80 research outputs found

    ๋“œ๋ก ์„ ํ™œ์šฉํ•œ ์œ„์„ฑ ์ง€ํ‘œ๋ฐ˜์‚ฌ๋„ ์‚ฐ์ถœ๋ฌผ ๊ณต๊ฐ„ ํŒจํ„ด ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ์ƒํƒœ์กฐ๊ฒฝยท์ง€์—ญ์‹œ์Šคํ…œ๊ณตํ•™๋ถ€(์ƒํƒœ์กฐ๊ฒฝํ•™), 2021.8. ์กฐ๋Œ€์†”.High-resolution satellites are assigned to monitor land surface in detail. The reliable surface reflectance (SR) is the fundamental in terrestrial ecosystem modeling so the temporal and spatial validation is essential. Usually based on multiple ground control points (GCPs), field spectroscopy guarantees the temporal continuity. Due to limited sampling, however, it hardly illustrates the spatial pattern. As a map, the pixelwise spatial variability of SR products is not well-documented. In this study, we introduced drone-based hyperspectral image (HSI) as a reference and compared the map with Sentinel 2 and Landsat 8 SR products on a heterogeneous rice paddy landscape. First, HSI was validated by field spectroscopy and swath overlapping, which assured qualitative radiometric accuracy within the viewing geometry. Second, HSI was matched to the satellite SRs. It involves spectral and spatial aggregation, co-registration and nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR) conversion. Then, we 1) quantified the spatial variability of the satellite SRs and the vegetation indices (VIs) including NDVI and NIRv by APU matrix, 2) qualified them pixelwise by theoretical error budget and 3) examined the improvement by BRDF normalization. Sentinel 2 SR exhibits overall good agreement with drone HSI while the two NIRs are biased up to 10%. Despite the bias in NIR, the NDVI shows a good match on vegetated areas and the NIRv only displays the discrepancy on built-in areas. Landsat 8 SR was biased over the VIS bands (-9 ~ -7.6%). BRDF normalization just contributed to a minor improvement. Our results demonstrate the potential of drone HSI to replace in-situ observation and evaluate SR or atmospheric correction algorithms over the flat terrain. Future researches should replicate the results over the complex terrain and canopy structure (i.e. forest).์›๊ฒฉํƒ์‚ฌ์—์„œ ์ง€ํ‘œ ๋ฐ˜์‚ฌ๋„(SR)๋Š” ์ง€ํ‘œ์ •๋ณด๋ฅผ ๋น„ํŒŒ๊ดด์ ์ด๊ณ  ์ฆ‰๊ฐ์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ์ „๋‹ฌํ•ด์ฃผ๋Š” ๋งค๊ฐœ์ฒด ์—ญํ• ์„ ํ•œ๋‹ค. ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” SR์€ ์œก์ƒ ์ƒํƒœ๊ณ„ ๋ชจ๋ธ๋ง์˜ ๊ธฐ๋ณธ์ด๊ณ , ์ด์— ๋”ฐ๋ผ SR์˜ ์‹œ๊ณต๊ฐ„์  ๊ฒ€์ฆ์ด ์š”๊ตฌ๋œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ SR์€ ์—ฌ๋Ÿฌ ์ง€์ƒ ๊ธฐ์ค€์ (GCP)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ํ˜„์žฅ ๋ถ„๊ด‘๋ฒ•์„ ํ†ตํ•ด์„œ ์‹œ๊ฐ„์  ์—ฐ์†์„ฑ์ด ๋ณด์žฅ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์žฅ ๋ถ„๊ด‘๋ฒ•์€ ์ œํ•œ์ ์ธ ์ƒ˜ํ”Œ๋ง์œผ๋กœ ๊ณต๊ฐ„ ํŒจํ„ด์„ ๊ฑฐ์˜ ๋ณด์—ฌ์ฃผ์ง€ ์•Š์•„, ์œ„์„ฑ SR์˜ ํ”ฝ์…€ ๋ณ„ ๊ณต๊ฐ„ ๋ณ€๋™์„ฑ์€ ์ž˜ ๋ถ„์„๋˜์ง€ ์•Š์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋“œ๋ก  ๊ธฐ๋ฐ˜์˜ ์ดˆ๋ถ„๊ด‘ ์˜์ƒ(HSI)์„ ์ฐธ๊ณ ์ž๋ฃŒ๋กœ ๋„์ž…ํ•˜์—ฌ, ์ด๋ฅผ ์ด์งˆ์ ์ธ ๋…ผ ๊ฒฝ๊ด€์—์„œ Sentinel 2 ๋ฐ Landsat 8 SR๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ์šฐ์„ , ๋“œ๋ก  HSI๋Š” ํ˜„์žฅ ๋ถ„๊ด‘๋ฒ• ๋ฐ ๊ฒฝ๋กœ ์ค‘์ฒฉ์„ ํ†ตํ•ด์„œ ๊ด€์ธก๊ฐ๋„ ๋ฒ”์œ„ ๋‚ด์—์„œ ์ •์„ฑ์ ์ธ ๋ฐฉ์‚ฌ ์ธก์ •์„ ๋ณด์žฅํ•œ๋‹ค๊ณ  ๊ฒ€์ฆ๋˜์—ˆ๋‹ค. ์ดํ›„, ๋“œ๋ก  HSI๋Š” ์œ„์„ฑ SR์˜ ๋ถ„๊ด‘๋ฐ˜์‘ํŠน์„ฑ, ๊ณต๊ฐ„ํ•ด์ƒ๋„ ๋ฐ ์ขŒํ‘œ๊ณ„๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๋งž์ถฐ์กŒ๊ณ , ๊ด€์ธก ๊ธฐํ•˜๋ฅผ ํ†ต์ผํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋“œ๋ก  HIS์™€ ์œ„์„ฑ SR์€ ๊ฐ๊ฐ ์–‘๋ฐฉํ–ฅ๋ฐ˜์‚ฌ์œจ๋ถ„ํฌํ•จ์ˆ˜ (BRDF) ์ •๊ทœํ™” ๋ฐ˜์‚ฌ๋„ (NBAR)๋กœ ๋ณ€ํ™˜๋˜์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, 1) APU ํ–‰๋ ฌ์œผ๋กœ ์œ„์„ฑ SR๊ณผ NDVI, NIRv๋ฅผ ํฌํ•จํ•˜๋Š” ์‹์ƒ์ง€์ˆ˜(VI)์˜ ๊ณต๊ฐ„๋ณ€๋™์„ฑ์„ ์ •๋Ÿ‰ํ™” ํ–ˆ๊ณ , 2) ๋Œ€๊ธฐ๋ณด์ •์˜ ์ด๋ก ์  ์˜ค์ฐจ๋ฅผ ๊ธฐ์ค€์œผ๋กœ SR๊ณผ VI๋ฅผ ํ”ฝ์…€๋ณ„๋กœ ํ‰๊ฐ€ํ–ˆ๊ณ , 3) BRDF ์ •๊ทœํ™”๋ฅผ ํ†ตํ•œ ๊ฐœ์„  ์‚ฌํ•ญ์„ ๊ฒ€ํ† ํ–ˆ๋‹ค. Sentinel 2 SR์€ ๋“œ๋ก  HSI์™€ ์ „๋ฐ˜์ ์œผ๋กœ ์ข‹์€ ์ผ์น˜๋ฅผ ๋ณด์ด๋‚˜, ๋‘ NIR ์ฑ„๋„์€ ์ตœ๋Œ€ 10% ํŽธํ–ฅ๋˜์—ˆ๋‹ค. NIR์˜ ํŽธํ–ฅ์€ ์‹์ƒ์ง€์ˆ˜์—์„œ ํ† ์ง€ ํ”ผ๋ณต์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. NDVI๋Š” ์‹์ƒ์—์„œ๋Š” ๋‚ฎ์€ ํŽธํ–ฅ์„ ๋ณด์—ฌ์คฌ๊ณ , NIRv๋Š” ๋„์‹œ์‹œ์„ค๋ฌผ ์˜์—ญ์—์„œ๋งŒ ๋†’์€ ํŽธํ–ฅ์„ ๋ณด์˜€๋‹ค. Landsat 8 SR์€ VIS ์ฑ„๋„์— ๋Œ€ํ•ด ํŽธํ–ฅ๋˜์—ˆ๋‹ค (-9 ~ -7.6%). BRDF ์ •๊ทœํ™”๋Š” ์œ„์„ฑ SR์˜ ํ’ˆ์งˆ์„ ๊ฐœ์„ ํ–ˆ์ง€๋งŒ, ๊ทธ ์˜ํ–ฅ์€ ๋ถ€์ˆ˜์ ์ด์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ‰ํƒ„ํ•œ ์ง€ํ˜•์—์„œ ๋“œ๋ก  HSI๊ฐ€ ํ˜„์žฅ ๊ด€์ธก์„ ๋Œ€์ฒดํ•  ์ˆ˜ ์žˆ๊ณ , ๋”ฐ๋ผ์„œ ์œ„์„ฑ SR์ด๋‚˜ ๋Œ€๊ธฐ๋ณด์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ‰๊ฐ€ํ•˜๋Š”๋ฐ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์˜€๋‹ค. ํ–ฅํ›„ ์—ฐ๊ตฌ์—์„œ๋Š” ์‚ฐ๋ฆผ์œผ๋กœ ๋Œ€์ƒ์ง€๋ฅผ ํ™•๋Œ€ํ•˜์—ฌ, ์ง€ํ˜•๊ณผ ์บ๋…ธํ”ผ ๊ตฌ์กฐ๊ฐ€ ๋“œ๋ก  HSI ๋ฐ ์œ„์„ฑ SR์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค.Chapter 1. Introduction 1 1.1 Background 1 Chapter 2. Method 3 2.1 Study Site 3 2.2 Drone campaign 4 2.3 Data processing 4 2.3.1 Sensor calibration 5 2.3.2 Bidirectional reflectance factor (BRF) calculation 7 2.3.3 BRDF correction 7 2.3.4 Orthorectification 8 2.3.5 Spatial Aggregation 9 2.3.6 Co-registration 10 2.4 Satellite dataset 10 2.4.2 Landsat 8 12 Chapter 3. Result and Discussion 12 3.1 Drone BRF map quality assessment 12 3.1.1 Radiometric accuracy 12 3.1.2 BRDF effect 15 3.2 Spatial variability in satellite surface reflectance product 16 3.2.1 Sentinel 2B (10m) 17 3.2.2 Sentinel 2B (20m) 22 3.2.3 Landsat 8 26 Chapter 4. Conclusion 28 Supplemental Materials 30 Bibliography 34 Abstract in Korean 43์„

