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    ์ง๊ด€๋‚ด ๊ธฐํฌ์˜ ํ๋ฆ„์— ๋Œ€ํ•œ 2์ฐจ์› ์ˆ˜์น˜ ๋ชจ์˜

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2016. 8. Van Thinh Nguyen.The main purpose of water aeration is to maintain healthy levels of dissolved oxygen (DO) concentration. Water aeration involves the injection of air or air bubbles into water treatment reservoir commonly through pipes. Fine bubble has higher mass transfer when its diameter gets smaller and smaller bubbles are more capable of enhancing DO concentration level. Two-phase flow consisting of air and water inside horizontal pipe with small diameter is capable of transferring fine bubbles into a body of water and its mechanism should be clearly understood for better system designing. Nevertheless, there are only a few studies that deal with the relationship between mathematical characteristics of two-phase flow inside horizontal pipe and DO concentration level. The main objective of this study is to perform 2-dimensional two-phase simulations inside horizontal pipe using the computational fluid dynamics (CFD) OpenFOAM (Open source Field Operation And Manipulation) tools to examine the effect of pipe wall shear stress on bubble size, which is the major factor effecting DO concentration level. Under different initial conditions, two-phase numerical simulations using Reynolds-averaged Navier-Stokes (RANS) combined with Eulerian-Eulerian method were performed to compute the axial Sauter Mean Diameter (SMD) of bubbles, water velocity, and wall shear stress within a 13.4 m long horizontal pipe with 50.3 mm inner diameter. The coalescence and breakage of bubbles caused by random collisions were considered during the simulations to predict the values of axial SMD. The water velocity and SMD were validated against the experimental data of Kocamustafaogullari and Wang (1991) and the relative errors ranged from 4% to 15% and 8% to 30%, respectively. Two additional experimental results obtained by Yin et al. (2012) and Water Supply Engineering Laboratory (WSEL) at SNU were gathered. These experiments deal with two-phase horizontal pipe flow under different configurations and DO concentration level. Their results were compared with the results obtained by Kocamustafaogullari and Wang (1991) and the aforementioned numerical analysis to determine the effect of pipe wall shear stress on bubble diameter and DO concentration level. As a result, the increase in pipe wall shear stress decreases bubble size and increases DO concentration level. By comparing the results and making links between them, it was concluded that the pipe wall shear stress plays a key role in breaking up the bubbles.CHAPTER 1. INTRODUCTION 1 1.1 General Introduction 1 1.2 Objective and Necessities 2 CHAPTER 2. THEORETICAL BACKGROUND 3 2.1 Previous Studies 3 2.1.1 Two-Phase Flow 3 2.1.2 Bubble Coalescence and Breakup 3 2.1.3 Bubble Diameter and DO Concentration Level 4 2.1.4 Two-Phase Flow Pipe Wall Shear Stress 4 CHAPTER 3. METHODOLOGIES 6 3.1 RANS Governing Equations 6 3.1.1 RANS Combined with Eulerian-Eulerian 6 3.2 Turbulence Model: k-ฮต Model 7 3.2.1 Dispersed k-ฮต Model 7 3.3 Bubble Coalescence 9 3.3.1 Mechanisms of Bubble Coalescence 9 3.3.2 Turbulent Collision Rate 10 