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    κ·Όμ ‘ ν‘œλ©΄ 원격 μ„Όμ‹± μ‹œμŠ€ν…œλ“€μ„ μ΄μš©ν•œ 지속적 식물 κ³„μ ˆ 및 νƒœμ–‘ μœ λ„ μ—½λ‘μ†Œ ν˜•κ΄‘λ¬Όμ§ˆ κ΄€μΈ‘

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : ν™˜κ²½λŒ€ν•™μ› ν˜‘λ™κ³Όμ • μ‘°κ²½ν•™, 2022.2. λ₯˜μ˜λ ¬.Monitoring phenology, physiological and structural changes in vegetation is essential to understand feedbacks of vegetation between terrestrial ecosystems and the atmosphere by influencing the albedo, carbon flux, water flux and energy. To this end, normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) from satellite remote sensing have been widely used. However, there are still limitations in satellite remote sensing as 1) satellite imagery could not capture fine-scale spatial resolution of SIF signals, 2) satellite products are strongly influenced by condition of the atmosphere (e.g. clouds), thus it is challenging to know physiological and structural changes in vegetation on cloudy days and 3) satellite imagery captured a mixed signal from over- and understory, thus it is difficult to study the difference between overstory and understory phenology separately. Therefore, in order to more accurately understand the signals observed from the satellite, further studies using near-surface remote sensing system to collect ground-based observed data are needed. The main purpose of this dissertation is continuous observation of vegetation phenology and SIF using near-surface remote sensing system. To achieve the main goal, I set three chapters as 1) developing low-cost filter-based near-surface remote sensing system to monitor SIF continuously, 2) monitoring SIF in a temperate evergreen needleleaf forest continuously, and 3) understanding the relationships between phenology from in-situ multi-layer canopies and satellite products. In Chapter 2, I developed the filter-based smart surface sensing system (4S-SIF) to overcome the technical challenges of monitoring SIF in the field as well as to decrease sensor cost for more comprehensive spatial sampling. I verified the satisfactory spectral performance of the bandpass filters and confirmed that digital numbers (DN) from 4S-SIF exhibited linear relationships with the DN from the hyperspectral spectroradiometer in each band (R2 > 0.99). In addition, we confirmed that 4S-SIF shows relatively low variation of dark current value at various temperatures. Furthermore, the SIF signal from 4S-SIF represents a strong linear relationship with QEpro-SIF either changing the physiological mechanisms of the plant using DCMU (3-(3, 4-dichlorophenyl)-1, 1-dimethyurea) treatment. I believe that 4S-SIF will be a useful tool for collecting in-situ data across multiple spatial and temporal scales. Satellite-based SIF provides us with new opportunities to understand the physiological and structural dynamics of vegetation from canopy to global scales. However, the relationships between SIF and gross primary productivity (GPP) are not fully understood, which is mainly due to the challenges of decoupling structural and physiological factors that control the relationships. In Chapter 3, I reported the results of continuous observations of canopy-level SIF, GPP, absorbed photosynthetically active radiation (APAR), and chlorophyll: carotenoid index (CCI) in a temperate evergreen needleleaf forest. To understand the mechanisms underlying the relationship between GPP and SIF, I investigated the relationships of light use efficiency (LUE_p), chlorophyll fluorescence yield (Ξ¦_F), and the fraction of emitted SIF photons escaping from the canopy (f_esc) separately. I found a strongly non-linear relationship between GPP and SIF at diurnal and seasonal time scales (R2 = 0.91 with a hyperbolic regression function, daily). GPP saturated with APAR, while SIF did not. In addition, there were differential responses of LUE_p and Ξ¦_F to air temperature. While LUE_p reached saturation at high air temperatures, Ξ¦_F did not saturate. I also found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level Ξ¦_F (R2 = 0.84) implying that Ξ¦_F could be more closely related to pigment pool changes rather than LUE_p. In addition, I found that the f_esc contributed to a stronger SIF-GPP relationship by partially capturing the response of LUE_p to diffuse light. These findings can help refine physiological and structural links between canopy-level SIF and GPP in evergreen needleleaf forests. We do not fully understand what satellite NDVI derived leaf-out and full leaf dates actually observe because deciduous broadleaf forest consists of multi-layer