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    ๋ถˆํ™•์‹ค์„ฑ์„ ๊ณ ๋ คํ•œ ๊ณ ๊ณ ๋„ ๊ณผํ•™ ๊ธฐ๊ตฌ์˜ ๊ถค์  ์˜ˆ์ธก ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2017. 2. ์ด๊ด€์ค‘.Before launching a scientific balloon, one needs to precisely predict its trajectory to avoid any possible accidents, as there are no means to control the horizontal motion of the balloon once it is launched. Although earlier studies developed simulation programs that predicted the trajectory of a scientific balloon with reasonable accuracy, the simulated results were hardly the same as the actual balloon flight because there existed uncertainties in balloon trajectory prediction. To address the difference between the predicted and actual flight trajectories, the prediction should be probabilistic, which means the uncertainties involved in the calculation should be taken into account. In the present work, a numerical simulation program is developed to predict the trajectories of a balloon, while considering various uncertainties, with the use of a Monte Carlo simulation. Sensitivity studies are performed to identify the most dominant uncertainty parameter in the distribution of landing points of the balloon flight. Operational uncertainty represented by the amount of buoyant gas is shown to be the most significant source of the prediction error, although it can be overcome by controlling the amount of lifting gas during the actual flight.I. Introduction 1 II. Simulation program development and its validation 6 A. Design of the Balloon 7 B. Equations of Motion 9 C. Thermal Model 11 D. Code Validation 14 E. Balloon Specifications and Flight Simulation Information 17 III. Uncertainty Analysis Based on Monte Carlo Simulation 24 A. Operational Uncertainty: Uncertainty in Helium Injection 27 B. Uncertainty in the Prediction Model: Uncertainty in the Drag Coefficient 28 C. Environmental Uncertainty: Uncertainty in the Wind Profile Data 30 D. Manufactural Uncertainty: Uncertainty in the Volume of the Balloon 33 E. Results and Discussions 34 IV. Conclusions 46 Appendix A: Specific Procedures to Design a Natural Shape Balloon 48 Appendix B: Equations to Calculate the Heat Fluxes in the Thermal Model 52 References 55Maste

