Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLin, Xiaopingen_US
dc.contributor.authorDuan, Peiyongen_US
dc.contributor.authorZheng, Yuanjieen_US
dc.contributor.authorCai, Wenjianen_US
dc.contributor.authorZhang, Xinen_US
dc.identifier.citationLin, X., Duan, P., Zheng, Y., Cai, W., & Zhang, X. (2020). Posting techniques in indoor environments based on deep learning for intelligent building lighting system. IEEE Access, 8, 13674-13682. doi:10.1109/access.2019.2959667en_US
dc.description.abstractRecently, with the rapid development of society, solutions to reduce energy consumption in the world have attracted a lot of attention, especial electric energy. In this regard, a system that can control light on and off by determining the location of the person to reduce the waste of electricity used in public buildings, called intelligent building lighting system. Following the practical requirements of the intelligent building lighting system, a technique for positioning in indoor environments is proposed, supporting the design of a positioning system based on deep learning and the Cerebellar Model Articulation Controller (CMAC), called Y-CMAC.This technique adopts YOLOv3 (the method in the paper of YOLOv3 : An Incremental Improvement) for object detections and makes the coordinate of a person in the image. On the other hand, using CMAC to calculate the actual position of the person in the indoor environment. Moreover, massive surveillance video is used to reduce the cost of equipment and facilitate the promotion of applications. The average positioning error is controlled at around 1m in this paper.en_US
dc.relation.ispartofIEEE Accessen_US
dc.rights© 2020 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titlePosting techniques in indoor environments based on deep learning for intelligent building lighting systemen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsIntelligent Building Lighting Systemen_US
dc.subject.keywordsIndoor Positioningen_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Journal Articles
Files in This Item:
File Description SizeFormat 
08933329.pdf6.76 MBAdobe PDFView/Open

Page view(s)

Updated on May 23, 2022


Updated on May 23, 2022

Google ScholarTM




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