Publication: Prediction of Gas Concentration Using Gated Recurrent Neural Networks
Prediction of Gas Concentration Using Gated Recurrent Neural Networks
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Wang, S., Hu, Y., Burgues, J., Marco, S., & Liu, S.-C. (2020, September 2). Prediction of Gas Concentration Using Gated Recurrent Neural Networks. 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Genova. https://doi.org/10.1109/aicas48895.2020.9073806
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Low-cost gas sensors allow for large-scale spatial monitoring of air quality in the environment. However they require calibration before deployment. Methods such as multivariate regression techniques have been applied towards sensor calibration. In this work, we propose instead, the use of deep learning methods, particularly, recurrent neural networks for predicting the gas concentrations based on the outputs of these sensors. This paper presents a first study of using Gated Recurrent Unit (GRU) neural network models for gas concentra
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- Wang, Shu
- Hu, Yuhuang
- Burgues, Javier
- Marco, Santiago
- Liu, Shih-Chii
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Wang, S., Hu, Y., Burgues, J., Marco, S., & Liu, S.-C. (2020, September 2). Prediction of Gas Concentration Using Gated Recurrent Neural Networks. 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Genova. https://doi.org/10.1109/aicas48895.2020.9073806
