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Sensors 2017, 17(11), 2476; https://doi.org/10.3390/s17112476

Indoor Air Quality Analysis Using Deep Learning with Sensor Data

1
Data Labs, Buzzni, Seoul 08788, Korea
2
Department of Computer Science and Engineering, Sogang University, Seoul 04107, Korea
*
Author to whom correspondence should be addressed.
Received: 21 July 2017 / Revised: 7 September 2017 / Accepted: 25 October 2017 / Published: 28 October 2017
(This article belongs to the Special Issue Air Pollution Sensors: A New Class of Tools to Measure Air Quality)
View Full-Text   |   Download PDF [4682 KB, uploaded 30 October 2017]   |  

Abstract

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. View Full-Text
Keywords: deep learning; time series prediction; atmospheric observation system deep learning; time series prediction; atmospheric observation system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Ahn, J.; Shin, D.; Kim, K.; Yang, J. Indoor Air Quality Analysis Using Deep Learning with Sensor Data. Sensors 2017, 17, 2476.

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