68 research outputs found

    ๊ณ ์† ์œ ๋™์—์„œ ์ถฉ๊ฒฉํŒŒ ๋ฐ ๊ฒฝ๊ณ„์ธต์— ์˜ํ•œ ๊ณต๊ธฐ๊ด‘ํ•™ ํšจ๊ณผ ๋น„๊ต

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2020. 8. ์ด๋ณต์ง.An antiballistic missile seeker is essential for a successful interception. However, aero-optical effects due to the flow field around the seeker window degrade the image captured using the seeker, making it difficult to identify the target. Thus, studying aero-optical phenomena and calibrating the images are necessary to improve the performance of seekers. Flight speed of antiballistic missiles are usually supersonic or hypersonic, where shock waves and boundary layers are inevitable and always exist. Therefore it is important to understand the aero-optical effects due to these flow features. In this dissertation, aero-optical phenomena due to the shock wave and boundary layer in supersonic and hypersonic flow are compared using numerical and experimental methods. In addition, a new experimental method based on background-oriented schlieren (BOS) is suggested and well validated using a Shack-Hartmann wavefront sensor in a subsonic heated jet. Because the BOS-based method could simultaneously visualize the flow and acquire optical characteristics, it is expected that flow properties could be more closely related to aero-optical effects. For supersonic flow, the flow field around the compression ramp is studied, and for hypersonic flow, the flow field around the wedge and cone model is investigated. To study the individual contribution of the shock wave and boundary layer to the wavefront distortion, numerical simulation is conducted. Flow is simulated by solving the two-dimensional Reynolds-averaged Navier-Stokes equations, and the ray-tracing method has been adopted to calculate the propagation of the optical wave. The deflection angle of the ray at the center of the laser beam is analyzed to assess the aero-optical effects caused by the shock wave and boundary layer. In the current wavefront measurement configuration, aero-optical effects due to two boundary layers cancel out and only shock wave effect remains. To compare aero-optical effects due to only one boundary layer and the shock wave, numerical simulations are conducted with a slip condition on the one side of the wall. From these numerical simulations, the aero-optical effects due to shock wave and boundary layer are comparable, Therefore the aero-optical effects due to the shock wave and the boundary layer are both important for the flow field around the window of the seekers.ํƒ„๋„ํƒ„ ์š”๊ฒฉ์œ ๋„ํƒ„์ด ์„ฑ๊ณต์ ์œผ๋กœ ์š”๊ฒฉํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํƒ์ƒ‰๊ธฐ์˜ ์„ฑ๋Šฅ์ด ์ค‘์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฐ๋ฐ, ๋น„ํ–‰ ์ค‘์—๋Š” ํƒ์ƒ‰๊ธฐ์ฐฝ ์ฃผ์œ„์— ์œ ๋™์— ์˜ํ•œ ๊ณต๊ธฐ๊ด‘ํ•™ ํšจ๊ณผ๋กœ ์ธํ•˜์—ฌ ํƒ์ƒ‰์˜ ๊ด‘ํ•™ ์„ผ์„œ๋กœ ์ดฌ์˜ํ•˜๋Š” ์ด๋ฏธ์ง€๊ฐ€ ์™œ๊ณก๋˜์–ด ๋ชฉํ‘œ๋ฅผ ๋ถ„๊ฐ„ํ•˜๊ธฐ ์–ด๋ ค์›Œ์ง„๋‹ค. ๋”ฐ๋ผ์„œ ํƒ์ƒ‰๊ธฐ์˜ ์„ฑ๊ณต์ ์ธ ์ž‘๋™์„ ์œ„ํ•ด์„œ๋Š” ๊ณต๊ธฐ๊ด‘ํ•™ ํ˜„์ƒ์„ ์—ฐ๊ตฌํ•˜์—ฌ ๊ด‘ํ•™์„ผ์„œ๋กœ ํš๋“ํ•œ ์ด๋ฏธ์ง€๋ฅผ ๋ณด์ •ํ•˜๋Š” ๊ณผ์ •์ด ํ•„์ˆ˜์ ์ด๋‹ค. ๋˜ํ•œ, ์ผ๋ฐ˜์ ์œผ๋กœ ํƒ„๋„ํƒ„ ์š”๊ฒฉ์œ ๋„ํƒ„์ด ์ดˆ์Œ์† ๋˜๋Š” ๊ทน์ดˆ์Œ์†์œผ๋กœ ๋น„ํ–‰ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ด๋Ÿฌํ•œ ๊ณ ์† ์œ ๋™์— ์˜ํ•ด ๋ฐœ์ƒํ•˜๋Š” ๊ณต๊ธฐ๊ด‘ํ•™ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•ด์•ผ ํ•œ๋‹ค. ํŠนํžˆ ์ดˆ์Œ์†๊ณผ ๊ทน์ดˆ์Œ์†์—์„œ๋Š” ์ถฉ๊ฒฉํŒŒ ๋ฐ ๊ฒฝ๊ณ„์ธต์ด ๋ฐ˜๋“œ์‹œ ๋ฐœ์ƒํ•˜๊ณ  ํ”ผํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์—, ์ถฉ๊ฒฉํŒŒ ๋ฐ ๊ฒฝ๊ณ„์ธต์— ์˜ํ•œ ๊ณต๊ธฐ๊ด‘ํ•™ ํšจ๊ณผ๋Š” ๋ฐ˜๋“œ์‹œ ์—ฐ๊ตฌํ•ด์•ผ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ดˆ์Œ์† ๋ฐ ๊ทน์ดˆ์Œ์† ์œ ๋™์—์„œ ์ถฉ๊ฒฉํŒŒ ๋ฐ ๊ฒฝ๊ณ„์ธต์ด ๊ฐ๊ฐ ๊ณต๊ธฐ๊ด‘ํ•™ ํšจ๊ณผ์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”๊ฐ€๋ฅผ ์‹คํ—˜ ๋ฐ ์ˆ˜์น˜ํ•ด์„์„ ํ†ตํ•ด ๋ถ„์„ํ•œ๋‹ค. ๋˜ํ•œ, ๋ฐฐ๊ฒฝ ์ง€ํ˜• ์Š๋ฆฌ๋ Œ(BOS)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ƒˆ๋กœ์šด ์‹คํ—˜ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ , ์ด ๊ณ„์ธก ๊ธฐ๋ฒ•์„ ๋‹ค๋ฅธ ํŒŒ๋ฉด ๊ณ„์ธก๊ธฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด์— ๋”ฐ๋ผ ์•„์Œ์† ๊ณ ์˜จ ์ œํŠธ์—์„œ ์ƒฅ-ํ•˜ํŠธ๋งŒ ํŒŒ๋ฉด ๊ณ„์ธก๊ธฐ ๊ฒฐ๊ณผ์™€ BOS ๊ธฐ๋ฐ˜ ์‹คํ—˜ ๊ธฐ๋ฐ˜ ๊ณ„์ธก ๊ฒฐ๊ณผ๊ฐ€ ๋Œ€์ฒด๋กœ ์ผ์น˜ํ•˜๋Š” ๊ฒƒ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. BOS ๊ธฐ๋ฐ˜ ๊ณ„์ธก ๊ธฐ๋ฒ•์€ ์œ ๋™ ์„ฑ์งˆ๊ณผ ๊ด‘ํ•™ ํŠน์„ฑ์„ ๋™์‹œ์— ํš๋“ํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์—, ์œ ๋™๊ณผ ๊ณต๊ธฐ๊ด‘ํ•™ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๋ฉด๋ฐ€ํžˆ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ์ดˆ์Œ์† ์œ ๋™์— ๋Œ€ํ•ด์„œ๋Š” ์••์ถ• ๋น„ํƒˆ๊ธธ ์ฃผ์œ„์˜ ์œ ๋™์— ์˜ํ•œ ๊ณต๊ธฐ๊ด‘ํ•™ ํšจ๊ณผ๋ฅผ ์—ฐ๊ตฌํ•˜์˜€์œผ๋ฉฐ ๊ทน์ดˆ์Œ์† ์œ ๋™์— ๋Œ€ํ•ด์„œ๋Š” ์๊ธฐ์™€ ์›๋ฟ”ํ˜• ๋ชจ๋ธ ์ฃผ์œ„์˜ ์œ ๋™์— ์˜ํ•œ ๊ณต๊ธฐ๊ด‘ํ•™ ํšจ๊ณผ๋ฅผ ํŒŒ์•…ํ•˜์˜€๋‹ค. ์ถฉ๊ฒฉํŒŒ ๋ฐ ๊ฒฝ๊ณ„์ธต ๊ฐ์ž์— ์˜ํ•œ ๊ณต๊ธฐ๊ด‘ํ•™ ํšจ๊ณผ๋ฅผ ๋น„๊ตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ˆ˜์น˜ํ•ด์„๋„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์œ ๋™์žฅ์€ Reynolds-averaged Navier-Stokes ์‹์„ ์ด์ฐจ์› ๊ฐ€์ •์„ ํ•˜์—ฌ ์ˆ˜์น˜์ ์œผ๋กœ ํ•ด์„ํ•˜์˜€์œผ๋ฉฐ ๊ด‘์„  ์ถ”์  ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๊ด‘ํ•™ํŒŒ์˜ ๊ฑฐ๋™์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ์ „์‚ฐํ•ด์„ ๊ฒฐ๊ณผ๋Š” ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋ ˆ์ด์ € ๋น”์˜ ์ค‘์‹ฌ์— ์œ„์น˜ํ•œ ๊ด‘์„ ์„ ๋”ฐ๋ผ์„œ ๊ตด์ ˆ๊ฐ์„ ์ถ”์ถœํ•˜์—ฌ ์ถฉ๊ฒฉํŒŒ ๋ฐ ๊ฒฝ๊ณ„์ธต ๊ฐ๊ฐ์— ์˜ํ•œ ๋น”์˜ ๊ตด์ ˆ๊ฐ์„ ๋น„๊ตํ•˜์˜€๋‹ค. ํ˜„์žฌ์˜ ํŒŒ๋ฉด ๊ณ„์ธก ๊ธฐ๋ฒ•์—์„œ๋Š” ํ•ญ์ƒ ๋‘ ๊ฐœ์˜ ๊ฒฝ๊ณ„์ธต์„ ์ง€๋‚˜๋ฉฐ, ์ด ๋‘ ๊ฐœ์˜ ๊ฒฝ๊ณ„์ธต์ด ๊ฐ๊ฐ ๋ฐ˜๋Œ€ ๋ฐฉํ–ฅ์˜ ๋ฐ€๋„ ๋ณ€ํ™”์œจ์„ ๊ฐ€์ง€๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋‘ ๊ฒฝ๊ณ„์ธต์— ์˜ํ•œ ๊ณต๊ธฐ๊ด‘ํ•™ ํšจ๊ณผ๊ฐ€ ์„œ๋กœ ์ƒ์‡„๋˜๋Š” ํ˜„์ƒ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ํ˜„์žฌ์˜ ํŒŒ๋ฉด ๊ณ„์ธก ๊ธฐ๋ฒ•๊ณผ ๊ฐ™์ด ๋‘ ๊ฐœ์˜ ๊ฒฝ๊ณ„์ธต์„ ์ง€๋‚˜๋Š” ๊ฒฝ์šฐ์—๋Š” ๊ฒฝ๊ณ„์ธต์˜ ํšจ๊ณผ๊ฐ€ ๋ฏธ๋ฏธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๊ณ , ์ถฉ๊ฒฉํŒŒ์˜ ํšจ๊ณผ๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋งŒ์•ฝ ์ถฉ๊ฒฉํŒŒ์™€ ๊ฒฝ๊ณ„์ธต์ด ํ•˜๋‚˜์”ฉ๋งŒ ์กด์žฌํ•œ๋‹ค๋ฉด ๊ฐ๊ฐ์— ์˜ํ•œ ๊ณต๊ธฐ๊ด‘ํ•™ ํšจ๊ณผ๋ฅผ ๋ฌด์‹œํ•  ์ˆ˜ ์—†๋Š” ์ˆ˜์ค€์ด๊ธฐ ๋•Œ๋ฌธ์— ๋‘˜ ๋‹ค ๋งŽ์€ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค.1 Introduction 1 1.1 Background 2 1.2 Introduction to Aero-optics 5 1.3 Previous Studies 10 1.3.1 Aero-optical effects due to the supersonic turbulent boundary layer 11 1.3.2 Aero-optical effects due to shock waves 14 1.3.3 Aero-optical effects due to hypersonic flow 19 1.3.4 Experimental methods for the aero-optics study 23 1.3.5 Numerical methods for the aero-optics study 26 1.4 Objectives 28 2 Experimental Methods 31 2.1 Experimental Facility 32 2.1.1 Heated subsonic jet 32 2.1.2 Supersonic windtunnel 33 2.1.3 Hypersonic shock tunnel 36 2.2 Experimental Configuration and Test Model 41 2.2.1 Heat gun for the subsonic experiment 41 2.2.2 Supersonic flow over a compression ramp 44 2.2.3 Hypersonic flow over a wedge 46 2.2.4 Hypersonic flow over an ogive nose cone 47 2.3 Data Acquisition 50 2.3.1 Flow visualization 50 2.3.2 Wavefront measurement system 54 3 Numerical Methods 59 3.1 Overview of the Numerical Simulation 60 3.2 Numerical Methods to Obtain the Flow Density 61 3.2.1 Density from the BOS technique 62 3.2.2 Flow-simulation method 66 3.3 Optical Calculation 69 4 Aero-optics in Subsonic Heated Flow 79 4.1 Flow Field of the Heated Subsonic Jet 80 4.2 Density Acquisition from Background-oriented Schlieren Images 84 4.3 Quantitative Evaluation of Background-oriented Schlieren for Aero-optics 90 5 Aero-optics in Supersonic Flow 97 5.1 Supersonic Flow Field Over the Compression Ramp 98 5.2 Wavefront Measured with the Shack-Hartmann Wavefront Sensor 101 5.3 Simulation of the Propagation of the Optical Wave 103 5.3.1 Validation of the numerical simulation result 103 5.3.2 Effect of the refraction due to windows 107 5.3.3 Deflection along