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    ํ•œ์ค‘์ผ ์›์ž๋ ฅ ์ •์ฑ… ๋น„๊ต ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ตญ์ œ๋Œ€ํ•™์› ๊ตญ์ œํ•™๊ณผ(๊ตญ์ œ์ง€์—ญํ•™์ „๊ณต), 2022.2. ๊น€ํƒœ๊ท .Soon after the Fukushima nuclear accident, countries in East Asia, Japan, South Korea, and China, each took different path on nuclear energy. Japan recently announced nuclear phase-out policy after the accident but soon reversed its policy and decided to re-boot its nuclear program. South Korea also announced nuclear phase-out policy whereas China has been firmly elevating nuclear power generation. To thoroughly probe such differences in their recent nuclear energy policies, nuclear institutions in these countries will be meticulously compared and examined in the historical perspective. This paper will be divided into three phases in an overall historical sequence of nuclear development to analyze nuclear institutions, various actors, and their interactions. For such an analysis, a path dependence framework will be used and then argued that nuclear paths in these East Asian countries are showing an institutional pattern of increasing return under self-reinforcing sequences. These nuclear institutions are reproduced by a group of elites and functional consequences, and their outcomes are top-down path dependent leading to seemingly different nuclear energy polices. From such a conclusion, this study will hopefully contribute to better understandings of nuclear energy policies in different regions and to predict future energy paths around the world.ํ›„์ฟ ์‹œ๋งˆ ์›์ „ ์‚ฌ๊ณ  ์งํ›„, ์ผ๋ณธ, ๋Œ€ํ•œ๋ฏผ๊ตญ, ์ค‘๊ตญ ๋“ฑ ๋™์•„์‹œ์•„ ๊ตญ๊ฐ€๋“ค์€ ๊ฐ๊ธฐ ๋‹ค๋ฅธ ์›์ž๋ ฅ ์ •์ฑ…์„ ํŽผ์ณค๋‹ค. ์ผ๋ณธ์˜ ๊ฒฝ์šฐ, ํƒˆ์›์ „ ์ •์ฑ…์„ ๋ฐœํ‘œํ•˜์˜€๋‹ค๊ฐ€ ์ดํ›„ ๋ฌดํšจํ™” ๊ฒฐ์ •์„ ๋‚ด๋ฆฌ๊ณ  ์›์ž๋ ฅ ๋ฐœ์ „์„ ๋Š˜๋ ค๊ฐ€๊ณ  ์žˆ์œผ๋ฉฐ ๋Œ€ํ•œ๋ฏผ๊ตญ์€ ์ตœ๊ทผ ํƒˆ์›์ „ ์ •์ฑ…์„ ๋ฐœํ‘œํ•œ ๋ฐ˜๋ฉด ์ค‘๊ตญ์€ ์›์ „ ๊ตด๊ธฐ ์ •์ฑ…์„ ํŽผ์น˜๋ฉฐ ๊ตณ๊ฑดํžˆ ์›์ž๋ ฅ ๋ฐœ์ „ ๋น„์ค‘์„ ๋Š˜๋ ค๊ฐ€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋‹ค๋ฅธ ์›์ „์ •์ฑ…๋“ค์˜ ์ถ”์ง„ ๋ฐฐ๊ฒฝ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ๊ฐ ๋‚˜๋ผ๋“ค์˜ ์›์ž๋ ฅ ์ œ๋„๋“ค์„ ์—ญ์‚ฌ์  ๊ด€์ ์œผ๋กœ ๊ฒฝ๋กœ ์˜์กด์„ฑ ํ‹€์„ ์ด์šฉํ•ด ๋น„๊ต ๋ถ„์„ํ•  ๊ฒƒ์ด๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๊ฐ ๋‚˜๋ผ๋“ค์˜ ์›์ž๋ ฅ ๊ฒฝ๋กœ๋“ค์€ ์ˆ˜ํ™• ์ฒด์ฆ ๊ณผ์ •์„ ํ†ตํ•ด ์ œ๋„๊ฐ€ ์ง€์†๋˜๊ณ  ์žฌ์ƒ์‚ฐ๋˜์—ˆ์œผ๋ฉฐ ๊ถŒ๋ ฅ๊ณผ ๊ธฐ๋Šฅ์„ฑ์— ๊ธฐ์ดˆํ•˜๋ฉฐ ์„ค๋ช…์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๋ก ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ด ์—ฐ๊ตฌ๊ฐ€ ์—ญ์‚ฌ์ ์œผ๋กœ ๋‹ค๋ฅธ ์›์ž๋ ฅ ์ •์ฑ…์˜ ๋ฐฐ๊ฒฝ์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋•๊ณ  ๋ฏธ๋ž˜์˜ ์—๋„ˆ์ง€ ์ •์ฑ… ๋ฐ ๊ฒฝ๋กœ ์˜ˆ์ธก์„ ๋•๊ธธ ํฌ๋งํ•œ๋‹ค.I. Introduction.. 5 II. Literature Review 1. Institutionalism.. 11 2. Comparative Historical Analysis... 16 III. Research Method 1. Path Dependency Framework.... 22 2. Time Frame and Variables. 29 IV. Analysis 1. Case Study 1-1. Phase 1: Early Development Stage.. 31 1-2. Phase 2: Acceleration Period.... 45 1-3. Phase 3: Stagnation Period.. 55 1-4. Summary... 67 V. Conclusion..... 72 VI. Bibliography... 76 VII. ๊ตญ๋ฌธ์ดˆ๋ก... 85์„

    ํ™œ์ฐจ ๊นŠ์ด ํ‰๊ฐ€์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ๋ฐฉ์‚ฌ์„  ์˜์ƒ ์ดฌ์˜ ๊ธฐ๋ฒ• ๋ฐ ๋ฐฉ์‚ฌ์„  ์˜์ƒ๊ณผ ์ „์‚ฐํ™”๋‹จ์ธต์ดฌ์˜์˜ ๋น„๊ต

