• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.3
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• pp.107-114
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• 2014

The purpose of this study is to explore a correlation model of traffic safety and personality in elderly and non-elderly people. The correlation model is constructed by a factor regression analysis with latent factors and items related to traffic safety consciousness. As a result, it is found that non-elderly people with positive, active, perfectionistic, and unforgiving personality are likely to speed, have a high chance to be involved in traffic accidents, and tend to give low scores to traffic conditions. However, elderly people who are highly educated are likely to give high scores to traffic conditions and do not speed. Instead, elderly people become more likely to be involved in traffic accidents when they are engaged in more social activities. The results could contribute to developing traffic safety education and policy that is better customized to the specific needs of different groups of road users.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1013-1024
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• 2015

We compare the performance of two representative estimation methods for the dynamic conditional correlation (DCC) GARCH model. The first method is the pairwise estimation which exploits partial information from the paired series, irrespective to the time series dimension. The second is the multi-dimensional estimation that uses full information of the time series. As a simulation for the comparison, we generate a multivariate time series similar to those observed in real markets and construct a DCC GARCH model. As an empirical example, we constitute various portfolios using real KOSPI 200 sector indices and estimate volatility and VaR of the portfolios. Through the estimated dynamic correlations from the simulation and the estimated volatility and value at risk (VaR) of the portfolios, we evaluate the performance of the estimations. We observe that the multi-dimensional estimation tends to be superior to pairwise estimation; in addition, relatively-uncorrelated series can improve the performance of the multi-dimensional estimation.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.533-541
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• 2011

Algal blooms have caused problems for drinking water as well as eutrophication. However it is difficult to control algal blooms by current warning manual in rainy season because the algal blooms happen in a few days. The water quality data, which have high correlations with Chlorophyll-a on Daecheongho station, were analyzed and chosen as input data of Artificial Neural Networks (ANN) for training pattern changes. ANN was applied to early forecasting of algal blooms, and ANN was assessed by forecasting errors. Water temperature, pH and Dissolved oxygen were important factors in the cross correlation analysis. Some water quality items like Total phosphorus and Total nitrogen showed similar pattern to the Chlorophyll-a changes with time lag. ANN model (No. 3), which was calibrated by water temperature, pH and DO data, showed lowest error. The combination of 1 day, 3 days, 7 days forecasting makes outputs more stable. When automatic monitoring data were used for algal bloom forecasting in Daecheong reservoir, ANN model must be trained by just input data which have high correlation with Chlorophyll-a concentration. Modular type model, which is combined with the output of each model, can be effectively used for stable forecasting.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.268-268
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• 2021

Groundwater resource is mostly used in Abomey-calavi (southern region of Benin) as main source of water for domestic, industrial, and agricultural activities. Groundwater intake across the region is not perfectly controlled by a network due to the presence of many private boreholes and traditional wells used by the population. After some decades, this important resource is becoming more and more vulnerable and needs more attention. For a better groundwater management in the region of Abomey-calavi, the present study attempts to predict a future probable groundwater drought using Recurrent Neural Network (RNN) for future groundwater level prediction. The RNN model was created in python using jupyter library. Six years monthly groundwater level data was used for the model calibration, two years data for the model test and the model was finaly used to predict two years future groundwater level (years 2020 and 2021). GRI was calculated for 9 wells across the area from 2012 to 2021. The GRI value in dry season (by the end of March) showed groundwater drought for the first time during the study period in 2014 as severe and moderate; from 2015 to 2021 it shows only moderate drought. The rainy season in years 2020 and 2021 is relatively wet and near normal. GRI showed no drought in rainy season during the study period but an important diminution of groundwater level between 2012 and 2021. The Pearson's correlation coefficient calculated between GRI and rainfall from 2005 to 2020 (using only three wells with times series long period data) proved that the groundwater drought mostly observed in dry season is not mainly caused by rainfall scarcity (correlation values between -0.113 and -0.083), but this could be the consequence of an overexploitation of the resource which caused the important spatial and temporal diminution observed from 2012 to 2021.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.89-102
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• 2014

In this study, we analyze the effect on the smoking status in the Seoul Metropolitan area using a multilevel logistic model with Community Health Survey data from the Korea Centers for Disease Control and Prevention. Intraclass correlation coefficient (ICC), profiling analysis and two types of predicted value were used to determine the appropriate multilevel analysis level. Sensitivity, specificity, percentage of correctly classified observations (PCC) and ROC curve evaluated model performance. We showed the applicability for multilevel analysis allowed for the possibility that different factors contribute to within group and between group variability using survey data.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.1
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• pp.241-251
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• 1991

The present simulation model relies on a new approach of the heavy particle motion in a turbulent flow considering the time and space correlation to the Lagrangian point of view. The turbulent field is, here, assumed that its characteristic scales are random and follow a Poisson's distribution. Using this model, we have computed the trajectory of each particle, that is, its velocity and position at each time in order to study the dispersion of particles in a grid turbulent flow. The computed results have been compared to the corresponding experimental data. Due to the complex mechanism of turbulence and the theoretically and experimentally lacking information, we had to make some assumptions for simplifying the situation, but we have found the good agreement between simulated and measured results. In particular, the application of the present method on the Lagrangian correlation of particle provides an interesting alternative to the usual computational methods.

