326 research outputs found

    ํ•ด์šด๋ฌผ๋ฅ˜์—์„œ์˜ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ ํšจ๊ณผ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2022.2. ๋ฌธ์ผ๊ฒฝ.์ปจํ…Œ์ด๋„ˆ ํ™” ์ดํ›„๋กœ ํ•ด์ƒ ๋ฌผ๋ฅ˜๋Š” ํญ๋ฐœ์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜์˜€๊ณ  ์„ธ๊ณ„ํ™”์™€ ์‚ฐ์—… ๋ฐœ์ „์„ ์„ ๋„ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ๋ฌด์—ญ๋Ÿ‰์˜ ์ฆ๊ฐ€์™€ ๋น„๋ก€ํ•˜์—ฌ ์ˆ˜์ถœ์ž… ๋ถˆ๊ท ํ˜•์œผ๋กœ ์ธํ•œ ์ปจํ…Œ์ด๋„ˆ์˜ ๋ถˆ๊ท ํ˜• ๋ฌธ์ œ๋„ ์‹ฌํ™”๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ์ž๋“ค์˜ ๋…ธ๋ ฅ์ด ์žˆ์—ˆ๊ณ , ๊ทธ ์ค‘ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๋ผ๋Š” ์ƒˆ๋กœ์šด ๊ฐœ๋…์˜ ์ปจํ…Œ์ด๋„ˆ๊ฐ€ ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์•„์ง ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๋Š” ์ƒ์šฉํ™” ์ดˆ๊ธฐ ๋‹จ๊ณ„์ด๋ฉฐ, ์ด๋ฅผ ํ™œ์šฉํ•œ ์—ฌ๋Ÿฌ ํšจ๊ณผ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•œ ์‹ค์ •์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๊ฐ€ ๋„์ž…๋˜์—ˆ์„ ๋•Œ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์˜ํ–ฅ๊ณผ ๊ทธ ํšจ๊ณผ์— ๋Œ€ํ•ด ๋‹ค๋ฃจ์—ˆ๋‹ค. ๋จผ์ € ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๊ฐ€ ํฌ๋ ˆ์ธ ํ™œ๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ณ , ์ „์—ญ์  ๊ด€์ ์œผ๋กœ ํฌ๋ ˆ์ธ ํ™œ๋™์„ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ ์œก์ƒ์—์„œ์˜ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ ์ ์šฉ์ด ํ•ด์ƒ๊ณผ๋Š” ๋‹ค๋ฅด๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•˜์—ฌ ๊ทธ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ 2008 ๊ธˆ์œต์œ„๊ธฐ์™€ COVID-19 ์ดํ›„์— ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋Š” ํ•ด์šด๋ฌผ๋ฅ˜์˜ ๊ฐ์ข… ๋ณ€๋™ํ•˜๋Š” ์ƒํ™ฉ ํ•˜์—์„œ์˜ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ ํšจ๊ณผ์— ๋Œ€ํ•ด ์ƒˆ๋กœ์šด ํ†ต์ฐฐ์„ ์ œ๊ณตํ•˜์˜€๋‹ค. 1์žฅ์—์„œ๋Š” ๊ฐ„๋‹จํ•˜๊ฒŒ ์ปจํ…Œ์ด๋„ˆํ™”์™€ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ์— ๋Œ€ํ•ด ์„ค๋ช…ํ•˜๊ณ  ๋ฌธ์ œ๋ฅผ ์ฃผ๋ชฉํ•˜๊ฒŒ ๋œ ์ด์œ ์™€ ๊ทธ ์„ฑ๊ณผ๋ฅผ ์„œ์ˆ ํ•˜์˜€๋‹ค. 2์žฅ์—์„œ๋Š” ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๊ฐ€ ๋„์ž…๋จ์— ๋”ฐ๋ผ ์ƒ๊ธธ ์ˆ˜ ์žˆ๋Š” โ€˜์ƒ๋‹จ ์ ์žฌ ๊ทœ์น™โ€™์ด ์ ์šฉ๋˜์—ˆ์„ ๋•Œ์˜ ํฌ๋ ˆ์ธ ํ™œ๋™์˜ ๋ณ€ํ™”๋ฅผ ์‚ดํŽด๋ณด๊ณ  ์ „์—ญ์  ์ตœ์ ํ™”๊ฐ€ ์ง€์—ญ์  ์ตœ์ ํ™”๋ณด๋‹ค ํšจ๊ณผ์ ์ž„์„ ๋ณด์˜€๋‹ค. ๋”๋ถˆ์–ด ์ „์—ญ์  ์ตœ์ ํ™”๋ฅผ ๋„์ž…ํ•˜์˜€์„ ๋•Œ ์ง๋ฉดํ•  ์ˆ˜ ์žˆ๋Š” ๋น„์šฉ ๋ถ„๋ฐฐ ๋ฌธ์ œ์— ๋Œ€ํ•ด์„œ๋„ ์กฐ๋งํ•˜์—ฌ ๊ทธ ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•˜์˜€๋‹ค. 3์žฅ์—์„œ๋Š” ์œก์ƒ์—์„œ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ๊ฐ€ ์ˆ˜์†ก๊ณต๊ฐ„์„ ์ค„์—ฌ์ฃผ๋Š” ์žฅ์  ์™ธ์— ๊ฒฝ๋กœ๋ฅผ ๋ฐ”๊พธ๋Š” ํšจ๊ณผ๊ฐ€ ์กด์žฌํ•จ์„ ๋ณด์ด๊ณ , ๋‹ค์–‘ํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค์™€ ์ •์ฑ…์— ๋”ฐ๋ผ ๊ทธ ํšจ๊ณผ๊ฐ€ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”ํ•˜๋Š”์ง€์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. 4์žฅ์—์„œ๋Š” ์ฆ๊ฐ€ํ•˜๋Š” ๋‹ค์–‘ํ•œ ๋ณ€๋™์ƒํ™ฉ ๊ฐ๊ฐ์— ๋Œ€ํ•ด ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ์˜ ํšจ๊ณผ์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ† ๋Œ€๋กœ ๊ฐ ์ƒํ™ฉ์— ๋งž๋Š” ์ตœ์  ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ ๊ฐœ์ˆ˜๋ฅผ ๋„์ถœํ•˜๊ณ  ์ž„๋Œ€ ์ •์ฑ…์„ ํ†ตํ•ด ๋Œ€์‘ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ํ†ต์ฐฐ์„ ๋„์ถœํ•˜์˜€๋‹ค. 5์žฅ์—์„œ๋Š” ๋ณธ ๋…ผ๋ฌธ์˜ ๊ฒฐ๋ก ๊ณผ ํ–ฅํ›„ ์—ฐ๊ตฌ ๋ฐฉ์•ˆ์— ๋Œ€ํ•ด ์„œ์ˆ ํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜๋Š” ๋ฌธ์ œ์™€ ๊ทธ ํ•ด๊ฒฐ ๋ฐฉ๋ฒ•์€ ํ•™์ˆ ์  ๋ฐ ์‚ฐ์—…์ ์œผ๋กœ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค. ํ•™๊ณ„์—๋Š” ์‹ค์ œ ์กด์žฌํ•˜๋Š” ํ˜„์žฅ์˜ ๋ฌธ์ œ๋“ค์„ ์ œ์‹œํ•˜๊ณ  ๋ฌธ์ œ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•๋“ค์„ ์ œ์•ˆํ•œ๋‹ค. ์‚ฐ์—…๊ณ„์—๋Š” ์‹ ๊ธฐ์ˆ ์ธ ์ ‘์ด์‹ ์ปจํ…Œ์ด๋„ˆ์˜ ๋„์ž…์— ๋”ฐ๋ผ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋ฌธ์ œ์— ๋Œ€ํ•ด ์ •๋Ÿ‰ํ™” ๋ฐ ๋ชจํ˜•ํ™”๋ฅผ ํ†ตํ•œ ํ•ด๊ฒฐ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์„ ํ†ตํ•ด ์‚ฐ์—…์˜ ๋ฐœ์ „๊ณผ ํ•™๋ฌธ์˜ ๋ฐœ์ „์ด ํ•จ๊ป˜ ์ด๋ฃจ์–ด์งˆ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.After containerization, maritime logistics experienced the substantial growth of trade volumes and led to globalization and industrial development. However, in proportion to the increase in the volume, the degree of container imbalance also intensified due to the disparity between importing and exporting sizes at ports in different continents. A group of researchers is digging into resolving this ongoing challenge, and a new concept of a container, called a foldable container, has been proposed. Nevertheless, foldable containers are still in the early stage of commercialization, and research on the various effects of using foldable containers seems insufficient yet. This dissertation considers the possible effects of the introduction of foldable containers. First, we analyze the effect of foldable containers on crane operation and reduce shifts from a global perspective. Second, the effect of using foldable containers in hinterland areas was analyzed by noting that the application of foldable containers on land was different from that of the sea. Finally, we provided new insights into the foldable container under plausible dynamic situations in the shipping industry during the COVID-19 and logistics that have increased since the 2008 financial crisis. A brief explanation of containerization and foldable containers is introduced in Chapter 1, along with the dissertation's motivations, contributions, and outlines. Chapter 2 examines changes in crane operation when the 'top stowing rule' that can be treated with foldable containers is applied and shows that global optimization is more effective than local optimization. In addition, we suggested the cost-sharing method to deal with fairness issues for additional costs between ports when the global optimization method is fully introduced. Chapter 3 shows that foldable containers in the hinterland have the effect of changing routes in addition to reducing transportation space and analyzes how the results change according to various scenarios and policies. Chapter 4 analyzes the effectiveness of foldable containers for different dynamic situations. Moreover, the managerial insight was derived that the optimal number of foldable containers suitable for each situation can be obtained and responded to leasing policies. Chapter 5 describes the conclusions of this dissertation and discusses future research. The problem definition and solution methods proposed