• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.69-78
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• 2015

Recently, research on the multi-agent system is developed actively in the various fields, especially on the control of complex system and optimization. In this study, we develop a multi-agent system for NPC simulation in game environment. The purpose of the development is to support quick and precise decision by inferencing the situation of the dynamic discrete domain, and to support an optimization process of the agent system. Our approach employed Petri-net as a basic agent model to simplify structure of the system, and used fuzzy inference engine to support decision making in various situation. Our experimentation describes situation of the virtual battlefield between the NPCs, which are divided two groups, such as fuzzy rule based agent and automata based agent. We calculate the percentage of winning and survival rate from the several simulations, and the result describes that the fuzzy rule based agent showed better performance than the automata based agent.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.3
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• pp.470-476
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• 1997

The dynamic characteristics of industrial processes frequently cause an abnormal situation which is undesirable in terms of the productivity and the safety of workers. The goal of fault-tolerance is to continue performing certain activities even after the failure of some system cononents. A fault-tolerant intelligent monitoring and control system which is robust under disturbances is proposed in this paper. Specifically, the fault-tolerant monitoring scheme proposed consists of two process models and the inference module to preserve such a robustness. The results of turning experiments demonstrate the effectiveness of the fault-tolerant scheme in the presence of built-up edge.

• 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 Quality Innovation
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• v.5 no.1
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• pp.1-9
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• 2004

The nonhomogeneous poisson process (NHPP) is often used to model repairable systems that are subject to a minimal repair strategy, with negligible repair times. In this situation, the system can be characterized by its intensity function. There have been many NHPP models according to intensity functions. However, the intensity function of system in use can be changed because of repair or its aging. We consider the single change point model as the modification of the power law process. The shape parameter of its intensity function is changed before and after the change point. We detect the presence of the change point using Bayesian methodology. Some numerical results are also presented.

• Korean Institute of Interior Design Journal
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• v.21 no.6
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• pp.186-193
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• 2012

This study is to reveal the periodical form of the 4 staged building sites with the size and the site composition of the master plan and floor plan of the Ungjindan (altar) from the Ungjindan excavation works in 2011. In order to project the research results aiming to the purpose of the study, the basic study was done with collecting data about shrine architecture for its architectural characteristics and case studies with ancestral facilities such as the Ak hae dok (national-level ancestral ritual to the big mountain, ocean and river) to understand the exact form of the site plan and architectural composition elements through the general information and excavation status. In addition, with the current situation and information from the excavation works the planned measurement scale will be calculated in inference for the size of the construction by stages and speculate the floor plan composition of the shrine architecture.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.291-301
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• 2017

It is well known in a small sample that the maximum likelihood (ML) approach for variance components in the general linear model yields estimates that are biased downward. The ML estimate of residual variance tends to be downwardly biased. The underestimation of residual variance, which has implications for the estimation of marginal effects and asymptotic standard error of estimates, seems to be more serious in some limited dependent variable models, as shown by some researchers. An alternative frequentist's approach may be restricted or residual maximum likelihood (REML), which accounts for the loss in degrees of freedom and gives an unbiased estimate of residual variance. In this situation, the REML estimator is derived in a censored regression model. A small sample the REML is shown to provide proper inference on regression coefficients.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.45-48
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• 2006

The system that can generate biological brain information processing mechanism more precisely may have several abilities such as exact cognition, situation decision, learning and inference, and output decision. In this paper, to implement high level information processing and thinking ability in a complex system, the information processing layer based on the biological brain is introduced. The biological brain information processing mechanism, which is analyzed in this paper, provides fundamental information about intelligent engineering system, and the design of the layer that can mimic the functions of a brain through engineering definitions can efficiently introduce an intelligent information processing method having a consistent flow in various engineering systems. The applications proposed in this paper are expected to take several roles as a unified model that generates information process in various areas, such as engineering and medical field, with a dream of implementing humanoid artificial intelligent system.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.994-996
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• 2016

글로벌 비즈니스 시장에서 IoT 산업은 급속도로 성장하고 있고 IoT에 대한 많은 연구가 진행되고 잇다. 그 중에서 스마트 홈 IoT 시스템이 대표적이며 이를 상용화하여 서비스를 제공하는 업체도 증가하고 있다. 홈 내부에 여러 IoT 장치와 센서들을 설치하고, 해당 장치와 센서에서 지속적으로 데이터가 생성되고 생성된 데이터를 저장하여 사용자에게 새로운 정보를 제공할 수 있다. 특히, 홈 내부에 설치된 여러 센서들의 데이터를 이용하여 현재 홈 내부 상황을 추론하여 집안 내부의 상황을 사용자에게 알려줄 수 있다. 본 논문에서는 스마트 홈 IoT 데이터를 베이지안 네트워크를 이용해 집안 내부의 쾌적 상황을 추론하여 사용자에게 정보를 제공하고 상황에 따른 행동을 할 수 있도록 정보를 제공하는 방법에 대한 연구의 결과에 대하여 소개한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.521-522
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• 2009

본 논문에서는 이동단말기로부터 수집 가능한 컨텍스트 정보를 기반으로 베이지안 네트워크 추론을 통해 송신자의 정황 정보를 생성하는 시스템을 제안한다. 축적된 데이터로부터 학습되는 베이지안 네트워크의 특성에 따라 설문조사를 통해 사용자의 정황 판단 기호를 수집하고 이를 기반으로 훈련 데이터를 생성하여 베이지안 네트워크를 구성한다. 추론 결과에 대한 사용자 피드백을 주기적인 학습에 사용하고 각 단계에서 정확도를 측정함으로써 개인화된 정황 정보 추론과 사용자의 정황 판단 기호 변화에 신속하게 적응함을 확인한다.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.287-295
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• 2005

This paper describes user context awareness system, which is one of the most essential technologies in various application services of ubiquitous computing. The proposed system used two-akial accelerometer, embedded in SenseWear(R)PRO2 Armband (BodyMedia). When it was worn on the right upper arm of the experiment subjects, MAD (mean of absolute difference) value of the sensor data was calculated to quantify the amount of the wear's activity. Using this data, PC-based fuzzy inference system was realized to distinguish human motion states, such as, lying, sitting, walking and running and to recognize the restricted emergency situations. In laboratory experiment, the amount of activities for tying, sitting, walking and running were 0.204 g/s, 0.373 g/s, 2.808 g/s and 16.243 g/s respectively. The recognition rates of human motion states were 96.7 %, 93.0 %, 95.2 % and 98.4 % respectively for lying, sitting, walking and running. The recognition rate of restricted emergency situation was 100%.