• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.1-12
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• 2006

Many software reliability growth models(SRGM) have been proposed since the software reliability issue was raised in 1972. The technology to estimate and grow the reliability of developing S/W to target value during testing phase were developed using them. Most of these propositions assumed the S/W debugging testing efforts be constant or even did not consider them. A few papers were presented as the software reliability evaluation considering the testing effort was important afterwards. The testing effort forms which have been presented by this kind of papers were exponential, Rayleigh, Weibull, or logistic functions, and one of these 4 types was used as a testing effort function depending on the S/W developing circumstances. I propose the methology to evaluate the SRGM using least square estimator and maximum likelihood estimator for those 4 functions, and then examine parameters applying actual data adopted from real field test of developing S/W.

• Journal of the Korean Data and Information Science Society
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• v.2
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• pp.23-39
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• 1991

The maximum likelihood estimators and Bayes estimators of the parameters and reliability function for the two-parameter Weibull distribution under the type-II progressively censoring schemes are derived when a shape parameter is known and unknown, respectively. Efficiencies for above estimators are also compared each other in terms of the mean square errors, and Bayes risk sensitivities of the Bayes estimators are investigated.

• IE interfaces
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• v.16 no.1
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• pp.27-33
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• 2003

The maintenance cost in K Steelworks has been continuously increased in proportion to the production cost. However, there seems to be a possibility of reducing cost through the optimization of maintenance actions. The failure types of the equipment in steelworks ate various with different failure cost. Thus the failure rate and cost of each type of failures should be considered simultaneously when the optimum maintenance period is to be determined. It is considered that the equipment undergoes periodic replacement and a specified number of incomplete preventive maintenance actions are performed during a replacement period. Assuming that the time to failure follows a Weibull distribution, the parameters of the failure rate are estimated using the maximum likelihood estimation. The optimal replacement period is determined to minimize the average cost per unit time. As the result of analysis it is suggested that the existing maintenance period for a hot-rolling equipment can be extended significantly.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.4
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• pp.466-475
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• 1999

A backward Dynamic Programming(DP) model for the optimal facility replacement decision problem during a finite planning horizon is presented. Multiple alternative challengers to a current defender are considered. All facilities are assumed to have finite service lives. The objective of the DP model is to maximize the profit over a finite planning horizon. As for the cost elements, purchasing cost, maintenance costs and repair costs as well as salvage value are considered. The time to failure is assumed to follow a weibull distribution and the maximum likelihood estimation of Weibull parameters is used to evaluate the expected cost of repair. To evaluate the revenue, the rate of operation during a specified period is employed. The cash flow component of each challenger can vary independently according to the time of occurrence and the item can be extended easily. The effects of inflation and the time value of money are considered. The algorithm is illustrated with a numerical example. A MATLAB implementation of the model is used to identify the optimal sequence and timing of the replacement.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.319-326
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• 2010

In this paper, a new group sampling plan for the lot acceptance is proposed for the time truncated life test, which can be utilized when multi-item testers are implemented. The design parameters are found using the two-point approach such that the producer's and consumer's risks are satisfied simultaneously at the acceptable reliability level and the lot tolerance reliability level, respectively. The case of Weibull distribution is described to illustrate the procedure that can be used when the quality level is expressed by a multiple of the specified life. The advantage of the proposed plan is demonstrated by comparing with the existing plan in terms of the sample size required. The tables are constructed and some examples are given to illustrate the procedure developed here.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.29 no.2
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• pp.152-161
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• 1993

This paper deals with the effect of spatial distribution of material properties on its statistical characteristics and numerical estimation method of reliability of fatigue sensitive structures with respect to the fatigue crack growth. A method is proposed to determine experimentally the probability distribution functions of material parameters of Paris law. da/dN=C(ΔK/K sub(0) ) super(m), using stress intensity factor controlled fatigue tests. The result with a high tensile strength steel shows that the distribution of the parameter m is approximately normal and that of 1/C, is a 3-parameter Weibull. The main result obtained are : (1) The theoretical autocorrelation of the resistance, 1/C, to fatigue crack growth are almost same for different lengths. (2) The variance decreases with the increasing a averaging length. When spatial correlation length is very small. the variane decreases significantly were the averaging length. (3) The probability distribution of load cycles or the number for a crack to reach a certain length can be estimated using these functions by simulation of non-Gaussian(expecially Weibull) Stochastic Process.

• Wind and Structures
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• v.28 no.6
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• pp.369-380
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• 2019

Statistical distributions are very useful in describing wind speed characteristics and in predicting wind power potential of a specified region. Although the Weibull distribution is the most popular one in wind energy literature, it does not seem to be able to perfectly fit all the investigated wind speed data in nature. Thus, many studies are still being conducted to find flexible distribution for modelling wind speed data. In this study, we propose a new Odd-Burr Rayleigh distribution for wind speed characterization. The Odd-Burr Rayleigh distribution with two shape parameters is flexible enough to model different shapes of wind speed data and thus it can be an alternative wind speed distribution for the assessment of wind energy potential. Therefore, suitability of the Odd-Burr Rayleigh distribution is investigated on real wind speed data taken from different regions in the South Africa. Numerical results of the conducted analysis confirm that the new Odd-Burr Rayleigh distribution is suitable for modelling most of the considered real wind speed cases and it also can be used for predicting wind power.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.665-677
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• 2022

This paper proposes a new estimation method based on the maximum product of spacings for estimating unknown parameters of the three-parameter Weibull distribution under a generalized Type-II progressive hybrid censoring scheme which guarantees a constant number of observations and an appropriate experiment duration. The proposed approach is appropriate for a situation where the maximum likelihood estimation is invalid, especially, when the shape parameter is less than unity. Furthermore, it presents the enhanced performance in terms of the bias through the Monte Carlo simulation. In particular, the superiority of this approach is revealed even under the condition where the maximum likelihood estimation satisfies the classical asymptotic properties. Finally, to illustrate the practical application of the proposed approach, the real data analysis is conducted, and the superiority of the proposed method is demonstrated through a simple goodness-of-fit test.

• Journal of Korean Society for Quality Management
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• v.27 no.3
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• pp.79-93
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• 1999

The common accelerated life test(ALT) consists of test methods applying a constant stress, higher than the use condition stress, to items. There we, however, situations for which a progressive stress ALT, in which the stress on a test item is continuously increased with time, Is more convenient to perform testing and simpler in analyzing data than a constant stress ALT. When a product under constant stress s follows a Weibull distribution with parameters $\theta$(5) and $\beta$, maximum likelihood(ML) estimators of parameters involved in the model are obtained and their asymptotic distributions are derived under stress bounded ramp tests in which the stress is increased linearly from use condition stress to the stress upper bound. The optimum test plans are also found which minimize the asymptotic variance of the ML estimator of the log mean life at design constant stress. For selected values of the design parameters, tables useful for finding optimal test plans are given. The effect of the pre-estimates of design parameters is studied.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.479-496
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• 2016

In this survey, we present a modified inverse moment estimation of parameters and its applications. We use a specific model to demonstrate its principle and how to apply this method in practice. The estimation of unknown parameters is considered. A necessary and sufficient condition for the existence and uniqueness of maximum-likelihood estimates of the parameters is obtained for the classical maximum likelihood estimation. Inverse moment and modified inverse moment estimators are proposed and their properties are studied. Monte Carlo simulations are conducted to compare the performances of these estimators. As far as the biases and mean squared errors are concerned, modified inverse moment estimator works the best in all cases considered for estimating the unknown parameters. Its performance is followed by inverse moment estimator and maximum likelihood estimator, especially for small sample sizes.