• Journal of the Korea Safety Management & Science
• /
• v.13 no.1
• /
• pp.195-202
• /
• 2011

The study interprets each of three classification models based on Bath-Tub Failure Rate (BTFR), Extreme Value Distribution (EVD) and Conjugate Bayesian Distribution (CBD). The classification model based on BTFR is analyzed by three failure patterns of decreasing, constant, or increasing which utilize systematic management strategies for reliability of time. Distribution model based on BTFR is identified using individual factors for each of three corresponding cases. First, in case of using shape parameter, the distribution based on BTFR is analyzed with a factor of component or part number. In case of using scale parameter, the distribution model based on BTFR is analyzed with a factor of time precision. Meanwhile, in case of using location parameter, the distribution model based on BTFR is analyzed with a factor of guarantee time. The classification model based on EVD is assorted into long-tailed distribution, medium-tailed distribution, and short-tailed distribution by the length of right-tail in distribution, and depended on asymptotic reliability property which signifies skewness and kurtosis of distribution curve. Furthermore, the classification model based on CBD is relied upon conjugate distribution relations between prior function, likelihood function and posterior function for dimension reduction and easy tractability under the occasion of Bayesian posterior updating.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.16 no.2
• /
• pp.104-110
• /
• 2016

Dense local image descriptors like SIFT are fruitful for capturing salient information about image, shown to be successful in various image-related tasks when formed in bag-of-words representation (i.e., histograms). In this paper we consider to utilize these dense local descriptors in the object tracking problem. A notable aspect of our tracker is that instead of adopting a point estimate for the target model, we account for uncertainty in data noise and model incompleteness by maintaining a distribution over plausible candidate models within the Bayesian framework. The target model is also updated adaptively by the principled Bayesian posterior inference, which admits a closed form within our Dirichlet prior modeling. With empirical evaluations on some video datasets, the proposed method is shown to yield more accurate tracking than baseline histogram-based trackers with the same types of features, often being superior to the appearance-based (visual) trackers.

• Journal of the Korea Safety Management & Science
• /
• v.9 no.5
• /
• pp.1-5
• /
• 2007

We construct the procedure to predict safety accidents following Bayesian approach. We make a model that can utilize the data to predict other levels of accidents. An event tree model which is a frequently used graphical tool in describing accident initiation and escalation to more severe accident is transformed into an influence diagram model. Prior distributions for accident occurrence rate and probabilities to escalating to more severe accidents are assumed and likelihood of number of accidents in a given period of time is assessed. And then posterior distributions are obtained based on observed data. We also points out the advantages of the bayesian approach that estimates the whole distribution of accident rate over the classical point estimation.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.3
• /
• pp.833-845
• /
• 2007

When X and Y have independent exponential distributions, we develop a Bayesian testing procedure for the ratio of two quantiles under reference prior. The noninformative prior such as reference prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we develop a Bayesian testing procedure based on fractional Bayes factor and intrinsic Bayes factor. We show that the posterior density under the reference prior is proper and propose the Bayesian testing procedure for the ratio of two quantiles using fractional Bayes factor and intrinsic Bayes factor. Simulation study and a real data example are provided.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.3
• /
• pp.852-861
• /
• 2000

Software reliability growth models are used in testing stages of software development to model the error content and time intervals betwewn software failures. In this paper, using priors for the number of fault with the negative binomial distribution nd the error rate with gamma distribution, Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability. For model selection, we explored the sum of the relative error, Braun statistic and median variation. In Bayesian computation process, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carolo method to compute the posterior distribution. Using simulated data, Bayesian inference and model selection is studied.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.7
• /
• pp.1750-1758
• /
• 2015

This paper provides Bayesian methodology to estimate the reliability for guided weapons which are not continuously operating. The posterior distribution of subsystems and components becomes the next prior distribution. By analyzing the results of the sub-systems and components presented a method for estimating the reliability of the entire guided weapons. Bayesian methodology using existing test data of subsystems may be used to reduce the sample sizes.

• Journal of Korean Society for Quality Management
• /
• v.37 no.4
• /
• pp.16-22
• /
• 2009

The inflection S-shaped software reliability growth model (SRGM) proposed by Ohba(1984) is one of the most commonly used models and has been discussed by many authors. The main purpose of this paper is to estimate the parameters of Ohba's SRGM within the Bayesian framework by applying the Markov chain Monte Carlo techniques. While the maximum likelihood estimates for these parameters are well known, the Bayesian method for the inflection S-shaped SRGM have not been discussed in the literature. The proposed methods can be quite flexible depending on the choice of prior distributions for the parameters of interests. We also compare the Bayesian methods with the maximum likelihood method numerically based on the real data.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2007.11a
• /
• pp.357-361
• /
• 2007

This paper presents a multi-robot localization based on Bayesian Multidimensional Scaling (BMDS). We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr${\ddot{o}}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• Communications for Statistical Applications and Methods
• /
• v.30 no.3
• /
• pp.311-330
• /
• 2023

The present work describes simulation studies to compare the performances in terms of averaged mean squared error of bayesian wavelet shrinkage methods in estimating component curves from aggregated functional data. Five bayesian methods available in the literature were considered to be compared in the studies: The shrinkage rule under logistic prior, shrinkage rule under beta prior, large posterior mode (LPM) method, amplitude-scale invariant Bayes estimator (ABE) and Bayesian adaptive multiresolution smoother (BAMS). The so called Donoho-Johnstone test functions, logit and SpaHet functions were considered as component functions and the scenarios were defined according to different values of sample size and signal to noise ratio in the datasets. It was observed that the signal to noise ratio of the data had impact on the performances of the methods. An application of the methodology and the results to the tecator dataset is also done.

• The Journal of Korea Robotics Society
• /
• v.19 no.2
• /
• pp.213-220
• /
• 2024

Fall detection and prevention technologies play a pivotal role in ensuring the well-being of individuals, particularly those living independently, where falls can result in severe consequences. This paper addresses the challenge of accurate and quick fall detection by proposing a Bayesian probability-based measure applied to surface electromyography (sEMG) signals. The proposed algorithm based on a Bayesian filter that divides the sEMG signal into transient and steady states. The ratio of posterior probabilities, considering the inclusion or exclusion of the transient state, serves as a scale to gauge the dominance of the transient state in the current signal. Experimental results demonstrate that this approach enhances the accuracy and expedites the detection time compared to existing methods. The study suggests broader applications beyond fall detection, anticipating future research in diverse human-robot interface benefiting from the proposed methodology.