• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.346-349
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• 2003

This paper is intended to assess a system reliability that is changed as time passes. The proposed methodology consists of two steps: (1) predicting probabilities that each component fails at specific time by using a Markov Chain model and (2) calculating reliability of the whole system via Bayesian network. The proposed methodology includes extended Bayesian network model reflecting the case that components are mutually dependent.

• Journal of the military operations research society of Korea
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• v.34 no.3
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• pp.79-92
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• 2008

The purpose of this paper is to design an algorithm for data fusion of sensors data in the helicopter ASE system, using Bayesian Network, which was selected among several knowledge base data fusion methods after consideration and applied to this study. The result of the algorithm analysis shows that Bayesian Network is effective method for solving this problem.

• Journal of Internet Computing and Services
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• v.4 no.2
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• pp.69-80
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• 2003

Program Behavior Intrusion Detection Technique analyses system calls that called by daemon program or root authority, constructs profiles, and detectes modificated anomaly intrusions effectively. In this paper, the relation among system calls of processes is represented by bayesian network and Multiple Sequence Alignment. Program behavior profiling by Bayesian Network classifies modified anomaly intrusion behaviors, and detects anomaly behaviors. we had simulation by proposed normal behavior profiling technique using UNM data.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.366-370
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• 2005

Tissue microarry is one of the high throughput technologies in the post-genomic era. Using tissue microarray, the researchers are able to investigate large amount of gene expressions at the level of DNA, RNA, and protein The important aspect of tissue microarry is its ability to assess a lot of biomarkers which have been used in clinical practice. To manipulate the categorical data of tissue microarray, we applied Bayesian network classifier algorithm. We identified that Bayesian network classifier algorithm could analyze tissue microarray data and integrating prior knowledge about gastric cancer could achieve better performance result. The results showed that relevant integration of prior knowledge promote the prediction accuracy of survival status of the immunohistochemical tissue microarray data of 18 tumor suppressor genes. In conclusion, the application of Bayesian network classifier seemed appropriate for the analysis of the tissue microarray data with clinical information.

• Journal of Computing Science and Engineering
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• v.9 no.4
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.61-69
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• 2010

The collapse of a slope or cut embankment brings much damage to life and property. Accordingly, it is very important to analyze the spatial distribution by calculating the landslide susceptibility in the estimation of the risk of landslide occurrence. The heuristic, statistic, deterministic, and probabilistic methods have been introduced to make landslide susceptibility maps. In many cases, however, the reliability is low due to insufficient field data, and the qualitative experience and knowledge of experts could not be combined with the quantitative mechanical?analysis model in the existing methods. In this paper, new modeling method for a probabilistic landslide susceptibility analysis combined Bayesian Network with ontology model about experts' knowledge and spatial data was proposed. The ontology model, which was made using the reasoning engine, was automatically converted into the Bayesian Network structure. Through conditional probabilistic reasoning using the created Bayesian Network, landslide susceptibility with uncertainty was analyzed, and the results were described in maps, using GIS. The developed Bayesian Network was then applied to the test-site to verify its effect, and the result corresponded to the landslide traces boundary at 86.5% accuracy. We expect that general users will be able to make a landslide susceptibility analysis over a wide area without experts' help.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.605-614
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• 2021

Water inrush from fault is one of the most severe hazards during tunnel excavation. However, the traditional evaluation methods are deficient in both quantitative evaluation and uncertainty handling. In this paper, a comprehensive methodology method combined intuitionistic fuzzy AHP with a Bayesian network for the risk assessment of water inrush from fault in the subsea tunnel was proposed. Through the intuitionistic fuzzy analytic hierarchy process to replace the traditional expert scoring method to determine the prior probability of the node in the Bayesian network. After the field data is normalized, it is classified according to the data range. Then, using obtained results into the Bayesian network, conduct a risk assessment with field data which have processed of water inrush disaster on the tunnel. Simultaneously, a sensitivity analysis technique was utilized to investigate each factor's contribution rate to determine the most critical factor affecting tunnel water inrush risk. Taking Qingdao Kiaochow Bay Tunnel as an example, by predictive analysis of fifteen fault zones, thirteen of them are consistent with the actual situation which shows that the IFAHP-Bayesian Network method is feasible and applicable. Through sensitivity analysis, it is shown that the Fissure development and Apparent resistivity are more critical comparing than other factor especially the Permeability coefficient and Fault dip. The method can provide planners and engineers with adequate decision-making support, which is vital to prevent and control tunnel water inrush.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.89-98
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• 2004

Conversational agent is a system that provides users with proper information and maintains the context of dialogue on the natural language. Analyzing and modeling process of user's query is essential to make it more realistic, for which Bayesian network is a promising technique. When experts design the network for a domain, the network is usually very complicated and is hard to be understood. The separation of variables in the domain reduces the size of networks and makes it easy to design the conversational agent. Composing Bayesian network as two stages, we aim to design conversational agent easily and analyze user's query in detail. Also, previous information of dialogue makes it possible to maintain the context of conversation. Actually implementing it for a guide of web pages, we can confirm the usefulness of the proposed architecture for conversational agent.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.264-266
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• 2009

A skill is defined as the special ability to do something well, especially as acquired by learning and practice. To learn a skill, a Bayesian network model for representing the skill is first learned. We will regard the Bayesian network for a skill as an affordance. We propose a soft Behavior Motivation(BM) switch as a method for ordering affordances to accomplish a task. Then, a skill is constructed as a combination of an affordance and a soft BM switch. To demonstrate the validity of our proposed method, some experiments were performed with GENIBO(Pet robot) performing a task using skills of Search-a-target-object, Approach-a-target-object, Push-up-in front of -a-target-object.