3.3.3 Collision Efficiency 11 3.4 Bubble Breakup 12 3.4.1 Mechanisms of Bubble Breakup 12 3.4.2 Breakup Efficiency 13 3.5 Sauter Mean Diameter (d32) 15 3.5.1 Interfacial Area Transport Equation (IATE) Model 15 3.5.2 One-Group ai Transport Equation 16 3.6 Wall Shear Stress 17 3.6.1 Circular Pipe Wall Shear Stress 17 CHAPTER 4. EXPERIMENTAL SETUP & DATA 25 4.1 Kocamustafaogullari and Wang (1991) 25 4.1.1 Experimental Setup & Procedure 25 4.1.2 Experimental Results 28 4.2 Yin et al. (2012) Numerical Model 30 4.2.1 Experimental Setup & Procedure 30 4.2.2 Experimental & Numerical Findings 32 4.3 Dissolved Oxygen Concentration Measurements 34 4.3.1 Experimental Setup & Procedure 34 4.3.2 Experimental Results 37 CHAPTER 5. NUMERICAL SIMULATION 39 5.1 Kocamustafaogullari and Wang (1991) 39 5.1.1 Computational Domain 39 5.1.2 Simulation Setup and Boundary Conditions 40 5.1.3 Simulation Results 41 5.2 Water Supplying Engineering Lab: Simulation 47 5.2.1 Simulation Results 47 CHAPTER 6. DISCUSSION 50 CHAPTER 7. CONCLUSION 60 CHAPTER 8. REFERENCES 61 ์ดˆ๋ก 67Maste

    ๊ธฐํ•˜ํ•™์  ๋ฐฉ๋ฒ•๋ก ์„ ์ด์šฉํ•œ ๋‹ค๋ฌผ์ฒด ์‹œ์Šคํ…œ ๋ชจ๋ธ ์ถ”์ • ๋ฐ ์ œ์–ด

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2019. 8. ๋ฐ•์ข…์šฐ.๋‹ค๋ฌผ์ฒด ๊ธฐ๊ณ„์‹œ์Šคํ…œ์€ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ๊ณ„ํš๊ณผ ์ œ์–ด๋ฅผ ํ•„์š”๋กœ ํ•˜๋Š” ๋‹ค์ˆ˜์˜ ์ž…๋ ฅ-์ถœ๋ ฅ ์‹œ์Šคํ…œ๋“ค์„ ์ด๋ฃฌ๋‹ค. ์ตœ๊ทผ ๋“ค์–ด, ํœด๋จธ๋…ธ์ด๋“œ๋‚˜ 4์กฑ ๋ณดํ–‰ ๋กœ๋ด‡๊ณผ ๊ฐ™์€ ๋ณต์žกํ•˜๊ณ  ๊ณ ์ฐจ์›์˜ ๋กœ๋ด‡๋“ค์ด ๋งค์šฐ ๋™์ ์ธ ์ž„๋ฌด์™€ ๋™์ž‘๋“ค์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์‹œ์ž‘ํ•˜๋ฉด์„œ ๋กœ๋ณดํ‹ฑ์Šค ๋ถ„์•ผ์—์„œ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ๊ณ„ํš๊ณผ ์ œ์–ด ๊ธฐ๋ฒ•๋“ค์ด ๋”์šฑ ์ฃผ๋ชฉ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฐ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋“ค์˜ ์„ฑ๋Šฅ์ด ๋ชจ๋ธ ์ถ”์ •์น˜์˜ ์ •ํ™•๋„์™€ ์ง๊ฒฐ๋˜๋Š” ๋ฐ˜๋ฉด, ๋ณต์žกํ•˜๊ณ  ๊ณ ์ฐจ์›์˜ ๋กœ๋ด‡๋“ค์— ๋Œ€ํ•œ ๋ชจ๋ธ ์ถ”์ •์€ ์ผ๋ฐ˜์ ์œผ๋กœ ์ˆ˜๋งŽ์€ ์–ด๋ ค์›€๋“ค์„ ์•ผ๊ธฐํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฐ ์–ด๋ ค์›€๋“ค์ด ์ขŒํ‘œ๊ณ„์— ๋ถˆ๋ณ€ํ•œ ๋ฏธ๋ถ„ ๊ธฐํ•˜ํ•™์ ์ธ ์ ‘๊ทผ์œผ๋กœ ํ•ด๊ฒฐ๋  ์ˆ˜ ์žˆ์Œ์„ ์ž…์ฆํ•œ๋‹ค. ์ฃผ ์š”์ ์€ ์งˆ๋Ÿ‰ ๊ด€์„ฑ ํŒŒ๋ผ๋ฏธํ„ฐ์™€ ๊ฐ™์€ ๋ฌผ๋ฆฌ์  ๊ฐ’์„ ์ง€๋‹Œ ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์ด ํœ˜์–ด์ง„ ๋ฆฌ๋งŒ ๊ณต๊ฐ„์ƒ์˜ ์š”์†Œ๋กœ ํ™•์ธ๋จ์— ๋”ฐ๋ผ ๋ฌผ๋ฆฌ์ ์œผ๋กœ ์ž์—ฐ์Šค๋Ÿฌ์šด ๊ฑฐ๋ฆฌ ์ธก๋Ÿ‰์ด ๊ฐ€๋Šฅํ•ด์ง์— ์žˆ๋‹ค. ์ด๋Ÿฐ ๊ธฐํ•˜ํ•™์ ์ธ ์ ‘๊ทผ์— ๊ธฐ๋ฐ˜์„ ๋‘์–ด, ๋‹ค์–‘ํ•œ ๋‹จ๊ณ„์™€ ์ƒํ™ฉ๋“ค์— ์žˆ์–ด์„œ ๊ฐ•๊ฑดํ•œ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๋Š” ์ขŒํ‘œ๊ณ„์— ๋ถˆ๋ณ€ํ•œ ๊ธฐํ•˜ํ•˜์ ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ €, ๋น„ ์‹ค์‹œ๊ฐ„ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •์„ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๊ธฐํ•˜ํ•™์  ๋ฌธ์ œ ์ •์˜์™€ ๊ทธ์— ๋”ฐ๋ฅธ ํšจ์œจ์ ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ํŠนํžˆ, ๋ฆฌ๋งŒ ๊ฑฐ๋ฆฌ ํ•จ์ˆ˜์˜ ์ด์ฐจ ๊ทผ์‚ฌ ํ•จ์ˆ˜๋“ค์„ ์ด์šฉํ•˜์—ฌ ๊ธฐํ•˜ํ•™์  ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ • ๋ฌธ์ œ๋ฅผ ๋ณผ๋ก ์ตœ์ ํ™” ๋ฌธ์ œ๋กœ ์žฌ์ •์˜ ํ•˜์˜€๋‹ค. ์ด๋กœ์จ ๋น ๋ฅด๊ณ  ์œ ์ผํ•ด๋กœ์˜ ์ˆ˜๋ ด์„ฑ์ด ๋ณด์žฅ๋˜๋Š” ๋ณผ๋ก ์ตœ์ ํ™” ๊ธฐ๋ฒ•๋“ค์˜ ์‚ฌ์šฉ์ด ๊ฐ€๋Šฅํ•ด์ง๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์ขŒํ‘œ๊ณ„ ๋ถˆ๋ณ€์˜ ์„ฑ์งˆ์ด ๋ณด์กด ๋˜๋ฉฐ, ์ถ”๊ฐ€์ ์ธ ๋ณผ๋ก ๊ตฌ์† ์กฐ๊ฑด์˜ ๋„์ž…์ด ์šฉ์ดํ•ด์ง€๊ฒŒ ๋œ๋‹ค. ๋กœ๋ด‡ํŒ” ๋ถ€ํ„ฐ ๋ณดํ–‰๋กœ๋ด‡ ๊ทธ๋ฆฌ๊ณ  ์ธ์ฒด ๋ชจ๋ธ์— ์ด๋ฅด๋Š” ์ œํ•œ๋œ ์„ผ์„œ ์ธก๋Ÿ‰ ๊ฐ’์„ ๊ฐ€์ง€๋Š” ๊ณ ์ฐจ์›์˜ ๋‹ค๋ฌผ์ฒด ์‹œ์Šคํ…œ๋“ค์— ๋Œ€ํ•ด ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•๋“ค์„ ์‹ฌ๋„ ์žˆ๊ฒŒ ๊ฒ€์ฆํ•˜์˜€์œผ๋ฉฐ, ๊ธฐ์กด์˜ ๋ฒกํ„ฐ ๊ณต๊ฐ„์ƒ์˜ ๋ฐฉ๋ฒ•๋“ค์— ๋น„ํ•˜์—ฌ ์ถ”์ •์น˜๋“ค์˜ ๊ฐ•๊ฑด์„ฑ๊ณผ ๋ณดํŽธ์„ฑ์ด ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •์— ํ•„์š”ํ•œ ์ตœ์  ๊ถค์  ๋ฐ์ดํ„ฐ ์ƒ์„ฑ์— ๋Œ€ํ•œ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ์—ˆ๋‹ค. ๊ถค์  ๋ฐ์ดํ„ฐ๋“ค์˜ ์ •๋ณด๋Ÿ‰์„ ์ขŒํ‘œ๊ณ„ ๋ถˆ๋ณ€ํ•œ ๋ฐฉ์‹์œผ๋กœ ์ธก๋Ÿ‰ํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ํ‘œ์ค€ ํ•จ์ˆ˜๋“ค์„ ์ •์˜ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ์ œ์•ˆ๋œ ํ‘œ์ค€ํ•จ์ˆ˜๋กœ ์ตœ์ ํ™”๋œ ๊ถค์  ๋ฐ์ดํ„ฐ๋“ค์€ ํ•ญ์ƒ ์ขŒํ‘œ๊ณ„์— ๋ถˆ๋ณ€ํ•˜๋‹ค. ๋˜ํ•œ, ์ตœ์  ๊ถค์  ์ƒ์„ฑ์„ ์œ„ํ•œ ํšจ์œจ์ ์ด๊ณ  ๊ฐ•๊ฑดํ•œ ๊ตฌ๋ฐฐ ๊ธฐ๋ฐ˜ ์ˆ˜์น˜์  ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ œ์•ˆ๋œ ๊ธฐํ•˜ํ•™์  ๋ฐฉ๋ฒ•์„ ๊ธฐ๋ฐ˜์œผ๋กœ, ์ฃผ์–ด์ง„ ๊ถค์ ๋ฐ์ดํ„ฐ๋กœ ๋ถ€ํ„ฐ ํšจ๊ณผ์ ์œผ๋กœ ์ถ”์ • ๊ฐ€๋Šฅํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์„ ์‹๋ณ„ํ•˜๋Š” ์ขŒํ‘œ๊ณ„ ๋ถˆ๋ณ€ํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ณ ์•ˆํ•˜์˜€๋‹ค. ์ด๋Š” ๋งค์šฐ ์ œํ•œ๋œ ๊ถค์ ์˜ ๊ตฌ๋™๋งŒ ๊ฐ€๋Šฅํ•œ ํœด๋จธ๋…ธ์ด๋“œ์™€ ๊ฐ™์€ ๊ณ ์ฐจ์› ๋กœ๋ด‡์˜ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •๋ฌธ์ œ์— ํŠน๋ณ„ํžˆ ์šฉ์ดํ•˜๋‹ค. ์‚ฐ์—…์šฉ ๋กœ๋ด‡๊ณผ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡์— ๋Œ€ํ•œ ์ˆ˜์น˜์  ์‹คํ—˜์„ ํ†ตํ•ด ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ๊ฒ€์ฆํ•œ ๊ฒฐ๊ณผ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •์˜ ๊ฐ•๊ฑด์„ฑ๊ณผ ์ •ํ™•๋„๊ฐ€ ํฌ๊ฒŒ ํ–ฅ์ƒ๋จ์„ ๋ณด์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์‹ค์‹œ๊ฐ„ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •์ด ์ˆ˜๋ฐ˜๋˜๋ฉฐ ํ๋ฃจํ”„ ์ œ์–ด ์‹œ์Šคํ…œ์˜ ์•ˆ์ „์„ฑ ๊นŒ์ง€ ๊ณ ๋ ค๋˜์–ด์•ผ ํ•˜๋Š” ๋กœ๋ด‡ ์ ์‘ ์ œ์–ด ๊ธฐ๋ฒ•์„ ์œ„ํ•œ ๊ธฐํ•˜ํ•™์  ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ €, ์งˆ๋Ÿ‰-๊ด€์„ฑ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ๋ฆฌ๋งŒ ๋‹ค์–‘์ฒด ๊ตฌ์กฐ๋ฅผ ๋” ์ผ๋ฐ˜์ ์ธ ๋ฌผ๋ฆฌ์  ํŒŒ๋ผ๋ฏธํ„ฐ ๊ณต๊ฐ„์œผ๋กœ ํ™•์žฅ ํ•˜์˜€๋‹ค. ๊ทธ ๋‹ค์Œ์œผ๋กœ, ์ขŒํ‘œ๊ณ„ ๋ถˆ๋ณ€ํ•˜๊ณ  ๊ธฐํ•˜ํ•™์ ์œผ๋กœ ์ž์—ฐ์Šค๋Ÿฌ์šด ๋ฆฌ์•„ํ‘ธ๋…ธํ”„ ํ•จ์ˆ˜๋ฅผ ์„ค์ •ํ•จ์— ๋”ฐ๋ผ ์ž์—ฐ์Šค๋Ÿฝ๊ณ  ์•ˆ์ •์„ฑ์ด ๋ณด์žฅ๋˜๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ ์ ์‘๋ฒ•์„ ์œ ๋„๋˜์—ˆ์œผ๋ฉฐ, ์‹ค์ œ๋กœ ๋ฆฌ๋งŒ ๊ณต๊ฐ„์ƒ์˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ๊ตฌ๋ฐฐ ํ๋ฆ„ ๋ฐฉ์ •์‹๊ณผ ๊ฐ™์€ ๊ผด๋กœ ๋‚˜ํƒ€๋‚จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํŠนํžˆ, ์ œ์•ˆ๋œ ์ ‘๊ทผ๋ฒ•์€ ๊ฒŒ์ธ ๋งคํŠธ๋ฆญ์Šค๋ฅผ ์ง์ ‘ ์‹œํ–‰์ฐฉ์˜ค๋ฅผ ํ†ตํ•ด ๊ณผ๋„ํ•˜๊ฒŒ ์กฐ์œจํ•ด์•ผํ•˜๋Š” ๊ธฐ์กด ๋ฐฉ๋ฒ•๋“ค์˜ ํ•œ๊ณ„์ ์„ ํ•ด๊ฒฐํ•˜์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ์ž„์˜์˜ ๋ณผ๋ก ๊ตฌ์† ์กฐ๊ฑด์„ ๋งŒ์กฑ์‹œํ‚ค๋Š” ๊ฐ•๊ฑด ์ ์‘ ์ œ์–ด ๊ธฐ๋ฒ•์œผ๋กœ ๋ณธ ๋ฐฉ๋ฒ•์„ ํ™•์žฅํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. 