in this dissertation can be seen as meaningful in both academic and industrial aspects. For academia, we presented real-world problems in the field and suggested ways to solve problems effectively. For industry, we offered solutions through quantification and modeling for real problems related to foldable containers. We expect that industrial development and academic achievement can be achieved together through this dissertation.Chapter 1 Introduction 1 1.1 Containerization and foldable container 1 1.2 Research motivations and contributions 3 1.3 Outline of the dissertation 6 Chapter 2 Efficient stowage plan with loading and unloading operations for shipping liners using foldable containers and shift cost-sharing 7 2.1 Introduction 7 2.2 Literature review 10 2.3 Problem definition 15 2.4 Mathematical model 19 2.4.1 Mixed-integer programming model 19 2.4.2 Cost-sharing 24 2.5 Computational experiment and analysis 26 2.6 Conclusions 34 Chapter 3 Effects of using foldable containers in hinterland areas 36 3.1 Introduction 36 3.2 Single depot repositioning problem 39 3.2.1 Problem description 40 3.2.2 Mathematical formulation of the single depot repositioning problem 42 3.2.3 Effects of foldable containers 45 3.3 Multi-depot repositioning problem 51 3.4 Computational experiments 56 3.4.1 Experimental design for the SDRP 57 3.4.2 Experimental results for the SDRP 58 3.4.3 Major and minor effects with the single depot repositioning problem 60 3.5 Conclusions 65 Chapter 4 Effect of foldable containers in dynamic situation 66 4.1 Introduction 66 4.2 Problem description 70 4.3 Mathematical model 73 4.4 Computational experiments 77 4.4.1 Overview 77 4.4.2 Experiment results 79 4.5 Conclusions 88 Chapter 5 Conclusion and future research 90 Bibliography 94 ๊ตญ๋ฌธ์ดˆ๋ก 99๋ฐ•

    ๊ตญ๋‚ด์™ธ ๊ณต๊ฐ„๋น…๋ฐ์ดํ„ฐ ์ •์ฑ… ๋ฐ ๊ธฐ์ˆ ๋™ํ–ฅ

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    ๋…ธํŠธ : [ํŠน์ง‘ | ๊ณต๊ฐ„๋น…๋ฐ์ดํ„ฐ์™€ ์ƒˆ๋กœ์šด ๊ตญํ† ๊ฐ€์น˜ ์ฐฝ์ถœ 4

    ์„ฑ๋Œ€ ์ฃผ์ž…์ˆ  ์ค‘ ์ง€์†์  ์••๋ ฅ ์ธก์ • ๋ฐ ์ˆœ์ฐจ์  ๋งˆ์ดํฌ๋กœ ๋‹จ์ธต ์ดฌ์˜์„ ์ด์šฉํ•œ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2021. 2. ๊ถŒํƒ๊ท .Injection laryngoplasty (IL) has been used to treat various types of glottal insufficiency. The precise volume and location of the injected materials impact the outcomes. However, exactly how increasing volumes of material are distributed is unknown. In fact, the amount of IL material required to medialize a vocal cord tends to be determined empirically. Thus, the goal of this study was to investigate the pattern of IL material distribution by checking serial microโ€“computed tomography (MCT) and pressure changes during ILs. This experimental study used 10 excised canine larynges. Experimental devices included the IL syringe, pressure sensor, infusion pump, fixed frame, and monitoring system. We injected calcium hydroxyapatite in the thyroarytenoid muscle; whenever 0.1 mL of material was injected, we obtained an MCT scan while simultaneously measuring the pressure. After the experiments, we performed histologic analyses. MCT analyses showed that materials initially expanded centrifugally and then expanded in all directions within the muscle. The pressure initially increased rapidly but then remained relatively constant until the point at which the materials expanded in multiple directions. Histologic analyses showed that the IL material tended to expand within the epimysium of the thyroarytenoid muscle. However, in some cases, the MCT revealed that there were leakages to the surrounding space with a corresponding pressure drop. If the IL material passes through the epimysium, leakage can occur in the surrounding space, which can account for the reduction in resistance during ILs.์„ฑ๋Œ€ ์ฃผ์ž…์ˆ ์€ ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ์„ฑ๋ฌธ ํ์‡„ ๋ถ€์ „์„ ์น˜๋ฃŒํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜์–ด ์™”๋‹ค. ์ฃผ์ž… ๋ฌผ์งˆ์˜ ์ •ํ™•ํ•œ ๋ถ€ํ”ผ์™€ ์œ„์น˜๋Š” ์Œ์„ฑ ๊ฒฐ๊ณผ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ฃผ์ž… ๋ฌผ์งˆ์˜ ์–‘์ด ์ฆ๊ฐ€ํ• ์ˆ˜๋ก ์–ด๋–ป๊ฒŒ ๋ถ„ํฌ๋˜๋Š”์ง€๋Š” ์ •ํ™•ํžˆ ์•Œ๋ ค์ง€์ง€ ์•Š์•˜๋‹ค. ์‹ค์ œ๋กœ ์„ฑ๋Œ€์˜ ๋‚ด์ธกํ™”์— ํ•„์š”ํ•œ ์ฃผ์ž… ๋ฌผ์งˆ์˜ ์–‘์€ ๊ฒฝํ—˜์ ์œผ๋กœ ๊ฒฐ์ •๋˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์„ฑ๋Œ€ ์ฃผ์ž…์ˆ  ์ค‘ ์ˆœ์ฐจ์  ๋งˆ์ดํฌ๋กœ ๋‹จ์ธต ์ดฌ์˜๊ณผ ์ง€์†์  ์••๋ ฅ ๋ณ€ํ™”๋ฅผ ํ™•์ธํ•จ์œผ๋กœ์จ ์„ฑ๋Œ€ ์ฃผ์ž… ๋ฌผ์งˆ์˜ ๋ถ„ํฌ ํŒจํ„ด์„ ์กฐ์‚ฌํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด ์‹คํ—˜ ์—ฐ๊ตฌ์—์„œ๋Š” 10๊ฐœ์˜ ์ ˆ์ œ๋œ ๊ฐœ ์‚ฌ์ฒด ํ›„๋‘๋ฅผ ์‚ฌ์šฉํ–ˆ๋‹ค. ์‹คํ—˜ ์žฅ์น˜๋กœ๋Š” ์„ฑ๋Œ€ ์ฃผ์ž…์ˆ  ์ฃผ์‚ฌ๊ธฐ, ์••๋ ฅ ์„ผ์„œ, ์ฃผ์ž… ํŽŒํ”„, ๊ณ ์ • ํ”„๋ ˆ์ž„ ๋ฐ ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ๋“ค์ด ํฌํ•จ๋˜์—ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ฃผ์ž… ๋ฌผ์งˆ๋กœ ๊ฐ‘์ƒํ”ผ์—ด๊ทผ์— ์ˆ˜์‚ฐํ™”์ธํšŒ์„ ์นผ์Š˜์„ ์ฃผ์ž…ํ–ˆ๋‹ค. 0.1 mL์˜ ๋ฌผ์งˆ์„ ์ฃผ์ž…ํ•  ๋•Œ๋งˆ๋‹ค ์••๋ ฅ์„ ์ธก์ •ํ•˜๋ฉด์„œ ๋งˆ์ดํฌ๋กœ ๋‹จ์ธต ์ดฌ์˜์„ ์‹œํ–‰ํ–ˆ๋‹ค. ์‹คํ—˜์ด ์ข…๋ฃŒ๋œ ํ›„, ์šฐ๋ฆฌ๋Š” ์ฃผ์ž…์ด ์™„๋ฃŒ๋œ ๊ฐœ ์‚ฌ์ฒด ํ›„๋‘๋“ค์— ๋Œ€ํ•œ ์กฐ์งํ•™์  ๋ถ„์„์„ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ๋งˆ์ดํฌ๋กœ ๋‹จ์ธต ์ดฌ์˜ ๋ถ„์„์—์„œ ์ฃผ์ž… ๋ฌผ์งˆ์€ ์ฃผ์ž… ์ดˆ๊ธฐ์—๋Š” ์›์‹ฌํ˜•์œผ๋กœ ํŒฝ์ฐฝํ•˜๋‹ค ์ดํ›„์— ๊ทผ์œก ๋‚ด ๋ชจ๋“  ๋ฐฉํ–ฅ์œผ๋กœ ํŒฝ์ฐฝํ–ˆ๋‹ค. ์••๋ ฅ์€ ์ฃผ์ž… ์ดˆ๊ธฐ์—๋Š” ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ–ˆ์ง€๋งŒ, ์ดํ›„์—๋Š” ์ฃผ์ž… ๋ฌผ์งˆ์ด ๊ทผ์œก๋‚ด์—์„œ ๋‹ค์–‘ํ•œ ๋ฐฉํ–ฅ์œผ๋กœ ํŒฝ์ฐฝํ•˜๋Š” ๋™์•ˆ ๋น„๊ต์  ์ผ์ •ํ•˜๊ฒŒ ์œ ์ง€๋˜์—ˆ๋‹ค. ์กฐ์งํ•™์  ๋ถ„์„์— ๋”ฐ๋ฅด๋ฉด ์„ฑ๋Œ€ ์ฃผ์ž… ๋ฌผ์งˆ์€ ๊ฐ‘์ƒํ”ผ์—ด๊ทผ์˜ ์™ธ๋ง‰๋‚ด์—์„œ ํŒฝ์ฐฝํ•˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์ผ๋ถ€์˜ ๊ฒฝ์šฐ์—์„œ๋Š” ๋งˆ์ดํฌ๋กœ ๋‹จ์ธต ์ดฌ์˜์ƒ ์ฃผ์ž… ๋ฌผ์งˆ์ด ์ฃผ๋ณ€ ๊ณต๊ฐ„์œผ๋กœ ์œ ์ถœ๋˜๋ฉด์„œ ๋™์‹œ์— ๋ฐœ์ƒํ•˜๋Š” ์••๋ ฅ์˜ ๊ฐ•ํ•˜๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋งŒ์•ฝ ์„ฑ๋Œ€ ์ฃผ์ž… ๋ฌผ์งˆ์ด ๊ทผ์œก ์™ธ๋ง‰์„ ๋šซ๊ณ  ํ†ต๊ณผ๋˜๋Š” ๊ฒฝ์šฐ์—๋Š” ์ฃผ๋ณ€ ๊ณต๊ฐ„์œผ๋กœ ์œ ์ถœ์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๊ณ  ์ด๊ฒƒ์€ ์šฐ๋ฆฌ๊ฐ€ ์ž„์ƒ์ ์œผ๋กœ ์„ฑ๋Œ€ ์ฃผ์ž…์ˆ ์‹œ ์ข…์ข… ๋Š๋ผ๋Š” ์ €ํ•ญ์˜ ๊ฐ์†Œ ํ˜„์ƒ์„ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.Abstract -------------------------------------------------------------------------------------------------- VI Table of Contents ------------------------------------------------------------------------------------ VIII List of Figures ------------------------------------------------------------------------------------------ IX Introduction ----------------------------- ----------------------------------------------------------------- 1 Materials & Methods ------------------------------------------------------------------------------------ 2 Results ----------------------------------------------------------------------------------------------------- 9 Discussion ----------------------------------------------------------------------------------------------- 12 Conclusions --------------------------------------------------------------------------------------------- 16 References ----------------------------------------------------------------------------------------------- 17 Korean Abstract ---------------------------------------------------------------------------------------- 19Docto

