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    ์œ ๊ธฐ ๋ถ„์ž๋ฅผ ์ด์šฉํ•œ ๋ฆฌํŠฌ-์‚ฐ์†Œ ์ „์ง€์™€ ๋ฆฌํŠฌ-์ด์˜จ ์ „์ง€์˜ ์–‘๊ทนโ”‚์ „ํ•ด์งˆ ๊ณ„๋ฉด ๊ฐœ์งˆ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€(์—๋„ˆ์ง€ํ™˜๊ฒฝ ํ™”ํ•™์œตํ•ฉ๊ธฐ์ˆ ์ „๊ณต),2020. 2. ์ตœ์žฅ์šฑ.ํ˜„๋Œ€ ์‚ฌํšŒ์—์„œ ์ „๊ธฐ ์ž๋™์ฐจ ์‹œ์žฅ์˜ ๊ธ‰์ž‘์Šค๋Ÿฐ ์„ฑ์žฅ๊ณผ ์ดˆ์†Œํ˜• ํœด๋Œ€ ์ „์ž ๊ธฐ๊ธฐ๋“ค์˜ ์ถœํ˜„์œผ๋กœ ์ธํ•˜์—ฌ ๋†’์€ ์—๋„ˆ์ง€ ๋ฐ€๋„๋ฅผ ๊ฐ€์ง€๋ฉฐ ์›์ž์žฌ ๊ฐ€๊ฒฉ์ด ์ €๋ ดํ•˜๊ณ  ์ถฉ๋ฐฉ์ „ ์ˆ˜๋ช…์ด ์šฐ์ˆ˜ํ•œ ๋ฆฌํŠฌ ์ „์ง€์˜ ํ•„์š”์„ฑ์ด ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ๋†’์€ ์—๋„ˆ์ง€ ๋ฐ€๋„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์ฐจ์„ธ๋Œ€ ๋ฆฌํŠฌ ์ „์ง€์˜ ์ถฉ๋ฐฉ์ „ ์ˆ˜๋ช…์„ ํ–ฅ์ƒ ์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ „ํ•ด์งˆ๊ณผ ์–‘๊ทน ๊ฐ„ ๊ณ„๋ช… ์ œ์–ด๋ฅผ ํ†ตํ•ด ๋ถ€๋ฐ˜์‘์„ ๋ง‰๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ์ œ 2์žฅ์—์„œ๋Š” ๋ฆฌํŠฌ-์‚ฐ์†Œ์—์„œ ๊ฐ€์žฅ ํฐ ์ด์Šˆ์ธ ์ถฉ์ „๊ณผ์ „์••์„ ๊ฐ์†Œ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ heme ๊ตฌ์กฐ์˜ ์ด‰๋งค๋ฅผ ์–‘๊ทน์— ๋ถ€์ฐฉํ•˜์˜€๋‹ค. Heme ๊ตฌ์กฐ์˜ ๋ฆฌ๊ฐ„๋“œ๋ฅผ azide์™€ thiocyanate๋กœ ๋ณ€ํ˜•์‹œํ‚ด์— ๋”ฐ๋ผ Fe ํ™œ์„ฑ์ ์—์„œ์˜ electrostatic potential์ด ๋‹ฌ๋ผ์ง€๊ฒŒ ๋œ๋‹ค. ์ด์— lewis acidity๊ฐ€ ๋‹ค๋ฅธ ๊ฐ๊ฐ์˜ ์ด‰๋งค๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ณผ์‚ฐํ™”๋ฆฌํŠฌ ๋ถ„ํ•ด ๋ฐ˜์‘์ด ๋‹ฌ๋ผ์ง€๋Š” ๊ฒƒ์„ ๋ฐ€๋„๋ฒ”ํ•จ์ˆ˜์ด๋ก  (Density functional Theory)๋กœ ๊ณ„์‚ฐํ•˜์˜€๊ณ  lewis acidity ์™€ ์ถฉ์ „๊ณผ์ „์•• ๊ฐ์†Œ ๋ฐ ์ˆ˜๋ช… ์„ฑ๋Šฅ ํ–ฅ์ƒ์˜ ๊ด€๊ณ„๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ์ œ 3์žฅ์—์„œ๋Š” high-Ni NCM ์˜ ์ถฉ๋ฐฉ์ „ ์‚ฌ์ดํด ์ค‘ ๊ตฌ์กฐ์  ๋ถˆ์•ˆ์ •์„ฑ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ์•ˆ์œผ๋กœ pyrazine-linked covalent organic framework (Pyr-2D) ์„ high-Ni NCM ์˜ ํ‘œ๋ฉด์— ์ฝ”ํŒ…ํ•˜์˜€๋‹ค. Pyr-2D ๋Š” ์ด์˜จ ๋ฐ ์ „์ž ์ „๋„๋„๊ฐ€ ์žˆ๊ณ  ๋งค์šฐ ์–‡์€ ์ฝ”ํŒ…์ธต์„ ํ˜•์„ฑ ํ•  ์ˆ˜ ์žˆ์–ด ๋ฌด๊ธฐ๋ฌผ๋กœ ์ด๋ฃจ์–ด์ง„ ๊ธฐ์กด ์ฝ”ํŒ… ๋ฐฉ๋ฒ•์— ๋น„ํ•ด ์ด์ ์ด ์žˆ๋‹ค. ๋”์šฑ์ด Pyr-2D ๋ฅผ ํ•ฉ์„ฑํ•˜๋Š” ๊ณผ์ •์—์„œ NCM ํ‘œ๋ฉด์— ํ˜•์„ฑ๋œ preformed TM mixed layer๋Š” Pyr-2D์™€ ๋”๋ถˆ์–ด ์ถฉ๋ฐฉ์ „ ์ค‘์— ์ „์ด๊ธˆ์† ํ˜ผํ•ฉ์ด ์ผ์–ด๋‚˜๋Š” ๊ฒƒ์„ ๋ฐฉ์ง€ํ•ด์ฃผ๋Š” ์—ญํ• ์„ ํ•˜์—ฌ ์ถฉ๋ฐฉ์ „ ์‚ฌ์ดํด ์ˆ˜๋ช… ํ–ฅ์ƒ ๋ฐ ์œจ์† ํŠน์„ฑ ํ–ฅ์ƒ์— ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐํ˜”๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฆฌํŠฌ ๋ฐฐํ„ฐ๋ฆฌ์˜ ์ „ํ•ด์งˆ/์–‘๊ทน ๊ณ„๋ฉด ์ œ์–ด๋ฅผ ์œ„ํ•œ ์ด‰๋งค ๊ฐœ๋ฐœ๊ณผ ํ‘œ๋ฉด ๊ฐœ์งˆ ๋ฐฉ๋ฒ•์˜ ๋…ผ๋ฆฌ์  ์ ‘๊ทผ ๋ฐฉํ–ฅ์„ ์ œ์‹œํ•˜๊ณ  ์ถฉ๋ฐฉ์ „ ์ˆ˜๋ช… ํŠน์„ฑ ํ–ฅ์ƒ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์—ฌ ํ–ฅํ›„ ์ „ํ•ด์งˆ/์–‘๊ทน ๊ณ„๋ฉด ์ œ์–ด ์—ฐ๊ตฌ์˜ ๋ฐœ์ „์— ๊ธฐ์—ฌ ํ•  ๊ฒƒ์ด๋ผ๊ณ  ๊ธฐ๋Œ€ํ•œ๋‹ค.The rapidly increasing usage of portable electronic devices and electric vehicles have consistently demanded high energy density batteries. While commercial lithium-ion batteries (LIBs) are consisted of layered oxide cathode materials, LiCoO2, the low specific capacity (145 mAh gโˆ’1) and scarcity of cobalt reserves have arisen the investigations on alternative cathode materials such as oxygen and high-Ni LiNixCoyMnzO2 (NCM). The lithium-oxygen (Liโˆ’O2) batteries have been a promising high energy density battery system attributed to the high theoretical specific energy of 3458 W h kgโˆ’1. However, their low cycle life originated from the large overpotential has inhibited them from practical use. Especially, the sluggish charging process has derived the charging overpotential larger than that of discharge. Thus, various catalysts were applied to the cathode electrode to reduce facilitate charging of lithium-oxygen battery. In chapter 2, the ligand modified heme catalysts were applied to the cathode of Liโˆ’O2 battery to reduce the charging overpotential and enhance the cycle life. According to the applied ligands of heme structure, the lewis acidity at the Fe active sites varied and affected the electrochemical performance of the cells. Furthermore, the origin of the different catalytic activity among various ligands were revealed using density functional theory (DFT) along with each charging steps. In addition, high-Ni NCM have attracted attentions owing to its high specific capacity of >200 mAh gโˆ’1 and comparably reasonable price of nickel. However, as the content of nickel becomes higher, the structural stability of the layered oxide decreases due to large volume change of the layer structure, transition metal (TM) mixing and TM dissolution. Thus, the surface engineering to protect the surface of the high-Ni NCM without disturbing the electronic/ionic conductivity is crucial in preventing the degradation of high-Ni NCM. In chapter 3, the pyrazine linked covalent organic frameworks (Pyr-2D) were coated on the high-Ni NCM. The Pyr-2D