• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.744-747
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• 2010

This paper presents a novel adaptive control method for DC-DC converters applied in PV generator systems. We derive an state-space average model of the converter system and then propose a adaptive control methodology to enhance transient response performance for time-varying PV systems. A well-knwon Lyapunov theory is utilized for constructing our control algorithm. Numerical simulation demonstrates reliability of our control methodology and its superiority by comparison to a traditional control strategy.

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.73-80
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• 2002

RFM is believed to be universally applicable to any type of the sensor. Most of researches carried out lately are concentrated on terrain-independent method, but the researches about approvement of accuracy by way of terrain-dependent method are required to increase a practical use of satellite imagery in nonprofessional groups. This research focused on a means to improve RFM solution, a matching technique, and a generation of DEM through a correlation analysis, with terrain-dependent solution. The result shows that accuracy problem which is caused by over-parameterization on RFCs was removed through correlation analysis, and it was possible to generate a accurate DEM with terrain-dependent solution. And also, the application of RFM with different satellite images show sensor independent characteristics of RFM

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.433-437
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• 2003

고해상도 위성영상을 이용하여 수치표고모델(DEM) 및 정사영상을 제작해서 수치지도의 갱신 및 지형공간정보체계의 자료기반으로써 활용할 수 있다. 본 연구에서는 Sobel 연산자를 이용하여 경계추출을 행한 후 스크린 디지타이징 방법으로 경계선을 추출하였다 이렇게 추출된 벡터자료와 기존수치지도와의 중첩을 통해서 건물, 도로, 임야의 평균위치오차를 분석해 보았다. 평균위치오차가 공공측량의 작업규정에 대한 1 : 5,000 수치지도 제작의 허용오차범위에는 들지 못하였지만, 특정 부분의 지형·지물의 경우에는 수정, 보완이 가능한 것으로 나타났다. 그리고, 산악지역 보다는 도심지에서의 경계추출이 뚜렷하기 때문에 위치정밀도가 향상됨을 알 수 있었다.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.224-227
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• 2007

마이크로파 센서 자료를 이용하여 태풍 강도를 산출하고자 TRMM TMI로부터 관측된 자료와 태풍 강도의 최대 상관성을 나타내는 지역올 찾고 최적의 상관 변수를 선정하였다. 분석기간은 2004년 6월부터 9월까지 발생된 태풍으로써 18개의 사례이다. TMI로부터 관측된 85 GHz 채널의 밝기온도，구름내 총 수증기량，얼음양，강우 강도，잠열방출양이 태풍 강도와의 상관성 분석을 위한 변수로 분석되었다. 태풍의 강도는 RSMC-Tokyo에서 발표된 Best track의 최대 풍속 자료를 이용하였다. 위성 관측 변수를 태풍 중심으로부터 공간 평균하였을 때 반경 2.0-2.5도 정도의 평균거리에서 최대의 상관성을 보였다. 위성 자료로부터 태풍 중심 풍속을 추정하기 위하여 회귀분석을 하였다. Best track과의 오차는 85 GHz 밝기온도와 수증기량을 이용한 다중 회귀 분석에서 오차가 최소를 보였다. 한편， 태풍강도 예측을 위한 통계모델에 마이크로파 위성 자료를 예측인자로 입력하여 태풍강도의 정확도가 3-6%정도 향상됨을 보였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.113-116
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• 2021

일반적으로 딥러닝 기반의 객체 검출(Object Detection)기법은 합성곱 신경망(Convolutional Neural Network, CNN)을 통해 입력된 영상의 특징(Feature)을 추출하여 이를 통해 객체 검출을 수행한다. 최근 자연어 처리 분야에서 획기적인 성능을 보인 트랜스포머(Transformer)가 영상 분류, 객체 검출과 같은 컴퓨터 비전 작업을 수행하는데 있어 경쟁력이 있음이 드러나고 있다. 본 논문에서는 YOLOv4-CSP의 CSP 블록을 개선한 one-stage 방식의 객체 검출 네트워크를 제안한다. 개선된 CSP 블록은 트랜스포머(Transformer)의 멀티 헤드 어텐션(Multi-Head Attention)과 CSP 형태의 공간 피라미드 풀링(Spatial Pyramid Pooling, SPP) 연산을 기반으로 네트워크의 Backbone과 Neck에서의 feature 학습을 돕는다. 본 실험은 MSCOCO test-dev2017 데이터 셋으로 평가하였으며 제안하는 네트워크는 YOLOv4-CSP의 경량화 모델인 YOLOv4s-mish에 대하여 평균 정밀도(Average Precision, AP)기준 2.7% 향상된 검출 정확도를 보인다.

• Journal of Korea Water Resources Association
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• v.56 no.spc1
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• pp.1059-1069
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• 2023

Dam-break flow occurs when an elevated dam suddenly collapses, resulting in the catastrophic release of rapid and uncontrolled impounded water. This study compares laminar and turbulent closure models for simulating three-dimensional dam-break flows using OpenFOAM. The Reynolds-Averaged Navier-Stokes (RANS) model, specifically the k-ε model, is employed to capture turbulent dissipation. Two scenarios are evaluated based on a laboratory experiment and a modified multi-layered block obstacle scenario. Both models effectively represent dam-break flows, with the turbulent closure model reducing oscillations. However, excessive dissipation in turbulent models can underestimate water surface profiles. Improving numerical schemes and grid resolution enhances flow recreation, particularly near structures and during turbulence. Model stability is more significantly influenced by numerical schemes and grid refinement than the use of turbulence closure. The k-ε model's reliance on time-averaging processes poses challenges in representing dam-break profiles with pronounced discontinuities and unsteadiness. While simulating turbulence models requires extensive computational efforts, the performance improvement compared to laminar models is marginal. To achieve better representation, more advanced turbulence models like Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) are recommended, necessitating small spatial and time scales. This research provides insights into the applicability of different modeling approaches for simulating dam-break flows, emphasizing the importance of accurate representation near structures and during turbulence.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.31 no.4
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• pp.326-339
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• 1995

In this paper, the validity of the numerical model of fishes' behavior presented in our earlier paper was examined by the whiteness test on the residual of numerical model and by the comparison between experiment and simulation on several indexes represented by fishes' swimming characteristics. The validity of the numerical model was proved statistically by means of the whiteness test of the residual. The similarity was confirmed by comparison between experiment and simulation for the swimming trajectory of fishes, the mean distance of individual from wall, the mean swimming speed and the mean distance between the nearest individuals. These results suggest that the behavior of fishes according to the flow speed in three-dimensional space can be estimated partially by the numerical model presented in our earlier paper. However, a long-term approach to improve the modeling technique on the behavior of fishes may be needed before applying the numerical model presented in our earlier paper to real fishing ground.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• Journal of IKEEE
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• v.27 no.4
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• pp.456-463
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• 2023

In this paper, we introduce the SSResUNet network model, which integrates the SPADE structure with the U-Net network model for accurate land cover classification using high-resolution satellite imagery without requiring user intervention. The proposed network possesses the advantage of preserving the spatial characteristics inherent in satellite imagery, rendering it a robust classification model even in intricate environments. Experimental results, obtained through training on KOMPSAT-3A satellite images, exhibit superior performance compared to conventional U-Net and U-Net++ models, showcasing an average Intersection over Union (IoU) of 76.10 and a Dice coefficient of 86.22.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1623-1631
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• 2022

Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.