27 research outputs found

    ์—ฐ์†ํ˜• ์ธก์ถ”๋ ฅ๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๋กœ์ผ“์˜ ๊ณต๋ ฅ ์ œํŠธ ๊ฐ„์„ญ๋ ฅ ์ˆ˜์น˜๋ถ„์„ ๋ฐ ๋Œ€์ฒด ๋ชจ๋ธ๋ง

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2017. 8. ์ด์ˆ˜๊ฐ‘.The supersonic jet interaction generated by a continuous type side-jet thruster of the missile was considered. Firstly, the jet interaction flow field was investigated using numerical simulations. The simulation was made use of the three-dimensional unstructured-based computational fluid dynamics (CFD) solver. The numerical simulation method was validated through comparison with wind tunnel test results. Flow fields investigation and jet interaction effects for various flow conditions, jet magnitude, and jet direction conditions were performed. Secondly, the jet interaction aerodynamic database based on CFD data was developed and assessed. The generation of the jet interaction aerodynamic database for the continuous type side-jet requires a large amount of simulation data owing to the complex nature of jet interaction. To reduce the required number of simulations, seven jet operating conditions were selected using geometrical symmetry at firstthen, three-dimensional numerical simulations were conducted to build the jet interaction aerodynamic database in the reduced design space. Two modeling approaches were used in developing the jet interaction aerodynamic database. One is CFD-based modeling with a full factorial sampling, and the other is surrogate modeling, based on the Latin hypercube sampling and Kriging method, for the interim database. The resulting two aerodynamic databases were assessed through comparison with flight test results. Based on the comparison, both models showed a suitable representation of the aerodynamic coefficients within 10\% error during the jet operation period. This assessment confirms that the jet interaction aerodynamic database for missiles with continuous type side-jet thruster can be constructed using the CFD-based modeling approach. The surrogate model was found to perform well compared with the CFD-based model within an acceptable error level.Chapter 1 Introduction 1 1.1 Research Background 1 1.1.1 Side-jet control of missile 1 1.1.2 Continuous type side-jet 3 1.1.3 Jet interaction aerodynamic database 4 1.2 Literature Review and Scope of Works 5 1.3 Objective of Research 7 1.4 Outline 7 Chapter 2 Numerical Method 9 2.1 Governing Equations 9 2.2 Gas Modeling 11 2.2.1 Calorically perfect gas 11 2.2.2 Thermally perfect gas, Multiple gases 12 2.3 Spatial Discretization 13 2.3.1 Convective uxes 14 2.3.2 Viscous uxes 16 2.4 Temporal Discretization 18 2.5 Turbulence Modeling 18 Chapter 3 Numerical Investigation of Continuous Type side-jet 21 3.1 Conguration and Computational Grid 21 3.2 Jet Interaction Parameters and Evaluation 23 3.3 Jet Direction and Scale of Continuous Type Side-jet 24 3.4 Simulation Conditions 25 3.5 Wind Tunnel Test and Validation of Numerical Method 27 3.5.1 Jet interaction similitude parameter 27 3.5.2 Jet-o cases 29 3.5.3 Jet-on cases 29 3.6 Investigation of Jet Interaction for Continuous Type Side-jet 43 3.6.1 Simulation results of continuous type side-jet 43 3.6.2 Flow Features of Jet Interaction for Continuous Type side-jet 49 3.6.3 Eect of jet interaction parameters 60 Chapter 4 Surrogate Modeling of Jet Interaction Aerodynamic Database for Continuous Type side-jet 66 4.1 Jet interaction aerodynamic database of continuous type side-jet 66 4.2 Dened Jet Direction Conditions 67 4.3 Jet interaction modeling strategy 70 4.4 CFD-Based Modeling of Jet Interaction 73 4.4.1 Numerical simulation for jet interaction modeling 73 4.4.2 CFD-based jet interaction modeling results 74 4.5 Surrogate Modeling Method 77 4.5.1 Design of experiments 77 4.5.2 Kriging predictor 78 4.6 Surrogate Modeling of Jet Interaction 81 4.6.1 Jet interaction modeling and evaluation 81 4.6.2 Surrogate modeling of jet interaction results 83 Chapter 5 Assessment of Jet Interaction Modeling Results 95 5.1 Post Flight Analysis for Jet Interaction Database Identication 95 5.2 Assessment of Jet Interaction Database 99 Chapter 6 Conclusion 106 Appendix Chapter A Extension Rules of Jet Directions 109 Bibliography 113 ๊ตญ๋ฌธ์ดˆ๋ก 120Docto