• Journal of computational fluids engineering
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• v.13 no.3
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• pp.62-68
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• 2008

This paper reports briefly on the computational results of a turbulent Rayleigh-Benard convection with the elliptic-blending second-moment closure (EBM). The primary emphasis of the study is placed on an investigation of accuracy and numerical stability of the elliptic-blending second-moment closure for the turbulent Rayleigh-Benard convection. The turbulent heat fluxes in this study are treated by the algebraic flux model with the temperature variance and molecular dissipation rate of turbulent heat flux. The model is applied to the prediction of the turbulent Rayleigh-Benard convection for Rayleigh numbers ranging from Ra=$2{\times}10^6$ to Ra=$10^9$ and the computed results are compared with the previous experimental correlations, T-RANS and LES results. The predicted cell-averaged Nusselt number follows the correlation by Peng et al.(2006) (Nu=$0.162Ra^{0.286}$) in the 'soft' convective turbulence region ($2{\times}10^6{\leq}Ra{\leq}4{\times}10^7$) and it follows the experimental correlation by Niemela et al. (2000) (N=$0.124Ra^{0.309}$) in the 'hard' convective turbulence region ($10^8{\leq}Ra{\leq}10^9$) within 5% accuracy. This results show that the elliptic-blending second-moment closure with an algebraic flux model predicts very accurately the Rayleigh-Benard convection.

• Structural Engineering and Mechanics
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• v.40 no.4
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• pp.517-539
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• 2011

This paper presents the seismic responses of a 1:5-scale five-story reinforced concrete building model, which represents a residential apartment building that has a high irregularity of weak story, soft story, and torsion simultaneously at the ground story. The model was subjected to a series of uni- and bi-directional earthquake simulation tests. Analysis of the test results leads to the following conclusions: (1) The model survived the table excitations simulating the design earthquake with the PGA of 0.187 g without any significant damages, though it was not designed against earthquakes; (2) The fundamental mode was the torsion mode. The second and third orthogonal translational modes acted independently while the torsion mode showed a strong correlation with the predominant translational mode; (3) After a significant excursion into inelastic behavior, this correlation disappeared and the maximum torsion and torsion deformation remained almost constant regardless of the intensity of the two orthogonal excitations; And, (4) the lateral resistance and stiffness of the critical columns and wall increased or decreased significantly with the large variation of acting axial forces caused by the high bi-directional overturning moments and rocking phenomena under the bi-directional excitations.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.33.2-33.2
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• 2008

Korea Astronomy and Space Science Institute (KASI) is developing an empirical model for Korean Space Weather Prediction Center (KSWPC). This model predicts the geomagnetic storm strength (Dst minimum) by using only CME parameters, such as the source location (L), speed (V), earthward direction (D), and magnetic field orientation of an overlaying potential field at CME source region. To derive an empirical formula, we considered that (1) the direction parameter has best correlation with the storm strength (2) west $15^{\circ}$ offset from the central meridian gives best correlation between the source location and the storm strength (3) consideration of two groups of CMEs according to their magnetic field orientation (southward or northward) provide better forecast. In this talk, we introduce current status of the empirical storm prediction model development.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1131-1134
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• 2003

Unknown parameters of a nonlinear system were estimated using a signal compression method. The estimated parameters were natural frequency and tile damping coefficient. This study applied a algorithm using tile comparison of the cross-correlation coefficient between the impulse response from a model and it from the signal compression method. The impulse through linear element included in a nonlinear system could be obtained by the signal compression method. The unknown parameters of the linear element could be estimated by comparing the Bode plots of system's impulse response with them of model's response. In this study, a LSCM(LabVIEW-Signal-Compression-Method) was developed to identify a nonlinear system. The LSCM consisted of National Instrument's (NI) Data Acquisition (DAQ) Board (Model PCI-1200), a monitoring program using LabVIEW software package, DAQ Signal Accessory Board, and 2nd-order electric circuits. The designed electric circuits consisted of resistors, inductors and capacitors. To evaluate the performance of the LSCM, the response from model with known parameters is compared with the response from the real system using the monitoring program. The results from simulation of experiment showed that the developed LSCM provided a reliable estimation performance.