120 research outputs found

    ํ™•๋ฅ ์ตœ๋Œ€ํ™” ์กฐํ•ฉ์ตœ์ ํ™” ๋ฌธ์ œ์— ๋Œ€ํ•œ ๊ทผ์‚ฌํ•ด๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ,2019. 8. ์ด๊ฒฝ์‹.In this thesis, we consider a variant of the deterministic combinatorial optimization problem (DCO) where there is uncertainty in the data, the probability maximizing combinatorial optimization problem (PCO). PCO is the problem of maximizing the probability of satisfying the capacity constraint, while guaranteeing the total profit of the selected subset is at least a given value. PCO is closely related to the chance-constrained combinatorial optimization problem (CCO), which is of the form that the objective function and the constraint function of PCO is switched. It search for a subset that maximizes the total profit while guaranteeing the probability of satisfying the capacity constraint is at least a given threshold. Thus, we discuss the relation between the two problems and analyse the complexities of the problems in special cases. In addition, we generate pseudo polynomial time exact algorithms of PCO and CCO that use an exact algorithm of a deterministic constrained combinatorial optimization problem. Further, we propose an approximation scheme of PCO that is fully polynomial time approximation scheme (FPTAS) in some special cases that are NP-hard. An approximation scheme of CCO is also presented which was derived in the process of generating the approximation scheme of PCO.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ผ๋ฐ˜์ ์ธ ์กฐํ•ฉ ์ตœ์ ํ™” ๋ฌธ์ œ(deterministic combinatorial optimization problem : DCO)์—์„œ ๋ฐ์ดํ„ฐ์˜ ๋ถˆํ™•์‹ค์„ฑ์ด ์กด์žฌํ•  ๋•Œ๋ฅผ ๋‹ค๋ฃจ๋Š” ๋ฌธ์ œ๋กœ, ์ด ์ˆ˜์ต์„ ์ฃผ์–ด์ง„ ์ƒ์ˆ˜ ์ด์ƒ์œผ๋กœ ๋ณด์žฅํ•˜๋ฉด์„œ ์šฉ๋Ÿ‰ ์ œ์•ฝ์„ ๋งŒ์กฑ์‹œํ‚ฌ ํ™•๋ฅ ์„ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ํ™•๋ฅ  ์ตœ๋Œ€ํ™” ์กฐํ•ฉ ์ตœ์ ํ™” ๋ฌธ์ œ(probability maximizing combinatorial optimization problem : PCO)์„ ๋‹ค๋ฃฌ๋‹ค. PCO์™€ ๋งค์šฐ ๋ฐ€์ ‘ํ•œ ๊ด€๊ณ„๊ฐ€ ์žˆ๋Š” ๋ฌธ์ œ๋กœ, ์ด ์ˆ˜์ต์„ ์ตœ๋Œ€ํ™”ํ•˜๋ฉด์„œ ์šฉ๋Ÿ‰ ์ œ์•ฝ์„ ๋งŒ์กฑ์‹œํ‚ฌ ํ™•๋ฅ ์ด ์ผ์ • ๊ฐ’ ์ด์ƒ์ด ๋˜๋„๋ก ๋ณด์žฅํ•˜๋Š” ํ™•๋ฅ  ์ œ์•ฝ ์กฐํ•ฉ ์ตœ์ ํ™” ๋ฌธ์ œ(chance-constrained combinatorial optimization problem : CCO)๊ฐ€ ์žˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋‘ ๋ฌธ์ œ์˜ ๊ด€๊ณ„์— ๋Œ€ํ•˜์—ฌ ๋…ผ์˜ํ•˜๊ณ  ํŠน์ • ์กฐ๊ฑด ํ•˜์—์„œ ๋‘ ๋ฌธ์ œ์˜ ๋ณต์žก๋„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ œ์•ฝ์‹์ด ํ•˜๋‚˜ ์ถ”๊ฐ€๋œ DCO๋ฅผ ๋ฐ˜๋ณต์ ์œผ๋กœ ํ’€์–ด PCO์™€ CCO์˜ ์ตœ์ ํ•ด๋ฅผ ๊ตฌํ•˜๋Š” ์œ ์‚ฌ ๋‹คํ•ญ์‹œ๊ฐ„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋” ๋‚˜์•„๊ฐ€, PCO๊ฐ€ NP-hard์ธ ํŠน๋ณ„ํ•œ ์ธ์Šคํ„ด์Šค๋“ค์— ๋Œ€ํ•ด์„œ ์™„์ „ ๋‹คํ•ญ์‹œ๊ฐ„ ๊ทผ์‚ฌํ•ด๋ฒ•(FPTAS)๊ฐ€ ๋˜๋Š” ๊ทผ์‚ฌํ•ด๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด ๊ทผ์‚ฌํ•ด๋ฒ•์„ ์œ ๋„ํ•˜๋Š” ๊ณผ์ •์—์„œ CCO์˜ ๊ทผ์‚ฌํ•ด๋ฒ• ๋˜ํ•œ ๊ณ ์•ˆํ•˜์˜€๋‹ค.Chapter 1 Introduction 1 1.1 Problem Description 1 1.2 Literature Review 7 1.3 Research Motivation and Contribution 12 1.4 Organization of the Thesis 13 Chapter 2 Computational Complexity of Probability Maximizing Combinatorial Optimization Problem 15 2.1 Complexity of General Case of PCO and CCO 18 2.2 Complexity of CCO in Special Cases 19 2.3 Complexity of PCO in Special Cases 27 Chapter 3 Exact Algorithms 33 3.1 Exact Algorithm of PCO 34 3.2 Exact Algorithm of CCO 38 Chapter 4 Approximation Scheme for Probability Maximizing Combinatorial Optimization Problem 43 4.1 Bisection Procedure of rho 46 4.2 Approximation Scheme of CCO 51 4.3 Variation of the Bisection Procedure of rho 64 4.4 Comparison to the Approximation Scheme of Nikolova 73 Chapter 5 Conclusion 77 5.1 Concluding Remarks 77 5.2 Future Works 79 Bibliography 81 ๊ตญ๋ฌธ์ดˆ๋ก 87Maste

    ๊ธฐํ›„๋ณ€ํ™”์— ๊ธฐ์ธํ•œ ์œ ๊ธฐ์˜ค์—ผ๋ฌผ์งˆ์˜ ํ™˜๊ฒฝ ๋™ํƒœ๋ณ€ํ™”์— ๋ฐฐ์ถœ์กฐ๊ฑด๊ณผ ํ† ์ง€ ํŠน์„ฑ์ด ๋ผ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ™˜๊ฒฝ๋Œ€ํ•™์› : ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2014. 2. ์ด๋™์ˆ˜.๋‹ค๋งค์ฒด๋ชจํ˜•์ธ KPOP-CC์— A1B ๊ธฐํ›„๋ณ€ํ™” ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ ์šฉํ•˜์—ฌ ๊ธฐํ›„๋ณ€ํ™”์— ๊ธฐ์ธํ•œ ์˜ค์—ผ๋ฌผ์งˆ์˜ ํ™˜๊ฒฝ ๋™ํƒœ ๋ณ€ํ™”์— ๋ผ์น˜๋Š” ๋ฐฐ์ถœ์กฐ๊ฑด๊ณผ ํ† ์–‘ ํ™˜๊ฒฝ์ธ์ž์˜ ์˜ํ–ฅ์ •๋„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. VOCs 4์ข…, PAHs 5์ข…, PCDDs/DFs 4์ข…์„ ๋Œ€์ƒ์œผ๋กœ ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๋†๋„๋ณ€ํ™”์— ๋ฏธ์น˜๋Š” ๋ฐฐ์ถœ์›์˜ ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ๋‚ด๋ถ€๋ฐฐ์ถœ๊ณผ ์™ธ๋ถ€๋ฐฐ์ถœ์˜ ์ƒ๋Œ€์ ์ธ ๋น„์ค‘์„ ๋‹ฌ๋ฆฌํ•˜์—ฌ ๋ชจํ˜•์„ ์‹คํ–‰ํ•˜์˜€๋‹ค. ํ† ์–‘ ํ™˜๊ฒฝ์ธ์ž์˜ ์˜ํ–ฅ์ •๋„๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด, ํ† ์–‘ ๊ฒฝ์‚ฌ์™€ ๊ธธ์ด๋ฅผ ํฌํ•จํ•˜๋Š” ํ† ์–‘ ํ™˜๊ฒฝ์ธ์ž๊ฐ€ ํ™•์—ฐํžˆ ๋‹ค๋ฅธ ์„œ์šธ๊ณผ ๊ฐ•์›๋„ ์ •์„ , ์ „๋ผ๋‚จ๋„ ๊ตฌ๋ก€ ์ง€์—ญ์„ ์„ ํƒํ•˜์—ฌ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ํ•œ ์ง€์—ญ ๋‚ด์—์„œ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๋Œ€๊ธฐ ์˜ค์—ผ๋ฌผ์งˆ์˜ ๋†๋„๋ณ€ํ™” ์–‘์ƒ์€ ๋‚ด๋ถ€๋ฐฐ์ถœ์›์ด ์žˆ์„ ๋•Œ์™€ ๋‚ด๋ถ€๋ฐฐ์ถœ์›์ด ์ „ํ˜€ ์—†์ด ์™ธ๋ถ€๋ฐฐ์ถœ์›๋งŒ ์žˆ์„ ๋•Œ์˜ ์กฐ๊ฑด์œผ๋กœ ๋‚˜๋‰˜์—ˆ๋‹ค. ๋‚ด๋ถ€๋ฐฐ์ถœ์ด ์กด์žฌํ•œ๋‹ค๋ฉด, ๊ทธ ์–‘์ด ์ „์ฒด ๋ฐฐ์ถœ๋Ÿ‰์— ๋น„ํ•ด ๋‚ฎ์€ ๋น„์œจ์ผ์ง€๋ผ๋„ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๋Œ€๊ธฐ ์˜ค์—ผ๋ฌผ์งˆ์˜ ๋†๋„๋ณ€ํ™” ์–‘์ƒ์ด ๋ชจ๋‘ ๋™์ผํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์™ธ๋ถ€๋ฐฐ์ถœ์›์ผ ๋•Œ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๋Œ€๊ธฐ ์ค‘ ์˜ค์—ผ๋ฌผ์งˆ์˜ ๋†๋„ ๊ฐ์†Œํญ์ด ์ปธ๋‹ค. ์™ธ๋ถ€๋ฐฐ์ถœ๋œ ์˜ค์—ผ๋ฌผ์งˆ์€ ๋ถ„์„์ง€์—ญ์œผ๋กœ์˜ ์ด๋™๊ณผ์ •์—์„œ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•ด ์ฆ๊ฐ€๋œ ๋Œ€๊ธฐ ์ค‘ ์ œ๊ฑฐ ํ”Œ๋Ÿญ์Šค๋ฅผ ๊ฑฐ์น˜๊ฒŒ ๋˜๋ฏ€๋กœ ๋ถ„์„์ง€์—ญ์œผ๋กœ์˜ ์œ ์ž…๋Ÿ‰์ด ๊ฐ์†Œ๋œ๋‹ค. ๋‚ด๋ถ€์™€ ์™ธ๋ถ€๋ฐฐ์ถœ์› ๋ชจ๋‘ ๋ถ„์„์ง€์—ญ ๋‚ด์—์„œ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•ด ์ฆ๊ฐ€๋œ ์ œ๊ฑฐ๊ธฐ์ž‘์„ ๊ฑฐ์น˜๊ฒŒ ๋˜๋Š”๋ฐ, ํ˜„์žฌ ์‹คํ—˜์กฐ๊ฑด์—์„œ ๋‚ด๋ถ€๋ฐฐ์ถœ์›์€ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๋Œ€๊ธฐ ์ค‘ ์˜ค์—ผ๋ฌผ์งˆ ์œ ์ž…๋Ÿ‰์— ๋ณ€ํ™”๊ฐ€ ์—†์ง€๋งŒ, ์™ธ๋ถ€๋ฐฐ์ถœ์›์€ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•ด ๋ถ„์„์ง€์—ญ์œผ๋กœ์˜ ๋Œ€๊ธฐ ์ค‘ ์˜ค์—ผ๋ฌผ์งˆ ์œ ์ž… ์ž์ฒด๊ฐ€ ๊ฐ์†Œํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋Œ€๊ธฐ ์ค‘ ์˜ค์—ผ๋ฌผ์งˆ์˜ ๋†๋„ ๊ฐ์†Œํญ์ด ๋” ํฌ๋‹ค. ํ•œํŽธ, ํ˜„์žฌ ๋ถ„์„ ์กฐ๊ฑด์—์„œ๋Š” ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๋Œ€๊ธฐ ์ค‘ ์˜ค์—ผ๋ฌผ์งˆ ๋†๋„ ๋ณ€ํ™”๋Š” ํ† ์–‘ ํ™˜๊ฒฝ์ธ์ž์˜ ์˜ํ–ฅ ๋ณด๋‹ค๋Š” ๊ธฐ์ƒ์ธ์ž์˜ ์˜ํ–ฅ์„ ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๋Œ€๊ธฐ ๋†๋„ ๊ฐ์†Œํญ์ด ์™ธ๋ถ€๋ฐฐ์ถœ์ผ ๋•Œ ๊ฐ€์žฅ ํฌ๊ธฐ ๋•Œ๋ฌธ์—, ์™ธ๋ถ€๋ฐฐ์ถœ์ผ ๋•Œ ํ•˜๋ถ€ ๋งค์งˆ๋กœ์˜ ํ”Œ๋Ÿญ์Šค ๊ฐ์†Œํญ์ด ๊ฐ€์žฅ ํฌ๊ฑฐ๋‚˜ ์ฆ๊ฐ€ํญ์ด ๊ฐ€์žฅ ์ž‘๋‹ค. ์ฆ‰, ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ํ† ์–‘ ๋†๋„ ๊ฐ์†Œํญ์ด ๊ฐ€์žฅ ํฌ๊ฑฐ๋‚˜ ๋†๋„ ์ฆ๊ฐ€ํญ์ด ๊ฐ€์žฅ ์ž‘๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ๋น„๊ต์  ๋ฌด๊ฑฐ์šด ๋ฌผ์งˆ๋“ค์˜ ๋†๋„ ๋ณ€ํ™”๋Š” ๊ธฐ์ƒ์ธ์ž๋ณด๋‹ค ํ† ์–‘ ํ™˜๊ฒฝ์ธ์ž์˜ ์˜ํ–ฅ์„ ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋Š”๋ฐ, ํŠนํžˆ PCDDs/DFs๋Š” ํ† ์ง€ ๊ฒฝ์‚ฌ๋„๋ฅผ ํฌํ•จํ•˜๋Š” ํ† ์–‘ ํ™˜๊ฒฝ์ธ์ž์˜ ์ฐจ์ด์— ๋”ฐ๋ผ ๊ธฐํ›„๋ณ€ํ™”๋กœ ์ธํ•œ ๋†๋„ ๋ณ€ํ™”์˜ ํ˜•ํƒœ๊ฐ€ ํฌ๊ฒŒ ๋‹ฌ๋ผ์กŒ๋‹ค.โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์  1 2. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„์™€ ๋ฐฉ๋ฒ• 3 3. KPOP-CC๋ชจํ˜• 9 โ…ก. ๊ธฐ์กด์—ฐ๊ตฌ์˜ ๊ณ ์ฐฐ 13 1. ๊ธฐํ›„๋ณ€ํ™”๊ฐ€ POPs์˜ ๋™ํƒœ๋ณ€ํ™”์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 13 2. A1B ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ฅธ ๊ธฐ์ƒ์ธ์ž ๋ณ€ํ™” 15 โ…ข. ๊ฒฐ๊ณผ์™€ ๊ณ ์ฐฐ 19 1. ๋Œ€๊ธฐ 19 1) ๋†๋„ 19 2) ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๋†๋„๋ณ€ํ™” 21 2. ํ† ์–‘ 32 1) ๋†๋„ 32 2) ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๋†๋„๋ณ€ํ™” 35 3. ์ˆ˜์ฒด 45 1) ๋†๋„ 45 2) ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๋†๋„๋ณ€ํ™” 48 โ…ฃ. ๊ฒฐ๋ก  54 โ–  ์ฐธ๊ณ ๋ฌธํ—Œ 56 ๋ถ„์„ ๋Œ€์ƒ๋ฌผ์งˆ์˜ ๋ฌผ๋ฆฌ์  ํ™”ํ•™์  ํŠน์„ฑ 59 ์ธ์ ‘์ง€์—ญ ๊ธฐ์ƒ์ธ์ž 61 ๋Œ€๊ธฐ TSP ๋น„์œจ 65 NCC ํ† ์–‘ ์ œ๊ฑฐ์†๋„์ƒ์ˆ˜๋น„๊ต 66Maste

    ํ•ญ์ธ์Š๋ฆฐ์œ ์‚ฌ์„ฑ์žฅ์ธ์ž์ˆ˜์šฉ์ฒด ๋‹จ์ผํด๋ก ํ•ญ์ฒด์— ๋Œ€ํ•œ ๋ฏธ์„ธ์ข…์–‘ํ™˜๊ฒฝ๋งค๊ฐœ ๋‚ด์„ฑ ๋ฐœ์ƒ๊ธฐ์ž‘

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๊ณผ, 2017. 2. ์ดํ˜ธ์˜.Drug resistance is a major impediment to a large repertoire of anticancer therapies. Hence, the rational design of anticancer therapies should include strategies that circumvent treatment-associated drug resistance. Recent studies have demonstrated the importance of the tumor microenvironment (TME) to innate resistance to molecularly targeted therapies. In this study, I investigated the role of the TME in innate resistance to insulin like growth factor receptor-1 (IGF-1R) targeting therapy based on monoclonal antibody (mAb) that has shown limited clinical efficacy. Anti-IGF-1R mAb treatment stimulated tumor progression with distant cancer metastasis and decreased survival in mouse models harboring orthotopic tumors of human cancer cell lines. In this models, increased tumor angiogenesis and stromal cell infiltration within the TME were concomitantly observed. Next I performed co-culture experiments with human cancer, vascular endothelial (VE) cells, fibroblasts and monocytes and found that IGF-1R ablated cancer cells recruited fibroblast and monocytes. Once fibroblasts and monocytes recruited to cancer cells, they were shown to stimulate angiogenic abilities of VE cells. From the signaling pathway array using protein lysates from IGF-1R blocked cancer cells, we