• Journal of Korea Water Resources Association
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• v.47 no.3
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• pp.297-306
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• 2014

In this study, EFDC (Environmental Fluid Dynamics Code) model was used to simulate the salinity distribution for sea water inflow and rainfall runoff. The flowrate was given to the boundary conditions, which can be calculated by areal-specific flowrate method from the measured flowrate of the representative outfall. The boundary condition of the water elevation can be obtained from the hourly tidal elevation. The flowrate from the outfall can be calculated using the condition of the 245 mm raifall. The simulation results showed that at Sites 1~2 and the Mangsan island (Site 4) the salinity becomes 0 ppt after the rainfall. However, the salinity is 30 ppt when there is no rainfall. Time series of the salinity changes were compared with the measured data from January 1 to December 31, 2010 at the four sites (Site 2~5) of Yongwon channel. Lower salinities are shown at the inner sites of Yongwon channel (Site 1~4) and the sites of Songjeong river (Site 7~8). The intensive investigation near the Mangsan island showed that the changes of salinity were 21.9~28.8 ppt after the rainfall of 17 mm and those of the salinity were 2.33~8.05 ppt after the cumulative rainfall of 160.5 mm. This means that the sea water circulation is blocked in Yongwon channel, and the salinity becomes lower rapidly after the heavy rain.

• Journal of Korea Water Resources Association
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• v.39 no.12 s.173
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• pp.1043-1056
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• 2006

Our ultimate purpose is to investigate the potential change in regional surface climate due to the global warming and to produce higher quality regional surface climate information over the Korean peninsula for comprehensive impact assessment. Toward this purpose, we carried out two 30-year long experiments, one for present day conditions (covering the period 1971-2000) and one for near future climate conditions (covering the period 2021-2050) with a regional climate model (RegCM3) using a one-way double-nested system. In order to obtain the confidence in a future climate projection, we first verify the model basic performance of how the reference simulation is realistic in comparison with a fairly dense observation network. We then examine the possible future changes in mean climate state as well as in the frequency and intensity of extreme climate events to be derived by difference between climate condition as a baseline and future simulated climate states with increased greenhouse gas. Emphasis in this study is placed on the high-resolution spatial/temporal aspects of the climate change scenarios under different climate settings over Korea generated by complex topography and coastlines that are relevant on a regional scale.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.223-235
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• 2020

In this study, we propose an approach for calibrating the elevation of a DEM (Digital Elevation Model), one of the key data in realizing unmanned aerial vehicle image-based precision agriculture. First of all, radiometric correction is performed on the orthophoto, and then ExG (Excess Green) is generated. The non-vegetation area is extracted based on the threshold value estimated by applying the Otsu method to ExG. Subsequently, the elevation of the DEM corresponding to the location of the non-vegetation area is extracted as EIFs (Elevation Invariant Features), which is data for elevation correction. The normalized Z-score is estimated based on the difference between the extracted EIFs to eliminate the outliers. Then, by constructing a linear regression model and correcting the elevation of the DEM, high-quality DEM is produced without GCPs (Ground Control Points). To verify the proposed method using a total of 10 DEMs, the maximum/minimum value, average/standard deviation before and after elevation correction were compared and analyzed. In addition, as a result of estimating the RMSE (Root Mean Square Error) by selecting the checkpoints, an average RMSE was derivsed as 0.35m. Comprehensively, it was confirmed that a high-quality DEM could be produced without GCPs.

• Journal of the Korean Society for Marine Environment & Energy
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• v.8 no.4
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• pp.165-173
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• 2005

Field observations have been conducted to investigate the physical environment around oyster farms in Gamak Bay. Tidal waves near the two channels at the northeast and south of the bay had almost the same amplitudes and phases. Water temperature responded sensibly to the tides, rising at high water and falling at low water, except for the northwest region. The currents more regularly varied in accordance with a tidal period as long as they are at the faster-flowing region. A considerable flow has been found near the seabed of the northwest of the bay, normally known to be a stagnant area, and also the flow was opposite to the surface flow. Average moving speeds and directions of the flow at each station coincided well with patterns of the residual currents computed by Lee ef al. [2004], except for the northwest region. The discrepancy for the northwest region is not clear but it may have resulted from the facts that the computed flow pattern represents only the case of spring tide and in addition, a northwesterly wind prevailed all the observation time.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.75-84
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• 2019

