• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.241-248
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• 2009

This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis of the proposed estimation to demonstrate its convergence property by using statistical convergence and system stability theories. We apply our fault detection approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields.

• Wind and Structures
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• v.4 no.1
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• pp.73-84
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• 2001

Analysis of pressures measured on the roof of the full-scale Texas Tech building and a 1/50 scale model of a typical house showed that the pressure fluctuations on cladding fastener and cladding-truss connection tributary areas have similar characteristics. The probability density functions of pressure fluctuations on these areas are negatively skewed from Gaussian, with pressure peak factors less than -5.5. The fluctuating pressure energy is mostly contained at full-scale frequencies of up to about 0.6 Hz. Pressure coefficients, $C_p$ and local pressure factors, $K_l$ given in the Australian wind load standard AS1170.2 are generally satisfactory, except for some small cladding fastener tributary areas near the edges.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.11 no.3
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• pp.165-172
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• 1999

A probability density function of reliability function is derived in this paper, by which the stability of armor units on the rubble-mound breakwater can be studied on the probabilistic approach. To obtain the distribution, each random variable of the reliability function is assumed to follow Gaussian distribution. The distribution function of reliability function is in agreement with the histogram simulated by the Monte-Carlo method. In addition, the failure probability of armor units on the rubble-mound breakwater evaluated by the derived probability density function is shown to have the same order of magnitude as those calculated by FMA and AFDA of moment method. In particular, it is important to note that random variables of the reliability function may be considered to be statistically independent in the reliability analysis of armor units on the rubble-mound breakwater. Therefore, the present approach may be straightforwardly applicable to all of the cases that any random variables in the reliability function are controlled by other distribution functions as well as normal distribution.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.183-191
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• 2014

We define a multivariate Cauchy distribution using a probability density function; subsequently, a Ferguson's definition of a multivariate Cauchy distribution can be viewed as a characterization theorem using the characteristic function approach. To clarify this characterization theorem, we construct two dependent Cauchy random variables, but their sum is not Cauchy distributed. In doing so the proofs depend on the characteristic function, but we use the cumulative distribution function to obtain the exact density of their sum. The derivation methods are relatively straightforward and appropriate for graduate level statistics theory courses.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.125-134
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• 2014

Kullback-Leibler (KL) information is a measure of discrepancy between two probability density functions. However, several nonparametric density function estimators have been considered in estimating KL information because KL information is not well-defined on the empirical distribution function. In this paper, we consider the KL information of the equilibrium distribution function, which is well defined on the empirical distribution function (EDF), and propose an EDF-based goodness of fit test statistic. We evaluate the performance of the proposed test statistic for an exponential distribution with Monte Carlo simulation. We also extend the discussion to the censored case.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.209-218
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• 2021

For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.

• Journal of the Chungcheong Mathematical Society
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• v.22 no.1
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• pp.1-5
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• 2009

Let {$X_n,\;n{\geq}1}$ be a sequence of independent identically distributed(i.i.d.) sequence of positive random variables with common absolutely continuous distribution function(cdf) F(x) and probability density function(pdf) f(x) and $E(X^2)<{\infty}$. The random variables $\frac{X_i{\cdot}X_j}{(\Sigma^n_{k=1}X_k)^{2}}$ and $\Sigma^n_{k=1}X_k$ are independent for $1{\leq}i if and only if {$X_n,\;n{\geq}1}$ have gamma distribution.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.1-9
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• 2003

In this article, we consider a Bayesian estimation method for the geometric mean of $textsc{k}$ exponential parameters, Using the Tibshirani's orthogonal parameterization, we suggest an invariant prior distribution of the $textsc{k}$ parameters. It is seen that the prior, probability matching prior, is better than the uniform prior in the sense of correct frequentist coverage probability of the posterior quantile. Then a weighted Monte Carlo method is developed to approximate the posterior distribution of the mean. The method is easily implemented and provides posterior mean and HPD(Highest Posterior Density) interval for the geometric mean. A simulation study is given to illustrates the efficiency of the method.

• Journal of Korea Water Resources Association
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• v.37 no.9
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• pp.759-769
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• 2004

This study proposes a new methodology to derive the areal reduction factor (ARF) using mixed probability density functions. Estimation of ARFs requires using the simultaneous rainfall data over the basin, which is rarely available in general. The new methodology Proposed in this study uses more available daily rainfall data during a given period, so the mixed probability density functions should be introduced to explain both the rainfall intermittency and variability. This study applied the mixed gamma distribution for the derivation of ARFs for the Keum river basin, and found that the new method is easier for application as well as it provides very comparable results.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.477-483
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• 2013

The purpose of this study was to calculate uncertainty of emission factor collected data and to evaluate the applicability of Monte Carlo simulation technique. To estimate the distribution of emission factors (Such as Basic wood density, Biomass expansion factor, and Root-to-shoot ratio), four probability density functions (Normal, Lognormal, Gamma, and Weibull) were used. The two sample Kolmogorov-Smirnov test and cumulative density figure were used to compare the optimal probability density function. It was observed that the basic wood density showed the gamma distribution, the biomass expansion factor results the log-normal distribution, and root-shoot ratio showd the normal distribution for Pinus densiflora in the Gangwon region; the basic wood density was the normal distribution, the biomass expansion factor was the gamma distribution, and root-shoot ratio was the gamma distribution for Pinus densiflora in the central region, respectively. The uncertainty assessment of emission factor were upper 62.1%, lower -52.6% for Pinus densiflora in the Gangwon region and upper 43.9%, lower -34.5% for Pinus densiflora in the central region, respectively.