14 research outputs found

    ๊น€์ˆ˜์˜ ์‹œ์— ๋‚˜ํƒ€๋‚œ `์‚ฌ๋ž‘`๊ณผ `์ฃฝ์Œ`์˜ ์˜๋ฏธ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ตญ์–ด๊ตญ๋ฌธํ•™๊ณผ ํ˜„๋Œ€๋ฌธํ•™์ „๊ณต,2002.Maste

    Collision analysis of ship structures considering dynamic contact behavior

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์กฐ์„ ํ•ด์–‘๊ณตํ•™๊ณผ,1999.Docto

    ์–ผ๊ตด ํŠน์ง• ์˜์—ญ์—์„œ์˜ ํ‘œ๋ฉด๊ณก๋ฅ ์ง€์ˆ˜๋ฅผ ์ด์šฉํ•œ 3์ฐจ์› ์–ผ๊ตด์ธ์‹

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    Dept. of Graduate Program in Biometrics/์„์‚ฌ[ํ•œ๊ธ€]๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์–ผ๊ตด ํ‘œ๋ฉด๊ณก๋ฅ ์ง€์ˆ˜(surface shape indexes)๋ฅผ ์ด์šฉํ•œ 3์ฐจ์› ์–ผ๊ตด์ธ์‹ ๊ธฐ์ˆ ์„ ์ œ์•ˆํ•œ๋‹ค. ๋ˆˆ, ์ฝ”, ์ž…๊ณผ ๊ฐ™์€ ์–ผ๊ตด ๊ตฌ์„ฑ์š”์†Œ์˜ ๊ตฌ์กฐ์  ์œ„์น˜ ์ •๋ณด์™€ ์ถ”์ถœ๋œ ๊ตฌ์„ฑ์š”์†Œ์˜ ์œ„์น˜์—์„œ ์–ป์–ด์ง„ ํ‘œ๋ฉด๊ณก๋ฅ ์ง€์ˆ˜๋ฅผ ์ด์šฉํ•˜์—ฌ ์–ผ๊ตด์˜ ๊ตฌ์กฐ์  ์ •๋ณด์™€ ๊ตฌ์„ฑ์š”์†Œ์˜ ํ˜•ํƒœ ์ •๋ณด๋ฅผ ๋™์‹œ์— ํฌํ•จํ•˜๋Š” ๊ณ ์ •๋œ ์ฐจ์›์˜ ํŠน์ง•๋ฒกํ„ฐ(feature vectors)๋ฅผ ์ œ๊ณตํ•จ์œผ๋กœ์จ ๊ฐœ์„ ๋œ ๋ถ„๋ฅ˜์„ฑ๋Šฅ๊ณผ ์ •ํ•ฉ์˜ ํŽธ๋ฆฌ์„ฑ์„ ์ œ๊ณตํ•˜๋Š” 3์ฐจ์› ์–ผ๊ตด์ธ์‹ ๊ธฐ์ˆ ์„ ์ œ์•ˆํ•œ๋‹ค.์ž…๋ ฅ๋‹จ๊ณผ DB๋‹จ์—์„œ๋Š” ๊ฐ๊ฐ ํš๋“๋œ ์–ผ๊ตด ๋ฐ์ดํ„ฐ์˜ ์ „์ฒ˜๋ฆฌ๋ฅผ ์œ„ํ•˜์—ฌ ํŠน์ง•์ ์„ ์ถ”์ถœํ•˜๋ฉฐ, ์ถ”์ถœ๋œ ํŠน์ง•์ ์„ ์ด์šฉํ•˜์—ฌ ๋จธ๋ฆฌ ๋ถ€๋ถ„์„ ์ œ๊ฑฐํ•˜๊ณ  ์ž…๋ ฅ๊ณผ DB์–ผ๊ตด ๋ฐ์ดํ„ฐ๊ฐ€ ๊ฐ™์€ ๊ณต๊ฐ„ ์ƒ์— ๋†“์ด๋„๋ก ์–ผ๊ตดํฌ์ฆˆ๋ณด์ • ๋ฐ ์ •๊ทœํ™” ๊ณผ์ •์„ ์ˆ˜ํ–‰ํ•œ๋‹ค.ํŠน์ง• ์ถ”์ถœ๋‹จ๊ณ„์—์„œ๋Š” ์–ผ๊ตด์˜ ๊ธฐํ•˜ํ•™์  ์ •๋ณด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜์—ฌ ๊นŠ์ด ์ •๋ณด๊ฐ€ ๊ฐ€์žฅ ํฐ ๊ฐ’์„ ๊ฐ–๋Š” ์ฝ”๋์ ์„ ์ค‘์‹ฌ์œผ๋กœ 3๊ฐœ์˜ ์–ผ๊ตด ์œค๊ณฝ ๊ณก์„ ๋“ค๊ณผ 10๊ฐœ์˜ ํŠน์ง•์ ๋“ค์„ ์ถ”์ถœํ•˜๊ฒŒ ๋œ๋‹ค. ๋˜ํ•œ, ์ถ”์ถœ๋œ 10๊ฐœ์˜ ํŠน์ง•์ ๋“ค๊ฐ„์˜ ๊ฑฐ๋ฆฌ์™€ ๊ฐ๋„, ๋น„์œจ ๋“ฑ์„ ์ด์šฉํ•œ 18๊ฐœ์˜ ์ƒ๊ด€ ํŠน์ง•๋“ค์„ ์–ป๊ฒŒ ๋œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ถ”์ถœ๋œ 10๊ฐœ์˜ ํŠน์ง•์  ์˜์—ญ์—์„œ ์–ผ๊ตดํ‘œ๋ฉด๊ณก๋ฅ ์ง€์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•˜์—ฌ ์–ผ๊ตด์ธ์‹์„ ์œ„ํ•œ ํŠน์ง•๋ฒกํ„ฐ(feature vector)๋ฅผ ๊ตฌ์„ฑํ•œ๋‹ค.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์–ผ๊ตด์ธ์‹ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ฐ€์ค‘์น˜๋ฒกํ„ฐ๊ฑฐ๋ฆฌ์ •ํ•ฉ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์˜ ๊ฒฐ๊ณผ๋Š” ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•์ธ SVM (Support Vector Machine)๊ณผ ICA (Independent Component Analysis)๋ฅผ ์ด์šฉํ•œ ๊ฒฐ๊ณผ์™€ ๋น„๊ตํ•œ๋‹ค.