• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.157-166
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• 2016

Aerodynamic loads for a horizontal axis wind turbine of the National Renewable Energy Laboratory (NREL) Phase VI rotor in yawed condition were predicted by using the blade element momentum theorem. The classical blade element momentum theorem was complemented by several aerodynamic corrections and models including the Pitt and Peters' yaw correction, Buhl's wake correction, Prandtl's tip loss model, Du and Selig's three-dimensional (3-D) stall delay model, etc. Changes of the aerodynamic loads according to the azimuth angle acting on the span-wise location of the NREL Phase VI blade were compared with the experimental data with various yaw angles and inflow speeds. The computational flow chart for the classical blade element momentum theorem was adequately modified to accurately calculate the combined functions of additional corrections and models stated above. A successive under-relaxation technique was developed and applied to prevent possible failure during the iteration process. Changes of the angle of attack according to the azimuth angle at the specified radial location of the blade were also obtained. The proposed numerical procedure was verified, and the predicted data of aerodynamic loads for the NREL Phase VI rotor bears an extremely close resemblance to those of the experimental data.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.3
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• pp.203-211
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• 2010

This study examines a cointegrated vector autoregressive (VAR) model where parameters are subject to switch across the regimes in the term structure of interest rates. To employ the regime switching framework, the Markov-switching vector error correction model (MS-VECM) is allowed to the regime shifts in the vector of intercept terms, the variance-covariance terms, the error correction terms, and the autoregressive coefficient parts. The corresponding approaches are illustrated using the term structure of interest rates in the US Treasury bonds over the period of 1958 to 2009. Throughout the modeling procedure, we find that the MS-VECM can form a statistically adequate representation of the term structure of interest rate in the US Treasury bonds. Moreover, the regime switching effects are analyzed in connection with the historical government monetary policy and with the recent global financial crisis. Finally, the results from the comparisons both in information criteria and in forecasting exercises with and without the regime switching lead us to conclude that the models in the presence of regime dependence are superior to the linear VECM model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.454-456
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• 2017

Current and capacitance-voltage characteristics of tunnel field effect transistor (TFET) with various quantum models were investigated. Density gradient, Bohm quantum potential (BQP), and Vandort quantum correction are used with calibrating against Schrodinger-Poisson model. Drive-currents in all models. are decreased. When only BQP is used, SS and $V_{onset}$ are fixed but drive-current is decreased 3 times more than those of no quantum model. And When BQP with Vandort and density gradient are used, SS increased more than 40 mV./dec and $V_{onset}$ shifted as 0.07 eV.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• The Korean Journal of Financial Management
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• v.21 no.1
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• pp.125-148
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• 2004

This paper deals with the relationship between money supply and the stock market. However, unlike past works, it has employed a rational expectation hypothesis and an efficient market hypothesis drawn from new classical macroeconomics and new Keynesian macro-economics, respectively. Accordingly, hypothesis 1 states that if economic subjects have rational expectation, they will immediately respond to a change in money supply. On the other hand, hypothesis 2 supposes that the expectation of economic subjects has changed after the currency crisis. This paper has first identified unit root by using the augmented Dickey-Fuller test and the Phillips-Perron test, then testing both hypotheses by employing the Johansen Procedure and vector error correction model for the periods before and after a currency crisis.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.419-426
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• 2022

In this paper, we propose a method to improve the accuracy of 3D human pose estimation model in various move motions. Existing human pose estimation models have some problems of jitter, inversion, swap, miss that cause miss coordinates when estimating human poses. These problems cause low accuracy of pose estimation models to detect exact coordinates of human poses. We propose a method that consists of detection and correction methods to handle with these problems. Deep learning-based outlier detection method detects outlier of human pose coordinates in move motion effectively and rule-based correction method corrects the outlier according to a simple rule. We have shown that the proposed method is effective in various motions with the experiments using 2D golf swing motion data and have shown the possibility of expansion from 2D to 3D coordinates.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.160-160
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• 2023

Generally, Global Climate Models (GCM) cannot be used directly due to their inherent error arising from over or under-estimation of climate variables compared to the observed data. Several bias correction methods have been devised to solve this problem. Most of the traditional bias correction methods are one dimensional as they bias correct the climate variables separately. One such method is the Quantile Mapping method which builds a transfer function based on the statistical differences between the GCM and observed variables. Laux et al. introduced a copula-based method that bias corrects simulated climate data by employing not one but two different climate variables simultaneously and essentially extends the traditional one dimensional method into two dimensions. but it has some limitations. This study uses objective functions to address specifically, the limitations of Laux's methods on the Quantile Mapping method. The objective functions used were the observed rank correlation function, the observed moment function and the observed likelihood function. To illustrate the performance of this method, it is applied to ten GCMs for 20 stations in South Korea. The marginal distributions used were the Weibull, Gamma, Lognormal, Logistic and the Gumbel distributions. The tested copula family include most Archimedean copula families. Five performance metrics are used to evaluate the efficiency of this method, the Mean Square Error, Root Mean Square Error, Kolmogorov-Smirnov test, Percent Bias, Nash-Sutcliffe Efficiency and the Kullback Leibler Divergence. The results showed a significant improvement of Laux's method especially when maximizing the observed rank correlation function and when maximizing a combination of the observed rank correlation and observed moments functions for all GCMs in the validation period.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.173-182
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• 2013

The early Sea-viewing Wide Field-of-view Sensor(SeaWiFS) atmospheric correction algorithm which is the basis of the atmospheric correction algorithm for Geostationary Ocean Color Imager(GOCI) assumes that water-leaving radiances is negligible at near-infrared(NIR) wavelengths. For this reason, all of the satellite measured radiances at the NIR wavelengths are assigned to aerosol radiances. However that assumption would cause underestimation of water-leaving radiances if it were applied to turbid Case-2 waters. To overcome this problem, Management Unit of the North Sea Mathematical Models(MUMM) atmospheric correction algorithm has been developed for turbid waters. This MUMM algorithm introduces new parameter ${\alpha}$, representing the ratio of water-leaving reflectance at the NIR wavelengths. ${\alpha}$ is calculated by statistical method and is assumed to be constant throughout the study area. Using this algorithm, we can obtain comparatively accurate water-leaving radiances in the moderately turbid waters where the NIR water-leaving reflectance is less than approximately 0.01. However, this algorithm still underestimates the water-leaving radiances at the extremely turbid water since the ratio of water-leaving radiance at two NIR wavelengths, ${\alpha}$ is changed with concentration of suspended particles. In this study, we modified the MUMM algorithm to calculate appropriate value for ${\alpha}$ using an iterative technique. As a result, the accuracy of water-leaving reflectance has been significantly improved. Specifically, the results show that the Root Mean Square Error(RMSE) of the modified MUMM algorithm was 0.002 while that of the MUMM algorithm was 0.0048.

• Proceedings of the KSRS Conference
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• v.1
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• pp.467-470
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• 2006

Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meteorological Satellite (COMS) is scheduled for launch in 2008. GOCI includes the eight visible-to-near-infrared (NIR) bands, 0.5km pixel resolution, and a coverage region of 2500 ${\times}$ 2500km centered at 36N and 130E. GOCI has had the scope of its objectives broadened to understand the role of the oceans and ocean productivity in the climate system, biogeochemical variables, geological and biological response to physical dynamics and to detect and monitor toxic algal blooms of notable extension through observations of ocean color. To achieve these mission objectives, it is necessary to develop an atmospheric correction technique which is capable of delivering geophysical products, particularly for highly turbid coastal regions that are often dominated by strongly absorbing aerosols from the adjacent continental/desert areas. In this paper, we present a more realistic and cost-effective atmospheric correction method which takes into account the contribution of NIR radiances and include specialized models for strongly absorbing aerosols. This method was tested extensively on SeaWiFS ocean color imagery acquired over the Northwest Pacific waters. While the standard SeaWiFS atmospheric correction algorithm showed a pronounced overcorrection in the violet/blue or a complete failure in the presence of strongly absorbing aerosols (Asian dust or Yellow dust) over these regions, the new method was able to retrieve the water-leaving radiance and chlorophyll concentrations that were consistent with the in-situ observations. Such comparison demonstrated the efficiency of the new method in terms of removing the effects of highly absorbing aerosols and improving the accuracy of water-leaving radiance and chlorophyll retrievals with SeaWiFS imagery.