the ray depending on the line of sight 108 5.3.4 Numerical simulation with slip conditions on the top wall 114 6 Aero-optics in Hypersonic Flow 119 6.1 Flow Conditions for the Hypersonic Experiment 120 6.2 Hypersonic Flow Field Around a Wedge 121 6.3 Wavefront Measurement in the Hypersonic Wedge Flow 124 6.4 Numerical Simulation for Hypersonic Wedge Flow 132 6.4.1 Numerical simulation considering only the flow field 132 6.4.2 Effect of refraction due to windows 133 6.4.3 Deflection depending on the line of sight 137 6.4.4 Numerical simulations with slip conditions on the up-plate 141 6.5 Aero-optical Experiments on the Ogive Nose Cone Model 146 7 Conclusions 149Docto

    ๊ทน์ดˆ์Œ์† ์œ ๋™ ๋‚ด ์ถฉ๊ฒฉํŒŒ์— ์˜ํ•œ ๊ณต๊ธฐ๊ด‘ํ•™ ํŠน์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2015. 2. ์ •์ธ์„.Hypersonic flow field around flight vehicle is complicated and induce various problem. Especially, flight vehicle with optical system experience aero-optical aberration induced by hypersonic flow field. Aero-optics is a field which studies interaction between flow field and electromagnetic wave propagation near flight vehicle. Turbulence, shock wave and boundary layer at near flow field of flight vehicle induce index of refraction variation, distorting electromagnetic wave propagation characteristics. Aero-optical phenomena decrease optical performance and limits accuracy of optical system loaded on flight vehicle. Research have been conducted to find out the relation between flow field characteristics and optical characteristics. Accordingly, various devices have been developed to measure and analyze aero-optical characteristics. Recently, an optical sensor called Shack-Hartmann sensor was developed and performance was compared with other optical sensor. In this thesis, hypersonic flow field is demonstrated through shock tunnel and aero-optics experiment is conducted. Shock tunnel performance is validated by analyzing pressure distribution inside driven tube and measuring test time inside test section. Conical nozzle and contoured nozzle is installed at the shock tunnel to generate Mach 7 flow. Nozzle flow characteristics is studied through stagnation pressure measurement with pitot rake and flow visualization of wedge model with shadowgraph or schlieren technique. Conical nozzle has 63% decrease of stagnation pressure during nozzle flow expansion and exit flow is Mach 6.8. Contoured nozzle has little loss of stagnation pressure and exit flow is Mach 6.9. After shock tunnel performance and nozzle performance is validated, aero-optical characteristics is measured using Shack-Hartmann sensor. Shack-Hartmann sensor performance is studied and validated with experiments on optical table. Wedged window and hot wire is used as a density disturbance. Wedged window tilts the laser to angle which is same as refractive angle calculated through refraction law. Hot wire experiment result reveals increase of optical aberration as power consumption of hot wire increase. Aero-optical characteristics induced by shock wave in hypersonic flow field is studied. Wedge model is installed inside shock tunnel test section and Shack-Hartmann sensor measured phase and intensity, which is converted to point spread function (PSF). From PSF, aero-optical parameter such as bore sight error (BSE), Strehl ratio is calculated. In the results, average BSE and tilt increased as stagnation pressure increased. Average BSE is 80 ~ 160ฮผrad, and Strehl ratio is 0.65 ~ 0.835. Shock wave in hypersonic flow increase BSE, tilt and decrease Strehl ratio.Abstract โ…ฐ Table of Contents iii List of Tables โ…ด List of Figures โ…ต Nomenclature โ…ท 1. Introduction 1 1.1 Background 1 1.2 Hypersonic flow field around flight vehicle 1 1.3 Aero-optics 2 1.4 Previous study 3 1.5 Research objectives 4 2. Experimental setup 6 2.1 Facility 6 2.1.1 Conical nozzle 8 2.1.2 Contoured nozzle 9 2.2 Model design