35 research outputs found

    ๋Œ€ํ˜• ๊ฐ•์ž…์ž ๊ฐ€์†๊ธฐ ๊ธฐ๋ฐ˜ ํƒ์ƒ‰์—์„œ ๊ธฐ๊ณ„ ํ•™์Šต ์‘์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€(๋ฌผ๋ฆฌํ•™์ „๊ณต), 2021. 2. ๊น€ํ˜•๋„.In this thesis, we study the application of machine learning for searches at the Large Hadron Collider without a sharp resonance peak. First, we use machine learning to find the best observables for the broad resonance earch. A vector resonance from the composite Higgs models in ttห‰t\bar{t} final state is considered as a benchmark. Various approaches are adopted to interpret the abstracted information by the machine, and we conclude that the resonance energy is still important for the broad resonance search, while the angular distributions and the transverse momenta of the decayed products have also great importance. Second, we use machine learning to extract information about the resonance from other than the final state. We show the correlation between the kinematics of jets from initial state radiation and the resonance particle. To demonstrate the experimental feasibility we perform the searching for invisible decay of Higgs by using machine learning. As a result, we show that the bound from gluon-fusion production mechanism can be improved even stronger than the other production mechanisms due to the correlation.์ด ๋…ผ๋ฌธ์€ ๋Œ€ํ˜• ๊ฐ•์ž…์ž ๊ฐ€์†๊ธฐ์—์„œ์˜ ํƒ์ƒ‰ ์ค‘, ์ค‘๊ฐ„ ์ž…์ž์˜ ๋‚ ์นด๋กœ์šด ๊ณต๋ช… ์ •์ ์ด ์—†๋Š” ๊ฒฝ์šฐ์— ๋Œ€ํ•œ ๊ธฐ๊ณ„ ํ•™์Šต์˜ ์‘์šฉ์„ ๋‹ค๋ฃฌ๋‹ค. ์‘์šฉ์˜ ํ•œ๊ฐ€์ง€ ์˜ˆ๋กœ์จ, ๊ธฐ๊ณ„ ํ•™์Šต์„ ์ด์šฉํ•˜์—ฌ ํƒ์ƒ‰ํ•˜๊ณ ์ž ํ•˜๋Š” ์ค‘๊ฐ„ ์ž…์ž์˜ ๊ณต๋ช…์˜ ํญ์ด ๋„“์„ ๋•Œ ๊ฐ€์žฅ ์ ํ•ฉํ•œ ๊ด€์ธก๋Ÿ‰์ด ๋ฌด์—‡์ธ๊ฐ€์— ๋Œ€ํ•˜์—ฌ ์กฐ์‚ฌํ•œ๋‹ค. ๋ณด๋‹ค ์ž์„ธํ•œ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•˜์—ฌ, ํ•ฉ์„ฑ ํž‰์Šค ๋ชจํ˜•์˜ ์ „๋ฐ˜์—์„œ ์˜ˆ์ธก ๋˜๋Š” ๋ฌด๊ฑฐ์šด ํ•ฉ์„ฑ ๋ฒกํ„ฐ ์ค‘๊ฐ„์ž๊ฐ€ ์œ„ ์ฟผํฌ์™€ ์œ„ ์ฟผํฌ์˜ ๋ฐ˜์ž…์ž๋กœ ๊ณต๋ช… ๋ถ•๊ดดํ•˜๋Š” ๊ณผ์ •์„ ์ƒ๊ฐํ•œ๋‹ค. ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•˜์—ฌ ๊ธฐ๊ณ„ ํ•™์Šต์„ ํ†ตํ•ด ์ถ”์ƒํ™”๋œ ์ •๋ณด๋ฅผ ํ•ด์„ํ•˜์—ฌ, ๊ฒฐ๊ณผ์ ์œผ๋กœ ๊ณต๋ช… ํญ์ด ๋„“์€ ๊ฒฝ์šฐ์—๋„ ์žฌ๊ตฌ์ถ• ๋œ ์ค‘๊ฐ„ ์ž…์ž์˜ ์งˆ๋Ÿ‰์ด ์œ ์šฉํ•˜๊ณ  ๋ถ•๊ดดํ•œ ์ž…์ž๋“ค์˜ ๊ฐ๋„ ๋ถ„ํฌ ๋ฐ ์ง๊ต ์šด๋™๋Ÿ‰ ๋˜ํ•œ ํ•ด๋‹น ํƒ์ƒ‰์—์„œ ์ค‘์š”์„ฑ์„ ๊ฐ–๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ์˜ˆ๋กœ์จ, ๊ธฐ๊ณ„ ํ•™์Šต์„ ์ด์šฉํ•˜์—ฌ ๊ณต๋ช…์„ ์œ ๋ฐœํ•˜๋Š” ์ค‘๊ฐ„ ์ž…์ž์˜ ๋ถ•๊ดด ํ›„ ์ตœ์ข… ์ƒํƒœ๊ฐ€ ์•„๋‹Œ ๊ณณ์—์„œ ๋ฐฉ์ถœ ๋œ ์ž…์ž๋ฅผ ํ†ตํ•ด ์ค‘๊ฐ„ ์ž…์ž์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์—ฐ๊ตฌํ•œ๋‹ค. ์ด๋ฅผ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ดˆ๊ธฐ ์ƒํƒœ ๋ณต์‚ฌ๋กœ ๋‚˜์˜จ ์ œํŠธ์™€ ์ค‘๊ฐ„ ์ž…์ž ์‚ฌ์ด์˜ ์ƒํ˜ธ ๊ด€๋ จ์„ฑ์ด ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ์ œ์‹œ๋œ ๋ถ„์„ ๋ฐฉ๋ฒ•์˜ ์‹ค์ œ ์‹คํ—˜์— ๋Œ€ํ•œ ์‘์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ์‹ค์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ํž‰์Šค ์ž…์ž์˜ ๊ด€์ธก ๋ถˆ๊ฐ€๋Šฅ ์ž…์ž๋“ค๋กœ์˜ ๋ถ•๊ดด์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ๊ธฐ๊ณ„ํ•™์Šต์„ ์ด์šฉํ•˜์—ฌ ์žฌํ˜„ํ•œ๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ๊ธ€๋ฃจ์˜จ ์œตํ•ฉ์œผ๋กœ ํž‰์Šค์ž…์ž๊ฐ€ ์ƒ์„ฑ๋˜๋Š” ๊ณผ์ •์—์„œ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ํž‰์Šค์ž…์ž์˜ ๊ด€์ธก ๋ถˆ๊ฐ€๋Šฅ ์ž…์ž๋กœ ๋ถ•๊ดดํ•˜๋Š” ํ™•๋ฅ ์— ๋Œ€ํ•œ ๊ตฌ์† ์กฐ๊ฑด์ด ํฌ๊ฒŒ ๊ฐœ์„ ๋˜๋ฉฐ, ๋‹ค๋ฅธ ์ƒ์„ฑ ๊ณผ์ •๋ณด๋‹ค ๊ฐ•ํ•œ ๊ตฌ์† ์กฐ๊ฑด์„ ์ค„ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค.Abstract i List of Figures v List of Tables x 1 Introduction 1 2 Reviews on the Standard Model and Neural Network 7 2.1 The Standard Model 7 2.2 Neural Network 25 3 Broad resonance in ttห‰t\bar{t} Final State 30 3.1 Introduction 31 3.2 Benchmark Model 33 3.3 Searching for a Broad ttห‰t\bar{t} Resonance 34 3.3.1 Breit-Wigner Parametrisation 34 3.3.2 Preparation of Training Data 35 3.3.3 Training the DNN 41 3.3.4 Setting Bounds for the Signal 46 3.4 Figuring out What the Machine Had Learned 50 3.4.1 Testing High-level Observables 50 3.4.2 Ranking Input Observables by Importance 55 3.4.3 Planing Away Mttห‰M_{t\bar{t}} 60 3.5 Conclusion 63 4 Invisible Higgs Decay 64 4.1 Introduction 64 4.2 Estimation on Leading Jet Kinematics 66 4.2.1 Higgs Produced via Gluon-Fusion 68 4.2.2 Massive Gauge Boson Production 72 4.2.3 Multiple Production 73 4.3 Phenomenology of