54 research outputs found

    ๊ทธ๋ผ๋””์–ธํŠธ ๊ฐœ์„  ๋ฐ ๋ช…์‹œ์  ์ •๊ทœํ™”๋ฅผ ํ†ตํ•œ ์‹ฌ์ธต ๋ชจ๋ธ ์••์ถ•์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•™๊ณผํ•™๋ถ€, 2022.2. ๊น€์žฅํ˜ธDeep Neural Network (DNN)์€ ๋น ๋ฅด๊ฒŒ ๋ฐœ์ „ํ•˜์—ฌ ์ปดํ“จํ„ฐ ๋น„์ „, ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๋ฐ ์Œ์„ฑ ์ฒ˜๋ฆฌ๋ฅผ ํฌํ•จํ•œ ๋งŽ์€ ์˜์—ญ์—์„œ ๋†€๋ผ์šด ์„ฑ๋Šฅ์„ ๋ณด์—ฌ ์™”๋‹ค. ์ด๋Ÿฌํ•œ DNN์˜ ๋ฐœ์ „์— ๋”ฐ๋ผ edge IoT ์žฅ์น˜์™€ ์Šค๋งˆํŠธํฐ์— DNN์„ ๊ตฌ๋™ํ•˜๋Š” ์˜จ๋””๋ฐ”์ด์Šค DNN์— ๋Œ€ํ•œ ์ˆ˜์š”๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ DNN์˜ ์„ฑ์žฅ๊ณผ ํ•จ๊ป˜ DNN ๋งค๊ฐœ๋ณ€์ˆ˜์˜ ์ˆ˜๊ฐ€ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ–ˆ๋‹ค. ์ด๋กœ ์ธํ•ด DNN ๋ชจ๋ธ์„ ๋ฆฌ์†Œ์Šค ์ œ์•ฝ์ด ์žˆ๋Š” ์—์ง€ ์žฅ์น˜์— ๊ตฌ๋™ํ•˜๊ธฐ๊ฐ€ ์–ด๋ ต๋‹ค. ๋˜ ๋‹ค๋ฅธ ๋ฌธ์ œ๋Š” ์—์ง€ ์žฅ์น˜์—์„œ DNN์˜ ์ „๋ ฅ ์†Œ๋น„๋Ÿ‰์ด๋‹ค ์™œ๋ƒํ•˜๋ฉด ์—์ง€ ์žฅ์น˜์˜ ์ „๋ ฅ์šฉ ๋ฐฐํ„ฐ๋ฆฌ๊ฐ€ ์ œํ•œ๋˜์–ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์œ„์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋ชจ๋ธ ์••์ถ•์ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์šฐ๋ฆฌ๋Š” ์ง€์‹ ์ฆ๋ฅ˜, ์–‘์žํ™” ๋ฐ ๊ฐ€์ง€์น˜๊ธฐ๋ฅผ ํฌํ•จํ•œ ๋ชจ๋ธ ์••์ถ•์˜ ์„ธ ๊ฐ€์ง€ ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ €, ์ง€์‹ ์ฆ๋ฅ˜๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ์จ, ๊ต์‚ฌ ๋„คํŠธ์›Œํฌ์˜ ์ถ”๊ฐ€ ์ •๋ณด๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ•™์ƒ ๋ชจ๋ธ์„ ํ•™์Šต์‹œํ‚ค๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์ด ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด ์ฃผ์–ด์ง„ ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ์ตœ๋Œ€ํ•œ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ด๋Š” ์žฅ์น˜์˜ ๋ฆฌ์†Œ์Šค๊ฐ€ ์ œํ•œ๋œ ์ƒํ™ฉ์—์„œ ์ค‘์š”ํ•˜๋‹ค. ๊ธฐ์กด ์ง€์‹ ์ฆ๋ฅ˜ ํ”„๋ ˆ์ž„์›Œํฌ์™€ ๋‹ฌ๋ฆฌ ๋„คํŠธ์›Œํฌ ๊ตฌ์กฐ, ๋ฐฐ์น˜ ๋ฌด์ž‘์œ„์„ฑ ๋ฐ ์ดˆ๊ธฐ ์กฐ๊ฑด๊ณผ ๊ฐ™์€ ๊ต์‚ฌ์™€ ํ•™์ƒ ๊ฐ„์˜ ๊ณ ์œ ํ•œ ์ฐจ์ด๊ฐ€ ์ ์ ˆํ•œ ์ง€์‹์„ ์ „๋‹ฌํ•˜๋Š” ๋ฐ ๋ฐฉํ•ด๊ฐ€ ๋  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ํ”ผ์ณ์—์„œ ์š”์†Œ๋ฅผ ์ถ”์ถœํ•˜์—ฌ ์ง€์‹์„ ๊ฐ„์ ‘์ ์œผ๋กœ ์ฆ๋ฅ˜ํ•˜๋Š” ๋ฐ ์ค‘์ ์„ ๋‘”๋‹ค. ๋‘˜์งธ, ์–‘์žํ™”๋ฅผ ์œ„ํ•œ ์ •๊ทœํ™” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์–‘์žํ™”๋œ ๋ชจ๋ธ์€ ์ž์›์ด ์ œํ•œ๋œ ์—์ง€ ์žฅ์น˜์— ์ค‘์š”ํ•œ ์ „๋ ฅ ์†Œ๋ชจ์™€ ๋ฉ”๋ชจ๋ฆฌ์— ์ด์ ์ด ์žˆ๋‹ค. ํŒŒ๋ผ๋ฏธํ„ฐ ๋ถ„ํฌ๋ฅผ ์–‘์žํ™” ์นœํ™”์ ์œผ๋กœ ๋งŒ๋“ค๊ธฐ ์œ„ํ•ด ํ›ˆ๋ จ ์‹œ๊ฐ„์— ๋ชจ๋ธ์˜ ๊ธฐ์šธ๊ธฐ๋ฅผ ๋ถˆ๊ท ์ผํ•˜๊ฒŒ ์žฌ์กฐ์ •ํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ๊ทธ๋ผ๋””์–ธํŠธ์˜ ํฌ๊ธฐ๋ฅผ ์žฌ์กฐ์ •ํ•˜๊ธฐ ์œ„ํ•ด position-based scaled gradient (PSG)๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค. Stochastic gradient descent (SGD) ์™€ ๋น„๊ตํ•˜์—ฌ, ์šฐ๋ฆฌ์˜ position-based scaled gradient descent (PSGD)๋Š” ๋ชจ๋ธ์˜ ์–‘์žํ™” ์นœํ™”์ ์ธ ๊ฐ€์ค‘์น˜ ๋ถ„ํฌ๋ฅผ ๋งŒ๋“ค๊ธฐ ๋•Œ๋ฌธ์— ์–‘์žํ™” ํ›„ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ์™„ํ™”ํ•œ๋‹ค. ์…‹์งธ, ์ค‘์š”ํ•˜์ง€ ์•Š์€ ๊ณผ์ž‰ ๋งค๊ฐœ ๋ณ€์ˆ˜ํ™” ๋ชจ๋ธ์„ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•ด, ๊ฐ€์ง€์น˜๊ธฐ๋œ ๊ฐ€์ค‘์น˜์˜ ๋Œ€๋žต์ ์ธ ๊ธฐ์šธ๊ธฐ์— Straight-Through-Estimator (STE)๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํ›ˆ๋ จ ์ค‘์— ๋‹ค์–‘ํ•œ ํฌ์†Œ์„ฑ ํŒจํ„ด์„ ์ฐพ์œผ๋ ค๊ณ  ํ•˜๋Š” ๋™์  ๊ฐ€์ง€์น˜๊ธฐ ๋ฐฉ๋ฒ•์ด ๋“ฑ์žฅํ–ˆ๋‹ค. STE๋Š” ๋™์  ํฌ์†Œ์„ฑ ํŒจํ„ด์„ ์ฐพ๋Š” ๊ณผ์ •์—์„œ ์ œ๊ฑฐ๋œ ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ๋˜์‚ด์•„๋‚˜๋„๋ก ๋„์šธ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๊ฑฐ์นœ ๊ธฐ์šธ๊ธฐ (coarse gradient)๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด STE ๊ทผ์‚ฌ์˜ ์‹ ๋ขฐํ•  ์ˆ˜ ์—†๋Š” ๊ธฐ์šธ๊ธฐ ๋ฐฉํ–ฅ์œผ๋กœ ์ธํ•ด ํ›ˆ๋ จ์ด ๋ถˆ์•ˆ์ •ํ•ด์ง€๊ณ  ์„ฑ๋Šฅ์ด ์ €ํ•˜๋œ๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์šฐ๋ฆฌ๋Š” ์ด์ค‘ ์ „๋‹ฌ ๊ฒฝ๋กœ๋ฅผ ํ˜•์„ฑํ•˜์—ฌ ์ œ๊ฑฐ๋œ ํŒŒ๋ผ๋ฏธํ„ฐ (pruned weights)๋ฅผ ์—…๋ฐ์ดํŠธํ•˜๊ธฐ ์œ„ํ•ด ์ •์ œ๋œ ๊ทธ๋ผ๋””์–ธํŠธ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๊ฐ€์ง€์น˜๊ธฐ์— ๊ฑฐ์นœ ๊ธฐ์šธ๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ธฐ ์œ„ํ•ด Dynamic Collective Intelligence Learning (DCIL)์„ ์ œ์•ˆํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•๋“ค์„ ์ด์šฉํ•˜์—ฌ ํ†ตํ•ฉ ๋ชจ๋ธ ์••์ถ• ํ›ˆ๋ จ ํ”„๋ ˆ์ž„์›Œํฌ๋กœ์„œ ๊ฒฐํ•ฉํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๊ทน๋„๋กœ ํฌ์†Œํ•˜๊ณ  ์–‘์žํ™” ์นœํ™”์ ์ธ ๋ชจ๋ธ์„ ํ›ˆ๋ จํ•  ์ˆ˜ ์žˆ๋‹ค.Deep neural network (DNN) has been developed rapidly and has shown remarkable performance in many domains including computer vision, natural language processing and speech processing. The demand for on-device DNN, i.e., deploying DNN on the edge IoT device and smartphone in line with this development of DNN has increased. However, with the growth of DNN, the number of DNN parameters has risen drastically. This makes DNN models hard to be deployed on resource-constraint edge devices. Another challenge is the power consumption of DNN on the edge device because edge devices have a limited battery for the power. To resolve the above issues model compression is very important. In this dissertation, we propose three novel methods in model compression including knowledge distillation, quantization and pruning. First, we aim to train the student model with additional information of the teacher network, named as knowledge distillation. This framework makes it possible to make the most of a given parameter, which is essential in situations where the device's resources are limited. Unlike previous knowledge distillation frameworks, we focus on distilling the knowledge indirectly by extracting the factor from features because the inherent differences between the teacher and the student, such as the network structure, batch randomness, and initial conditions, can hinder the transfer of appropriate knowledge. Second, we propose the regularization method for quantization. The quantized model has advantages in power consumption and memory which are essential to the resource-constraint edge device. We non-uniformly rescale the gradient of the model in the training time to make a weight distribution quantization-friendly. We use position-based scaled gradient (PSG) for rescaling the gradient. Compared with the stochastic gradient descent (SGD), our position-based scaled gradient descent (PSGD) mitigates the performance degradation after quantization because it makes a quantization-friendly weight distribution of the model. Third, to prune the unimportant overparameterized model dynamic