canopies typically and mixed-signal from multi-layer canopies could affect satellite observation. Ultimately, we have the following question: What phenology do we actually see from space compared to ground observations on multi-layer canopy phenology? In Chapter 4, I reported the results of 8 years of continuous observations of multi-layer phenology and climate variables in a deciduous broadleaf forest, South Korea. Multi-channel spectrometers installed above and below overstory canopy allowed us to monitor over- and understory canopy phenology separately, continuously. I evaluated the widely used phenology detection methods, curvature change rate and threshold with NDVI observed above top of the canopy and compared leaf-out and full leaf dates from both methods to in-situ observed multi-layer phenology. First, I found that NDVI from the above canopy had a strong linear relationship with satellites NDVI (R2=0.95 for MODIS products and R2= 0.85 for Landsat8). Second, leaf-out dates extracted by the curvature change rate method and 10% threshold were well matched with understory leaf-out dates. Third, the full-leaf dates extracted by the curvature change rate method and 90% threshold were similar to overstory full-leaf dates. Furthermore, I found that overstory leaf-out dates were closely correlated to accumulated growing degree days (AGDD) while understory leaf-out dates were related to AGDD and also sensitive to the number of chill days (NCD). These results suggest that satellite-based leaf-out and full leaf dates represent understory and overstory signals in the deciduous forest site, which requires caution when using satellite-based phenology data into future prediction as overstory and understory canopy show different sensitivities to AGDD and NCD.식물 κ³„μ ˆ 및 μ‹μƒμ˜ 생리학적, ꡬ쑰적인 λ³€ν™”λ₯Ό μ§€μ†μ μœΌλ‘œ λͺ¨λ‹ˆν„°λ§ ν•˜λŠ” 것은 μœ‘μƒμƒνƒœκ³„μ™€ λŒ€κΈ°κΆŒ μ‚¬μ΄μ˜ μ—λ„ˆμ§€, νƒ„μ†Œ μˆœν™˜ λ“±μ˜ ν”Όλ“œλ°±μ„ μ΄ν•΄ν•˜λŠ”λ° ν•„μˆ˜μ μ΄λ‹€. 이λ₯Ό κ΄€μΈ‘ν•˜κΈ° μœ„ν•˜μ—¬ μœ„μ„±μ—μ„œ κ΄€μΈ‘λœ μ •κ·œν™” 식생 μ§€μˆ˜ (NDVI) νƒœμ–‘ μœ λ„ μ—½λ‘μ†Œ ν˜•κ΄‘λ¬Όμ§ˆ (SIF)λŠ” λŒ€μ€‘μ μœΌλ‘œ μ‚¬μš©λ˜κ³  μžˆλ‹€. κ·ΈλŸ¬λ‚˜, 우주 μœ„μ„± 기반의 μžλ£ŒλŠ” λ‹€μŒκ³Ό 같은 ν•œκ³„μ λ“€μ΄ μ‘΄μž¬ν•œλ‹€. 1) μ•„μ§κΉŒμ§€ κ³ ν•΄μƒλ„μ˜ μœ„μ„± 기반 SIF μžλ£ŒλŠ” μ—†κ³ , 2) μœ„μ„± μžλ£Œλ“€μ€ λŒ€κΈ°μ˜ 질 (예, ꡬ름)에 영ν–₯을 λ°›μ•„, 흐린 λ‚ μ˜ μ‹μƒμ˜ 생리학적, ꡬ쑰적 λ³€ν™”λ₯Ό νƒμ§€ν•˜κΈ° νž˜λ“€λ‹€. λ˜ν•œ, 3) μœ„μ„± μ΄λ―Έμ§€λŠ” 상뢀 식생과 ν•˜λΆ€ 식생이 ν˜Όν•©λ˜μ–΄ μ„žμΈ μ‹ ν˜Έλ₯Ό νƒμ§€ν•˜κΈ° λ•Œλ¬Έμ—, 각 측의 식물 κ³„μ ˆμ„ 각각 μ—°κ΅¬ν•˜κΈ°μ— 어렀움이 μžˆλ‹€. κ·ΈλŸ¬λ―€λ‘œ, μœ„μ„±μ—μ„œ νƒμ§€ν•œ μ‹ ν˜Έλ₯Ό ν‰κ°€ν•˜κ³ , μ‹μƒμ˜ 생리학적, ꡬ쑰적 λ³€ν™”λ₯Ό 보닀 μ •ν™•νžˆ μ΄ν•΄ν•˜κΈ° μœ„ν•΄μ„œλŠ” κ·Όμ ‘ ν‘œλ©΄ 원격 μ„Όμ‹± μ‹œμŠ€ν…œμ„ μ΄μš©ν•œ μ‹€μΈ‘ 자료 기반의 연ꡬ듀이 μš”κ΅¬λœλ‹€. λ³Έ ν•™μœ„λ…Όλ¬Έμ˜ μ£Ό λͺ©μ μ€ κ·Όμ ‘ ν‘œλ©΄ 원격 μ„Όμ‹± μ‹œμŠ€ν…œμ„ μ΄μš©ν•˜μ—¬ 식물 κ³„μ ˆ 및 SIFλ₯Ό ν˜„μž₯μ—μ„œ μ§€μ†μ μœΌλ‘œ μ‹€μΈ‘ν•˜κ³ , μœ„μ„± μ˜μƒ 기반의 연ꡬ가 κ°–κ³  μžˆλŠ” ν•œκ³„μ  및 κΆκΈˆμ¦λ“€μ„ ν•΄κ²° 및 λ³΄μ™„ν•˜λŠ” 것이닀. 이 λͺ©μ μ„ λ‹¬μ„±ν•˜κΈ° μœ„ν•˜μ—¬, μ•„λž˜μ™€ 같은 세가지 Chapter: 1) SIFλ₯Ό κ΄€μΈ‘ν•˜κΈ° μœ„ν•œ ν•„ν„° 기반의 μ €λ ΄ν•œ κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œ 개발, 2)μ˜¨λŒ€ μΉ¨μ—½μˆ˜λ¦Όμ—μ„œμ˜ 연속적인 SIF κ΄€μΈ‘, 3)μœ„μ„± 기반의 식물 κ³„μ ˆκ³Ό μ‹€μΈ‘ν•œ λ‹€μΈ΅ μ‹μƒμ˜ 식물 κ³„μ ˆ λΉ„κ΅λ‘œ κ΅¬μ„±ν•˜κ³ , 이λ₯Ό μ§„ν–‰ν•˜μ˜€λ‹€. SIFλŠ” μ‹μƒμ˜ ꡬ쑰적, 생리학적 λ³€ν™”λ₯Ό μ΄ν•΄ν•˜κ³ , μΆ”μ •ν•˜λŠ” 인자둜 μ‚¬μš©λ  수 μžˆμ–΄, SIFλ₯Ό ν˜„μž₯μ—μ„œ κ΄€μΈ‘ν•˜κΈ° μœ„ν•œ λ‹€μ–‘ν•œ κ·Όμ ‘ ν‘œλ©΄ 원격 μ„Όμ‹± μ‹œμŠ€ν…œλ“€μ΄ 졜근 μ œμ‹œλ˜μ–΄ 였고 μžˆλ‹€. κ·ΈλŸ¬λ‚˜, μ•„μ§κΉŒμ§€ SIFλ₯Ό κ΄€μΈ‘ν•˜κΈ° μœ„ν•œ μƒμ—…μ μœΌλ‘œ μœ ν†΅λ˜λŠ” κ΄€μΈ‘ μ‹œμŠ€ν…œμ€ ν˜„μ €νžˆ λΆ€μ‘±ν•˜λ©°, λΆ„κ΄‘κ³„μ˜ ꡬ쑰적 νŠΉμ„±μƒ ν˜„μž₯μ—μ„œ κ΄€μΈ‘ μ‹œμŠ€ν…œμ„ 보정 및 κ΄€λ¦¬ν•˜κΈ°κ°€ μ–΄λ €μ›Œ 높은 질의 SIFλ₯Ό μ·¨λ“ν•˜λŠ” 것은 맀우 도전 적인 뢄야이닀. λ³Έ ν•™μœ„ λ…Όλ¬Έμ˜ Chapter 2μ—μ„œλŠ” SIFλ₯Ό ν˜„μž₯μ—μ„œ 보닀 μ†μ‰½κ²Œ κ΄€μΈ‘ν•˜κΈ° μœ„ν•œ ν•„ν„° 기반의 κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œ(Smart Surface Sensing System, 4S-SIF)을 κ°œλ°œν•˜μ˜€λ‹€. μ„Όμ„œλŠ” λŒ€μ—­ ν•„ν„°λ“€κ³Ό ν¬ν† λ‹€μ΄μ˜€λ“œκ°€ κ²°ν•©λ˜μ–΄ 있으며, μ„œλ³΄ λͺ¨ν„°λ₯Ό μ‚¬μš©ν•˜μ—¬ λŒ€μ—­ ν•„ν„° 및 κ΄€μΈ‘ λ°©ν–₯을 μžλ™μ μœΌλ‘œ λ³€κ²½ν•¨μœΌλ‘œμ¨, ν•œ 개의 ν¬ν† λ‹€μ΄μ˜€λ“œκ°€ 3개의 파μž₯ λ²”μœ„(757, 760, 770 nm)의 λΉ› 및 νƒœμ–‘μœΌλ‘œλΆ€ν„° μž…μ‚¬λ˜λŠ” κ΄‘λŸ‰κ³Ό μ‹μƒμœΌλ‘œ λ°˜μ‚¬/방좜된 κ΄‘λŸ‰μ„ κ΄€μΈ‘ν•  수 μžˆλ„λ‘ κ³ μ•ˆλ˜μ—ˆλ‹€. ν¬ν† λ‹€μ΄μ˜€λ“œλ‘œλΆ€ν„° μΈμ‹λœ 디지털 수치 값은 μƒμ—…μ μœΌλ‘œ νŒλ§€λ˜λŠ” μ΄ˆκ³ ν•΄μƒλ„ 뢄광계(QE Pro, Ocean Insight)와 λšœλ ·ν•œ μ„ ν˜• 관계λ₯Ό λ³΄μ΄λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€ (R2 > 0.99). μΆ”κ°€μ μœΌλ‘œ, 4S-SIFμ—μ„œ κ΄€μΈ‘λœ SIF와 μ΄ˆκ³ ν•΄μƒλ„ 뢄광계λ₯Ό μ΄μš©ν•˜μ—¬ μΆ”μΆœν•œ SIFκ°€ μ„ ν˜•μ μΈ 관계λ₯Ό