provides electronic/ionic conductive layer on the high-Ni NCM through its unique rigid and porous structure. Moreover, the preformed transition metal mixed layer combined with Pyr-2D showed improved cycle life and rate capability due to prevention of TM mixing and dissolution of transition metal during cycling. This thesis on the cathode catalysts design (chapter 2) and surface modification of cathode materials (chapter 3) would provide logical designing strategies toward electrolyte/cathode interface engineering to enhance the charge/discharge cycle life of lithium batteries.Chapter 1. Introduction. 1 1.1 Lithium-Oxygen Battery. 1 1.2 High-Nickel Li[Ni,Co,Mn]O2 Battery. 3 1.3 CathodeElectrolyte Interface Engineering in Battery. 5 Chapter 2. Lewis Acidity Controlled Heme Catalyst for Lithium-Oxygen Battery. 6 2.1 Introduction. 6 2.2 Experimental. 8 2.2.1 Synthesis of Heme, Heme+NCS and Heme+N3 Catalysts 8 2.2.2 Nitroblue Tetrazolium (NBT) Reduction Test. 8 2.2.3 Characterization of Heme, Heme+NCS and Heme+N3. 9 2.2.4 LiO2 Battery Preparation and Electrochemical Measurements. 10 2.2.5 Computational Details . 10 2.3 Results and Discussion. 12 2.3.1 Characterization of Heme Catalysts. 12 2.3.2 Electrochemical Performance. 16 2.3.3 Density Functional Theory (DFT) Calculations. 30 2.4 Conclusion. 40 Chapter 3. Pyrazine-linked 2D Covalent Organic Frameworks as Coating Material for High-Nickel Layered Oxide Cathodes in Lithium-Ion Batteries. 41 3.1 Introduction. 41 3.2 Experimental. 44 3.2.1 Synthesis of the Materials. 44 3.2.2 Characterization of Materials. 44 3.2.3 Electrochemical Evaluation. 46 3.3 Results and Discussion. 47 3.3.1 Characterization of NCM811-Pyr-2D. 47 3.3.2 Electrochemical Performance. 59 3.3.3 Effect of Pyr-2D Coating. 68 3.4 Conclusion. 72 Chapter 4. Summary. 73 Bibliography. 74 Abstract in Korean. 91Docto

    ํ”Œ๋กœํ‚น ๋ฐ ๋™๊ธฐํ™” ๋ชจ๋ธ๋“ค์˜ ํŒจ์ŠคํŠธ-์Šฌ๋กœ์šฐ ์—ญํ•™๊ณ„ ์ด๋ก 

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ˆ˜๋ฆฌ๊ณผํ•™๋ถ€, 2013. 8. ํ•˜์Šน์—ด.