Invisible Decay of Higgs 76 4.4 Data Preparation and Multi-variate Analysis 78 4.5 Analysis Method 83 4.6 Result and Conclusion 85 5 Conclusion 91 Bibliography 95 A Profile Likelihood Ratio Test 116 B Collider Phenomenology 121 B.1 Parton Density Function 121 B.2 Partonic Cross Section 123 B.3 Hadronic Cross Section 125 C Loop Functions 129 D Jet Tagging Algorithm for Simulated Events 133 ์ดˆ๋ก 137Docto

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    ใ€ˆ์ผยทํ•™์Šต ์—ฐ๊ณ„๋ฅผ ์œ„ํ•œ ํ•€๋ž€๋“œ ํ›„๊ธฐ์ค‘๋“ฑ์ง์—…๊ต์œก ํ˜„ํ™ฉใ€‰ ๋ณธ๊ณ ๋Š” ํ•€๋ž€๋“œ ํ›„๊ธฐ์ค‘๋“ฑ์ง์—…๊ต์œก์˜ ์ผยทํ•™์Šต ์—ฐ๊ณ„ ํ˜•ํƒœ์ธ ํ˜„์žฅํ•™์Šต๊ณผ ๋„์ œํ›ˆ๋ จ์— ๊ด€ํ•˜์—ฌ ์„œ๋กœ ๋‹ค๋ฅธ ์ดํ•ด๋‹น์‚ฌ์ž๋“ค์ธ ํ•€๋ž€๋“œ ์ค‘์•™๋…ธ๋™์กฐํ•ฉ ์—ฐ๋งน๊ณผ ๋ฐ˜๋”ฐ ์ง์—…๊ณ ๋“ฑํ•™๊ต, ๋•€ํŽ˜๋ ˆ ์ง€์—ญ ์ง์—…๊ต์œกํ›ˆ๋ จ ์ปจ์†Œ์‹œ์›€์˜ ์ง์—…๊ณ ๋“ฑํ•™๊ต ๊ต์‚ฌ๋“ค, ๊ทธ๋ฆฌ๊ณ  ํ•™์ƒ๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์˜๊ฒฌ์„ ๋“ค์–ด๋ณด์•˜๋‹ค. ์†Œ๊ฐœ ์ž๋ฃŒ๋Š” ๋ณธ์ธ์˜ ใ€Œํ•€๋ž€๋“œ ์ง์—…๊ต์œกํ›ˆ๋ จ์˜ ์ž๊ฒฉ๊ณผ ์งˆ ๋ณด์ฆ๋ฐฉ์‹ใ€ ์—ฐ๊ตฌ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋‚ด์šฉ์„ ๋ง๋ถ™์ด๊ณ  ์žฌํŽธ์ง‘ํ•œ ๊ฒƒ์ด๋‹ค. โ… . ํ•€๋ž€๋“œ ์ค‘๋“ฑ์ง์—…๊ต์œกํ›ˆ๋ จ ๊ฐœ์š” โ–ก ์ง์—…๊ต์œกํ›ˆ๋ จ โ–ก ํ•™๊ต๊ธฐ๋ฐ˜ ์ง์—…๊ต์œก โ–ก ๋„์ œํ›ˆ๋ จ ํ˜•ํƒœ์˜ ์ง์—…๊ต์œก โ…ก. ์ง์—…๊ณ ์˜ ์ผยทํ•™์Šต ์—ฐ๊ณ„ โ–ก ์ง์—…๊ณ  ๊ต๊ณผ๊ณผ์ • โ–ก ํ˜„์žฅ ํ•™์Šต(On-the-job learning) โ–ก ๋„์ œ ํ›ˆ๋ จ(Apprenticeship) โ–ก ํ˜„์žฅํ•™์Šต์˜ ๊ณ„์•ฝ๊ณผ ํ‰๊ฐ€ โ…ข. ์„œ๋กœ ๋‹ค๋ฅธ ์ดํ•ด ๋‹น์‚ฌ์ž๋“ค์˜ ๊ฒฌํ•ด โ–ก ํ•€๋ž€๋“œ ์ค‘์•™๋…ธ๋™์กฐํ•ฉ์—ฐ๋งน(Suomen Ammattiliittojen Keskusjarjesto, SAK) โ–ก ํ›„๊ธฐ์ค‘๋“ฑ์ง์—…๊ณ ๋“ฑํ•™๊ต ๊ต์‚ฌ โ–ก ์ง์—…๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ โ…ฃ. ์‹œ์‚ฌ์  ใ€ˆ๋…์ผ์˜ ๊ณต๋ฌด์› ์ด์›ํ™” ๊ต์œก : ๊ณต๋ฌด์›๋Œ€ํ•™(Verwaltungsfachhochschulen)์„ ์ค‘์‹ฌ์œผ๋กœใ€‰ ๋…์ผ์˜ ๊ณต๋ฌด์› ์–‘์„ฑ๊ณผ์ •์€ ์ด์›ํ™” ๊ณต๋ฌด์›์ง์—…ํ•™๊ต๋ฅผ ์กธ์—…ํ•˜๊ณ  ์ค‘๊ฐ„์ง ๋ฐ ๋‹จ์ˆœ์ง ๊ณต๋ฌด์›์ด ๋˜๋Š” ๊ฒฝ๋กœ์™€ ์ด์›ํ™”์ง์—…๋Œ€ํ•™์ธ ๊ณต๋ฌด์›๋Œ€ํ•™์„ ํ†ตํ•ด ์ƒ๊ธ‰์ง ๊ณต๋ฌด์›์ด ๋˜๋Š” ๊ฒฝ๋กœ, ์ผ๋ฐ˜์ •๊ทœ๋Œ€ํ•™์„ ์กธ์—…ํ•˜๊ณ  ๊ณ ๊ธ‰์ง ํ›ˆ๋ จ๊ณผ์ •(Referendariat)์„ ๊ฑฐ์ณ ๊ณ ๊ธ‰๊ณต๋ฌด์›์ด ๋˜๋Š” ๊ฒฝ๋กœ๊ฐ€ ์žˆ์Œ. ์ด ๊ธ€์€ ๋…์ผ ๊ณต๋ฌด์›๋Œ€ํ•™์„ ์ค‘์‹ฌ์œผ๋กœ ๊ณต๋ฌด์›์˜ ์ด์›ํ™” ๊ต์œกํ›ˆ๋ จ๊ณผ์ •์„ ์†Œ๊ฐœํ•จ. ๊ณต๋ฌด์›๋Œ€ํ•™ ๊ต์œก๊ธฐ๊ฐ„์€ 3๋…„ ๋˜๋Š” 4๋…„์œผ๋กœ ๊ต์œก๊ณผ์ •์˜ ์ ˆ๋ฐ˜์€ ํ˜„์žฅ, ์ฆ‰ ๊ณต๊ณต๊ธฐ๊ด€์˜ ๊ต์œก๊ณผ ํ›ˆ๋ จ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ๊ณ , ๋‚˜๋จธ์ง€ ์ ˆ๋ฐ˜์€ ๋Œ€ํ•™์˜ ์ด๋ก ๊ต์œก์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋Š” ๊ฒƒ์ด ํŠน์ง•์ž„. ๊ณต๋ฌด์›๋Œ€ํ•™์˜ ํ•™์ƒ ์ˆ˜๋Š” ์ƒ๊ธ‰์ง ๊ณต๋ฌด์›์˜ ์ธ๋ ฅ์ˆ˜์š”๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์ •ํ•ด์ง€๋ฉฐ, ์ด๋Š” ๊ณต๋ฌด์›์ธ๋ ฅ์˜ ์ˆ˜์š”์™€ ๊ณต๊ธ‰์˜ ๊ธ‰๊ฒฉํ•œ ๋ถˆ๊ท ํ˜•์„ ๋ง‰๋Š” ์—ญํ• ์„ ํ•จ. ๊ทธ๋Ÿฌ๋‚˜ ๊ณต๋ฌด์›๋Œ€ํ•™์˜ ์กธ์—…์ž๊ฐ€ ๊ณต๋ฌด์›์œผ๋กœ ์ž„์šฉ๋  ์ˆ˜ ์žˆ๋Š”์ง€ ์—ฌ๋ถ€๋Š” ๋Œ€ํ•™ ๋ฐ ํ›ˆ๋ จ๊ธฐ๊ด€์˜ ์„ฑ์ทจ๋„์™€ ๊ฒฝ๋ ฅ์— ๋”ฐ๋ผ ๊ฒฐ์ •๋˜๊ธฐ ๋•Œ๋ฌธ์— ๊ณต๋ฌด์›๋Œ€ํ•™ ์กธ์—… ์ž์ฒด๊ฐ€ ์ทจ์—…์„ ๋ณด์žฅํ•˜๋Š” ๊ฒƒ์€ ์•„๋‹˜. ์ด ๊ธ€์˜ ์ฃผ์š” ์ฐธ๊ณ ์ž๋ฃŒ๋Š” 2004๋…„ ์˜ˆ๋‚˜ ํ”„๋ฆฌ๋“œ๋ฆฌํžˆ ์‰ด๋Ÿฌ๋Œ€ํ•™ ๋ฐ•์‚ฌํ•™์œ„๋…ผ๋ฌธ โ€œ๊ณ ๋“ฑ๊ต์œก์œผ๋กœ์„œ ์ฃผ์ •๋ถ€์˜ ๊ณต๋ฌด์›๋Œ€ํ•™๊ต์œก (Die Ausbildung in den Verwaltungsfachhochschulen der Laender als Bildungseinrichtung des tertiaeren Bereichs)โ€๊ณผ ์—ฐ๋ฐฉํ†ต๊ณ„์ฒญ์˜ ๋Œ€ํ•™๊ต์œกํ†ต๊ณ„, ๊ทธ๋ฆฌ๊ณ  ๊ฐ์ข… ์ •๋ถ€์˜ ํ†ต๊ณ„์ž๋ฃŒ์ž„. โ… . ๊ณต๋ฌด์›๋Œ€ํ•™ ๊ฐœ๊ด„ โ–ก ๋ฐฐ๊ฒฝ โ–ก ๊ณ ๋“ฑ๊ต์œก๊ธฐ๊ด€๊ณผ ๊ณต๋ฌด์›๋Œ€ํ•™ ๋น„๊ต โ–ก ๊ณต๋ฌด์›๋Œ€ํ•™์˜ ํ˜„ํ™ฉ โ–ก ๋…์ผ์˜ ๊ณต๊ณต๋ถ€๋ฌธ ์ธ๋ ฅํ˜„ํ™ฉ๊ณผ ๊ณต๋ฌด์› ์ž„์šฉ โ–ก ๊ณต๋ฌด์›์˜ ๊ต์œกํ›ˆ๋ จ๊ณผ ๊ฒฝ๋ ฅ์›์น™(Laufbahnprinzip) โ…ก. ๋ฐ”๋ด-๋ทฐํ…œ๋ฒ ๋ฅด๊ทธ์ฃผ ๊ณต๋ฌด์›๋Œ€ํ•™ ๊ต์œก๊ณผ์ • โ–ก ์˜ˆ๊ณผ ๊ต์œก๊ณผ์ •์˜ ๊ต์œก๊ณผ ํ›ˆ๋ จ โ–ก ๋ณธ๊ณผ ๊ต์œก๊ณผ์ •์˜ ๊ต์œก๊ณผ ํ›ˆ๋ จ โ–ก ์กธ์—…์ƒ์˜ ์ž๊ฒฉ๊ณผ ๋…ธ๋™์‹œ์žฅ๊ธฐ

    [๋™ํ–ฅ] ํ•ด์™ธ

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    ใ€ˆ๋ฏธ๊ตญ ์ปค๋ฎค๋‹ˆํ‹ฐ ์นผ๋ฆฌ์ง€์˜ 4๋…„์ œ ํ•™์‚ฌํ•™์œ„ ๊ณผ์ • ์ œ๊ณต ํ˜„ํ™ฉ๊ณผ ์˜์˜ใ€‰ 1990๋…„๋Œ€ ์ดˆ ๋ฏธ๊ตญ์—์„œ๋Š” ๊ต์œก, ๊ณผํ•™๊ธฐ์ˆ , ๋ณด๊ฑด ๋“ฑ์˜ ์‚ฐ์—… ๋ถ„์•ผ๊ฐ€ ํ™•๋Œ€๋จ์— ๋”ฐ๋ผ ํ•ด๋‹น ๋ถ„์•ผ์˜ ํ•™์‚ฌํ•™์œ„ ์†Œ์ง€์ž์— ๋Œ€ํ•œ ์ˆ˜์š”๊ฐ€ ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ์ด์— ๋”ํ•ด ๋ฌผ๋ฆฌ์ ยท์‹œ๊ฐ„์  ์ œํ•œ์„ ๋ฐ›๋Š” ๋น„์ „ํ†ต์  ์„ฑ์ธ ํ•™์Šต์ž์˜ ํ•™์‚ฌํ•™์œ„์— ๋Œ€ํ•œ ์š”๊ตฌ๋„ ํ™•๋Œ€๋˜์—ˆ์Œ. ์ด๋Ÿฌํ•œ ์š”๊ตฌ๋ฅผ ์ถฉ์กฑ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋ฏธ๊ตญ์˜ ๋Œ€ํ‘œ์ ์ธ ๊ณ ๋“ฑ์ง์—…๊ต์œก ๊ธฐ๊ด€์ธ ์ปค๋ฎค๋‹ˆํ‹ฐ ์นผ๋ฆฌ์ง€(Community College)์—์„œ๋Š” 1990๋…„๋Œ€ ์ดˆ๋ถ€ํ„ฐ ๊ธฐ์กด์— ์ œ๊ณตํ•˜๋˜ 2๋…„์ œ ์ค€ํ•™์‚ฌํ•™์œ„ ์™ธ์— 4๋…„์ œ ํ•™์‚ฌํ•™์œ„ ๊ณผ์ •๋„ ์ œ๊ณตํ•˜๊ธฐ ์‹œ์ž‘ํ•˜์˜€์Œ. ์ปค๋ฎค๋‹ˆํ‹ฐ ์นผ๋ฆฌ์ง€ ํ•™์‚ฌํ•™์œ„๋Š” ๊ทธ ์ˆ˜์™€ ์ „๊ณต์˜ ์ข…๋ฅ˜๊ฐ€ ๊พธ์ค€ํžˆ ์ฆ๊ฐ€ํ•˜์—ฌ 2010๋…„์—๋Š” 18๊ฐœ์ฃผ 54๊ฐœ ์ปค๋ฎค๋‹ˆํ‹ฐ ์นผ๋ฆฌ์ง€์—์„œ 465๊ฐœ์˜ ํ•™์‚ฌํ•™์œ„ ๊ณผ์ •์„ ์ œ๊ณตํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ์กฐ์‚ฌ๋จ. ์ปค๋ฎค๋‹ˆํ‹ฐ ์นผ๋ฆฌ์ง€ ํ•™์‚ฌํ•™์œ„๋Š” ์ƒ๋‹นํ•œ ํ•„์š”์„ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์‹œ์ž‘๋œ ์ œ๋„์ด๋‚˜ ๊ธฐ์กด 4๋…„์ œ ๋Œ€ํ•™๊ณผ์˜ ์—ญํ•  ์ถฉ๋Œ๊ณผ ๊ฐ™์€ ์šฐ๋ ค์˜ ๋ชฉ์†Œ๋ฆฌ๋„ ์ œ๊ธฐ๋˜์–ด์˜ด. ๊ทธ๋Ÿฌ๋‚˜ ์ง€๋‚œ 20์—ฌ๋…„๊ฐ„ ์ปค๋ฎค๋‹ˆํ‹ฐ ์นผ๋ฆฌ์ง€ ํ•™์‚ฌํ•™์œ„์˜ ์ˆ˜์™€ ์ข…๋ฅ˜๋Š” ๊พธ์ค€ํžˆ ์ฆ๊ฐ€ํ•˜์˜€์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ์ปค๋ฎค๋‹ˆํ‹ฐ ์นผ๋ฆฌ์ง€๋Š” ์ƒˆ๋กœ์šด ๊ต์œก๊ธฐ๊ด€์œผ๋กœ์„œ์˜ ์ •์ฒด์„ฑ์„ ํ™•๋ฆฝํ•ด ๋‚˜๊ฐ€๋Š” ๊ณผ์ •์— ์žˆ์Œ. ํ˜„์žฌ ์šฐ๋ฆฌ๋‚˜๋ผ ์ „๋ฌธ๋Œ€ํ•™ ์—ญ์‹œ ๋‹ค๊ฐ€์˜ฌ ๋ฏธ๋ž˜ ์‚ฌํšŒ์—์„œ์˜ ํ™•๊ณ ํ•œ ์ •์ฒด์„ฑ ํ™•๋ฆฝ์„ ์œ„ํ•ด ์ˆ˜์—…์—ฐํ•œ ๋ฐ ํ•™์œ„๊ณผ์ • ๋‹ค์–‘ํ™”๋ฅผ ํ†ตํ•œ ํ‰์ƒ์ง์—…๊ณ ๋“ฑ๊ต์œก๊ธฐ๊ด€์œผ๋กœ์˜ ๋ณ€ํ™”๋ฅผ ๊พ€ํ•˜๊ณ  ์žˆ์Œ. ๋ณธ๊ณ ๋Š” 2010๋…„ ํ•œ๊ตญ์ „๋ฌธ๋Œ€ํ•™๊ต์œกํ˜‘์˜ํšŒ ์ฃผ๊ด€์œผ๋กœ ์ˆ˜ํ–‰๋œ ใ€Ž์„ ์ง„ํ•ด์™ธ๊ณ ๋“ฑ๊ต์œก๊ธฐ๊ด€์กฐ์‚ฌ์—ฐ๊ตฌใ€๋ฅผ ์ฐธ๊ณ ํ•˜์—ฌ ๋ฏธ๊ตญ์˜ ์ปค๋ฎค๋‹ˆํ‹ฐ ์นผ๋ฆฌ์ง€ ํ•™์‚ฌํ•™์œ„ ์ œ๊ณต ์‚ฌ๋ก€๊ฐ€ ์šฐ๋ฆฌ๋‚˜๋ผ ์ „๋ฌธ๋Œ€ํ•™์— ์–ด๋– ํ•œ ์‹œ์‚ฌ์ ์„ ์ฃผ๊ณ  ์žˆ๋Š”์ง€ ๋ถ„์„ํ•จ. ใ€ˆ๋…์ผ์˜ ์‚ฐํ•™์—ฐ๊ณ„ํ˜• ๊ณ ๋“ฑ๊ต์œกใ€‰ ์ด ๊ธ€์€ ์‚ฐ์—… ํ˜„์žฅ๊ณผ์˜ ์—ฐ๊ณ„์„ฑ์„ ์ค‘์‹œํ•˜๋Š” ๋…์ผ์˜ ๊ณ ๋“ฑ๊ต์œก์„ ๋…์ผ์‚ฐ์—…๋Œ€ํ•™(Fachhochschule)๊ณผ ์ง์—…์•„์นด๋ฐ๋ฏธ(Berufsakademie)์ œ๋„๋ฅผ ํ†ตํ•ด ์†Œ๊ฐœํ•จ. ์‚ฐ์—…๋Œ€ํ•™์ด ๋Œ€ํ•™์ž…ํ•™์‹œํ—˜(Abitur)์„ ์น˜๋ฅด์ง€ ์•Š์€ ๋…ธ๋™์ž์—๊ฒŒ๋„ ์ง์—…ํ›ˆ๋ จ ์ž๊ฒฉ๊ณผ ์‚ฐ์—… ํ˜„์žฅ ๊ฒฝํ—˜์„ ๊ธฐ์ค€์œผ๋กœ ๋Œ€ํ•™๊ต์œก์˜ ๊ธธ์„ ์—ด์–ด์ฃผ๊ณ  ์žˆ๋‹ค๋ฉด ์ง์—…์•„์นด๋ฐ๋ฏธ๋Š” ๋Œ€ํ•™์ž…ํ•™์ž๊ฒฉ์‹œํ—˜์— ํ•ฉ๊ฒฉํ•œ ํ•™์ƒ๋“ค์—๊ฒŒ ์‚ฐ์—… ํ˜„์žฅ ์—ฐ๊ณ„ํ˜• ๊ณ ๊ธ‰๊ณ ๋“ฑ๊ต์œก์„ ์ œ๊ณตํ•˜์—ฌ ์ด๋ก ๊ณผ ์‹ค์ œ์˜ ์‹œ๋„ˆ์ง€ํšจ๊ณผ๋ฅผ ๊ฐ•ํ™”ํ•˜๋Š” ๊ต์œก ๋ชจ๋ธ์ž„. ์ด ๊ธ€์˜ ์ฃผ์š” ์ฐธ๊ณ ์ž๋ฃŒ๋Š” 2002๋…„ ์—ฐ๋ฐฉ์ •๋ถ€ ๊ต์œก๋ฌธํ™”๋ถ€์—์„œ ๋ฐœ๊ฐ„๋œ ์ž๋ฃŒ์ง‘ โ€œFachhochschulen in Germany(๋…์ผ ์‚ฐ์—…๋Œ€ํ•™)โ€๊ณผ Asia-Pacific Journal of Cooperative Education์— ์ˆ˜๋ก๋œ ๋ผ์ธํ•˜๋ฅด๋“œ(Reinhard)์˜ 2006๋…„ ๋…ผ๋ฌธ โ€œThe German Berufsakademie Work-Integrated Learning Program: A Potential Higher Education Model for West and East(๋…ธ๋™์—ฐ๊ณ„ํ˜• ๊ต์œก ์ œ๋„์ธ ๋…์ผ ์ง์—…์•„์นด๋ฐ๋ฏธ: ๋™์–‘๊ณผ ์„œ์–‘์—์„œ ์ž ์žฌ๋ ฅ์„ ๋ฐœํœ˜ํ•˜๋Š” ๊ณ ๋“ฑ๊ต์œก ๋ชจ๋ธ)โ€์ž„. ๊ทธ ์™ธ ๋…์ผ ํ†ต๊ณ„์ฒญ์˜ ๊ต์œกํ†ต๊ณ„, ์—ฐ๋ฐฉ์ •๋ถ€ ๊ต์œก์—ฐ๊ตฌ๋ถ€ ์›น์‚ฌ์ดํŠธ์—์„œ ์ œ๊ณตํ•˜๋Š” ๋…์ผ ๊ณ ๋“ฑ๊ต์œก์— ๋Œ€ํ•œ ์ž๋ฃŒ๊ฐ€ ์ด ๊ธ€์˜ ์ฐธ๊ณ ์ž๋ฃŒ์ž„. ใ€ˆํ•€๋ž€๋“œ ์ „๋ฌธ๋Œ€ํ•™(Ammattikorkeakoulu) ํ˜„ํ™ฉ ๊ณ ๋“ฑ๊ต์œก์˜ ๋Œ€์ค‘ํ™”๋กœ ์ธํ•ด ์ง์—…๊ต์œกํ›ˆ๋ จ์€ ๊ฐ ๋‚˜๋ผ์˜ ์—ญ์‚ฌ์™€ ๋ฌธํ™”, ์‚ฌํšŒ๊ฒฝ์ œ, ์ธ๊ตฌ, ์ง€๋ฆฌ์ •์น˜์ ์ธ ์š”์ธ์— ๋”ฐ๋ผ โ€˜Fachhochschulenโ€™, โ€˜Ammattikorkeakouluโ€™, โ€˜University Collegeโ€™๋ผ๋Š” ๊ฐ๊ธฐ ๋‹ค๋ฅธ ์ด๋ฆ„ํ•˜์— ๊ณ ๋“ฑ๊ต์œก์ˆ˜์ค€๊นŒ์ง€ ์ƒํ–ฅ๋˜์—ˆ๋‹ค. ์ด๋Š” ๋ˆ„๊ฐ€, ๊ทธ๋ฆฌ๊ณ  ์–ด๋–ป๊ฒŒ ํ‰์ƒ๊ต์œก ์„ ์ƒ์—์„œ ๊ณ ๋“ฑ์ง์—…๊ต์œก์˜ ๊ฒฝ๊ณ„๋ฅผ ๋‚˜๋ˆ„๊ณ  ์ •๋‹นํ™”ํ•˜๋ฉฐ ํ‹€์„ ๋‹ค์‹œ ์งœ๋Š”์ง€์— ๋Œ€ํ•œ ๋ฌธ์ œ์™€ โ€œํ•™๋ฌธ์  ํ‘œ๋ฅ˜(academic drift)โ€๋ฅผ ๊ฒช๋Š” ์ง์—…๊ณ ๋“ฑ๊ต์œก์˜ ๊ต์ˆ˜์™€ ํ•™์Šต์˜ ์งˆ์„ ์–ด๋–ป๊ฒŒ ํ–ฅ์ƒ์‹œํ‚ค๋Š”์ง€์— ๊ด€ํ•œ ์ค‘์š”ํ•œ ๋…ผ์˜๊ฐ€ ๋œ๋‹ค. ํ•€๋ž€๋“œ ์ •๋ถ€๋Š” โ€˜์ง์—…๊ณ ๋“ฑ๊ต์œก์˜ ํšจ์œจ์„ฑ๊ณผ ๊ณ ์šฉ๋ฅ , ์งˆ, ๊ตญ์ œ ๊ฒฝ์Ÿ๋ ฅโ€™์„ ๊ฐ•ํ™”ํ•˜๊ธฐ ์œ„ํ•ด 2011~2014๋…„์— ๊ฑธ์ณ ์ง€๋ฐฐ๊ตฌ์กฐ๋ฅผ ์ค‘์•™์ง‘๊ถŒํ™”ํ•˜๊ณ , ๋ชจ๋“  ์ „๋ฌธ๋Œ€ํ•™์„ ์œ ํ•œ๋ฒ•์ธํ™”ํ•˜๋Š” ๊ตฌ์กฐ๊ฐœํ˜์„ ๋‹จํ–‰ํ•˜๊ณ  ์žˆ๋‹ค. ์ „๋ฌธ๋Œ€ํ•™์˜ ์„ฑ๊ณผ์ค‘์‹ฌ ํฌ๋ฎฌ๋Ÿฌ ์žฌ์ •์€ ๋ฏธ๋ž˜ ์ง์—…๊ณ ๋“ฑ๊ต์œก์˜ ์ฑ…๋ฌด์„ฑ๊ณผ ์„œ๋กœ๊ฐ€ ์ง๋ฉดํ•œ ๋”œ๋ ˆ๋งˆ์— ์„œ๋กœ ๋‹ค๋ฅธ ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•ด ์ค„ ์ˆ˜ ์žˆ๋‹ค

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    ์ถ”๋…€์™€ ์„œ๊นŒ๋ž˜๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์ถ•ํ•™๊ณผ, 2012. 2. ์ „๋ด‰ํฌ.์ตœ๊ทผ ํ•œ์˜ฅ์— ๋Œ€ํ•œ ๊ด€์‹ฌ๊ณผ ์ˆ˜์š”๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋‚˜ ์—ฌ์ „ํžˆ ๋น„์‹ผ ๊ฑด์ถ•๋น„๋กœ ์ธํ•˜์—ฌ ํ™œ์„ฑํ™”๊ฐ€ ์ €ํ•ด ๋˜๊ณ  ์žˆ๋‹ค. ์ด์— 3์ฐจ์› ๊ฐ์ฒด์ง€ํ–ฅ ๋ชจ๋ธ๋ง์ธ BIM๊ธฐ์ˆ ์„ ๋„์ž…ํ•˜์—ฌ ํ•œ์˜ฅ ์ƒ์‚ฐ์˜ ํ•ฉ๋ฆฌํ™”๊ฐ€ ํ•„์š”ํ•œ ์‹œ์ ์œผ๋กœ ํ•œ์˜ฅ ๋ชธ์ฒด ๊ตฌ์กฐ, ์ž…๋ฉด์— ๊ด€๋ จ๋œ ํŒŒ๋ผ๋ฉ”ํŠธ๋ฆญ ๋ชจ๋ธ๋ง ์—ฐ๊ตฌ๋Š” ์ง„ํ–‰๋˜๊ณ  ์žˆ์œผ๋‚˜ ์•„์ง ์ง€๋ถ•์— ๋Œ€ํ•œ ํŒŒ๋ผ๋ฉ”ํŠธ๋ฆญ ๋ชจ๋ธ๋ง ๋ฐฉ๋ฒ•๋ก ์€ ์—ฐ๊ตฌ๊ฐ€ ๋ถ€์กฑํ•œ ์‹ค์ •์ด๋‹ค. ์ด๋Š” ํ•œ์˜ฅ์˜ ์ง€๋ถ•์ด 3์ฐจ์›์˜ ๊ณก์„ ์˜ ํ˜•ํƒœ๋กœ ๋น„์ •ํ˜• ๋ถ€์žฌ๋“ค๋กœ ์ด๋ฃจ์–ด์ ธ 2์ฐจ์›์  ๋ฐฉ๋ฒ•์˜ ์ ‘๊ทผ์ด ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ์˜ฅ์˜ ์ง€๋ถ• ์ค‘ ๊ฐ€์žฅ ๊ธฐ๋ณธ์ ์ธ ๋ผˆ๋Œ€๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ์ถ”๋…€์™€ ์„œ๊นŒ๋ž˜๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์ง€๋ถ•์— ๋Œ€ํ•œ ํŒŒ๋ผ๋ฉ”ํŠธ๋ฆญ ๋ชจ๋ธ๋ง ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ํ•œ์˜ฅ์˜ ์ง€๋ถ•์€ ํฌ๊ฒŒ ๋„๋ฆฌ ์œ„์— ์˜ฌ๋ผ๊ฐ€๋Š” ์ถ”๋…€์™€ ์„œ๊นŒ๋ž˜๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ์œผ๋ฉฐ ์‚ฌ์šฉ์œ„์น˜์™€ ํ˜•ํƒœ์— ๋”ฐ๋ผ ์ถ”๋…€์™€ ํšŒ์ฒจ์ถ”๋…€, ์„ ์ž์—ฐ, ์žฅ์—ฐ, ๋‹จ์—ฐ์œผ๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋“ค ๋ถ€์žฌ๋Š” ๋„๋ฆฌ ๊ฐ„ ์ˆ˜ํ‰๊ฐ„๊ฒฉ๊ณผ ์ˆ˜์ง๊ฐ„๊ฒฉ, ์ฒ˜๋งˆ๋‚ด๋ฐ€๊ธฐ, ์•™๊ณก, ์•ˆํ—ˆ๋ฆฌ๊ณก ๋“ฑ์— ์˜ํ–ฅ์„ ๋ฐ›์œผ๋ฉฐ ๊ทธ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ๋ฐ€์ ‘ํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ์˜ฅ์˜ ์ง€๋ถ•์€ ์žฅ์—ฐ์ด ์œ„์น˜ํ•˜๋Š” ์ฒ˜๋งˆ๋ถ€์™€ ๋‹จ์—ฐ์ด ์œ„์น˜ํ•˜๋Š” ๋‹จ์—ฐ๋ถ€๋กœ ๊ตฌ๋ถ„ํ•˜๊ณ , ์ฒ˜๋งˆ๋ถ€๋Š” ์ž…๋ฉด๋ณ„๋กœ ์‚ฌ์šฉ๋œ ๋ถ€์žฌ๋“ค์˜ ์กฐํ•ฉ๊ณผ ํ˜•ํƒœ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ 9๊ฐ€์ง€ ์กฐํ•ฉ๋‹จ์œ„๋กœ, ๋‹จ์—ฐ ๋ถ€๋ถ„์€ ํ˜•ํƒœ๋ณ„๋กœ 6๊ฐ€์ง€ ์กฐํ•ฉ๋‹จ์œ„๋กœ ๊ตฌ์„ฑํ•œ๋‹ค. ์ด๋ฅผ ๋‹ค์‹œ ์ „ํ†ต ํ•œ์˜ฅ ์‚ฌ๋ก€๋ฅผ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์—ฌ 29๊ฐ€์ง€ ๋‹จ์œ„๋กœ ํ™•์žฅํ•œ๋‹ค. ์„ ํ–‰์—ฐ๊ตฌ์™€ ์‹ค์ธก์กฐ์‚ฌ๋ณด๊ณ ์„œ๋ฅผ ํ†ตํ•ด ๊ฐ ์š”์†Œ๋“ค ๊ฐ„์˜ ์ตœ์ ํ™”๋œ ํ•จ์ˆ˜์‹๊ณผ ๋กœ์ง์„ ์ถ”์ถœํ•˜์—ฌ ๋ถ€์žฌ ํŒŒ๋ผ๋ฉ”ํŠธ๋ฆญ ๋ชจ๋ธ๋กœ ๊ตฌ์ถ•ํ•˜๊ณ  ์ด๋“ค์„ ๋‹ค์‹œ ์กฐํ•ฉํ•˜์—ฌ 9๊ฐ€์ง€ ์ฒ˜๋งˆ๋ถ€ ์กฐํ•ฉ๋‹จ์œ„์™€ 6๊ฐ€์ง€ ๋‹จ์—ฐ๋ถ€ ์กฐํ•ฉ๋‹จ์œ„๋ฅผ ์™„์„ฑํ•œ๋‹ค. ์ด ์กฐํ•ฉ๋‹จ์œ„๋ฅผ ์ „ํ†ต ํ•œ์˜ฅ ์‚ฌ๋ก€์— ์ ์šฉํ•˜์—ฌ ์กฐํ•ฉ๋‹จ์œ„ ๋ชจ๋ธ๋ง ๋ฐฉ๋ฒ•๋ก ์˜ ์œ ์šฉ์„ฑ๊ณผ ํšจ์œจ์„ฑ์„ ๊ฒ€์ฆํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์กฐํ•ฉ๋‹จ์œ„ ํŒŒ๋ผ๋ฉ”ํŠธ๋ฆญ ๋ชจ๋ธ๋ง ๋ฐฉ๋ฒ•๋ก ์€ ๊ธฐ์กด์˜ ์„ค๊ณ„๋ฐฉ์‹๋ณด๋‹ค ์‰ฝ๊ณ  ์ •๋ฐ€ํ•˜๊ณ  ๋น ๋ฅธ ์„ค๊ณ„๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์—ฌ ์„ค๊ณ„์™€ ์‹œ๊ณต์˜ ๊ฒฉ์ฐจ๋ฅผ ์ค„์ด๊ณ  ์ผ๋ฐ˜ ๊ฑด์ถ• ์„ค๊ณ„์‚ฌ๋ฌด์†Œ์˜ ํ•œ์˜ฅ ์‹œ์žฅ ์ง„์ถœ์— ๋„์›€์„ ์ค„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.This study is assembly unit parametric modeling methodology of eaves and rafter which are composing 3-dimensional curve of Hanok roof Hanok roof is basically composed of angle rafter, Seonjayeon, common rafter, short rafter and corner angle rafter. Hanok roof is classified eaves part and short rafter part which covers the roof with 5-purlins structure. Eaves part and short rafter part are constructed first of all in the roof and very important because they are basis of roof framework. They are affected by body elements (horizontal gap of purlins, vertical gap of purlins) and roof elements (overhang distance of eaves, Anggok, Anheorigok) For efficient application of parametric modeling, elements modeling is not used but assembly unit modeling is used. So eaves part is defined 9 assembly units and short rafter part is defined 6 assembly units. Totally 29 detail assembly units are deduced by case analysis of traditional Hanok. Optimized parametric functional formula and logic of relation of elements in eaves and short rafter part are deduced by prior studies and reports. Those formula and logic build elements parametric models. Those elements parametric models are used making of 9 eaves assembly units and 6 short rafter assembly units. They embody 29 assembly units and are verified by modeling some cases of Hanok roof. This assembly unit modeling methodology is faster, more efficient, precise and easier than common 2-dimensional design methods and can narrow the gap between design and construction. Because people who have basic knowledge of Hanok can design Hanok, common architecture firm can enter the market of Hanok. Because this study deals with only core elements of Hanok roof, wide studies that deal with parametric modeling of completed whole Hanok with decorative and other elements and connection of construction and automated production system are needed.Maste
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