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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：IEEE ACCESS
关键字：Ensemble occupancy inference visible light sensing
摘要：As a key component of building management and security, occupancy inference through smart sensing has attracted a lot of research attention for nearly two decades. Recently, a cutting edge technique visible light sensing (VLS) that utilizes the LED luminaires as light sensors has shown its promising application potentials in occupancy inference as it piggybacks on pervasive lighting infrastructure without extra equipment deployment. Although existing inference algorithms based on the VLS data set can achieve high accuracy, the performance degrades when the occupants are moving. This paper focuses on the occupancy inference issue and presents an ensemble learning algorithm to improve the inference accuracy. We use heterogeneous learning algorithms to generate diverse learners. Consequently, we adopt forward sequential pruning to enhance the ensemble that pursues inference error minimization. We conduct extensive experiments based on the field data. The experiment results show that the proposed algorithm is able to improve inference accuracy, especially for highly dynamic occupancy data set.
ISSN号：2169-3536
是否译文：否
发表时间：2018-01-01
合写作者：Yuan, Xiaoming,Yang, Yanbing,F70206470,庄毅,Luo, Jun
通讯作者：郝洁