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    ์ปฌ๋Ÿฌ ์นด๋ฉ”๋ผ ์ด๋ฏธ์ง• ๋ฎฌ๋Ÿฌ ํ–‰๋ ฌ ์—ก๋ฆฝ์†Œ๋ฏธํ„ฐ๋ฅผ ์ด์šฉํ•œ ์˜์—ญ ๋ถ„๋ฅ˜์™€ ๋ฐ•๋ง‰ ๋‘๊ป˜ ์ธก์ •์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2019. 2. ๋ฐ•ํฌ์žฌ.ํƒ€์›๊ณ„๋Š” ๊ด‘ํ•™ ์ธก์ • ๋ฐฉ์‹์œผ๋กœ ๋ฐ˜๋„์ฒด, ๋””์Šคํ”Œ๋ ˆ์ด ๊ณต์ • ๊ฒ€์‚ฌ์—์„œ ๋น„์ ‘์ด‰, ๋น„ํŒŒ๊ดด, ์‹ ์† ์ธก์ •์ด ๊ฐ€๋Šฅํ•œ ์žฅ์ ์ด ์žˆ๋‹ค. ๋ถ„๊ด‘ ๋ฐฉ์‹์˜ ๊ฒฝ์šฐ ๋†’์€ ํŒŒ์žฅ ํ•ด์ƒ๋ ฅ์„ ํ†ตํ•ด ๋†’์€ ์ •๋ฐ€๋„๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค๋Š” ์žฅ์ ์ด ์žˆ์œผ๋‚˜, ๋‹จ์ผ ์˜์—ญ ์ธก์ • ํ•œ๊ณ„์™€ ์ข์€ ์˜์—ญ์˜ ์ธก์ •์ด ๋ถˆ๊ฐ€๋Šฅํ•˜๋ฉฐ ์ธก์ • ์˜์—ญ ๋‚ด์˜ ๋ชจํ˜ธ์„ฑ ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. 2D CCD๋ฅผ ์ด์šฉํ•œ ์ด๋ฏธ์ง• ๋ฐฉ์‹์˜ ์ธก์ • ๋ฐฉ๋ฒ•์€ ๊ณต๊ฐ„ ํ•ด์ƒ๋ ฅ์„ ํ”ฝ์…€ ์ˆ˜์ค€๊นŒ์ง€ ๋†’์ผ ์ˆ˜ ์žˆ์–ด ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๊ธฐ์กด์˜ ๋‹จํŒŒ์žฅ ์ด๋ฏธ์ง• ๋ฐฉ์‹์€ ํŒŒ์žฅ ๋ฐ์ดํ„ฐ์˜ ๋ถ€์กฑ์œผ๋กœ ์ธก์ • ๋ชจํ˜ธ์„ฑ์ด ์žˆ์œผ๋ฉฐ, ๋‹คํŒŒ์žฅ ์ด๋ฏธ์ง• ๋ฐฉ์‹์˜ ๊ฒฝ์šฐ ํŒŒ์žฅ์„ ๋ฐ”๊พธ๋Š” ๊ณผ์ •์œผ๋กœ ์ธํ•ด ์ธก์ • ์‹œ๊ฐ„์ด ๊ธธ๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ปฌ๋Ÿฌ ์นด๋ฉ”๋ผ๋ฅผ ์ด์šฉํ•œ ์ด๋ฏธ์ง• ๋ฎฌ๋Ÿฌ ํ–‰๋ ฌ ํƒ€์›๊ณ„๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•œ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ ์ด๋ฏธ์ง• ๋ฐฉ์‹์˜ ๊ฒฝ์šฐ ์ด๋ฏธ์ง€ ์˜์—ญ ์ „์ฒด ํ”ฝ์…€ ๋ถ„์„์˜ ๊ฒฝ์šฐ ๋งŽ์€ ์‹œ๊ฐ„์ด ๊ฑธ๋ ค ๋ถˆ๊ฐ€๋Šฅํ•œ ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ์ œ์•ˆํ•˜๋Š” ํด๋Ÿฌ์Šคํ„ฐ๋ง ๋ฐฉ์‹์„ ์‚ฌ์šฉํ•˜์—ฌ ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•œ๋‹ค. ์—ฐ๊ตฌ์— ์‚ฌ์šฉ๋˜๋Š” ์ปฌ๋Ÿฌ ์นด๋ฉ”๋ผ๋Š” ๋ฒ ์ด์–ด ํ•„ํ„ฐ๋กœ ์ธํ•ด ์—ฌ๋Ÿฌ ํŒŒ์žฅ์˜ ๋ฎฌ๋Ÿฌ ํ–‰๋ ฌ์ด ์ค‘์ฒฉ๋œ ๊ฒฐ๊ณผ๊ฐ€ ์ธก์ •๋˜๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ด‘๋Œ€์—ญ ํŒŒ์žฅ ํ•„ํ„ฐ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํ•„ํ„ฐ์˜ ํˆฌ๊ณผ๋„๋ฅผ ๊ณ ๋ คํ•œ ์ƒˆ๋กœ์šด ๋ฎฌ๋Ÿฌ ํ–‰๋ ฌ ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•œ ๋ฐฉ์‹์„ ์ด์šฉํ•˜์—ฌ ๋‘๊ป˜ ์ธก์ •์„ ์ง„ํ–‰ํ•˜๊ณ , ๊ฒ€์ฆ๋œ ํ•˜๋“œ์›จ์–ด๋กœ ์ธก์ •๋œ ๊ฒฐ๊ณผ์™€ ๋น„๊ตํ•˜์—ฌ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์— ์‚ฌ์šฉ๋œ ํด๋Ÿฌ์Šคํ„ฐ๋ง ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ์ด๋ฏธ์ง€ ์˜์—ญ์„ ๋น„์Šทํ•œ ๋ฎฌ๋Ÿฌ ํ–‰๋ ฌ์„ ๊ฐ–๋Š” ์˜์—ญ๋ผ๋ฆฌ ๋ถ„๋ฅ˜ํ•˜์˜€๊ณ , ๋ถ„๋ฅ˜ํ•œ ์˜์—ญ์˜ ํ‰๊ท  ๋‘๊ป˜๋ฅผ ์ธก์ •ํ•˜์˜€๋‹ค. ๊ฐ ํด๋Ÿฌ์Šคํ„ฐ ๋‚ด์˜ ํ‘œ์ค€ํŽธ์ฐจ๋ฅผ ํ™•์ธํ•˜์—ฌ ํ‰๊ท ๊ฐ’์ด ํด๋Ÿฌ์Šคํ„ฐ๋ฅผ ๋Œ€ํ‘œํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.The ellipsometer, one of the optical measurement systems, are widely used because of the advantages of non-contact, nondestructive and rapid measurement. the spectroscopic method has the superiority of high precision because of high wavelength resolution, but the system can not measure narrow areas and has ambiguity in the measurement area and a limit for measuring only a single area. Imaging systems using 2D CCD are a technique to increase spatial resolution to pixel levels. Traditional monochromatic imaging methods have uncertainty in measurement because of the lack of wavelength data, and multi-wavelength imaging methods have long measurement time due to wavelength changes. In this study, A color camera imaging Mueller matrix ellipsometer is proposed to solve the problem. Further, imaging methods have a problem that takes a long time to analyze the entire pixel of an image, and a clustering method is proposed to solve the analysis time problem. The measured data with the color camera is the result of a superposition of different wavelengths due to the Bayer filter. Therefore, a new Mueller matrix equation is proposed considering the transmittance of the filter that can be applied to a broadband wavelength filter. Thickness measurements were implemented using the proposed method and verified by comparing with the measured results with validated hardware. Through the clustering method used in the study, the image was classified into regions having similar Mueller matrices, and the average thicknesses of each class were measured. By examining the standard deviation within each cluster, we have verified that the mean of the Mueller matrix of the class can represent the classChapter 1. Introduction 1 1.1 Research background. 