• 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.

• 지구물리
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• 제4권3호
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• pp.175-182
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• 2001

본 논문에서는 의료영상의 응용분야로서 방출전산화단증 영상에 사용되는 베이지안 방법을 위한 Gibbs 사전정보의 평활 파라미터를 결정하는 문제를 다룬다. 특히, 광역 하이퍼파라미터(평활 파라미터)가 해외 편향과 분산의 균형을 조절하는 단순 평활사전정보(일명 멤브레인)를 연구 대상으로 한다. 본 논문에서 사용된 방법은 관측된 훈련데이터에 MI. 방법을 적용한 하이퍼파라미터 추정법에 기반을 두며, 이러한 접근방법에 대한 동기에 대하여도 논한다. 멤브레인 사전정보를 위한 평활 파라미터의 경우 단순한 ML 추정법을 적용하여도 파라미터가 쉽게 추정될 수 있음을 보인다.

• 공학논문집
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• 제4권1호
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• pp.47-54
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• 2002

본 논문에서는 의료영상의 응용분야로서 방출전산화단층 영상에 사용되는 베이지안 방법을 위한 Gibbs 사전정보의 평활 파라미터를 결정하는 문제를 다룬다. 특히, 광역 하이퍼파라미터(평활 파라미터)가 해의 편향과 분산의 균형을 조절하는 단순 평활사전정보(일명 멤브레인)를 연구 대상으로 한다. 본 논문에서 사용된 방법은 관측된 훈련데이터에 ML 방법을 적용한 하이퍼파라미터 추정법에 기반을 두며, 이러한 접근방법에 대한 동기에 대하여도 논한다. 멤브레인 사전정보를 위한 평활 파라미터의 경우 단순한 ML 추정법을 적용하여도 파라미터가 쉽게 추정될 수 있음을 보인다.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.945-953
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• 2007

In this paper, we propose a smoothing and thus noise-reducing method of data sequences for stock price prediction with hidden Markov models, HMMs. The suggested method just uses simple moving average. A proper average size is obtained from forecasting experiments with stock prices of bank sector of Korean Exchange. Forecasting method with HMM and moving average smoothing is compared with a conventional method.

• Journal of Power Electronics
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• 제9권2호
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• pp.320-333
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• 2009

A Photovoltaic (PV) system's power output varies with the change of climate. Frequency deviations, tie line voltage swings are caused by the varying PV power when large PV power from several PV systems is fed in the utility. In this paper, to overcome these problems, a simple coordinated control method for smoothing the variations of combined PV power from multiple PV systems is proposed. Here, output power command is formed in two steps: central and local. Fuzzy control is used to produce the central smoothing output power command considering insolation, variance of insolation and absolute average of frequency deviation. In local step, a simple coordination is kept between the central power command and the local power commands by producing a common tuning factor. Power converters are used to achieve the same output power as local command power employing PI control law for each of the PV generation systems. The proposed method is compared with the method where conventional Maximum Power Point Tracking (MPPT) control is used for each of the PV systems. Simulation results show that the proposed method is effective for smoothing the output power variations and feasible to reduce the frequency deviations of the power utility.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.177.4-177
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• 2001

In this paper, we propose a new type of nonlinear fixed interval smoothing filter which is modified from the existing nonlinear smoothing filter. A nonlinear smoothing filter is derived from two-filter formulas. For the backward filter, the propagation and update equation of error states are derived. Particularly the modified update equation of the backward filter use the estimated error terms from the forward filter. Smoothing algorithm is altered into the compatible form with the new type of the backward fitter. An advantage of the proposed algorithm is more efficient than the existing one because propagation in backward filter is very simple from the implementation point of view. We apply the proposed nonlinear smoothing ...

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.279-282
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• 1997

Bayesian reconstruction methods for emission computed tomography have been a topic of interest in recent years, partly because they allow for the introduction of prior information into the reconstruction problem. Early formulations incorporated priors that imposed simple spatial smoothness constraints on the underlying object using Gibbs priors in the form of four-nearest or eight-nearest neighbors. While these types of priors, known as "membrane" priors, are useful as stabilizers in otherwise unstable ML-EM reconstructions, more sophisticated prior models are needed to model underlying source distributions more accurately. In this work, we investigate whether the "thin plate" model has advantages over the simple Gibbs smoothing priors mentioned above. To test and compare quantitative performance of the reconstruction algorithms, we use Monte Carlo noise trials and calculate bias and variance images of reconstruction estimates. The conclusion is that the thin plate prior outperforms the membrane prior in terms of bias and variance.

• Industrial Engineering and Management Systems
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• 제8권4호
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• pp.257-263
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• 2009

Focusing on the idea that the equation of exponential smoothing method (ESM) is equivalent to (1, 1) order ARMA model equation, new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Theoretical solution was derived in a simple way. Mere application of ESM does not make good forecasting accuracy for the time series which has non-linear trend and/or trend by month. A new method to cope with this issue is required. In this paper, combining the trend removal method with this method, we aim to improve forecasting accuracy. An approach to this method is executed in the following method. Trend removal by a linear function is applied to the original shipping data of consumer goods. The combination of linear and non-linear function is also introduced in trend removal. For the comparison, monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful especially for the time series that has stable characteristics and has rather strong seasonal trend and also the case that has non-linear trend. The effectiveness of this method should be examined in various cases.

• 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.