• Honam Mathematical Journal
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• v.31 no.4
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• pp.591-599
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• 2009

For given ${\alpha},{\omega}\;{\in}\;{\mathbb{R}}$ and ${\beta}$ > 1, let $T_{{\beta},{\alpha},{\omega}}$ be the skew-product transformation on the torus, [0, 1) ${\times}$ [0, 1) defined by (x, y) ${\longmapsto}\;({\beta}x,y+{\alpha}x+{\omega})$ (mod 1). In this paper, we give a criterion of ergodicity and weakly mixing for the transformation $T_{{\beta},{\alpha},{\omega}}$ when the natural extension of the given ${\beta}$-transformation can be viewed as a generalized baker's transformation, i.e., they flatten and stretch and then cut and stack a two-dimensional domain. This is a generalization of theorems in [10].

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.329-340
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• 1999

We consider a Bayesian forcasting method for the analysis of repeated surveys. It is assumed that the parameters of the superpopulation model at each time follow a stochastic model. We propose Bayesian prediction procedures for the finite population total under multiprocess dynamic generalized linear models. The multiprocess dynamic model offers a powerful framework for the modelling and analysis of time series which are subject to a abrupt changes in pattern. Some numerical studies are provided to illustrate the behavior of the proposed predictors.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1537-1545
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• 2015

Support vector quantile regression (SVQR) can be obtained by applying support vector machine with a check function instead of an e-insensitive loss function into the quantile regression, which still requires to solve a quadratic program (QP) problem which is time and memory expensive. In this paper we propose an SVQR whose objective function is composed of an asymmetric quadratic loss function. The proposed method overcomes the weak point of the SVQR with the check function. We use the iterative procedure to solve the objective problem. Furthermore, we introduce the generalized cross validation function to select the hyper-parameters which affect the performance of SVQR. Experimental results are then presented, which illustrate the performance of proposed SVQR.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1353-1360
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• 2008

An estimating procedure is introduced for the nonlinear mixed-effect Poisson regression, for longitudinal study, where data from different subjects are independent whereas data from same subject are correlated. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation function is introduced to choose optimal hyper-parameters in the procedure. Experimental results are then presented, which indicate the performance of the proposed estimating procedure.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.271-278
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• 2005

In this paper, we present an adaptive observer for multi-output nonlinear systems that include unknown constant parameters and are not necessarily observable. Based on generalized nonlinear observer canonical form, new adaptive observer canonical form is proposed. Sufficient conditions are given for a nonlinear system to be transformed into the proposed adaptive observer canonical form. The existence of the proposed adaptive observer is given in terms of Lyapunov-like condition and SPR condition. An illustrative example is presented to show the design procedure of the proposed method.

• 한국데이터정보과학회:학술대회논문집
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• 2001.10a
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• pp.25-33
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• 2001

This paper suggests a generalized logit model with mixed effects for analysing frequency data in multi-contingency table. In this model nominal response variable is assumed to be polychotomous. When some factors are fixed but condisered as ordinal and others are random, this paper shows how to use baseline-category logits to incoporate the mixed-effects of those factors into the model. A numerical algorithm was used to estimate model parameters by using marginal log-likelihood.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.393-401
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• 2012

This paper deals with the estimations of the least squares support vector regression when the responses are subject to randomly right censoring. The estimation is performed via two steps - the ordinary least squares support vector regression and the least squares support vector regression with censored data. We use the empirical fact that the estimated regression functions subject to randomly right censoring are close to the true regression functions than the observed failure times subject to randomly right censoring. The hyper-parameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation function. Experimental results are then presented which indicate the performance of the proposed procedure.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.735-744
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• 2007

Support vector machine(SVM) is capable of providing a more complete description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse kernel regression(SKR) to overcome a weak point of SVM, which is, the steep growth of the number of support vectors with increasing the number of training data. The iterative reweighted least squares(IRWLS) procedure is used to solve the optimal problem of SKR with a Laplacian prior. Furthermore, the generalized cross validation(GCV) function is introduced to select the hyper-parameters which affect the performance of SKR. Experimental results are then presented which illustrate the performance of the proposed procedure.

• Journal of Mechanical Science and Technology
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• v.18 no.7
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• pp.1150-1158
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• 2004

In this paper, we present an adaptive observer for nonlinear systems that include unknown constant parameters and are not necessarily observable. Sufficient conditions are given for a nonlinear system to be transformed by state-space change of coordinates into an adaptive observer canonical form. Once a nonlinear system is transformed into the proposed adaptive observer canonical form, an adaptive observer can be designed under the assumption that a certain system is strictly positive real. An illustrative example is included to show the effectiveness of the proposed method.

• Nonlinear Functional Analysis and Applications
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• v.26 no.3
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• pp.497-512
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• 2021

In this paper, we study the analytical and approximate solutions for a fractional quadratic integral equation involving Katugampola fractional integral operator. The existence and uniqueness results obtained in the given arrangement are not only new but also yield some new particular results corresponding to special values of the parameters 𝜌 and ϑ. The main results are obtained by using Banach fixed point theorem, Picard Method, and Adomian decomposition method. An illustrative example is given to justify the main results.