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ˆ˜์˜๊ณผ๋Œ€ํ•™ ์ˆ˜์˜ํ•™๊ณผ, 2021. 2. ์ตœ๋ฏผ์ฒ .Objective: The purpose of this study was to verify the effectiveness of skyline view radiography by comparing trochlear depth, patellar thickness, and the trochlear depth to patellar thickness ratio in radiographs and computed tomography (CT) scans. Study design: The trochlear depth, patellar thickness, and the trochlear depth to patellar thickness ratio of 10 stifle joints were measured in radiographs and CT images that were acquired with the joints fully extended. Results: The patella was located in the distal trochlear groove at the level of intercondylar fossa with the stifle joint flexed but in the more proximal trochlear groove with the stifle joint extended. The trochlear depth assessed in this posture is more similar to the depth of middle trochlear groove. There was no significant difference in the values obtained from radiographs and those obtained from CT images. Conclusion: Conventional skyline view stifle radiography with the stifle in the flexed position appears less suitable for measuring trochlear depth but the view with the stifle fully extended is rather considered more useful for this purpose. Although CT is accurate for evaluating trochlear depth, extension skyline view stifle radiography can be used as an alternative to CT.๋ชฉ์ : ์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ํ™œ์ฐจ ๊นŠ์ด, ์Šฌ๊ฐœ๊ณจ์˜ ๋‘๊ป˜, ๊ทธ๋ฆฌ๊ณ  ํ™œ์ฐจ ๊นŠ์ด์™€์Šฌ๊ฐœ๊ณจ์˜ ๋‘๊ป˜์˜ ๋น„์œจ์„ skyline view ๋ฐฉ์‚ฌ์„  ์˜์ƒ๊ณผ ์ „์‚ฐํ™”๋‹จ์ธต์ดฌ์˜์—์„œ ๋น„๊ตํ•จ์œผ๋กœ์จ, skyline view ๋ฐฉ์‚ฌ์„  ์˜์ƒ ๊ธฐ๋ฒ•์˜ ์œ ํšจ์„ฑ์„ ์ž…์ฆํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์—ฐ๊ตฌ ์„ค๊ณ„: ํ™œ์ฐจ ๊นŠ์ด, ์Šฌ๊ฐœ๊ณจ ๋‘๊ป˜, ๊ทธ๋ฆฌ๊ณ  ํ™œ์ฐจ ๊นŠ์ด์™€ ์Šฌ๊ฐœ๊ณจ ๋‘๊ป˜์˜๋น„์œจ์€ ๋‹ค๋ฆฌ๋ฅผ ์™„์ „ํžˆ ํŽธ ์ƒํƒœ์—์„œ ํš๋“๋œ 10๊ฐœ์˜ ๋ฌด๋ฆŽ ๊ด€์ ˆ ๋ฐฉ์‚ฌ์„  ์˜์ƒ๊ณผ ์ „์‚ฐํ™”๋‹จ์ธต์ดฌ์˜์—์„œ ์ธก์ •๋˜์—ˆ๋‹ค. ๊ฒฐ๊ณผ: ๋ฌด๋ฆŽ์„ ๊ตฝํžŒ ์ƒํƒœ์—์„œ ์Šฌ๊ฐœ๊ณจ์€ ์œต๊ธฐ์‚ฌ์ด ์˜ค๋ชฉ์ˆ˜์ค€์˜ ์›์œ„ ํ™œ์ฐจ๊ตฌ์— ์œ„์น˜ํ•˜์˜€์œผ๋ฉฐ, ๋ฌด๋ฆŽ์„ ํŽธ ์ƒํƒœ์—์„œ๋Š” ๋” ๊ทผ์œ„ ํ™œ์ฐจ๊ตฌ์— ์œ„์น˜ํ•˜์˜€๋‹ค. ๋ฌด๋ฆŽ์„ ํŽธ ์ž์„ธ์—์„œ์˜ ํ™œ์ฐจ ๊นŠ์ด๋Š” ์ค‘์•™ ๋ถ€์œ„์˜ ํ™œ์ฐจ ๊นŠ์ด์™€ ๋” ์œ ์‚ฌํ•˜์˜€๋‹ค. ๋ฐฉ์‚ฌ์„  ์˜์ƒ๊ณผ ์ „์‚ฐํ™”๋‹จ์ธต์ดฌ์˜์—์„œ์˜ ์ธก์ •๊ฐ’์„ ๋น„๊ต ์‹œ ์œ ์˜์ ์ธ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. ๊ฒฐ๋ก : ๋‹ค๋ฆฌ๋ฅผ ๊ตฝํžŒ ์ž์„ธ์—์„œ ์ดฌ์˜ํ•œ ๊ธฐ์กด์˜ skyline view ๋ฌด๋ฆŽ ๋ฐฉ์‚ฌ์„  ์˜์ƒ๋ณด๋‹ค ๋‹ค๋ฆฌ๋ฅผ ํŽธ ์ž์„ธ์—์„œ ์ดฌ์˜ํ•œ extension skyline view ๋ฌด๋ฆŽ ๋ฐฉ์‚ฌ์„  ์˜์ƒ์ด ํ™œ์ฐจ ๊นŠ์ด ์ธก์ •์— ๋” ์ ํ•ฉํ•˜๋‹ค. ์ „์‚ฐํ™”๋‹จ์ธต์ดฌ์˜์ด ํ™œ์ฐจ ๊นŠ์ด๋ฅผ ํ‰๊ฐ€ํ•˜๋Š” ๋ฐ ์žˆ์–ด ์ •ํ™•ํ•˜๋‚˜, extension skyline view ๋ฌด๋ฆŽ ๋ฐฉ์‚ฌ์„  ์˜์ƒ์ด ์ „์‚ฐํ™”๋‹จ์ธต์ดฌ์˜์„ ๋Œ€์ฒดํ•˜์—ฌ ์ด์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.Abstract i Table of Contents iii List of Abbreviations โ…ณ Introduction 1 Materials and Methods 3 1. Animals 3 2. Radiographic and CT scanning settings 3 3. Conventional method 4 4. New method 4 5. Statistical analysis 5 Result 9 Discussion 13 Reference 18 Abstract in Korean 20Maste

    ์—ผ๋ฃŒ๊ฐ์‘ํ˜• ํƒœ์–‘์ „์ง€๋ฅผ ์œ„ํ•œ ํ–ฅ์ƒ๋œ ๋น›์˜ ํ™œ์šฉ์„ฑ์„ ๊ฐ€์ง„ ๊ด‘์ „๊ทน ์ œ์กฐ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€(์—๋„ˆ์ง€ํ™˜๊ฒฝ ํ™”ํ•™์œตํ•ฉ๊ธฐ์ˆ ์ „๊ณต), 2015. 2. ์„ฑ์˜์€.An increase in global energy demand, environmental concerns and the finite nature of fossil fuels have led to a great interest and development in the field of renewable energy sources. Solar energy is one of the most promising renewable energy sources because of its cleanliness and abundance. Among photovoltaic devices, dye-sensitized solar cells (DSSCs) have attracted substantial attention as a renewable energy conversion device because of their low production cost, easy fabrication process, and aesthetically appealing design. However, in order for DSSCs to realistically be considered an alternative to conventional solid-state photovoltaic devices, improvements in power conversion efficiency must be made. For improving the efficiency of DSSCs, the light absorption performance of photoanode, a key component of DSSCs, should be enhanced