• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.999-1005
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• 2011

This paper proposes a weighted least squares support vector machine for asymmetric least squares regression. This method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. The cross validation function is introduced to choose optimal hyperparameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.949-954
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• 2009

The autoregressive process is applied in this paper to kernel regression in order to infer nonlinear models for predicting responses. We propose a kernel method for the autoregressive data which estimates the mean function by kernel machines. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which affect the performance of kernel regression. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of mean function in the presence of autocorrelation between data.

• 한국전산유체공학회:학술대회논문집
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• 2003.10a
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• pp.43-45
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• 2003

Nonlinear relationship between Reynolds stresses and the rate of strain for nonlinear${\kappa}-{\varepsilon}$ turbulence models is validated theoretically by using the boundary layer assumptions against the turbulence­driven secondary flows in noncircular ducts and then the prediction performance for several nonlinear models is evaluated numerically through the application to the turbulent flow in a square duct.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1045-1052
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• 2005

This paper deals with the estimations of kernel ridge regression when the responses are subject to randomly right censoring. The weighted data are formed by redistributing the weights of the censored data to the uncensored data. Then kernel ridge regression can be taken up with the weighted data. The hyperparameters of model which affect the performance of the proposed procedure are selected by a generalized approximate cross validation(GACV) function. Experimental results are then presented which indicate the performance of the proposed procedure.

• Proceedings of the KSR Conference
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• 2003.05a
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• pp.462-467
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• 2003

According to the computerization of railway signalling system, necessity of communication protocol for interface between these equipments is increasing. Therefore the research on communication protocol structure that is required according to computerization trend of these signalling goes. In our research the existed protocol currently used was analyzed, and the simulation modeling for performance analysis is deduced. Based on this simulation model the performance analysis and comparison is carried out, and also validation test is executed about new suggested protocol.

• Structural Engineering and Mechanics
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• v.40 no.5
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• pp.617-636
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• 2011

We present the numerical implementation, simulation, and validation of the high-velocity impact experiments that have been described in the companion article. In this part, numerical investigations and simulations performed to mimic the tests are presented. The experiments were analyzed by the explicit integration-based software ABAQUS for improved simulations. Targets were modeled with a damaged plasticity model for concrete. Computational results of residual velocity and crater dimensions yielded acceptable results.

• Genomics & Informatics
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• v.14 no.4
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• pp.138-148
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• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.261-272
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• 2009

Kolmogorov-Smirnov (K-S) statistic has been widely used for testing homogeneity of two distributions in the credit rating models. Joseph (2005) used K-S statistic to obtain validation criteria which is most well-known. There are other homogeneity test statistics such as the Cramer-von Mises, Anderson-Darling, and Watson statistics. In this paper, these statistics are introduced and applied to obtain criterion of these statistics by extending Joseph (2005)'s work. Another set of alternative criterion is suggested according to various sample sizes, type a error rates, and the ratios of bads and goods by using the simulated data under the similar situation as real credit rating data. We compare and explore among Joseph's criteria and two sets of the proposed criterion and discuss their applications.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.1
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• pp.32-40
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• 2018

In this research, a method of deriving statistical weight prediction formula which is used during the conceptual design phase was studied and it was programmed using Microsoft Excel and verified by applying to jet transport aircraft. The database was built while referencing the variables of conventional wing weight estimation formulas and it was used for modeling the jet transport wing weight regression equation. The model was evaluated using the K-fold cross validation method to solve the overfitting problem of the model.

• Water Engineering Research
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• v.4 no.3
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• pp.141-154
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• 2003

Nonpoint source pollutants from agriculture are identified as one of the main causes of water quality degradation in the United States. The Hydrological Simulation Program-Fortran (HSPF) was used to simulate runoff, nitrogen, and sediment loads from an urbanizing watershed; the Polecat Creek watershed located in Virginia. Model parameters related to hydrology and water quality were calibrated and validated using observed hydrologic and water quality data collected at the watershed outlet and at several sub-watershed outlets. A comparison of measured and simulated monthly runoff at the outlet of the watershed resulted in a correlation coefficient of 0.94 for the calibration period and 0.74 for the validation period. The annual observed and simulated sediment loads for the calibration period were 220.9 kg/ha and 201.5 kg/ha, respectively. The differences for annual nitrate nitrogen ($NO_3$) loads between the observed and simulated values at the outlet of the watershed were 5.1% and 42.1% for the calibration and validation periods, respectively. The corresponding values for total Kjeldahl nitrogen (TKN) were 60.9% and 40.7%, respectively. Based on the simulation results, the calibrated HSPF input parameters were considered to adequately represent the Polecat Creek watershed.