• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2457-2464
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• 2015

Distributed Networked Control Systems (NCSs) through wireless networks have a tremendous potential to improve the efficiency of various control systems. In this paper, we define the State Update Interval (SUI) as the elapsed time between successful state vector reports derived from the NCSs. A simple expression of the SUI is derived to characterize the key interactions between the control and communication layers. This performance measure is used to formulate a novel optimization problem where the objective function is the probability to meet the SUI constraint and the decision parameter is the channel access probability. We prove the existence and uniqueness of the optimal channel access probability of the optimization problem. Furthermore, the optimal channel access probability for NCSs is lower than the channel access probability to maximize the throughput. Numerical results indicate that the improvement of the success probability to meet the SUI constraint using the optimal channel access probability increases as the number of nodes increases with respect to that using the channel access probability to maximize the throughput.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1474-1479
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• 2003

This paper proposes a transmission interval which includes collisions and retransmissions in the IEEE 802.11 DCF. The transmission probability is used for the calculation of collisions and retransmissions in the DCF. Since the noises are considered to analyze the transmission interval, retransmissions by the noises are included in the proposed transmission interval. The proposed transmission interval takes account of the retransmissions by packet errors, and makes it possible to analyze the maximum throughput of the DCF.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2750-2767
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• 1996

In this papr, a new lossless compression method is presented based on the Binary Adaptive Arithmetic Coder(BAAC). A simple unbalanced binary tree is created by recursively dividing the BAAC unit interval into two probability sub-inervals. On the tree the More Probable Predicted Value(MPPV) and Less Probable Predicated Value(LPPV) estimated by local statistics of the image pixels are arranged in decreasing order. The BAAC or Huffman coder is thus applied to the branches of the tree. The proposed method allows the coder be directly applied to the full bit-plane medical image without a decomposition of the full bit-planes into a series of binary bit-planes. The use of the full bit model template improves the compresion ratio. In addition, a fast computation for adjusting the interval is possible since a simple arithmetic operation based on probability interval estimation state machine is used for interval sub-division within the BAAC unit interval.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3221-3223
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• 2007

Recently, nuclear power plant companies have been extending the turbine valve test interval to reduce the potential of the reactor trip accompanied with a turbine valve test and to improve the NPP's economy through the reduction of unexpected plant trip or decreased operation. In these regards, the extension of the test interval for turbine valves was reviewed in detail. The effect on the destructive overspeed probability due to the test interval change of turbine valves is evaluated by Fault Tree Analysis(FTA) method. Even though the test interval of turbine valves is changed from 1 month to 3 months, the analysis result shows that the reliability of turbine over speed protection system meets acceptance criteria of 1.0E-4/yr. This result will be used as the technical basis on the extension of the test interval for turbine valves. In this paper, the propriety of the turbine valve test interval extension is explained through the review on the turbine valve test interval status of turbine overspeed protection system, the analysis on the annual turbine missile frequency and the probability evaluation of the destructive overspeed due to the test interval extension.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1175-1182
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• 2011

In this paper, properties of exact confidence interval and some approximate confidence intervals of hyper-geometric parameter, that is the probability of success p in the population is discussed. Usually, binomial distribution is a well known discrete distribution with abundant usage. Hypergeometric distribution frequently replaces a binomial distribution when it is desirable to make allowance for the finiteness of the population size. For example, an application of the hypergeometric distribution arises in describing a probability model for the number of children attacked by an infectious disease, when a fixed number of them are exposed to it. Exact confidence interval estimation of hypergeometric parameter is reviewed. We consider the approximation of hypergeometirc distribution to the binomial and normal distribution respectively. Approximate confidence intervals based on these approximation are also adequately discussed. The performance of exact confidence interval estimates and approximate confidence intervals of hypergeometric parameter is compared in terms of actual coverage probability by small sample Monte Carlo simulation.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1289-1300
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• 2009

The double sampling scheme is effective in reducing the sampling cost. However, the doubly sampled data is contaminated by two types of error, namely false-positive and false-negative errors. These would make the statistical analysis more difficult, and it would require more sophisticate analysis tools. For instance, the Wald method for the interval estimation of a proportion would not work well. In fact, it is well known that the Wald confidence interval behaves very poorly in many sampling schemes. In this note, the property of the Wald interval is investigated in terms of the coverage probability and the expected width. An alternative confidence interval based on the Agresti-Coull's approach is recommended.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.73-78
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• 2002

