Journal
SENSORS
Volume 17, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/s17112476
Keywords
deep learning; time series prediction; atmospheric observation system
Funding
- MSIT(Ministry of Science and ICT), Korea under ITRC(Information Technology Research Center) [IITP-2017-2015-0-00369, R7117-16-0098]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [R7117-16-0098, 2015-0-00369-003, 2016-0-00073-002] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Indoor air quality analysis is of interest to understand the abnormal atmospheric phenomena and external factors that affect air quality. By recording and analyzing quality measurements, we are able to observe patterns in the measurements and predict the air quality of near future. We designed a microchip made out of sensors that is capable of periodically recording measurements, and proposed a model that estimates atmospheric changes using deep learning. In addition, we developed an efficient algorithm to determine the optimal observation period for accurate air quality prediction. Experimental results with real-world data demonstrate the feasibility of our approach.
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