28 research outputs found

    ์ž„์ƒ์ˆ ๊ธฐ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฒ• ์—ฐ๊ตฌ: ๋Œ€์žฅ๋‚ด์‹œ๊ฒฝ ์ง„๋‹จ ๋ฐ ๋กœ๋ด‡์ˆ˜์ˆ  ์ˆ ๊ธฐ ํ‰๊ฐ€์— ์ ์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์˜์šฉ์ƒ์ฒด๊ณตํ•™์ „๊ณต, 2020. 8. ๊น€ํฌ์ฐฌ.This paper presents deep learning-based methods for improving performance of clinicians. Novel methods were applied to the following two clinical cases and the results were evaluated. In the first study, a deep learning-based polyp classification algorithm for improving clinical performance of endoscopist during colonoscopy diagnosis was developed. Colonoscopy is the main method for diagnosing adenomatous polyp, which can multiply into a colorectal cancer and hyperplastic polyps. The classification algorithm was developed using convolutional neural network (CNN), trained with colorectal polyp images taken by a narrow-band imaging colonoscopy. The proposed method is built around an automatic machine learning (AutoML) which searches for the optimal architecture of CNN for colorectal polyp image classification and trains the weights of the architecture. In addition, gradient-weighted class activation mapping technique was used to overlay the probabilistic basis of the prediction result on the polyp location to aid the endoscopists visually. To verify the improvement in diagnostic performance, the efficacy of endoscopists with varying proficiency levels were compared with or without the aid of the proposed polyp classification algorithm. The results confirmed that, on average, diagnostic accuracy was improved and diagnosis time was shortened in all proficiency groups significantly. In the second study, a surgical instruments tracking algorithm for robotic surgery video was developed, and a model for quantitatively evaluating the surgeons surgical skill based on the acquired motion information of the surgical instruments was proposed. The movement of surgical instruments is the main component of evaluation for surgical skill. Therefore, the focus of this study was develop an automatic surgical instruments tracking algorithm, and to overcome the limitations presented by previous methods. The instance segmentation framework was developed to solve the instrument occlusion issue, and a tracking framework composed of a tracker and a re-identification algorithm was developed to maintain the type of surgical instruments being tracked in the video. In addition, algorithms for detecting the tip position of instruments and arm-indicator were developed to acquire the movement of devices specialized for the robotic surgery video. The performance of the proposed method was evaluated by measuring the difference between the predicted tip position and the ground truth position of the instruments using root mean square error, area under the curve, and Pearsons correlation analysis. Furthermore, motion metrics were calculated from the movement of surgical instruments, and a machine learning-based robotic surgical skill evaluation model was developed based on these metrics. These models were used to evaluate clinicians, and results were similar in the developed evaluation models, the Objective Structured Assessment of Technical Skill (OSATS), and the Global Evaluative Assessment of Robotic Surgery (GEARS) evaluation methods. In this study, deep learning technology was applied to colorectal polyp images for a polyp classification, and to robotic surgery videos for surgical instruments tracking. The improvement in clinical performance with the aid of these methods were evaluated and verified.๋ณธ ๋…ผ๋ฌธ์€ ์˜๋ฃŒ์ง„์˜ ์ž„์ƒ์ˆ ๊ธฐ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ์ƒˆ๋กœ์šด ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฒ•๋“ค์„ ์ œ์•ˆํ•˜๊ณ  ๋‹ค์Œ ๋‘ ๊ฐ€์ง€ ์‹ค๋ก€์— ๋Œ€ํ•ด ์ ์šฉํ•˜์—ฌ ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋Œ€์žฅ๋‚ด์‹œ๊ฒฝ์œผ๋กœ ๊ด‘ํ•™ ์ง„๋‹จ ์‹œ, ๋‚ด์‹œ๊ฒฝ ์ „๋ฌธ์˜์˜ ์ง„๋‹จ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์šฉ์ข… ๋ถ„๋ฅ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๊ณ , ๋‚ด์‹œ๊ฒฝ ์ „๋ฌธ์˜์˜ ์ง„๋‹จ ๋Šฅ๋ ฅ ํ–ฅ์ƒ ์—ฌ๋ถ€๋ฅผ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋Œ€์žฅ๋‚ด์‹œ๊ฒฝ ๊ฒ€์‚ฌ๋กœ ์•”์ข…์œผ๋กœ ์ฆ์‹ํ•  ์ˆ˜ ์žˆ๋Š” ์„ ์ข…๊ณผ ๊ณผ์ฆ์‹์„ฑ ์šฉ์ข…์„ ์ง„๋‹จํ•˜๋Š” ๊ฒƒ์€ ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ˜‘๋Œ€์—ญ ์˜์ƒ ๋‚ด์‹œ๊ฒฝ์œผ๋กœ ์ดฌ์˜ํ•œ ๋Œ€์žฅ ์šฉ์ข… ์˜์ƒ์œผ๋กœ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง์„ ํ•™์Šตํ•˜์—ฌ ๋ถ„๋ฅ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ž๋™ ๊ธฐ๊ณ„ํ•™์Šต (AutoML) ๋ฐฉ๋ฒ•์œผ๋กœ, ๋Œ€์žฅ ์šฉ์ข… ์˜์ƒ์— ์ตœ์ ํ™”๋œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ฅผ ์ฐพ๊ณ  ์‹ ๊ฒฝ๋ง์˜ ๊ฐ€์ค‘์น˜๋ฅผ ํ•™์Šตํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ธฐ์šธ๊ธฐ-๊ฐ€์ค‘์น˜ ํด๋ž˜์Šค ํ™œ์„ฑํ™” ๋งตํ•‘ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๊ฐœ๋ฐœํ•œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๊ฒฐ๊ณผ์˜ ํ™•๋ฅ ์  ๊ทผ๊ฑฐ๋ฅผ ์šฉ์ข… ์œ„์น˜์— ์‹œ๊ฐ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋„๋ก ํ•จ์œผ๋กœ ๋‚ด์‹œ๊ฒฝ ์ „๋ฌธ์˜์˜ ์ง„๋‹จ์„ ๋•๋„๋ก ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ˆ™๋ จ๋„ ๊ทธ๋ฃน๋ณ„๋กœ ๋‚ด์‹œ๊ฒฝ ์ „๋ฌธ์˜๊ฐ€ ์šฉ์ข… ๋ถ„๋ฅ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฒฐ๊ณผ๋ฅผ ์ฐธ๊ณ ํ•˜์˜€์„ ๋•Œ ์ง„๋‹จ ๋Šฅ๋ ฅ์ด ํ–ฅ์ƒ๋˜์—ˆ๋Š”์ง€ ๋น„๊ต ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๊ณ , ๋ชจ๋“  ๊ทธ๋ฃน์—์„œ ์œ ์˜๋ฏธํ•˜๊ฒŒ ์ง„๋‹จ ์ •ํ™•๋„๊ฐ€ ํ–ฅ์ƒ๋˜๊ณ  ์ง„๋‹จ ์‹œ๊ฐ„์ด ๋‹จ์ถ•๋˜์—ˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋กœ๋ด‡์ˆ˜์ˆ  ๋™์˜์ƒ์—์„œ ์ˆ˜์ˆ ๋„๊ตฌ ์œ„์น˜ ์ถ”์  ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๊ณ , ํš๋“ํ•œ ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์›€์ง์ž„ ์ •๋ณด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ˆ˜์ˆ ์ž์˜ ์ˆ™๋ จ๋„๋ฅผ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๋Š” ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์›€์ง์ž„์€ ์ˆ˜์ˆ ์ž์˜ ๋กœ๋ด‡์ˆ˜์ˆ  ์ˆ™๋ จ๋„๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ์ฃผ์š”ํ•œ ์ •๋ณด์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์ž๋™ ์ˆ˜์ˆ ๋„๊ตฌ ์ถ”์  ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜์˜€์œผ๋ฉฐ, ๋‹ค์Œ ๋‘๊ฐ€์ง€ ์„ ํ–‰์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์ ์„ ๊ทน๋ณตํ•˜์˜€๋‹ค. ์ธ์Šคํ„ด์Šค ๋ถ„ํ•  (Instance Segmentation) ํ”„๋ ˆ์ž„์›์„ ๊ฐœ๋ฐœํ•˜์—ฌ ํ์ƒ‰ (Occlusion) ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์˜€๊ณ , ์ถ”์ ๊ธฐ (Tracker)์™€ ์žฌ์‹๋ณ„ํ™” (Re-Identification) ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ๊ตฌ์„ฑ๋œ ์ถ”์  ํ”„๋ ˆ์ž„์›์„ ๊ฐœ๋ฐœํ•˜์—ฌ ๋™์˜์ƒ์—์„œ ์ถ”์ ํ•˜๋Š” ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์ข…๋ฅ˜๊ฐ€ ์œ ์ง€๋˜๋„๋ก ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋กœ๋ด‡์ˆ˜์ˆ  ๋™์˜์ƒ์˜ ํŠน์ˆ˜์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์›€์ง์ž„์„ ํš๋“ํ•˜๊ธฐ์œ„ํ•ด ์ˆ˜์ˆ ๋„๊ตฌ ๋ ์œ„์น˜์™€ ๋กœ๋ด‡ ํŒ”-์ธ๋””์ผ€์ดํ„ฐ (Arm-Indicator) ์ธ์‹ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์€ ์˜ˆ์ธกํ•œ ์ˆ˜์ˆ ๋„๊ตฌ ๋ ์œ„์น˜์™€ ์ •๋‹ต ์œ„์น˜ ๊ฐ„์˜ ํ‰๊ท  ์ œ๊ณฑ๊ทผ ์˜ค์ฐจ, ๊ณก์„  ์•„๋ž˜ ๋ฉด์ , ํ”ผ์–ด์Šจ ์ƒ๊ด€๋ถ„์„์œผ๋กœ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์›€์ง์ž„์œผ๋กœ๋ถ€ํ„ฐ ์›€์ง์ž„ ์ง€ํ‘œ๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ  ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ๋กœ๋ด‡์ˆ˜์ˆ  ์ˆ™๋ จ๋„ ํ‰๊ฐ€ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœํ•œ ํ‰๊ฐ€ ๋ชจ๋ธ์€ ๊ธฐ์กด์˜ Objective Structured Assessment of Technical Skill (OSATS), Global Evaluative Assessment of Robotic Surgery (GEARS) ํ‰๊ฐ€ ๋ฐฉ๋ฒ•๊ณผ ์œ ์‚ฌํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์˜๋ฃŒ์ง„์˜ ์ž„์ƒ์ˆ ๊ธฐ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ๋Œ€์žฅ ์šฉ์ข… ์˜์ƒ๊ณผ ๋กœ๋ด‡์ˆ˜์ˆ  ๋™์˜์ƒ์— ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ์ˆ ์„ ์ ์šฉํ•˜๊ณ  ๊ทธ ์œ ํšจ์„ฑ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ํ–ฅํ›„์— ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ์ž„์ƒ์—์„œ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ์ง„๋‹จ ๋ฐ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•์˜ ๋Œ€์•ˆ์ด ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.Chapter 1 General Introduction 1 1.1 Deep Learning for Medical Image Analysis 1 1.2 Deep Learning for Colonoscipic Diagnosis 2 1.3 Deep Learning for Robotic Surgical Skill Assessment 3 1.4 Thesis Objectives 5 Chapter 2 Optical Diagnosis of Colorectal Polyps using Deep Learning with Visual Explanations 7 2.1 Introduction 7 2.1.1 Background 7 2.1.2 Needs 8 2.1.3 Related Work 9 2.2 Methods 11 2.2.1 Study Design 11 2.2.2 Dataset 14 2.2.3 Preprocessing 17 2.2.4 Convolutional Neural Networks (CNN) 21 2.2.4.1 Standard CNN 21 2.2.4.2 Search for CNN Architecture 22 2.2.4.3 Searched CNN Training 23 2.2.4.4 Visual Explanation 24 2.2.5 Evaluation of CNN and Endoscopist Performances 25 2.3 Experiments and Results 27 2.3.1 CNN Performance 27 2.3.2 Results of Visual Explanation 31 2.3.3 Endoscopist with CNN Performance 33 2.4 Discussion 45 2.4.1 Research Significance 45 2.4.2 Limitations 47 2.5 Conclusion 49 Chapter 3 Surgical Skill Assessment during Robotic Surgery by Deep Learning-based Surgical Instrument Tracking 50 3.1 Introduction 50 3.1.1 Background 50 3.1.2 Needs 51 3.1.3 Related Work 52 3.2 Methods 56 3.2.1 Study Design 56 3.2.2 Dataset 59 3.2.3 Instance Segmentation Framework 63 3.2.4 Tracking Framework 66 3.2.4.1 Tracker 66 3.2.4.2 Re-identification 68 3.2.5 Surgical Instrument Tip Detection 69 3.2.6 Arm-Indicator Recognition 71 3.2.7 Surgical Skill Prediction Model 71 3.3 Experiments and Results 78 3.3.1 Performance of Instance Segmentation Framework 78 3.3.2 Performance of Tracking Framework 82 3.3.3 Evaluation of Surgical Instruments Trajectory 83 3.3.4 Evaluation of Surgical Skill Prediction Model 86 3.4 Discussion 90 3.4.1 Research Significance 90 3.4.2 Limitations 92 3.5 Conclusion 96 Chapter 4 Summary and Future Works 97 4.1 Thesis Summary 97 4.2 Limitations and Future Works 98 Bibliography 100 Abstract in Korean 116 Acknowledgement 119Docto

    ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก์˜ ํ’๋ฏธ ๊ตฌ๋ช… ๋ฐ ์กฐ๋ฆฌ์กฐ๊ฑด์— ์˜ํ•œ ํ’๋ฏธ์ฆ์ง„ ํšจ๊ณผ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์ƒ๋ช…๊ณตํ•™๋ถ€, 2021. 2. ์กฐ์ฒ ํ›ˆ.Dry-aged beef is well known for its unique flavor compared to unaged or wet-aged beef. However, in spite of the importance, little research has been conducted to identify the origin of the characteristic flavor of dry-aged beef. In order to understand and to even enhance it, the analysis on the formation of aroma volatile compounds in dry-aged beef under various conditions is necessary. Therefore, the objectives of this study were โ…ฐ) to investigate the change of volatile compounds in dry-aged beef during aging process, and โ…ฑ) to suggest the optimal cooking conditions for intensifying the flavor of dry-aged beef. For this, two consecutive experiments were conducted as follows: Experiment โ… . Effect of different aging methods on the formation of aroma volatiles in beef strip loins The effects of different aging methods on the changes in the concentrations of aroma volatiles of beef were investigated. Each of fifteen strip loins were dry- and wet-aged for 28 days and their aroma volatiles were analyzed at a 7-day interval (n = 3 for each aging period). As the aging period increased, dry-aged beef showed higher concentrations of volatile compounds than those in wet-aged beef (p < 0.05). Most changes in the concentrations of aroma volatiles of dry-aged beef were associated with propanal, 2-methylbutanal, 2-methylpropanal, 1-butanamine, trimethylamine, 2-methyl-2-propanethiol, and ethyl propanoate, which were mainly produced by lipid oxidation and/or microbial activity (e.g., proteolysis and lipolysis) during the dry aging period. These compounds