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    ์ผ๋ฐ˜ํ™” ๋Šฅ๋ ฅ์˜ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๊นŠ์€ ์‹ ๊ฒฝ๋ง ์–‘์žํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ์„ฑ์›์šฉ.์ตœ๊ทผ ๊นŠ์€ ์‹ ๊ฒฝ๋ง(deep neural network, DNN)์€ ์˜์ƒ, ์Œ์„ฑ ์ธ์‹ ๋ฐ ํ•ฉ์„ฑ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์ด๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๋Œ€๋ถ€๋ถ„์˜ ์ธ๊ณต์‹ ๊ฒฝ๋ง์€ ๋งŽ์€ ๊ฐ€์ค‘์น˜(parameter) ์ˆ˜์™€ ๊ณ„์‚ฐ๋Ÿ‰์„ ์š”๊ตฌํ•˜์—ฌ ์ž„๋ฒ ๋””๋“œ ์‹œ์Šคํ…œ์—์„œ์˜ ๋™์ž‘์„ ๋ฐฉํ•ดํ•œ๋‹ค. ์ธ๊ณต์‹ ๊ฒฝ๋ง์€ ๋‚ฎ์€ ์ •๋ฐ€๋„์—์„œ๋„ ์ž˜ ๋™์ž‘ํ•˜๋Š” ์ธ๊ฐ„์˜ ์‹ ๊ฒฝ์„ธํฌ๋ฅผ ๋ชจ๋ฐฉํ•˜์˜€๊ธฐ ๋–„๋ฌธ์— ๋‚ฎ์€ ์ •๋ฐ€๋„์—์„œ๋„ ์ž˜ ๋™์ž‘ํ•  ๊ฐ€๋Šฅ์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ธ๊ณต์‹ ๊ฒฝ๋ง์˜ ์–‘์žํ™”(quantization)๋Š” ์ด๋Ÿฌํ•œ ํŠน์ง•์„ ์ด์šฉํ•œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ๊นŠ์€ ์‹ ๊ฒฝ๋ง ๊ณ ์ •์†Œ์ˆ˜์  ์–‘์žํ™”๋Š” 8-bit ์ด์ƒ์˜ ๋‹จ์–ด๊ธธ์ด์—์„œ ๋ถ€๋™์†Œ์ˆ˜์ ๊ณผ ์œ ์‚ฌํ•œ ์„ฑ๋Šฅ์„ ์–ป์„ ์ˆ˜์žˆ์ง€๋งŒ, ๊ทธ๋ณด๋‹ค ๋‚ฎ์€ 1-, 2-bit์—์„œ๋Š” ์„ฑ๋Šฅ์ด ๋–จ์–ด์ง„๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ๋ถˆ๊ท ํ˜• ์–‘์žํ™”๊ธฐ๋‚˜ ์ ์‘์  ์–‘์žํ™” ๋“ฑ์˜ ๋” ์ •๋ฐ€ํ•œ ์ธ๊ณต์‹ ๊ฒฝ๋ง ์–‘์žํ™” ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์™€ ๋งค์šฐ ๋‹ค๋ฅธ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ณ ์ • ์†Œ์ˆ˜์  ๋„คํŠธ์›Œํฌ์˜ ์ผ๋ฐ˜ํ™”๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š”๋ฐ ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ์žฌํ›ˆ๋ จ(retraining) ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์–‘์žํ™”๋œ ์ธ๊ณต์‹ ๊ฒฝ๋ง์˜ ์„ฑ๋Šฅ์„ ๋ถ„์„ํ•œ๋‹ค. ์„ฑ๋Šฅ ๋ถ„์„์€ ๋ ˆ์ด์–ด๋ณ„ ๋ฏผ๊ฐ๋„ ์ธก์ •(layer-wise sensitivity analysis)์— ๊ธฐ๋ฐ˜ํ•œ๋‹ค. ๋˜ํ•œ ์–‘์žํ™” ๋ชจ๋ธ์˜ ๋„“์ด์™€ ๊นŠ์ด์— ๋”ฐ๋ฅธ ์„ฑ๋Šฅ๋„ ๋ถ„์„ํ•œ๋‹ค. ๋ถ„์„๋œ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์–‘์žํ™” ์Šคํ… ์ ์‘ ํ›ˆ๋ จ๋ฒ•(quantization step size adaptation)๊ณผ ์ ์ง„์  ์–‘์žํ™” ํ›ˆ๋ จ ๋ฐฉ๋ฒ•(gradual quantization)์„ ์ œ์•ˆํ•œ๋‹ค. ์–‘์žํ™”๋œ ์‹ ๊ฒฝ๋ง ํ›ˆ๋ จ์‹œ ์–‘์žํ™” ๋…ธ์ด์ฆˆ๋ฅผ ์ ๋‹นํžˆ ์กฐ์ •ํ•˜์—ฌ ์†์‹ค ํ‰๋ฉด(loss surface)์ƒ์— ํ‰ํ‰ํ•œ ๋ฏธ๋‹ˆ๋งˆ(minima)์— ๋„๋‹ฌ ํ•  ์ˆ˜ ์žˆ๋Š” ์–‘์žํ™” ํ›ˆ๋ จ ๋ฐฉ๋ฒ• ๋˜ํ•œ ์ œ์•ˆํ•œ๋‹ค. HLHLp (high-low-high-low-precision)๋กœ ๋ช…๋ช…๋œ ํ›ˆ๋ จ ๋ฐฉ๋ฒ•์€ ์–‘์žํ™” ์ •๋ฐ€๋„๋ฅผ ํ›ˆ๋ จ์ค‘์— ๋†’๊ฒŒ-๋‚ฎ๊ฒŒ-๋†’๊ฒŒ-๋‚ฎ๊ฒŒ ๋ฐ”๊พธ๋ฉด์„œ ํ›ˆ๋ จํ•œ๋‹ค. ํ›ˆ๋ จ๋ฅ (learning rate)๋„ ์–‘์žํ™” ์Šคํ… ์‚ฌ์ด์ฆˆ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์œ ๋™์ ์œผ๋กœ ๋ฐ”๋€๋‹ค. ์ œ์•ˆํ•˜๋Š” ํ›ˆ๋ จ๋ฐฉ๋ฒ•์€ ์ผ๋ฐ˜์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ํ›ˆ๋ จ๋œ ์–‘์žํ™” ๋ชจ๋ธ์— ๋น„ํ•ด ์ƒ๋‹นํžˆ ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ์„ ํ›ˆ๋ จ๋œ ์„ ์ƒ ๋ชจ๋ธ๋กœ ํ•™์ƒ ๋ชจ๋ธ์„ ํ›ˆ๋ จํ•˜๋Š” ์ง€์‹ ์ฆ๋ฅ˜(knowledge distillation, KD) ๊ธฐ์ˆ ์„ ์ด์šฉํ•˜์—ฌ ์–‘์žํ™”์˜ ์„ฑ๋Šฅ์„ ๋†’์ด๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ํŠนํžˆ ์„ ์ƒ ๋ชจ๋ธ์„ ์„ ํƒํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ ์ง€์‹ ์ฆ๋ฅ˜์˜ ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ์„ฑ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•œ๋‹ค. ๋ถ€๋™์†Œ์ˆ˜์  ์„ ์ƒ๋ชจ๋ธ๊ณผ ์–‘์žํ™” ๋œ ์„ ์ƒ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ํ›ˆ๋ จ ์‹œํ‚จ ๊ฒฐ๊ณผ ์„ ์ƒ ๋ชจ๋ธ์ด ๋งŒ๋“ค์–ด๋‚ด๋Š” ์†Œํ”„ํŠธ๋งฅ์Šค(softmax) ๋ถ„ํฌ๊ฐ€ ์ง€์‹์ฆ๋ฅ˜ํ•™์Šต ๊ฒฐ๊ณผ์— ํฌ๊ฒŒ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ์†Œํ”„ํŠธ๋งฅ์Šค ๋ถ„ํฌ๋Š” ์ง€์‹์ฆ๋ฅ˜์˜ ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์„ ํ†ตํ•ด ์กฐ์ ˆ๋ ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ์ง€์‹์ฆ๋ฅ˜ ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ๋“ค๊ฐ„์˜ ์—ฐ๊ด€๊ด€๊ณ„ ๋ถ„์„์„ ํ†ตํ•ด ๋†’์€ ์„ฑ๋Šฅ์„ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ์ ์ง„์ ์œผ๋กœ ์†Œํ”„ํŠธ ์†์‹ค ํ•จ์ˆ˜ ๋ฐ˜์˜ ๋น„์œจ์„ ํ›ˆ๋ จ์ค‘์— ์ค„์—ฌ๊ฐ€๋Š” ์ ์ง„์  ์†Œํ”„ํŠธ ์†์‹ค ๊ฐ์†Œ(gradual soft loss reducing)๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์—ฌ๋Ÿฌ ์–‘์žํ™”๋ชจ๋ธ์„ ํ‰๊ท ๋‚ด์–ด ๋†’์€ ์ผ๋ฐ˜ํ™” ๋Šฅ๋ ฅ์„ ๊ฐ–๋Š” ์–‘์žํ™” ๋ชจ๋ธ์„ ์–ป๋Š” ํ›ˆ๋ จ ๋ฐฉ๋ฒ•์ธ ํ™•๋ฅ  ์–‘์žํ™” ๊ฐ€์ค‘์น˜ ํ‰๊ท (stochastic quantized weight averaging, SQWA) ํ›ˆ๋ จ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์€ (1) ๋ถ€๋™์†Œ์ˆ˜์  ํ›ˆ๋ จ, (2) ๋ถ€๋™์†Œ์ˆ˜์  ๋ชจ๋ธ์˜ ์ง์ ‘ ์–‘์žํ™”(direct quantization), (3) ์žฌํ›ˆ๋ จ(retraining)๊ณผ์ •์—์„œ ์ง„๋™ ํ›ˆ๋ จ์œจ(cyclical learning rate)์„ ์‚ฌ์šฉํ•˜์—ฌ ํœธ๋ จ์œจ์ด ์ง„๋™๋‚ด์—์„œ ๊ฐ€์žฅ ๋‚ฎ์„ ๋•Œ ๋ชจ๋ธ๋“ค์„ ์ €์žฅ, (4) ์ €์žฅ๋œ ๋ชจ๋ธ๋“ค์„ ํ‰๊ท , (5) ํ‰๊ท  ๋œ ๋ชจ๋ธ์„ ๋‚ฎ์€ ํ›ˆ๋ จ์œจ๋กœ ์žฌ์กฐ์ • ํ•˜๋Š” ๋‹ค์ค‘ ๋‹จ๊ณ„ ํ›ˆ๋ จ๋ฒ•์ด๋‹ค. ์ถ”๊ฐ€๋กœ ์–‘์žํ™” ๊ฐ€์ค‘์น˜ ๋„๋ฉ”์ธ์—์„œ ์—ฌ๋Ÿฌ ์–‘์žํ™” ๋ชจ๋ธ๋“ค์„ ํ•˜๋‚˜์˜ ์†์‹คํ‰๋ฉด๋‚ด์— ๋™์‹œ์— ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์žˆ๋Š” ์‹ฌ์ƒ(visualization) ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์‹ฌ์ƒ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด SQWA๋กœ ํ›ˆ๋ จ๋œ ์–‘์žํ™” ๋ชจ๋ธ์€ ์†์‹คํ‰๋ฉด์˜ ๊ฐ€์šด๋ฐ ๋ถ€๋ถ„์— ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์˜€๋‹ค.Deep neural networks (DNNs) achieve state-of-the-art performance for various applications such