• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.4
• /
• pp.254-259
• /
• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• The Korean Journal of Applied Statistics
• /
• v.33 no.3
• /
• pp.309-320
• /
• 2020

Bayesian networks, also known as directed acyclic graphs (DAG), are used in many areas of medicine, meteorology, and genetics because relationships between variables can be modeled with graphs and probabilities. In particular, Bayesian network classifiers, which are used to predict discrete data, have recently become a new method of data mining. Bayesian networks can be grouped into different models that depend on structured learning methods. In this study, Bayesian network models are learned with various properties of structure learning. The models are compared to the simplest method, the naïve Bayes model. Classification results are compared by applying learned models to various real data. This study also compares the relationships between variables in the data through graphs that appear in each model.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.10
• /
• pp.901-906
• /
• 2013

Chaff is a kind of matter spreading atmosphere with the purpose of preventing aircraft from detecting by radar. The chaff is commonly composed of small aluminum pieces, metallized glass fiber, or other lightweight strips which consists of reflecting materials. The chaff usually appears on the radar images as narrow bands shape of highly reflective echoes. And the chaff echo has similar characteristics to precipitation echo, and it interrupts weather forecasting process and makes forecasting accuracy low. In this paper, the chaff echo recognizing and removing method is suggested using Bayesian network. After converting coordinates from spherical to Cartesian in UF (Universal Format) radar data file, the characteristics of echoes are extracted by spatial and temporal clustering. And using the data, as a result of spatial and temporal clustering, a classification process for analyzing is performed. Finally, the inference system using Bayesian network is applied. As a result of experiments with actual radar data in real chaff echo appearing case, it is confirmed that Bayesian network can distinguish between chaff echo and non-chaff echo.

• Journal of Korea Multimedia Society
• /
• v.16 no.3
• /
• pp.269-280
• /
• 2013

In this paper, we suggested the possibility of using an efficient classifier for the dependency analysis of retinal disease. First, we analyzed the classification performance and the prediction accuracy of GBN (General Bayesian Network), GBN with reduced features by Markov Blanket and TAN (Tree-Augmented Naive Bayesian Network) among the various bayesian networks. And then, for the first time, we applied TAN showing high performance to the dependency analysis of the clinical data of retinal disease. As a result of this analysis, it showed applicability in the diagnosis and the prediction of prognosis of retinal disease.

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

• Journal of KIISE:Software and Applications
• /
• v.31 no.12
• /
• pp.1569-1580
• /
• 2004

One commonly used approach to deal with uncertainty is Bayesian network which represents joint probability distributions of domain. There are some attempts to team the structure of Bayesian networks automatically and recently many researchers design structures of Bayesian network using evolutionary algorithm. However, most of them use the only one fittest solution in the last generation. Because it is difficult to combine all the important factors into a single evaluation function, the best solution is often biased and less adaptive. In this paper, we present a method of generating diverse Bayesian network structures through fitness sharing and combining them by Bayesian method for adaptive inference. In order to evaluate performance, we conduct experiments on learning Bayesian networks with artificially generated data from ASIA and ALARM networks. According to the experiments with diverse conditions, the proposed method provides with better robustness and adaptation for handling uncertainty.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.911-919
• /
• 2011

In recent medical analysis, it becomes more important to looking for risk factors related to mental illness. If we find and identify their relevant characteristics of the risk factors, the disease can be prevented in advance. Moreover, the study can be helpful to medical development. These kinds of studies of risk factors for mental illness have mainly been discussed by using the logistic regression model. However in this paper, data mining techniques such as CART, C5.0, logistic, neural networks and Bayesian network were used to search for the risk factors. The Bayesian network of the above data mining methods was selected as most optimal model by applying delirium data. Then, Bayesian network analysis was used to find risk factors and the relationship between the risk factors are identified through a radial graph.

• Korean Management Science Review
• /
• v.34 no.2
• /
• pp.15-33
• /
• 2017

This paper considers a Bayesian Network analysis for understanding the heterogeneous cross-category effects of different promotion activities and developing an efficient overall promotion strategy for a large retail store. More specifically we differentiate price reduction promotion and floor promotion and study their heterogeneous effect on consumer purchase behavior under a market basket setting. We then utilize Bayesian networks in identifying complex association structure in market basket dataset by analyzing the effects of different promotional activities and also include the effects of time, family income and size. We find from our Bayesian network analysis that the dominant cross-category promotion effect of price promotion is the indirect effect whereas the dominant cross-category promotion effect of floor promotion is the direct effect. Also, among the demographic variables we find that family size of the household is linked with more product categories compared to income and see that there are differences in the extent of the effects by product category. Finally, we also show the existence of products acting as a network hub and how they can be utilized by retailers faced with a limited marketing budget and suggest a more efficient promotion strategy.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.105-105
• /
• 2017

최근 우리나라에서는 국지성호우로 인해 발생하는 돌발홍수에 방어하지 못하는 소규모 저수지에 대한 붕괴사고가 빈발하고 있다. 붕괴된 저수지를 살펴보면, 대체적으로 규모가 작아 체계적인 안전관리가 이루어지지 않거나 경과연수가 50년 이상인 필댐(fill dam) 형식으로 축조된 노후저수지로서 갑작스러운 홍수를 대응하는데 있어 매우 취약한 상태이다. 체계적으로 운영되는 대형댐에 비해 축조기간이 오래된 소규모 저수지의 경우, 저수지에 대한 수문학적 정보가 거의 없거나 미계측되어 보수보강이 필요한 저수지를 선정하거나 정량적인 위험도를 분석하는데 매우 어려운 실정이다. 이러한 이유로 본 연구에서는 노후된 소규모 저수지에 대한 수문학적 파괴인자들을 선정하여 Bayesian Network기반의 소규모 저수지 위험도 분석 모형을 구축하였다. 구축된 모형을 기준으로 고려될 수 있는 다양한 위험인자 및 이들 인자간의 연관성을 평가하였으며, 각각의 노드에 파괴인자를 노드로 할당하여 소규모 저수지의 위험도를 분석하였다. Bayesian Network기법의 도입으로 불확실한 상황을 확률로 표시하고, 복잡한 추론을 정량화된 노드의 관계로 단순화시켜 노드의 연결 관계로 표현하였다. 본 연구에서 제안된 모형은 노후된 소규모 저수지의 수문학적 위험도를 정량으로 분석하는 모형으로서 활용성이 높을 것으로 기대된다.

• ETRI Journal
• /
• v.29 no.2
• /
• pp.249-251
• /
• 2007

A novel idea is presented to locate intersections in a video sequence captured from a moving vehicle. More specifically, we propose a Bayesian network approach to combine evidence extracted from a video sequence and evidence from a database, maximizing evidence from various sensors in a systematic manner and locating intersections robustly.