and installation 10 2.2.1 Laser 12 2.2.2 Optical components 12 2.2.3 Sensor 15 2.2.4 Model 17 2.3 Measurement 19 2.3.1 Aero-optics measurement 19 2.3.2 Hypersonic flow field measurement 22 2.3.3 Measurement trigger system 25 3. Experiments results 28 3.1 Aero-optical characteristics in static flow 28 3.1.1 Experiment using wedged window 28 3.1.2 Experiment using hot wire 30 3.2 Hypersonic flow field characteristics 33 3.2.1 Shock tunnel performance validation 33 3.2.2 Nozzle flow characteristics 35 3.3 Aero-optical characteristics in hypersonic flow field 42 4. Conclusion 47 Bibliography 48Maste

    ์ธ๊ฐ„ PD-L1 ํ•ญ์ฒด์™€ ์ธํ„ฐํŽ˜๋ก  ๋ฒ ํƒ€ ๋ณ€์ด์ฒด๋ฅผ ํ†ตํ•œ ๋ฉด์—ญ ์‚ฌ์ดํ† ์นด์ธ ๊ฐœ๋ฐœ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์•ฝํ•™๋Œ€ํ•™ ์•ฝํ•™๊ณผ, 2017. 8. ์‹ ์˜๊ธฐ.๋ฉด์—ญ ํ•ญ์•”์š”๋ฒ•(Immunotherapy)์€ ๊ธฐ์กด ํ•ญ์•”์š”๋ฒ•์— ๋น„ํ•ด ์•” ํ™˜์ž์˜ ์žฅ๊ธฐ ์ƒ์กด์œจ์„ ํฌ๊ฒŒ ์ฆ๋Œ€์‹œ์ผœ ์ค€๋‹ค๋Š” ์ ์—์„œ ํฐ ์ฃผ๋ชฉ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ ์ค‘ ๋Œ€ํ‘œ์ ์ธ ํƒ€๊ฒŸ์œผ๋กœ ์•Œ๋ ค์ง„ PD-L1 (Programmed cell death Ligand 1)์€ PD-1์— ๊ฒฐํ•ฉํ•˜์—ฌ ์•”์„ธํฌ์˜ ๋ฉด์—ญํšŒํ”ผ๋ฅผ ์ผ์œผํ‚ค๊ณ  ํ•ญ์›์ œ์‹œ์„ธํฌ์˜ ์กฐ์ ˆ T ์„ธํฌ ์„ฑ์ˆ™๋Šฅ๋ ฅ์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ๋“ฑ ๋‹ค๊ฐ์ ์œผ๋กœ ์ž‘์šฉํ•˜๋Š” ๋‹จ๋ฐฑ์งˆ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํŒŒ์ง€ ๋””์Šคํ”Œ๋ ˆ์ด ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์ธ๊ฐ„ PD-L1์— ์„ ํƒ์ ์œผ๋กœ ๊ฒฐํ•ฉํ•˜๋Š” ๋‹จ์ผ ํด๋ก  ํ•ญ์ฒด ํ›„๋ณด๋ฌผ์งˆ ๋‘ ๊ฐ€์ง€๋ฅผ ์„ ๋ณ„ํ•˜์˜€์œผ๋ฉฐ, ์ด๊ฒƒ๋“ค์€ ๋ฏธFDA๊ฐ€ ์Šน์ธํ•œ PD-L1 ํ•ญ์ฒด Atezolizumab๋ณด๋‹ค ๋” ๋‚ฎ์€ ๋†๋„์—์„œ PD-1/PD-L1 ์‹ ํ˜ธ๋ฅผ ์ฐจ๋‹จํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ PD-L1 ํ•ญ์ฒด๋Š” ๋ฏธ์„ธ์ข…์–‘ํ™˜๊ฒฝ์—์„œ์˜ ์ข…์–‘ ์นจ์œค ๋ฆผํ”„๊ตฌ ๋ถ€์กฑ์œผ๋กœ ์ธํ•ด ๋งŽ์€ ํ™˜์ž๋“ค์—์„œ ํšจ๊ณผ๋ฅผ ๋ณด์ด์ง€ ์•Š๋Š”๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ข…์–‘ ์นจ์œค ๋ฆผํ”„๊ตฌ๋ฅผ ์ฆ๊ฐ€์‹œํ‚ฌ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋˜๋Š” ์ธํ„ฐํŽ˜๋ก  ๋ฒ ํƒ€ ๋ณ€์ด์ฒด๋ฅผ PD-L1 ํ•ญ์ฒด์— ์—ฐ๊ฒฐ์‹œํ‚จ ํ˜•ํƒœ์ธ ๋ฉด์—ญ์‚ฌ์ดํ† ์นด์ธ์„ ๊ตฌ์ƒํ•˜์˜€๊ณ , ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ๋ฉด์—ญ ์‚ฌ์ดํ† ์นด์ธ์„ ๋””์ž์ธํ•˜์—ฌ ์ƒ์‚ฐ ํ›„ ๊ฐ ํ˜•ํƒœ๋ณ„ ํŠน์„ฑ์„ ์ธก์ •ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, scFv-Fc-์ธํ„ฐํŽ˜๋ก  ๋ฒ ํƒ€ ๋ณ€์ด์ฒด ํ˜•ํƒœ์˜ ๋ฉด์—ญ์‚ฌ์ดํ† ์นด์ธ์ด ์ƒ์‚ฐ์„ฑ, ์ƒ๋ฌผํ•™์  ํ™œ์„ฑ, PD-1/PD-L1 ์‹ ํ˜ธ ์ฐจ๋‹จ ๋Šฅ๋ ฅ ๊ทธ๋ฆฌ๊ณ  PD-L1 ํƒ€๊ฒŸํŒ… ๋Šฅ๋ ฅ์—์„œ ๋‹ค๋ฅธ ํ˜•ํƒœ์˜ ๋ฉด์—ญ์‚ฌ์ดํ† ์นด์ธ๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” PD-L1 ํ•ญ์ฒด๋ฅผ ์„ ๋ณ„ํ•˜๊ณ  ์ด๋ฅผ ๋ฉด์—ญ์‚ฌ์ดํ† ์นด์ธ์œผ๋กœ ํ™œ์šฉํ•˜๊ธฐ ์œ„ํ•œ ์ตœ์ ์˜ ํ˜•ํƒœ๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค.Immunotherapy has received a great deal of attention due to its great enhancement of the long term survival rate of patients compared to conventional anti-cancer therapy. PD-L1(Programmed cell death Ligand 1), a typical target for immunotherapy, is a protein that binds to PD-1 and has various wide-reaching functions, which can cause avoiding immune destruction of cancer cells and increase regulatory T cell maturation by antigen presenting cells. In this study, two antibody candidates selectively bound to human PD-L1 were screened using Phage display technique, which confirmed that these antibodies neutralized PD-1/PD-L1 signal at a lower concentration compared to Atezolizumab, a FDA approved PD-L1 antibody. However, PD-L1 antibodies are known to be ineffective in more than half of the patients due to the lack of TIL(tumor infiltrating lymphocyte cells) in the tumor micro-environment. In order to solve this problem, immunocytokines were designed by linking interferon-ฮฒ-1b mutein, which is known to increase TIL, to the PD-L1 antibody and various types of immunocytokines were designed. In addition, their properties were measured after production to find the most optimal forms of immunocytokine. From our results, we found that scfv-Fc-interferon-ฮฒ-1b