10 research outputs found

    ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ํ˜ˆ์•• ์˜ˆ์ธก ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ์œค์„ฑ๋กœ.While COVID-19 is changing the world's social profile, it is expected that the telemedicine sector, which has not been activated due to low regulation and reliability, will also undergo a major change. As COVID-19 spreads in the United States, the US Department of Health \& Human Services temporarily loosens the standards for telemedicine, while enabling telemedicine using Facebook, Facebook Messenger-based video chat, Hangouts, and Skype. The expansion of the telemedicine market is expected to quickly transform the existing treatment-oriented hospital-led medical market into a digital healthcare service market focused on prevention and management through wearables, big data, and health records analysis. In this prevention and management-oriented digital healthcare service, it is very important to develop a technology that can easily monitor a person's health status. One of the vital signs that can be used for personal health monitoring is blood pressure. High BP is a common and dangerous condition. About 1 out of 3 adults in the U.S. (about 75 million people) have high BP. This common condition increases the risk of heart disease and stroke, two of the leading causes of death for Americans. High BP is called the silent killer because it often has no warning signs or symptoms, and many people are not aware they have it. For these reasons, it is important to develop a technology that can easily and conveniently check BP regularly. In biomedical data analysis, various studies are being attempted to effectively analyze by applying machine learning to biomedical big data accumulated in large quantities. However, collecting blood pressure-related data at the level of big data is very difficult and very expensive because it takes a lot of manpower and time. So in this dissertation, we proposed a three-step strategy to overcome these issues. First, we describe a BP prediction model with extraction and concentration CNN architecture, to process publicly disclosed sequential ECG and PPG dataset. Second, we evaluate the performance of the developed model by applying the developed model to privately measured data. To address the third issue, we propose the knowledge distillation method and input pre-processing method to improve the accuracy of the blood pressure prediction model. All the methods proposed in this dissertation are based on a deep convolutional neural network (CNN). Unlike other studies based on manual recognition of the features, by utilizing the advantage of deep learning which automatically extracts features, raw biomedical signals are used intact to reflect the inherent characteristics of the signals themselves.