as image recognition and speech synthesis across different fields. However, their implementation in embedded systems is difficult owing to the large number of associated parameters and high computational costs. In general, DNNs operate well using low-precision parameters because they mimic the operation of human neurons; therefore, quantization of DNNs could further improve their operational performance. In many applications, word-length larger than 8 bits leads to DNN performance comparable to that of a full-precision model; however, shorter word-length such as those of 1 or 2 bits can result in significant performance degradation. To alleviate this problem, complex quantization methods implemented via asymmetric or adaptive quantizers have been employed in previous works. In contrast, in this study, we propose a different approach for quantization of DNNs. In particular, we focus on improving the generalization capability of quantized DNNs (QDNNs) instead of employing complex quantizers. To this end, first, we analyze the performance characteristics of quantized DNNs using a retraining algorithm; we employ layer-wise sensitivity analysis to investigate the quantization characteristics of each layer. In addition, we analyze the differences in QDNN performance for different quantized network sizes. Based on our analyses, two simple quantization training techniques, namely \textit{adaptive step size retraining} and \textit{gradual quantization} are proposed. Furthermore, a new training scheme for QDNNs is proposed, which is referred to as high-low-high-low-precision (HLHLp) training scheme, that allows the network to achieve flat minima on its loss surface with the aid of quantization noise. As the name suggests, the proposed training method employs high-low-high-low precision for network training in an alternating manner. Accordingly, the learning rate is also abruptly changed at each stage. Our obtained analysis results include that the proposed training technique leads to good performance improvement for QDNNs compared with previously reported fine tuning-based quantization schemes. Moreover, the knowledge distillation (KD) technique that utilizes a pre-trained teacher model for training a student network is exploited for the optimization of the QDNNs. We explore the effect of teacher network selection and investigate that of different hyperparameters on the quantization of DNNs using KD. In particular, we use several large floating-point and quantized models as teacher networks. Our experiments indicate that, for effective KD training, softmax distribution produced by a teacher network is more important than its performance. Furthermore, because softmax distribution of a teacher network can be controlled using KD hyperparameters, we analyze the interrelationship of each KD component for QDNN training. We show that even a small teacher model can achieve the same distillation performance as a larger teacher model. We also propose the gradual soft loss reducing (GSLR) technique for robust KD-based QDNN optimization, wherein the mixing ratio of hard and soft losses during training is controlled. In addition, we present a new QDNN optimization approach, namely \textit{stochastic quantized weight averaging} (SQWA), to design low-precision DNNs with good generalization capability using model averaging. The proposed approach includes (1) floating-point model training, (2) direct quantization of weights, (3) capture of multiple low-precision models during retraining with cyclical learning rate, (4) averaging of the captured models, and (5) re-quantization of the averaged model and its fine-tuning with low learning rate. Additionally, we present a loss-visualization technique for the quantized weight domain to elucidate the behavior of the proposed method. Our visualization results indicate that a QDNN optimized using our proposed approach is located near the center of the flat minimum on the loss surface.1.Introduction 1 1.1 Quantization of Deep Neural Networks 1 1.2 Generalization Capability of DNNs 3 1.3 Improved Generalization Capability of QDNNs 3 1.4 Outline of the Dissertation 5 2. Analysis of Fixedpoint Quantization of Deep Neural Networks 6 2.1 Introduction 6 2.2 Fixedpoint Performance Analysis of Deep Neural Networks 8 2.2.1 Model Design of Deep Neural Networks 8 2.2.2 Retrainbased Weight Quantization 10 2.2.3 Quantization Sensitivity Analysis 12 2.2.4 Empirical Analysis 13 2.3 Step Size Adaptation and Gradual Quantization for Retraining of DeepNeural Networks 22 2.3.1 Stepsize adaptation during retraining 22 2.3.2 Gradual quantization scheme 24 2.3.3 Experimental Results 24 2.4 Concluding remarks 30 3. HLHLp:Quantized Neural Networks Training for Reaching Flat Minimain Loss Surface 32 3.1 Introduction 32 3.2 Related Works 33 3.2.1 Quantization of Deep Neural Networks 33 3.2.2 Flat Minima in