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전산유동가시화를 활용한 웨이퍼 이송장치의 복사열전달에 관한 연구
추민기,정지홍,손동기,고한서 한국가시화정보학회 2022 한국가시화정보학회지 Vol.20 No.3
The high heat emitted from the process module and heat jacket may cause errors in semiconductor process equipment. Barriers were designed to reduce the temperature of surface on transfer module. A designed barrier was compared and analyzed by numerical analysis using ANSYS Fluent. The average temperature of barrier and effect of radiation heat transfer were also compared through absorbed radiative heat flux of the barrier. The adoption of the barrier had an effect on the radiative heat transfer reduction of the transfer module rod. The effect of the angles of barrier from 50° to 90° on the heat transfer was investigated using the absorbed radiative heat flux with the average temperature. The angle of barrier of 50° reduced the temperature up to 9.6 %.
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MODIS 시계열 위성영상을 이용한 한라산과 지리산 구상나무 식생 변동 추세 분석
추민기,유철희,임정호,조동진,강유진,오현경,이종성 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.3
Korean fir (Abies koreana Wilson) is one of the most important environmental indicatortree species for assessing climate change impacts on coniferous forests in the Korean Peninsula. However, due to the nature of alpine and subalpine regions, it is difficult to conduct regular field surveysof Korean fir, which is mainly distributed in regions with altitudes greater than 1,000 m. Therefore,this study analyzed the vegetation change trend of Korean fir using regularly observed remote sensingdata. Specifically, normalized difference vegetation index (NDVI) from Moderate Resolution ImagingSpectroradiometer (MODIS), land surface temperature (LST), and precipitation data from GlobalPrecipitation Measurement (GPM) Integrated Multi-satellitE Retrievalsfor GPM from September 2003to 2020 for Hallasan and Jirisan were used to analyze vegetation changes and their association withenvironmental variables. We identified a decrease in NDVI in 2020 compared to 2003 for both sites. Based on the NDVI difference maps, areas for healthy vegetation and high mortality of Korean fir were selected. Long-term NDVI time-series analysis demonstrated that both Hallasan and Jirisan had adecrease in NDVI at the high mortality areas (Hallasan: –0.46, Jirisan: –0.43). Furthermore, whenanalyzing the long-term fluctuations of Korean fir vegetation through the Hodrick-Prescott filter-appliedNDVI, LST, and precipitation, the NDVI difference between the Korean fir healthy vegetation and highmortality sitesincreased with the increasing LST and decreasing precipitation in Hallasan. Thissuggeststhat the increase in LST and the decrease in precipitation contribute to the decline of Korean fir inHallasan. In contrast, Jirisan confirmed a long-term trend of declining NDVI in the areas of Korean firmortality but did not find a significant correlation between the changes in NDVI and environmentalvariables (LST and precipitation). Further analyses of environmental factors, such as soil moisture,insolation, and wind that have been identified to be related to Korean fir habitats in previous studiesshould be conducted. This study demonstrated the feasibility of using satellite data for long-termmonitoring of Korean fir ecosystems and investigating their changes in conjunction with environmentalconditions. Thisstudy provided the potential forsatellite-based monitoring to improve our understandingof the ecology of Korean fir.
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기계학습법을 이용한 동해 울릉분지의 봄과 여름 순군집생산 추정
함도식,이인희,추민기 한국해양학회 2024 바다 Vol.29 No.1
동해 남서부해역은 대마난류나 연안 용승에 의한 영양염 공급 등으로 동해 북부나 동부에 비해 일차생산력이 높은 것으로 알려져 있지만, 이 해역의 생물 펌프에 관한 연구는 제한적이다. 본 연구에서는 O2/Ar 측정으로 산출한 고해상도 순군집생산 현장 관측 결과와 기계학습 모형을 결합하여 시공간 해상도가 8일 간격, 4 km인 봄과 여름 순군집생산 시계열 자료를 추정하였다. 기계 모형의 예측과 실측의 평균 제곱근 오차는 6 mmol O2 m-2 d-1로 관측값 평균의 15%에 해당했다. 울릉분지 중앙부의 순군집생산은 3월에 49 mmol O2 m-2 d-1로 가장 높았고, 6월과 7월에 18 mmol O2 m-2 d-1로 가장 낮았다. 이 같은 계절 변화는 3He 기체교환율로 추정한 질산염 공급률이나 234Th 비평형법으로 추정한 입자유기탄소 방출률과 유사하였다. 봄과 여름의 순군집생산 추정으로 한정된 이 연구방법을 가을과 겨울로 확대하기 위해서는 아표층수의 표층 혼입에 따른 O2/Ar 순군집생산의 오차를 보정하는 연구가 필요하다. The southwestern part of the East Sea is known to have a high primary productivity compared to those in the northern and eastern parts, which is attributed to nutrients supplies either by Tsushima Warm Current or by coastal upwelling. However, research on the biological pump in this area is limited. We developed machine learning models to estimate net community production (NCP), a measure of biological pump, with high spatial and time scales of 4 km and 8 days, respectively. The models were fed with the input parameters of sea surface temperature, chlorophyll-a, mixed layer depths, and photosynthetically active radiation and trained with observed NCP derived from high resolution measurements of surface O2/Ar. The root mean square error between the predicted values by the best performing machine model and the observed NCP was 6 mmol O2 m-2 d-1, corresponding to 15% of the average of observed NCP. The NCP in the central part of the Ulleung Basin was highest in March at 49 mmol O2 m-2 d-1 and lowest in June and July at 18 mmol O2 m-2 d-1. These seasonal variations were similar to the vertical nitrate flux based on the 3He gas exchange rate and to the particulate organic carbon flux estimated by the 234Th disequilibrium method. To expand this method, which produces NCP estimate for spring and summer, to autumn and winter, it is necessary to devise a way to correct bias in NCP by the entrainment of subsurface waters during the seasons.