In this paper we develop a method for constructing a Bayesian HPD (highest probability density) interval of a ratio of two multivariate normal generalized variances. The method gives a way of comparing two multivariate populations in terms of their dispersion or spread, because the generalized variance is a scalar measure of the overall multivariate scatter. Fully parametric frequentist approaches for the interval is intractable and thus a Bayesian HPD(highest probability densith) interval is pursued using a variant of weighted Monte Carlo (WMC) sampling based approach introduced by Chen and Shao(1999). Necessary theory involved in the method and computation is provided.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.743-752
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• 2004

In this paper, we develop the Bayesian model selection procedure using the reference prior for comparing two nested model such as the independent and intraclass models using the distance or divergence between the two as the basis of comparison. A suitable criterion for this is the power divergence measure as introduced by Cressie and Read(1984). Such a measure includes the Kullback -Liebler divergence measures and the Hellinger divergence measure as special cases. For this problem, the power divergence measure turns out to be a function solely of $\rho$, the intraclass correlation coefficient. Also, this function is convex, and the minimum is attained at $\rho=0$. We use reference prior for $\rho$. Due to the duality between hypothesis tests and set estimation, the hypothesis testing problem can also be solved by solving a corresponding set estimation problem. The present paper develops Bayesian method based on the Kullback-Liebler and Hellinger divergence measures, rejecting $H_0:\rho=0$ when the specified divergence measure exceeds some number d. This number d is so chosen that the resulting credible interval for the divergence measure has specified coverage probability $1-{\alpha}$. The length of such an interval is compared with the equal two-tailed credible interval and the HPD credible interval for $\rho$ with the same coverage probability which can also be inverted into acceptance regions of $H_0:\rho=0$. Example is considered where the HPD interval based on the one-at- a-time reference prior turns out to be the shortest credible interval having the same coverage probability.

• Journal of Environmental Health Sciences
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• v.45 no.5
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• pp.497-510
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• 2019

Objectives: The aim of this study was to secure the reliability of using the CalTOX model when evaluating LADD (or ADD) and Risk (or HQ) among local residents for the emission of BTEX (Benzene, Toluene, Ethylbenzene, Xylene) and by closely examining the difference in the confidence interval of the assessment outcomes according to the difference in the probability density function of input variables. Methods: The assessment was made by dividing it according to the method ($I^{\dagger}$) of inputting the probability density function in meteorological variables of the model with log-normal distribution and the method of inputting ($II^{\ddagger}$) after grasping the optimal probability density function using @Risk. A T-test was carried out in order to analyze the difference in confidence interval of the two assessment results. Results: It was evaluated to be 1.46E-03 mg/kg-d in LADD of Benzene, 1.96E-04 mg/kg-d in ADD of Toluene, 8.15E-05 mg/kg-d in ADD of Ethylbenzene, and 2.30E-04 mg/kg-d in ADD of Xylene. As for the predicted confidence interval in LADD and ADD, there was a significant difference between the $I^{\dagger}$ and $II^{\ddagger}$ methods in $LADD_{Inhalation}$ for Benzene, and in $ADD_{Inhalation}$ and ADD for Toluene and Xylene. It appeared to be 3.58E-05 for risk in Benzene, 3.78E-03 for HQ in Toluene, 1.48E-03 for HQ in Ethylbenzene, and 3.77E-03 for HQ in Xylene. As a result of the HQ in Toluene and Xylene, the difference in confidence interval between the $I^{\dagger}$ and $II^{\ddagger}$ methods was shown to be significant. Conclusions: The human risk assessment for BTEX was made by dividing it into the method ($I^{\dagger}$) of inputting the probability density function of meteorological variables for the CalTOX model with log-normal distribution, and the method of inputting ($II^{\ddagger}$) after grasping the optimal probability density function using @Risk. As a result, it was identified that Risk (or HQ) is the same, but that there is a significant difference in the confidence interval of Risk (or HQ) between the $I^{\dagger}$ and $II^{\ddagger}$ methods.

• Structural Engineering and Mechanics
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• v.62 no.6
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• pp.703-708
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• 2017

The goal of this study is to evaluate behavior uncertainties of structures by using interval finite element analysis based on interval factor method as a specific non-stochastic tool. The interval finite element method, i.e., interval FEM, is a finite element method that uses interval parameters in situations where it is not possible to get reliable probabilistic characteristics of the structure. The present method solves the uncertainty problems of a 2D solid structure, in which structural characteristics are assumed to be represented as interval parameters. An interval analysis method using interval factors is applied to obtain the solution. Numerical applications verify the intuitive effectiveness of the present method to investigate structural uncertainties such as displacement and stress without the application of probability theory.