• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.419-430
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• 2014

An accurate prediction of electricity supply and demand is important for daily life, industrial activities, and national management. In this paper electricity sales is predicted by time series modelling. Real data analysis shows the transfer function model with cooling and heating days as an input time series and a pulse function as an intervention variable outperforms other time series models for the root mean square error and the mean absolute percentage error.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.103-109
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• 1995

In this paper, we propose a postprocessing method that neatly removes blocking effect but retains visually important image details and edges. The iterative thresholded lowpass filter is basically a low pass filter whose ouput depends on three variable elements. I.e. iteration number, threshold value and passband width. The threshold value restricts the difference between the output of the proposed filter and the original input independent of the iteration number. With this property, the iterative thresholded lowpass filter can retain most of the image details while smoothing the block boundaries. The other two variable elements, i.e. iteration number and passband width, can determine the convergence speed of the proposed filter. In this paper, we also propose several adaptive filtering techniques based on the iterative thresholded lowpass filter with their simulation results.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.04a
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• pp.7-12
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• 1992

The variable node plate bending element, ie, the element with one or two additional mid-side nodes is used in the analysis of mat foundation to generate the nearly ideal grid model in which more nodes are defined near the column location. The plate bending element used in this study is the one based on Mindlin/Reissner plate theory with substitute shear strain field and the nodal stresses of that element are obtained by the local smoothing technique. The interaction of the soil material with the mat foundation is modeled with Winkler springs connected to the nodal points in the mat model. The vertical stiffness of the soil material are represented in terms of a modulus of subgrade reaction and are computed in the same way as to the computation of consistent nodal force of uniform surface loading. Several mesh schemes were proposed and tested to find the most suitable scheme for mat foundation analysis.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.511-521
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• 2020

In this paper, a partially linear multivariate model with error in the explanatory variable of the nonparametric part, and an m dimensional response variable is considered. Using the uniform consistency results found for the estimator of the nonparametric part, we derive an estimator of the parametric part. The dependence of the convergence rates on the errors distributions is examined and demonstrated that proposed estimator is asymptotically normal. In main results, both ordinary and super smooth error distributions are considered. Moreover, the derived estimators are applied to the economic behaviors of consumers. Our method handles contaminated data is founded more effectively than the semiparametric method ignores measurement errors.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.143-154
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• 2024

This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the differenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.971-980
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• 2017

The varying coefficient regression model has gained lots of attention since it is capable to model dynamic changes of regression coefficients in many regression problems of science. In this paper we propose a varying coefficient regression model that effectively considers the errors on both input and response variables, which utilizes the kernel method in estimating the varying coefficient which is the unknown nonlinear function of smoothing variables. We provide a generalized cross validation method for choosing the hyper-parameters which affect the performance of the proposed model. The proposed method is evaluated through numerical studies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11C
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• pp.94-101
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• 2001

In this paper, we propose WRR cell scheduling algorithm that improve current smoothing scheme. In proposed cell scheduling algorithm, using the number of practical input cell in each VC and variable thats indicate weight and state of queue, we could service VC of buffer efficiently that input cells over weight value and input cells smaller than weight value. And, we could service multimedia data by providing remained bandwidth after that allocate to real time traffic with non-real time traffic. In this result, the number of serviceable average cells were increased and length of buffer was decreased. Through the computer simulation, we evaluated the performance of proposed algorithm. According to the results, the proposed algorithm showed good performance.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.451-459
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• 2017

There are many researches showing that when a process change has occurred, variable sampling intervals (VSI) control chart is better than the fixed sampling interval (FSI) control chart in terms of reducing the required time to signal. When the process engineers use VSI control procedure, frequent switching between different sampling intervals can be a complicating factor. However, average number of samples to signal (ANSS), which is the amount of required samples to signal, and average time to signal (ATS) do not provide any control statistics about switching performances of VSI charts. In this study, we evaluate numerical switching performances of multivariate VSI EWMA chart including average number of switches to signal (ANSW) and average switching rate (ASWR). In addition, numerical study has been carried out to examine how to improve the performance of considered chart with accumulate-combine approach under several different smoothing constant and sample size. In conclusion, process engineers, who want to manage the correlation coefficients of related quality variables, are recommended to make sample size as large and smoothing constant as small as possible under permission of process conditions.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.502-509
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• 2005

In this paper, a 4-wheel vehicle model including the effects of tire slip was considered, along with variable parameter sliding control, in order to improve the performance of the vehicle longitudinal response. The variable sliding parameter is made to be proportional to the square root of the pressure derivative at the wheel, in order to compensate for large pressure changes in the brake cylinder. A typical tire force-relative slip curve for dry road conditions was used to generate an analytical tire force-relative slip function, and an antilock sliding control process based on the analytical tire force-relative slip function was used. A retrofitted brake system, with the pushrod force as the end control parameter, was employed, and an average decay function was used to suppress the simulation oscillations. The simulation results indicate that the velocity and spacing errors were slightly larger than those obtained when the wheel slip effect was not considered, that the spacing errors of the lead and follower were insensitive to the adhesion coefficient up to the critical wheel slip value, and that the limit for the antilock control under non-constant adhesion road conditions was determined by the minimum value of the equivalent adhesion coefficient.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.937-944
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• 2021

The problem of selecting variables is addressed in partial linear regression. Model selection for partial linear models is not easy since it involves nonparametric estimation such as smoothing parameter selection and estimation for linear explanatory variables. In this work, several approaches for variable selection are proposed using a fast forward selection algorithm, least angle regression (LARS). The proposed procedures use t-test, all possible regressions comparisons or stepwise selection process with variables selected by LARS. An example based on real data and a simulation study on the performance of the suggested procedures are presented.