• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.30 no.9
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• pp.551-557
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• 2017

It is extremely important to improve methodologies for the lifetime assessment of porcelain insulators. While there has been a considerable amount of work regarding the phenomena of lifetime distributions, most of the studies assume that aging distributions follow the Weibull distribution. However, the true underlying distribution is unknown, giving rise to unrealistic inferences, such as parameter estimations. In this article, we review several distributions that are commonly used in reliability and survival analysis, such as the exponential, Weibull, log-normal, and gamma distributions. Some properties, including the characteristics of failure rates of these distributions, are presented. We use a Bayesian approach for model selection and parameter estimation procedures. A well-known measure, called the Bayes factor, is used to find the most plausible model among several contending models. The posterior mean can be used as a parameter estimate for unknown parameters, once a model with the highest posterior probability is selected. Extensive simulation studies are performed to demonstrate our methodologies.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.6
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• pp.27-35
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• 2008

This study aims to estimate the optimal probability distribution of daily electricity generation by wind power, in order to contribute in rural green-village planning. Wind power generation is now being recognized as one of the most popular sources for renewable resources over the country. Although it is also being adapted to rural area for may reasons, it is important to estimate the magnitudes of power outputs with reliable statistical methodologies while applying historical daily wind data, for correct feasibility analysis. In this study, one of the well-known statistical methodology is employed to define the appropriate statistical distributions for monthly power outputs for specific rural areas. The results imply that the assumption of normal distributions for many cases may lead to incorrect decision-making and therefore lead to the unreliable feasibility analysis. Subjective methodology for testing goodness of fit for normal distributions on all the cases in this study, provides possibilities to consider the other various types of statistical distributions for more precise feasibility analysis.

• Journal of the Korean Mathematical Society
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• v.38 no.1
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• pp.101-123
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• 2001

For a linear operator Q from R(sup)d into R(sup)d and 0$\alpha$ and parameter b on the other. characterization of strictly (Q,b)-semi-stable distributions among (Q,b)-semi-stable distributions is made. Existence of (Q,b)-semi-stable distributions which are not translation of strictly (Q,b)-semi-stable distribution is discussed.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.1073-1080
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• 1996

The impact of the input and output probability distributions on the performance of neural networks to forecast two year peak stream flow (cubic meters per second) is examined for two major river basins of the US. The neural network input consisted of drainage area(square kilometers ) and elevation (meters). When data are normally distributed , the neural networks predict much better than when the data are non-normal and have larger tails in their distributions.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.177-189
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• 2003

For a positive integer m, operator m-semi-stability and the strict operator m-semi-stability of probability measures on R^d$ are defined. The operator m-semi-stability is a generalization of the definition of operator semi-stability with exponent Q. Characterization of strictly operator na-semi-stable distributions among operator m-semi-stable distributions is given. Translation of strictly operator m-semi-stable distribution is discussed.

• Journal of the Chungcheong Mathematical Society
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• v.29 no.3
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• pp.385-396
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• 2016

Characterizations of probability distributions by different regression conditions on generalized order statistics has attracted the attention of many researchers. We present here, characterization of Pareto and Weibull distributions based on the conditional expectation of generalized order statistics extending the characterization results reported by Jin and Lee (2014). We also present a characterization of the power function distribution based on the conditional expectation of lower generalized order statistics.

• Magazine of the Korean Society of Agricultural Engineers
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• v.17 no.2
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• pp.3772-3777
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• 1975

This paper reports on the analysis of the distributions of probable days being good for mechanical rice harvesting and the method of determining the capacity of rice harvesting machinery for the given harvesting duration. In the analysis of the probability distribution of days being good for rice harvesting, the daily rainfalls above which mechanical field work may be impracticable were specified and their frequency of occurances was analyzed by using the weather records during past twenty-one years measured at five different locations. The conclusions being drawn from the analysis are as follows: 1. The distributions of probable workable days in different region and harvesting duration are very distinct and are different to set a uniform trend (refer to Fig. 1-4). 2. The occurance of probable days being good for mechanical field work under 66% confidence level are quite variable by region and by ten-day period. The analysis indicates that the probable workable days may range from 7.5 to 8.5 days of 10-day span within optimum harvesting duration (refer to Table 1). 3. Based on the probability distributions analyzed, the optimun capacities of harvesting machinery required for different harvesting areas and harvesting start-date were estimated as a function of operating duration (refer to Fig. 5 and Table 2)

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.537-537
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• 2015

In recent decades, the independence and identical distribution (iid) assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino-Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, in the current study we propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

• KIEE International Transactions on Power Engineering
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• v.3A no.3
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• pp.155-160
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• 2003

Reliability assessment of power systems has been an important topic for the past several decades. It is becoming even more important nowadays as the power market moves toward a new competitive environment. This paper deals with two topics on reliability assessment. The first is how to select probability distributions and determine their parameters to model the probabilistic events in a power system. The second is how to consider restorative actions in the assessment, which directly influence reliability indices. This paper proposes simple but convincing alternative solutions on the two topics. In the case study, this paper shows the influences of the probability distributions that are used in power system modeling.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.351-359
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• 1999

We present a new approach to the analysis of mobile cellular communication systems under the environment of mixed platforms adopting a guard channel scheme. We assume general cell residence time distributions according to platform-types. Our system model is based on a heterogeneous M/G/c loss system with customer-dependent guard channels, where heterogeneous customers with different service time distributions have different numbers of their own guard channels. We develop the general formula of steady state probabilities for the heterogeneous M/G/c loss system with customer-dependent guard channels. The mean channel occupancy times of new and handoff calls are rigorously derived under a general setting. Finally, our numerical results show that the blocking probability and the forced termination probability are sensitive to the cell residence time distributions.