as much as possible to increase the production of photocurrent. Thus far, various attempts have been made to enhance the light harvesting efficiency of photoanodes. These efforts include developing new sensitizers with a broader range of absorption wavelength and a higher extinction coefficient, construction of new semiconducting electrode structures with light scattering properties, mixing different dyes for co-sensitization, and introducing non-radiative resonance energy transfer concepts. Nevertheless, it is still difficult to attain the desired level of light efficiency that would lead to significantly improved efficiency. In this study, new approaches for obtaining enhanced light utilization in photoanodes of DSSCs are demonstrated by incorporating optical-active inorganic materials such as quantum dots and precious metal nanoparticles into the photoanode of DSSCs. The first part presents dye-sensitized solar cells with silica-coated quantum dot embedded nanoparticles (SiO2/QD@SiO2 NPs). QDs have been considered promising materials with potential to be applied to photovoltaic devices owing to their powerful light absorption property. However, it is hard to apply QDs to DSSCs because of their instability in iodide pair electrolyte system which is most commonly used in DSSCs. To overcome this problem, QDs were embedded in SiO2 nanoparticle and coated with thin SiO2 layer. SiO2/QD@SiO2 NPs were incorporated into the photoanode of DSSCs. The enhanced performance of the SiO2/QD@SiO2 NP containing DSSC was believed to be mainly due to the improved short-circuit current density (JSC), which was a direct result of enhanced light utilization in the photoanode. Rather than working as a sensitizer, QDs in DSSCs act as a light reservoir that absorb the extra light and re-emit the absorbed light. The second part discusses plasmon-enhanced dye-sensitized solar cells using SiO2 spheres decorated with tightly assembled silver nanoparticles (Ag NPs). The plasmonic enhancement effects of the photoanode in DSSCs were investigated. To activate the strong plasmon coupling, new structure of Ag NPs assembly was designed through electromagnetic wave simulation. Tightly assembled Ag NPs on a SiO2 core showed broadband plasmonic absorption developed by coupled plasmon modes, which was not limited to a specific wavelength. By incorporating SiO2-t-Ag@SiO2 into the photoanodes of DSSCs, light absorption by the photoanode thin films definitely increased and overall power conversion efficiencies of DSSCs were improved.Contents Abstract ............................... i List of Tables ......................... ix List of Figures ........................ x Chapter 1. Introduction ................ 1 1.1 Solar energy and solar cells ....... 1 1.2 Dye-sensitized solar cells (DSSCs) ........................................ 5 1.2.1 Components and working principles of DSSC ........................................ 6 1.2.2 Characterization techniques.............................. 12 1.2.2.1 The current -voltage (I-V) characteristics ........................................ 12 1.2.2.2 Incident photon-to-current conversion efficiency (IPCE) and absorbed photon-to-current conversion efficiency (APCE) ........................................ 16 1.2.3 Light utilization in photoanode ........................................ 17 1.3 Objectives of this dissertation ........................................ 20 Chapter 2. Dye-sensitized solar cells with silica coated quantum dot ............................ 23 2.1 Introduction ........................23 2.2 Experimental section ............... 27 2.2.1 Preparation of silica-coated quantum dot-embedded silica nanoparticles (SiO2/QD@SiO2 NPs) ........................................ 