์ด ๋…ผ๋ฌธ์—์„œ๋Š”, ์šฐ๋ฆฌ๋Š” ํ”Œ๋กœํ‚น๊ณผ ๋™๊ธฐํ™” ๋ชจ๋ธ๋“ค์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•œ๋‹ค. ํŠนํžˆ, ์ฟ ๋ผ๋ชจํ†  ๋ชจ๋ธ, ๊ด€์„ฑ์ด ์žˆ๋Š” ์ฟ ๋ผ๋ชจํ†  ๋ชจ๋ธ, ์ฟ ์ปค-์Šค๋งค์ผ ๋ชจ๋ธ๊ณผ ๋ ˆ์ผ๋ ˆ์ด ๋งˆ์ฐฐ์ด ์žˆ๋Š” ๋‰ดํ„ด ํƒ€์ž… ๋ชจ๋ธ์„ ๋‹ค๋ฃฌ๋‹ค. ์šฐ๋ฆฌ๋Š” ํŠน์ด ๊ทนํ•œ์—์„œ์˜ ์—ญํ•™๊ณ„์˜ ์งˆ์ ์ธ ๊ธฐ์ˆ ์„ ์œ ๋„ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์•Œ์Šคํ…Œ์ธ- ์ผ€๋ธŒ๋ฆฌํ‚ค๋””์Šค-์Šฌ๋ ˜๋กœ๋“œ- ํ‹ฐํ‹ฐ์˜ ํŠน์ด ์„ญ๋™์„ ์œ„ํ•œ ํ†ต์ผ๋œ ์ ‘๊ทผ๋ฒ”์„ ์ด์šฉํ•œ๋‹ค. ์ œ 2์žฅ์—์„œ๋Š” ์•Œ์Šคํ…Œ์ธ-์ผ€๋ธŒ ๋ฆฌํ‚ค๋””์Šค- ์Šฌ๋ ˜๋กœ๋“œ-ํ‹ฐํ‹ฐ์˜ ํŠน์ด ์„ญ๋™ ์ด๋ก ๊ณผ ํ‰๋ฉด์—์„œ์˜ ํฌ์•™์นด๋ ˆ-๋ฒค๋”•์Šจ ์ด๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ์ œ 3์žฅ์—์„œ๋Š” ๋น„๋™๋“ฑ ์ฟ ๋ผ๋ชจํ†  ์ง„๋™์ž์˜ ์•™์ƒ๋ธ”๋กœ๋ถ€ํ„ฐ ์œ ๋„๋œ ์œ„์ƒ ๋™๊ธฐ ์ƒํƒœ์˜ ์ ๊ทผ์  ํ˜•์„ฑ์„ ๋…ผ์˜ํ•œ๋‹ค. ์œ„์ƒ ๋™๊ธฐ ์ƒํƒœ์˜ ํ˜• ์„ฑ ๊ณผ์ •์—์„œ, ์šฐ๋ฆฌ๋Š” ์ง„๋™์ž ์‚ฌ์ด์˜ ์ง„๋™ ์ˆ˜์™€ ํšก๋‹จ ์œ„์ƒ ์ฐจ์˜ ํ•˜ํ•œ-์ƒํ•œ ์„ ์ถ”์ •ํ•œ๋‹ค. ์ œ 4์žฅ์—์„œ, ์šฐ๋ฆฌ๋Š” ๊ด€์„ฑ์ด ์žˆ๋Š” ์ฟ ๋ผ๋ชจํ†  ํƒ€์ž… ๋ชจ๋ธ์„ ์œ„ ํ•œ ํŒจ์ŠคํŠธ-์Šฌ๋กœ์šฐ ์—ญํ•™ ์‹œ์Šคํ…œ์„ ์ œ์‹œํ•œ๋‹ค. ์šฐ๋ฆฌ์˜ ์ƒˆ๋กœ์šด ํ˜•ํƒœ์—์„œ, ์ˆœ์„œ ๋งค๊ฐœ๋ณ€์ˆ˜๋Š” ํŠน์ด ์„ญ๋™์˜ ์•Œ์Šคํ…Œ์ธ-์ผ€๋ธŒ๋ฆฌํ‚ค๋””์Šค-์Šฌ๋ ˜๋กœ๋“œ-ํ‹ฐํ‹ฐ์˜ ์ด๋ก ์˜ ์ฒด๊ณ„์—์„œ ์ง๊ฐ์˜ ํ•˜์ธก์น˜๋กœ์„œ์˜ ์—ญํ• ์„ ํ•œ๋‹ค. ์ œ 5์žฅ์—์„œ ์šฐ๋ฆฌ๋Š” ํ‰๋ฉด์—์„œ ์˜ ์ž…์ž๋ชจ๋ธ(์ฟ ์ปค-์Šค๋งค์ผ ๋ชจ๋ธ๊ณผ ๋ ˆ์ผ๋ ˆ์ด ๋งˆ์ฐฐ์ด ์žˆ๋Š” ๋‰ดํ„ด ํƒ€์ž… ๋ชจ๋ธ)์˜ ํŒจ์ŠคํŠธ- ์Šฌ๋กœ์šฐ ์—ญํ•™๊ณ„๋ฅผ ๋…ผ์˜ํ•œ๋‹ค. ์šฐ๋ฆฌ์˜ ๋ถ„์„์€ ์˜์‚ฌ์†Œํ†ต ํ•˜์ค‘์˜ ์ตœ์†Œ ํ•œ์˜ ๊ฐ€์ •์„ ์‚ฌ์šฉํ•œ๋‹ค. ์ œ 6์žฅ์—์„œ๋Š” ํ”Œ๋กœํ‚น ๋ชจ๋ธ๊ณผ ๊ด€๋ จ๋œ ์ˆ˜ํ•™์  ๋ฌธ์ œ๋ฅผ ๊ฐ„๋‹จํžˆ ์ œ์‹œํ•˜๊ณ  ์ด ๋…ผ๋ฌธ์„ ์š”์•ฝํ•œ๋‹ค.In this thesis, we study about the flocking and synchronization models. In specially, we deal with Kuramoto model, Kuramoto model with inertia, Cucker-Smale type model and Newtonian type model with Rayleigh friction. We employ Artstein-Kevrekidis-Slemrod-Titis unified approach for the singular perturbation to derive a qualitative description of the dynamics in the singular limit. In chapter 2, we review AKSTs singular perturbation theory and planar Poincare-Bendixson theory. In chapter 3, we discuss the asymptotic formation of phase-locked states arising from the ensemble of non-identical Kuramoto oscillators. In the formation process of phase-locked states, we estimate the number of collisions between oscillators, and lower- upper bounds of the transversal phase differences. In chapter 4, we present a fast-slow dynamical systems theory for a Kuramoto type model with inertia. In our new formation, order parameters serve as orthogonal observables in the framework of AKSTs theory of singular perturbation. In chapter 5, we discuss