๋ณธ ์‹คํ—˜์—์„œ๋Š” BERC(Biometrics Engineering Research Center)์—์„œ ์ œ๊ณตํ•˜๋Š” 300๋ช…์˜ ์–ผ๊ตด ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ฐ€์ค‘์น˜๋ฒกํ„ฐ๊ฑฐ๋ฆฌ์ •ํ•ฉ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ธฐ๋ฐ˜์˜ ์–ผ๊ตด ์ธ์‹์˜ ๊ฒฝ์šฐ ์ •ํ•ฉ์„ ์ˆ˜ํ–‰ํ•œ ๊ฒฐ๊ณผ 97.6%์˜ ์ธ์‹๋ฅ ์„ ๋ณด์˜€๋‹ค. SVM ๊ธฐ๋ฐ˜ ์–ผ๊ตด ์ธ์‹์˜ ๊ฒฝ์šฐ์—๋Š” 98.6%์˜ ์ธ์‹๋ฅ ์„ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ICA์˜ ๊ฒฝ์šฐ๋Š” 96.3%์˜ ์ธ์‹๋ฅ ์„ ๋ณด์˜€๋‹ค. SVM๋ฐฉ์‹์ด ๊ฐ€์žฅ ์ข‹์€ ์ธ์‹๋ฅ ์„ ๋ณด์ด์ง€๋งŒ ๊ฐ€์ค‘์น˜๋ฒกํ„ฐ๊ฑฐ๋ฆฌ์ •ํ•ฉ ๋ฐฉ์‹์€ ํ›ˆ๋ จ๊ณผ์ •์ด ์—†๊ณ , SVM์ด๋‚˜ ICA๋ณด๋‹ค ๋“ฑ๋ก๊ณผ์ •์ด ๊ฐ„๋‹จํ•œ ์žฅ์ ์ด ์žˆ๋‹ค.๋˜ํ•œ ๊ฐ€์ค‘์น˜๋ฒกํ„ฐ๊ฑฐ๋ฆฌ์ •ํ•ฉ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ํ‘œ๋ฉด๊ณก๋ฅ ์ง€์ˆ˜๋งŒ์œผ๋กœ ๊ตฌ์„ฑ๋œ ํŠน์ง•๋ฒกํ„ฐ์™€ ์–ผ๊ตด ๊ตฌ์„ฑ์š”์†Œ์˜ ์œ„์น˜์™€ ์ƒ๋Œ€์  ์ •๋ณด๋งŒ์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ํŠน์ง•๋ฒกํ„ฐ๋กœ ๋‚˜๋ˆ„์–ด ์ธ์‹์„ ์ˆ˜ํ–‰ํ•œ ๊ฒฐ๊ณผ ๊ฐ๊ฐ 83% ์™€ 89%์˜ ์ธ์‹๋ฅ ์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ชจ๋“  ํŠน์ง•์„ ๋‹ค ์‚ฌ์šฉํ•˜์—ฌ ์ธ์‹์„ ์ˆ˜ํ–‰ํ•œ ๊ฒฐ๊ณผ 97.6%์˜ ์ธ์‹๋ฅ ์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์‹คํ—˜์„ ํ†ตํ•ด ์–ผ๊ตด๊ตฌ์„ฑ์š”์†Œ์˜ ์œ„์น˜๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํ˜•ํƒœ ์ •๋ณด๋„ ์ค‘์š”ํ•œ ์–ผ๊ตด์˜ ํŠน์ง•์ด๋ผ๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. [์˜๋ฌธ]In this dissertation, we propose a pose invariant three-dimensional (3D) face recognition method using distinctive facial features. A face has its structural components like the eyes, nose and mouth. The positions and the shapes of the facial components are very important characteristics of a face. We extract invariant facial feature points on those components using the facial geometry from a normalized face data and calculate relative features using these feature points. We also calculate a shape index on each facial feature point to represent curvature characteristics of facial components. When facial shape index and facial feature points with relative features are used separately, face recognition rates are 83% and 89% at first top rank by the weighted distance matching on average for seven different poses for 300 different people, respectively. However, the recognition rate goes up to 96.7% when they are used together.Proposed feature vector can be applied to