Earticle

초록

영어

Context-awareness is the key element for building a smart home environment. The goal of a smart home is to predict the demand of home users and proactively provides the proper services by considering the user’s context information. Several methods are used in context-aware system to provide services. Machine learning based approaches are capable to make better prediction and adaptation than others. In this paper, we present machine learning based context-aware system which can provide service according to the trained model. Two effective learning algorithms: Back propagation Neural Network, and Temporal Differential (TD) class of reinforcement learning are used for prediction and adaptation respectively. This ap

목차

Abstract
 1. Introduction
 2. Proposed Method
  2.1. Context Receiver Module
  2.2. Service Selection Context Module
  2.3. Service Selection Module
  2.4. Service Provider Module
  2.5. Service Execution Module
  2.6. User Feedback Module
  2.7. Adaptation Module
  2.8. Knowledge Base
 3. The Implementation Environment
 4. Conclusion
 References

키워드

저자

• M. Humayun Kabir [ Department of Electronic Engineering, Kwangwoon University Seoul, Republic of Korea ]
• M. Robiul Hoque [ Department of Electronic Engineering, Kwangwoon University Seoul, Republic of Korea ]
• Hyungyu Seo [ Department of Electronic Engineering, Kwangwoon University Seoul, Republic of Korea ]
• Sung-Hyun Yang [ Department of Electronic Engineering, Kwangwoon University Seoul, Republic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605