• Atmosphere
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• v.31 no.3
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• pp.327-340
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• 2021

In this study, the impact of soil moisture initialization in GloSea5, the operational climate prediction system of the Korea Meteorological Administration (KMA), has been investigated for the period of 1991~2010. To overcome the large uncertainties of soil moisture in the reanalysis, JRA55 reanalysis and CMAP precipitation were used as input of JULES land surface model and produced soil moisture initial field. Overall, both mean and variability were initialized drier and smaller than before, and the changes in the surface temperature and pressure in boreal summer and winter were examined using ensemble prediction data. More realistic soil moisture had a significant impact, especially within 2 months. The decreasing (increasing) soil moisture induced increases (decreases) of temperature and decreases (increases) of sea-level pressure in boreal summer and its impacts were maintained for 3~4 months. During the boreal winter, its effect was less significant than in boreal summer and maintained for about 2 months. On the other hand, the changes of surface temperature were more noticeable in the southern hemisphere, and the relationship between temperature and soil moisture was the same as the boreal summer. It has been noted that the impact of land initialization is more evident in the summer hemispheres, and this is expected to improve the simulation of summer heat wave in the KMA's operational climate prediction system.

• Composites Research
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• v.31 no.6
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• pp.371-378
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• 2018

In this study, the thermoelastic properties of poly lactic acid (PLA) based nanocomposites are predicted by molecular dynamics (MD) simulation and a micromechanics model. The stereocomplex mixed with L-lactic acid (PLLA) and D-lactic acid (PDLA) is modeled as matrix phase and a single walled carbon nanotube is embedded as reinforcement. The glass transition temperature, elastic moduli and thermal expansion coefficients of pure matrix and nanocomposites unit cells are predicted though ensemble simulations according to the hydrolysis. In micromechanics model, the double inclusion (D-I) model with a perfect interface condition is adopted to predict the properties of nanocomposites at the same composition. It is found that the stereocomplex nanocomposites show prominent improvement in thermal stability and interfacial adsorption regardless of the hydrolysis. Moreover, it is confirmed from the comparison of MD simulation results with those from the D-I model that the interface between CNT and the stereocomplex matrix is slightly weak in nature.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.215-222
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• 2022

The purpose of air traffic flow management is to balance demand and capacity in the national airspace, and its performance relies on an accurate capacity prediction of the airport or airspace. This paper developed a regression model that predicts the number of aircraft actually departing and arriving in a terminal maneuvering area. The regression model is based on a boosting ensemble learning algorithm that learns past aircraft operational data such as time, weather, scheduled demand, and unfulfilled demand at a specific airport in the terminal maneuvering area. The developed model was tested using historical departure and arrival flight data at Incheon International Airport, and the coefficient of determination is greater than 0.95. Also, the capacity of the terminal maneuvering area of interest is implicitly predicted by using the model.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.249-255
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• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• Journal of Korea Water Resources Association
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• v.48 no.7
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• pp.581-593
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• 2015

This study constructs the drought outlook system using ESP(Ensemble Streamflow Prediction) method and evaluates its utilization for drought prediction. Historical Runoff(HR) was estimated by employing LSM(Land Surface Model) and the observed meteorological, hydrological and topographical data in South Korea. Also Predicted Runoff(PR) was produced for different lead times(i.e. 1-, 2-, 3-month) using 30-year past meteorological data and the initial soil moisture condition. The HR accuracy was higher during MAM, DJF than JJA, SON, and the prediction accuracy was highly decreased after 1 month outlook. SRI(Standardized Runoff Index) verified for the feasibility of domestic drought analysis was used for drought outlook, and PR_SRI was evaluated. The accuracy of PR_SRI with lead times of 1- and 2-month was highly increased as it considered the accumulated 1- and 2-month HR, respectively. The Correlation Coefficient(CC) was 0.71, 0.48, 0.00, and Root Mean Square Error(RMSE) was 0.46, 0.76, 1.01 for 1-, 2- and 3-month lead times, respectively, and the accuracy was higher in arid season. It is concluded that ESP method is applicable to domestic drought prediction up to 1- and 2-month lead times.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.2
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• pp.79-88
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• 2015

