• 대기
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• 제32권2호
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• pp.103-118
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• 2022

This study evaluates the simulated meteorological fields with a particular focus on the low-level wind, which plays an important role in air pollutants dispersion, under the varying synoptic environment. Additionally, the effects of subgrid-scale orography parameterization and improved topography/land-use data on the simulated low-level wind is investigated. The WRF model version 4.1.3 is utilized to simulate two cases that were affected by different synoptic environments. One case from 2 to 6 April 2012 presents the substantial low-level wind speed over the Korean peninsula where the synoptic environment is characterized by the baroclinic instability. The other case from 14 to 18 April 2012 presents the relatively weak low-level wind speed and distinct diurnal cycle of low-level meteorological fields. The control simulations of both cases represent the systematic overestimation of the low-level wind speed. The positive bias for the case under the baroclinic instability is considerably alleviated by applying the subgrid-scale orography parameterization. However, the improvement of wind speed for the other case showing relatively weak low-level wind speed is not significant. Applying the high-resolution topography and land-use data also improves the simulated wind speed by reducing the positive bias. Our analysis shows that the increased roughness length in the high-resolution topography and land-use data is the key contributor that reduces the simulated wind speed. The simulated wind direction is also improved with the high-resolution data for both cases. Overall, our study indicates that wind forecasts can be improved through the application of the subgrid-scale orography parameterization and high-resolution topography/land-use data.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.329-329
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• 2022

Accurate characterization of terrestrial precipitation variation from high spatial resolution satellite sensors is beneficial for urban hydrology and microscale agriculture modeling, as well as natural disasters (e.g., urban flooding) early warning. However, the widely-used top-down approach for precipitation retrieval from microwave satellites is limited in several hydrological and agricultural applications due to their coarse spatial resolution. In this research, we aim to apply a novel bottom-up method, the parameterized SM2RAIN, where precipitation can be estimated from soil moisture signals based on an inversion of water balance model, to generate high spatial resolution terrestrial precipitation estimates at 0.01º grid (roughly 1-km) from the C-band SAR Sentinel-1. This product was then tested against a common reanalysis-based precipitation data and a domestic rain gauge network from the Korean Meteorological Administration (KMA) over central South Korea, since a clear difference between climatic types (coasts and mainlands) and land covers (croplands and mixed forests) was reported in this area. The results showed that seasonal precipitation variability strongly affected the SM2RAIN performances, and the product derived from separated parameters (rainy and non-rainy seasons) outperformed that estimated considering the entire year. In addition, the product retrieved over the mainland mixed forest region showed slightly superior performance compared to that over the coastal cropland region, suggesting that the 6-day time resolution of S1 data is suitable for capturing the stable precipitation pattern in mainland mixed forests rather than the highly variable precipitation pattern in coastal croplands. Future studies suggest comparing this product to the traditional top-down products, as well as evaluating their integration for enhancing high spatial resolution precipitation over entire South Korea.

• 대한공간정보학회지
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• 제15권4호
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• pp.103-109
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• 2007