μ΄λ£¨λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. μ‹μƒμ˜ 생리학적 λ³€ν™”λ₯Ό μΌμœΌν‚€λŠ” ν™”ν•™ 물질인 DCMU(3-(3, 4-dichlorophenyl)-1, 1-dimethyurea)을 μ²˜λ¦¬ν–ˆμŒμ—λ„ λΆˆκ΅¬ν•˜κ³ , μ‚°μΆœλœ SIF듀은 μ„ ν˜• 관계λ₯Ό λ³΄μ˜€λ‹€. λ³Έ μ„Όμ„œλŠ” κΈ°μ‘΄ μ‹œμŠ€ν…œλ“€μ— λΉ„ν•΄ μ‚¬μš©ν•˜κΈ° 쉽고 κ°„λ‹¨ν•˜λ©°, μ €λ ΄ν•˜κΈ° λ•Œλ¬Έμ— λ‹€μ–‘ν•œ μ‹œκ³΅κ°„μ  μŠ€μΌ€μΌμ˜ SIFλ₯Ό κ΄€μΈ‘ν•  수 μžˆλ‹€λŠ” μž₯점이 μžˆλ‹€. μœ„μ„± 기반의 SIFλ₯Ό μ΄μš©ν•˜μ—¬ 총일차생산성(gross primary productivity, GPP)을 μΆ”μ •ν•˜λŠ” μ—°κ΅¬λŠ” 졜근 νƒ„μ†Œ μˆœν™˜ 연ꡬ λΆ„μ•Όμ—μ„œ 각광받고 μžˆλŠ” 연ꡬ μ£Όμ œμ΄λ‹€. κ·ΈλŸ¬λ‚˜, SIF와 GPP의 κ΄€κ³„λŠ” μ—¬μ „νžˆ λ§Žμ€ λΆˆν™•μ‹€μ„±μ„ μ§€λ‹ˆκ³  μžˆλŠ”λ°, μ΄λŠ” SIF-GPP 관계λ₯Ό μ‘°μ ˆν•˜λŠ” μ‹μƒμ˜ ꡬ쑰적 및 생리학적 μš”μΈμ„ λ”°λ‘œ λΆ„λ¦¬ν•˜μ—¬ κ³ μ°°ν•œ 연ꡬ듀이 λΆ€μ‘±ν•˜κΈ° λ•Œλ¬Έμ΄λ‹€. λ³Έ ν•™μœ„ λ…Όλ¬Έμ˜ Chapter 3μ—μ„œλŠ” μ§€μ†μ μœΌλ‘œ SIF, GPP, 흑수된 κ΄‘ν•©μ„±μœ νš¨λ³΅μ‚¬λŸ‰ (absorbed photosynthetically active radiation, APAR), 그리고 ν΄λ‘œλ‘œν•„κ³Ό μΉ΄λ‘œν‹°λ…Έμ΄λ“œμ˜ λΉ„μœ¨ 인자 (chlorophyll: carotenoid index, CCI)λ₯Ό μ˜¨λŒ€μΉ¨μ—½μˆ˜λ¦Όμ—μ„œ μ—°μ†μ μœΌλ‘œ κ΄€μΈ‘ν•˜μ˜€λ‹€. SIF-GPP κ΄€κ³„μ˜ ꡬ체적인 λ©”μ»€λ‹ˆμ¦˜ 관계λ₯Ό 밝히기 μœ„ν•˜μ—¬, κ΄‘ 이용효율 (light use efficiency, LUE_p), μ—½λ‘μ†Œ ν˜•κ΄‘ μˆ˜λ“λ₯  (chlorophyll fluorescence yield, Ξ¦_F) 그리고 SIF κ΄‘μžκ°€ κ΅°λ½μœΌλ‘œλΆ€ν„° λ°©μΆœλ˜λŠ” λΉ„μœ¨ (escape fraction, f_esc)을 각각 λ„μΆœν•˜κ³  νƒκ΅¬ν•˜μ˜€λ‹€. SIF와 GPP의 κ΄€κ³„λŠ” λšœλ ·ν•œ λΉ„ μ„ ν˜•μ μΈ 관계λ₯Ό λ³΄μ΄λŠ” 것을 ν™•μΈν–ˆμœΌλ©°(R2 = 0.91 with a hyperbolic regression function, daily), 일주기 λ‹¨μœ„μ—μ„œ SIFλŠ” APAR에 λŒ€ν•΄ μ„ ν˜•μ μ΄μ—ˆμ§€λ§Œ GPPλŠ” APAR에 λŒ€ν•΄ λšœλ ·ν•œ 포화 관계λ₯Ό λ³΄μ΄λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. μΆ”κ°€μ μœΌλ‘œ LUE_p 와 Ξ¦_F κ°€ λŒ€κΈ° μ˜¨λ„μ— 따라 λ°˜μ‘ν•˜λŠ” 정도가 λ‹€λ₯Έ 것을 ν™•μΈν•˜μ˜€λ‹€. LUE_pλŠ” 높은 μ˜¨λ„μ—μ„œ 포화 λ˜μ—ˆμ§€λ§Œ, Ξ¦_FλŠ” 포화 νŒ¨ν„΄μ„ 확인할 수 μ—†μ—ˆλ‹€. λ˜ν•œ, ꡰ락 μˆ˜μ€€μ—μ„œμ˜ CCI와 Ξ¦_Fκ°€ λšœλ ·ν•œ 상관 관계λ₯Ό λ³΄μ˜€λ‹€(R2 = 0.84). μ΄λŠ” Ξ¦_Fκ°€ μ—½λ‘μ†Œ μƒ‰μ†Œμ— 영ν–₯을 LUE_p에 λΉ„ν•΄ 더 κ°•ν•œ 관계가 μžˆμ„ 수 μžˆμŒμ„ μ‹œμ‚¬ν•œλ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ, f_escκ°€ νƒœμ–‘κ΄‘μ˜ μ‚°λž€λœ 정도에 따라 λ°˜μ‘μ„ ν•˜μ—¬, Ξ¦_F와 LUE_p의 κ°•ν•œ 상관 관계λ₯Ό ν˜•μ„±ν•˜λŠ”λ° κΈ°μ—¬ν•˜λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. μ΄λŸ¬ν•œ λ°œκ²¬μ€ μ˜¨λŒ€ μΉ¨μ—½μˆ˜λ¦Όμ—μ„œ ꡰ락 μˆ˜μ€€μ˜ SIF-GPP관계λ₯Ό 생리학적 및 ꡬ쑰적 μΈ‘λ©΄μ—μ„œ μ΄ν•΄ν•˜κ³  규λͺ…ν•˜λŠ”λ° 큰 도움이 될 것이닀. 식물 κ³„μ ˆμ€ 식생이 철을 따라 주기적으둜 λ‚˜νƒ€λ‚΄λŠ” λ³€ν™”λ₯Ό κ΄€μΈ‘ν•˜λŠ” λ°˜μ‘μ΄λ‹€. 식물 κ³„μ ˆμ€ μœ‘μƒμƒνƒœκ³„μ™€ λŒ€κΈ°κΆŒ μ‚¬μ΄μ˜ 물질 μˆœν™˜μ„ μ΄ν•΄ν•˜λŠ”λ° 맀우 μ€‘μš”ν•˜λ‹€. μœ„μ„± 기반의 NDVIλŠ” 식물 κ³„μ ˆμ„ νƒμ§€ν•˜κ³  μ—°κ΅¬ν•˜λŠ”λ° κ°€μž₯ λŒ€μ€‘μ μœΌλ‘œ μ‚¬μš©λœλ‹€. κ·ΈλŸ¬λ‚˜, ν™œμ—½μˆ˜λ¦Όμ—μ„œμ˜ μœ„μ„± NDVI 기반의 κ°œμ—½ μ‹œκΈ° 및 μ„±μˆ™ μ‹œκΈ°κ°€ μ‹€μ œ μ–΄λŠ μ‹œμ μ„ νƒμ§€ν•˜λŠ”μ§€λŠ” λΆˆλΆ„λͺ…ν•˜λ‹€. μ‹€μ œ ν™œμ—½μˆ˜λ¦Όμ€ λ‹€μΈ΅ 식생 ꡬ쑰의 μ‚Όμ°¨μ›μœΌλ‘œ 이루어져 μžˆλŠ” 반면, μœ„μ„± μ˜μƒμ€ λ‹€μΈ΅ μ‹μƒμ˜ μ‹ ν˜Έκ°€ μ„žμ—¬ μžˆλŠ” μ΄μ°¨μ›μ˜ 결과물이기 λ•Œλ¬Έμ΄λ‹€. λ”°λΌμ„œ, μœ„μ„± NDVI 기반의 식물 κ³„μ ˆμ΄ λ‹€μΈ΅ 식생 ꡬ쑰λ₯Ό 이루고 μžˆλŠ” ν™œμ—½μˆ˜λ¦Όμ—μ„œ μ‹€μ œ ν˜„μž₯ κ΄€μΈ‘κ³Ό λΉ„κ΅ν•˜μ˜€μ„ λ•Œ μ–΄λŠ μ‹œμ μ„ νƒμ§€ν•˜λŠ”μ§€μ— λŒ€ν•œ ꢁ금증이 λ‚¨λŠ”λ‹€. λ³Έ ν•™μœ„ λ…Όλ¬Έμ˜ Chapter 4μ—μ„œλŠ” μ§€μ†μ μœΌλ‘œ 8λ…„ λ™μ•ˆ ν™œμ—½μˆ˜λ¦Όλ‚΄μ˜ λ‹€μΈ΅ μ‹μƒμ˜ 식물 κ³„μ ˆμ„ κ·Όμ ‘ ν‘œλ©΄ 원격 μ„Όμ‹± μ‹œμŠ€ν…œμ„ μ΄μš©ν•˜μ—¬ κ΄€μΈ‘ν•˜κ³ , μœ„μ„± NDVI 기반의 식물 κ³„μ ˆκ³Ό λΉ„κ΅ν•˜μ˜€λ‹€. 닀채널 뢄광계λ₯Ό 상뢀 μ‹μƒμ˜ μœ„μ™€ μ•„λž˜μ— μ„€μΉ˜ν•¨μœΌλ‘œμ¨, 상뢀 식생과 ν•˜λΆ€ μ‹μƒμ˜ 식물 κ³„μ ˆμ„ 각각 μ—°μ†μ μœΌλ‘œ κ΄€μΈ‘ν•˜μ˜€λ‹€. 식물 κ³„μ ˆμ„ νƒμ§€ν•˜κΈ° μœ„ν•˜μ—¬ κ°€μž₯ 많이 μ‚¬μš©λ˜λŠ” 방법인 1) μ—­μΉ˜λ₯Ό μ΄μš©ν•˜λŠ” 방법과 2) μ΄κ³„λ„ν•¨μˆ˜λ₯Ό μ΄μš©ν•˜λŠ” 방법을 μ‚¬μš©ν•˜μ—¬ κ°œμ—½ μ‹œκΈ° 및 μ„±μˆ™ μ‹œκΈ°λ₯Ό κ³„μ‚°ν•˜κ³  이λ₯Ό λ‹€μΈ΅ μ‹μƒμ˜ 식물 κ³„μ ˆκ³Ό λΉ„κ΅ν•˜μ˜€λ‹€. λ³Έ 연ꡬ κ²°κ³Ό, 첫번째둜, ꡰ락의 μƒμΈ΅λΆ€μ—μ„œ μ‹€μΈ‘ν•œ NDVI와 μœ„μ„± 기반의 NDVIκ°€ κ°•ν•œ μ„ ν˜• 관계λ₯Ό λ³΄μ΄λŠ” 것을 ν™•μΈν–ˆλ‹€ (R2=0.95 λŠ” MODIS μ˜μƒλ“€ 및 R2= 0.85 λŠ” Landsat8). λ‘λ²ˆμ§Έλ‘œ, μ΄κ³„λ„ν•¨μˆ˜ 방법과 10%의 μ—­μΉ˜ 값을 μ΄μš©ν•œ 방법이 λΉ„μŠ·ν•œ κ°œμ—½ μ‹œκΈ°λ₯Ό μΆ”μ •ν•˜λŠ” 것을 ν™•μΈν•˜μ˜€μœΌλ©°, ν•˜λΆ€ μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°μ™€ λΉ„μŠ·ν•œ μ‹œκΈ°μž„μ„ ν™•μΈν•˜μ˜€λ‹€. μ„Έλ²ˆμ§Έλ‘œ, μ΄κ³„λ„ν•¨μˆ˜ 방법과 90%의 μ—­μΉ˜ 값을 μ΄μš©ν•œ 방법이 λΉ„μŠ·ν•œ μ„±μˆ™ μ‹œκΈ°λ₯Ό μ‚°μΆœν•˜μ˜€μœΌλ©°, μ΄λŠ” 상뢀 μ‹μƒμ˜ μ„±μˆ™ μ‹œκΈ°μ™€ λΉ„μŠ·ν•˜μ˜€λ‹€. μΆ”κ°€μ μœΌλ‘œ 상뢀 μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°μ™€ ν•˜λΆ€ μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°κ°€ μ˜¨λ„μ™€ λ°˜μ‘ν•˜λŠ” 정도가 λšœλ ·ν•˜κ²Œ 차이가 λ‚˜λŠ” 것을 확인할 수 μžˆμ—ˆλ‹€. 