์ฝ”๋กœ๋‚˜ 19์— ์˜ํ•œ ์ „ ์„ธ๊ณ„์˜ ์‚ฌํšŒ์  ํ”„๋กœํ•„ ๋ณ€ํ™”๋กœ, ๊ทœ์ œ์™€ ์‹ ๋ขฐ์„ฑ์ด ๋‚ฎ๊ธฐ ๋•Œ๋ฌธ์— ํ™œ์„ฑํ™” ๋˜์ง€ ์•Š์€ ์›๊ฒฉ ์˜๋ฃŒ ๋ถ„์•ผ๋„ ํฐ ๋ณ€ํ™”๋ฅผ ๊ฒช์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋ฉ๋‹ˆ๋‹ค. ์ฝ”๋กœ๋‚˜ 19๊ฐ€ ๋ฏธ๊ตญ์— ํผ์ง์— ๋”ฐ๋ผ ๋ฏธ๊ตญ ๋ณด๊ฑด๋ณต์ง€๋ถ€๋Š” ์›๊ฒฉ ์ง„๋ฃŒ์˜ ํ‘œ์ค€์„ ์ผ์‹œ์ ์œผ๋กœ ์™„ํ™”ํ•˜๋ฉด์„œ ํŽ˜์ด์Šค๋ถ, ํŽ˜์ด์Šค๋ถ ๋ฉ”์‹ ์ € ๊ธฐ๋ฐ˜ ํ™”์ƒ ์ฑ„ํŒ…, ํ–‰์•„์›ƒ, ์Šค์นด์ดํ”„๋ฅผ ์‚ฌ์šฉํ•œ ์›๊ฒฉ ์ง„๋ฃŒ๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ–ˆ์Šต๋‹ˆ๋‹ค. ์›๊ฒฉ์˜๋ฃŒ ์‹œ์žฅ์˜ ํ™•์žฅ์€ ๊ธฐ์กด์˜ ์น˜๋ฃŒ์ค‘์‹ฌ ๋ณ‘์›์ฃผ๋„์˜ ์˜๋ฃŒ์‹œ์žฅ์„ ์›จ์–ด๋Ÿฌ๋ธ”, ๋น… ๋ฐ์ดํ„ฐ ๋ฐ ๊ฑด๊ฐ•๊ธฐ๋ก ๋ถ„์„์„ ํ†ตํ•œ ์˜ˆ๋ฐฉ ๋ฐ ๊ด€๋ฆฌ์— ์ค‘์ ์„ ๋‘” ๋””์ง€ํ„ธ ์˜๋ฃŒ ์„œ๋น„์Šค ์‹œ์žฅ์œผ๋กœ ๋น ๋ฅด๊ฒŒ ๋ณ€ํ™”์‹œํ‚ฌ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์˜ˆ๋ฐฉ ๋ฐ ๊ด€๋ฆฌ ์ค‘์‹ฌ์˜ ๋””์ง€ํ„ธ ํ—ฌ์Šค์ผ€์–ด ์„œ๋น„์Šค์—์„œ๋Š” ์‚ฌ๋žŒ์˜ ๊ฑด๊ฐ• ์ƒํƒœ๋ฅผ ์‰ฝ๊ฒŒ ๋ชจ๋‹ˆํ„ฐ๋ง ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ˆ  ๊ฐœ๋ฐœ์ด ๋งค์šฐ ์ค‘์š”ํ•œ๋ฐ ํ˜ˆ์••์€ ๊ฐœ์ธ ๊ฑด๊ฐ• ๋ชจ๋‹ˆํ„ฐ๋ง์— ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ํ•„์ˆ˜ ์ง•ํ›„ ์ค‘ ํ•˜๋‚˜ ์ž…๋‹ˆ๋‹ค. ๊ณ ํ˜ˆ์••์€ ์•„์ฃผ ํ”ํ•˜๊ณ  ์œ„ํ—˜ํ•œ ์งˆํ™˜์ž…๋‹ˆ๋‹ค. ๋ฏธ๊ตญ ์„ฑ์ธ 3๋ช…์ค‘ 1๋ช…(์•ฝ 7,500๋งŒ๋ช…)์ด ๊ณ ํ˜ˆ์••์„ ๊ฐ€์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๋ฏธ๊ตญ์ธ์˜ ์ฃผ์š” ์‚ฌ๋ง ์›์ธ ์ค‘ ๋‘๊ฐ€์ง€์ธ ์‹ฌ์žฅ์งˆํ™˜๊ณผ ๋‡Œ์กธ์ค‘์˜ ์œ„ํ—˜์„ ์ฆ๊ฐ€ ์‹œํ‚ต๋‹ˆ๋‹ค. ๊ณ ํ˜ˆ์••์€ ์‹ ์ฒด์— ๊ฒฝ๊ณ  ์‹ ํ˜ธ๋‚˜ ์ž๊ฐ ์ฆ์ƒ์ด ์—†์–ด ๋งŽ์€ ์‚ฌ๋žŒ๋“ค์ด ์ž์‹ ์ด ๊ณ ํ˜ˆ์••์ธ ๊ฒƒ์„ ์ธ์ง€ํ•˜์ง€ ๋ชปํ•˜๊ธฐ ๋•Œ๋ฌธ์— "์‚ฌ์ผ๋ŸฐํŠธ ํ‚ฌ๋Ÿฌ"๋ผ ๋ถˆ๋ฆฌ์›๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ ์ •๊ธฐ์ ์œผ๋กœ ์‰ฝ๊ณ  ํŽธ๋ฆฌํ•˜๊ฒŒ ํ˜ˆ์••์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ˆ ์˜ ๊ฐœ๋ฐœ์ด ๋งค์šฐ ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ์ƒ์ฒด์˜ํ•™ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋ถ„์•ผ์—์„œ๋Š” ๋จธ์‹  ๋Ÿฌ๋‹์„ ๋Œ€๋Ÿ‰์œผ๋กœ ์ˆ˜์ง‘๋œ ์ƒ์ฒด์˜ํ•™ ๋น… ๋ฐ์ดํ„ฐ์— ์ ์šฉํ•˜๋Š” ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํšจ๊ณผ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋น… ๋ฐ์ดํ„ฐ ์ˆ˜์ค€์œผ๋กœ ๋‹ค๋Ÿ‰์˜ ํ˜ˆ์•• ๊ด€๋ จ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๋Š” ๊ฒƒ์€ ๋งŽ์€ ์ „๋ฌธ์ ์ธ ์ธ๋ ฅ๋“ค์ด ์˜ค๋žœ์‹œ๊ฐ„์„ ํ•„์š”๋กœ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋งค์šฐ ์–ด๋ ต๊ณ  ๋น„์šฉ ๋˜ํ•œ ๋งŽ์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ 3๋‹จ๊ณ„ ์ „๋žต์„ ์ œ์•ˆํ–ˆ์Šต๋‹ˆ๋‹ค. ๋จผ์ € ๋ˆ„๊ตฌ๋‚˜ ์‹œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ณต๊ฐœ๋˜์–ด ์žˆ๋Š” ์‹ฌ์ „๋„, ๊ด‘์šฉ์ ๋งฅํŒŒ ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉ, ์ˆœ์ฐจ์ ์ธ ์‹ฌ์ „๋„, ๊ด‘์šฉ์ ๋งฅํŒŒ ์‹ ํ˜ธ์—์„œ ํ˜ˆ์••์„ ์ž˜ ์˜ˆ์ธกํ•˜๋„๋ก ๊ณ ์•ˆ๋œ ์ถ”์ถœ ๋ฐ ๋†์ถ• ์ž‘์—…์„ ๋ฐ˜๋ณตํ•˜๋Š” ํ•จ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ–ˆ์Šต๋‹ˆ๋‹ค. ๋‘๋ฒˆ์งธ๋กœ ์ œ์•ˆ๋œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์„ ๊ฐœ์ธ์—๊ฒŒ์„œ ์ธก์ •ํ•œ ๊ด‘์šฉ์ ๋งฅํŒŒ ์‹ ํ˜ธ๋ฅผ ์ด์šฉํ•ด ์ œ์•ˆ๋œ ํ•จ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ–ˆ์Šต๋‹ˆ๋‹ค. ์„ธ๋ฒˆ์งธ๋กœ ํ˜ˆ์••์˜ˆ์ธก ๋ชจ๋ธ์˜ ์ •ํ™•์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ์ง€์‹ ์ฆ๋ฅ˜๋ฒ•๊ณผ ์ž…๋ ฅ์‹ ํ˜ธ ์ „์ฒ˜๋ฆฌ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ ๋ชจ๋“  ํ˜ˆ์••์˜ˆ์ธก ๋ฐฉ๋ฒ•์€ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•ฉ๋‹ˆ๋‹ค. ํ˜ˆ์•• ์˜ˆ์ธก์— ํ•„์š”ํ•œ ํŠน์ง•๋“ค์„ ์ˆ˜๋™์œผ๋กœ ์ถ”์ถœํ•ด์•ผ ํ•˜๋Š” ๋‹ค๋ฅธ ์—ฐ๊ตฌ๋“ค๊ณผ ๋‹ค๋ฅด๊ฒŒ ํŠน์ง•์„ ์ž๋™์œผ๋กœ ์ถ”์ถœํ•˜๋Š” ๋”ฅ๋Ÿฌ๋‹์˜ ์žฅ์ ์„ ํ™œ์šฉ, ์•„๋ฌด๋Ÿฐ ์ฒ˜๋ฆฌ๋„ ํ•˜์ง€ ์•Š์€ ์›๋ž˜ ๊ทธ๋Œ€๋กœ์˜ ์ƒ์ฒด ์‹ ํ˜ธ์—์„œ ์‹ ํ˜ธ ์ž์ฒด์˜ ๊ณ ์œ ํ•œ ํŠน์ง•์„ ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.1 