found that anti-IGF-1R mAb treatment stimulated signal transducer and activator of transcription 3 (STAT3)-dependent transcriptional up-regulation of IGF-2 in cancer cells, enabling communication with fibroblast and monocytes through their insulin like growth factor receptor 2 (IGF-2R). Upon the interaction with IGF-1R ablated cancer cells, fibroblasts and monocytes produced potent proangiogenic cytokine, CXCL8. Silencing IGF-2 or STAT3 expression in cancer cells or IGF-2R or CXCL8 expression in stromal cells markedly inhibited communication between cancer and stromal cells and vascular endothelial cells angiogenic activities. Moreover, tumor tissue derived STAT3 knocked down cancer cells revealed impairment of anti-IGF-1R mAbs ability to recruit stromal cells. In conclusion, IGF-1R blockade reprograms cancer cells to produce IGF-2, which alters the TME, thereby stimulating tumor angiogenesis and metastasis. Targeting the STAT3/IGF2/IGF-2R/CXCL8 intercellular signaling loop may overcome the adverse consequences of anti-IGF-1R mAb-based therapies.I. INTRODUCTION 1 1. Molecular targeted cancer therapy 2 2. Insulin-like Growth Factor (IGF) axis in cancer therapeutics 7 3. Tumor Microenvironment (TME). 10 4. TME-mediated drug resistance 13 II. PURPOSE OF THIS STUDY 16 III. MATERIALS AND METHODS 18 1. Cell culture and reagents 19 2. Mouse studies 20 3. Immunofluorescence and immunohistochemistry (IHC) assays. 21 4. Establishment of silenced stable cell line. 22 5. Isolation of primary monocytes. 23 6. In vitro migration and tube formation assay 23 7. RT-PCR and real-time PCR analysis 24 8. Western blotting and RTK array 24 9. ELISA 25 10. Plasmids and luciferase assay 26 11. Cell proliferation/viability assay 26 12. Statistical analysis 26 IV. RESULTS 31 1. Increased cancer metastasis after blockade of IGF-1R 32 1.1. Increased metastasis after anti IGF-1R mAb treatment in orthotopic breast cancer model 32 1.2. Response to anti IGF-1R mAb in lung cancer models 39 1.3. Increased metastasis after cixutumumab treatment in orthotopic HNSCC tumor models 42 1.4. Increased metastasis by cixutumumab treatment in humanized mice with orthotopic breast tumors models 46 2. Alteration of TME under the IGF-1R blockade 49 2.1. IGF-1R blockade does not induce aggressive phenotype of cancer cells in vitro 49 2.2. The IGF-1R blockade alters infiltration of stromal cells into tumors 53 3. IGF-1R blockade stimulates tumor angiogenesis through fibroblast and macrophages 56 3.1. IGF-1R blockade fails to enhance angiogenic activity of VE cells 56 3.2. IGF-1R blockade of cancer cells stimulates fibroblasts and macrophages 59 3.3. Interaction between cixutumumab-treated cancer cells and stromal cells induced tumor angiogenesis 63 4. Cancer interacts with stromal cells through IGF-2/IGF-2R pathway 67 4.1. IGF-1R blockade activates transcription of IGF-2 in cancer cells 67 4.2. IGF-2 delivers signal to stromal cells through IGF-2R 74 5. Ablation of IGF-1R increases IGF-2 transcription via STAT3 activation 79 5.1. IGF-1R blockade induces STAT3 phosphorylation in cancer cells 79 5.2. STAT3 knockdown inhibits interaction between cancer and stromal cells 85 6. Stromal cell-derived CXCL8 stimulates tumor angiogenesis 89 6.1. Increased production of CXCL8 from stromal cells upon interaction with cixutumumab-treated cancer cells 89 6.2. CXCL8 from stromal cells stimulates tumor angiogenesis 94 7. Increased infiltration of stromal cells under cixutumumab clinical trial 99 V. DISCUSSION 103 VI. REFERENCES 113 VII. ๊ตญ๋ฌธ์ดˆ๋ก 130Docto

    ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์ด ์˜๋ฃŒ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋ณด๊ฑด๋Œ€ํ•™์› : ๋ณด๊ฑดํ•™๊ณผ(๋ณด๊ฑด์ •์ฑ…๊ด€๋ฆฌํ•™์ „๊ณต), 2013. 8. ์กฐ๋ณ‘ํฌ.๊ฑด๊ฐ•์ˆ˜์ค€์€ ์–‘ํ˜ธํ•˜๋‚˜ ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์ด ๋‚˜์˜๊ณ  ์™ธ๋ž˜์ด์šฉ์ด ๋งŽ์€ ์šฐ๋ฆฌ๋‚˜๋ผ์—์„œ ๊ฑด๊ฐ•์ƒํƒœ์™€ ์˜๋ฃŒ์ด์šฉ์— ๋Œ€ํ•˜์—ฌ ์งˆ๋ณ‘ ์ด์™ธ์˜ ์–ด๋–ค ์š”์ธ์ด ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์•Œ์•„๋ณด๊ณ ์ž ํ•œ๋‹ค. ๋น„๊ต์  ๊ฑด๊ฐ•ํ•œ ์„ฑ์ธ์œผ๋กœ ์—ฐ๊ตฌ๋Œ€์ƒ์ž๋ฅผ ์ œํ•œํ•˜์—ฌ ์งˆํ™˜์ž๋“ค์˜ ํ•„์š”์— ์˜ํ•œ ์˜๋ฃŒ์ด์šฉ์ด ์•„๋‹ˆ๋ผ, ์˜ํ•™์ ์œผ๋กœ ์งˆ๋ณ‘์„ ์ง„๋‹จ๋ฐ›์ง€ ์•Š์€ ์‚ฌ๋žŒ๋“ค์˜ ์˜๋ฃŒ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•˜์—ฌ ์•Œ์•„๋ณด๊ณ ์ž ํ•œ๋‹ค. ์„ ํ–‰์—ฐ๊ตฌ์—์„œ ๋Œ€๋ถ€๋ถ„์˜ ์˜ํ–ฅ๋ ฅ์„ ์ฐจ์ง€ํ•œ ์งˆ๋ณ‘๋ณ€์ˆ˜๋ฅผ ๋ฐฐ์ œํ•จ์œผ๋กœ์จ ์งˆ๋ณ‘ ์ด์™ธ์˜ ์–ด๋– ํ•œ ์‚ฌํšŒ์  ์š”์ธ๋“ค์ด ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€, ์งˆ๋ณ‘ ์š”์ธ์ด ๋ฐฐ์ œ๋œ ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์ด ์˜๋ฃŒ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์—ฌ ๋น„๊ต์  ๊ฑด๊ฐ•ํ•œ ์‚ฌ๋žŒ๋“ค์˜ ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹๊ณผ ์˜๋ฃŒ์ด์šฉ๊ฐ„์˜ ์—ฐ๊ด€์„ฑ์„ ํŒŒ์•…ํ•˜๋ ค๊ณ  ํ•œ๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ์€ ํ•œ๊ตญ๋ณต์ง€ํŒจ๋„ 1์ฐจ๋ถ€ํ„ฐ 7์ฐจ ์ž๋ฃŒ๊นŒ์ง€ ๋ชจ๋‘ ์‘๋‹ตํ•œ ๋Œ€์ƒ์ž ์ค‘ ๋งŒ์„ฑ์งˆํ™˜๊ณผ ์ฃผ์š”๋ณ‘๋ช…์ด ์กด์žฌํ•œ ์ž๋ฅผ ์ œ์™ธํ•˜์—ฌ ์ตœ๊ทผ 7๋…„ ๊ฐ„์˜ ์งˆ๋ณ‘์„ ํ†ต์ œํ•˜์˜€๋‹ค. ์™ธ๋ž˜์ด์šฉ๋ฐฉ๋ฌธ์ด ์ž์œจ์ ์ธ 18์„ธ ์ด์ƒ ๋น„์งˆํ™˜์ž๋ฅผ ์„ ํƒํ•˜๊ณ  1๋…„๊ฐ„ ๋ณ‘์› ์ž…์› ์ด์œ ๊ฐ€ ์ž์˜์  ๋ฐฉ๋ฌธ์ด ์•„๋‹Œ ์งˆ๋ณ‘, ์‚ฌ๊ณ  ๋ฐ ์ถœ์‚ฐ์ธ ๊ฒฝ์šฐ๋ฅผ ์ œ์™ธํ•œ ์ตœ์ข… 1619๋ช…์ด๋‹ค. ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์— ๋ฏธ์น˜๋Š” ์œ ์˜ํ•œ ๋ณ€์ˆ˜๋ฅผ ๋‹จ์ˆœ/์œ„๊ณ„์  ํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•ด ์•Œ์•„๋ณด๊ณ , ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์ด ์˜๋ฃŒ ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•ด ์•Œ์•„๋ณด์•˜๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ๊ฑด๊ฐ• ๋งŒ์กฑ๋„, ์—ฐ๋ น, ์ž์•„์กด์ค‘๊ฐ, ๊ฐ€์กฑ๊ฐˆ๋“ฑ ๋Œ€์ฒ˜๋ฐฉ๋ฒ•, ์œ ๋ฐฐ์šฐ์ž, ์œ„๊ธ‰ํ•œ ์‚ฌ๋žŒ์„ ๋„์™€์ค„ ์šฉ์˜ ๋ฐ ์„ฑ๋ณ„์ด ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์— ํฐ ์˜ํ–ฅ๋ ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์—ฐ๋ น, ์„ฑ๋ณ„, ๊ต์œก์ˆ˜์ค€, ํ˜ผ์ธ์ƒํƒœ, ์˜๋ฃŒ๋ณดํ—˜ํ˜•ํƒœ, ์˜๋ฃŒ์„œ๋น„์Šค๋งŒ์กฑ๋„๋ฅผ ๋ณด์ •ํ•œ ์ƒํƒœ์—์„œ ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์„ ๋ถ€์ •์ ์œผ๋กœ ๋Œ€๋‹ตํ•œ ๊ตฐ์ผ์ˆ˜๋ก ์™ธ๋ž˜๋ฅผ ์ž์ฃผ ์ด์šฉํ•  ํ™•๋ฅ ์ด ๋†’๊ฒŒ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค.I. ์„œ๋ก  1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 1. ์ฃผ๊ด€์  ๊ฑด๊ฐ• 1) ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹ 2) ์ฃผ๊ด€์  ๊ฑด๊ฐ•์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ 2. ์˜๋ฃŒ์ด์šฉ 1) ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์˜๋ฃŒ์ด์šฉ ํ˜„ํ™ฉ 2) ์˜๋ฃŒ์ด์šฉ ๋ชจํ˜• ๋ฐ ์˜ํ–ฅ์š”์ธ 3) ์˜๋ฃŒ์ด์šฉ์˜ ์ ์ ˆ์„ฑ III. ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ• 1. ์—ฐ๊ตฌ๋ชจํ˜• 2. ์—ฐ๊ตฌ๊ฐ€์„ค 3. ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ์ž๋ฃŒ 4. ๋ณ€์ˆ˜ ์ •์˜ ๋ฐ ์ธก์ • ๋„๊ตฌ 1) ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ (์—ฐ๊ตฌ๋ชจํ˜•1) (1) ๋…๋ฆฝ๋ณ€์ˆ˜ (2) ์ข…์†๋ณ€์ˆ˜ 2) ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์ด ์˜๋ฃŒ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ (์—ฐ๊ตฌ๋ชจํ˜• 2) (1) ๋…๋ฆฝ๋ณ€์ˆ˜ (2) ์ข…์†๋ณ€์ˆ˜ (3) ํ†ต์ œ๋ณ€์ˆ˜ 5. ๋ถ„์„ ๋ฐฉ๋ฒ• IV. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 1. ์—ฐ๊ตฌ๋Œ€์ƒ์ž์˜ ํŠน์„ฑ 1) ์ธ๊ตฌ์‚ฌํšŒํ•™์  ํŠน์„ฑ 2) ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹ ํŠน์„ฑ 3) ์˜๋ฃŒ์ด์šฉ ํŠน์„ฑ 4) ๊ฒฝ์ œ์  ํŠน์„ฑ 5) ๊ฑด๊ฐ• ์œ„ํ•ด ํ–‰์œ„ ํŠน์„ฑ 6) ์ •์‹  ์‹ฌ๋ฆฌ์  ํŠน์„ฑ 7) ์‚ฌํšŒ์  ์ธ์‹ ๋ฐ ํ™˜๊ฒฝ ํŠน์„ฑ 2. ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์š”์ธ 1) ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋‹จ์ˆœํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ 2) ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์œ„๊ณ„์  ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ 3. ์ฃผ๊ด€์  ๊ฑด๊ฐ• ์ธ์‹์ด ์™ธ๋ž˜ ์ง„๋ฃŒ ํšŸ์ˆ˜์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ V. ๊ณ ์ฐฐ 1. ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ ๋ฐ ๋…ผ์˜ 2. ์—ฐ๊ตฌ์˜ ํ•จ์˜ ๋ฐ ํ•œ๊ณ„ ์ฐธ๊ณ ๋ฌธํ—Œ AbstractMaste

    ์‚ฌํšŒ๋ถˆ์•ˆ ์ƒํ™ฉ์ฐจ์›๊ณผ ์ธ์ง€์ ยท์ •์„œ์  ์š”์ธ์˜ ๊ด€๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‹ฌ๋ฆฌํ•™๊ณผ, 2017. 2. ๊ถŒ์„๋งŒ.์‚ฌํšŒ๋ถˆ์•ˆ์žฅ์• ๋Š” ์‚ฌํšŒ์  ์ƒํ™ฉ์— ๋…ธ์ถœ๋˜๋Š” ๊ฒƒ์„ ๊ทน๋„๋กœ ๋‘๋ ค์›Œํ•˜๊ฑฐ๋‚˜ ๋ถˆ์•ˆํ•ดํ•˜๋Š” ์‹ฌ๋ฆฌ์  ๋ถ€์ ์‘ ๋ฌธ์ œ๋กœ ์ตœ๊ทผ DSM-5์—์„œ๋Š” ๋ถˆ์•ˆ์„ ์œ ๋ฐœํ•˜๋Š” ์‚ฌํšŒ์  ์ƒํ™ฉ์„ ๊ตฌ์ฒด์ ์œผ๋กœ ์†Œ๊ฐœํ•˜๊ณ  ๊ทธ์— ๋”ฐ๋ฅธ ํ•˜์œ„์œ ํ˜•์„ ์ œ์‹œํ•œ ๋ฐ” ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์‚ฌํšŒ๋ถˆ์•ˆ์„ ์œ ๋ฐœํ•˜๋Š” ๊ตฌ์ฒด์ ์ธ ์ƒํ™ฉ์ฐจ์›์„ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ๊ด€์  ์ธก์ •๋„๊ตฌ๊ฐ€ ๋“œ๋ฌผ๊ณ , ํŠนํžˆ ์‚ฌํšŒ๋ถˆ์•ˆ ์ƒํ™ฉ์ฐจ์›๊ณผ ์ธ์ง€์  ๋ฐ ์ •์„œ์  ์š”์ธ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ํƒ์ƒ‰ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋ถ€์žฌํ•˜๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ์ƒํ™ฉ์ฐจ์›์„ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋Š” ๋„๊ตฌ๋ฅผ ๋ฒˆ์•ˆ ๋ฐ ํƒ€๋‹นํ™”ํ•˜๊ณ , ์‚ฌํšŒ๋ถˆ์•ˆ ์ƒํ™ฉ์ฐจ์›๊ณผ ์ฐจ๋ณ„์ ์œผ๋กœ ๊ด€๋ จ๋˜๋Š” ์ธ์ง€์  ๋ฐ ์ •์„œ์  ์š”์ธ์„ ํƒ์ƒ‰ํ•ด ๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ โ… ์—์„œ๋Š” ์‚ฌํšŒ๋ถˆ์•ˆ ์ƒํ™ฉ์ฐจ์›์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋Š” ์‚ฌํšŒ๋ถˆ์•ˆ์ฒ™๋„(Social Anxiety Questionnaire: SAQ)๋ฅผ ๋ฒˆ์•ˆํ•˜๊ณ  ์š”์ธ๊ตฌ์กฐ, ์‹ ๋ขฐ๋„ ๋ฐ ํƒ€๋‹น๋„๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ด 302๋ช…์˜ ๋Œ€ํ•™์ƒ ํ‘œ๋ณธ์„ ๋Œ€์ƒ์œผ๋กœ ํƒ์ƒ‰์  ์š”์ธ๋ถ„์„์„ ์‹ค์‹œํ•œ ๊ฒฐ๊ณผ Caballo ๋“ฑ(2015)์ด ๊ฐœ๋ฐœํ•œ ์›์ฒ™๋„์˜ ์š”์ธ๊ตฌ์กฐ์™€ ๋™์ผํ•œ ์š”์ธ๊ตฌ์กฐ๋ฅผ ๋”ฐ๋ฅด๋Š” ๊ฒƒ์œผ๋กœ ํŒ๋ช…๋˜์—ˆ๋‹ค. 262๋ช…์˜ ๋Œ€ํ•™์ƒ ํ‘œ๋ณธ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ํ™•์ธ์  ์š”์ธ๋ถ„์„ ๊ฒฐ๊ณผ์—์„œ๋„, SAQ๊ฐ€ 5๊ฐœ์˜ ์‚ฌํšŒ๋ถˆ์•ˆ ์ƒํ™ฉ์š”์ธ(์‚ฌ๊ตํ™œ๋™, ์ˆ˜ํ–‰ํ‰๊ฐ€, ์ด์„ฑ๊ด€๊ณ„, ๋น„๋‚œ๋ฌด์‹œ, ์ž๊ธฐ์ฃผ์žฅ)์œผ๋กœ ๊ตฌ์„ฑ๋˜๋Š” ํƒ€๋‹นํ•œ ์ฒ™๋„์ž„์ด ๋ฐํ˜€์กŒ์œผ๋ฉฐ, ๋‚ด์  ํ•ฉ์น˜๋„์™€ ๊ฒ€์‚ฌ-์žฌ๊ฒ€์‚ฌ ์‹ ๋ขฐ๋„ ์—ญ์‹œ ์ ์ ˆํ•œ ์ˆ˜์ค€์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์—ฐ๊ตฌ โ…ก์—์„œ๋Š” ์—ฐ๊ตฌ โ… ์—์„œ ํƒ€๋‹นํ™”ํ•œ ํ•œ๊ตญํŒ SAQ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ธ์ง€์  ๋ฐ ์ •์„œ์  ์š”์ธ๋“ค์ด 5๊ฐœ ์ƒํ™ฉ์ฐจ์›์˜ ์‚ฌํšŒ๋ถˆ์•ˆ ์ˆ˜์ค€๊ณผ ์–ด๋–ป๊ฒŒ ๊ด€๋ จ๋˜๋Š” ์ง€ ํƒ์ƒ‰ํ•ด ๋ณด์•˜๋‹ค. ์ด 476๋ช…์˜ ๋Œ€ํ•™์ƒ๋“ค์„ ๋Œ€์ƒ์œผ๋กœ SAQ์˜ ์ƒํ™ฉ์š”์ธ๋ณ„ ๋ถˆ์•ˆ ์ˆ˜์ค€๊ณผ ์ธ์ง€์  ๋ฐ ์ •์„œ์  ๋ณ€์ธ๋“ค ๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ–ˆ๋‹ค. ๋ถ€๋ถ„์ƒ๊ด€ ๋ถ„์„ ๊ฒฐ๊ณผ, ์ƒํ™ฉ์ฐจ์›์— ๋”ฐ๋ผ ์ธ์ง€์ ยท์ •์„œ์  ํŠน์„ฑ์ด ์ฐจ๋ณ„์ ์œผ๋กœ ๊ด€๋ จ๋˜์–ด ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด์— ์ƒํ™ฉ์ฐจ์›๋ณ„ ์‚ฌํšŒ๋ถˆ์•ˆ ์ˆ˜์ค€์„ ์˜ˆ์ธกํ•˜๋Š” ์ธ์ง€์  ๋ฐ ์ •์„œ์  ์š”์ธ์„ ๋ณด๋‹ค ๊ตฌ์ฒด์ ์œผ๋กœ ํ™•์ธํ•ด๋ณด๊ณ ์ž ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ธ์ง€์  ์š”์ธ ์ค‘์—์„œ๋Š” ์ฃผ๊ด€์  ๋ถ€๋‹ด๊ฐ์ด 5๊ฐœ ์ƒํ™ฉ์ฐจ์›(์‚ฌ๊ตํ™œ๋™, ์ˆ˜ํ–‰ํ‰๊ฐ€, ์ด์„ฑ๊ด€๊ณ„, ๋น„๋‚œ๋ฌด์‹œ, ์ž๊ธฐ์ฃผ์žฅ)์˜ ๋ถˆ์•ˆ์„ ๊ณตํ†ต์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜์˜€๋‹ค. ๋ฐ˜๋ฉด, ๋˜ ๋‹ค๋ฅธ ์ธ์ง€์  ์š”์ธ์ธ ์‚ฌํšŒ์  ์ž๊ธฐ ํšจ๋Šฅ๊ฐ์€ 3๊ฐœ ์ƒํ™ฉ์ฐจ์›(์‚ฌ๊ตํ™œ๋™, ์ˆ˜ํ–‰ํ‰๊ฐ€, ์ด์„ฑ๊ด€๊ณ„)์˜ ๋ถˆ์•ˆ ์ˆ˜์ค€์„ ์œ ์˜ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜์˜€๊ณ , ์—ญ๊ธฐ๋Šฅ์  ์‹ ๋…์˜ ๊ฒฝ์šฐ ์‚ฌ๊ตํ™œ๋™ ์ƒํ™ฉ์—์„œ์˜ ๋ถˆ์•ˆ ์ˆ˜์ค€์„ ์œ ์˜ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜์˜€๋‹ค. ํ•œํŽธ, ์ •์„œ์  ์š”์ธ์ธ ์ •์„œํ‘œํ˜„ ์–‘๊ฐ€์„ฑ์˜ ๊ฒฝ์šฐ ์ž๊ธฐ์ฃผ์žฅ ์ƒํ™ฉ์—์„œ์˜ ๋ถˆ์•ˆ ์ˆ˜์ค€์„ ์œ ์˜ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์ƒํ™ฉ๋ณ„ ์‚ฌํšŒ๋ถˆ์•ˆ ์ˆ˜์ค€์ด ๊ฐœ์ธ์˜ ์ธ์ง€์  ๋ฐ ์ •์„œ์  ํŠน์„ฑ์— ๋”ฐ๋ผ ์ฐจ๋ณ„์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ด๋Ÿฐ ๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ ์‚ฌํšŒ๋ถˆ์•ˆ์„ ํšจ๊ณผ์ ์œผ๋กœ ์น˜๋ฃŒํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ƒํ™ฉ์ฐจ์›๋ณ„ ๋ถˆ์•ˆ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ธ์ง€์  ๋ฐ ์ •์„œ์  ์š”์ธ์— ์ดˆ์ ์„ ๋งž์ถฐ ๊ฐœ์ž…ํ•  ํ•„์š”์„ฑ์ด ์žˆ๋‹ค๋Š” ์ž„์ƒ์  ํ•จ์˜๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์˜ ์‹œ์‚ฌ์  ๋ฐ ํ›„์† ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์ œ์–ธ์ด ๋…ผ์˜๋˜์—ˆ๋‹ค.์„œ ๋ก  1 ์‚ฌํšŒ๋ถˆ์•ˆ์žฅ์• ์˜ ํ•˜์œ„์œ ํ˜• 2 ์‚ฌํšŒ๋ถˆ์•ˆ์žฅ์• ์˜ ์ธก์ •: ์ƒํ™ฉ์ฐจ์›์˜ ๋ถ„๋ฅ˜ 4 ์‚ฌํšŒ๋ถˆ์•ˆ์žฅ์• ์˜ ์ธ์ง€์  ์š”์ธ 6 ์‚ฌํšŒ๋ถˆ์•ˆ์žฅ์• ์˜ ์ •์„œ์  ์š”์ธ 10 ์—ฐ๊ตฌ๋ชฉ์  ๋ฐ ๊ฐœ์š” 13 ์—ฐ๊ตฌ 1. ์‚ฌํšŒ๋ถˆ์•ˆ์ฒ™๋„(SAQ)์˜ ๋ฒˆ์•ˆ ๋ฐ ํƒ€๋‹นํ™” 15 ๋ฐฉ๋ฒ• 16 ๊ฒฐ๊ณผ 21 ๋…ผ์˜ 31 ์—ฐ๊ตฌ 2. ์‚ฌํšŒ๋ถˆ์•ˆ ์ƒํ™ฉ์ฐจ์›๊ณผ ์ธ์ง€์ ์ •์„œ์  ์š”์ธ์˜ ์ฐจ๋ณ„์  ๊ด€๊ณ„ 34 ๋ฐฉ๋ฒ• 36 ๊ฒฐ๊ณผ 40 ๋…ผ์˜ 47 ์ข…ํ•ฉ๋…ผ์˜ 52 ์ฐธ๊ณ ๋ฌธํ—Œ 56 ๋ถ€ ๋ก 65 ์˜๋ฌธ์ดˆ๋ก 82Maste

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ™˜๊ฒฝ๋Œ€ํ•™์› : ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2015. 2. ์ด๋„์›.๊ฑด์กฐ์ง€(drylands)๋Š” ๊ธฐํ›„์  ์š”์ธ๊ณผ ์ธ๊ฐ„์˜ ํ™œ๋™์œผ๋กœ ์ธํ•œ ์‚ฌ๋ง‰ํ™”์™€ ํ† ์ง€ ํ™ฉํํ™”์— ํŠนํžˆ ์ทจ์•ฝํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ฑด์กฐ ๋ฐ ๋ฐ˜๊ฑด์กฐ ์ƒํƒœ๊ณ„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๊ธฐํ›„ ๋ณ€ํ™”์— ๋Œ€์‘๊ณผ ์ ์‘์— ๊ด€๋ จํ•˜์—ฌ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค. 500 mm ๋ฏธ๋งŒ์˜ ๋‚ฎ์€ ์—ฐ๊ฐ•์ˆ˜๋Ÿ‰์„ ๋ณด์ด๋Š” ์ง€์—ญ์—์„œ๋Š” ๋งŽ์€ ๊ฒฝ์šฐ ๊ฐ•์ˆ˜๋Ÿ‰์ด ์‹์ƒ ์ƒ์‚ฐ์„ฑ์˜ ์ œํ•œ ์š”์ธ์ด๋‹ค. ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋“ค์€ ๋Œ€๊ทœ๋ชจ์˜ ํ† ์ง€ ํ™ฉํํ™”์™€ ๊ธฐํ›„ ๋ณ€ํ™”์— ๋Œ€ํ•œ ์ƒํƒœ๊ณ„์˜ ๋ฐ˜์‘์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด์„œ ์ˆœ์ผ์ฐจ ์ƒ์‚ฐ์„ฑ๊ณผ ์—ฐ๊ฐ„ ๊ฐ•์ˆ˜๋Ÿ‰์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ ๊ฐ•์ˆ˜์ด์šฉํšจ์œจ์ง€์ˆ˜(Rain Use EfficiencyRUE)๋ฅผ ์‚ฌ์šฉํ–ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ชฝ๊ณจ์—์„œ์˜ ๊ฐ€๋ญ„ ์ŠคํŠธ๋ ˆ์Šค์™€ ๊ฐ€๋ญ„ ์ทจ์•ฝ์„ฑ(๋ฏผ๊ฐ์„ฑ)์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด Do and Kang(2014)์˜ ํ† ์–‘์ˆ˜๋ถ„ํšจ์œจ์ง€์ˆ˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ ๊ฐ€๋ญ„ ์ŠคํŠธ๋ ˆ์Šค ์ง€์ˆ˜์™€ ๊ฐ€๋ญ„ ์ทจ์•ฝ์„ฑ ์ง€์ˆ˜๋ฅผ ๊ณ„์‚ฐ๋ฒ•์„ RUE๋กœ ๋ณ€ํ™˜์‹œ์ผœ ์ ์šฉํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋จผ์ €, ๋ชฝ๊ณจ์—์„œ 1982๋…„์—์„œ 2008๋…„๊นŒ์ง€(27๋…„ ๊ฐ„)์˜ ๊ฐ•์ˆ˜์ด์šฉํšจ์œจ์ง€์ˆ˜ ์™€ ๊ฐ€๋ญ„ ์ทจ์•ฝ์„ฑ์˜ ์‹œ๊ฐ„์  ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋Š” ์ฒจ๋‹จ ๊ณ ํ•ด์ƒ ๋ฐฉ์‚ฌ๊ณ„ ์œ„์„ฑ ์ž๋ฃŒ(Advanced Very High Resolution Radiometer)๋ฅผ ์‚ฌ์šฉํ•ด์„œ ์„ฑ์žฅ๊ธฐ์ธ 6์›”์—์„œ 9์›”์— ์ธก์ •๋œ ์ •๊ทœ์‹์ƒ์ง€์ˆ˜์™€ 63๊ฐœ์˜ ์ง€์—ญ ๊ธฐ์ƒ ๊ด€์ธก์†Œ์˜ ๊ฐ•์ˆ˜๋Ÿ‰ ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉํ–ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ๊ฐ•์ˆ˜์ด์šฉํšจ์œจ์ง€์ˆ˜ ์™€ ๊ฐ€๋ญ„ ์ทจ์•ฝ์„ฑ์˜ ๊ณต๊ฐ„์  ๋ถ„ํฌ๋ฅผ ๋ถ„์„ ํ•˜์˜€๋‹ค. ์ด๋Š” 1998๋…„์—์„œ 2008๋…„, ์ฆ‰ 11๋…„์— ๋™์•ˆ์˜ ํ”ฝ์…€ ์ž๋ฃŒ์ธ ์ •๊ทœ์‹์ƒ์ง€์ˆ˜ ์ง€๋„์™€ ์—ด๋Œ€ ๊ฐ•์šฐ ๊ด€์ธก ์œ„์„ฑ(Tropical Rainfall Measuring Mission)์˜ ๊ฐ•์ˆ˜๋Ÿ‰ ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ–ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์•ฝ 300๊ฐœ์˜ ์จ ๋‹จ์œ„์˜ ํšŒ๊ท€ ๋ถ„์„์„ ํ†ตํ•ด ๊ธฐํ›„, ์ง€๋ฆฌ, ํ† ์ง€ ์ด์šฉ ์š”์†Œ๋“ค์ด ๊ฐ€๋ญ„ ์ŠคํŠธ๋ ˆ์Šค์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ–ˆ๋‹ค. 63๊ฐœ ์ง€์—ญ ๊ธฐ์ƒ๊ด€์ธก์†Œ๋ฅผ 4๊ฐœ์˜ ์ดˆ์ง€(steppe) ์ง€์—ญ์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๋ถ„์‚ฐ๋ถ„์„์„ ์‹œํ–‰ํ–ˆ๋‹ค. ์‚ฌ๋ง‰ ์ง€์—ญ์ด ๊ฐ€์žฅ ๋†’์€ ๊ฐ€๋ญ„ ์ŠคํŠธ๋ ˆ์Šค์™€ ๊ฐ€๋ญ„ ์ทจ์•ฝ์„ฑ์„ ๋ณด์˜€๊ณ , ์ดˆ์ง€ ์ง€์—ญ์€ ๊ฐ€์žฅ ๋‚ฎ์€ ๊ฐ€๋ญ„ ์ŠคํŠธ๋ ˆ์Šค๋ฅผ, ๊ทธ๋ฆฌ๊ณ  ์‚ผ๋ฆผ ์ดˆ์ง€๊ฐ€ ๊ฐ€์žฅ ๋‚ฎ์€ ๊ฐ€๋ญ„ ์ทจ์•ฝ์„ฑ์„ ๋ณด์˜€๋‹ค. ํŠนํžˆ ๋ฑŒ์–€์ฝฉ๊ณ ๋ฅด(Bayankhongor) ์•„์ด๋ง‰ ๋‚จ๋ถ€ ์ง€๋ฐฉ์ธ ๋ฐ”์–€ ์˜จ๋„๋ฅด(Bayan Ondor) ์จ๊ณผ ์‹œ๋„ค์‹ ์ŠคํŠธ(Shinejinst) ์จ, ๊ทธ๋ฆฌ๊ณ  ๊ณ ๋น„ ์•Œํƒ€์ด(Gobi-Altai) ์•„์ด๋ง‰์˜ ์—๋ฅด๋ด(Erdene) ์จ์˜ ๋‚จ์ชฝ ์ง€์—ญ์ด ํ”ฝ์…€ ์ง€๋„๋ฅผ ํ†ตํ•ด ๋ณผ ๋•Œ ๋†’์€ ๊ฐ€๋ญ„ ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ๋ณด์˜€๋‹ค. ๊ฐ๊ฐ ์จ์˜ ํ‰๊ท  ๊ฐ’์„ ์ด์šฉํ•œ ํšŒ๊ท€๋ถ„์„์˜ ๊ฒฐ๊ณผ๋กœ๋Š” ๋†’์€ ์ธ๊ตฌ๋ฐ€๋„, ์˜จ๋„, ๊ทธ๋ฆฌ๊ณ  ๊ฐ•์ˆ˜์ด์šฉํšจ์œจ์ง€์ˆ˜๊ฐ€ ๋†’์€ ๊ฐ€๋ญ„ ์ŠคํŠธ๋ ˆ์Šค๋กœ ์ด์–ด์ง€๊ณ , ๋†’์€ ๋ชฉ์ถ•๋ฐ€๋„์™€ ๊ฐ•์ˆ˜๋Ÿ‰์€ ๊ฐ€๋ญ„ ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ๋‚ฎ์ถ”๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ฐ€๋ญ„ ์ŠคํŠธ๋ ˆ์Šค๋Š” ์‚ฌ๋ง‰๊ณผ ๋น„๊ตํ•ด ๋ณผ ๋•Œ ์Šคํ… ์ง€์—ญ๊ณผ ์‚ฌ๋ง‰ ์Šคํ…์—์„œ ๋ณด๋‹ค ๋‚ฎ์•˜๋‹ค. ๊ฐ•์ˆ˜์ด์šฉํšจ์œจ์ง€์ˆ˜์˜ ์‚ฌ์šฉ์€ ๋ชฝ๊ณจ์˜ ์ดˆ๋ชฉ์ƒ์‚ฐ์„ฑ ์˜ˆ์ธก, ํ† ์ง€ ํ™ฉํํ™” ํ‰๊ฐ€์™€ ๊ฐ€๋ญ„๋ฐ˜์‘์˜ˆ์ธก์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ๋ชฝ๊ณจ์ง€์—ญ์˜ 30์—ฌ๋…„์˜ ์ธ๊ณต์œ„์„ฑ ์ž๋ฃŒ๋Š” ์žฅ๊ธฐ๊ฐ„์˜ ๋ถ„์„๊ณผ ๋„“์€ ๋ฒ”์œ„์˜ ๊ณต๊ฐ„ ๋ถ„์„์—๋Š” ์œ ์šฉํ•˜์ง€๋งŒ ์ข€ ๋” ์ •ํ™•ํ•œ ํ‰๊ฐ€ ๋ฐ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ํ™•์ฆ์„ ์œ„ํ•ด์„œ๋Š” ๊ณ ํ•ด์ƒ๋„ ๋ฐ์ดํ„ฐ์˜ ๋น„๊ต์™€ ํ˜„์ง€์กฐ์‚ฌ๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์ด๋‹ค. ๊ฑด์กฐ์ง€ ์ƒ์‚ฐ์„ฑ๊ณผ ๊ฐ€๋ญ„ ์ทจ์•ฝ์„ฑ์— ๋Œ€ํ•œ ํ‰๊ฐ€๋Š” ๊ฐ•์ˆ˜๋Ÿ‰ ๋ณ€ํ™”์™€ ์˜จ๋„์ƒ์Šน๊ณผ ๊ฐ™์€ ๊ธฐํ›„๋ณ€ํ™”์— ๋Œ€์‘ํ•˜๊ธฐ ์œ„ํ•œ ๋ฏธ๋ž˜ ํ† ์ง€๊ด€๋ฆฌ ๊ณ„ํš์— ์œ ์šฉํ•  ๊ฒƒ์ด๋‹ค. ์ฃผ์š”์–ด : ๊ฐ€๋ญ„ ์ทจ์•ฝ์„ฑ, ๊ฐ•์ˆ˜์ด์šฉํšจ์œจ์ง€์ˆ˜(Rain Use Efficiency), ๋ชฝ๊ณจ, AVHRR NDVI, ๊ธฐํ›„๋ณ€ํ™”Studies of arid and semi-arid ecosystems are meaningful in relation to climate change as drylands are especially vulnerable to desertification and land degradation caused by climatic factors and human activities. Precipitation is often the limiting factor for vegetation productivity in such regions, as they experience low annual rainfall, often less than 500 mm. Numerous studies used the Rain Use Efficiency (RUE), an index derived from vegetation productivity and annual precipitation, as a potential indicator for assessing large scale degradation and for evaluating responses of ecosystems to climate change. This study adopted Soil Moisture Use Efficiency based Drought Stress Index and Drought Vulnerability Index by Do and Kang (2014) and modified it for RUE to assess drought stress and drought vulnerability (i.e. sensitivity) in Mongolia. The objective of this study was to analyze first, the temporal patterns of RUE and drought vulnerability in respective weather station regions over 27 year period, 1982-2008, in Mongolia by utilizing satellite derived vegetation index, namely NDVI (Normalized Difference Vegetation Index) from the Advanced Very High Resolution Radiometer (AVHRR), of growing seasons (June - September) and 63 local weather stations precipitation data. Secondly, the spatial pattern of RUE and drought vulnerability was analyzed by mapping with pixel based NDVI and Tropical Rainfall Measuring Mission (TRMM) precipitation data during 1998 to 2008, 11 year period. Lastly, climatic, geological, and land use factors attributing to drought stress were identified through multiple regression analysis. Analysis of variance (ANOVA) was conducted among four steppe zones represented in 63 local weather stations, and the result showed desert having the most drought stress and drought vulnerability while steppe had the least drought stress but forest steppe had the least drought vulnerability. Notable regions with high drought stress were found in pixel based mapBayan Ondor and Shinejinst soums in southern part of Bayankhongor aimag and southern part of Erden soum of Gobi-Altai aimag. Most drought vulnerable soums were Altai, Cogt, and Erdene soums in the most southern part of Gobi-Altai