• Journal of the Korean Data and Information Science Society
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• 제15권4호
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• pp.899-910
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• 2004

In this paper we have proposed a new loss function, namely, non-linear exponential(NLINEX) loss function, which is quite asymmetric in nature. We obtained the Bayes estimator under exponential(LINEX) and squared error(SE) loss functions. Moreover, a numerical comparison among the Bayes estimators of power function distribution under SE, LINEX, and NLINEX loss function have been made.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.327-336
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• 2001

This work concerns estimating a regression function, which is not linear, using aggregate data. In much of the empirical research, data are aggregated for various reasons before statistical analysis. In a traditional parametric approach, a linear estimation of the non-linear function with aggregate data can result in unstable estimators of the parameters. More serious consequence is the bias in the estimation of the non-linear function. The approach we employ is the kernel regression smoothing. We describe the conditions when the aggregate data can be used to estimate the regression function efficiently. Numerical examples will illustrate our findings.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.1007-1015
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• 2003

In this paper we introduced a new score generating function for the rank dispersion function in a general linear model. Based on the new score function, we derived the null asymptotic theory of the rank-based hypothesis testing in a linear model. In essence we showed that several rank test statistics, which are primarily focused on our new score generating function and new dispersion function, are mainly distribution free and asymptotically converges to a chi-square distribution.

• 대한수학회논문집
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• 제38권2호
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• pp.377-387
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• 2023

In this paper, we study a uniqueness problem of entire functions that share two linear polynomials with its linear differential polynomial. We deduce two theorems which improve some previous results given by I. Lahiri [7].

• 한국정밀공학회지
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• 제15권8호
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• pp.183-192
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• 1998

In this paper, we measure the back-EMF and the thrust of a linear brushless DC motor along the relative position between coils and magnets in various speed environments in order to obtain the back-EMF and the thrust as a function of a motor position. The measured back-EMF function and thrust function of the position differ from the analytical ones within 5%. The measured back-EMF and thrust function can, then, be employed in controlling the thrust ripple of the linear motor. Furthermore, to minimize the torque ripple of the linear motor, we suggest the design method to shape the back-EMF and thrust function of the linear motor.

• 제어로봇시스템학회논문지
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• 제4권5호
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• pp.644-650
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• 1998

In this paper , a state space model for flexible beam is presented using the assumed-modes approach. The state space equation is derived for a flexible beam in which one end is connected to a motor and is driven by a torque equation and the other end is free. Many of the transfer function proposed thus far use the torque to the flexible beam as the input and the tip deflection of the flexible beam as the output. The Technique for the analysis and synthesis of the dual-input describing function(DIDF) is introduced here and the construction of a non-linear compensator, based on this technique, is proposed. This non-linear compensator, properly connected in the direct path of a closed-loop linear or non-linear control system. The above non-linear network is used to compensate linear and non-linear systems for instability, limit cycles, low speed of response and static accuracy. The effectiveness of the proposed scheme is demonstrated through computer simulation and experimental results.

• Journal of Power Electronics
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• 제9권4호
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• pp.567-574
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• 2009

A linear transfer function for the output current control of frequency-controlled resonant inverters is proposed in this paper. The circuit of resonant inverters can be transformed into two coupled circuits through the complex phasor transform. The circuits consist of cross-coupled power sources and passive elements. The circuits are used to induce the state space equation, which is transformed into the $4^{th}$ order cross-coupled transfer function. The $4^{th}$ order cross-coupled transfer function is modeled into a $2^{nd}$ order linear transfer function based on a behavior analysis of the pole and zero locations that facilitate a simple and intuitive linear transfer function. The feasibility and validity of the proposed linear transfer function were verified by simulation and experiment.

• 대한수학회보
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• 제43권2호
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• pp.253-263
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• 2006

In this paper we introduce a new concept, a 'periodicizing' function for the linear differential equation with the periodic forcing function. Moreover, we construct this function, which is closely related with the solution of a difference equation and an indefinite sum. Using this function, we can obtain a representation of solutions from which we see immediately the asymptotic behavior of the solutions.

• 한국전자통신학회논문지
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• 제6권1호
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• pp.20-26
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• 2011

본 논문에서는 NM(node monitoring) 알고리듬과 NP(Piecewise Linear Function Approximation)를 사용해서 LDPC 코드 복호의 복잡도를 감소시키기 위한 효율적인 알고리듬을 제안한다. 이 NM 알고리듬은 새로운 node-threshold 방법과 message passing 알고리듬에 근거해서 제안되었는데, 이에 NP 방법을 사용해서 알고리듬의 복잡도를 더 줄일 수 있었다. 이 알고리듬의 효율성을 입증하기 위해서 모의 실험을 하였다. 모의실험결과, 기존에 잘 알려진 방법에 비해서 20% 정도 더 효율적이었다.

• Communications for Statistical Applications and Methods
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• 제19권6호
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• pp.885-898
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• 2012

In this paper, we consider Bayesian approaches to partially linear models, in which a regression function is represented by a semiparametric additive form of a parametric linear regression function and a nonparametric regression function. We make a comparative study on the performance of widely used Bayesian partially linear models in terms of empirical analysis. Specifically, we deal with three Bayesian methods to estimate the nonparametric regression function, one method using Fourier series representation, the other method based on Gaussian process regression approach, and the third method based on the smoothness of the function and differencing. We compare the numerical performance of three methods by the root mean squared error(RMSE). For empirical analysis, we consider synthetic data with simulation studies and real data application by fitting each of them with three Bayesian methods and comparing the RMSEs.