    RNA ์‹œํ€€์‹ฑ ๋ฐ์ดํ„ฐ์˜ ํ•ด๋…๊ณผ ํ™œ์šฉ์„ ์œ„ํ•œ ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์ƒ๋ฌผ์ •๋ณดํ•™์ „๊ณต,2019. 8. ๊น€์„ .์ง„ํ•ต ์„ธํฌ ์‹œ์Šคํ…œ์—์„œ๋Š” mRNA ๋ถ„์ž๊ฐ€ ์ „์‚ฌ๋œ ์ดํ›„ ์™„์ „ํžˆ ์ฒ˜๋ฆฌ๋˜์–ด ๋‹จ๋ฐฑ์งˆ๋กœ ๋ฒˆ์—ญ๋  ๋•Œ๊นŒ์ง€ ์—ฌ๋Ÿฌ ๋‹จ๊ณ„์˜ ์ „์‚ฌ ํ›„ ์กฐ์ ˆ ๊ณผ์ •์„ ๊ฑฐ์น˜๊ฒŒ ๋œ๋‹ค. ์ „์‚ฌ ํ›„ ์กฐ์ ˆ ๊ณผ์ •์€ RNA ํŽธ์ง‘, ์„ ํƒ์  ์ ‘ํ•ฉ, ์„ ํƒ์  ์•„๋ฐ๋‹ํ™” ๋“ฑ์„ ํฌํ•จํ•œ๋‹ค. ์ฆ‰ ์–ด๋Š ํ•œ ์‹œ์ ์—์„œ ์ „์‚ฌ์ฒด๋ฅผ ๋“ค์—ฌ๋‹ค๋ณด๋ฉด ๊ทธ ๋‚ด๋ถ€๋Š” ๋‹ค์–‘ํ•œ ์ค‘๊ฐ„์ฒด๋“ค์˜ ํ˜ผํ•ฉ๋ฌผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ๋ณต์žกํ•œ ์กฐ์ ˆ ์‹œ์Šคํ…œ ๋•Œ๋ฌธ์— ์ „์‚ฌ์ฒด๋ฅผ ์ „์ฒด์ ์ธ ์ˆ˜์ค€์—์„œ ์ดํ•ดํ•˜๊ธฐ๊ฐ€ ์‰ฝ์ง€ ์•Š๋‹ค. ๋ณธ ํ•™์œ„ ์—ฐ๊ตฌ๋Š” RNA ์‹œํ€€์‹ฑ ๋ฐ์ดํ„ฐ๋ฅผ ํ•ด๋…ํ•˜๊ณ  ํ™œ์šฉํ•˜๊ธฐ ์œ„ํ•œ ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฒ•๋“ค์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์ด๋ฉฐ RNA ํŽธ์ง‘, ์„ ํƒ์  ์ ‘ํ•ฉ ๋ฐ ์œ ์ „์ž ๋ฐœํ˜„์˜ ๊ด€์ ์—์„œ ์ˆ˜ํ–‰๋œ ์„ธ ๊ฐ€์ง€ ์—ฐ๊ตฌ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. RNA ํŽธ์ง‘์€ ADAR(A=>I) ๊ณผ APOBEC(C=>U) ๋‘ ๊ฐ€์ง€ ํšจ์†Œ์— ์˜ํ•ด ์ด‰๋งค ๋˜๋Š” ์ „์‚ฌ ํ›„ RNA ์„œ์—ด ์กฐ์ ˆ ๊ธฐ์ž‘์ด๋‹ค. RNA ํŽธ์ง‘์€ ๋‹จ๋ฐฑ์งˆ ํ™œ์„ฑ๋„, ์„ ํƒ์  ์ ‘ํ•ฉ ๋ฐ miRNA ํ‘œ์  ์กฐ์ ˆ ๋“ฑ ๋‹ค์–‘ํ•œ ์„ธํฌ ๊ธฐ์ž‘์„ ์ œ์–ดํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ง„ ์ค‘์š”ํ•œ ์ƒˆํฌ ๋‚ด ์กฐ์ ˆ ์‹œ์Šคํ…œ์ด๋‹ค. RNA ์‹œํ€€์‹ฑ์„ ์ด์šฉํ•ด RNA ํŽธ์ง‘ ํ˜„์ƒ์„ ๊ฒ€์ถœํ•˜๋Š” ๊ฒƒ์€ RNA ํŽธ์ง‘ ํ˜„์ƒ์˜ ์ƒ๋ฌผํ•™์  ๊ธฐ๋Šฅ์„ ์ดํ•ดํ•˜๋Š” ๋ฐ์— ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ๋ฌธ์ œ๋Š” ์ด ๊ณผ์ •์—์„œ ์ƒ๋‹นํ•œ ์–‘์˜ ์œ„์–‘์„ฑ์ด ๋ฐœ์ƒํ•œ๋‹ค๋Š” ์ ์ด๋‹ค. ์ƒ˜ํ”Œ๋‹น ์ˆ˜๋งŒ ๊ฐœ ์ด์ƒ ๋ฐœ์ƒํ•˜๋Š” RNA ํŽธ์ง‘ ์ž”๊ธฐ๋“ค ๋ชจ๋‘๋ฅผ ์‹คํ—˜์ ์œผ๋กœ ๊ฒ€์ฆํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ์ด๋ฅผ ๊ฑธ๋Ÿฌ๋‚ด๊ธฐ ์œ„ํ•œ ์ „์‚ฐํ•™์  ๋ชจ๋ธ์ด ์š”๊ตฌ๋œ๋‹ค. RDDpred๋Š” RNA ์‹œํ€€์‹ฑ ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ RNA ํŽธ์ง‘ ํ˜„์ƒ์„ ๊ฒ€์ถœํ•˜๋Š” ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์œ„์–‘์„ฑ ์ž”๊ธฐ๋“ค์„ ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ์ˆ ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๊ตฌ๋ถ„ํ•˜๋Š” ๋ชจ๋ธ์ด๋‹ค. RDDpred๋Š” ๋‘ ๊ฐœ์˜ ๊ธฐ ๋ฐœํ‘œ๋œ RNA ํŽธ์ง‘ ์—ฐ๊ตฌ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฒ€์ฆ๋˜์—ˆ๋‹ค. RNA ์‹œํ€€์‹ฑ ๊ธฐ์ˆ ์ด ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ๋˜ ํ•˜๋‚˜์˜ ๋ณต์žกํ•œ ๋ฌธ์ œ๋กœ ์ ‘ํ•ฉ์ฒด ์ฐจ์›์—์„œ์˜ ์ข…์–‘ ์ด์งˆ์„ฑ (ITH) ์ธก์ • ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ITH๋Š” ์•” ์กฐ์ง์„ ๊ตฌ์„ฑํ•˜๋Š” ์„ธํฌ ์ง‘๋‹จ์˜ ๋‹ค์–‘์„ฑ์˜ ์ง€ํ‘œ์ด๋ฉฐ, ์ตœ๊ทผ ์ถœํŒ๋œ ์—ฐ๊ตฌ๋“ค์˜ ๊ฒฐ๊ณผ๋Š” ์œ ์ „์ž ๋ฐœํ˜„๋Ÿ‰ ๋ฐ์ดํ„ฐ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ธก์ •๋œ ์ „์‚ฌ์ฒด ์ˆ˜์ค€์—์„œ์˜ ITH๊ฐ€ ์•” ํ™˜์ž์˜ ์˜ˆํ›„์˜ˆ์ธก์— ์œ ์šฉํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ ‘ํ•ฉ์ฒด๋Š” ์œ ์ „์ž ๋ฐœํ˜„๋Ÿ‰๊ณผ ํ•จ๊ป˜ ์ „์‚ฌ์ฒด๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ์ฃผ์š” ์š”์†Œ ์ค‘ ํ•˜๋‚˜์ด๋ฉฐ ๋”ฐ๋ผ์„œ ์ ‘ํ•ฉ์ฒด ์ˆ˜์ค€์—์„œ ITH๋ฅผ ์ธก์ •ํ•˜๋Š” ๊ฒƒ์€ ๋ณด๋‹ค ์ „์ฒด์ ์ธ ์ˆ˜์ค€์—์„œ ์ „์‚ฌ์ฒด ITH๋ฅผ ์—ฐ๊ตฌํ•˜๊ธฐ ์œ„ํ•œ ์ž์—ฐ์Šค๋Ÿฌ์šด ํ๋ฆ„์ด๋‹ค. RNA ์‹œํ€€์‹ฑ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์•” ์ ‘ํ•ฉ์ฒด ์ˆ˜์ค€์—์„œ ITH๋ฅผ ์ธก์ •ํ•˜๋Š” ๊ณผ์ •์—๋Š” ๋ณต์žกํ•œ ์ ‘ํ•ฉ ํŒจํ„ด๊ณผ ๊ด‘๋ฒ”์œ„ํ•œ ์ธํŠธ๋ก  ์—ฐ์žฅ ๋ณ€์ด ๋ฐ ์งง์€ ์‹œํ€€์‹ฑ ํŒ๋… ๊ธธ์ด ๋“ฑ์˜ ์‹ฌ๊ฐํ•œ ๊ธฐ์ˆ ์  ๋‚œ๊ด€๋“ค์ด ์žˆ๋‹ค. SpliceHetero๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋“ค์„ ๊ณ ๋ คํ•˜์—ฌ ์ ‘ํ•ฉ์ฒด ์ˆ˜์ค€์—์„œ์˜ ITH (์ฆ‰, sITH)๋ฅผ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ์ด๋ฉฐ ๋‚ด๋ถ€์ ์œผ๋กœ ์ •๋ณด์ด๋ก ์„ ํ™œ์šฉํ•œ๋‹ค. SpliceHetero๋Š” ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ์ดํ„ฐ, ์ด์ข…์ด์‹ ์ข…์–‘ ๋ฐ์ดํ„ฐ ๋ฐ TCGA pan-cancer ๋ฐ์ดํ„ฐ ๋“ฑ์„ ํ™œ์šฉํ•˜์—ฌ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ๊ฒ€์ฆ๋˜์—ˆ์œผ๋ฉฐ ITH๋ฅผ ์ž˜ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด๋ฟ ์•„๋‹ˆ๋ผ sITH๋Š” ์•”์˜ ์ง„ํ–‰๊ณผ ์•” ํ™˜์ž์˜ ์˜ˆํ›„ ๋ฐ PAM50์™€ ๊ฐ™์€ ์ž˜ ์•Œ๋ ค์ง„ ๋ถ„์ž ์•„ํ˜•๋“ค๊ณผ๋„ ๋†’์€ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ€์ง€๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰ ์—ฐ๊ตฌ ์ฃผ์ œ๋Š” ์œ ์ „์ž ๋ฐœํ˜„๋Ÿ‰ ๋ฐ์ดํ„ฐ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ํŠน์ • ์•” ํ‘œํ˜„ํ˜•์— ํŠน์ด์ ์ธ ํ™˜์ž ๋ถ€๋ถ„ ๊ณต๊ฐ„์„ ์ •์˜ํ•˜๋Š” ๊ธฐ๊ณ„ํ•™์Šต ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์ด๋‹ค. RNA ์‹œํ€€์‹ฑ ๋ฐ์ดํ„ฐ๋Š” ์•” ํ™˜์ž์˜ ์œ ์ „์ž ๋ฐœํ˜„๋Ÿ‰ ํ”„๋กœํŒŒ์ผ์„ ์–ป๋Š” ๋ฐ์— ์œ ์šฉํ•œ ๋„๊ตฌ์ด์ง€๋งŒ, 2๋งŒ ๊ฐœ ์ด์ƒ์˜ ์ฐจ์›์„ ๊ฐ€์ง„ ๋งค์šฐ ๊ณ ์ฐจ์›์˜ ๋ฐ์ดํ„ฐ์ด๊ธฐ ๋•Œ๋ฌธ์— ์‹ค์งˆ์ ์ธ ์šฉ๋„๋กœ ์‚ฌ์šฉ๋˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ทธ ์ฐจ์›์˜ ํฌ๊ธฐ๋ฅผ ์ถ•์†Œํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด๋•Œ ๊ฐ ์œ ์ „์ž๋“ค์€ ๋ณต์žกํ•˜์ง€๋งŒ ๊ณ ์œ ํ•œ ๋ฐฉ์‹์œผ๋กœ ์„œ๋กœ ์ƒํ˜ธ์ž‘์šฉํ•œ๋‹ค๋Š” ์ ์„ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹คํ—˜์ ์œผ๋กœ ๊ฒ€์ฆ๋œ ๋‹จ๋ฐฑ์งˆ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ ์ •๋ณด๋ฅผ ๋ชจ์•„ ๋„คํŠธ์›Œํฌ ํ˜•ํƒœ๋กœ ๋ฌถ์€ ๊ฒƒ์„ ๋‹จ๋ฐฑ์งˆ ์ƒํ˜ธ์ž‘์šฉ ๋„คํŠธ์›Œํฌ (ํ˜น์€ PIN)๋ผ ๋ถ€๋ฅธ๋‹ค. ์ด PIN์„ ํ™œ์šฉํ•˜์—ฌ RNA ์‹œํ€€์‹ฑ ๋ฐ์ดํ„ฐ์˜ ์ฐจ์›์„ ์ค„์ด๋ฉด์„œ๋„ ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ์ƒ๋ฌผํ•™์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ํŠน์ง•๋“ค์„ ์ถ”์ถœํ•  ์ˆ˜ ์žˆ๋‹ค. Tumor2Vec์€ ์ด๋ ‡๊ฒŒ ์ถ”์ถœ๋œ PIN ์ˆ˜์ค€์˜ ํŠน์ง•๋“ค์„ ํ™œ์šฉํ•˜์—ฌ ํŠน์ • ์•” ํ‘œํ˜„ํ˜•์— ํŠน์ด์ ์ธ ํ™˜์ž ๋ถ€๋ถ„ ๊ณต๊ฐ„์„ ์ •์˜ํ•œ๋‹ค. Tumor2Vec์€ ์กฐ๊ธฐ ๊ตฌ๊ฐ• ์•”์—์„œ ๋ฆผํ”„์ ˆ ์ „์ด๋ฅผ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•œ ํŒŒ์ผ๋Ÿฟ ์—ฐ๊ตฌ์— ์ ์šฉ๋˜์—ˆ์œผ๋ฉฐ ๊ทธ ๊ฒฐ๊ณผ RNA ์‹œํ€€์‹ฑ ๋ฐ์ดํ„ฐ์˜ ์ฐจ์›์„ ์ค„์—ฌ ๋ฆผํ”„์ ˆ ์ „์ด ์˜ˆ์ธก ๋ชจ๋ธ์„ ์ƒ์„ฑํ–ˆ๊ณ  ์ด ๊ณผ์ •์—์„œ ์•” ํ‘œํ˜„ํ˜•์„ ์ž˜ ์„ค๋ช…ํ•˜๋Š” PIN ์ˆ˜์ค€์˜ ํŠน์ง•๋“ค์„ ๋ณด์กดํ•˜๋Š” ๋ฐ์—๋„ ์„ฑ๊ณตํ–ˆ๋‹ค.In eukaryotic cells, there are several post-transcriptional modification steps such as RNA editing and alternative splicing, until mRNA molecules are fully matured and translated into proteins. Thus, the transcriptome is a complex mixture of various intermediates that are processed in multiple steps. This complex regulatory structure makes it difficult to fully understand the landscape of transcriptome. My doctoral study consists of three studies that enable RNA-seq to be decoded and utilized in terms of RNA editing, alternative splicing, and gene expression. RNA editing is a post-transcriptional RNA sequence modification performed by two catalytic enzymes ADAR (A-to-I) and APOBEC (C-to-U). RNA editing is considered an important regulatory system that controls a variety of cellular functions such as protein activation, alternative splicing, and miRNA targeting. Therefore, detecting RNA editing events in RNA-seq data is important for understanding its biological functions. However, it is known that a significant amount of false-positives occur when detecting RNA editing in RNA-seq. Since it is not possible to experimentally validate all RNA editing residues extracted from RNA-seq, a computational model is needed to filter potential false-positive RNA editing calls. RDDpred, an RNA editing predictor based on machine learning techniques, was developed to filter out false-positive RNA editing calls in RNA-seq. It uses prior knowledge bases to collect training instances directly from the input data, and then trains the random forest (RF) predictors that are specific to the input data. RDDpred was tested using two publicly available datasets of RNA editing studies and has shown good performance. Another complex problem in RNA-seq decoding is spliceomic intratumor heterogeneity (ie, sITH). Intratumor heterogeneity (ITH) represents the diversity of cell populations that make up the cancer tissue. Recent studies have identified ITH at the transcriptome level and suggested that ITH at gene expression levels is useful for predicting prognosis. Measuring ITH levels at the spliceome level is a natural extension. There is a serious technical challenge in measuring sITH from bulk tumor RNA-seq, such as complex splicing patterns, widespread intron retentions, and short sequencing read lengths. SpliceHetero, an information-theoretic method for measuring the sITH of a tumor, was developed to address the aforementioned technical problems. SpliceHetero was extensively tested in experiments using synthetic data, xenograft tumor data and TCGA pan-cancer data and measured sITH successfully. Also, sITH was shown to be closely related to cancer progression and clonal heterogeneity, along with clinically significant features such as cancer stage, survival outcome, and PAM50 subtype. The last research topic is to develop a machine learning algorithm that defines patient subspaces specific to particular cancer phenotypes based on gene expression data. Since RNA-seq data is high-dimensional data composed of 20,000 or more genes in general, it is not easy to apply a machine learning algorithm. A network that collects information of experimentally verified interaction of proteins is called a Protein Interaction Network (PIN). Tumor2Vec defines the patient subspace by defining the subnetwork communities that interact with each other by applying the Graph Embedding technique to PIN. Tumor2Vec proposed a clinical model by defining a subspace for patients with different lymph node metastases in early oral cancer and found biologically significant features in the PIN subnetwork unit in the process.Chapter 1 Introduction 1 1.1 Biological background . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Challenges in decoding and utilizing RNA-seq data . . . . . . . . 5 1.2.1 false-positives in RNA editing calls . . . . . . . . . . . . . 6 1.2.2 Absence of a model for measuring spliceomic intratumor heterogeneity considering complex cancer spliceome . . . 6 1.2.3 Lack of biological interpretation of dimension reduction techniques using gene expression . . . . . . . . . . . . . . 8 1.3 Machine learning techniques to solve difficulties in using RNA-seq 9 1.4 Outline of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter 2 RDDpred: A condition specific machine learning model for filtering false-positive RNA editing calls in RNAseq data 11 2.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 A preliminary study . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.5.1 Design of experiments for evaluation . . . . . . . . . . . . 18 2.5.2 Evaluation using data from Bahn et al. . . . . . . . . . . 19 2.5.3 Evaluation using data from Peng et al. . . . . . . . . . . . 19 2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Chapter 3 SpliceHetero: An information-theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq data 24 3.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3 A preliminary study . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.1 Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.2 Xenograft tumor data . . . . . . . . . . . . . . . . . . . . 36 3.5.3 TCGA pan-cancer data . . . . . . . . . . . . . . . . . . . 38 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Chapter 4 Tumor2Vec: A supervised learning algorithm for extracting subnetwork representations of cancer RNAseq data using protein interaction networks 48 4.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.4 Results & Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4.1 Lymph node metastasis in early oral cancer . . . . . . . . 57 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Chapter 5 Conclusion 62 ์ดˆ๋ก 78Docto

    ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง์˜ ํšจ์œจ์ ์ธ ์‹คํ–‰์„ ์œ„ํ•œ ์‹คํ–‰ ๊ณ„ํš ์ž๋™ ์ƒ์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2020. 8. Bernhard Egger.Over the past years, a large number of architectures and accelerators for Deep Neural Networks (DNNs) have been proposed. While exhibiting common features, the number and arrangement of processing elements, the sizes and types of on-chip memory, and the possibilities of parallel execution vary significantly especially in the embedded system domain. The number of off-chip memory accesses and the performance of a DNN on a given accelerator depends not only on the supported computational patterns and the available on-chip memory but also on the sizes and shapes of each layer. Finding a computational pattern that minimizes off-chip memory accesses while maximizing performance is thus a tedious and error-prone task. This thesis presents e-PlaNNer, a compiler framework that generates an optimized execution plan for a given embedded accelerator and Convolutional Neural Network (CNN). For each layer, e-PlaNNer determines the performance-optimal configuration by considering the data movement, tiling, and work distribution. The generated execution plan is transformed to code, allowing for a fast development cycle with different CNNs and hardware accelerators. Evaluated with five neural networks under varying memory configurations and compared to previous works on the Nvidia Jetson TX2, e-PlaNNer achieves 6x speedup and 21.14% reduction of off-chip memory access volume on average. In addition, e-PlaNNer shows meaningful performance compared to well-known deep learning frameworks in terms of end-to-end execution.์ง€๋‚œ ๋ช‡ ๋…„๊ฐ„ ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์„ ์œ„ํ•œ ์ˆ˜๋งŽ์€ ์•„ํ‚คํ…์ฒ˜์™€ ๊ฐ€์†๊ธฐ๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด, ์ผ๋ฐ˜์ ์ธ ์‹ฌ์ธต์‹ ๊ฒฝ๋ง ์ˆ˜ํ–‰ ๋ฐฉ์‹๋“ค์ด ํ•จ๊ป˜ ์ œ์•ˆ๋˜์—ˆ์œผ๋‚˜, ๊ตฌ์ฒด์ ์ธ ์—ฐ์‚ฐ ๋ฐฐ์น˜ ๋ฐฉ์‹๊ณผ ์˜จ์นฉ ๋ฉ”๋ชจ๋ฆฌ์˜ ํฌ๊ธฐ ๋ฐ ์ข…๋ฅ˜, ๊ทธ๋ฆฌ๊ณ  ๋ณ‘๋ ฌ ์‹คํ–‰ ๋ฐฉ์‹์€ ํŠนํžˆ ๋‚ด์žฅํ˜• ์‹œ์Šคํ…œ์—์„œ ๋‹ค์–‘ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์˜คํ”„์นฉ ๋ฉ”๋ชจ๋ฆฌ ์ ‘๊ทผ ํฌ๊ธฐ ๋ฐ ์‹ ๊ฒฝ๋ง์˜ ์„ฑ๋Šฅ์€ ์—ฐ์‚ฐ ํ˜•ํƒœ ๋ฐ ์˜จ์นฉ ๋ฉ”๋ชจ๋ฆฌ์˜ ํฌ๊ธฐ ๋ฟ ์•„๋‹ˆ๋ผ ์‹ ๊ฒฝ๋ง ๊ฐ ๊ณ„์ธต์˜ ํฌ๊ธฐ ๋ฐ ํ˜•ํƒœ์— ๋”ฐ๋ผ์„œ ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ, ์ตœ๋Œ€ ์„ฑ๋Šฅ์„ ๋‚ด๋ฉด์„œ ์˜คํ”„์นฉ ๋ฉ”๋ชจ๋ฆฌ ์ ‘๊ทผ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ์—ฐ์‚ฐ ํ˜•ํƒœ๋ฅผ ์ผ์ผ์ด ์ฐพ๋Š” ๊ฒƒ์€ ์ƒ๋‹นํžˆ ๋ฒˆ๊ฑฐ๋กœ์šด ์ž‘์—…์ด๋ฉฐ, ๋งŽ์€ ์˜ค๋ฅ˜๋ฅผ ๋ฐœ์ƒ ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์†Œ๊ฐœํ•  e-PlaNNer๋Š” ์ฃผ์–ด์ง„ ๋‚ด์žฅํ˜• ํ•˜๋“œ์›จ์–ด ๊ฐ€์†๊ธฐ์™€ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง์— ๋Œ€ํ•˜์—ฌ ์ตœ์ ํ™”๋œ ์‹คํ–‰ ๊ณ„ํš์„ ์ƒ์„ฑํ•ด์ฃผ๋Š” ์ปดํŒŒ์ผ๋Ÿฌ ํ”„๋ ˆ์ž„์›Œํฌ์ด๋‹ค. e-PlaNNer๋Š” ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์˜ ๊ฐ ์‹ ๊ฒฝ๋ง ๊ณ„์ธต์— ๋Œ€ํ•˜์—ฌ ๋ฐ์ดํ„ฐ ์ด๋™, ํƒ€์ผ๋ง, ๊ทธ๋ฆฌ๊ณ  ์ž‘์—… ๋ฐฐ๋ถ„์„ ๊ณ ๋ คํ•œ ์„ฑ๋Šฅ ์ตœ์ ํ™”๋œ ์‹คํ–‰ ๊ณ„ํš์„ ๊ฒฐ์ •ํ•œ๋‹ค. ๋˜ํ•œ, ์ƒ์„ฑ๋œ ์‹คํ–‰ ๊ณ„ํš์„ ์‹ค์ œ ์ปดํŒŒ์ผ ๊ฐ€๋Šฅํ•œ ์ฝ”๋“œ๋กœ ๋ณ€ํ™˜ํ•จ์œผ๋กœ์จ, ์„œ๋กœ ๋‹ค๋ฅธ ๋‹ค์–‘ํ•œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง๊ณผ ํ•˜๋“œ์›จ์–ด ๊ฐ€์†๊ธฐ์— ๋Œ€ํ•˜์—ฌ ๋น ๋ฅธ ๊ฐœ๋ฐœ ์ฃผ๊ธฐ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๋‹ค์–‘ํ•œ ๋ฉ”๋ชจ๋ฆฌ ๊ตฌ์„ฑ์œผ๋กœ ๋‹ค์„ฏ ๊ฐ€์ง€ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ์‘์šฉ์„ Nvidia์˜ Jetson TX2 ์—์„œ ๊ฒ€์ฆํ•˜์—ฌ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์™€ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, e-PlaNNer๋Š” ํ‰๊ท ์ ์œผ๋กœ 6๋ฐฐ์˜ ์„ฑ๋Šฅ ํ–ฅ์ƒ๊ณผ 21.14% ์˜ ์˜คํ”„์นฉ ๋ฉ”๋ชจ๋ฆฌ ๋ฐ์ดํ„ฐ ์ ‘๊ทผ๋Ÿ‰ ๊ฐ์†Œ๋ฅผ ๋ณด์˜€๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, e-PlaNNer๋Š” ์ „์ฒด ์‹ฌ์ธต์‹ ๊ฒฝ๋ง์˜ ์‹คํ–‰ ๊ด€์ ์—์„œ ๊ธฐ์กด์— ์ž˜ ์•Œ๋ ค์ง„ ๋”ฅ๋Ÿฌ๋‹ ํ”„๋ ˆ์ž„์›Œํฌ์™€์˜ ๋น„๊ต์—์„œ๋„ ์˜๋ฏธ์žˆ๋Š” ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค.Chapter 1 Introduction 1 Chapter 2 Related Work 5 Chapter 3 Background 8 3.1 Convolutional Neural Networks 8 3.2 DNN Accelerator 9 3.3 Roofline Model 11 Chapter 4 Graph Level Processing 13 4.1 Graph Construction 13 4.2 Schedule Caching 14 Chapter 5 Convolutional Layer Analysis 15 5.1 Loop Structure 16 5.2 Loop Tiling 17 5.3 Dataflow 18 Chapter 6 Execution Planning 20 6.1 Architecture Con figurations 20 6.2 Modeling Off-Chip Memory Accesses 22 6.3 Modeling Performance 24 6.4 Search Space Exploration 25 Chapter 7 Code Generation 32 7.1 Intermediate Representation 33 7.2 Target Code Generation 34 Chapter 8 Evaluation 36 8.1 Experimental Setup 36 8.2 Performance Results 39 8.3 Comparison of Off-chip Memory Access 40 8.4 Framework Results 42 Chapter 9 Discussion 46 Chapter 10 Conclusion 47 Bibliography 48 ์š”์•ฝ 57Maste

    Volume-Controlled Versus Dual-Controlled Ventilation during Robot-Assisted Laparoscopic Prostatectomy with Steep Trendelenburg Position: A Randomized-Controlled Trial

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    Dual-controlled ventilation (DCV) combines the advantages of volume-controlled ventilation (VCV) and pressure-controlled ventilation (PCV). Carbon dioxide (CO2) pneumoperitoneum and steep Trendelenburg positioning for robot-assisted laparoscopic radical prostatectomy (RALRP) has negative effects on the respiratory system. We hypothesized that the use of autoflow as one type of DCV can reduce these effects during RALRP. Eighty patients undergoing RALRP were randomly assigned to receive VCV or DCV. Arterial oxygen tension (PaO2) as the primary outcome, respiratory and hemodynamic data, and postoperative fever rates were compared at four time points: 10 min after anesthesia induction (T1), 30 and 60 min after the initiation of CO2 pneumoperitoneum and Trendelenburg positioning (T2 and T3), and 10 min after supine positioning (T4). There were no significant differences in PaO2 between the two groups. Mean peak airway pressure (Ppeak) was significantly lower in group DCV than in group VCV at T2 (mean difference, 5.0 cm H2O; adjusted p < 0.001) and T3 (mean difference, 3.9 cm H2O; adjusted p < 0.001). Postoperative fever occurring within the first 2 days after surgery was more common in group VCV (12%) than in group DCV (3%) (p = 0.022). Compared with VCV, DCV did not improve oxygenation during RALRP. However, DCV significantly decreased Ppeak without hemodynamic instability.ope

    Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm

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    Background: Intraoperative neurophysiological monitoring is essential in neurosurgical procedures. In this study, we built and evaluated the performance of a deep neural network in differentiating between the presence and absence of a lateral spread response, which provides critical information during microvascular decompression surgery for the treatment of hemifacial spasm using intraoperatively acquired electromyography images. Methods and findings: A total of 3,674 image screenshots of monitoring devices from 50 patients were prepared, preprocessed, and then adopted into training and validation sets. A deep neural network was constructed using current-standard, off-the-shelf tools. The neural network correctly differentiated 50 test images (accuracy, 100%; area under the curve, 0.96) collected from 25 patients whose data were never exposed to the neural network during training or validation. The accuracy of the network was equivalent to that of the neuromonitoring technologists (p = 0.3013) and higher than that of neurosurgeons experienced in hemifacial spasm (p < 0.0001). Heatmaps obtained to highlight the key region of interest achieved a level similar to that of trained human professionals. Provisional clinical application showed that the neural network was preferable as an auxiliary tool. Conclusions: A deep neural network trained on a dataset of intraoperatively collected electromyography data could classify the presence and absence of the lateral spread response with equivalent performance to human professionals. Well-designated applications based upon the neural network may provide useful auxiliary tools for surgical teams during operations.ope

    57a-69e์— ๋Œ€ํ•œ ๋ถ„์„์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ธ๋ฌธ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์„œ์–‘๊ณ ์ „ํ•™์ „๊ณต, 2021. 2. ๊ฐ•์„ฑํ›ˆ.ใ€ŽํŒŒ์ด๋ˆใ€์˜ ์ „๋ฐ˜๋ถ€๋ฅผ ์ค‘์ ์ ์œผ๋กœ ๋ถ„์„ํ•˜๋ ค๋Š” ๋ณธ ๋…ผ๋ฌธ์€ ๋Œ€ํ™”ํŽธ์—์„œ ๋“œ๋Ÿฌ๋‚˜๋Š” ์ฒ ํ•™์ž์˜ ์†Œ์ž„์ด ๋ฌด์—‡์ธ์ง€ ๋ฐํžˆ๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜๊ณ  ์žˆ๋‹ค. ใ€ŽํŒŒ์ด๋ˆใ€์—์„œ ์ฃฝ์Œ์„ ์•ž๋‘” ์ฒ ํ•™์ž๋Š”, ๋Œ€ํ™”ํŽธ ๋‚ด์˜ ๋ณธ๊ฒฉ์ ์ธ ๋…ผ์˜๊ฐ€ ์‹œ์ž‘๋˜๋Š” ์ง€์ ์—์„œ๋ถ€ํ„ฐ, ์˜ํ˜ผ์˜ ๋ถˆ๋ฉธ์„ฑ์„ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•ด ๊ทธ์˜ ๋Œ€ํ™”์ƒ๋Œ€์ž๋“ค๊ณผ ๊ฝค ๊ธด ๋Œ€ํ™”๋ฅผ ํ•ด๋‚˜๊ฐ„๋‹ค. ์˜ํ˜ผ๋ถˆ๋ฉธ๋…ผ์ฆ์„ ๋งˆ์น˜๋ฉฐ ์†Œํฌ๋ผํ…Œ์Šค๋Š” ์˜ํ˜ผ์ด ์ฃฝ์Œ ์ดํ›„์—๋„ ๊ณ„์†ํ•˜์—ฌ ์กด์žฌํ•œ๋‹ค๋Š” ๊ฒฐ๋ก ์„ ์ด๋Œ์–ด๋‚ด๊ณ , ๊ทธ์˜ ๋Œ€ํ™”์ƒ๋Œ€์ž๋“ค๋„ ์ฒ ํ•™์ž์˜ ๋…ผ์ฆ๊ณผ ๊ฒฐ๋ก ์— ์„ค๋“๋˜๋Š” ์„ฑ๊ณผ๋ฅผ ์ด๋ค„๋‚ธ๋‹ค. ์†Œํฌ๋ผํ…Œ์Šค์˜ ์ฃผ๋„ ์•„๋ž˜ ์ˆ˜ํ–‰๋˜๋Š” ์˜ํ˜ผ๋ถˆ๋ฉธ๋…ผ์ฆ์€ ์˜ค๋Š˜๋‚  ์ฒ ํ•™์— ์–ด๋Š ์ •๋„์— ์ต์ˆ™ํ•œ ์‚ฌ๋žŒ๋“ค์—๊ฒŒ๋Š” ์ž˜ ์•Œ๋ ค์ ธ ์žˆ๋Š” ๋‚ด์šฉ์ด๋ฉฐ, ์•„๋งˆ๋„ ์˜ํ˜ผ์˜ ๋ถˆ๋ฉธ์„ฑ ๋…ผ์˜๊ฐ€ ใ€ŽํŒŒ์ด๋ˆใ€์—์„œ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์ด์•ผ๊ธฐ๋ผ๋Š” ๋ฐ์— ํฐ ์ด๊ฒฌ์€ ์—†์„ ๊ฒƒ ๊ฐ™๋‹ค. ํ•˜์ง€๋งŒ ํ•„์ž๊ฐ€ ๋ณด๊ธฐ์— ์ด ๋Œ€ํ™”ํŽธ์—๋Š” ์˜ํ˜ผ๋ถˆ๋ฉธ๋…ผ์ฆ ์ด์™ธ์—๋„ ์ถฉ๋ถ„ํžˆ ์ฃผ๋ชฉ๋ฐ›์„ ๋งŒํ•œ ๋งŽ์€ ์ด์•ผ๊นƒ๊ฑฐ๋ฆฌ๋“ค์ด ๋งˆ๋ จ๋˜์–ด ์žˆ์œผ๋ฉฐ, ๋ณธ ๋…ผ๋ฌธ์€ ํŠนํžˆ ๋Œ€ํ™”ํŽธ ์ „๋ฐ˜๋ถ€์ธ 57a-69e๋ฅผ ๊ฒ€ํ† ํ•จ์œผ๋กœ์จ, ์ž‘๊ฐ€ ํ”Œ๋ผํ†ค์ด ์ฃฝ์Œ์„ ์•ž๋‘” ์ฒ ํ•™์ž์˜ ๋ง๊ณผ ํ–‰๋™์„ ํ†ตํ•ด โ€˜์ฒ ํ•™์ž์˜ ์†Œ์ž„โ€™์ด ๋ฌด์—‡์ธ์ง€๋ฅผ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค๋Š” ์ด์•ผ๊ธฐ๋ฅผ ํ’€์–ด๋‚˜๊ฐˆ ๊ฒƒ์ด๋‹ค. ๋Œ€ํ™”ํŽธ ๋„์ž…๋ถ€์—๋Š” ์–ผํ• ์˜ํ˜ผ๋ถˆ๋ฉธ๋…ผ์ฆ๊ณผ ๋ฐ”๋กœ ์—ฐ๊ฒฐ๋˜๊ธฐ ์–ด๋ ค์›Œ ๋ณด์ด๋Š” ์ด๋Ÿฌ์ €๋Ÿฌํ•œ ์ด์•ผ๊ธฐ๋“ค์ด ์ œ์‹œ๋œ๋‹ค. ์ด๊ฒƒ๋“ค์„ ๋งž์ดํ•˜๋ฉด์„œ ๋…์ž๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์งˆ๋ฌธ์„ ๋– ์˜ฌ๋ ค ๋ณผ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ์™œ ์ž‘ํ’ˆ์˜ ์ œ๋ชฉ์ด ใ€ŽํŒŒ์ด๋ˆใ€์ด๋ฉฐ, ์†Œํฌ๋ผํ…Œ์Šค์˜ ์žฌํŒ ์ „๋‚  ์™œ ํ…Œ์„ธ์šฐ์Šค์˜ ๋ฐฐ๊ฐ€ ๋ธ๋กœ์Šค๋กœ ๋– ๋‚ฌ๋Š”์ง€, ๋ฌด์Šจ ์ด์œ ๋กœ ์†Œํฌ๋ผํ…Œ์Šค๊ฐ€ ๊ฐ์˜ฅ ์•ˆ์—์„œ ๋Œ€์ค‘ ์‹œ๊ฐ€๋ฅผ ์ง€์—ˆ๋˜ ๊ฒƒ์ธ์ง€, ๋˜ ๋ณด๋‹ค ์ค‘์š”ํ•œ ๋‚ด์šฉ๋“ค, ๊ฐ€๋ น ์™œ ์†Œํฌ๋ผํ…Œ์Šค๋Š” ์ฃฝ์Œ์„ ์ถ”๊ตฌํ•˜๋Š” ์ฒ ํ•™์ž๊ฐ€ ์ž์‚ดํ•ด์„œ๋Š” ์•ˆ ๋œ๋‹ค๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ, ์–ด๋–ค ์ด์œ ์—์„œ ์ฃฝ์Œ๊ณผ ์ฃฝ์–ด ์žˆ์Œ์„ ๊ตฌ๋ถ„ํ•˜๋Š” ๊ฒƒ์ด๊ณ , ๋˜ํ•œ ์ฒ ํ•™์ž๊ฐ€ ๋งํ•˜๋Š” ์˜ฌ๋ฐ”๋ฅธ ์ •ํ™”๋ผ๋Š” ๊ฒƒ์€ ๋ฌด์—‡์ธ๊ฐ€? ๊ทธ๋ฆฌ๊ณ  ๋Œ€ํ™”ํŽธ ๋ง๋ฏธ, ๊ทธ๋Ÿฌ๋‹ˆ๊นŒ ์†Œํฌ๋ผํ…Œ์Šค๊ฐ€ ์˜ํ˜ผ๋ถˆ๋ฉธ๋…ผ์ฆ์„ ๋งˆ์นœ ๋’ค, ์ฒ ํ•™์ž๊ฐ€ ์˜ํ˜ผ์˜ ์‚ฌํ›„ ์—ฌ์ •์— ๊ด€ํ•ด ๋“ค๋ ค์ฃผ๋Š” ์ด์•ผ๊ธฐ๋Š” ๋Œ€ํ™”ํŽธ์—์„œ ์–ด๋–ค ์—ญํ• ์„ ํ•˜๋Š”๊ฐ€? ์ด๋Ÿฌํ•œ ์˜๋ฌธ๋“ค์€ ํ”Œ๋ผํ†ค์˜ ์˜๋„ ํ•˜์— ์„œ๋กœ ์—ฐ๊ด€ ๋งบ์€ ์ฑ„๋กœ ์ƒ๋‹นํžˆ ํฅ๋ฏธ๋กญ๊ฒŒ ๋ฐฐ์น˜๋œ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด๋Ÿฌํ•œ ์ด์•ผ๊ธฐ๋“ค์— ๋Œ€ํ•œ ๋ถ„์„์„ ์‹œ๋„ํ•˜๊ณ , ๊ทธ ๋ถ„์„์„ ํ†ตํ•ด ใ€ŽํŒŒ์ด๋ˆใ€์—์„œ ๋“œ๋Ÿฌ๋‚˜๋Š” ์ฒ ํ•™์ž์˜ ์†Œ์ž„์ด๋ž€ ์‹ ์œผ๋กœ๋ถ€ํ„ฐ ๋ช…๋ น์„ ๋ถ€์—ฌ ๋ฐ›์•„ ๋กœ๊ณ ์Šค์™€ ๋ฎˆํ† ์Šค๋ฅผ ๊ฐ€์ง€๊ณ ์„œ ์ž์‹ ๊ณผ ๋‹ค๋ฅธ ์ฒ ํ•™์ž๋“ค, ๊ทธ๋ฆฌ๊ณ  ๋Œ€์ค‘์˜ ์˜ํ˜ผ์„ ๋Œ๋ณด๋Š” ๊ฒƒ์ด๋ผ๋Š” ๊ฒฐ๋ก ์„ ์ด๋Œ์–ด๋‚ด๊ณ ์ž ๋…ธ๋ ฅํ•  ๊ฒƒ์ด๋‹ค.The purpose of this thesis is to clarify the concept of โ€˜the mission of philosopherโ€™, by reading of the first-half of Phaedo 57a-69e. In the dialogue, the philosopher nearing death has a lengthy conversation with his interlocutors to prove the immortality of the soul from the point where a full-scale discussion in the dialogue begins. The philosopher who unfolds the โ€˜the argument of the immortality of the soulโ€™, comes to the conclusion that the soul continues to exist after death, and his interlocutors are persuaded by his arguments. Socratesโ€™ argument for the immortality of the soul is relatively well known to those who are familiar with philosophy to some extent today, and there seems to be no great disagreement that the argument for the immortality of the soul is perhaps the most important story in the Phaedo. In this dialogue, however, in addition to the argument for the immortality of the soul, there are sufficiently remarkable stories in the first half of the dialogue 57a-69e, and by reviewing this part, we might find that Plato shows what the philosopher's mission is. At the beginning of the dialogue, a series of stories are presented that seem difficult to connect directly to the argument for the immortality of the soul. But as the story progresses, we the reader might think of questions like: Why is the dialogue titled โ€œPhaedoโ€, why Theseus' ship left for Delos the day before Socrates' trial, why Socrates built the popular music in prison, and more importantly, why does Socrates, the philosopher seeking death, assert that suicide is not allowed, and for what reason are death and being dead different, and what is the proper purification that the philosopher pursues? And at the end of the dialogue, after Socrates's argument for the immortality of the soul, what is the purpose of the philosopherโ€™s story about the soul's death journey? These questions seem to have been arranged quite interestingly linked to each other under Plato's intention. This study tries to analyze these stories, and through the analysis leads the conclusion that the philosopher's mission in Phaedo is to get the word by God and to take care of the soul of Socrates himself and other people with the philosophical tool logos and mythos.