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    CUDA Unified Memory๋ฅผ ์œ„ํ•œ ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ ๋ฐ ํ”„๋ฆฌํŽ˜์นญ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2022. 8. ์ด์žฌ์ง„.Unified Memory (UM) is a component of CUDA programming model which provides a memory pool that has a single address space and can be accessed by both the host and the GPU. When UM is used, a CUDA program does not need to explicitly move data between the host and the device. It also allows GPU memory oversubscription by using CPU memory as a backing store. UM significantly lessens the burden of a programmer and provides great programmability. However, using UM solely does not guarantee good performance. To fully exploit UM and improve performance, the programmer needs to add user hints to the source code to prefetch pages that are going to be accessed during the CUDA kernel execution. In this thesis, we propose three frameworks that exploits UM to improve the ease-of-programming while maximizing the application performance. The first framework is HUM, which hides host-to-device memory copy time of traditional CUDA program without any code modification. It overlaps the host-to-device memory copy with host computation or CUDA kernel computation by exploiting Unified Memory and fault mechanisms. The evaluation result shows that executing the applications under HUM is, on average, 1.21 times faster than executing them under original CUDA. The speedup is comparable to the average speedup 1.22 of the hand-optimized implementations for Unified Memory. The second framework is DeepUM which exploits UM to allow GPU memory oversubscription for deep neural networks. While UM allows memory oversubscription using a page fault mechanism, page fault handling introduces enormous overhead. We use a correlation prefetching technique to solve the problem and hide the overhead. The evaluation result shows that DeepUM achieves comparable performance to the other state-of-the-art approaches. At the same time, our framework can run larger batch size that other methods fail to run. The last framework is SnuRHAC that provides an illusion of a single GPU for the multiple GPUs in a cluster. Under SnuRHAC, a CUDA program designed to use a single GPU can utilize multiple GPUs in a cluster without any source code modification. SnuRHAC automatically distributes workload to multiple GPUs in a cluster and manages data across the nodes. To manage data efficiently, SnuRHAC extends Unified Memory and exploits its page fault mechanism. We also propose two prefetching techniques to fully exploit UM and to maximize performance. The evaluation result shows that while SnuRHAC significantly improves ease-of-programming, it shows scalable performance for the cluster environment depending on the application characteristics.Unified Memory (UM)๋Š” CUDA ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๋ชจ๋ธ์—์„œ ์ œ๊ณตํ•˜๋Š” ๊ธฐ๋Šฅ ์ค‘ ํ•˜๋‚˜๋กœ ๋‹จ์ผ ๋ฉ”๋ชจ๋ฆฌ ์ฃผ์†Œ ๊ณต๊ฐ„์— CPU์™€ GPU๊ฐ€ ๋™์‹œ์— ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ด์ค€๋‹ค. ์ด์— ๋”ฐ๋ผ, UM์„ ์‚ฌ์šฉํ•  ๊ฒฝ์šฐ CUDA ํ”„๋กœ๊ทธ๋žจ์—์„œ ๋ช…์‹œ์ ์œผ๋กœ ํ”„๋กœ์„ธ์„œ๊ฐ„์— ๋ฐ์ดํ„ฐ๋ฅผ ์ด๋™์‹œ์ผœ์ฃผ์ง€ ์•Š์•„๋„ ๋œ๋‹ค. ๋˜ํ•œ, CPU ๋ฉ”๋ชจ๋ฆฌ๋ฅผ backing store๋กœ ์‚ฌ์šฉํ•˜์—ฌ GPU์˜ ๋ฉ”๋ชจ๋ฆฌ ํฌ๊ธฐ ๋ณด๋‹ค ๋” ๋งŽ์€ ์–‘์˜ ๋ฉ”๋ชจ๋ฆฌ๋ฅผ ํ•„์š”๋กœ ํ•˜๋Š” ํ”„๋กœ๊ทธ๋žจ์„ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ด์ค€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ, UM์€ ํ”„๋กœ๊ทธ๋ž˜๋จธ์˜ ๋ถ€๋‹ด์„ ํฌ๊ฒŒ ๋œ์–ด์ฃผ๊ณ  ์‰ฝ๊ฒŒ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋„์™€์ค€๋‹ค. ํ•˜์ง€๋งŒ, UM์„ ์žˆ๋Š” ๊ทธ๋Œ€๋กœ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์€ ์„ฑ๋Šฅ ์ธก๋ฉด์—์„œ ์ข‹์ง€ ์•Š๋‹ค. UM์€ page fault mechanism์„ ํ†ตํ•ด ๋™์ž‘ํ•˜๋Š”๋ฐ page fault๋ฅผ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋งŽ์€ ์˜ค๋ฒ„ํ—ค๋“œ๊ฐ€ ๋ฐœ์ƒํ•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. UM์„ ์‚ฌ์šฉํ•˜๋ฉด์„œ ์ตœ๋Œ€์˜ ์„ฑ๋Šฅ์„ ์–ป๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ”„๋กœ๊ทธ๋ž˜๋จธ๊ฐ€ ์†Œ์Šค ์ฝ”๋“œ์— ์—ฌ๋Ÿฌ ํžŒํŠธ๋‚˜ ์•ž์œผ๋กœ CUDA ์ปค๋„์—์„œ ์‚ฌ์šฉ๋  ๋ฉ”๋ชจ๋ฆฌ ์˜์—ญ์— ๋Œ€ํ•œ ํ”„๋ฆฌํŽ˜์น˜ ๋ช…๋ น์„ ์‚ฝ์ž…ํ•ด์ฃผ์–ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ UM์„ ์‚ฌ์šฉํ•˜๋ฉด์„œ๋„ ์‰ฌ์šด ํ”„๋กœ๊ทธ๋ž˜๋ฐ๊ณผ ์ตœ๋Œ€์˜ ์„ฑ๋Šฅ์ด๋ผ๋Š” ๋‘๋งˆ๋ฆฌ ํ† ๋ผ๋ฅผ ๋™์‹œ์— ์žก๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•๋“ค์„ ์†Œ๊ฐœํ•œ๋‹ค. ์ฒซ์งธ๋กœ, HUM์€ ๊ธฐ์กด CUDA ํ”„๋กœ๊ทธ๋žจ์˜ ์†Œ์Šค ์ฝ”๋“œ๋ฅผ ์ˆ˜์ •ํ•˜์ง€ ์•Š๊ณ  ํ˜ธ์ŠคํŠธ์™€ ๋””๋ฐ”์ด์Šค ๊ฐ„์— ๋ฉ”๋ชจ๋ฆฌ ์ „์†ก ์‹œ๊ฐ„์„ ์ตœ์†Œํ™”ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, UM๊ณผ fault mechanism์„ ์‚ฌ์šฉํ•˜์—ฌ ํ˜ธ์ŠคํŠธ-๋””๋ฐ”์ด์Šค ๊ฐ„ ๋ฉ”๋ชจ๋ฆฌ ์ „์†ก์„ ํ˜ธ์ŠคํŠธ ๊ณ„์‚ฐ ํ˜น์€ CUDA ์ปค๋„ ์‹คํ–‰๊ณผ ์ค‘์ฒฉ์‹œํ‚จ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด HUM์„ ํ†ตํ•ด ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์‹คํ–‰ํ•˜๋Š” ๊ฒƒ์ด ๊ทธ๋ ‡์ง€ ์•Š๊ณ  CUDA๋งŒ์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์— ๋น„ํ•ด ํ‰๊ท  1.21๋ฐฐ ๋น ๋ฅธ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, Unified Memory๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ”„๋กœ๊ทธ๋ž˜๋จธ๊ฐ€ ์†Œ์Šค ์ฝ”๋“œ๋ฅผ ์ตœ์ ํ™”ํ•œ ๊ฒƒ๊ณผ ์œ ์‚ฌํ•œ ์„ฑ๋Šฅ์„ ๋‚ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘๋ฒˆ์งธ๋กœ, DeepUM์€ UM์„ ํ™œ์šฉํ•˜์—ฌ GPU์˜ ๋ฉ”๋ชจ๋ฆฌ ํฌ๊ธฐ ๋ณด๋‹ค ๋” ๋งŽ์€ ์–‘์˜ ๋ฉ”๋ชจ๋ฆฌ๋ฅผ ํ•„์š”๋กœ ํ•˜๋Š” ๋”ฅ ๋Ÿฌ๋‹ ๋ชจ๋ธ์„ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. UM์„ ํ†ตํ•ด GPU ๋ฉ”๋ชจ๋ฆฌ๋ฅผ ์ดˆ๊ณผํ•ด์„œ ์‚ฌ์šฉํ•  ๊ฒฝ์šฐ CPU์™€ GPU๊ฐ„์— ํŽ˜์ด์ง€๊ฐ€ ๋งค์šฐ ๋นˆ๋ฒˆํ•˜๊ฒŒ ์ด๋™ํ•˜๋Š”๋ฐ, ์ด๋•Œ ๋งŽ์€ ์˜ค๋ฒ„ํ—ค๋“œ๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ๋‘๋ฒˆ์งธ ๋ฐฉ๋ฒ•์—์„œ๋Š” correlation ํ”„๋ฆฌํŽ˜์นญ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ์ด ์˜ค๋ฒ„ํ—ค๋“œ๋ฅผ ์ตœ์†Œํ™”ํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด DeepUM์€ ๊ธฐ์กด์— ์—ฐ๊ตฌ๋œ ๊ฒฐ๊ณผ๋“ค๊ณผ ๋น„์Šทํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ด๋ฉด์„œ ๋” ํฐ ๋ฐฐ์น˜ ์‚ฌ์ด์ฆˆ ํ˜น์€ ๋” ํฐ ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๋ชจ๋ธ์„ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, SnuRHAC์€ ํด๋Ÿฌ์Šคํ„ฐ์— ์žฅ์ฐฉ๋œ ์—ฌ๋Ÿฌ GPU๋ฅผ ๋งˆ์น˜ ํ•˜๋‚˜์˜ ํ†ตํ•ฉ๋œ GPU์ฒ˜๋Ÿผ ๋ณด์—ฌ์ค€๋‹ค. ๋”ฐ๋ผ์„œ, ํ”„๋กœ๊ทธ๋ž˜๋จธ๋Š” ์—ฌ๋Ÿฌ GPU๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ํ•˜์ง€ ์•Š๊ณ  ํ•˜๋‚˜์˜ ๊ฐ€์ƒ GPU๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ”„๋กœ๊ทธ๋ž˜๋ฐํ•˜๋ฉด ํด๋Ÿฌ์Šคํ„ฐ์— ์žฅ์ฐฉ๋œ ๋ชจ๋“  GPU๋ฅผ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Š” SnuRHAC์ด Unified Memory๋ฅผ ํด๋Ÿฌ์Šคํ„ฐ ํ™˜๊ฒฝ์—์„œ ๋™์ž‘ํ•˜๋„๋ก ํ™•์žฅํ•˜๊ณ , ํ•„์š”ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์ž๋™์œผ๋กœ GPU๊ฐ„์— ์ „์†กํ•˜๊ณ  ๊ด€๋ฆฌํ•ด์ฃผ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋˜ํ•œ, UM์„ ์‚ฌ์šฉํ•˜๋ฉด์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์˜ค๋ฒ„ํ—ค๋“œ๋ฅผ ์ตœ์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ํ”„๋ฆฌํŽ˜์นญ ๊ธฐ๋ฒ•์„ ์†Œ๊ฐœํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด SnuRHAC์ด ์‰ฝ๊ฒŒ GPU ํด๋Ÿฌ์Šคํ„ฐ๋ฅผ ์œ„ํ•œ ํ”„๋กœ๊ทธ๋ž˜๋ฐ์„ ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋„์™€์ค„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ํŠน์„ฑ์— ๋”ฐ๋ผ ์ตœ์ ์˜ ์„ฑ๋Šฅ์„ ๋‚ผ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค.1 Introduction 1 2 Related Work 7 3 CUDA Unified Memory 12 4 Framework for Maximizing the Performance of Traditional CUDA Program 17 4.1 Overall Structure of HUM 17 4.2 Overlapping H2Dmemcpy and Computation 19 4.3 Data Consistency and Correctness 23 4.4 HUM Driver 25 4.5 HUM H2Dmemcpy Mechanism 26 4.6 Parallelizing Memory Copy Commands 29 4.7 Scheduling Memory Copy Commands 31 5 Framework for Running Large-scale DNNs on a Single GPU 33 5.1 Structure of DeepUM 33 5.1.1 DeepUM Runtime 34 5.1.2 DeepUM Driver 35 5.2 Correlation Prefetching for GPU Pages 36 5.2.1 Pair-based Correlation Prefetching 37 5.2.2 Correlation Prefetching in DeepUM 38 5.3 Optimizations for GPU Page Fault Handling 42 5.3.1 Page Pre-eviction 42 5.3.2 Invalidating UM Blocks of Inactive PyTorch Blocks 43 6 Framework for Virtualizing a Single Device Image for a GPU Cluster 45 6.1 Overall Structure of SnuRHAC 45 6.2 Workload Distribution 48 6.3 Cluster Unified Memory 50 6.4 Additional Optimizations 57 6.5 Prefetching 58 6.5.1 Static Prefetching 58 6.5.2 Dynamic Prefetching 61 7 Evaluation 62 7.1 Framework for Maximizing the Performance of Traditional CUDA Program 62 7.1.1 Methodology 63 7.1.2 Results 64 7.2 Framework for Running Large-scale DNNs on a Single GPU 70 7.2.1 Methodology 70 7.2.2 Comparison with Naive UM and IBM LMS 72 7.2.3 Parameters of the UM Block Correlation Table 78 7.2.4 Comparison with TensorFlow-based Approaches 79 7.3 Framework for Virtualizing Single Device Image for a GPU Cluster 81 7.3.1 Methodology 81 7.3.2 Results 84 8 Discussions and Future Work 91 9 Conclusion 93 ์ดˆ๋ก 111๋ฐ•

