• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.256-259
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• 1996

수명시험에서는 시험기간을 단축하기 위해 중도절단(consoring) 방법을 사용한다. 혼합 관측중단방법은 정시에 시험을 끝낼 수 있으며 고장시간을 관찰할 필요가 없다는 장점 때문에 널리 사용되고 있다. 본 논문에서는 제품의 수명이 와이블 분포를 따르고 형상모수를 알고 있다는 가정아래, 혼합 관측중단하에서 생산자 위험과 소비자 위험을 고려한 신뢰도 합격판정 샘플링 계획을 개발하였다. 아울러, 형상모수값에 개재된 불확실성이 실제 생산자 위험과 소비자 위험, 그리고 의사결정까지의 평균 고장개수에 미치는 영향을 민감도 분석을 통해 파악하였다.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.125-130
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• 2001

본 논문에서는 혼합보증기간이 있는 수리 가능한 시스템에 대한 최적의 교체정책을 제안한다. 이 교체정책은 보증기간이 재생되는 경우와 재생되지 않는 두 가지 경우에 대해서 고려되며, 최적의 교체주기를 결정하기 위해서 사용자 관점에서의 단위 시간당 기대비용을 사용한다. 시스템의 고장시간이 와이블 분포를 할 때 수치적 예를 통해서 제안된 최적의 교체정책을 설명한다.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.107-117
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• 2002

In this paper we present the optimal replacement policies following the expiration of combination warranty. We consider two types of combination warranty policies: renewing warranty and non-renewing warranty. The criterion used to determine the optimal replacement period is the expected cost rate per unit time from the user'perspective. The optimal maintenance period following the expiration of combination warranty is obtained. Some numerical examples are presented for illustrative purpose.

• Journal of the Korean Solar Energy Society
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• v.33 no.2
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• pp.18-27
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• 2013

This paper presents a method for assessing the wind energy potential at complex terrain using probability distribution. And the proper probability models of the parameters estimating the wind energy are presented. Finally a mixture-Weibull determined by numerical methods procedure are proposed to assess the probability distribution of the energy potential at a site. The developed method is applied to the Kwanjungchun Bridge and compared with wind records which the neighboring weather station.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.943-953
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• 2010

From a mixture distribution of the score random variable for credit evaluation, there are many methods of estimating optimal thresholds. Most the research news is based on the assumption of normal distributions. In this paper, we extend non-normal distributions such as Weibull, Logistic and Gamma distributions to estimate an optimal threshold by using a hypotheses test method and other methods maximizing the total accuracy and the true rate. The type I and II errors are obtained and compared with their sums. Finally we discuss their e ciency and derive conclusions for non-normal distributions.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.1001-1009
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• 2019

Due to recent increasing heavy snow in Korea, the damage caused by heavy snow is also increasing. In Korea, there are many efforts including establishing disaster prevention measures to reduce the damage throughout the country, but it is difficult to establish the design criteria due to the characteristics of heavy snow. In this study, snowfall frequency analysis was performed to estimate design snow depths using observed snow depth data at Jinju, Changwon and Hapcheon stations. The conventional frequency analysis is sometime limted to apply to the snow depth data containing zero values which produce unrealistc estimates of distributon parameters. To overcome this problem, this study employed mixed distributions based on Lognormal, Generalized Pareto (GP), Generalized Extreme Value (GEV), Gamma, Gumbel and Weibull distribution. The results show that the mixed distributions produced smaller design snow depths than single distributions, which indicated that the mixed distributions are applicable and practical to estimate design snow depths.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.909-923
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• 2008

This paper considers the replacement model and the preventive maintenance model following the expiration of combination warranty for a repairable system. If the system fails after the combination warranty is expired, then it is minimally repaired at each failure. The criterion used to determine the optimal replacement policy and the optimal preventive maintenance policy is the overall value function based on the expected cost rate per unit time and the expected downtime per unit time. The numerical examples are presented for illustrative purpose when the failure time follows a Weibull distribution.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.865-877
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• 2010

This paper considers the periodic preventive maintenance model for a repairable system following warranty expiration. We consider three types of warranty policies: free repair warranty, pro-rata repair warranty, and combination repair warranty. Under these preventive maintenance models, we derive the expressions for the expected cycle length, the total expected cost, and the expected cost rate per unit time. In addition, we explain the optimal preventive maintenance period and the optimal preventive maintenance number by minimizing the expected cost rate per unit time. Finally, the optimal periodic preventive maintenance policy is given for a Weibull distribution case.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.873-882
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• 2010

This paper considers a Bayesian approach to replacement policy following the expiration of non-renewing combination warranty. The non-renewing combination warranty is the combination of the non-renewing free replacement warranty and the non-renewing pro-rata replacement warranty. We use the criterion based on the expected cost and the expected downtime to determine the optimal replacement period. To do so, we obtain the expected cost rate per unit time and the expected downtime per unit time, respectively. When the failure times are assumed to follow a Weibull distribution with uncertain parameters, we propose the optimal replacement policy based on the Bayesian approach. The overall value function suggested by Jiang and Ji (2002) is utilized to determine the optimal replacement period. Also, the numerical examples are presented for illustrative purpose.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.3
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• pp.500-506
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• 2017

In 2014, ISO 55000s has been enacted and the power plant asset management is becoming a hot issue for all over the world. The asset management system is being developed as a combination of CBM(Condition Based Maintenance) and RCM(Reliability Centered Maintenance). Therefore, the research on the calculation of the failure rate which is the most basic index of RCM is actively carried out. The failure rate calculation has been going on for a long time, and the most widely used probability distribution is the Weibull distribution. In the Weibull distribution, the failure rate function is determined in three types according to the value of the shape parameter. However, the Weibull distribution has a limitation that it is difficult to apply it when the trend of failure rate changes-such as bathtub curves. In this paper, the failure rate is calculated using the mixture Weibull distribution which can appropriately express the change of the shape of the failure rate. Based on these results, we propose the necessity and validity of applying mixture Weibull distribution.