    ๊ด€์ ˆ์—ฐ๊ณจ์˜ T1ฯ ์ด์™„์‹œ๊ฐ„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋‹ค์–‘ํ•œ ์š”์†Œ๋“ค์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ, 2014. 8. ํ™์„ฑํ™˜.Introduction: Degenerative arthritis is a very common and important disease that is characterized by decreasing proteoglycan and collagen fibers in the articular cartilage. Early detection of degeneration and prediction of the development of advanced osteoarthritis are important for planning the treatment of osteoarthritis patients. The purpose of this study is to evaluate various factors that contribute to variations in the T1 ฯ value of articular cartilage. Methods: This study was exempted from institutional and animal review board reviews, and informed consent was not required. Twelve porcine patellae were assigned into the following three groups: trypsin-treated (proteoglycan-degraded), collagenase-treated (collagen-degraded), and control groups. T1 ฯ imaging was obtained using 3 Tesla magnetic resonance imaging(MRI) scanners with a single loop coil at the three different orientations with respect to the main magnetic field. The T1 ฯ relaxation map of articular cartilage was constructed with a homemade mapping program using Mat lab R2013. Significant differences were explored using the ANCOVA test to evaluate the effects of the enzyme and orientation on the T1 ฯ relaxation time. Results: There was a statistically significant difference in the T1 ฯ values among the three different enzyme groups (P<0.001). However, there was no significant difference in the T1 ฯ values among the three different orientations (P=0.220). Conclusions: Degradation of the proteoglycans or collagen fibers in the articular cartilage caused an increase in the T1 ฯ value of articular cartilage, but the orientation dependence of the T1 ฯ value was not proven.Abstract i Contents iii List of tables and figures iv List of abbreviations v Introduction 1 Material and Methods 4 Results 11 Discussion 18 References 22 Abstract in Korean 25Maste

    Hiring experienced and its outcome in terms of innovation decision process: Evidence from Hyundai Heavy Industries

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์˜ํ•™๊ณผ, 2017. 2. ๋ฐ•๋‚จ๊ทœ.Previous research on hiring have focused on knowledge transfer, patent, business and etc., and there has been no attention to the subject of hiring outcome in the context of innovation decision process. In this paper, I look for factors which can influence on the hiring experienced and factors impacting on hiring outcomes in the perspective of innovation decision process by examining the case of Hyundai Heavy Industries. The case analysis shows that when exploring new business and restructuring business, a company hires experienced. The factors that affect hiring outcomes in the perspective of innovation decision process are found to be the hierarchical position of employee.1. Introduction 1 2. Theory 4 3. Case Research(Hyundai Heavy Industries) 11 4. Results 28 Reference 31 Abstract in Korean 35Maste