    ์กฐ์„ ํ›„๊ธฐ ์™•์‹ค ๅฟŒ่พฐ็ฅญ์˜ ์„คํ–‰๊ณผ ์šด์˜

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    ๅฟŒ่พฐ็ฅญ๋Š” ์ฃฝ์€ ์‚ฌ๋žŒ์˜ ๅฟŒๆ—ฅ์— ์ง€๋‚ด๋Š” ์ œ์‚ฌ์ด๋‹ค. ๊ธฐ์ผ์— ์ง€๋‚ด๋Š” ์ œ์‚ฌ๋Š” ์‹œ๋Œ€์— ๋”ฐ๋ผ ํ˜น์€ ์œ ํ–‰ํ•˜๋Š” ์ข…๊ต ํ˜น์€ ์‚ฌ์ƒ์— ์˜ํ•ด ๋‹ค๋ฅธ ํ˜•ํƒœ๋กœ ์น˜๋ฅด๊ฒŒ ๋˜์—ˆ๋‹ค. ๋ถˆ๊ต๊ฐ€ ์ง€๋ฐฐํ•˜์˜€๋˜ ์‹œ๋Œ€์—๋Š” ๅฟŒๆ™จ้ฝ‹์˜ ํ˜•ํƒœ๋กœ ์™•์‹ค์—์„œ ๅ…ˆ็Ž‹๊ณผ ๅ…ˆๅŽ์˜ ๅฟŒๆ—ฅ์„ ๋งž์•„ ์ด๋ฅผ ์ถ”๋ชจํ•˜์—ฌ ์ง€๋‚ธ ๋ถˆ๊ต์‹ ๅฟŒ็ฅญ็ฅ€๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ด์— ๋น„ํ•ด ์œ ๊ต์‹์˜ ๊ธฐ์ œ์‚ฌ์—๋Š” ๋ถˆ๊ต์™€๋Š” ๋‹ค๋ฅธ ํ˜•ํƒœ๋กœ ๋ด‰ํ–‰ํ•˜๋Š” ๅฟŒ่พฐ็ฅญ๊ฐ€ ์žˆ๋‹ค. ์ด ๋‘˜์€ ๋ถˆ๊ต์™€ ์œ ๊ต๋ผ๋Š” ์ข…๊ต์  ์ฐจ์ด์ ๋งŒ์ด ์žˆ๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์‹œํ–‰ํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ ์žฅ์†Œ ๋“ฑ์—์„œ ๋งŽ์€ ๋‹ค๋ฅธ ์ ์ด ์žˆ์—ˆ๋‹ค. ๊ณ ๋ ค์‹œ๋Œ€์—๋Š” ๋ถˆ๊ต๊ฐ€ ์ˆญ์ƒ๋˜์—ˆ์œผ๋ฏ€๋กœ ๋ถˆ๊ต์‹์œผ๋กœ ๊ธฐ์‹ ์žฌ๊ฐ€ ์น˜๋ฃจ์–ด์ ธ ๋ฌด๋ค์—์„œ ์ง€๋‚ด๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์‚ฌ์›์—์„œ ํ–‰ํ•ด์กŒ๋‹ค. ์ด์— ๋น„ํ•ด ์กฐ์„ ์‹œ๋Œ€์— ํ–‰ํ•ด์ง„ ์œ ๊ต์˜ ๊ธฐ์‹ ์ œ๋Š” ๊ธฐ๋ณธ์ ์œผ๋กœ๋Š” ์‚ฌ๋‹น์—์„œ ์ง€๋ƒˆ์œผ๋ฉฐ, ์™•์˜ ๊ฒฝ์šฐ ๋Šฅ์—์„œ ์ง€๋‚ด๊ธฐ๋„ ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์กฐ์„ ์ „๊ธฐ๊นŒ์ง€ ๊ธฐ์‹ ์ œ๋Š” ๋ถˆ๊ต์‹์ธ ๊ธฐ์‹ ์žฌ์˜ ํ˜•ํƒœ๋กœ ๊ฑฐํ–‰๋˜๋‹ค๊ฐ€ ์ค‘์ข… ๋•Œ์—์•ผ ํ˜ํŒŒ๋˜์—ˆ๋‹ค. ์ดํ›„ ๊ธฐ์‹ ์ œ๋Š” ์œ ๊ต์˜ ๋ฐฉ์‹๋Œ€๋กœ ์ง€๋‚ด๊ฒŒ ๋˜์—ˆ์œผ๋‚˜ ์กฐ์„ ํ›„๊ธฐ์—๋Š” ์™•๋ฆ‰์—์„œ ์ง€๋‚ด๊ฒŒ ๋จ์œผ๋กœ์จ ํŠน๋ณ„ํ•œ ์˜๋ฏธ๋ฅผ ์ง€๋‹ˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ์„ ์กฐ ์ดํ›„์—๋Š” ๋ถˆ๊ต์‹ ๊ธฐ์‹ ์žฌ๋ฅผ ๋Œ€์ฒดํ•˜์—ฌ ์™•๋ฆ‰์—์„œ ๊ธฐ์‹ ์ œ๊ฐ€ ํ–‰ํ•ด์กŒ๋‹ค. ๋•Œ๋กœ๋Š” ๊ธฐ์‹ ์ œ๊ฐ€ ์›๋ฌ˜์—์„œ ๊ฑฐํ–‰๋˜๊ธฐ๋„ ํ•˜์˜€์œผ๋‚˜ ์™•๋ฆ‰์—์„œ ๊ฑฐํ–‰๋˜๋Š” ๊ฒƒ์ด ์ผ๋ฐ˜ํ™”๋˜์—ˆ๋‹ค. ํŠนํžˆ ์˜์กฐ๋Š” ์™•๋ฆ‰์—์„œ ํ–‰ํ•˜๋Š” ๊ธฐ์‹ ์ œ์— ์ง์ ‘ ์ฐธ์—ฌํ•˜์—ฌ ์นœํ–‰ํ•˜๋Š” ์˜ˆ๋ฅผ ํ–‰ํ•˜์˜€๋‹ค. ์ด์— ๋”ฐ๋ผ ๊ตญ์™•์ด ์™•๋ฆ‰ ๊ธฐ์‹ ์ œ์— ์ฐธ์—ฌํ•˜๋Š” ์˜ˆ๊ฐ€ ๋“ฑ์žฅํ•˜์˜€๊ณ , ์ด๋ฅผ ์˜์ ˆ๋กœ ๋งŒ๋“ค์—ˆ์œผ๋ฉฐ, ๊ตญ์™• ๋ฟ๋งŒ์ด ์•„๋‹ˆ๋ผ ์™•์„ธ์ž๊ฐ€ ์ฃผ์ฒด๊ฐ€ ๋˜๋Š” ๊ธฐ์‹  ์ œ์˜๊ฐ€ ๋“ฑ์žฅํ•˜์˜€๋‹ค. ๋˜ํ•œ ์˜์กฐ ๋•Œ์— ์ƒˆ๋กญ๊ฒŒ ์ •๋น„๋œ ๅœ’ๅˆถ์— ์˜ํ•ด ๅœ’์—์„œ ์น˜๋Ÿฌ์ง€๋Š” ๊ธฐ์‹ ์ œ์˜๋„ ์ •๋น„๋˜์—ˆ๋‹ค

    ๊ด‘์„  ์ถ”์  ๊ธฐ๋ฒ•์— ๊ธฐ๋ฐ˜ํ•œ ์‹ค๋‚ด ๋ฌด์„  ์ฑ„๋„์—์„œ์˜ ์‚ฌ์šฉ์ž ์‰๋„์ž‰ ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2015. 8. ๊น€์„ฑ์ฒ .In this dissertation, the effects of user body on radio wave propagation in indoor wireless channels are analyzed. The user who is nearly always being close to mobile device influences very strongly and consistently on propagation channel. Therefore, exclusively focusing on the user body separately from other bodies, the shadowing effects caused by the user are investigated at 2.4 GHz by using the uniform theory of diffraction (UTD) and the ray-tracing technique. First of all, the user body shadowing (UBS) effects on a single ray path are investigated deterministically by using the UTD. The UTD scattering solutions for diffraction at a smooth convex surface are adopted to analyze the effects of user body modeled as a circular cylinder. The UTD-based model for a single ray path is defined as the relative received signal power according to the relative position of user, which is validated by measurements in an anechoic chamber. The validated UTD-based model is combined with the indoor ray-tracing technique in order to examine the UBS effects on multipath channels. Since the ray-tracing provides not only the powers of multipaths but also their angular profiles, it is possible to apply the UTD single path model according to the relationship between the users position and the direction of rays. This combination method is also verified by in-building measurements. In realistic communications, however, the users position can be neither fixed at any one value and nor can its exact value be provided to systems in real time. Thus, a statistical analysis for the UBS is conducted taking into consideration the randomness of users position. First, the K-factor, defined as the ratio of the power in the dominant path and the sum of the powers in the other paths, is proposed as the most significant factor to determine the UBS effects. Because the UBS effects considerably depend on the extent of the dominant path and whether the dominant path is blocked. As a result, the distributions of total power losses caused by the UBS are link-by-link modeled by Nakagami-m distributions. Additionally, the estimated parameter m is proposed as a function of K-factor. Finally, the enhanced UBS stochastic model is proposed based on the bimodal characteristics of UBS. The UBS model based on Nakagami-m distribution has a drawback of inaccuracies for the links with high K-factor because the distribution of total UBS losses for links with high K-factors has a bimodal shape that has two peaks in its histogram. Therefore, the distributions of total UBS losses were classified into unimodal and bimodal groups with the quantitative decision criterion of K-factor. For the unimodal model, Rician distribution is used to achieve the best accuracy, whereas Gaussian mixture model is exploited for the bimodal UBS model. The validity of these proposed models is verified using the ray-tracing simulation in various indoor environments.Chapter 1 Introduction....................................................1 1.1 Indoor Wireless Propagation Channel ........................1 1.2 User Body Effects on Wireless Propagation Channel....2 1.3 Dissertation Outline..................................................4 Chapter 2 User Body Shadowing Effects on Single Ray Path based on UTD..........................5 2.1 Introduction.............................................................5 2.2 Uniform Theory of Diffraction (UTD)............................6 2.3 UTD Solutions at a Smooth Convex Surface................7 2.4 UBS Effects on a Single Ray Path.............................12 2.5 Conclusion............................................................16 Chapter 3 Analysis of UBS Effects on Indoor Wireless Multipath Channels..............17 3.1 Introduction...........................................................17 3.2 Ray-Tracing Technique..........................................18 3.2.1 Image Method.....................................................18 3.2.2 Reliability ...........................................................19 3.3 Application of Single Ray UBS Model to Multipath Channel.........24 3.3.1 Methodology........................................................24 3.3.2 Validation............................................................27 3.4 Link-by-Link Model using Nakagami-m Distribution....29 3.5 Conclusion............................................................35 Chapter 4 Enhanced Statistical Model for UBS based on Bimodal Characteristics............36 4.1 Introduction...........................................................36 4.2 Methodology for Enhancement of UBS Model............39 4.2.1 Bimodal Characteristics of UBS Model...................42 4.2.2 Data Grouping.....................................................43 4.2.3 Other Factors......................................................45 4.3 Ray-Tracing Simulation..........................................48 4.4 Enhanced Statistical UBS Model..............................51 4.4.1 Bimodality in terms of K-factor..............................51 4.4.2 The Unimodal UBS Model....................................54 4.4.3 The Bimodal UBS Model......................................57 4.4.4 Application of the Proposed Model for Other Environments...61 4.5 Conclusion...........................................................64 Chapter 5 Conclusion ..............................................65 5.1 Summary...............................................................65 5.2 Expansion and Application of User Body Effects..........66 5.2.1 Other Frequency Bands.........................................66 5.2.2 Device-to-Device (D2D) Communications................67 5.2.3 Temporal Variation of UBS.....................................71 Bibliography................................................................72 Abstract in Korean.......................................................80Docto

