• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.602-607
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• 2008

A technique for reliability-based design optimization(RBDO) is developed based on the Bayesian approach, which can deal with the epistemic uncertainty arising due to the limited number of data. Until recently, the conventional RBDO was implemented mostly by assuming the uncertainty as aleatory which means the statistical properties are completely known. In practice, however, this is not the case due to the insufficient data for estimating the statistical information, which makes the existing RBDO methods less useful. In this study, a Bayesian reliability is introduced to take account of the epistemic uncertainty, which is defined as the lower confidence bound of the probability distribution of the original reliability. In this case, the Bayesian reliability requires double loop of the conventional reliability analyses, which can be computationally expensive. Kriging based dimension reduction method(KDRM), which is a new efficient tool for the reliability analysis, is employed to this end. The proposed method is illustrated using a couple of numerical examples.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.221-227
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• 2007

This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant(with unknown time-varying parameters) and a particle filtering(PF) algorithm to estimate the probability density function(pdf) of the state in real-time. The state pdf estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the pdf of the remaining useful life(RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary carrier plate are used to validate the proposed methodology.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.20 no.4
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• pp.14-20
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• 2012

A properly determined separation minima applied in Air Traffic Management(ATM) is critical for safe and efficient aircraft operations. The separation minima is primarily determined by the accuracy performance of surveillance system, and, due to the stringent aviation safety standard, the position accuracy of the surveillance system must be estimated with a high level of reliability. This study proposed a method for estimating the position accuracy of surveillance system with a relatively small amount of data by finding upper confidence limit instead of maximum likelihood values of unknown parameters. Through the proposed method, it is possible to determine a required separation minima with a more reliability in the face of data scarcity which often occurs when we implement a new surveillance system such as Automatic Dependent Surveillance-Broadcast (ADS-B).

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.382-387
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• 2002

In this paper, we propose a geostatistical approach incorporated to the Bayesian data fusion technique for supervised classification of multi-sensor remote sensing data. Traditional spectral based classification cannot account for the spatial information and may result in unrealistic classification results. To obtain accurate spatial/contextual information, the indicator kriging that allows one to estimate the probability of occurrence of classes on the basis of surrounding observations is incorporated into the Bayesian framework. This approach has its merit incorporating both the spectral information and spatial information and improves the confidence level in the final data fusion task. To illustrate the proposed scheme, supervised classification of multi-sensor test remote sensing data set was carried out.

• Journal of Korean Institute of Industrial Engineers
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• v.5 no.1
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• pp.19-25
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• 1979

This paper presents an analysis of the contents of job for the sensory inspection on the basis of the probability theory, and the new determination for an index(d') of the testing power precision in carrying out sensory inspections. Also presented are the evaluation method of determining the ability of inspector by presuming the confidence interval for the average record of inspector, and the computation method for the index (de') of the testing power precision required as the goal-value in accordance with quality character, process inferior ratio, and required AOQ.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.825-833
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• 2003

Knowing the time of the process change could lead to quicker identification of the responsible special cause and less process down time, and it could help to reduce the probability of incorrectly identifying the special cause. In this paper, we propose a maximum likelihood estimator of the process change point when a Shewhart $\bar{X}$ chart with variable sample size (VSS) scheme signals a change in the process mean. Also we build a confidence interval for the process change point by using the likelihood function.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.181-187
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• 2004

One of objectives in epidemiologic studies is to detect the amount of change caused by a specific risk factor. Risk ratio is one of the most useful measurements in epidemiology. When we perform the inference for this measurement with rare events, the standard approach based on the normal approximation may fail, in particular when there are no disease cases observed. In this paper, we discuss and evaluate several existing methods for constructing a confidence interval of risk ratio through simulation when the disease of interest is a rare event. The results in this paper provide guidance with how to construct interval estimates for risk difference and risk ratio when there are no disease cases observed.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.4
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• pp.5-14
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• 2006

The correct and opportune decision of reengaging the intercepted target is required in order to enhance the engagement performance of the surface to air missile systems that has the ability to defense or attack against various targets at the same time. The engagement efficiency and success of these systems will be largely enhanced by assigning quickly its system resources to the intercepted target and minimizing the waste of system resources for the target which is not able to attack any more. The kill-assessment algorithm has to be able to evaluate automatically whether various targets intercepted by missiles are killed or not on the basis of the reasonable confidence level. The definition of kill assessment is discussed and the kill assessment algorithm is designed reliably by using Kalman filter and a probability theory. Finally its performance is evaluated and analyzed by the Monte Carlo simulation.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.625-636
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• 2013

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require the explicit form of nonlinear mapping function. Moreover they need no assumption about the underlying probability distribution of errors. Through numerical studies on two artificial an two real data sets we show their effectiveness on the estimation performance at various confidence levels.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.262-267
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• 2001

In this study, the Reliability of degraded steam turbine blade was evaluated using the limited fatigue data. The statistical estimation of limited fatigue data implies that some unknown uncertainties which may be involved in fatigue reliability analysis. Therefore, an appropriate distribution in the fatigue strength was determined by the characteristic distribution - linear correlation coefficient, fatigue physics, error parameter. 3-parameter Weibull distribution is the most appropriate distribution to assume for infinite region. The load applied on the blade is mainly tensile. The maximum Von-Mises stress is 219.4 MPa at the steady state service condition. The failure probability($F_p$) derived from the strength-stress interference model using Monte carlo simulation under variable service condition is 0.25% at the 99.99% confidence level.