• 대한원격탐사학회지
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• 제36권5_3호
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• pp.1053-1066
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• 2020

이산화황(SO₂)은 대기 중 화학 반응을 통해 2차 대기오염물질을 생성하는 전구체로, 주로 산업활동이나 주거 및 교통 활동 등을 통해 배출된다. 장기간 노출 시 호흡기 질환이나 심혈관 질환 등을 유발하여 인체 건강에 부정적인 영향을 미칠 수 있기 때문에 이에 대한 지속적인 모니터링이 필요하다. 우리나라에서는 SO₂에 대해 관측소 기반의 모니터링이 수행되고 있으나 이는 공간적으로 연속적인 정보를 제공하는 데에 한계가 있다. 따라서, 본 연구에서는 위성자료와 수치모델 자료를 융합하여 일별 13시를 타겟으로 하는 1 km의 고해상도로 공간적으로 연속적인 SO₂ 지상농도를 산출하였다. 2015년 1월부터 2019년 4월까지의 기간 동안 남한 지역에 대하여 스태킹 앙상블 기법을 이용하여 SO₂ 지상농도 추정 모델을 개발하였다. 스태킹 앙상블 기법이란 여러가지 기계학습 기법을 두 단계로 쌓는 방식으로 융합하여 단일 모델 대비 더 향상된 성능을 도출하는 방법이다. 본 연구에서는 베이스 모델로는 RF (Random Forest)와 XGB (eXtreme Gradient BOOSTing) 기법이, 메타 모델로는 MLR (Multiple Linear Regression) 기법이 사용되었다. 구축된 모델의 교차검증 결과 메타 모델은 상관계수(R) = 0.69와 root-mean-squared-error(RMSE) = 0.0032 ppm의 결과를 보였으며 이는 베이스 모델의 평균 대비 약 25% 향상된 안정성을 보였다. 또한 모델 구축에 사용되지 않은 기간에 대한 예측 검증을 수행하여 모델의 일반화 가능성을 평가하였다. 구축된 모델을 이용하여 남한 지역의 SO₂ 지상농도 공간분포를 분석한 결과 일반적인 계절성과 배출원의 변화를 잘 반영하는 패턴을 보임을 확인하였다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제19권4호
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• pp.31-44
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• 2014

In the present study, a few of recently developed geostatistical models are comparatively studied. The models are two-point statistics based sequential indicator simulation (SISIM) and generalized coupled Markov chain (GCMC), multi-point statistics single normal equation simulation (SNESIM), and object based model of FLUVSIM (fluvial simulation) that predicts structures of target object from the provided geometric information. Out of the models, SNESIM and FLUVSIM require additional information other than conditioning data such as training map and geometry, respectively, which generally claim demanding additional resources. For the comparative studies, three-dimensional fluvial reservoir model is developed considering the genetic information and the samples, as input data for the models, are acquired by mimicking realistic sampling (i.e. random sampling). For SNESIM and FLUVSIM, additional training map and the geometry data are synthesized based on the same information used for the objective model. For the comparisons of the predictabilities of the models, two different measures are employed. In the first measure, the ensemble probability maps of the models are developed from multiple realizations, which are compared in depth to the objective model. In the second measure, the developed realizations are converted to hydrogeologic properties and the groundwater flow simulation results are compared to that of the objective model. From the comparisons, it is found that the predictability of GCMC outperforms the other models in terms of the first measure. On the other hand, in terms of the second measure, the both predictabilities of GCMC and SNESIM are outstanding out of the considered models. The excellences of GCMC model in the comparisons may attribute to the incorporations of directional non-stationarity and the non-linear prediction structure. From the results, it is concluded that the various geostatistical models need to be comprehensively considered and comparatively analyzed for appropriate characterizations.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3330-3344
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• 2023

This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.

• Bulletin of the Korean Chemical Society
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• 제32권11호
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• pp.3909-3913
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• 2011

In this paper we have carried out molecular dynamics simulations (MD) for model systems of liquid n-alkane oligomers ($C_{12}{\sim}C_{80}$) at high temperatures (~2300 K) in a canonical ensemble to calculate viscosity ${\eta}$, self-diffusion constants D, and monomeric friction constant ${\zeta}$. We found that the long chains of these n-alkanes at high temperatures show an abnormality in density and in monomeric friction constant. The behavior of both activation energies, $E_{\eta}$ and $E_D$, and the mass and temperature dependence of ${\eta}$, D, and ${\zeta}$ are discussed.