    ๋ฐฐ์–‘ ์ธ๊ฐ„์„ฌ์œ ๋ชจ์„ธํฌ์™€ ์ง€๋ฐฉ์ค„๊ธฐ์„ธํฌ์˜ ๊ด‘๋…ธํ™” ํ”ผ๋ถ€์ฃผ๋ฆ„ ๊ฐœ์„ ํšจ๊ณผ์— ๋Œ€ํ•œ ๋น„๊ต

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ, 2015. 2. ๊น€์„ํ™”.Introduction: We investigated the anti-wrinkle effects of cultured human fibroblasts and adipose-derived stem cells (ADSCs) and the mechanisms underlying the reduction of wrinkles in photoaged skin. Methods: The fibroblasts and ADSCs were isolated from human tissue and cultured. A total of 28 six-week-old female BALB/c-nude mice were classified into 4 groups, including the normal control group and 3 groups that were irradiated 6 times a week for 6 weeks using ultraviolet B radiation to induce photoaged wrinkles. ADSCs were injected into the wrinkles in the skin of the second group and fibroblasts were injected into the wrinkles in the skin of the third group. The fourth group was the irradiated negative control group (no therapy). After 4 weeks of injections, wrinkles were compared by replica analysis, biopsies were performed, and the dermal thickness and collagen densities were measured. We determined the amounts of type 1 collagen and matrix metalloproteinases (MMPs) 1, 2, 3, 9, and 13 using real-time polymerase chain reaction and western blot analysis and assessed tropoelastin and fibrillin-1 expression in the dermis by immunohistochemistry. Results: Replica analysis showed significant wrinkle reduction in the fibroblast group and the ADSC group. ADSCs stimulated collagen expression and decreased MMP expression. Although fibroblasts stimulated more collagen expression than ADSCs, they also increased MMP expression. Overall, the ADSC group showed higher collagen density and had better outcomes in the tropoelastin and fibrillin-1 assessments. Conclusions: Cultured fibroblasts and ADSCs could both play an important role in wrinkle reduction despite differences in their mechanisms of action.Abstract โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ ั– List of table โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โ…ฒ List of figures โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โ…ณ โ… . Introduction โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ 1 โ…ก. Materials and Methods โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ 4 โ…ข. Results โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ 11 โ…ฃ. Discussion โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ 25 โ…ค. Conclusion โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ 30 References โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ 31 Abstract in Korean โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ โˆ™ 40Docto