aimag. Regression analysis conducted with aggregated values within each soum illustrated that higher population density, temperature, and RUE indicate higher drought stress condition while higher livestock density and precipitation range lowers drought stress, and drought stress is less experienced in land cover of desert steppe and steppe regions compared to desert. Utilization of RUE index allowed vegetation productivity forecast, land degradation assessment, and drought response projection in Mongolia. While the satellite derived data sets were useful in long term and regional analyses in Mongolia over three decades, for more accurate assessment and verification of results, comparison to higher resolution data and field survey must be accompanied. In response to changing rainfall patterns and temperature rise, assessment of dryland vegetation productivity and drought vulnerability will be relevant to planning future land management.1. Introduction.......................................1 2. Materials and Methods..............................5 2.1. Site Description................................5 2.2. Data Description................................7 2.2.1. NASA GIMMS NDVI data 2.2.2. Local Weather Stations' Precipitation data 2.2.3. TRMM data 2.3. Data Analysis...................................12 2.3.1. Weather Station based RUE & Drought Vulnerability 2.3.2. Map based RUE & Drought Vulnerability 2.3.3. Multiple Regression Analysis 3. Results............................................20 3.1. Weather Station based RUE.......................20 3.2. Weather Station based Drought Vulnerability.....24 3.3. Map based RUE...................................26 3.4. Map based Drought Vulnerability.................30 3.5. Multiple Regression Analysis by soums...........32 4. Discussion.........................................35 4.1. RUE and DVI among different zones...............35 4.2. Comparison to other DSI & NDVI..................36 4.3. Comparison to published desertification map.....36 5. Conclusion.........................................38 Reference.............................................39 Appendix..............................................45 Abstract in Korean....................................48Maste

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    OAIID:oai:osos.snu.ac.kr:snu2010-01/104/0000025799/7SEQ:7PERF_CD:SNU2010-01EVAL_ITEM_CD:104USER_ID:0000025799ADJUST_YN:NEMP_ID:A075458DEPT_CD:611CITE_RATE:0FILENAME:๋””์ž์ธ_์‚ฌ๊ณ _๊ธฐ๋ฐ˜์˜_์†Œ์…œ_๋„คํŠธ์›Œํฌ๋ฅผ_ํ™œ์šฉํ•œ_์ฐฝ์˜๋ฐœ์ƒ_๋ฐฉ๋ฒ•_์ œ์•ˆ.pdfDEPT_NM:๋””์ž์ธํ•™๋ถ€EMAIL:[email protected]:

    A Study of Open Collaboration Creative Thinking System Based on Design Thinking

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    ํ…Œํฌ๋†€๋กœ์ง€๊ฐ€ ๊ธ‰์†๋„๋กœ ๋ฐœ์ „ํ•˜๋ฉด์„œ ์‹ ์‚ฌ์—… ๋ฐœ๊ตด์— ๋Œ€ํ•œ ์š•๊ตฌ๊ฐ€ ๋†’์•„์ง€๊ณ  ์ด์— ๋Œ€ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๋””์ž์ธ์˜ ์ฐฝ์˜์  ๋ฐœ์ƒ๋ฐฉ๋ฒ•์ด ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด์— ๋Œ€ํ•œ ๊ตฌ์ฒด์ ์ธ ๋ฐฉ๋ฒ•์€ ์•„์ง ์ œ์•ˆ๋˜๊ณ  ์žˆ์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋Š” ์ƒํƒœ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋””์ž์ธ๊ณผ ๋ฐ€์ ‘ํ•œ ๋ฐœ์ „๊ด€๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ํ…Œํฌ๋†€๋กœ์ง€ ๋ถ„์•ผ์˜ ์•„์ด๋””์–ด ๋ฐœ์ƒ ํ”„๋กœ์„ธ์Šค์˜ ๋ณ€ํ™”์™€ ๋””์ž์ธ ํ”„๋กœ์„ธ์Šค์˜ ๋ณ€ํ™”๋ฅผ ๋น„๊ตํ•˜์—ฌ ๋””์ž์ธ ์‚ฌ๊ณ ๋ฅผ ๊ธฐ๋ฐ˜์„ ๋‘” ์ฐฝ์˜ ๋ฐœ์ƒ์˜ ๊ตฌ์ฒด์ ์ธ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋””์ž์ธ ์‚ฌ๊ณ ์— ๊ธฐ๋ฐ˜์„ ๋‘” ์ด ๋ฐฉ๋ฒ•๋ก ์€ ์‹œ๊ฐํ™” ๊ธฐ๋ฒ•๊ณผ, ๋‹ค์–‘ํ•œ ํ˜‘์—… ๋ฐฉ๋ฒ•, ๊ตฌ์ฒด์ ์ธ ํ”„๋กœ์„ธ์Šค ๋‚ด์šฉ์„ ํฌํ•จํ•˜๊ณ  ์žˆ๋‹ค. ๊ตฌ์ฒด์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ์จ ๋ฐฐ์šฐ๊ณ , ์˜๊ฐํ•˜๊ณ , ์•„์ด๋””์–ด๋ฅผ ๊ตฌ์ฒดํ™”ํ•˜๊ณ , ์„ ํƒํ•˜๋ฉฐ, ํ˜‘์—…์„ ํ†ตํ•ด์„œ ๋น„์ฆˆ๋‹ˆ์Šค๋ฅผ ๋ก ์นญ ์‹œํ‚ค๋Š” ํ”„๋กœ์„ธ์Šค๋ฅผ ๊ฐ€์ง„ ๋ฐฉ๋ฒ•๋ก ๊ณผ ์ด๋ฅผ ์ ์šฉํ•œ ํ”„๋กœํ† ํƒ€์ž…์„ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•๋ก ๊ณผ ํ”„๋กœํ† ํƒ€์ž… ์ œ์•ˆ์€ ๋””์ž์ธ์‚ฌ๊ณ ์˜ ๊ธฐ์—… ๋‚ด ์‹ค์งˆ ์ ์šฉ์— ๋Œ€ํ•œ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œ ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.As technology advances rapidly, demand for discovery of new businesses are increasing, and creative thinking methods of design are receiving attention. However, specific methods for such purposes are yet to be developed. The following studies annalize changes in thinking processes, by comparing design and technology driven methodologies. Through results, the studies suggest a detailed method for creative thinking based on design thinking. As a detailed method, a methodology for learning, inspiration, ideation, selection of ideas, and