7 ์ž์œ ๋„ ๋กœ๋ด‡ํŒ”์˜ ๊ถค์  ์ถ”์  ์ ์‘์ œ์–ด ์ž‘์—…์— ๋Œ€ํ•œ ์ˆ˜์น˜์  ์‹คํ—˜๊ณผ ์‹ค์ œ ์‹คํ—˜์„ ํ†ตํ•ด ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ๊ฒ€์ฆํ•˜์˜€์œผ๋ฉฐ, ๊ฒŒ์ธ ์ˆ˜์น˜์˜ ๊ณผ๋„ํ•œ ์กฐ์œจ ์—†์ด๋„ ์ถ”์  ์—๋Ÿฌ๊ฐ€ ํฌ๊ฒŒ ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.Multibody mechanical systems constitute a large and important class of input-output systems that are the subject of model-based planning and control. Model-based control and planning methods in robotics have recently gained more attention, as complex, high-dimensional robots, e.g., humanoids and quadruped robots, are beginning to perform highly dynamic tasks. While the performance of these methods is in general affected by the accuracy of the model, common situations for complex high-dimensional systems raise a number of difficulties in estimating the model parameters in a robust and generalizable manner. In this thesis, we demonstrate that many of the challenges can be mitigated by appealing to coordinate-invariant, differential geometric methods. The key lies in the finding that mass-inertial parameters reside in a curved space of positive definite matrices endowed with a natural Riemannian metric which captures the distance between the parameter pairs in a physically meaningful, coordinate-invariant way. Taking this geometric perspective as our point of departure, we present geometric, coordinate-invariant algorithms that allow robust estimation of the parameters in various stages and situations of estimation. We first propose geometric formulations and algorithms for robust offline parameter identification of multibody mechanical systems. In particular, we provide a convex programming approach to the geometric dynamic parameter identification through the use of second-order approximations of the Riemannian distance. Not only does this allow for the use of fast convex optimization algorithms that are guaranteed to converge to a global solution, but also ensures coordinate-invariance while allowing for the inclusion of additional convex constraints as imposed by physical considerations and other practical requirements. Our geometric identification methods are validated through extensive experiments on a wide range of systems ranging from a robot manipulator to a legged robot and a human subject. The results show markedly improved robustness and generalizability vis-\`a-vis existing vector space methods. Then we address the problem of generating optimal excitation trajectories for parameter identification. We suggest a new set of optimality criteria that encodes the information from the trajectory samples in a coordinate-invariant way. The resulting optimal excitation trajectories are coordinate invariant and can be obtained efficiently and robustly using recursive analytic gradients of the criteria. The proposed geometric framework is also used to devise a coordinate-invariant algorithm for characterizing the effectively identifiable set of parameters given a set of excitation trajectory samples. The suggested method is particularly useful for robust identification of high-dimensional systems like humanoid robot that can execute only a limited range of feasible trajectories. The improved robustness and accuracy of our geometric approach in comparison to existing methods is demonstrated through numerical experiments involving industrial manipulators and a humanoid robot. Finally, we propose a geometric parameter adaptation law for adaptive control of robot manipulators. Toward deriving our geometric adaptation law, we extend the way of defining Riemannian manifold structure on the space of feasible inertial