73 research outputs found

    ์‹ค๋ฌด์ž๋“ค์„ ์œ„ํ•œ ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ์‚ฌ์„ ๋ฐ ํ…ŒํŠธ๋ผํฌ๋“œ ํ”ผ๋ณต์žฌ์˜ ์•ˆ์ •์ˆ˜ ์ธ๊ณต์‹ ๊ฒฝ๋ง ๋ชจ๋ธ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2018. 2. ํ™ฉ์ง„ํ™˜.The stability number of rubble mound breakwaters determines the appropriate weight of armor units of concrete or rock required to resist the wave condition. Therefore, the prediction of suitable stability number is necessary for the stability of the breakwaters. Many empirical formulas have been developed for the stability number since Hudson (1959). To improve the empirical formulas which had significant differences between observed data and prediction data, the machine learning, ANN in particular, has been used during the last two decades. However, most of ANN models did not deal with reliability assessment such as confidence interval. In addition, they are seldom used by practicing engineers probably because most of them did not provide them with an explicit calculation method. In this study, to solve these problems, bootstrap resampling technique was used to make the information or assessment of the reliability in prediction. Also, Excel files made with the by-products of the ANN model such as weights and biases are provided, so that practicing engineers can easily use ANN model.CHAPTER 1. INTRODUCTION 1 1.1 Background 1 1.2 Previous studies 2 1.2.1 The formulas of stability 2 1.2.2 The reliability of ANN prediction 2 1.3 Objectives and research overview 3 CHAPTER 2. THEORETICAL BACKGROUDS 6 2.1 Stability number 6 2.2 Parameters 7 2.3 Artificial neural networks (ANN) 12 CHAPTER 3. METHODOLOGY 16 3.1 Data 16 3.2 Preparation of data 18 3.3 ANN configuration 20 3.3.1 Classification of data 20 3.3.2 The number of Hidden neurons 22 3.4 Uncertainty assessment 25 3.4.1 Bootstrap resampling 25 3.4.2 Prediction value and confidence interval 26 3.5 Verification of ANN prediction method 29 3.6 Performance evaluation of method 30 CHAPTER 4. RESULTS 33 4.1 Classification of data 33 4.2 The number of hidden neurons 43 4.3 Prediction value and confidence interval 47 4.4 Verification of ANN prediction model 54 4.4.1 Sensitivity analysis 54 4.4.2 The number of models 56 4.5 Performance evaluation of method 57 4.6 How to use Excel files 61 CHAPTER 5. CONCLUSIONS 64 5.1 Research summary 64 5.2 Research limitations and future study 65 REFERENCES 66 ๊ตญ๋ฌธ์ดˆ๋ก 69Maste