mutein type of immunocytokine is superior than other forms of immunocytokines in productivity, biological activity, PD-1/PD-L1 signal neutralizing ability, and PD-L1 targeting ability. This study is significant in that it has searched for an effective human PD-L1 antibody and found an optimal form to be utilized as an immunocytokine.INTRODUCTION 1 Materials and Methods 3 Molecular cloning and expression of PD-L1 antigen 3 Cleavage of Fc domain from PD-L1 antigen 3 Phage display 4 Expression of single chain fragment variables 4 ELISA for PD-L1 candidate screening 4 PD-L1 candidates CDR sequencing 5 IGG conversion and expression of PD-L1 antibody candidates 5 Cell culture 6 Flow cytometry 6 PD-1/PD-L1 competitive inhibition ELISA 6 Type 1 IFN responsive Luciferase reporter assay 7 Molecular cloning and expression of various immunocytokines 7 RESULT 8 DISCUSSION 26 REFERENCES 30 ๊ตญ ๋ฌธ ์ดˆ ๋ก 34Maste

    Pamidronate Down-regulates Tumor Necrosis Factor-alpha Induced Matrix Metalloproteinases Expression in Human Intervertebral Disc Cells

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    BACKGROUND: N-containing bisphosphonates (BPs), such as pamidronate and risedronate, can inhibit osteoclastic function and reduce osteoclast number by inducing apoptotic cell death in osteoclasts. The aim of this study is to demonstrate the effect of pamidronate, second generation nitrogen-containing BPs and to elucidate matrix metallo-proteinases (MMPs) mRNA expression under serum starvation and/or tumor necrosis factor alpha (TNF-ฮฑ) stimulation on metabolism of intervertebral disc (IVD) cells in vitro. METHODS: Firstly, to test the effect of pamidronate on IVD cells in vitro, various concentrations (10(-12), 10(-10), 10(-8), and 10(-6) M) of pamidronate were administered to IVD cells. Then DNA and proteoglycan synthesis were measured and messenger RNA (mRNA) expressions of type I collagen, type II collagen, and aggrecan were analyzed. Secondly, to elucidate the expression of MMPs mRNA in human IVD cells under the lower serum status, IVD cells were cultivated in full serum or 1% serum. Thirdly, to elucidate the expression of MMPs mRNA in IVD cells under the stimulation of 1% serum and TNF-ฮฑ (10 ng/mL) In this study, IVD cells were cultivated in three dimensional alginate bead. RESULTS: Under the lower serum culture, IVD cells in alginate beads showed upregulation of MMP 2, 3, 9, 13 mRNA. The cells in lower serum and TNF-ฮฑ also demonstrated upregulation of MMP-2, 3, 9, and 13 mRNA. The cells with various doses of pamidronate and lower serum and TNF-ฮฑ were reveled partial down-regulation of MMPs. CONCLUSIONS: Pamidronate, N-containing second generation BPs, was safe in metabolism of IVD in vitro maintaining chondrogenic phenotype and matrix synthesis, and down-regulated TNF-ฮฑ induced MMPs expression.ope

    ํž˜์ค„ ์†์ƒ ๋ฐฑ์„œ ๋ชจ๋ธ์—์„œ์˜ ์ธ๊ฐ„ ์ง€๋ฐฉ์œ ๋ž˜ ์ค‘๊ฐ„์—ฝ ์ค„๊ธฐ์„ธํฌ์˜ ์น˜๋ฃŒ ๊ธฐ์ „

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ ์žฌํ™œ์˜ํ•™์ „๊ณต, 2016. 2. ์ •์„ ๊ทผ.Introduction: Mesenchymal stem cell (MSC) in treating tendon injury is well investigated by experimental animal models and several clinical trials have also reported safety and efficacy of the MSC therapy. However, therapeutic mechanisms of MSC for tendon injury have not fully understood and there is no in vivo study that MSCs can function as differentiated cells after transplantation. We aimed to investigate whether MSCs can differentiate into the tenogenic lineage and secrete their own proteins using a xenogeneic MSC transplantation model. Methods: Bilateral Achilles tendons of 57 SD rats were given full-thickness rectangular injuries at the tendon insertion site to the midsubstance. They were randomly assigned to 3 groups after the modeling: 1) human adipose-derived mesenchymal stem cells (hASC) implantation with fibrin glue (106 cells in 60 ฮผL) (Cell group), 