상뢀 μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°λŠ” 적산 생μž₯ μ˜¨λ„ 일수 (AGDD)와 κ°•ν•œ 상관성을 λ³΄μ˜€κ³ , ν•˜λΆ€ μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°λŠ” AGDD와 연관성을 κ°–κ³  μžˆμ„ 뿐만 μ•„λ‹ˆλΌ μΆ”μœ„ 일수(NCD)에도 λ―Όκ°ν•˜κ²Œ λ°˜μ‘ν•˜λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. μ΄λŸ¬ν•œ κ²°κ³ΌλŠ” μœ„μ„± NDVI 기반의 κ°œμ—½ μ‹œκΈ°λŠ” ν•˜λΆ€ μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°μ™€ 연관성이 λ†’κ³ , μ„±μˆ™ μ‹œκΈ°λŠ” 상뢀 μ‹μƒμ˜ μ„±μˆ™ μ‹œκΈ°μ™€ λΉ„μŠ·ν•˜λ‹€λŠ” 것을 μ˜λ―Έν•œλ‹€. λ˜ν•œ, 상뢀 식생과 ν•˜λΆ€ 식생이 μ˜¨λ„μ— λ‹€λ₯Έ 민감성을 κ°–κ³  μžˆμ–΄, μœ„μ„±μ—μ„œ μ‚°μΆœλœ 식물 κ³„μ ˆμ„ μ΄μš©ν•˜μ—¬ κΈ°ν›„λ³€ν™”λ₯Ό μ΄ν•΄ν•˜κ³ μž ν•  λ•Œ, μ–΄λ–€ 측의 식생이 μœ„μ„± μ˜μƒμ— 주된 영ν–₯을 λ―ΈμΉ˜λŠ”μ§€ κ³ λ €ν•΄μ•Ό ν•œλ‹€λŠ” 것을 μ‹œμ‚¬ν•œλ‹€. μœ„μ„±μ€ 넓은 μ§€μ—­μ˜ λ³€ν™”λ₯Ό μ†μ‰½κ²Œ λͺ¨λ‹ˆν„°λ§ν•  수 μžˆμ–΄ λ§Žμ€ κ°€λŠ₯성을 κ°–κ³  μžˆλŠ” λ„κ΅¬μ΄μ§€λ§Œ, 보닀 μ •ν™•ν•œ μœ„μ„± κ΄€μΈ‘ 값을 μ΄ν•΄ν•˜κΈ° μœ„ν•΄μ„œλŠ” ν˜„μž₯μ—μ„œ κ΄€μΈ‘λœ 자료λ₯Ό 기반으둜 ν•œ 검증이 μš”κ΅¬λœλ‹€. λ³Έ ν•™μœ„ λ…Όλ¬Έμ—μ„œλŠ” 1) κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œμ„ 개발, 2) κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œμ„ ν™œμš©ν•œ μ‹μƒμ˜ 생리학적 ꡬ쑰적 λ³€ν™”μ˜ 지속적인 κ΄€μΈ‘, 3) λ‹€μΈ΅ 식생 κ΅¬μ‘°μ—μ„œ κ΄€μΈ‘λ˜λŠ” 식물 κ³„μ ˆ 및 μœ„μ„±μ—μ„œ μΆ”μ •λœ 식물 κ³„μ ˆμ˜ μ—°κ΄€μ„± 평가λ₯Ό μˆ˜ν–‰ν•˜μ˜€λ‹€. κ°œλ°œν•œ κ·Όμ ‘ ν‘œλ©΄ μ„Όμ„œλŠ” 상업 μ„Όμ„œλ“€κ³Ό λΉ„κ΅ν–ˆμ„ λ•Œ, κ°€κ²©μ μœΌλ‘œ μ €λ ΄ν•˜κ³  손 μ‰½κ²Œ μ‚¬μš©ν•  수 μžˆμ—ˆμœΌλ©°, μ„±λŠ₯μ μœΌλ‘œλ„ 뢀쑱함이 μ—†μ—ˆλ‹€. κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œμ„ μ΄μš©ν•˜μ—¬ SIFλ₯Ό μ˜¨λŒ€ μΉ¨μ—½μˆ˜λ¦Όμ—μ„œ μ§€μ†μ μœΌλ‘œ κ΄€μΈ‘ν•œ κ²°κ³Ό, 총일차생산성과 SIFλŠ” λΉ„μ„ ν˜• 관계λ₯Ό κ°–λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. μ΄λŠ” λ§Žμ€ μ„ ν–‰ μ—°κ΅¬λ“€μ—μ„œ λ°œν‘œν•œ μœ„μ„± 기반의 SIF와 GPPκ°€ μ„ ν˜•μ μΈ 관계λ₯Ό λ³΄μΈλ‹€λŠ” κ²ƒκ³ΌλŠ” λ‹€μ†Œ μƒλ°˜λœ 결과이닀. λ‹€μΈ‘ μ‹μƒμ˜ λ΄„μ²  식물 κ³„μ ˆμ„ μ—°μ†μ μœΌλ‘œ κ΄€μΈ‘ν•˜κ³ , μœ„μ„± 기반의 식물 κ³„μ ˆκ³Ό λΉ„κ΅ν‰κ°€ν•œ μ—°κ΅¬μ—μ„œλŠ” μœ„μ„± 기반의 κ°œμ—½ μ‹œκΈ°λŠ” ν•˜λΆ€ 식생에 영ν–₯을 주둜 λ°›κ³ , μ„±μˆ™ μ‹œκΈ°λŠ” 상뢀 μ‹μƒμ˜ μ‹œκΈ°μ™€ λΉ„μŠ·ν•œ 것을 ν™•μΈν•˜μ˜€λ‹€. 즉, κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œμ„ μ΄μš©ν•˜μ—¬ ν˜„μž₯μ—μ„œ μ‹€μΈ‘ν•œ κ²°κ³ΌλŠ” μœ„μ„± μ˜μƒμ„ ν™œμš©ν•œ μ—°κ΅¬λ“€κ³ΌλŠ” λ‹€λ₯Έ κ²°κ³Όλ₯Ό 보일 μˆ˜λ„ 있으며, μœ„μ„± μ˜μƒμ„ 평가 및 μ΄ν•΄ν•˜λŠ”λ° μ‚¬μš©λ  수 μžˆλ‹€. λ”°λΌμ„œ, 보닀 μ •ν™•ν•œ μ‹μƒμ˜ ꡬ쑰적, 생리학적 λ©”μ»€λ‹ˆμ¦˜μ„ μ΄ν•΄ν•˜κΈ° μœ„ν•΄μ„œλŠ” κ·Όμ ‘ ν‘œλ©΄ 센싱을 ν™œμš©ν•œ ν˜„μž₯μ—μ„œ κ΅¬μΆ•ν•œ 자료 기반의 더 λ§Žμ€ 연ꡬ듀이 ν•„μš”ν•˜λ‹€λŠ” 것을 μ‹œμ‚¬ν•œλ‹€.Abstract i Chapter 1. Introduction 2 1. Background 2 2. Purpose 5 Chapter 2. Monitoring SIF using a filter-based near surface remote sensing system 9 1. Introduction 9 2. Instrument desing and technical spefications of the filter-based smart surface sensing system (4S-SIF) 12 2.1. Ultra-narrow band pass filter 14 2.2. Calibration of 4S-SIF 15 2.3. Temperature and humidity response 16 2.4. Evaluate SIF quality from 4S-SIF in the field 17 3. Results 20 4. Discussion 23 Chapter 3. SIF is non-linearly related to canopy photosynthesis in a temperate evergreen needleleaf forest during fall transition 27 1. Introduction 27 2. Methods and Materials 31 2.1. Study site 31 2.2. Leaf-level fluorescence measurement 32 2.3. Canopy-level SIF and spectral reflectance measurement 34 2.4. SIF retrieval 37 2.5. Canopy-level photosynthesis estimates 38 2.6. Meteorological variables and APAR 39 2.7. Statistical analysis 40 3. Results 41 4. Discussion 48 4.1. Non-linear relationships between SIF and GPP 49 4.2. Role of f_esc in SIF-GPP relationship 53 4.3. Implications of non-linear SIF-GPP relationship in temperate ENF 54 5. Conclusion 57 6. Appendix 59 Chapter 4. Monitoring spring phenology of multi-layer canopy in a deciduous broadleaf forest: What signal do satellites actually see in space 65 1. Introduction 65 2. Materials and Methods 69 2.1. Study site 69 2.2. Multi-layer spectral reflectance and transmittance measurement 70 2.3. Phenometrics detection 72 2.4. In-situ multi-layer phenology 74 2.5. Satellite remote sensing data 75 2.6. Meteorological variables 75 3. Results 76 3.1. Seasonal to interannual variations of NDVI, 1-transmittance, and air temperature 76 3.2. Inter-annual variation of leaf-out and full-leaf dates 78 3.3. The relationships between dates calculated according tothreshold and in-situ multi-layer phenology 80 3.4. The relationship between multi-layer phenology, AGDD and NCD 81 4. Discussion 82 4.1. How do satellite-based leaf-out and full-leaf dates differ from in-situ multi-layer phenology 83 4.2. Are the 10 % and 90 % thresholds from satellite-basedNDVI always well matched with the leaf-out and full-leaf dates calculated by the curvature change rate 86 4.3. What are the implications of the difference between satellite-based and multi-layer phenology 87 4.4. Limitations and implications for future studies 89 5. Conclusion 91 6. Appendix 92 Chapter 5. Conclusion 114 Abstract in Korean 115λ°•