Introduction 1 2 Background 5 2.1 Cuff-based BP measurement methods 9 2.1.1 Auscultatory method 9 2.1.2 Oscillometric method 10 2.1.3 Tonometric method 11 2.2 Biomedical signals used in cuffless BP prediction methods 13 2.2.1 Electrocardiography (ECG) 13 2.2.2 Photoplethysmography (PPG) 20 2.3 Cuffless BP measurement methods 21 2.3.1 PWV based BP prediction methods 25 2.3.2 Machine learning based pulse wave analysis methods 26 2.4 Deep learning for sequential biomedical data 30 2.4.1 Convolutional neural networks 31 2.4.2 Recurrent neural networks 32 3 End-to-end blood pressure prediction via fully convolutional networks 33 3.1 Introduction 35 3.2 Method 38 3.2.1 Data preparation 38 3.2.2 CNN based prediction model 41 3.2.3 Detailed architecture 45 3.3 Experimental results 47 3.3.1 Setup 47 3.3.2 Model evaluation & selection 48 3.3.3 Calibration-based method 51 3.3.4 Performance comparison 52 3.3.5 Verification using international standards for BP measurement grading criteria 54 3.3.6 Performance comparison by the input signal combinations 56 3.3.7 An ablation study of each architectural component of extraction-concentration blocks 58 3.3.8 Preprocessing of input signal to improve blood pressure prediction performance 59 3.4 Discussion 61 3.5 Summary 63 4 Blood pressure prediction by a smartphone sensor using fully convolutional networks 64 4.1 Introduction 66 4.2 Method 69 4.2.1 Data acquisition 71 4.2.2 Preprocessing of the PPG signals 71 4.2.3 PPG signal selection 71 4.2.4 Data preparation for CNN model training 72 4.2.5 Network architectures 72 4.3 Experimental results 75 4.3.1 Implementation details 75 4.3.2 Effect of PPG combination on BP prediction 75 4.3.3 Performance comparison with other related works 76 4.3.4 Verification using international standards for BP measurement grading criteria 77 4.3.5 Preprocessing of input signal to improve blood pressure prediction performance 79 4.4 Discussion 81 4.5 Summary 83 5 Improving accuracy of blood pressure prediction by distilling the knowledge of neural networks 84 5.1 Introduction 85 5.2 Methods 87 5.3 Experimental results 88 5.4 Discussion & Summary 89 6 Conclusion 90 6.1 Future work 92 Bibliography 93 Abstract (In Korean) 106Docto

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    Design and characteristic analysis of permanent magnet synchronous motor for unmanned marine exploration considering propulsor performance

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    ๋ณธ ๋…ผ๋ฌธ์€ ๋ฌด์ธ ํ•ด์–‘ ํƒ์‚ฌ์šฉ ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ์˜ ์„ค๊ณ„ ๋ฐ ์ œ์–ด ํŠน์„ฑ ํ•ด์„์— ๊ด€ํ•œ ๊ฒƒ์ด๋ฉฐ ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ์˜ ์„ ์ •์€ ์ œ์ž‘์„ฑ์ด ๋น„๊ต์  ๊ฐ„๋‹จํ•˜๊ณ  ๊ณ ํšจ์œจ ๊ตฌํ˜„์ด ๊ฐ€๋Šฅํ•˜๋ฉฐ ์ฒด์ ์— ๋Œ€ํ•œ ๋ฐ€๋„๊ฐ€ ๋†’์€ ํ‘œ๋ฉด๋ถ€์ฐฉํ˜• ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ(SPMSM_Surface-mounted Permanent Magnet synchronous motor)๋กœ ์ฑ„ํƒํ•˜์˜€๋‹ค. ๋ฌด์ธ ํ•ด์–‘ ํƒ์‚ฌ์šฉ ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ์˜ ์„ค๊ณ„๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ถ”์ง„๊ธฐ์— ์š”๊ตฌ๋˜๋Š” ์‚ฌ์–‘ ๊ฒ€ํ† ๊ฐ€ ์„ ํ–‰์œผ๋กœ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ”„๋กœํŽ ๋Ÿฌ์— ๋Œ€ํ•œ ์ถ”๋ ฅ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ธฐ๋ณธ ์„ค๊ณ„๋ฅผ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ, ์ถ”์ง„๊ธฐ๊ฐ€ ์š”๊ตฌ๋˜๋Š” ์ถ”๋ ฅ์„ ์–ป๊ธฐ ์œ„ํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์„ ์ •์— ๋Œ€ํ•ด ์ˆ˜์‹์ ์œผ๋กœ ์‚ดํŽด๋ณด๊ณ  ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ํ—ˆ๋ธŒ(Hub)์™€ ์ƒคํ”„ํŠธ(Shaft)๊ฐ€ ์—†๋Š” ์ถ”์ง„์šฉ ์ „๋™๊ธฐ์˜ ํ”„๋กœํŽ ๋Ÿฌ ๊ฒฐํ•ฉ์„ ๊ณ ๋ คํ•˜์—ฌ ํšŒ์ „์ž์˜ ํฌ๊ธฐ๋ฅผ ์„ ์ •ํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ์ƒคํ”„ํŠธ๋ฆฌ์Šค(Shaft-less)ํ˜• ์ถ”์ง„์šฉ ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ๋Š” ์Šคํฌ๋ฅ˜ ๋ฐฉ์‹์˜ ์ถ”์ง„๊ธฐ๋ณด๋‹ค ์ปดํŒฉํŠธ(Compact)ํ•œ ๋ฐฐ์น˜์™€ ๊ตฌ์กฐ์  ๋ถ€ํ’ˆ ๋‹จ์ˆœํ™”, ๋น„์šฉ๊ณผ ๋ฌด๊ฒŒ ์ ˆ๊ฐ ๋“ฑ ๋งŽ์€ ์žฅ์ ์„ ๊ธฐ๋Œ€ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ถ”์ง„ ์—ญํ• ์„ ํ•˜๋Š” ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ๋Š” ์ „์ž๊ธฐ์  ํŠน์„ฑ ๊ฒ€ํ† ๋ฅผ ํ•„์ˆ˜์  ์š”์†Œ๋กœ ์ง„ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค. ํšŒ์ „์ž ๋ถ€ํ”ผ์— ๋Œ€ํ•œ ์ถœ๋ ฅ ํ† ํฌ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ ๊ณ ์ •์ž ์ขŒํ‘œ๊ณ„์˜ ์ „์••๋ฐฉ์ •์‹, ์˜๊ตฌ์ž์„ ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์ž”๋ฅ˜์ž์†๋ฐ€๋„์™€ ๊ทน ์ˆ˜์™€ ์Šฌ๋กฏ ์ˆ˜์˜ ์กฐํ•ฉ๋ฒ•, ๊ณต๊ทน ์ž์†๋ฐ€๋„์— ๋Œ€ํ•ด ์ˆ˜์‹์ ์œผ๋กœ ์‚ดํŽด๋ณด๊ณ  ์ž๊ธฐ๋“ฑ๊ฐ€ํšŒ๋กœ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๋ฌด๋ถ€ํ•˜ ์‹œ ์ž๊ธฐ ๋ฐ์ดํ„ฐ์™€ ์ฝ”๊น…ํ† ํฌ, ์—ญ๊ธฐ์ „๋ ฅ์— ๋Œ€ํ•œ ํ•ด์„ ๊ฒฐ๊ณผ์™€ ์ •๊ฒฉ ๋ถ€ํ•˜ ์‹œ ์„ฑ๋Šฅ ํ•ด์„ ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฌด์ธ ํ•ด์–‘ ํƒ์‚ฌ์šฉ ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ์˜ ๊ธฐ์ดˆ ์„ค๊ณ„ ๋ฐ์ดํ„ฐ๋ฅผ ๊ฒ€์ฆํ•˜๊ณ  ์ œ์–ด ๋ฐฉ์‹์— ๋Œ€ํ•œ ์„ฑ๋Šฅ ํ™•์ธ์„ ์œ„ํ•ด ์œ ํ•œ์š”์†Œ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์ •ํ˜„ํŒŒ ๋ฐ PWM ๊ตฌ๋™์— ๋”ฐ๋ฅธ ์ „์ž๊ธฐ์  ํŠน์„ฑ๊ณผ ๊ณต๊ทน ์ž์†๋ฐ€๋„์— ๋Œ€ํ•œ ๋ถ„ํฌ, ์ „์ž๊ธฐ์  ์†์‹ค์„ ๊ฒ€ํ† ํ•˜์—ฌ ์„ค๊ณ„ ๋ชจ๋ธ์˜ ์ „์ž๊ธฐ์  ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋์œผ๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ „๋™๊ธฐ ์„ค๊ณ„์— ๋Œ€ํ•œ ๊ฒฐ๊ณผ ๊ฒ€์ฆ์„ ์œ„ํ•ด ์ œ์•ˆ๋œ ๋ฌด์ธ ํ•ด์–‘ ํƒ์‚ฌ์šฉ ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ ์‹œ์ž‘ํ’ˆ์„ ์ œ์ž‘ํ•˜์—ฌ ๋ฌด๋ถ€ํ•˜ ์‹คํ—˜์„ ํ†ตํ•ด ๊ถŒ์„ ์ €ํ•ญ๊ณผ ์—ญ๊ธฐ์ „๋ ฅ์„ ์ธก์ •ํ•˜์—ฌ ๊ธฐ๋ณธ ์„ค๊ณ„ ๋ฐ์ดํ„ฐ๋ฅผ ํ™•์ธํ•˜๊ณ  ๋‹ค์ด๋‚˜๋ชจ๋ฏธํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ œ์–ด ๋ฐฉ์‹ ๋”ฐ๋ฅธ ๋ถ€ํ•˜ ์‹คํ—˜์„ ํ†ตํ•ด ์ „๋™๊ธฐ ์„ค๊ณ„ ํŠน์„ฑ์— ๋Œ€ํ•ด ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ถ”์ง„ ์„ฑ๋Šฅ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ํ•ด์ˆ˜ ํ™˜๊ฒฝ ๊ตฌํ˜„์šฉ ๋ชจ์˜ ์ˆ˜์กฐ๋ฅผ ์ œ์ž‘ํ•˜๊ณ  ๋กœ๋“œ์…€์„ ์ด์šฉํ•˜์—ฌ ์ œ์–ด ๋ฐฉ์‹์— ๋”ฐ๋ฅธ ์ถ”๋ ฅ์„ ์ธก์ •ํ•˜์—ฌ ์ œ์•ˆ๋œ ์„ค๊ณ„ ๋ชจ๋ธ์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.1. ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ๋‚ด์šฉ 4 1.3 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 5 2. ๋ฌด์ธ ํ•ด์–‘ ํƒ์‚ฌ์šฉ ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ ์„ค๊ณ„ 6 2.1 ์„ค๊ณ„ ์‚ฌ์–‘ 6 2.2 ์„ค๊ณ„ ์š”๊ตฌ ์กฐ๊ฑด 7 2.2.1 ์„ค๊ณ„ ์ˆœ์„œ 7 2.2.2 ํšŒ์ „์ž ์„ค๊ณ„ 8 2.2.3 ๊ณ ์ •์ž ์„ค๊ณ„ 15 3. ๋ฌด์ธ ํ•ด์–‘ ํƒ์‚ฌ์šฉ ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ ํ•ด์„ 21 3.1 ํ•ด์„ ๋ชจ๋ธ 21 3.2 ์ž๊ธฐ๋“ฑ๊ฐ€ํšŒ๋กœ๋ฒ•์— ์˜ํ•œ ํŠน์„ฑ ํ•ด์„ 24 3.3 ์œ ํ•œ์š”์†Œ๋ฒ•์— ์˜ํ•œ ํŠน์„ฑ ํ•ด์„ 27 3.3.1 ์ •ํ˜„ํŒŒ ์ž…๋ ฅ ์œ ํ•œ์š”์†Œ ํ•ด์„ 28 3.3.2 PWM ์ž…๋ ฅ ์œ ํ•œ์š”์†Œ ํ•ด์„ 34 4. ๋ฌด์ธ ํ•ด์–‘ ํƒ์‚ฌ์šฉ ์˜๊ตฌ์ž์„ ๋™๊ธฐ์ „๋™๊ธฐ ์‹คํ—˜ 41 4.1 ์‹œ์ž‘ํ’ˆ ์ œ์ž‘ 41 4.2 ์‹คํ—˜ ์žฅ์น˜ ๊ตฌ์„ฑ 43 4.3 ํŠน์„ฑ ๋ถ„์„ 46 4.3.1 ๋ฌด๋ถ€ํ•˜ ์‹คํ—˜ 46 4.3.2 ๋‹ค์ด๋‚˜๋ชจ๋ฏธํ„ฐ ๋ถ€ํ•˜ ์‹คํ—˜ 48 4.3.3 ๋ชจ์˜ ์ˆ˜์ค‘ ๋ถ€ํ•˜ ์‹คํ—˜ 50 5. ๊ฒฐ๋ก  60 ์ฐธ๊ณ ๋ฌธํ—Œ 62Maste

