• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.259-269
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• 1997

Bayesian inference for a record value statistics(RVS) model of nonhomogeneous Poisson process is considered. We seal with Bayesian inference for double exponential, Gamma, Rayleigh, Gumble RVS models using Gibbs sampling and Metropolis algorithm and also explore Bayesian computation and model selection.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.49
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• pp.23-31
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• 1999

Bayesian approach using nonhomogeneous Poisson process is considered for modelling software reliability problem. The usefulness of the iterative sampling-based method increases greatly as the dimension of a problem increases. Maximum likelihood estimator and Gibbs estimator are derived. Model selection based on a predictive likelihood is studied. A numerical example is given.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.457-463
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• 2008

In this study, the mean time to failures of a system under shock models are derived. The system receives shocks according to a stochastic process. The expected system lifetime under homogeneous Poisson shock process, nonhomogeneous Poisson shock process, and a general renewal shock process are derived. Some numerical examples are presented.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.1-9
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• 2004

The nonhomogeneous poisson process (NHPP) is often used to model repairable systems that are subject to a minimal repair strategy, with negligible repair times. In this situation, the system can be characterized by its intensity function. There have been many NHPP models according to intensity functions. However, the intensity function of system in use can be changed because of repair or its aging. We consider the single change point model as the modification of the power law process. The shape parameter of its intensity function is changed before and after the change point. We detect the presence of the change point using Bayesian methodology. Some numerical results are also presented.

• Journal of Korean Society for Quality Management
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• v.39 no.2
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• pp.170-178
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• 2011

In general, a software fault detection phenonenon is described by a software reliability model based on a nonhomogeneous Poisson process(NHPP). In this paper, we propose a software reliability growth model considering the differences of the software environments in both the testing phase and the operational phase. Also, we consider the problem of determining the optimal release time and the optimal warranty period that minimize the total expected software cost which takes account of periodic software maintenance(e.g. patch, update, etc). Finally, we analyze the sensitivity of the optimal release time and warranty period based on the fault data observed in the actual testing process.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.2 s.25
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• pp.51-59
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• 2006

The purpose of this paper is to present reliability analysis procedures for repairable systems and apply the procedures for assessing the reliabilities of two subsystems of a specific group of military equipment based on field failure data. The mean cumulative function, M(t), the average repair rate, ARR(t), and analytic test methods are used to determine whether a failure process follows a renewal or non-renewal process. For subsystem A, the failure process turns out to follow a homogeneous Poisson process, and subsequently, its mean time between failures, availability, and the necessary number of spares are estimated. For subsystem B, the corresponding M(t) plot shows an increasing trend, indicating that its failure process follows a non-renewal process. Therefore, its M(t) is modeled as a power function of t, and a preventive maintenance policy is proposed based on the annual mean repair cost.

• The KIPS Transactions:PartD
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• v.10D no.5
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• pp.805-812
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• 2003

This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP) and performs Bayesian inference using prior information. The failure process is analyzed to develop a suitable mean value function for the NHPP; expressions are given for several performance measure. The parametric inferences of the model using Logarithmic Poisson model, Crow model and Rayleigh model is discussed. Bayesian computation and model selection using the sum of squared errors. The numerical results of this models are applied to real software failure data. Tools of parameter inference was used method of Gibbs sampling and Metropolis algorithm. The numerical example by T1 data (Musa) was illustrated.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.277-286
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• 1998

This paper investigates the problem of determining optimal replacement policies for equipment subject to failures with cyclic rates. In many situations, the system failures depend on the operating environmental conditions that vary on time, usually with periodical manners. We use nonhomogeneous Poisson processes whose rate functions exhibit cyclic behavior as well as a long-term evolutionary trend to model the stochastic process of the failures when the rate of occurrence of the failures varies periodically, for example from day to day or between seasons. In this study, we compare optimal policies under the nonhomogeneous process with/without a cyclic component in the failure rate function. The analytical results for various situations are presented along with numerical examples using simulated data.

• Spatial Information Research
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• v.6 no.1
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• pp.77-90
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• 1998

The complex interactions of climate, topography, geology, biota and hwnan activities result in the land cover patterns, which are impacted by natural disturbances such as fire, earthquake and flood. Natural disturbances disrupt ecosystem communities and change the physical environment, thereby generating a new landscape. Community ecologists believe that disturbance is critical in determining how diverse ecological systems function. Fires were once a major agent of disturbance in the North American tall grass prairies, African savannas, and Australian bush. The major focus of this research was to develop stochastic model of spatial process of disturbance or spatial events and simulate the process based on the developed model and it was applied to the fire arrival process in the Great Victoria Desert of Australia, where wildfires generate a mosaic of patches of habitat at various stages of post-fire succession. For this research, Landsat Multi-Spectral Scanner(MSS) data covering the period from 1972 to 1994 were utilized. Fire arrival process is characterized as a spatial point pattern irregularly distributed within a region of space. Here, nonhomogeneous planar Poisson process is proposed as a model for the fire arrival process and rejection sampling thinning the homogeneous Poisson process is used for its simulation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.1-10
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• 2012

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and the life distribution applied exponential and log shaped type hazard function. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model could be confirmed. This paper, a failure data analysis of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and coefficient of determination.