Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145756
Title: Posting techniques in indoor environments based on deep learning for intelligent building lighting system
Authors: Lin, Xiaoping
Duan, Peiyong
Zheng, Yuanjie
Cai, Wenjian
Zhang, Xin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Lin, 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.2019.2959667
Journal: IEEE Access
Abstract: Recently, 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.
URI: https://hdl.handle.net/10356/145756
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2959667
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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