• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.17-24
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• 1994

This paper is a study on Korean word recognition and suggest the method that stabilizes the state-transition in the HMM by applying the `inertia' to the feature vector sequences. In order to reduce the quantized distortion considering probability distribution of input vectors, we used SOFM, an unsupervised learning method, as a vector quantizer, By applying inertia to the feature vector sequences, the overlapping of probability distributions for the response path of each word on the self organizing feature map can be reduced and the state-transition in the Hmm can be Stabilized. In order to evaluate the performance of the method, we carried out experiments for 50 DDD area names. The results showed that applying inertia to the feature vector sequence improved the recognition rate by 7.4% and can make more HMMs available without reducing the recognition rate for the SOFM having the fixed number of neuron.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1391-1396
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• 2008

In this paper, we consider two components system which the lifetimes have Marshall and Olkin's bivariate exponential model with bivariate type I censored data. We propose a Bayesian independent test procedure for above model using fractional Bayes factor method by O'Hagan based on improper prior distributions. And we compute the fractional Bayes factor and the posterior probabilities for the hypotheses, respectively. Also we select a hypothesis which has the largest posterior probability. Finally a numerical example is given to illustrate our Bayesian testing procedure.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.508-512
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• 2004

An aerospace vehicle in flight can be exposed to random gust which may cause critical structural failure. In this paper, the failure analysis is conducted for composite wing subjected to random gust. For this, the profile of random gust is idealized as a stationary Gaussian random process and the power spectral density (PSD) of wing bending moment induced by gust is obtained. The PSD function is converted to probabilistic distributions and the failure probability during total flight time is calculated by Monte Carlo simulation.

• Wind and Structures
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• v.14 no.4
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• pp.321-335
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• 2011

The vulnerability of roofing components of contemporary houses built in cyclonic regions of Australia is assessed for increasing wind speeds. The wind loads and the component strengths are treated as random variables with their probability distributions derived from available data, testing, structural analysis and experience. Design details including types of structural components of houses are obtained from surveying houses and analyzing engineering drawings. Wind load statistics on different areas of the roof are obtained by wind tunnel model studies and compared with Australian/New Zealand Standard, AS/NZS 1170.2. Reliability methods are used for calculating the vulnerability of roofing components independently over the roof. Cladding and batten fixings near the windward gable edge are found to experience larger negative pressures than prescribed in AS/NZS 1170.2, and are most vulnerable to failure.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1183-1197
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• 2011

The prior distribution is the probability distribution we have before observing data. Using Bayes' rule, we can compute the posterior distribution, the new probability distribution, after observing data. Computing the posterior distribution is much easier than before by using Excel VBA macro. In addition, we can conveniently compute the successive updating posterior distributions after observing the independent and sequential outcomes. In this paper we compose some Excel VBA macros for applying Bayes' rule and give some examples.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.357-367
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• 2021

In general, common cause failures (CCFs) have been modeled with the assumption that components within the same group are symmetric. This assumption reduces the number of parameters required for the CCF probability estimation and allows us to use a parametric model, such as the alpha factor model. Although there are various asymmetric conditions in nuclear power plants (NPPs) to be addressed, the traditional CCF models are limited to symmetric conditions. Therefore, this paper proposes the copulabased CCF model to deal with asymmetric as well as symmetric CCFs. Once a joint distribution between the components is constructed using copulas, the proposed model is able to provide the probability of common cause basic events (CCBEs) by formulating a system of equations without symmetry assumptions. In addition, Bayesian inferences for the parameters of the marginal and copula distributions are introduced and Markov Chain Monte Carlo (MCMC) algorithms are employed to sample from the posterior distribution. Three example cases using simulated data, including asymmetry conditions in total failure probabilities and/or dependencies, are illustrated. Consequently, the copula-based CCF model provides appropriate estimates of CCFs for asymmetric conditions. This paper also discusses the limitations and notes on the proposed method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.9-16
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• 2011

An approach to understand the phase distribution in a multi-phase polycrystalline material is important since it can affect material properties and mechanical behaviors. A proper method is needed to describe the phase distribution. For this purpose, contiguity and probability functions(two-point correlation and lineal-path functions) are investigated for representing the phase distributions of microstructures. The mechanical behaviors are evaluated using the finite element method. The characteristics of probability functions and mechanical reponses of virtual samples are represented. It is confirmed that the topology of phase clustering affects the mechanical behavior of materials and that the strength is reduced as the clustering size increases.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.6
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• pp.739-746
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• 2004

The plastic deformation behavior of an aluminum alloy containing a particle and porosities was investigated at room temperature during equal channel angular pressing (ECAP). Finite element analysis by using ABAQUS shows that ECAP is a useful tool for eliminating residual porosity in the specimen, and more effective under friction condition. The simulation, however, shows considerably low density distributions for matrix near a particle at which many defects may occur during severe deformation. Finite element results of effective strains and deformed shapes for matrix with a particle were compared with theoretical calculations under simple shear stress. Also, based on the distribution of the maximum principal stress in the specimen, Weibull fracture probability was obtained for particle sizes and particle-coating layer materials. The probability was useful to predict the trend of more susceptible failure of a brittle coating layer than a particle without an interphase in metal matrix composites.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1731-1740
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• 2013

The design life and durability of the bridges are strongly affected by the Gross Vehicle Weight(GVW) of heavyweight trucks. The Weigh-In-Motion(WIM) systems are typically used to collect information on truck total weight and speed. The statistical analysis of the GVW measured using High Speed WIM systems showed that most of heavy vehicles were from Vehicle Type 7, 10, and 12. The analysis was also carried out to determine goodness of fit with theoretical probability distributions. The normal distribution was shown to best describe the overall distribution of GVW. The top 10% of the GVW appeared to best fit by the Weibull 3 probability distribution.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.602-606
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• 2005

A systematic procedure of probability analysis for general distributions is developed based on the first four moments estimated from polynomial interpolation of the system response function and the Pearson system. The function approximation is based on a specially selected experimental region for accuracy and the number of function evaluations is taken equal to that of the unknown coefficient for efficiency. For this purpose, three error-minimizing conditions are proposed and corresponding canonical experimental regions are formed for popular probability. This approach is applied to study the stochastic properties of the performance functions of a MEMS structure, which has quite large fabrication errors compared to other structures. Especially, the vibratory micro-gyroscope is studied using the statistical moments and probability density function (PDF) of the performance function to be the difference between resonant frequencies corresponding to sensing and driving mode. The results show that it is very sensitive to the fabrication errors and that the types of PDF of each variable also affect the stochastic properties of the performance function although they have same the mean and variance.