pruning methods have emerged, which try to find diverse sparsity patterns during training by utilizing Straight-Through-Estimator (STE) to approximate gradients of pruned weights. STE can help the pruned weights revive in the process of finding dynamic sparsity patterns. However, using these coarse gradients causes training instability and performance degradation owing to the unreliable gradient signal of the STE approximation. To tackle this issue, we propose refined gradients to update the pruned weights by forming dual forwarding paths. We propose a Dynamic Collective Intelligence Learning (DCIL) to avoid using coarse gradients for pruning. Lastly, we combine proposed methods as a unified model compression training framework. This method can train a drastically sparse and quantization-friendly model.Abstract i Contents iii List of Tables vii List of Figures x 1 Introduction 1 1.1 Motivation 1 1.2 Tasks 4 1.3 Contributions and Outline 7 2 Related work 11 2.1 Knowledge Distillation 11 2.2 Quantization 13 2.2.1 Sparse training 14 2.3 Pruning 15 3 Factor Transfer (FT) for Knowledge Distillation 17 3.1 Introduction 17 3.2 Proposed method 19 3.2.1 Teacher Factor Extraction with Paraphraser 20 3.2.2 Factor Transfer with Translator 21 3.3 Experiments 23 3.3.1 CIFAR-10 24 3.3.2 CIFAR-100 26 3.3.3 Ablation Study 28 3.3.4 ImageNet 29 3.3.5 Object Detection 29 3.3.6 Discussion 31 3.4 Conclusion 31 4 Position based Scaled Gradients (PSG) for Quantization 33 4.1 Introduction 33 4.2 Proposed method 37 4.2.1 Optimization in warped space 38 4.2.2 Position-based scaled gradient 39 4.2.3 Target points 43 4.2.4 PSGD for deep networks 44 4.2.5 Geometry of the Warped Space 45 4.3 Experiments 50 4.3.1 Implementation details 51 4.3.2 Pruning 53 4.3.3 Quantization 56 4.3.4 Knowledge Distillation 58 4.3.5 Various architectures with PSGD 60 4.3.6 Adam optimizer with PSG 60 4.4 Discussion 61 4.4.1 Toy Example 61 4.4.2 Weight Distributions 62 4.4.3 Quantization-aware training vs PSGD 64 4.4.4 Post-training with PSGD-trained model 65 4.5 Conclusion 65 5 Dynamic Collective Intelligence Learning (DCIL) for Pruning 69 5.1 Introduction 69 5.2 Proposed method 73 5.2.1 Backgrounds 73 5.2.2 Dynamic Collective Intelligence Learning 74 5.2.3 Convergence analysis 79 5.3 Experiments 80 5.3.1 Experiment Setting 81 5.3.2 Experiment Results 84 5.3.3 Differences between Dense and pruned model 87 5.3.4 Analysis of the stability 87 5.3.5 Cost of training 90 5.3.6 Fast convergence of DCIL 92 5.3.7 Tendency of warm-up 93 5.3.8 CIFAR10 94 5.3.9 ImageNet 94 5.3.10 Analysis of training and inference overheads 95 5.4 Conclusion 96 6 Deep Model Compression via KD, Quantization and Pruning (KQP) 97 6.1 Method 97 6.2 Experiment 98 6.3 Conclusion 102 7 Conclusion 103 7.1 Summary 103 7.2 Limitations and Future Directions 105 Abstract (In Korean) 118 ๊ฐ์‚ฌ์˜ ๊ธ€ 120๋ฐ•

    Humanistic Corporate Paradigm and Comprehensive Learning Society

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    โ… . ์„œ๋ก  / 95 โ…ก. ๅ‹žๅ‹•์˜ ่ฝ‰ๆ›: ๆ„ๅ‘ณ์™€ ็คบๅ”†้ปž / 99 1. ํฌ๋“œ์ฃผ์˜ ๋…ธ๋™ํŽธ์„ฑ์›๋ฆฌ์˜ ๋ถ•๊ดด / 99 2. ์ง€์‹๊ธฐ๋ฐ˜๊ฒฝ์ œ์˜ ๋Œ€๋‘ / 101 3. ๋น„์ •ํ˜• ๊ณ ์šฉํ˜•ํƒœ์˜ ํ™•๋Œ€ / 103 โ…ข. ่ณ‡ๆœฌไธป็พฉ ็™ผๅฑ•๊ณผ ไผๆฅญํŒจ๋Ÿฌ๋‹ค์ž„์˜ ่ฎŠๅŒ– / 104 โ…ฃ. ไบบๆœฌไธป็พฉ ไผๆฅญํŒจ๋Ÿฌ๋‹ค์ž„๊ณผ ็ธฝ้ซ”็š„ ๅญธ็ฟ’็คพๆœƒ์˜ ๅ…ท็พ / 108 1. ์ธ๋ณธ์ฃผ์˜ ๊ธฐ์—…ํŒจ๋Ÿฌ๋‹ค์ž„ / 108 2. ่„ซํฌ๋“œ์ฃผ์˜ ์ž‘์—…์กฐ์ง ๋ฐ ๋…ธ์‚ฌ๊ด€๊ณ„ / 110 3. ์ธ๋ณธ์ฃผ์˜ ๋…ธ๋™๊ฒฝ์ œ์˜ ๋น„์ „ / 112 4. ์ด์ฒด์  ํ•™์Šต์‚ฌํšŒ์˜ ๊ตฌํ˜„ / 114 โ…ค. ๆŒ็บŒ็š„ ้›‡ๅ‚ญ๊ณผ ๅญธ็ฟ’์˜ ๅ–„ๅพช็’ฐ ๆจกๅž‹: ์œ ํ•œํ‚ด๋ฒŒ๋ฆฌ ๆˆๅŠŸไบ‹ไพ‹ / 115 โ…ฅ. ๊ฒฐ๋ก  / 119 ์ฐธ๊ณ ๋ฌธํ—Œ / 121 Abstract / 122This paper examined the significance and meaning of characteristical transformation of work that has come into the limelight recently and suggested the humanistic corporate paradigm, vision of work system principle and basic policy direction required in the 21st century knowledge-based society. Recently, the change in the technology paradigm of informationalization globalization and the change in social economic conditions are transforming the characteristic of labor, thus requiring the establishment of a new corporate paradigm and work system principle. This paper emphasized that the basic ideological and philosophical condition of the production system that actively conforms to the new social economic condition has to be human oriented, and that the post-Fordism work system principle that backs this is needed not only to overcome labor alienation and to raise equity, but also in order to secure economic efficiency in a long term point of view. This paper also pointed out that such a vision is not only something that is desirable, but is a reality of historical trend, considering the changes expected in the 21st century including the transition in technical condition, corporate environment and labor market conditions. It also stressed that in order to meet the humanistic corporate paradigm and post-Fordism's work system principle, the whole society needs to outgrow from the existing labor-centered society and become a comprehensive learning society in which learning keeps pace with labor in all units of organizations. This paper asserted that in order to speed up the realization of such comprehensive learning society, the nation, market, businesses and labor unions should all be approached with a new principle and way of thinking. The business reengineering success case of Yuhan-Kimberly, Inc., which is growing strong through consistent employment security and lifelong learning, is an important case that proves the potential of humanistic learning corporations. It hints that the employment crisis that we face now could be overcome with a paradigm shift

    [๊ถŒ๋‘์–ธ] ใ€Ž์‚ฌ๋žŒ์ž…๊ตญใ€์„ ์œ„ํ•œ ๋‰ดํŒจ๋Ÿฌ๋‹ค์ž„

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    Improving Korean-to-Chinese Phrase-based Statistical Machine Translation Using Enhanced Word Alignment