    Experimental Exploitation of Random and Deterministic Data Patterns for Stringent DDR4 I/O Timing Margins

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    In this paper, I/O timing margins are experimentally measured by DQS groups, for a DDR4 RDIMM with 2133 Mbps data rate, to study the margin effects of the special combination and sequence of random and fault-based deterministic data patterns. The most effective 94 data patterns are newly developed after experimentally investigating three test patterns factors, which consist of test algorithms, address directions, and data patterns; the most influential factor was data patterns, which resulted in the average margin reduction of 15.2%. The maximum of 11.8% margin was reduced by the proposed 94 patterns (in comparison to 28-bit PRBS pattern), which was from both selected PRBS and fault-based deterministic data patterns

    ํด๋ฆฌํ”„๋กœํ•„๋ Œ ํ•ฉ์„ฑ์„ฌ์œ ๋ณด๊ฐ• ์ฝ˜ํฌ๋ฆฌํŠธ์˜ ๊ฐ•๋„ ํŠน์„ฑ ๋ฐ ๊ฑด์กฐ์ˆ˜์ถ•๊ท ์—ด์ œ์–ด ํŠน์„ฑ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ† ๋ชฉ๊ณตํ•™๊ณผ,1996.Maste

    Dielectrophoretic technique for measurement of chemical biological interactions

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    ์˜๊ณตํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€]๋ณธ ์—ฐ๊ตฌ๋Š” ์‘๊ธ‰์‹ค ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค, ์ง๋ฌด๋งŒ์กฑ, ์ด์ง์˜๋„ ๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๊ณ ์ž ์‹œ๋„๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ์ž๋Š” ์„œ์šธ?์ธ์ฒœ ์†Œ์žฌ 9๊ฐœ ์ข…ํ•ฉ์ „๋ฌธ์š”์–‘๊ธฐ๊ด€์˜ ์‘๊ธ‰์‹ค ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ๋กœ์„œ ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ๋ฅผ ์œ„ํ•œ ์˜ค๋ฆฌ์—”ํ…Œ์ด์…˜ ๊ธฐ๊ฐ„์„ ๋งˆ์น˜๊ณ  ๋…๋ฆฝ์ ์ธ ๊ฐ„ํ˜ธ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ์‘๊ธ‰์‹ค ๊ทผ๋ฌด ๊ฒฝ๋ ฅ 12๊ฐœ์›” ์ดํ•˜์ธ ๊ฐ„ํ˜ธ์‚ฌ 81๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ์ž๋ฃŒ์ˆ˜์ง‘์€ 2009๋…„ 5์›” 1์ผ๋ถ€ํ„ฐ 5์›” 20์ผ๊นŒ์ง€ 20์ผ๊ฐ„ ์‹ค์‹œ๋˜์—ˆ์œผ๋ฉฐ ๊ตฌ์กฐํ™”๋œ ์„ค๋ฌธ์ง€๋ฅผ ์ž๊ฐ€๋ณด๊ณ  ํ˜•์‹์œผ๋กœ ์ˆ˜์ง‘ํ•˜์˜€์œผ๋ฉฐ, ์ž๋ฃŒ๋ถ„์„ ๋ฐฉ๋ฒ•์€ SAS 8.1์„ ์ด์šฉํ•˜์—ฌ ๋ฐฑ๋ถ„์œจ, ํ‰๊ท ๊ณผ ํ‘œ์ค€ํŽธ์ฐจ, Chi-square test, t-test, Pearson correlation ๋“ฑ์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค.1. ์‘๊ธ‰์‹ค ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค ์ •๋„๋Š” 2.79์ (4์  ์ฒ™๋„)์œผ๋กœ ์ค‘๊ฐ„์ˆ˜์ค€ ์ด์ƒ์ด์—ˆ๋‹ค. ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค ์ •๋„๊ฐ€ ๊ฐ€์žฅ ๋†’์€ ์˜์—ญ์€ โ€˜๋Œ€์ธ๊ด€๊ณ„์ƒ์˜ ๋ฌธ์ œโ€™(2.84์ )์ด์—ˆ์œผ๋ฉฐ, ๊ทธ ๋‹ค์Œ์ด โ€˜์˜์‚ฌ์™€์˜ ๋Œ€์ธ๊ด€๊ณ„์ƒ ๊ฐˆ๋“ฑโ€™(2.83์ ), โ€˜์—…๋ฌด ์™ธ์˜ ์ฑ…์ž„โ€™(2.83์ ), โ€˜์˜์‚ฌ์™€์˜ ์—…๋ฌด์ƒ ๊ฐˆ๋“ฑโ€™(2.81์ ), โ€˜์—…๋ฌด๋Ÿ‰ ๊ณผ์ค‘โ€™(2.80์ ), โ€˜๋ถ€์ ์ ˆํ•œ ๋ณด์ƒโ€™(2.72์ ), โ€˜์ „๋ฌธ์ง ์—ญํ•  ๊ฐˆ๋“ฑโ€™(2.69์ ), โ€˜์ „๋ฌธ์ง€์‹๊ณผ ๊ธฐ์ˆ  ๋ถ€์กฑโ€™(2.69์ ), โ€˜๋ถ€์ ์ ˆํ•œ ๋ฌผ๋ฆฌ์  ํ™˜๊ฒฝโ€™(2.66์ ), โ€˜์ต์ˆ™ํ•˜์ง€ ์•Š์€ ์ƒํ™ฉโ€™(2.63์ ), โ€˜๋ถ€์ ์ ˆํ•œ ๋Œ€์šฐโ€™(2.60์ ), โ€˜์ƒ์‚ฌ์™€์˜ ๊ด€๊ณ„โ€™(2.58์ ), โ€˜์น˜๋ฃŒ์˜ ํ•œ๊ณ„์— ๋Œ€ํ•œ ์‹ฌ๋ฆฌ์  ๋ถ€๋‹ดโ€™(2.49์ ), โ€˜๋ถ€ํ•˜์ง์›๊ณผ์˜ ๋ถˆ๋งŒ์Šค๋Ÿฐ ๊ด€๊ณ„โ€™(2.20์ ), โ€˜๋ฐค ๊ทผ๋ฌดโ€™(2.00์ ) ์ˆœ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค.2. ์‘๊ธ‰์‹ค ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ง๋ฌด๋งŒ์กฑ ์ •๋„๋Š” 2.92์ (5์  ์ฒ™๋„)์œผ๋กœ โ€˜์ƒํ˜ธ์ž‘์šฉโ€™ ์˜์—ญ์ด 3.69์ ์œผ๋กœ ๊ฐ€์žฅ ๋†’์•˜๋‹ค. ๋‹ค์Œ์œผ๋กœ๋Š” โ€˜์ž์œจ์„ฑโ€™(3.14์ ), โ€˜์ „๋ฌธ์ง ์ƒํƒœโ€™(2.85์ ), โ€˜์—…๋ฌด์š”๊ตฌ๋„โ€™(2.76์ ), โ€˜๊ฐ„ํ˜ธ์‚ฌ-์˜์‚ฌ ๊ด€๊ณ„โ€™(2.74์ ), โ€˜๋ณด์ˆ˜โ€™(2.57์ ), โ€˜์กฐ์ง์š”๊ตฌ๋„โ€™(2.55์ ) ์ˆœ์ด์—ˆ๋‹ค.3. ์‘๊ธ‰์‹ค ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ 81๋ช… ์ค‘ ๋ถ€์„œ์ด๋™์€ 68๋ช…(83.95%)์ด, ๋ณ‘์›์ด๋™์€ 69๋ช…(85.19%)์ด, ์ง์ข…์ด๋™์€ 74๋ช…(91.36%)์ด ์ด์ง์˜๋„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋ณด๊ณ ํ•˜์˜€๋‹ค.4. ์‘๊ธ‰์‹ค ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค, ์ง๋ฌด๋งŒ์กฑ, ์ด์ง์˜๋„์˜ ์ฐจ์ด๋ฅผ ์‚ดํŽด๋ณด์•˜ ์„ ๋•Œ ๋Œ€์ƒ์ž์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ ์ค‘ ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค์™€ ์œ ์˜ํ•œ ๊ด€๋ จ์š”์ธ์€ ์—†์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ง๋ฌด๋งŒ์กฑ์€ ์‘๊ธ‰์‹ค ๊ทผ๋ฌด ๊ฒฝ๋ ฅ(p=.02)๊ณผ ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ๋ฅผ ์œ„ํ•œ ์‘๊ธ‰์‹ค ์˜ค๋ฆฌ์—”ํ…Œ์ด์…˜ ๊ธฐ๊ฐ„(p=.03)๊ณผ ๊ด€๋ จ์ด ์žˆ์—ˆ์œผ๋ฉฐ ์ด์ง์˜๋„๋Š” ์ตœ์ข…ํ•™๋ ฅ(p=.006)๊ณผ ๊ด€๋ จ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค.5. ๋ถ€์„œ์ด๋™, ๋ณ‘์›์ด๋™, ์ง์ข…์ด๋™ ์ด์ง์˜๋„๋ฅผ ๊ฐ€์ง„ ๋Œ€์ƒ์ž์˜ ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค์™€ ์ง๋ฌด๋งŒ์กฑ ๊ฐ„์—๋Š” ์œ ์˜ํ•œ ์Œ์˜ ์ƒ๊ด€๊ด€๊ณ„(r=-0.36, p=.0022, r=-0.41, p=.0004, r=-0.28, p=.0125)๊ฐ€ ์žˆ๊ณ , ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค์™€ ์ด์ง์˜๋„ ๊ฐ„์—๋Š” ์œ ์˜ํ•œ ์–‘์˜ ์ƒ๊ด€๊ด€๊ณ„(r=.40, p=.0007, r=.40, p=.0006, r=.42, p=.0002)๊ฐ€ ์žˆ๋‹ค. ๋ถ€์„œ์ด๋™, ๋ณ‘์›์ด๋™ ์ด์ง์˜๋„๋ฅผ ๊ฐ€์ง„ ๋Œ€์ƒ์ž์˜ ์ง๋ฌด๋งŒ์กฑ๊ณผ ์ด์ง์˜๋„ ๊ฐ„์—๋Š” ์œ ์˜ํ•œ ์Œ์˜ ์ƒ๊ด€๊ด€๊ณ„(r=-0.34, p=.0044, r=-0.42, p=.0003)๊ฐ€ ์žˆ๋‹ค. ์ด์ƒ์˜ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•˜๋ฉด ์‘๊ธ‰์‹ค ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ๋Š” ๋Œ€์ธ๊ด€๊ณ„์ƒ์˜ ๋ฌธ์ œ, ์˜์‚ฌ์™€์˜ ๋Œ€์ธ๊ด€๊ณ„์ƒ ๊ฐˆ๋“ฑ๊ณผ ๊ฐ™์€ ์ธ๊ฐ„๊ด€๊ณ„์˜ ๋ฌธ์ œ์—์„œ ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ๊ฐ€์žฅ ๋งŽ์ด ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ , ์ƒํ˜ธ์ž‘์šฉ๊ณผ ์ž์œจ์„ฑ์— ์ง๋ฌด๋งŒ์กฑ์„ ๋†’๊ฒŒ ๋Š๊ผˆ๋‹ค. ๋˜ํ•œ ์‘๊ธ‰์‹ค ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ์˜ ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค๊ฐ€ ๋†’๊ณ  ์ง๋ฌด๋งŒ์กฑ์ด ๋‚ฎ์„์ˆ˜๋ก ์ด์ง์˜๋„๊ฐ€ ๋†’์•˜๋‹ค. ๋”ฐ๋ผ์„œ ์‘๊ธ‰์‹ค ์‹ ๊ทœ ๊ฐ„ํ˜ธ์‚ฌ์˜ ๋Œ€์ธ๊ด€๊ณ„์—์„œ ์˜ค๋Š” ์ง๋ฌด ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ์ค„์ด๊ณ  ์ง๋ฌด๋งŒ์กฑ์„ ๋†’์—ฌ ์ด์ง์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ์ง๋ฌด ์ ์‘ ํ”„๋กœ๊ทธ๋žจ ๊ฐœ๋ฐœ์ด ํ•„์š”ํ•˜๋‹ค๊ณ  ์‚ฌ๋ฃŒ๋œ๋‹ค. [์˜๋ฌธ]The purpose of this study was to investigate the relationships among job stress, job satisfaction, and turnover intention of new nurses at the emergency department (ED). The