parameters to more general types of mechanical parameters including, e.g., joint frictions and stiffness. Then we show that a coordinate-invariant choice of a Lyapunov function that can be naturally defined on the so-called Hessian manifold of mechanical parameters leads to an adaptation law that can be viewed as a natural gradient descent flow on the corresponding manifold. Perhaps most importantly, our geometric approach considerably reduces the degree to which engineering choices must be made in the adaptation gain matrix compared to the existing methods. Our geometric adaptive control framework is further extended to robust adaptive control where arbitrary convex constraints imposed on the parameters can be taken into account with geometric projection methods. The efficacy of our method is verified with adaptive trajectory tracking control task involving a seven-dof robot manipulator through both simulation and real experiment.1 Introduction 1 1.1 A Geometric Approach 4 1.2 Organization 7 2 Preliminaries 11 2.1 Introduction 11 2.2 Symmetric Positive Definite Matrix Manifold 12 2.2.1 Affine-invariant Riemannian Metric 13 2.2.2 Log-det Bregman Divergence 14 2.3 Matrix Lie Groups 16 2.3.1 The Rotation Group 17 2.3.2 The Euclidean Group 18 3 Geometric Dynamic Identification of Multibody Systems 21 3.1 Introduction 21 3.2 Preliminaries 24 3.2.1 Physically Consistent Rigid Body Inertial Parameters 24 3.2.2 Linear Least Squares based Identification 27 3.3 Geometry of Rigid Body Inertial Parameters 31 3.3.1 Riemannian Distance Metric 32 3.3.2 Entropic Divergence 37 3.3.3 Constant Pullback Metric 38 3.3.4 Distribution Awareness of Geometric Distances 39 3.4 Geometric Identification with Geodesic Least Squares 43 3.4.1 Intrinsic Riemannian Error Criterion 43 3.4.2 Cyclic Optimization Algorithm 46 3.5 Geometric Identification with Convex Relaxations 48 3.5.1 Provable Comparative Analysis Scheme 50 3.6 Experiments 56 3.6.1 AMBIDEX Robot Manipulator 56 3.6.2 MIT Cheetah3 Robot 63 3.6.3 Human with Low-Cost Affordable Sensors 71 3.7 Discussion 75 3.8 Conclusion 77 4 Geometric Criteria for Excitation Trajectory Optimization 79 4.1 Introduction 79 4.2 Preliminaries 81 4.2.1 Optimal Design of Experiments 81 4.2.2 Excitation Criteria for Multibody Systems 84 4.2.3 Coordinate Invariance and Normalization 86 4.3 Geometric Excitation Criteria 89 4.3.1 Motivation 90 4.3.2 Pushforward Metric on Observable Parameters 90 4.3.3 Coordinate-invariant Criterion 94 4.4 Optimal Excitation Trajectory Generation 97 4.5 Determination of Effectively Identifiable Parameter Set 99 4.5.1 Reduced Identification 100 4.5.2 Reduced Optimal Excitation 102 4.6 Simulation Study 102 4.6.1 SCARA with Unknown Payload 103 4.6.2 KUKA iiwa R280 Manipulator 107 4.6.3 Atlas V5 Humanoid Robot 111 4.7 Conclusion 116 5 Geometric Robust Adaptive Control of Robot Manipulators 117 5.1 Introduction 117 5.2 Preliminaries 120 5.2.1 Adaptive Control of Robot Manipulators 121 5.3 Barrier-Hessian Manifolds 125 5.3.1 Rigid Body Inertial Parameters 127 5.3.2 Joint Friction/Stiffness Parameters 130 5.4 Geometric Parameter Update Laws 132 5.4.1 Geometric Adaptation Law 134 5.4.2 Geometric Projection Law 138 5.5 Simulation Study: Barret WAM7 Manipulator 140 5.5.1 Full