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    ๋ผ์ง€์‹ฌ๋ถ€์ „๋ชจ๋ธ์„ ์ด์šฉํ•œ ๋™์ •๋งฅ ์ฒด์™ธ๋ง‰ํ˜• ์‚ฐ์†Œํ™”์žฅ์น˜์—์„œ ๋Œ€๋™๋งฅ๋‚ด ํ’์„ ์žฅ์น˜์˜ ํ˜ˆ์—ญํ•™์ ํšจ๊ณผ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2019. 2. ๊น€๊ฒฝํ™˜.์„œ๋ก : ์‹ฌ๋ถ€์ „ ํ™˜์ž์—๊ฒŒ์„œ ๋™์ •๋งฅํ˜• ์ฒด์™ธ๋ง‰ํ˜•์‚ฐ์†Œํ™”์žฅ์น˜๋Š” ๋งค์šฐ ์œ ์šฉํ•˜๋‚˜, ์ขŒ์‹ฌ์‹ค ํ›„๋ถ€ํ•˜๋ฅผ ์ƒ์Šน์‹œ์ผœ ์ขŒ์‹ฌ์‹คํšŒ๋ณต์— ๋‚˜์œ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ์ด์— ์ขŒ์‹ฌ์‹คํ›„๋ถ€ํ•˜ ๊ฐ์†Œ๋ฅผ ๋ชฉ์ ์œผ๋กœ ๋Œ€๋™๋งฅ๋‚ด ํ’์„ ์žฅ์น˜๋ฅผ ์ถ”๊ฐ€ ์‚ฝ์ž…ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์œผ๋‚˜, ๊ทธ ํšจ๊ณผ์— ๋Œ€ํ•ด์„œ๋Š” ํ˜ˆ์—ญํ•™์ ์œผ๋กœ ์ฆ๋ช…๋œ ๋ฐ”๊ฐ€ ์—†์–ด ์ด๋ฅผ ๋ผ์ง€์‹ฌ๋ถ€์ „ ๋ชจ๋ธ์„ ํ†ตํ•˜์—ฌ ํ™•์ธํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ฐฉ๋ฒ•: 80kg ๋‚ด์™ธ์˜ ๋ผ์ง€๋ฅผ ์ด์šฉํ•˜์—ฌ ์ •์ƒ ์‹ฌ๊ธฐ๋Šฅ์ƒํƒœ์—์„œ ๋Œ€ํ‡ด๋™๋งฅ๊ณผ ์ •๋งฅ์„ ์ด์šฉํ•˜์—ฌ ์ฒด์™ธ ๋ง‰ํ˜• ์‚ฐ์†Œํ™”์žฅ์น˜๋ฅผ ์‚ฝ์ž…ํ•˜๊ณ , ์ขŒ์ธก 4๋ฒˆ์งธ ๋Š‘๊ฐ„์„ ํ†ตํ•œ ๊ฐœํ‰์ˆ ์„ ์‹œํ–‰ํ•˜์—ฌ ์‹ฌ์žฅ์„ ๋…ธ์ถœํ•œ ํ›„, ์ขŒ์‹ฌ๋ฐฉ์••๋ ฅ, ์ƒ๋Œ€์ •๋งฅ์••๋ ฅ์ธก์ •์„ ์‹œํ–‰ํ•œ๋‹ค. ๊ด€์ƒ๋™๋งฅํ˜ˆ๋ฅ˜๋ฅผ ๋Œ€ํ‘œํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ขŒ์ „ํ•˜ ๊ด€์ƒ๋™๋งฅ์„ ๋…ธ์ถœํ•˜๊ณ , ๋‡Œํ˜ˆ๋ฅ˜๋ฅผ ๋Œ€ํ‘œํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ขŒ์‡„๊ณจํ•˜๋™๋งฅ์„ ๋…ธ์ถœ์‹œ์ผœ ์ดˆ์ŒํŒŒ ํ˜ˆ๋ฅ˜๋ฅผ ์ธก์ •ํ•œ๋‹ค. 1์‹œ๊ฐ„์ •๋„ ์ฒด์™ธ๋ง‰ํ˜• ์‚ฐ์†Œํ™”์žฅ์น˜๋ฅผ ์šด์šฉ์„ ํ•˜๊ณ  ๋‚˜์„œ ๋Œ€๋™๋งฅํ’์„ ์žฅ์น˜๋ฅผ ๋ฐ˜๋Œ€์ชฝ ๋Œ€ํ‡ด๋™๋งฅ์— ๊ฑฐ์น˜ํ•˜์—ฌ 1์‹œ๊ฐ„ ํ›„์— ๋™๋งฅ์••, ์ขŒ์‹ฌ๋ฐฉ์••, ์ขŒ์ „ํ•˜๊ด€์ƒ๋™๋งฅํ˜ˆ๋ฅ˜, ์ขŒ์‡„๊ณจํ•˜๋™๋งฅํ˜ˆ๋ฅ˜๋ฅผ ์ธก์ •ํ•˜์—ฌ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•œ๋‹ค. ์‹ฌ๋ถ€์ „ ๋ชจ๋ธ์—์„œ๋Š” ํ๋™๋งฅ์„ ์กฐ์—ฌ์„œ ํ‰๊ท ๋™๋งฅ์••์ด 60mmHg์ดํ•˜๋กœ ๋˜๊ฒŒ ์œ ๋„ํ•œ ํ›„, ์ฒด์™ธ๋ง‰ํ˜• ์‚ฐ์†Œํ™” ์žฅ์น˜๋ฅผ ๊ฐ€๋™ํ•˜๊ณ , ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ 1์‹œ๊ฐ„ ํ›„์— ๋Œ€๋™๋งฅํ’์„ ์žฅ์น˜๋ฅผ ๊ฑฐ์น˜ํ•˜์—ฌ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ํ˜ˆ์—ญํ•™์  ์ง€ํ‘œ๋“ค์˜ ๋ณ€ํ™”๋ฅผ 1์‹œ๊ฐ„ ํ›„์— ๊ด€์ฐฐํ•œ๋‹ค. ์ •์ƒ ์‹ฌ๊ธฐ๋Šฅ ๋™๋ฌผ๋ชจ๋ธ 4๋งˆ๋ฆฌ์™€ ์‹ฌ๋ถ€์ „ ๋™๋ฌผ๋ชจ๋ธ 4๋งˆ๋ฆฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ง€ํ‘œ๋“ค์˜ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜์—ฌ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•œ๋‹ค. ๊ฒฐ๊ณผ: ์ •์ƒ ์‹ฌ์žฅ๋ชจ๋ธ์—์„œ ๋Œ€๋™๋งฅ๋‚ด ํ’์„ ์žฅ์น˜๋Š” ํ‰๊ท  ๋™๋งฅ์••์„ ์ƒ์Šน์‹œํ‚ค์ง€ ์•Š์•˜๊ณ  (118.3ยฑ25.2 ์ฒด์™ธ๋ง‰ํ˜• ์‚ฐ์†Œํ™” ์žฅ์น˜ํ›„ vs. 86.8ยฑ17.1mmHg ๋Œ€๋™๋งฅ๋‚ด ํ’์„ ์žฅ์น˜ ๊ฑฐ์น˜ ํ›„, p=0.068), ์ขŒ์‹ฌ๋ฐฉ์••์€ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€์œผ๋‚˜ (9.3ยฑ7.4 vs. 3.3ยฑ2.6mmHg, p=0.068) ํ†ต๊ณ„์  ์œ ์˜์„ฑ์€ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. ๋˜ํ•œ ๊ด€์ƒ๋™๋งฅํ˜ˆ๋ฅ˜๋‚˜ (28.4ยฑ5.4 vs 23.6ยฑ7.7mL/min, p=0.068) ์ขŒ์‡„๊ณจํ•˜๋™๋งฅํ˜ˆ๋ฅ˜๋Š”(161.9ยฑ55.8 vs. 123.3ยฑ45.9 mL/min, p=0.465) ๋Œ€๋™๋งฅ๋‚ด ํ’์„ ์žฅ์น˜ ํ›„์— ์˜คํžˆ๋ ค ๊ฐ์†Œํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€์œผ๋‚˜ ์—ญ์‹œ ํ†ต๊ณ„์  ์œ ์˜์„ฑ์€ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. ์‹ฌ๋ถ€์ „ ๋ชจ๋ธ์—์„œ๋„ ๋Œ€๋™๋งฅํ’์„ ์žฅ์น˜๋Š” ์ฒด์™ธ๋ง‰ํ˜• ์‚ฐ์†Œํ™”์žฅ์น˜๋งŒ ์šด์šฉํ•˜์˜€์„๋•Œ์™€ ๋น„๊ตํ•˜์—ฌ ํ‰๊ท ๋™๋งฅ์•• (93.8ยฑ20.5 vs. 89.3ยฑ19.6mmHg, p=0.716), ์ขŒ์‹ฌ๋ฐฉ์••์˜ ๋ณ€ํ™”๋‚˜ (7.5ยฑ3.3 vs. 5.5ยฑ2.5mmHg, p=0.144) ๊ด€์ƒ๋™๋งฅํ˜ˆ๋ฅ˜ (9.4 vs. 21.7ยฑ3.4mL/min, p=0.197), ์ขŒ์‡„๊ณจํ•˜๋™๋งฅํ˜ˆ๋ฅ˜ (125.6ยฑ52.4 vs. 122.3ยฑ53.4mL/min, p=1.000)์˜ ์ƒ์Šน์„ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. ๊ฒฐ๋ก : ๋Œ€๋™๋งฅ๋‚ด ํ’์„ ์žฅ์น˜๋Š”, ๋™์ •๋งฅํ˜• ์ฒด์™ธ๋ง‰ ์‚ฐ์†Œํ™” ์žฅ์น˜๋ฅผ ์‹œํ–‰ํ•˜๊ณ  ์žˆ๋Š” ์‹ฌ๋ถ€์ „ ๋ชจ๋ธ์—์„œ ํ˜ˆ์—ญํ•™์  ์ง€ํ‘œ์— ์˜ํ–ฅ์„ ์ฃผ์ง€ ์•Š์•˜๋‹ค.Background: Intra-aortic balloon pump (IABP) may reduce the afterload on the left ventricle as a result of pulmonary congestion from veno-arterial extracorporeal membrane oxygenation (ECMO) support. This animal experiment aims to elucidate the hemodynamic impacts of IABP on ECMO with porcine heart failure model. Methods: The femoral artery and vein of 69~85kg pigs were cannulated for ECMO support by percutaneous or open technique. Left lateral thoracotomy via 4th intercostal level was performed for invasive catheterization and cardiac intervention. We checked various hemodynamic parameters, including left atrial pressure, coronary blood flow on the left anterior descending coronary artery, and left subclavian artery blood flow using a Doppler. The ECMO was applied for one hour and IABP was added on the counterpart femoral artery for another one hour. In the heart failure model, we induced heart failure by pulmonary artery banding to maintain the mean arterial blood pressure below 60mmHg prior to starting ECMO support. We checked same parameters before and after the IABP support (n=4). Results: In the normal heart model, IABP had no impact on hemodynamic change, such as mean blood pressure (118.3ยฑ25.2 ECMO only vs. 86.8ยฑ17.1mmHg ECMO+IABP, p=0.068), left atrial pressure (9.3ยฑ7.4 vs. 3.3ยฑ2.6mmHg, p=0.068), coronary blood flow (28.4ยฑ5.4 vs 23.6ยฑ7.7mL/min, p=0.068), or left subclavian artery flow (161.9ยฑ55.8 vs. 123.3ยฑ45.9 mL/min, p=0.465). In the heart failure model, IABP showed no significant hemodynamic advantage over ECMO only supportthe mean blood pressure (93.8ยฑ20.5 vs. 89.3ยฑ19.6mmHg, p=0.716), left atrial pressure (7.5ยฑ3.3 vs. 5.5ยฑ2.5mmHg, p=0.144), coronary blood flow (26.4ยฑ9.4 vs. 21.7ยฑ3.4mL/min, p=0.197), and left subclavian artery flow (125.6ยฑ52.4 vs. 122.3ยฑ53.4mL/min, p=1.000). Conclusion: IABP seems to not affect hemodynamic change on veno-arterial ECMO support even in the heart failure model. The effect of IABP appears to be minimal during maximal ECMO support1. Introduction ------------------------------------------------------------------ 1 2. Materials and Methods ---------------------------------------------------- 3 3. Results ------------------------------------------------------------------------ 10 4. Discussions ------------------------------------------------------------------ 17 5. References ------------------------------------------------------------------- 23 6. ๊ตญ๋ฌธ์ดˆ๋ก -------------------------------------------------------------------- 27Docto

    ์ด์ข… ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๋ชจ๋ธ์„ ์œ„ํ•œ ํ™•์žฅํ˜• ์ปดํ“จํ„ฐ ์‹œ์Šคํ…œ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021. 2. ๊น€์žฅ์šฐ.Modern neural-network (NN) accelerators have been successful by accelerating a small number of basic operations (e.g., convolution, fully-connected, feedback) comprising the specific target neural-network models (e.g., CNN, RNN). However, this approach no longer works for the emerging full-scale natural language processing (NLP)-based neural network models (e.g., Memory networks, Transformer, BERT), which consist of different combinations of complex and heterogeneous operations (e.g., self-attention, multi-head attention, large-scale feed-forward). Existing acceleration proposals cover only the proposal-specific basic operations and/or customize them for specific models only, which leads to the low performance improvement and the narrow model coverage. Therefore, an ideal NLP accelerator should first identify all performance-critical operations required by different NLP models and support them as a single accelerator to achieve a high model coverage, and can adaptively optimize its architecture to achieve the best performance for the given model. To address these scalability and model/config diversity issues, the dissertation introduces two novel projects (i.e., MnnFast and NLP-Fast) to efficiently accelerate a wide spectrum of full-scale NLP models. First, MnnFast proposes three novel optimizations to resolve three major performance problems (i.e., high memory bandwidth, heavy computation, and cache contention) in memory-augmented neural networks. Next, NLP-Fast adopts three optimization techniques to resolve the huge performance variation due to the model/config diversity in emerging NLP models. We implement both MnnFast and NLP-Fast on different hardware platforms (i.e., CPU, GPU, FPGA) and thoroughly evaluate their performance improvement on each platform.์ž์—ฐ์–ด ์ฒ˜๋ฆฌ์˜ ์ค‘์š”์„ฑ์ด ๋Œ€๋‘๋จ์— ๋”ฐ๋ผ ์—ฌ๋Ÿฌ ๊ธฐ์—… ๋ฐ ์—ฐ๊ตฌ์ง„๋“ค์€ ๋‹ค์–‘ํ•˜๊ณ  ๋ณต์žกํ•œ ์ข…๋ฅ˜์˜ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๋ชจ๋ธ๋“ค์„ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค. ์ฆ‰ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๋ชจ๋ธ๋“ค์€ ํ˜•ํƒœ๊ฐ€ ๋ณต์žกํ•ด์ง€๊ณ ,๋กœ๊ทœ๋ชจ๊ฐ€ ์ปค์ง€๋ฉฐ, ์ข…๋ฅ˜๊ฐ€ ๋‹ค์–‘ํ•ด์ง€๋Š” ์–‘์ƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์€ ์ด๋Ÿฌํ•œ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๋ชจ๋ธ์˜ ๋ณต์žก์„ฑ, ํ™•์žฅ์„ฑ, ๋‹ค์–‘์„ฑ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์—ฌ๋Ÿฌ ํ•ต์‹ฌ ์•„์ด๋””์–ด๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ๊ฐ๊ฐ์˜ ํ•ต์‹ฌ ์•„์ด๋””์–ด๋“ค์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. (1) ๋‹ค์–‘ํ•œ ์ข…๋ฅ˜์˜ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ ์˜ค๋ฒ„ํ—ค๋“œ ๋ถ„ํฌ๋„๋ฅผ ์•Œ์•„๋‚ด๊ธฐ ์œ„ํ•œ ์ •์ /๋™์  ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. (2) ์„ฑ๋Šฅ ๋ถ„์„์„ ํ†ตํ•ด ์•Œ์•„๋‚ธ ์ฃผ๋œ ์„ฑ๋Šฅ ๋ณ‘๋ชฉ ์š”์†Œ๋“ค์˜ ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ์„ ์ตœ์ ํ™” ํ•˜๊ธฐ ์œ„ํ•œ ์ „์ฒด๋ก ์  ๋ชจ๋ธ ๋ณ‘๋ ฌํ™” ๊ธฐ์ˆ ์„ ์ œ์‹œํ•œ๋‹ค. (3) ์—ฌ๋Ÿฌ ์—ฐ์‚ฐ๋“ค์˜ ์—ฐ์‚ฐ๋Ÿ‰์„ ๊ฐ์†Œํ•˜๋Š” ๊ธฐ์ˆ ๊ณผ ์—ฐ์‚ฐ๋Ÿ‰ ๊ฐ์†Œ๋กœ ์ธํ•œ skewness ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ dynamic scheduler ๊ธฐ์ˆ ์„ ์ œ์‹œํ•œ๋‹ค. (4) ํ˜„ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ ๋‹ค์–‘์„ฑ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ ๋ชจ๋ธ์— ์ตœ์ ํ™”๋œ ๋””์ž์ธ์„ ์ œ์‹œํ•˜๋Š” ๊ธฐ์ˆ ์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ํ•ต์‹ฌ ๊ธฐ์ˆ ๋“ค์€ ์—ฌ๋Ÿฌ ์ข…๋ฅ˜์˜ ํ•˜๋“œ์›จ์–ด ๊ฐ€์†๊ธฐ (์˜ˆ: CPU, GPU, FPGA, ASIC) ์—๋„ ๋ฒ”์šฉ์ ์œผ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋งค์šฐ ํšจ๊ณผ์ ์ด๋ฏ€๋กœ, ์ œ์‹œ๋œ ๊ธฐ์ˆ ๋“ค์€ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๋ชจ๋ธ์„ ์œ„ํ•œ ์ปดํ“จํ„ฐ ์‹œ์Šคํ…œ ์„ค๊ณ„ ๋ถ„์•ผ์— ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ์ ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•ด๋‹น ๊ธฐ์ˆ ๋“ค์„ ์ ์šฉํ•˜์—ฌ CPU, GPU, FPGA ๊ฐ๊ฐ์˜ ํ™˜๊ฒฝ์—์„œ, ์ œ์‹œ๋œ ๊ธฐ์ˆ ๋“ค์ด ๋ชจ๋‘ ์œ ์˜๋ฏธํ•œ ์„ฑ๋Šฅํ–ฅ์ƒ์„ ๋‹ฌ์„ฑํ•จ์„ ๋ณด์—ฌ์ค€๋‹ค.1 INTRODUCTION 1 2 Background 6 2.1 Memory Networks 6 2.2 Deep Learning for NLP 9 3 A Fast and Scalable System Architecture for Memory-Augmented Neural Networks 14 3.1 Motivation & Design Goals 14 3.1.1 Performance Problems in MemNN - High Off-chip Memory Bandwidth Requirements 15 3.1.2 Performance Problems in MemNN - High Computation 16 3.1.3 Performance Problems in MemNN - Shared Cache Contention 17 3.1.4 Design Goals 18 3.2 MnnFast 19 3.2.1 Column-Based Algorithm 19 3.2.2 Zero Skipping 22 3.2.3 Embedding Cache 25 3.3 Implementation 26 3.3.1 General-Purpose Architecture - CPU 26 3.3.2 General-Purpose Architecture - GPU 28 3.3.3 Custom Hardware (FPGA) 29 3.4 Evaluation 31 3.4.1 Experimental Setup 31 3.4.2 CPU 33 3.4.3 GPU 35 3.4.4 FPGA 37 3.4.5 Comparison Between CPU and FPGA 39 3.5 Conclusion 39 4 A Fast, Scalable, and Flexible System for Large-Scale Heterogeneous NLP Models 40 4.1 Motivation & Design Goals 40 4.1.1 High Model Complexity 40 4.1.2 High Memory Bandwidth 41 4.1.3 Heavy Computation 42 4.1.4 Huge Performance Variation 43 4.1.5 Design Goals 43 4.2 NLP-Fast 44 4.2.1 Bottleneck Analysis of NLP Models 44 4.2.2 Holistic Model Partitioning 47 4.2.3 Cross-operation Zero Skipping 51 4.2.4 Adaptive Hardware Reconfiguration 54 4.3 NLP-Fast Toolkit 56 4.4 Implementation 59 4.4.1 General-Purpose Architecture - CPU 59 4.4.2 General-Purpose Architecture - GPU 61 4.4.3 Custom Hardware (FPGA) 62 4.5 Evaluation 64 4.5.1 Experimental Setup 65 4.5.2 CPU 65 4.5.3 GPU 67 4.5.4 FPGA 69 4.6 Conclusion 72 5 Related Work 73 5.1 Various DNN Accelerators 73 5.2 Various NLP Accelerators 74 5.3 Model Partitioning 75 5.4 Approximation 76 5.5 Improving Flexibility 78 5.6 Resource Optimization 78 6 Conclusion 80 Abstract (In Korean) 106Docto

    ๋…ธ์ธ์—์„œ ์กฐ์ ˆ ์ค‘์ธ ๊ณ ํ˜ˆ์••์ด ๋Œ€๋‡Œ๋ฐฑ์งˆ๊ณ ๊ฐ•๋„์‹ ํ˜ธ์™€ ์ธ์ง€๊ธฐ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋‡Œ์ธ์ง€๊ณผํ•™๊ณผ, 2021.8. ์ด์˜์ง€.์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ : ๊ณ ํ˜ˆ์••์€ ์ธ์ง€์žฅ์• ์˜ ์œ„ํ—˜์ธ์ž์ด๋‹ค. ๋˜ํ•œ, ๊ณ ํ˜ˆ์••์€ ๋Œ€๋‡Œ๋ฐฑ์งˆ๊ณ ๊ฐ•๋„์‹ ํ˜ธ (WMH)์˜ ์œ„ํ—˜์ธ์ž์ด๊ณ  WMH๋Š” ์ธ์ง€์žฅ์• ์˜ ์œ„ํ—˜์ธ์ž์ด์ง€๋งŒ, WMH์˜ ๊ณ ํ˜ˆ์••๊ณผ ์ธ์ง€๊ธฐ๋Šฅ๊ฐ„์˜ ๋งค๊ฐœํšจ๊ณผ๋Š” ์•„์ง ์ถฉ๋ถ„ํžˆ ๊ฒ€์ฆ๋œ ์ ์ด ์—†๋‹ค. ๊ณ ํ˜ˆ์••ํ™˜์ž์—์„œ WMH๊ฐ€ ์ธ์ง€์žฅ์• ๋ฅผ ๋งค๊ฐœํ•œ๋‹ค๋ฉด, WMH์˜ ์กด์žฌ๋‚˜ ํฌ๊ธฐ๋Š” ์ธ์ง€์žฅ์• ๋ฅผ ํŒ๋‹จํ•  ์ˆ˜ ์žˆ๋Š” ์ค‘์š”ํ•œ ์ง€ํ‘œ๊ฐ€ ๋  ๊ฒƒ์ด๋‹ค. ํ•˜์ง€๋งŒ ๊ฑด๊ฐ•ํ•œ ๋…ธ์ธ์˜ WMH ํ™•๋ฅ ์ง€๋„ (WMHPM)๋‚˜ WMHPM์„ ํ™œ์šฉํ•œ ์ธ์ง€์žฅ์• ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์•„์ง๊นŒ์ง€ ์ง„ํ–‰๋œ๋ฐ” ์—†๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ๊ฐœ์˜ ๊ฐ€์„ค์„ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ•œ๋‹ค. 1) ๋น„์น˜๋งค ๋…ธ์ธ์—์„œ์˜ WMH๊ฐ€ ์กฐ์ ˆ๋œ ๊ณ ํ˜ˆ์••์ด ์ธ์ง€๊ธฐ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์ •ํ•˜๋Š”๊ฐ€? 2) WMHPM๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ถ”์ •๋œ WMH ๋‚˜์ด๊ฐ€ ์กฐ์ ˆ๋œ ๊ณ ํ˜ˆ์•• ๋…ธ์ธ์˜ ํ˜„์žฌ์˜ ์ธ์ง€์žฅ์• ์™€ ๋ฏธ๋ž˜์˜ ์ธ์ง€์ €ํ•˜๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€? ์—ฐ๊ตฌ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฃผ์š” ์ •์‹ ํ•™์  ๋˜๋Š” ์‹ ๊ฒฝํ•™์  ์งˆํ™˜์ด ์—†๋Š” 890๋ช…์˜ ์ง€์—ญ์‚ฌํšŒ ๊ฑฐ์ฃผ 60์„ธ ์ด์ƒ์˜ ๋น„์น˜๋งค ๋…ธ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ ์ค‘ 368๋ช…์ด 2๋…„ํ›„ ์ถ”์ ๊ฒ€์‚ฌ๋ฅผ ํ•˜์˜€๋‹ค. WMHPM์€ 300๋ช…์˜ ์ฃผ์š” ์ •์‹ ํ•™์  ๋˜๋Š” ์‹ ๊ฒฝํ•™์  ์งˆํ™˜์ด ์—†๊ณ  ์ธ์ง€๊ธฐ๋Šฅ์ด ์ •์ƒ์ธ ๊ฑด๊ฐ•ํ•œ 60์„ธ ์ด์ƒ ์ง€์—ญ์‚ฌํšŒ ๊ฑฐ์ฃผ๋…ธ์ธ์œผ๋กœ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๋Œ€์ƒ์ž์˜ ํ˜ˆ์••์€ ์ขŒ์œ„ ์ž์„ธ์—์„œ ์ž๋™ํ˜ˆ์••์ธก์ •๊ธฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์„ธ ๋ฒˆ ์ธก์ •๊ฐ’์˜ ํ‰๊ท ๊ฐ’์„ ์ด์šฉํ•˜์˜€๋‹ค. ์กฐ์ ˆ๋œ ๊ณ ํ˜ˆ์•• (cHT)์€ ๊ณ ํ˜ˆ์••๋ณ‘๋ ฅ์ด ์žˆ๊ณ  ์ธก์ •๋œ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••์ด 140 mm Hg ๋ฏธ๋งŒ์ด๋ฉด์„œ ์ด์™„๊ธฐ ํ˜ˆ์••์€ 90 mm Hg ๋ฏธ๋งŒ์ธ ์ž๋กœ ์ •์˜ํ•˜์˜€๋‹ค. ๋‚ฎ์€ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์•• (LSBP)๋Š” ์ธก์ •๋œ ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••์ด 110 mm Hg ์ดํ•˜์ธ ์ž๋กœ ์ •์˜ํ•˜์˜€๊ณ , ๋‚ฎ์€ ์ด์™„๊ธฐ ํ˜ˆ์•• (LDBP)๋Š” ์ธก์ •๋œ ์ด์™„๊ธฐ ํ˜ˆ์••์ด 60 mm Hg ์ดํ•˜์ธ ์ž๋กœ ์ •์˜ํ•˜์˜€๋‹ค. ์ธ์ง€๊ธฐ๋Šฅ์€ CERAD-K ์‹ ๊ฒฝ์‹ฌ๋ฆฌ๊ฒ€์‚ฌ, ์ „๋‘์—ฝ๊ธฐ๋Šฅํ‰๊ฐ€, ์ˆซ์ž์™ธ์šฐ๊ธฐ ๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. CERAD-TS ์ ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. WMH ์ถ”์ถœ์€ 3.0T ์•ก์ฒด๊ฐ์‡ ์—ญ์ „ํšŒ๋ณต ์ž๊ธฐ๊ณต๋ช…์˜์ƒ์„ ์ด์šฉํ•˜์˜€๋‹ค. ๊ฐœ์ธ์˜ WMH ์˜์ƒ๊ณผ 5๊ฐœ์˜ ์—ฐ๋ น๋Œ€์˜ WMHPM ์‚ฌ์ด์˜ ์ตœ์ € ํŽธ์ฐจ ๊ฐ’์„ ๊ณ„์‚ฐํ•˜์—ฌ ๊ฐœ์ธ์˜ WMH ์—ฐ๋ น์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. WMH์—ฐ๋ น์ด ์‹ค์ œ์—ฐ๋ น๊ณผ ๊ฐ™์„ ์‹œ normal WMH ๋‚˜์ด, ๋†’์„ ์‹œ older WMH ๋‚˜์ด, ๋‚ฎ์„ ์‹œ younger WMH ๋‚˜์ด๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. WMH๊ฐ€ ๊ณ ํ˜ˆ์••์ด ์ธ์ง€๊ธฐ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์ •ํ•˜๋Š”์ง€ Baron๊ณผ Kenny ๋ฐฉ๋ฒ•์œผ๋กœ ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋กœ์ง€์Šคํ‹ฑํšŒ๊ท€๋ถ„์„์„ ์ด์šฉํ•˜์—ฌ ๊ณ ํ˜ˆ์••๊ณผ WMH๋‚˜์ด๊ฐ€ ์ธ์ง€์žฅ์• ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ: cHT (p < .001), LSBP (p = .018)์™€ ์ƒํ˜ธ์ž‘์šฉ (p < .001)์€ WMH์šฉ์ ์˜ ์ปค์ง๊ณผ ๊ด€๋ จ์ด ์žˆ๋‹ค. WMH์šฉ์ ์€ ์ธ์ง€๊ธฐ๋Šฅ์˜ ๋‚ฎ์€ ์ˆ˜ํ–‰์ ์ˆ˜์™€ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค (๋ชจ๋“  ์ธ์ง€๊ฒ€์‚ฌ: p < .001). WMH๋Š” ์ˆ˜์ถ•๊ธฐ ํ˜ˆ์••์ด 1 mm Hg ๊ฐ์†Œํ•  ๋•Œ ์ธ์ง€๊ธฐ๋Šฅ์ ์ˆ˜๊ฐ€ 0.016 ~ 0.030 ํฌ์ธํŠธ ๊ฐ์†Œํ•˜๋Š” ๊ด€๊ณ„์— ๋งค๊ฐœํ•˜์˜€๋‹ค. Younger ํ˜น์€ normal WMH ๋‚˜์ด์— ๋น„ํ•ด older WMH ๋‚˜์ด ๊ตฐ์ด ๋ชจ๋“  ์ธ์ง€๊ธฐ๋Šฅ ๊ฒ€์‚ฌ์—์„œ ๋‚ฎ์€ ์ˆ˜ํ–‰๋Šฅ๋ ฅ์„ ๋ณด์˜€๋‹ค. (๋ชจ๋“  ์ธ์ง€๊ฒ€์‚ฌ: p < .001; DST: p = .002 for DST). cHT (p = .002), LSBP (p = .003), LDBP (p = .013), ์ƒํ˜ธ์ž‘์šฉ (p = .010)์ด older WMH์™€ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค. cHT๊ตฐ ์ค‘ older WMH ๋‚˜์ด์ธ ์‚ฌ๋žŒ๋“ค์€ ์ •์ƒํ˜ˆ์••์„ ๊ฐ€์ง„ normal ํ˜น์€ younger WMH ๋‚˜์ด์ธ ์‚ฌ๋žŒ๋“ค์— ๋น„ํ•ด 2๋…„ํ›„ ์ธ์ง€๊ธฐ๋Šฅ์ €ํ•˜๊ฐ€ ๋น ๋ฅด๊ณ  ๊ฒฝ๋„์ธ์ง€์žฅ์• ๊ฐ€ ๋ฐœ๋ณ‘ํ•  ํ™•๋ฅ ์ด 8๋ฐฐ ๋†’์•˜๋‹ค. ๊ฒฐ๋ก : cHT ํ™˜์ž์—์„œ LSBP๋Š” WMH ์šฉ์ ์„ ์ฆ๊ฐ€์‹œํ‚ด์œผ๋กœ์จ ์ธ์ง€๊ธฐ๋Šฅ์ €ํ•˜์™€ ๊ด€๋ จ์ด ์žˆ์—ˆ๋‹ค. ๊ฑด๊ฐ•ํ•œ ๋…ธ์ธ์˜ WMHPM์„ ์‚ฌ์šฉํ•˜๋ฉด ์ž„์ƒํ™˜๊ฒฝ์—์„œ WMH ์—ฐ๋ น์„ ์ถ”์ •ํ•˜์—ฌ ์ธ์ง€์ €ํ•˜ ์œ„ํ—˜์ด ์žˆ๋Š” ๊ณ ํ˜ˆ์••ํ™˜์ž๋ฅผ ๊ตฌ๋ณ„ํ•ด๋‚ผ ์ˆ˜ ์žˆ๋‹ค.Background and Objectives: Hypertension, even when controlled, is associated with cognitive impairment. Although hypertension is also associated with white matter hyperintensity (WMH) and WMH is associated with cognitive impairments, the mediation role of WMH in the association of hypertension with cognitive impairments has never been directly investigated. If WMH shows to mediate the cognitive impairments in participants with controlled hypertension, presence or volume of WMH may be a good biomarker of those who are at risk of cognitive impairments. However, neither the WMH probability map (WMHPM) of healthy older adults nor the predictive validity of WMH age estimated by WMHPM for cognitive impairments has been investigated. This study examined two main hypotheses; 1) Does cerebral WMH mediate the effect of controlled hypertension on cognitive function in nondemented older adults?; 2) Does WMH age estimated using the WMHPM predict current cognitive impairment and future cognitive decline in older adults with controlled hypertension? Methods: We recruited 890 community-dwelling nondemented Koreans aged 60 years or older; 505 from the participants of the Korean Longitudinal Study on Cognitive Aging and Dementia and 385 from the visitors to the Dementia Clinic of the Seoul National University Bundang Hospital. Among them, 368 participants completed 2-year follow-up assessment. We constructed WMHPM using 300 community-dwelling cognitively and physically healthy Koreans aged 60 years or older; 228 from the KLOSCAD and 72 from the Gwangju Alzheimerโ€™s & Related Dementias Study. We defined controlled hypertension (cHT) as having history of hypertension, however, office-measured systolic blood pressure (SBP) less than 140 mm Hg and office-measured diastolic blood pressure (DBP) less than 90 mm Hg; low systolic blood pressure (LSBP) as having