• 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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• 제16권4호
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• pp.201-212
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• 2004

Properties of polymer based nanocomposites depend on dispersion state of embedded fillers. In order to examine the effect of dispersion state on rheological properties, a new bi-mode FENE dumbbell model was proposed. The FENE dumbbell model includes two separate ensemble sets of dumbbells with different fric­tion coefficients, which simulate behavior of well dispersed and aggregated carbon nanotubes (CNTs). A new parameter indicating dispersion state of the CNT was proposed to account for degree of dispersion quantitatively as well as qualitatively. Rheological material functions in elongational, steady shear, and oscillatory shear flows were obtained numerically. The CNT/epoxy nanocomposites with different dis­persion state were prepared depending on whether a solvent is used for the dispersion of CNTs or not. Dis­persion state of the CNT in the epoxy nanocomposites was morphologically characterized by the field emission scanning electronic microscope and the transmission electron microscope images. It was found that the numerical prediction was in a good agreement with experimental results especially for steady state shear flow.

• 시스템엔지니어링학술지
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• 제17권2호
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• pp.98-105
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• 2021

As the average life expectancy is rising, the population is aging and the number of chronic diseases is increasing. This has increased the importance of healthy life and health management, and interest in mobile health services is on the rise thanks to the development of ICT(Information and communication technologies) and the smartphone use expansion. In order to meet these interests, many mobile services related to daily health are being launched in the market. Therefore, in this study, the characteristics of users who actually use mobile health services were analyzed and a predictive model applied with machine learning modeling was developed. As a result of the study, we developed a prediction model to which the decision tree and ensemble methods were applied. And it was found that the mobile health service users' continued use can be induced by providing features that require frequent visit, suggesting achievable activity missions, and guiding the sensor connection for user's activity measurement.

• Ocean and Polar Research
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• 제42권3호
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• pp.179-194
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• 2020

The present study aims to evaluate the effects of satellite-based SST (OSTIA) assimilation on a regional ocean circulation model for the Yellow and East China Seas (YECS), using three different assimilation methods: the Ensemble Optimal Interpolation (EnOI), Ensemble Kalman Filter (EnKF), and 4-Dimensional Variational (4DVAR) techniques, which are widely used in the ocean modeling communities. The model experiments show that an improved initial condition by assimilating the SST affects the seasonal water temperature and water mass distributions of the YECS. In particular, the SST data assimilation influences the temperature structures horizontally and vertically in winter, thereby improving the behavior of the YS warm current water. This is due to the fact that during wintertime the water column is well mixed, which is directly updated by the SST assimilation. The model comparisons indicate that the SST assimilation can improve the model performance in resolving the subsurface structures in wintertime, but has a relatively small impact in summertime due to the strong stratification. The differences among the different assimilation experiments are obvious when the SST was sharply changed due to a typhoon passage. Overall, the EnKF and 4DVAR show better agreement with the observations than the EnOI. The relatively low performance of EnOI under storm conditions may be related with a limitation of EnOI method whereby an analysis is obtained from a number of climatological fields, and thus the typhoon-induced SST changes in short-time scales may not be adequately reflected in the data assimilation.

• 한국환경복원기술학회지
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• 제21권1호
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• pp.103-118
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• 2018

Ecosystems in subalpine regions are recognized as areas vulnerable to climatic changes because rainfall and the possibility of flora migration are very low due to the characteristics of topography in the regions. In this context, habitat niche was formulated for representative species of arbors in subalpine regions in order to understand the effects of climatic changes on alpine arbor ecosystems. The current potential habitats were modeled as future change areas according to the climatic change scenarios. Based on the growth conditions and environmental characteristics of the habitats, the study was conducted to identify direct and indirect causes affecting the habitat reduction of Abies nephrolepis. Diverse model algorithms for explanation of the relationship between the emergence of biological species and habitat environments were reviewed to construct the environmental data suitable for the six models(GLM, GAM, RF, MaxEnt, ANN, and SVM). Weights determined through TSS were applied to the six models for ensemble in an attempt to minimize the uncertainty of the models. Based on the current climate determined by averaging the climates over the past 30years(1981~2010) and the HadGEM-RA model was applied to fabricate bioclimatic variables for scenarios RCP 4.5 and 8.5 on the near and far future. The results of models of the alpine region tree species studied were put together and evaluated and the results indicated that a total of eight national parks such as Mt. Seorak, Odaesan, and Hallasan would be mainly affected by climatic changes. Changes in the Baekdudaegan reserves were analyzed and in the results, A. nephrolepis was predicted to be affected the most in the RCP8.5. The results of analysis as such are expected to be finally utilizable in the survey of biological species in the Korean peninsula, restoration and conservation strategies considering climatic changes as the analysis identified the degrees of impacts of climatic changes on subalpine region trees in Korean peninsula with very high conservation values.

• 대기
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• 제31권2호
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• pp.185-198
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• 2021

In this study, we examined a spatial downscaling method based on Gradient and Inverse Distance Squared (GIDS) weighting to produce high-resolution grid data from a numerical weather prediction model over Korean Peninsula with complex terrain. The GIDS is a simple and effective geostatistical downscaling method using horizontal distance gradients and an elevation. The predicted meteorological variables (e.g., temperature and 3-hr accumulated rainfall amount) from the Limited-area ENsemble prediction System (LENS; horizontal grid spacing of 3 km) are used for the GIDS to produce a higher horizontal resolution (1.5 km) data set. The obtained results were compared to those from the bilinear interpolation. The GIDS effectively produced high-resolution gridded data for temperature with the continuous spatial distribution and high dependence on topography. The results showed a better agreement with the observation by increasing a searching radius from 10 to 30 km. However, the GIDS showed relatively lower performance for the precipitation variable. Although the GIDS has a significant efficiency in producing a higher resolution gridded temperature data, it requires further study to be applied for rainfall events.