1 1.2 Research Trends 2 1.2.1 Mueller matrix ellipsometry 2 1.2.2 Imaging reflectometry. 3 1.2.3 Imaging ellipsometry . 4 1.3 Research topic 5 Chapter 2. Theoretical Background. 7 2.1 Research background. 7 2.1.1 The law of refraction and reflection. 7 2.1.2 Multiplt reflections in thin-film 8 2.2 Jones and Mueller matrix. 9 2.3 Mueller matrix ellipsometry 10 2.3.1 Ellipsometry configuration 10 2.3.2 MME parts 12 2.3.3 Decomposition 13 2.4 Clustering 14 2.4.1 Principal component analysis 14 2.4.2 K-means clustering 16 Chapter 3. Data Acquisition Method 17 3.1 Beam drifting correction 17 3.2 Mueller matrix calculation . 17 3.3 Calibration. 18 3.4 Mueller matrix decomposition . 20 3.5 Mueller matrix in a RGB color space 21 3.6 Thickness measurement 22 3.7 Clustering 23 Chapter 4. Result. 27 4.1 Measurement sample . 27 4.2 Classification and thickness measurement . 28 Chapter 5. Conclusion 30 Bibliography. 31 Abstract in Korean 34Maste

    ๋‘๋ถ€๊ณ„์ธก๋ฐฉ์‚ฌ์„  ์‚ฌ์ง„ ๊ณ„์ธก์  ์ž๋™ ์‹๋ณ„์˜ ์ตœ์‹  ๊ธฐ๊ณ„ ํ•™์Šต ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐ„ ์ •ํ™•๋„ ๋ฐ ์—ฐ์‚ฐ ์„ฑ๋Šฅ ๋น„๊ต ์—ฐ๊ตฌ โ€“ YOLOv3 vs SSD

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์น˜์˜ํ•™๋Œ€ํ•™์› ์น˜์˜ํ•™๊ณผ,2019. 8. ์ด์‹ ์žฌ.Introduction: The purpose of this study was to compare two of the latest deep learning algorithms for automatic identification of cephalometric landmarks in their accuracy and computational efficiency. This study uses two different algorithms for automated cephalometric landmark identification with an extended number of landmarks: 1) You-Only-Look-Once version 3 (YOLOv3) based method with modification, and 2) the Single Shot Detector (SSD) based method. Materials and methods: A total of 1,028 cephalometric radiographic images were selected as learning data that trained YOLOv3 and SSD methods. The number of target labelling was 80 landmarks. After the deep learning process, the algorithms were tested using a new test data set comprised of 283 images. The accuracy was determined by measuring the mean point-to-point error, success detection rate (SDR), and visualized by drawing 2-dimensional scattergrams. Computational time of both algorithms were also recorded. Results: YOLOv3 algorithm outperformed SSD in accuracy for 38/80 landmarks. The other 42/80 landmarks did not show a statistically significant difference between YOLOv3 and SSD. Error plots of YOLOv3 showed not only a smaller error range, but also a more isotropic tendency. Mean computational time spent per image was 0.05 seconds and 2.89 seconds for YOLOv3 and SSD, respectively. YOLOv3 showed approximately 5% higher accuracy compared with the top benchmarks in the literature. Conclusions: Between the two algorithms applied, YOLOv3 seems to be promising as a fully automated cephalometric landmark identification system for use in clinical practice.์—ฐ๊ตฌ ๋ชฉ์ : ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋‘๋ถ€๊ณ„์ธก๋ฐฉ์‚ฌ์„  ์‚ฌ์ง„ ๊ณ„์ธก์  ์ž๋™ ์‹๋ณ„์— ์žˆ์–ด, ์ตœ๊ทผ ๊ฐœ๋ฐœ๋œ ๋‘ ๊ฐ€์ง€ ๋”ฅ ๋Ÿฌ๋‹ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์ •ํ™•๋„์™€ ์—ฐ์‚ฐ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹ค์Œ ๋‘ ๊ฐ€์ง€์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ณ„์ธก์  ์ž๋™ ์‹๋ณ„์— ์ ์šฉํ•˜์˜€๋‹ค. 1) You-Only-Look-Once version 3 (YOLOv3) ๋ฐ 2) the Single Shot Detector (SSD). ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ•: ์ด 1,028 ๊ฐœ์˜ ๋‘๋ถ€๊ณ„์ธก๋ฐฉ์‚ฌ์„  ์‚ฌ์ง„ ์˜์ƒ์ด YOLOv3 ์™€ SSD๋ฐฉ์‹์˜ ํ•™์Šต ๋ฐ์ดํ„ฐ๋กœ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๋Œ€์ƒ ๊ณ„์ธก์ ์€ 80๊ฐœ์˜€๋‹ค. ํ•™์Šต ๊ณผ์ •์„ ๊ฑฐ์นœ ํ›„, ๊ฐ๊ฐ์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ƒˆ๋กœ์šด 283 ๊ฐœ์˜ ํ…Œ์ŠคํŠธ ์˜์ƒ์—์„œ ๋น„๊ต ๋ถ„์„ํ•˜์˜€๋‹ค. ์ •ํ™•๋„๋Š” 1) ํ‰๊ท ์ ์ธ point-to-point error, 2) success detection rate (SDR), ๊ทธ๋ฆฌ๊ณ  3) 2์ฐจ์› ํ‰๋ฉด์—์„œ ์‹œ๊ฐํ™”ํ•œ scattergram ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ‰๊ฐ€ํ–ˆ๋‹ค. ๊ฐ๊ฐ์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ‰๊ท  ์—ฐ์‚ฐ ์‹œ๊ฐ„ ์—ญ์‹œ ๊ธฐ๋กํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: YOLOv3 ๋Š” SSD ์— ๋น„ํ•ด ์ด 38/80 ๊ฐœ์˜ ๊ณ„์ธก์ ์—์„œ ๋” ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€๋‹ค. ๋‚˜๋จธ์ง€ 