office-measured SBP of 110 mm Hg or below; low diastolic blood pressure (LDBP) as having office-measured DBP of 60 mm Hg or below. We measured blood pressure three times in a sitting position using an automated blood pressure monitoring device. We evaluated cognitive performance using the CERAD-K Neuropsychological Assessment Battery, Frontal Assessment Battery and Digit Span Test. We calculated Consortium to Establish a Registry for Alzheimer Disease neuropsychological battery total score (CERAD-TS). We segmented and quantified WMH from 3.0 Tesla fluid attenuated inversion recovery magnetic resonance images. We estimated WMH age using the WMHPM by calculating the lowest deviance between individualโ€™s WMH and each of the 5 age-banded WMHPMs. We classified the participants into three WMH age group; normal WMH age group whose WMH age is equal to their chronological age, younger WMH age group whose WMH age is younger than their chronological age, and the older WMH age group whose WMH age is older than their chronological age. We analyzed the mediation role of WMH on the effect of controlled hypertension on cognitive function using Baron and Kenny method of mediation analysis. We examined the effect of controlled hypertension and WMH age on the risk of incident mild cognitive impairment (MCI) using logistic regression analysis. Results: cHT (p < .001), LSBP (p = .018), and their interaction (p < .001) were associated with WMH volume, and WMH volume was associated with negative cognitive performance (p < .001 for all cognitive performance). WMH mediated the association of LSBP on the performance of neuropsychological tests with 1 mm Hg decrease of SBP affect 0.016 to 0.030 points decrease in various cognitive tests. Compared to the younger or normal WMH age groups, the older WMH age group performed worse in all neuropsychological tests (p = .002 for DST; p < .001 for other tests). cHT (p = .002), LSBP (p = .003), LDBP (p = .013) and their interaction (p = .010) were associated with older WMH age. The cHT with the older WMH age group showed the faster cognitive decline and 8 times higher risk of incident MCI after two years than normotensive participants with the normal or younger WMH age. Conclusion: In the cHT patients, LSBP was associated with worse cognitive performance by increasing WMH volume. If we use WMHPM of healthy older adults, we can identify older adults with controlled hypertension who are at risk of cognitive decline by estimating their WMH age in clinical settings.1. Introduction 1 1.1. Study Background 1 1.2. Purpose of Research 3 2. Methods 4 2.1. Study population 4 2.1.1. Hypothesis 1. Does cerebral WMH mediate the effect of controlled hypertension on cognitive function in nondemented older adults 4 2.1.2. Hypothesis 2. Does WMH age estimated using the WMH probability map (WMHPM) predict current cognitive impairment and future cognitive decline in older adults with controlled hypertension 4 2.2. Research ethics 5 2.3. Assessments 6 2.4. Diagnoses 6 2.5. Acquisition of brain MRI 7 2.6. Processing of brain MRI 7 2.7. Segmentation of WMH. 8 2.8. Visual rating of WMH 8 2.9. Construction of WMHPM 9 2.10. Estimation of WMH age 9 2.11. Statistical analyses 10 3. Results 12 3.1. Hypothesis 1. Does cerebral WMH mediate the effect of controlled hypertension on cognitive function in nondemented older adults 12 3.2. Hypothesis 2. Does WMH age estimated using the WMH probability map (WMHPM) predict current cognitive impairment and future cognitive decline in older adults with controlled hypertension 14 4. Discussions 17 5. Conclusions 24 Bibliography 49 ๊ตญ๋ฌธ์ดˆ๋ก 57๋ฐ•

    ํ•™๋ถ€์ƒ์„ ์œ„ํ•œ ๋ถ„์•ผ๋ณ„ ๋ฆฌํฌํŠธ ์ž‘์„ฑ๋ฒ•์˜ ์„ฑ๊ณผ์™€ ์ „๋ง

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    ๊ธ€์“ฐ๊ธฐ๊ต์‹ค์€ ์ง€๋‚œ 4๋…„ ๊ฐ„ ๋งค ํ•™๊ธฐ ๋ฆฌํฌํŠธ ์ž‘์„ฑ๋ฒ• ์ฝ”์Šค์›Œํฌ๋ฅผ ์šด์˜ํ•˜์—ฌ ํ•™๋ถ€ ํ•™์ƒ๋“ค์ด ๋ฆฌํฌํŠธ ์ž‘์„ฑ์˜ ๋ฐฉ๋ฒ•์„ ๋ฐฐ์šฐ๊ณ  ์—ฐ์Šตํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๋ฅผ ๋งˆ๋ จํ•˜์˜€๋‹ค. ์ฝ”์Šค์›Œํฌ๋Š” ์ฃผ์ œ ์„ ์ •๊ณผ ์•„์ด๋””์–ด ๋งŒ๋“ค๊ธฐ, ํ•ต์‹ฌ์ฃผ์žฅ๊ณผ ๊ธ€์˜ ๊ตฌ์กฐ ๋งŒ๋“ค๊ธฐ, ๋ฌธ์žฅ๋ก , ์ธ์šฉ๊ณผ ๋งž์ถค๋ฒ• ๋“ฑ์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ๋ฆฌํฌํŠธ ์ž‘์„ฑ์˜ ์ผ๋ฐ˜์ ์ธ ๋ฐฉ๋ฒ•์„ ์ „๋‹ฌํ•˜๋Š” ๋ฐ ์ดˆ์ ์ด ๋งž์ถ”์–ด์กŒ๋‹ค

    A Study on the Characteristics of an Organic Rankine Cycle for Ocean Thermal Energy Conversion According to Pinch Point Analysis and a Transcritical Cycle

