๋ณ‘์šฉ์ฒ˜๋ฆฌ 4.5. Maleic acid, ์‹œํŠธ๋ฅด์‚ฐ๊ณผ NaOCl์˜ ๋ณ‘์šฉ์ฒ˜๋ฆฌ 4.6. EDTA์™€ NaOCl์˜ ๋ณ‘์šฉ์ฒ˜๋ฆฌ 4.7. MTAD์™€ Nisin์˜ ๋ณ‘์šฉ์ฒ˜๋ฆฌ 4.8. ๊ทผ๊ด€์„ธ์ฒ™์ œ ๋ณ‘์šฉ์ฒ˜๋ฆฌ์— ๋Œ€ํ•œ ์ „๋งMaste
    corecore