School of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, China.
School of Electric and Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China.
Sensors (Basel). 2019 Nov 18;19(22):5033. doi: 10.3390/s19225033.
Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function = 10 and the degree of freedom = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.
工业气体的快速检测和识别是一个具有挑战性的问题。它们的组成复杂，规格多样。本文提出了一种基于核判别分析（KDA）算法的工业气体识别方法。使用电子鼻采集了四种典型工业气体的气味图谱。使用不同的分类算法，包括主成分分析（PCA）、线性判别分析（LDA）、PCA+LDA 和 KDA，对采集到的气体特征进行气体识别。为了获得更好的分类结果，我们降低了原始高维数据的维度，并选择了一个好的分类器。通过选择核函数的偏移量 = 10 和自由度 = 5，KDA 算法提供了 100%的高分类准确率。结果表明，这一准确率比使用 PCA 获得的准确率高 4.17%。在标准差方面，KDA 算法具有最高的识别率和最短的时间消耗。