    Prefetching Framework for General Workloads Using Breakpoint

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    ํ”„๋กœ๊ทธ๋žจ์˜ ๋กœ๋”ฉ ์†๋„๋Š” ํ”„๋กœ๊ทธ๋žจ์ด ์š”์ฒญํ•˜๋Š” ๋””์Šคํฌ ๋ธ”๋ก์„ ๋ฏธ๋ฆฌ ์ฝ์–ด ๋“ค์ž„์œผ๋กœ์จ(ํ”„๋ฆฌํŽ˜์นญ) ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ ํ”„๋ฆฌํŽ˜์นญ ๊ด€๋ จ ๊ธฐ๋ฒ•๋“ค์€ ํŠน์ • ํ”„๋กœ๊ทธ๋žจ์— ์ตœ์ ํ™”๋œ ๊ฒฝ์šฐ๋ฅผ ์ œ์™ธํ•˜๋ฉด ์ƒ๋‹นํ•œ ์˜ค๋ฒ„ํ—ค๋“œ๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ํŠนํžˆ ์š”์ฒญ๋ธ”๋ก์„ ์ •ํ™•ํžˆ ์ถ”์ ํ•˜๋Š”๋ฐ ์–ด๋ ค์›€์ด ์žˆ์—ˆ๋‹ค. ์–ด๋–ค ๋ธ”๋ก๋“ค์€ ์—ฌ๋Ÿฌ ์‹œํ€€์Šค(๋‹จ์œ„์‹œ๊ฐ„ ๋‚ด์— ์ถ”์ ๋œ ๋ธ”๋ก๋“ค)์— ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๊ณ  ๋‘ ์ ‘๊ทผ ์‹œํ€€์Šค๊ฐ€ ๋™์ผ ํ•˜๋”๋ผ๋„ ๋ฒ„ํผ ์บ์‹œ์— ์˜ํ•ด์„œ ์ ‘๊ทผ ์‹œ๊ฐ„๊ณผ ์ˆ˜์ง‘๋˜๋Š” ๋ธ”๋ก ์ •๋ณด๊ฐ€ ๋‹ค๋ฅผ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋ถ„์„์ด ๊นŒ๋‹ค๋กญ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์†Œํ”„ํŠธ์›จ์–ด์  ์ ‘๊ทผ ๋ฐฉ๋ฒ•์œผ๋กœ ์ƒˆ๋กœ์šด ๋ฒ”์šฉ ์›Œํฌ๋กœ๋“œ ํ”„๋ฆฌํŽ˜์นญ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ํ”„๋ฆฌํŽ˜์นญ ๊ธฐ๋ฒ•์€ ๋ธŒ๋ ˆ์ดํฌํฌ์ธํŠธ๋ฅผ ํ”„๋กœ๊ทธ๋žจ์˜ ์ ์žฌ ์ ์†Œ์— ๋ฐฐ์น˜ํ•จ์œผ๋กœ์จ ์š”์ฒญ ๋ธ”๋ก์˜ ์ƒ๊ด€๊ด€๊ณ„ ์ •๋ณด๋ฅผ ์ˆ˜์ง‘ํ•˜๊ณ , ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ”„๋ฆฌํŽ˜์นญ์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ƒ์šฉ ํ•˜๋“œ๋””์Šคํฌ๋ฅผ ์ด์šฉํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ, ๋ถˆํ•„์š”ํ•œ ์˜ค๋ฒ„ํ—ค๋“œ๊ฐ€ ๊ฐ์†Œ๋˜์—ˆ์œผ๋ฉฐ ๊ธฐ๋™ ์‹œ๊ฐ„์€ ํ‰๊ท  30%, ๋กœ๋”ฉ์€ ํ‰๊ท  15% ๋‹จ์ถ•๋˜์—ˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.Application loading speed can be improved by timely prefetching disk blocks likely to be needed by an application. However, existing prefetchers if they are not specialized to a particular application incur high overheads and are poor at identifying the blocks that will actually be required. There are many sequences in which blocks may be needed and, even if two access sequences are identical, block tracing and access timings can be affected significantly by the state of the buffer cache. We propose a new application independent software based prefetching technique, in which breakpoints are inserted at appropriate places in an application to collect the information on correlations between the blocks and to prefetch the potential blocks ahead of their schedule based on it. Experiments on an HDD based desktop PC demonstrated an average 30% reduction in application launch time and 15% in general I/O, while reducing the wasted overhead.OAIID:oai:osos.snu.ac.kr:snu2014-01/102/0000001265/3SEQ:3PERF_CD:SNU2014-01EVAL_ITEM_CD:102USER_ID:0000001265ADJUST_YN:YEMP_ID:A002514DEPT_CD:4190FILENAME:๊ณ ๊ด‘์ง„๋…ผ๋ฌธ_09(833-838) csts14-01.pdfDEPT_NM:์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€SCOPUS_YN:NCONFIRM:

    ๋‚ด์žฅํ˜• ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ๋ฆฌ๋ˆ…์Šค ๊ฐœ๋ฐœ ๋ฐ ์‚ฌ๋ก€ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ „๊ธฐ.์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2001.Maste

    ไบ‹ๅ‹™ๆ‰€ๅปบ็‰ฉ์˜ ์ฝ”์•„ ๏งๅž‹์— ๋”ฐ๋ฅธ FCU์‹œ์Šคํ…œ ่ฎŠๆต้‡ๅˆถๅพก ๆ–นๆณ•์˜ ็ถ“ๆฟŸๆ€ง์— ๊ด€ํ•œ ็ก็ฉถ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธๅคงๅญธๆ ก ๅคงๅญธ้™ข :ๅปบ็ฏ‰ๅญธ็ง‘,1995.Maste

    3์„ธ๋Œ€ ๋ฐฉ์†ก๋ง์—์„œ์˜ FGS ๋น„๋””์˜ค ์ „์†ก์„ ์œ„ํ•œ ํšจ์œจ์ ์ธ ์—๋Ÿฌ์ฒ˜๋ฆฌ ๋ฐ ์Šค์ผ€์ค„๋ง

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ „๊ธฐ. ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2007.Docto

    Intentionality-related Deep Learning Method in Web Prefetching

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    Many prediction models have been proposed to improve the effectiveness of web prefetching for reducing the response time perceived by users when browsing the web. Most of these models are based on structure learning and are applied at the client side. Currently, considerable attention is being paid to proxy-based prefetching because it is more effective and accurate in predicting the correlated pages of many websites of similar interest for more homogeneous users. Compared with client-based prefetching, more complex prediction tasks must run in the proxy, which implies that a more powerful prediction model is required. Thus, based on the time-series characteristics of browsing records, we proposed the intentionality-related long short-term memory (Ir-LSTM) model, which combines both the Skip-Gram embedding method and the LSTM model while expanding the input features with user information. We also propose a novel dynamic allocation module for detecting real-time traffic bursts and correspondingly adjusting the correlation coefficient of the model's output to achieve higher server-side resource utilization while fully maximizing hit ratio

    Waveguide-type strongly coupled oscillator์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2002.Maste

    ํ’๋ ฅ ๋ฐœ์ „ ํšŒ์ „์ต์˜ ๊ณต๋ ฅ ์ตœ์ ํ™” ์„ค๊ณ„์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2003.Maste

    Efficiency analysis by the change of public service management method

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ธฐ์—…์ •์ฑ…ํ•™๊ณผ, 2011.8. ๊น€์ค€๊ธฐ.Maste

    File-System-Level SSD Caching for Improving Application Launch Time

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    ์‘์šฉํ”„๋กœ๊ทธ๋žจ์˜ ๊ธฐ๋™ ์‹œ๊ฐ„์€ ๊ธฐ๊ธฐ์— ๋Œ€ํ•œ ์‚ฌ์šฉ์ž ์ฒดํ—˜์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ์ค‘์š”ํ•œ ์ง€ํ‘œ๋กœ ๋ณด์กฐ ๊ธฐ์–ต ์žฅ์น˜์˜ ์„ฑ๋Šฅ์— ์˜ํ•ด ํฐ ์˜ํ–ฅ์„ ๋ฐ›๋Š”๋‹ค. ํ•˜๋“œ๋””์Šคํฌ ๋Œ€์‹  SSD๋ฅผ ์‚ฌ์šฉํ•˜๊ฒŒ ๋˜๋ฉด ๊ธฐ๋™ ์‹œ๊ฐ„์„ ํฌ๊ฒŒ ๋‚ฎ์ถœ์ˆ˜ ์žˆ์ง€๋งŒ ๋น„์šฉ ๋Œ€๋น„ ์„ฑ๋Šฅ์„ ๊ณ ๋ คํ•˜๋ฉด ์ž‘์€ ์šฉ๋Ÿ‰์˜ SSD๋ฅผ ํ•˜๋“œ๋””์Šคํฌ์˜ ์บ์‹œ๋กœ ์“ฐ๋Š” ๊ฒƒ์ด ํ˜„์‹ค์ ์ธ ๋Œ€์•ˆ์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํŒŒ์ผ์‹œ์Šคํ…œ ์ˆ˜์ค€์—์„œ ํ•˜๋“œ๋””์Šคํฌ ์ƒ์˜ ๋ธ”๋ก์„ SSD๋กœ ์ด์ฃผ์‹œํ‚ค๋Š” ๊ธฐ๋ฒ•์„์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์€ ๊ธฐ์กด์˜ SSD ์บ์‹ฑ ๊ธฐ๋ฒ•๋“ค์—์„œ ์š”๊ตฌ๋˜๋˜ ์บ์‹œ ๋ฐ์ดํ„ฐ์˜ ์‚ฌ์ƒ์— ํ•„์š”ํ•œ ์ฃผ ๋ฉ”๋ชจ๋ฆฌ, CPU, ๊ทธ๋ฆฌ๊ณ  ์‚ฌ์ƒ ์ •๋ณด์˜ ์œ ์ง€๋ฅผ ์œ„ํ•œ SSD ๊ณต๊ฐ„ ์‚ฌ์šฉ์˜ ๋ถ€๊ฐ€์ ์ธ ์˜ค๋ฒ„ํ—ค๋“œ๊ฐ€ ์—†๋‹ค. 8๊ฐœ์˜ ์‘์šฉํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•œ ์‹คํ—˜์—์„œ ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ์™€ ๋ฐ์ดํ„ฐ ๋ธ”๋ก์„ ๋ชจ๋‘ SSD์— ์บ์‹ฑํ•œ ๊ฒฝ์šฐ์— ๊ธฐ๋™์‹œ๊ฐ„์ด ํ‰๊ท  56% ๋‹จ์ถ•๋จ์„ ํ™•์ธํ•˜์˜€๋‹ค. Application launch time is an important performance metric to user experience in desktop and laptop environment, which mostly depends on the performance of secondary storage. Application launch times can be reduced by utilizing solid-state drive (SSD) instead of hard disk drive (HDD). However, considering a cost-performance trade-off, utilizing SSDs as caches for slow HDDs is a practicable alternative in reducing the application launch times. We propose a new SSD caching scheme which migrates data blocks from HDDs to SSDs. Our scheme operates entirely in the file system level and does not require an extra layer for mapping SSD-cached data that is essential in most other schemes. In particular, our scheme does not incur mapping overheads that cause significant burdens on the main memory, CPU, and SSD space for mapping table. Experimental results conducted with 8 popular applications demonstrate our scheme yields 56% of performance gain in application launch, when data blocks along with metadata are migrated.N
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