    ๊ต์œก์˜ ๊ณต๊ฐ„ ๋ถˆํ‰๋“ฑ ์š”์ธ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ™˜๊ฒฝ๋Œ€ํ•™์› : ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2015. 2. ๊น€๊ฒฝ๋ฏผ.๋„์‹œ๋Š” ์ง€์—ญ์˜ ๊ฒฝ์ œ์„ฑ์žฅ์„ ๋„๋ชจํ•˜๊ธฐ ์œ„ํ•ด์„œ ์ƒ์‚ฐ์  ์ž์›์ธ ์ธ์ ์ž๋ณธ์„ ์—ญ๋‚ด๋กœ ์œ ์น˜ํ•˜๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์„ ํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•˜์—ฌ ์ง€์—ญ๊ฒฝ์ œ๊ฐ€ ์„ฑ์žฅํ•˜๋ฉด ์ง€๋ฐฉ์ •๋ถ€์˜ ์„ธ์›์ด ํ™•๋Œ€๋˜๊ณ , ๊ณ ์šฉ์ด ์ฆ๋Œ€๋˜๋ฉฐ, ๋ถ€๋™์‚ฐ ๋“ฑ ๋„์‹œ์˜ ์ž์‚ฐ ๊ฐ€์น˜๊ฐ€ ์ƒ์Šน๋˜๋Š” ๋“ฑ ์ „๋ฐ˜์ ์ธ ๊ฐ€์น˜๊ฐ€ ํ–ฅ์ƒ๋  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ธ์ ์ž๋ณธ์„ ํ†ตํ•œ ๊ฒฝ์ œ์„ฑ์žฅ์€ ๋„์‹œ์˜ ์ „์ฒด์ ์ธ ์ด์ต์ด์ž ๋„์‹œ์ •์ฑ…์˜ ๋ชฉํ‘œ๊ฐ€ ๋œ๋‹ค. ๋„์‹œ์ •์ฑ… ์ค‘ ์ธ์ ์ž๋ณธ ํ˜•์„ฑ์— ์‚ฌ์šฉ๋˜๋Š” ๊ต์œก์ง€์ถœ์€ ์ €์†Œ๋“ ๊ณ„์ธต์˜ ํ•™์ƒ๋“ค์—๊ฒŒ ๊ต์œก์˜ ๊ธฐํšŒ๋ฅผ ์ œ๊ณตํ•˜๋Š” ์žฌ๋ถ„๋ฐฐ ์ •์ฑ…์˜ ํšจ๊ณผ๊ฐ€ ์žˆ์ง€๋งŒ, ๊ฒฝ์ œ์„ฑ์žฅ์„ ์œ„ํ•œ ์ธ์  ์ธํ”„๋ผ๋ฅผ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฐœ๋ฐœ์ •์ฑ…์˜ ํšจ๊ณผ๋„ ์žˆ๋‹ค. ๊ต์œก์ •์ฑ…์˜ ํšจ๊ณผ๋Š” ๊ต์œก์˜ ์„ฑ๊ณผ์— ์˜ํ–ฅ์„ ์ฃผ๋ฉฐ, ๊ต์œก์˜ ์„ฑ๊ณผ๋Š” ๊ฐœ์ธ์˜ ์†Œ๋“์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ธฐ ๋•Œ๋ฌธ์— ๊ต์œก ์ •์ฑ…์ด ๊ฐœ๋ฐœ์ •์ฑ…์ธ์ง€ ์žฌ๋ถ„๋ฐฐ์ •์ฑ…์ธ์ง€ ์—ฌ๋ถ€์— ๋”ฐ๋ผ์„œ ์ •์ฑ…์˜ ์ˆ˜ํ˜œ๋ฅผ ๋ฐ›๋Š” ๊ณ„์ธต์ด ๋‹ฌ๋ผ์ง„๋‹ค. ์ด๋Ÿฌํ•œ ์ •์ฑ…์˜ ํšจ๊ณผ๋Š” ๊ต์œก์˜ ์„ฑ๊ณผ๋ฅผ ๋งค๊ฐœ์ฒด๋กœ ํ•˜์—ฌ ๊ฐœ์ธ์˜ ์†Œ๋“์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ฒŒ ๋˜๊ธฐ ๋•Œ๋ฌธ์—, ๊ต์œก ์ •์ฑ…์€ ๊ฐœ์ธ์˜ ์†Œ๋“์—๋„ ์ค‘์š”ํ•œ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค. ๋˜ํ•œ ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ผ์„œ ๊ณต๊ฐ„์ ์œผ๋กœ ๊ต์œก์„ฑ๊ณผ์˜ ๋ถˆํ‰๋“ฑ์ด ์™„ํ™”๋˜๊ณ  ์žˆ๋Š”์ง€ ์‹ฌํ™”๋˜๊ณ  ์žˆ๋Š”์ง€ ์—ฌ๋ถ€๋Š” ๋งค์šฐ ์ค‘์š”ํ•œ๋ฐ, ์ž๋…€ ๊ต์œก์„ ์œ„ํ•œ ๊ฑฐ์ฃผ์ง€ ์ด๋™์„ ํ†ตํ•ด์„œ ๋ถ€๋™์‚ฐ ์ž์‚ฐ๊ฐ€์น˜ ๋“ฑ์˜ ๋„์‹œ ๋ณ€ํ™”๊ฐ€ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๊ต์œก ๊ฒฉ์ฐจ๊ฐ€ ๋„์‹œ์˜ ์–ด๋ฉ”๋„ˆํ‹ฐ์— ์˜ํ–ฅ์„ ์ฃผ๋ฉด์„œ ๊ณ„์ธต๊ฐ„ ๊ฑฐ์ฃผ์ง€ ๋ถ„ํ™” ํ˜„์ƒ์ด ์ผ์–ด๋‚˜๊ณ , ์ง€์—ญ๊ฐ„ ์–‘๊ทนํ™”์˜ ์‚ฌํšŒ ๋ฌธ์ œ๋กœ ํ™•๋Œ€๋  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. 2003๋…„์—๋Š” ์ง€์—ญ๊ฐ„ ๊ต์œก ๊ฒฉ์ฐจ๋ฅผ ์™„ํ™”์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ ์žฌ๋ถ„๋ฐฐ์ •์ฑ…์œผ๋กœ์„œ์˜ ๊ต์œก๋ณต์ง€ํˆฌ์ž์šฐ์„ ์ง€์—ญ ์ง€์›์‚ฌ์—…์ด ์‹œ์ž‘๋˜์—ˆ์œผ๋ฉฐ, ๋งค๋…„ ๊ทธ ๋ฒ”์œ„๊ฐ€ ํ™•์žฅ๋˜์–ด 41๊ฐœ ํ•™๊ต์—์„œ 2008๋…„์—๋Š” 222๊ฐœ๋กœ 441.5% ์ฆ๊ฐ€ํ–ˆ์œผ๋ฉฐ, ์˜ˆ์‚ฐ์•ก ๋˜ํ•œ 2003๋…„ 30์–ต3์ฒœ1๋ฐฑ๋งŒ์›์—์„œ 2008๋…„ 208์–ต5์ฒœ3๋ฐฑ๋งŒ์›์œผ๋กœ 687.9% ๋Œ€ํญ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ, ์„œ์šธ์‹œ ์ˆ˜๋Šฅ์ƒ์œ„๊ตฌ์™€ ํ•˜์œ„๊ตฌ์˜ ๊ต์œก๊ฒฉ์ฐจ๋Š” 2000, 2005, 2009๋…„ ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ผ ๋”์šฑ ๋ฒŒ์–ด์ง€๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์ด๋‹ค. 2000ํ•™๋…„๋„์˜ ๊ฒฝ์šฐ, ๊ฐ•๋‚จ๊ตฌ์˜ ์˜์–ด 1๋“ฑ๊ธ‰ ๋น„์œจ์€ 8.2%์ธ๋ฐ ๋น„ํ•ด, ๊ธˆ์ฒœ๊ตฌ๋Š” 1.3%๋กœ, ๋‘˜๊ฐ„์˜ ๊ฒฉ์ฐจ๋Š” 5.93๋ฐฐ์— ๋ถˆ๊ณผํ•˜์˜€์ง€๋งŒ, 2005ํ•™๋…„์ด ๋˜๋ฉด ๊ฐ•๋‚จ๊ตฌ๋Š” 11.94%, ๊ธˆ์ฒœ๊ตฌ๋Š” 1.17๋กœ ๊ฒฉ์ฐจ๊ฐ€ 10๋ฐฐ๋กœ ๋Š˜์–ด๋‚˜๊ฒŒ ๋˜์—ˆ์œผ๋ฉฐ, 2009ํ•™๋…„๋„์—๋Š” ๊ฐ•๋‚จ๊ตฌ์˜ ์ˆ˜๋Šฅ ์˜์–ด 1๋“ฑ๊ธ‰ ๋น„์œจ์ด ๋ฌด๋ ค 14%์ธ๋ฐ ๋น„ํ•ด ๊ธˆ์ฒœ๊ตฌ๋Š” ๊ณ ์ž‘ 0.6%๋กœ ๋‘˜๊ฐ„์˜ ๊ต์œก๊ฒฉ์ฐจ๊ฐ€ 21.95๋ฐฐ๋กœ ํฌ๊ฒŒ ํ™•๋Œ€๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์–‘๊ทนํ™” ํ˜„์ƒ์˜ ์ง„ํ–‰์„ ๋‹จ์ ˆ์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ ์ง€์—ญ๊ฐ„ ๊ต์œก๊ฒฉ์ฐจ๋ฅผ ํ•ด์†Œํ•ด์•ผ ํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ง€์—ญ๊ฐ„ ๊ต์œก ๊ฒฉ์ฐจ๋ฅผ ์ผ์œผํ‚ค๋Š” ์š”์ธ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ•„์ˆ˜์ ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ต์œก์˜ ๊ณต๊ฐ„ ๋ถˆํ‰๋“ฑ์„ ์•ผ๊ธฐํ•˜๋Š” ์š”์ธ์— ๋Œ€ํ•ด์„œ ๊ณต๊ฐ„๊ณ„๋Ÿ‰๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•˜์—ฌ ์‹ค์ฆ์ ์œผ๋กœ ๋ฐํ˜€๋ƒˆ๋‹ค. ์šฐ์ˆ˜ ํ•™์ƒ ์ง‘๋‹จ์˜ ๊ฒฝ์šฐ ํ•™๊ต ๋ถ„์œ„๊ธฐ์™€ ๊ฐœ์ธํ•™์Šต๋Šฅ๋ ฅ์„ ํ†ต์ œํ–ˆ์„ ๋•Œ, ์†Œ๋“์ด๋ผ๋Š” ํ•™์Šต ์™ธ์  ํ™˜๊ฒฝ์ด ์ˆ˜๋Šฅ ์˜์–ด์„ฑ์ ์— ์˜ํ–ฅ์„ ์ฃผ๊ณ  ์žˆ์Œ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ์—ˆ๋Š”๋ฐ, ์ด๋Š” ์ˆ˜๋Šฅ ์›์ž๋ฃŒ์˜ ๋ถ„์„๊ณผ ๊ต์œก๋ณต์ง€ ํˆฌ์ž์šฐ์„ ์ง€์—ญ์˜ ์žฌ๋ถ„๋ฐฐ ์ •์ฑ…์˜ ํ™•์ธ์—์„œ ์•Œ ์ˆ˜ ์žˆ๋“ฏ์ด, ์ˆ˜๋Šฅ ์˜์–ด๊ณผ๋ชฉ์— ๋Œ€ํ•œ ์ง€์—ญ๊ฐ„ ๊ต์œก๊ฒฉ์ฐจ๋Š” ๊ฐœ์ธ์ด ๊ณต๋ถ€๋ฅผ ์—ด์‹ฌํžˆ ํ•ด์„œ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐœ์ธ๋Šฅ๋ ฅ ์ฐจ์›์˜ ๋ฌธ์ œ๋ฅผ ๋ฒ—์–ด๋‚ฌ๋‹ค๊ณ  ํŒ๋‹จํ•  ์ˆ˜ ์žˆ๋‹ค. 2003๋…„๋ถ€ํ„ฐ ์‹œ์ž‘๋œ ์žฌ๋ถ„๋ฐฐ ์ •์ฑ… ์—ญ์‹œ ์ง€์—ญ๊ฐ„ ๊ต์œก ๊ฒฉ์ฐจ๋ฅผ ํ•ด์†Œํ•˜๊ธฐ์—๋Š” ๋ถ€์กฑํ•œ ๋ฉด์ด ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋‹จ๊ธฐ์ ์ธ ์ •์ฑ…์ˆ˜๋‹จ์œผ๋กœ ์ง€์—ญ๊ฐ„ ์†Œ๋“ ๊ฒฉ์ฐจ์˜ ์ฐจ์ด๊ฐ€ ๊ต์œก ์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์™„ํ™”์‹œ์ผœ ์ค„ ์ˆ˜ ์žˆ๋Š” ๋ณด๋‹ค ๊ฐ•๋ ฅํ•œ ์žฌ๋ถ„๋ฐฐ์ •์ฑ…์˜ ์‹คํ–‰์„ ๊ณ ๋ คํ•ด ๋ณผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์žฅ๊ธฐ์ ์œผ๋กœ๋Š” ๊ต์œก์ด ๊ณ„์ธต ๊ฐ„ ์‚ฌ๋‹ค๋ฆฌ ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๋„๋ก ์†Œ๋“๊ณผ ๊ต์œก์„ฑ๊ณผ์˜ ์—ฐ๊ฒฐ๊ณ ๋ฆฌ ๋ˆ์„ ๋Š์–ด์ค„ ์ˆ˜ ์žˆ๋Š” ํ‰๊ฐ€์ œ๋„ ๋ณ€๊ฒฝ๊ณผ ๊ฐ™์€ ๊ฒŒ์ž„์˜ ๋ฃฐ์˜ ๊ฐœ์„ ์„ ์ƒ๊ฐํ•ด ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋” ๋‚˜์•„๊ฐ€ ๊ต์œก์ •์ฑ…๋งŒ์œผ๋กœ๋Š” ์†Œ๋“์˜ ๊ฒฉ์ฐจ๋ฅผ ํ•ด์†Œํ•˜๊ธฐ์—๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๊ธฐ ๋•Œ๋ฌธ์—, ๊ต์œก ์™ธ์ ์ธ ํ†ตํ•ฉ์  ์ •์ฑ… ์„ค๊ณ„๋ฅผ ํ†ตํ•˜์—ฌ ์†Œ๋“๊ฒฉ์ฐจ๊ฐ€ ๊ต์œก๊ฒฉ์ฐจ๋กœ ์ด์–ด์ง€์ง€ ์•Š๋„๋ก ๊ต์œก๋‹น๊ตญ๊ณผ ์ •๋ถ€์˜ ์„ธ์‹ฌํ•œ ๋Œ€์ฑ…์ด ํ•„์š”ํ•œ ์‹œ์ ์ด๋‹ค.I. ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1) ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 2) ์—ฐ๊ตฌ์˜ ๋ชฉ์  4 3) ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 6 2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 7 1) ์—ฐ๊ตฌ์ž๋ฃŒ์™€ ์ธก์ • 7 2) ๋ถ„์„ ๋ฐฉ๋ฒ• 8 II. ์ด๋ก ์  ๊ณ ์ฐฐ 12 1. ๋„์‹œํ•œ๊ณ„๋ก ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ 12 2. ๊ณต๊ฐ„๊ณ„๋Ÿ‰ ๋ถ„์„๋ชจ๋ธ์— ๋Œ€ํ•œ ์ด๋ก ์  ๊ณ ์ฐฐ 14 1) ๊ณต๊ฐ„์ž๊ธฐ์ƒ๊ด€ 14 2) ๊ณต๊ฐ„์ž๊ธฐ์ƒ๊ด€์˜ ๊ฒ€์ • 15 3) ๊ณต๊ฐ„๊ฐ€์ค‘์น˜ํ–‰๋ ฌ 18 4) ๊ณต๊ฐ„๊ณ„๋Ÿ‰ ๋ถ„์„๋ชจ๋ธ 19 III. ๊ธฐ์กด์—ฐ๊ตฌ์˜ ๊ณ ์ฐฐ 20 1. ์ง€์—ญ๊ฐ„ ๊ต์œก ๊ฒฉ์ฐจ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ 20 1) ๊ต์œก ๊ฒฉ์ฐจ์˜ ๊ฐœ๋… 20 2) ๊ต์œก ๊ฒฉ์ฐจ์˜ ์›์ธ 22 (1) ๊ฐœ์ธ์  ํŠน์„ฑ ๋ณ€์ธ 22 (2) ํ•™๊ต ๋‚ด์  ์š”์ธ 23 (3) ํ•™๊ต ์™ธ์  ์š”์ธ 25 โ‘  ๊ฐ€์ •์˜ ์‚ฌํšŒยท๊ฒฝ์ œ์  ์ž๋ณธ๊ณผ ์ง€์—ญ์˜ ์‚ฌํšŒ์  ์ž๋ณธ 25 โ‘ก ์ง€์—ญ์†Œ๋“์„ ํฌํ•จํ•œ ์ง€์—ญ์‚ฌํšŒ์˜ ํŠน์„ฑ 26 3) ๊ต์œก ๊ฒฉ์ฐจ ํ•ด์†Œ๋ฅผ ์œ„ํ•œ ์žฌ๋ถ„๋ฐฐ์ •์ฑ… 27 2. ์ง€์—ญ๊ฐ„ ์†Œ๋“ ๋ถˆํ‰๋“ฑ ๋Œ€ํ•œ ์—ฐ๊ตฌ 29 1) ์†Œ๋“ ๋ถˆํ‰๋“ฑ์˜ ๊ฐœ๋… 29 2) ์†Œ๋“ ๋ถˆํ‰๋“ฑ์˜ ์›์ธ 30 (1) ์ธ์ ์ž๋ณธ์ด๋ก  30 (2) ์ด์ค‘๋…ธ๋™์‹œ์žฅ์ด๋ก  31 (3) ์‹ ์ž์œ ์ฃผ์˜์  ์„ธ๊ณ„ํ™”๋ก  32 3. ๊ต์œก๋ณต์ง€ ํˆฌ์ž์šฐ์„ ์ง€์—ญ์˜ ์žฌ๋ถ„๋ฐฐ์ •์ฑ… ํ˜„ํ™ฉ 35 1) ๊ต์œก๋ณต์ง€์˜ ๊ฐœ๋… 35 2) ๊ต์œก๋ณต์ง€ํˆฌ์ž์šฐ์„ ์ง€์—ญ ์ง€์›์‚ฌ์—…์˜ ๋ฐฐ๊ฒฝ 36 3) ๊ต์œก๋ณต์ง€ํˆฌ์ž์šฐ์„ ์ง€์—ญ ์ง€์›์‚ฌ์—…์˜ ํ˜„ํ™ฉ 37 4) ๊ต์œก๋ณต์ง€ํˆฌ์ž์šฐ์„ ์ง€์—ญ ์ง€์›์‚ฌ์—…์˜ ํ•œ๊ณ„ 40 IV. ์—ฐ๊ตฌ ๋ถ„์„ 42 1. ์ˆ˜๋Šฅ ์›์ž๋ฃŒ ๋ถ„์„์„ ํ†ตํ•œ ์ง€์—ญ๊ฐ„ ๊ต์œก๊ฒฉ์ฐจ ํ™•์ธ 42 2. ์ง€์—ญ๊ฐ„ ๊ต์œก๊ฒฉ์ฐจ์˜ ์š”์ธ ๋ถ„์„ 44 1) ์ข…์†๋ณ€์ˆ˜์— ๋Œ€ํ•œ ๋ถ„์„ 44 (1) ์ˆ˜๋Šฅ ์˜์–ด 1๋“ฑ๊ธ‰ ๋น„์œจ 44 (2) ์ˆ˜๋Šฅ ์˜์–ด 1๋“ฑ๊ธ‰ ๋น„์œจ๋ฐฐ์ˆ˜์˜ ์ •์˜ 46 (3) ์ข…์†๋ณ€์ˆ˜์˜ ์ž๊ธฐ์ƒ๊ด€์„ฑ ์—ฌ๋ถ€ 48 (4) ์ˆ˜๋Šฅ ์˜์–ด์„ฑ์ ์˜ LISA๊ฒฐ๊ณผ 48 2) ๋…๋ฆฝ๋ณ€์ˆ˜์— ๋Œ€ํ•œ ๋ถ„์„ 50 (1) ํ•™์—…์„ฑ์ทจ๋„ํ‰๊ฐ€ ์„ค๋ฌธ์ž๋ฃŒ์— ๋Œ€ํ•œ ์ธ์ž๋ถ„์„ 50 (2) ์ธ์ž๋ถ„์„์„ ํ†ตํ•œ ๋…๋ฆฝ๋ณ€์ˆ˜์˜ ์ธ์ž๋ช…๋ช… 53 (3) ์†Œ๋“์ถ”์ •๋ณ€์ธ์— ๋Œ€ํ•œ ๋ถ„์„ 54 3) ๊ณต๊ฐ„๊ณ„๋Ÿ‰ ๋ถ„์„๊ฒฐ๊ณผ 56 V. ๊ฒฐ๋ก  63 โ–  ์ฐธ๊ณ ๋ฌธํ—Œ 66Maste