Loss Surfaces 34 3.3 Training QDNN for IMproved Generalization Capability 35 3.3.1 Analysis of Training with Quantized Weights 35 3.3.2 Highlowhighlowprecision Training 38 3.4 Experimental Results 40 3.4.1 Image Classification with CNNs 41 3.4.2 Language Modeling on PTB and WikiText2 44 3.4.3 Speech Recognition on WSJ Corpus 48 3.4.4 Discussion 49 3.5 Concluding Remarks 55 4 Knowledge Distillation for Optimization of Quantized Deep Neural Networks 56 4.1 Introduction 56 4.2 Quantized Deep Neural Netowrk Training Using Knowledge Distillation 57 4.2.1 Quantization of deep neural networks and knowledge distillation 58 4.2.2 Teacher model selection for KD 59 4.2.3 Discussion on hyperparameters of KD 62 4.3 Experimental Results 62 4.3.1 Experimental setup 62 4.3.2 Results on CIFAR10 and CIFAR100 64 4.3.3 Model size and temperature 66 4.3.4 Gradual Soft Loss Reducing 68 4.4 Concluding Remarks 68 5 SQWA: Stochastic Quantized Weight Averaging for Improving the Generalization Capability of LowPrecision Deep Neural Networks 70 5.1 Introduction 70 5.2 Related works 71 5.2.1 Quantization of deep neural networks for efficient implementations 71 5.2.2 Stochastic weight averaging and losssurface visualization 72 5.3 Quantization of DNN and loss surface visualization 73 5.3.1 Quantization of deep neural networks 73 5.3.2 Loss surface visualization for QDNNs 75 5.4 SQWA algorithm 76 5.5 Experimental results 80 5.5.1 CIFAR100 80 5.5.2 ImageNet 87 5.6 Concluding remarks 90 6 Conclusion 92 Abstract (In Korean) 110Docto

    ๋‹ค์„ฑ๋ถ„ ๋ฐ ์Šคํ•€-์Œ๊ทน์ž ๋ณด์ฆˆ ์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ๊ธฐ์ดˆ ๋ฌผ๋ฆฌ ๋ฐ ๊ณ„์ธกํ•™ ์—ฐ๊ตฌ๋กœ์˜ ํ™œ์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€(๋ฌผ๋ฆฌํ•™์ „๊ณต), 2022. 8. Uwe R. Fischer.This thesis is focused on theoretical studies of the possibility of applying Bose-Einstein condensates (BEC) as laboratories for fundamental physics and metrology. First topic is how one can get the value of the damping parameter in the modified mean-field theory of the spinor BEC. Mean-field theory of BEC [1, 2] is widely used to study behaviors and characteristics of BEC, but they have one major problem: it cannot explain the collective damping of BEC. To remedy this problem, Pitaevskiห‡ฤฑ introduced dimensionless phenomenological damping parameter [3] and Choi, Morgan, and Burnett estimated its value to be about 0.03 [4] from the date of scalar 23Na BEC experiment [5]. Later, people tried to derive this phenomenological equations for scalar BEC [6] but so far it has been done with introducing correction factor to match the value of the damping parameter to be 0.03 [7, 8]. In other words, no complete microscopic derivation for the damping parameter is done yet. Moreover, we find out that the damping parameter for spinor BEC is commonly set to be 0.03 without any justifications, e.g. [9, 10], although there is a possiblity that the damping parameter might be different on different systems and it may depend on spin indices. Based on our Physical Review A paper [11], we show that one may get the value of the damping parameter by measuring the switching time of the direction of the spin of the spinordipolar BEC if its local spin orientation is homogeneous. By assuming that the damping paramter for spinor BEC does not depend on spin indices [9, 10], we were able to derive the Landau-Lifshitz-Gilbert equation which is phenomenological equation to describe the behavior of the ferromagnets under external magnetic field. We also obtain Stoner-Wohlfarth Hamiltonian if there is no dissipation in the spinor-dipolar BEC and if local spin direction of the spinor-dipolar BEC is same everywhere. It has been verified experimentally that spinor-dipolar BEC with homogeneous local spin orientation can be made [12], so our suggestion to get damping parameter from the switching time of the direction of the spin of the spinor-dipolar BEC is not just a theoretical toy model. Second topic is the possilibity of estimating the magnitude of the external perturbation by measuring the number of BEC molecules created by ultracold chemical reaction. There are proposals that BEC can act as sensors for measuring the acceleration [13], for measuring the detection of gravitational waves (GW) [14, 15, 16, 17], for measuring the gravitational field gradient on a millimetre scale [18], and for the detection of dark matter [19], but they do not calculate classical Fisher information and hence the lower bound of the variance of the estimation could be bigger. Moreover, those proposed sensors are based on measuring number of phonons in BEC but single phonon detection in condensates has been achieved experimentally so far only in the superfluid helium II [20] and there is no report of achieving single phonon detection in BECs yet (it is difficult to measure the number of phonons in BEC, for example, see [21]). In