subjects were 81 new nurses who have been working at EDs for 12 months or less at 9 tertiary hospitals located in Seoul and Incheon. Data were collected from May 1 to May 20, 2009 with a structured survey questionnaire and were analyzed by using SAS 8.1 program. The results were as follows; 1. The average score of job stress of new nurses was 2.79, ranging from 1 to 4. The highest job stressors were related to interpersonal problems and work load. 2. The average score of job satisfaction was 2.92, ranging from 1 to 5. The factors related to the highest job satisfaction was interaction (3.69) then autonomy (3.14). 3. Turnover intention was classified into 3 categories: transfer to other departments, transfer to other hospitals, and leave nursing job. Among 81 new nurses, with multiple responses, 68 (83.95%) reported intention to transfer to other departments, 69 (85.19%) transfer to other hospitals, and 74 (91.36%) of leaving nursing job. 4. There was no significant difference in job stress according to general characteristics but the level of job satisfaction was higher when they had longer than 4 weeks of orientation period. Turnover intention of transferring to other hospitals was higher among 3-year program graduates than 4-year program graduates. 5. There were significant negative correlations between job stress and job satisfaction among the subjects who had turnover intention of transferring to other departments (r=-0.36, p=.0022), transferring to other hospitals (r=-0.41, p=.0004), and leaving nursing job (r=-0.28, p=.0125). And there were significant positive correlations between job stress and turnover intention among the subjects who had turnover intention of transferring to other departments (r=.40, p=.0007), transferring to other hospitals (r=.40, p=.0006), and leaving nursing job (r=.42, p=.0002). There were significant negative correlations between job satisfaction and turnover intention among the subjects who had turnover intention of transferring to other departments (r=-0.34, p=.0044) and transferring to other hospitals (r=-0.42, p=.0003). According to the results of this study, the higher job stress of new nurses had, the lower job satisfaction and the higher turnover intention existed. It is suggested that the orientation program for the new nurses at ED should include programs to reduce job stress and to increase job satisfaction.ope

    ์ฝ”์–ด ํ˜•ํƒœ์™€ ์‹œ๋ฉ˜ํŠธ ์ข…๋ฅ˜์— ๋”ฐ๋ฅธ ์ „๋ถ€์ฃผ์กฐ๊ธˆ๊ด€์˜ ์œ ์ง€๋ ฅ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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

    'Symptomatic Reading' On the <Uniform>, <Night Train>, <Black Coal, Thin Ice> of Diao yi nan

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    A three-dimensional finite-element analysis of influence of splinting in mandibular posterior implants

