• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1993.10a
• /
• pp.24-30
• /
• 1993

When modeling a complex system we use an event tree to analyze propagation of failure. An event tree cannot represent the statistical interrelationships among parameters, but it can be represented as a statistically identical influence diagram so that parameter updating can be easily performed. After updating parameters we can calculate posterior distribution of the failure rate for each path. But exact distribution requires considerably complex numerical integration. We propose an approximation method to calculate the posterior and derive the predictive distribution of the time to next failure. Finally we introduce the system which implements our methodology.

• Journal of Korean Society for Quality Management
• /
• v.16 no.2
• /
• pp.56-67
• /
• 1988

It is wellknown that the left truncated exponential distribution with positivity constraint on the location parameter is appropriate as a lifetime distribution model, In this paper, some Bayes estimators of the parameters and reliability for the left truncated exponential lifetime distribution when an unidentified-failure outlier is included and it is excluded in the exchangeable outlier model are proposed, and the performances of these proposed Bayes estimators are also discussed.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.18 no.1
• /
• pp.71-78
• /
• 1993

A Baysian approach is proposed is estimating the mission failure rates by criticalities. A mission failure which occurs according to a Poisson process with unknown rate is assumed to be classified as one of the criticality levels with an unknown probability. We employ the Gamma prior for the mission failure rate and the Dirichlet prior for the criticality probabilities. Posterior distributions of the mission rates by criticalities and predictive distributions of the time to failure are derived.

• Communications for Statistical Applications and Methods
• /
• v.13 no.1
• /
• pp.89-99
• /
• 2006

This article deals with the problem of estimating parameters of Burr Type XII distribution, on the basis of a general progressive Type II censored sample using Bayesian viewpoints. The maximum likelihood estimator does not admit closed form but explicit sharp lower and upper bounds are provided. Assuming squared error loss and linex loss functions, Bayes estimators of the parameter k, the reliability function, and the failure rate function are obtained in closed form. Finally, a simulation study is also included.

• Proceedings of the Safety Management and Science Conference
• /
• 2000.11a
• /
• pp.53-62
• /
• 2000

다양한 컴포넌트들로 구성된 시스템의 수명 데이터는 시스템 컴포넌트들의 신뢰성을 추정하는데 많이 사용된다. 하지만 비용이나 고장진단의 기술적 문제 때문에 시스템 고장의 정확한 원인을 밝혀내기는 어렵다. 시스템이나 컴포넌트의 수명 데이터 중 정확한 고장원인을 알 수 없는 데이터를 마스크 데이터라 한다. 본 연구는 마스크데이터와 베이지안 추정의 연구방향을 살펴보고, 그리고 고장률의 비정보 사전분포를 이용하여, 컴포넌트가 직렬로 구성된 시스템의 수명 데이터가 마스크 데이터를 갖는 지수분포의 시스템 컴포넌트 고장률을 추정 한다.

• International Journal of Advanced Culture Technology
• /
• v.2 no.2
• /
• pp.7-10
• /
• 2014

In order to develop smart grid communications infrastructure, a high level of interconnectivity and reliability among its nodes is required. Sensors, advanced metering devices, electrical appliances, and monitoring devices, just to mention a few, will be highly interconnected allowing for the seamless flow of data. Reliability and security in this flow of data between nodes is crucial due to the low latency and cyber-attacks resilience requirements of the Smart Grid. In particular, Artificial Intelligence techniques such as Fuzzy Logic, Bayesian Inference, Neural Networks, and other methods can be employed to enhance the security gaps in conventional IDSs. A distributed FPGA-based network with adaptive and cooperative capabilities can be used to study several security and communication aspects of the smart grid infrastructure both from the attackers and defensive point of view. In this paper, the vital issue of security in the smart grid is discussed, along with a possible approach to achieve this by employing FPGA based Radial Basis Function (RBF) network intrusion.

• Journal of the Korea Society of Computer and Information
• /
• v.4 no.1
• /
• pp.68-75
• /
• 1999

In this paper, we propose a Baysian method estimating system reliability which is more effective and precise than conventional methods using prior information. This technique estimates system reliabilities that an entire system and multiprocessing system is normally working in multiprocessor system and multiple port connected memory architecture. The reason is why internetwork with multiprocessor system is mainly connected as multiple bus structure, crossbar switching structure and multiport connected memory structure.

• Journal of applied mathematics & informatics
• /
• v.8 no.1
• /
• pp.243-252
• /
• 2001

The points of failure of a decomposition process are defined to be the union of the points of failure from two component point processes for software reliability systems. Because sampling from the likelihood function of the decomposition model is difficulty, Gibbs Sampler can be applied in a straightforward manner. A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For model determination, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. A numerical example with a simulated data set is given.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.22 no.3
• /
• pp.123-129
• /
• 2014

SCC (Smart Cruise Control) and AEBS (Autonomous Emergency Braking System) are using various types of sensors data, so it is important to consider about sensor data reliability. In this paper, data from radar and vision sensor is fused by applying a Bayesian sensor fusion technique to improve the reliability of sensors data. Then, it presents a sensor fusion verification tool developed to monitor acquired sensors data and to verify sensor fusion results, efficiently. A parallel computing method was applied to reduce verification time and a series of simulation results of this method are discussed in detail.

• Journal of Integrative Natural Science
• /
• v.7 no.2
• /
• pp.138-144
• /
• 2014

Fisher information matrix plays an important role in statistical inference of unknown parameters. Especially, it is used in objective Bayesian inference where we calculate the posterior distribution using a noninformative prior distribution, and also in an example of metric functions in geometry. To estimate parameters in a distribution, we can use the Fisher information matrix. The more the number of parameters increases, the more its matrix form gets complicated. In this paper, by using Mathematica programs we derive the Fisher information matrix for 4-parameter generalized gamma distribution which is used in reliability theory.