Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHao, Jieen
dc.contributor.authorYuan, Xiaomingen
dc.contributor.authorYang, Yanbingen
dc.contributor.authorWang, Ranen
dc.contributor.authorZhuang, Yien
dc.contributor.authorLuo, Junen
dc.identifier.citationHao, J., Yuan, X., Yang, Y., Wang, R., Zhuang, Y., & Luo, J. (2018). Visible light based occupancy inference using ensemble learning. IEEE Access, 6, 16377-16385.en
dc.description.abstractAs 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.en
dc.format.extent9 p.en
dc.relation.ispartofseriesIEEE Accessen
dc.rights© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.en
dc.subjectOccupancy Inferenceen
dc.titleVisible light based occupancy inference using ensemble learningen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.description.versionPublished versionen
item.fulltextWith Fulltext-
Appears in Collections:SCSE Journal Articles
Files in This Item:
File Description SizeFormat 
Visible Light Based Occupancy Inference Using Ensemble Learning.pdf9.38 MBAdobe PDFThumbnail

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.