52 research outputs found

    ๋ ˆ์ด์ € ์Šค์บ๋„ˆ ๊ธฐ๋ฐ˜ ์ž์œจ์ฃผํ–‰์šฉ ๊ต์ฐจ๋กœ ๋‚ด ์ฃผ๋ณ€ ์ฐจ๋Ÿ‰ ์ธ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2017. 8. ์ด๊ฒฝ์ˆ˜.The aim of this study is designing surrounding vehicle movement perception algorithm in urban condition. In recent autonomous vehicle industry, many researchers are focusing on three major topics which is called environment perception, localization and designing controller for autonomous vehicle these days. Especially for the perception technology, the design of the algorithm follows the characteristics of the target environment or objects. In Urban condition, to design safe drivable path for autonomous vehicle, the objects position is much important than high way conditions. Also, various objects appear which cannot be detected with RADAR or yield fault case with camera. Because of these reasons, many research are trying to fuse different sensors including camera, RADAR and LiDAR to overcome the challenges that can occur in urban conditions, therefore, laser scanner based target detecting technology is needed to perceive in city road. The tracking filter consists of two parts, shape estimation and tracking filter. To fuse with other sensors or designing target filter, there should be a step for compressing point cloud group information into some representative point or state. Thus in shape estimation parts, we transform the laser scanners point cloud data into vehicle position state measurement value. Vehicle shape estimation also consists of two parts, clustering and shape extraction. Clustering classify the total point cloud into object level and shape extraction estimates the vehicle liked objects position information. The clustering part works based on Euclidean Minimum Spanning Tree (EMST), and for the shape extraction, Random Sample Consensus (RANSAC) method is used to estimate the target objects rear and side edge. The second part, tracking filter, has two different filters. Particle filter estimates the target vehicles position including heading angle. To improve the tracking performance of the particle filter, Kalman filter is also designed to estimate the velocity and yaw rate recursively to update the process model of the particle filter. The performance of the proposed algorithm has been verified with several stages. To check quantitative error level, off-line simulation is held for profile based motion tracking case and designed intersection simulator with simple path tracking algorithm for the target vehicle. In these conditions, the exact target vehicles position information was known, thus we verified the error level of the lateral/longitudinal direction of target vehicles local coordinate which is important information when designing driving path or controller. For the second step, simulation with point cloud data which is collected from the test vehicle was held to verify its performance for actual vehicle condition. As a final stage, for integrating into autonomous vehicle, the proposed algorithm evaluated into the test vehicle for guaranteeing on-line performance.Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Research Overview 3 Chapter 2 Target Vehicle Tracking Filter 4 2.1 Laser scanner Data Post-processing 6 2.2 Vehicle Tracking with Particle Filter 9 2.3 Process model Input Update with Kalman Filter 11 Chapter 3 Simulation 14 3.1 Pre-defined Profile based Simulation 14 3.2 Intersection Environment based Simulation 22 Chapter 4 Vehicle Experiment Data Test 28 4.1 Test Vehicle Configuration 28 4.2 Vehicle Experiment data based Simulation 30 4.3 Actual Vehicle Test 40 Chapter 5 Conclusion 45 Bibliography 46 ๊ตญ๋ฌธ์ดˆ๋ก 49Maste

    Estudio sobre la Peregrinaciรณn de la Leyenda de Buda a Espaรฑa: Sobre la Primera Versiรณn Cristianizada

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    ์ค‘์„ธ์— ์ŠคํŽ˜์ธ์„ ๋น„๋กฏํ•œ ์ „ ์œ ๋Ÿฝ์—์„œ ๋„๋ฆฌ ํผ์กŒ๋˜ ์„ฑ์ธ ์—ด์ „ ์€ ๋ถ€์ฒ˜ ์„คํ™”์—์„œ ๊ทธ ์›ํ˜•์„ ์ฐพ์„ ์ˆ˜ ์žˆ๋‹ค. ์ธ๋„์—์„œ ์‚ฐ์Šคํฌ๋ฆฌํŠธ์–ด๋กœ ๊ธฐ๋ก๋œ ์ด ์„คํ™”๋Š” ์‹œ๊ฐ„์˜ ํ๋ฆ„์— ๋”ฐ๋ผ ์„œ์ง„(่ฅฟ้€ฒ)ํ•˜์—ฌ, ๋จผ์ € ๋งˆ๋‹ˆ๊ต ํŒ๋ณธ์œผ๋กœ ๊ณ ๋Œ€ ํŽ˜๋ฅด์‹œ์•„์–ด์™€ ๊ณ ๋Œ€ ํ„ฐํ‚ค์–ด์˜ ํŒ๋ณธ์œผ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค. ์ดํ›„ ์•„๋ž ์ง€์—ญ์— ์œ ์ž…๋˜์–ด ์ด์Šฌ๋žŒ๊ต์ ์ธ ์„ฑ๊ฒฉ์— ๋งž๊ฒŒ ๋ณ€ํ˜•๋˜์–ด ์œ ํฌ๋˜๋‹ค๊ฐ€, ์•ฝ 10-11์„ธ๊ธฐ๊ฒฝ ๊ทธ๋ฃจ์ง€์•ผ์™€ ๊ทธ๋ฆฌ์Šค ์ง€์—ญ์œผ๋กœ ์ „ํ•ด์ ธ ๊ธฐ๋…๊ตํ™” ๋œ๋‹ค. ์ธ๋„์—์„œ ์ถœ๋ฐœํ•œ ๋ถ€์ฒ˜ ์„คํ™”๊ฐ€ ๊ธด ์—ฌ์ •์„ ๊ฑฐ์น˜๋ฉด์„œ ์—ฌ๋Ÿฌ ๋ฌธํ™”๊ถŒ์œผ๋กœ ํก์ˆ˜, ๋ณ€ํ˜•๋˜๋‹ค๊ฐ€ ์ข…๊ตญ์—๋Š” ๊ทธ๋ฃจ์ง€์•ผ์™€ ๊ทธ๋ฆฌ์Šค๋ฅผ ๊ฑฐ์น˜๋ฉด์„œ ์‹ฏ๋‹ค๋ฅดํƒ€๊ฐ€ ๊ธฐ๋…๊ต ์„ฑ์ธ ํ˜ธ์‚ฌํŒฅ์œผ๋กœ ํƒˆ๋ฐ”๊ฟˆํ•˜๊ฒŒ ๋œ๋‹ค..

