论文标题
气味标签标记卷积编码器，用于机器嗅觉中的气味传感
An Odor Labeling Convolutional Encoder-Decoder for Odor Sensing in Machine Olfaction
论文作者
论文摘要
深度学习方法已广泛应用于视觉和声学技术。在本文中，我们提出了一种气味标记卷积编码器（OLCE），以用于机器嗅觉中的气味鉴定。 OLCE组成了卷积神经网络编码器和解码器，其中编码器输出约束至气味标签。电子鼻被用于气体响应的数据收集，然后进行规范实验程序。计算了几个评估指数以评估算法效率：精度为92.57％，精度为92.29％，召回率为92.06％，F1分数91.96％和Kappa系数为90.76％。我们还将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在这些算法中的性能最佳。在本文中，还讨论了机器嗅觉的一些观点。
Deep learning methods have been widely applied to visual and acoustic technology. In this paper, we proposed 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. In the paper, some perspectives of machine olfactions have been also discussed.