42/80 ๊ฐœ์˜ ๊ณ„์ธก์ ์€ ๋‘ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐ„์— ์ •ํ™•๋„์— ์žˆ์–ด ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋‚˜ํƒ€๋‚ด์ง€ ์•Š์•˜๋‹ค. Error plot ์—์„œ๋Š” YOLOv3 ๊ฐ€ SSD ์— ๋น„ํ•ด์„œ error ์˜ ๋ฒ”์œ„๊ฐ€ ๋” ์ž‘์„ ๋ฟ ์•„๋‹ˆ๋ผ, 2์ฐจ์› ํ‰๋ฉด์—์„œ ๋ฐฉํ–ฅ์„ฑ์˜ ์˜ํ–ฅ์„ ๋œ ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•˜๋‚˜์˜ ์˜์ƒ์—์„œ ๊ณ„์ธก์ ์„ ์ž๋™ ์‹๋ณ„ํ•˜๋Š”๋ฐ ์†Œ์š”๋œ ํ‰๊ท  ์‹œ๊ฐ„์€ YOLOv3 ์™€ SSD ๊ฐ€ ๊ฐ๊ฐ 0.05 ์ดˆ, 2.89 ์ดˆ๋กœ ๊ธฐ๋ก๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ YOLOv3 ๋Š” ๊ธฐ์กด ๋ฌธํ—Œ์—์„œ ์ตœ์ƒ์˜ ์ •ํ™•๋„๋ฅผ ๊ธฐ๋กํ–ˆ๋˜ ์—ฐ๊ตฌ์— ๋น„ํ•ด ์•ฝ 5% ๊ฐ€๋Ÿ‰ ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€๋‹ค. ๊ฒฐ๋ก : ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ ์šฉ๋œ ๋‘ ๊ฐœ์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ค‘, YOLOv3 ๊ฐ€ ๋‘๋ถ€๊ณ„์ธก๋ฐฉ์‚ฌ์„  ์‚ฌ์ง„ ๊ณ„์ธก์  ์™„์ „ ์ž๋™ ์‹๋ณ„์˜ ์ž„์ƒ์ ์ธ ์ ์šฉ์— ๊ฐ€๋Šฅ์„ฑ ๋†’์€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค.Abstract Contents I. INTRODUCTION 1 II. MATERIALS AND METHODS 4 1. Subjects 4 2. Manual identification of cephalometric landmarks 4 3. Two Deep Learning Systems 5 4. Test Procedures and Comparisons between the two systems 6 III. RESULTS 7 IV. DISCUSSION 8 V. CONCLUSIONS 13 REFERENCES 14 TABLES 20 FIGURES 24 ๊ตญ๋ฌธ์ดˆ๋ก 46Docto

    Analysis Technology Development of Cause Investigation for Ship Collisionโ‹…Grounding Accident

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    ์ตœ๊ทผ ๋Œ€์šฉ๋Ÿ‰์˜ ์ „์‚ฐ์‹œ์Šคํ…œ์˜ ๊ฐœ๋ฐœ๊ณผ ๋”๋ถˆ์–ด LS-DYNA ์ฝ”๋“œ์™€ ๊ฐ™์€ ๊ณ ๋„ ๋น„์„ ํ˜• ๋™์  ์ƒ์šฉํ•ด์„ ํ”„๋กœ๊ทธ๋žจ์ด ํฌ๊ฒŒ ๋ฐœ์ „๋จ์— ๋”ฐ๋ผ ๊ตฌ์กฐ ์•ˆ์ „์„ฑ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ๋‚ด์ถฉ๊ฒฉ ์‘๋‹ตํ•ด์„ ๊ธฐ๋ฒ•์ด ๋งค์šฐ ํ™œ๋ฐœํžˆ ์ˆ˜ํ–‰๋˜๊ณ  ์žˆ์œผ๋ฉฐ ์‹คํ—˜์ด๋‚˜ ์‹œํ—˜์„ ๋ณด์กฐํ•˜๊ฑฐ๋‚˜ ๋Œ€์ฒดํ•  ์ˆ˜ ์žˆ๋Š” ๋‹จ๊ณ„์— ์žˆ๋‹ค. ํญ๋ฐœ, ์Šฌ๋กœ์‹ฑ, ์ถฉ๋Œ, ์ขŒ์ดˆ, ์ „๋ณต, ์นจ์ˆ˜ ๋ฐ ์นจ๋ชฐ ๋“ฑ๊ณผ ๊ฐ™์ด ์œ ์ฒด์™€ ๊ด€๋ จ๋œ ๋‚ด์ถฉ๊ฒฉ ์‘๋‹ต์„ ์ •ํ™•ํ•˜๊ณ  ํ•ฉ๋ฆฌ์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์œ ์ฒด-๊ตฌ์กฐ ์—ฐ์„ฑ ํ•ด์„๊ธฐ๋ฒ•์˜ ๊ณ ๋„ ์ •๋ฐ€ M&S ์‹œ์Šคํ…œ์„ ์ด์šฉํ•œ ์‹ค์„  ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€์žฅ ์ตœ์„ ์˜ ์ ‘๊ทผ ๋ฐฉ๋ฒ•์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์œ ์ฒด-๊ตฌ์กฐ ์—ฐ์„ฑ ํ•ด์„๊ธฐ๋ฒ•์˜ ๊ณ ๋„ ์ •๋ฐ€ M&S ์‹œ์Šคํ…œ์€ ํ•ด์ˆ˜์—์„œ ์„ ๋ฐ•์ด ๋ถ€์–‘(floating)๋˜๊ณ , 6์ž์œ ๋„์˜ ์šด๋™(motion)๋„ ํ•˜๊ณ , ์„ ๋‚ด์— ํ•ด์ˆ˜๋„ ์นจ์ˆ˜(flooding)๋˜๊ณ , ์šดํ•ญ ์ค‘์ผ ๋•Œ ํŒŒ๋„(wave)๋„ ์ƒ์„ฑํ•˜๊ณ , ๋‘ ๋ฌผ์ฒด๊ฐ€ ๊ทผ์ ‘ํ•  ๊ฒฝ์šฐ์—๋Š” ์••์ฐฉ์••๋ ฅ(squeezing pressure)์ด ๋ฐœ์ƒํ•˜๊ณ , ๋‘ ๋ฌผ์ฒด๊ฐ€ ์Šค์ณ ์ง€๋‚˜๊ฑฐ๋‚˜ ์•ˆ๋ฒฝ๊ณผ ํ•ด์ €์— ๊ทผ์ ‘ํ•˜๊ฒŒ ์šดํ•ญํ•  ๊ฒฝ์šฐ์—๋Š” ์••๋ ฅ์ด ์ €ํ•˜(banking effect)๋˜๋Š” ๋“ฑ ํ•ด์ˆ˜์—์„œ์˜ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์—ฐ์„ฑํšจ๊ณผ๋ฅผ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์†์ƒ์ž๋ฃŒ๋ฅผ ์„ธ์‹ฌํžˆ ๋ถ„์„ํ•˜๋ฉด์„œ ์ถฉ๋Œ์†๋„์— ๋”ฐ๋ฅธ ๋ณ€ํ˜•๋ฅ  ์˜์กด ์žฌ๋ฃŒ ๋ฌผ์„ฑ์น˜๋ฅผ ๊ณ ๋ คํ•˜๋Š” ํŒŒ๋‹จ๋ชจ๋ธ์„ ํ•ฉ๋ฆฌ์ ์œผ๋กœ ๊ตฌ์กฐ ์†์ƒ์„ ๊ตฌํ˜„ํ•˜๋Š” ๊ฒƒ๋„ ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์†Œํ˜• ์–ด์„ ์˜ ์ถฉ๋Œ์‚ฌ๊ณ ์˜ ์›์ธ๋ถ„์„๊ณผ ๋ฐฉ์‚ฌ์„ฑํ๊ธฐ๋ฌผ ์šด๋ฐ˜์„ ์˜ ์ขŒ์ดˆ์— ๋Œ€ํ•œ ๊ตฌ์กฐ ์•ˆ์ „์„ฑ ํ‰๊ฐ€๋ฅผ ์œ„ํ•ด ์œ ์ฒด-๊ตฌ์กฐ ์—ฐ์„ฑ ํ•ด์„๊ธฐ๋ฒ•์˜ ๊ณ ๋„ ์ •๋ฐ€ M&S ์‹œ์Šคํ…œ์„ ์ด์šฉํ•œ ์‹ค์„  ์ถฉ๋Œ ๋ฐ ์ขŒ์ดˆ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ˆ˜ํ–‰ํ•˜์˜€์œผ๋ฉฐ, ํ•ด์ˆ˜ ์ค‘์—์„œ ๋ณด๋‹ค ์ •ํ™•ํ•˜๊ณ  ์‹ค์ œ์ ์ธ ์ถฉ๋Œ ๋ฐ ์ขŒ์ดˆ ๊ฑฐ๋™๊ณผ ์†์ƒ ์‘๋‹ต์„ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. |With the advent and ongoing advances in numerical simulation capabilities and its sophisticated tools, such as highly accurate dynamic nonlinear simulation hydrocode LS-DYNA, development of shock response analysis techniques has been actively carried out for the structural safety assessment with viable, less costly alternatives as well as more reliable aids to the tests and/or experiments. To ensure an accurate and reasonable prediction of shock response with relation to the fluid, such as explosion, sloshing, collision, grounding, capsize, flooding and sinking etc., full-scale simulations would be the best approach using highly advanced M&S (Modeling & Simulation) of its Fluid-Structure