    ํ•˜์ด๋ฐ๊ฑฐ ์‚ฌ์œ ์—์„œ ์ƒ๊ธฐ(Ereignis)์™€ ๋ฌด(Nichts)์˜ ๊ด€๊ณ„์— ๋Œ€ํ•˜์—ฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ธ๋ฌธ๋Œ€ํ•™ ์ฒ ํ•™๊ณผ(์„œ์–‘์ฒ ํ•™์ „๊ณต), 2020. 8. ๋ฐ•์ฐฌ๊ตญ.์ธ๊ฐ„์€ ์–ด๋–ป๊ฒŒ ๊ณ ์œ ํ•œ ์ž๊ธฐ๋กœ ๊ฑฐ๋“ญ๋‚˜๊ฒŒ ๋˜๋Š”๊ฐ€? ๊ทธ๊ฐ€ ์กด์žฌ์˜ ์œ ํ•œํ•จ์„ ์ž๊ฐํ•˜๋Š” ๊ฒƒ์œผ๋กœ๋ถ€ํ„ฐ ๊ณ ์œ ํ•œ ์ž๊ธฐ๊ฐ€ ๋˜๊ณ ์ž ํ•˜๋Š” ์ž๊ฐ์œผ๋กœ์˜ ์ด๋Œ๋ฆผ์€ ์–ด๋–ป๊ฒŒ ๊ฐ€๋Šฅํ•œ๊ฐ€? ์ธ๊ฐ„(ํ˜„์กด์žฌ)์ด ์ž์‹ ์˜ ๊ณ ์œ ํ•œ ์กด์žฌ๋ฅผ ํš๋“ํ•จ์— ๋”ฐ๋ผ์„œ ์„ธ๊ณ„์™€ ์กด์žฌ๊ฐ€, ๊ทธ๋ฆฌ๊ณ  ๊ทธ๊ฐ€ ๊ฒฝํ—˜ํ•˜๋Š” ์‹œ๊ฐ„์ด ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”ํ•˜๋Š”๊ฐ€? ์ด ์งˆ๋ฌธ๋“ค์— ๋Œ€ํ•œ ํƒ๊ตฌ๋กœ์จ ๋ณธ๊ณ ๋Š” ํ•˜์ด๋ฐ๊ฑฐ ์‚ฌ์œ ์—์„œ ์ƒ๊ธฐ(Ereignis)์™€ ๋ฌด(Nichts)์˜ ์—ฐ๊ด€์„ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ํ˜„์กด์žฌ๊ฐ€ ์ž์‹ ์˜ ์‹ค์กด์„ ๊ณ ์œ ํ•˜๊ฒŒ ์‚ด์•„๋‚ธ๋‹ค๋Š” ์˜๋ฏธ๋ฅผ ์ง€๋‹Œ ๋ณธ๋ž˜์„ฑ(Eigentlichkeit) ๊ฐœ๋…์œผ๋กœ๋ถ€ํ„ฐ ๋ฐœ์ „๋œ ์ƒ๊ธฐ ๊ฐœ๋…์€ ํ›„๊ธฐ ํ•˜์ด๋ฐ๊ฑฐ์˜ ์ฃผ์š” ๊ฐœ๋…์ด๋‹ค. ์ƒ๊ธฐ๋Š” ์ด๋ฟ ์•„๋‹ˆ๋ผ ์„ธ๊ณ„์˜ ๊ณ ์œ ํ•œ ์ƒ๊ธฐ๋ฅผ ์˜๋ฏธํ•˜๊ธฐ๋„ ํ•˜๋Š”๋ฐ, ํ˜„์กด์žฌ์™€ ์„ธ๊ณ„์˜ ๊ณ ์œ ํ•œ ํ˜„์„ฑ์„ ์•„์šฐ๋ฅด๋Š” ์ƒ๊ธฐ์˜ ์˜๋ฏธ๋Š” ์กด์žฌ์˜ ํ˜„์„ฑ์ด๋‹ค. ํ•˜์ด๋ฐ๊ฑฐ ์‚ฌ์œ ์—์„œ ์กด์žฌ์˜ ์—ด๋ ค ์žˆ์Œ, ๊ทธ๋ฆฌ๊ณ  ํ˜„์„ฑ์€ ์กด์žฌ์˜ ์€๋‹‰๋œ ์ฐจ์›์œผ๋กœ์„œ์˜ ๋ฌด๋กœ๋ถ€ํ„ฐ ์ดํ•ด๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์กด์žฌ์˜ ์€๋‹‰, ๊ณง ์กด์žฌ์˜ ๋ฌด์„ฑ์ด ์กด์žฌ์˜ ์—ด๋ ค์žˆ์Œ์˜ ๊ทผ๊ฑฐ๋ผ๋Š” ๊ฒƒ์€ ๋ฌด์—‡์„ ์˜๋ฏธํ•˜๋Š”๊ฐ€? ์ด ์งˆ๋ฌธ์— ๋Œ€ํ•œ ํƒ๊ตฌ๋ฅผ ์œ„ํ•ด ๋ณธ๊ณ ๋Š” ์ „๊ธฐ ํ•˜์ด๋ฐ๊ฑฐ์˜ ๋ฌด ๊ฐœ๋…์—์„œ๋ถ€ํ„ฐ ํ›„๊ธฐ ํ•˜์ด๋ฐ๊ฑฐ์˜ ๋ฌด ๊ฐœ๋…์œผ๋กœ ๋„˜์–ด๊ฐ€๋Š” ๊ณผ์ •์„ ์‚ดํˆ๋‹ค. ๋ฌด๊ฐ€ ํ˜„์กด์žฌ์˜ ๊ณ ์œ ํ•œ ์ž๊ธฐ์— ๋Œ€ํ•œ ๋ฐœ๊ฒฌ์„ ์ด‰๊ตฌํ•  ๋ฟ ์•„๋‹ˆ๋ผ ํ˜„์กด์žฌ์˜ ๊ณ ์œ ํ•จ์˜ ๊ทผ๊ฑฐ๊ฐ€ ๋˜๋Š” ์กด์žฌ์˜ ๊ณ ์œ ํ•จ์ด ๋ฌด๋กœ๋ถ€ํ„ฐ ๋ฐœ์›ํ•œ๋‹ค๋Š” ์ดํ•ด๋กœ ๋‚˜์•„๊ฐ€๋Š” ๊ณผ์ •์„ ์ถ”์ ํ–ˆ๋‹ค. ์ด์–ด ์กด์žฌ๋Š” ์œ ํ•œ์„ฑ์— ์˜ํ•ด ๊ฐ์ธ๋˜์–ด ์žˆ๋Š”๋ฐ ๋”ฐ๋ผ์„œ ์กด์žฌ์˜ ๋ฌด์„ฑ์€ ์‹œ๊ฐ„์˜ ์œ ํ•œ์„ฑ์— ์˜ํ•ด ๊ทผ๊ฑฐ์ง€์–ด์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋“œ๋Ÿฌ๋‚œ๋‹ค. ์‹œ๊ฐ„์˜ ์œ ํ•œ์„ฑ์€ ์กด์žฌ ์ดํ•ด์˜ ์ง€ํ‰์ผ ๋ฟ ์•„๋‹ˆ๋ผ ์กด์žฌ๊ฐ€ ํƒˆ์ž์ ์œผ๋กœ ํ˜„์„ฑํ•˜๋„๋ก ํ•˜๋Š” ๊ทผ๊ฑฐ๋กœ์„œ ๋“œ๋Ÿฌ๋‚œ๋‹ค. ์กด์žฌ์˜ ์ž๊ธฐ ์ž์‹ ๊ณผ์˜ ๊ด€๊ณ„๊ฐ€ ๋™์ผ์ž๋กœ์˜ ํšŒ๊ท€๊ฐ€ ๋˜์ง€ ๋ชปํ•˜๋„๋ก ํ•˜๋Š” ๊ฒƒ์ด ์กด์žฌ์˜ ๋ฌด์„ฑ์ด๋ฉฐ, ๋‚˜์•„๊ฐ€ ์กด์žฌ์˜ ๋ฌด์„ฑ์ด ์‹œ๊ฐ„์˜ ์œ ํ•œ์„ฑ ์†์—์„œ ๊ทผ๊ฑฐ์ง€์–ด์ง€๊ธฐ์— ํ˜„์กด์žฌ์˜ ๊ณ ์œ ํ•œ ์ƒ๊ธฐ๋Š” ์‹œ๊ฐ„์˜ ์œ ํ•œ์„ฑ์— ๋Œ€ํ•œ ์ˆ˜์šฉ ์†์—์„œ ๊ฐ€๋Šฅํ•˜๋‹ค. ์š”์•ฝํ•˜๊ฑด๋Œ€ ์ƒ๊ธฐ ๊ฐœ๋…์˜ ํƒ์ƒ‰์„ ์œ„ํ•ด์„œ ๋ฌด์— ๋Œ€ํ•œ ํƒ์ƒ‰์ด ํ•„์š”ํ–ˆ์œผ๋ฉฐ, ์ƒ๊ธฐ๋Š” ์กด์žฌ์˜ ์€๋‹‰์œผ๋กœ์„œ์˜ ๋ฌด๊ฐ€ ์กด์žฌ์˜ ์—ด๋ ค์žˆ์Œ์— ๋Œ€ํ•œ ๊ทผ๊ฑฐ๊ฐ€ ๋˜๋Š” ์‚ฌ๊ฑด์„ ๊ฐ€๋ฆฌํ‚จ๋‹ค. ์ด๋Š” ์กด์žฌ๊ฐ€ ์‹œ๊ฐ„์˜ ๋ฌด์„ฑ(์œ ํ•œ์„ฑ) ์†์—์„œ ๊ณ ์œ ํ•˜๊ฒŒ ํ˜„์„ฑํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ์กด์žฌ์˜ ํ˜„์„ฑ์œผ๋กœ์„œ์˜ ์ƒ๊ธฐ๋Š” ๋ฌด๊ฐ€ ๊ฐ–๊ณ  ์žˆ๋Š” ์ƒ์„ฑ์˜ ์„ฑ๊ฒฉ์— ๋Œ€ํ•œ ๊ธ์ •์ ์ธ ํ‘œํ˜„์ด ๋œ๋‹ค.1.์„œ๋ก  5 2.์ƒ๊ธฐ๋กœ๋ถ€ํ„ฐ์˜ ๋ฌด 8 2.1.๋ณธ๋ž˜์„ฑ(Eigentlichkeit)๊ณผ ์ƒ๊ธฐ(Ereignis) 8 2.2.ํ˜„์กด์žฌ์˜ ๋ฌด๊ทœ์ •์„ฑ๊ณผ ์‹œ๊ฐ„ 16 2.3.๋ชฉ์  ๊ฐœ๋…์—์„œ์˜ ๋Šฅ๋™์„ฑ๊ณผ ์ˆ˜๋™์„ฑ 27 2.4.๋ฌด๋กœ์˜ ์ดˆ์›”๊ณผ ์ƒ๊ธฐ 34 3.๋ฌด๋กœ์„œ์˜ ๋ฌด 41 3.1.๋ถˆ์•ˆ๊ณผ ๋ฌด: ์ „๊ธฐ ํ•˜์ด๋ฐ๊ฑฐ์˜ '๋ถˆ์•ˆ์˜ ๋ฌด'์˜ ํ•œ๊ณ„ ๊ฐ€๋Š  41 3.2.์กด์žฌ์™€ ๋ฌด์˜ ๊ณต์† 47 3.3.์‹œ๊ฐ„์˜ ์œ ํ•œ์„ฑ๊ณผ ๋ฌด 56 3.4.์œ ํ•œํ•จ๊ณผ ๊ณ ์œ ํ•จ: ๋ฌด์™€ ์ƒ๊ธฐ 62 4.์ƒ๊ธฐ์™€ ๋ฌด์˜ ๋™์ผ์„ฑ 66 4.1.์กด์žฌ์™€ ์กด์žฌ์ž์—์„œ ๋™์ผ์„ฑ๊ณผ ์ฐจ์ด์˜ ์ธต์œ„ 66 4.2.์‹œ๊ฐ„์˜ ์‹œ๊ฐ„ํ™”์—์„œ์˜ ์ƒ๊ธฐ์™€ ๋ฌด 72 4.3.์„ธ๊ณ„์˜ ์„ธ๊ณ„ํ™”์—์„œ์˜ ์ƒ๊ธฐ์™€ ๋ฌด 81 4.4.์‚ฌ๋ฌผ์˜ ์‚ฌ๋ฌผํ™”์—์„œ์˜ ์ƒ๊ธฐ์™€ ๋ฌด 87 5.๊ฒฐ๋ก  93Maste

