• 산업기술연구
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• 제15권
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• pp.41-50
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• 1995

This paper presents a technique for estimating distribution of data stored in a database. This technique is very useful for accurate selectivity estimation, which is essential in query optimization and physical database design. To maintain data distribution, we employ the directory of the multilevel grid file, a multidimensional dynamic file organization. The major advantage of our technique is that data distribution information is maintained dynamically in the multilevel grid file. In contrast, other static methods such as the histogram method use static date structures, which requires periodic restructuring. Furthermore, we propose a method for keeping the abstract information of data distribution in main memory. This is advantageous in the situation where the size of main memory is not sufficient. Finally, We also suggest formulas for calculating selectivies of various queries based on our data distribution information.

• Communications for Statistical Applications and Methods
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• 제23권3호
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• pp.241-250
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• 2016

In this paper, we consider statistical inferences on the estimation of the parameters of a Weibull distribution when data are randomly censored. Maximum likelihood estimators (MLEs) and approximate MLEs are derived to estimate the parameters. We consider two cases for the censoring model: the assumption that the censoring distribution does not involve any parameters of interest and a censoring distribution that follows a Weibull distribution. A simulation study is conducted to compare the performances of the estimators. The result shows that the MLEs and the approximate MLEs are similar in terms of biases and mean square errors; in addition, the assumption of the censoring model has a strong influence on the estimation of scale parameter.

• Wind and Structures
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• 제33권6호
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1802-1811
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• 2016

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

• 품질경영학회지
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• 제30권4호
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• pp.68-77
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• 2002

EM algorithm has good convergence rate for numerical procedures which converges on very small step. In the case of proportion estimation in a mixed distribution which has very big incomplete data or of update of new data continuously, however, EM algorithm highly depends on a initial value with slow convergence ratio. There have been many studies to improve the convergence rate of EM algorithm in estimating the proportion parameter of a mixed data. Among them, dynamic EM algorithm by Hurray Jorgensen and Titterington algorithm by D. M. Titterington are proven to have better convergence rate than the standard EM algorithm, when a new data is continuously updated. In this paper we suggest dynamic EM algorithm and Titterington algorithm for the estimation of a mixed Poisson distribution and compare them in terms of convergence rate by using a simulation method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1830-1835
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• 2003

A novel index representing burden distribution form in the blast furnace is developed and index estimation model is built with an empirical modeling method to monitor inner condition of the furnace without expensive sensors. To find the best combination of index and modeling method, two candidates for the index and four modeling methods have been examined. Results have shown that 3-D index have more resolution in describing the distribution form than 1-D index and ANN model produces smallest RMSE due to nonlinearity between the indices and charging mode. Although ANN has shown the best prediction accuracy in this study, PLS can be a good alternative due to its advantages in generalization capability, consistency, simplicity and training time. The second best result of PLS in the prediction results supports this fact.

• Communications for Statistical Applications and Methods
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• 제14권2호
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• pp.355-363
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• 2007

In this paper a Bayesian estimation of the two-parameter kappa distribution was discussed under the noninformative prior. The Bayesian estimators are obtained by the Gibbs sampling. The generation of the shape parameter and scale parameter in the Gibbs sampler is implemented using the adaptive rejection Metropolis sampling algorithm of Gilks et al. (1995). A Monte Carlo study showed that the Bayesian estimators proposed outperform other estimators in the sense of mean squared error.

• 전자공학회논문지S
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• 제35S권5호
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• pp.94-102
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• 1998

In this paper, we proposed a dynamic selection scheme of advnaced prediction mode(DAPM), which reduces computational cost and improves coding efficiency. We can select the mode between default prediction mode (DPM) and advanced prediction mode (APM) according to motion componenets in a frame dynamically. For this purpose, we defined error distribution of motion estimation (EDME) as sum of absolute difference(SAD) for each searching points. This distribution region is divided to four subregions. We calculate minimum values in each subregions and then, we determine whether block motion estimation is performed or not depending on the results. As a result, we reduced computational complexity to 30% without degradation of image quality compared to fixed APM(FAPM) by selecting DPM for linear movement.

• International Journal of Reliability and Applications
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• 제4권1호
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• pp.41-50
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• 2003

This paper represents the first comprehensive comparison of the Newton-Raphson's method and Simple Iterative Procedure (SIP) in the maximum likelihood estimation of the two-parameter Weibull distribution. Computer simulation is employed to compare these two methods for multiply censored, singly censored data (Type I or Type Ⅱ censoring) and complete data. Results indicate the Newton-Raphson's with the Menon's estimated value, as an initial point remains the effective iterative procedure for estimating the parameters.

• 한국산학기술학회논문지
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• 제5권6호
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• pp.533-538
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• 2004

전력 소비자 근처에 설치, 운영하는 소규모 발전 형태인 분산형 전원에 대한 관심이 최근 들어 커지고 있다. 전력산업 구조개편이 진행되고 있는 현 상황에서 분산형 전원을 배전계통 내에 설치하고, 배전계통의 상태추정 기법을 적용하여 배전계통 운영을 안정적이고 신뢰성 높게 하려는 시도가 이루어지고 있다. 본 논문에서는 배전계통에 분산형 전원을 추가하였을 때 계통 손실에 미치는 영향을 분석하여 분산형 전원의 최적위치를 선정하고, 분산형 전원이 포함된 배전계통을 위한 상태추정 기법을 개발하였다. 불량데이터에 대한 강건성이 뛰어난 WLAV 상태추정기를 구현하였으며, 단일, 다중, 상호작용 불량 데이터에 대한 테스트를 실시하였다. IEEE 34 모선 수지상 계통을 사용하여 사례연구를 수행함으로써 제안한 알고리즘의 타당성을 확인하였다.