Adaptation 142 5.5.2 Unknown Payload Adaptation 143 5.6 Experiment: AMBIDEX Robot Manipulator 147 5.7 Conclusion 150 6 Conclusion 153 6.1 Summary 153 6.2 Future Work 156 6.3 Concluding Remark 158 A Proofs and Supplemental Derivations 159 A.1 Supplemental Derivations 159 A.2 Proof of Proposition32 161 A.3 Supplemental Propositions for Section 3.5.1 164 A.4 Proof of Proposition 4.1 166 A.5 Proof of Proposition 5.1 168 A.6 Proof of Proposition 5.2 169 A.7 Proof of Proposition 5.5 170 A.8 Proof of Proposition 5.6 171 A.9 Proof of Proposition 5.7 171 A.10 Proof of Proposition 5.8 172 A.11 Proof of Proposition 5.9 172 B Algorithms and Implementation Details 173 B.1 Mappings Associated with Inertial Parameters 173 B.2 Inertial Parameter Perturbation Strategy 175 B.3 Recursive Regressor Gradient for Multibody Systems 175 B.3.1 Joint Torque Regressor 178 B.3.2 Ground Reaction Wrench Regressor 181 B.4 Matrix Inverse-free Computation of Geometric Adaptation Laws 181 Bibliography 183 Abstract 196Docto

    ์‹ค๋ฆฌ์ฝ˜์ด ๋„ํ•‘๋œ ์‚ฐํ™”๋ฌผ ํ•˜ํ”„๋Š„์—์„œ์˜ ๊ฐ•์œ ์ „์„ฑ ๋ถ„๊ทน ์Šค์œ„์นญ ๋™์—ญํ•™๊ณผ wake-up ํšจ๊ณผ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ๊ณผํ•™๊ต์œก๊ณผ(๋ฌผ๋ฆฌ์ „๊ณต), 2018. 8. ์ฑ„์Šน์ฒ .Ferroelectricity in ultra-thin HfO2 offers a viable alternative for the ferroelectric random access memory. The reliable switching behavior is highly required before commercial applications, whereas many intriguing features have not been understood yet clearly. Herein, we report an increase in the remnant polarization after electric field cycling, known as the wake-up effect, in terms of the change in the polarization switching dynamics of a Si-doped HfO2 thin film. Compared with the pristine specimen, the Si-doped HfO2 thin film exhibited a partial increase in the polarization value after a finite number of ferroelectric switching behaviors. Polarization switching behaviors were analyzed using the nucleation limited switching model, accompanied by defects charged randomly. The polarization switching was simulated using the Monte Carlo method with respect to the effect of defects. Comparing the experimental results with the simulations revealed that the wake-up effect of the HfO2 thin film was due to suppression of chemical disorder.โ… . Introduction 1 โ…ก. Structure analysis of Si:HfO2 thin films 5 2.1 Structure analysis using XRD and TEM 5 2.2 Details of the geometric phase analysis (GPA) used for domain mapping 8 โ…ข. Electric properties of Si:HfO2 thin films 11 3.1 Ferroelectric hysteresis and wake-up effect in Si:HfO2 11 3.2 Double switching current induced by non-uniform dipole defect 13 3.3 AC frequency dependence of polarization-voltage hysteresis 15 โ…ฃ. Ferroelectric polarization switching dynamics of Si:HfO2 thin films 17 4.1 Time-dependent ferroelectric polarization switching behavior 17 4.2 E-field cycling effect on ferroeletric polarization switching behavior 20 4.3 Details of the experimental method used for measuring the time-dependent switched polarization under various external voltages 23 โ…ค. Monte Carlo simulation of ferroelectric properties as a function of defect density 25 โ…ฅ. Conclusion 30 References 32 Appendix 39 ๊ตญ๋ฌธ์ดˆ๋ก 47Maste

    Varios Fenรณmenos del Uso del Gerundio en el Espaรฑol Americano