Interaction (FSI) analysis technique. Several coupling effects in the water could be conceptualized in this highly advanced M&S system of FSI analysis technique, such as floating, motion, wave making, squeezing pressure, bank effect and realistic ship velocity. Fracture criteria have to be also suitably applied to the structural damage considering strain rate effect, together with careful investigation of damage information. In this study, using highly advanced M&S system of FSI analysis technique, full-scale ship collision & grounding simulations were performed for the investigation of small fishing ship collision and capsize accident and for the structural safety assessment of radioactive waste matter carrying vessel. More accurate and realistic collision and grounding behaviors, damage responses could be found in water condition using highly advanced M&S system of FSI analysis technique.1. ์„œ ๋ก  1 2. ์„ ๋ฐ• ์žฌ๋ฃŒ์˜ ํŒŒ๋‹จ๊ธฐ์ค€ 7 3. ์ถฉ๋Œ์‚ฌ๊ณ ์˜ ์›์ธ๊ทœ๋ช… ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฒ• 11 4. ์ขŒ์ดˆ์‚ฌ๊ณ ์˜ ๊ตฌ์กฐ ์•ˆ์ „์„ฑ ํ‰๊ฐ€ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฒ• 23 5. ๊ณ ์ฐฐ ๋ฐ ๊ฒฐ๋ก  32 Reference 34 ๊ฐ์‚ฌ์˜ ๊ธ€ 35Maste

    A Study on the Design of Hybrid Energy Storage System combined by Pumped Hydro Storage and Compressed Air Energy Storage Technology

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    Storing energy is important since electricity should be available whenever needed and must be used or stored immediately after being generated. Many renewable energy technologies such as solar and wind energy, provide intermittent power generation and sometimes produce surplus electricity when demand is low. Current growth of renewable energy systems are subjected to the issues of higher costs and power instability which makes energy storage systems essential. This research work focuses on a mechanical hybrid energy storage system which uses the concepts of combined pumped hydro storage (PHS) and compressed air energy storage (CAES). The system consists of one open tank to the air and one closed tank which stores water and compressed air. The multistage pump and hydro turbine are used for the charging and discharging process respectively, similar to pumped hydro storage operation in hydropower plants. When the grid power is at surplus, the unused power can be utilized to operate the multistage pump and store water and compressed air in the pressure vessel. The energy of compressed air can be released to drive water which passes through the hydro turbine, resulting in the generation of electricity when the grid power is insufficient. A major disadvantage of the conventional PHS and CAES is that the site where the systems can be installed is rare and have environmental side effects. As an alternative, this energy storage system is capable of overcoming the difficulties posed by PHS and CAES. This system can be used regardless of site conditions, since it uses a pressure vessel instead of two reservoirs of pumped hydro storage. This study was carried out to verify the operating principle and analyze the characteristics of energy charging and discharging of the mechanical hybrid energy storage system. Firstly, the characteristics of energy charging and discharging of lab-scale model was analyzed and additionally CFD analysis and experimental test were performed on the charging and discharging process. It was found that the pressure in the vessel depends only on stored volume and air compression ratio of water at isothermal state without the loss to outside. Therefore, it is more effective to control the discharging flow rate from the pressure vessel in the operation. Secondly, the characteristics study of the charging process by multistage pump depends on variable speed which was carried out. In addition, a numerical model of the multistage pump was made and analysed using CFD and the performance and characteristics of the pump were determined and plotted. By using the plotted data and related formulas, a more efficient charging process by pump operation was found. In this thesis, the submerged floating-type mechanical hybrid energy storage system that can minimize the pressure differences between inside and outside of pressure vessel, by installing it in the sea was suggested. The submerged floating-type mechanical hybrid energy storage system has advantages such as the size reduction of pressure vessel and ensuring stability of the pressure vessel. Regardless, it should be further investigated for stability of mooring line considering effect of ocean current.1. ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ๋ชฉ์  3 1.3 ์—๋„ˆ์ง€ ์ €์žฅ ๊ธฐ์ˆ ์˜ ๊ฐœ์š”์™€ ์„ค์น˜ ์‚ฌ๋ก€ 4 1.3.1 ์–‘์ˆ˜๋ฐœ์ „ 3 1.3.2 ์••์ถ•๊ณต๊ธฐ์—๋„ˆ์ง€์ €์žฅ 6 1.3.3 ํ”Œ๋ผ์ดํœ  ์—๋„ˆ์ง€ ์ €์žฅ์žฅ์น˜ 8 1.3.4 ๋‚ฉ์ถ•์ „์ง€ 10 1.3.5 Nas์ „์ง€ 12 1.3.6 ๋ฆฌํŠฌ์ „์ง€ 14 1.3.7 ํ๋ฆ„์ „์ง€ 18 1.3.8 ESS์˜ ๊ธฐ์ˆ ๋ณ„ ํŠน์„ฑ๊ณผ ์ ์šฉ๋ถ„์•ผ 21 2. ๊ธฐ๊ณ„์  Hybrid ์—๋„ˆ์ง€ ์ €์žฅ ์žฅ์น˜ 24 2.1 ์žฅ์น˜์˜ ๊ตฌ์กฐ ๋ฐ ๊ฐœ๋… 24 2.2 ์—๋„ˆ์ง€ ์ €์žฅ ์šฉ๋Ÿ‰์— ๋”ฐ๋ฅธ ์••๋ ฅ์šฉ๊ธฐ์˜ ํฌ๊ธฐ ๊ฒฐ์ • 27 2.3 ํŽŒํ”„์˜ ์„ ์ • 29 2.4 ์ˆ˜์ฐจ์˜ ์„ ์ • 32 3. ๊ธฐ๊ณ„์  Hybrid ESS์˜ Lab-scale ๋ชจ๋ธ ์ˆ˜์น˜ํ•ด์„ 34 3.1 ์ˆ˜์น˜ํ•ด์„ ๊ธฐ๋ฒ• 34 3.1.1 ์ง€๋ฐฐ๋ฐฉ์ •์‹ 35 3.1.2 ์ด์‚ฐํ™”๋ฐฉ๋ฒ• 36 3.1.3 ๋‚œ๋ฅ˜๋ชจ๋ธ๋ง 39 3.2 ์—๋„ˆ์ง€ ์ €์žฅ์— ๋”ฐ๋ฅธ ์••๋ ฅ์šฉ๊ธฐ์˜ ๋‚ด๋ถ€ ์œ ๋™์žฅ ๋ถ„์„ 41 3.2.1 3Dํ˜•์ƒ ๋ฐ ๊ฒฉ์ž 41 3.2.2 ๊ฒฝ๊ณ„์กฐ๊ฑด 41 3.2.3 ๋‚ด๋ถ€ ์œ ๋™์žฅ ๋ถ„์„๊ฒฐ๊ณผ 44 3.3 ์—๋„ˆ์ง€ ๋ฐฉ์ถœ์— ๋”ฐ๋ฅธ ์••๋ ฅ์šฉ๊ธฐ์˜ ๋‚ด๋ถ€ ์œ ๋™์žฅ ๋ถ„์„ 51 3.3.1 3Dํ˜•์ƒ ๋ฐ ๊ฒฉ์ž 51 3.3.2 ๊ฒฝ๊ณ„์กฐ๊ฑด 51 3.3.3 ๋‚ด๋ถ€ ์œ ๋™์žฅ ๋ถ„์„๊ฒฐ๊ณผ 53 3.4 ์—๋„ˆ์ง€ ์ €์žฅ์šฉ ํŽŒํ”„์˜ ์„ฑ๋Šฅํ•ด์„ 66 3.4.1 3Dํ˜•์ƒ ๋ฐ ๊ฒฉ์ž 66 3.4.2 ๊ฒฝ๊ณ„์กฐ๊ฑด 66 3.4.3 ์„ฑ๋Šฅํ•ด์„๊ฒฐ๊ณผ 69 3.4.4 ์—๋„ˆ์ง€ ์ €์žฅ ๊ณผ์ •์—์„œ์˜ ์—๋„ˆ์ง€ ์†Œ๋ชจ๋Ÿ‰ ๋น„๊ต 72 4. ๊ธฐ๊ณ„์  Hybrid ESS์˜ Lab-scale ๋ชจ๋ธ์‹คํ—˜ 76 4.1 ์‹คํ—˜์žฅ์น˜ 76 4.2 ๊ณ„์ธก์‹œ์Šคํ…œ 76 4.3 ์‹คํ—˜๊ฒฐ๊ณผ 79 5. ์ˆ˜์ค‘๋ถ€์œ ์‹ ๊ธฐ๊ณ„์  Hybrid ESS 83 5.1 ์žฅ์น˜์˜ ๊ตฌ์กฐ์™€ ๊ฐœ๋… 83 5.2 ์œ„์น˜ํ•˜๋Š” ์ˆ˜์‹ฌ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์••๋ ฅ์šฉ๊ธฐ์˜ ํฌ๊ธฐ ์ถ”์‚ฐ 86 6. ๊ฒฐ๋ก  90 ์ฐธ๊ณ ๋ฌธํ—Œ 92Docto

    Uncertainty Quantification of Reservoir Performances Using Streamline Based Inversion and Distance Based Method

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์—๋„ˆ์ง€์‹œ์Šคํ…œ๊ณตํ•™๋ถ€, 2014. 2. ์ตœ์ข…๊ทผ.For decision makings, it is crucial to have proper reservoir characterization and uncertainty assessment of reservoir performances. Since an initial model constructed with limited data has high uncertainty, it is essential to integrate both static and dynamic data for reliable prediction. Uncertainty quantification is computationally demanding because it requires a lot of iterative forward simulations and optimizations in a single history matching. Multiple realizations of reservoir models should be history matched. In addition, history matching is mathematically a highly ill-posed problem. In this paper, a methodology is proposed to rapidly quantify uncertainties by combining streamline based inversion and distance based method. First, a distance between each model is defined as the norm of differences in generalized travel time vectors. Second, they are grouped according to distances and representative models are selected instead of matching all models. Third, generalized travel time inversion is applied for integration of dynamic data and a streamline simulator is adopted as a forward simulator to take advantage of computational efficiency. It is verified that the proposed method gathers models with similar dynamic responses and permeability distribution. It also assesses the uncertainty of reservoir performances fairly well, while reducing the amount of calculations significantly by using the representative models.Abstract โ…ฐ Table of Contents โ…ฑ List of Tables โ…ฒ List of Figures โ…ณ 1. Introduction 1 2. Theoretical backgrounds 7 2.1 Streamline simulation 7 2.2 Generalized travel time inversion 14 2.3 Distance based method 25 2.4 Randomized maximum likelihood 28 3. Quantifying uncertainty with GTTI, RML, and distance based method 30 4. Results 35 4.1 Reference field 35 4.2 Sensitivity calculations 43 4.3 Application of distance based method, RML and GTTI 46 4.4 Misfit reduction and improvement of computational efficiency 73 5. Conclusions 76 References 78 ๊ตญ๋ฌธ์ดˆ๋ก 84Maste