    ์ˆ˜์ˆ  ํ›„ ์‚ฌ๋ง๋ฅ ์„ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•œ ๋‹ค๋ฉด์  ๋…ธ์‡  ํ‰๊ฐ€ ๋„๊ตฌ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ, 2015. 2. ๊น€๊ด‘์ผ.Background: The number of geriatric patients who undergo surgery has been increasing, and they show an increased mortality rate after surgery compared to younger patients. However there are insufficient tools to predict post-operative outcomes in older surgical patients. We aimed to design a predictive model for adverse outcomes in older surgical patients. Methods: From October 19, 2011, to July 31, 2012, we enrolled 275 consecutive elderly patients (aged โ‰ฅ 65 years) undergoing intermediate-risk or high-risk elective operations in the Department of Surgery of single tertiary hospital. Comprehensive geriatric assessment (CGA) was performed before surgery, and we developed a new scoring model to predict 1-year all-cause mortality using the results of CGA. The secondary outcomes were postoperative complications (eg, pneumonia, urinary tract infection, delirium, acute pulmonary thromboembolism, and unplanned intensive care unit admission), length of hospital stay, and discharge to nursing facility. Results: Twenty-five patients (9.1%) died during the follow-up period (median [interquartile range], 13.3 [11.5-16.1] months), including 4 in-hospital deaths after surgery. Twenty-nine patients (10.5%) experienced at least 1 complication after surgery and 24 (8.7%) were discharged to nursing facilities. Malignant disease and low serum albumin levels were more common in the patients who died. Among the geriatric assessment domains, Charlson Comorbidity Index, dependence in activities of daily living, dependence in instrumental activities of daily living, dementia, risk of delirium, short midarm circumference, and malnutrition were associated with increased mortality rates. A multidimensional frailty score model composed of the above items predicted all-cause mortality rates more accurately than the American Society of Anesthesiologists classification (area under the receiver operating characteristic curve, 0.821 vs 0.647P = .01). The sensitivity and specificity for predicting all-cause mortality rates were 84.0% and 69.2%, respectively, according to the models cutoff point (>5 vs โ‰ค5). High-risk patients (multidimensional frailty score >5) showed increased postoperative mortality risk (hazard ratio, 9.0195% CI, 2.15-37.78P = .003) and longer hospital stays after surgery (median [interquartile range], 9 [5-15] vs 6 [3-9] daysP < .001). Conclusions: The multidimensional frailty score based on comprehensive geriatric assessment is more useful than conventional methods for predicting outcomes in geriatric patients undergoing surgery.Abstract i Contents iii List of tables and figures iv Introduction 1 Methods 3 Results 8 Discussion 22 References 26 Abstract in Korean 30Maste