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    Master์ธํ„ฐ๋„ท์ด ๊ธ‰์†๋„๋กœ ๋ฐœ์ „ํ•จ์— ๋”ฐ๋ผ ์„ธ๊ณ„๋Š” ์ ์  ๋” ๊ฐ€๊นŒ์›Œ์ง€๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์—ฌ๋Ÿฌ ๋‚˜๋ผ ์‚ฌ์ด์˜ ๊ต๋ฅ˜๊ฐ€ ๋นˆ๋ฒˆํ•ด์ง€๋ฉด์„œ ๋น„๋‹จ ์ž๊ตญ์˜ ๋ชจ๊ตญ์–ด๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‹ค๋ฅธ ๋‚˜๋ผ์˜ ์–ธ์–ด๋กœ ๋œ ์ •๋ณด๋ฅผ ์ ‘ํ•ด์•ผ ํ•  ํ•„์š”์„ฑ์ด ๋”์šฑ ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ํ˜„์žฌ ์ธ๊ฐ„์˜ ๋ฒˆ์—ญ๋Šฅ๋ ฅ์€ ๋งค์ผ ๋„˜์ณ๋‚˜๋Š” ๊ฐ์ข… ๋‰ด์Šค๋ฅผ ์ „๋ถ€ ๋ฒˆ์—ญํ•˜๊ธฐ์—๋Š” ์—ญ๋ถ€์กฑ์ด๋‹ค. ๋•Œ๋ฌธ์— ํ˜„์žฌ ์ด๋Ÿฌํ•œ ์ˆ˜์š”๋ฅผ ๊ฐ๋‹นํ•˜๊ธฐ ์œ„ํ•œ ์ž๋™๋ฒˆ์—ญ๊ธฐ์ˆ ์€ ๊ทธ ์ง€์œ„๊ฐ€ ๋‚ ๋กœ ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋‹ค. ์ž๋™๋ฒˆ์—ญ๊ธฐ์ˆ ์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๊ทœ์น™๊ธฐ๋ฐ˜๊ณผ ํ†ต๊ณ„๊ธฐ๋ฐ˜ ๋ฐฉ์‹์œผ๋กœ ๋‚˜๋ˆŒ ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ ์ตœ๊ทผ์—๋Š” ์ธํ„ฐ๋„ท์˜ ๋ฐœ๋‹ฌ๋กœ ๋Œ€๋Ÿ‰์˜ ๋ง๋ญ‰์น˜๋ฅผ ์ž๋™์ ์œผ๋กœ ๊ตฌ์ถ•ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜๋ฉด์„œ ์ˆ˜ํ•™์  ๋ชจ๋ธ๊ณผ ๋Œ€๋Ÿ‰์˜ ๋ง๋ญ‰์น˜๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฒˆ์—ญ์„ ์ง„ํ–‰ํ•˜๋Š” ํ†ต๊ณ„๊ธฐ๋ฐ˜ ๋ฒˆ์—ญ๋ฐฉ์‹์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ ์  ๋” ํ™œ๋ฐœํžˆ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ํ†ต๊ณ„๊ธฐ๋ฐ˜ ๋ฒˆ์—ญ์‹œ์Šคํ…œ์€ ๊ตฌ ๊ธฐ๋ฐ˜๊ณผ ๊ตฌ๋ฌธ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์œผ๋กœ ๋‚˜๋ˆŒ ์ˆ˜ ์žˆ์œผ๋ฉฐ ํ˜„์žฌ ๊ฐ€์žฅ ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์ด๊ณ  ์žˆ๋Š” ๋ฐฉ์‹์€ ๊ตฌ ๊ธฐ๋ฐ˜ ํ†ต๊ณ„๊ธฐ๊ณ„๋ฒˆ์—ญ ์‹œ์Šคํ…œ์ด๋‹ค.๊ตฌ ๊ธฐ๋ฐ˜ ํ†ต๊ณ„๊ธฐ๊ณ„๋ฒˆ์—ญ ์‹œ์Šคํ…œ[1]์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๋‹จ์–ด์ •๋ ฌ, ๊ตฌ ์ถ”์ถœ ๋ฐ ๋””์ฝ”๋”ฉ(๋ฒˆ์—ญ๊ณผ์ •) ๋“ฑ ์„ธ ๊ฐ€์ง€ ๋ถ€๋ถ„์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ๋‹จ์–ด์ •๋ ฌ์€ ๋‘ ์–ธ์–ด์˜ ๋‹จ์–ด ์‚ฌ์ด์˜ ๋Œ€์‘๊ด€๊ณ„๋ฅผ ์ •ํ•ด์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋‹จ์–ด์ •๋ ฌ์˜ ์ข‹๊ณ  ๋‚˜์จ์€ ๋‹ค์Œ ๋‹จ๊ณ„์ธ ๊ตฌ ์ถ”์ถœ์— ์ง์ ‘ ์˜ํ–ฅ์„ ์ฃผ๋Š” ์•„์ฃผ ์ค‘์š”ํ•œ ์š”์†Œ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋‹จ์–ด์ •๋ ฌ์˜ ์„ฑ๋Šฅ์„ ์ œ๊ณ ํ•˜๋Š” ์—ฐ๊ตฌ๋Š” ํ†ต๊ณ„๊ธฐ๊ณ„๋ฒˆ์—ญ์˜ ์ฒซ ๋‹จ๊ณ„๋กœ ์•„์ฃผ ํฐ ๋น„์ค‘์„ ์ฐจ์ง€ํ•˜๋ฉฐ ์—ฐ๊ตฌ๋˜์–ด ์™”๋‹ค. ํ•˜์ง€๋งŒ ํ˜„์žฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ๋‹จ์–ด์ •๋ ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜[2]์€ ๋‘ ์–ธ์–ด์— ๋Œ€ํ•œ ์–ธ์–ดํ•™์  ๋ถ„์„์ด ์—†์ด ๋‹จ์ˆœํžˆ ์ˆ˜ํ•™์  ๋ชจ๋ธ์„ ํ†ตํ•˜์—ฌ ๊ณ„์‚ฐํ•œ ํ™•๋ฅ ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๋‘ ๋‹จ์–ด์˜ ๋Œ€์‘๊ด€๊ณ„๋ฅผ ๊ฒฐ์ •ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ์‹์€ ์–ธ์–ดํ•™์  ์ฐจ์ด๊ฐ€ ํฐ ๋‘ ์–ธ์–ด ์‚ฌ์ด์—์„œ ์ƒ๋Œ€์–ธ์–ด์— ๋Œ€์‘๋˜๋Š” ๋‹จ์–ด๊ฐ€ ์กด์žฌํ•˜์ง€ ์•Š์„ ๋•Œ ๋ง๋ญ‰์น˜์—์„œ ๋™์‹œ์— ๋‚˜ํƒ€๋‚˜๋Š” ๋นˆ๋„์ˆ˜๊ฐ€ ๋†’์€ ๋‹จ์–ด์— ๋Œ€์‘๊ด€๊ณ„๋ฅผ ์„ค์ •ํ•ด ์ฃผ๊ธฐ์— ๋‹จ์–ด์ •๋ ฌ์— ๋งŽ์€ ์˜ค๋ฅ˜๊ฐ€ ์ƒ๊ธด๋‹ค.ํ•œ๊ตญ์–ด์™€ ์ค‘๊ตญ์–ด๋Š” ์–ธ์–ดํ•™์  ์ฐจ์ด๊ฐ€ ํฐ ๋Œ€ํ‘œ์ ์ธ ์–ธ์–ด ์Œ์ด๋‹ค. ํ•œ๊ตญ์–ด๋Š” ๊ธฐ๋ณธ์ ์œผ๋กœ ์–ด์ ˆ๋กœ ์ด๋ฃจ์–ด์กŒ๊ณ  ์–ด์ ˆ์€ ๋‹จ์–ด์˜ ๋œป์„ ๊ฒฐ์ •ํ•˜๋Š” ์–ด๊ฐ„๊ณผ ๋ฌธ๋ฒ•์  ๊ธฐ๋Šฅ์„ ํ•˜๋Š” ์กฐ์‚ฌ, ์–ด๋ฏธ, ํŒŒ์ƒ์ ‘์‚ฌ์˜ ์กฐํ•ฉ์œผ๋กœ ์ด๋ฃจ์–ด์กŒ๋‹ค. ํŠนํžˆ ํ•œ๊ตญ์–ด ๋ฌธ์žฅ์—์„œ ์กฐ์‚ฌ, ์–ด๋ฏธ, ํŒŒ์ƒ์ ‘์‚ฌ๋Š” ํŠน์ •๋œ ๋œป์„ ๊ฐ€์ง€๊ณ  ์žˆ์ง€ ์•Š์ง€๋งŒ ์ „์ฒด ํ•œ๊ตญ์–ด ๋ฌธ์žฅ์—์„œ ์•ฝ 40%๋ฅผ ์ฐจ์ง€ํ•˜๋ฉฐ ์•„์ฃผ ํฐ ๋ฌธ๋ฒ•์  ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ค‘๊ตญ์–ด๋Š” ํ•œ๊ตญ์–ด์— ๋น„ํ•˜์—ฌ ์ด๋Ÿฌํ•œ ๋ฌธ๋ฒ•์  ๊ธฐ๋Šฅ์„ ํ•˜๋Š” ๋‹จ์–ด๊ฐ€ ๊ฑฐ์˜ ์กด์žฌํ•˜์ง€ ์•Š๊ณ  ๊ทธ ๊ธฐ๋Šฅ์„ ๋‹จ์–ด์˜ ์œ„์น˜๋‚˜ ์ˆœ์„œ์— ์˜ํ•˜์—ฌ ํ‘œํ˜„ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋งŽ์€ ํ•œ๊ตญ์–ด์˜ ์กฐ์‚ฌ๋‚˜ ์–ด๋ฏธ๋Š” ์ค‘๊ตญ์–ด์—์„œ ๋Œ€์‘๋˜๋Š” ๋‹จ์–ด๋ฅผ ๊ฐ€์ง€์ง€ ์•Š์œผ๋ฉฐ ์ด๋Ÿฌํ•œ ์ฐจ์ด๋Š” ํ˜„์žฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ๋‹จ์–ด์ •๋ ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ ๋งŽ์€ ์˜ค๋ฅ˜๋ฅผ ์œ ๋ฐœํ•œ๋‹ค. ํŠนํžˆ ๋‹จ์–ด ์‚ฌ์ด์˜ ์ž˜๋ชป๋œ ๋Œ€์‘๊ด€๊ณ„๋Š” ๊ตฌ ๊ธฐ๋ฐ˜ ํ†ต๊ณ„๊ธฐ๊ณ„๋ฒˆ์—ญ์— ํ•„์š”ํ•œ ์ •ํ™•ํ•œ ๊ตฌ๋ฅผ ์ถ”์ถœํ•˜์ง€ ๋ชปํ•˜๊ฑฐ๋‚˜ ํ‹€๋ฆฌ๊ฒŒ ์ถ”์ถœํ•จ์œผ๋กœ์จ ์ „์ฒด ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ์— ๋‚˜์œ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋”ฐ๋ผ์„œ ๋‹จ์–ด์ •๋ ฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ๊ฐœ์„ ํ•˜๋Š” ๊ฒƒ์€ ๊ตฌ ๊ธฐ๋ฐ˜ ํ†ต๊ณ„๊ธฐ๊ณ„๋ฒˆ์—ญ ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ์„ ์ œ๊ณ ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋ฐ˜๋“œ์‹œ ์„ ํ–‰๋˜์–ด์•ผ ํ•˜๋Š” ๋‹จ๊ณ„์ด๋‹ค.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ตฌ ๊ธฐ๋ฐ˜ ํ†ต๊ณ„๊ธฐ๊ณ„๋ฒˆ์—ญ ์‹œ์Šคํ…œ์—์„œ ๊ฐ€์žฅ ๋งŽ์ด ์‚ฌ์šฉํ•˜๋Š” GIZA++๋ฅผ ์ด์šฉํ•˜์—ฌ ํ•œ๊ตญ์–ด-์ค‘๊ตญ์–ด ๋‹จ์–ด์ •๋ ฌ์˜ ์ง„ํ–‰ํ•˜๊ณ  ๊ทธ ๊ฒฐ๊ณผ์— ๋Œ€ํ•˜์—ฌ ์˜ค๋ฅ˜๊ฐ€ ๋‚˜ํƒ€๋‚˜๋Š” ์›์ธ์„ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ ํ•ด๊ฒฐ์ฑ…์œผ๋กœ ํ˜•ํƒœ์†Œ๋ฅผ ์‚ญ์ œํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ ํ†ต๊ณ„์  ์ˆ˜์ •๊ทœ์น™์„ ์ด์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. 2์žฅ์—์„œ๋Š” ํ•œ๊ตญ์–ด-์ค‘๊ตญ์–ด ๋‹จ์–ด์ •๋ ฌ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋ฌธ์ œ์ ์„ ๋ถ„์„ํ•˜๊ณ  3์žฅ์—์„œ๋Š” ๋‹จ์–ด์ •๋ ฌ์— ๊ด€๋ จ๋œ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋“ค์„ ์‚ดํŽด๋ณธ๋‹ค. 4์žฅ์—์„œ๋Š” ๋‹จ์–ด์ •๋ ฌ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ œ์•ˆํ•œ ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด์„œ ์ž์„ธํ•˜๊ฒŒ ์„ค๋ช…ํ•˜๊ณ  5์žฅ์—์„œ๋Š” ์œ„์—์„œ ์ œ์•ˆํ•œ ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ œ์‹œํ•˜๋ฉฐ 6์žฅ์—์„œ๋Š” ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ๊ณผ์ œ์— ๋Œ€ํ•ด์„œ ์–ธ๊ธ‰ํ•œ๋‹ค.In the statistical machine translation, correspondences between the words in the source and the target language are learned from parallel corpora and often no morph-syntactic gap was considered to structure underlying models. In particular, Korean and Chinese which belong to extremely different language families in terms of typology and genealogy cause many errors in these models. In this thesis, we describe two methods to improve word alignment quality as well as Korean to Chinese phrase based machine translation quality. One is to remove Korean morphemes which have no correspondence Chinese word in the Chinese sentence. The other one is automatically extract correction rule to refine word alignment result output from GIZA++. The correction rule was generated from gold standard set of word alignment and automatically applied to the phrase based SMT system. The experiments result show that the first approach improve machine translation quality significantly. Second approach lead to 17.6% relative decrease in alignment error rate compared to baseline system. We also demonstrate that combination with two approaches yields up 1.11 BLEU point improvement over the baseline system