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    Due to the recent energy shortage, global warming, and environment pollution, the importance of energy saving and environment regulation is rapidly increasing. One of the methods to resolve these problems is using new renewable energy. Ocean thermal energy conversion, which is one way of using new renewable energy, is a power cycle utilizing the temperature difference between surface water and deep water. As ocean thermal energy conversion uses a heat source at low temperature, it is essential to use an organic Rankine cycle. Thus, this study examined the characteristics of an organic Rankine cycle for ocean thermal energy conversion according to pinch point analysis and a transcritical cycle. First, thermal efficiency analysis on an organic Rankine cycle depending on various types of working fluid and cycle was conducted. A classic simple Rankine cycle, regenerative Rankine cycles, and a Kalina cycle were considered in the analysis. In addition, nine types of single working fluid and three types of mixed working fluid were selected. For cycle analysis methods, pinch point analysis was conducted. As for single working fluid, thermal efficiency was the highest in RE245fa2 in a simple Rankine cycle and regenerative Rankine cycles. As for mixed working fluid, thermal efficiency was the highest when the composition ratio of NH3 to H2O was 0.9:0.1 in a Kalina cycle. Compared to a simple Rankine cycle, a Rankine cycle with open feedliquid heater, a Rankine cycle with integrated regenerator, and a Kalina cycle showed thermal efficiency increase rates of approx. 2.0%, 1.0%, and 10%, respectively. Second, exergy analysis on the cycles at each heat exchanger was conducted considering the influence of pinch point temperature difference and that of outlet temperatures of a heat source and a heat sink. Thermodynamic performance was analyzed by applying seven types of working fluid to the cycles designed according to pinch point analysis. As a result of performance analysis, as pinch point temperature difference and the temperature difference between inlet and outlet of a heat source or a heat sink were low at each heat exchanger, second law efficiency increased but cycle irreversibility and exergy destruction factor decreased. In addition, cycle irreversibility largely changed where thermodynamic change occurred. Of the selected types of working fluid, RE245fa2 showed the most excellent thermodynamic performance. Lastly, recent research related to a transcritical cycle of an organic Rankine cycle using a heat source at low temperature was reviewed. A transcritical cycle was made up of an solar-boosted ocean thermal energy conversion system using R744, economical and stable working fluid, and then thermodynamic performance analysis was conducted according to the state of turbine inlet. As a result, a transcritical cycle showed better thermodynamic performance as turbine inlet temperature was high. On the other hand, turbine inlet pressure of a transcritical cycle showed better thermodynamic performance than a subcritical cycle only in the optimized state. Compared to an optimized transcritical simple Rankine cycle, an optimized transcritical Rankine cycle with open feedliquid heater showed increased second law efficiency and reduced cycle irreversibility.List of Tables โ…ฒ List of Figures โ…ณ Nomenclature โ…ท Abstract โ…ธ 1. ์„œ ๋ก  1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ๋ชฉ์  4 2. ์‚ฌ์ดํด ๋ฐ ์ž‘๋™์œ ์ฒด์— ๋”ฐ๋ฅธ ํ•ด์–‘์˜จ๋„์ฐจ๋ฐœ์ „์šฉ ์‚ฌ์ดํด์˜ ์„ฑ๋Šฅ๋ถ„์„ 2.1 ๊ฐœ์š” 6 2.2 ํ•ด์–‘์˜จ๋„์ฐจ๋ฐœ์ „์šฉ ์‚ฌ์ดํด์˜ ์ข…๋ฅ˜ 8 2.3 ๋ถ„์„์กฐ๊ฑด 14 2.4 ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 19 2.5 ์š”์•ฝ 27 3. ํ•€์น˜ํฌ์ธํŠธ์˜จ๋„์ฐจ์— ๋”ฐ๋ฅธ ์œ ๊ธฐ๋žญํ‚จ์‚ฌ์ดํด์˜ ์„ฑ๋Šฅ๋ถ„์„ 3.1 ๊ฐœ์š” 28 3.2 ํ•ด์–‘์˜จ๋„์ฐจ๋ฐœ์ „์šฉ ์‚ฌ์ดํด์˜ ์—ด์—ญํ•™์  ํ•ด์„ 30 3.3 ๋ถ„์„์กฐ๊ฑด 33 3.4 ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 35 3.4.1 ์ฆ๋ฐœ๊ธฐ ๋ฐ ์‘์ถ•๊ธฐ ํ•€์น˜ํฌ์ธํŠธ์˜จ๋„์ฐจ์˜ ์˜ํ–ฅ 35 3.4.2 ์—ด์› ๋ฐ ์—ด์นจ ์ถœ๊ตฌ์˜จ๋„์˜ ์˜ํ–ฅ 40 3.5 ์š”์•ฝ 45 4. ์ดˆ์ž„๊ณ„ ์œ ๊ธฐ๋žญํ‚จ์‚ฌ์ดํด์— ๋”ฐ๋ฅธ ์„ฑ๋Šฅ๋ถ„์„ 4.1 ๊ฐœ์š” 46 4.2 ํ•ด์–‘์˜จ๋„์ฐจ๋ฐœ์ „์šฉ ์ดˆ์ž„๊ณ„ ์‚ฌ์ดํด 48 4.3 ๋ถ„์„์กฐ๊ฑด 51 4.4 ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 54 4.5 ์š”์•ฝ 64 5. ๊ฒฐ ๋ก  65 ์ฐธ๊ณ ๋ฌธํ—Œ 68 ๊ฐ์‚ฌ์˜ ๊ธ€ 74Maste

    ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ์ธ๊ณผ์„ฑ์„ ํ•ด๋ช…ํ•˜๋Š”๊ฐ€?: ๋งฅ๋ฝ์ผ์น˜์›๋ฆฌ์™€ ์ธ๊ณผ์„ฑ์— ๋Œ€ํ•œ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ด๋ก ์˜ ์ดํ•ด์˜ ๋ฌธ์ œ