fast-slow dynamic of planar particle models (Cucker-Smale type model and Newtonian type model with Rayleigh friction).Our analysis em- ploys minimal assumptions on the communication weight. In chapter 6, we briefly present a mathematical problems related by the flocking models and summary this thesis.Abstract i 1 Introduction 1 2 Preliminaries 5 2.1 Flocking and synchronization models 5 2.2 A fast-slow dynamical system 7 2.2.1 Invariant measures and Young measures 7 2.2.2 Review on AKSTs unified approach 9 2.2.3 Limit dynamics of a planar dynamical system 11 3 Asymptotic formation of phase-locked states for the Kuramoto oscillators 13 3.1 Definitions and Motivating problems 13 3.1.1 Dynamics of phase diameter in a stable regime 15 3.2 Formationofphase-locked states 18 3.2.1 Overview of our strategy 18 3.2.2 Basic apriori estimates 21 3.2.3 Convergence toward phase-locked states 25 3.3 Quantitative estimates toward the phase-locked states 27 3.3.1 Finiteness on the number of collisions 28 3.3.2 Estimate on the transversal phase differences 32 3.4 Numerical Simulations 35 3.4.1 Formation of phase-locked states 35 3.4.2 Estimate on the number of collisions 36 3.4.3 Estimate on the evolution of phase-differences 37 3.4.4 Transition and relaxation stages 38 4 Kuramoto oscillators with inertia 43 4.1 Derivation of fast-slow dynamics 43 4.2 Invariant measure for the fast system 47 4.2.1 Subcritical regime(Kr0>ฮฉ) 48 4.2.2 Critical regime(Kr0=ฮฉ) 49 4.2.3 Supercritical regime(Kr0<ฮฉ) 49 4.3 Limit dynamics of order parameters 50 4.4 Numerical Simulations 54 4.4.1 Subcritical case 55 4.4.2 Critical case 56 4.4.3 Supercritical case 56 5 Planar particle models for flocking and swarming 59 5.1 A Cucker-Smale type model 59 5.2 A Newtonian model for swarms with Rayleigh friction 64 5.2.1 Description of model system 64 5.2.2 Classification of equilibria 68 5.2.3 Limit dynamics of the system (5.2.3) 69 5.3 Numerical Simulations 71 6 Conclusion and future project 74 7 Appendix 76 7.1 Appendix A 76 7.1.1 The proof of Proposition 3.2.1 76 7.1.2 The proof of Proposition 3.2.2 79 7.2 Appendix B 81 7.2.1 The proof of Lemma 3.3.1 81 7.2.2 The proof of Lemma 3.3.2 83 7.2.3 The proof of Proposition 3.3.1 85 7.3 Appendix C. Elementary estimates 88 Abstract (in Korean) 101Docto

