• Journal of Astronomy and Space Sciences
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• v.28 no.1
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• pp.71-77
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• 2011

Ionospheric error modeling is necessary to create reliable global navigation satellite system (GNSS) signals using a GNSS simulator. In this paper we developed algorithms to generate Klobuchar coefficients ${\alpha}_n$, ${\beta}_n$ (n = 1, 2, 3, 4) for a GNSS simulator and verified accuracy of the algorithm. The eight Klobuchar coefficients were extracted from three years of global positioning system broadcast (BRDC) messages provided by International GNSS service from 2006 through 2008 and were fitted with Fourier series. The generated coefficients from our developed algorithms are referred to as Fourier Klobuchar model (FOKM) coefficients, while those coefficients from BRDC massages are named as BRDC coefficients. The correlation coefficient values between FOKM and BRDC were higher than 0.97. We estimated total electron content using the Klobuchar model with FOKM coefficients and compared the result with that from the BRDC model. As a result, the maximum root mean square was 1.6 total electron content unit.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.449-451
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• 2006

With the increasing danger of personal information being exposed, a technique to protect personal information by identifying a non-user in case it is exposed. A study to construct a neural network recognizer for developing a economical and effective user protecting system. For this, time variables regarding user typing patterns from a pattern extraction device. With the variations in the standard deviation for the collected time variables, non-user patterns were generated. The recognition performance increased with the increase in the standard deviation and a higher recognition was achieved at 2.5. Also, five types of training data were generated and the recognition performance was examined as a function of the number of non-user patterns. With the increase in non-suer patterns, the recognition error quantified in the root mean square error (RMSE) was reduced. The smallest RMSE was obtained at the type 5 and 90 non-user patterns. In overall, the type 3 model yielded the highest recognition accuracy Particularly, a perfect recognition of 100% was achieved at 45 non-user patterns.

• Smart Structures and Systems
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• v.16 no.3
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• pp.401-414
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• 2015

A developed hybrid method for crack identification of beams is presented. Based on the Euler-Bernouli beam theory and concepts of fracture mechanics, governing equation of the cracked beams is reformulated. Finite element (FE) method as a powerful numerical tool is used to discritize the equation in space domain. After transferring the equations from time domain to frequency domain, frequencies and mode shapes of the beam are obtained. Efficiency of the governed equation for free vibration analysis of the beams is shown by comparing the results with those available in literature and via ANSYS software. The used equation yields to move the influence of cracks from the stiffness matrix to the mass matrix. For crack identification measured data are produced by applying random error to the calculated frequencies and mode shapes. An objective function is prepared as root mean square error between measured and calculated data. To minimize the function, hybrid genetic algorithms (GAs) and particle swarm optimization (PSO) technique is introduced. Efficiency, Robustness, applicability and usefulness of the mixed optimization numerical tool in conjunction with the finite element method for identification of cracks locations and depths are shown via solving different examples.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.19-24
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• 1997

In this study, we extend the application of the interface-matrix(IM) method for reflector modeling to Analytic Flux Expansion Nodal (AFEN) method. This include the modifications of the surface-averaged net current continuity and the net leakage balance conditions for IM method in accordance with AFEN fomular. AFEN-interface matrix (AFEN-IM) method has been tested against ZION-1 benchmark problem. The numerical result AFEN-IM method shows 1.24% of maximum error and 0.42% of root-mean square error in assembly power distribution, and 0.006%Δk of neutron multiplication factor. This result proves that the interface-matrix method for reflector modeling can be useful in AFEN method.

• Transactions of Materials Processing
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• v.23 no.7
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• pp.446-452
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• 2014

In the current study finite element forming analysis is performed to understand the final geometric accuracy limitations for the stamping of an automotive S-rail from four different steel sheets having tensile strengths of 340MPa, 440MPa, 590MPa and 780MPa. Comparisons between the analysis and the experiments for both springback and formability as measured by the amount of edge draw-in and the thickness distribution were conducted. The springback modes were classified according to a scheme proposed in the current investigation and the error was calculated using the normalized root mean square error method. While the analysis results show fairly good agreement with the experimental data for deformation and formability, the simulation accuracy is lower for predicting wall curl, camber and section twist as the UTS of steel sheet increases.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.378-383
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• 2000

The daily streamflow in the Yalu watershed located in the north-estern part of China was simulated by the DAWAST model. The parameters of model were calibrated by optimization technique with the input data of daily rainfall and pan evaporation occurred from 1997 to 1998, and they were Umax of 404mm, Lmax of 39mm, FC of 104mm, CP of 0.018, and CE of 0.003, respectively. Model verification tests were carried out with a data of 1996, and the results were generally satisfactory. Root mean square error was 0.3mm and Percent error in volume was 9.7%, and Correlation coefficient was 0.941.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.4
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• pp.377-382
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• 2008

For stator winding protection of AC generator, KCL(Kirchhoff's Current Law) is widely applied. Actually a CRDR(Current Ratio Differential Relay) based on DFT(Discrete Fourier Transform) has been used for protecting generator. It has been pointed out that defects can occur during the process of transforming a time domain signal into a frequency domain one which can lead to loss of time domain information. Wavelets techniques are proposed for the analysis of power system transients. This paper introduces an algorithm to choose a suitable Mother Wave1et for generator stator fault detection. For optimal selection, we analyzed db(Daubechies), sym(Symlets), and coif(Coiflects) of Mother Wavelet. And we compared with performance of the choice algorithm using detail coefficients energy and RMS(root mean square) error. It can be improved the reliability of the conventional DFT based CRDR. The feasibility and effectiveness of the proposed scheme is proved with simulation using collected data obtained from ATP (Alternative Transient Program) package.

• Journal of the Korean Solar Energy Society
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• v.39 no.2
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• pp.71-80
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• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

• Journal of Forest and Environmental Science
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• v.34 no.2
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• pp.119-125
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• 2018

The tropical forests in developing countries are faced with the problem of illegal exploitation of trees. However, dearth of empirical means of expressing the dimensions, structure, quality and quantity of a removed tree has imped conviction of offenders. This study aimed at developing a model that can effectively estimate individual tree basal area (BA) from stump diameter (Ds) for Tectona grandis stands in Omo Forest Reserve, Nigeria, for timber valuation in case of illegal felling. Thirty-six $25m{\times}25m$ temporary sample plots (TSPs) were laid randomly in six age strata; 26, 23, 22, 16, 14, and 12 years specifically. BA, Ds and diameter at breast height were measured in all living T. grandis trees within the 36 TSPs. Least square method was used to convert the counted stumps into harvested stem cross-sectional areas. Six basal area models were fitted and evaluated. The BA-Ds relationship was best described by power model which gave least values of Root mean square error (0.0048), prediction error sum of squares (0.0325) and Akaike information criterion (-15391) with a high adjusted coefficient of determination (0.921). This study revealed that basal area estimation was realistic even when the only information available was stump diameter. The power model was validated using independent data obtained from additional plots and was found to be appropriate for estimating the basal area of Tectona grandis stands in Omo Forest Reserve, Nigeria.

• 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.