    ๊ฒฉ์ž ๋น„์„ญ๋™์  ์žฌ๊ทœ๊ฒฉํ™”์™€ ์•ฝ๋ ฅ์˜ CP ๊นจ์ง์— ๋Œ€ํ•œ ์ ์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€,2015. 2. ์ด์›์ข….CP violation was first discovered in the Kaon system in 1964. K^0 and \bar{K}^0 are flavor eigenstates. They are mixed together by weak interaction in nature. The mass eigenstates K_L can decay to two pion state in two ways. First, the CP odd component in KL decay to the two pion states. It is called direct CP violation and parametrized by ฮต'. Another way is that the CP even component in K_L decay to the two pion states. it does not breaking CP symmetry but the existence of CP even compoenet in K_L indicates the CP violation. Therefore, it is called indirect CP violation and parametrized by ฮต_K . ฮต_K is the indirect CP violation parameter and very well known from experiments. ฮต_K can be written in terms of B_K which contains all the non-perturbative QCD contributions for ฮต_K. Current estimate of ฮต_K with exclusive V_cb and B_K computed in SWME collaboration has 3.7 ฯƒ difference between the result of experiment and those of standard model theory. Hence, it is very important to reduce the theoratical error of ฮต_K . The most dominant error of ฮต_K comes fromVcbbut the second dominant error comes from B_K. In our calculation, one of the dominant source of B_K error comes from the matching factor. One-loop perturbative matching has 4.4% systematic error for B_K. The non-perturbative renormalization (NPR) method can reduce this error down to 2% level. Using NPR method with Regularization Independent Momentum Subtraction (RI-MOM) scheme, we calculate the wave function renormalization factor Z_q from conserved vector and axial currents and the mass renormalization factor Z_m from scalar and pseudo-scalar staggered bilinear operators. We obtain the results of the matching factor for the staggered four-fermion operators relevant to B_K using NPR method in RI-MOM scheme. We compare the NPR results with those of one-loop perturbation theory and they are consistent within 2ฯƒ. furthermore, the error of the NPR results are about half of the those of one- loop perturbation theory. For further research, we plan to apply NPR method in RI-SMOM (symmetric momentumi condition) scheme.CP ๊นจ์ง์€ 1964๋…„์— ์ผ€์ด์˜จ ์‹œ์Šคํ…œ์—์„œ ์ฒ˜์Œ์œผ๋กœ ๋ฐœ๊ฒฌ๋˜์—ˆ๋‹ค. K^0์™€ \bar{K}^0๋Š” ์•ฝ๋ ฅ์— ์˜ํ•ด์„œ ์„œ๋กœ ์„ž์ด๊ฒŒ ๋˜๊ณ  ์งˆ๋Ÿ‰ ๊ณ ์œ ์ƒํƒœ์ธ K_L๊ณผ K_S ์œผ๋กœ ์กด์žฌํ•˜๊ฒŒ ๋œ๋‹ค. K_L์ด ํŒŒ์ด์˜จ ๋‘๊ฐœ๋กœ ๋ถ•๊ดดํ•˜๋Š” ๊ณผ์ •์—์„œ CP ๊นจ์ง์ด ๋ฐœ์ƒํ•˜๊ฒŒ ๋˜๋Š”๋ฐ ์ง์ ‘ ๊นจ์ง๊ณผ ๊ฐ„์ ‘ ๊นจ์ง์ด ์žˆ๋‹ค. ์—ฌ๊ฐ€์„œ ๊ฐ„์ ‘ ๊นจ์ง์„ ์„ค๋ช…ํ•˜๋Š” ๋งค๊ฐœ๋ณ€์ˆ˜๊ฐ€ ฮต_K์ด๋‹ค. CP ๊นจ์ง ๋งค๊ฐœ๋ณ€์ˆ˜์ธ ฮต_K ๋Š” ์‹คํ—˜์ ์œผ๋กœ ๋งค์šฐ ์ž˜ ์•Œ๋ ค์ ธ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ฮต_K ๋ฅผ ๊ฒฉ์ž ๊ฒŒ์ด์ง€ ์ด๋ก ์„ ์ด์šฉํ•ด์„œ ์ •๋ฐ€ํ•˜๊ฒŒ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์€ ํ‘œ์ค€๋ชจํ˜•์„ ๊ฒ€์ฆํ•  ์ˆ˜ ์žˆ๋Š” ์•„์ฃผ ์ค‘์š”ํ•œ ์ˆ˜๋‹จ์ด๋‹ค. ฮต_K ์˜ ํ˜„์žฌ ์ด๋ก ์ ์ธ ๊ณ„์‚ฐ ๊ฒฐ๊ณผ๋Š” ์‹คํ—˜๊ฐ’๊ณผ 3.7 ฯƒ์˜ ๊ฒฉ์ž๋ฅผ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ์ด๋ก ์ ์ธ ๊ณ„์‚ฐ์˜ ์ฃผ์š” ์˜ค์ฐจ ์›์ธ ์ค‘์— ํ•˜๋‚˜๋Š” B_K ์˜ ๋งž์ถค์ธ์ž๋กœ๋ถ€ํ„ฐ ์˜จ๋‹ค. ๊ฒฉ์ž ์„ญ๋™ ์ด๋ก ์„ ์ด์šฉํ•ด์„œ ๊ณ„์‚ฐ๋œ B_K ์˜ ์ผ์ฐจ ๋งž์ถค์ธ์ž์—์„œ ์˜ค๋Š” ๊ณ„ํ†ต ์˜ค์ฐจ๋Š” 4.4%์ด๋‹ค. ๋ฐ˜๋ฉด์— ๋น„์„ญ๋™์  ์žฌ๊ทœ๊ฒฉํ™” ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜๋ฉด ์ด ๊ณ„ํ†ต ์˜ค์ฐจ๋ฅผ 2% ์•„๋ž˜๋กœ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋ณด์กด๋˜๋Š” ๋ฒกํ„ฐ ํ๋ฆ„๊ณผ ์ถ•๋ฒกํ„ฐ ํ๋ฆ„ ์—ฐ์‚ฐ์ž๋ฅผ ์ด์šฉํ•˜์—ฌ ํŒŒ๋™ ํ•จ์ˆ˜์˜ ์žฌ๊ทœ๊ฒฉํ™” ์ธ์ž๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๊ณ  ์Šค์นผ๋ผ์™€ ์œ ์‚ฌ์Šค์นผ๋ผ ์—ฐ์‚ฐ์ž๋ฅผ ์ด์šฉํ•˜์—ฌ ์งˆ๋Ÿ‰ ์žฌ๊ทœ๊ฒฉํ™” ์ธ์ž๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๋˜ํ•œ 4์ฐจ ํŽ˜๋ฅด๋ฏธ์˜จ ์—ฐ์‚ฐ์ž๋ฅผ ์ด์šฉํ•˜์—ฌ B_K ์˜ ๋งž์ถค์ธ์ž๋„ ๊ณ„์‚ฐ์„ ํ•˜์˜€๋‹ค. ์šฐ๋ฆฌ๋Š” ์ด ๊ฒฐ๊ณผ๋ฅผ ์ผ์ฐจ ์„ญ๋™ ์ด๋ก ์œผ๋กœ ๊ณ„์‚ฐ๋œ ๊ฐ’๊ณผ ๋น„ ๊ต๋ฅผ ํ•˜์˜€์œผ๋ฉฐ 2ฯƒ ์ด๋‚ด์—์„œ ์ผ์น˜ํ•จ์„ ๋ณด์˜€๋‹ค. ์ด๋Š” ๋น„์„ญ๋™์  ๋ฐฉ๋ฒ•์œผ๋กœ ์–ป์€ ๊ฒฐ๊ณผ๊ฐ€ ํƒ€๋‹นํ•จ์„ ์ผ๊ฑท๋Š”๋‹ค. ๋˜ํ•œ ์ผ์ฐจ ์„ญ๋™ ์ด๋ก ์˜ ๊ฒฐ๊ณผ๋ณด๋‹ค ์˜ค์ฐจ๊ฐ€ ์ ˆ๋ฐ˜์ด์ƒ ์ค„์–ด๋“  ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค.1 Introduction 1 1.1 Motivation 1 1.2 QCD 2 1.3 Lattice QCD 4 1.4 Staggered Fermions 7 1.5 CP Violation 11 2 RI-MOM Scheme for Quark Propagators in Continuum 17 3 RI-MOM Scheme for Bilinear Operator in Continuum 21 3.1 Wave Function Renormalization Factor Z_q in continuum 29 3.2 Quark Mass Renormalization Factor Z_m in Continuum 31 3.3 Other Bilinear Operators in Continuum 32 4 RI-MOM Scheme for Four Fermion Operator in Continuum 33 5 Non-perturbative Renormalization for Staggered Quark Propagators 39 5.1 NPR for Staggered Quark Propagator 39 5.2 Results of Self-energy Analysis 43 6 Non-perturbative Renormalization for Staggered Bilinear Operators 51 6.1 NPR for Staggered Bilinear Operators 51 6.1.1 Simulation Details 54 6.2 Wave function renormalization 55 6.2.1 Z_q from Conserved Vector Current 55 6.2.1.1 Pole-fit method for Conserved Vector Current . . 62 6.2.2 Z_q from Conserved Axial Current 67 6.2.2.1 Pole-fit method for Conserved Axial Current . . . 72 6.2.3 Systematic Error of Z_q 76 6.3 Mass Renormalization 79 6.3.1 Mass Renormalization Factor from S โŠ— S Operator 80 6.3.2 Mass Renormalization Factor from P โŠ— P Operator 85 6.3.3 Error Budget of Z_m 92 6.4 Renormalization Factor of Other Bilinear Operators 94 6.4.1 Renormalization Factor of Tensor Operator with Scalar taste 94 6.4.2 Renormalization Factor of Tensor Operator with pseudo-scalar taste 100 6.4.3 Systematic Error of the Renormalization Factor of Tensor Operator 104 6.5 Off-diagonal Analysis 106 6.6 Result 112 7 Non-perturbative Renormalization for Staggered Four-fermion Operators 119 7.1 NPR for Staggered Four-fermion Operators 119 7.2 NPR for B_K operator 122 7.3 Results 127 7.3.1 Diagonal Terms 127 7.3.2 Off-diagonal Terms 134 7.3.3 Systematic Error 139 8 Conclusion and Discussion 143 A Ward-Takahashi Identity in Continuum 145 A.1 Conserved Vector Current 145 A.2 Conserved Axial Current 148 B Renormalization Factor for Conserved Currents in Continuum 151 B.1 Conserved Vector Current 151 B.2 Conserved Axial Current 152 C Ward-Takahashi Identity for Staggered Fermions 153 C.1 Conserved Vector Current 153 C.2 Conserved Axial Current 156 D Renormalization Factor for Conserved Currents for Staggered Fermions 159 D.1 Conserved Vector Current. . . . . . . . . . . . . . . . . . . . . . . 159 D.2 Conserved Axial Current .. . . . . . . . . . . . . . . . . . . . . . . 160 E Amputated Greens Function for Staggered Bilinear Operators 163 F The RG Running Formula 167 F.1 ฮฒ-function 167 F.2 Anomalous Dimension for Quark Mass 167 F.3 Anomalous Dimension for Quark Field 168 F.4 Anomalous Dimension for Bilinear Operators 170 F.5 RG Running for B_K 171 G The Conversion Factors from RI-MOM to MSbar 175 G.1 The Conversion Factor of Wave Function Renormalization Factor Zq 175 G.2 The Conversion Factor of Mass Renormalization Factor Z_m 175 G.3 The Conversion Factor of Tensor Bilinear Operator Renormalization Factor Z_T 176 G.4 RI-MOM โ†’ MSbar Scheme using Fixed Point for B_K 176 H Weinberg Theorem 179 I One-loop Perturbation Theory 181 ๊ตญ๋ฌธ์ดˆ๋ก 189Docto