various conventional classifiers because it has a fixed dimension. We propose weighted vector distance matching. We also applied proposed feature vector to support vector machine (SVM) and independent component analysis (ICA). We have 97.6% recognition rate from our proposed weighted distance matching, 98.6% at first top rank by the SVM and 97.3% by the ICA on average for seven different poses for 300 different people. Although SVM shows the highest recognition rate, the weighted vector distance matching shows a satisfactory recognition rate without any training process. Moreover, the proposed method can be used with incomplete feature vector. When some features are failed to be extracted, the proposed recognition algorithm can still process the incomplete information. But it is still valid for the proposed recognition algorithm. From the experimental results, we have effectively utilized facial shape indexes, geometrical feature points and its relational features for pose invariant face recognition.ope

    Modeling of heat transfer and fluid flow model in weldzone considering arc weaving

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2018. 2. ์ด๊ฒฝ์šฐ.์šฉ์ ‘์€ ์˜ค๋Š˜๋‚  ๋งŽ์€ ์‚ฐ์—…์˜ ์‘์šฉ๋˜์–ด ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ์ ‘ํ•ฉ ๊ณต์ • ์ค‘ ํ•˜๋‚˜๋กœ ํŠนํžˆ ์šด์†ก, ๋ฐœ์ „์†Œ, ๋“ฑ ์•ˆ์ „์„ฑ์ด ๊ฐ€์žฅ ์ค‘์š”์‹œ ๋˜๋Š” ์‚ฐ์—…์— ๋„๋ฆฌ ์‚ฌ์šฉ๋œ๋‹ค. ์šฉ์ ‘์ด๋ž€ ๋‘ ์žฌ๋ฃŒ์— ๋ง‰๋Œ€ํ•œ ์—ด์„ ์ฃผ์ž…ํ•˜๋Š” ๊ณต์ •์ด๊ธฐ ๋•Œ๋ฌธ์— ์žฌ๋ฃŒ์˜ ์—ด์  ํŠน์„ฑ์— ๋”ฐ๋ผ ์ƒ๋ณ€ํƒœ๊ฐ€ ๋ฐœ์ƒํ•˜๊ณ  ๋ฏธ์„ธ์กฐ์ง์ด ๋ณ€ํ™”ํ•˜์—ฌ ์„ฑ์งˆ์ด ์ €ํ•˜๋˜๋Š” ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•˜๊ฒŒ ๋˜๋Š”๋ฐ ๋”ฐ๋ผ์„œ ์—ด ์ฃผ์ž…์— ๋”ฐ๋ฅธ ์—ด์  ํŠน์„ฑ ๋ถ„์„ ๋ฐ ๋ฏธ์„ธ์กฐ์ง ๋ถ„์„์ด ํ•„์ˆ˜์ ์ด๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋ฒˆ ์—ฐ๊ตฌ์—์„œ๋Š” ์ƒ์šฉ์ฝ”๋“œ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์šฉ์ ‘๋ถ€์˜ ์œ ๋™์„ ํŒŒ์•…ํ•˜๊ณ  ์œ„ ๋ฐ์ดํ„ฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‘๊ณ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ์šฉ์ ‘๋ถ€์˜ ๋ฏธ์„ธ์กฐ์ง์„ ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. ์šฉ์ ‘๋ถ€์˜ ๋ฏธ์„ธ์กฐ์ง์€ ์šฉ์ ‘๋ถ€์˜ ์ฃผ์ž…๋˜๋Š” ์—ด์˜ ํฌ๊ธฐ์™€ ์ฃผ์ž…๋˜๋Š” ์šฉ์ ‘๋ฒ•, ๋ƒ‰๊ฐ์†๋„ ๋“ฑ ์—ฌ๋Ÿฌ ๋ณ€์ˆ˜์˜ ์˜ํ–ฅ์„ ๋ฐ›๋Š”๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ •ํ™•ํ•œ ์—ด ํ•ด์„์„ ์œ„ํ•ด ์•„ํฌ ์œ„๋น™ ์šฉ์ ‘๋ฒ•์„ ๊ณ ๋ คํ•œ ์—ด์› ๋ชจ๋ธ๋ง์„ ์ง„ํ–‰ํ•˜์—ฌ ๊ณ„์‚ฐ์˜ ์ •ํ™•๋„ ๋ฐ ๊ณ„์‚ฐ์†๋„๋ฅผ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋ชฉ์ ์œผ๋กœ ํ•˜๋Š” ์šฉ์ ‘์ด ์ด 3๋ฒˆ์˜ ์šฉ์ ‘์„ ํ†ตํ•ด ์ด๋ฃจ์–ด์ง€๋Š” ๋ฉ€ํ‹ฐํŒจ์Šค ์šฉ์ ‘์ด๊ธฐ ๋•Œ๋ฌธ์— ์šฉ์ ‘๋ถ€๊ฐ€ ์šฉ์ ‘์—ด์˜ ์—ฌ๋Ÿฌ ๋ฒˆ ์˜ํ–ฅ์„ ๋ฐ›๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ•ด์„ํ•˜๊ธฐ ์œ„ํ•ด ์šฉ์ ‘๋ถ€๋ฅผ ๊ฒฝํ—˜ํ•œ ์˜จ๋„ ๋ฒ”์œ„์— ๋”ฐ๋ผ ๋ชจ์žฌ, ์—ด ์˜ํ–ฅ๋ถ€, ์ฒซ๋ฒˆ์งธ ์šฉ์ ‘ํŒจ์Šค์— ์šฉ์œต๋œ ์˜์—ญ, ๋‘๋ฒˆ์งธ ์šฉ์ ‘ํŒจ์Šค์— ์šฉ์œต๋œ ์˜์—ญ, ์„ธ๋ฒˆ์งธ ์šฉ์ ‘ํŒจ์Šค์— ์šฉ์œต๋œ ์˜์—ญ ์ด 5๊ฐ€์ง€๋กœ ๋ถ„๋ฅ˜ํ•˜์—ฌ ๊ฐ๊ฐ์˜ ์˜์—ญ์—์„œ์˜ ์—ด ํ•ด์„๊ณผ ๋ฏธ์„ธ์กฐ์ง ๋ถ„์„์„ ๋น„๊ต ๋ถ„์„ํ•˜์˜€๋‹ค.1. ์„œ๋ก  1 1.1. ์šฉ์ ‘๋ถ€ ์˜จ๋„ ๋ถ„ํฌ ํ•ด์„ 1 1.2. ๊ธฐ์กด ์šฉ์ ‘๊ตฌ ์˜จ๋„ ๋ถ„ํฌ ์ˆ˜์น˜ํ•ด์„ ์—ฐ๊ตฌ 4 1.3. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  10 2. ์‹คํ—˜๋ฐฉ๋ฒ• 11 2.1. ์šฉ์ ‘๋ถ€ ์˜จ๋„ ๋ถ„ํฌ ํ•ด์„ 12 2.1.1. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 13 2.1.2. ๊ณ„์‚ฐ ์˜์—ญ 19 2.1.3. ๊ฒฝ๊ณ„์กฐ๊ฑด 26 2.1.4. ๊ณ„์‚ฐ๋ฐฉ๋ฒ• 32 2.2. ์šฉ์ ‘ ์‹œํŽธ์„ ํ†ตํ•œ ์šฉ์ ‘๋ถ€ ์šฉ์œต ์˜์—ญ ๋ฐ ๋ฏธ์„ธ์กฐ์ง ๋ถ„์„ 33 2.2.1. ์‹คํ—˜๋ฐฉ๋ฒ• 33 3. ๊ฒฐ๊ณผ ๋ฐ ํ† ์˜ 34 3.1. ์šฉ์ ‘๋ถ€ ์—ด ์œ ๋™ ๋ฐ ์˜จ๋„๋ถ„ํฌ 34 3.1.1. ์šฉ์ ‘๋ถ€ ํ‘œ๋ฉด์—์„œ์˜ ์˜จ๋„๋ถ„ํฌ ๋ฐ ์—ด ์œ ๋™ ๊ด€์ฐฐ 36 3.1.2. ์šฉ์ ‘๋ถ€ ๋‹จ๋ฉด์˜ ์˜จ๋„๋ถ„ํฌ ๋ฐ ์šฉ์œต ์˜์—ญ์˜ ํฌ๊ธฐ ๋ฐ ํ˜•ํƒœ ๋น„๊ต 39 3.2. ์„ธ ์šฉ์ ‘ ํŒจ์Šค๋ฅผ ๊ณ ๋ คํ•œ ์šฉ์ ‘ ํ•ด์„ ๋ชจ๋ธ 44 3.2.1. ์šฉ์ ‘๋ถ€ ์˜์—ญ ๋ถ„๋ฅ˜ 44 3.2.2. ์˜จ๋„ ์ด๋ ฅ์„ ํ†ตํ•œ ๋ฏธ์„ธ์กฐ์ง ์˜ˆ์ธก 47 4. ๊ฒฐ๋ก  52 ์ฐธ๊ณ ๋ฌธํ—Œ 55 Abstract 57Maste

    ์ด์ƒ ๋ฌธํ•™์˜ ์—ญ์‚ฌ์ฒ ํ•™์  ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ตญ์–ด๊ตญ๋ฌธํ•™๊ณผ, 2012. 8. ์‹ ๋ฒ”์ˆœ.๋ณธ ๋…ผ๋ฌธ์€ ์ด์ƒ ๋ฌธํ•™์˜ ์„ฑ๊ฒฉ์„ ์ƒˆ๋กญ๊ฒŒ ์žฌ๋ฐฐ์น˜ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค๋Š” ๋ฌธ์ œ์˜์‹ ํ•˜์—, ์ด์ƒ ๋ฌธํ•™ ์•ˆ์— ๋‚ดํฌ๋ผ ์žˆ์œผ๋‚˜ ์ถฉ๋ถ„ํžˆ ๋…ผ๊ตฌ๋˜์ง€ ๋ชปํ•œ ์—ญ์‚ฌ์ โ€ค์ •์น˜์  ์š”์†Œ๋“ค์„ ์‚ดํŽด ์ด์ƒ ๋ฌธํ•™์˜ ์—ญ์‚ฌ์ฒ ํ•™์  ์ขŒํ‘œ๋ฅผ ๋„์ถœํ•ด ๋‚ด๊ณ , ์ด๋ฅผ ๊ทผ๊ฑฐ๋กœ ๊ทธ๋™์•ˆ ์ด์ƒ ๋ฌธํ•™์˜ ํ•ต์‹ฌ์œผ๋กœ ๊ฐ„์ฃผ๋˜์–ด ์˜จ ๋ช‡๋ช‡ ์ฃผ์ œ๋“ค์„ ๋‹ค์‹œ ํƒ๊ตฌํ–ˆ๋‹ค. ๋„“์€ ์˜๋ฏธ์—์„œ์˜ ์—ญ์‚ฌ์ฒ ํ•™์ด๋ž€ ์—ญ์‚ฌ์˜ ์˜๋ฏธ, ๋ฒ•์น™, ๋ฐฉํ–ฅ์— ๋Œ€ํ•œ ๋ชจ์ข…์˜ ๋ฉ”ํƒ€ ์„œ์‚ฌ๋กœ ๊ทœ์ •๋  ์ˆ˜ ์žˆ๋‹ค. ๋ฉ”ํƒ€ ์„œ์‚ฌ๋ผ๊ณ  ๋ถ€๋ฅผ ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์€ ์—ญ์‚ฌ์ฒ ํ•™์ด ์—ญ์‚ฌ๋ผ๋Š” ์„œ์‚ฌ์˜ ์›๋ฆฌ์— ๋Œ€ํ•œ ์„œ์‚ฌ์ด๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ํŠน์ •ํ•œ ์ž‘๊ฐ€ ํ˜น์€ ์ž‘ํ’ˆ์ด ์—ญ์‚ฌ์ฒ ํ•™์„ ๊ฐ–๊ณ  ์žˆ๋‹ค๋Š” ๊ฒƒ์€ ๊ทธ ์ž‘ํ’ˆ์ด ์—ญ์‚ฌ์„ฑ์„ ๊ฐ–๊ณ  ์žˆ๋‹ค๊ฑฐ๋‚˜ ๊ทธ ์ž‘๊ฐ€๊ฐ€ ๋ฐ”๋žŒ์งํ•œ ์—ญ์‚ฌ์˜์‹์˜ ์†Œ์œ ์ž๋ผ๊ฑฐ๋‚˜ ํ•˜๋Š” ๊ฒƒ๊ณผ๋Š” ์ „ํ˜€ ๋‹ค๋ฅธ ๊ฒƒ์ด๋‹ค. ์š”์ปจ๋Œ€ ์—ญ์‚ฌ์„ฑ์ด ๋ชจ๋“  ์ž‘ํ’ˆ์˜ ์ˆ™๋ช…์  ์กฐ๊ฑด์ด๋ผ๋ฉด, ์—ญ์‚ฌ์˜์‹์€ ํŠน์ •ํ•œ ์—ญ์‚ฌ์  ์‹œ๊ธฐ๋ฅผ ์‚ด์•„๊ฐ€๋Š” ์ฐฝ์ž‘์ž์˜ ์ฒดํ—˜์  ๋ฐ˜์‘์ด๋ฉฐ, ์—ญ์‚ฌ์ฒ ํ•™์€ ์—ญ์‚ฌ์— ๋Œ€ํ•œ ๋‹น๋Œ€์˜ ํ†ต๋…์„ ๊ฑฐ์Šค๋ฅด๋Š” ์„œ์‚ฌ์  ๊ตฌ์ถ•์ด๋‹ค. 2์žฅ์€ ๋ณธ๋ก (3, 4, 5์žฅ)์˜ ์„œ๋ก ์œผ๋กœ์„œ, ์ด์ƒ์˜ ์ตœ์ดˆ ๋ฐœํ‘œ์ž‘ํ’ˆ์ธ ์„ ์— ๊ด€ํ•œ ๊ฐ์„œ ์—ฐ์ž‘์„ ๋Œ€์ƒ์œผ๋กœ ์ด์ƒ์˜ ์—ญ์‚ฌ์ฒ ํ•™์  ์‚ฌ์œ ์˜ ๋ฐ‘๊ทธ๋ฆผ์„ ์žฌ๊ตฌ์„ฑํ•ด๋ณธ ์ž‘์—…์ด๋‹ค. ์ด ์—ฐ์ž‘์‹œ๋Š” ์‚ฌ๋žŒ์ด ๋น›๋ณด๋‹ค ๋นจ๋ฆฌ ๋‹ฌ๋ ค ๊ณผ๊ฑฐ๋กœ ๋˜๋Œ์•„๊ฐˆ ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•œ ๋ชฝ์ƒ์„ ๊ธฐํ•˜ํ•™์ ์ด๋ฉด์„œ๋„ ๋น„์˜์ ์ธ ๋ฐฉ์‹์œผ๋กœ ํ‘œ๋ช…ํ•œ ์ž‘ํ’ˆ์ด๋‹ค. ์ธ๊ฐ„์˜ ๋น›โ€”๋˜๊ธฐ์— ๋Œ€ํ•œ ์ƒ์ƒ์ด ์‹œ๊ฐ„์—ฌํ–‰์— ๋Œ€ํ•œ ์ƒ์ƒ์œผ๋กœ ์ด์–ด์ง€๊ณ , ์ด๋Š” ๊ทผ๋Œ€๊ณผํ•™์˜ ์ผ๋ฐ˜ ๊ณต๋ฆฌ๋“ค์„ ์˜๋ฌธ์— ๋ถ€์น˜๋Š” ๋น„ํŒ์  ์„ฑ์ฐฐ๋กœ ์ด์–ด์ง€๋ฉฐ, ๊ทธ ๊ณต๋ฆฌ๋“ค ์œ„์— ๊ตฌ์ถ•๋ผ ์žˆ๋Š” ํ˜„์žฌโ€”๊ณผ๊ฑฐโ€”๋ฏธ๋ž˜๋ผ๋Š” ํ†ต๋…์  ์‹œ๊ฐ„๊ด€์„ ์žฌ์กฐ์ •ํ•˜๋Š” ์ž‘์—…์œผ๋กœ ์ด์–ด์ ธ์„œ, ํ˜„์žฌ๋ฅผ ์‚ฌ๋Š” ์ธ๊ฐ„์˜ ์‚ถ์— ์ธ์‹์  ์ถฉ๊ฒฉ๊ณผ ์ƒ์ƒ๋ ฅ์˜ ํ˜์‹ ์„ ์ดˆ๋ž˜ํ•˜๋Š” ๊ฒฐ๊ณผ๋ฅผ ๋„๋ชจํ•˜๊ฒ ๋‹ค๋Š” ๊ฒƒ์ด ์ด ์„ค๊ณ„๋„โ€”์‹œ์˜ ํ•ต์‹ฌ์ ์ธ ์ทจ์ง€๋‹ค. ์ผ๋‹จ์€ ๋น›์— ๋Œ€ํ•œ ์‹œ์ด๋˜, ๊ถ๊ทน์ ์œผ๋กœ๋Š” ์ƒˆ๋กœ์šด ์‹œ๊ฐ„๊ด€, ์ƒˆ๋กœ์šด ์ธ๊ฐ„๊ด€์— ๋Œ€ํ•œ ์‹œ๋กœ ๊ฐ„์ฃผ๋  ์ˆ˜ ์žˆ๋‹ค. ์ด ์ž‘ํ’ˆ์—์„œ ์ด์ƒ์€ ์ง์„ ์ฒ˜๋Ÿผ ๋ฏธ๋ž˜๋ฅผ ํ–ฅํ•ด์„œ๋งŒ ํ˜๋Ÿฌ๊ฐ€๋Š” ๊ทผ๋Œ€์  ์‹œ๊ฐ„๊ณผ๋Š” ๋‹ฌ๋ฆฌ ๊ณผ๊ฑฐ๋กœ ๊ฑฐ๊พธ๋กœ ํ˜๋Ÿฌ๊ฐ€๋Š” ์‹œ๊ฐ„์—์„œ ์–ด๋–ค ๊ตฌ์›์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๋ฐœ๊ฒฌํ•˜๊ธฐ๋ฅผ ์›ํ•œ๋‹ค. 3์žฅ์—์„œ๋Š” 2์žฅ์— ์žฌ๊ตฌ์„ฑํ•œ ์ด์ƒ์˜ ์—ญ์‚ฌ์ฒ ํ•™์  ์„ค๊ณ„๋„๊ฐ€ ๋‹น๋Œ€์˜ ๊ตฌ์ฒด์ ์ธ ์—ญ์‚ฌ์  ํ˜„์‹ค๊ณผ ๋ถ€๋”ช์น˜๋ฉด์„œ ์‚ฐ์ถœ๋œ ์ž‘ํ’ˆ๋“ค์„ ๋ถ„์„ํ–ˆ๋‹ค. ์ด์ƒ์€ 1930๋…„ ์ดˆ์—์„œ 1937๋…„ ์ดˆ๊นŒ์ง€ 7๋…„ ๋™์•ˆ ์ž‘ํ’ˆ ํ™œ๋™์„ ํ–ˆ๋‹ค. ์ด ๊ธฐ๊ฐ„์€ ๋งŒ์ฃผ์‚ฌ๋ณ€(1931)์—์„œ ์ œ1์ฐจ ์ƒํ•ด์‚ฌ๋ณ€(1932)์„ ๊ฑฐ์ณ ์ค‘์ผ์ „์Ÿ(1937)์— ์ด๋ฅด๋Š” ๊ธฐ๊ฐ„๊ณผ ์ผ์น˜ํ•œ๋‹ค. ๋‹น๋Œ€ ์ตœ๊ณ ์˜ ์—˜๋ฆฌํŠธ ์ง€์‹์ธ์ด์—ˆ๋˜ ์ด์ƒ์ด ๋™์•„์‹œ์•„ ๊ตญ์ œ ์ •์„ธ์— ๋‘”๊ฐํ–ˆ์„ ๋ฆฌ ์—†์Œ์—๋„ ๊ทธ์˜ ๋ฌธํ•™์ด ๋†“์—ฌ ์žˆ๋Š” ์ด์™€ ๊ฐ™์€ ์ขŒํ‘œ๋Š” ๊ทธ์˜ ํŒŒ๊ฒฉ์ ์ธ ๋ฌธํ•™์  ์Šคํƒ€์ผ์ด๋‚˜ ๋– ๋“ค์ฉํ•œ ์Šค์บ”๋“ค์— ๊ฐ€๋ ค ์ถฉ๋ถ„ํžˆ ์ฃผ๋ชฉ๋ฐ›์ง€ ๋ชปํ–ˆ๋‹ค. 3์žฅ์€ ๊ทธ์˜ ์ž‘ํ’ˆ์— ์ƒˆ๊ฒจ์ ธ ์žˆ๋Š” 1930๋…„๋Œ€ ๋™์•„์‹œ์•„ ๊ตญ์ œ ์ •์„ธ์˜ ํ”์ ์„ ์ถ”์ถœํ•˜๊ณ  ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ด์ƒ ๋ฌธํ•™์— ์ˆจ๊ฒจ์ ธ ์žˆ๋Š” ์ •์น˜์„ฑ์˜ ์ธต์œ„๋ฅผ ํƒ๊ตฌํ•œ ์ž‘์—…์ด๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ด์ƒ์˜ ์ž‘ํ’ˆ๋“ค์—์„œ ๋‹น๋Œ€ ๋™์•„์‹œ์•„ ์ •์„ธ์— ๋Œ€ํ•œ ์€๋ฐ€ํ•œ ์ฐธ๊ณ ์™€ ์ฃผ์˜์˜ ํ™˜๊ธฐ๊ฐ€ ๋นˆ๋ฒˆํžˆ ์ด๋ฃจ์–ด์กŒ๋‹ค๋Š” ์‚ฌ์‹ค์„ ํŒŒ์•…ํ–ˆ๊ณ , ๊ทธ๊ฐ„ ์ฃผ๋ชฉ๋ฐ›์ง€ ๋ชปํ•œ ๋ช‡๋ช‡ ์ž‘ํ’ˆ๋“ค์ด ์ด๋Ÿฐ ์—ญ์‚ฌ์  โ€ง ์ •์น˜์  ๋งฅ๋ฝ ์†์—์„œ ์ƒˆ๋กญ๊ฒŒ ์ฝํž ์ˆ˜ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์„ ๋…ผ์ฆํ–ˆ๋‹ค. 4์žฅ์—์„œ๋Š” ์ด์ƒ ๋ฌธํ•™ ํŠน์œ ์˜ ๋ณ‘๋ฆฌ์„ฑ์„ ๊ทธ์˜ ๋ณ‘๋ ฅ์˜ ์‚ฐ๋ฌผ๋กœ ํ™˜์›ํ•˜์ง€ ์•Š๊ณ  2์žฅ๊ณผ 3์žฅ์—์„œ ๋…ผ์˜ํ•œ ๊ทธ์˜ ์—ญ์‚ฌ์ฒ ํ•™์  ๊ตฌ์ƒ๊ณผ ๊ทธ ์ขŒ์ ˆ์˜ ํ•„์—ฐ์  ์‚ฐ๋ฌผ๋กœ ๋‹ค์‹œ ์ฝ์—ˆ๋‹ค. ์‹๋ฏผ์ง€ ์กฐ์„ ์€ ์—ญ์‚ฌ์ฒ ํ•™์˜ ์ฃผ์ฒด๊ฐ€ ๋˜๊ธฐ๋ณด๋‹ค๋Š”, ๋งŒ์ฃผ์‚ฌ๋ณ€(1931)๊ณผ ์ƒํ•ด์‚ฌ๋ณ€(1932) ์ดํ›„ ์กฐ์งํ™”๋˜๊ธฐ ์‹œ์ž‘ํ•œ ์‹๋ฏผ์ง€ ๋ณธ๊ตญ ์—ญ์‚ฌ์ฒ ํ•™์˜ ์˜ˆ์†์  ๊ฐ์ฒด ํ˜น์€ ํ•˜์œ„์ฃผ์ฒด๋กœ ํ˜ธ๋ช…๋  ์ˆ˜๋ฐ–์— ์—†๋Š” ์ฒ˜์ง€์— ๋†“์—ฌ ์žˆ์—ˆ๋‹ค. ์ด ๊ณผ์ •์—์„œ ๊ทธ์— ๊ฑธ๋งž์€ ์ •์ฒด์„ฑ์ด ์ง€์†์ ์œผ๋กœ ํ”ผ์‹๋ฏผ ์ฃผ์ฒด๋“ค์—๊ฒŒ ๋ถ€๊ณผ๋˜์—ˆ์Œ์€ ๋ฌผ๋ก ์ด๋‹ค. ์—ญ์‚ฌ์˜ ์˜๋ฏธโ€ค๋ฒ•์น™โ€ค๋ฐฉํ–ฅ์— ๋Œ€ํ•œ ๋…์ž์ ์ธ ๋ฉ”ํƒ€ ์„œ์‚ฌ๋ฅผ ๊ฐ–๊ณ  ์žˆ๋Š” ์ฃผ์ฒด์—๊ฒŒ ๊ทธ์™€ ๊ฐ™์€ ์ •์ฒด์„ฑ์˜ ๊ฐ•์š”๋Š” ์„ฑ๊ณตํ•  ์ˆ˜ ์—†์„ ๊ฒƒ์ด๊ณ . ์ •์ฒด์„ฑ์˜ ๋ถ„์—ด์„ ๋‚ณ์„ ๊ฒƒ์ด๋‹ค. ์ด์ƒ์˜ ์ดˆ๊ธฐ ์‹œ์ธ ๏ฝข์ด์ƒํ•œ ๊ฐ€์—ญ๋ฐ˜์‘๏ฝฃ์—์„œ๋ถ€ํ„ฐ ์ด๋ฏธ ๋‚˜ํƒ€๋‚˜๋Š” ์ฃผ์ฒด์˜ ๋ณ‘๋ฆฌ์  ๋ถ„์—ด์€ ๊ทธ์™€ ๊ฐ™์€ ์—ญ์‚ฌ์  ๋งฅ๋ฝ ์†์—์„œ ์ดํ•ด๋˜์–ด์•ผ ํ•˜๋ฉฐ, ๊ทธ๋Ÿฐ ๋งฅ๋ฝ์—์„œ ๋ณผ ๋•Œ ํ™ฉ(็š)์ด๋ผ๋Š” ์ด๋ฆ„์˜ ๊ฐœ๋ฅผ ์†Œ์žฌ๋กœ ํ•œ ์ด์ƒ์˜ ๋ฏธ๋ฐœํ‘œ ์œ ๊ณ  ์—ฐ์ž‘์€ ์ด์ƒ ๋ฌธํ•™์˜ ๋ณ‘๋ฆฌ์„ฑ์ด ์–ด๋–ป๊ฒŒ ์—ญ์‚ฌ์ โ€ง์ •์น˜์  ์กฐ๊ฑด๊ณผ ๋ฐ€์ ‘ํ•œ ๊ด€๋ จ์„ ๋งบ๊ณ  ์žˆ๋Š”์ง€๋ฅผ ํ•ต์‹ฌ์ ์œผ๋กœ ๋ณด์—ฌ์ค€๋‹ค๋Š” ์ ์—์„œ ์ค‘์š”ํ•œ ํ…์ŠคํŠธ๋กœ ๊ฐ„์ฃผ๋˜์–ด์•ผ ํ•œ๋‹ค. ์ด์ƒ ๋ฌธํ•™์˜ ๋ณ‘๋ฆฌ์„ฑ์€ ์˜๋„๋œ ๋ณ‘๋ฆฌ์„ฑ์ด๊ณ , ์—ฐ๊ธฐ์™€ ์œ„์žฅ์˜ ํ•œ ์–‘์ƒ์ด๋ฉฐ, ์ •์น˜์  ์•Œ๋ ˆ๊ณ ๋ฆฌ๋กœ ํŒŒ์•…๋˜์–ด์•ผ ํ•  ์„ฑ์งˆ์˜ ๊ฒƒ์ด๋‹ค. 5์žฅ์—์„œ๋Š” ์ด์ƒ์˜ ํ›„๊ธฐ ์ž‘ํ’ˆ๋“ค์„ ์šด๋ช…์— ๋Œ€ํ•œ ์„ฑ์ฐฐ๊ณผ ์กด์žฌ๋ฏธํ•™(aesthetic of existence)์  ๊ธ€์“ฐ๊ธฐ์—์˜ ์‹œ๋„๋กœ ๊ทœ์ •ํ–ˆ๋‹ค. ์—ญ์‚ฌ์˜ ์ถœ๊ตฌ์™€ ์‚ถ์˜ ์ถœ๊ตฌ๊ฐ€ ๋™์‹œ์— ๋ด‰์‡„๋ผ ์žˆ๋Š” ์ƒํ™ฉ์—์„œ ์ด์ƒ์€ ์šด๋ช…์ด๋ผ๋Š” ์ฃผ์ œ๋ฅผ ๊ธ€์“ฐ๊ธฐ์˜ ํ•œ๋ณตํŒ์œผ๋กœ ๋Œ์–ด๋“ค์ธ๋‹ค. ๏ฝข์—ญ๋‹จ๏ฝฃ๊ณผ ๏ฝข์œ„๋…๏ฝฃ ์—ฐ์ž‘์—์„œ ์ด์ƒ์€ ์šด๋ช…์ด๋ผ๋Š” ํ˜•์‹์œผ๋กœ ์ด๋ฏธ ์“ฐ์—ฌ ์žˆ๋Š” ์‚ถ์„ ๊ธ€์“ฐ๊ธฐ๋ฅผ ํ†ตํ•ด ์–ด๋–ป๊ฒŒ ๋‹ค์‹œ ์“ธ ์ˆ˜ ์žˆ๋Š”๊ฐ€๋ผ๋Š” ๋ฌผ์Œ์— ์ฒœ์ฐฉํ–ˆ๋‹ค. ์ดํ›„ ์ด์ƒ์ด ์‹œ์—์„œ ์†Œ์„ค๋กœ ์ด๋™ํ•˜๊ฒŒ ๋œ ๊ฒƒ, ๊ทธ์ค‘์—์„œ๋„ ํŠนํžˆ ์—ฐ์• ๋‹ด์˜ ํ˜•์‹์œผ๋กœ ์ด๋™ํ•œ ๊ฒƒ์€ ์ด๋Ÿฐ ๋งฅ๋ฝ์—์„œ๋‹ค. ์—ฌ๋Ÿฌ ํŽธ์˜ ์—ฐ์• ๋‹ด์—์„œ ์ด์ƒ์€ ์ž์‹ ์˜ ์กด์žฌ๊ฐ€ ์œ„๊ธฐ์— ์ฒ˜ํ•ด์žˆ์Œ์„ ํ† ๋กœํ•˜๊ณ , ๊ทธ ์œ„๊ธฐ๋ฅผ ํƒ€๊ฐœํ•˜๊ธฐ ์œ„ํ•ด, ์ž‘ํ’ˆ์ด ์‚ถ๋ณด๋‹ค ์•ž์„œ ๋‚˜๊ฐ€์„œ ์‚ถ์„ ๋ฏธ๋ฆฌ ์™„์„ฑํ•˜๋„๋ก ํ•˜๋Š” ๊ธ€์“ฐ๊ธฐ๋ฅผ ์‹œ๋„ํ•œ๋‹ค. ๋„“๊ฒŒ ๋ณด๋ฉด ์ด์™€ ๊ฐ™์€ ๊ธ€์“ฐ๊ธฐ๋Š” ์‚ถ์„ ์˜ˆ์ˆ ์ž‘ํ’ˆ์œผ๋กœ ๋งŒ๋“ค๊ณ ์ž ํ•œ19์„ธ๊ธฐ ํ›„๋ฐ˜ ๋Œ„๋””์ฆ˜์˜ ์กด์žฌ๋ฏธํ•™์œผ๋กœ๋ถ€ํ„ฐ ๊ธฐ์›ํ•˜๋Š” ๊ฒƒ์ด์ง€๋งŒ, ๋™์‹œ์—, ๋‹น๋Œ€์˜ ์—ญ์‚ฌ์ฒ ํ•™์  ์ง€๋ฐฐ๋‹ด๋ก ์„ ๋๊นŒ์ง€ ๊ฑฐ์ ˆํ•œ ์ง€์ ์—์„œ ์ž์‹ ์˜ ์‚ถ์„ ์ฃผ์ฒด์ ์œผ๋กœ ์ข…๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ฃผ์ฒดํ™”(subjectification) ์ „๋žต์ด์ž ๋ฌธํ•™์  ์‹ค์ฒœ์˜ ๋งˆ์ง€๋ง‰ ํ˜•์‹์œผ๋กœ ํ‰๊ฐ€๋  ์ˆ˜ ์žˆ๋‹ค.๋ชฉ ์ฐจ โ–  ๊ตญ๋ฌธ์ดˆ๋ก 1 ์„œ๋ก  1. ์—ฐ๊ตฌ์‚ฌ ๊ฒ€ํ†  โ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€ง 1 2. ์—ฐ๊ตฌ์˜ ์‹œ๊ฐ โ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€ง 8 2 ์ดˆ๊ธฐ์‹œ์˜ ์—ญ์‚ฌ์ฒ ํ•™์  ์ขŒํ‘œ์™€ ๋ฐ˜๊ทผ๋Œ€์ฃผ์˜์˜ ์–‘์ƒ : ๏ฝข์‚ผ์ฐจ๊ฐ ์„ค๊ณ„๋„โ€”์„ ์— ๊ด€ํ•œ ๊ฐ์„œ๏ฝฃ ์—ฐ์ž‘์„ ์ค‘์‹ฌ์œผ๋กœ โ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€ง 16 3 ์‹ค์žฌ๋กœ์„œ์˜ ์—ญ์‚ฌ์™€ ํ˜„์‹ค ์žฌํ˜„์˜ ์ •์น˜ํ•™ 1. ๋งŒ์ฃผโ€ค์ƒํ•ด ๊ด€๋ จ ํ…์ŠคํŠธ์˜ ํ˜„์‹ค ์ฐธ์กฐ ์–‘์ƒ : ๊ฑด์ถ•๋ฌดํ•œ์œก๋ฉด๊ฐ์ฒด ๊ณ„์—ด์‹œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ โ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€ง 32 2. ์˜ค๊ฐ๋„ ์—ฐ์ž‘์— ๋‚˜ํƒ€๋‚œ ๊ฒฝ์„ฑ๊ณผ ์žฌํ˜„์˜ ์ •์น˜ํ•™ : ์˜ค๊ฐ๋„ ์—ฐ์ž‘์‹œ์™€ ๊ทธ ์ฃผ๋ณ€ โ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€ง 47 4 ๋ณ‘๋ฆฌํ•™์  ๊ธ€์“ฐ๊ธฐ์™€ ์ •์ฒด์„ฑ์˜ ์•Œ๋ ˆ๊ณ ๋ฆฌ 1. ์ดˆ๊ธฐ์‹œ์˜ ์กด์žฌ๋ก ์  ํ•จ์˜์— ๋Œ€ํ•œ ํ•ด์„ํ•™์  ๊ฒ€ํ†  : ๏ฝข์ด์ƒํ•œ ๊ฐ€์—ญ๋ฐ˜์‘๏ฝฃ์—์„œ ๊ฐ€์—ญ๋ฐ˜์‘์˜ ์˜๋ฏธ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ โ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€ง 59 2. ๋ณ‘๋ฆฌํ•™์  ๋ชจํ‹ฐํ”„์™€ ์ •์น˜์  ์•Œ๋ ˆ๊ณ ๋ฆฌ : ํ™ฉ ์—ฐ์ž‘์„ ์ค‘์‹ฌ์œผ๋กœ โ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€ง 78 5 ๊ธ€์“ฐ๊ธฐ์˜ ์กด์žฌ๋ฏธํ•™๊ณผ ์•„์ด๋Ÿฌ๋‹ˆ์˜ ์œค๋ฆฌํ•™ 1. ์กด์žฌ๋ก ์  ์œ„๊ธฐ์™€ ์šด๋ช…์— ๋งž์„œ๋Š” ๊ธ€์“ฐ๊ธฐ : ๏ฝข12์›” 12์ผ๏ฝฃ์—์„œ ๏ฝข์œ„๋…๏ฝฃ๊นŒ์ง€ โ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€ง 91 2. ์—ฐ์• ๋‹ด์˜ ์˜๋ฏธ์™€ ๋™๊ฒฝ ํ…์ŠคํŠธ์˜ ์—ญ์‚ฌ์ฒ ํ•™์  ์ขŒํ‘œ : ๏ฝข๋ด‰๋ณ„๊ธฐ๏ฝฃ์™€ ๏ฝข์‹คํ™”๏ฝฃ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ โ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€ง 108 6 ๊ฒฐ๋ก  โ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€งโ€ง 125 โ–  ์ฐธ๊ณ ๋ฌธํ—Œ โ–  AbstractDocto

