• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.39-49
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• 1996

비모수적 커널 회귀함수 추정법에서 평활량(bandwidth of smoothing parameter)의 선택은 아주 중요한 문제이다. 교차타당성(cross-validation) 방법에 의한 평활량은 최적평활량으로의 상대적 수렴속도(relative convergence rate)가 $n^{-1/10}$로 상당히 느리다는 것을 알고 있다. 본 연구는 삽입방법(plug-in method)에 의해 선택된 평활량의 상대적 수렴속도가 교차타당성 방법보다 더 빠른 $n^{-2/7}$이 됨을 보였다. 그리고 모의실험을 통하여 소 표본에서도 삽입방법이 교차타당성 방법보다 우수함을 입증하였다.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.891-901
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• 1997

The objective of this research is to investigate the problem of goodness of fit testing based on nonparametric density estimation with a data-driven smoothing parameter. The small and large smaple properties of the proposed test statistic $Z_{mn}$ are investigated with the minimizer $\widehat{m}$ of the estimated mean integrated squared error by the Diggle and Hall (1986) method.

• Journal of applied mathematics & informatics
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• v.11 no.1_2
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• pp.327-340
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• 2003

For motion analysis electromagnetic sensors are often used to measure positions and orientations of human subjects. It is observed from several experiments of the Ergonomics Research group that there exist systematic errors and unexpected serious distortions due to some metal masses in the test area. A calibration process is necessary to fix these errors. In this article three models are proposed to correct position measurement errors based on observations from calibration experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.502-509
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• 2005

In this paper, a 4-wheel vehicle model including the effects of tire slip was considered, along with variable parameter sliding control, in order to improve the performance of the vehicle longitudinal response. The variable sliding parameter is made to be proportional to the square root of the pressure derivative at the wheel, in order to compensate for large pressure changes in the brake cylinder. A typical tire force-relative slip curve for dry road conditions was used to generate an analytical tire force-relative slip function, and an antilock sliding control process based on the analytical tire force-relative slip function was used. A retrofitted brake system, with the pushrod force as the end control parameter, was employed, and an average decay function was used to suppress the simulation oscillations. The simulation results indicate that the velocity and spacing errors were slightly larger than those obtained when the wheel slip effect was not considered, that the spacing errors of the lead and follower were insensitive to the adhesion coefficient up to the critical wheel slip value, and that the limit for the antilock control under non-constant adhesion road conditions was determined by the minimum value of the equivalent adhesion coefficient.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.243-255
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• 1997

Forecasting the future values of an observed time series is an important problem in many areas, including economics, traffic engineering, production planning, sales forecasting, and stock control. The purpose of this paper is aimed to discover the more efficient forecasting model through the parameter estimation and residual analysis among the quantitative method such as Winters' exponential smoothing model, Box-Jenkins' model, and Kalman filtering model. The mean of the time series is assumed to be a linear combination of known functions. For a parameter estimation and residual analysis, Winters', Box-Jenkins' model use Statgrap and Timeslab software, and Kalman filtering utilizes Fortran language. Therefore, this paper can be used in real fields to obtain the most effective forecasting model.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.399-402
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• 2000

In this paper, we present a regularized mixed norm multichannel image restoration algorithm. The problem of multichannel restoration using both within- and between- channel deterministic information is considered. For each channel a functional which combines the least mean squares (LMS), the least mean fourth(LMF), and a smoothing functional is proposed, We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter that defines the degree of smoothness of the solution, both updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required, and the parameters mentioned above are adjusted based on the partially restored image.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.164-171
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• 2012

ESPRIT(Estimation of Signal Parameter via Rotational Invariance Techniques) estimates DOAs(directions of arrival) of the incident signals on a sensor array by exploiting the shift invariance between its two subarrays. This paper suggests an efficient DOA estimation method based on ESPRIT when coherent signals impinge on the sensor array. When applying ESPRIT, it is necessary to find a signal subspace. Though the widely known SS(spatial smoothing) method allows us to obtain a signal subspace in the presence of coherent signals, its computational complexity is very high. Recently a CV(correlation vector) based method has been presented which is computationally simple. However, the number of resolvable signals in the method is smaller than that in the SS based method when multiple coherent signal groups are present. The proposed method in this paper, which obtains a signal subspace by utilizing only part of the correlation matrix, significantly reduces the computational complexity as compared with the SS based one, while the former is resolving the same number of coherent signals as the latter,

• The Journal of the Acoustical Society of Korea
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• v.30 no.7
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• pp.383-389
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• 2011

In this paper, we propose a improved speech absence probability estimation algorithm by applying environmental noise classification for speech enhancement. The previous speech absence probability required to seek a priori probability of speech absence was derived by applying microphone input signal and the noise signal based on the estimated value of a posteriori SNR threshold. In this paper, the proposed algorithm estimates the speech absence probability using noise classification algorithm which is based on Gaussian mixture model in order to apply the optimal parameter each noise types, unlike the conventional fixed threshold and smoothing parameter. Performance of the proposed enhancement algorithm is evaluated by ITU-T P.862 PESQ (perceptual evaluation of speech quality) and composite measure under various noise environments. It is verified that the proposed algorithm yields better results compared to the conventional speech absence probability estimation algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2337-2342
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• 2008

In this paper, we propose the novel approach to enhance the visual quality of the digital image with adaptively sharpening and removing the noise. Image enhancement is performed in two ways. The pixels in the high dynamics area are sharpened by the adaptive unsharp mask with the parameter, which is derived using the statistical information of the image. On the other hand, the proposed algorithm do not perform the sharpening process in the uniform area that may cause the undesired artifact due to noise amplification, rather it performs smoothing to suppress the noise in this area. The decision, which process will be applied at the pixel, is also controlled by the statistics of the pixel dynamics. The proposed algorithm enhances the visual quality almost automatically by sharpening and smoothing at the same time with less parameter selection.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.50-58
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• 2018

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston's method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston's method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands' interval separately, as in Croston's method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.