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ฐ”์ด์˜ค์‹œ์Šคํ…œยท์†Œ์žฌํ•™๋ถ€(๋ฐ”์ด์˜ค์‹œ์Šคํ…œ๊ณตํ•™์ „๊ณต), 2014. 8. ์ •์ข…ํ›ˆ.์‚ด์•„ ์žˆ๋Š” ์„ธํฌ๋Š” ํ™”ํ•™ ๋ฐ ๋ฌผ๋ฆฌํ•™์  ์ž๊ทน ๊ทธ๋ฆฌ๊ณ  ์ฃผ๋ณ€ ์„ธํฌ ํ˜น์€ ์„ธํฌ์™ธ๊ธฐ์งˆ๊ฐ„์˜ ์ƒํ˜ธ ๊ด€๊ณ„ ๋“ฑ ๋ณต์žกํ•˜๊ณ  ๊ธฐ๋Šฅ์ ์ธ ๋ฏธ์„ธ ํ™˜๊ฒฝ์— ๋…ธ์ถœ๋˜์–ด ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋Ÿฌํ•œ ๋ฏธ์„ธ ํ™˜๊ฒฝ๊ณผ ์œ ์‚ฌํ•œ ํ˜น์€ ๋˜‘๊ฐ™์€ ์กฐ๊ฑด์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ํ”Œ๋žซํผ์˜ ๊ฐœ๋ฐœ์€ ์ƒ๋ฌผํ•™, ์น˜๋ฃŒ ๋ฐ ์ง„๋‹จ ์—ฐ๊ตฌ, ์ค„๊ธฐ์„ธํฌ ๋ฐ ์žฌ์ƒ์˜ํ•™, ์งˆ๋ณ‘ ๋ชจ๋ธ ๊ฐœ๋ฐœ ๋“ฑ ๋‹ค์–‘ํ•œ ์ธ๊ฐ„, ๋™๋ฌผ ๋ฐ ์‹๋ฌผ์˜ ์ƒ๋ฌผํ•™์  ์‘์šฉ์— ์žˆ์–ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์„ธํฌ๋Š” ๋ฏธ์„ธ ํ™˜๊ฒฝ ๋‚ด ์ฝœ๋ผ๊ฒ, ์—˜๋ผ์Šคํ‹ด ๋“ฑ์˜ ๋งˆ์ดํฌ๋กœ ๋ฐ ๋‚˜๋…ธ๋ฏธํ„ฐ ์ˆ˜์ค€์˜ ๊ณ ๋ถ„์ž ํŒŒ์ด๋ฒ„ ํ˜•ํƒœ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋Š” ์„ธํฌ์™ธ๊ธฐ์งˆ ๋ฐ ํŠน์ด์  ์กฐ์ง ๊ตฌ์กฐ์— ๋‘˜๋Ÿฌ ์Œ“์—ฌ ์žˆ์œผ๋ฉฐ ์ด๋Š” ์„ธํฌ์˜ ๊ธฐ๋Šฅ์  ์กฐ์ ˆ์— ์žˆ์–ด ๊ตฌ์กฐ์  ์ž๊ทน์˜ ์ค‘์š”์„ฑ์„ ์ผ๋Ÿฌ ์ค€๋‹ค. ํ•˜์ง€๋งŒ ํ™”ํ•™ ๋ฐ ๋ฌผ๋ฆฌํ•™์  ์ž๊ทน์— ๋”ฐ๋ฅธ ์„ธํฌ์˜ ๊ธฐ๋Šฅ์  ์ดํ•ด ๋ฐ ์‘์šฉ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์— ๋น„ํ•ด์„œ ์„ธํฌ ์™ธ๊ธฐ์งˆ ํ˜น์€ ์กฐ์ง ๋‚ด ํŠน์ด์  ๊ตฌ์กฐ ์ž๊ทน์— ๋”ฐ๋ฅธ ์„ธํฌ์˜ ๊ธฐ๋Šฅ์  ์ดํ•ด๋Š” ๊ฑฐ์˜ ๋˜์–ด ์žˆ์ง€ ์•Š๋‹ค. ์ฆ‰, ๋‹ค์–‘ํ•œ ์ƒ๋ฌผํ•™์  ์—ฐ๊ตฌ ๋ฐ ์‘์šฉ์— ์žˆ์–ด์„œ ์„ธํฌ๊ฐ€ ๋…ธ์ถœ๋˜์–ด ์žˆ๋Š” ๊ตฌ์กฐ์  ์ž๊ทน์€ ๊ฑฐ์˜ ๋ฌด์‹œ๋˜๊ฑฐ๋‚˜ ์ตœ์†Œํ™” ๋˜๊ณ  ์žˆ๋Š”๋ฐ ์ด๋Š” ์„ธํฌ ๋ฏธ์„ธ ํ™˜๊ฒฝ ๋‚ด ๋‚˜๋…ธ๋ฏธํ„ฐ ์ˆ˜์ค€์˜ ๋งค์šฐ ๋ฏธ์„ธํ•˜๊ณ  ๋ณต์žกํ•œ ํ˜•ํƒœ์˜ ํŠน์ด์  ๊ตฌ์กฐ๋ฅผ ์ง€๋‹ˆ๋Š” ํ”Œ๋žซํผ ๊ฐœ๋ฐœ์— ์žˆ์–ด ๊ธฐ์ˆ ์  ํ•œ๊ณ„๊ฐ€ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ํ•œ๊ณ„์ ์„ ๊ทน๋ณตํ•˜๊ณ ์ž ์ƒ์ฒด์žฌ๋ฃŒ ๊ฐ€๊ณต๊ธฐ์ˆ ์„ ์ด์šฉํ•˜์—ฌ ํŠน์ด์  ์กฐ์ง ๋ฐ ์„ธํฌ์™ธ๊ธฐ์งˆ์˜ ๊ตฌ์กฐ์  ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์ƒ์ฒด๋ชจ๋ฐฉํ˜• ๋‚˜๋…ธ๊ตฌ์กฐ ํ”Œ๋žซํผ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋ชจ์„ธ๊ด€๋ ฅ ๋ฆฌ์†Œ๊ทธ๋ž˜ํ”ผ, ๋‚˜๋…ธ๋ฌผ์งˆ ๋“ฑ์„ ํ™œ์šฉํ•œ ๋‹ค์–‘ํ•œ ๋‚˜๋…ธ๊ธฐ์ˆ ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์‚ด์•„ ์žˆ๋Š” ์„ธํฌ์—๊ฒŒ ์ƒ์ฒด ๋‚ด ๊ตฌ์กฐ์  ์ž๊ทน๊ณผ ์œ ์‚ฌํ•œ ํ™˜๊ฒฝ์„ ์ œ๊ณตํ•ด ์ค„ ์ˆ˜ ์žˆ๋Š” ํ”Œ๋žซํผ์„ ๊ฐœ๋ฐœํ•˜๊ณ , ์ด๋ฅผ ์‘์šฉํ•˜์—ฌ ์„ธํฌ ๊ธฐ๋Šฅ์„ ์กฐ์ ˆํ•˜๊ฑฐ๋‚˜ ์ด‰์ง„ ๊ทธ๋ฆฌ๊ณ  ์กฐ์ง์„ ์žฌ์ƒ ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ˆ ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ด์— ๋”ฐ๋ฅธ ๋ณธ ๋…ผ๋ฌธ์˜ ๊ตฌ์ฒด์ ์ธ ๋ชฉ์ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. (1) ์„ธํฌ ๊ธฐ๋Šฅ ์กฐ์ ˆ ๋ฐ ์ด‰์ง„์„ ์œ„ํ•œ ํ˜•ํƒœ๊ฐ€ ์ •๋ฐ€ํ•˜๊ฒŒ ์ปจํŠธ๋กค๋œ ์ƒ์ฒด๋ชจ๋ฐฉํ˜• ๋‚˜๋…ธ๊ตฌ์กฐ ํ”Œ๋žซํผ ๊ฐœ๋ฐœ, (2) ์„ธํฌ ๊ธฐ๋Šฅ ์ด‰์ง„ ๋ฐ ์ƒ๋ฌผํ•™์  ํ˜„์ƒ์— ์žˆ์–ด ๋‚˜๋…ธ๊ตฌ์กฐ์˜ ์—ญํ•  ๊ตฌ๋ช…, (3) ๋ณต์žกํ•œ ์ˆ˜์ˆ ์  ์น˜๋ฃŒ ์—†์ด๋„ ์กฐ์ง์„ ์žฌ์ƒ ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ์ƒ์ฒด๋ชจ๋ฐฉํ˜• ๋‚˜๋…ธ๊ตฌ์กฐ ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ. ์ด๋ฅผ ์œ„ํ•ด์„œ ๋ณธ ๋…ผ๋ฌธ์€ 3๊ฐœ์˜ ํŒŒํŠธ ๋‚ด 7๊ฐœ์˜ ๋…๋ฆฝ์ ์ธ ์—ฐ๊ตฌ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ์œผ๋ฉฐ ๋ณธ ๋…ผ๋ฌธ์˜ ์ฃผ์š” ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ ๋ฒˆ์งธ ํŒŒํŠธ ์—ฐ๊ตฌ์—์„œ ๋ชจ์„ธ๊ด€๋ ฅ ๋ฆฌ์†Œ๊ทธ๋ž˜ํ”ผ ๊ธฐ์ˆ  ๊ธฐ๋ฐ˜์œผ๋กœ ๋‹ค์–‘ํ•œ ๋‚˜๋…ธ๋ฏธํ„ฐ ์‚ฌ์ด์ฆˆ์™€ ํ•จ๊ป˜ ์ผ๋ ฌ๋กœ ๋ฐฐ์—ด๋˜์–ด ์žˆ๋Š” ํŠน์ด์  ์กฐ์ง ๋ฐ ์„ธํฌ์™ธ๊ธฐ์งˆ ๊ตฌ์กฐ๋ฅผ ๋ชจ๋ฐฉํ•œ ์„ธํฌ ๋ฐฐ์–‘ ํ”Œ๋žซํผ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœํ•œ ๋‚˜๋…ธ๊ตฌ์กฐ ํ”Œ๋žซํผ์„ ์‘์šฉํ•˜์—ฌ ์„ธํฌ์˜ ๊ตฌ์กฐ ๋ฐ ๊ธฐ๋Šฅ์ด ๋‚˜๋…ธ๊ตฌ์กฐ์˜ ๋ฐ€๋„์— ๋งค์šฐ ๋ฏผ๊ฐํ•˜๊ฒŒ ๋ฐ˜์‘ํ•จ์„ ๊ทœ๋ช…ํ•˜์˜€๊ณ , ํ”Œ๋žซํผ ๋‚ด ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋Š” ๋‚˜๋…ธ๊ตฌ์กฐ ๋ฐ€๋„์˜ ์ตœ์ ํ™”์— ๋”ฐ๋ผ ์ƒ์ฒ˜ ์น˜์œ , ์ค„๊ธฐ์„ธํฌ ๋ถ„ํ™” ๋“ฑ ์„ธํฌ ๊ธฐ๋Šฅ์„ ์ด‰์ง„ ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ํŒŒํŠธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋งˆ์ดํฌ๋กœ ๋ฐ ๋‚˜๋…ธ๋ฏธํ„ฐ ํ˜น์€ ๋‹ค์–‘ํ•œ ๋‚˜๋…ธ๋ฏธํ„ฐ ์‚ฌ์ด์ฆˆ๋ฅผ ๋™์‹œ์— ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์กฐ์ง ๋ฐ ์„ธํฌ์™ธ๊ธฐ์งˆ์˜ ๊ณ„์ธต ๊ตฌ์กฐ๋ฅผ ๋ชจ๋ฐฉํ•œ ์„ธํฌ ๋ฐฐ์–‘ ํ”Œ๋žซํผ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋ชจ์„ธ๊ด€๋ ฅ ๋ฆฌ์†Œ๊ทธ๋ž˜ํ”ผ ๋ฐ ๋งˆ์ดํฌ๋กœ ํ‘œ๋ฉด์ฃผ๋ฆ„ ๊ตฌ์กฐ ๊ธฐ์ˆ ์„ ์‘์šฉํ•˜์—ฌ ๋งˆ์ดํฌ๋กœ ๋ฐ ๋‚˜๋…ธ๊ตฌ์กฐ๊ฐ€ ๊ณ„์ธต์ ์œผ๋กœ ์ •๋ฐ€ํ•˜๊ฒŒ ์ปจํŠธ๋กค๋œ ํ”Œ๋žซํผ์„ ์ œ์ž‘ํ•˜์˜€๊ณ , ์ด๋ฅผ ํ†ตํ•˜์—ฌ ๋‚˜๋…ธ๊ตฌ์กฐ๊ฐ€ ์„ธํฌ-์ง€์ง€์ฒด ๋ฐ ์„ธํฌ-์„ธํฌ๊ฐ„ ์ƒํ˜ธ ๊ด€๊ณ„๋ฅผ ์กฐ์ ˆํ•˜๊ณ  ์ด์— ๋”ฐ๋ผ ์„ธํฌ์˜ ๊ธฐ๋Šฅ์„ ์ด‰์ง„ ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ๊ตฌ๋ช…ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ชจ์„ธ๊ด€๋ ฅ ๋ฆฌ์†Œ๊ทธ๋ž˜ํ”ผ ๊ธฐ์ˆ  ๊ธฐ๋ฐ˜์— ๋‚˜๋…ธ๋ฌผ์งˆ์ธ ๊ทธ๋ž˜ํ•€์„ ์ด์šฉํ•˜์—ฌ ์ˆ˜๋ฐฑ ๋‚˜๋…ธ ๋ฐ ์ˆ˜์‹ญ ๋‚˜๋…ธ๋ฏธํ„ฐ ๊ตฌ์กฐ๋ฅผ ๋™์‹œ์— ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ํ”Œ๋žซํผ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ๊ตฌ์กฐ์  ์ž๊ทน์— ๋”ฐ๋ผ์„œ ์„ธํฌ์˜ ๊ธฐ๋Šฅ์„ ๋”์šฑ๋” ์ด‰์ง„ ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰ ์„ธ ๋ฒˆ์งธ ํŒŒํŠธ ์—ฐ๊ตฌ์—์„  ์ƒ์ฒด๋ชจ๋ฐฉํ˜• ๋‚˜๋…ธ๊ตฌ์กฐ ํ”Œ๋žซํผ๊ณผ ์„ธํฌ ๋ฏธ์„ธ ํ™˜๊ฒฝ ๋‚ด ๋‹ค์–‘ํ•œ ํ™”ํ•™์  ๋ฌผ์งˆ๊ณผ ์„ธํฌ๊ฐ€ ์‹œ์Šคํ…œ์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ์„ ๋•Œ ์„ธํฌ์˜ ๊ธฐ๋Šฅ ์ด‰์ง„์„ ๊ทน๋Œ€ํ™” ์‹œํ‚ฌ ์ˆ˜ ์žˆ๊ณ , ์ด ์‹œ์Šคํ…œ์  ๊ด€์ ์— ๋”ฐ๋ผ ์†์ƒ๋œ ์กฐ์ง์˜ ์น˜๋ฃŒ ๋ฐ ์žฌ์ƒ์„ ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ผˆ ์กฐ์ง ๋‚ด ํ˜ˆ๊ด€์ด ํ•จ๊ป˜ ์กด์žฌํ•จ์— ์˜๊ฐ์„ ์–ป์–ด ์„ธํฌ์™ธ๊ธฐ์งˆ์„ ๋ชจ๋ฐฉํ•œ ์„ธํฌ ๋ฐฐ์–‘ ํ”Œ๋žซํผ์— ์ค„๊ธฐ์„ธํฌ์™€ ํ˜ˆ๊ด€์„ธํฌ๋ฅผ ๋™์‹œ์— ๋ฐฐ์–‘ํ•˜์˜€๊ณ , ์ด์— ๋”ฐ๋ผ ์ค„๊ธฐ์„ธํฌ์˜ ๊ณจ์„ธํฌ๋กœ์˜ ๋ถ„ํ™”๊ฐ€ ๋”์šฑ๋” ์ด‰์ง„ ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์— ๊ธฐ๋ฐ˜์„ ๋‘์–ด ์ƒ์ฒด์ ํ•ฉ์„ฑ ๋ฌผ์งˆ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋Š” ์ƒ์ฒด๋ชจ๋ฐฉํ˜• ๋‚˜๋…ธ๊ตฌ์กฐ ํ”Œ๋žซํผ๊ณผ ์ค„๊ธฐ์„ธํฌ๊ฐ€ ๊ฒฐํ•ฉ๋˜์–ด ์žˆ๋Š” ์ค„๊ธฐ์„ธํฌ ํŒจ์น˜๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๊ณ , ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์†์ƒ๋œ ๋ผˆ๋ฅผ ์ˆ˜์ˆ ์  ์น˜๋ฃŒ ์—†์ด ์žฌ์ƒ ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ๋™๋ฌผ๋ชจ๋ธ์„ ํ†ตํ•˜์—ฌ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ฐ•ํ…Œ๋ฆฌ์•„๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฐœ๋ฐœํ•œ ์…€๋ฃฐ๋กœ์˜ค์Šค ๋‚˜๋…ธ๊ตฌ์กฐ ํŒจ์น˜๋ฅผ ์ด์šฉํ•˜์—ฌ ์ฒœ๊ณต๋œ ๊ณ ๋ง‰ ์žฌ์ƒ์„ ์ด‰์ง„ ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„  ๋‚˜๋…ธ๊ณตํ•™ ๋ฐ ์ƒ์ฒด๋ชจ๋ฐฉํ•™์  ์ ‘๊ทผ๋ฒ•์„ ํ†ตํ•˜์—ฌ ์ƒ์ฒด๋ชจ๋ฐฉํ˜• ๋‚˜๋…ธ๊ตฌ์กฐ ํ”Œ๋žซํผ์„ ๊ฐœ๋ฐœํ•˜์˜€๊ณ  ์ด์˜ ์„ธํฌ ๊ธฐ๋Šฅ ์กฐ์ ˆ ๋ฐ ์ด‰์ง„ ๊ทธ๋ฆฌ๊ณ  ์กฐ์ง ์žฌ์ƒ์„ ์œ„ํ•œ ๊ณตํ•™์  ๋„๊ตฌ๋กœ์„œ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ๊ฐœ๋ฐœ๋œ ๋‚˜๋…ธ๊ตฌ์กฐ ๊ฐœ๋ฐœ ๊ธฐ์ˆ ์€ ๋‹ค์–‘ํ•œ ์ƒ์ฒด์žฌ๋ฃŒ ๋ฐ ์ƒ๋ฌผ์žฌ๋ฃŒ์˜ ๋‚˜๋…ธ ๋ฐ ๋งˆ์ดํฌ๋กœ๋ฏธํ„ฐ ์ˆ˜์ค€์˜ ๋ฏธ์„ธ๊ฐ€๊ณต ๊ธฐ์ˆ  ๊ฐœ๋ฐœ์— ์‘์šฉ ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ฉฐ, ์ธ๊ฐ„ ํ˜น์€ ๋™๋ฌผ์˜ ์งˆ๋ณ‘ ๋ชจ๋ธ, ์ง„๋‹จ ๋ฐ ์น˜๋ฃŒ ๊ฐœ๋ฐœ ๋“ฑ ๋‹ค์–‘ํ•œ ์ƒ๋ฌผํ•™์  ์‘์šฉ์— ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Living cells are exposed to complex and functional microenvironment including soluble macromolecules, biophysical cues, and interactions between cell-cell and cell-extracellular matrix (ECM), suggesting that the design and manipulation of engineered cellular microenvironments is of great important in a wide variety of biological applications such as fundamental biology, therapeutic and diagnostic research, stem cells and regenerative medicine, and developing in vitro disease models. Compared with the effects of soluble macromolecules and biophysical cues, the extent and importance of architecture of ECMs in defining cellular function is currently poorly understood in spite of the essential fact that living cells display high sensitivity to the ECM composed of complex and well-defined topographies in protein fibers such as fibrillar collagens and elastins with feature sizes ranging from tens to several hundreds of nanometers in vivo. Namely, the architecture effects of ECMs are mainly neglected or minimized as a considering factor for most in vitro and in vivo experimentation. In this dissertation, we developed a series of advanced nanopatterned platforms inspired by the unique architectures of native tissues and ECMs in a detailed and comprehensive fashion using nanofabrication technologies such as capillary force lithography (CFL) to provide cells the in vivo-like topographical cell environment. Using the nanoengineered biomimetic platforms, the roles of nanotopography in regulation of cellular and multicellular structure and function were investigated. We also utilized the biomimetic systems composed of a nanoengineered substrate, specific soluble macromolecules, and cells to achieve desired phenotypic responses toward tissue regeneration. The specific aims of my thesis are as follows are: (1) to design and manipulate nanotopographically defined platforms with precisely controlled topographical architectures as a synthetic ECM for regulating structure and function of cells in single (i.e., single adherent cells) and multi-cellular (i.e., cohesive groups of cells) levels, (2) to investigate the detailed role of nanotopography in cellular behavior for developing a methodology for promoting cell function and modeling biological processing such as stem cell differentiation and would repair, and (3) to develop a strategy for tissue regeneration such as bone and tympanic membrane using nanoengineered biomimetic systems. The working hypothesis underlying my research is that nanoengineered biomimetic platforms can (i) provide cells in vivo-like topographical cues that control cellular and multicellular structure and function, and (ii) allow appropriate environments for repair or regeneration of damaged tissues without surgical treatments. The main results of my dissertation research can be summarized as follows. First, inspired by the architectures of native ECMs in various tissues, nanotopographically defined ridge/groove patterned substrata with precisely controlled sizes were developed using CFL. Using these platforms, it was found that nanotopographical density can control the morphology, focal adhesion formation, migration, ECM molecule production of fibroblast cells, and would healing as well as the adhesion, migration, and differentiation of mesenchymal stem cells (MSCs). Second, nanopatterned hierarchical platforms (i.e., multiscale topography) were developed for better mimicking architectures of ECMs using CFL in combination with micro wrinkling technique and nanomaterials. Using anisotropically multiscale patterned substrata with precisely defined micro- and nanotopography, the potential role of nanotopography in ECMs were investigatedthe nanotopography can regulate the cell-substrate or cell-cell interactions, which may eventually promote the function of cells including NIH3T3 fibroblast cells, MG-63 cells, and MSCs. Furthermore, it was found that graphene-matrix nanotopography hybrid substrata with nano and sub-nanopatterned hierarchical features can promote the functions of cells including differentiation of MSCs, enhanced mineralization of MC-3T3 cells, and capillary tube formation of HUVEC. Finally, nanoengineered biomimetic systems composed of the ECM-like topographical substrate, chemical molecules, and cells were proposed as a strategy for repair or regeneration of damaged tissues. Inspired by the aligned nanostructures and co-existence of vascular cells and stem cells in bone tissues, the systems comprised of nanotopography and co-culture platforms were developed, showing that the osteogenesis of MSCs was further enhanced by the two factors in combination whereas both nanotopography and co-culture independently enhanced the osteogenesis. In addition, a stem cell patch that integrates MSCs into the nanopatterned hierarchical substrate was developed using a Food and Drug Administration (FDA)-approved poly(lacticco-glycolic acid) (PLGA) polymer. It was demonstrated that the nanopatterned stem cell patches can guide the bone regeneration and the nanofibrillar patch synthesized from bacterial cellulose can promote the tympanic membrane regeneration without complex surgical treatments or tissue transplantation.Abstract i Table of Contents iii List of Table vi List of Figures vii Scope and Format of Dissertation x 1 Introduction โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ......................... 