• International Journal of Aeronautical and Space Sciences
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• 제3권2호
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• pp.88-94
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• 2002

This paper describes attitude determination algorithm for the low earth orbit(LEO) spacecraft using stellar inertial sensors. The cascaded gyro/star tracker extended Kalman filter is constructed to fuse two sensor data. And then the smoothing of the measurement are proposed for an unreasonable jump of star tracker. The smoothing algorithm for the rejection of star tracker error jumps is designed by scalar adaptive filter. The proposed algorithms operate to process the measurement of gyro/star tracker Kalman filter, therefore, it is comparatively simple to apply these methods to other integration systems. Simulations to gyro/star tracker integrated system show that the proposed method is effective.

• 한국정보과학회논문지:정보통신
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• 제29권5호
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• pp.495-500
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• 2002

We propose a new estimation method for rate adjustment in the face of a packet loss in the TFRC protocol, a TCP-Friendly congestion control protocol for UDP flows. Previous methods respond in a sensitive way to a single packet loss, resulting in oscillatory transmission behavior. This is an undesirable for multimedia services demanding constant bandwidth. The proposed TFRC provides more smooth and fair (against TCP flows) transmission through collective response based on multiple packets loss events. We show our "Exponential smoothing method" performs better than known "Weight smoothing method" in terms of smoothness and fairness.

• 전자공학회논문지T
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• 제36T권4호
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• pp.24-29
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• 1999

In this paper, a simple contour-preserving filtering algorithm is proposed. The goal of the contour-preserving filtering method is to remove noise ad granularity as the preprocessing for the image segmentation procedure. Our method finds edge map and separates the image into the edge region and the non-edge region using this edge map. For the non-edge region, typical smoothing filters could be used to remove the noise and the small areas during the segmentation procedure. The result of simulation shows that our method is slightly better than the typical methods such as the median filtering and gradient inverse weighted filtering in the point of view of analysis of variance (ANOVA).

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.713-722
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• 2009

The estimation of odds ratio and corresponding confidence intervals for case-control data have been done by traditional generalized linear models which assumed that the logarithm of odds ratio is linearly related to risk factors. We adapt a lower-dimensional approximation of Gu and Kim (2002) to provide a faster computation in nonparametric method for the estimation of odds ratio by allowing flexibility of the estimating function and its Bayesian confidence interval under the Bayes model for the lower-dimensional approximations. Simulation studies showed that taking larger samples with the lower-dimensional approximations help to improve the smoothing spline estimates of odds ratio in this settings. The proposed method can be used to analyze case-control data in medical studies.

• Journal of Mechanical Science and Technology
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• 제18권3호
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• pp.460-470
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• 2004

A computational analysis of engineering problems with moving domain or/and boundary according to either Lagrangian or Eulerian approach may encounter inherent numerical difficulties, the extreme mesh distortion in the former and the material boundary indistinctness in the latter. In order to overcome such defects in classical numerical approaches, the ALE(arbitrary Lagrangian Eulerian) method is widely being adopted in which the finite element mesh moves with arbitrary velocity. This paper is concerned with the ALE finite element formulation, aiming at the dynamic response analysis of baffled fuel-storage container in yawing motion, for which the coupled time integration scheme, the remeshing and smoothing algorithm and the mesh velocity determination are addressed. Numerical simulation illustrating theoretical works is also presented.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.231-242
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• 1995

In this paper we consider kernel type estimator of the spectral density at a point in the analysis of stationary time series data. The kernel entails choice of smoothing parameter called bandwidth. A data-based bandwidth choice is proposed, and it is obtained by solving an equation similar to Sheather(1986) which relates to the probability density estimation. A Monte Carlo study is done. It reveals that the spectral density estimates using the data-based bandwidths show comparatively good performance.

• 응용통계연구
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• 제26권1호
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• pp.71-79
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• 2013

지역 또는 도메인에 작은 크기의 표본이 배정되어 추정의 정도가 나쁜 경우에 사용되는 준모수적 또는 비모수적 소지역 추정법은 최근 많은 연구가 진행되고 있다. 본 논문에서는 커널을 이용한 국소다항 혼합모형 소지역 추정법과 벌점 스플라인을 이용한 혼합모형 소지역 추정법이 연구되었다. 이 두 방법과 소지역추정에 흔히 사용되고 있는 선형 혼합모형을 모의실험을 통해 그 우수성을 비교하였다.

• 대한전기학회논문지:전력기술부문A
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• 제48권4호
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• pp.365-370
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• 1999

This paper describes the Weekly Load Forecasting Expert System(Named WLoFy) which was developed and implemented for Korea Electric Power Corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial nerual networks, rult-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results form WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remakably.

• Communications for Statistical Applications and Methods
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• 제26권1호
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• pp.69-78
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• 2019

A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.III
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• pp.560-568
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• 2000

The aims of this research were to investigate the preprocessing effect of spectrum data on prediction performance and to develop a robust model to predict SSC in intact apple. Spectrum data of 320 Fuji apples were measured with the on-line transmittance measurement system at the wavelength range of 550∼1100nm. Preprocess methods adopted for the tests were Savitzky Golay, MSC, SNV, first derivative and OSC. Several combinations of those methods were applied to the raw spectrum data set to investigate the relative effect of each method on the performance of the calibration model. PLS method was used to regress the preprocessed data set and the SSCs of samples, and the cross-validation was to select the optimal number of PLS factors. Smoothing and scattering corection were essential in increasing the prediction performance of PLS regression model and the OSC contributed to reduction of the number of PLS factors. The first derivative resulted in unfavorable effect on the prediction performance. MSC and SNV showed similar effect. A robust calibration model could be developed by the preprocessing combination of Savitzky Golay smoothing, MSC and OSC, which resulted in SEP= 0.507, bias=0.032 and R$^2$=0.8823.