Indoor Air Quality Analysis Using Deep Learning with Sensor Data

Indoor Air Quality Analysis Using Deep Learning with Sensor Data

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 mic...

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Bibliographic Details
Main Authors: Jaehyun Ahn, Dongil Shin, Kyuho Kim, Jihoon Yang
Format: Article
Language:English
Published: MDPI AG 2017-10-01
Series:Sensors
Subjects:
deep learning
time series prediction
atmospheric observation system
Online Access:https://www.mdpi.com/1424-8220/17/11/2476
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record_format Article
spelling doaj-3a75161793564165afb54df335fc9c732020-11-24T22:08:53ZengMDPI AGSensors1424-82202017-10-011711247610.3390/s17112476s17112476Indoor Air Quality Analysis Using Deep Learning with Sensor DataJaehyun Ahn0Dongil Shin1Kyuho Kim2Jihoon Yang3Data Labs, Buzzni, Seoul 08788, KoreaDepartment of Computer Science and Engineering, Sogang University, Seoul 04107, KoreaDepartment of Computer Science and Engineering, Sogang University, Seoul 04107, KoreaDepartment of Computer Science and Engineering, Sogang University, Seoul 04107, KoreaIndoor 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.https://www.mdpi.com/1424-8220/17/11/2476deep learningtime series predictionatmospheric observation system
collection DOAJ
language English
format Article
sources DOAJ
author Jaehyun Ahn
Dongil Shin
Kyuho Kim
Jihoon Yang
spellingShingle Jaehyun Ahn
Dongil Shin
Kyuho Kim
Jihoon Yang
Indoor Air Quality Analysis Using Deep Learning with Sensor Data
Sensors
deep learning
time series prediction
atmospheric observation system
author_facet Jaehyun Ahn
Dongil Shin
Kyuho Kim
Jihoon Yang
author_sort Jaehyun Ahn
title Indoor Air Quality Analysis Using Deep Learning with Sensor Data
title_short Indoor Air Quality Analysis Using Deep Learning with Sensor Data
title_full Indoor Air Quality Analysis Using Deep Learning with Sensor Data
title_fullStr Indoor Air Quality Analysis Using Deep Learning with Sensor Data
title_full_unstemmed Indoor Air Quality Analysis Using Deep Learning with Sensor Data
title_sort indoor air quality analysis using deep learning with sensor data
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2017-10-01
description 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.
topic deep learning
time series prediction
atmospheric observation system
url https://www.mdpi.com/1424-8220/17/11/2476
work_keys_str_mv AT jaehyunahn indoorairqualityanalysisusingdeeplearningwithsensordata
AT dongilshin indoorairqualityanalysisusingdeeplearningwithsensordata
AT kyuhokim indoorairqualityanalysisusingdeeplearningwithsensordata
AT jihoonyang indoorairqualityanalysisusingdeeplearningwithsensordata
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