    ๋™์ถ• ์ „๊ธฐ๋ฐฉ์‚ฌ๋ฅผ ์ด์šฉํ•œ Core/Sheath ๊ตฌ์กฐ์˜ ์ž์„ฑ ๋‚˜๋…ธ์„ฌ์œ  ์ œ์กฐ ๋ฐ ํ˜•์ƒ ๊ธฐ์–ต ํŠน์„ฑ ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2013. 2. ๊ฐ•ํƒœ์ง„.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋™์ถ• ์ „๊ธฐ๋ฐฉ์‚ฌ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด, ์ž๊ธฐ์žฅ์— ๋ฐ˜์‘ํ•˜์—ฌ ๋ฌผ์„ฑ์ด ํ–ฅ์ƒ๋˜๊ณ , ์—ด์— ๋ฐ˜์‘ํ•˜์—ฌ ํ˜•์ƒ๊ธฐ์–ต์„ฑ์งˆ์„ ๋ณด์ด๋Š” ์ด์ค‘ ๊ตฌ์กฐ(core/sheath)์˜ ์›น์„ ์ œ์ž‘ํ•˜์˜€๋‹ค. Core๋กœ๋Š” N,N-dimethylformamide(DMF)์™€ tetrahydrofuran (THF)๋ฅผ ํ˜ผํ•ฉํ•œ ์šฉ๋งค์— ํด๋ฆฌ์šฐ๋ ˆํƒ„์„ ๋…น์ธ ์šฉ์•ก์—, ์˜ค์ผ ๊ธฐ๋ฐ˜์˜ magnetorheological fluid(MRF)์ธ EFH-1์„ ํ˜ผํ•ฉํ•ด์„œ ์‚ฌ์šฉํ•˜์˜€๋‹ค. THF๋Š” DMF์— ๋น„ํ•ด์„œ ํด๋ฆฌ์šฐ๋ ˆํƒ„์„ ๋…น์ด๋Š” ์„ฑ๋Šฅ์ด ๋›ฐ์–ด๋‚˜์ง€๋งŒ ๋ฐฉ์‚ฌ๊ณผ์ •์—์„œ ์ฆ๋ฐœ์ด ์›ํ™œํ•˜๊ฒŒ ์ผ์–ด๋‚˜์ง€ ์•Š๋Š”๋‹ค. ์ด๋ฅผ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด DMF์™€ THF๋ฅผ ํ˜ผํ•ฉํ•˜์—ฌ ์‚ฌ์šฉํ•˜์˜€๋‹ค. Sheath ๋ฌผ์งˆ๋กœ๋Š” DMF์™€ THF๋ฅผ ํ˜ผํ•ฉํ•œ ์šฉ๋งค์— ํด๋ฆฌ์šฐ๋ ˆํƒ„๋งŒ์„ ๋…น์—ฌ ๋งŒ๋“  ์šฉ์•ก์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. Core ๋ฌผ์งˆ์˜ ๊ฒฝ์šฐ, 15 wt% ํด๋ฆฌ์šฐ๋ ˆํƒ„์šฉ์•ก๊ณผ MRF์˜ ๋น„์œจ์ด 10 wt%, 20 wt%์ธ sample์ด ๋ฐฉ์‚ฌ์— ๊ฐ€์žฅ ์ ํ•ฉํ•˜์˜€๊ณ , sheath ๋ฌผ์งˆ์€ DMF์™€ THF์˜ ๋น„์œจ์ด 5:5์—์„œ 6:4 ์‚ฌ์ด์ธ ๊ฒฝ์šฐ๊ฐ€ ๊ฐ€์žฅ ๋ฐฉ์‚ฌ์— ์ ํ•ฉํ•˜์˜€๋‹ค. Sheath ๋ฌผ์งˆ์ธ ํด๋ฆฌ์šฐ๋ ˆํƒ„์šฉ์•ก์€ ๋†๋„๊ฐ€ 14 wt%์—์„œ 22 wt%๊นŒ์ง€ ๋ฐฉ์‚ฌ ๊ฐ€๋Šฅํ–ˆ๋Š”๋ฐ, ๊ทธ ์ค‘์—์„œ๋„ 17 wt%์ผ ๊ฒฝ์šฐ์— ๊ฐ€์žฅ ๋ฐฉ์‚ฌ์— ์ ํ•ฉํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ๋งŒ๋“ค์–ด์ง„ ์ด์ค‘ ๊ตฌ์กฐ์˜ web์€ scanning electron microscope(SEM)๊ณผ transmission electron microscope(TEM)์„ ํ†ตํ•ด ์•ˆ์ •์ ์ธ core/sheath ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , dynamic mechanical analyzer(DMA)๋ฅผ ํ†ตํ•ด ์—ด์— ์˜ํ•œ ํ˜•์ƒ๊ธฐ์–ต ์„ฑ์งˆ์„, ๊ทธ๋ฆฌ๊ณ  ์ „์ž์„์„ ์ด์šฉํ•œ universal testing machine(UTM)๊ณผ atomic force microscope(AFM)์„ ์ด์šฉํ•ด ์ž๊ธฐ์žฅ์— ์˜ํ•œ ๋ฌผ์„ฑ์˜ ํ–ฅ์ƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.CONTENTS ABSTRACT โ…ฐ CONTENTS โ…ฒ LIST OF FIGURES โ…ด LIST OF TABLES โ…ท 1. Introduction 1 2. Literature Review 3 2.1. Nanoparticles 3 2.2. Electrospinning 6 2.3. Magnetorheological fluid 8 2.4. Shape memory polyurethane 10 2.5. Nanoindentation 12 3. Experimental 13 3.1. Materials 13 3.2. Preparation of electrospinning solution 15 3.2.1. Blended type I(BF) 15 3.2.2. Blended type II(BM) 16 3.3. Electrospinning condition 18 3.4. Characterization 20 3.4.1. Morphology and mechanical properties 20 3.4.2. Rheological properties 21 3.4.3. Nanoindentation 21 3.4.4. Shape recovery properties 23 4.1. Rheological properties 24 4.1.1. Blend type I 24 4.1.2. Blend type II 27 4.2. Morphology 30 4.2.1. Blend type I 30 4.2.2. Blend type II 33 4.2.3. Core-sheath type 36 4.3. Shape recovery properties 38 4.4. Mechanical properties 40 5. Conclusions 43 6. References 45Maste