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    Indigenismo en el Espaรฑol Americano: Desde la Perspectiva del Quechua

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    Effects of culture temperatures and media on morphological changes of leptospira interrogans isolated in Korea

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    ์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] Leptospirosis์˜ ์›์ธ์ฒด์ธ Leptospira interrogans (์ดํ•˜ L.interrogans๋กœ ์•ฝํ•จ)๋Š” ๊ฐ€๋Š” ๋‚˜์„ ํ˜• ์„ธ๊ท ์œผ๋กœ์„œ ๋‚˜์„ ์˜ ๋ฐฉํ–ฅ์€ ๊ฑฐ์˜ ๋Œ€๋ถ€๋ถ„์˜ ๊ฒฝ์šฐ ์˜ค๋ฅธ์ชฝ์ธ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ตœ๊ทผ์— ํ•œ๊ตญ์—์„œ ๋ถ„๋ฆฌ๋œ L.interrogans์˜ ํŠน์ง•์€ ๊ทธ ํ˜•ํƒœ๊ฐ€ ๋‹ค์–‘ํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋ถ„๋ฆฌ ๊ท ์ฃผ๋Š” L.interrogans์˜ ๋ฐฐ์–‘์— ํ˜ธ์ ์กฐ๊ฑด์ธ 30โ„ƒ, EMJH(Ellinghausen and McCullough modified by Johnson and Harris)๋ฐฐ์ง€์—์„œ๋„ ๊ฐ„๊ท , ์˜ค๋ฅธ์ชฝ ํ˜น์€ ์™ผ์ชฝ ๋ฐฉํ–ฅ์˜ ๋‚˜์„ ๊ท , ๊ทธ๋ฆฌ๊ณ  ๊ตฌํ˜•์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ๋‹ค์–‘ํ•œ ํ˜•ํƒœ๋ฅผ ๋ณด์ด์—ˆ๋‹ค. ํ˜„์žฌ๊นŒ์ง€ ๋ณด๊ณ ๋œ ๋ฌธํ—Œ๋“ค ์ค‘ ์™ผ์ชฝ๋ฐฉํ–ฅ์˜ ๋‚˜์„ ๊ท , ์žฅ๊ฐ„๊ท , ๊ทธ๋ฆฌ๊ณ  ๊ตฌํ˜•๋“ฑ์ด L.interrogans ๋ถ„๋ฆฌ๊ท ์ฃผ๋“ค์—์„œ ๊ด€์ฐฐ๋œ ๋ณด๊ณ ๋ฅผ ์ฐพ์„ ์ˆ˜๋Š” ์žˆ์œผ๋‚˜, ์ด๋Ÿฌํ•œ ํ˜•ํƒœํ•™์  ๋‹ค์–‘์„ฑ์„ ์œ ๋ฐœํ•˜๋Š” ์š”์ธ์— ๊ด€ํ•˜์—ฌ๋Š” ๊ฑฐ์˜ ๋ณด๊ณ ๋œ ๋ฐ”๊ฐ€ ์—†๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์‹œ๋„๋กœ์จ ๋ณธ ์—ฐ๊ตฌ ์—์„œ๋Š” ๋ฐฐ์–‘์˜จ๋„์™€ ๋ฐฐ์ง€์— ๋”ฐ๋ฅธ L.interrogans์˜ ํ˜•ํƒœ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜์˜€๋‹ค. 4๊ฐ€์ง€ ๋ฐฐ์–‘์˜จ๋„ ์ฆ‰ 5โ„ƒ, 15โ„ƒ, 30โ„ƒ ๋ฐ 37โ„ƒ์™€ ๋‘ ์ข…๋ฅ˜์˜ ๋ฐฐ์ง€(Flecher ๋ฐ EMJH๋ฐฐ์ง€), ๊ทธ๋ฆฌ๊ณ  1์ฃผ์˜ ํ•œ๊ตญ๋ถ„๋ฆฌ๊ท ์ฃผ(UM-19)์™€ Pasteur ์—ฐ๊ตฌ์†Œ(Paris, France)์—์„œ ๋ถ„์–‘๋ฐ›์€ L.interrogans (ํ˜ˆ์ฒญํ˜• canicola)๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. 1.ํ•œ๊ตญ์—์„œ ๋ถ„๋ฆฌ๋œ L.interrogant(UM-19)๋Š” Pasteur ์—ฐ๊ตฌ์†Œ์˜ ๊ท ์ฃผ์™€ ๋น„๊ตํ•˜์—ฌ ์„ธ๊ท ์˜ ์ง๊ฒฝ(0.25โˆผ30ฮผm : 0.10โˆผ0 15ฮผm)๊ณผ ๋‚˜์„ ์˜ ์ง๊ฒฝ(0.10โˆผ0.60ฮผm : 0.10 โˆผ 0.15ฮผm)์ด ๋” ์ปธ๊ณ , ๋‚˜์„ ๊ฐ„ ๊ฑฐ๋ฆฌ(0.30โˆผ1.07ฮผm : 0.50โˆผ0.70ฮผm)๋Š” ๋‹ค์–‘ํ•˜์˜€๋‹ค. 2. UM-19 ๊ท ์ฃผ๋ฅผ 5โ„ƒ๋‚˜ 15โ„ƒ์—์„œ 3๊ฐœ์›” ์ด์ƒ ๋ฏธ๋ฆฌ ๋ฐฐ์–‘ํ•œ ํ›„ 37โ„ƒ์—์„œ ๋ฐฐ์–‘ํ•œ ๊ฒฝ์šฐ, ๊ท ์ฃผ์˜ ๋Œ€๋ถ€๋ถ„์€ ๋‚˜์„ ํ˜•์˜ ํ˜•ํƒœ๋ฅผ ์ทจํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, 37โ„ƒ ๋ฐฐ์–‘๊ธฐ๊ฐ„์ด 1์ฃผ์ผ์„ ๋„˜๊ฑฐ๋‚˜ 30โ„ƒ ํ˜น์€ 37โ„ƒ์—์„œ ๊ณ„๋Œ€๋ฐฐ์–‘ํ•œ ๊ฒฝ์šฐ์—๋Š” ๋‹จ๊ฐ„ํ˜•์œผ๋กœ ๋ฐ”๋€Œ์—ˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” Fletcher๋‚˜ EMJH๋ฐฐ์ง€์—์„œ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. 3. Pasteur์—ฐ๊ตฌ์†Œ์˜ L.interrogans๋ฅผ Fletcher๋ฐฐ์ง€์—์„œ 37โ„ƒ๋กœ 10ํšŒ์ด์ƒ ๊ณ„๋Œ€๋ฐฐ์–‘ํ•œ ๊ฒฝ์šฐ, ์ผ๋ถ€ ์„ธ๊ท ๋“ค์€ ํ•œ์ชฝ ํ˜น์€ ์–‘์ชฝ ๋์˜ hook๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์šด๋™์„ฑ๋„ ์ƒ์‹คํ•˜๋Š” ๊ฒƒ์„ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ์„ธ๊ท ์˜ ์ˆ˜ํšจ๋Š” ๊ณ„๋Œ€๋ฐฐ์–‘ ํšŸ์ˆ˜๊ฐ€ ๋Š˜์–ด๋‚จ์— ๋”ฐ๋ผ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์ด์ƒ์˜ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•˜์—ฌ ๋ณผ ๋•Œ, ํ•œ๊ตญ์—์„œ ๋ถ„๋ฆฌ๋œ L.interrogans๊ท ์ฃผ์ธ UM-19์€ ํ˜•ํƒœํ•™์ ์œผ๋กœ Pasteur์—ฐ๊ตฌ์†Œ์˜ L.interrogans์™€ ์ฐจ์ด๋ฅผ ๋ณด์ด์—ˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๋‹ค์–‘ํ•œ ํ˜•ํƒœ๋“ค์€ ์ด ์„ธ๊ท ์ด ๊ฐ€์ง€๊ณ  ์žˆ์„ ์ˆ˜ ์žˆ๋Š” ์ƒํ™œ๊ด€(life cycle)์—์„œ ๊ฐ๊ฐ์˜ ์‹œ๊ธฐ๋ฅผ ๋Œ€ํ‘œํ•˜๋ฉฐ, ๊ทธ ํ˜•ํƒœ๋Š” ์˜จ๋„์— ๋”ฐ๋ผ ๋ณ€ํ•œ๋‹ค๊ณ  ์ถ”์ •๋˜๋‚˜, ์ด๋ฅผ ํ™•์‹คํžˆ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ๋Š” ๋™์ผํ•œ ํ˜•ํƒœํ•™์  ๋‹ค์–‘์„ฑ์„ ๋ณด์ด๋Š” L.interrogans์˜ ์žฌ๋ถ„๋ฆฌ ๋ฐ ๋™์ •, ๊ทธ๋ฆฌ๊ณ  ์ด๋Ÿฌํ•œ ์„ธ๊ท ๋“ค์— ๊ฐ์ˆ˜์„ฑ์„ ๊ฐ€์ง„ ๋™๋ฌผ์„ ์‚ฌ์šฉํ•œ ์‹คํ—˜์ด ์ถ”๊ฐ€๋˜์–ด์•ผ ํ•  ๊ฒƒ์œผ๋กœ ์‚ฌ๋ ค๋œ๋‹ค. [์˜๋ฌธ] Leptospira interrogans(L.interrogans), the causative organism of leptospirosis, is characterized by a fine helical morphology, and the helix is almost always right-handed. However, one of the striking features of recent isolates of L.interrogans in Korea was the heterogeneity in their morphology. Even under optimal culture conditions (30โ„ƒ, EMJH medium), rods, spiral forms with right or left-handed helices, and even spherical forms of we L.interrogans were Present. Although the literature notes the presence of Left-handed helices, long rods, and spherical forms in cultures of L.interrogans isolates, little is known about the cause of this morphlogic heterogeneity. In an attempt to answer this question, this study was initiated to examine the effects of culture conditions, especially temperature and medium, on the morphology of L.interrogans. Four temperatures (5โ„ƒ, 15โ„ƒ, 30โ„ƒ, and 37โ„ƒ)and two types of media(Fletcher and EMJH) were used; one strain from the Korean isolates and L.interrogans serovar canicola obtained from the Pasteur Institute(Paris, France) were employed throughout the study. The findings are as follows: 1. The L.interrogans isolated in Korea(UM-19)had a larger cell diameter (0.25-0.30um:0.10-0.15um), and helix diameter (0.1O-0.60um:0.10-0.15um)than that obtained from the Pasteur Institute, but they varied in their distances between the helices(0.30-1.OOum:0.50-0.70um). 2. When UM-19 was grown at 37โ„ƒ after a 3 month or longer preincubation at 5โ„ƒ or 15โ„ƒ, the majority of the organisms were spiral forms; however, they became rods when subcultured at 30โ„ƒ or 37โ„ƒ. No significant morphological differences were found between Fletcher and EMJH media. 3. When L.interrogans serovar canicola was subcultured more han ten times at 37โ„ƒ, some of the organism lost their motility as well as the hooks at either one or both ends, but only in Fletcher medium. The number of variants increased with the frequency of subculturing. These findings suggested that L.interrogans strain(UM-19)is different , in their morphology, from that of the Pasteur Institute, and its various morphologies may represent stages of the life cycle and vary with incubation temperature.restrictio

    ์€ ๋‚˜๋…ธ์ž…์ž๋ฅผ ํ•จ์œ ํ•œ ์ŠคํŒ๋ฑ์Šค ์„ฌ์œ ์— ๋‚˜์„ ํ˜• ๊ตฌ์กฐ๋ฅผ ์ ์šฉํ•œ ์ดˆ์‹ ์ถ•์„ฑ ์ „๋„์„ฑ ์„ฌ์œ 

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    ๋ณธ ๋ฐœ๋ช…์€ ๋‚˜์„ ํ˜• ์ „๋„์„ฑ ์„ฌ์œ  ๋ฐ ์ด์˜ ์ œ์กฐ ๋ฐฉ๋ฒ•์— ๊ด€ํ•œ ๊ฒƒ์œผ๋กœ์„œ, ์ผ์‹ค์‹œ์˜ˆ์— ๋”ฐ๋ฅธ ๋‚˜์„ ํ˜• ์ „๋„์„ฑ ์„ฌ์œ ๋กœ ์—ฐ๊ฒฐ๋œ ์ „์ž ์†Œ์ž๋Š” PDMS(Poly-dimethylsiloxane)๋กœ ์ฝ”ํŒ…๋œ ๋‚˜์„ ํ˜• ์ „๋„์„ฑ ์„ฌ์œ ์™€, ๋‚˜์„ ํ˜• ์ „๋„์„ฑ ์„ฌ์œ ์˜ ๋‚ด๋ถ€ ๋‚˜์„  ์ง๊ฒฝ(Helical diameter)๊ณผ ๋™์ผํ•œ ์ง๊ฒฝ์„ ๊ฐ–๋Š” ์›๊ธฐ๋‘ฅ ์—ฐ๊ฒฐ๋ถ€๋ฅผ ํ†ตํ•ด ๋‚˜์„ ํ˜• ์ „๋„์„ฑ ์„ฌ์œ ์™€ ์—ฐ๊ฒฐ๋œ ๋ฉ”ํƒˆ ํ™€๋” ๋ฐ ๋ฉ”ํƒˆ ํ™€๋”์™€ ์—ฐ๊ฒฐ๋œ ์ „์ž ์†Œ์ž๋ฅผ ํฌํ•จํ•  ์ˆ˜ ์žˆ๋‹ค
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