    A Study on the Hepatic Glucose Balance after Glucose Loading in Diabetic Dogs

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    After an intravenous glucose load, a series of serum glucose, and insulin concentration ๏ฟฆere measured. in 3 diabetic dogs previously treated with alloxan, and 3 normal control dogs. Glucose disappearance rate , integrated insulin concentration, insulinogenic index, glucose uptake at tissue sites, and hepatic glucose balance were calculated and compared. Followings were the results; 1. The fasting concentration of insulin was 7.4ยฑ 231'u/ml in control group , and 3. 5ยฑ1. 31'u/ml in diabetic group (p<O. 1). Nine minutes after glucose load, the maximal insulin concentration in control group was 15.2ยฑ1. 10l'u/ml, but there was no acute response in diabetics. 2. The glucose disappearance rate was 2. 68ยฑ0. 75 %/min in control group and 2. 24ยฑ0. 51%/min, showing lower value in diabetics (p<0.15). 3. The integrated insulin concentration of the first 5 minutes was 8. 15ยฑ0. 3751lU/ml in control group, and 4. 07ยฑ0. 7161'u/ml in diabetics (p<0.05). Those of the first 10 minutes were 10.6ยฑ0. 4561'u/ml in controls, and 4. 18 ๅœŸO. 3351'u/ml in diabetics (p<O.01) and those of the 60 minutes for total response were 12.1ยฑ1. 328ฮผ๋ /ml in controls, and 4. 42ยฑ0. 445ฮผu/ml in diabetics (p<O. 025). 4. The insulinogenic index during 0~5 min and 0~10 min period for acute response and 0~60 min period for total response were as follows; 0.0147ยฑ 0.003, O. 0288ยฑ0. 009 and 0.051 ยฑO. 017 in controls and O. 01l5ยฑ0. 002, O. 0101ยฑ0. 002 and O. 0207ยฑ0. 013 in diabetics. (0~5 ฯ€in.; p<O. 4, 0~10 min.; p<O.l, 0~60 min.; p<0.4). 5. The glucose uptake at tissue sites was less in diabetics than in controls. 6. The hepatic glucose balance in every 10 minutes was as follows; 29.9mg/min, -45.7mg/min, -0.9 mg/min, -18.9mg/min, 45.7mg/min, 29.8mg/min and 49.0mg/min in control group , and 11. 4mg/min, 187.4mg/min, 31. 7mg/min, 16.9mg/min, 37.8mg/ min, 86.9mg/min and 78.0mg/min in diabetic dogs. From these data, it was concluded that diabetes was characterized not only by increased fasting blood glucose, lowered glucose disappearance rate, lowered insulinogenic index and glucose uptake at tissue sites but also by decreased hepatic glucose balance after intravenous glucose load

    ๊ธˆ์œต ์‹œ์žฅ ๋ชจ๋ธ๋ง์œผ๋กœ ๋ถ€ํ„ฐ ์œ ๋„๋œ ๋น„ํ‰ํ˜• ๋ฐฉ์ •์‹