고해상도 위성영상으로부터 수치고도자료와 정사영상을 생성하기 위해서는 센서모델을 수립해야 한다. 센서모델 수립에 필요한 지상기준점은 실측을 통해서 획득할 수 있지만, 이를 위해서는 많은 시간과 비용이 소요된다. 본 연구에서는 기존의 정사영상과 위성영상 간의 정합을 통해서 얻은 지상좌표와 그에 대응하는 높이를 Digital Terrain Elevation(DTED) Level 2 자료로부터 추출하여 IKONOS 위성영상 센서모델을 수립하기 위한 기준점을 획득하였다. 획득한 기준점으로 IKONOS 센서 모델을 수립하고 그 결과를 분석하여 이로부터 추출한 기준점이 IKONOS 위성영상의 센서모델 수립에 적합한지 여부를 알아보았다. 본 연구를 위해서 사용된 DTED Level 2는 공간해상도가 약30m이고, 절대 수평 정확도는 원형오차로 23m(WGS84 기준)이하이고 절대 수직 정확도가 평균해수면 기준으로 18m 이하인 수치고도모델이다. 정합에 사용된 기존 정사영상의 공간해상도는 1m이다. 본 연구에서는 DTED와 정사영상을 이용해서 추출한 기준점으로 수립한 IKONOS 센서모델의 성능을 분석하였다. 실측 기준점을 검사점으로 했을 때 수립된 모델의 독립적 성능은 약 $4{\sim}5$ 픽셀 정도였다. 또한 수치고도모델을 생성하고 이를 실측 기준점으로 생성한 수치고도모델과 육안으로 비교했을 때 서로 유사함을 알 수 있었으며, DTED 자료를 기준으로 산출한 높이 RMS 오차는 약 9 m였다. 이 결과로 보아 DTED Level 2와 정사영상을 이용해서 추출한 기준점이 IKONOS 센서 모델 수립에 적용될 수 있음을 알 수 있었다.

• 대한원격탐사학회지
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• 제37권6_3호
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• pp.1931-1942
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• 2021

최근 고해상도 광학 위성영상의 활용성이 강조되면서 이를 이용한 지표 모니터링 연구가 활발히 수행되고 있다. 그러나 고해상도 위성영상은 낮은 시간 해상도에서 획득되기 때문에 그 활용성에 한계가 있다. 이러한 한계를 보완하기 위해 서로 다른 시간 및 공간 해상도를 갖는 다중 위성영상을 융합해 높은 시공간 해상도의 합성 영상을 생성하는 시공간 자료 융합을 적용할 수 있다. 기존 연구에서는 중저해상도의 위성영상을 대상으로 시공간 융합 모델이 개발되어 왔기 때문에 고해상도 위성영상에 대한 기개발된 융합 모델의 적용성을 평가할 필요가 있다. 이를 위해 이 연구에서는 KOMPSAT-3A 영상과 Sentinel-2 영상을 대상으로 기개발된 시공간 융합 모델의 적용성을 평가하였다. 여기에는 예측을 위해 사용하는 정보가 다른 Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM)과 Spatial Time-series Geostatistical Deconvolution/Fusion Model (STGDFM)을 적용하였다. 연구 결과, 시간적으로 연속적인 반사율 값을 결합하는 STGDFM의 예측 성능이 ESTARFM 보다 높은 것으로 나타났다. 특히 KOMPSAT 영상의 낮은 시간 해상도로 같은 시기에서 KOMPSAT 및 Sentinel-2 영상을 동시에 획득하기 어려운 경우, STGDFM의 예측 성능 향상이 더욱 크게 나타났다. 본 실험 결과를 통해 연속적인 시간 정보를 결합해 상대적으로 높은 예측 성능을 가지는 STGDFM을 이용해 낮은 재방문 주기로 인한 고해상도 위성영상의 한계를 보완할 수 있음을 확인하였다.

• Nuclear Engineering and Technology
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• 제56권7호
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• pp.2498-2515
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• 2024

In this study, we propose a fully parallelized adaptive particle refinement (APR) algorithm for smoothed particle hydrodynamics (SPH) to construct a stable and efficient multi-resolution computing system for nuclear safety analysis. The APR technique, widely employed by SPH research groups to adjust local particle resolutions, currently operates on a serialized algorithm. However, this serialized approach diminishes the computational efficiency of the system, negating the advantages of acceleration achieved through high-performance computing devices. To address this drawback, we propose a fully parallelized APR algorithm designed to enhance both efficiency and computational accuracy, facilitated by a new adaptive smoothing length model. For model validation, we simulated both hydrostatic and hydrodynamic benchmark cases in 2D and 3D environments. The results demonstrate improved computational efficiency compared to the conventional SPH method and APR with a serialized algorithm, and the model's accuracy was confirmed, revealing favorable outcomes near the resolution interface. Through the analysis of jet breakup, we verified the performance and accuracy of the model, emphasizing its applicability in practical nuclear safety analysis.