    A Study on the Activation of Integrated Freight Information Network in the Cargo Transportation

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    4์ฐจ ์‚ฐ์—…ํ˜๋ช…๊ณผ ์ „์ž์ƒ๊ฑฐ๋ž˜ ํ™•์‚ฐ์œผ๋กœ ์ธํ•œ ์ „ํ†ต์  ์‹œ์žฅ๊ตฌ์กฐ ๋ณ€๊ฒฝ์ด๋ผ๋Š” ํ™˜๊ฒฝ๋ณ€ํ™”์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ํ™”๋ฌผ์ž๋™์ฐจ ์šด์†ก์‹œ์žฅ์˜ ๊ตฌ์กฐ์  ๋ฌธ์ œ์ ์ธ ์ง€์ž…์ œ๋„, ๋‹ค๋‹จ๊ณ„ ์šด์†ก๊ฑฐ๋ž˜๋กœ ์ธํ•˜์—ฌ ๋ฌผ๋ฅ˜ ํ™˜๊ฒฝ๋ณ€ํ™”๋ฅผ ํ™”๋ฌผ์ž๋™์ฐจ ์šด์†ก์‹œ์žฅ ๋ฐœ์ „์˜ ์ „ํ™˜์ ์œผ๋กœ ์‚ด๋ฆฌ์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ™˜๊ฒฝ๋ณ€ํ™”์— ๋Œ€์‘ํ•˜์—ฌ ์ •๋ถ€๋„ ๋ฌผ๋ฅ˜ ์‚ฐ์—…ํ˜์‹ ๋ฐฉ์•ˆ์„ ๋ฐœํ‘œํ•˜์˜€๋‹ค. ์ฃผ์š” ๋‚ด์šฉ์€ ์‹œ์žฅ์งˆ์„œ ํ˜์‹ ๋ฐฉ์•ˆ์œผ๋กœ ์ง€์ž…์ œ๋„ ํ์ง€ ๊ฒ€ํ† , ๋‹ค๋‹จ๊ณ„ ์šด์†ก ์ œํ•œ์„ ์œ„ํ•œ ์ง์ ‘์šด์†ก์˜๋ฌด๋น„์œจ ์ƒํ–ฅ๊ณผ ํ™”๋ฌผ์ •๋ณด๋ง ํ™œ์„ฑํ™”๋ฅผ ์œ„ํ•œ ์šด์†ก๊ฐ€๋งน์  ์ฐจ๋Ÿ‰ ํ—ˆ๊ฐ€ ๋Œ€์ˆ˜ ๊ธฐ์ค€ ์™„ํ™”, ๋ฌผ๋ฅ˜ ์ •๋ณดํ™” ์‚ฌ์—… ํˆฌ์ž์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ํ™”๋ฌผ์ž๋™์ฐจ ์šด์†ก์‹œ์žฅ์˜ ๊ตฌ์กฐ์  ๋ฌธ์ œ ํ•ด๊ฒฐ์„ ์œ„ํ•ด ํ™”๋ฌผ์ •๋ณด๋ง์ด๋ผ๋Š” ์ œ๋„๋ฅผ ๋„์ž…ํ•˜์˜€์œผ๋‚˜ ์•„์ง ํ™œ์„ฑํ™”๊ฐ€ ๋ถ€์กฑํ•œ ํ˜„์‹ค์ด๋‹ค. ํ™”๋ฌผ์ž๋™์ฐจ ์šด์†ก์‹œ์žฅ์˜ ์„ ์ง„ํ™” ๋ฐฉ์•ˆ์œผ๋กœ ํ™”๋ฌผ์ •๋ณด๋ง์„ ๋„์ž…ํ•˜์˜€์Œ์—๋„ ํ™œ์„ฑํ™”๋˜์ง€ ๋ชปํ•œ ํ•œ๊ณ„์ ์„ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ํ˜„์žฌ ์šด์˜๋˜๊ณ  ์žˆ๋Š” ํ™”๋ฌผ์ •๋ณด๋ง์˜ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ํŠน์ง• ๋ฐ ํ•œ๊ณ„์ ์„ ๋ถ„์„ํ•˜๊ณ  ์ตœ๊ทผ ๋ฌผ๋ฅ˜๊ธฐ์ˆ  ๋™ํ–ฅ, ํ™”๋ฌผ์ž๋™์ฐจ์šด์†ก์‹œ์žฅ ํŠน์„ฑ ๋ฐ ์ •๋ถ€์˜ ์ •์ฑ… ๋ฐฉํ–ฅ์„ ๊ณ ๋ คํ•œ ํ™”๋ฌผ์ •๋ณด ํ†ตํ•ฉ๋ง ํ™œ์„ฑํ™” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜๋Š” ๊ฒƒ์ด ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์ด๋‹ค. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ํ†ต๊ณ„์ž๋ฃŒ๋ฅผ ํ†ตํ•œ ํ™”๋ฌผ์ž๋™์ฐจ ์šด์†ก์‹œ์žฅ์˜ ํŠน์„ฑ๊ณผ ๋ฌธ์ œ์ ์„ ๋„์ถœํ•˜๊ณ , ์‹œ์‚ฌ์ ์„ ํ†ตํ•œ ํ™”๋ฌผ์ •๋ณด๋ง์˜ ํ•„์š”์„ฑ์— ๊ด€ํ•˜์—ฌ ์—ฐ๊ตฌํ•˜์˜€์œผ๋ฉฐ, ํ™”๋ฌผ์ •๋ณด๋ง ์‚ฌ์—…์ž์— ๋Œ€ํ•œ ์‚ฌ๋ก€๋ถ„์„์„ ํ†ตํ•˜์—ฌ ๊ธฐ์กด ํ™”๋ฌผ์ •๋ณด๋ง์˜ ํŠน์ง• ๋ฐ ํ•œ๊ณ„์ ์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ํ†ต๊ณ„์ž๋ฃŒ๋กœ ํ™•์ธํ•œ ์ง์ ‘์šด์†ก๊ฒฝ๋กœ๋Š” 42.4%๋กœ ์ ˆ๋ฐ˜์— ๋ฏธ์น˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ์œผ๋ฉฐ ์‹ค์ œ๋กœ ์ฃผ์„ ์‚ฌ ๊ฐ„์˜ ๊ฑฐ๋ž˜๋Š” ์ง์ ‘์šด์†ก์˜๋ฌด ๋‹จ๊ณ„์—์„œ ์ œ์™ธ๋œ๋‹ค๋Š” ์ ์„ ๊ณ ๋ คํ•˜๋ฉด ํ†ต๊ณ„์น˜๋Š” ๋”์šฑ ๋‚ฎ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฑฐ๋ž˜๋‹จ๊ณ„ ์ฆ๊ฐ€๋Š” ์šด์†ก์„œ๋น„์Šค ํ’ˆ์งˆ ์ €ํ•˜, ์šด์†ก์—…์ฒด์˜ ๊ฒฝ์˜์•…ํ™”๋กœ ์ด์–ด์ง€๊ณ  ์žˆ๋‹ค. ํ™”๋ฌผ์ •๋ณด๋ง ์‚ฌ์—…์ž์˜ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•œ ํ™”๋ฌผ์ •๋ณด๋ง์€ ๊ฐœ๋ฐฉํ˜•๊ณผ ํ์‡„ํ˜•์œผ๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๊ณ  ์ด๋Ÿฌํ•œ ๊ฐœ๋ฐฉํ˜•์€ ๋น„์ˆ˜์ตํ˜•๊ณผ ์ˆ˜์ตํ˜•์œผ๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ํ™”๋ฌผ์ •๋ณด๋ง ์‹œ์žฅ์˜ ํ•œ๊ณ„์ ์€ ํ™”๋ฌผ์ •๋ณด๋ง ์‚ฌ์—…์ž์˜ ๋ฌผ๋Ÿ‰ํ™•๋ณด๊ฐ€ ๋ฏธํกํ•˜๊ณ , ์ˆ˜์ˆ˜๋ฃŒ ์ˆ˜์ต ์œ„์ฃผ์˜ ํš์ผํ™”๋œ ํ”Œ๋žซํผ์— ์žˆ๋‹ค. ๋˜ํ•œ, ์ •๋ถ€์˜ ์ •์ฑ… ๋˜ํ•œ ์‹ค์ ์‹ ๊ณ , ์ •๋ณด๋ง ์ธ์ฆ์— ๊ทธ์ณ ์ •์ฑ… ์ž์ฒด๊ฐ€ ๋ฏธํกํ•˜๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ํ™”๋ฌผ์ •๋ณด ํ†ตํ•ฉ๋ง์˜ ์ถ”์ง„๋ฐฉ์•ˆ์€ 1๋‹จ๊ณ„ ๋ฌผ๋ฅ˜๊ธฐ์ˆ  ๋™ํ–ฅ๋ฐ˜์˜, 2๋‹จ๊ณ„ ์ •๋ถ€ ๋ฌผ๋ฅ˜ ์„ ์ง„ํ™” ์ •์ฑ… ๋ฐ˜์˜, 3๋‹จ๊ณ„ ์˜์„ธ์ฐจ์ฃผ ์ง€์›, 4๋‹จ๊ณ„ ๊ฐœ๋ฐฉํ˜• ์—ฐ๊ณ„, 5๋‹จ๊ณ„ ์ •๋ถ€ ๊ธฐ๊ด€๊ณผ ๊ธˆ์œต๊ธฐ๊ด€ ์—ฐ๊ณ„๋˜์–ด์•ผ ํ•˜๋ฉฐ ๊ธฐ์กด ์ •๋ณด๋ง๊ณผ๋Š” ๋‹ฌ๋ฆฌ ํ™”์ฃผ, ์šด์†ก ์ฃผ์„ ์—…์ฒด, ๋ฌผ๋ฅ˜ ์„ผํ„ฐ, ํ™”๋ฌผ์ฐจ์ฃผ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ •๋ณด๋ง์‚ฌ์—…์ž, ์ •๋ถ€ ๊ธฐ๊ด€์ด ํ†ตํ•ฉ๋œ ์‹œ์Šคํ…œ์œผ๋กœ์„œ ํ™”๋ฌผ์ •๋ณด ํ†ตํ•ฉ๋ง ํ˜•ํƒœ๋กœ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ ์•ž์œผ๋กœ์˜ ํ™”๋ฌผ์ •๋ณด๋ง์€ ํ™”๋ฌผ์ •๋ณด ํ†ตํ•ฉ๋ง ํ˜•ํƒœ๋กœ ๊ตฌํ˜„๋˜์–ด์•ผ ํ•˜๋ฉฐ, ๋น„์ˆ˜์ตํ˜• ์ •๋ณด์ œ๊ณตํ˜•์ธ ํ™”๋ฌผ๋ณต์ง€์žฌ๋‹จ์˜ ์‹œ์Šคํ…œ๊ณผ ์—ฐ๊ณ„ํ•˜๋ฉด ์‹ ๊ทœ ๊ฐœ๋ฐœ์— ํ•„์š”ํ•œ ํ•˜๋“œ์›จ์–ด ๋ฐ ์ธํ”„๋ผ ๋น„์šฉ์„ ์ ˆ๊ฐํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ •๋ณด๋ง ์ฐธ์—ฌ๋ฅผ ์œ„ํ•˜์—ฌ ์˜๋ฌด๊ฐ€์ž… ์ •์ฑ… ๋˜๋Š” ์ฐธ์—ฌ ๊ฑฐ๋ž˜์—…์ฒด์— ๋Œ€ํ•œ ์„ฑ๊ณผ๋ณด์ˆ˜ ์ œ๋„๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ํ™”๋ฌผ์ •๋ณด ํ†ตํ•ฉ๋ง์„ ์ด์šฉํ•˜๊ฒŒ ๋˜๋ฉด ํ™”๋ฌผ ์šด์†ก์˜ ๋น…๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ณต์ฐจ ๊ฐ์†Œ๋ฅผ ์œ„ํ•œ ์ตœ์  ๊ฒฝ๋กœ ๋„์ถœ ๋ฐ ํ™”๋ฌผ ์šด์†ก๊ณผ ๊ด€๋ จ๋œ ์ถ”๊ฐ€์ •๋ณด ๋“ฑ์„ ํ™•๋ณดํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์ ์œผ๋กœ๋Š” ์‹œ์Šคํ…œ ๋„์ž…์— ๋”ฐ๋ฅธ ์‚ฌํšŒ์  ๋น„์šฉ ์ ˆ๊ฐ๊ณผ ๊ฒฝ์˜์ˆ˜์ง€ ๊ฐœ์„  ํšจ๊ณผ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋Š” ์ˆ˜ํ–‰ํ•˜์ง€ ๋ชปํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ–ฅํ›„ ํ™”๋ฌผ์ •๋ณด ํ†ตํ•ฉ๋ง ๋„์ž…์˜ ์˜ํ–ฅ๋ ฅ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์ด๋‹ค. |Despite the environmental change of the traditional market structure caused by the 4th industrial revolution and the spread of e-commerce, the logistics environment change is not a turning point in the development of the Transportation Industry due to the structural problems of the Transportation Industry and multi-level transportation transactions. In response to these environmental changes, the government also announced measures to innovate logistics industries. The main contents include consideration of abolishing the land entry system as a means of market order innovation, raising the ratio of direct transport obligations to restrict multistage transport, easing the criteria for the number of license permits issued by the carrier to activate the freight information network, and investing in logistics and information service businesses. The purpose of this study was to solve structural problems in the Transportation Industry, but the system is still not active. In the preceding research, most of the research related to the freight information network was done as an auxiliary means of government policy and the need for the information network. It wanted to find out the status of the Transportation Industry and the limitations that could not be activated despite the introduction of the freight information network as a way of advancing the Transportation Industry. In addition, the purpose of this study is to analyze characteristics and limitations through the case study of the currently operating freight information network business model and to present measures to activate the freight information network as implemented by the integrated freight information network system considering the recent trends in logistics technology, characteristics of the freight Transportation Industry and the government's policy direction. The research method is to draw out the characteristics and problems of the Transportation Industry through statistical data, study the need for the freight information network in the Transportation Industry through implications, study the characteristics and limitations of the existing freight information network through case analysis for the freight information network operators, and propose a joint network model of freight information reflecting improvement plans and changes in logistics environment. The statistics are even lower, considering that direct transport routes identified by the statistics are less than half the 42.4 percent, and that transactions between the consignors are actually excluded from the direct transport obligation phase. This increase in the level of transactions is leading to poor quality of transportation services and poor management of carriers. In this study, we proposed ways to utilize the freight information network. By utilizing the freight information network, we can upgrade and streamline transportation services and improve the balance of freight owners by reducing freight forwarding fees by simplifying the middle stage of freight transport transactions. The freight information network through the case study of the freight information network operators can be divided into open and closed types, and open types can be divided into non-profitable and profitable types. The limitation of the freight information network market is that the freight information network operators do not secure enough supplies and are on a uniform platform focused on commission revenue. In addition, the government's policies are not enough to report performance and certify the information network. Therefore, the proposed method of promoting the freight information network is to reflect trends in logistics technology in the first stage, to reflect