27 2.2.2 Preparation of photoanodes with SiO2/QD@SiO2 NPs ........................................ 28 2.2.3 Fabrication of SiO2/QD@SiO2 DSSCs ........................................ 29 2.2.4 Instruments ........................................ 29 2.3 Results and discussion ............. 31 2.3.1 Preparation of silica-coated quantum dot-embedded silica nanoparticles (SiO2/QD@SiO2 NPs) ........................................ 31 2.3.2 UV-vis spectra of modified photoanode ........................................ 35 2.3.3 Confocal laser scanning microscopy (CLSM) ........................................ 38 2.3.4 Photovoltaic characteristics ........................................ 45 2.3.5 Photovoltaic performance without sensitizer.............................. 55 2.4 Conclusions ........................ 58 Chapter 3. Plasmon-enhanced dye-sensitized solar cells ............................ 59 3.1 Introduction ....................... 59 3.2 Experimental section ............... 64 3.2.1 Materials ........................ 64 3.2.2 Preparation of SiO2 spheres decorated with assembled silver nanoparticles .......................... 64 3.2.3 Preparation of the photoanodes ........................................ 66 3.2.4 Assembly of dye-sensitized solar cells ........................................ 67 3.2.5 Instruments ...................... 68 3.3 Results and discussion ........................................ 69 3.3.1 Preparation of SiO2 spheres decorated with tightly assembled silver nanoparticles ........................................ 69 3.3.2 Discrete dipole approximation (DDA) simulation ........................................ 72 3.3.3 UV-vis absorption spectra of plasmonic particles in solution ............................... 79 3.3.4 UV-Vis absorption and reflectance spectra of photoelectrodes ........................................ 84 3.3.5 Photovoltaic characteristics ........................................ 88 3.3.6 Absorbed photon-to-current conversion efficiency (APCE) ................................. 97 viii 3.4 Conclusions ........................ 99 References ............................. 101 ๊ตญ๋ฌธ์ดˆ๋ก ............................... 115Docto

    Acceleration of CNN Computation on a PIM-enabled GPU system

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2022. 8. ์ดํ˜์žฌ.์ตœ๊ทผ, convolutional neural network (CNN)์€ image processing ๋ฐ computer vision ๋“ฑ์—์„œ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. CNN์€ ์—ฐ์‚ฐ ์ง‘์•ฝ์ ์ธ convolutional layer์™€ ๋ฉ”๋ชจ๋ฆฌ ์ง‘์•ฝ์ ์ธ fully connected layer, batch normalization layer ๋ฐ activation layer ๋“ฑ ๋‹ค์–‘ํ•œ layer๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ CNN์„ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด GPU๊ฐ€ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์ง€๋งŒ, CNN์€ ์—ฐ์‚ฐ ์ง‘์•ฝ์ ์ธ ๋™์‹œ์— ๋ฉ”๋ชจ๋ฆฌ ์ง‘์•ฝ์ ์ด๊ธฐ์— ์„ฑ๋Šฅ์ด ์ œํ•œ๋œ๋‹ค. ๋˜ํ•œ, ๊ณ ํ™”์งˆ์˜ image ๋ฐ video application์˜ ์‚ฌ์šฉ์€ GPU์™€ ๋ฉ”๋ชจ๋ฆฌ ๊ฐ„์˜ data ์ด๋™์— ์˜ํ•œ ๋ถ€๋‹ด์„ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค. Processing-in-memory๋Š” ๋ฉ”๋ชจ๋ฆฌ์— ์—ฐ์‚ฐ๊ธฐ๋ฅผ ํƒ‘์žฌํ•˜์—ฌ data ์ด๋™์— ์˜ํ•œ ๋ถ€๋‹ด์„ ์ค„์ผ ์ˆ˜ ์žˆ์–ด, host GPU์™€ PIM์„ ํ•จ๊ป˜ ์‚ฌ์šฉํ•˜๋Š” system์€ CNN์„ ์ฒ˜๋ฆฌํ•˜๊ธฐ์— ์ ํ•ฉํ•˜๋‹ค. ๋จผ์ € convolutional layer์˜ ์—ฐ์‚ฐ๋Ÿ‰์„ ๊ฐ์†Œ์‹œํ‚ค๊ธฐ ์œ„ํ•ด, ๊ทผ์‚ฌ ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ ๊ทผ์‚ฌ ์—ฐ์‚ฐ์€ host GPU๋กœ data๋ฅผ load ํ•œ ํ›„ data ๊ฐ„ ์œ ์‚ฌ๋„๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ์—, GPU์™€ DRAM ๊ฐ„์˜ data ์ด๋™๋Ÿ‰์„ ์ค„์ด์ง€๋Š” ๋ชปํ•œ๋‹ค. ์ด๋Š” ๋ฉ”๋ชจ๋ฆฌ intensity๋ฅผ ์ฆ๊ฐ€์‹œ์ผœ ๋ฉ”๋ชจ๋ฆฌ bottleneck์„ ์œ ๋ฐœํ•œ๋‹ค. ๊ฒŒ๋‹ค๊ฐ€, ๊ทผ์‚ฌ ์—ฐ์‚ฐ์œผ๋กœ ์ธํ•ด warp ๊ฐ„ load imbalance ๋˜ํ•œ ๋ฐœ์ƒํ•˜๊ฒŒ ๋˜์–ด ์„ฑ๋Šฅ์ด ์ €ํ•˜๋œ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” data ๊ฐ„ ๊ทผ์‚ฌ ๋น„๊ต๋ฅผ PIM์—์„œ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์€ PIM์—์„œ data๊ฐ„ ์œ ์‚ฌ๋„๋ฅผ ํŒŒ์•…ํ•œ ํ›„, ๋Œ€ํ‘œ data์™€ ์œ ์‚ฌ๋„ ์ •๋ณด๋งŒ์„ GPU๋กœ ์ „์†กํ•œ๋‹ค. GPU๋Š” ๋Œ€ํ‘œ data์— ๋Œ€ํ•ด์„œ๋งŒ ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•˜๊ณ , ์œ ์‚ฌ๋„ ์ •๋ณด์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ํ•ด๋‹น ๊ฒฐ๊ณผ๋ฅผ ์žฌ์‚ฌ์šฉํ•˜์—ฌ ์ตœ์ข… ๊ฒฐ๊ณผ๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ์ด๋•Œ, ๋ฉ”๋ชจ๋ฆฌ์—์„œ์˜ data ๋น„๊ต๋กœ ์ธํ•œ latency ์ฆ๊ฐ€๋ฅผ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•ด DRAM์˜ bank ๋‹จ๊ณผ TSV ๋‹จ์„ ๋ชจ๋‘ ํ™œ์šฉํ•˜๋Š” 2-level PIM ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, ๋Œ€ํ‘œ data๋ฅผ ์ ๋‹นํ•œ address์— ์žฌ๋ฐฐ์น˜ํ•œ ํ›„ GPU๋กœ ์ „์†กํ•˜์—ฌ GPU์—์„œ์˜ ๋ณ„๋„ ์ž‘์—… ์—†์ด load balancing์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, batch normalization ๋“ฑ non-convolutional layer์˜ ๋†’์€ ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์œผ๋กœ ์ธํ•œ ๋ฉ”๋ชจ๋ฆฌ bottleneck ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด PIM์—์„œ non-convolutional layer๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” PIM์œผ๋กœ non-convolutional layer๋ฅผ ๊ฐ€์†ํ•˜์˜€์ง€๋งŒ, ๋‹จ์ˆœํžˆ GPU์™€ PIM์ด ์ˆœ์ฐจ์ ์œผ๋กœ ๋™์ž‘ํ•˜๋Š” ์ƒํ™ฉ์„ ๊ฐ€์ •ํ•˜์—ฌ ์„ฑ๋Šฅ ํ–ฅ์ƒ์— ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์€ non-convolutional layer๊ฐ€ ouptut feature map์˜ channel ๋‹จ์œ„๋กœ ์ˆ˜ํ–‰๋œ๋‹ค๋Š” ์ ์— ์ฐฉ์•ˆํ•˜์—ฌ host์™€ PIM์„ pipeline์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ CNN ํ•™์Šต์„ ๊ฐ€์†ํ•œ๋‹ค. PIM์€ host์—์„œ convolution ์—ฐ์‚ฐ์ด ๋๋‚œ output feature map์˜ channel์— ๋Œ€ํ•ด non-convolution ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์—ญ์ „ํŒŒ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” weight update์™€ feature map gradient ๊ณ„์‚ฐ์—์„œ์˜ convolution๊ณผ non-convolution ๊ฐ„ job ๊ท ํ˜•์„ ์œ„ํ•ด, ์ ์ ˆํ•˜๊ฒŒ non-convolution job์„ ๋ถ„๋ฐฐํ•˜์—ฌ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค. ์ด์— ๋”ํ•ด, host์™€ PIM์ด ๋™์‹œ์— memory์— accessํ•˜๋Š” ์ƒํ™ฉ์—์„œ ์ „์ฒด ์ˆ˜ํ–‰ ์‹œ๊ฐ„์„ ์ตœ์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•ด bank ์†Œ์œ ๊ถŒ ๊ธฐ๋ฐ˜์˜ host์™€ PIM ๊ฐ„ memory scheduling ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, image processing application ์ฒ˜๋ฆฌ๋ฅผ ์œ„ํ•ด logic die์— ํƒ‘์žฌ ๊ฐ€๋Šฅํ•œ PIM GPU ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. GPU ๊ธฐ๋ฐ˜์˜ PIM์€ CUDA ๊ธฐ๋ฐ˜์˜ application์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์–ด ๋”ฅ๋Ÿฌ๋‹ ๋ฐ image application์˜ ์ฒ˜๋ฆฌ์— ์ ํ•ฉํ•˜์ง€๋งŒ, GPU์˜ ํฐ ์šฉ๋Ÿ‰์˜ on-chip SRAM์€ logic die์— ์ถฉ๋ถ„ํ•œ ์ˆ˜์˜ computing unit์˜ ํƒ‘์žฌ๋ฅผ ์–ด๋ ต๊ฒŒ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” PIM์— ์ ํ•ฉํ•œ ์ตœ์ ์˜ lightweight GPU ๊ตฌ์กฐ์™€ ํ•จ๊ป˜ ์ด๋ฅผ ํ™œ์šฉํ•˜๊ธฐ ์œ„ํ•œ ์ตœ์ ํ™” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. Image processing application์˜ ๋ฉ”๋ชจ๋ฆฌ ์ ‘๊ทผ ํŒจํ„ด๊ณผ data locality๊ฐ€ ๋ณด์กด๋˜๋„๋ก ๊ฐ computing unit์— data๋ฅผ ํ• ๋‹นํ•˜๊ณ , ์˜ˆ์ธก ๊ฐ€๋Šฅํ•œ data์˜ ํ• ๋‹น์„ ๊ธฐ๋ฐ˜์œผ๋กœ prefetcher๋ฅผ ํƒ‘์žฌํ•˜์—ฌ lightweightํ•œ ๊ตฌ์กฐ์ž„์—๋„ ์ถฉ๋ถ„ํ•œ ์ˆ˜์˜ computing unit์„ ํƒ‘์žฌํ•˜์—ฌ ๋†’์€ ์„ฑ๋Šฅ์„ ํ™•๋ณดํ•œ๋‹ค.Recently, convolutional neural networks (CNN) have been widely used in image processing and computer vision. CNNs are composed of various layers such as computation-intensive convolutional layer and memory-intensive fully connected layer, batch normalization layer, and activation layer. GPUs are often used to accelerate the CNN, but performance is limited by high computational costs and memory usage of