• 한국측량학회지
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• 제40권1호
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• pp.23-30
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• 2022

최근 딥러닝을 이용한 이미지 매팅 방법에 관한 다양한 연구가 진행되고 있다. 특히, 사진측량 분야에서도 고품질의 실감모형을 제작하기 위해서는 촬영된 이미지에서 유물 정보를 추출하는 과정이 필요하며, 이와 같은 과정은 많은 시간과 인력이 들어 기존에는 크로마키를 이용하여 추출하는 방법이 많이 활용되고 있다. 그러나, 기존의 방법은 세부 분류에 대한 정확도가 떨어져 고품질 실감모형에 적용하기에는 어려움이 있었다. 본 연구에서는 사전배경정보와 훈련된 학습데이터를 이용하여 고해상도 유물 이미지에서 배경정보를 제거하고 추출된 유물 이미지에 대하여 정성적, 정량적 결과를 평가하였다. 그 결과 제안된 방법과 FBA(매뉴얼 트라이맵)이 정량적으로 높은 결과를 나타냈으며, 정성적 평가에서도 유물 주변부의 분류도가 높은 정확도를 보였다. 따라서 제안된 방법은 고해상도 유물 이미지 분류에 있어 사전배경정보 취득을 통하여 높은 정확도와 빠른 처리 속도를 나타냈으며, 실내 유물 촬영에서 그 활용 가능성을 확인하였다.

• Mass Spectrometry Letters
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• 제6권4호
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• pp.91-98
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• 2015

The Brownian motion or Wiener process, as the physical model of the stochastic procedure, is observed as an indexed collection random variables. Stochastic procedure are quite influential on the confinement potential fluctuation in the quadrupole ion trap (QIT). Such effect is investigated for a high fractional mass resolution Δm/m spectrometry. A stochastic procedure like the Wiener or Brownian processes are potentially used in quadrupole ion traps (QIT). Issue examined are the stability diagrams for noise coefficient, η=0.07;0.14;0.28 as well as ion trajectories in real time for noise coefficient, η=0.14. The simulated results have been obtained with a high precision for the resolution of trapped ions. Furthermore, in the lower mass range, the impulse voltage including the stochastic potential can be considered quite suitable for the quadrupole ion trap with a higher mass resolution.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1319-1322
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• 2003

According to the evolutive theory of saline, combined with field survey data, the authors have discussed the theoretical model of recording past climatic change of Luobupo saline. After interpreted and analyzed the causes of the ringy image, the authors have mapped high resolution climatic changing graph of Luobupo saline during past 2000 years by using remote sensing method. Contrast to the known results, it is proved that the research results have comparability and continuity. The resolution of special climatic event can reach in one year, and in general, the resolution of climatic change can reach in ten to twenty years.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2008년도 추계학술대회 논문집
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• pp.274-277
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• 2008

To evaluate high-resolution wind resources for local and coastal area with complex terrain was attemped to combine the prognostic MM5 mesoscale model with CALMET diagnostic modeling this study. Firstly, MM5 was simulated for 1km resolution, nested fine domain, with FDDA using QuikSCAT seawinds data was employed to improve initial meteorological fields. Wind field and other meteorological variables from MM5 with all vertical levels used as initial guess field for CALMET. And 5 surface and 1 radio sonde observation data is performed objective analysis whole domain cells. Initial and boundary condition are given by 3 hourly RDAPS data of KMA in prognostic MM5 simulation. Geophysical data was used high-resolution terrain elevation and land cover(30 seconds) data from USGS with MM5 simulation. On the other hand SRTM 90m resolution and EGIS 30m landuse was adopted for CALMET diagnostic simulation. The simulation was performed on whole year for 2007. Vertical wind field a hour from CALMET and latest results of MM5 simulation was comparison with wind profiler(KEOP-2007 campaign) data at HAENAM site.

• 대한원격탐사학회지
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• 제33권4호
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• pp.423-436
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• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.