by Keyword: Predictive models
Wang, S., Hu, Y., Burgués, J., Marco, S., Liu, S.-L., (2020). Prediction of gas concentration using gated recurrent neural networks 
2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) , IEEE (Genova, Italy) , 178-182
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 concentration prediction. The GRU networks achieve on average, a 44.69% and a 25.17% RMSE improvement in concentration prediction on a gas dataset when compared with Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models respectively. With the current advances in deep network hardware accelerators, these networks can be combined with the sensors for a compact embedded system suitable for edge applications.
JTD Keywords: Robot sensing systems, Predictive models, Logic gates, Gas detectors, Training, Temperature measurement, Support vector machines