    ์ฐจ๋Ÿ‰ ํ›„๋ฐฉํ˜•์ƒ ์ตœ์ ์„ค๊ณ„์™€ ๊ณต๋ ฅ์ €๊ฐ์žฅ์น˜๋ฅผ ํ†ตํ•œ ์ดˆ์ €๊ณต๋ ฅ์ฐจ๋Ÿ‰ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2015. 2. ๊น€๊ทœํ™.In this study, design of ultra-low aerodynamic drag vehicle was performed through optimization of rear shape and aerodynamic drag reduction devices. Sedan type vehicle was selected as a base model, which is being manufactured in these days. To reduce the aerodynamic drag, its rear shape was redesigned to square back type which is widely known as having low aerodynamic drag. Square back shape was designed by considering only the aerodynamic perspective. As a result, the aerodynamic drag was reduced. In order to minimize the aerodynamic drag of redesigned square back vehicle, optimization was performed. Roof angle, side angle and diffuser angle, which are the component of square back shape, were selected as the design variables. As a result of optimization, aerodynamic drag was decreased by 10.85% compared to initial square back shape. The relation between design variables and aerodynamic drag, together with the aerodynamic characteristics were obtained. Aerodynamic drag of optimal design was reduced by 29.25% compared to sedan type vehicle. To additionally reduce the aerodynamic drag, aerodynamic drag reduction devices(side air dam, under deflector, wheel arch cover) were installed to optimal design of square back type vehicle. As a result of installing aerodynamic drag reduction devices, aerodynamic drag was improved by 33.15% compared to sedan type vehicle.Abstract โ…  Table of Contents โ…ข List of Figures โ…ฃ List of Tables โ…ฅ 1. Indroduction 1 1.1 Research Background 1 1.2 Research Objective 9 2. Physical Modeling and Numerical Methods 10 2.1 Physical Modeling 10 2.2 Numerical Methods 13 3. Design of Square Back Type Vehicle 22 3.1 Base Model 22 3.2 Redesigning Rear Shape of Sedan Type to Square Back Type 24 3.3 Flow Analysis 30 3.4 Optimization of Square Back Rear Shape 36 4. Installation of Aerodynamic Drag Reduction Devices 51 4.1 Modeling 51 4.2 Flow Analysis Result 54 5. Conclusion 56 References 58 ๊ตญ๋ฌธ ์ดˆ๋ก 65Maste

    System Calibration between Non-metric Optical Camera and Range Camera

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2013. 8. ๊น€์šฉ์ผ.์ตœ๊ทผ ์‹ค๋‚ด 3์ฐจ์› ๋ชจ๋ธ๋ง์€ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ์œผ๋ฉฐ, ์ด์— ๋”ฐ๋ผ ๊ด€๋ จ ์—ฐ๊ตฌ์— ๋Œ€ํ•œ ํ•„์š”์„ฑ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์ด์ข… ์„ผ์„œ ์˜์ƒ๊ฐ„์˜ ์œตํ•ฉ์„ ํ†ตํ•œ ์‹ค๋‚ด 3์ฐจ์› ๋ชจ๋ธ๋ง ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์ด ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ์ด๋•Œ ์ด์ข… ์„ผ์„œ ์˜์ƒ๊ฐ„์˜ ์œตํ•ฉ์— ์žˆ์–ด, ์ •๋ฐ€ํ•œ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜์€ ๋งค์šฐ ํ•„์ˆ˜์ ์ธ ์š”์†Œ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋น„์ธก์ •์šฉ ๊ด‘ํ•™ ์นด๋ฉ”๋ผ์™€ ๋ ˆ์ธ์ง€ ์นด๋ฉ”๋ผ๋กœ ๊ตฌ์„ฑ๋œ ์นด๋ฉ”๋ผ ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ธฐ์กด์˜ ๊ด‘ํ•™ ์นด๋ฉ”๋ผ์™€ ๋ ˆ์ธ์ง€ ์นด๋ฉ”๋ผ๊ฐ„์˜ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ํŠน์ • ๊ฐ์ฒด๋งŒ์„ 3์ฐจ์›์œผ๋กœ ๊ตฌ์„ฑํ•˜๋Š” ๊ฒƒ์— ์—ฐ๊ตฌ๊ฐ€ ์ง‘์ค‘๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ๋ฐฉ๋ฒ• ๊ฐ„์˜ ๋น„๊ต ํ‰๊ฐ€๊ฐ€ ๋ถ€์กฑํ•˜์˜€์œผ๋ฉฐ, ๊ฒ€์ • ๋Œ€์ƒ์ง€ ์„ค๊ณ„๊ฐ€ ๋ฏธํกํ•˜์˜€๋‹ค๋Š” ํ•œ๊ณ„๊ฐ€ ์กด์žฌํ•œ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„์ธก์ •์šฉ ๊ด‘ํ•™ ๋ฐ ๋ ˆ์ธ์ง€ ์นด๋ฉ”๋ผ์˜ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๊ธฐ์กด๊ณผ ๋‹ค๋ฅธ ๊ฒ€์ • ๋Œ€์ƒ์ง€๋ฅผ ์ƒˆ๋กญ๊ฒŒ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋‹จ์‚ฌ์ง„ ํ‘œ์ • ๋ฐ ๋ธ”๋ก ์กฐ์ •์˜ ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์„ ํ†ตํ•˜์—ฌ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ƒ๋Œ€ํ‘œ์ •์š”์†Œ๋ฅผ ๋„์ถœํ•˜๊ณ , ๊ทธ ๊ฒฐ๊ณผ์— ๋Œ€ํ•˜์—ฌ ๋น„๊ต ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ์ƒ๋Œ€ํ‘œ์ •์š”์†Œ๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„ ๋ฐ ํ‘œ์ค€ํŽธ์ฐจ๋ฅผ ๋‚ฎ์ถ”๋Š” ๊ฒƒ์ด ์ •ํ™•๋„์— ํฐ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ๋ธ”๋ก ์กฐ์ •์„ ํ†ตํ•˜์—ฌ ๋ณด๋‹ค ์‹ ๋ขฐ๋„ ๋†’์€ ๊ฒฐ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋ณด๋‹ค ํšจ์œจ์ ์ธ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ์ˆ˜ํ–‰ ๋ฐฉ๋ฒ• ๋ฐ ๊ฒ€์ • ๋Œ€์ƒ์ง€ ์„ค๊ณ„์™€ ์˜์ƒ ์ดฌ์˜ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค.Recently, indoor 3D modeling has attracted attention in various fields, and the needs of its related research needs is increasing. Especially, indoor 3D modeling studies by using image fusion technique with different types of sensors are becoming a necessity. For a image fusion between two kinds of sensors, precise system calibration is essential. Therefore, system calibration was performed on the camera system consisting of non-metric optical camera and range camera in this study. Previous studies about system calibration between non-metric optical camera and range camera were mainly focused on constructing certain object in 3d model. And previous test-bed design was not sufficient for system calibration. In this study, test-bed for calibration was designed by considering the characteristics of non-metric optical camera and range camera. Also, relative orientation parameters were derived by performing a system calibration using single photo resection and block adjustment. As a result, it was confirmed that it is important to reduce correlation between relative orientation parameters and standard deviation to obtain result with high accuracy. Also, it was confirmed that through block-adjustment method to get more reliable results. Finally, a efficient way to perform system calibration, test-bed design and image shooting methods were proposed.1. ์„œ๋ก  1 1.1. ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ 1 1.2. ์—ฐ๊ตฌ๋™ํ–ฅ 3 1.3. ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๋ฐ ๋ฒ”์œ„ 5 2. ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ์ˆ˜ํ•™์  ๋ชจ๋ธ 7 2.1. ๊ด‘ํ•™ ์นด๋ฉ”๋ผ ์˜์ƒ์˜ ์ˆ˜ํ•™์  ๋ชจ๋ธ์‹ 7 2.2. ๋ ˆ์ธ์ง€์นด๋ฉ”๋ผ ์˜์ƒ์˜ ์ˆ˜ํ•™์  ๋ชจ๋ธ์‹ 8 2.2.1. ๊ฑฐ๋ฆฌ ๊ด€์ธก๊ฐ’ 8 2.2.2. ์ •์˜ค์ฐจ(systematic error) 8 2.3. ๋‹จ์‚ฌ์ง„ํ‘œ์ •์˜ ์ˆ˜ํ•™์  ๋ชจ๋ธ์‹ 10 2.4. ๋ธ”๋ก์กฐ์ •์—์„œ์˜ ์ˆ˜ํ•™์  ๋ชจ๋ธ์‹ 13 3. ์นด๋ฉ”๋ผ์˜ ํŠน์ง• ๋ฐ ๊ฒ€์ • ๋Œ€์ƒ์ง€ ๊ตฌ์„ฑ 14 3.1. ๊ด‘ํ•™ ๋ฐ ๋ ˆ์ธ์ง€ ์นด๋ฉ”๋ผ์˜ ์ œ์› ๋ฐ ํŠน์ง• 14 3.1.1. ๊ด‘ํ•™์นด๋ฉ”๋ผ 14 3.1.2. ๋ ˆ์ธ์ง€ ์นด๋ฉ”๋ผ 16 3.2. ์นด๋ฉ”๋ผ ๋ฐ ๊ฒ€์ • ๋Œ€์ƒ์ง€ ์ขŒํ‘œ๊ณ„ ์„ค์ • 19 3.3. ๊ฒ€์ • ๋Œ€์ƒ์ง€ ๊ตฌ์„ฑ 20 3.3.1. ๊ด‘ํ•™์นด๋ฉ”๋ผ์šฉ ๊ฒ€์ • ๋Œ€์ƒ์ง€ ๊ตฌ์„ฑ 20 3.3.2. ๋ ˆ์ธ์ง€์นด๋ฉ”๋ผ์šฉ ๊ฒ€์ • ๋Œ€์ƒ์ง€ ๊ตฌ์„ฑ 21 4. ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 25 4.1. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ง„ํ–‰ ์„ค๊ณ„ ๋ฐ ์ˆœ์„œ 25 4.2. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒ€์ • ๋Œ€์ƒ์ง€ ๋ฐ ํ‘œ์ •์š”์†Œ ๊ฒฐ์ • 27 4.2.1. ๊ฒ€์ • ๋Œ€์ƒ์ง€ ๋ฐ ์ง€์ƒ ๊ธฐ์ค€์  ์„ค๊ณ„ 27 4.2.2. ๋‚ด๋ถ€ํ‘œ์ •์š”์†Œ 29 4.2.3. ์™ธ๋ถ€ํ‘œ์ •์š”์†Œ ๋ฐ ์ƒ๋Œ€ํ‘œ์ •์š”์†Œ์˜ ๊ฒฐ์ • 31 4.3. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์˜์ƒ ์ œ์ž‘ 36 4.3.1. ๊ด‘ํ•™ ์นด๋ฉ”๋ผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์˜์ƒ ์ œ์ž‘ 36 4.3.2. ๋ ˆ์ธ์ง€ ์นด๋ฉ”๋ผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์˜์ƒ ์ œ์ž‘ 39 4.4. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ์ˆ˜ํ–‰ 43 4.4.1. ๋‹จ์‚ฌ์ง„ ํ‘œ์ • ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 43 4.4.2. ๋ธ”๋ก์กฐ์ • ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 45 5. ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ 46 5.1. ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ์ง„ํ–‰ ์„ค๊ณ„ ๋ฐ ์ˆœ์„œ 46 5.2. ๊ฒ€์ • ๋Œ€์ƒ์ง€ ๊ตฌํ˜„ 47 5.2.1. ๊ฒ€์ • ๋Œ€์ƒ์ง€ ํ”„๋ ˆ์ž„ 47 5.2.2. ๊ด‘ํ•™ ์นด๋ฉ”๋ผ์šฉ ์ง€์ƒ ๊ธฐ์ค€์  48 5.2.3. ๋ ˆ์ธ์ง€ ์นด๋ฉ”๋ผ์šฉ ์ง€์ƒ ๊ธฐ์ค€์  50 5.2.4. ์ง€์ƒ ๊ธฐ์ค€์  ์ขŒํ‘œ ์ธก์ • 55 5.3. ์‹ค์˜์ƒ ์ดฌ์˜ 56 5.3.1. ์นด๋ฉ”๋ผ๊ฐ„์˜ ์œ„์น˜ ๊ด€๊ณ„ 56 5.3.2. ์‹ค์˜์ƒ ์ดฌ์˜ 57 5.4. ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ์ˆ˜ํ–‰ 60 5.4.1. ๋‚ด๋ถ€ํ‘œ์ •์š”์†Œ ๋„์ถœ 60 5.4.2. ๋‹จ์‚ฌ์ง„ ํ‘œ์ • 61 5.4.3. ๋ธ”๋ก์กฐ์ • 61 6. ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ๋ฐ ํ‰๊ฐ€ 62 6.1. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ 62 6.1.1. ๋‹จ์‚ฌ์ง„ ํ‘œ์ •์„ ํ†ตํ•œ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 62 6.1.2. ๋ธ”๋ก ์กฐ์ •์„ ํ†ตํ•œ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ 75 6.1.3. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋‹จ์‚ฌ์ง„ ํ‘œ์ •๊ณผ ๋ธ”๋ก ์กฐ์ • ๊ฒฐ๊ณผ ๋น„๊ต 81 6.2. ์‹ค์ œ ์˜์ƒ์„ ์ด์šฉํ•œ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ 83 6.2.1. ๋‹จ์‚ฌ์ง„ ํ‘œ์ •์„ ํ†ตํ•œ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ 83 6.2.2. ๋ธ”๋ก ์กฐ์ •์„ ํ†ตํ•œ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ 93 6.2.3. ๋‹จ์‚ฌ์ง„ ํ‘œ์ • ๋ฐ ๋ธ”๋ก ์กฐ์ • ๊ฒฐ๊ณผ ๋น„๊ต 99 6.3. ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹ค์ œ ์‹œ์Šคํ…œ ์บ˜๋ฆฌ๋ธŒ๋ ˆ์ด์…˜ ๋น„๊ต ํ‰๊ฐ€ 101 7. ๊ฒฐ๋ก  103 ์ฐธ๊ณ ๋ฌธํ—Œ 107 ๋ถ€ ๋ก 118 A.1. ์ตœ์†Œ์ œ๊ณฑ๋ฒ• 118 A.2. ์ถ•์ฐจ๊ทผ์‚ฌ๋ฒ• 120 A.3. ๊ณต์„ ์กฐ๊ฑด์‹(collinearity condition) 121 A.4. ๊ด‘ํ•™์นด๋ฉ”๋ผ ๋‹จ์‚ฌ์ง„ ํ‘œ์ • ๋ชจ๋ธ์‹ ๋ฐ ํ–‰๋ ฌ๊ตฌ์„ฑ 125 A.5. ๋ ˆ์ธ์ง€์นด๋ฉ”๋ผ ๋‹จ์‚ฌ์ง„ ํ‘œ์ • ๋ชจ๋ธ์‹ 131 A.6. ๋ธ”๋ก์กฐ์ • ์ˆ˜ํ•™์  ๋ชจ๋ธ 135Maste