    ๊ณ ์ฐจ ๋„คํŠธ์›Œํฌ์—์„œ์˜ ์ƒ์ „์ด

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€(๋ฌผ๋ฆฌํ•™์ „๊ณต), 2022. 8. ๋ฐฑ์šฉ์ฃผ.๋ณต์žก๊ณ„๋Š” ๋น„๊ท ์งˆ์ ์ธ ๊ตฌ์„ฑ์›๋“ค์ด ๋‹ค์–‘ํ•œ ์ƒํ˜ธ์ž‘์šฉ์„ ์ฃผ๊ณ ๋ฐ›๋Š” ์‹œ์Šคํ…œ์ด๋‹ค. ๋„ค ํŠธ์›Œํฌ๋Š” ์ด๋Ÿฌํ•œ ์‹œ์Šคํ…œ์˜ ๊ฐ ์š”์†Œ๋ฅผ ์ , ๊ทธ ์‚ฌ์ด์˜ ์ƒํ˜ธ์ž‘์šฉ์„ ์„ ์œผ๋กœ ํ‘œํ˜„ํ•จ์œผ ๋กœ์จ ๋ณต์žก๊ณ„ ๊ตฌ์กฐ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋ณดํŽธ์ ์ธ ํŠน์„ฑ๊ณผ ๊ทธ ๋™์—ญํ•™์  ํšจ๊ณผ๋ฅผ ๊ธฐ์ˆ ํ•˜๋Š”๋ฐ ๋„๋ฆฌ ์“ฐ์—ฌ์™”๋‹ค. ํ•˜์ง€๋งŒ ๋„คํŠธ์›Œํฌ๋Š” ์ •์˜๋กœ๋ถ€ํ„ฐ ๊ธฐ์ธํ•˜๋Š” ๋‚ด์žฌ์ ์ธ ์ œ์•ฝ์„ ์ง€๋‹ˆ๊ณ  ์žˆ๋‹ค. ์—ฐ๊ฒฐ์„ ์€ ์˜ค์ง ๋‘ ์š”์†Œ์˜ ๊ด€๊ณ„๋งŒ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๊ธฐ์— ์Œ์œผ๋กœ ์ƒํ˜ธ์ž‘์šฉ(pairwise interaction)ํ•˜์ง€ ์•Š๋Š” ์š”์†Œ๋“ค์„ ํ‘œํ˜„ํ•˜๋Š” ๊ฒƒ์— ์–ด๋ ค์›€์„ ๊ฐ–๊ณ ์žˆ๋‹ค. ๊ณ ์ฐจ ๋„คํŠธ์›Œํฌ๋Š” ์ •์ ๊ณผ ๊ณ ์ฐจ์—ฐ๊ฒฐ์„ (higher-order edge)์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ๋Š” ๋„คํŠธ์›Œํฌ์˜ ์ผ๋ฐ˜ํ™”์ธ๋ฐ, ์ด๊ฒƒ์€ ์…‹ ์ด์ƒ์˜ ๊ณ ์ฐจ ์—ฐ๊ฒฐ์„ ๊ณ ๋ คํ•˜๊ธฐ์— ์ด ์ œ์•ฝ์—์„œ ์ž์œ ๋กญ๋‹ค. ์ฒซ์งธ๋กœ ์šฐ๋ฆฌ๋Š” ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ ์„ฑ์žฅํ•˜๋Š” ๋ณต์žก๊ณ„์˜ ๋ฐ์ดํ„ฐ ๋ฐ ๋ชจํ˜•์„ ๊ณ ์ฐจ ๋„ค ํŠธ์›Œํฌ์˜ ๊ด€์ ์—์„œ ๋ถ„์„ํ•˜์—ฌ ๋ณต์žก๊ณ„์˜ ๋‹จ๊ณ„์  ๊ตฌ์กฐ ๋ณ€ํ™”๋ฅผ ๋‹จ์ฒด ๋ณตํ•ฉ์ฒด(simplicial complex) ๊ด€์ ์œผ๋กœ ๊ธฐ์ˆ ํ•˜์˜€๋‹ค. ํƒœ๋™์˜ ๋‹จ๊ณ„๋ฅผ ๋น„๋กฏํ•˜์—ฌ ์—ฐ๊ฒฐ์„ฑ(connectivity)์ด ํ™• ๋ฆฝ๋˜๋Š” ๋‹จ๊ณ„์™€ ๊ฐ•๊ฑด์„ฑ(robustness)์ด ํ™•๋ฆฝ๋˜๋Š” ๋‹จ๊ณ„๋ฅผ ์œ„์ƒ์ ์ธ ์–‘์ธ ๋ฒ ํ‹ฐ ์ˆ˜(Betti number)๋กœ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค. ์—ฐ๊ฒฐ์„ฑ์ด ํ™•๋ฆฝ๋˜๋Š” ๋‹จ๊ณ„์˜ ํŠน์ง•์ธ ๊ฑฐ์‹œ์ ์ธ ๊ณ ๋ฆฌ ํ˜•์„ฑ์„ ์ฒซ ์งธ ๋ฒ ํ‹ฐ ์ˆ˜๋กœ, ๊ฐ•๊ฑด์„ฑ์ด ํ™•๋ฆฝ๋˜๋Š” ๋‹จ๊ณ„์—์„œ ๊ณ„์˜ ๋ฐ€๋„๊ฐ€ ๋†’์•„์ง์— ๋”ฐ๋ผ ๋‚˜ํƒ€๋‚˜๋Š” ๊ตญ์†Œ์ ์ธ ํ๊ณก๋ฉด(void) ํ˜•์„ฑ์„ ๋‘˜์งธ ๋ฒ ํ‹ฐ ์ˆ˜๋กœ ์ •๋Ÿ‰ํ™” ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์˜€๋‹ค. ์‹œ๊ฐ„์— ๋Œ€ํ•ด ์„ฑ์žฅํ•˜๋Š” ๊ณ ์ฐจ ๋„คํŠธ์›Œํฌ์—์„œ ๋ฒ ํ‹ฐ ์ˆ˜๋“ค์ด ์ˆœ์ฐจ์ ์œผ๋กœ ์ƒ๊ธฐ๋ฉฐ ์ฆ๊ฐ€ ํ•œ๋‹ค๋Š” ๊ฒƒ์ด ์ผ๋ฐ˜์ ์ด๋ผ๋Š” ๊ฒƒ์„ ์—ฌ๋Ÿฌ ๊ณ ์ฐจ ๋„คํŠธ์›Œํฌ ๋ชจ๋ธ ๊ณต๋ถ€๋ฅผ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ํŠนํžˆ ์„ฑ์žฅํ•˜๋Š” ์ฒ™๋„์—†๋Š” ๊ณ ์ฐจ ๋„คํŠธ์›Œํฌ์—์„œ ์ •์˜๋˜๋Š” ๋ฒ ํ‹ฐ ์ˆ˜๋“ค ๋˜ํ•œ ๊ฐ๊ฐ ์ƒ์ „์ด๋ฅผ ๋ณด์ธ๋‹ค๋Š” ๊ฒƒ์„ ์ˆ˜์น˜์ ์œผ๋กœ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ฒซ์งธ ๋ฒ ํ‹ฐ์ˆ˜๋Š” ๋„คํŠธ์›Œํฌ์™€ ๋‹จ์ฒด ๋ณตํ•ฉ ์ฒด ๋ชจ๋‘์—์„œ ์—ฌ๊ณผ ์ƒ์ „์ด์™€ ์ •ํ™•ํžˆ ๊ฐ™์€ ์ƒ์ „์ด ์–‘์ƒ์„ ๋ณด์ธ๋‹ค๋Š” ๊ฒƒ์„ ํ•ด์„์ ์œผ๋กœ๋„ ๋ณด์˜€๋‹ค. ๋‘˜์งธ๋กœ ๋ณต์žก๊ณ„์˜ ๊ณ ์ฐจ ์ƒํ˜ธ์ž‘์šฉ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ํ—ˆ๋ธŒ ๊ตฌ์กฐ๊ฐ€ ์ƒ์ „์ด์™€ ์ž„๊ณ„ํ˜„์ƒ ์— ์–ด๋–ค ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ๋™๊ธฐํ™” ๋ชจํ˜•์˜ ํ•ด์„์ , ์ˆ˜์น˜์  ๋ถ„์„์„ ํ†ตํ•ด ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ์ด์ค‘ ์•ˆ์ •์„ฑ, ๋‹ค์ค‘ ์•ˆ์ •์„ฑ์„ ๋ณด์ด๋Š” ๋‘ ๊ฐ€์ง€์˜ ์ „์—ญ ๊ฒฐํ•ฉ(globally coupled) ๋ชจํ˜•์„ ๋‹ค๋ฃจ์—ˆ๋Š”๋ฐ, ๊ณตํ†ต์ ์œผ๋กœ ๊ณ ์ฐจ ์ƒํ˜ธ์ž‘์šฉ์ด ๋ถˆ์—ฐ์† ์ƒ์ „์ด์™€ ์ž„๊ณ„ ํ˜„์ƒ์„ ๋™์‹œ์— ํ•จ ์œ ํ•˜๋Š” ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์ƒ์ „์ด๋ฅผ ์œ ๋ฐœํ•œ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋น„๊ท ์งˆ์  ๊ตฌ์กฐ๋ฅผ ์ง€๋‹Œ ์ฒ™๋„์—†๋Š” ๊ณ ์ฐจ ๋„คํŠธ์›Œํฌ์—์„œ ์ •์˜๋˜๋Š” ์ด์ค‘ ์•ˆ์ • ๋ชจํ˜•์— ์„œ์—ฐ๊ฒฐ์„ ์ˆ˜๋ถ„ํฌ์ง€์ˆ˜์˜ํŠน์ •๊ฐ’(ฮปc = 2+1/(dโˆ’1))์„๊ธฐ์ค€์œผ๋กœ๋™๊ธฐํ™”ํ•ด์˜ ์–‘์ƒ(์ž„๊ณ„ํ˜„์ƒ) ์ด ๊ธ‰๊ฒฉํ•˜๊ฒŒ ๋ณ€ํ•œ๋‹ค๋Š” ์‚ฌ์‹ค์„ ์ˆ˜์น˜์  ๋ฐ ํ•ด์„์ ์œผ๋กœ ๋„์ถœํ–ˆ๋‹ค. ๋„ ์ˆ˜๋ถ„ํฌ์ง€์ˆ˜๊ฐ€์ž„๊ณ„์ง€์ˆ˜๋ณด๋‹ค์ž‘์€๊ฒฝ์šฐ์ธฮป ฮปc ์ผ ๋•Œ ํญ๋ฐœ์ ์ธ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ๋™๊ธฐํ™” ์ƒ์ „์ด(hybrid synchronization transition)๊ฐ€ ๋ฐœ๊ฒฌ๋œ๋‹ค.A complex system is a system in which many elements interact one another in heterogeneous forms. Networks have been widely used to describe the universal charateristics of the structural properties and dynamical behavior of the system by representing each element as a vertex and their interactions as edges. However, network representation has an inherent limitation that stems from the definition. Since an edge can only express the relationship between two elements, it is difficult to express elements that do not show pairwise interaction. A higher-order network is a generalization of a net- work consisting of vertices and higher-order connections, which is not restricted from this limitation as it considers interactions of three or more elements. First, in this dissertation, we analyzed the empirical data and model of a growing complex system from the perspective of a higher-order network and described the evolutionary stages of the complex system in simplicial complex representation. The stages of establishing connectivity and robustness, including the stage of the birth, were separated by the topological quantity, the Betti numbers. It was shown that loop formations in the macroscopic length scale, which is a typical characteristic of the stage where connectivity is established, can be quantified by the first Betti number. Furthermore, the formation of locally closed surface which begins to appear as the density of the system increases, which can be interpreted as robustness enhancement, can be quantified by the second Betti number. It has been confirmed that the Betti numbers emerge and increase successively in growing higher-order interacting systems by studying several simplicial complexes models. In particular, it was numerically confirmed that the Betti numbers defined in the growing scale-free simplicial complexes also exhibit phase transitions, homological percolation transitions. Furthermore, we analytically