Preparations need to be made for Korean pine(Pinus koraiensis) in anticipation of climate change because Korean pine is an endemic species of South Korea and the source of timber and pine nut. Therefore, climate change adaptation policy has been established to conduct an impact assessment on the distribution of Korean pine. Our objective was to predict the distribution of Korean pine while taking into account uncertainty and afforestation conditions. We used the 5th forest types map, a forest site map and BIOCLIM variables. The climate scenarios are RCP 4.5 and RCP 8.5 for uncertainty and the climate models are 5 regional climate models (HadGEM3RA, RegCM4, SNURCM, GRIMs, WRF). The base period for this study is 1971 to 2000. The target periods are the mid-21st century (2021-2050) and the end of the 21st century (2071-2100). This study used the MaxEnt model, and 50% of the presences were randomly set as training data. The remaining 50% were used as test data, and 10 cross-validated replicates were run. The selected variables were the annual mean temperature (Bio1), the precipitation of the wettest month (Bio13) and the precipitation of the driest month (Bio14). The test data's ROC curve of Korean pine was 0.689. The distribution of Korean pine in the mid-21st century decreased from 11.9% to 37.8% on RCP 4.5 and RCP 8.5. The area of Korean pine at an artificial plantation occupied from 32.1% to 45.4% on both RCPs. The areas at the end of the 21st century declined by 53.9% on RCP 4.5 and by 86.0% on RCP 8.5. The area of Korean pine at an artificial plantation occupied 23.8% on RCP 4.5 and 7.2% on RCP 8.5. Private forests showed more of a decrease than national forests for all subsequent periods. Our results may contribute to the establishment of climate change adaptation policies for considering various adaptation options.

• Journal of the Society of Naval Architects of Korea
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• v.39 no.3
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• pp.48-56
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• 2002

The flow characteristics around KRISO 3600TEU container ship model have been experimentally investigated in a circulating water channel. The instantaneous velocity vectors were measured using 2-frame PIV measurement system. The mean velocity fields and turbulent statistics including turbulent kinetic energy and vorticity were obtained by ensemble-averaging 400 instantaneous velocity fields. The free stream velocity was fixed at 0.6m/s and the corresponding Reynolds number was $9{\times}10^5$. The test sections were divided into two regions, three transverse sections of the wake region(Station -0.5767, -1, -3) and five longitudinal sections of the wake((Z/(B/2)=0, 0.1, 0.2, 0.4, 0.6). In the wake region, large-scale longitudinal vortices of nearly same strength are symmetric with respect to the wake centerline and a relatively weak secondary vortex is formed near the waterline. With going downstream, the strength of longitudinal vortex is decreased and the wake region expands.

• The Korean Journal of Quaternary Research
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• v.20 no.1 s.26
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• pp.9-27
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• 2006

The East Asian (China, Korea and Japan) summer monsoon precipitation and its variability are examined from the outputs of the 22 coupled climate models performing coordinated experiments leading to the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) following the multi-model ensemble (MME) technique. Results are based on averages of all the available models. The shape of the annual cycle with maximum during the summer monsoon period is simulated by the coupled climate models. However, models fail to simulate the minimum peak in July which is associated with northward shifts of the Meiyu-Changma-Baiu precipitation band. The MME precipitation pattern is able to capture the spatial distribution of rainfall associated with the location of the north Pacific subtropical high and the Meiyu-Changma-Baiu frontal zone. However precipitation over the east coast of China, Korea-Japan peninsular and the adjoining oceanic regions is underestimated. Future projections to the radiative forcing of doubled $CO_2$ scenario are examined. The MME reveals an increase in precipitation varying from 5 to 10 %, with an average of 7.8 % over the East Asian region at the time of $CO_2$ doubling. However the increases are statistically significant only over the Korea-Japan peninsula and the adjoining north China region. The increase in precipitation may be attributed to the projected intensification of the subtropical high, and thus the associated influx of moist air from the Pacific to inland. The projected changes in the amount of precipitation are directly proportional to the changes in the strength of the subtropical high. Further a possible increase in the length of the summer monsoon precipitation period from late spring through early autumn is suggested.

• Atmosphere
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• v.29 no.3
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• pp.257-267
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• 2019

This study is to assess the applicability of the Extreme Forecast Index (EFI) algorithm of the ECMWF seasonal forecast system to the Global Seasonal Forecasting System version 5 (GloSea5), operational seasonal forecast system of the Korea Meteorological Administration (KMA). The EFI is based on the difference between Cumulative Distribution Function (CDF) curves of the model's climate data and the current ensemble forecast distribution, which is essential to diagnose the predictability in the extreme cases. To investigate its applicability, the experiment was conducted during the heat-wave cases (the year of 1994 and 2003) and compared GloSea5 hindcast data based EFI with anomaly data of ERA-Interim. The data also used to determine quantitative estimates of Probability Of Detection (POD), False Alarm Ratio (FAR), and spatial pattern correlation. The results showed that the area of ERA-Interim indicating above 4-degree temperature corresponded to the area of EFI 0.8 and above. POD showed high ratio (0.7 and 0.9, respectively), when ERA-Interim anomaly data were the highest (on Jul. 11, 1994 (> $5^{\circ}C$) and Aug. 8, 2003 (> $7^{\circ}C$), respectively). The spatial pattern showed a high correlation in the range of 0.5~0.9. However, the correlation decreased as the lead time increased. Furthermore, the case of Korea heat wave in 2018 was conducted using GloSea5 forecast data to validate EFI showed successful prediction for two to three weeks lead time. As a result, the EFI forecasts can be used to predict the probability that an extreme weather event of interest might occur. Overall, we expected these results to be available for extreme weather forecasting.