Indoor Air Quality Analysis Using Deep Learning with Sensor Data

被引：74

作者：

Ahn, Jaehyun ^{[1
]}

Shin, Dongil ^{[2
]}

Kim, Kyuho ^{[2
]}

Yang, Jihoon ^{[2
]}

机构：

[1] Buzzni, Data Labs, Seoul 08788, South Korea

[2] Sogang Univ, Dept Comp Sci & Engn, Seoul 04107, South Korea

来源：

SENSORS | 2017年 / 17卷 / 11期

关键词：

deep learning; time series prediction; atmospheric observation system;

D O I：

10.3390/s17112476

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：13

共 13 条

[1] Use of real-time light scattering data to estimate the contribution of infiltrated and indoor-generated particles to indoor air [J].