    ๋ณดํŽธ์  ์œ ์ „ ๋ณ€์ด๋ฅผ ์ด์šฉํ•œ ์œ ์ „์œ„ํ—˜์ ์ˆ˜์˜ ์‹ฌ๋ฐฉ์„ธ๋™ ์ „๊ทน๋„์ž์ ˆ์ œ์ˆ  ํ›„ ์žฌ๋ฐœ ์˜ˆ์ธก๋ ฅ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ, 2017. 2. ์˜ค์„ธ์ผ.Background: Genetic predisposition plays a substantial role in the development and progression of atrial fibrillation (AF). However, the association of AF susceptibility loci with recurrence after catheter ablation has been reported with controversial results. We sought to find out the impact of cumulative genetic risk score (GRS) on response to AF catheter ablation. Methods: We determined the association between the 20 AF-susceptible single nucleotide polymorphisms (SNPs) and AF recurrence after catheter ablation in 746 patients (74% malesage, 59ยฑ11 years56% paroxysmal AF). A GRS was calculated by summing the unweighted numbers of risk alleles of SNPs, which showed at least borderline significant association with AF recurrence. The primary outcome was AF recurrence after a 3-month blanking period. A Cox proportional hazard model was used to identify the association between the GRS and risk of AF recurrence after catheter ablation. Results: During median 23 months of follow-up, 168 (23%) patients showed clinical recurrence. The GRS was calculated using 5 SNPs (rs1448818, rs2200733, rs6843082, rs6838973 at chromosome 4q25 [PITX2] and rs2106261 at chromosome 16q22 [ZFHX3]), which showed modest associations with AF recurrence. The GRS was significantly associated with AF recurrence (hazard ratio [HR] per each score, 1.1495% confidence interval [CI] 1.04-1.24). Patients with high risks (GRS 6-10) showed HR of 1.50 (95% CI 1.06-2.11), compared to patients with low risk (GRS 0-5). Conclusion: Our novel GRS using 5 AF-susceptible SNPs strongly associated with AF recurrence after catheter ablation, with patients with a high GRS being at particularly high risk.Introduction 1 Methods 3 Study population 3 Mapping and catheter ablation procedure 4 Single nucleotide polymorphism (SNP) selection and genotyping 4 Genetic risk score construction 5 Statistical analysis 5 Results 7 Study population and AF recurrence 7 Target SNP identification for GRS modeling 7 Association between the GRS and AF recurrence 8 Discussion 10 Conclusion 13 References 14 ๊ตญ๋ฌธ์ดˆ๋ก 30Maste

    ๋†์šฉ ํŠธ๋ž™ํ„ฐ์˜ ์•ˆ์ „์บก์— ๋Œ€ํ•œ ์ •ํ•˜์ค‘ ์‹œํ—˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋†๊ณตํ•™๊ณผ ๋†์—…๊ธฐ๊ณ„์ „๊ณต,2001.Maste

    A low optical loss liquid crystal lens array fabricated by soft imprinting lithography technique

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