• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.324-330
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• 2023

Aids to navigation, which provide information about a ship's position, direction, and the location of obstacles, are crucial for uninterrupted maritime services. This study aimed to analyze accidents involving aids to navigation that resulted in service disruptions and identify the key factors associated with these accidents. Aids to navigation accident data from 2000 to 2022 were utilized to achieve this. We categorized accidents by accident type, cause, region, season, and type of navigation aid and established a network through correlation analysis. Bayesian networks based on aids to navigation accidents were assigned prior probabilities, and the factors that increased the probability of accidents for different types of aids to navigation were identified. The findings can be used to infer the causes of unreported aids to navigation accidents and serve as foundational data for the prevention of such accidents.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.179-184
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• 2007

This paper deals with a context-aware application based on Bayesian network in the smart home. Bayesian network is a powerful graphical tool for learning casual dependencies between various context events and obtaining probability distributions. So we can recognize the resident's activities and home environment based on it. However as the sensors become various, learning the structure become difficult. We construct Bayesian network simple and efficient way with mutual information and evaluated the method in the virtual smart home.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.293-305
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• 2011

The volatility is one of most important parameters in the areas of pricing of financial derivatives an measuring risks arising from a sudden change of economic circumstance. We propose a Bayesian approach to estimate the volatility varying with time under a linear model with ARMA(p, q)-GARCH(r, s) errors. This Bayesian estimate of the volatility is compared with the ML estimate. We also present the probability of existence of the unit root in the GARCH model.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.341-350
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• 2012

Composite quantile regression model is considered for iid error case. Since the regression coefficients are the same across different quantiles, composite quantile regression can be used to combine the strength across multiple quantile regression models. For the composite quantile regression, bootstrap method is examined for statistical inference including the selection of the number of quantiles and confidence intervals for the regression coefficients. Feasibility of the bootstrap method is demonstrated through a simulation study.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.104-112
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• 2016

The Adaboost chooses a good set of features in rounds. On each round, it chooses the optimal feature and its threshold value by minimizing the weighted error of classification. The involved process of classification performs a hard decision. In this paper, we expand the process of classification to a soft fuzzy decision. We believe this expansion could allow some flexibility to the Adaboost algorithm as well as a good performance especially when the size of a training data set is not large enough. The typical Adaboost algorithm assigns a same weight to each training datum on the first round of a training process. We propose a new algorithm to assign different initial weights based on some statistical properties of involved features. In experimental results, we assess that the proposed method shows higher performance than the traditional one.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1167-1176
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• 2009

Few studies have investigated the quantitative relationship between port ownership structure and port efficiency with mixed results. This paper therefore contributes to the empirical literature by investigating the impact of port privatization on port efficiency using sample data drawn from the world's major ports. Moreover, this study applies the Bayesian approach to estimate the impact of port ownership on port efficiency. We fit Bayesian stochastic frontier model which is introduced by Griffin and Steel (2007) by WinBUGS. World's 25 main ports data are used for analysis. Based on MCMC sampling, we estimate parameters of the model and efficiency index of each ports. Moreover, we add estimates from package Frontier 4.1c in order to compare them with Bayesian results.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.539-544
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• 2004

We investigate a visual object tracking algorithm based upon particle filters, namely CONDENSATION, in order to combine multiple observation models such as active contours of digitally subtracted image and the particle measurement of object color. The former is applied to matching the contour of the moving target and the latter is used to independently enhance the likelihood of tracking a particular color of the object. Particle filters are more efficient than any other tracking algorithms because the tracking mechanism follows Bayesian inference rule of conditional probability propagation. In the experimental results, it is demonstrated that the suggested contour tracking particle filters prove to be robust in the cluttered environment of robot vision.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.64-73
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• 2004

세포 내에서 일어나는 신호 전달 과정은 단백질간의 상호작용을 통해 수행되고 조절된다. 단백질 상호작용 데이터를 활용하여 수행된 연구로는 단백질의 기능을 유추하거나 전체 네트워크 중 다른 지역보다 더 조밀한 상호작용을 추출하여 complex 혹은 pathway를 발견하고 진화 과정을 이해하는 바탕이 되고 있다. 본 연구에서는 신호 전달 경로에 대한 사전 정보 없이 yeast 상호작용 정보와 녹색형광단백질(GFP)을 이용하여 밝혀진 4000여 개의 yeast 단백질 위치 분포 data를 이용하여 신호전달경로를 찾는 방법을 시도했다. 기존 연구에 의해 밝혀진 yeast 내의 단백질 위치 분포 결과를 보면 21개의 category에 대해 각 단백질 상호작용 분포가 다양하게 나타나고, 특정 위치에서 상호작용 빈도수가 현저히 크다는 것을 알 수 있다. 특히 두 단백질이 같은 장소에 있을 경우 상호작용 확률이 높으며, 세포 내 소기관 사이에도 상호작용의 정도가 다양함이 알려져 있다. 따라서 이러한 분포상의 특성을 고려하여 상호작용을 기반으로 하여 세포막 단백질을 출발점으로, 핵에 있는 단백질을 도착점으로 잡고, 그 사이에 존재하는 다양한 가능 경로 중에서 단백질의 위치 정보를 가중치로 사용하여 그 중 최대 가능 경로를 찾도록 구현하였다. 이와 같은 pathway 모델링은 기존에 밝혀진 pathway와의 비교를 통해 알려지지 않은 새로운 경로를 발견하고, 이전에 경로에 참여하지 않은 단백질들을 발견할 수 있고, 이미 알려진 단백질들의 새로운 기능들에 대해서도 추론할 수 있을 것이라 기대한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.756-760
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• 2010

This paper is proposed to an novel facial behavior recognition system for driver's fatigue detection. Facial behavior is shown in various facial feature such as head expression, head pose, gaze, wrinkles. But it is very difficult to clearly discriminate a certain behavior by the obtained facial feature. Because, the behavior of a person is complicated and the face representing behavior is vague in providing enough information. The proposed system for facial behavior recognition first performs detection facial feature such as eye tracking, facial feature tracking, furrow detection, head orientation estimation, head motion detection and indicates the obtained feature by AU of FACS. On the basis of the obtained AU, it infers probability each state occur through Bayesian network.

• Tunnel and Underground Space
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• v.15 no.6 s.59
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• pp.441-451
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• 2005

It is the unavoidable problem that the RMR rock classification method has the uncertainty resulted from uncertain definition of measured value in RMR grade table, hence in this paper, the estimation of probability density function$(p{\cdot}d{\cdot}f)$ graph with the evaluation of continuos RMR and the Monte Carlo Simulation and statistic reasoning were carried out to evaluate the uncertainty quantitatively. Also, the modified RMR rock classification table was presented in order to apply the uncertainty of RMR to the practice, and then the design process of standard support pattern and the tunnel support material was proposed.