    Design Optimization of Diffuser and Overall Performance Analysis for Tidal current turbine by CFD

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    ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์—ฐ์•ˆ์€ ์ด 1,400๋งŒkW์˜ ํ•ด์–‘์—๋„ˆ์ง€๊ฐ€ ๋ถ€์กด๋˜์–ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜๋ฉฐ, ํŠนํžˆ ์„ธ๊ณ„์ ์œผ๋กœ ๋ณด๊ธฐ ๋“œ๋ฌธ ์กฐ๋ฅ˜๋ฐœ์ „์˜ ์ ์ง€์ด๋‹ค. ์‚ผ๋ฉด์ด ๋ฐ”๋‹ค์ธ ์šฐ๋ฆฌ๋‚˜๋ผ๋Š” ํ•ด์–‘์—๋„ˆ์ง€์˜ ์ž ์žฌ๋Ÿ‰์ด ๋†’๊ณ , ์ด์šฉํ•˜๋Š”๋ฐ ๋งค์šฐ ์œ ๋ฆฌํ•œ ํ™˜๊ฒฝ์„ ๊ฐ–๊ณ  ์žˆ๋‹ค. ์กฐ๋ฅ˜๋ฐœ์ „์€ ์กฐ๋ ฅ๋ฐœ์ „๊ณผ ๋‹ฌ๋ฆฌ ๋Œ์„ ๋งŒ๋“ค ํ•„์š”๊ฐ€ ์—†์œผ๋ฉฐ, ์กฐ๋ฅ˜์˜ ํ๋ฆ„์ด ๋น ๋ฅธ ๊ณณ์„ ์„ ์ •ํ•˜์—ฌ ๊ทธ ์ง€์ ์— ์ˆ˜์ฐจ๋ฐœ์ „๊ธฐ๋ฅผ ์„ค์น˜ํ•˜๊ณ , ์ž์—ฐ์ ์ธ ์กฐ๋ฅ˜์˜ ํ๋ฆ„์„ ์ด์šฉํ•˜์—ฌ ์„ค์น˜๋œ ์ˆ˜์ฐจ๋ฐœ์ „๊ธฐ๋ฅผ ๊ฐ€๋™์‹œ์ผœ ๋ฐœ์ „ํ•˜๋Š” ๊ธฐ์ˆ ์ด๋‹ค. ์กฐ๋ฅ˜ํ„ฐ๋นˆ์€ ์กฐ๋ฅ˜์†๋„๊ฐ€ ๋น ๋ฅผ์ˆ˜๋ก ๋ฐœ์ „๋Ÿ‰์ด ์ฆ๊ฐ€ํ•˜๋ฉฐ ๊ทธ์— ๋”ฐ๋ผ ๊ฒฝ์ œ์„ฑ์ด ํ™•๋ณด๋œ๋‹ค. ํ•˜์ง€๋งŒ ์„ ํ–‰์—ฐ๊ตฌ๋“ค์˜ ๊ฒฐ๊ณผ์—์„œ ์กฐ๋ฅ˜ํ„ฐ๋นˆ์˜ ๊ฒฝ์ œ์„ฑ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ‰๊ท  ์œ ์†์ด 2m/s ์ด์ƒ์ธ ๊ณณ์— ์„ค์น˜๋˜์–ด์•ผ ํ•˜๋ฏ€๋กœ ์„ค์น˜ ์žฅ์†Œ์— ๋Œ€ํ•œ ์ œํ•œ์ด ์žˆ๋‹ค. ์กฐ๋ฅ˜ํ„ฐ๋นˆ์‹œ์Šคํ…œ์˜ ์ถœ๋ ฅํ–ฅ์ƒ์„ ์œ„ํ•œ ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋“ค์€ ํ„ฐ๋นˆ์œผ๋กœ ์œ ์ž…๋˜๋Š” ์œ ์†์„ ์ฆ๊ฐ€์‹œํ‚ค๊ฑฐ๋‚˜ ์ƒ๋ฐ˜์ „๊ณผ ๊ฐ™์ด ํ„ฐ๋นˆ์„ ์ถ”๊ฐ€ํ•˜์—ฌ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ์ฃผ๋ฅผ ์ด๋ฃฌ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‚ฎ์€ ์œ ์†์ง€์—ญ์— ์ ํ•ฉํ•œ ์กฐ๋ฅ˜๋ฐœ์ „์žฅ์น˜ ๊ฐœ๋ฐœ์„ ๋ชฉํ‘œ๋กœ ํ•˜๋ฉฐ, ์กฐ๋ฅ˜ํ„ฐ๋นˆ์˜ ์„ฑ๋Šฅํ–ฅ์ƒ์„ ์œ„ํ•ด ๋‹จ๋ฐฉํ–ฅ์˜ ๋””ํ“จ์ €๋ฅผ ๊ฒฐํ•ฉํ•˜์—ฌ ์œ ์ฒด์˜ ํ๋ฆ„์— ๋”ฐ๋ผ ๋ฐœ์ƒํ•˜๋Š” ๋””ํ“จ์ € ์ „ํ›„๋‹จ์˜ ์••๋ ฅ์ฐจ๋ฅผ ์ด์šฉํ•˜์—ฌ ์กฐ๋ฅ˜ํ„ฐ๋นˆ์œผ๋กœ ์œ ์ž…๋˜๋Š” ์œ ์†์„ ์ฆ๊ฐ€์‹œํ‚ค๊ณ ์ž ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•ด์„์œผ๋กœ NACA ๋ฐ S์‹œ๋ฆฌ์ฆˆ ์—์–ดํฌ์ผ์„ ์ ์šฉํ•œ ์กฐ๋ฅ˜๋ฐœ์ „ํ„ฐ๋นˆ์„ ๊ฐ๊ฐ ์„ค๊ณ„ํ•˜์˜€์œผ๋ฉฐ, ์ด ํ„ฐ๋นˆ๋“ค์— ๋Œ€ํ•œ ์„ฑ๋Šฅํ•ด์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์œ ์ž… ์œ ์†์˜ ์ฆ๊ฐ€๋ฅผ ์œ„ํ•ด ์กฐ๋ฅ˜๋ฐœ์ „์— ์ ํ•ฉํ•œ ๋””ํ“จ์ €๋ฅผ ์„ค๊ณ„ํ•˜์—ฌ CFDํ•ด์„์„ ํ†ตํ•ด ๋‚ด๋ถ€์œ ์† ๋ณ€ํ™”๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋””ํ“จ์ €๋ฅผ ์ ์šฉํ•œ ์กฐ๋ฅ˜๋ฐœ์ „ํ„ฐ๋นˆ์˜ ์„ฑ๋Šฅํ•ด์„์„ ์ˆ˜ํ–‰ํ•˜์˜€์œผ๋ฉฐ, ์žฅ์น˜์˜ ์Šค์ผ€์ผ์— ๋”ฐ๋ฅธ CFDํ•ด์„์„ ํ†ตํ•ด ํ„ฐ๋นˆ์˜ ์ถœ๋ ฅ๊ณ„์ˆ˜์˜ ๊ฒฝํ–ฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. | It is estimated that there are 14,000,000 kW of marine energy in the coastal area of โ€‹โ€‹Korea, especially in the case of low tidal currents in the world (Yang et al., 2006). So, there is a very favorable environment for use that energy potential. Unlike tidal power generation, tidal current generation does not need to build a dam, requirements are to select a place where the flow of the tidal current is fast enough to install and operate a water turbine generator. Faster the flow velocity, greater the power generation of the tidal turbine and the economic efficiency is secured accordingly. However, according to the results of previous studies, it is necessary to install the turbine at an average flow rate of 2 m/s or more in order to economically secure the turbine. Studies are underway to improve the output of the turbine system. These studies are mainly focused on increasing the flow rate to the turbine or adding the turbine as before. In this paper, it is aimed to develop a tidal power generation device suitable for low flow area. To improve the performance of a tidal turbine, a unidirectional diffuser is combined into a tidal turbine in order to increase the flow rate. In the first analysis, a tidal power turbine with NACA and S series airfoils was designed, and the performance of these turbines was analyzed. In order to increase the inflow velocity, a diffuser suitable for tidal power generation was designed and CFD analysis was used to confirm the internal velocity change. The performance analysis of the turbine with diffuser was performed and the trend of the turbine power factor was confirmed by CFD analysis according to the scale of the device.1. ์„œ ๋ก  1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ๋™ํ–ฅ 3 1.3 ์—ฐ๊ตฌ๋ชฉ์  7 2. ์ˆ˜ํ‰์ถ• ํ„ฐ๋นˆ ๋ธ”๋ ˆ์ด๋“œ ์„ค๊ณ„ 2.1 ์ˆ˜ํ‰์ถ• ํ„ฐ๋นˆ์˜ ๊ณต๊ธฐ์—ญํ•™ 8 2.1.1 ์šด๋™๋Ÿ‰ ์ด๋ก  8 2.1.2 Actuator disk ์ด๋ก  9 2.1.3 ๊ฐ์šด๋™๋Ÿ‰ ์ด๋ก  11 2.1.4 ๋‚ ๊ฐœ์š”์†Œ ์ด๋ก  13 2.1.5 ๋‚ ๊ฐœ์š”์†Œ ์šด๋™๋Ÿ‰ ์ด๋ก  14 2.2 7kW ๋กœํ„ฐ ๋ธ”๋ ˆ์ด๋“œ ์„ค๊ณ„ 16 3. CFD๋ฅผ ์ด์šฉํ•œ ์กฐ๋ฅ˜๋ฐœ์ „ ํ„ฐ๋นˆ์˜ ์„ฑ๋Šฅ ํ•ด์„ 3.1 S822 ์ตํ˜• 19 3.1.1 ๊ฒฉ์ž์ƒ์„ฑ ๋ฐ CFD ๊ฒฝ๊ณ„์กฐ๊ฑด 19 3.1.2 ๋‚ด๋ถ€ ์œ ๋™์žฅ ๋ถ„์„๊ฒฐ๊ณผ 23 3.2 NACA-63421 ์ตํ˜• 36 3.2.1 ๊ฒฉ์ž์ƒ์„ฑ ๋ฐ CFD ๊ฒฝ๊ณ„์กฐ๊ฑด 36 3.2.2 ๋‚ด๋ถ€ ์œ ๋™์žฅ ๋ถ„์„๊ฒฐ๊ณผ 38 4. ๋””ํ“จ์ € ์„ค๊ณ„ ๋ฐ ๋ฐ˜์‘ํ‘œ๋ฉด๋ฒ•์„ ์ด์šฉํ•œ ํ˜•์ƒ ์ตœ์ ํ™” 4.1 ๋””ํ“จ์ € ๊ธฐ๋ณธ์„ค๊ณ„ 51 4.2 ๋””ํ“จ์ € ๊ธฐ๋ณธ์„ค๊ณ„์— ๋Œ€ํ•œ CFD ์„ฑ๋Šฅํ•ด์„ 56 4.2.1 ๊ฒฉ์ž์ƒ์„ฑ ๋ฐ CFD ๊ฒฝ๊ณ„์กฐ๊ฑด 56 4.2.2 ๋‚ด๋ถ€ ์œ ๋™์žฅ ๋ถ„์„๊ฒฐ๊ณผ 58 4.3 ๋ฐ˜์‘ํ‘œ๋ฉด๋ฒ•์„ ํ™œ์šฉํ•œ ๋””ํ“จ์ €์˜ ํ˜•์ƒ ์ตœ์ ํ™” 66 4.3.1 ๋ฐ˜์‘ํ‘œ๋ฉด๋ฒ• 66 4.3.2 ์„ค๊ณ„ ์š”์†Œ ์„ ์ • ๋ฐ CFD ๊ฒฝ๊ณ„์กฐ๊ฑด 67 4.3.3 ๋‚ด๋ถ€ ์œ ๋™์žฅ ๋ถ„์„๊ฒฐ๊ณผ ๋ฐ ๋ฐ˜์‘ํ‘œ๋ฉด ์„ค์ • 71 4.3.4 ๋ฐ˜์‘ํ‘œ๋ฉด๋ฒ• ํ•ด์„๊ฒฐ๊ณผ 72 5. ๋””ํ“จ์ €๋ฅผ ์ ์šฉํ•œ ์กฐ๋ฅ˜๋ฐœ์ „ ํ„ฐ๋นˆ์˜ CFD ์„ฑ๋Šฅํ•ด์„ 5.1 ๊ฒฉ์ž์ƒ์„ฑ ๋ฐ CFD ๊ฒฝ๊ณ„์กฐ๊ฑด 85 5.2 ์กฐ๋ฅ˜ํ„ฐ๋นˆ์˜ ์„ฑ๋Šฅํ‰๊ฐ€ 90 5.2.1 ๊ธฐ๋ณธ ๋””ํ“จ์ € ํ˜•์ƒ์— ๋Œ€ํ•œ ์กฐ๋ฅ˜ํ„ฐ๋นˆ์˜ ์„ฑ๋Šฅํ‰๊ฐ€ 90 5.2.2 ๋””ํ“จ์ € ์ตœ์ ํ™” ํ˜•์ƒ์— ๋Œ€ํ•œ ์กฐ๋ฅ˜ํ„ฐ๋นˆ(S822)์˜ ์„ฑ๋Šฅํ‰๊ฐ€ 94 5.2.3 ๋””ํ“จ์ € ์ตœ์ ํ™” ํ˜•์ƒ์— ๋Œ€ํ•œ ์กฐ๋ฅ˜ํ„ฐ๋นˆ(NACA-63421)์˜ ์„ฑ๋Šฅํ‰๊ฐ€ 104 5.3 ๋””ํ“จ์ €๋ฅผ ์ ์šฉํ•œ ์กฐ๋ฅ˜๋ฐœ์ „ ํ„ฐ๋นˆ์˜ ์šฉ๋Ÿ‰๋ณ„ ์„ฑ๋Šฅ ๋น„๊ต 114 6. ๊ฒฐ๋ก  120 References 121 Bibliography 123Docto

    A study on Motion Control of Manta-type Unmanned Undersea Vehicle with Influence of Current

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    This is a basic study on motion control of Manta-type Unmanned Undersea Vehicle(UUV). At first irregular current will be supposed as operating environment condition of Manta-type UUV. Here irregular current is composed of two parts. One is static component and the other variable one. And then the six-degree-of-freedom motion mathematical models of manta-type UUV are suggested. For this the static model test at large angles of attack and four quadrant propeller open water test are carried out respectively. Proportional and Derivative(PD) controls are designed as control devices. The paper deals with thrust control and motion controls in horizontal plane and vertical plane respectively. Numerical simulations of motion control of manta-type UUV are carried out and the effects of motion control are discussed.๋ชฉ ์ฐจ Nomenclature III List of Figures IV List of Tables V Abstract VI ์ œ 1์žฅ ์„œ๋ก  ์ œ 2์žฅ ๋งŒํƒ€ ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ํ˜•์ƒ ๋ฐ ์ฃผ์š”๋ชฉ ์ œ 3์žฅ 6์ž์œ ๋„ ์šด๋™ ์ˆ˜ํ•™๋ชจ๋ธ 3.1 ์ขŒํ‘œ๊ณ„ 3.2 ๋Œ€๊ฐ๋„ ์‚ฌํ•ญ์‹œํ—˜ 3.2.1 ์‹คํ—˜์— ์‚ฌ์šฉ๋œ ๋ชจํ˜•์„ ์˜ ๋ช…์„ธ 3.2.2 ์žฅ์น˜ ๋ฐ ์‹คํ—˜๋ฐฉ๋ฒ• 3.2.3 ์‹œํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 3.3 4์ƒํ•œ ์˜์—ญ ์ถ”์ง„๊ธฐ ๋‹จ๋…์‹œํ—˜ 3.3.1 ์‹คํ—˜์— ์‚ฌ์šฉ๋œ ๋ชจํ˜• ์ถ”์ง„๊ธฐ์˜ ๋ช…์„ธ 3.3.2 ์‹คํ—˜์žฅ์น˜ ๋ฐ ์‹คํ—˜๋ฐฉ๋ฒ• 3.3.3 ์‹œํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 3.4 ๋งŒํƒ€ ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ์šด๋™ ์ˆ˜ํ•™๋ชจ๋ธ 3.4.1 ์กฐ๋ฅ˜ ์ค‘์—์„œ์˜ ์šด๋™ ์ˆ˜ํ•™๋ชจ๋ธ 3.4.2 ๋งŒํƒ€ ๋ฌด์ธ์ž ์ˆ˜์ •์— ์ž‘์šฉํ•˜๋Š” ๋™์œ ์ฒด๋ ฅ ๋ฏธ๊ณ„์ˆ˜ 3.4.3 ์ˆ˜ํ•™๋ชจ๋ธ์˜ ๊ฒ€์ฆ 3.4.4 ์ˆ˜์น˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฒ•์— ์˜ํ•œ ๊ธฐ๋ณธ ์กฐ์ข…์šด๋™์˜ ๊ฒ€ํ†  ์ œ 4์žฅ ๋งŒํƒ€ ๋ฌด์ธ์ž ์ˆ˜์ •์˜ ์šด๋™ ์ œ์–ด ์ˆ˜์น˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 4.1 ๋ถˆ๊ท ์ผ ์กฐ๋ฅ˜ ์ค‘์—์„œ์˜ PD ์ œ์–ด ์‹œ์Šคํ…œ 4.2 ์ถ”๋ ฅ ์ œ์–ด 4.2.1 ์ถ”๋ ฅ ์ œ์–ด ๋ชจ๋ธ 4.2.2 ์ถ”๋ ฅ ์ œ์–ด ์ˆ˜์น˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ 4.3 ์ˆ˜ํ‰๋ฉด ์šด๋™ ์ œ์–ด 4.3.1 ์ˆ˜ํ‰๋ฉด ์šด๋™ ์ œ์–ด ๋ชจ๋ธ 4.3.2 ์ˆ˜ํ‰๋ฉด ์šด๋™ ์ œ์–ด ์ˆ˜์น˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ 4.4 ์ˆ˜์ง๋ฉด ์šด๋™ ์ œ์–ด 4.4.1 ์ˆ˜์ง๋ฉด ์šด๋™ ์ œ์–ด ๋ชจ๋ธ 4.4.2 ์ˆ˜์ง๋ฉด ์šด๋™ ์ œ์–ด ์ˆ˜์น˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ์ œ 5์žฅ ๊ฒฐ๋ก  ์ฐธ๊ณ ๋ฌธ

    Benefit of Four-Dimensional Computed Tomography Derived Ejection Fraction of the Left Atrial Appendage to Predict Thromboembolic Risk in the Patients with Valvular Heart Disease

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    BACKGROUND AND OBJECTIVES: Decreased left atrial appendage (LAA) emptying velocity in transesophageal echocardiography (TEE) is related with higher incidence of thrombus and increased risk of stroke. Patients with valve disease are at higher risk of thrombus formation before and after surgery. The aim of this study was to investigate the role of 4-dimensional cardiac computed tomography (4DCT) to predict the risk of thrombus formation. METHODS: Between March 2010 to March 2015, total of 62 patients (mean 60ยฑ15 years old, male: 53.2%) who underwent 4DCT and TEE for cardiac valve evaluation before surgery were retrospectively included in the current study. Fractional area change in TEE view and emptying velocity at left atrial appendage in TEE view (VeTEE) were measured. Ejection fraction (EF) of left atrial appendage in computed tomography (EFCT) was calculated by 4DCT with full volume analysis. The best cut-off value of EFCT predicting presence of spontaneous echo contrast (SEC) or thrombus was evaluated, and correlation between the parameters were also estimated. RESULTS: SEC or thrombus was observed in 45.2%. EFCT and VeTEE were significantly correlated (r=0.452, p<0.001). However, fractional area change measured by TEE showed no correlation with VeTEE (r=0.085, p=0.512). EFCT <37.5% best predicted SEC or thrombus in the patients with valve disease who underwent 4DCT and TEE (area under the curve, 0.654; p=0.038). CONCLUSIONS: In the patients who underwent 4DCT for cardiac valve evaluation before surgery, EFCT by volume analysis might have additional role to evaluate LAA function and estimate the risk of thrombus.ope

    Long-Term Prognosis of Patients with an Implantable Cardioverter-Defibrillator in Korea

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    PURPOSE: The objective of this study was to elucidate the long-term prognosis of patients with implantable cardioverter-defibrillators (ICDs) in Korea. MATERIALS AND METHODS: We enrolled 405 patients (age, 57.7ยฑ16.7 years; 311 men) who had undergone ICD implantation. The patients were divided into three groups: heart failure (HF) and ICD for primary (group 1, n=118) and secondary prevention (group 2, n=93) and non-HF (group 3, n=194). We compared appropriate and inappropriate ICD therapy delivery among the groups and between high- (heart rate โ‰ฅ200 /min) and low-rate (<200 /min) ICD therapy zones. RESULTS: During the follow-up period (58.9ยฑ49.8 months), the annual appropriate ICD therapy rate was higher in group 2 (10.4%) than in groups 1 and 3 (6.1% and 5.9%, respectively, p<0.001). There were no significant differences in annual inappropriate ICD therapy rate among the three groups. In group 1, the annual appropriate ICD therapy rate was significantly lower in patients with a high-rate versus a low-rate therapy zone (4.5% and 9.6%, respectively, p=0.026). In group 3, the annual inappropriate ICD therapy rate was significantly lower in patients with a high-rate versus a low-rate therapy zone (3.1% and 4.0%, respectively, p=0.048). CONCLUSION: Appropriate ICD therapy rates are not low in Korean patients with ICD, relative to prior large-scale studies in Western countries. Appropriate and inappropriate ICD therapy could be reduced by a high-rate therapy zone in patients with HF and ICD for primary prevention, as well as non-HF patients, respectively.ope
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