• 대한산업공학회지
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• 제16권2호
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• pp.129-134
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• 1990

Exponential smoothing methods have enjoyed a long history of successful applications and have been used in forecasting for many years. However, it has been long known that one of the deficiencies of the method is an inability to respond quickly to interventions to interruptions, or to large changes in level of the underlying process. An exponential smoothing method adaptive to repeated random level changes is proposed using a change-detection statistic derived from a simple dynamic linear model. The results are compared with Trigg and Leach's and the exponential smoothing methods.

• Communications for Statistical Applications and Methods
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• 제5권2호
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• pp.277-285
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• 1998

The smoothing parameter $\lambda$ in smoothing spline regression is usually selected by minimizing cross-validation (CV) or generalized cross-validation (GCV). But, simple CV or GCV is poor candidate for estimating prediction error. We defined MGCV (Multifold Generalized Cross-validation) as a criterion for selecting smoothing parameter in smoothing spline regression. This is a version of cross-validation using $leave-\kappa-out$ method. Some numerical results comparing MGCV and GCV are done.

• International Journal of Contents
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• 제5권4호
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• pp.1-6
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• 2009

In this paper, we propose a mesh smoothing method for mesh models with noise. The proposed method enables not only the removal of noise from the vertexes but the preservation and smoothing of shape recognized as edges and comers. The magnitude ratio of 2D area and 3D volume in mesh data is adopted for the smoothing of noise. Comparing with previous smoothing methods, this method does not need many iteration of the smoothing process and could preserve the shape of original model. Experimental results demonstrate improved performance of the proposed approach in 3D mesh smoothing.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.585-596
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• 2000

We developed a macro for prediction and classification using profection pursuit regression based on Friedman (1984b) and Hwang, et al. (1994). In the macro, the order of the Hermite functions can be selected automatically. In projection pursuit regression, we compare several smoothing methods such as super smoothing, smoothing with the Hermite functions. Also, classification methods applied to German credit data are compared.

• Journal of applied mathematics & informatics
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• 제22권1_2호
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• pp.133-148
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• 2006

This paper proposes a smoothing method for symmetric conic linear programming (SCLP). We first characterize the central path conditions for SCLP problems with the help of Chen-Harker-Kanzow-Smale smoothing function. A smoothing-type algorithm is constructed based on this characterization and the global convergence and locally quadratic convergence for the proposed algorithm are demonstrated.

• 대한지리학회지
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• 제49권3호
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• pp.339-356
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• 2014

DEM의 보급과 처리 기술의 발전으로 DEM으로부터 하천종단을 추출하는 것이 과거 어느 때보다 용이해졌다. 하지만 DEM에서 추출된 하천종단은 인위적인 평지 및 계단과 같은 문제점들도 포함하고 있기 때문에, 자연스런 하천종단을 얻어내기 위해서는 평활화 과정을 거쳐야 한다. 그러나 평활화 자체는 원본 하천종단의 변형을 필연적으로 동반하기 마련이다. 지금까지는 평활화로 인한 원자료의 변형에 대해 객관적이고 정량적인 평가가 없었다. 이에 본 연구는 주요 평활화 방법의 특성을 파악하고, 이들의 한계점을 극복하는 새로운 평활화 방법을 제안하며, 이의 유용성을 검증하였다. 본 연구는 보다 정밀한 평활화 방법을 제시할 뿐만 아니라 연구목적에 부합하는 평활화 방법을 선택하는데도 도움이 될 것이다.

• Journal of Advanced Marine Engineering and Technology
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• 제38권2호
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• pp.182-187
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• 2014

We propose nonlinear GMM-based transformation functions in an attempt to deal with the over-smoothing effects of linear transformation for voice processing. The proposed methods adopt RBF networks as a local transformation function to overcome the drawbacks of global nonlinear transformation functions. In order to obtain high-quality modifications of speech signals, our voice conversion is implemented using the Harmonic plus Noise Model analysis/synthesis framework. Experimental results are reported on the English corpus, MOCHA-TIMIT.

• Industrial Engineering and Management Systems
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• 제7권1호
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• pp.44-50
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• 2008

Focusing on the exponential smoothing method equivalent to (1, 1) order ARMA model equation, a new method of estimating smoothing constant using exponential smoothing method is proposed. This study goes beyond the usual method of arbitrarily selecting a smoothing constant. First, an estimation of the ARMA model parameter was made and then, the smoothing constants. The empirical example shows that the theoretical solution satisfies minimum variance of forecasting error. The new method was also applied to the stock market price of electrical machinery industry (6 major companies in Japan) and forecasting was accomplished. Comparing the results of the two methods, the new method appears to be better than the ARIMA model. The result of the new method is apparently good in 4 company data and is nearly the same in 2 company data. The example provided shows that the new method is much simpler to handle than ARIMA model. Therefore, the proposed method would be better in these general cases. The effectiveness of this method should be examined in various cases.

• 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.