    μ €λ ΄ν•œ κ·Όμ ‘ ν‘œλ©΄ μ„Όμ„œλ₯Ό μ΄μš©ν•œ μ‹μƒμ§€μˆ˜, 엽면적 μ§€μˆ˜, κ΄‘ν•©μ„±μœ νš¨λ³΅μ‚¬λŸ‰μ˜ 흑수λ₯  κ΄€μ°°

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› 농업생λͺ…κ³Όν•™λŒ€ν•™ μƒνƒœμ‘°κ²½Β·μ§€μ—­μ‹œμŠ€ν…œκ³΅ν•™λΆ€, 2017. 8. λ₯˜μ˜λ ¬.Monitoring vegetation indices, fraction of absorbed photosynthetically active radiation (fPAR) and leaf area index (LAI) has advanced our understanding of biosphere-atmosphere interactions. Although there are continuous observations for each variable, monitoring vegetation indices, fPAR and LAI simultaneously is still lacking. Recent advances of technology provide unprecedented opportunities to integrate various low-cost sensors as an intelligent near surface observation system for monitoring ecosystem structure and functions. In this study, we developed a Smart Surface Sensing System (4S), which can automatically collect, transfer, process and analyze data, and then publish time series results on public-available website. The system is composed of micro-computers, micro-controllers, multi-spectral spectrometers made from Light Emitting Diode (LED), micro cameras, and Internet module. We did intensive tests and calibrations in the lab. Then, we conducted in-situ observations of normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), fraction of absorbed photosynthetically active radiation (fPAR), and leaf area index (LAI) continuously at a rice paddy field during the growing season. NDVI and EVI obtained by 4S showed linear relationships with those from a reference hyperspectrometer (R2 = 0.98NDVI, R2 = 0.96EVI). 4S derived fPAR and LAI were comparable to LAI-2200 and destructive measurements in both magnitude and seasonal trajectory. We retrieved vegetation indices, fPAR and LAI independently and continuously and show that after the reproductive stage, fPAR remained constant, whereas LAI and NDVI decreased continuously after their peak because of non-photosynthetic materials such as grain and yellow leaf. In addition, using vegetation index to estimate fPAR has limitation because the spectral reflectance could not capture the diurnal pattern. On the other hand, fPAR changes abruptly depending on the sky conditions and the amount of light transmitted. We believe that 4S will be useful in the expansion of ecological sensing networks across multiple spatial and temporal scales.1 Introduction 1 2 Method and materials 4 2.1 Development and calibration of 4S 4 2.2 Testing the 4S LED spectrometer 6 2.2.1 Site description 10 2.2.2 4S in-situ 12 2.2.3 Reference data collection 15 2.2.4 Satellite remote sensing data 16 3 Results 17 3.1 Seasonal variation in 4S LED sensor 17 3.2 Seasonal variation in 4S camera sensor 20 3.3 Comparison of NDVI obtained from 4S and satellite with different resolutions 22 4 Discussion 23 4.1 What are the advantages of 4S development 23 4.2 What are the advantages of observing vegetation indices, fPAR and LAI independently 25 4.3 What are the advantages of continuous observation compared to different sensors 29 5 Conclusion 32 6 References 33 7 Abstract (Korean) 38Maste