โ… . ์„œ๋ก  1 โ…ก. ๋ณธ๋ก  9 1. ๋ฌด๋Œ€์„ค์ • 9 1.1. ์ฒซ ๋ฒˆ์งธ ์งˆ๋ฌธ 9 1.2. ํŒŒ์ด๋ˆ์ด๋ผ๋Š” ์ธ๋ฌผ 12 1.3. ํ”Œ๋ ˆ์ด์šฐ์Šค๋ผ๋Š” ์žฅ์†Œ์™€ ํ”ผํƒ€๊ณ ๋ผ์Šคํ•™ํŒŒ 16 1.4. ์–ด๋–ค ์šฐ์—ฐํ•œ ์ผ 21 1.5. ํ…Œ์„ธ์šฐ์Šค ์‹ ํ™”์™€ ์•„ํด๋ก  ์ถ•์ œ 25 2. ๊ฐ์˜ฅ ์•ˆ์œผ๋กœ 30 2.1. ์ด์•ผ๊ธฐ๊พผ์œผ๋กœ์˜ ์ „ํšŒ 31 2.2. ์ž์‚ด ๊ธˆ์ง€ 41 2.3. ๋น„๋ฐ€์Šค๋Ÿฐ ๊ฐ€๋ฅด์นจ 47 3. ์†Œํฌ๋ผํ…Œ์Šค์˜ ๋‘ ๋ฒˆ์งธ ๋ณ€๋ก  61 3.1. ์ฃฝ์Œ๊ณผ ์ฃฝ์–ด ์žˆ์Œ 72 3.1.1. ์ˆœํ™˜ ๋…ผ์ฆ 75 3.1.2. ์œ ์‚ฌ์„ฑ ๋…ผ์ฆ 78 3.1.3. ์†Œํฌ๋ผํ…Œ์Šค์˜ ์ „ํšŒ์™€ ๋งˆ์ง€๋ง‰ ๋…ผ์ฆ 81 3.2. ์ฒ ํ•™์ž๊ฐ€ ์ถ”๊ตฌํ•˜๋Š” ์ฃฝ์Œ 85 3.2.1. ๊ธˆ์š•์  ํ•ด์„๊ณผ ํ‰๊ฐ€์  ํ•ด์„ 86 3.2.2. ์ƒ๊ธฐ ๋…ผ์ฆ 95 3.3. ์ •ํ™”์˜์‹๊ณผ ์ •ํ™” 104 3.4. ๋•์„ ์œ„ํ•œ ์˜ฌ๋ฐ”๋ฅธ ๊ตํ™˜ 113 4. ์ฒ ํ•™์ž์˜ ๋ฎˆํ† ์Šค 117 4.1. ์˜ํ˜ผ์˜ ์‚ฌํ›„ ์—ฌ์ • ๋‚ด์šฉ ์š”์•ฝ 117 4.2. ๋‹จ์ˆœํ•˜์ง€ ์•Š์€ ๊ธธ 123 4.3. ์ดํ•ญ๋Œ€๋ฆฝ์ด๋ก  126 4.4. ์ง€๊ตฌ์˜ ๋ชจ์Šต๊ณผ ๊ทธ๊ณณ์˜ ๊ฑฐ์ฃผ์ž๋“ค 128 โ…ข. ๊ฒฐ๋ก  136 ์ฐธ๊ณ ๋ฌธํ—Œ 141 Abstract 147Maste

    ์„œ์šธ~๋ถ€์‚ฐ ๊ตฌ๊ฐ„์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ๊ณต๊ธฐ์—…์ •์ฑ…ํ•™๊ณผ, 2020. 8. ๊ณ ๊ธธ๊ณค.๊ตํ†ต์ˆ˜๋‹จ ํŠน์„ฑ์š”์ธ์ธ ์šด์ž„, ํ†ตํ–‰์‹œ๊ฐ„, ์šดํ–‰ํšŸ์ˆ˜๋Š” ๊ตํ†ต์ˆ˜๋‹จ ์šด์˜๊ณ„ํš ์ˆ˜๋ฆฝ๊ณผ ํ†ตํ–‰์ž ๊ตํ†ต์ˆ˜๋‹จ์„ ํƒ ๋“ฑ ์—ฌ๊ฐ์ˆ˜์†ก์‹ค์ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ํฐ ์ค‘์š” ์ฒ™๋„์ด๋‹ค. ํ•œํŽธ, ์„œ์šธ-๋ถ€์‚ฐ ๋…ธ์„ ์€ ์šฐ๋ฆฌ๋‚˜๋ผ ๋‚ด๋ฅ™๋…ธ์„  ์ค‘ ๊ฐ€์žฅ ํฐ ์—ฌ๊ฐ์‹œ์žฅ์ด๋ฉฐ ๊ฐ€์žฅ ๊ธด ๋…ธ์„ ์œผ๋กœ ๊ตํ†ต์ˆ˜๋‹จ๊ฐ„ ์šด์ž„๊ณผ ์‹œ๊ฐ„, ๊ณต๊ธ‰์„ ์ง€์†์ ์œผ๋กœ ์ฐจ๋ณ„ํ™”ํ•˜๋ฉฐ ๊ฒฝ์Ÿํ•˜๊ณ  ์žˆ๋Š” ๊ตํ†ต์‹œ์žฅ์ด๋‹ค. ์ด์—, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„œ์šธ-๋ถ€์‚ฐ ๋…ธ์„ ์„ ์ค‘์‹ฌ์œผ๋กœ ๊ฐ ๊ตํ†ต์ˆ˜๋‹จ์˜ ์šด์ž„, ํ†ตํ–‰์‹œ๊ฐ„, ์šดํ–‰ํšŸ์ˆ˜๊ฐ€ ์—ฌ๊ฐ์ˆ˜์†ก์‹ค์ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ์ธ๊ณผ๊ด€๊ณ„๋ฅผ ์‹ค์ฆ์ ์œผ๋กœ ๋ถ„์„ํ•จ์œผ๋กœ์จ ๊ตญ๋‚ด ์žฅ๊ฑฐ๋ฆฌ ๊ตํ†ต์ˆ˜๋‹จ์— ๋Œ€ํ•œ ํšจ์œจ์  ์šด์˜ ๋ฐ ๊ฒฝ์Ÿ๋ ฅ ๊ฐ•ํ™”๋ฅผ ์œ„ํ•œ ์ •์ฑ…์  ํ•จ์˜๋ฅผ ๋„์ถœํ•˜๋Š”๋ฐ ๋ชฉ์ ์ด ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ณต๊ฐ„์  ๋ฒ”์œ„๋Š” ์„œ์šธ-๋ถ€์‚ฐ ๊ตฌ๊ฐ„์ด๋ฉฐ, ์‹œ๊ฐ„์  ๋ฒ”์œ„๋Š” 2005๋…„ 1์›”๋ถ€ํ„ฐ 2019๋…„ 12์›”๊นŒ์ง€๋กœ 15๋…„๊ฐ„์˜ ์›”๋ณ„ 180๊ฐœ ๊ด€์ฐฐ์ ์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๊ณ ์†์ฒ ๋„, ์ผ๋ฐ˜์ฒ ๋„, ๊ณ ์†๋ฒ„์Šค, ํ•ญ๊ณต 4๊ฐ€์ง€ ๋Œ€์ค‘๊ตํ†ต ์ˆ˜๋‹จ์„ ์—ฐ๊ตฌ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•์€ ์ˆ˜์ง‘ํ•œ ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœํ•œ ๊ธฐ์ดˆํ†ต๊ณ„์™€ ๊ธฐ์ˆ ํ†ต๊ณ„ ๋ถ„์„์„ ์ง„ํ–‰ํ•˜๊ณ , ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์ธ ๊ตํ†ต์ˆ˜๋‹จ ํŠน์„ฑ์š”์ธ์ด ์—ฌ๊ฐ์ˆ˜์†ก์‹ค์ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ์ƒํ˜ธ์ž‘์šฉํ•ญ์ด ํฌํ•จ๋œ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋…๋ฆฝ๋ณ€์ˆ˜๋Š” 4๊ฐ€์ง€ ๊ตํ†ต์ˆ˜๋‹จ์˜ ์šด์ž„, ํ†ตํ–‰์‹œ๊ฐ„, ์šดํ–‰ํšŸ์ˆ˜, ์ˆ˜๋‹จํŠน์„ฑ*์ˆ˜๋‹จ์˜ ์ƒํ˜ธ์ž‘์šฉํ•ญ์ด๊ณ , ์ข…์†๋ณ€์ˆ˜๋Š” ๊ฐ ์ˆ˜๋‹จ์˜ ์—ฌ๊ฐ์ˆ˜ ๋ฐ ์ˆ˜์†ก๋ถ„๋‹ด๋ฅ , ์ขŒ์„ํƒ‘์Šน๋ฅ ์ด๋‹ค. ๋˜ํ•œ, 1์ธ๋‹น ์‹ค์งˆ ์ฒ˜๋ถ„๊ฐ€๋Šฅ์†Œ๋“์„ ํ†ต์ œ๋ณ€์ˆ˜๋กœ ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ์ •๋ฆฌํ•˜๋ฉฐ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ฐ ๊ตํ†ต์ˆ˜๋‹จ ์šด์ž„์ด ์ฆ๊ฐ€๋˜๋ฉด ์—ฌ๊ฐ์ˆ˜, ์ˆ˜์†ก๋ถ„๋‹ด๋ฅ , ์ขŒ์„ํƒ‘์Šน๋ฅ ์— ์Œ์˜(-) ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์„ ๋Œ€๋ถ€๋ถ„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋‚˜, ๊ณ ์†์ฒ ๋„์˜ ๊ฒฝ์šฐ ์šด์ž„์ด ์ฆ๊ฐ€ํ•จ์—๋„ ์ˆ˜์†ก๋ถ„๋‹ด๋ฅ ์ด ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜์™”๋‹ค. ์ด๋Š” ์„œ์šธ-๋ถ€์‚ฐ ๊ตํ†ต์‹œ์žฅ์—์„œ ๊ณ ์†์ฒ ๋„์˜ ์‹œ์žฅ์ง€๋ฐฐ๋ ฅ์ด ๋งค์šฐ ๋†’์Œ์— ๋”ฐ๋ผ, ์†Œ๋น„์ž๋Š” ์šด์ž„์ด ์˜ฌ๋ผ๊ฐ€๋„ ๊ณ ์†์ฒ ๋„๋ฅผ ๋Œ€์ฒดํ•  ๊ตํ†ต์ˆ˜๋‹จ์„ ์ฐพ์ง€ ๋ชปํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ๋‘˜์งธ, ๊ฐ ๊ตํ†ต์ˆ˜๋‹จ ํ†ตํ–‰์‹œ๊ฐ„์ด ์ฆ๊ฐ€๋˜๋ฉด ์—ฌ๊ฐ์ˆ˜, ์ˆ˜์†ก๋ถ„๋‹ด๋ฅ , ์ขŒ์„ํƒ‘์Šน๋ฅ ์— ์Œ์˜(-) ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์„ ๋Œ€๋ถ€๋ถ„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์…‹์งธ, ๊ฐ ๊ตํ†ต์ˆ˜๋‹จ ์šดํ–‰ํšŸ์ˆ˜๊ฐ€ ์ฆ๊ฐ€๋˜๋ฉด ์—ฌ๊ฐ์ˆ˜, ์ˆ˜์†ก๋ถ„๋‹ด๋ฅ , ์ขŒ์„ํƒ‘์Šน๋ฅ ์— ์–‘์˜(+) ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์„ ๋Œ€๋ถ€๋ถ„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋‚˜, ํ•ญ๊ณต์˜ ๊ฒฝ์šฐ ์šดํ–‰ํšŸ์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ•จ์—๋„ ์ขŒ์„ํƒ‘์Šน๋ฅ ์ด ๊ฐ์†Œํ•˜๋Š” ๊ฒฐ๊ณผ๊ฐ€ ๋„์ถœ๋˜์—ˆ๋‹ค. ์ด๋Š” ํ•ญ๊ณต์˜ ์šด์˜ํŠน์„ฑ์ƒ ์ •ํ™•ํ•œ ์—ฌ๊ฐ์ˆ˜์š” ์˜ˆ์ธก์„ ํ†ตํ•œ ํƒ„๋ ฅ์  ์šดํ–‰ํšŸ์ˆ˜ ๋ณ€ํ™”๊ฐ€ ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์„œ์šธ-๋ถ€์‚ฐ ์žฅ๊ฑฐ๋ฆฌ ๋…ธ์„ ์—์„œ ๊ฐ ๊ตํ†ต์ˆ˜๋‹จ๋ณ„ ์šด์ž„, ํ†ตํ–‰์‹œ๊ฐ„, ์šดํ–‰ํšŸ์ˆ˜๊ฐ€ ์—ฌ๊ฐ์ˆ˜์†ก์‹ค์ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ†ต๊ณ„์ ์œผ๋กœ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•ด ์ƒํ˜ธ์ž‘์šฉํ•ญ์ด ํฌํ•จ๋œ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•ด ๋งŽ์€ ๋ถ€๋ถ„์—์„œ ์œ ์˜๋ฏธํ•œ ๋ถ„์„๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•จ์œผ๋กœ์จ ๋ณ€์ˆ˜๊ฐ„ ์ธ๊ณผ๊ด€๊ณ„๋ฅผ ์‹ค์ฆ์ ์œผ๋กœ ์ž…์ฆํ•œ ์ ์—์„œ ์˜์˜๊ฐ€ ์žˆ๋‹ค.Transportation cost, travel time, and frequency of driving, which are characteristics of transportation, are important measures that have a significant impact on passenger transportation performance, such as establishing a transportation operation plan and choosing a transportation method for passengers. On the other hand, of the inland routes in Korea, the Seoul-Busan route is the largest passenger market and the longest route, futhermore, a transportation market that continuously competes with differentiating fare, time, and supply between transportation. Therefore, this study empirically examines the relationship between the effects of changes in fare, travel time, and frequency of transportation, which are characteristic factors of each transportation method, competing on the Seoul-Busan route. The purpose of this study is to derive policy implications for efficient operation of domestic long-distance transportation and strengthening competitiveness. The spatial range of this study is the Seoul-Busan section, and the temporal range is from January 2005 to December 2019, and 180 observation points per month were used for 15 years of transportation performance data. In addition, four types of public transportation were used: high-speed rail, general rail, high-speed bus, and aviation. The research method of this study is as follows that it is based on collected data, and then multiple regression analysis with interaction terms is used to investigate the effects of transportation characteristics. The independent variables are the interaction terms of the fare, travel time, number of operations, and means characteristics * means of the four modes of transportation, and the dependent variables are the number of passengers, the ratio of transportation, and the load factor of each means. In addition, real disposable income per person was used as a control variable. The results of the study on the effect of the characteristics of each transportation method on the passenger transportation performance are as follows. First, it was found that the increase in fare for each means of transportation has a negative (-) effect on the number of passengers, the share of transportation, and the occupancy of seats. But when it comes to the high -speed rail, the opposite result was drawn. As the market dominance of the high-speed rail is very high in the Seoul -Busan transportation market, it is judged that consumers cannot find a way to replace the high-speed rail even if the fare increases. Second, it was found that the increase in the transit time of each means of transportation has a negative (-) effect on the number of passengers, transportation share, and seating rate. Third, it was found that the increase in the number of operation of each means of transportation has a positive (+) effect on the number of passengers, the share of transportation, and the rate of seating. But, in case of seating rate of airplane , the opposite result was obtained. This is due to the fact that it is difficult to change the number of flexible flights through accurate passenger demand prediction due to the operational characteristics of the airline. In this study, in order to statistically demonstrate the effect of fare, travel time, and number of operations for each transportation method on the passenger transport performance on the Seoul-Busan long-distance route, the results of meaningful analysis in many areas were analyzed through multiple regression analysis with interaction terms. It is significant in that it proves the relationship between variables by deriving.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  6 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 7 1. ์—ฐ๊ตฌ ๋ฒ”์œ„ 7 2. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 8 ์ œ 2 ์žฅ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 10 ์ œ 1 ์ ˆ ๊ตํ†ต์ˆ˜๋‹จ ํŠน์„ฑ์š”์ธ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 10 ์ œ 2 ์ ˆ ์ง€์—ญ๊ฐ„ ๊ตํ†ต์ˆ˜๋‹จ ์„ ํƒ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 12 ์ œ 3 ์ ˆ ์„ ํ–‰์—ฐ๊ตฌ์™€์˜ ์ฐจ์ด์  15 ์ œ 3 ์žฅ ์—ฐ๊ตฌ์˜ ์„ค๊ณ„ ๋ฐ ๋ถ„์„๋ฐฉ๋ฒ• 16 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋‚ด์šฉ ๋ฐ ์„ค๊ณ„ 16 1. ์—ฐ๊ตฌ ๋‚ด์šฉ 16 2. ์—ฐ๊ตฌ ์„ค๊ณ„ 17 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€ 22 1. ์—ฐ๊ตฌ๊ฐ€์„ค ์„ค์ • 22 2. ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€ 23 3. ๋ณ€์ˆ˜์˜ ์กฐ์ž‘์  ์ •์˜ 24 4. ์ž๋ฃŒ์˜ ๊ตฌ์ถ• 32 ์ œ 4 ์žฅ ์—ฐ๊ตฌ๋Œ€์ƒ ๊ธฐ์ดˆํ†ต๊ณ„ 34 ์ œ 1 ์ ˆ ๋…๋ฆฝ๋ณ€์ˆ˜ 34 1. ์šด์ž„ 34 2. ํ†ตํ–‰์‹œ๊ฐ„ 39 3. ์šดํ–‰ํšŸ์ˆ˜ 41 ์ œ 2 ์ ˆ ์ข…์†๋ณ€์ˆ˜ 45 1. ์—ฌ๊ฐ ์ˆ˜ 45 2. ์ˆ˜์†ก๋ถ„๋‹ด๋ฅ  48 3. ์ขŒ์„ํƒ‘์Šน๋ฅ  50 ์ œ 3 ์ ˆ ๊ตํ†ต์ˆ˜๋‹จ ํŠน์„ฑ๋ถ„์„ 53 1. ๊ณ ์†์ฒ ๋„ ์šด์˜๋น„์šฉ 53 2. ํ•ญ๊ณต ์šด์˜๋น„์šฉ 55 3. ํ•ญ๊ณต ๋Œ€๊ธฐ์˜ค์—ผ ๋น„์šฉ 56 4. ํ•ญ๊ณต ์†Œ์Œํ”ผํ•ด ๋น„์šฉ 57 ์ œ 5 ์žฅ ์—ฐ๊ตฌ๊ฒฐ๊ณผ 58 ์ œ 1 ์ ˆ ๊ธฐ์ˆ ํ†ต๊ณ„ ๋ถ„์„ 58 1. ๊ณ ์†์ฒ ๋„ ๊ธฐ์ˆ ํ†ต๊ณ„ 58 2. ์ผ๋ฐ˜์ฒ ๋„ ๊ธฐ์ˆ ํ†ต๊ณ„ 60 3. ๊ณ ์†๋ฒ„์Šค ๊ธฐ์ˆ ํ†ต๊ณ„ 61 4. ํ•ญ๊ณต ๊ธฐ์ˆ ํ†ต๊ณ„ 63 ์ œ 2 ์ ˆ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ 65 1. ์—ฌ๊ฐ์ˆ˜ ์ฆ๊ฐ๋ชจํ˜• 66 2. ์ˆ˜์†ก๋ถ„๋‹ด๋ฅ  ์ฆ๊ฐ๋ชจํ˜• 70 3. ์ขŒ์„ํƒ‘์Šน๋ฅ  ์ฆ๊ฐ๋ชจํ˜• 73 ์ œ 6 ์žฅ ๊ฒฐ ๋ก  76 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ ์š”์•ฝ 76 ์ œ 2 ์ ˆ ์ •์ฑ…์  ํ•จ์˜ 81 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์  85 ์ œ 4 ์ ˆ ๊ฒฐ ๋ก  87 ์ฐธ๊ณ ๋ฌธํ—Œ 88 ๋ถ€ ๋ก 92 Abstract 106Maste

    ๋ธ”๋ฃจํˆฌ์Šค์™€ ์—ฐ๋™ํ•˜๋Š” LED๊ธฐ๋ฐ˜ ์ˆ˜์ค‘๊ฐ€์‹œ๊ด‘ ํ†ต์‹  ์‹œ์Šคํ…œ ์—ฐ๊ตฌ

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    Recently, a variety of research are proceeding for underwater environment monitoring system and resource development. Underwater Communication infrastructures are required for underwater wireless sensor networks. Method of Underwater wireless communications are radio frequency communications, acoustic communications and optical communications. Underwater communication is severely limited when compared to communications in air because water is essentially opaque to electromagnetic radiation except in visible range. Acoustic systems are capable of long range communication, but offer limited data rates and significant latency due to the speed of sound in water. On the other hand, optical wireless communication has been proposed as one of the best alternatives to meet the requirements of the underwater observation and subsea monitoring systems. Therefore, visible light wireless communications is an alternative in underwater. Visible wavelength range have a lower absorption than electromagnetic wave at underwater. And it transmits big data such as photos and videos by using a wide bandwidth. In this paper, we use the lens for connecting in parallel LED sending away the light. An interface between LED lighting communication system and Bluetooth module is presented to support the underwater-to-air communications. Error free image and text transmission at 3 m of water were achieved at bit rates of 230.4 kbps.1. ์„œ ๋ก  1 2. ๊ฐ€์‹œ๊ด‘ ๋ฌด์„ ํ†ต์‹  2.1 ๊ฐ€์‹œ๊ด‘ ๋ฌด์„ ํ†ต์‹ ์˜ ๊ฐœ๋… 4 2.2 ๊ฐ€์‹œ๊ด‘ ํ†ต์‹ ์˜ ์—ฐ๊ตฌ๋™ํ–ฅ 5 2.2.1 ๊ตญ๋‚ด ๊ธฐ์ˆ  ๋™ํ–ฅ 5 2.2.2 ๊ตญ์™ธ ๊ธฐ์ˆ  ๋™ํ–ฅ 7 2.3 ๋ฌด์„  ๊ด‘์ฑ„๋„ 9 2.4 ์ˆ˜์ค‘ ๊ฐ€์‹œ๊ด‘ ํ†ต์‹  14 3. ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 3.1 ์‹คํ—˜ ์‹œ์Šคํ…œ ๊ตฌ์„ฑ 16 3.2 ์ง€์ƒ ๊ฐ€์‹œ๊ด‘ ๋ฌด์„ ํ†ต์‹  ์‹œ์Šคํ…œ ์„ฑ๋Šฅ ์‹คํ—˜ 23 3.2.1 ์‹คํ—˜ ์„ค์ • ํ™˜๊ฒฝ 23 3.2.2 ์‹คํ—˜ ๊ฒฐ๊ณผ 26 3.3 ์ˆ˜์ค‘ ๊ฐ€์‹œ๊ด‘ ๋ฌด์„ ํ†ต์‹ ๊ณผ ๋ธ”๋ฃจํˆฌ์Šค ์—ฐ๋™ ์‹คํ—˜ 29 3.3.1 ์ˆ˜์ค‘ ์‹คํ—˜ ๊ตฌ์„ฑ๋„ 29 3.3.2 ์‹คํ—˜ ๋‚ด์šฉ ๋ฐ ๊ฒฐ๊ณผ 30 4. ๊ฒฐ ๋ก  36 ๊ฐ์‚ฌ์˜ ๊ธ€ 37 ์ฐธ๊ณ ๋ฌธํ—Œ 3
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