Han Lu, Yu Chongchong, Xiao Kaitai, Zhao Xia
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China.
Shenyang Research Institute of China Coal Technology and Engineering Group, Fushun 113122, China.
Sensors (Basel). 2019 Apr 26;19(9):1960. doi: 10.3390/s19091960.
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.
本文提出了一种基于卷积神经网络的用于时间序列分类的混合气体识别新方法。鉴于卷积神经网络在计算机视觉领域的优越性，我们将该概念应用于由八个金属氧化物半导体（MOX）气体传感器阵列收集的五种混合气体时间序列数据的分类。现有的卷积神经网络大多用于处理视觉数据，很少用于气体数据分类且有很大局限性。因此，提出了将时间序列数据映射为类似图像矩阵数据的想法。然后，使用五种卷积神经网络——VGG - 16、VGG - 19、ResNet18、ResNet34和ResNet50——对五种混合气体进行分类和比较。通过调整卷积神经网络的参数，最终气体识别率达到96.67%。实验结果表明，该方法能够快速有效地对气体数据进行分类，并有效地将气体时间序列数据与经典卷积神经网络相结合，为混合气体的识别提供了新思路。
