• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.111-118
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• 2016

Artificial intelligence (AI) is making computer systems intelligent to do right thing. The AI is used today in a variety of fields, such as journalism, medical, industry as well as entertainment. The impact of AI is becoming larger day after day. In general, the AI system has to lead the optimal decision under uncertainty. But it is difficult for the AI system can derive the best conclusion. In addition, we have a trouble to represent the intelligent capacity of AI in numeric values. Statistics has the ability to quantify the uncertainty by two approaches of frequentist and Bayesian. So in this paper, we propose a methodology of the connection between statistics and AI efficiently. We compute a fixed value for estimating the population parameter using the frequentist learning. Also we find a probability distribution to estimate the parameter of conceptual population using Bayesian learning. To show how our proposed research could be applied to practical domain, we collect the patent big data related to Apple company, and we make the AI more intelligent to understand Apple's technology.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.1-11
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• 2012

Data association plays an important role in intelligent systems. This paper presents the Bayesian formulation of data association and its applications to intelligent systems. We first describe the Bayesian formulation of data association developed for solving multi-target tracking problems in a cluttered environment. Then we review applications of data association in intelligent systems, including surveillance using wireless sensor networks, identity management for air traffic control, camera network localization, and multi-sensor fusion.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.518-523
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• 2008

In this paper, we present the development of an intelligent diagnosis system for detecting faults of the transmission line. Based on the TFDR (Time-Frequency Domain Reflectometry), the fault detecting performs to measure the location of fault line. We analyze the reflected signal which is sent from the wire detecting system and classify the fault type of the wires by using intelligent diagnosis system. In order to analyze effectively, we construct the intelligent diagnosis system which is based on the fuzzy-bayesian algorithm. Finally, we provide the simulation results which are performed at transmission line to evaluate the feasibility and generality of the proposed method in this paper.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.135-142
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• 2011

Unlike using the sequence-based representation for a chromosome in previous genetic algorithms for Bayesian structure learning, we proposed a matrix representation-based genetic algorithm. Since a good chromosome representation helps us to develop efficient genetic operators that maintain a functional link between parents and their offspring, we represent a chromosome as a matrix that is a general and intuitive data structure for a directed acyclic graph(DAG), Bayesian network structure. This matrix-based genetic algorithm enables us to develop genetic operators more efficient for structuring Bayesian network: a probability matrix and a transpose-based mutation operator to inherit a structure with the correct edge direction and enhance the diversity of the offspring. To show the outstanding performance of the proposed method, we analyzed the performance between two well-known genetic algorithms and the proposed method using two Bayesian network scoring measures.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.45-48
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• 2003

We propose some properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses H$\sub$0/($\theta$) versus H$_1$($\theta$) on $\theta$ with loss function.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.287-290
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• 2001

Bishop이 제안한 generative Topographic Mapping(GTM)은 Kohonen이 제안한 자율 학습 신경망인 Self Organizing Maps(SOM)의 확률적 버전이다. 본 논문에서는 이러한 GTM 모형에 베이지안 추론을 결합하여 작은 오분류율을 가지는 분류 알고리즘인 베이지안 GTM(Bayesian GTM)을 제안한다. 이 방법은 기존의 GTM의 빠른 계산 처리 능력과 베이지안 추론을 이용하여 기존의 분류 알고리즘보다 우수한 결과가 나타남을 실험을 통하여 확인하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.349-352
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• 2001

We propose one properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses H$\sub$0/($\theta$) versus H$_1$($\theta$) on $\theta$.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.316-321
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• 2007

We propose a quatitative annotation method for edges in Bayesian networks using given sets of condition-specific data. Bayesian network model has been used widely in various fields to infer probabilistic dependency relationships between entities in target systems. Besides the need for identifying dependency relationships, the annotation of edges in Bayesian networks is required to analyze the meaning of learned Bayesian networks. We assume the training data is composed of several condition-specific data sets. The contribution of each condition-specific data set to each edge in the learned Bayesian network is measured using the ratio of likelihoods between network structures of including and missing the specific edge. The proposed method can be a good approach to make quantitative annotation for learned Bayesian network structures while previous annotation approaches only give qualitative one.

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

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.277-281
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• 2006

Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.