show that the first Betti number exhibits the same transitional behavior as the percolation phase transition in both the graph and the simplicial complex. Second, we investigated the effect of the hub structure in higher-order interactions of complex systems on phase transitions and critical phenomena through analytical and numerical analysis of the higher-order synchronization models. Two models of globally coupled oscillators showing bistability and multistability were dealt with. It was confirmed that higher-order interactions promote a discontinuous synchronization transition with critical phenomena, a hybrid synchronization transition. It was derived numerically and analytically that the behavior of the synchronization transition changes abruptly based on a specific value of the exponent of degree distri- bution (ฮปc = 2 + 1/(d โˆ’ 1)) in the bistable model defined in a scale-free higher-order network with a heterogeneous structure. When the exponent of degree distribution is smaller than the critical value (ฮป ฮปc, an explosive hybrid synchronization transition emerges.1 Introduction 1 1.1 Percolation 3 1.1.1 Overview on percolation theory 3 1.1.2 Topological viewpoint in percolation transition 4 1.1.3 Simplicial complex and simplicial homology 5 1.2 Synchronization 6 1.2.1 Kuramoto model 6 1.2.2 Synchronization transition 7 1.2.3 Hybrid synchronization transition 7 2 Homological percolations in coauthorship relations 9 2.1 Homological percolation transitions 10 2.2 Facet degree distribution 15 2.3 Minimal model 16 2.4 Kahle localization 19 2.5 Remark 20 3 Percolation of simplicial complex models 25 3.1 Percolation of static simplicial complex 25 3.1.1 Model description 25 3.1.2 Percolation threshold 28 3.1.3 Cluster size distribution 29 3.2 Percolation of growing simplicial complex 32 3.2.1 Model description 33 3.2.2 Percolation threshold 34 3.2.3 Percolation phase transition 36 3.2.4 Degree distributions 41 3.2.5 Cluster size distribution 44 3.3 Homological percolation of simplicial complex models 46 3.3.1 The first Betti number of d-GSC 46 3.3.2 The first Betti number of d-SSC 52 3.4 Remark 54 4 Higher-order interacting oscillators 55 4.1 Globally coupled higher-order interaction 55 4.1.1 Model of bistable synchronization 55 4.1.2 Model of multistable synchronization 63 4.2 Heterogeneous higher-order interaction 70 4.2.1 Heterogeneous mean-field theory 72 4.2.2 Critical behavior 77 4.2.3 Correlation size 79 4.3 Numerical simulation 81 4.4 Remark 83 5 Conclusion 85 Appendices 87 Appendix A Computation of the Betti numbers 88 Appendix B Simplifying homology via strong collapse 90 Appendix C Lagrange inversion formula 91 Bibliography 92 Abstract in Korean 98๋ฐ•