    New Methods of Efficient Base Station Control for Green Wireless Communications

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2014. 2. ์ด๋ณ‘๊ธฐ.This dissertation reports a study on developing new methods of efficient base station (BS) control for green wireless communications. The BS control schemes may be classified into three different types depending on the time scale โ€” hours based, minutes based, and milli-seconds based. Specifically, hours basis pertains to determining which BSs to switch on or offminutes basis pertains to user equipment (UE) associationand milli-seconds basis pertains to UE scheduling and radio resource allocation. For system model, the dissertation considers two different models โ€” heterogeneous networks composed of cellular networks and wireless local area networks (WLANs), and cellular networks adopting orthogonal frequency division multiple access (OFDMA) with carrier aggregation (CA). By combining each system model with a pertinent BS control scheme, the dissertation presents three new methods for green wireless communications: 1) BS switching on/off and UE association in heterogeneous networks, 2) optimal radio resource allocation in heterogeneous networks, and 3) energy efficient UE scheduling for CA in OFDMA based cellular networks. The first part of the dissertation presents an algorithm that performs BS switchingon/off and UE association jointly in heterogeneous networks composed of cellular networks and WLANs. It first formulates a general problem which minimizes the total cost function which is designed to balance the energy consumption of overall network and the revenue of cellular networks. Given that the time scale for determining the set of active BSs is much larger than that for UE association, the problem may be decomposed into a UE association algorithm and a BS switching on/off algorithm, and then an optimal UE association policy may be devised for the UE association problem. Since BS switching-on/off problem is a challenging combinatorial problem, two heuristic algorithms are proposed based on the total cost function and the density of access points of WLANs within the coverage of each BS, respectively. According to simulations, the two heuristic algorithms turn out to considerably reduce energy consumption when compared with the case where all the BSs are always turned on. The second part of the dissertation presents an energy-per-bit minimized radioresource allocation scheme in heterogeneous networks equipped with multi-homing capability which connects to different wireless interfaces simultaneously. Specifically, an optimization problem is formulated for the objective of minimizing the energy-per-bit which takes a form of nonlinear fractional programming. Then, a parametric optimization problem is derived out of that fractional programming and the original problem is solved by using a double-loop iteration method. In each iteration, the optimal resource allocation policy is derived by applying Lagrangian duality and an efficient dual update method. In addition, suboptimal resource allocation algorithms are developed by using the properties of the optimal resource allocation policy. Simulation results reveal that the optimal allocation algorithm improves energy efficiency significantly over the existing resource allocation algorithms designed for homogeneous networks and its performance is superior to suboptimal algorithms in reducing energy consumption as well as in enhancing network energy efficiency. The third part of the dissertation presents an energy efficient scheduling algorithm for CA in OFDMA based wireless networks. In support of this, the energy efficiency is newly defined as the ratio of the time-averaged downlink data rate and the time-averaged power consumption of the UE, which is important especially for battery-constrained UEs. Then, a component carrier and resource block allocation problem is formulated such that the proportional fairness of the energy efficiency is guaranteed. Since it is very complicated to determine the optimal solution, a low complexity energy-efficient scheduling algorithm is developed, which approaches the optimal algorithm. Simulation results demonstrate that the proposed scheduling scheme performs close to the optimal scheme and outperforms the existing scheduling schemes for CA.Abstract i List of Figures viii List of Tables x 1 Introduction 1 2 A Joint Algorithm for Base Station Operation and User Association in Heterogeneous Networks 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 UE Association Algorithm . . . . . . . . . . . . . . . . . . . . . . 14 2.5 BS Switching-on/off Algorithm . . . . . . . . . . . . . . . . . . . . 17 2.5.1 Cost Function Based (CFB) Algorithm . . . . . . . . . . . 19 2.5.2 AP Density Based (ADB) Algorithm . . . . . . . . . . . . 19 2.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 20 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 Energy-per-Bit Minimized Radio Resource Allocation in Heterogeneous Networks 27 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 System Model and Problem Formulation . . . . . . . . . . . . . . . 30 3.3 Parametric Approach to Fractional Programming . . . . . . . . . . 36 3.3.1 Parametric Approach . . . . . . . . . . . . . . . . . . . . . 37 3.3.2 Double-Loop Iteration to Determine Optimal ฮธ . . . . . . . 38 3.4 Optimal Resource Allocation Algorithm . . . . . . . . . . . . . . . 39 3.4.1 Optimal Allocation of Subcarrier and Power . . . . . . . . . 41 3.4.2 Optimal Allocation of Time Fraction . . . . . . . . . . . . . 44 3.4.3 Lagrangian Multipliers Update Algorithm . . . . . . . . . . 48 3.5 Design of Suboptimal Algorithms . . . . . . . . . . . . . . . . . . 51 3.5.1 Time-Fraction Allocation First (TAF) Algorithm . . . . . . 51 3.5.2 Normalized Time-Fraction Allocation (NTA) Algorithm . . 53 3.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 54 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4 Energy Efficient Scheduling for Carrier Aggregation in OFDMA Based Wireless Networks 68 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.3 Energy Efficiency Proportional Fairness (EEPF) Scheduling . . . . 74 4.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 78 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5 Conclusion 87 5.1 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . 87 5.2 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . 91 References 93Docto

    ์ŠคํŽ˜์ธ ํŽ ๋ฆฌํŽ˜ 2์„ธ๊ฐ€ ๋ช…๋‚˜๋ผ ํ™ฉ์ œ์—๊ฒŒ ๋ณด๋‚ด๋Š” ํŽธ์ง€

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    ์‹ ์˜ ์€์ด์œผ๋กœ ์ŠคํŽ˜์ธ๊ณผ ๋‚˜ํด๋ฆฌ, ์‹œ์น ๋ฆฌ์•„, ์˜ˆ๋ฃจ์‚ด๋ ˜ ๋“ฑ์˜ ์™•์ด์ž, ๋˜ ํ•œ (์„œ)์ธ๋„์™€ ๋Œ€์–‘์˜ ๋Œ€๋ฅ™๊ณผ ์„ฌ๋“ค์˜ ์™•์ด๋ฉฐ, ์˜ค์ŠคํŠธ๋ฆฌ์•„์˜ ๋Œ€๊ณต์ด๊ณ , ๋ฐ€ ๋ž€๊ณผ ๋ธŒ๋ผ๋ฐ˜ํ…Œ์˜ ๋ถ€๋ฅด๊ณ ๋‰ด ๊ณต์ž‘์ด๊ณ , ํ•ฉ์Šค๋ถ€๋ฅดํฌ์™€ ํ”Œ๋ž‘๋“œ๋ฅด์™€ ํ‹ฐ๋กค ๋“ฑ์˜ ๋ฐฑ์ž‘์ธ ๋ˆ ํŽ ๋ฆฌํŽ˜๋Š” ๊ฐ•๋ ฅํ•˜๊ณ  ๊ฒฝ์• ํ•˜๋Š” ์ค‘๊ตญ์˜ ์™•์—๊ฒŒ ์ง„์‹ฌ์œผ๋กœ ๋ฒˆ์˜๊ณผ ๋ฒˆ์ฐฝ์„ ์ถ•์›ํ•˜๋ฉฐ, ์ข‹์€ ์ผ๋“ค์ด ๋งŽ์ด ์ผ์–ด๋‚˜๊ธธ ๊ธฐ์›ํ•ฉ๋‹ˆ๋‹ค. ํ•„๋ฆฌํ•€ ์„ฌ์— ํŒŒ๊ฒฌ๋œ ๋‚˜์˜ ์ด๋…๋“ค์˜ ๋ณด๊ณ ์™€ ์ข…๊ต์ธ๋“ค์„ ํ†ตํ•ด์„œ ๊ตญ์™•๊ป˜ ์„œ ์œ„๋Œ€ํ•œ ์™•๊ตญ์„ ํ˜„๋ช…ํ•˜๊ณ  ์ •์˜๋กญ๊ฒŒ ๋‹ค์Šค๋ฆฌ๊ณ  ๊ณ„์‹œ๋‹ค๋Š” ์‚ฌ์‹ค๊ณผ ์šฐ๋ฆฌ๊ฐ€ ๋„์ฐฉํ•ด์„œ ์‰ฌ๊ณ  ์žˆ๋Š” ํ•ญ๊ตฌ์™€ ์—ฌ๋Ÿฌ ๊ณณ์—์„œ ๊ท€๊ตญ์˜ ์‹ ํ•˜๋“ค์ด ์šฐ๋ฆฌ ์‹ ํ•˜๋“ค์„ ์•„์ฃผ ์ž˜ ๋Œ€ํ•ด์ฃผ๊ณ  ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์ž˜ ์•Œ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด์— ๋Œ€ํ•ด ๊นŠ์€ ๊ฐ์‚ฌ๋ฅผ ๋“œ๋ฆฌ๋ฉฐ ๊ท€๊ตญ์˜ ์šฐ์ •์— ๋Œ€๋‹จํžˆ ๊ธฐ์œ ๋งˆ์Œ์„ ๊ฐ€์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค

    TSV ๋ชจ๋ธ์„ ํฌํ•จํ•˜๋Š” ์ƒ์œ„ ๋ ˆ๋ฒจ 3D-IC ์—ด ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2014. 8. ์ตœ๊ธฐ์˜.์ˆ˜์‹ญ ๋…„ ๋™์•ˆ ๋ฐ˜๋„์ฒด ๊ธฐ์ˆ ์—์„œ ์ฃผ์š” ๊ด€์‹ฌ์‚ฌ๋Š” ์ง‘์ ๋„(degree of integration)๋ฅผ ๋†’์ด๋Š”๋ฐ ์žˆ์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ 2D ์ง‘์ ํšŒ๋กœ(Integrated Circuit)์—์„œ์˜ ์ง‘์ ๋„ ํ–ฅ์ƒ์€ ์ด์ œ ํ•œ๊ณ„์— ๋‹ค๋‹ค๋ฅด๊ณ  ์žˆ๋‹ค. ์ด์— 2D์˜ die๋ฅผ ์Œ“์•„ ์˜ฌ๋ฆฌ๋Š” ๋ฐฉ๋ฒ•์„ ๊ณ ์•ˆํ•˜๊ฒŒ ๋˜์—ˆ์œผ๋ฉฐ ์ด 3D stacking ๋ฐฉ๋ฒ•์€ ์œ ๋งํ•œ ๊ธฐ์ˆ ๋กœ ์ „๋ง๋˜๊ณ  ์žˆ๋‹ค. 3D๋กœ ์Œ“์•„ ์˜ฌ๋ ค์ง„ die๋Š” ํ•œ ํ‰๋ฉด์— ๋‚˜๋ž€ํžˆ ์žˆ์„ ๋•Œ๋ณด๋‹ค ๊ฐ™์€ ๋ฉด์ ์— ๋Œ€ํ•ด ์ˆ˜ ๋ฐฐ ์ด์ƒ ๊ทธ ์ง‘์ ๋„๊ฐ€ ์ปค์ง€๊ณ  ๋‚˜๋ž€ํžˆ ์—ฐ๊ฒฐ๋˜๋Š” ๋Œ€์‹  ์ˆ˜์ง์œผ๋กœ ์ „์„ ์ด ์—ฐ๊ฒฐ๋˜๊ธฐ ๋•Œ๋ฌธ์— ๊ทธ ๊ธธ์ด๋„ ํ˜„์ €ํžˆ ์งง์•„์ง„๋‹ค. ์ด ์ „์„ ๊ธธ์ด ๊ฐ์†Œ๋Š” ์‹ค์งˆ์ ์œผ๋กœ ์ž„๊ณ„ ๊ฒฝ๋กœ ์ง€์—ฐ์‹œ๊ฐ„(critical path delay)์˜ ๋‹จ์ถ•๊ณผ ์ „์„ ์—์„œ ์†Œ๋ชจํ•˜๋Š” ์—๋„ˆ์ง€ ๊ฐ์†Œ ๋“ฑ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์žฅ์ ๋“ค์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฐ˜๋Œ€๋กœ 2D ์ƒํƒœ์— ๋น„ํ•ด ์ „๋ ฅ ์†Œ๋ชจ์˜ ์ง‘์ ๋„๊ฐ€ ์˜ฌ๋ผ๊ฐ€๊ณ  ๊ณต๊ธฐ ์ ‘์ด‰ ๋ฉด์ ์ด ์ƒ๋Œ€์ ์œผ๋กœ ๋งŽ์ด ๊ฐ์†Œํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋” ํฐ ์—ด ๋ฌธ์ œ๋ฅผ ๋งž์ดํ•˜๊ฒŒ ๋œ๋‹ค. ์—ด ๋ฌธ์ œ๋ฅผ ๋ถ„์„ํ•˜๊ณ  ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์˜จ๋„๋ฅผ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋Š” ์—ด ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์—ด ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋Š” ํฌํ™” ์˜จ๋„๋ฅผ ์ธก์ •ํ•˜๋Š” steady state(์ •์ƒ ์ƒํƒœ) ๋ถ„์„๊ณผ ์ˆœ๊ฐ„ ์˜จ๋„๋ฅผ ์ธก์ •ํ•˜์—ฌ ์˜จ๋„๋ณ€ํ™”์˜ ์ถ”์ด๋ฅผ ๋ณผ ์ˆ˜ ์žˆ๋Š” transient ๋ถ„์„ ๋‘ ๊ฐ€์ง€์˜ ๊ฒฝ์šฐ๊ฐ€ ์žˆ๋‹ค. ๊ธฐ์กด์— ์žˆ๋˜ ๋งŽ์€ ์—ด ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋“ค์€ 2D-IC๋ฅผ ๋‹ค๋ฐฉ๋ฉด์—์„œ ๋ถ„์„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 3D-IC๋ฅผ ๋ถ„์„ํ•˜๋Š” ์—ด ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋Š” ๋งŽ์ง€ ์•Š๊ณ  ๊ทธ ์ค‘์— TSV๋ฅผ ๊ณ ๋ คํ•˜๋Š” ๋™์‹œ์— steady state์™€ transient ๋ถ„์„์„ ๋ชจ๋‘ ํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋Š” ์—†์—ˆ๋‹ค. TSV์™€ ์˜จ๋„๋ฅผ ๊ณ ๋ คํ•˜๋Š” ๊ฒƒ์€ ์ถฉ๋ถ„ํžˆ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค. TSV(through silicon via)๋Š” 3D๋กœ ์˜ฌ๋ ค์ง„ die๋“ค์˜ ์ „์„ ์„ ์„œ๋กœ ์—ฐ๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์žฅ์น˜๋กœ ๊ตฌ๋ฆฌ(๊ฐ„ํ˜น ํ……์Šคํ…์ด ์‚ฌ์šฉ๋˜๊ธฐ๋„ ํ•จ)๋กœ ์ด๋ฃจ์–ด์ง„ ์›๊ธฐ๋‘ฅ ํ˜•ํƒœ์ด๋ฉฐ ์ด๋Š” die๋“ค์„ ๊ด€ํ†ตํ•˜์—ฌ ์„ค์น˜๋œ๋‹ค. ์ด ๋•Œ ๊ตฌ๋ฆฌ๋Š” TSV ์›๊ธฐ๋‘ฅ์˜ ์•ˆ ์ชฝ์— ์œ„์น˜ํ•œ๋‹ค. TSV๋Š” ์ „์„ ์„ ์—ฐ๊ฒฐํ•˜๋Š” ์—ญํ• ์„ ํ•  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ „์ฒด ์นฉ์˜ ์—ด ๋ฌธ์ œ๋ฅผ ํ•ด์†Œํ•˜๋Š” ์—ญํ• ๋„ ํ•œ๋‹ค. TSV๋Š” ๋ณดํ†ต ํŒจํ‚ค์ง€์—์„œ spreader์™€ heatsink๋ฅผ ์ œ์™ธํ•œ die๋“ค์„ ๊ด€ํ†ตํ•˜์—ฌ ์—ฐ๊ฒฐํ•˜๊ณ  ์žˆ์–ด์„œ spreader๋‚˜ heatsink์™€ ๋‹ค๋ฅธ die ์‚ฌ์ด์˜ ์—ด ๊ตํ™˜์„ ์‰ฝ๊ฒŒ ํ•ด ์ค€๋‹ค. TSV์˜ ์—ด ์ „๋„์œจ์€ ์นฉ์˜ ์—ด ์ „๋„์œจ๋ณด๋‹ค ํฌ๋‹ค. ๋˜ํ•œ 3D stack ์ค‘๊ฐ„์— ํ•„์š”์— ์˜ํ•ด ์กด์žฌํ•˜๋Š” ์—ฌ๋Ÿฌ ๋‹จ์—ด๋ฌผ์งˆ๋“ค์„ ๊ด€ํ†ตํ•จ์œผ๋กœ์จ TSV๋Š” ํฐ ๋ƒ‰๊ฐ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋œ๋‹ค. ์ด์— TSV๋กœ ์—ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•ด ๋ณด๋ ค๋Š” ์‹œ๋„๊ฐ€ ์ ์ฐจ ๋งŽ์•„์ง€๊ณ  ์žˆ๋‹ค [4]. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” transient ์—ด ๋ถ„์„๊ณผ steady state ์—ด ๋ถ„์„์ด ๋ชจ๋‘ ๊ฐ€๋Šฅํ•œ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋ฅผ ์ œ์‹œํ•˜์˜€์œผ๋ฉฐ ๊ทธ ์„ฑ๋Šฅ์„ ์—ฌ๋Ÿฌ ์‹คํ—˜๋“ค๋กœ ์ฆ๋ช…ํ•˜์˜€๋‹ค.A High Level Thermal 3D-IC simulator including TSV model ์„ฑ ๋ช… Sunwook Kim ํ•™๊ณผ ๋ฐ ์ „๊ณต Electrical Engineering The Graduate School Seoul National University For decades, the main interest in semiconductor technology has been in increasing the degree of integration. However, the increase in integration on 2D ICs (integrated circuits) is approaching to its limit. So methods of stacking dies are considered to be a promising technology. 3D stacked die is far more scalable and allows higher degree of integration compared to 2D die. It shortens wire lengths and decreases not only critical path delays but also power consumed by wires. But as the result of stacking, the density of power dissipation rises whereas the area of the surface contacting air decreases. To analyze and solve the thermal problems, a thermal simulator that can estimate temperature is required. There are two aspects of thermal analysis. One is steady state analysis and the other is transient analysis. There are many existing simulators for thermal analysis of 2D-ICs. But there are a few simulators that can analyze 3D-ICs and none of them can perform both steady state and transient analysis while considering TSVs. TSVs made up of copper connect wires on different stacked dies using cylindrical holes through the dies. The copper of the TSVs fills up the cylindrical holes for the connection. As well as connecting wires, TSVs also help dissipating the heat. TSVs commonly connect multiple dies within a package to the spreader and heatsink then they makes it easy to exchange heat. In fact, the thermal conductance of a TSV of copper is higher than that of a chip of silicon. Moreover, since TSVs connect different dies through BEOL(back end of line) and TIM(thermal interface material) layers which blocks heat flow, they help a lot with heat dissipation. As a result there have been many attempts of solving the heat problems with TSVs. This paper presents an approach to the development of a simulator that can analyze both steady state and transient temperatures in 3D ICs with TSVs and shows the effectiveness with some experiments.์ œ1์žฅ ์„œ๋ก  1 ์ œ2์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ 3 ์ œ1์ ˆ Hotspot simulator 3 ์ œ2์ ˆ 3D-ICE 9 ์ œ3์ ˆ 3D-acme 9 ์ œ3์žฅ TSV ๊ตฌํ˜„ 10 ์ œ1์ ˆ TSV์™€ ๊ธฐ์กด ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ์— ๋Œ€ํ•œ ๊ณ ์ฐฐ 10 ์ œ2์ ˆ TSV ๊ตฌํ˜„ ๋ฐฉ์‹ 10 ์ œ4์žฅ ์‹คํ—˜ ๋ฐฉ์‹ ๋ฐ ๊ฒฐ๊ณผ 20 ์ œ1์ ˆ ์‹คํ—˜ ๋ชฉํ‘œ 20 ์ œ2์ ˆ Steady state ์—ด ๋ถ„์„ 20 ์ œ3์ ˆ Transient ์—ด ๋ถ„์„ 25 ์ œ5์žฅ ๊ฒฐ๋ก ๊ณผ ํ–ฅํ›„ ๊ณผ์ œ 30Maste

