Department of Electromechanical Engineering, Guangdong University of Technology, 100, Waihuan Rd. W., Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Sensors (Basel). 2021 Jan 8;21(2):388. doi: 10.3390/s21020388.
Deep learning methods have been widely applied to visual and acoustic technology. In this paper, we propose an odor labeling convolutional encoder-decoder (OLCE) for odor identification in machine olfaction. OLCE composes a convolutional neural network encoder and decoder where the encoder output is constrained to odor labels. An electronic nose was used for the data collection of gas responses followed by a normative experimental procedure. Several evaluation indexes were calculated to evaluate the algorithm effectiveness: accuracy 92.57%, precision 92.29%, recall rate 92.06%, F1-Score 91.96%, and Kappa coefficient 90.76%. We also compared the model with some algorithms used in machine olfaction. The comparison result demonstrated that OLCE had the best performance among these algorithms.
深度学习方法已广泛应用于视觉和声学技术。在本文中，我们提出了一种用于机器嗅觉中气味识别的气味标记卷积编解码器（OLCE）。OLCE 由卷积神经网络编码器和解码器组成，其中编码器输出受到气味标签的约束。电子鼻用于收集气体响应数据，并遵循规范的实验程序。计算了几个评估指标来评估算法的有效性：准确率为 92.57%，精密度为 92.29%，召回率为 92.06%，F1 分数为 91.96%，kappa 系数为 90.76%。我们还将该模型与机器嗅觉中使用的一些算法进行了比较。比较结果表明，OLCE 在这些算法中具有最佳性能。