    Castor oil๊ณผ TDI๋กœ ํ•ฉ์„ฑํ•œ ํด๋ฆฌ์šฐ๋ ˆํƒ„ ๋ฐฉ์ˆ˜์ฝ”ํŒ…์ด ์ „๋ถ„๊ธ€๋ฆฌ์„ธ๋กค ํ•„๋ฆ„์˜ ์Šต๋„๋ฐ˜์‘ํŠน์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ฒœ์—ฐ์„ฌ์œ ํ•™๊ณผ,2002.Maste

    Fabrication of Depth Probe Type Semiconductor Microelectrode Arrays for Neural Recording Using Both Dry and wet Etching of Silicon

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    ๋Œ€๋‡Œ ํ”ผ์งˆ์— ์‚ฝ์ž…ํ•˜์—ฌ ๊นŠ์ด์— ๋”ฐ๋ผ ์‹ ๊ฒฝ ์‹ ํ˜ธ๋ฅผ ๊ธฐ๋กํ•˜๊ธฐ ์œ„ํ•œ ํƒ์นจํ˜• ๋ฐ˜๋„์ฒด ๋ฏธ์„ธ์ „๊ทน ์–ด๋ ˆ์ด(depth-type silicon microelectrode array, ์ผ๋ช… SNU probe)๋ฅผ ์ œ์ž‘ํ•˜์˜€๋‹ค. ๋ถ•์†Œ๋ฅผ ํ™•์‚ฐ์‹œ์ผœ ์ƒ์„ฑ๋œ ๊ณ ๋†๋„ p-type doping ๋œ p+ ์˜์—ญ์„ ์Šต์‹์‹๊ฐ ์ •์ง€์ ์œผ๋กœ ์‚ฌ์šฉํ•˜๋Š” ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๊ณผ ๋‹ฌ๋ฆฌ ์‹ค๋ฆฌ์ฝ˜ ์›จ์ดํผ์˜ ์•ž๋ฉด์„ ๊ฑด์‹์‹๊ฐํ•˜์—ฌ ์›ํ•˜๋Š” ํƒ์นจ ๋‘๊ป˜๋งŒํผ์˜ ๊นŠ์ด๋กœ ํŠธ๋ Œ์น˜(trench)๋ฅผ ํ˜•์„ฑํ•œ ํ›„ ๋’ท๋ฉด์„ ์Šต์‹์‹๊ฐํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ํƒ์นจํ˜•ํƒœ์˜ ๋ฏธ์„ธ ๊ตฌ์กฐ๋ฅผ ๋งŒ๋“ค์—ˆ๋‹ค. ์ œ์ž‘๋œ ๋ฐ˜๋„์ฒด ๋ฏธ์„ธ์ „๊ทน ์–ด๋ ˆ์ด์˜ ํƒ์นจ ๋‘๊ป˜๋Š” 30um ์ด๋ฉฐ ์‹ค๋ฆฌ์ฝ˜ ๊ฑด์‹์‹๊ฐ์„ ์œ„ํ•œ ๋งˆ์Šคํฌ๋กœ 6um ๋‘๊ป˜์˜ LTO(low teemperature oxide)๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ํƒ์นจ์˜ ๋‘๊ป˜๋Š” ๊ฐœ๋ฐœ๋œ ๋ณธ ๊ณต์ •์„ ์ด์šฉํ•ด์„œ 5~90um ๋ฒ”์œ„๊นŒ์ง€ ์‰ฝ๊ฒŒ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํƒ์นจ์˜ ๋‘๊ป˜๋ฅผ ๋ณด๊ฐ€ ์‰ฝ๊ฒŒ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋จ์— ๋”ฐ๋ผ ์—ฌ๋Ÿฌ ์‹ ๊ฒฝ ์กฐ์ง์— ํ•„์š”ํ•œ ๋‹ค์–‘ํ•œ ๊ตฌ์กฐ์˜ ๋ฐ˜๋„์ฒด ๋ฏธ์„ธ์ „๊ทน ์–ด๋ ˆ์ด๋ฅผ ๊ฐœ๋ฐœํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ๋ณธ ๊ณต์ •์„ ์ด์šฉํ•ด์„œ ๊ฐœ๋ฐœ๋œ 4์ฑ„๋„ SNU probe๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํฐ์ฅ์˜ ์ œ1์ฐจ ์ฒด๊ฐ๊ฐ ํ”ผ์งˆ์—์„œ 4์ฑ„๋„ ์‹ ๊ฒฝ์‹ ํ˜ธ๋ฅผ ๋™์‹œ์— ๊ธฐ๋กํ•˜์˜€์œผ๋ฉฐ, ์ „๊ธฐ์  ํŠน์„ฑ๊ฒ€์‚ฌ์—์„œ ๊ธฐ์กด์˜ ํƒ์นจํ˜• ๋ฐ˜๋„์ฒด ๋ฏธ์„ธ์ „๊ทน, ํ……์Šคํ… ์ „๊ทน๊ณผ ๋Œ€๋“ฑํ•˜๊ฑฐ๋‚˜ ์šฐ์ˆ˜ํ•œ ์‹ ํ˜ธ๋Œ€ ์žก์Œ๋น„(signal to noise ratio, SNR)ํŠน์„ฑ์„ ๊ฐ€์ง์„ ํ™•์ธํ•˜์˜€๋‹ค.๋ณธ ์—ฐ๊ตฌ๋Š” 2000๋…„๋„ ๋ณด๊ฑด๋ณต์ง€๋ถ€ ๋ณด๊ฑด์˜๋ฃŒ๊ธฐ์ˆ ์ง„ํฅ์‚ฌ์—… ์—ฐ๊ตฌ(๊ณผ์ œ๋ฒˆํ˜ธ:HMP-00-B-31400-00174)์ง€์›์— ์˜ํ•ด ์ˆ˜ํ–‰๋˜์—ˆ
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