represent malty, nutty, fruity, ammoniacal and fermented flavors. Therefore, it is suggested that the differences in aroma between dry- and wet-aged beef could result from increased lipid oxidation and microbial activity in dry-aged beef possibly owing to its ambient exposure to oxygen. Experiment โ…ก. Effects of cooking conditions on the physicochemical and sensory characteristics of dry- and wet-aged beef The effects of cooking conditions on the physicochemical and sensory characteristics of dry- and wet-aged beef strip loins were evaluated. Dry- and wet-aged beef aged for 28 days were cooked at different combinations of cooking method (grilling or oven roasting) ร— cooking temperatures (150ยฐC or 230ยฐC), and their cooking time, pH, cooking loss, 2-thiobarbituric acid reactive substances (TBARS), volatile compounds, and color were measured. Cooking conditions did not affect pH, however, grilling resulted in lower TBARS but higher cooking doneness at the surface of dry-aged beef compared to oven roasting (p < 0.05). In descriptive sensory analysis, the roasted flavor of dry-aged beef was significantly stronger when grill-cooked compared to oven roasting. Dry-aged beef grill-cooked at 150ยฐC presented a higher intensity of cheesy flavor, and that grilled at 230ยฐC showed a greater intensity of roasted flavor compared to wet-aged beef at the same condition, respectively. Therefore, we suggest that grilling may be effective for enhancing the unique flavor in dry-aged beef.๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก์€ ์ˆ™์„ฑ๋˜์ง€ ์•Š์€ ์šฐ์œก ๋ฐ ์Šต์‹์ˆ™์„ฑ ์šฐ์œก๊ณผ ๋šœ๋ ทํ•˜๊ฒŒ ๊ตฌ๋ณ„๋˜๋Š” ๋…ํŠนํ•œ ํ’๋ฏธ๋ฅผ ๊ฐ€์ง€๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก์˜ ํ’๋ฏธ๊ฐ€ ๊ฐ€์ง€๋Š” ์ค‘์š”์„ฑ์— ๋น„ํ•ด ํ’๋ฏธ๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ์ค‘์š”ํ•œ ์š”์†Œ์ธ ํœ˜๋ฐœ์„ฑ ํ–ฅ ํ™”ํ•ฉ๋ฌผ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ฏธํกํ•œ ์‹ค์ •์ด๋‹ค. ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก ํŠน์œ ์˜ ํ’๋ฏธ๊ฐ€ ํ˜•์„ฑ๋˜๋Š” ๊ธฐ์ž‘์„ ์ดํ•ดํ•˜๊ณ  ํ’๋ฏธ๋ฅผ ์ฆ์ง„์‹œ์ผœ ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก์˜ ๊ด€๋Šฅ์  ํ’ˆ์งˆ ๋ฐ ๊ฒฝ์Ÿ๋ ฅ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ˆ™์„ฑ์กฐ๊ฑด, ์กฐ๋ฆฌ์กฐ๊ฑด ๋“ฑ ํ’๋ฏธ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์กฐ๊ฑด์—์„œ์˜ ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก ๋‚ด ํœ˜๋ฐœ์„ฑ ํ–ฅ ํ™”ํ•ฉ๋ฌผ์˜ ํ˜•์„ฑ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” โ…ฐ) ๊ฑด์‹์ˆ™์„ฑ ๊ณผ์ •์—์„œ ์ˆ™์„ฑ๊ธฐ๊ฐ„์— ๋”ฐ๋ฅธ ํœ˜๋ฐœ์„ฑ ํ–ฅ ํ™”ํ•ฉ๋ฌผ์˜ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•˜๊ณ  โ…ฑ) ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก์˜ ํ’๋ฏธ๋ฅผ ๋‹๋ณด์ผ ์ˆ˜ ์žˆ๋Š” ์ตœ์ ์˜ ์กฐ๋ฆฌ์กฐ๊ฑด์„ ์ฐพ๊ณ ์ž ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ์‹คํ—˜ โ… ์—์„œ๋Š” ์ˆ™์„ฑ๋ฐฉ๋ฒ•์— ๋”ฐ๋ฅธ ์šฐ์œก ๋‚ด ํœ˜๋ฐœ์„ฑ ํ–ฅ ํ™”ํ•ฉ๋ฌผ์˜ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์šฐ์œก ์ฑ„๋์„ ๊ฐ๊ฐ ๊ฑด์‹ ๋˜๋Š” ์Šต์‹์ˆ™์„ฑํ•˜์—ฌ 28์ผ ๊ธฐ๊ฐ„ ๋™์•ˆ 7์ผ ๊ฐ„๊ฒฉ์œผ๋กœ ํœ˜๋ฐœ์„ฑ ํ–ฅ ํ™”ํ•ฉ๋ฌผ์„ ๋ถ„์„๋น„๊ตํ•˜์˜€๋‹ค. ์ˆ™์„ฑ๊ธฐ๊ฐ„์ด ๊ธธ์–ด์งˆ์ˆ˜๋ก ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก ๋‚ด ํœ˜๋ฐœ์„ฑ ํ–ฅ ํ™”ํ•ฉ๋ฌผ ๋†๋„๊ฐ€ ์Šต์‹์ˆ™์„ฑ ์šฐ์œก์— ๋น„ํ•ด ๋†’์€ ๊ฐ’์„ ๊ธฐ๋กํ•˜์˜€๋‹ค(p < 0.05). ๊ฑด์‹์ˆ™์„ฑ ๊ณผ์ • ์ค‘ ๋ฐœ์ƒํ•œ ํœ˜๋ฐœ์„ฑ ํ–ฅ ํ™”ํ•ฉ๋ฌผ์˜ ๋ณ€ํ™”๋Š” ์ง€๋ฐฉ ์‚ฐํ™” ๋˜๋Š” ๋ฏธ์ƒ๋ฌผ ํšจ์†Œ ์ž‘์šฉ์— ์˜ํ•ด ์ƒ์„ฑ๋  ์ˆ˜ ์žˆ๋Š” propanal, 2-methylbutanal, 2-methylpropanal, 1-butanamine, trimethylamine, 2-methyl-2-propanethiol, ethyl propanoate์—์„œ ์ง‘์ค‘์ ์œผ๋กœ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์ด๋“ค ํ™”ํ•ฉ๋ฌผ์€ malty, nutty, fruity, ammoniacal, fermentedํ•œ ํ–ฅ์„ ๋‚ผ ์ˆ˜ ์žˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•ด๋ณผ ๋•Œ ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก์˜ ํœ˜๋ฐœ์„ฑ ํ–ฅ ํ™”ํ•ฉ๋ฌผ์ด ์Šต์‹์ˆ™์„ฑ ์šฐ์œก๊ณผ ์ฐจ์ด๋ฅผ ๋ณด์ด๋Š” ์ด์œ ๋Š” ์™ธ๋ถ€์—์˜ ๋…ธ์ถœ์— ๋”ฐ๋ฅธ ์ง€๋ฐฉ ์‚ฐํ™” ๋ฐ ๋ฏธ์ƒ๋ฌผ ํ™œ์„ฑ์˜ ์ฆ๊ฐ€์— ์˜ํ•œ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค. ์‹คํ—˜ โ…ก์—์„œ๋Š” ์กฐ๋ฆฌ์กฐ๊ฑด์ด ๊ฑด์‹ ๋ฐ ์Šต์‹์ˆ™์„ฑ ์šฐ์œก ์ฑ„๋์˜ ๋ฌผ๋ฆฌํ™”ํ•™์  ํ’ˆ์งˆ๊ณผ ๊ด€๋Šฅ์  ํ’ˆ์งˆ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ฐ๊ฐ 28์ผ๊ฐ„ ์ˆ™์„ฑ์‹œํ‚จ ๊ฑด์‹ ๋ฐ ์Šต์‹์ˆ™์„ฑ ์šฐ์œก์„ 4์ข…๋ฅ˜์˜ ์กฐ๋ฆฌ์กฐ๊ฑด[2์ข…๋ฅ˜์˜ ์กฐ๋ฆฌ๋ฐฉ๋ฒ•(๊ทธ๋ฆด ์กฐ๋ฆฌ, ์˜ค๋ธ ์กฐ๋ฆฌ) ร— 2์ข…๋ฅ˜์˜ ์กฐ๋ฆฌ์˜จ๋„(150ยฐC, 230ยฐC)]์œผ๋กœ ์กฐ๋ฆฌ์‹œ๊ฐ„, ์กฐ๋ฆฌ ํ›„ pH, ์กฐ๋ฆฌ๊ฐ๋Ÿ‰, ์ง€์งˆ์‚ฐํŒจ๋„, ํœ˜๋ฐœ์„ฑ ํ–ฅ ํ™”ํ•ฉ๋ฌผ, ์œก์ƒ‰์„ ์ธก์ •ํ•˜์˜€๋‹ค. ์กฐ๋ฆฌ์กฐ๊ฑด์€ pH์— ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š์•˜์œผ๋‚˜, ๊ทธ๋ฆด ์กฐ๋ฆฌ ์‹œ ์˜ค๋ธ ์กฐ๋ฆฌ์— ๋น„ํ•˜์—ฌ ๋‚ฎ์€ ์ง€์งˆ ์‚ฐํŒจ๋„์™€ ๋†’์€ ์ˆ˜์ค€์˜ ๊ฒ‰๋ฉด ์ตํž˜ ์ •๋„๋ฅผ ๋ณด์˜€๋‹ค(p < 0.05). ๋ฌ˜์‚ฌ๋ถ„์„ ๊ฒฐ๊ณผ ๊ทธ๋ฆด์กฐ๋ฆฌ์œก์ด ์˜ค๋ธ์กฐ๋ฆฌ์œก์— ๋น„ํ•ด roasted flavor๊ฐ€ ๊ฐ•ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก์„ 150ยฐC์—์„œ ๊ทธ๋ฆด ์กฐ๋ฆฌํ•˜์˜€์„ ๊ฒฝ์šฐ ๊ฐ™์€ ์กฐ๊ฑด์—์„œ ์กฐ๋ฆฌํ•œ ์Šต์‹์ˆ™์„ฑ ์šฐ์œก์— ๋น„ํ•ด cheesy flavor๊ฐ€ ๋” ๊ฐ•ํ–ˆ์œผ๋ฉฐ, ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก์„ 230ยฐC์—์„œ ๊ทธ๋ฆด ์กฐ๋ฆฌํ•˜์˜€์„ ๊ฒฝ์šฐ roasted flavor ๊ฐ•๋„๊ฐ€ ๋” ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก์„ ๊ทธ๋ฆด ์กฐ๋ฆฌํ•˜๋Š” ๊ฒƒ์ด ๊ฑด์‹์ˆ™์„ฑ ์šฐ์œก ํŠน์œ ์˜ ํ’๋ฏธ๋ฅผ ์ฆ์ง„์‹œํ‚ค๋Š” ๋ฐ ํšจ๊ณผ์ ์ผ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค.Abstract i Contents iv List of Tables vii List of Figures x List of Abbreviations xi Chapter I. General Introduction 1 Chapter II. Effect of different aging methods on the formation of aroma volatiles in beef strip loins 2.1. Introduction 3 2.2. Materials and methods 6 2.2.1. Raw material and aging process 6 2.2.2. Volatile compounds analysis 6 2.2.3. Mold distribution 9 2.2.4. Free fatty acids 9 2.2.5. Statistical analysis 10 2.3. Results and discussion 11 2.3.1. Volatile profiling of aged beef 11 2.3.1.1. Aldehydes, furan and ketone 16 2.3.1.2. N-compounds 22 2.3.1.3. S-compounds 26 2.3.1.4. Alcohols 29 2.3.1.5. Hydrocarbons, esters, and