In this study, the random forest approach was used to predict the national mean rice yield of South Korea by using mean climatic factors at a national scale. A random forest model that used monthly climate variable and year as an important predictor in predicting crop yield. Annual yield change would be affected by technical improvement for crop management as well as climate. Year as prediction factor represent technical improvement. Thus, it is likely that the variables of importance identified for the random forest model could result in a large error in prediction of rice yield in practice. It was also found that elimination of the trend of yield data resulted in reasonable accuracy in prediction of yield using the random forest model. For example, yield prediction using the training set (data obtained from 1991 to 2005) had a relatively high degree of agreement statistics. Although the degree of agreement statistics for yield prediction for the test set (2006-2015) was not as good as those for the training set, the value of relative root mean square error (RRMSE) was less than 5%. In the variable importance plot, significant difference was noted in the importance of climate factors between the training and test sets. This difference could be attributed to the shifting of the transplanting date, which might have affected the growing season. This suggested that acceptable yield prediction could be achieved using random forest, when the data set included consistent planting or transplanting dates in the predicted area.

• Geophysics and Geophysical Exploration
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• v.22 no.3
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• pp.107-115
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• 2019

Recently, since earthquakes have happened frequently in Gyeongju and Pohang areas in Korea, the earthquake detection research gets lots of attention. Geophysical monitoring data have been changed during the earthquake activity because the huge amount of energy is accumulated. The change of telluric current can be predicted by both of piezoelectric and electrokinetic effects before or during the earthquake occurrence, and if the change value exceeds the conventional telluric current noise, we can measure changes in the electric field associated with earthquakes. In this study, we have self-developed and verified the system that can monitor the telluric current. In order to verify our telluric current monitoring system, we installed lines of 40 m (E-W direction) and 28 m (N-S direction) on the site in Pohang. The telluric currents were sampled at 1 kHz for about a month. We have compared and analyzed the data of earthquake signals and electrical noises based on the earthquakes that occurred during the monitoring period. We have monitored if there were significant signals related to the earthquake on measured time series data. Through this study, we will suggest the direction of continuous research in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.583-592
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• 2020

This study assessed source depletion in the vadose zones of contaminated sites. The possible range of infiltration rate in Korea was statistically analyzed. The results showed a trend of decreasing leachate concentration of 13 pollutants used for risk assessment. Among them, benzene, ethylbenzene, toluene, and xylene showed a lower leachate concentration in groundwater over time due to their low distribution coefficient and also possible biodegradation effects. The average values of the relative concentration could be taken as a default index due to a very small range of uncertainties. In the case of heavy metals, it was shown that the leachate concentration in a pollutant does not decrease over time. Considering the annually different infiltration, a site-specific source-depletion scenario was applied to Cheongju in North Chungcheong Province. The result was expressed as a time series of the relative concentration of the leachate concentration, and this was compared to the trend by averaged Korean infiltration. Finally, an open-source code that used Python was used to help calculate the leachate concentration by this site-specific infiltration scenario.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.229-250
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• 2022

Since the publication of Markowitz's (1952) mean-variance portfolio model, research on portfolio optimization has been conducted in many fields. The existing mean-variance portfolio model forms a nonlinear convex problem. Applying Dantzig's linear programming method, it was converted to a linear form, which can effectively reduce the algorithm computation time. In this paper, we proposed a Dantzig perturbation portfolio model that can reduce management costs and transaction costs by constructing a portfolio with stable and small (sparse) assets. The average return and risk were adjusted according to the purpose by applying a perturbation method in which a certain part is invested in the existing benchmark and the rest is invested in the assets proposed as a portfolio optimization model. For a covariance estimation, we proposed a Gaussian kernel weight covariance that considers time-dependent weights by reflecting time-series data characteristics. The performance of the proposed model was evaluated by comparing it with the benchmark portfolio with 5 real data sets. Empirical results show that the proposed portfolios provide higher expected returns or lower risks than the benchmark. Further, sparse and stable asset selection was obtained in the proposed portfolios.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.