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http://dx.doi.org/10.22156/CS4SMB.2021.11.11.057

A Study on the Air Pollution Monitoring Network Algorithm Using Deep Learning  

Lee, Seon-Woo (Electric Computer Engineering, Inha University)
Yang, Ho-Jun (Electric Computer Engineering, Inha University)
Lee, Mun-Hyung (Computer Engineering, Inha University)
Choi, Jung-Moo (Computer Engineering, Inha University)
Yun, Se-Hwan (Computer Engineering, Inha University)
Kwon, Jang-Woo (Computer Engineering, Inha University)
Park, Ji-Hoon (Air Quality Research Department, Air Quality Research Division)
Jung, Dong-Hee (Air Quality Research Department, Air Quality Research Division)
Shin, Hye-Jung (Air Quality Research Department, Air Quality Research Division)
Publication Information
Journal of Convergence for Information Technology / v.11, no.11, 2021 , pp. 57-65 More about this Journal
Abstract
We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.
Keywords
Air Quality; Deep Learning; Abnormal Detection; Air Pollution Monitoring Network; Machine Learning;
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