    Highly efficient gene knockout in mice and zebrafish with RNA-guided endonucleases

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : ν™”ν•™λΆ€(생화학전곡), 2014. 2. κΉ€μ§„μˆ˜.Zinc finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) , engineered nucleases, are composed of designable DNA-binding domains and a non-specific nuclease domain and enable a broad range of genomic modification by inducing double-strand breaks (DSBs) that stimulate intrinsic cellular repair mechanisms such as non-homologous recombination (NHEJ) and homologous recombination (HR) at specific genomic locations. This technology has been described earlier as promising tools for targeted genome engineering in cells and many organisms. Recently, RNA-guided endonucleases (RGENs) derived from bacterial type-II CRISPR/Cas system, have been described as site-specific endonucleases whose specificities are programmed by small RNA components. RGEN also has been applied in cells and organisms as genome engineering tool. Here in this study, injection of RGENs as Cas9 protein: guide RNA complexes or Cas9 mRNA plus guide RNA into one-cell stage embryos of mice and zebrafish efficiently disrupts a target gene in both species. RGENs efficiently generated germ-line transmittable mutations in up to 93% of newborn mice with minimal toxicity. RGEN-induced mutations in the mouse Prkdc gene that encodes an enzyme critical for DNA double strand break repair resulted in immunodeficiency both in F0 and F1 mice. I propose that RGEN-mediated mutagenesis in animals will greatly expedite the creation of genetically-engineered model organisms accelerating functional genomic research.Table of contents ABSTRACT .............................................................................................. 1 TABLE OF CONTENTS ......................................................................... 3 LIST OF FIGURES .................................................................................. 5 LIST OF TABLES .................................................................................... 7 I. INTRODUCTION ................................................................................ 8 II. MATERIALS AND METHODS ....................................................... 11 1. RGEN COMPONENTS. ..................................................................... 11 2. In vitro cleavage reactions. ................................................................. 11 3. Microinjection of RGENs into mouse embryos. .............................. 12 4. Fluorescent PCR. ................................................................................ 13 5. Genotyping, sequence analyses, and phenotyping of mutant mice. 14 6. Characterization of immune cells by flow cytometry. ..................... 15 7. Procedures of RGEN-mediated mutagenesis in zebrafish embryos. .................................................................................................................. 16 III. RESULTS ......................................................................................... 17 4 1. RGEN Design and Production. ......................................................... 17 2. Generation of Founder Mice with RGEN-induced Mutations. ...... 18 3. Gene Disruption by Cas9 protein-sgRNA Complex in Mice and Zebrafish. ................................................................................................ 20 4. Analysis of Off-target Effects of RGENs. ......................................... 22 5. Phenotype Analysis and Germ-line Transmission of Prkdc-mutant Mice. ........................................................................................................ 23 IV. DISCUSSION ................................................................................... 26 V. REFERENCE ..................................................................................... 55 Abstract in Korean………………………………………………........61Maste