    Where was the Birthplace of Tรผrk Ashina Clan?

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    ์กฐ์„ ํ›„๊ธฐ ์ •ํ†ต๋ก ์˜ ์ˆ˜์šฉ๊ณผ ๊ทธ ๋ณ€ํ™” -์ˆ˜์‚ฐ ์ด์ข…ํœ˜์˜ ใ€Ž๋™์‚ฌใ€๋ฅผ ์ค‘์‹ฌ์œผ๋กœ-

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    The purpose of this study is to examine the historical position of the historical work Tongsa (ๆฑๅฒ) by Yi Chong-hwi in the late Choson dynasty. Yi Chong-hwi was affected by the historical conception of orthodoxy in late Choson. In particular, he accepted the Tan-Ki orthodoxy (็ฆฎ็ฎ•ๆญฃ็ตฑ่ซ–) that was explained in Tongguk Yoktae Chongmok (ๆฑๅœ‹ๆญทไปฃ็ธฝ็›ฎ) written by Hong Man-jong (ๆดช่ฌๅฎ—). Yi Chong-hwis Choson Chunghwaron (ๆœ้ฎฎไธญ่ฏ่ซ–) is important in that it focuses on Tangun in ethnology, the North geographically, and the inheritance of kija culture (็ฎ•ๅญ ๆ–‡ๅŒ–๏ผŒ Confucianism), That is different form the Chinese-Barbarian Thought that focused on Hanjok(ๆผขๆ—) in ethnology. Chungguk(ไธญๅœ‹) geographically, and Kija culture. Such a differentiation illustrates the understanding and adaption of Chinese thought according to the local conditions of Choson. which shows that Choson of the 18th century was culturally mature

    A Study on Characteristics of Autonomous Coordination and Aesthetic Values of Colors in Residential Townscape