our to-be-submitted paper, we study scalar BEC system under ultracold chemical reaction with homogeneous but time-dependent density perturbation being applied to that system at some time t = 0. By calculating quantum Fisher information (QFI) and the lower bound of the classical Fisher information (CFI) when estimating the maximum magnitude of that perturbation by measuring the number of BEC molecules created by ultracold chemical reaction, we found out that the sensitivity of this method can be close to the ultimate possible limit. In addition, since number of BEC molecules created by ultracold chemical reaction can be measured (for example, see [22, 23, 24, 25]), our scheme implies that there could be BEC sensors more easy to implement than previous BEC sensors based on phonons.๋ณธ ๋…ผ๋ฌธ์€ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ๊ธฐ์ดˆ ๋ฌผ๋ฆฌ ๋ฐ ๊ณ„์ธกํ•™ ์—ฐ๊ตฌ๋กœ์˜ ํ™œ์šฉ ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด ๋‹ค๋ฃฌ๋‹ค. ์ฒซ๋ฒˆ์งธ ์ฃผ์ œ์—์„œ๋Š” ๊ฐ์‡„์— ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฐ˜์˜ํ•œ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ํ‰๊ท ์žฅ ์ด๋ก ์— ๋“ฑ์žฅํ•˜๋Š” ๊ฐ์‡„ ์ง€์ˆ˜๋ฅผ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ์‹คํ—˜ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์ œ์•ˆ์— ๋Œ€ํ•ด ๋‹ค๋ฃฌ๋‹ค. ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ํ‰๊ท ์žฅ ์ด๋ก  [1, 2]์€ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ์˜ ํŠน์„ฑ์„ ์—ฐ๊ตฌํ•˜๋Š” ๋ฐ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ์ง‘๋‹จ์  ์ง„๋™(collective oscillation)์ด ์ œํ•œ๋œ ์‹œ๊ฐ„ ๋™์•ˆ๋งŒ ์กด์žฌํ•˜๋Š” ์ ์„ ์„ค๋ช…ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ด๋ฅผ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด ํ”ผํƒ€์—ํ”„์Šคํ‚ค๋Š” ๊ฐ์‡„ ์ง€์ˆ˜๋ฅผ ๋„์ž…ํ•˜์˜€๊ณ  [3] ์ดํ›„ ์Šค์นผ๋ผ 23Na ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ์‹คํ—˜ ๋ฐ์ดํ„ฐ [5]๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋ฒ„๋„ท์˜ ์—ฐ๊ตฌํŒ€์ด ๊ฐ์‡„ ์ง€์ˆ˜์˜ ํฌ๊ธฐ๊ฐ€ ์•ฝ 0.03์ด๋ผ๋Š” ์ถ”์ • ๊ฒฐ๊ณผ๋ฅผ ๋ฐœํ‘œํ•˜์˜€๋‹ค [4]. ์ด ๊ฐ์‡„ ์ง€์ˆ˜ ๋„์ž…์„ ์Šค์นผ๋ผ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์— ๋Œ€ํ•ด ์ด๋ก ์ ์œผ๋กœ ์„ค๋ช…ํ•˜๊ธฐ ์œ„ํ•œ ์‹œ๋„๊ฐ€ ์žˆ์—ˆ์œผ๋‚˜ [6] ์•„์ง๊นŒ์ง€๋Š” ๊ฐ์‡„ ์ง€์ˆ˜์˜ ๊ฐ’์„ 0.03์œผ๋กœ ๋งž์ถ”๊ธฐ ์œ„ํ•ด ์ธ์œ„์ ์œผ๋กœ ๋น„๋ก€ ์ƒ์ˆ˜๋ฅผ ๋„์ž…ํ•œ ๋ถˆ์™„์ „ํ•œ ์„ค๋ช…๋งŒ ์กด์žฌํ•œ๋‹ค [7, 8]. ๋˜ํ•œ ์ด ๊ฐ์‡„ ์ง€์ˆ˜๋Š” ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ์ข…๋ฅ˜์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์งˆ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๊ณ , ์Šคํ•€์„ ๊ฐ€์ง„ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์—์„œ๋Š” ์ด ๊ฐ์‡„ ์ง€์ˆ˜๊ฐ€ ์Šคํ•€์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์งˆ ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋Š”๋ฐ๋„ ์ ์ ˆํ•œ ์„ค๋ช… ์—†์ด ์œ„์—์„œ ์–ธ๊ธ‰ํ•œ ์ถ”์ •๊ฐ’(0.03)์„ ๊ทธ๋Œ€๋กœ ์‚ฌ์šฉํ•˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋‹ค [9,10]. ํ”ผ์ง€์ปฌ ๋ฆฌ๋ทฐ A์— ๊ฒŒ์žฌ๋œ ๋ณธ ์ €์ž์˜ ๋…ผ๋ฌธ [11]์„ ๋ฐ”ํƒ•์œผ๋กœ, ์ €์ž๋Š” ์Šคํ•€์˜ ๋ฐฉํ–ฅ์ด ๊ท ์ผํ•œ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ์Šคํ•€์˜ ๋ฐฉํ–ฅ์ด ์™ธ๋ถ€ ์ž๊ธฐ์žฅ์— ์˜ํ•ด ๋ฐ”๋€Œ๋Š” ์‹œ๊ฐ„์„ ์ธก์ •ํ•˜๋ฉด ์ด ๊ฐ์‡„ ์ง€์ˆ˜๋ฅผ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ๋‹ค. ์ด ๊ฒฐ๊ณผ๋Š” ๊ฐ์‡„ ์ง€์ˆ˜๊ฐ€ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ์Šคํ•€๊ณผ ๋ฌด๊ด€ํ•˜๋‹ค๋Š” ๊ธฐ์กด์˜ ๊ฐ€์„ค [9,10]์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜์˜€๋Š”๋ฐ, ๊ทธ ๊ณผ์ •์—์„œ ๋ณธ ์ €์ž๋Š” ๊ฐ•์ž์„ฑ์ฒด๋ฅผ ์—ฐ๊ตฌํ•˜๋Š” ๋ฐ ์žˆ์–ด์„œ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์ง€๋งŒ ์ด๋ก ์ ์œผ๋กœ ์œ ๋„๋˜์ง€๋Š” ์•Š์•˜๋˜ ๋ž€๋‹ค์šฐ-๋ฆฌํ”„์‰ฌ์ธ -๊ธธ๋ฒ„ํŠธ ๋ฐฉ์ •์‹์„ ์ด๋ก ์ ์œผ๋กœ ์œ ๋„ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ๊ฐ•์ž์„ฑ์ฒด์˜ ์ž๊ธฐ์ด๋ ฅํ˜„์ƒ์„ ์„ค๋ช…ํ•˜๋Š” ์Šคํ† ๋„ˆ-๋ณผํŒŒ๋ฅดํŠธ ๋ชจ๋ธ๋„ ์œ„์˜ ๊ฐ€์ •์— ๊ฐ์‡„ ํ˜„์ƒ์ด ์—†๋‹ค๋Š” ๊ฐ€์ •์„ ์ถ”๊ฐ€ํ•˜๋ฉด ์ด๋ก ์ ์œผ๋กœ ์œ ๋„ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ์Šคํ•€์˜ ๋ฐฉํ–ฅ์ด ๋ชจ๋‘ ๊ท ์ผํ•œ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด๊ฐ€ ๋งŒ๋“ค์–ด์งˆ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์ด ์ž…์ฆ๋˜์—ˆ์œผ๋ฏ€๋กœ [12], ์œ„์—์„œ ์†Œ๊ฐœํ•œ ๊ฐ์‡„ ์ง€์ˆ˜๋ฅผ ์‹คํ—˜์ ์œผ๋กœ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์€ ํ˜„์‹ค์„ฑ์ด ์ „ํ˜€ ์—†์ง€๋Š” ์•Š์„ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๋‘๋ฒˆ์งธ ์ฃผ์ œ์—์„œ๋Š” ์ดˆ์ €์˜จ ํ™”ํ•™ ๋ฐ˜์‘์ด ์ผ์–ด๋‚˜๋Š” ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์— ์™ธ๋ถ€ ์ž๊ทน์ด ๊ฐ€ํ•ด์กŒ์„ ๋•Œ ์ƒ์„ฑ๋˜๋Š” ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ๋ถ„์ž ์‘์ง‘์ฒด์˜ ๊ฐฏ์ˆ˜๋ฅผ ์ธก์ •ํ•˜์—ฌ ์™ธ๋ถ€ ์ž๊ทน์˜ ํฌ๊ธฐ๋ฅผ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๋‚ด์šฉ์— ๋Œ€ํ•ด ๋‹ค๋ฃฌ๋‹ค. ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฐ€์†๋„์˜ ํฌ๊ธฐ๋ฅผ ์ธก์ •ํ•˜๊ฑฐ๋‚˜ [13] ์ค‘๋ ฅํŒŒ๋ฅผ ๊ฒ€์ถœํ•˜๋Š” ์„ผ์„œ๋ฅผ ๋งŒ๋“ค๊ฑฐ๋‚˜ [14, 15, 16, 17] ์ค‘๋ ฅ์žฅ์˜ ๊ณต๊ฐ„์— ๋”ฐ๋ฅธ ๋ณ€ํ™”๋Ÿ‰์„ ๋ฐ€๋ฆฌ๋ฏธํ„ฐ ๋‹จ์œ„๋กœ ์ธก์ •ํ•˜๊ฑฐ๋‚˜ [18] ์•”ํ‘ ๋ฌผ์งˆ์„ ๊ฒ€์ถœํ•˜๋Š” ์„ผ์„œ๋ฅผ ๋งŒ๋“ค ์ˆ˜ ์žˆ๋‹ค๋Š” [19] ์ด๋ก ์  ์ œ์•ˆ๋“ค์€ ์กด์žฌํ•˜์˜€์œผ๋‚˜, ์ด๋“ค์€ ๊ณ ์ „์  ํ”ผ์…” ์ •๋ณด๋Ÿ‰์„ ๊ณ„์‚ฐํ•˜์ง€ ์•Š์•˜๊ณ  ์ด๋กœ ์ธํ•ด ํ•ด๋‹น ์ธก์ •์˜ ์ •๋ฐ€๋„๊ฐ€ ์˜ˆ์ƒ๋ณด๋‹ค ๋‚ฎ์„ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์ด ์กด์žฌํ•œ๋‹ค. ๋˜ํ•œ ์ด๋“ค์€ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ํฌ๋…ผ์˜ ๊ฐฏ์ˆ˜๋ฅผ ์ธก์ •ํ•˜๋Š” ๋ฐฉ์‹์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š”๋ฐ, ์•„์ง๊นŒ์ง€๋Š” ์˜ค์ง ์ดˆ์œ ์ฒด ํ—ฌ๋ฅจ II์—์„œ๋งŒ ํฌ๋…ผ์˜ ๊ฐฏ์ˆ˜๋ฅผ ์ธก์ •ํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ  [20] ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ํฌ๋…ผ์˜ ๊ฐฏ์ˆ˜๋ฅผ ์ธก์ •ํ–ˆ๋‹ค๋Š” ๋ณด๊ณ ๋Š” ์กด์žฌํ•˜์ง€ ์•Š๋Š”๋‹ค.(์ด๋ก ์ ์œผ๋กœ๋„ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ํฌ๋…ผ์˜ ๊ฐฏ์ˆ˜๋ฅผ ์ธก์ •ํ•˜๋Š” ๊ฒƒ์€ ์–ด๋ ค์šด ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค [21]). ๊ณง ํˆฌ๊ณ ํ•  ๋ณธ ์ €์ž์˜ ์—ฐ๊ตฌ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ๊ณต๊ฐ„์ƒ ๊ท ์ผํ•˜์ง€๋งŒ ์‹œ๊ฐ„์— ๋”ฐ๋ผ ์‹œ์Šคํ…œ์˜ ๋ฐ€๋„๋ฅผ ๋ฐ”๊พธ๋Š” ์™ธ๋ถ€ ์ž๊ทน์ด ์ดˆ์ €์˜จ ํ™”ํ•™ ๋ฐ˜์‘์ด ์ผ์–ด๋‚˜๋Š” ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์— ๊ฐ€ํ•ด์ง„ ์ƒํ™ฉ์— ๋Œ€ํ•œ ์ด๋ก ์  ์—ฐ๊ตฌ๋ฅผ ์†Œ๊ฐœํ•œ๋‹ค. ๋ณธ ์ €์ž๋Š” ํ•ด๋‹น ์ƒํ™ฉ์—์„œ ์ƒ์„ฑ๋˜๋Š” ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด ๋ถ„์ž์˜ ๊ฐฏ์ˆ˜๋ฅผ ์ธก์ •ํ•˜์—ฌ ์™ธ๋ถ€ ์ž๊ทน์˜ ํฌ๊ธฐ๋ฅผ ์ธก์ •ํ•  ๋•Œ์˜ ์–‘์ž ํ”ผ์…” ์ •๋ณด๋Ÿ‰๊ณผ ๊ณ ์ „์  ํ”ผ์…” ์ •๋ณด๋Ÿ‰์˜ ํ•˜ํ•œ๊ฐ’์„ ๊ณ„์‚ฐํ•˜์˜€๊ณ , ์ด๋ฅผ ํ† ๋Œ€๋กœ ํ•ด๋‹น ์ธก์ • ๋ฐฉ์‹์˜ ์ •๋ฐ€๋„๊ฐ€ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด๋ฅผ ์‚ฌ์šฉํ–ˆ์„ ๋•Œ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ์ •๋ฐ€๋„์˜ ์ด๋ก ์  ํ•œ๊ณ„์น˜์— ๊ทผ์ ‘ํ•œ๋‹ค๋Š” ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ๋‹ค. ์ดˆ์ €์˜จ ํ™”ํ•™ ๋ฐ˜์‘์ฒด์„ ํ†ตํ•ด ์ƒ์„ฑ๋˜๋Š” ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด ๋ถ„์ž์˜ ๊ฐฏ์ˆ˜๋Š” ์ธก์ •ํ•  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ [22, 23, 24, 25], ์ด๋ฅผ ํ†ตํ•ด ์™ธ๋ถ€ ์ž๊ทน์˜ ํฌ๊ธฐ๋ฅผ ์ธก์ •ํ•˜๋Š” ๋ฐฉ์‹์€ ๋ณด์ฆˆ-์•„์ธ์Šˆํƒ€์ธ ์‘์ง‘์ฒด์˜ ํฌ๋…ผ์˜ ๊ฐฏ์ˆ˜๋ฅผ ์ธก์ •ํ•˜๋Š” ๋ฐฉ์‹์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ๊ธฐ์กด์˜ ์ œ์•ˆ๋“ค๋ณด๋‹ค ์‹คํ˜„๋  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Abstract i Table of Contents iii List of Tables vii List of Figures viii Introduction 1 0.1 Structure of the Thesis and How to Read 5 1 Theoretical Framework 10 1.1 General Theory on Multicomponent and Spinor-Dipolar Bose-Einstein Condensates 11 1.1.1 Hamiltonian 11 1.1.2 Mean-field Description and Its Limitations 14 1.1.3 Bogoliubov Theory 16 1.2 Time Evolution Operator 24 1.2.1 Dyson Series Expansion 26 1.2.2 Symplectic Formalism 26 1.3 Parameter Estimation Theory 27 1.3.1 Cramer-Rao Theorem and Classical Fisher Information 28 1.3.2 Quantum Cramer-Rao Theorem and Quantum Fisher Information 29 2 Stoner-Wohlfarth Switching and Damping Parameter Measurement 31 2.1 The Stoner-Wohlfarth Model and the Landau-Lifshitz-Gilbert Equation 32 2.2 Spinor-Dipolar Bose-Einstein Condensates as Detector 33 2.2.1 Derivations on Landau-Lifshitz-Gilbert equation 34 2.2.2 Derivations on the Hamiltonian of the Stoner-Wohlfarth Model 37 2.2.3 Measuring the Damping Parameter 38 3 BEC as Detector Ultracold Chemical Reaction 44 3.1 Short Introduction on Ultracold Chemical Reaction 45 3.2 Reaction Rate Operator and Reactons 46 3.3 BEC as Detector - Estimating External Perturbation 49 Conclusion 54 A Quasi-1D Gross-Pitaevskiฤฑ equation with dissipation 56 A.1 Step 1. Dipole-Dipole Interaction Term 57 A.1.1 Fourier Space Representation 58 A.1.2 Final Result 63 A.2 Step 2. Final Result 64 B Derivation of the Landau-Lifshitz-Gilbert Equation in Spinor-Dipolar BEC with homogeneous local spin orientations 67 C Description of magnetostriction 70 D Analytic Expressions of wp (k), upq (k), and vpq (k) 75 D.0.1 Part 1 - Theta^T(k)MB;1(k) Theta(k) 76 D.0.2 Part 2 - Theta^T(k)MB;2 Theta(k) 76 D.0.3 Determining Rotation Angle phi(k) 77 D.1 Analytic Expressions of w_p(k) 78 D.2 Analytic Expressions of u_{pq}(k), and v_{pq}(k) 79 E Definitions of Xi_{pq} (k) 80 E.1 Analytic Expressions 82 E.1.1 Part 1 - Theta^T (k)MR;1 Theta(k) 82 E.1.2 Part 2 - Theta^T (k)MR;2 Theta(k) 82 E.1.3 Final Results - Xi_{pp}(k) and Xi_{12}(k) 82 E.1.4 Final Results - Xi_{13}(k), Xi_{14}(k), and Xi_{24}(k) 87 F Fisher Information Calculated by Using Dyson Series Expansion 92 F.1 Time Evolution Operator 93 F.1.1 Second Order Terms 95 F.2 Reaction Rate - Initially in Bogoliubov Vacuum State 103 F.3 Quantum Fisher information 105 F.4 Lower bound of Classical Fisher Information 108 F.4.1 Final Results 121 F.5 Summary and Specific Examples 123 F.5.1 Delta Function Perturbation 124 F.5.2 Step Function Perturbation 125 F.5.3 Sinusoidal Perturbation 126 G Fisher Information Calculated by Using Symplectic Formalism 129 G.1 Time Evolution Operator 129 G.2 Quantum Fisher Information 136 G.3 Lower bound of Classical Fisher Information 144 Bibliography 149 Abstract in Korean 157 Acknowledgments 160๋ฐ•