the government logistics advancement policy in the second stage, to support small car owners in the third stage, to open the fourth stage, and to link the government agencies and financial institutions in the fifth stage. The information network proposed in this study is a co-network model for integrating freight information. Unlike the existing information network, this concept is a model that integrates not only shippers, transportation agents, logistics centers, and truck owners, but also information network operators and government agencies. As a result of this study, the future freight information network should be implemented in the form of a shared freight information network, and the non-revenue information provided by the Korea Cargo Welfare Foundation can reduce the hardware and infrastructure costs required for new development. In order to participate in the information network, the mandatory input policy of the information network or the performance compensation system for the vendors participating in the information network is required. This integrated network enables you to utilize big data from freight transport to derive the optimal route for reducing tolerance and to obtain additional information related to freight transport. As a limitation of this research, we believe that the impact of introducing the freight information network should be studied, such as the effect of reducing social costs or improving the balance of business, or conflicts of interest between industries. Key Words : integrated Freight Information Network์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ๋ชฉ์  4 1.3 ์—ฐ๊ตฌ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• 5 ์ œ 2 ์žฅ ํ™”๋ฌผ์ž๋™์ฐจ ์šด์†ก์‹œ์žฅ ํ˜„ํ™ฉ ์—ฐ๊ตฌ 7 2.1 ํ™”๋ฌผ์ž๋™์ฐจ ์šด์†ก์‹œ์žฅ์˜ ์‚ฐ์—…์  ํŠน์„ฑ 7 2.2 ํ™”๋ฌผ์ž๋™์ฐจ ์šด์ˆ˜์‚ฌ์—…์˜ ์œ ํ˜• 11 ์ œ 3 ์žฅ ํ™”๋ฌผ์ž๋™์ฐจ์šด์†ก์‹œ์žฅ์˜ ๊ตฌ์กฐ ๋ฐ ๋ฌธ์ œ์  ๋ถ„์„ 17 3.1 ํ™”๋ฌผ์ž๋™์ฐจ ์šด์†ก์‹œ์žฅ์˜ ๊ตฌ์กฐ 17 3.2 ํ™”๋ฌผ์ž๋™์ฐจ ์šด์†ก์‹œ์žฅ ๊ตฌ์กฐ์ƒ์˜ ๋ฌธ์ œ์  22 3.3 ํ™”๋ฌผ ์šด์†ก์‹œ์žฅ์˜ ๋ฌธ์ œ์ ์„ ํ†ตํ•œ ์‹œ์‚ฌ์  29 ์ œ 4 ์žฅ ํ™”๋ฌผ์ •๋ณด๋ง ์ผ๋ฐ˜ํ˜„ํ™ฉ ๋ฐ ์‚ฌ๋ก€๋ถ„์„ 31 4.1 ํ™”๋ฌผ์ •๋ณด๋ง ์ผ๋ฐ˜ํ˜„ํ™ฉ 31 4.2 ํ™”๋ฌผ์ •๋ณด๋ง ์‚ฌ์—…์ž ์‚ฌ๋ก€๋ถ„์„ 33 4.3 ํ™”๋ฌผ์ •๋ณด๋ง ์‹œ์žฅ์˜ ํŠน์ง• ๋ฐ ๋ฌธ์ œ์  43 ์ œ 5 ์žฅ ํ™”๋ฌผ์ •๋ณด ํ†ตํ•ฉ๋ง ํ™œ์„ฑํ™” ๋ฐฉ์•ˆ 46 5.1 ํ™”๋ฌผ์ •๋ณด ํ†ตํ•ฉ๋ง ๊ตฌํ˜„ ๋ฐ ์ถ”์ง„๋ฐฉ์•ˆ 46 5.2 ํ™”๋ฌผ์ •๋ณด ํ†ตํ•ฉ๋ง ํ™œ์„ฑํ™” ๋ฐฉ์•ˆ 49 ์ œ 6 ์žฅ ๊ฒฐ ๋ก  54 6.1 ์—ฐ๊ตฌ์š”์•ฝ 54 6.2 ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์  ๋ฐ ํ–ฅํ›„ ๊ณผ์ œ 56 ์ฐธ๊ณ ๋ฌธํ—Œ 58Maste

    The Expression of Programmed Death-Ligand 1 on Immune Cells Is Related to a Better Prognosis in Biliary Tract Cancer

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    Background/Aims: Programmed death-ligand 1 (PD-L1) expression in tumor cells is associated with a poor biliary tract cancer (BTC) prognosis; tumor-infiltrating immune cells in the tumor microenvironment are associated with a better prognosis. The effect of PD-L1 expression on immune cells on survival is unclear. We investigated the relationship between PD-L1 expression in immune cells and BTC prognosis. Methods: PD-L1 expression was evaluated using an anti-PD-L1 22C3 mouse monoclonal primary antibody, and its relationships with clinical characteristics and prognosis were analyzed using the Cox proportional hazard model to investigate the prognostic performance of PD-L1 in BTC. Results: Among 144 analyzed cases, patients with positive PD-L1 expression in tumor cells and negative PD-L1 expression in immune cells showed poorer overall survival rates than those exhibiting other expressions (tumor cells: hazard ratio [HR]=1.023, p<0.001; immune cells: HR=0.983, p=0.021). PD-L1 expression in tumor cells was an independent predictor of poor overall survival (HR=1.024, p<0.001). In contrast, PD-L1 expression in immune cells was a predictive marker of good prognosis (HR=0.983, p=0.018). Conclusions: PD-L1 expression in immune cells may be used as an independent factor to evaluate the prognosis of patients with BTC. (Gut Liver, Published online December 13, 2022)ope
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