• Proceedings of the Korean Reliability Society Conference
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• 2011.06a
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• pp.79-85
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• 2011

LED(Light Emitting Diode) is a powerful device used in applications as diverse as replacements for aviation lighting, automotive lighting as well as in traffic signals. This study is to propose a prediction system based on the degradation model of LED which is determined by combining scale and shape parameter. The degradation model is analysed goodness of fit test using calculated R-square, and is compared with previous models. A LED prediction system using degradation model is developed to automate estimations of degradation parameters and lifetimes.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.713-723
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• 2013

An objective Bayesian estimation procedure of the two-parameter Pareto distribution is presented under the reference prior and the noninformative prior. Bayesian estimators are obtained by Gibbs sampling. The steps to generate parameters in the Gibbs sampler are from the shape parameter of the gamma distribution and then the scale parameter by the adaptive rejection sampling algorism. A numerical study shows that the proposed objective Bayesian estimation outperforms other estimations in simulated bias and mean squared error.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.733-740
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• 2009

We obtain a skewed uniform distribution by a uniform distribution, and evaluate its coeffcient of skewness. And we obtain the approximate maximum likelihood estimator (AML) and moment estimator of skew parameter in the skewed uniform distribution. And we compare simulated mean squared errors (MSE) of those estimators, and also compare MSE of two proposed reliability estimators in two independent skewed uniform distributions each with different skew parameters.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.319-326
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• 1999

We shall derive several estimators for the minimum and maximum of two generalized uniform scale parameters with a common known shape parameter when the order of the scales is unknown and sample sizes are equal. Also we shall obtain the biases and mean squared errors for the proposed several estimators and compare numerically performances for the preposed several estimators.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.989-995
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• 2003

Inference for probability P(Y

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.829-836
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• 2000

We shall derive Bayes estimators for he smaller and larger of two Pareto scale parameters with a common known shape parameter when the order of the scales is unknown and sample sizes are equal under squared error loss function. Also, we shall obtain biases and man squared errors for proposed Bayes estimators, and compare numerically performances for the proposed Bayes estimators.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.79-84
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• 1990

The modal parameter estimations of linear multi-degree-of-freedom structural dynamic systems are carried out in time domain. For this purpose, the equation of motion is transformed into the autoregressive and moving average model with auxiliary stochastic input (ARMAX) model. The parameters of the ARMAX model are estimated by using the sequential prediction error method. Then, the modal parameters of the system are obtained thereafter. Experimental results are given for a 3-story building model subject to ground exitations.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.2
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• pp.122-129
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• 1988

A design method of decentralized state observer for large scale interconnected systems is proposed by the use of interconnection rejection approach and interconnection modelling technique. The proposed design method is developed based on the interconnection partitioning. Therefore partitioning conditions are suggested. And the conditions for observer pole assignment and observer parameter determination procedures are described for possible interconnection patterns. The decentralized state observer gives good estimates without any information on the interconnection variables and estimations. In addition, a numerical example is given to explain the design procedures and to show the estimation performance of the decentralized observer.

• Journal of the Korean Operations Research and Management Science Society
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• v.42 no.2
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• pp.35-48
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• 2017

As the technology convergence paradigm emerges, the need for "CIA techniques" to analyze the mutual effects of technology is increasing. However, since the CIA input parameter estimation is difficult, the present study suggests a "CIA input parameter setting model" to alleviate the difficulty of CIA input parameter estimation. This paper is focused on the difference of measurement difficulty by each scale which expert's estimation behavior was defined as measurement activity quantifying the judgment of future technology. Therefore, this model is designed to estimate the input variable as a sequence or isometric scale that is relatively easy to measure, and then converts it into a probability value. The input parameter setting model of the CIA technique consists of three sub-models : 'probability value derivation model', 'influence estimation model', and 'impact value calculation model', in order to develop a series of models the Thurstone V model, Regression Analysis, etc has been used.

• Transactions on Electrical and Electronic Materials
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• v.17 no.6
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• pp.311-316
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• 2016

Batteries are critical components of electric vehicles and energy storage systems. The connection of a battery to the power grid for charge and discharge greatly affects energy storage. Therefore, an accurate and easy-to-observe battery model should be established to achieve systematic design, simulation, and SOC (state of charge) estimations. In this review, several equivalent circuit models of representative significance are explained, and their respective advantages and disadvantages are compared to determine and outline their reasonable applications to Li-ion batteries. Numerous commonly used model parameter identification principles are summarized as well, and basic model verification methods are briefly introduced for the convenient use of such models.