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    Thesis(masters) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ˆ˜๋ฆฌ๊ณผํ•™๋ถ€,2009.8.Maste

    ๋Œ€์—ญํญ ์ œํ•œ ์ฑ„๋„์—์„œ์˜ ๊ณ ์ฃผํŒŒ ์ˆ˜์‹ ๊ธฐ ์‹ ํ˜ธ ๊ฐ•ํ™” ๊ธฐ์ˆ 

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2002.Maste

    ํ•ด์•ˆ ๊ฐ„์ฒ™์ง€์—์„œ์˜ ์กฐ๋ฅ˜์„œ์‹์ฒ˜ ๋ณต์›์— ๊ด€ํ•œ ์—ฐ๊ตฌ : ๋Œ€ํ˜ธ ๊ฐ„์ฒ™์ง€์˜ ์ˆ˜์กฐ๋ฅ˜ ์„œ์‹์ฒ˜ ๊ธฐ๋ฐ˜์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ƒํƒœ์กฐ๊ฒฝํ•™๊ณผ,2001.Maste

    ํšจ์œจ์ ์ธ ํšŒ์ „์šด๋™ ๋ณด์ƒ์„ ์œ„ํ•œ ISAR๋ณ‘์ง„์šด๋™ ๋ณด์ƒ๊ธฐ๋ฒ•

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    MasterThis paper introduces a translational motion compensation method for generating a focused inverse synthetic aperture radar (ISAR) image of a target that has complex motion. Translational motion which is a target motion along radar line of sight (RLOS) and non-uniform rotational motion that causes non-uniform change rate of relative aspect angle between the radar and targets blur the ISAR images. Therefore motion compensation (MOCOM) which is composed of translational MOCOM (TMC) and rotational MOCOM (RMC) is necessary to eliminate those undesired motion components and generate a focused ISAR image. Generally we use the TMC methods that optimize cost functions of the ISAR images, but these methods distort the rotational motion components in received signals and the distortion causes errors during RMC. We proposed a new TMC method that provides data more efficiently to conduct RMC. We use range alignment (RA) and particle swarm optimization (PSO) to estimate the translational motion of the target and eliminate the translational motion components. The proposed method eliminates only translational motion components and remains the rotational motion without distortion. So after TMC, the RMC works successfully and can generate a focused ISAR image.์—ญํ•ฉ์„ฑ ๊ฐœ๊ตฌ๋ฉด ๋ ˆ์ด๋”(Inverse Synthetic Aperture Radar: ISAR) ์˜์ƒ์˜ ์ดˆ์ ์„ ๋งž์ถ”๊ธฐ ์œ„ํ•œ ์š”๋™๋ณด์ƒ(Motion compensation) ๊ณผ์ •์€ ํ‘œ์ ์˜ ๋ ˆ์ด๋‹ค ๊ฐ€์‹œ์„ (Radar Line Of Sight: RLOS) ๋ฐฉํ–ฅ์œผ๋กœ์˜ ์›€์ง์ž„์„ ์ œ๊ฑฐํ•˜๋Š” ๋ณ‘์ง„์šด๋™ ์„ฑ๋ถ„ ๋ณด์ƒ(Translational Motion Compensation: TMC)๊ณผ ๊ด€์ธก๊ฐ๋„ ๋ณ€ํ™”์œจ์„ ์ผ์ •ํ•˜๊ฒŒ ๋งŒ๋“œ๋Š” ํšŒ์ „ ์šด๋™ ์„ฑ๋ถ„ ๋ณด์ƒ(Rotational Motion Compensation: RMC)์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ TMC๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ISAR ์˜์ƒ์˜ ๋น„์šฉํ•จ์ˆ˜(cost function)๋ฅผ ์ตœ์ ํ™”ํ•˜๋Š” ๊ธฐ๋ฒ•๋“ค์ด ๋„๋ฆฌ ํ™œ์šฉ๋œ๋‹ค. ํ•˜์ง€๋งŒ, ์ƒ๊ธฐ์˜ ๊ธฐ๋ฒ•๋“ค์€ TMC ์ˆ˜ํ–‰ ํ›„ ํšŒ์ „์šด๋™ ์„ฑ๋ถ„์„ ์™œ๊ณก์‹œํ‚ค๊ธฐ ๋•Œ๋ฌธ์— RMC์„ ์ˆ˜ํ–‰ํ•˜๋Š”๋ฐ ํฐ ์–ด๋ ค์›€์„ ์ค€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ณ‘์ง„์šด๋™ ์„ฑ๋ถ„ ์ถ”์ •์— ๊ธฐ๋ฐ˜ํ•œ TMC ๊ธฐ๋ฒ•์„ ์ œ์‹œํ•จ์œผ๋กœ์จ TMC ํ›„ RMC๋ฅผ ์„ฑ๊ณต์ ์œผ๋กœ ์ˆ˜ํ–‰ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•œ๋‹ค. ๊ฑฐ๋ฆฌ์ •๋ ฌ ๋ฐฉ๋ฒ•(Range Alignment: RA)๊ณผ particle swarm optimization(PSO)๋ฅผ ํ†ตํ•œ ๋ณ‘์ง„์šด๋™ ์„ฑ๋ถ„์„ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜๊ณ  ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ ๊ธฐ์กด์˜ ๋น„์šฉํ•จ์ˆ˜๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” TMC ๊ธฐ๋ฒ•๊ณผ ๊ทธ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•œ๋‹ค

    ISAR ์˜์ƒ ํ˜•์„ฑ์„ ์œ„ํ•œ ํšŒ์ „์šด๋™๋ณด์ƒ ๊ธฐ๋ฒ• ์—ฐ๊ตฌ

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    ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ‘œ์ ์˜ ๊ด€์ธก๊ฐ๋„ ๋ณ€ํ™”์œจ์ด ์ผ์ •ํ•˜์ง€ ์•Š์€ ํ‘œ์ ์˜ ํšŒ์ „์šด๋™ ์„ฑ๋ถ„์˜ ๊ฒฐ๊ณผ๋กœ ์ธํ•œ, ์—ญํ•ฉ์„ฑ ๊ฐœ๊ตฌ๋ฉด ๋ ˆ์ด๋‹ค(Inverse Synthetic Aperture Radar: ISAR) ์˜์ƒ์˜ ์ดˆ์  ์ €ํ•˜ ํ˜„์ƒ์„ ํ•ด๊ฒฐํ•˜๋Š” ํšŒ์ „์šด๋™๋ณด์ƒ(Rotational Motion Compensation: RMC) ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ €, ํ•˜๋‚˜์˜ ์‚ฐ๋ž€์›์ด ์กด์žฌํ•˜๋Š” ๋ ˆ์ธ์ง€ ๋นˆ(range bin)์„ ์„ ํƒํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ํ“จ๋ฆฌ์— ๋ณ€ํ™˜(Fourier Transform: FT)๊ณผ ๋‹คํ•ญ์‹-์œ„์ƒ ๋ณ€ํ™˜(Polynomial-Phase Transform: PPT)๋ฅผ ํ™œ์šฉํ•˜์—ฌ, ์„ ํƒ๋œ ๋ ˆ์ธ์ง€ ๋นˆ์— ๋Œ€ํ•œ ์œ„์ƒํ•จ์ˆ˜๋ฅผ์ถ”์ •ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๊ด€์ธก ๊ฐ๋„์˜ ๋ณ€ํ™”์œจ์„ ์ผ์ •ํ•˜๊ฒŒ ํ•˜๋Š” ์ƒˆ๋กœ์šด ์‹œ๊ฐ„ ๋ณ€์ˆ˜๋ฅผ ์ •์˜ํ•œ ํ›„, ๋ณด๊ฐ„๋ฒ•(interpolation)์„ ํ†ตํ•ด์ƒˆ๋กญ๊ฒŒ ์ •์˜๋œ ์‹œ๊ฐ„๋ณ€์ˆ˜์— ๋Œ€ํ•œ ๋ ˆ์ด๋‹ค ์‹ ํ˜ธ๋ฅผ ํš๋“ํ•œ๋‹ค. ์ด์— ๋Œ€ํ•œ ๊ฒฐ๊ณผ๋กœ, ๊ด€์ธก ๊ฐ๋„์˜ ๋ณ€ํ™”์œจ์„ ์ผ์ •ํ•˜์ง€ ์•Š๊ฒŒ ํ•˜๋Š”ํ‘œ์ ์˜ ํšŒ์ „์šด๋™ ์„ฑ๋ถ„์„ ์ œ๊ฑฐํ•จ์œผ๋กœ์จ, ์ดˆ์ ์ด ๋งž๋Š” ISAR ์˜์ƒ์„ ํš๋“ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ „ํ•จ(battleship) ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ RMC ๊ธฐ๋ฒ•์˜ ํšจ์šฉ์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.22Nkc
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