• Journal of applied mathematics & informatics
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• 제33권3_4호
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• pp.387-399
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• 2015

In this paper, a new smoothing approximation to the l₁ exact penalty function for constrained optimization problems (COP) is presented. It is shown that an optimal solution to the smoothing penalty optimization problem is an approximate optimal solution to the original optimization problem. Based on the smoothing penalty function, an algorithm is presented to solve COP, with its convergence under some conditions proved. Numerical examples illustrate that this algorithm is efficient in solving COP.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.273-273
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• 2000

We present a new scheme to increase the performance of edge-preserving image smoothing from the parameter tuning of a Markov random field (MRF) function. The method is based on automatic control of the image smoothing-strength in MRF model ing in which an introduced parameter function is based on control of enforcing power of a discontinuity-adaptive Markov function and edge magnitude resulted from discontinuities of image intensity. Without any binary decision for the edge magnitude, adaptive control of the enforcing power with the full edge magnitude could improve the performance of discontinuity-preserving image smoothing.

• Journal of applied mathematics & informatics
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• 제17권1_2_3호
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• pp.401-418
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• 2005

In this paper, a dual algorithm, based on a smoothing function of Bertsekas (1982), is established for solving unconstrained minimax problems. It is proven that a sequence of points, generated by solving a sequence of unconstrained minimizers of the smoothing function with changing parameter t, converges with Q-superlinear rate to a Kuhn-Thcker point locally under some mild conditions. The relationship between the condition number of the Hessian matrix of the smoothing function and the parameter is studied, which also validates the convergence theory. Finally the numerical results are reported to show the effectiveness of this algorithm.

• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.137-141
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• 2009

We study on selecting the kernel weighting function in smoothing moment conditions for dependent processes. For hypothesis testing in Generalized Method of Moments or Generalized Empirical Likelihood context, we find that smoothing moment conditions by Bartlett kernel delivers smallest size distortions based on empirical Edgeworth expansions of the long-run variance estimator.

• 대한의용생체공학회:의공학회지
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• 제38권5호
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• pp.219-226
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• 2017

Penalized-likelihood (PL) reconstruction methods for transmission tomography are known to provide improved image quality for reduced dose level by efficiently smoothing out noise while preserving edges. Unfortunately, however, most of the edge-preserving penalty functions used in conventional PL methods contain at least one free parameter which controls the shape of a non-quadratic penalty function to adjust the sensitivity of edge preservation. In this work, to avoid difficulties in finding a proper value of the free parameter involved in a non-quadratic penalty function, we propose a new adaptive method of space-variant smoothing with a simple quadratic penalty function. In this method, the smoothing parameter is adaptively selected for each pixel location at each iteration by using the image roughness measured by a pixel-wise standard deviation image calculated from the previous iteration. The experimental results demonstrate that our new method not only preserves edges, but also suppresses noise well in monotonic regions without requiring additional processes to select free parameters that may otherwise be included in a non-quadratic penalty function.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.811-816
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• 2000

The purpose of this paper is to study of data-driven smoothing goodness of it test, when the hypothesis is complete. The smoothing goodness of fit test statistic by nonparametric function estimation techniques is proposed in this paper. The results of simulation studies for he powers of show that the proposed test statistic compared well to other.

• Journal of applied mathematics & informatics
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• 제32권1_2호
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• pp.211-225
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• 2014

A new class of smoothing functions is introduced in this paper, which includes some important smoothing complementarity functions as its special cases. Based on this new smoothing function, we proposed a smoothing Newton method. Our algorithm needs only to solve one linear system of equations. Without requiring the nonemptyness and boundedness of the solution set, the proposed algorithm is proved to be globally convergent. Numerical results indicate that the smoothing Newton method based on the new proposed class of smoothing functions with ${\theta}{\in}(0,1)$ seems to have better numerical performance than those based on some other important smoothing functions, which also demonstrate that our algorithm is promising.

• Journal of applied mathematics & informatics
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• 제29권5_6호
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• pp.1511-1523
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• 2011

In this paper, we consider the smoothing Newton method for the nonlinear complementarity problems with $P_0$-function. The proposed algorithm is based on a new smoothing function and it needs only to solve one linear system of equations and perform one line search per iteration. Under the condition that the solution set is nonempty and bounded, the proposed algorithm is proved to be convergent globally. Furthermore, the local superlinearly(quadratic) convergence is established under suitable conditions. Preliminary numerical results show that the proposed algorithm is very promising.

• 전자공학회논문지S
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• 제36S권6호
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• pp.87-95
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• 1999

본 논문에서는 ATM망에서 3차원 동영상 데이터의 시뮬레이션 모델을 제시한다. 이 모델은 슬라이스 레벨에 기초를 두며, PVAR(Projected Vector Autoregressive)모델이라고 명한다. PVAR 모델은 자기상관성(Autocorrelation)과 히스토그램(Histogram)특성을 만족하기 위해 AR(Autoregressive)모델에 기초로 모델링 되고 프로젝션 함수(Projection function)에 의해 실제 데이터를 매핑 한다. 프로젝션 함수로는 CDPF(cumulative distribution probability function)를 사용한다. 이때 과정은 슬라이스 단위로 수행된다. 제안된 모델은 자기 상관성과 히스토그램을 만족시키는데 좋은 성능을 보여주고, 네트워크 성능 분석에 중요하다. 이어서 이것을 주기적 평균값에 의한 Smoothing 방법에 적용한다. 일반적으로 QoS는 버퍼(buffer)에서의 셀 손신과 최대 지연에 관계된 CLR에 달려 있다. 따라서 제안한 Smoothing 기법은 QoS를 향상시키는데 이용할 수 있다.

• Journal of the Korean Data and Information Science Society
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• 제7권2호
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• pp.257-262
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• 1996

본 논문은 스플라인 희귀모형에서 평활모수를 추정할 때 사전 작업으로 영향력 진단을 하는 문제를 다룬다. 평활모수의 추정방법으로 일반화최대우도함수법을 사용할 때, 얻어지는 추정 치에 영향을 주는 관측치를 진단하는 측도를 제안하고, 찾아낸 영향력 관측치를 수정하여 올바른 평활모수 추정치를 찾는 방법을 소개한다.