    The Effect of Symptom Recognition on Pre-hospital Delay in Patients with Acute Coronary Syndrome

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    Purpose: This study was performed to determine the association of symptom recognition with pre-hospital delay in patients with acute coronary syndrome (ACS), and to determine the factors influencing symptom recognition. Methods: A prospective study from June 1, 2009 to July 31, 2009 was performed. The pre-hospital delay was calculated by subtraction of the hospital-arrival time from the symptom-onset time. The pre-hospital delay of the patients that recognized the symptoms as cardiovascular in origin was compared to the patients that did not recognize the symptoms as cardiac in origin. In addition, the socioeconomic indexes and risk factors were evaluated. Results: Eighty three subjects were enrolled from a total of 205 patients suspected of having an ACS during the study period. No statistical differences were identified in the comparison of the pre-hospital delay by socioeconomic and risk factors of ischemic heart disease. The median pre-hospital delay of the patients that recognized the symptoms as cardiac was 2.9 hours compared to 11.9 hours among the patients that did not recognize the symptoms as cardiac; this difference was statistically significant (p=0.003). There were statistically significant differences in symptom recognition between the patients that had a history of cardiovascular disease and those that did not (p=0.037), and between the patients that took aspirin and those that did not (p=0.014). In addition, the severity of symptoms differed between the patients that recognized their symptoms and those that did not; this difference was statistically significant (p=0.019). Only the severity of symptoms was statistically significant by the logistic regression analysis (p=0.018). Conclusion: The pre-hospital delay was shorter, if patients that recognized the symptoms as cardiac in origin. A history of cardiovascular disease, taking aspirin and severity of symptoms were factors influencing the recognition of symptomsope

    Assessment of Severity Scoring Systems for Predicting the Prognosis of Early Goal Directed Therapy (EGDT) Enrolled Patients

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    Purpose: Mortality in emergency department sepsis (MEDS), sepsis-related organ failure assessment (SOFA), multiple organ dysfunction score (MODS), and serum lactate levels have shown their efficacy in the early detection of patients with a bad prognosis. However, those studies did not consider differences in treatment protocols and could not rule out the interference of these differences in treatment modalities. Hence, we aimed to assess the performance of MEDS, MODS, SOFA, and serum lactate levels for predicting a bad prognosis in patients scheduled for identical, standardized treatment protocols, EGDT. Methods: Medical records of patients who visited a tertiary level teaching hospital and were enrolled in an EGDT program between October 2009 and May 2010, were retrospectively reviewed. MEDS, SOFA, and MODS scores were calculated and recorded along with serum lactate levels. Receiver operating characteristics (ROC) curves of those predictors of mortality were plotted, Bivariate correlation analyses with overall lengths of admission and ICU lengths of stay were done for surviving patients. Results: None of the diagnostic methods (serum lactate level, MEDS, SOFA, MODS) showed a significant difference difference on ROC analysis (p=0.819, 0.506, 0.811, 0.873, respectively). Bivariate correlation analyses of MEDS, SOFA, MODS and overall lengths of admission showed significant results (p=0.048, 0.018, and 0.003, respectively. Pearson correlation coefficients were, 0.263, 0.312, and 0.381). Only MEDS showed a significant correlation with intensive care unit (ICU) length of stay (p=0.032, Pearson correlation coefficient = 0.332). Conclusion: Neither MEDS, SOFA, MODS, nor serum lactate level can predict mortality in EGDT-enrolled patients. MEDS may be correlated with ICU length of stayope

    Predicting the Airway Patency using the Parameters of Soft-tissue Lateral Neck Radiography in Adult Patients with Acute Epiglottitis

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    Purpose: We wanted to predict the high risk group that requires urgent airway intervention by using the parameters of the soft-tissue lateral neck radiographs of adult acute epiglottitis patients. Methods: This retrospective study was conducted in two teaching hospitals. The patients who were diagnosed with acute epiglottitis from June, 2007 to May, 2009 were enrolled and their medical records and x-ray films were reviewed. The width of the epiglottis at the widest point (EW), the width of the arytenoid at the widest point (AW), the prevertebral soft tissue distance at the third cervical spine (PSTD), the shortest distance from the epiglottis to the hypopharyngeal wall (EHD) and the shortest distance from the epiglottic root to the arytenoids` tip (EAD) were investigated and we performed regression analyses of these parameters of the patients in the high risk group that required urgent airway intervention. Results: A total of 42 patients were enrolled. Dyspnea and hoarseness were more frequent in the high risk group that required urgent airway intervention (p=0.008, 0.040, respectively). The EW was significantly longer (p=0.001) in the high risk group. The EHD and EAD were significantly shorter (p=0.012, <0.001, respectively) in the high risk group. Only the EAD showed significant correlation with the percent of airway patency on linear regression analysis (p=0.003) and the EAD was the only significant predictor for the high risk group on multivariate logistic regression analysis (p=0.043). The receiver operating characteristics curve of the EW/EAD for the high risk group was obtained and it showed the best predictive power (AUC: 0.977, p<0.001). Conclusion: The EAD noted on soft-tissue lateral neck radiography is an important predictor of high risk patients who require urgent airway intervention. The cut-off value of the EW/EAD for the predicting the high risk group is 2.44 (sensitivity 100%, specificity 85.7%).ope
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