the convolution. Also, increasing demand for high resolution image applications increases the burden of data movement between GPU and memory. By performing computations on the memory, processing-in-memory (PIM) is expected to mitigate the overhead caused by data transfer. Therefore, a system that uses a PIM is promising for processing CNNs. First, prior studies exploited approximate computing to reduce the computational costs. However, they only reduced the amount of the computation, thereby its performance is bottlenecked by the memory bandwidth due to an increased memory intensity. In addition, load imbalance between warps caused by approximation also inhibits the performance improvement. This dissertation proposes a PIM solution that reduces the amount of data movement and computation through the Approximate Data Comparison (ADC-PIM). Instead of determining the value similarity on the GPU, the ADC-PIM located on memory compares the similarity and transfers only the selected data to the GPU. The GPU performs convolution on the representative data transferred from the ADC-PIM, and reuses the calculated results based on the similarity information. To reduce the increase in memory latency due to the data comparison, a two-level PIM architecture that exploits both the DRAM bank and TSV stage is proposed. To ease the load balancing on the GPU, the ADC-PIM reorganizes data by assigning the representative data to proposer addresses that are computed based on the comparison result. Second, to solve the memory bottleneck caused by the high memory usage, non-convolutional layers are accelerated with PIM. Previous studies also accelerated the non-convolutional layers by PIM, but there was a limit to performance improvement because they simply assumed a situation in which the GPU and PIM operate sequentially. The proposed method accelerates the CNN training with a pipelined execution of GPU and PIM, focusing on the fact that the non-convolution operation is performed in units of channels of the output feature map. PIM performs non-convolutional operations on the output feature map where the GPU has completed the convolution operation. To balance the jobs between convolution and non-convolution in weight update and feature map gradient calculation that occur in the back propagation process, non-convolution job is properly distributed to each process. In addition, a memory scheduling algorithm based on bank ownership between the host and PIM is proposed to minimize the overall execution time in a situation where the host and PIM simultaneously access memory. Finally, a GPU-based PIM architecture for image processing application is proposed. Programmable GPU-based PIM is attractive because it enables the utilization of well-crafted software development kits (SDKs) such as CUDA and openCL. However, the large capacity of on-chip SRAM of GPU makes it difficult to mount a sufficient number of computing units in logic die. This dissertation proposes a GPU-based PIM architecture and well-matched optimization strategies considering both the characteristics of image applications and logic die constraints. Data allocation to the computing unit is addressed to maintain the data locality and data access pattern. By applying a prefetcher that leverages the pattern-aware data allocation, the number of active warps and the on-chip SRAM size of the PIM are significantly reduced. This enables the logic die constraints to be satisfied and a greater number of computing units to be integrated on a logic die.์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ 3 1.3 ๋…ผ๋ฌธ ๊ตฌ์„ฑ 4 ์ œ 2 ์žฅ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ์ง€์‹ 5 2.1 High Bandwidth Memory 5 2.2 Processing-In-Memory 6 2.3 GPU์˜ ๊ตฌ์กฐ ๋ฐ ๋™์ž‘ ๋ชจ๋ธ 7 ์ œ 3 ์žฅ PIM์„ ํ™œ์šฉํ•œ ๊ทผ์‚ฌ์  ๋ฐ์ดํ„ฐ ๋น„๊ต ๋ฐ ๊ทผ์‚ฌ ์—ฐ์‚ฐ์„ ํ†ตํ•œ Convolution ๊ฐ€์† 9 3.1 ๊ด€๋ จ ์—ฐ๊ตฌ 10 3.1.1 CNN์—์„œ์˜ Approximate Computing 10 3.1.2 Processing In Memory๋ฅผ ํ™œ์šฉํ•œ CNN ๊ฐ€์† 11 3.2 Motivation 13 3.2.1 GPU์—์„œ Convolution ์—ฐ์‚ฐ ์‹œ์˜ Approximation ๊ธฐํšŒ 13 3.2.2 Approxiamte Convolution ์—ฐ์‚ฐ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ฌธ์ œ์  14 3.3 ์ œ์•ˆํ•˜๋Š” ADC-PIM Design 18 3.3.1 Overview 18 3.3.2 Data ๊ฐ„ ์œ ์‚ฌ๋„ ๋น„๊ต ๋ฐฉ๋ฒ• 19 3.3.3 ADC-PIM ์•„ํ‚คํ…์ฒ˜ 21 3.3.4 Load Balancing์„ ์œ„ํ•œ Data Reorganization 27 3.4 GPU์—์„œ์˜ Approximate Convolution 31 3.4.1 Instruction Skip์„ ํ†ตํ•œ Approximate Convolution 31 3.4.2 Approximate Convolution์„ ์œ„ํ•œ ๊ตฌ์กฐ์  ์ง€์› 32 3.5 ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 36 3.5.1 ์‹คํ—˜ ํ™˜๊ฒฝ ๊ตฌ์„ฑ 36 3.5.2 ์ œ์•ˆํ•˜๋Š” ๊ฐ ๋ฐฉ๋ฒ•์˜ ์˜ํ–ฅ ๋ถ„์„ 38 3.5.3 ๊ธฐ์กด ์—ฐ๊ตฌ์™€์˜ ์„ฑ๋Šฅ ๋น„๊ต 41 3.5.4 ์—๋„ˆ์ง€ ์†Œ๋ชจ๋Ÿ‰ ๋น„๊ต 44 3.5.5 Design Overhead ๋ถ„์„ 44 3.5.6 ์ •ํ™•๋„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 46 3.6 ๋ณธ ์žฅ์˜ ๊ฒฐ๋ก  47 ์ œ 4 ์žฅ Convolutional layer์™€ non-Convolutional Layer์˜ Pipeline ์‹คํ–‰์„ ํ†ตํ•œ CNN ํ•™์Šต ๊ฐ€์† 48 4.1 ๊ด€๋ จ ์—ฐ๊ตฌ 48 4.1.1 Non-CONV Lasyer์˜ Memory Bottleneck ์™„ํ™” 48 4.1.2 Host์™€ PIM ๊ฐ„ Memory Scheduling 49 4.2 Motivation 51 4.2.1 CONV์™€ non-CONV์˜ ๋™์‹œ ์ˆ˜ํ–‰ ์‹œ ์„ฑ๋Šฅ ํ–ฅ์ƒ ๊ธฐํšŒ 51 4.2.2 PIM ์šฐ์„ ๋„์— ๋”ฐ๋ฅธ host ๋ฐ PIM request์˜ ์ฒ˜๋ฆฌ ํšจ์œจ์„ฑ ๋ณ€ํ™” 52 4.3 ์ œ์•ˆํ•˜๋Š” host-PIM Memory Scheduling ์•Œ๊ณ ๋ฆฌ์ฆ˜ 53 4.3.1 host-PIM System Overview 53 4.3.2 PIM Duration Based Memory Scheduling 53 4.3.3 ์ตœ์  PD_TH ๊ฐ’์˜ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ• 56 4.4 ์ œ์•ˆํ•˜๋Š” CNN ํ•™์Šต ๋™์ž‘ Flow 62 4.4.1 CNN ํ•™์Šต ์ˆœ์ „ํŒŒ ๊ณผ์ • 62 4.4.2 CNN ํ•™์Šต ์—ญ์ „ํŒŒ ๊ณผ์ • 63 4.5 ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 67 4.5.1 ์‹คํ—˜ ํ™˜๊ฒฝ ๊ตฌ์„ฑ 67 4.5.2 Layer ๋‹น ์ˆ˜ํ–‰ ์‹œ๊ฐ„ ๋ณ€ํ™” 68 4.5.3 ์—ญ์ „ํŒŒ ๊ณผ์ •์—์„œ์˜ non-CONV job ๋ฐฐ๋ถ„ ํšจ๊ณผ 70 4.5.4 ์ „์ฒด Network Level์—์„œ์˜ ์ˆ˜ํ–‰ ์‹œ๊ฐ„ ๋ณ€ํ™” 72 4.5.5 ์ œ์•ˆํ•˜๋Š” ์ตœ์  PD_TH ์ถ”์ • ๋ฐฉ๋ฒ•์˜ ์ •ํ™•๋„ ๋ฐ ์„ ํƒ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์ˆ˜๋ ด ์†๋„ 74 4.6 ๋ณธ ์žฅ์˜ ๊ฒฐ๋ก  75 ์ œ 5 ์žฅ Image processing์˜ ๋ฐ์ดํ„ฐ ์ ‘๊ทผ ํŒจํ„ด์„ ํ™œ์šฉํ•œ PIM์— ์ ํ•ฉํ•œ lightweight GPU ๊ตฌ์กฐ 76 5.1 ๊ด€๋ จ ์—ฐ๊ตฌ 77 5.1.1 Processing In Memory 77 5.1.2 GPU์—์„œ์˜ CTA Scheduling 78 5.1.3 GPU์—์„œ์˜ Prefetching 78 5.2 Motivation 79 5.2.1 PIM GPU system์—์„œ Image Processing ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ฒ˜๋ฆฌ ์‹œ ๊ธฐ์กด GPU ๊ตฌ์กฐ์˜ ๋น„ํšจ์œจ์„ฑ 79 5.3 ์ œ์•ˆํ•˜๋Š” GPU ๊ธฐ๋ฐ˜ PIM System 82 5.3.1 Overview 82 5.3.2 Access Pattern์„ ๊ณ ๋ คํ•œ CTA ํ• ๋‹น 83 5.3.3 PIM GPU ๊ตฌ์กฐ 90 5.4 ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 94 5.4.1 ์‹คํ—˜ ํ™˜๊ฒฝ ๊ตฌ์„ฑ 94 5.4.2 In-Depth Analysis 95 5.4.3 ๊ธฐ์กด ์—ฐ๊ตฌ์™€์˜ ์„ฑ๋Šฅ ๋น„๊ต 98 5.4.4 Cache Miss Rate ๋ฐ Memory Traffic 102 5.4.5 ์—๋„ˆ์ง€ ์†Œ๋ชจ๋Ÿ‰ ๋น„๊ต 103 5.4.6 PIM์˜ ๋ฉด์  ๋ฐ ์ „๋ ฅ ์†Œ๋ชจ๋Ÿ‰ ๋ถ„์„ 105 5.5 ๋ณธ ์žฅ์˜ ๊ฒฐ๋ก  107 ์ œ 6 ์žฅ ๊ฒฐ๋ก  108 ์ฐธ๊ณ ๋ฌธํ—Œ 110 Abstract 118๋ฐ•

    Corporate Intellectual Property Strategies on Software Inventions