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K-Means Clustering 기법과 원격탐사 자료를 활용한 탄소기반 글로벌 해양 생태구역 분류
김영준,배덕원,임정호,정시훈,추민기,한대현,Young Jun Kim,Dukwon Bae,Jungho Im,Sihun Jung,Minki Choo,Daehyeon Han 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
An acceleration of climate change in recent years has led to increased attention towards 'blue carbon' which refers to the carbon captured by the ocean. However, our comprehension of marine ecosystems is still incomplete. This study classified and analyzed global marine eco-provinces using k-means clustering considering carbon cycling. We utilized five input variables during the past 20 years (2001-2020): Carbon-based Productivity Model (CbPM) Net Primary Production (NPP), particulate inorganic and organic carbon (PIC and POC), sea surface salinity (SSS), and sea surface temperature (SST). A total of nine eco-provinces were classified through an optimization process, and the spatial distribution and environmental characteristics of each province were analyzed. Among them, five provinces showed characteristics of open oceans, while four provinces reflected characteristics of coastal and high-latitude regions. Furthermore, a qualitative comparison was conducted with previous studies regarding marine ecological zones to provide a detailed analysis of the features of nine eco-provinces considering carbon cycling. Finally, we examined the changes in nine eco-provinces for four periods in the past (2001-2005, 2006-2010, 2011-2015, and 2016-2020). Rapid changes in coastal ecosystems were observed, and especially, significant decreases in the eco-provinces having higher productivity by large freshwater inflow were identified. Our findings can serve as valuable reference material for marine ecosystem classification and coastal management, with consideration of carbon cycling and ongoing climate changes. The findings can also be employed in the development of guidelines for the systematic management of vulnerable coastal regions to climate change.
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다종 위성자료와 인공지능 기법을 이용한 한반도 주변 해역의 고해상도 해수면온도 자료 생산
정시훈 ( Sihun Jung ),추민기 ( Minki Choo ),임정호 ( Jungho Im ),조동진 ( Dongjin Cho ) 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.5
위성기반 해수면온도는 광역 모니터링이 가능한 장점이 있지만, 다양한 환경적 그리고 기계적 이유로 인한 시공간적 자료공백이 발생한다. 자료공백으로 인한 활용성의 한계가 있으므로, 공백이 없는 자료 생산이 필수적이다. 따라서 본 연구에서는 한반도 주변 해역에 대해 극궤도와 정지궤도 위성에서 생산되는 해수면온도 자료를 두 단계의 기계학습을 통해 융합하여 4 km의 공간해상도를 가지는 일별 해수면온도 합성장을 만들었다. 첫번째 복원 단계에서는 Data INterpolate Convolutional AutoEncoder (DINCAE) 모델을 이용하여 다종 위성기반 해수면온도 자료를 합성하여 복원하였고, 두번째 보정 단계에서는 복원된 해수면온도 자료를 현장관측자료에 맞춰 Light Gradient Boosting Machine (LGBM) 모델로 학습시켜 최종적인 일별 해수면온도 합성장을 만들었다. 개발된 모델의 검증을 위해 복원 단계에서 무작위 50일의 자료 중 일부분을 제거하여 복원한 뒤 제거된 영역에 대해 검증하였으며, 보정 단계에서는 Leave One Year Out Cross Validation (LOYOCV) 기법을 이용하여 현장자료와의 정확도를 검증하였다. DINCAE 모델의 해수면온도 복원 결과는 상당히 높은 정확도(R<sup>2</sup>=0.98, bias=0.27℃, RMSE=0.97℃, MAE=0.73℃)를 보였다. 두번째 단계의 LGBM 보정 모델의 정확도 개선은 표층 뜰개 부이와 계류형 부이 현장자료와의 비교에서 모두 상당한 향상(RMSE=Δ0.21-0.29℃, rRMSE=Δ0.91-1.65%, MAE=Δ0.17-0.24℃)을 보여주었다. 특히, 모든 현장 자료를 이용한 보정 모델의 표층 뜰개 부이와의 정확도는 동일한 현장 자료가 동화된 기존 해수면온도 합성장보다 나은 정확도를 보였다. 또한 LGBM 보정 모델은 랜덤포레스트(random forest)를 사용한 선행연구에서 보고된 과적합의 문제를 상당부분 해결하였다. 보정된 해수면온도는 기존의 초고해상도 해수면온도 합성장들과 유사한 수준으로 수온 전선과 와동 등의 중규모 해양현상을 뚜렷하게 모의하였다. 본 연구는 다종위성 자료와 기계학습 기법을 사용해 시공간적 공백 없는 고해상도 해수면온도 합성장 제작 방법을 제시하였다는 점에서 가치가 있다. Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R<sup>2</sup> of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.
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