acids 32 2.3.2. Volatile patterns of aged beef 40 2.3.3. Correlation analysis with mold distribution and UFA 45 2.4. Conclusion 53 Chapter III. Effects of cooking conditions on the physicochemical and sensory characteristics of dry- and wet-aged beef 3.1. Introduction 54 3.2. Materials and methods 57 3.2.1. Sample preparation 57 3.2.1.1. Raw material and aging process 57 3.2.1.2. Cooking process 57 3.2.2. pH 58 3.2.3. Cooking loss 59 3.2.4. Lipid oxidation 59 3.2.5. Volatile compound analysis 60 3.2.6. Meat color 61 3.2.7. Descriptive sensory analysis 61 3.2.8. Statistical analysis 64 3.3. Results and discussion 65 3.3.1. Cooking time 65 3.3.2. pH 68 3.3.3. Cooking loss 71 3.3.4. Lipid oxidation 74 3.3.5. Volatile compound analysis 78 3.3.6. Meat color 86 3.3.7. Descriptive sensory analysis 91 3.4. Conclusion 98 References 99 Summary in Korean 113Maste

    Investigation of medium-range order in amorphous GeTe and Ge2Sb2Te5 using density functional theory and neural network potential

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2020. 8. ํ•œ์Šน์šฐ.Phase change memory (PCM) is a promising non-volatile memory. Among emerging memories, PCM has been successfully commercialized and mature technology. However, there is still a lack of understanding of the phase transition process at the atomic scale. Since molecular dynamics simulation can provide insight into crystallization kinetics of phase change materials, we perform the crystallization simulations and show that the medium-range orders in amorphous phase change materials are critical in crystallization kinetics. We develop neural network potentials (NNP) for GeTe as a representative phase change material and investigate the crystallization process of amorphous GeTe. With the accuracy of density functional theory (DFT) level and much cheaper computational cost, we achieve the realistic simulations using the NNP. In developing the NNP, we find that overly flattened fourfold rings in the amorphous structure exaggerate the crystallization process, especially for nucleation. By explicitly including relaxation paths from flat to puckered fourfold rings, we obtain a modified NNP, which produces medium-range orders that are more consistent with DFT. This structural change increases interfacial energy between crystalline and amorphous phases and suppresses the nucleation. Using the modified NNP, we perform crystallization simulations at two densities (equilibrium density and crystalline density) and temperatures ranging from 500 to 650 K. We observe finite incubation times at both densities. In particular, the incubation time at the equilibrium density is found to be 7 or 17 ns, which is consistent with experiments. In practice, properties of the phase change materials are tuned by doping and Ge-Sb-Te alloys are mainly used. However, developing NNP for the multi-component systems is challenging at the present, we study the effects of Al and Ga dopants using ab initio calculations. We find that the two dopants behave similarly in amorphous Ge2Sb2Te5 (GST), and they are mostly coordinated by Te atoms in a tetrahedral geometry, which is similar to those in crystalline MxTey (M=Al or Ga). The number of wrong bonds increases as dopant atoms predominantly bond with Te atoms, which affects the medium-range order structures. The number of fourfold ring structures, especially ABAB-type, decreases significantly and the number of odd-numbered rings is increased, explaining the enhanced thermal stability and slow crystallization speed of doped amorphous GST in the experiment.์ƒ๋ณ€ํ™” ๋ฉ”๋ชจ๋ฆฌ๋Š” ์ฐจ์„ธ๋Œ€ ๋น„ํœ˜๋ฐœ์„ฑ ๋ฉ”๋ชจ๋ฆฌ ๊ธฐ์ˆ ๋กœ ์œ ๋งํ•œ ๊ธฐ์ˆ ์ด๋‹ค. ์ƒˆ๋กœ์šด ๋ฉ”๋ชจ๋ฆฌ ๊ธฐ์ˆ ๋“ค ์ค‘์—์„œ ์ƒ๋ณ€ํ™” ๋ฉ”๋ชจ๋ฆฌ๋Š” ์ด๋ฏธ ์„ฑ๊ณต์ ์œผ๋กœ ์ƒ์šฉํ™”๋  ์ •๋„๋กœ ์„ฑ์ˆ™ํ•œ ๊ธฐ์ˆ ์ด๋‹ค. ํ•˜์ง€๋งŒ, ์—ฌ์ „ํžˆ ์›์ž์ˆ˜์ค€์˜ ์ƒ์ „์ด ๊ฑฐ๋™์— ๋Œ€ํ•œ ๊ธฐ์ดˆ์ ์ธ ์ดํ•ด๊ฐ€ ๋ถ€์กฑํ•˜๋‹ค. ๋ถ„์ž๋™์—ญํ•™์„ ์ด์šฉํ•˜์—ฌ ์ƒ๋ณ€ํ™” ๋ฌผ์งˆ์˜ ์ƒ์ „์ด ๊ณผ์ •์„ ํšจ๊ณผ์ ์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์—, ๊ฒฐ์ •ํ™” ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ง„ํ–‰ํ•˜์˜€๊ณ  ๋น„์ •์งˆ์˜ ์ค‘๊ฑฐ๋ฆฌ ์ฐจ์ˆ˜ ๊ตฌ์กฐ๊ฐ€ ๊ฒฐ์ •ํ™” ๊ณผ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•ด ์‚ดํŽด๋ณด์•˜๋‹ค. ๋จผ์ €, ๋Œ€ํ‘œ์ ์ธ ์ƒ๋ณ€ํ™” ๋ฌผ์งˆ์ธ GeTe์— ๋Œ€ํ•˜์—ฌ ์ธ๊ณต์‹ ๊ฒฝ๋ง ํผํ…์…œ์„ ๊ฐœ๋ฐœํ•˜์˜€๊ณ , ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ๋น„์ •์งˆ GeTe์˜ ๊ฒฐ์ •ํ™”์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋ฐ€๋„ ๋ฒ”ํ•จ์ˆ˜ ์ด๋ก ์˜ ์ •ํ™•๋„์™€ ๊ฐ’์‹ผ ๊ณ„์‚ฐ ๋น„์šฉ์œผ๋กœ ์†Œ์ž์—์„œ ์ผ์–ด๋‚˜๋Š” ์ƒ์ „์ด ์กฐ๊ฑด์— ๊ฐ€๊น๊ฒŒ ๋ชจ๋ธ๋งํ•  ์ˆ˜๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ด ํผํ…์…œ์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ณผ์ •์—์„œ ๋น„์ •์งˆ ๊ตฌ์กฐ์˜ ์‚ฌ๊ฐํ˜• ๋ง ๊ตฌ์กฐ์˜ ์ž…์ฒด์  ํ˜•ํƒœ๊ฐ€ ๊ฒฐ์ •ํ™”์˜ ํ•ต์ƒ์„ฑ ๊ณผ์ •์— ์ค‘๋Œ€ํ•œ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด์ฐจ์›์— ๊ฐ€๊นŒ์šด ์‚ฌ๊ฐํ˜• ๋ง์ด ๋น„์ •์งˆ ์ƒ์—์„œ ๋งŽ์ด ์กด์žฌํ• ์ˆ˜๋ก ํ•ต์ƒ์„ฑ ๊ณผ์ •์ด ์ž˜ ์ผ์–ด๋‚˜๋Š” ๊ฒƒ์„ ๋ณด์•˜๋‹ค. ๊ตฌ๋ถ€๋ ค์ง„ ์‚ฌ๊ฐํ˜• ๋ง ๊ตฌ์กฐ๋ฅผ ํฌํ•จ์™€ ๊ฐ™์€ ์ž…์ฒด์ ์ธ ๋ง ๊ตฌ์กฐ๋“ค์„ ํฌํ•จํ•œ ๋น„์ •์งˆ ๊ตฌ์กฐ๋ฅผ ํ•™์Šต ๋ฐ์ดํ„ฐ์— ์ถ”๊ฐ€ํ•จ์œผ๋กœ์จ ๋ฐ€๋„ ๋ฒ”ํ•จ์ˆ˜ ์ด๋ก ์˜ ๊ฒฐ๊ณผ์— ๊ฐ€๊นŒ์šด ๋น„์ •์งˆ ๊ตฌ์กฐ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ตฌ์กฐ์ ์ธ ๊ฐœ์„ ์ด ๋น„์ •์งˆ๊ณผ ๊ฒฐ์ •์งˆ์˜ ๊ณ„๋ฉด ์—๋„ˆ์ง€๋ฅผ ๋†’์—ฌ์ฃผ์—ˆ๋‹ค. ๊ฐœ์„ ๋œ ์ธ๊ณต์‹ ๊ฒฝ๋ง ํผํ…์…œ์„ ์ด์šฉํ•˜์—ฌ ๊ฒฐ์ •ํ™” ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ๋‘ ๊ฐ€์ง€ ๋ฐ€๋„์™€ ๋„ค ๊ฐ€์ง€ ์˜จ๋„ ์กฐ๊ฑด์—์„œ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋ชจ๋“  ๊ฒฐ์ •ํ™” ๊ณผ์ •์—์„œ ํ•ต์ƒ์„ฑ์— ๊ฑธ๋ฆฌ๋Š” ์œ ํšจํ•œ ์‹œ๊ฐ„์ด ์กด์žฌํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๊ณ , ํŠนํžˆ ์†Œ์ž ๋‚ด๋ถ€์˜ ํ‰ํ˜• ๋ฐ€๋„๋ฅผ ๊ณ ๋ คํ•œ ๊ฒฝ์šฐ ์‹คํ—˜์—์„œ ๋ณด๊ณ ๋˜๋Š” 30 ns์— ์œ ์‚ฌํ•œ ์ •๋„๋กœ 7๊ณผ 17 ns์˜ ์‹œ๊ฐ„์ด ํ•ต์ƒ์„ฑ์— ์†Œ์š”๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์‹ค์งˆ์ ์œผ๋กœ ์†Œ์ž์—์„œ๋Š” Ge-Sb-Te ํ™”ํ•ฉ๋ฌผ์— ๋„ํ•‘์„ ํ•˜์—ฌ ๋ฌผ์„ฑ์„ ์กฐ์ ˆํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฐ ๋‹ค์„ฑ๋ถ„๊ณ„์˜ ๊ฒฝ์šฐ๊นŒ์ง€ ์ธ๊ณต์‹ ๊ฒฝ๋ง ํผํ…์…œ์„ ๊ฐœ๋ฐœํ•˜๋Š” ๋ฐ๋Š” ํ˜„์žฌ ์ˆ˜์ค€์œผ๋กœ๋Š” ๊ณผ๋„ํ•œ ๊ณ„์‚ฐ ๋น„์šฉ์„ ์†Œ๋ชจํ•˜๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ, Al๊ณผ Ga ๋„ํ•‘๋œ GST์— ๋Œ€ํ•ด์„œ๋Š” ์ œ์ผ์›๋ฆฌ ๊ณ„์‚ฐ์„ ํ†ตํ•ด์„œ ๋„ํ•‘ ํšจ๊ณผ์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. Al๊ณผ Ga์€ ๋น„์ •์งˆ GST์—์„œ ๋„ค ๊ฐœ์˜ Te์— ์‚ฌ๋ฉด์ฒด ํ˜•ํƒœ๋กœ ๋‘˜๋Ÿฌ ์Œ“์ธ ๊ตฌ์กฐ๋ฅผ ๋งŽ์ด ๊ฐ–๊ฒŒ ๋˜๋ฉฐ ์–‘์ด์˜จ์  ์„ฑ์งˆ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์ด๋Š” GST์—์„œ Ge-Ge, Ge-Sb, Sb-Sb์™€ ๊ฐ™์€ ๋™์ข…๊ฒฐํ•ฉ ์ˆ˜๋ฅผ ์ฆ๊ฐ€์‹œ์ผฐ๊ณ , ํ™€์ˆ˜ ๊ฐœ์˜ ๋ง ๊ตฌ์กฐ๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ฒฐ์ •ํ™”๊ฐ€ ๋˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ง์ˆ˜ ๊ฐœ์˜ ๋ง ๊ตฌ์กฐ๋กœ ๋ณ€ํ™”ํ•ด์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ด๋Ÿฌํ•œ ํ™€์ˆ˜ ๋ง์˜ ์ฆ๊ฐ€๋Š” ๊ฒฐ์ •ํ™”๋ฅผ ์–ด๋ ต๊ฒŒ ๋งŒ๋“ค๊ณ  ๋น„์ •์งˆ ์ƒ์˜ ์•ˆ์ •์„ฑ์„ ๋†’์ด๋Š” ํšจ๊ณผ๋ฅผ ์ฃผ๋Š” ๊ฒƒ์„ ์˜ˆ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค.1 Introduction 1 1.1 Phase change memory 1 1.2 Goal of the dissertation 7 1.3 Organization of the dissertation 8 2 Theoretical background 9 2.1 Molecular dynamics 9 2.1.1 Classical molecular dynamics 9 2.1.2 Ab initio molecular dynamics (AIMD) 12 2.2 Neural network potential 16 2.2.1 Neural network model 16 2.2.2 Atom-centered symmetry function 20 2.2.3 Training method 24 2.3 Classical nucleation theory 32 3 Crystallization of amorphous GeTe 36 3.1 Introduction 36 3.2 Computational details 40 3.2.1 Training set 40 3.2.2 Training method 45 3.3 Validation 48 3.3.1 Bulk properties of crystalline phases 48 3.3.2 Bulk properties of liquid phase 50 3.3.3 Bulk properties of amorphous phase 52 3.4 Crystallization simulation 58 3.4.1 Equilibrium volume condition 60 3.4.2 Crystalline volume condition 62 3.5 Summary 65 4 Al- and Ga-doped Ge2Sb2Te5 (GST) 66 4.1 Introduction 66 4.2 Computational details 69 4.3 Structural properties 71 4.3.1 Local structures of Al- and Ga-doped amorphous GST 71 4.3.2 Ring statistics 77 4.3.3 Dopants in the crystalline phase 81 4.4 Dynamical properties 82 4.4.1 Diffusivity 82 4.4.2 Interface-growth simulation . 84 4.5 Summary 86 5 Conclusion 87 Bibliography 89 Abstract (In Korean) 98Docto

    ๋ฌด์„ ํ†ต์‹ ๋ง์—์„œ ์ฒ˜๋ฆฌ์œจ ๊ฐœ์„ ์„ ์œ„ํ•œ ์‹ ํ˜ธ์ „๋‹ฌ ๋ถ€ํ•˜์˜ ์ €๊ฐ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2014. 2. ์ „ํ™”์ˆ™.๋ฌด์„ ํ†ต์‹ ๋ง(wireless networks)์€ ๋ฌด์„  ์ฑ„๋„์˜ ์ƒํƒœ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด ๋งํฌ ์ ์‘(link adaptation) ๊ธฐ์ˆ ์„ ๊ธฐ๋ณธ์ ์œผ๋กœ ์‚ฌ์šฉํ•œ๋‹ค. ๋งํฌ ์ ์‘ ๊ธฐ์ˆ ์„ ์œ„ํ•ด์„œ๋Š” ์ฑ„๋„ ์ƒํƒœ ์ •๋ณด๋ฅผ ์ถ”์ •ํ•˜๊ณ  ์ˆ˜์ง‘ํ•ด์•ผํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ด์— ๋”ฐ๋ฅธ ์‹ ํ˜ธ์ „๋‹ฌ ๋ถ€ํ•˜(signaling overhead)๊ฐ€ ๋ฐœ์ƒํ•˜๊ฒŒ ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฌด์„ ํ†ต์‹ ๋ง์—์„œ์˜ ์‹ ํ˜ธ์ „๋‹ฌ ๋ถ€ํ•˜๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•œ ๋‘ ๊ฐ€์ง€ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ € ํ˜‘๋ ฅ ํ†ต์‹  ๋„คํŠธ์›Œํฌ(cooperative communication networks)์—์„œ์˜ ์ ์‘์ ์ธ ์ „์†ก ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋Š” ํ˜‘๋ ฅ ํ†ต์‹  ๋„คํŠธ์›Œํฌ๋Š” ACK(positive acknowledgement)/NACK(negative ACK)์™€ ๊ฐ™์€ ์ œํ•œ๋œ ํ”ผ๋“œ๋ฐฑ ์ •๋ณด๋กœ๋ถ€ํ„ฐ ์ถ”์ •๋œ ์ฑ„๋„ ์ƒํƒœ์— ๊ธฐ๋ฐ˜์„ ๋‘์–ด ์ „์†ก ์†๋„๋ฅผ ์กฐ์ ˆํ•˜๋ฉด์„œ ๋ฆด๋ ˆ์ด(relay)์˜ ์‚ฌ์šฉ์—ฌ๋ถ€๋„ ํ•จ๊ป˜ ๊ฒฐ์ •ํ•œ๋‹ค. ์ œํ•œ๋œ ํ”ผ๋“œ๋ฐฑ ์ •๋ณด๋Š” ์‹ค์ œ ์ฑ„๋„ ์ƒํƒœ์— ๋Œ€ํ•œ ๋ถ€๋ถ„์ ์ธ ์ •๋ณด๋งŒ์„ ์ œ๊ณตํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•์„ ๋ถˆํ™•์‹ค์„ฑ ๋งˆ์ฝ”๋ธŒ ์˜์‚ฌ ๊ฒฐ์ •(partially observable Markov decision process)์— ๋”ฐ๋ผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์…€๋ฃฐ๋Ÿฌ ๋„คํŠธ์›Œํฌ์—์„œ์˜ ๊ธฐ๊ธฐ ๊ฐ„(D2D, device-to-device) ํ†ต์‹ ์„ ์œ„ํ•œ ์ž์› ๊ด€๋ฆฌ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์€ ๋‘ ๋‹จ๊ณ„๋กœ ๊ตฌ์„ฑ๋˜๊ณ  ์ค€ ๋ถ„์‚ฐ์ (semi-distributed)์œผ๋กœ ๋™์ž‘ํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋‹จ๊ณ„์—์„œ๋Š” ์ค‘์•™ ์ง‘์ค‘์ (centralized)์œผ๋กœ ๊ธฐ์ง€๊ตญ์ด ์ž์› ๋ธ”๋ก์„ B2D(BS-to-user device) ๋งํฌ์™€ D2D ๋งํฌ์—๊ฒŒ ํ• ๋‹นํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋‹จ๊ณ„์—์„œ๋Š” ๋ถ„์‚ฐ์ (distributed)์œผ๋กœ ๊ธฐ์ง€๊ตญ์€ B2D ๋งํฌ์— ํ• ๋‹น๋œ ์ž์› ๋ธ”๋ก๋“ค์„ ์‚ฌ์šฉํ•˜์—ฌ ์ „์†ก ์Šค์ผ€์ค„์„ ๊ฒฐ์ •(scheduling)ํ•˜๊ณ , ๊ฐ D2D ๋งํฌ์˜ ์ œ 1 ์‚ฌ์šฉ์ž ๊ธฐ๊ธฐ(primary user device)๋Š” ํ•ด๋‹น D2D ๋งํฌ์— ํ• ๋‹น๋œ ์ž์› ๋ธ”๋ก๋“ค์—์„œ์˜ ๋งํฌ ์ ์‘์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ž์› ๊ด€๋ฆฌ ๊ตฌ์กฐ๋Š” ์ค‘์•™ ์ง‘์ค‘์  ๊ธฐ๋ฒ•์ฒ˜๋Ÿผ ๋†’์€ ๋„คํŠธ์›Œํฌ ์šฉ๋Ÿ‰์„ ๋‹ฌ์„ฑํ•  ๋ฟ ์•„๋‹ˆ๋ผ ๋ถ„์‚ฐ์  ๊ธฐ๋ฒ•์ฒ˜๋Ÿผ ๋‚ฎ์€ ์‹ ํ˜ธ์ „๋‹ฌ ๋ฐ ๊ณ„์‚ฐ(computational) ๋ถ€ํ•˜๋ฅผ ํ•„์š”๋กœ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ œ์•ˆํ•œ ์ž์› ๊ด€๋ฆฌ ๊ตฌ์กฐ์—์„œ ์ฃผํŒŒ์ˆ˜ ์ž์› ํšจ์œจ์„ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ์ž์› ๋ธ”๋ก ํ• ๋‹น ๋ฌธ์ œ๋“ค์„ ๋‘ ๊ฐ€์ง€ ์„œ๋กœ ๋‹ค๋ฅธ ์ž์› ํ• ๋‹น ์ •์ฑ…์— ๋Œ€ํ•˜์—ฌ ๋งŒ๋“ค๊ณ  ์ด ๋ฌธ์ œ๋“ค์„ ํ’€๊ธฐ ์œ„ํ•ด ํƒ์š•(greedy) ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ์—ด ์ถ”๊ฐ€ ๊ธฐ๋ฐ˜(column generation-based) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋˜ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•๋“ค์ด ์„ค๊ณ„ ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ณ  ๊ธฐ์กด์˜ ๊ธฐ๋ฒ•๋ณด๋‹ค ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์ด๋ฉด์„œ๋„ ์‹ ํ˜ธ์ „๋‹ฌ ๋ถ€ํ•˜๋ฅผ ์ค„์ผ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค.Wireless networks usually adopt some link adaptation techniques to mitigate the performance degradation due to the time-varying characteristics of wireless channels. Since the link adaptation techniques require to estimate and collect channel state information, signaling overhead is inevitable in wireless networks. In this thesis, we propose