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2021. 2. ๊น€์—ฐ๋ฐฐ.This study analyzes the influence of intellectual property strategy on the innovation performance of companies related to software inventions. The software inventions are mainly used in the information and communication industry, and may be directly related to the software industry and partially related to the ICT manufacturing industry according to the Korean Standard Industry Classification (KSIC). This study analyzes effective intellectual property strategies for the software industry compared with the ICT manufacturing industry by dividing the cases where patent protection mechanisms and informal protection mechanisms are applied individually and in combination. Additionally, this study determines whether firm size has a moderating effect for each intellectual property strategy. The result shows that the combination of patent protection mechanisms and informal protection mechanisms has a positive effect on innovation performance in both the software industry and the ICT manufacturing industry. Meanwhile, the intellectual property strategy using only patent protection mechanisms has a positive effect on the innovation performance in the ICT manufacturing industry. However, in the software industry, the intellectual property strategy using only patent protection mechanisms has different effects on the innovation performance depending on the size of the company. In conclusion, the intellectual property strategy related to the software inventions is desirable to use both patent protection mechanisms and informal protection mechanisms. When only patent protection mechanisms are used, the type of the industry and the company size should be considered.๋ณธ ์—ฐ๊ตฌ๋Š” ์ง€์‹ ์žฌ์‚ฐ ์ „๋žต์ด ์†Œํ”„ํŠธ์›จ์–ด ๋ฐœ๋ช…์— ๊ด€ํ•œ ๊ธฐ์—…์˜ ํ˜์‹  ์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•œ๋‹ค. ์†Œํ”„ํŠธ์›จ์–ด ๋ฐœ๋ช…์€ ์ •๋ณด ํ†ต์‹  ์‚ฐ์—…์—์„œ ์ฃผ๋กœ ์ด์šฉ๋˜๋ฉฐ, ํ•œ๊ตญํ‘œ์ค€์‚ฐ์—…๋ถ„๋ฅ˜(KSIC)์— ๋”ฐ๋ผ ์†Œํ”„ํŠธ์›จ์–ด ์‚ฐ์—…๊ณผ ์ง์ ‘์ ์œผ๋กœ ๊ด€๋ จ๋  ์ˆ˜ ์žˆ๊ณ , ICT ์ œ์กฐ์—…์—๋„ ๋ถ€๋ถ„์ ์œผ๋กœ ๊ด€๋ จ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํŠนํ—ˆ ๋ณดํ˜ธ ์ˆ˜๋‹จ๊ณผ ๋น„๊ณต์‹์  ๋ณดํ˜ธ ์ˆ˜๋‹จ์ด ๊ฐ๊ฐ ๊ฐœ๋ณ„ ์ ์šฉ๋œ ๊ฒฝ์šฐ์™€ ๊ฒฐํ•ฉ ์ ์šฉ๋œ ๊ฒฝ์šฐ๋ฅผ ๋‚˜๋ˆ„์–ด ์†Œํ”„ํŠธ์›จ์–ด ์‚ฐ์—…๊ณผ, ์ด์— ๋Œ€๋น„๋˜๋Š” ICT ์ œ์กฐ์—…์— ๋Œ€ํ•ด ์œ ํšจํ•œ ์ง€์‹ ์žฌ์‚ฐ ์ „๋žต์„ ๋ถ„์„ํ•œ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธฐ์—… ๊ทœ๋ชจ๊ฐ€ ๊ฐ๊ฐ์˜ ์ง€์‹ ์žฌ์‚ฐ ์ „๋žต์— ๋Œ€ํ•ด ์กฐ์ ˆ ํšจ๊ณผ๊ฐ€ ์žˆ๋Š”์ง€ ํ™•์ธํ•œ๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ํŠนํ—ˆ ๋ณดํ˜ธ ์ˆ˜๋‹จ๊ณผ ๋น„๊ณต์‹์  ๋ณดํ˜ธ ์ˆ˜๋‹จ์„ ํ•จ๊ป˜ ์‚ฌ์šฉํ•œ ๊ฒฝ์šฐ, ์†Œํ”„ํŠธ์›จ์–ด ์‚ฐ์—…๊ณผ ICT ์ œ์กฐ์—… ๋ชจ๋‘์—์„œ ํ˜์‹  ์„ฑ๊ณผ์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ํ•œํŽธ, ํŠนํ—ˆ ๋ณดํ˜ธ ์ˆ˜๋‹จ๋งŒ ์‚ฌ์šฉํ•œ ๊ฒฝ์šฐ, ICT ์ œ์กฐ์—…์—์„œ๋Š” ํ˜์‹  ์„ฑ๊ณผ์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์ณค์œผ๋‚˜, ์†Œํ”„ํŠธ์›จ์–ด ์‚ฐ์—…์—์„œ๋Š” ๊ธฐ์—… ๊ทœ๋ชจ์— ๋”ฐ๋ผ ํ˜์‹  ์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ์ƒ์ดํ–ˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ์†Œํ”„ํŠธ์›จ์–ด ๋ฐœ๋ช…์˜ ์ง€์‹ ์žฌ์‚ฐ ์ „๋žต์€ ํŠนํ—ˆ ๋ณดํ˜ธ ์ˆ˜๋‹จ๊ณผ ๋น„๊ณต์‹์  ๋ณดํ˜ธ ์ˆ˜๋‹จ์„ ํ•จ๊ป˜ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด ๋ฐ”๋žŒ์งํ•˜๋ฉฐ, ํŠนํ—ˆ ๋ณดํ˜ธ ์ˆ˜๋‹จ๋งŒ ์‚ฌ์šฉํ•  ๊ฒฝ์šฐ ์‚ฐ์—… ์œ ํ˜•๊ณผ ๊ธฐ์—… ๊ทœ๋ชจ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ๊ฒฐ์ •ํ•ด์•ผ ํ•œ๋‹ค.1. ์„œ๋ก  1 2. ์—ฐ๊ตฌ ๊ฐ€์„ค 5 2.1 ์†Œํ”„ํŠธ์›จ์–ด ํŠนํ—ˆ์˜ ์œ ํšจ์„ฑ 5 2.2 ํŠนํ—ˆ ๋ณดํ˜ธ ์ˆ˜๋‹จ๊ณผ ๋น„๊ณต์‹์  ๋ณดํ˜ธ ์ˆ˜๋‹จ์˜ ๊ฒฐํ•ฉ ํšจ๊ณผ 9 2.3 ๊ธฐ์—… ๊ทœ๋ชจ์— ๋”ฐ๋ฅธ ์ง€์‹ ์žฌ์‚ฐ ์ „๋žต 12 3. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 13 3.1 ์ž๋ฃŒ 13 3.2 ๋ณ€์ˆ˜ 16 3.3 ๋ถ„์„ ๋ชจํ˜• 21 3.4 ์ถ”์ • ๋ฐฉ๋ฒ• 22 4. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 23 4.1 ์‚ฐ์—…๋ณ„ ์ง€์‹ ์žฌ์‚ฐ ์ „๋žต ํ™œ์šฉ ํ˜„ํ™ฉ 23 4.2 ์ง€์‹ ์žฌ์‚ฐ ์ „๋žต๊ณผ ํ˜์‹  ์„ฑ๊ณผ 25 4.3 ๊ธฐ์—… ๊ทœ๋ชจ์™€ ์ง€์‹ ์žฌ์‚ฐ ์ „๋žต 28 5. ๊ฒฐ๋ก  35 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 38 Abstract 43Maste

    A Study on the private-initiated urban design : Focusing on the exterior space of the buildings

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