    적은 수의 μ‚¬μš©μž μž…λ ₯μœΌλ‘œλΆ€ν„° 인간 λ™μž‘μ˜ ν•©μ„± 및 νŽΈμ§‘

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : 전기·컴퓨터곡학뢀, 2014. 8. 이제희.An ideal 3D character animation system can easily synthesize and edit human motion and also will provide an efficient user interface for an animator. However, despite advancements of animation systems, building effective systems for synthesizing and editing realistic human motion still remains a difficult problem. In the case of a single character, the human body is a significantly complex structure because it consists of as many as hundreds of degrees of freedom. An animator should manually adjust many joints of the human body from user inputs. In a crowd scene, many individuals in a human crowd have to respond to user inputs when an animator wants a given crowd to fit a new environment. The main goal of this thesis is to improve interactions between a user and an animation system. As 3D character animation systems are usually driven by low-dimensional inputs, there is no method for a user to directly generate a high-dimensional character animation. To address this problem, we propose a data-driven mapping model that is built by motion data obtained from a full-body motion capture system, crowd simulation, and data-driven motion synthesis algorithm. With the data-driven mapping model in hand, we can transform low-dimensional user inputs into character animation because motion data help to infer missing parts of system inputs. As motion capture data have many details and provide realism of the movement of a human, it is easier to generate a realistic character animation than without motion capture data. To demonstrate the generality and strengths of our approach, we developed two animation systems that allow the user to synthesize a single character animation in realtime and edit crowd animation via low-dimensional user inputs interactively. The first system entails controlling a virtual avatar using a small set of three-dimensional (3D) motion sensors. The second system manipulates large-scale crowd animation that consists of hundreds of characters with a small number of user constraints. Examples show that our system is much less laborious and time-consuming than previous animation systems, and thus is much more suitable for a user to generate desired character animation.Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Background 10 2.1 Performance Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.1 Performance-based Interfaces for Character Animation . . . . . . . 11 2.1.2 Statistical Models for Motion Synthesis . . . . . . . . . . . . . . . 12 2.1.3 Retrieval of Motion Capture Data . . . . . . . . . . . . . . . . . . 13 2.2 Crowd Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1 Crowd Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 Motion Editing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.3 Geometry Deformation . . . . . . . . . . . . . . . . . . . . . . . . 15 3 Realtime Performance Animation Using Sparse 3D Motion Sensors 17 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.3 Sensor Data and Calibration . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.4 Motion Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.4.1 Online Local Model . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.4.2 Kernel CCA-based Regression . . . . . . . . . . . . . . . . . . . . 25 3.4.3 Motion Post-processing . . . . . . . . . . . . . . . . . . . . . . . 27 3.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 Interactive Manipulation of Large-Scale Crowd Animation 40 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2 Crowd Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.3 Cage-based Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3.1 Cage Construction . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.3.2 Cage Representation . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4 Editing Crowd Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4.1 Spatial Manipulation . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4.2 Temporal Manipulation . . . . . . . . . . . . . . . . . . . . . . . . 57 4.5 Collision Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5 Conclusion 69 Bibliography I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIIIDocto

    ν˜‘μƒμ—μ„œμ˜ κ΄€ν–‰ ν˜•μ„±κ³Όμ •

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    λ‚΄μ‰¬μ˜ νŒŒμ΄κ²Œμž„μ€ μˆ˜λ§Žμ€ κ· ν˜•μ„ 가지고 μžˆλ‹€. κ·ΈλŸ¬λ‚˜ ν˜„μ‹€μ—μ„œλŠ” 절반으둜 λ‚˜λˆ„ λŠ” λΆ„λ°°κ°€ κ΄€ν–‰μ μœΌλ‘œ 이루어지고 μžˆμŒμ„ μ•Œ 수 μžˆλ‹€. λ³Έ μ—°κ΅¬μ—μ„œλŠ” 내쉬 λͺ¨ν˜•μ˜ λ³€ν˜•λœ ν˜•νƒœλ₯Ό λΆ„μ„ν•˜μ—¬ 절반으둜 λ‚˜λˆ„λŠ” λΆ„λ°°κ°€ λ‹€λ₯Έ κ· ν˜•λ³΄λ‹€ μ™œ 더 μ•ˆμ •μ μΈ κ²° κ³Όλ₯Ό λ‚³λŠ”μ§€μ— λŒ€ν•΄ 진화 κ²Œμž„μ  μ ‘κ·Ό 방식을 μ΄μš©ν•˜μ—¬ λΆ„μ„ν•˜κ³ μž ν•œλ‹€. λ³Έ 연ꡬ에 μ„œλŠ” λ‹€λ₯Έ κ²½κΈ°μžλ“€μ„ λͺ¨λ°©ν•˜λŠ” ν–‰νƒœλ₯Ό λ³΄μ΄λŠ” κ²½κΈ°μžλ“€μ΄ λ•Œλ•Œλ‘œ 선택에 μžˆμ–΄μ„œ μ˜λ„ν•˜μ§€ μ•Šμ€ μ‹€μˆ˜λ₯Ό ν•œλ‹€λŠ” 점을 κ°€μ •ν•œ κ²°κ³Ό, 균등뢄할이 μœ μΌν•˜κ²Œ ν™•λ₯ μ  μ•ˆμ •μ„±μ„ μ§€λ‹ˆκ³  μžˆλŠ” ν•΄μž„μ„ 보이고 μžˆλ‹€.이 μ—°κ΅¬λŠ” μ„œμšΈλŒ€ν•™κ΅ κ²½μ œμ—°κ΅¬μ†Œ κΈ°μ—…κ²½μŸλ ₯ 연ꡬ센터에 μ§€μ›λœ μ„œμšΈλŒ€ν•™κ΅ λ°œμ „κΈ°κΈˆμ˜ μ—° ꡬ비 지원을 톡해 μˆ˜ν–‰λ˜μ—ˆλ‹€

    λͺ©μ§ˆκ³„ κΈ°μ§ˆλ‘œλΆ€ν„° 응집성 효λͺ¨λ₯Ό μ΄μš©ν•œ μ—°λ£Œμš© μ—νƒ„μ˜¬ 생산에 κ΄€ν•œ 연ꡬ

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :곡업화학과,1997.Maste

    Development and characterization of artificial nuclear receptor and target reporter.