    ๋Œ€์žฅ๊ท  dnaA ํ”„๋กœ๋ชจํ„ฐ์™€ DnaA, IciA ๋‹จ๋ฐฑ์งˆ์˜ ์ƒํ˜ธ์ž‘์šฉ

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    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ฏธ์ƒ๋ฌผํ•™๊ณผ,1998.Docto

    La Realidad Histรณrica de Amรฉrica Latina y su Configuraciรณn Literaria: En torno a la Narrativa Contemporรกnea

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    En la historia del pensamiento de Amรฉrica Latina, la bรบsqueda de la propia identidad cultural ocupa un lugar importante. Podemos darnos cuenta de que los intelectuales se preocupan por reflexionar sobre el modo de ser propio de los pueblos latinoamericanos. La realidad histรณrica latinoamericana se ha caracterizado desde siempre por el enfrentamiento dualista en parejas de valores, conceptos y tendencias polรญticas, filosรณficas y estรฉticas de carรกcter antinรณmico, cuando no abiertamente contradictorias. La dicotomรญa de la identidad cultural nos permite entender c1aramnete el problema tan polรฉmico en la bรบsqueda de la identidad cultural

    IciA ่›‹็™ฝ่ณช์— ์˜ํ•œ dnaA ้บๅ‚ณๅญ์˜ ่ฝ‰ๅฏซ่ชฟ็ฏ€ๆฉŸไฝœ์— ๊ด€ํ•œ ็ก็ฉถ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธๅคงๅญธๆ ก ๅคงๅญธ้™ข :ๅพฎ็”Ÿ็‰ฉๅญธ็ง‘,1995.Maste

    Case study on the changes in privatization policy applying policy window model

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ธฐ์—…์ •์ฑ…ํ•™๊ณผ, 2011.8. ์ž„๋„๋นˆ.Maste

    (A) study of the `Five Shakespeare Songs, op.23` composed by Roger Quilter

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์Œ์•…๊ณผ(์„ฑ์•…์ „๊ณต),2009.8.Maste

    Method to activate the learning organization of a dental laboratory in a dental hospital

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์น˜์˜ํ•™๊ณผ, 2011.2. ๊น€๋ช…๊ธฐ.Maste
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