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    ์ด ๊ธ€์€ ์ž์—ฐ์„ ํƒ์˜ ๊ณผ์ •์„ ๋ฉ”์ปค๋‹ˆ์ฆ˜์œผ๋กœ ํ•ด๋ช…ํ•˜๋ ค๋Š” ์ธ๊ณผ์ด๋ก  ์— ๋Œ€ํ•œ ๋น„ํŒ์  ๊ฒ€ํ† ์ด๋‹ค. ํ•„์ž๋Š” ๋„ค ๊ฐ€์ง€๋ฅผ ๋…ผ์˜ํ•œ๋‹ค. ์ฒซ์งธ, ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ด๋ก  ์€ ๋งฅ๋ฝ์ผ์น˜์›๋ฆฌ์™€ ์ด ์›๋ฆฌ์— ํ† ๋Œ€ํ•œ ํ™•๋ฅ ์ธ๊ณผ์ด๋ก ์— ๋Œ€ํ•œ ๋น„ํŒ์—์„œ ์ถœ๋ฐœ ํ•œ๋‹ค. ํ•„์ž๋Š” ์ด ๋น„ํŒ์ด ๋งฅ๋ฝ์ผ์น˜์›๋ฆฌ์™€ ํ™•๋ฅ ์ธ๊ณผ์ด๋ก ์— ๋Œ€ํ•œ ์ž˜๋ชป๋œ ์ดํ•ด ๋ผ๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋‘˜์งธ, ๋ฉ”์ปค๋‹ˆ์ฆ˜์˜ ๋‘ ๊ฐ€์ง€ ๊ฐœ๋…์„ โ€• ์ธ๊ณผ๊ณผ์ •์œผ๋กœ์„œ ๋ฉ”์ปค๋‹ˆ์ฆ˜๊ณผ ์ฒด๊ณ„๋กœ์„œ ๋ฉ”์ปค๋‹ˆ์ฆ˜ โ€• ๋น„ํŒ์ ์œผ๋กœ ๊ฒ€ํ† ํ•œ๋‹ค. ํ•„์ž๋Š” ์ธ๊ณผ๊ณผ์ •์œผ ๋กœ์„œ ๋ฉ”์ปค๋‹ˆ์ฆ˜์œผ๋กœ๋Š” ์‚ฌ๊ฑด ์ˆ˜์ค€์—์„œ์˜ ๋ณต์žกํ•œ ์ธ๊ณผ์œ ๊ด€์„ฑ์„ ํ•ด๋ช…ํ•  ์ˆ˜ ์—† ๊ณ  ์ฒด๊ณ„๋กœ์„œ ๋ฉ”์ปค๋‹ˆ์ฆ˜์˜ ์‹ ๋ขฐ์„ฑ์€ ๊ธ€๋ Œ๋‚œ์ด ๋น„ํŒํ•œ ํ™•๋ฅ ์ธ๊ณผ์ด๋ก ์œผ๋กœ ํ•ด๋ช… ๋œ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ์…‹์งธ, ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ด๋ก ์— ๊ธฐ์ดˆํ•œ ์ธ๊ณผ์˜ ๋‘ ์ˆ˜์ค€์˜ ๊ด€ ๊ณ„์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ โ€• ์†์„ฑ ์ˆ˜์ค€์˜ ์ธ๊ณผ๋Š” ์‚ฌ๊ฑด ์ˆ˜์ค€์˜ ์ธ๊ณผ์—์„œ ๋„์ถœ๋˜๊ณ  ์‚ฌ๊ฑด ์ˆ˜์ค€์˜ ์ธ๊ณผ๋Š” ์†์„ฑ ์ˆ˜์ค€์˜ ์ธ๊ณผ์— ๊ฐœ๋…์ ์œผ๋กœ ์šฐ์„ ํ•œ๋‹ค โ€• ๋น„ํŒํ•œ๋‹ค. ๊ธ€๋ Œ๋‚œ์˜ ์ฃผ์žฅ๊ณผ ๋ฐ˜๋Œ€๋กœ ๋‘ ์ˆ˜์ค€์˜ ์ธ๊ณผ๋Š” ๊ฐœ๋…์ ์œผ๋กœ ์„œ๋กœ ๋…๋ฆฝ์ ์ธ ๊ด€๊ณ„ ๋ผ๋Š” ๊ฒƒ์„ ํ™•์ธํ•œ๋‹ค. ๋„ท์งธ, ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ์ž์—ฐ์„ ํƒ ๊ณผ์ •์˜ ์ƒ๋ฌผํ•™์  ์‚ฌ์‹ค์„ ๋ณด๋‹ค ์ž˜ ํ•ด๋ช…ํ•˜๊ณ  ์†Œ๋ฒ„ใ†๋ฅด์›ํ‹ด์˜ ์›๋ฆฌ๋ฅผ ๋ณด์™„ํ•œ๋‹ค๋Š” ์ฃผ์žฅ์„ ๋น„ํŒ์ ์œผ๋กœ ํ‰๊ฐ€ํ•œ๋‹ค.์ด ์—ฐ๊ตฌ๋Š” 2005๋…„ ๊ธฐ์ดˆํ•™๋ฌธ์œก์„ฑ ์ธ๋ฌธ์‚ฌํšŒ๋ถ„์•ผ์ง€์›์‚ฌ์—…์— ์˜ํ•ด ์ด๋ฃจ์–ด์กŒ๋‹ค. (KRF2005-079-AS0034

    ๊ณ ์ฝœ๋ ˆ์Šคํ…Œ๋กค์ด ์œ ๋„ํ•˜๋Š” ์ค‘๊ฐ„์—ฝ ์ค„๊ธฐ์„ธํฌ ์ž๋ฉธ์‚ฌ์— ๋Œ€ํ•œ ๋ฉœ๋ผํ† ๋‹Œ์˜ ๋ณดํ˜ธ ํšจ๊ณผ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ˆ˜์˜๊ณผ๋Œ€ํ•™ ์ˆ˜์˜ํ•™๊ณผ, 2020. 8. ํ•œํ˜ธ์žฌ.High cholesterol levels of patients with obesity are associated with insufficient efficacy of transplantation therapy using umbilical cord blood-derived mesenchymal stem cells (UCB-MSCs). Further, the regulation of intracellular cholesterol levels is important for reducing apoptosis of engrafted donor stem cells. The endogenous hormone melatonin contributes to the prevention of cholesterol accumulation in patients with obesity through a poorly understood mechanism. Therefore, this study investigated the regulatory mechanism of melatonin that inhibits cholesterol-induced apoptosis in vitro and in vivo. Melatonin increased the expression of ATP-binding cassette subfamily A member 1 (ABCA1) in UCB-MSCs, which reduced cholesterol accumulation and cholesterol-induced apoptosis. In addition, pretreatment with disodium 4,4โ€ฒ-diisothiocyanatostilbene-2,2โ€ฒ-disulfonate (DIDS), an ABCA1 inhibitor, diminished these effects. Skin wound healing to show that melatonin treatment restored the survival rate of transplanted UCB-MSCs and wound-healing capacity, which was lowered in high-fat-diet-induced obese mice. In the presence of high concentrations of cholesterol, melatonin receptor 2 (MT2) level increased, and melatonin treatment inhibited the expression of binding immunoglobulin protein (BiP) through regulation of the MT2/Sp1-dependent microRNA-597. Melatonin decreased co-localization of BiP with nuclear factor erythroid 2-related factor 1 (NRF1), which is required for the translocation of NRF1. Inhibition of the nuclear translocation of NRF1 increased ABCA1 expression and cholesterol efflux and inhibited apoptosis in the presence of high cholesterol concentrations. In conclusion, these findings indicate that melatonin induced the efflux of intracellular cholesterol through ABCA1 to decrease the apoptosis of UCB-MSCs in the presence of high cholesterol concentrations through an MT2-dependent BiP/NRF1 pathway.์ œ๋Œ€ํ˜ˆ ์œ ๋ž˜ ์ค‘๊ฐ„์—ฝ ์ค„๊ธฐ์„ธํฌ ์ด์‹์น˜๋ฃŒ๋Š” ์กฐ์ง ์žฌ์ƒ์„ ์ด‰์ง„ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ๋น„๋งŒ ํ™˜์ž์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์ด์‹์น˜๋ฃŒ ํšจ์œจ ์ €ํ•˜๋Š” ๊ณ ์ฝœ๋ ˆ์Šคํ…Œ๋กค ํ™˜๊ฒฝ๊ณผ ์—ฐ๊ด€์ด ์žˆ๋‹ค. ๋˜ํ•œ ์„ธํฌ ๋‚ด ์ฝœ๋ ˆ์Šคํ…Œ๋กค ์กฐ์ ˆ์€ ์ด์‹์„ธํฌ์˜ ์„ธํฌ์ž๋ฉธ์‚ฌ๋ฅผ ์ค„์ด๋Š”๋ฐ ์ค‘์š”ํ•˜๋‹ค. ๋‚ด๋ถ„๋น„ ํ˜ธ๋ฅด๋ชฌ์ธ ๋ฉœ๋ผํ† ๋‹Œ์€ ์ฝœ๋ ˆ์Šคํ…Œ๋กค ์ˆ˜์ค€์„ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณด๊ณ ๋˜์—ˆ์œผ๋‚˜ ๊ทธ ๊ธฐ์ „์€ ์ž˜ ์•Œ๋ ค์ ธ์žˆ์ง€ ์•Š๋‹ค. ๋”ฐ๋ผ์„œ ์ด ์—ฐ๊ตฌ๋Š” ๋ฉœ๋ผํ† ๋‹Œ์˜ ์ฝœ๋ ˆ์Šคํ…Œ๋กค ์œ ๋„์„ฑ ์„ธํฌ์‚ฌ๋ฉธ์‚ฌ ์กฐ์ ˆ ๊ธฐ์ „์„ ๊ทœ๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๋ฉœ๋ผํ† ๋‹Œ์˜ ์ฒ˜๋ฆฌ๋Š” ์ค„๊ธฐ์„ธํฌ์˜ ์ฝœ๋ ˆ์Šคํ…Œ๋กค ์ˆ˜์†ก๋‹จ๋ฐฑ์งˆ์ธ ABCA1 (ATP-binding cassette subfamily A member 1)์˜ ๋ฐœํ˜„์„ ์ฆ๊ฐ€์‹œ์ผœ ์„ธํฌ ๋‚ด ์ฝœ๋ ˆ์Šคํ…Œ๋กค ์ถ•์ ๊ณผ ์„ธํฌ์‚ฌ๋ฉธ์„ ๊ฐ์†Œ์‹œ์ผฐ์œผ๋ฉฐ, ABCA1์˜ ์–ต์ œ์ œ ์ฒ˜๋ฆฌ๋ฅผ ํ†ตํ•ด ๋ฉœ๋ผํ† ๋‹Œ์˜ ๋ณดํ˜ธํšจ๊ณผ๊ฐ€ ABCA1์— ์˜ํ•œ ๊ฒƒ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ํ”ผ๋ถ€์ƒ์ฒ˜ํšŒ๋ณต ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ๋ฉœ๋ผํ† ๋‹Œ์˜ ์ฒ˜๋ฆฌ๊ฐ€ ๋น„๋งŒ์ฅ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์ค„๊ธฐ์„ธํฌ ์ด์‹ํšจ์œจ ๊ฐ์†Œ๋ฅผ ์ •์ƒํ™”ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ณ ์ฝœ๋ ˆ์Šคํ…Œ๋กค ์ƒํ™ฉ์—์„œ ์ค„๊ธฐ์„ธํฌ์˜ ๋ฉœ๋ผํ† ๋‹Œ ์ˆ˜์šฉ์ฒด์ธ MT2์˜ ๋ฐœํ˜„์ด ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ๋ฉœ๋ผํ† ๋‹Œ์˜ ์ฒ˜๋ฆฌ๋Š” Sp1 ์ „์‚ฌ์ธ์ž๋ฅผ ํ†ตํ•œ microRNA-597-5p์˜ ๋ฐœํ˜„ ์ฆ๊ฐ€๋กœ ํ‘œ์  ๋‹จ๋ฐฑ์งˆ์ธ BiP (binding immunoglobulin protein)์˜ ๋ฐœํ˜„์„ ๊ฐ์†Œ์‹œ์ผฐ๋‹ค. ๋ฉœ๋ผํ† ๋‹Œ์˜ ์ฒ˜๋ฆฌ๋Š” NRF1 (nuclear factor erythroid 2-related factor 1)์˜ ํ•ต๋‚ด ์ด๋™์— ํ•„์š”ํ•œ BiP๊ณผ NRF1์˜ ๊ฒฐํ•ฉ์„ ๊ฐ์†Œ์‹œ์ผฐ์œผ๋ฉฐ, ์ด๋กœ ์ธํ•œ NRF1์˜ ํ•ต๋‚ด ์ด๋™ ์–ต์ œ๋Š” ABCA1 ๋ฐœํ˜„๊ณผ ์„ธํฌ ๋‚ด ์ฝœ๋ ˆ์Šคํ…Œ๋กค ๋ฐฐ์ถœ์„ ์ฆ๊ฐ€์‹œ์ผœ ๊ณ ์ฝœ๋ ˆ์Šคํ…Œ๋กค์— ์˜ํ•œ ์„ธํฌ์ž๋ฉธ์‚ฌ๋ฅผ ์–ต์ œํ•˜์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ฉœ๋ผํ† ๋‹Œ์€ MT2/BiP/NRF1 ๊ฒฝ๋กœ๋ฅผ ํ†ตํ•ด ABCA1์— ์˜ํ•œ ์„ธํฌ๋‚ด ์ฝœ๋ ˆ์Šคํ…Œ๋กค ๋ฐฐ์ถœ์„ ์ฆ๊ฐ€์‹œ์ผœ ๊ณ ์ฝœ๋ ˆ์Šคํ…Œ๋กค์— ์˜ํ•œ ์ค‘๊ฐ„์—ฝ์ค„๊ธฐ์„ธํฌ์˜ ์„ธํฌ์ž๋ฉธ์‚ฌ๋ฅผ ๊ฐ์†Œ์‹œํ‚จ๋‹ค.INTRODUCTION 1 MATERIALS AND METHODS 5 RESULTS 18 DISCUSSION 56 REFERENCES 63 ABSTRACT IN KOREAN (๊ตญ๋ฌธ์ดˆ๋ก) 73Maste

    ์‚ฌํšŒ ๋ณต์ง€ ์ œ๋„์™€ ์‚ฐ์—…์ƒ์‚ฐ์„ฑ๊ณผ์˜ ๊ด€๊ณ„์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ–‰์ •๋Œ€ํ•™์› : ํ–‰์ •ํ•™๊ณผ(์ •์ฑ…ํ•™์ „๊ณต), 2015. 8. ๋ฐ•์ƒ์ธ.์ด ์—ฐ๊ตฌ๋Š” ์œ ๋Ÿฝ 21๊ฐœ๊ตญ์„ ๋Œ€์ƒ์œผ๋กœ 1990๋…„๋ถ€ํ„ฐ 2010๋…„๊นŒ์ง€์˜ ํŒจ๋„์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ณต๊ณต ์‚ฌํšŒ ์ง€์ถœ๊ณผ ์‚ฐ์—… ์ƒ์‚ฐ์„ฑ ๊ฐ„์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์‹ค์ฆ์  ๋ถ„์„์„ ์‹œ๋„ํ•œ ์—ฐ๊ตฌ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ธฐ์กด ์‚ฌํšŒ ๋ณต์ง€์™€ ๊ฒฝ์ œ ์„ฑ์žฅ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๋‹ค๋ฃฌ ์—ฐ๊ตฌ๋“ค์„ ๊ฒ€ํ† ํ•œ ํ›„ ์‚ฐ์—… ๋‹จ์œ„์˜ ๊ธฐ์—…๊ฐ€ ํ™œ๋™์˜ ๊ฒฐ๊ณผ๋ฅผ ์‚ฐ์—… ๋‹จ์œ„์˜ ์ƒ์‚ฐ์„ฑ ํ–ฅ์ƒ์œผ๋กœ ์กฐ์ž‘ํ™”ํ•˜์—ฌ ๋ถ„์„ ๋Œ€์ƒ์œผ๋กœ ์‚ผ์•˜๋‹ค. ํŠนํžˆ 90๋…„๋Œ€ ์ดํ›„ ๊ธ‰์„ฑ์žฅํ•œ ICT์‚ฐ์—…์˜ ์ƒ์‚ฐ์„ฑ๊ณผ ์‚ฌํšŒ ์ •์ฑ… ๊ฐ„์˜ ๊ด€๊ณ„ ๋ถ„์„์„ ์ฃผ๋กœ ์‹œ๋„ํ•˜์—ฌ ์ƒˆ๋กœ์šด ์‚ฐ์—…์˜ ์„ฑ์žฅ๊ณผ ์‚ฌํšŒ ๋ณต์ง€ ์ œ๋„์™€์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. GLS๋ฅผ ํ™œ์šฉํ•œ ๋ถ„์„๋ชจํ˜•์—์„œ ์–‘์ž ๊ฐ„์˜ ์–‘์˜ ๊ด€๊ณ„๋ฅผ ํ™•์ธํ•˜์˜€๊ณ , ICT ์‚ฐ์—…์˜ ์ƒ์‚ฐ์„ฑ๊ณผ ๋‹ค๋ฅธ ์‚ฐ์—…์˜ ์„ฑ์žฅ๋ฅ ๊ณผ์˜ ๊ฒฉ์ฐจ์™€๋„ ์–‘์˜ ๊ด€๊ณ„๊ฐ€ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ •๋ถ€ ์ง€์ถœ ๋น„์ค‘ ์ค‘ ๊ณต๊ณต ์‚ฌํšŒ ์ง€์ถœ์˜ ๋น„์ค‘์ด ๋†’์„์ˆ˜๋ก ICT์‚ฐ์—…์„ ํฌํ•จํ•œ ์—ฌ๋Ÿฌ ์‚ฐ์—…์˜ ์ƒ์‚ฐ์„ฑ์ด ๋†’์•„์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๋ถ„์„ ๊ฒฐ๊ณผ ์‚ฌํšŒ ๋ณต์ง€ ์ง€์ถœ๊ณผ ์‹ ์ƒ ์‚ฐ์—…์˜ ์ƒ์‚ฐ์„ฑ ํ–ฅ์ƒ์˜ ์–‘์˜ ๊ด€๊ณ„๋ฅผ ํ™•์ธํ–ˆ์œผ๋ฉฐ, ๋ถ„์„๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•ด๋ณผ ๋•Œ ์‚ฌํšŒ ๋ณต์ง€ ์ œ๋„๊ฐ€ ํ˜์‹  ์ฃผ๋„ ๋‹จ๊ณ„์˜ ๊ฒฝ์ œ์„ฑ์žฅ์— ๊ธ์ •์ ์œผ๋กœ ๊ธฐ๋Šฅํ•  ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.๋ชฉ ์ฐจ ์ œ1์žฅ ์„œ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ ๋ชฉ์ ๊ณผ ํ•„์š”์„ฑ 1 ์ œ2์žฅ ์ด๋ก ์  ๋…ผ์˜ ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  3 ์ œ1์ ˆ ํ˜์‹  ์ฃผ๋„ ๊ฒฝ์ œ์˜ ์ •์˜์™€ ๊ตฌ์„ฑ 3 ์ œ2์ ˆ ํ˜์‹  ์ฃผ๋„ ๊ฒฝ์ œ์˜ ์„ฑ๊ณผ์™€ ํ˜•ํƒœ 5 ์ œ3์ ˆ ํ˜์‹  ์ฃผ๋„ ๊ฒฝ์ œ์™€ ์‚ฌํšŒ์  ์•ˆ์ „๋ง 7 ์ œ3์žฅ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  10 ์ œ1์ ˆ ์‚ฌํšŒ๋ณต์ง€์™€ ๊ฒฝ์ œ์„ฑ๊ณผ ๊ฐ„์˜ ๊ด€๊ณ„์— ๊ด€ํ•œ ๊ฒ€ํ†  10 ์ œ2์ ˆ ๊ตญ๊ฐ€ ๊ฐœ์ž…๊ณผ ๊ธฐ์—…๊ฐ€ ํ™œ๋™์— ๊ด€ํ•œ ๊ฒ€ํ†  13 ์ œ3์ ˆ ์‚ฌํšŒ์  ์•ˆ์ „๋ง๊ณผ ๊ธฐ์—…๊ฐ€ ํ™œ๋™์— ๊ด€ํ•œ ๊ฒ€ํ†  15 ์ œ4์ ˆ ์„ ํ–‰์—ฐ๊ตฌ์™€ ๋ชฉํ‘œ์ ํ•ฉ์„ฑ์˜ ๊ด€๊ณ„์— ๊ด€ํ•œ ๊ฒ€ํ†  17 ์ œ4์žฅ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 18 ์ œ1์ ˆ ๋ถ„์„๋ฐฉ๋ฒ• 18 1 ๋ถ„์„ ๋Œ€์ƒ ๊ตญ๊ฐ€ 19 2 ๋ถ„์„ ๊ธฐ๊ฐ„ 19 3 ๋ถ„์„ ์ž๋ฃŒ 19 4 ๊ฐ€์„ค ์„ค์ • 20 5 ๋ถ„์„ ๋ชจํ˜• 22 ์ œ2์ ˆ ๋ณ€์ˆ˜ 22 1 ์„ค๋ช…๋ณ€์ˆ˜ 22 2 ์ข…์†๋ณ€์ˆ˜ 24 3 ํ†ต์ œ๋ณ€์ˆ˜ 26 ์ œ5์žฅ ๋ถ„์„๊ฒฐ๊ณผ 31 ์ œ1์ ˆ ๊ธฐ์ˆ  ํ†ต๊ณ„๋Ÿ‰ 31 ์ œ2์ ˆ ๋ณ€์ˆ˜ ๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„ 33 ์ œ3์ ˆ ๋ถ„์„ ์ž๋ฃŒ์˜ ํŠน์„ฑ๊ณผ GLS๋ฅผ ์ด์šฉํ•œ ๋ถ„์„ ๊ฒฐ๊ณผ 34 1 ๋ถ„์„ ์ž๋ฃŒ์˜ ํŠน์„ฑ๊ณผ ๋ถ„์„ ๋ฐฉ๋ฒ• 34 2 ๋ถ„์„ ๊ฒฐ๊ณผ 35 ์ œ6์žฅ ๊ฒฐ๋ก  52 ์ œ1์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 52 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ์‹œ์‚ฌ์  ๋ฐ ํ•œ๊ณ„ 53Maste

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    Anisotropic Dirac Fermions in a Bi Square Net of AMnBi2 Single Crystals

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