Lagged encoding for image‐based time series classification using convolutional neural networks

2020 ◽  
Vol 13 (3) ◽  
pp. 245-260
Author(s):  
Agnieszka Jastrzebska
Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1960 ◽  
Author(s):  
Lu Han ◽  
Chongchong Yu ◽  
Kaitai Xiao ◽  
Xia Zhao

This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks—VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50—were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.


2017 ◽  
Vol 28 (1) ◽  
pp. 162-169 ◽  
Author(s):  
Bendong Zhao ◽  
◽  
Huanzhang Lu ◽  
Shangfeng Chen ◽  
Junliang Liu ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document