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  4. Hierarchical Context Discovery, Recognition and Adaptation in Smart Environment
 
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Hierarchical Context Discovery, Recognition and Adaptation in Smart Environment

Date Issued
2016
Date
2016
Author(s)
Xie, Yifei
DOI
10.6342/NTU201603016
URI
http://ntur.lib.ntu.edu.tw//handle/246246/275384
Abstract
As we are moving towards the time of Internet of Things(IoT), a significant number of heterogeneous low-power sensors and actuators are deployed around the world. The data from them, which we called Big Data, are generated for further analy-sis in human-centric applications in IoT. One of the applications in IoT is in Smart Home. In smart home environment, the application provides context-aware services and context discovery after recognizing user activities through obtained sensory data. However, there are still some challenges in activity context discovery and recognizing. With the sensors on large number of types and places to locate, the negative effects from heterogeneous features will arise, and may lead to confusing results. Besides, it is difficult to label the large quantity of sensor data quickly generated. How the appli-cation recognizes existing activities and discovers potential ones within only part of sensor data is still challenging. So, we propose an unsupervised context discovery and recognition based on hierarchical fusion of heterogeneous features in real smart envi-ronment. Also, the active learning with feedbacks presents user’s activity and reinforce the existing models. There are three major contributions in this thesis: Firstly, a hierarchical structure of models of contexts from unsupervised algorithms is proposed to discover the poten-tial contexts within heterogeneous sensors. Secondly, we propose an online context recognizing to check whether it is unseen context hierarchically. Thirdly, the active learning as well as outlier detection with percentage and frequency of context help adapt new unseen context into recognition instead of random outlier.
Subjects
Smart Home
Activity Recognition
Activity Discovery
Unsupervised Learning
Semi-Supervised Learning
Active Learning
Novelty Detection
Outlier Detection
Type
thesis
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ntu-105-R03922143-1.pdf

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