• 응용통계연구
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• 제34권2호
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• pp.153-166
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• 2021

약물유전체학 연구의 주요 목표는 고차원의 유전 변수를 기반으로 개인의 약물 반응성을 예측하는 것이다. 변수의 개수가 많기 때문에 변수의 개수를 줄이기 위해서는 변수 선택이 필요하며, 선택된 변수들은 머신러닝 알고리즘을 사용하여 예측 모델을 구축하는데 사용된다. 본 연구에서는 400명의 뇌전증 환자의 차세대 염기서열 분석 데이터에 로지스틱 회귀, ReliefF, TurF, 랜덤 포레스트, LASSO의 조합과 같은 여러 가지 혼합 변수 선택 방법을 적용하였다. 선택된 변수들에 랜덤포레스트, 그래디언트 부스팅, 서포트벡터머신을 포함한 머신러닝 방법들을 적용했고 스태킹을 통해 앙상블 모형을 구축하였다. 본 연구의 결과는 랜덤포레스트와 ReliefF의 혼합 변수 선택 방법을 이용한 스태킹 모형이 다른 모형보다 더 좋은 성능을 보인다는 것을 보여주었다. 5-폴드 교차 검증을 기반으로 하여 적합한 최적 모형의 평균 검증 정확도는 0.727이고 평균 검증 AUC 값은 0.761로 나타났다. 또한, 동일한 변수를 사용할 때 스태킹 모델이 단일 머신러닝 예측 모델보다 성능이 우수한 것으로 나타났다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권2호
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• pp.57-64
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• 2021

자율주행차 시대가 도래하면서 ECU (Electronic Control Unit)는 점차 고도화되고 있고, 이에 따라 차량에서 정확한 데이터를 추출하고 분석하려는 연구가 다양하게 시도되어 왔다. 그러나 ECU는 차량 제조사별로 상이한 프로토콜을 가지고 있어 상용 단말기로는 정확한 데이터 추출과 분석이 어렵다. 본 연구에서는 정확한 차량 데이터를 추출하기 위하여 전용 펌웨어를 개발하여 차량의 2019년 1월부터 2월의 실제 주행데이터 53,580건의 데이터를 추출하였으며, 20회가 넘는 실제 도로 주행을 통해서 데이터의 정확도를 검증하였다. 이러한 데이터를 바탕으로 실시간 연료 소비량 예측 모델의 정확도를 높이기 위하여 스태킹 앙상블 기법을 이용하였다. 본 연구에서는 베이스 모델로 Ridge, Lasso, XGBoost, LightGBM이 사용되고 메타 모델은 Ridge가 사용되었으며, 예측 성능은 MAE 0.011, RMSE 0.017로 최적의 결과를 보였다.

• 한국정보통신학회논문지
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• 제26권1호
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• pp.70-75
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• 2022

위치를 정확하게 측정하는 것은 다양한 서비스를 제공하는 데 필요하다. 실내 측위를 위한 데이터는 스마트 폰의 앱을 통해 WiFi 장치로부터 RSSI 값을 측정한다. 이렇게 측정된 데이터는 기계학습의 원시 데이터가 된다. 특징 데이터는 측정된 RSSI 값이고, 레이블은 측정한 위치에 대한 공간의 이름으로 한다. 이를 위한 기계학습 기법은 분류에 효율적인 기법을 적용하여 WiFi 신호만으로 정확한 위치를 예측하는 기법을 연구하고자 한다. 앙상블은 하나의 모델보다 다양한 모델을 통하여 더 정확한 예측값을 구하는 기법으로, bagging과 boosting이 있다. 이 중 Boosting은 샘플링한 데이터를 바탕으로 모델링한 결과를 통해 모델의 가중치를 조정하는 기법으로, 다양한 알고리즘이 있다. 본 연구는 위 기법 중 XGboost를 이용하고, 다른 앙상블 기법과 이용한 수행결과를 바탕으로 성능을 평가한다.

• 한국지반신소재학회논문집
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• 제23권1호
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• pp.17-25
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• 2024

골재 간극률은 구조적 강도, 내구성, 배수 및 투수성 등 다양한 아스팔트의 특성에 직접적인 영향을 미친다. 따라서 아스팔트 포장이 사용되는 위치, 기후, 환경 등에 적절하도록 골재 간극률이 설계되어야한다. 하지만 골재 간극률은 다양한 요인들에 의해 영향을 받으므로 그 설계가 쉽지 않다. 예를 들어 골재 입자의 크기 분포, 구성이나 아스팔트 바인더의 양, 다짐 수준 등 다양한 영향인자가 존재한다. 본 연구에서는 골재 간극률에 영향을 미치는 요인들로부터 골재 간극률을 예측하고자 하였다. 이를 위해 다양한 기계학습 모델 방법을 적용하였고 단일 기계학습 모델을 적용했을 때보다 높은 정확도로 골재 간극률을 예측할 수 있음을 보였다. 본 연구의 결과는 경험과 노동집약적인 실험에 의존하는 골재 간극률 예측에 데이터 기반의 접근방법을 적용할 수 있음을 보였으며 향후 최적 골재 간극률 설계 등에 활용 가능할 것으로 기대된다.