원문정보
초록
영어
Context-awareness is an important characteristic of smart home. Several methods are used in context-aware application to provide services. The main target of smart home is to predict the demand of home users and proactively provide the proper services by computing user’s context information. In this paper, we present a context-aware application which can provide service according to predefined choice of user. It uses Mahalanobis distance based k nearest neighbors classifier technique for inference of predefined service. We combine the features of supervised and unsupervised machine learning in the proposed application. This application can also adapt itself when the choice of user is changed by using Q-learning reinforcement learning algorithm.
목차
1. Introduction
2. Proposed Method
2.1 Context Receiver Module:
2.2 Service-triggering Context Module:
2.3 Service Form-changing Context Module:
2.4 Learning and Mapping:
2.5 Inference Engine:
2.6 Service Provider Module:
2.7 Service Execution Module:
2.8 Adaption Module:
2.9 User Feedback Module:
2.10 Knowledge Base:
3. The Implementation Environment
4. Conclusion
Acknowledgements
References