two schemes to reduce the signaling overhead in wireless networks. First, we design an adaptive transmission scheme for cooperative communication networks. The cooperative network with the proposed scheme chooses the transmission rate and decides to involve the relay in transmission, adapting to the channel state estimated from limited feedback information (e.g., ACK/NACK feedback). Considering that the limited feedback information provides only partial knowledge about the actual channel states, we design a decision-making algorithm on cooperative transmission by using a partially observable Markov decision process (POMDP) framework. Next, we also propose a two-stage semi-distributed resource management framework for the device-to-device (D2D) communication in cellular networks. At the first stage of the framework, the base station (BS) allocates resource blocks (RBs) to BS-to-user device (B2D) links and D2D links, in a centralized manner. At the second stage, the BS schedules the transmission using the RBs allocated to B2D links, while the primary user device of each D2D link carries out link adaptation on the RBs allocated to the D2D link, in a distributed fashion. The proposed framework has the advantages of both centralized and distributed design approaches, i.e., high network capacity and low signaling/computational overhead, respectively. We formulate the problems of RB allocation to maximize the radio resources efficiency, taking account of two different policies on the spatial reuse of RBs. To solve these problems, we suggest a greedy algorithm and a column generation-based algorithm. By simulation, it is shown that the proposed schemes achieve their design goal properly and outperform existing schemes while reducing the signaling overhead.1 Introduction 1 1.1 Background and Motivation 1 1.2 Approaches to Reduce Signaling Overhead 5 1.3 Proposed Schemes 7 1.3.1 Adaptive Transmission Scheme for Cooperative Communication 7 1.3.2 Resource Management Scheme for D2D Communication in Cellular Networks 8 1.4 Organization 10 2 Adaptive Transmission Scheme for Cooperative Communication 11 2.1 System Model 11 2.2 Cooperative Networks with Limited Feedback 12 2.2.1 Operation of the Proposed Cooperative Network 12 2.2.2 Finite-State Markov Channel Model 15 2.2.3 Packet Error Probability 16 2.2.4 Channel Feedback Schemes 18 2.3 Adaptive Transmission Scheme for Cooperative Communication 19 2.3.1 POMDP Formulation 19 2.3.2 Solution to POMDP 22 3 Resource Management Scheme for D2D Communication in Cellular Networks 25 3.1 System Model 25 3.1.1 Network Model 25 3.1.2 Radio Resource Model 27 3.2 Proposed Resource Management Framework 28 3.2.1 Framework Overview 28 3.2.2 Two-Stage Resource Management 29 3.2.3 Advantages of the Proposed Framework 31 3.3 Conditions for Simultaneous Transmission of B2D and D2D Links 33 3.3.1 Analysis of Interference on B2D and D2D Links 33 3.3.2 Conditions for Simultaneous Transmission of B2D and D2D Links 36 3.4 Resource Block Allocation 38 3.4.1 Resource Block Allocation with Conservative Reuse Policy 39 3.4.2 Resource Block Allocation with Aggressive Reuse Policy 44 4 Performance Evaluation 52 4.1 Adaptive Transmission Scheme for Cooperative Communication 52 4.1.1 Simulation Model 52 4.1.2 Simulation Results 53 4.2 Resource Management Scheme for D2D Communication in Cellular Networks 62 4.2.1 Simulation Model 62 4.2.2 Simulation Results 64 5 Conclusion 75 Bibliography 77 Abstract 85Docto

    Accuracy Enhancement of Vision-based Surgical Instrument Segmentation using Background Simplification in Robot-assisted Laparoscopic Surgery

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต, 2015. 2. ๊น€ํฌ์ฐฌ.๋‹ค์–‘ํ•œ ์ด์ ์„ ๊ฐ€์ง„ ๋ณต๊ฐ•๊ฒฝ ๋กœ๋ด‡ ์ˆ˜์ˆ ์€ ๋ณต๊ฐ•๊ฒฝ์„ ํ†ตํ•œ ์ข์€ ์‹œ์•ผ, ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์ด‰๊ฐ(Haptic) ์‹œ์Šคํ…œ์˜ ๋ถ€์žฌ ๋“ฑ์œผ๋กœ ์‘๊ธ‰์ƒํ™ฉ ๋ฐœ์ƒ ์‹œ ์ˆ˜์ˆ ์ž์˜ ์‹ ์†ํ•œ ์ƒํ™ฉ ํŒ๋‹จ์ด ์–ด๋ ค์šด ํ•œ๊ณ„์ ๋“ค์ด ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ˆ˜์ˆ  ์ค‘ ์œ„ํ—˜ ์ƒํ™ฉ ๊ฐ์ง€๋ฅผ ์œ„ํ•ด ์ˆ˜์ˆ  ์˜์ƒ์—์„œ ์ž๋™์œผ๋กœ ์ˆ˜์ˆ ๋„๊ตฌ๋ฅผ ์ธ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ๋ณต๊ฐ•๊ฒฝ ๋กœ๋ด‡ ์ˆ˜์ˆ  ์‹œ์Šคํ…œ์˜ ๊ตฌํ˜„์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ณต๊ฐ•๊ฒฝ ์ˆ˜์ˆ  ํ™”๋ฉด ๋‚ด์—์„œ ์ˆ˜์ˆ ๋„๊ตฌ์˜ ์œ„์น˜๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ˆ˜์ˆ ๋„๊ตฌ ๊ฐ„์˜ ์ถฉ๋Œ ๋ฐ ์ˆ˜์ˆ ๋„๊ตฌ์™€ ์กฐ์ง์˜ ์ถฉ๋Œ์„ ์ •ํ™•ํžˆ ๊ฐ์ง€ํ•˜๊ธฐ ์œ„ํ•œ ์ˆ˜์ˆ ๋„๊ตฌ์˜ ๋ถ„ํ• (Segmentation) ์ •ํ™•๋„๊ฐ€ ๋†’์€ ์˜์ƒ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์ด๋‹ค. ์ œ์•ˆํ•œ ์˜์ƒ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์€, ์ˆ˜์ˆ  ์˜์ƒ์—์„œ ๋ฐฐ๊ฒฝ์„ ๋‹จ์ˆœํ™”ํ•˜์—ฌ ์ˆ˜์ˆ ๋„๊ตฌ ๋ถ„ํ•  ์˜์ƒ์ฒ˜๋ฆฌ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์žก์Œ์„ ํšจ๊ณผ์ ์œผ๋กœ ์ œ๊ฑฐํ•˜์˜€๊ณ  K-ํ‰๊ท  ๊ตฐ์ง‘ํ™”(K-means Clustering), ์ฃผ์„ฑ๋ถ„ ๋ถ„์„(PCA) ๋“ฑ์˜ ๊ธฐ๋ฒ•์„ ์กฐํ•ฉํ•œ ์ƒˆ๋กœ์šด ๋ณตํ•ฉ ์˜์ƒ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์ด๋‹ค. ๊ฐœ๋ฐœ๋œ ๋ฐฉ๋ฒ•์€ ์„ธ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์œผ๋กœ ์„ฑ๋Šฅ ํ‰๊ฐ€๋ฅผ ํ•˜์˜€์œผ๋ฉฐ, 7๊ฐ€์ง€์˜ ์ˆ˜์ˆ  ํ™˜๊ฒฝ์—์„œ ์ œ์•ˆํ•œ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๊ณผ ๋‘ ๊ฐ€์ง€ ์„ ํ–‰์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•œ ์˜์ƒ์„ ์ˆ˜๋™์œผ๋กœ ์ˆ˜์ˆ ๋„๊ตฌ๋ฅผ ๋ถ„ํ• ํ•œ ๊ธฐ์ค€ ์˜์ƒ๊ณผ ๋น„๊ตํ•˜์—ฌ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•์€ ์ˆ˜์ˆ ๋„๊ตฌ ๋ถ„ํ•  ์ •ํ™•๋„๋ฅผ ๋น„๊ตํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ, ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋“ค์„ ์ ์šฉํ•˜์—ฌ ์–ป์€ ์ˆ˜์ˆ ๋„๊ตฌ ๋ถ„ํ•  ์˜์ƒ๊ณผ ๊ธฐ์ค€ ์˜์ƒ์˜ ํ™”์†Œ(pixel)๊ฐ„์˜ ์ฐจ์ด๋ฅผ ํ˜ผ๋™ํ–‰๋ ฌ์„ ์ด์šฉํ•˜์—ฌ ๋ฏผ๊ฐ๋„์™€ ํŠน์ด๋„๋ฅผ ๊ตฌํ•˜์˜€๋‹ค. ์ œ์•ˆํ•œ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์˜ ํ‰๊ท  ๋ฏผ๊ฐ๋„์™€ ํŠน์ด๋„๋Š” 84.81ยฑ5.96%, 98.36ยฑ0.737%๋ฅผ ๋ณด์ž„์œผ๋กœ์จ ์„ ํ–‰์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋“ค๋ณด๋‹ค ๋” ๋†’์€ ์ •ํ™•๋„(Accuracy)์™€ ๊ฐ•์ธํ•จ(Robustness)์„ ๋ณด์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•์€ ์ˆ˜์ˆ ๋„๊ตฌ ๋(Tip) ์ขŒํ‘œ์˜ ์˜ค์ฐจ๋ฅผ ๋น„๊ตํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ, ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ์–ป์€ ์ˆ˜์ˆ ๋„๊ตฌ ๋ถ„ํ•  ์˜์ƒ๊ณผ ๊ธฐ์ค€ ์˜์ƒ์˜ ์ˆ˜์ˆ ๋„๊ตฌ ๋ ์ขŒํ‘œ์˜ ํ‰๊ท  ์ œ๊ณฑ๊ทผ ํŽธ์ฐจ(RMSE)๋ฅผ ํ†ตํ•ด ์˜ค์ฐจ๋ฅผ ๊ตฌํ•˜์˜€๋‹ค. ์ œ์•ˆํ•œ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์˜ ํ‰๊ท  ์˜ค์ฐจ๋Š” ์ˆ˜์ˆ ๋„๊ตฌ 1~3์—์„œ ๊ฐ๊ฐ 1.07ยฑ0.83mm, 1.34ยฑ0.83mm, 0.08ยฑ0.63mm๋ฅผ ๋ณด์ž„์œผ๋กœ์จ ์„ ํ–‰์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋“ค๋ณด๋‹ค ๋” ๋‚ฎ์€ ์˜ค์ฐจ์œจ๊ณผ ๋†’์€ ๊ฐ•์ธํ•จ์„ ๋ณด์˜€๋‹ค. ๋งˆ์ง€๋ง‰ ํ‰๊ฐ€๋ฐฉ๋ฒ•์œผ๋กœ ์ˆ˜ํ–‰์‹œ๊ฐ„ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•˜์˜€์œผ๋ฉฐ ์ œ์•ˆํ•œ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์˜ ์ˆ˜ํ–‰์‹œ๊ฐ„์ด 0.214์ดˆ๋ฅผ ๋ณด์ž„์œผ๋กœ์จ ์„ ํ–‰ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋“ค๊ณผ ์œ ์‚ฌํ•œ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. ์ด์™€ ๊ฐ™์ด ์ œ์•ˆํ•œ ๋ณตํ•ฉ ์˜์ƒ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์ด ์„ฑ๋Šฅ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ์ˆ˜์ˆ  ํ™˜๊ฒฝ์—์„œ ์ž๋™์œผ๋กœ ์ˆ˜์ˆ ๋„๊ตฌ๋ฅผ ์ธ์ง€ํ•˜์—ฌ ์œ„ํ—˜ ์ƒํ™ฉ์„ ๊ฐ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ๋ณต๊ฐ•๊ฒฝ ์ˆ˜์ˆ  ๋กœ๋ด‡ ์‹œ์Šคํ…œ ๊ตฌํ˜„์˜ ๋ณดํŽธ์ ์ธ ๊ธฐ๋ฒ•์œผ๋กœ ์œ ์šฉํ•˜๊ฒŒ ํ™œ์šฉ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.์ดˆ ๋กโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ…ฐ ํ‘œ ๋ชฉ์ฐจโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ…ด ๊ทธ๋ฆผ ๋ชฉ์ฐจโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ…ด 1. ์„œ ๋ก โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ1 1.1. ๋ณต๊ฐ•๊ฒฝ ๋กœ๋ด‡ ์ˆ˜์ˆ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ1 1.1.1. ๋ฐฐ๊ฒฝโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ1 1.1.2. ํ•œ๊ณ„์  ๋ฐ ํ•„์š”์„ฑโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ5 1.2. ์ˆ˜์ˆ ๋„๊ตฌ ๋ถ„ํ•  ๊ธฐ๋ฒ•์˜ ์„ ํ–‰ ์—ฐ๊ตฌโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ6 1.2.1. ๋งˆ์ปค๋ฅผ ์ด์šฉํ•œ ๋ฐฉ๋ฒ•โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ7 1.2.2. ์˜์ƒ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ8 1.3. ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ10 2. ๋ฐฉ ๋ฒ•โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ11 2.1. ๊ฐœ๋ฐœ ํ™˜๊ฒฝ ๋ฐ ํ”„๋กœ๊ทธ๋žจ ๊ฐœ์š”โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ11 2.2. ์ˆ˜์ˆ ๋„๊ตฌ ๋ถ„ํ•  ๊ธฐ๋ฒ•โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ13 2.2.1. ๋ฐฐ๊ฒฝ ๋ถ„ํ• โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ13 2.2.1.1. L*a*b* ์ƒ‰ ๊ณต๊ฐ„ ๋ณ€ํ™˜โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 14 2.2.1.2. ๊ทธ๋ฆผ์ž ์ œ๊ฑฐโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ17 2.2.1.3. ๋ฐ˜์‚ฌ๊ด‘ ์ œ๊ฑฐโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ20 2.2.1.4. HSV ์ƒ‰ ๊ณต๊ฐ„ ๋ณ€ํ™˜ ๋ฐ ๊ฒฝ๊ณ„ํ™”โ€ฆโ€ฆ22 2.2.2. ๋ฐฐ๊ฒฝ ๋‹จ์ˆœํ™”โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ27 2.2.2.1. ๋‹จ์ผ ์กฐ์ง ์ƒ‰์ƒ ํ•ฉ์„ฑโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ27 2.2.2.2. XYZ ์ƒ‰ ๊ณต๊ฐ„ ๋ณ€ํ™˜โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ28 2.2.2.3. K-ํ‰๊ท  ๊ตฐ์ง‘ํ™”โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 30 2.2.3. ์žก์Œ ์ œ๊ฑฐโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ33 2.2.3.1. ์ฃผ์„ฑ๋ถ„ ๋ถ„์„โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ33 2.3. ์„ฑ๋Šฅ ํ‰๊ฐ€ ๋ฐฉ๋ฒ•โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ37 2.3.1. ์ˆ˜์ˆ ๋„๊ตฌ ๋ถ„ํ•  ์ •ํ™•๋„โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 37 2.3.2. ์ˆ˜์ˆ ๋„๊ตฌ ๋ ์ขŒํ‘œ ์˜ค์ฐจโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ40 2.3.3. ์ˆ˜ํ–‰์‹œ๊ฐ„โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 42 3. ๊ฒฐ ๊ณผโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ43 3.1. ์ˆ˜์ˆ ๋„๊ตฌ ๋ถ„ํ•  ์ •ํ™•๋„ ๋น„๊ต ๊ฒฐ๊ณผโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ47 3.2. ์ˆ˜์ˆ ๋„๊ตฌ ๋ ์ขŒํ‘œ ์˜ค์ฐจ ๋น„๊ต ๊ฒฐ๊ณผโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ62 3.3. ์ˆ˜ํ–‰์‹œ๊ฐ„ ๋น„๊ต ๊ฒฐ๊ณผโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ79 4. ๊ณ  ์ฐฐโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ80 5. ๊ฒฐ ๋ก โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ85 ์ฐธ๊ณ  ๋ฌธํ—Œโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ87 Abstractโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ93 ๊ฐ์‚ฌ์˜ ๊ธ€โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ97Maste

    DBMS๊ฐœ๋ฐœ์„ ์œ„ํ•œ ์„ฑ๋Šฅ ์ด์ƒ ๊ฒ€์ถœ ๋ฐ ๋ถ„์„ ํ”„๋ ˆ์ž„์›Œํฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐ. ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2011.8. ์ฐจ์ƒ๊ท .Docto

    QoS-guaranteed delivery of biomedical information for telemedicine applications over mobile networks

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    ์ƒ์ฒด๊ณตํ•™ ํ˜‘๋™๊ณผ์ •/์„์‚ฌ[ํ•œ๊ธ€]์ด๋™ํ†ต์‹ ๋ง์„ ์‚ฌ์šฉํ•œ ์›๊ฒฉ ์ง„๋ฃŒ ์‹œ์Šคํ…œ์€ ์ƒ์ฒด ์‹ ํ˜ธ, ํ™˜์ž ๋น„๋””์˜ค, ์˜ค๋””์˜ค, ์˜ํ•™ ์Šค์บ” ์ด๋ฏธ์ง€๋“ฑ์„ ๋‹ค๋ฃฌ๋‹ค. ์ด๋™ํ†ต์‹ ๋ง์€ ๊ธฐ์กด์˜ ์œ ์„ ๋ง๊ณผ ๋‹ฌ๋ฆฌ ๋†’์€ ์—๋Ÿฌ์œจ๊ณผ ์ง€์—ฐ์˜ ๋ณ€ํ™”๊ฐ€ ์‹ฌํ•œ ํŠน์„ฑ์„ ๊ฐ€์ง„๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋™ํ†ต์‹ ๋ง์˜ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ƒ์ฒด ์ •๋ณด์˜ QoS๋ฅผ ๋ณด์žฅํ•  ์ˆ˜ ์žˆ๋Š” ์ „์†ก ํ”„๋กœํ† ์ฝœ์ด ํ•„์š”ํ•˜๋‹ค. ํ•˜์ง€๋งŒ ์ด๋Ÿฌํ•œ ์ „์†ก ๊ณ„์ธต์˜ QoS ๋ณด์žฅ ์ •๋„๋Š” ์ด๋™ํ†ต์‹ ๋ง ๋„คํŠธ์›Œํฌ, ์ธํ„ฐ๋„ท๊ณผ ๊ฐ™์€ ๋„คํŠธ์›Œํฌ ๊ณ„์ธต์—์„œ์˜ QoS ๋ณด์žฅ ์ •๋„์— ํฌ๊ฒŒ ์˜ํ–ฅ์„ ๋ฐ›๋Š”๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋™ํ†ต์‹ ๋ง์˜ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ƒ์ฒด ์‹ ํ˜ธ์˜ QoS๋ฅผ ๋ณด์žฅ ํ•  ์ˆ˜ ์žˆ๋Š” ์ƒ์ฒด ์‹ ํ˜ธ ์ „์†ก ํ”„๋กœํ† ์ฝœ์„ ์„ค๊ณ„ํ•˜์˜€๊ณ  ์ด๋™ํ†ต์‹ ๋ง ํŒจํ‚ท ์ฝ”์–ด ๋„คํŠธ์›Œํฌ์—์„œ ์ƒ์ฒด ์ •๋ณด์˜ QoS ๋ณด์žฅ ๋ฐฉ์•ˆ์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค.์›๊ฒฉ์ง„๋ฃŒ์‹œ์Šคํ…œ์˜ ์ƒ์ฒด ์‹ ํ˜ธ๋Š” ํ™˜์ž ๋น„๋””์˜ค ๊ฐ™์€ ๋‹ค๋ฅธ ์›๊ฒฉ์ง„๋ฃŒ์˜ ์š”์†Œ๋“ค๊ณผ ๋น„๊ตํ•ด์„œ ์ง€์—ฐ์ด๋‚˜ ์†์‹ค์— ๋ฏผ๊ฐํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์‹ ๋ขฐ์ ์ด๊ณ  ์ง€์—ฐ์„ ์ตœ์†Œ๋กœ ํ•˜๋Š” ์ „์†ก์„ ์š”๊ตฌํ•œ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ตœ๋Œ€ํ•œ ์‹ ๋ขฐ์ ์œผ๋กœ ์ง€์—ฐ์„ ์ตœ์†Œํ™”ํ•˜๋ฉฐ ์ƒ์ฒด ์‹ ํ˜ธ๋ฅผ ์ „์†กํ•˜๋Š” ์ „์†ก ํ”„๋กœํ† ์ฝœ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ํฌ๋ฐ•ํ•œ ์‹ค์ •์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ ํ•œ๊ตญ ์ „์—ญ์— ๊ฑธ์ณ ๊ตฌ์ถ•๋œ CDMA ๋ชจ๋ฐ”์ผ ๋„คํŠธ์›Œํฌ์—์„œ ์‹ ๋ขฐ์ ์ด๊ณ  ์ง€์—ฐ์„ ์ตœ์†Œํ™”ํ•˜๋ฉฐ ์ƒ์ฒด ์‹ ํ˜ธ๋ฅผ ์ „์†กํ•  ์ˆ˜ ์žˆ๋Š” ์ƒ์ฒด์‹ ํ˜ธ์ „์†กํ”„๋กœํ† ์ฝœ(vital sign transmission protocol : VSTP)์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. VSTP๋Š” ์ง€์—ฐ ์ œ์•ฝ ์‹œ๊ฐ„ ๋‚ด์—์„œ ์ƒ์ฒด ์‹ ํ˜ธ๋ฅผ ์ตœ๋Œ€ํ•œ ๋งŽ์ด ์ „์†กํ•˜๊ธฐ ์œ„ํ•ด ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์—๋Ÿฌ ๋ณด์ • ๊ธฐ์ˆ ๊ณผ ์Šค์œ„์นญ ๋ฒ„ํผ ๊ด€๋ฆฌ ๊ธฐ์ˆ ์„ ์‚ฌ์šฉํ•œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ ํ˜„์กดํ•˜๋Š” ์ „์†ก ํ”„๋กœํ† ์ฝœ์ธ TCP, UDP๋ณด๋‹ค ํŠนํžˆ ๋†’์€ ์—๋Ÿฌ์œจ์ธ ํ™˜๊ฒฝ์—์„œ ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ž„์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ์ง€์—ฐ ๋ฒ„ํผ๋ฅผ ์‚ฌ์šฉํ•œ ์Šค์œ„์นญ ๋ฒ„ํผ ๊ด€๋ฆฌ ๊ธฐ์ˆ ์€ ์ง€์—ฐ ์ œ์•ฝ์ด ์žˆ๋Š” ์‹ ๋ขฐ์ ์ธ ์ „์†ก์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์‹ ๋ขฐ์ ์ธ ์ „์†ก๊ณผ ์ง€์—ฐ์„ ์ตœ์†Œ๋กœ ํ•˜๋Š” ์ „์†ก ์‚ฌ์ด์— ์ ์ ˆํ•œ ๊ท ํ˜•์„ ์ด๋ฃฐ ์ˆ˜ ์žˆ์—ˆ๋‹ค.์—ฌ๋Ÿฌ ์ข…๋ฅ˜์˜ ์‚ฌ์šฉ์ž ํŠธ๋ž˜ํ”ฝ์ด ์กด์žฌํ•˜๋Š” ์ด๋™ํ†ต์‹ ๋ง ๋‚ด์—์„œ ์ƒ์ฒด ์ •๋ณด ํŠธ๋ž˜ํ”ฝ์˜ QoS๋ฅผ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•ด DiffServ ๊ตฌ์กฐ๋ฅผ ๋”ฐ๋ฅธ ์ด๋™ํ†ต์‹ ๋ง ํŒจํ‚ท ์ฝ”์–ด ๋„คํŠธ์›Œํฌ์—์„œ์˜ ์ƒ์ฒด ์ •๋ณด QoS ๋ณด์žฅ ๋ฐฉ์•ˆ์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. 3GPP2 QoS ํด๋ž˜์Šค, DiffServ PHB, ์ƒ์ฒด ์ •๋ณด ํŠธ๋ž˜ํ”ฝ ๊ฐ„์˜ ๋งตํ•‘ ๊ด€๊ณ„๋ฅผ ์ œ์•ˆํ•˜์˜€๊ณ  ์ด๋™ํ†ต์‹ ๋ง ํŒจํ‚ท ์ฝ”์–ด ๋„คํŠธ์›Œํฌ์˜ ์ƒ์ฒด ์ •๋ณด QoS ๋ณด์žฅ ๊ตฌ์กฐ๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ด๊ฒƒ์ด ์›๊ฒฉ ์ง„๋ฃŒ ์„œ๋น„์Šค์— ์ฃผ๋Š” ์ด์ ์„ Network Simulator (NS)๋ฅผ ์ด์šฉํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ตœ์•…์˜ ์ƒํ™ฉ (๋†’์€ ์šฐ์„ ์ˆœ์œ„๋ฅผ ๊ฐ€์ง„ ํŠธ๋ž˜ํ”ฝ์— ์˜ํ•œ ํ˜ผ์žก ์ƒํ™ฉ)์—์„œ ์ œ์•ˆ๋œ ๋งตํ•‘ ๊ด€๊ณ„๊ฐ€ ์šฐ์„ ์ˆœ์œ„๊ฐ€ ๋†’์€ ์ƒ์ฒด ์‹ ํ˜ธ์˜ ์ „์†ก์„ ๋ณด์žฅํ•˜๋„๋ก ํ•˜์—ฌ ํ™˜์ž์˜ ์ง€์†์ ์ธ ๋ชจ๋‹ˆํ„ฐ๋ง์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•จ์„ ๋ณด์˜€๋‹ค. ์ด๋™ํ†ต์‹ ๋ง์„ ์‚ฌ์šฉํ•œ ์›๊ฒฉ ์ง„๋ฃŒ ์‹œ์Šคํ…œ์ด ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ ์ด๋™ํ†ต์‹ ๋ง ํŒจํ‚ท ์ฝ”์–ด ๋„คํŠธ์›Œํฌ์—์„œ์˜ ์ƒ์ฒด ์ •๋ณด QoS ๋ณด์žฅ ๊ตฌ์กฐ๋ฅผ ๋”ฐ๋ฅธ๋‹ค๋ฉด ์ƒ์ฒด ์ •๋ณด๊ฐ„์˜ ์šฐ์„ ์ˆœ์œ„ ์ฐจ๋ณ„ํ™”๋ฅผ ํ†ตํ•ด ๋„คํŠธ์›Œํฌ์˜ ์ƒํ™ฉ์— ์ƒ๊ด€์—†์ด ํ™˜์ž์˜ ์ง€์†์ ์ธ ๋ชจ๋‹ˆํ„ฐ๋ง์ด ๊ฐ€๋Šฅํ•  ๊ฒƒ์ด๋‹ค. [์˜๋ฌธ]Telemedicine systems over mobile networks use biomedical information such as vital signs, patient video, audio and medical data between a patient and a specialist for rendering a diagnosis and treatment plan. mobile telemedicine systems are prone to be affected by delays and data corruptions due to fading channel characteristics of mobile network. Thus, a transmission protocol, which guarantees a good quality of biomedical information deliveries(QoS-guarantee) as well as considers characteristics of mobile networks, is needed. However, these QoS-guarantee on transport layer is highly affected by QoS-guarantee on network layers such as mobile networks and internet. The paper introduces a QoS-guaranteed vital sign transmission protocol considering the characteristics of mobile networks, and represents how to guarantee QoS of biomedical information deliveries in the mobile packet core network environment.Telemedicine systems require reliable and instant transmission of vital signs, particularly when managing emergency patients. Compared with other telemedicine components such as patient videos and vital signs are sensitive to delays and data corruptions. Delays should be minimized in time-critical patient care. Additionally, the reliable transmission of vital signs is also important for diagnosing a patient remotely, since corrupted vital signs sometimes can cause misdiagnosis. Nonetheless, few studies have been performed regarding vital sign transmission protocol that maximizes the amount of reliable transmission within an acceptable delay bound.In this paper, the VSTP running over a CDMA 1xEVDO mobile network is proposed to comply with both the reliability and instantaneity requirements. The switching buffer management scheme is combined with a hybrid error control scheme, consisting of FEC and ARQ. Throughout the simulation, it is demonstrated that the VSTP performs better than existing network protocols, including TCP and UDP, for a high noise environment.In mobile networks where various kinds of user traffic exist simultaneously, QoS-guaranteed delivery of biomedical information is researched over the mobile packet core network, in an IP-based backbone which employs DiffServ architecture. The class mapping between 3GPP2 QoS classes, DiffServ PHB, and biomedical information traffics is proposed and QoS-guaranteed architecture for biomedical information in the packet core network is designed. The paper also examines advantages of the proposed QoS-guaranteed architecture using Network Simulator. Through the simulation in the worst case of networks conditions, it was possible to have a continuous patient monitoring with guaranteed deliveries of highly prioritized vital signs by the proposed class mapping. Mobile telemedicine systems employing the proposed QoS-guaranteed architecture for biomedical information can successfully operate patient monitoring continuously by different prioritization of biomedical informations, regardless of network conditions.ope

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