    (A) study of the hardness of metal-ceramic precious gold alloys popularly used in Korea

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    ์น˜์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] metal-ceramic ์น˜๊ด€ ๋ณด์ฒ ์šฉ ๊ธˆํ•ฉ๊ธˆ์€ ๋„์žฌ์˜ ์—ดํŒฝ์ฐฝ๊ณ„์ˆ˜์™€ ์กฐํ™”๋ฅผ ์ด๋ฃจ์–ด์„œ, ๊ณ„๋ฉด์— ์ƒ๊ธฐ๋Š” ์‘๋ ฅ์„ ์ตœ๋Œ€ํ•œ์œผ๋กœ ๊ทน์†Œํ™”์‹œ์ผœ ํ•˜๋ฉฐ ๊ฐ•๋„, ๊ฒฝ๋„, ๊ณ ํƒ„์„ฑ๊ณ„์†Œ, ๊ณ ์˜จ๊ฐ•๋„, ๊ณ ์šฉ์œต์  ๋“ฑ ์ ๋‹นํ•œ ๋ฌผ๋ฆฌ์  ์„ฑ์งˆ์„ ๊ฐ–์ถ”์–ด์•ผ ํ•˜๋ฉฐ, ํŠนํžˆ ๊ฒฝ๋„๋Š” ๋งˆ๋ชจ์ €ํ•ญ๋„์™€ ๊ฐ•๋„๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ๋ฌผ๋ฆฌ์  ์„ฑ์งˆ๋กœ์„œ ์ž„์ƒ์ ์œผ๋กœ ์ค‘์š”ํ•œ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๊ตญ๋‚ด์—์„œ ์ƒ์šฉ๋˜๊ณ  ์žˆ๋Š” metal-ceramic ์น˜๊ด€๋ณด์ฒ ์šฉ ๊ธˆํ•ฉ๊ธˆ์˜ ์ถ”๊ฐ€ ์—ด์ฒ˜๋ฆฌ์— ๋”ฐ๋ฅธ ๊ฒฝ๋„์˜ ๋ณ€ํ™”์™€ ๊ฐ ์ œํ’ˆ๊ฐ„์— ๋‚˜ํƒ€๋‚œ ๋ฌผ๋ฆฌ์  ๋ณ€ํ™”๋ฅผ ์ƒํ˜ธ๋น„๊ต ์—ฐ๊ตฌํ•˜๊ธฐ ์œ„ํ•จ์ด๋‹ค. ์ €์ž๋Š” ๊ตญ๋‚ด์ œํ’ˆ 4๊ฐ€์ง€์™€ ์™ธ๊ตญ์ œํ’ˆ 1๊ฐ€์ง€๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ ๊ฐ ์ œํ’ˆ์— ๋Œ€ํ•ด regular porcelain firing cycle ํ›„์™€ 1100หšF์—์„œ 15๋ถ„๊ฐ„ ์ถ”๊ฐ€ ์—ด์ฒ˜๋ฆฌํ•œ ๊ฒƒ๊ณผ ์•„์šธ๋Ÿฌ ๋„์žฌ์†Œ๋ถ€ ์—ฌ๋ถ€์— ๋”ฐ๋ฅธ ๊ฒฝ๋„ ๋ณ€์ด๋ฅผ Microvicker'w Hardness Tester๋กœ ์ธก์ •ํ•˜๊ณ  ๊ด‘ํ•™ํ˜„๋ฏธ๊ฒฝ์œผ๋กœ ๊ธˆ์†์กฐ์ง์„ ๊ด€์ฐฐํ•œ ๊ฒฐ๊ณผ, ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๋ก ์„ ์–ป์—ˆ๋‹ค. 1. ๊ฐ ์ œํ’ˆ๊ฐ„์˜ ๊ฒฝ๋„๋Š” ์„œ๋กœ ๋‹ฌ๋ž์œผ๋ฉฐ ๊ฒฝ๋„๊ฐ€ ๋†’์€ ์ˆœ์„œ๋Š” C, E, A, D, B ์ˆœ์„œ์˜€๋‹ค. 2. ์ถ”๊ฐ€์ ์ธ ์—ด์ฒ˜๋ฆฌ ์˜ํ–ฅ์— ๋”ฐ๋ผ์„œ ๊ฒฝ๋„์˜ ๋ณ€ํ™”๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ, ๊ทธ ๋ณ€ํ™”๋Š” ์ œํ’ˆ๋งˆ๋‹ค ๋‹ฌ๋ž๊ณ , ์ „์ฒด์ ์œผ๋กœ ๋ณด์•„ ์ฆ๊ฐ€ํ•˜์˜€์œผ๋‚˜ ์ œํ’ˆ B๋Š” ์ „ํ˜€ ๋ณ€ํ™”๊ฐ€ ์—†์—ˆ๋‹ค. 3. ๋„์žฌ์ˆ˜๋ถ€ ์—ฌ๋ถ€์— ๋”ฐ๋ฅธ ๊ฒฝ๋„์˜ ๋ณ€ํ™”๋Š” ์—†์—ˆ๋‹ค. 4. ์‹คํ—˜์ œํ’ˆ ์ค‘ ๊ฒฝ๋„์  ์ธก๋ฉด์—์„œ ์ž„์ƒ์ ์œผ๋กœ ์ƒ์šฉํ•˜๊ธฐ ๊ณค๋ž€ํ•œ ์ œํ’ˆ์€ B์˜€๋‹ค. [์˜๋ฌธ] The metal ceramic precious gold alloys should minimize the stress occurs on the interface by harmonizing the modulus of thermal expansion of porcelains, and have proper physical properties as like strength, stress, high modulus of elasticity, high temperature strength, and high modulus of elasticity, high temperature strength, and high melting point, and especially, hardness, which is a physical property showing potential abrasion resistance and strength, has an important clinical meaning. The purpose of this study is to study the physical properties of commercially marketing metal ceramic precious gold alloys in Korea by comparing the changes of hardness after additional heat treatment. The author of this study has collected four Korean made and one foreign country's made metal ceramic preciour gold alloys, and used them as the materials of this study by testing the changes of each of their hardness variations after the regular porcelain firing cycle, after the additional heat treatment for 15 minutes at 1100หšF, being porcelain venered or not by Microvicker's hardness tester, and observed their metallographic structures by optical microscopes, and the results are as follows. 1. The hardness of the five metal ceramic precious gold alloty are different from each other, and the order from the strongest to the weakest is C.E.A.D.B. 2. According to the additional heat treatment, there are the hardness variations but the amount of them are different from each other, and the hardness are increased generally except B. 3. There are no changes according to the fact that whether they are porcelain veneered or not. 4. There are some Korea made goods which are not proper to use in prosthodontic fields.restrictio

    ํ‰ํŒ์œ„ ์ฒœ์ด๊ฒฝ๊ณ„์ธต์˜ ์œ ๋™ํŠน์„ฑ์— ๋Œ€ํ•œ ์‹คํ—˜์  ์—ฐ๊ตฌ

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

    US-China Rivalry and Security Studies in South Korea

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    ์ตœ๊ทผ ๋ฏธ๊ตญ ํ•™๊ณ„์˜ 21์„ธ๊ธฐ ์•ˆ๋ณด์—ฐ๊ตฌ๋Š” ์ค‘๊ตญ์˜ ๋ถ€์ƒ์— ๋”ฐ๋ฅธ ์„ธ๋ ฅ ์ „์ด์™€ ์ด๊ฒƒ์ด ์ดˆ๋ž˜ํ•˜๋Š” ๋ฏธ์ค‘ ํŒจ๊ถŒ๊ฒฝ์Ÿ์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ์ด๋ก ์  ๊ฐœ๋…์  ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๊ตญ์ œ์งˆ์„œ์—์„œ ํž˜์˜ ๊ตฌ์กฐ์  ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๊ตญ๊ฐ€ ๊ฐ„ ์œ„์ƒ์˜ ๋ณ€ํ™”๋ฅผ ๋‘˜๋Ÿฌ์‹ผ ๊ฐˆ๋“ฑ๊ณผ ์กฐ์ •, ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•œ ์‚ฌํšŒ์ •์ฒด์„ฑ ์ด๋ก ์˜ ์ ์šฉ ๋“ฑ์ด ๊ทธ๊ฒƒ์ด๋‹ค. ๋™์‹œ์— ์ „ํ†ต์ ์ธ ์•ˆ๋ณด์—ฐ๊ตฌ์—์„œ ์ค‘์‹ฌ์„ ์ด๋ฃจ๋˜ ๊ตญ๊ฐ€ ํ–‰์œ„์ž์™€ ๋”๋ถˆ์–ด ์ƒˆ๋กœ์šด ๊ตญ์ œ์•ˆ๋ณด์˜ ์ค‘์š”ํ•œ ํ–‰์œ„์ž๋กœ ๋น„๊ตญ๊ฐ€ ํ–‰์œ„์ž์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ์ƒˆ๋กœ์ด ์ฃผ๋ชฉ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ๋ƒ‰์ „์ดํ›„ ์ˆ˜๋งŽ์€ ๋ถ„์Ÿ์ด ๊ตญ๊ฐ€ ๊ฐ„ ๋ณด๋‹ค๋Š” ๋‹ค์–‘ํ•œ ๋น„ ๊ตญ๊ฐ€ ํ–‰์œ„์ž์— ์˜ํ•ด ์ฃผ๋„๋˜๊ณ  ์žˆ๋Š” ํ˜„์‹ค์„ ๋ฐ˜์˜ํ•œ๋‹ค. ํ•œํŽธ 4์ฐจ ์‚ฐ์—…ํ˜๋ช…์œผ๋กœ ์ด‰๋ฐœ๋œ ์ƒˆ๋กœ์šด ๊ตฐ์‚ฌ๊ธฐ์ˆ ์ด ์ „์Ÿ์˜ ์–‘์ƒ๊ณผ ์ˆ˜๋‹จ์„ ํ˜๋ช…์ ์œผ๋กœ ๋ฐ”๊ฟ€ ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•œ ๊ด€์‹ฌ๋„ ๋‚ ๋กœ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์‚ฌ์ด๋ฒ„ ์•ˆ๋ณด์˜ ์ค‘์š”์„ฑ๊ณผ ๊ด€๋ จ ๊ธฐ์ˆ , ์ „๋žต์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ๊ด€์‹ฌ์ด ๊ทธ ์ข‹์€ ์˜ˆ์ด๋‹ค. ์ด๋Ÿฌํ•œ ๋ฏธ๊ตญ์˜ 21์„ธ๊ธฐ ์•ˆ๋ณด์—ฐ๊ตฌ๋Š” ํ•œ๋ฐ˜๋„๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ๋ฏธ์ค‘ํŒจ๊ถŒ๊ฒฝ์Ÿ์ด ๊ฒฉํ™”๋˜๋Š” ๊ฐ€์šด๋ฐ, ๋ถํ•ต๊ณผ ๋”๋ถˆ์–ด ํ•œ๋ฏธ๋™๋งน์˜ ๊ทผ๋ณธ์  ๋ณ€ํ™”, 4์ฐจ ์‚ฐ์—…ํ˜๋ช… ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์„ ๋™์‹œ์— ๊ฒช๊ณ  ์žˆ๋Š” ํ•œ๊ตญ์˜ ์•ˆ๋ณด์—ฐ๊ตฌ์—๋„ ์ค‘์š”ํ•œ ๊ณผ์ œ๋ฅผ ์ œ๊ธฐํ•œ๋‹ค. ๋ฏธ์ค‘ํŒจ๊ถŒ๊ฒฝ์Ÿ๊ณผ ์‹ ๊ตฐ์‚ฌํ˜์‹ ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฟ ์•„๋‹ˆ๋ผ์ด๋“ค์— ๋Œ€ํ•œ ํ•œ๊ตญ์˜ ๋…์ž์ ์ธ ์ ‘๊ทผ๊ณผ ํ•ด์„์„ ํ† ๋Œ€๋กœ 21์„ธ๊ธฐ ํ•œ๋ฐ˜๋„ ์•ˆ๋ณด๋ฌธ์ œ ํ•ด๊ฒฐ์„ ์œ„ํ•œ ์šฐ๋ฆฌ ์Šค์Šค๋กœ์˜ ์ฐฝ์กฐ์ ์ธ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•  ์ƒˆ๋กœ์šด ์ฑ…์ž„์ด ๊ทธ๊ฒƒ์ด๋‹ค. Recent research trends in the US security studies tend to focus on theory and conceptual analysis of power transition and rivalry between the US and rising China. Using social identity theory, they discuss conflicts, tension, and coordination regarding the issue of status, reputation, legitimacy in international relations. At the same time, some others focus on new dynamics between nation and non-nation actors in security problem in 21st century. Another research deals with new military technology and revolution in military affairs driven by the 4th industrial technology. Rise of cyber space and its related technology and strategic dilemma represent the new era of security study. Such new trends in security studies suggest the same issues will be an important topic for the Korean Peninsula situation where the US-China rivalry, 4th industrial revolution, alliance transformation and North Korean nuclear issue are causing profound security challenges for the region. The security studies in South Korea need to come up with its own independent approach and analysis to those issues in order to provide creative solutions for solving outstanding security problem on the Korean Peninsula.N

    The Middle East Peace Negotiations and the US

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    ํ”ํžˆ ์ด์Šค๋ผ์—˜๊ณผ ํŒ”๋ ˆ์Šคํƒ€์ธ๊ฐ„์˜ ๊ฐˆ๋“ฑ์œผ๋กœ ์•Œ๋ ค์ง„ ์ค‘๋™๋ถ„์Ÿ์€ ๋ณด๋‹ค ๋ณต์žกํ•œ ์ฃผ์ฒด์™€ ๊ฐˆ๋“ฑ์˜ ์š”์†Œ๋“ค์ด ๋‚œ๋งˆ์ฒ˜๋Ÿผ ์–ฝํ˜€์žˆ๋‹ค. ๊ฐˆ๋“ฑ์˜ ์ฃผ์ฒด์ธ ์ด์Šค๋ผ์—˜๊ณผ ํŒ”๋ ˆ์Šคํƒ€์ธ์„ ๋‘˜๋Ÿฌ์‹ธ๊ณ  ๋‹ค์–‘ํ•œ ์ž…์žฅ์˜ ์ฃผ๋ณ€ ์•„๋ž๊ตญ๊ณผ ํŒ”๋ ˆ์Šคํƒ€์ธ ์ž์น˜์ง€์—ญ ๋‚ด ์ด์Šค๋ผ์—˜ ์ •์ฐฉ๋ฏผ์ด ๋˜ ๋‹ค๋ฅธ ๊ฐˆ๋“ฑ์˜ ์ถ•์„ ํ˜•์„ฑํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋“ค ๊ฐ๊ธฐ์˜ ์ฃผ์ฒด ์—ญ์‹œ ๋‚ด๋ถ€์ ์œผ๋กœ ๊ฐ•์˜จํŒŒ๋ฅผ ๋น„๋กฏํ•œ ๋‹ค์–‘ํ•œ ์„ธ๋ ฅ์œผ๋กœ ๋‚˜๋‰˜์–ด ๋ณต์žกํ•œ ์—ญํ•™๊ด€๊ณ„๋ฅผ ํ˜•์„ฑํ•œ๋‹ค. ์—ฌ๊ธฐ์— ์˜ํ† ๋‚˜ ์ข…๊ต๋ฌธ์ œ ๋“ฑ ๊ทผ๋ณธ์ ์œผ๋กœ ํƒ€๊ฒฐํ•˜๊ธฐ ํž˜๋“  ์ƒํ™ฉ์— ๋ง๋ถ™์—ฌ ๊ฐ ์ดํ•ด ๋‹น์‚ฌ์ž๋“ค ๊ฐ„ ํ˜น์€ ์ž์ฒด ๋‚ด๋ถ€์˜ ๋‹ค์–‘ํ•œ ๊ตญ๋‚ด์ •์น˜, ๊ฒฝ์ œ, ์‚ฌํšŒ๋ฌธ์ œ๋ฅผ ๋‘˜๋Ÿฌ์‹ผ ์„ธ๋ ฅ๋‹คํˆผ๊นŒ์ง€ ์–ฝํžˆ๋ฉด์„œ ์ด ์ง€์—ญ์„ ํ‰ํ™”๋Š” ์—ฌ์ „ํžˆ ์š”์›ํ•ด ๋ณด์ธ๋‹ค. ๋ฌธ์ œ๋Š” ์ด ์ง€์—ญ์˜ ๋ฌธ์ œ๊ฐ€ ์„์œ , ์ข…๊ต, ํ…Œ๋Ÿฌ๋“ฑ๊ณผ ๊ฒฐ๋ถ€๋˜์–ด ๊ตญ์ œ์ •์น˜์˜ ํ•ต์‹ฌ์œผ๋กœ ๋“ฑ์žฅํ•˜๋ฉด์„œ ๋ฏธ๊ตญ์„ ํฌํ•จํ•œ ์„ธ๊ณ„๊ฐ€ ๋” ์ด์ƒ ์ด๋“ค์˜ ๋ถ„์Ÿ์„ ์™ธ๋ฉดํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋‹น์‚ฌ์ž๋“ค ์—ญ์‹œ ํ”ผ์˜ ๋ณด๋ณต๊ณผ ๊ฐˆ๋“ฑ์˜ ์•…์ˆœํ™˜์„ ๋” ์ด์ƒ ๊ณ„์†ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๊ณต๊ฐ๋Œ€๊ฐ€ ํ˜•์„ฑ๋˜๋ฉด์„œ ํ‰ํ™”๋ฅผ ์œ„ํ•œ ๋…ธ๋ ฅ์ด ์‹œ์ž‘๋˜์—ˆ์œผ๋ฉฐ, ์ด ๊ณผ์ •์—์„œ ๊ตญ์ œ์‚ฌํšŒ๊ฐ€ ์ค‘์žฌ์ž์˜ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ๋ฏธ๊ตญ์€ ๊ตญ์ œ์ •์น˜์˜ ์ฃผ๋„๊ตญ์œผ๋กœ 1978๋…„ ์บ ํ”„๋ฐ์ด๋น„๋“œ ํ˜‘์ƒ์„ ์‹œ๋ฐœ๋กœ ์ด์Šค๋ผ์—˜๊ณผ ์ฃผ๋ณ€ ์•„๋ž๊ตญ ๋ฐ ํŒ”๋ ˆ์Šคํƒ€์ธ์‚ฌ์ด์˜ ํ‰ํ™”ํ˜‘์ƒ์— ์ ๊ทน์ ์œผ๋กœ ๋‚˜์„œ์™”๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์—ฌ๋Ÿฌ ์–ด๋ ค์›€ ์†์—์„œ๋„ ์ด์Šค๋ผ์—˜๊ณผ ์ด์ง‘ํŠธ, ์š”๋ฅด๋‹จ๊ฐ„์— ํ‰ํ™”ํ˜‘์ •์ด ์ฒด๊ฒฐ๋˜์—ˆ๊ณ , ์ด์Šค๋ผ์—˜๊ณผ ํŒ”๋ ˆ์Šคํƒ€์ธ ์‚ฌ์ด์—๋„ ๋‘ ๊ตญ๊ฐ€ ํ‰ํ™”์  ์ƒํ˜ธ๊ณต์กด์„ ์›์น™์ ์œผ๋กœ ํ•ฉ์˜ํ•˜๋Š” ์„ ๊นŒ์ง€ ์ง„์ „์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ๋ฌธ์ œ๋Š” ์ด๋Ÿฌํ•œ ์›์น™์  ํ•ฉ์˜์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ทธ ์‹คํ–‰์ด ์—ฌ์ „ํžˆ ์–ด๋ ต๋‹ค๋Š” ํ˜„์‹ค์ด๋‹ค. ํŠนํžˆ ์–‘์ž๋‚ด๋ถ€์˜ ๊ฐ•๊ฒฝํŒŒ๊ฐ€ ๋“์„ธํ•˜๋ฉด์„œ ์ด์Šค๋ผ์—˜๊ณผ ํŒ”๋ ˆ์Šคํƒ€์ธ๊ฐ„์˜ ํญ๋ ฅ๊ณผ ๋ณด๋ณต์˜ ๊ฐˆ๋“ฑ์€ ์ง€์†๋˜๊ณ  ์žˆ๋‹ค. 9/11 ์ดํ›„ ํ…Œ๋Ÿฌ์™€์˜ ์ „์Ÿ์†์— ์ค‘๋™์— ๋„๋ฆฌ ํผ์ง„ ๋ฐ˜๋ฏธ๊ฐ์ •์„ ์ˆœํ™”ํ•˜๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์˜ ์ผํ™˜์œผ๋กœ ์ค‘๋™ํ‰ํ™”์— ๋Œ€ํ•œ ๋ฏธ๊ตญ ์ •๋ถ€์˜ ์ƒˆ๋กœ์šด ๋…ธ๋ ฅ์ด ์‹œ๋„ ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฏธ๊ตญ์ด ์›๋ž˜ ๋ถ„์Ÿ์˜ ์›์ธ ์ œ๊ณต์ž๊ฐ€ ์•„๋‹Œ ์ œ3์ž์  ์ž…์žฅ, ํ˜‘์ƒ์˜ ์‹ค์ฒœ์ด ๋‹น์‚ฌ์ž์— ์˜ํ•ด ๊ฒฐ์ •๋  ์ˆ˜๋ฐ–์— ์—†๋Š” ๊ตฌ์กฐ, ๊ทธ๋ฆฌ๊ณ  ๋ถ„์Ÿ์˜ ๋ฟŒ๋ฆฌ๊ฐ€ ์ˆ˜ ์ฒœ๋…„์— ๊ฑธ์ณ ํ˜•์„ฑ๋œ ์ข…๊ต์ , ์ธ์ข…์ , ๋ฏผ์กฑ์  ๊ฐˆ๋“ฑ์— ์žˆ๋‹ค๋Š” ์  ๋“ฑ์—์„œ ๋ฏธ๊ตญ์˜ ์ค‘์žฌ๋…ธ๋ ฅ์€ ๋งŽ์€ ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. The Israel-Palestine conflict has evolved around many actors. It includes