1 1.1 General introduction โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 2 1.2 Objectives โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 2 Micro- and nanoengineered biomimetic platforms for modeling biological processing and tissue regeneration โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ... 4 2.1 Summary โ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...โ€ฆโ€ฆโ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 4 2.2 Histological background โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 4 2.3 Micro- and nanofabrication technology for biomimetic microenvironment โ€ฆโ€ฆ.. 5 2.4 Microfluidics for physiologically relevant microenvironmentโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 8 2.5. Discussion โ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 9 2.6 References โ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 10 Part I Designing and manipulating nanopatterned platforms with precisely controlled topographical architectures and sizes โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 15 3 Nanotopographic pattern arrays with variable local sizes for engineering adhesion and migration of fibroblast cells โ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.. 17 3.1 Summary โ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...โ€ฆโ€ฆโ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 17 3.2 Introduction โ€ฆ...โ€ฆโ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.. 18 3.3 Materials and methods โ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ... 19 3.4 Results โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ... 22 3.5 Discussion โ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 29 3.6 References โ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 30 4 Designing nanotopographical density of extracellular matrix for controlled morphology and function of mesenchymal stem cells โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 40 4.1 Summary โ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..... 40 4.2 Introduction โ€ฆ...โ€ฆโ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.. 41 4.3 Materials and methods โ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ... 43 4.4 Results โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ... 49 4.5 Discussion โ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 57 4.6 References โ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 63 Part II Designing and manipulating nanopatterned hierarchical platforms with precisely controlled topographical architectures and sizes โ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ... 82 5 Bioinspired configurable hierarchical micro- and nanostructure with precisely controlled sizes for functional alignment and guided orientation of cells โ€ฆโ€ฆโ€ฆโ€ฆ 84 5.1 Summary โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 84 5.2 Introduction โ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..... 85 5.3 Materials and methods โ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..... 86 5.4 Results โ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.. 91 5.5 Discussion โ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 96 5.6 References โ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 98 6 Nano- and sub-nanopatterned hierarchical platforms composed of graphene and matrix nanotopography for promoting cellular function โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ............... 110 7.1 Summary โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..... 110 7.2 Introduction โ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..... 111 7.3 Materials and methods โ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...... 112 7.4 Results โ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.. 116 7.5 Discussion โ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 121 7.6 References โ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 122 Part III Development of nanoengineered biomimetic systems for tissue regeneration โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 135 7 Synergistic effects of nanotopography and co-culture with endothelial cells on osteogenesis of mesenchymal stem cells โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ... 136 7.1 Summary โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 136 7.2 Introduction โ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..... 137 7.3 Materials and methods โ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..... 138 7.4 Results โ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.. 144 7.5 Discussion โ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 151 7.6 References โ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 156 8 Multiscale patterned transplantable stem cell patches for bone tissue regeneration โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 170 8.1 Summary โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 170 8.2 Introduction โ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 170 8.3 Materials and methods โ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..... 173 8.4 Results โ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.. 179 8.5 Discussion โ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 185 8.6 References โ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 189 9 Bacterial cellulose nanofibrillar patch as a wound healing platform of tympanic membrane perforation โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ... 199 9.1 Summary โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 199 9.2 Introduction โ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 199 9.3 Materials and methods โ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..... 201 9.4 Results โ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.. 205 9.5 Discussion โ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 211 9.6 References โ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ. 211 10 Concluding remarks โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ... 223 10.1 Introduction โ€ฆ...โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..... 223 10.2 Summary of dissertation work โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ..โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ...โ€ฆโ€ฆ... 224 10.3 Broad implications of this dissertation work for biological engineering โ€ฆโ€ฆ..โ€ฆ. 230 Abstract (Korean) โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ.. 232 Curriculum Vitae โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ... 235Docto

