• Communications for Statistical Applications and Methods
• /
• v.10 no.2
• /
• pp.268-275
• /
• 2003

We consider the Bayesian accelerated failure time model. The error distribution is assigned a skewed normal distribution which is including normal distribution. For noninformative priors of regression coefficients, we show the propriety of posterior distribution. A Markov Chain Monte Carlo algorithm(i.e., Gibbs Sampler) is used to obtain a predictive distribution for a future observation and Bayes estimates of regression coefficients.

• Journal of Mechanical Science and Technology
• /
• v.18 no.12
• /
• pp.2080-2094
• /
• 2004

A model predictive controller is designed to control thermal power in a nuclear reactor. The basic concept of the model predictive control is to solve an optimization problem for finite future time steps at current time, to implement only the first optimal control input among the solved control inputs, and to repeat the procedure at each subsequent instant. A controller design model used for designing the model predictive controller is estimated every time step by applying a recursive parameter estimation algorithm. A 3-dimensional nuclear reactor analysis code, MASTER that was developed by Korea Atomic Energy Research Institute (KAERI), was used to verify the proposed controller for a nuclear reactor. It was known that the nuclear power controlled by the proposed controller well tracks the desired power level and the desired axial power distribution.

• Communications for Statistical Applications and Methods
• /
• v.4 no.2
• /
• pp.465-471
• /
• 1997

This paper deals with the problems of obtaining some Bayesian and empirical Bayesian Predictive densities and prediction intervals of a future observation $X_{(\tau+\gamma)}$ in the Rayleigh distribution. Using an inverse gamma prior distribution, some prodictive densities and prodiction intervals are proposed and studied. Also the behaviors of the proposed results are examined via numerical examples.

• Journal of Distribution Research
• /
• v.4 no.1
• /
• pp.17-29
• /
• 1999

We investigated the consumer behavior of store choice using consumer panel data. The NBD-Dirichlet model known to be predictive of the consumer's brand choice was also found to be well fitted for the store choice behavior. Understanding the regularity in the store choice will provide both manufacturers and sistributors with the necessary guidelines for their competitive strategies.

• Proceedings of the KWS Conference
• /
• 2009.11a
• /
• pp.102-102
• /
• 2009

The purpose of this study is to establish the predictive equation of transversal residual stress at the thick weldment of large container ship. In order to do it, the variables used for this study were restraint degree, yield strength of base material, thickness of weldment and welding heat input. Here, the level of restraint degree at the thick weldment of container ship having the various welding sequence was calculated using FEA. From the result, the h-type specimen was designed to simulate the level of restraint degree at the actual weldment of containership. With H-type test specimen designed, the effect of the variables on the distribution of transversal residual stress at the weldment in a container ship was evaluated using the comprehensive FEA. Based on the results, the predictive equations of mean value and the distribution of transverse residual stress in each location of residual stress were established using dimensional analysis and multiple-regression method. The validation of predictive equations was verified by comparing with measured results by XRD in the actual weldment of the ship.

• 한국전산유체공학회:학술대회논문집
• /
• 2010.05a
• /
• pp.444-445
• /
• 2010

Predictive modeling schemes have been developed to characterize the heat Transfer and thermo-mechanical behavior for the plasma display panel (PDP) in operation. The inverse approach was adopted to predict the accurate temperature distribution and deformation in PDP. The predictive models were validated with the measurements from real panel. The developed models could be utilized to predict and/or improve the product quality of PDP.

• Communications for Statistical Applications and Methods
• /
• v.20 no.1
• /
• pp.29-39
• /
• 2013

This paper derives maximum likelihood estimators (MLEs) and some approximate MLEs (AMLEs) of unknown parameters of the generalized exponential distribution when data are lower record values. We derive approximate Bayes estimators through importance sampling and obtain corresponding Bayes predictive intervals for unknown parameters for lower record values from the generalized exponential distribution. For illustrative purposes, we examine the validity of the proposed estimation method by using real and simulated data.

• Journal of the Optical Society of Korea
• /
• v.9 no.4
• /
• pp.157-161
• /
• 2005

Medical infrared imaging is obtained by measuring the self-emitted infrared radiance from the human body. Infrared emission is related to surface temperature and temperature is one of the most important physiological parameters related to health. Though recent applications such as security identification and oriental medicine have provided new fields of biomedical applications, infrared thermography has had ups and downs in its usages in cancer detection. Some of the main difficulties include finding proper applications and efficient diagnostic algorithms. In this study, infrared thermal imaging was used to detect regional metastasis of breast cancer. Our measurements were done for 110 women. From 63 individuals of a Healthy Group and a Benign Breast Disease Group, we developed algorithms for differentiating malignant regional metastasis based on temperature difference and asymmetry of temperature distribution. Testing with 47 cancer patients, we achieved a positive predictive value of $87.5\%$ and a negative predictive value of $95.6\%$. The results were better than for mammogram examination. A proper analysis of infrared imaging proved to be a highly informative and sensitive method for differentiating regional cancer metastasis from normal regions.

• Proceedings of the KIEE Conference
• /
• 2006.11a
• /
• pp.396-398
• /
• 2006

Predictive maintenance is not an unfamiliar concept because it has been used to predict the failures of electrical equipment such as transformers, motors and so on. By thoroughly monitoring the status of individual equipment and tracing how the various characteristics change over time, we can be aware of its exact condition and prevent the impending failure by taking appropriate actions. In this paper, I will extend the concept of predictive maintenance for individual electrical equipment to the power distribution system and show how to use the data obtained from power quality monitors to improve the power system.

• 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.