• Interaction and multiscale mechanics
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• 제1권2호
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• pp.303-315
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• 2008

In this paper, thermodynamical properties of crystalline silicon under strain are calculated using classical molecular dynamics (MD) simulations based on the Tersoff interatomic potential. The Helmholtz free energy of the silicon crystal under strain is calculated by using the ensemble method developed by Frenkel and Ladd (1984). To account for quantum corrections under strain in the classical MD simulations, we propose an approach where the quantum corrections to the internal energy and the Helmholtz free energy are obtained by using the corresponding energy deviation between the classical and quantum harmonic oscillators. We calculate the variation of thermodynamic properties with temperature and strain and compare them with results obtained by using the quasi-harmonic model in the reciprocal space.

• Korea-Australia Rheology Journal
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• 제17권1호
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• pp.27-32
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• 2005

We determine the effective viscosity of suspensions with bidisperse particle size distribution by modifying an effective-medium theory that was proposed by Acrivos and Chang (1987) for monodisperse suspensions. The modified theory uses a simple model that captures some important effects of multi-particle hydrodynamic interactions. The modifications are described in detail in the present study. Estimations of effective viscosity by the modified theory are compared with the results of prior work for monodisperse and bidisperse suspensions. It is shown that the estimations agree very well with experimental or other calculated results up to approximately 0.45 of normalized particle volume fraction which is the ratio of volume faction to the maximum volume fraction of particles for bidisperse suspensions.

• 대한조선학회논문집
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• 제40권5호
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• pp.17-25
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• 2003

Velocity field behind a container ship model with a rotating propeller has been investigated using PIV (particle image velocimetry) system. Four hundred instantaneous velocity fields were measured at 4 different blade phases and ensemble-averaged to investigate the spatial evolution of vortical structure of near wake within one propeller diameter downstream. The phase-averaged mean velocity fields show the potential wake and the viscous wake formed due to the boundary layers developed on the blade surfaces. The interaction between bilge vortex developed along the hull surface and the tangential velocity component of incoming flow causes to have asymmetric flow structure in the transverse plane.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.606-611
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• 2007

Proper orthogonal decomposition (POD) is a method for extracting bases for modal decomposition from the ensemble of dynamic signals. Using the POD method, we analyzed the proper orthogonal modes (POMs) of AFM microcantilevers in dynamic mode operations such as Tapping Mode. The POMs and POVs (proper orthogonal values) were computed through MATLAB simulation for the 5-mode model of the microcantilever. We found that the POV portion of the higher POMs of the tapping microcanilever slightly increased in comparison with no tapping. This implies that the modal energy in the fundamental mode can be transferred to the higher modes during tapping.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1186-1190
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• 2009

강수량이 예년에 비해 적은 양상은 여름강수량에 대한 부족으로 기인한다. 우리나라의 경우 장마기간의 강수와 태풍으로 인해 발생하는 강수가 전체 강수량에 많은 부분을 차지하고 있기 때문에 여름강수량이 적게 나타나게 되면 가을 가뭄 및 봄 가뭄에 대한 발생 압력도 그 만큼 커지게 되는 것이 일반적이다. 기존 연구들이 단순히 강수량을 가정하거나 시나리오를 기반으로 가뭄을 전망하는데 그치고 있으나 본 연구에서는 2009년 가뭄전망을 위해서 전지구기후모형(GCMs)의 3개월 기상예측 결과를 활용하고자 한다. 즉, APEC 기후예측 센터로부터 제공 받은 3개월 GCM Multi-Model Ensemble 예측 결과를 바탕으로 가뭄상태를 평가하였다. 따라서 본 연구의 목적은 Large-scale의 기후예측 시스템과 기상관측지점의 강수 및 온도를 연결시켜 가뭄을 전망할 수 있는 시스템을 구축하는데 있다. GCM 예측 결과를 바탕으로 2009년도 매월 강수량 및 평균 온도를 추정하여 PDSI 가뭄지수 산정에 이용하였다.