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2017. 2. ์ตœ๋ง‰์ค‘.๊ฒฝ๊ด€์—์„œ ์ƒ‰์ฑ„๋ฅผ ๊ฐœ์ธ์˜ ์ž์œจ์  ์„ ํƒ์ด ๋งŒ๋“ค์–ด๋‚ธ ๊ฒฐ๊ณผ๋ฌผ๋กœ ์ดํ•ดํ•˜๊ณ  ์ƒ‰์ฑ„๊ฒฐ์ •์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ์š”์ธ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ธฐ์กด์˜ ์ž์—ฐ๋ฐœ์ƒ์ ์ธ ๋„์‹œ๊ฒฝ๊ด€์˜ ๊ตฌ์„ฑ์›๋ฆฌ๋Š” ์ง€๊ธˆ๊ป ์•Œ๋ ค์ง„ ๋ฐ”๊ฐ€ ๊ฑฐ์˜ ์—†๊ณ , ๋”ฐ๋ผ์„œ ์ดํ•ด์„ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋ถ„์„์„ ์‹œ๋„ํ•˜์˜€๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ, ์ƒ‰์ฑ„์˜ ๊ฒฐ์ •์€ ์ง€์—ญ์„ฑ, ๊ฐœ๋ณ„ ๋ฌผ๋ฆฌ์กฐ๊ฑด, ์ธ์ ‘ํ•œ ์ฃผ๋ณ€๊ณผ์˜ ๊ด€๊ณ„ ๋“ฑ์˜ ์˜ํ–ฅ์„ ๋ฐ›๊ณ  ์žˆ์—ˆ๋‹ค. ์ด ์ค‘ ์ƒ‰์ฑ„๊ตฌ์„ฑ ๋ฐ ์กฐํ˜•์  ์งˆ์„œ๋ฅผ ๋งŒ๋“œ๋Š” ์ฃผ์š” ์ธ์ž๊ฐ€ ์ฃผ๋ณ€์ƒ‰์ฑ„์™€์˜ ๊ด€๊ณ„์— ์˜ํ•œ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋˜์—ˆ๊ณ  ์ƒํ˜ธ๊ด€๊ณ„์„ฑ์„ ์ค‘์‹ฌ์œผ๋กœ ๋ณด๋‹ค ๋ฉด๋ฐ€ํ•œ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์ƒ‰์ฑ„๊ด€๊ณ„๋ถ„์„์€ ์กฐ์‚ฌ๋œ ์ƒ‰์ฑ„์ž๋ฃŒ๋ฅผ ์ƒ‰์ƒ๊ด€๊ณ„, ํ•œ ์Œ์”ฉ์˜ ๋ฐฐ์ƒ‰๊ด€๊ณ„, ๊ตฌ์„ฑ์  ์กฐํ™”๊ด€๊ณ„์˜ ์„ธ ๊ฐ€์ง€ ์ธก๋ฉด์—์„œ ์‚ดํŽด๋ณด๊ณ  ๊ตฌ์„ฑํŠน์„ฑ์„ ๋„์ถœํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ด€๊ณ„์„ฑ์ด ๋งŒ๋“ค์–ด์ง€๋Š” ์š”์ธ๊ณผ ๊ตฌ์„ฑ์›๋ฆฌ๋ฅผ ๋ฐํ˜€ ์ž์œจ์  ์ƒ‰์ฑ„๊ฒฝ๊ด€์— ๋‚ด์žฌ๋œ ์งˆ์„œ์˜ ํŠน์„ฑ์„ ์ •์˜ํ•˜์˜€๋‹ค. ์ดํ›„ ํ†ต๊ณ„์  ํ•ด์„์„ ํ†ตํ•ด ์ž์œจ์  ์งˆ์„œ๋Š” ์ƒ‰์ฑ„์„ ํƒ์—์„œ ์ƒํ˜ธ ์กฐํ™”ํ•˜๋ ค๋Š”, ๋˜๋Š” ๋ณด์™„ํ•˜๋ ค๋Š” ์˜์ง€๊ฐ€ ๋ฐ˜์˜๋œ ๊ฒฐ๊ณผ๋ฌผ์ž„์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋‹ค์Œ ๋‹จ๊ณ„๋กœ ์ž์œจ์ ์œผ๋กœ ๊ตฌ์„ฑ๋œ ์ƒ‰์ฑ„๊ฒฝ๊ด€์ด ์–ด๋–ค ์กฐํ˜•๊ฐ€์น˜๋ฅผ ๊ฐ€์ง€๋Š”๊ฐ€๋ฅผ ๊ณ ์ „์ ์ธ ์ƒ‰์ฑ„์กฐํ™”์ด๋ก ์— ๋น„์ถ”์–ด ๋ถ„์„ํ•˜๊ณ , ์ด๊ฒƒ์ด ์œ ์˜ํ•œ ๊ฒฐ๊ณผ์ธ์ง€ ์‹œ๋ฏผ ์„ค๋ฌธํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ์„ค๋ฌธ์€ ์กฐํ™”์ด๋ก ์—์„œ ์กฐํ™”๋กญ๊ฒŒ ํ‰๊ฐ€ํ•˜๋Š” ์ƒ‰์ฑ„๊ตฌ์„ฑ์„ ์‹ค์ œ๋กœ ์กฐํ™”๋กญ๋‹ค๊ณ  ๋Š๋ผ๋Š”๊ฐ€์™€ ์ž์œจ์ ์œผ๋กœ ๊ตฌ์„ฑ๋œ ๊ฒฝ๊ด€๊ณผ ๊ณ„ํš๊ฒฝ๊ด€์„ ๋น„๊ตํ•˜์˜€์„ ๋•Œ ์–ด๋–ค ์‹ฌ์ƒ์˜ ์ฐจ์ด๋ฅผ ๊ฐ€์ง€๋Š”๊ฐ€๋ฅผ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ž์œจ์ ์œผ๋กœ ๊ตฌ์„ฑ๋œ ์ƒ‰์ฑ„๊ฒฝ๊ด€์—์„œ ๋ฐœ๊ฒฌ๋œ ์‹ฌ๋ฏธ์ , ๊ธฐ๋Šฅ์  ๊ฐ€์น˜์™€ ๊ฒฝ๊ด€๊ณ„ํš์—์„œ ์ถ”๊ตฌํ•˜๋Š” ๊ฐ€์น˜ ๋ฐ ์ง€์†๊ฐ€๋Šฅ์„ฑ์„ ๋น„๊ตํ•˜์—ฌ ์ •์ฑ…์  ์‹œ์‚ฌ์ ์„ ๋„์ถœํ•˜์˜€๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ๊ตฌ์„ฑ 3 ์ œ 2 ์žฅ ์ด๋ก  ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 5 ์ œ 1 ์ ˆ ๋ถ„์„์˜ ๊ทผ๊ฑฐ์ด๋ก  5 1. ๊ฒฝ๊ด€์—ฐ๊ตฌ ๊ด€๋ จ ์ด๋ก  6 2. ์ƒ‰์ฑ„์กฐํ™”์ด๋ก  8 3. ์ง€๊ฐ ๋ฐ ์ธ์ง€์ด๋ก  14 ์ œ 2 ์ ˆ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 19 1. ๊ฒฝ๊ด€๊ณ„ํš ๊ด€๋ จ ์—ฐ๊ตฌ 19 2. ํ™˜๊ฒฝ๊ฒฐ์ •์˜ ์ž์œจ์„ฑ ๊ด€๋ จ ์—ฐ๊ตฌ 25 3. ์ƒ‰์ฑ„๊ฒฝ๊ด€์˜ ์ง€๊ฐ ๋ฐ ์ธ์ง€ ๊ด€๋ จ ์—ฐ๊ตฌ 28 ์ œ 3 ์ ˆ ๊ฒฝ๊ด€์ƒ‰์ฑ„๊ณ„ํš์ œ๋„ ๊ณ ์ฐฐ 30 1. ๊ฒฝ๊ด€์ƒ‰์ฑ„๊ณ„ํš์˜ ์ „๊ฐœ๊ณผ์ • 30 2. ๊ฒฝ๊ด€์ƒ‰์ฑ„ ๊ด€๋ จ๋ฒ•๋ฅ  ๋ฐ ๊ด€๋ฆฌ์ฒด๊ณ„ 32 ์ œ 3 ์žฅ ๋ถ„์„์˜ ํ‹€ 35 ์ œ 1 ์ ˆ ๋Œ€์ƒ์ง€ ์„ ์ • 35 1. ๋ถ„์„๋Œ€์ƒ์ง€ 35 2. ๋น„๊ต๋ถ„์„ ๋Œ€์ƒ์ง€ 40 ์ œ 2 ์ ˆ ์กฐ์‚ฌ ๋ฐ ๋ถ„์„๋ฐฉ๋ฒ• 42 1. ๋ถ„์„์ฒด๊ณ„ 42 2. ์ƒ‰์ฑ„์กฐ์‚ฌ ๋ฐ ์ƒ‰์ฑ„์ถ”์ถœ 44 ์ œ 3 ์ ˆ ๋ถ„์„๊ธฐ์ค€ 47 1. ์ƒ‰์ƒ๊ด€๊ณ„ ๋ถ„์„๊ธฐ์ค€ 47 2. ์˜ค์ŠคํŠธ๋ฐœํŠธ ์ƒ‰์ฑ„์กฐํ™”๊ธฐ์ค€ 50 3. ๋ฌธ-์ŠคํŽœ์„œ ๋ฏธ๋„ ๊ณ„์‚ฐ์‹ 53 ์ œ 4 ์žฅ ์ž์œจ์  ์ƒ‰์ฑ„๊ฒฝ๊ด€์˜ ๊ตฌ์„ฑํŠน์„ฑ 60 ์ œ 1 ์ ˆ ์ƒ‰์ฑ„์ •๋ณด ๊ธฐ์ดˆํ†ต๊ณ„ 60 1. ์กฐ์‚ฌ์ง€์—ญ ํ‘œ๋ณธ์ถ”์ถœ 60 2. ์ƒ‰์ƒยท๋ช…๋„ยท์ฑ„๋„์˜ ๊ตฌ์„ฑ ๋ฐ ๋ถ„ํฌ 61 ์ œ 2 ์ ˆ ์ฃผํƒ๊ทœ๋ชจ์™€์˜ ์ƒ๊ด€๊ด€๊ณ„ 64 1. ์ƒ‰์ƒ๊ณผ ์ฃผํƒ๊ทœ๋ชจ์˜ ์ƒ๊ด€๊ด€๊ณ„ 64 2. ๋ช…๋„ยท์ฑ„๋„์™€ ์ฃผํƒ๊ทœ๋ชจยท์ง€๊ฐ€์˜ ์ƒ๊ด€๊ด€๊ณ„ 65 3. ์กฐ์‚ฌํ‘œ๋ณธ ์ƒ‰์ฑ„๋ฉด์ ์˜ ํ•ด์„ 66 ์ œ 3 ์ ˆ ์ƒ‰์ƒ๊ตฌ์„ฑ์˜ ํŒจํ„ด๋ถ„์„ 68 1. ์ƒ‰์ƒ๊ด€๊ณ„ ํ†ต๊ณ„ ๋ฐ ๋ถ„์„ 68 2. ์ƒ‰์ƒํ๋ฆ„ ๋ถ„์„ ๋ฐ ํŒจํ„ด์ •์˜ 72 ์ œ 4 ์ ˆ ์†Œ ๊ฒฐ 76 ์ œ 5 ์žฅ ์ž์œจ์  ์ƒ‰์ฑ„๊ฒฝ๊ด€์˜ ์กฐํ˜•์„ฑ 78 ์ œ 1 ์ ˆ ์กฐํ˜•์„ฑ ๋ถ„์„ 78 1. ์˜ค์ŠคํŠธ๋ฐœํŠธ ์ƒ‰์ฑ„์กฐํ™”๋„ ๋ถ„์„ 78 2. ๋ฌธ-์ŠคํŽœ์„œ ๋ฏธ๋„ํ‰๊ฐ€ 80 3. ์˜ค์ŠคํŠธ๋ฐœํŠธ์™€ ๋ฌธ-์ŠคํŽœ์„œ ์ƒ‰์ฑ„์กฐํ™”์˜ ์ƒ๊ด€์„ฑ 83 ์ œ 2 ์ ˆ ์ฃผ๋ฏผ์„ค๋ฌธํ‰๊ฐ€ 86 1. ์ฃผ๋ฏผ์„ค๋ฌธ์กฐ์‚ฌ1 - ์–ด์˜์ฐจ์ฒ™๋„ ์ด๋ฏธ์ง€ ํ‰๊ฐ€ 86 2. ์ฃผ๋ฏผ์„ค๋ฌธ์กฐ์‚ฌ2 - ์ƒ‰์ฑ„๊ตฌ์„ฑ์— ๋Œ€ํ•œ ์„ ํ˜ธ๋„ 87 3. ์ฃผ๋ฏผ์„ค๋ฌธ์กฐ์‚ฌ3 โ€“์ƒ‰์ฑ„๋ธ”๋ก๋ณ„ ์กฐํ™”๋„ 89 ์ œ 3 ์ ˆ ์ƒ‰์ฑ„๊ณ„ํš์ดํ›„ ์ž์œจ์  ๋ณ€๊ฒฝ 92 1. ๊ฒฝ๊ด€์ƒ‰์ฑ„๊ณ„ํš์˜ ์กฐํ˜•์„ฑ 92 2. ์ฃผ๋ฏผ์˜ ์ƒ‰์ฑ„๋ณ€๊ฒฝ๊ณผ ์กฐํ˜•์„ฑ์˜ ๋ณ€ํ™” 95 ์ œ 4 ์ ˆ ์†Œ ๊ฒฐ 100 ์ œ 6 ์žฅ ๊ฒฐ๋ก  102 ์ฐธ๊ณ ๋ฌธํ—Œ 108 ๋ถ€ ๋ก 112 Abstract 113Docto

    Effect of Na+,K+-ATPase inhibition by ouabain and [K+]e deprivation in cofilin regulatory mechanism

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ œ์•ฝํ•™๊ณผ ๋ฏธ์ƒ๋ฌผ์•ฝํ’ˆํ™”ํ•™์ „๊ณต,2006.Docto

    A study on the current situations and issues in relation to TESOL teachers and prograrns in Australia

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    This paper aims at (a) investigating the current situations and issues in relation to TESOL teachers and programs in Australia and (b) making a suggestion for developing TKFL (Teaching Korean as a Foreign Language) teacher training programs in Korea This ~ consists of three parts In the part one, the historical background of Australian ESL/EFL programs is discussed in terms of multiculturalism and globalisation. In the part two, Australian TESOL degrees and certificates, including the relevant institutions and associations along with their program details, are investigated. It also deals with the qualifications of TESOL teachers and their employment conditions. The current issues and general concerns in Australian TESOL programs and teachers are also outlined in the part two. In the part three, this paper notes the current issues of TKFL programs and teachers in Australia As a conclusion, this paper suggests the establishment of linkage systems for the mutual recognition of academic credits and exchanges of TKFL teachers between Australia and Korea, including the mutual recognition of TKFL curriculum and TKFL teacher practicum in both Australia and Korea
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