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    Statement of problem: Over the past two decades, implant supported fixed prosthesis have been widely used. However, there are few studies conducted systematically and intensively on the splinting effect of implant systems in mandible. Purpose: The purpose of this study was to investigate the changes in stress distributions in the mandibular implants with splinting or non-splinting crowns by performing finite element analysis. Materials and methods: Cortical and cancellous bone were modeled as homogeneous, transversely isotropic, linearly elastic. Perfect bonding was assumed at all interfaces. Implant models were classified as follows. Group 1: length 8.5mm 13mm splinting type Group 2: length 8.5mm 13mm Non-splinting type Group 3: ITI length 8.5mm 13mm splinting type Group 4: ITI length 8.5mm 13mm Non-splinting type An load of 100N was applied vertically and horizontally. Stress levels were calculated using von Mises stresses values. Results: 1. The stress distribution and maximum von Mises stress of two-length implants (8.5mm, 13mm) was similar. 2. The stress of vertical load concentrated on mesial side of implant while the stress of horizontal load was distributed on both side of implant. 3. Stress of internal connection type was spreading through abutment screw but the stress of external connection type was concentrated on cortical bone level. 4. Degree of stress reduction was higher in the external connection type than in the internal connection type

    ์ฝ”์–ด ํ˜•ํƒœ์™€ ์‹œ๋ฉ˜ํŠธ ์ข…๋ฅ˜์— ๋”ฐ๋ฅธ ์ „๋ถ€์ฃผ์กฐ๊ธˆ๊ด€์˜ ์œ ์ง€๋ ฅ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    The purpose of this study was to compare the retention of complete cast crown over amalgam ores, composite resin cores, and cast gold cores when cemented with three different luting agents. Eighteen core specimens each of amalgam(Bestaloy, Dong Myung, Seoul, Korea), composite resin (Z100, 3M Dental product, st. Paul, Minn) and type IV gold alloy (Ba-4, Heesung Engelhard Corp., Korea) were made in a customized milling stainless steel die. A wax pattern with a loop attached to occlusal surface was made for each core and a type II gold alloy casting was fabricated. The castings which had clinically acceptable marginal fit were used as test samples. The following luting cements were used to cement cast crowns on each core material : (1) zinc phosphate cement (Confi-dental Products Co., USA) (2) glass-ionomer cement (Fuji Plus, GC Industrial Corp., Tokyo, Japan) (3) resin cement (Panavia 21, Kuraray Co., USA). All cements were mixed according to manufacturers' instructions. A static load of 5kg was then applied for 10 minutes on the crowns. All specimens were stored in saline solution for 24 hours at and thermocycled for 500 cycles. After storage and cycling, the tensile bond strengths were measured by using a universal testing machine (Instron Corp., Canton, Mass.) at a crosshead speed of 0.5mm/min. The results were as follows 1. The retentive strength of resin cement was the highest of alt three types of cement for resin core (p0.05). 3. The retentive strength of resin cement was higher than that of zinc phosphate for cast core, but there was no difference between the retentive strength of glass ionomer cement and those of rein and zinc phosphate cement. 4. The retentive strength of the zinc phosphate cement for amalgam core was the highest of all type of cores
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