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    μ˜κ³Όν•™μ‚¬μ—…λ‹¨/석사[ν•œκΈ€] νŠΉμ • μœ μ „μžμ˜ 생리적인 κΈ°λŠ₯을 μ—°κ΅¬ν•˜λŠ” κΈ°μ΄ˆμ—°κ΅¬λ‚˜ μœ μ „μžμΉ˜λ£Œ 같은 μ‘μš©μ—°κ΅¬μ— μ‚¬μš©ν•˜κΈ° μœ„ν•œ inducible expression system을 κ°œλ°œν•˜κΈ° μœ„ν•˜μ—¬, 기쑴의 GAL4 DNA binding domain (DBD)을 μ΄μš©ν•œ inducible expression system을 κ°œλŸ‰ν•œ artificial nuclear receptor와 target reporterλ₯Ό κ°œλ°œν•˜μ˜€λ‹€. ν˜„μž¬ λ‹€μ–‘ν•œ μ’…λ₯˜μ˜ inducible expression system이 개발 λ˜μ—ˆλŠ”λ°, κ·Έλ“€ λŒ€λΆ€λΆ„μ€ μœ μ „μžμ˜ λ°œν˜„μ„ νŠΉμ •ligand둜 μ‘°μ ˆν•˜κΈ° μœ„ν•΄ prokaryoteλ‚˜ eukaryoteμ—μ„œ 유래된 ligand binding domain (LBD)을 μ‚¬μš©ν•˜λ©°, activation domain (AD)으둜 herpes simplex virus의 VP16 ADλ₯Ό 주둜 μ‚¬μš©ν•œλ‹€. μƒˆλ‘œμ΄ κ°œλ°œν•œ inducible expression systemμ—μ„œ artificial nuclear receptorλŠ” yeast의 GAL4 DBD와 human progesterone/estrogen receptor LBD, SREBP1a의 ADλ₯Ό μ‚¬μš©ν•˜μ˜€λ‹€. Estrogen receptor LBD의 κ²½μš°μ— estrogenκ³Ό anti-estrogen인 4-OHT (hydroxytamoxifen)이 λͺ¨λ‘ κ²°ν•©ν•˜μ§€λ§Œ, human progesterone receptor LBD의 경우 C-terminal λ§λ‹¨μ˜ 892-933 아미노산을 μ œκ±°ν•˜μ—¬ progesteroneκ³ΌλŠ” κ²°ν•©ν•˜μ§€ μ•Šκ³  progesterone antagonist인 RU486 (mifepristone)μ—λ§Œ 특이적인 λ°˜μ‘μ„ λ‚˜νƒ€λ‚΄κ²Œ ν•œ LBDλ₯Ό μ‚¬μš©ν•˜μ˜€λ‹€. Reporter gene은 GAL4 response element (RE)λ₯Ό 5번 λ°˜λ³΅ν•œ λΆ€μœ„μ™€ ACL minimal promoter -60/+67, luciferase gene을 μ—°κ²°ν•˜μ˜€λ‹€. μƒˆλ‘œ κ°œλ°œν•œ inducible expression system은 두 가지 κ°œμ„ μ μ΄ μžˆμ—ˆλ‹€. 첫째둜, artificial nuclear receptor에 SREBP1a AD을 μ‚¬μš©ν•¨μœΌλ‘œμ¨ μœ μ „μžλ°œν˜„μ„ λ”μš± ν™œμ„±ν™” μ‹œν‚¬ 수 μžˆμ—ˆκ³  λ‘˜μ§Έλ‘œ, reporter gene의 promoter인 minimal TATA-boxλ₯Ό ACL minimal promoter -60/+67둜 λŒ€μ²΄μ‹œν‚€κ³ , 여기에 SV40 enhancerλ₯Ό μ—°κ²°ν•˜μ—¬ μœ μ „μžμ˜ λ°œν˜„μ„ λ”μš± 증가 μ‹œν‚¬ 수 μžˆμ—ˆλ‹€. μ΄λŸ¬ν•œ κ°œλŸ‰λœ inducible expression system은 exogenous gene의 κΈ°λŠ₯을 μ—°κ΅¬ν•˜λŠ” κΈ°μ΄ˆμ—°κ΅¬λ‚˜ μœ μ „μžμΉ˜λ£Œ 같은 μ‘μš©μ—°κ΅¬μ—μ„œ 맀우 μœ μš©ν•˜κ²Œ 이용될 수 μžˆμ„ 것이닀. [영문] To develop an inducible expression system for use in basic research and applied research, we generated the artificial nuclear receptor and target reporter. A number of ligand-regulated artificial nuclear receptor have been generated by various means, using ligand binding domain (LBD) derived from either prokaryotes or eukaryotes, and activation domain (AD) derived from herpes simplex virus. In our inducible expression system, the artificial nuclear receptor contains the yeast GAL4 DNA binding domain, LBD of human progesterone/estrogen receptor and SREBP1a AD. The estrogen receptor LBD bind to estrogen and anti-estrogen (4-OHT). But progesterone receptor LBD specifically bind to anti-progesterone (mifepristone, RU486) instead of endogenous hormone. The target reporter construct contains the five copies of GAL4 responsive element, ACL minimal promoter -60/+67 and luciferase gene. In the presence of ligand, artificial nuclear receptor specifically activates the expression of luciferase gene. Two major improvements of this system were made. First, artificial nuclear receptor GAL4-PR/ER was rendered more potent by fusing the strong activation domain, SREBP1a AD. Second, target reporter gene expression was more increase by replaced minimal TATA box promoter with ACL minimal promoter -60/+67, and fusing the SV40 enhancer. This enhanced inducible expression system can restrict its specificity to the target gene, and enable to provide powerful tools that may be applied to the study of exogenous gene function and gene therapy.ope

    λ„λ†ν†΅ν•©μ „ν›„μ˜ μž¬μ •μ§€μΆœλ³€ν™”μ— κ΄€ν•œ 연ꡬ

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ ν™˜κ²½λŒ€ν•™μ› :ν™˜κ²½κ³„νšν•™κ³Ό λ„μ‹œλ°μ§€μ—­κ³„νšμ „κ³΅,1997.Maste

    생뢄해성 iodophor microsphere μœ λ‘μΉ¨μ§€μ œμ˜ 건유기 유우 유방 감염 예방 효과

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :μˆ˜μ˜ν•™κ³Ό μˆ˜μ˜λ‚΄κ³Όν•™μ „κ³΅,2002.Maste

    (A)Study on the debarment and the suspension in the U.S. government procurement contract law

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :법학과,2005.Maste
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