launching businesses based on collaboration, the studies will suggest a prototype applying the methodology. Based on creative thinking, the methodology includes visualization techniques, various methods for collaboration, and specific contents in processes. Recommendations for this methodology and prototype are expected to suggest actual application of design thinking in businesses.OAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000025799/1SEQ:1PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000025799ADJUST_YN:YEMP_ID:A075458DEPT_CD:611CITE_RATE:0FILENAME:๋””์ž์ธ_์‚ฌ๊ณ ๋ฅผ_๋ฐ”ํƒ•์œผ๋กœ_ํ•œ_๊ฐœ๋ฐฉํ˜•_ํ˜‘์—…_์ฐฝ์˜๋ฐœ์ƒ_์‹œ์Šคํ…œ_์—ฐ๊ตฌ.pdfDEPT_NM:๋””์ž์ธํ•™๋ถ€EMAIL:[email protected]_YN:NCONFIRM:

    Design Thinking Adaptation for Creative Emergence in Technology Industry

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    ๊ธ‰๋ณ€ํ•˜๋Š” ํ™˜๊ฒฝ๊ณผ ํ…Œํฌ๋†€๋กœ์ง€์˜ ๋ฐœ์ „์œผ๋กœ ์ธํ•œ ๊ธฐ์—…์˜ ์ง€์†์ ์ธ ๊ฐ€์น˜์ฐฝ์ถœ๊ณผ ํ˜์‹ ์ด ์š”๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ์—”์ง€๋‹ˆ์–ด ์ค‘์‹ฌ์˜ ํ”„๋กœ์„ธ์Šค๋ฅผ ๊ฐ–์ถ˜ ํ…Œํฌ๋†€๋กœ์ง€ ๋ถ„์•ผ์˜ ๊ธฐ์—…๋“ค์€ ์—”์ง€๋‹ˆ์–ด์˜ ํ•œ์ •๋œ ๋ฐœ์ƒ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ  ์ƒˆ๋กœ์šด ์•„์ด๋””์–ด๋กœ ์‹œ์žฅ์—์„œ ์Šน๋ถ€ํ•˜๊ธฐ ์œ„ํ•œ ๊ธฐ์—… ํ˜์‹ ์„ ๊ฐํ–‰ํ•˜๊ณ ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ฐฝ์˜์  ์ธ์žฌ์˜ ํ™•๋ณด์™€ ์–‘์„ฑ ๋ฐ ๋ฐฉ๋ฒ•๋ก ๊ณผ ํ”„๋กœ์„ธ์Šค ๋„์ž…์— ๋Œ€๊ทœ๋ชจ ํˆฌ์ž๊ฐ€ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ์œผ๋‚˜์‹ค์šฉ์  ์ฐฝ์˜์‚ฌ๊ณ  ๊ธฐ๋ฒ•์ด๋‚˜ ๋ฐœ์ƒ๋ฐฉ๋ฒ•๋ก , ํ”„๋กœ์„ธ์Šค๊ฐ€ ์•„์ง๊นŒ์ง€ ํšจ๊ณผ์ ์œผ๋กœ ์ œ์‹œ๋˜๊ณ  ์žˆ๋Š” ๋ชปํ•œ ์‹ค์ •์ด๋‹ค. ์ด์— ๋ณธ์—ฐ๊ตฌ์—์„œ๋Š” ํ…Œํฌ๋†€๋กœ์ง€ ๊ธฐ๋ฐ˜ ๊ธฐ์—… ๊ตฌ์„ฑ์›์˜ ์ฐฝ์˜์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•๋ก  ์ค‘ ํ˜„์žฌ์˜ ํ…Œํฌ๋†€๋กœ์ง€ ์ค‘์‹ฌ์˜ ๋ฐœ์ƒ๋ฒ•์— ๋””์ž์ธ ์‚ฌ์ƒ ๋ฐ ์ ‘๊ทผ ๋ฐฉ๋ฒ•๋ก ์„ ์ ‘๋ชฉํ•˜์—ฌํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ ํ˜„์žฌ ํ…Œํฌ๋†€๋กœ์ง€ ์ค‘์‹ฌ์˜ ์•„์ด๋””์–ด ๋ฐœ์ƒ ๋ฐฉ๋ฒ•๋ก ์˜ ๋ฌธ์ œ์ ๊ณผ ๋””์ž์ธ ์‚ฌ๊ณ ๋ฅผ ํ…Œํฌ๋†€๋กœ์ง€ ์ค‘์‹ฌ ๊ธฐ์—…์— ์ ์šฉํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ์˜ ๋ฌธ์ œ์ ์„ ํŒŒ์•…ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ๊ทน๋ณต ํ•  ์ˆ˜ ์žˆ๋Š” ํ…Œํฌ๋†€๋กœ์ง€๋ฅผ ํ•™์Šต, ํ™œ์šฉ, ๋ฐœ์ „์˜ ์„ ์ˆœํ™˜ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง„ ํ…Œํฌ๋†€๋กœ์ง€ ์ฐธ์—ฌ์™€ ๊ณต์œ ์˜ ๊ฒฝํ–ฅ๊ณผ ๋””์ž์ธ ์ธก๋ฉด์˜ ๋ฐฉ๋ฒ•๋ก ์ ๋ฌธ์ œ์ ์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ์‹ค์šฉํ™”์™€ ๊ตฌ์ฒดํ™”์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ์˜ˆ์‹œ๋“ค์„ ์ œ์‹œํ•จ์œผ๋กœ์จ ๋””์ž์ธ๊ณผ ํ…Œํฌ๋†€๋กœ์ง€ ์–‘์ธก๋ฉด์˜ ์žฅ์ ์„ ๊ฐ–์ถ˜ ์ฐฝ์˜์  ์•„์ด๋””์–ด ๋ฐœ์ƒ ๋ฐฉ๋ฒ•๋ก ์— ๋Œ€ํ•œ ๋ฐฉํ–ฅ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค.Sudden changes in technology require companies to have a workforce that can continually create innovative high concept products and services. Companies that rely on their technology divisions to drive innovation require their engineers to have creative design abilities as well technical prowess. The reliance on engineers with these dual skill sets does not guarantee success and often results in failure due to restrictively systematic methodologies that stifle creativity, engineering monoculture, and the assumption that technology is a specialists field. Design driven companies, on the other hand, often lack a concrete methodology that can be applied to other companies due to a lack of knowledge of the definition of what design is and the ineffective conversion of employees into creative design forces. Therefore, this paper suggests to introduce a solution that can increase creativity within the technology centers of the world by analyzing both technology and design driven methodologies. By comparing design and technology driven methodologies, we will look to generate a solution to overcome the shortcomings of both approaches that will result in facilitating the creative process within corporate environments.OAIID:oai:osos.snu.ac.kr:snu2009-01/102/0000025799/1SEQ:1PERF_CD:SNU2009-01EVAL_ITEM_CD:102USER_ID:0000025799ADJUST_YN:YEMP_ID:A075458DEPT_CD:611CITE_RATE:0FILENAME:ํ…Œํฌ๋†€๋กœ์ง€_๋ถ„์•ผ์˜_์ฐฝ์˜์ _๋ฐœ์ƒ์„_์œ„ํ•œ_๋””์ž์ธ_์‚ฌ๊ณ _์ ์šฉ.pdfDEPT_NM:๋””์ž์ธํ•™๋ถ€EMAIL:[email protected]_YN:NCONFIRM:

    Design Thinking Adaptation for Creative Emergence in Technology Industry

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    OAIID:oai:osos.snu.ac.kr:snu2009-01/104/0000025799/10SEQ:10PERF_CD:SNU2009-01EVAL_ITEM_CD:104USER_ID:0000025799ADJUST_YN:NEMP_ID:A075458DEPT_CD:611CITE_RATE:0FILENAME:ํ…Œํฌ๋†€๋Ÿฌ์ง€_๋ถ„์•ผ์˜_์ฐฝ์˜์ _๋ฐœ์ƒ์„_์œ„ํ•œ_๋””์ž์ธ_์‚ฌ๊ณ _์ ์šฉ.pdfDEPT_NM:๋””์ž์ธํ•™๋ถ€EMAIL:[email protected]:
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