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Smart Control System Using Fuzzy and Neural Network Prediction System

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Author(s)
Tae Yeun Kim Sang Hyun Bae
Issued Date
2019
Keyword
Smart Control System Fuzzy Prediction System Neural Network Prediction System Motion Vector Rough Set
Abstract
In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.
Publisher
조선대학교 기초과학연구원
Citation
Tae Yeun Kim. (2019). Smart Control System Using Fuzzy and Neural Network Prediction System, 조선자연과학논문집 | Vol.12, No.4 p.105 ~ p.115
Type
Laboratory article
ISSN
2005-1042
URI
https://oak.chosun.ac.kr/handle/2020.oak/16449
http://www.chosun.ac.kr/user/indexSub.do?codyMenuSeq=24427455&siteId=ricns&dum=dum&boardId=175013&page=1&command=view&boardSeq=310691&categoryId=291762&categoryDepth=00130004
Appears in Collections:
2019 > Vol.12, No.4
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