• Journal of KIISE:Computing Practices and Letters
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• v.15 no.1
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• pp.72-76
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• 2009

Recently, as the interest of ubiquitous computing has been increased there has been lots of research about recognizing human activities to provide services in this environment. Especially, in mobile environment, contrary to the conventional vision based recognition researches, lots of researches are sensor based recognition. In this paper we propose to recognize the user's activity with multi-modal sensors using hierarchical dynamic Bayesian networks. Dynamic Bayesian networks are trained by the OVR(One-Versus-Rest) strategy. The inferring part of this network uses less calculation cost by selecting the activity with the higher percentage of the result of a simpler Bayesian network. For the experiment, we used an accelerometer and a physiological sensor recognizing eight kinds of activities, and as a result of the experiment we gain 97.4% of accuracy recognizing the user's activity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.232-235
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• 2004

차세대 정보통신 기술의 가장 중요한 패러다임으로 유비쿼터스 컴퓨팅이 새롭게 주목 받고있다. 그러나 현재 유비쿼터스 환경에서 축적되어 있는 분산데이터베이스의 구체적인 활용 방안에 관한 연구는 아직 불충분하다. 본 논문에서는 분산환경 데이터베이스에 축적되어 있는 데이터를 베이지안 네트워크를 이용하여 인간의 동작이나 행동에 대한 상황 적응형 서비스를 실행하는 방법을 제안한다. 베이지안 네트워크는 변수들 사이의 인과 관계를 표현하기 때문에 사용자의 행동이나 특성들을 기술하는 것이 용이하다 유비쿼터스 환경에서 인간이나 사물의 동작, 행동 등을 축적한 데이터베이스로부터 현재 인간의 상황을 예측하여 인간이 필요로 하는 적절한 서비스를 실행하는 작업이 요구된다. 유비쿼터스 환경 내에서 발생하는 이벤트를 인지하고 인간과 사물간의 대화 생성의 중개역할자로 베이지안 네트워크를 이용하여 적절한 서비스를 추론하고 실행하는 방법을 제시한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.834-839
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• 2005

When a great variety of services become available to user through the broadband convergence network in the ubiquitous home environment, an intelligent agent is required to deal with the complexity of services and perceive intension of a user. Different from the old-fashioned command-based user interface for selecting services, conversation enables flexible and rich interactions between human and agents, but diverse expressions of the user's background and context make conversation hard to implement by using either user-initiative or system-initiative methods. To deal with the ambiguity of diverse expressions between user and agents, we have to apply hierarchial bayesian networks for the mixed initiative conversation. Missing information from user's query is analyzed by hierarchial bayesian networks to inference the user's intension so that can be collected through the agent's query. We have implemented this approach in ubiquitous home environment by implementing simulation program.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.907-912
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• 2007

The present level of ubiquitous computing technology have developed to the point where Home-server provides services that user require directly for user in the intelligent space. But it will need intelligent system to provides more active services for user in the near future. In this paper, we define the environment information about situation that user is in as Context, and collect the Context that stereotype as 4W1H form for construct the system that can decision service will be provide from information about a situation that user is in, without user's involvement. Additionally we collect information about user's emotional state, use these informations as nodes of Bayesian network for probabilistic reasoning. From that, we materialize Context Awareness system about it that what kind of situation user is in. And, we propose the Context-based Service reasoning model using Bayesian Network from the result of Context Awareness.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.381-386
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• 2006

오늘날 분산 컴퓨팅 환경에서 운용되는 시스템이 증가함에 따라 시스템의 관리작업은 고수준(high-level)의 자동화에 대한 요구가 증가하고 있다. 이에 따라 시스템 관리방식이 전통적인 관리자 중심의 방식에서 시스템 스스로가 자신의 문제를 인식하고 상황을 분석하여 해결하는 자율 컴퓨팅 방식으로 변화하고 있으며, 이에 대한 연구가 많은 연구기관에서 다양한 방법으로 이루어지고 있다. 그러나 이러한 대부분의 기존 연구들은 문제가 발생한 이후의 치유에 주로 초점이 맞추어져 있다. 이러한 문제를 해결하기 위해서는 시스템 스스로가 동작환경을 인식하고 에러의 발생을 예측하기 위한 예측 모델이 필요하다. 따라서 본 논문에서는 자율 컴퓨팅환경에서 자가 치유를 지원하는 4가지의 예측 모델 설계 방법을 제안한다. 본 예측 모델은 ID3 알고리즘, 퍼지 추론, 퍼지 뉴럴 네트워크 그리고 베이지안 네트워크가 각 시스템 상황에 맞춰 적절하게 사용되는 방식이며, 이를 통해 보다 정확한 에러 예측이 가능해진다. 우리는 제안모델의 평가를 위해 본 예측모델을 자가치유 시스템에 적용하여 기존 연구와 예측의 효율을 비교하였으며, 그 결과를 통해 제안 모델의 유효성을 증명하였다.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.137-146
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• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.