• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.411-425
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• 2021

Accelerated failure time (AFT) model represents a linear relationship between the log-survival time and covariates. We are interested in the inference of covariate's effect affecting the variation of survival times in the AFT model. Thus, we need to model the variance as well as the mean of survival times. We call the resulting model mean and variance AFT (MV-AFT) model. In this paper, we propose a variable selection procedure of regression parameters of mean and variance in MV-AFT model using penalized likelihood function. For the variable selection, we study four penalty functions, i.e. least absolute shrinkage and selection operator (LASSO), adaptive lasso (ALASSO), smoothly clipped absolute deviation (SCAD) and hierarchical likelihood (HL). With this procedure we can select important covariates and estimate the regression parameters at the same time. The performance of the proposed method is evaluated using simulation studies. The proposed method is illustrated with a clinical example dataset.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.1
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• pp.85-90
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• 2013

PID control for unstable processes with time delay is not easy to apply because of unstability due to the poles existing on left-hand side in s-plane and the effect of time delay. In this paper, the authors consider the PID controller design technique in case of predefining overshoot or rising time by designer according to control environment. To deal with constraint problem like this, in this paper, the RCGA incorporating the penalty strategy is used. This is the method that if the RCGA violates given constraints, the defined penalty function is summed to the evaluation function depending on the severity and then the given constraint problem is converted to non-constraints optimization problem. The proposed method is applied to the unstable FOPTD(First Order Plus Time Delay) system and simulations are accomplished to illustrate the set-point tracking performance.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1361-1371
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• 2016

Quantile regression provides a variety of useful statistical information to examine how covariates influence the conditional quantile functions of a response variable. However, traditional quantile regression (which assume a linear model) is not appropriate when the relationship between the response and the covariates is a nonlinear. It is also necessary to conduct variable selection for high dimensional data or strongly correlated covariates. In this paper, we propose a penalized quantile regression tree model. The split rule of the proposed method is based on residual analysis, which has a negligible bias to select a split variable and reasonable computational cost. A simulation study and real data analysis are presented to demonstrate the satisfactory performance and usefulness of the proposed method.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.227-243
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• 2008

This study investigates an event detection method with the aim of generating an event-focused news summary from a set of news articles on a certain event using a multi-document summarization technique. The event detection method first classifies news articles into the event related topic categories by employing a SVM classifier and then creates event clusters containing news articles on an event by a modified single pass clustering algorithm. The clustering algorithm applies a time penalty function as well as cluster partitioning to enhance the clustering performance. It was found that the event detection method proposed in this study showed a satisfactory performance in terms of both the F-measure and the detection cost.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.330-338
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• 2018

The proportional-integral-derivative(PID) controller has been widely used in the industry because of its robust performance and simple structure in a wide range of operating conditions. However, the AVR(Automatic Voltage Regulator) as a control system is not robust to variations of the power system parameters. Therefore, it is necessary to use PID controller to increase the stability and performance of the AVR system. In this paper, a novel design method for determining the optimal PID controller parameters of an AVR system using the particle swarm optimization(PSO) algorithm is presented. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. In order to assist estimating the performance of the proposed PSO-PID controller, a new performance criterion function is also defined. This evaluation function is intended to reflect when the maximum percentage overshoot, the settling time are given as design specifications. The ITAE evaluation function should impose a penalty if the design specifications are violated, so that the PSO algorithm satisfies the specifications when searching for the PID controller parameter. Finally, through the computer simulations, the proposed PSO-PID controller not only satisfies the given design specifications for the terminal voltage step response, but also shows better control performance than other similar recent studies.