neighboring arab countries, the general arab world, and Israeli settlers in the occupied territories as well. The complex dynamics of interaction among and within these various actors creates fundamental challenges for the efforts for the Middle East peace. And the challenges are even more complicated by a myriad of issues involving territorial, national, ethnic, religious, and socio-political conflict among those actors. After three major wars between Israel and neighboring Egypt, Jordan, and Syria, there have been efforts to make a peace in this region. The United States has led the difficult peace negotiations since the 1978 Camp David Talks initiated by President Jimmy Carter. Since then Israel made a peace agreement with Egypt and Jordan, and agreed to a two state solution acknowledging the Palestinian authority. Yet, each agreement had to experience a set back and the challenge of implementing the deal. In particular, the Israeli settlers inside supposedly Palestinian territory of the West Bank has become a major obstacle to the two state solution as they refuse to evacuate from the land they claim their home after decades of settlement. The division among the moderates and the conservatives in Israeli domestic politics makes the issue very much contentious. Add to that, the infight over Palestine leadership between Hamas in Gaza Strip and the Palestine Authority led by President Mahmoud Abbas in the West Bank further acerbate the conflict. The involvement of foreign influence, in particular, by the Syrian and the Iranian government makes the situation even more complicated. As a result, the US role in peace negotiations has been limited at best

    Obama Administrations Cyber Security Policy and Challenges

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    ๋ฏธ๊ตญ์€ ์‚ฌ์ด๋ฒ„ ๊ณต๊ฐ„์˜ ๊ฐ€์žฅ ํฐ ๊ธฐ์ˆ ์  ๋ฆฌ๋”์ด์ž, ์ˆ˜ํ˜œ์ž์ด๋ฉฐ, ๋˜ํ•œ ๊ฐ์ข… ์‚ฌ์ด๋ฒ„ ๊ณต๊ฒฉ์˜ ๊ฐ€์žฅ ํฐ ๋Œ€์ƒ์ด๊ธฐ๋„ ํ•˜๋‹ค. ๋ฏธ๊ตญ์€ ์ •๋ณด์˜ ์ž์œ ๋กœ์šด ์†Œํ†ต๊ณผ ์ ‘๊ทผ, ๊ฐœ์ธ์˜ ์˜์‚ฌํ‘œํ˜„๊ณผ ์ •๋ณด ์Šต๋“ ๊ถŒํ•œ ๋ณด์žฅ, ์—ด๋ฆฐ ์‚ฌ์ด๋ฒ„ ๊ณต๊ฐ„์„ ํ†ตํ•œ ๊ฐœ์ธ๊ณผ ๋ฏผ๊ฐ„, ๊ตญ๊ฐ€ ์ด์ต์˜ ์ฆ์ง„ ๋“ฑ์„ ๋ชฉํ‘œ๋กœ ์‚ฌ์ด๋ฒ„๋ฒ”์ฃ„๋กœ๋ถ€ํ„ฐ ์ด๋“ค ๊ฐ€์น˜์™€ ์›์น™์„ ์ง€ํ‚ค๊ธฐ ์œ„ํ•œ ๊ตญ๋‚ด์ •์ฑ…, ๊ตญ์ œํ˜‘๋ ฅ, ๊ตญ์ œ๊ทœ๋ฒ” ์ฐฝ์ถœ์— ๋…ธ๋ ฅํ•˜๊ณ  ์žˆ๋‹ค. ๋ฏธ๊ตญ ์ •๋ถ€๊ฐ€ ์ •์˜ํ•˜๋Š” ์‚ฌ์ด๋ฒ„์•ˆ๋ณด ์ •์ฑ…์€ ์‚ฌ์ด๋ฒ„ ๊ณต๊ฐ„๊ณผ ๊ทธ ๋‚ด๋ถ€์˜ ์šด์˜์— ๋Œ€ํ•œ ๋ณด์•ˆ์— ๊ด€๊ณ„๋œ ๋ชจ๋“  ๊ธฐ์ค€๊ณผ ์ •์ฑ…, ์ „๋žต์„ ํฌ๊ด„ํ•˜๋ฉฐ, ์ง€๊ตฌ์  ์ •๋ณดํ†ต์‹  ์ธํ”„๋ผ์˜ ๋ณด์•ˆ๊ณผ ์•ˆ์ •์— ๊ด€์—ฌ๋œ ์ปดํ“จํ„ฐ ๋„คํŠธ์›Œํฌ ์šด์˜, ์ •๋ณด ๋ณด์•ˆ, ๋ฒ• ์ง‘ํ–‰, ์™ธ๊ต, ๊ตฐ์‚ฌ, ์ฒฉ๋ณด ํ™œ๋™ ๋“ฑ์„ ํฌํ•จํ•˜๋Š” ๋ชจ๋“  ๋ฒ”์œ„์˜ ์œ„ํ˜‘์ถ•์†Œ, ์ทจ์•ฝ์„ฑ ๊ฐ์†Œ, ์–ต์ œ, ๊ตญ์ œ ๊ต๋ฅ˜, ์‚ฌ๊ณ ๋Œ€์‘, ๋ณต์›๋ ฅ, ๋ณต๊ตฌ ์ •์ฑ…๊ณผ ์ผ์ฒด์˜ ํ™œ๋™์„ ํฌ๊ด„ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฏธ๊ตญ์˜ ์‚ฌ์ด๋ฒ„์•ˆ๋ณด ์ •์ฑ…์€ ์‚ฌ์ด๋ฒ„ ๊ณต๊ฐ„์ด ๊ฐ€์ง€๋Š” ๊ธฐ์ˆ ์  ํŠน์„ฑ์œผ๋กœ ์ธํ•ด ๊ธฐ์กด์˜ ์—ฌํƒ€ ์•ˆ๋ณด์ •์ฑ…๊ณผ ๋‹ค๋ฅธ ๋งŽ์€ ๋„์ „๊ณผ ๊ณผ์ œ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ํ•œํŽธ ๋ฏธ๊ตญ์€ ์‚ฌ์ด๋ฒ„ ๊ณต๊ฐ„๊ณผ ์ธํ„ฐ๋„ท ์ƒ์˜ ํ‘œํ˜„์˜ ์ž์œ , ๊ฐœ๋ฐฉ, ์‹ ๋ขฐ์˜ ๊ธฐ๋ณธ์›์น™์— ๋ฐ”ํƒ•ํ•œ ๊ตญ์ œ ๊ทœ๋ฒ”๊ณผ ํ†ต์น˜์ œ๋„์˜ ์ฐฝ์ถœ์„ ์œ„ํ•ด ์ง€์—ญํ˜‘๋ ฅ๊ณผ ์ง€๊ตฌ์  ์ฐจ์›์˜ ๊ฑฐ๋ฒ„๋„Œ์Šค ํ˜•์„ฑ์— ๋…ธ๋ ฅ์„ ๊ฒฝ์ฃผํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฏธ๊ตญ์˜ ๋…ธ๋ ฅ์€ ์ด์— ๋Œ€ํ•œ ๋‹ค๋ฅธ ์ดํ•ด๊ด€๊ณ„์™€ ์ ‘๊ทผ์„ ์ถ”๊ตฌํ•˜๋Š” ์ค‘๊ตญ์ด๋‚˜ ๋Ÿฌ์‹œ์•„์™€์˜ ๊ฐˆ๋“ฑ์„ ์•ผ๊ธฐํ•˜๊ธฐ๋„ ํ•œ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ์—ฌ์ „ํžˆ ์‚ฌ์ด๋ฒ„์•ˆ๋ณด ๊ด€๋ จ ์ •์ฑ…๊ณผ ์ œ๋„, ๊ธฐ์ˆ ๊ฐœ๋ฐœ, ๊ตญ์ œ๊ทœ๋ฒ” ์„ค๋ฆฝ ๋“ฑ์—์„œ ์„ ๋„์  ์—ญํ• ์„ ์ถ”๊ตฌํ•˜๋Š” ๋ฏธ๊ตญ์˜ ์‚ฌ์ด๋ฒ„์•ˆ๋ณด ์ „๋žต์€ ํ–ฅํ›„ ํ•œ๊ตญ์„ ๋น„๋กฏํ•œ ๊ฐ๊ตญ์˜ ์‚ฌ์ด๋ฒ„์•ˆ๋ณด ์ „๋žต์€ ๋ฌผ๋ก  ๊ตญ์ œ ์‚ฌ์ด๋ฒ„ ์งˆ์„œ ํ™•๋ฆฝ์— ๋งŽ์€ ์‹œ์‚ฌ์ ์„ ๊ฐ€์งˆ ๊ฒƒ์ด๋‹ค. The United States has been the biggest leader and beneficiary of cyber space. Yet, it has been also the biggest target of cyber attack. The US government has worked hard to promote free exchange and free access of information, guarantee of individual right of free expression and acquiring information and open cyber space. For this, the US government pursues various policies in domestic and international arena. Especially, under the Obama administration, the United States initiated a comprehensive policy review and published two major reports on cyber security policy: Cyberspace policy Review: Assuring a Trusted and Resilient Information and Communications Infrastructure (2009) and International Strategy for Cyberspace: Prosperity, Security and Openness in a Networked World (2011). This paper discusses the US efforts to deal with cyber security issues, challenges, and its implications in ever more complex and rapidly changing environment of cyber space in the 21st century

    Bioremediation and Risk Assessment of Polycyclic Aromatic Hydrocarbons

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    Maste

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