2) fibrin glue injection with cell media by the same volume (Fibrin group), and 3) identical surgical procedure without any treatment (Sham group). After 2 and 4 weeks after modeling, all groups were evaluated by morphological, biomechanical, and histopathological (using modified Bonar score) analyses. Viability of tagged hASC was observed by immunofluorescence staining and protein expressions (collagen type I/III and tenascin-C) were evaluated by immunohistochemistry and Western blot analyses. Results: Rupture rate of Cell group (11.1%) was lower than the rates of Sham (25.0%) and Fibrin (22.2%) groups. Cross sectional areas of tendons in Cell group were decreased (P = 0.008) while those in Sham group were increased (P = 0.005) from 2 to 4 weeks. At 2 weeks, ultimate tensile strength and stiffness of Cell group (49.4 ยฑ 17.4 N and 10.1 ยฑ 3.9 N/mm) were significantly higher than those of Sham group (31.2 ยฑ 7.5 N, P = 0.037 and 4.7 ยฑ 1.4 N/mm, P = 0.010, respectively). Stiffnesses of Cell group at 2 and 4 weeks were also significantly higher than those of Fibrin group (P = 0.037 in both). However, there were no significant differences of total modified Bonar score among three groups at both 2 and 4 weeks. From the immunofluorescent scanning at 1, 2, and 4 weeks after cell implantations, tagged hASCs were all observed. Cell group showed higher optical densities in immunohistochemistry than those of Sham and Fibrin groups in human-specific collagen type I at both 2 and 4 weeks and in human-specific tenascin-C at 2 weeks. Western blot analysis also revealed human-specific collagen type I expression was higher than that of Sham group. Conclusions: Implanted hASCs to rat tendon injury model survived for 4 weeks and secreted human-specific collagen type I and tenascin-C. Human stem cells biomechanically enhanced rat tendon healing superior to sham and active control groups. To the best of the author's knowledge, this is the first in vivo report which proves the cell-originated protein synthesis by the implanted stem cells.Introduction 1 Materials and Methods 3 1. Study design 3 2. Stem cell preparation 3 3. Fluorescent cell labeling and immunofluorescence staining 5 4. Surgical procedure and treatment 6 5. Rupture rate and cross sectional area calculation 9 6. Biomechanical test 9 7. Tissue preparation and histological analysis 12 8. Immunohistochemistry 12 9. Western blot analysis 14 10. Statistical analysis 15 Results 16 1. Rupture rate and cross sectional area of tendon 16 2. Biomechanical test 19 3. Histological analysis (modified Bonar score) 21 4. Immunohistochemistry and immunofluorescent staining 24 5. Western blot analysis 29 Discussion 31 Conclusion 39 References 40 Abstract in Korean 46Docto

    Block๊ณผ particle ๊ณจ์ด์‹์žฌ์˜ pore density์™€ interconnectivity์˜ ์ฐจ์ด ๋ฐ ๊ณจํ˜•์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    Thesis(masters) --์„œ์šธ๋Œ€ํ•™๊ต ์น˜์˜ํ•™๋Œ€ํ•™์› :์น˜์˜ํ•™๊ณผ,2010.2.Maste

    ์ž‘์—…์žฅ ๋ˆ„์ ์†Œ์Œ๋…ธ์ถœ๊ณผ ํ˜ˆ์••๊ณผ์˜ ๊ด€๋ จ์„ฑ์— ๋Œ€ํ•œ ๋‹จ๋ฉด ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ์˜ˆ๋ฐฉ์˜ํ•™์ „๊ณต,2000.Maste

    L1210์„ธํฌ ํฌ์Šคํฌ๋ฆฌํŒŒ์ œ D์˜ ํŠน์„ฑ ์—ฐ๊ตฌ

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    Thesis (master`s)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ™”ํ•™๊ณผ ์ƒํ™”ํ•™์ „๊ณต,1997.Maste

