• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.713-716
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• 2005

In this paper, in order to identify and recognize attack patterns, we propose a Bayesian classification using frequent patterns. In theory, Bayesian classifiers guarantee the minimum error rate compared to all other classifiers. However, in practice this is not always the case owing to inaccuracies in the unrealistic assumption{ class conditional independence) made for its use. Our method addresses the problem of attribute dependence by discovering frequent patterns. It generates frequent patterns using an efficient FP-growth approach. Since the volume of patterns produced can be large, we propose a pruning technique for selection only interesting patterns. Also, this method estimates the probability of a new case using different product approximations, where each product approximation assumes different independence of the attributes. Our experiments show that the proposed classifier achieves higher accuracy and is more efficient than other classifiers.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.8-20
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• 1999

In this paper, a design and estimation procedure for the stochastically dependent nonstationary multiple acceptance sampling plans is developed. At first, the rough-cut acceptance and rejection numbers are given as an initial solution from the corresponding sequential sampling plan. A Monte-Carlo algorithm is used to find the acceptance and rejection probabilities of a lot. The conditional probability formula for a sample path is found. The acceptance and rejection probabilities are found when a decision boundary is given. Several decision criteria and the design procedure to select optimal plans are suggested. The formula for measuring performance of these sampling plans is developed. Type I and II error probabilities are also estimated. As a special case, by setting the stage size as 1 in a dependent sampling plan, a sequential sampling plan satisfying type I and II error probabilities is more accurate and a smaller average sample number can be found. In a numerical example, a Polya dependent process is examined. The sampling performances are shown to compare the selection scheme and the effect of the change of the dependency factor.

• Bulletin of the Korean Mathematical Society
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• v.26 no.2
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• pp.151-158
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• 1989

Let (H, B, .nu.) be an abstract Wiener space where H is a separable Hilbert space with the inner product <.,.> and the norm vertical bar . vertical bar=.root.<.,.>, which is densely and continuously imbedded into a separable Banach space B with the norm ∥.∥ , and .nu. is a probability measure on the Borel .sigma.-algebra B(B) of B which satisfies (Fig.) where $B^{*}$ is the topological dual of B and (.,.) is the natural dual pairing between B and $B^{*}$. We will regard $B^{*}$.contnd.H.contnd.B in the natural way. Thus we have =(y, x) for all y in $B^{*}$ and x in H. Let $R^{n}$ and C denote the n-dimensional Euclidean space and the complex numbers respectively.ctively.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.265-272
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• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1477-1481
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• 2010

In this paper, we propose the classification and tracking method of the hand region using deformable template and condensation. To do this, first, we extract the hand region by using the fuzzy color filter and HCbCr color model. Second, we extract the edge of hand by applying the Canny edge algorithm. Third, we find the first template by calculating the conditional probability between the extracted edge and the model edge. If the accurate template of the first object is decided, the condensation algorithm tries to track it. Finally, we demonstrate the effectiveness and feasibility of the proposed method through some experiments.

• Wind and Structures
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• v.12 no.5
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• pp.425-439
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• 2009

The expediency of revising universal rules for the combination of gravity and lateral actions of wind force-resisting steel structures recommended by the Standards EN 1990 and ASCE/SEI 7-05 is discussed. Extreme wind forces, gravity actions and their combinations for the limit state design of structures are considered. The effect of statistical uncertainties of extreme wind pressure and steel yield strength on the structural safety of beam-column joints of wind force-resisting multistory steel frames designed by the partial factor design (PFD) and the load and resistance factor design (LRFD) methods is demonstrated. The limit state criterion and the performance process of steel frame joints are presented and considered. Their long-term survival probability analysis is based on the unsophisticated method of transformed conditional probabilities. A numerical example illustrates some discrepancies in international design standards and the necessity to revise the rule of universal combinations of loads in wind and structural engineering.

• Smart Structures and Systems
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• v.11 no.1
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• pp.103-122
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• 2013

It is essential to update the model with reflecting observation or inspection data for reliability estimation of existing structures. Authors proposed updated reliability analysis by using Particle Filter. We discuss how to apply the proposed method through numerical examples on reinforced concrete structures after verification of the method with hypothetical linear Gaussian problem. Reinforced concrete structures in a marine environment deteriorate with time due to chloride-induced corrosion of reinforcing bars. In the case of existing structures, it is essential to monitor the current condition such as chloride-induced corrosion and to reflect it to rational maintenance with consideration of the uncertainty. In this context, updated reliability estimation of a structure provides useful information for the rational decision. Accuracy estimation is also one of the important issues when Monte Carlo approach such as Particle Filter is adopted. Especially Particle Filter approach has a problem known as degeneracy. Effective sample size is introduced to predict the covariance of variance of limit state exceeding probabilities calculated by Particle Filter. Its validity is shown by the numerical experiments.

• Journal of the Korean Society of Combustion
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• v.6 no.2
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• pp.43-49
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• 2001

Instantaneous, three-dimensional scalar dissipation rates of the reaction progress variable are measured in turbulent premixed Bunsen flames of lean hydrocarbon/air mixtures with the two-sheet, two-dimensional Rayleigh scattering technique. The flames investigated are located in the turbulent flame-front regime on a newly proposed combustion diagram for premixed flames. The conditionally-averaged mean scalar dissipation rates, $N_{\zeta}$ are found to be lower than the calculated laminar values, indicating a locally broadened flame front. In agreement with previous measurements, the maximum of $N_{\zeta}$, decreases strongly with increasing Karlovitz numbers. The conditional probability density functions are close to a log-normal distribution for scalar dissipation rates conditioned at the progress variable value where the scalar dissipation is maximum in unstretched laminar flame calculations. The time scale for the Favre-averaged mean scalar dissipation rate decreases in general across the turbulent flame brush from the unburnt to burnt side.

• Journal of Information Processing Systems
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• v.3 no.2
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• pp.73-81
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• 2007

In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naive Bayesian classification algorithms. The architecture of our system is simple and modularized in that each sub-module of the system could be changed or improved efficiently. Moreover, it provides various feature selection mechanisms to be applied to optimize the general-purpose classifiers for a specific domain. As for the enhanced classification performance, our system provides conditional probability boosting (CPB) mechanism which could be used in various domains. In the mood classification domain, our optimized framework using the CPB algorithm showed 1% of improvement in precision and 2% in recall compared with the baseline.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.523-532
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• 1999

Generalized linear models have various applications for data arising from many kinds of statistical studies. Although the response variable is generally assumed to be generated from a wide class of probability distributions we focus on count data that are most often analyzed using binomial models for proportions or poisson models for rates. The methods and results presented here also apply to many other categorical data models in general due to the relationship between multinomial and poisson sampling. The novelty of the approach suggested here is that all conditional distribution s can be specified directly so that staraightforward Gibbs sampling is possible. The prior distribution consists of two stages. We rely on a normal nonconjugate prior at the first stage and a vague prior for hyperparameters at the second stage. The methods are demonstrated with an illustrative example using data collected by Rosenkranz and raftery(1994) concerning the number of hospital admissions due to back pain in Washington state.