    Visions and Strategies for a Human Resources-Centered Society

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    ์‚ฐ์—…ํ™” ์‹œ๋Œ€์— ๊ฐœ์ธ์˜ ๊ต์œก์—ด์— ์˜์กดํ–ˆ๋˜ ์ธ์ ์ž์› ํˆฌ์ž๋Š” ์ง€์‹๊ธฐ๋ฐ˜๊ฒฝ์ œ์—์„œ๋Š” ๊ตญ๊ฐ€์  ์ฐจ์›์—์„œ ์ฒด๊ณ„์ ์ด๊ณ  ์ „๋žต์ ์ธ ํˆฌ์ž๋กœ ์ „ํ™˜๋˜์–ด์•ผ ํ•จ์„ ์ „์ œ๋กœ ํ•˜์—ฌ ๋ณธ์„œ์—์„œ๋Š” ์ธ์ ์ž์› ์ค‘์‹ฌ์˜ ์ƒˆ๋กœ์šด ๊ตญ๊ฐ€๋ฐœ์ „ํŒจ๋Ÿฌ๋‹ค์ž„์„ โ€˜์ธ์ ์ž์›์ž…๊ตญโ€™์œผ๋กœ ๋ช…๋ช…ํ•˜๊ณ , ์ƒˆ๋กœ์šด ๋ฐœ์ „ ํŒจ๋Ÿฌ๋‹ค์ž„์˜ ๋ฐฐ๊ฒฝ๊ณผ ํ•„์š”์„ฑ์— ๋Œ€ํ•œ ์ง„์ง€ํ•œ ์„ฑ์ฐฐ์„ ํ†ตํ•ด ๊ทธ ๋…ผ๋ฆฌ๋ฅผ ๊ตฌ์„ฑํ•˜๋ฉฐ ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ธ์ ์ž์›์ž…๊ตญ์˜ ๋น„์ „๊ณผ ์‹ค์ฒœ์ „๋žต์„ ์ง„์ง€ํ•˜๊ฒŒ ๋ชจ์ƒ‰ํ•œ ๊ฒฐ๊ณผ๋ฌผ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ตญ๊ฐ€๋ฐœ์ „ํŒจ๋Ÿฌ๋‹ค์ž„์˜ ์ „ ์˜์—ญ์„ ๋ง๋ผํ•˜๊ธฐ ๋ณด๋‹ค๋Š” ํ•™๊ต๊ต์œก, ๊ธฐ์—…์กฐ์ง๊ณผ ์ง€์—ญ ์ฐจ์› ์˜ ํ‰์ƒํ•™์Šต, ๊ต์œก๊ณผ ๋…ธ๋™์‹œ์žฅ์˜ ์—ฐ๊ณ„, ๊ทธ๋ฆฌ๊ณ  ๊ตญ๊ฐ€์ธ์ ์ž์›๊ด€๋ฆฌ์˜ ๋„ค ์˜์—ญ์„ ์ธ์ ์ž์›์ž…๊ตญ์˜ ํ•ต์‹ฌ์ ์ธ ์˜์—ญ์œผ๋กœ ์„ค์ •ํ•˜๊ณ , ๊ฐ ์˜์—ญ์„ ์ค‘์‹ฌ์œผ๋กœ ํ˜„ํ™ฉ๊ณผ ๋ฌธ์ œ์ ์„ ์‚ดํŽด๋ณธ ๋‹ค์Œ ์ค‘์š”ํ•œ ์ •์ฑ…์  ์ด์Šˆ๋“ค์„ ๋‹ค๋ฃจ๋Š” ์„ ํƒ๊ณผ ์ง‘์ค‘์˜ ์ „๋žต์ ์ธ ์ ‘๊ทผ๋ฐฉ์‹์„ ์ทจํ•˜์˜€๋‹ค.์š” ์•ฝ ์ œ1์žฅ ์„œ๋ก  ์ œ2์žฅ ์ธ์ ์ž์›์ž…๊ตญ์˜ ๋น„์ „๊ณผ ์ „๋žต ์ œ1์ ˆ ์™œ ์ธ์ ์ž์›์ž…๊ตญ์ธ๊ฐ€? 7 1. ๊ฒฉ๋ž‘์†์˜ ํ•œ๊ตญํ˜ธ: ์„ฑ์žฅ๊ณผ ํ†ตํ•ฉ์˜ ์œ„๊ธฐ 7 2. ์„ฑ์žฅ๊ณผ ํ†ตํ•ฉ์˜ ์œ„๊ธฐ ๊ทน๋ณต ๋ฐฉํ–ฅ: ์ธ์ ์ž์›์— ์žˆ๋‹ค 25 3. ์ด๋Œ€๋กœ๋Š” ์•ˆ ๋œ๋‹ค: ์ˆ˜์ถœ์ž…๊ตญ์—์„œ ์ธ์ ์ž์›์ž…๊ตญ์œผ๋กœ ํŒจ๋Ÿฌ๋‹ค์ž„ ์ „ํ™˜ํ•„์š” 26 4. ์„ธ๊ณ„๋Š” ์ง€๊ธˆ ์ธ์ ์ž์›ํ˜๋ช… ์ค‘ 30 ์ œ2์ ˆ ์ธ์ ์ž์›์ž…๊ตญ์ด๋ž€ ๋ฌด์—‡์ธ๊ฐ€? 34 1. ์ƒˆ๋กœ์šด ๋ฐœ์ „ํŒจ๋Ÿฌ๋‹ค์ž„: ์ธ์ ์ž์›์ž…๊ตญ 34 2. ์ธ์ ์ž์›์ž…๊ตญ์˜ ๋น„์ „: โ€˜ํ•™์Šต, ํ˜์‹ , ํ†ตํ•ฉ์˜ ์—ญ๋™์  ์„ ์ง„๊ตญ๊ฐ€โ€™37 3. ์ธ์ ์ž์›์ž…๊ตญ์˜ ๋ชฉํ‘œ: ์ด์ฒด์  ํ•™์Šต์‚ฌํšŒ ๊ตฌ์ถ• 38 4. ์ธ์ ์ž์›์ž…๊ตญ์˜ ์ •์ฑ…์  ์‹œ์‚ฌ์ : ๊ตญ๊ฐ€๋ฐœ์ „์ •์ฑ…์˜ ํ•ต์‹ฌ์€ ์ธ์ ์ž์›์ •์ฑ… 41 ์ œ3์ ˆ ๋ฌด์—‡์ด ์ธ์ ์ž์›์ž…๊ตญ์„ ๊ฐ€๋กœ๋ง‰๋Š”๊ฐ€? 42 1. ๊ณต๊ธ‰์ž ์ค‘์‹ฌ์˜ ํš์ผํ™”๋œ ํ•™๊ต๊ต์œก 42 2. ์‹ค์† ์—†๋Š” ํ‰์ƒํ•™์Šต 45 3. ๊ต์œก๊ณผ ์ง์—…์„ธ๊ณ„์˜ ์œ ๋ฆฌ 47 4. ๋น„์ฒด๊ณ„์ , ๋น„ํšจ์œจ์ ์ธ ๊ตญ๊ฐ€์ธ์ ์ž์›์ •์ฑ… 49 ์ œ4์ ˆ ์šฐ๋ฆฌ๋Š” ๋ฌด์—‡์„ ํ•ด์•ผ ํ•˜๋Š”๊ฐ€? 50 1. ์ •์ฑ…๊ธฐ์กฐ์˜ ์ „ํ™˜: ์‚ฌ๋žŒ ์ค‘์‹ฌ์˜ ์ •์ฑ… ํŒจ๋Ÿฌ๋‹ค์ž„์˜ ๊ธฐ์กฐ 51 2. ์ธ์ ์ž์›์ž…๊ตญ์„ ์œ„ํ•œ ์ธ์ ์ž์›์ •์ฑ… ์˜์—ญ 54 3. ์ธ์ ์ž์›์ •์ฑ…์˜ 7๋Œ€ ์ •์ฑ… ๋ฐฉํ–ฅ 56 ์ œ3์žฅ ํ•™๊ต๊ต์œก ์ œ1์ ˆ ๊ฐœ๊ด€ 61 ์ œ2์ ˆ ํ•™๊ต์˜ ํ˜์‹  63 1. ์ฐฝ์˜์ ์ธ ๊ต์œก๊ณผ์ • ๊ฐœ๋ฐœ 69 2. ๊ต์›์˜ ์ „๋ฌธ์„ฑ ์ œ๊ณ ์™€ ์‹ ๋ถ„๋ณด์žฅ 73 3. ๊ณ ๋“ฑ๊ต์œกํ‰๊ฐ€์˜ ์ƒˆ๋กœ์šด ๋ฐฉํ–ฅ 78 4. ์ด๊ณต๊ณ„ ์œ„๊ธฐ์™€ ๋Œ€ํ•™๊ต์œก์˜ ํ˜์‹  ๋ฐฉ์•ˆ 82 5. ๋Œ€ํ•™์—์„œ์˜ ์„ฑ์ธ๊ต์œก ํ™œ์„ฑํ™” 85 6. ๋Œ€ํ•™์˜ ์—ฐ๊ตฌ๊ฐœ๋ฐœ ๊ธฐ๋Šฅํ˜์‹  89 7. ์ง์—…๊ต์œก ์ œ์ž๋ฆฌ ์ฐพ๊ธฐ 93 ์ œ3์ ˆ ๊ต์œก์ œ๋„์™€ ์ •์ฑ…์˜ ๊ฐœํ˜ 99 1. ํ‰์ค€ํ™”์ œ๋„ ๋ฐ ๋Œ€ํ•™์ž…์‹œ์ œ๋„ ๊ฐœ์„  105 2. ์ •๋ถ€์˜ ๊ต์œก๊ด€๋ฆฌ ์ฒด์ œ ํ˜์‹  110 3. ๊ต์œก์†Œ์™ธ๊ณ„์ธต์˜ ๊ต์œก๋ณต์ง€ ๋ฐœ์ „๋ฐฉ์•ˆ 115 4. ์‚ฐ์—… ์š”๊ตฌ๋ฅผ ๋ฐ˜์˜ํ•œ ์ง์—…์ „๋ฌธํ•™์œ„์˜ ์‹ ์„คใ†๊ด€๋ฆฌ์ฒด์ œ ๊ตฌ์ถ• 120 5. ๋Œ€ํ•™๊ตญ์ œํ™”์˜ ํ˜„ํ™ฉ๊ณผ ์ „๋ง, ๋Œ€์ฑ… 125 6. ์ง€์‹๊ธฐ๋ฐ˜์‚ฌํšŒ์—์„œ์˜ ํ•™์ œ ๋ฐœ์ „ ๋ฐฉ์•ˆ 130 ์ œ4์žฅ ํ‰์ƒํ•™์Šต ์ œ1์ ˆ ๊ฐœ๊ด€ 137 ์ œ2์ ˆ ํ‰์ƒํ•™์Šต์˜ ํ˜„ํ™ฉ ๋ฐ ๋ฌธ์ œ์  138 1. ํ‰์ƒํ•™์Šต์— ๋Œ€ํ•œ ์ด๋ถ„๋ฒ•์  ์ ‘๊ทผ 138 2. ํ•™๋ น๊ธฐ ๊ณผ๋‹ค ํˆฌ์ž / ๋…ธ๋™์‹œ์žฅ ์ง„์ž… ์ดํ›„ HRD ๊ณผ์†Œํˆฌ์ž 140 3. ์„ฑ์ธ์˜ ํ‰์ƒํ•™์Šต ์ฐธ์—ฌ์œจ ์ €์กฐ 141 4. ๊ธฐ์—…์˜ ๊ต์œกํ›ˆ๋ จ ๊ฐ์†Œ ์ถ”์„ธ 144 5. ํ‰์ƒํ•™์Šต ๊ธฐํšŒ์˜ ๋ถˆ๊ท ๋“ฑ ํ˜„์ƒ ์‹ฌํ™” 145 ์ œ3์ ˆ ์ผ๊ณผ ํ•™์Šต์˜ ๋ณ‘ํ–‰ 147 1. ์ง์žฅ๋‚ด ํ‰์ƒํ•™์Šต์ฒด์ œ ๊ตฌ์ถ• 149 2. ๋…ธ์‚ฌ์ฐธ์—ฌ์— ๊ธฐ์ดˆํ•œ ํ˜‘๋ ฅ์  ์ธ์ ์ž์›๊ฐœ๋ฐœ 152 3. ํ˜„์žฅํ•™์Šต(OJT) ์ด‰์ง„ 155 4. ์ค‘์†Œ๊ธฐ์—… ์ธ์ ์ž์›๊ฐœ๋ฐœ ํ™œ์„ฑํ™” 159 5. ์ธ์ ์ž์›๊ฐœ๋ฐœ ์ธ์ฆ์ œ 161 6. ๊ฐœ์ธ์ฃผ๋„ ํ•™์Šต์˜ ํ™œ์„ฑํ™” 164 ์ œ4์ ˆ ํ•™์Šต ์ค‘์‹ฌ์˜ ์ง€์—ญ์‚ฌํšŒ : ํ•™์Šต์„ ํ†ตํ•œ ํ˜์‹ , ํ†ตํ•ฉ, ์„ฑ์žฅ 169 1. ์ง€์—ญ HRD ์—ญ๋Ÿ‰์˜ ๊ฐ•ํ™” 173 2. ์ง€์—ญ์˜ ํ•™์Šต ํŒŒํŠธ๋„ˆ์‹ญ ๊ตฌ์ถ• 177 3. ๊ณต๊ณต๊ธฐ๊ด€์˜ ํ•™์Šต ๊ฑฐ์ ํ™” 182 4. ์ธ์ ์ž์›๊ฐœ๋ฐœ๊ณผ ์ง€์—ญ๋ฐœ์ „์˜ ์—ฐ๊ฒฐ๊ณ ๋ฆฌ๋กœ์„œ์˜ ํ•™์Šต๋™์•„๋ฆฌ 186 5. ํ•™์Šตํ˜• ์‚ฌํšŒ์  ์ผ์ž๋ฆฌ ์ฐฝ์ถœ 190 6. ํ‰์ƒํ•™์Šต ๊ณต๋™์ฒด ๋งŒ๋“ค๊ธฐ 194 ์ œ5์žฅ ํ†ตํ•ฉ์  ์ธ์ ์ž์›๊ฐœ๋ฐœ ์ œ1์ ˆ ๊ฐœ๊ด€ 199 ์ œ2์ ˆ ๊ต์œก-๋…ธ๋™ ์—ฐ๊ณ„ 204 1. ์ž๊ฒฉ์ œ๋„์˜ ์žฌ์ •๋ฆฝ 206 2. ์ง„๋กœ ๋ฐ ๊ฒฝ๋ ฅ๊ฐœ๋ฐœ ์ฒด์ œ์˜ ์žฌํ™•๋ฆฝ 210 3. ํ•™์Šต๊ฒฐ๊ณผ ์ธ์ฆ์ฒด์ œ ์žฌ๊ตฌ์ถ• 214 4. HRD ์ค‘์‹ฌ์˜ ์‚ฐํ•™์—ฐ๊ณ„ ํ™œ์„ฑํ™” 217 ์ œ3์ ˆ ์ •๋ณดใ†์„œ๋น„์Šค์˜ ์ „๋‹ฌ 221 1. ์ธ์ ์ž์› ์ •๋ณด์ธํ”„๋ผ ๊ตฌ์ถ• 222 2. ํ•™์Šต-๊ณ ์šฉ-๋ณต์ง€ ์—ฐ๊ณ„์ฒด๊ณ„ 227 ์ œ4์ ˆ ์ธ์ ์ž์›์˜ ์œ ์ถœ์ž… 232 1. ๊ณ ๊ธ‰๋‘๋‡Œ ํ™œ์šฉ์˜ ๊ตญ์ œํ™” 234 2. ์™ธ๊ตญ์ธ ์ธ๋ ฅ์˜ ๊ด€๋ฆฌ์ฒด๊ณ„ 237 ์ œ5์ ˆ ์ธ์ ์ž์›์ •์ฑ… ์—ญ๋Ÿ‰ 242 1. ๊ณต๋ฌด์› ์ž„์šฉ์ œ๋„์˜ ํ˜์‹  244 2. ์ธ์ ์ž์›์ •์ฑ… ์ถ”์ง„์ฒด์ œ ํ˜์‹  247 ์ œ6์žฅ ๊ฒฐ๋ก : ์ธ์ ์ž์›์ž…๊ตญ ์ œ์•ˆ 2005 ์ œ1์ ˆ ์š”์•ฝ 254 ์ œ2์ ˆ ์ œ์–ธ: ์ธ์ ์ž์›์ž…๊ตญ ์ œ์•ˆ 2005 257 1. ์ธ์ ์ž์›์ž…๊ตญ์˜ ๋ฐฉํ–ฅ 258 2. ๊ต์œก์˜ ๋‚ด์šฉ๊ณผ ๋ฐฉ๋ฒ•์˜ ํ˜์‹  259 3. ํ•ต์‹ฌ์ธ์žฌ ์–‘์„ฑ๊ณผ ๊ต์œก ๊ธ€๋กœ๋ฒŒํ™” 261 4. ๊ต์œก์˜ ์งˆ ๋ณด์žฅ ์ฒด์ œ ๊ตฌ์ถ• 262 5. ํ•™์Šต์ค‘์‹ฌใ†๋Šฅ๋ ฅ์ค‘์‹ฌ์˜ ์‚ฌํšŒ๋กœ ์žฌ๊ตฌ์กฐํ™” 264 6. ์ •๋ณด ๋ฐ ํ•™์Šต์ง€์› ์ธํ”„๋ผ ๊ตฌ์ถ• 266 7. ์žฌ์ •์ธํ”„๋ผ ๊ตฌ์ถ•๊ณผ ๊ฑฐ๋ฒ„๋„Œ์Šค ํšจ์œจํ™” 267 ์ฐธ๊ณ ๋ฌธํ—Œ 26

    [ํŠน๋ณ„๊ธฐ๊ณ ] ๋™๋ฐ˜์„ฑ์žฅ์„ ์œ„ํ•œ ํ‰์ƒ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ ์ฒด์ œ ํ˜์‹ 

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    ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ์ •์ฑ…์˜ ์ „๊ฐœ๊ณผ์ • ํ‰์ƒ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ ๊ฐœ๋…๊ณผ ํ•„์š”์„ฑ ํ‰์ƒ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ์˜ ํ˜„์ฃผ์†Œ ๋น„์ „ ยท ๋ชฉํ‘œ ์ •์ฑ…๊ณผ์ œ ๋ณดํŽธ์  ๊ถŒ๋ฆฌ๋กœ์„œ์˜ ์ง์—…๋Šฅ๋ ฅ๊ฐœ๋ฐœ ๊ฒฝ์Ÿ๋ ฅ ์žˆ๋Š” ์ง€์‹๊ทผ๋กœ์ž ์œก์„ฑ ์‹œ์žฅ์ˆ˜์š”๋ฅผ ๋ฐ˜์˜ํ•˜๋Š” ์ง€์›์ฒด๊ณ„ ๊ตฌ์ถ• ํŒŒํŠธ๋„ˆ์‹ญ๊ณผ ๋ฏผ๊ฐ„์ฐธ์—ฌ์— ์˜ํ•œ ์ถ”์ง„์ฒด์ œ ๋Šฅ๋ ฅ์ค‘์‹ฌ ๋ฌธํ™” ํ™•
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