    ์žฌ๊ณ ๊ด€๋ฆฌ ๋ฌธ์ œ์— ๋Œ€ํ•œ ๋ถ„ํฌ ๊ฐ•๊ฑด ์ตœ์ ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ,2020. 2. ๋ฌธ์ผ๊ฒฝ.The inventory problem is a classical problem in the operations management society to decide an optimal order policy under demand uncertainty. A decision maker chooses order quantities over the planning horizon to achieve the company's objective with respect to performance measures. Classical inventory management researches assume that complete information about the probability distribution of random demand is known, however, only limited information of the probability distribution is available in practice. To tackle this difficulty, a decision maker considers an ambiguity set which is a set of candidate distributions that may contain the unknown true distribution, and minimizes the worst-case expected cost over the ambiguity set. This approach is called distributionally robust optimization (DRO) and widely applied to many operations management problems. We adopt the distributionally robust approach to inventory problems to handle distributional ambiguity. In this dissertation, we consider three different but closely related problems: newsvendor problem, inventory problem, and empty container repositioning problem. For all three problems, we study decision making under demand uncertainty, but limited information about probability distributions of random demand is given. Hence, we adopt the distributionally robust approach and analyze various aspects of distributionally robust models. First, we study the data-driven distributionally robust newsvendor model with a set of distributions close to the empirical distribution in terms of the Wasserstein distance, and derive the closed-form solution of an optimal order quantity. Second, the inventory problem is considered, which is an extension of the newsvendor problem to the multistage setting. In the multistage setting of distributionally robust inventory problems, the decision maker carefully considers time consistency issue. Time consistency means that the optimal policy derived in the first period maintains its optimality through the planning horizon. We analyze the time consistency issue of the distributionally robust inventory model with a Wasserstein ambiguity set. Third, the empty container repositioning problem with foldable containers is considered, which is a practical application of the inventory problem. We propose a mathematical model of the empty container repositioning problem considering the use of foldable containers under demand uncertainty. To tackle the intractability of the multistage stochastic programming formulation, the linear decision rule formulation is proposed for the tractable and distributionally robust approximation of the multistage stochastic programming formulation. We also conduct computational experiments to validate respective models and findings.์žฌ๊ณ ๊ด€๋ฆฌ๋Š” ์šด์˜ ๊ด€๋ฆฌ ๋ถ„์•ผ์—์„œ ์ „ํ†ต์ ์ธ ๋ฌธ์ œ๋กœ, ์ˆ˜์š”์˜ ๋ถˆํ™•์‹ค์„ฑ ํ•˜์—์„œ ์ตœ์ ์˜ ์ฃผ๋ฌธ ์ •์ฑ…์„ ๊ฒฐ์ •ํ•˜๋Š” ๋ฌธ์ œ๋‹ค. ์˜์‚ฌ๊ฒฐ์ •์ž๋Š” ์„ฑ๊ณผ ์ฒ™๋„๋กœ ํ‘œํ˜„๋˜๋Š” ํšŒ์‚ฌ์˜ ๋ชฉ์ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ๊ณ„ํš ๊ธฐ๊ฐ„ ๋™์•ˆ์˜ ์ฃผ๋ฌธ๋Ÿ‰์„ ์„ ํƒํ•œ๋‹ค. ์ „ํ†ต์ ์ธ ์žฌ๊ณ  ๊ด€๋ฆฌ ์—ฐ๊ตฌ์—์„œ๋Š” ์ˆ˜์š”์˜ ํ™•๋ฅ  ๋ถ„ํฌ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ์ •๋ณด๋ฅผ ์•Œ๊ณ  ์žˆ๋‹ค๊ณ  ๊ฐ€์ •ํ•˜์ง€๋งŒ, ํ˜„์‹ค์—์„œ๋Š” ํ™•๋ฅ  ๋ถ„ํฌ์— ๋Œ€ํ•œ ์ œํ•œ๋œ ์ •๋ณด๋งŒ ์ด์šฉ๊ฐ€๋Šฅํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ์–ด๋ ค์›€์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์˜์‚ฌ๊ฒฐ์ •์ž๋Š” ์•Œ๋ ค์ง€์ง€ ์•Š์€ ์ˆ˜์š”์˜ ํ™•๋ฅ  ๋ถ„ํฌ๋ฅผ ํฌํ•จํ•  ์ˆ˜ ์žˆ๋Š” ํ›„๋ณด ๋ถ„ํฌ๋“ค์˜ ์ง‘ํ•ฉ์ธ ๋ชจํ˜ธ์„ฑ ์ง‘ํ•ฉ์„ ๊ณ ๋ คํ•˜๊ณ , ์ด ์ง‘ํ•ฉ ์œ„์—์„œ ์ตœ์•…์˜ ํ‰๊ท  ๋น„์šฉ์„ ์ตœ์†Œํ™”ํ•œ๋‹ค. ์ด ์ ‘๊ทผ๋ฐฉ๋ฒ•์„ ๋ถ„ํฌ ๊ฐ•๊ฑด ์ตœ์ ํ™”๋ผ๊ณ  ํ•˜๋ฉฐ, ๋งŽ์€ ์šด์˜ ๊ด€๋ฆฌ ๋ฌธ์ œ์— ๋„๋ฆฌ ์ ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์žฌ๊ณ ๊ด€๋ฆฌ ๋ฌธ์ œ์˜ ๋ถ„ํฌ์— ๋Œ€ํ•œ ์ •๋ณด ๋ถ€์กฑ์„ ๋‹ค๋ฃจ๊ธฐ ์œ„ํ•ด ๋ถ„ํฌ ๊ฐ•๊ฑด ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹ ๋ฌธ๊ฐ€ํŒ์› ๋ฌธ์ œ, ์žฌ๊ณ ๊ด€๋ฆฌ ๋ฌธ์ œ, ๊ณต์ปจํ…Œ์ด๋„ˆ ์žฌ๋ฐฐ์น˜ ๋ฌธ์ œ ๋“ฑ ์„ธ ๊ฐ€์ง€ ์„œ๋กœ ๋ฐ€์ ‘ํ•˜๊ฒŒ ๊ด€๋ จ๋œ ๋ฌธ์ œ๋ฅผ ๊ณ ๋ คํ•œ๋‹ค. ์„ธ ๊ฐ€์ง€ ๋ฌธ์ œ ๋ชจ๋‘ ์ˆ˜์š”์˜ ๋ถˆํ™•์‹ค์„ฑ ํ•˜์—์„œ ์˜์‚ฌ๊ฒฐ์ •์„ ์—ฐ๊ตฌํ•˜์ง€๋งŒ, ์ˆ˜์š”์˜ ํ™•๋ฅ  ๋ถ„ํฌ์— ๋Œ€ํ•œ ์ œํ•œ๋œ ์ •๋ณด๋งŒ ์ฃผ์–ด์ง„๋‹ค. ์ด์— ๋”ฐ๋ผ ๋ถ„ํฌ ๊ฐ•๊ฑด ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜๊ณ  ๋ถ„ํฌ ๊ฐ•๊ฑด ๋ชจํ˜•๋“ค์˜ ๋‹ค์–‘ํ•œ ์ธก๋ฉด์„ ๋ถ„์„ํ•œ๋‹ค. ์ฒซ์งธ, ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ๋งŒ๋“ค์–ด์ง„ ๊ฒฝํ—˜์  ๋ถ„ํฌ๋กœ๋ถ€ํ„ฐ Wasserstein ๊ฑฐ๋ฆฌ ๊ธฐ์ค€์œผ๋กœ ๊ฐ€๊นŒ์šด ํ™•๋ฅ  ๋ถ„ํฌ๋“ค์„ ๊ณ ๋ คํ•œ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜์˜ ๋ถ„ํฌ ๊ฐ•๊ฑด ์‹ ๋ฌธ๊ฐ€ํŒ์› ๋ชจํ˜•์„ ์—ฐ๊ตฌํ•˜๊ณ , ์ตœ์  ์ฃผ๋ฌธ๋Ÿ‰์˜ ๋‹ซํžŒ ํ˜•ํƒœ์˜ ํ‘œํ˜„์„ ๋„์ถœํ•œ๋‹ค. ๋‘˜์งธ, ์‹ ๋ฌธ๊ฐ€ํŒ์› ๋ชจํ˜•์ด ๋‹ค๋‹จ๊ณ„ ๋ฌธ์ œ๋กœ ํ™•์žฅ๋œ ์žฌ๊ณ ๊ด€๋ฆฌ ๋ฌธ์ œ๋ฅผ ๊ณ ๋ คํ•œ๋‹ค. ๋ถ„ํฌ ๊ฐ•๊ฑด ์žฌ๊ณ  ๋ฌธ์ œ์˜ ๋‹ค๋‹จ๊ณ„ ํŠน์„ฑ์—์„œ ์˜์‚ฌ๊ฒฐ์ •์ž๊ฐ€ ์‹ ์ค‘ํ•˜๊ฒŒ ๊ณ ๋ คํ•ด์•ผํ•  ์‚ฌํ•ญ์€ ์‹œ๊ฐ„ ์ผ๊ด€์„ฑ์ด๋‹ค. ์‹œ๊ฐ„ ์ผ๊ด€์„ฑ์€ ์ฒซ ์‹œ์ ์— ๋„์ถœํ•œ ์ตœ์  ์ •์ฑ…์ด ๊ณ„ํš ๊ธฐ๊ฐ„ ๋™์•ˆ ์ตœ์ ์„ฑ์„ ์œ ์ง€ํ•ด์•ผ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. Wasserstein ๋ชจํ˜ธ์„ฑ ์ง‘ํ•ฉ์„ ๊ณ ๋ คํ•œ ๋ถ„ํฌ ๊ฐ•๊ฑด ์žฌ๊ณ  ๋ชจํ˜•์˜ ์‹œ๊ฐ„ ์ผ๊ด€์„ฑ์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์…‹์งธ, ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๋ฅผ ๊ณ ๋ คํ•œ ๊ณต์ปจํ…Œ์ด๋„ˆ ์žฌ๋ฐฐ์น˜ ๋ฌธ์ œ๋ฅผ ๊ณ ๋ คํ•˜๋Š”๋ฐ, ์ด๋Š” ์žฌ๊ณ  ๊ด€๋ฆฌ ๋ฌธ์ œ์˜ ํ˜„์‹ค์ ์ธ ์‘์šฉ ๋ฌธ์ œ์ด๋‹ค. ์ˆ˜์š” ๋ถˆํ™•์‹ค์„ฑ ํ•˜์—์„œ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ์˜ ์‚ฌ์šฉ์„ ๊ณ ๋ คํ•œ ๊ณต์ปจํ…Œ์ด๋„ˆ ์žฌ๋ฐฐ์น˜ ๋ฌธ์ œ์˜ ์ˆ˜๋ฆฌ์  ๋ชจํ˜•์„ ์ œ์•ˆํ•œ๋‹ค. ๋‹ค๋‹จ๊ณ„ ์ถ”๊ณ„ ๊ณ„ํš์˜ ๊ณ„์‚ฐ ๋ณต์žก๋„ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์„ ํ˜• ๊ฒฐ์ • ๊ทœ์น™์— ๊ธฐ๋ฐ˜ํ•œ ์ˆ˜๋ฆฌ ๋ชจํ˜•์„ ์ œ์‹œํ•˜๋Š”๋ฐ, ์ด๋Š” ๋‹ค๋‹จ๊ณ„ ์ถ”๊ณ„ ๊ณ„ํš์˜ ๊ณ„์‚ฐ๊ฐ€๋Šฅํ•˜๋ฉฐ ๋ถ„ํฌ ๊ฐ•๊ฑดํ•œ ๊ทผ์‚ฌ๊ฐ€ ๋œ๋‹ค. ๋˜ํ•œ ๊ฐ๊ฐ์˜ ๋ชจํ˜•๊ณผ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜์น˜ ์‹คํ—˜์„ ์ง„ํ–‰ํ•œ๋‹ค.Chapter 1 Introduction 1 1.1 Inventory Problems 1 1.2 Distributionally Robust Optimization 4 1.3 Research Motivations and Contributions 7 1.4 Outline of the Dissertation 10 Chapter 2 Distributionally Robust Newsvendor Model with a Wasser stein Ambiguity Set 11 2.1 Problem Description and Literature Review 11 2.2 Distributionally Robust Optimization with the Wasserstein Distance 16 2.3 Distributionally Robust Newsvendor Model 22 2.3.1 Wasserstein Order p = 1 24 2.3.2 Wasserstein Order p > 1 29 2.4 Risk-averse Newsvendor Model 40 2.4.1 Wasserstein Order p = 1 42 2.4.2 Wasserstein Order p > 1 45 2.5 Computational Experiments 48 2.5.1 Out-of-sample Performance 48 2.5.2 Convergence Property 51 2.5.3 Risk-aversion of the CVaR Solution 55 2.6 Summary 56 Chapter 3 Distributionally Robust Inventory Model with a Wasserstein Ambiguity Set 58 3.1 Problem Description and Literature Review 58 3.2 Distributionally Robust Inventory Model and Time Consistency 63 3.3 Sufficient Condition for Weak Time Consistency 73 3.3.1 Newsvendor Policy 77 3.3.2 Distributionally Robust Newsvendor Model with Unit Purchase Cost 84 3.4 Further Analysis About the Dynamic Programming Formulation 87 3.4.1 Computation of Base-Stock Levels for Dynamic Programming Formulations 87 3.4.2 Non-zero Fixed Order Cost 89 3.4.3 Desirable Properties of Wasserstein DRO 91 3.5 Computational Experiments 93 3.5.1 Monotonicity 93 3.5.2 Conservativeness 96 3.5.3 Out-of-sample Performance Guarantee 99 3.5.4 Convergence Property 101 3.6 Summary 103 Chapter 4 Empty Container Repositioning with Foldable Containers 104 4.1 Problem Description and Literature Review 104 4.2 Multistage Stochastic Programming Formulation 112 4.2.1 Cycle of Container Flows 112 4.2.2 Assumptions and Nomenclature 114 4.2.3 Deterministic Formulation 118 4.2.4 Multistage Stochastic Programming Formulation 121 4.3 Affinely Adjustable Robust Formulation 125 4.3.1 Factor-Based Demand Model 125 4.3.2 Bound on Expectations of Positive Parts 126 4.3.3 Linear Decision Rule Formulation 130 4.3.4 Restricted Linear Decision Rule Formulation 136 4.4 Computational Experiments 141 4.4.1 Experimental Setting 142 4.4.2 Computational Results 146 4.4.3 Simulation Results 152 4.5 Summary 157 Chapter 5 Conclusions 158 5.1 Summary and Contributions 158 5.2 Future Research 160 Bibliography 162 ๊ตญ๋ฌธ์ดˆ๋ก 177Docto
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