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Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines
Sensors and Actuators B: Chemical ( IF 8.4 ) Pub Date : 2020-09-21 , DOI: 10.1016/j.snb.2020.128921
Juan C. Rodriguez Gamboa , Adenilton J. da Silva , Ismael C. S. Araujo , Eva Susana Albarracin E. , Cristhian M. Duran A.

Real-time gas classification is an essential issue and challenge in applications such as food and beverage quality control, accident prevention in industrial environments, for instance. In recent years, the Deep Learning (DL) models have shown great potential to classify and forecast data in diverse problems, even in the electronic nose (E-Nose) field. In this work, a Support Vector Machine (SVM) algorithm and three different DL models were used to validate the rapid detection approach (based on processing an early portion of raw signals and a rising window protocol) over diverse measurement conditions. We performed a set of experiments with five different E-Nose databases, including fifteen datasets to be used with these algorithms. Based on the obtained results, we concluded that the proposed approach has a high potential and reduces the response time for making E-nose forecasts. Because in more than 60 % of the cases, it achieved reliable estimates using only the first 30 % or fewer of measurement data (counted after the gas injection starts). The findings suggest that the rapid detection approach generates reliable forecasting models using different classification methods. Moreover, SVM seems to achieve the best accuracy and better training time.



中文翻译:

使用不同的深度学习模型和支持向量机验证了用于增强电子鼻系统性能的快速检测方法

在诸如食品和饮料质量控制,工业环境中的事故预防等应用中,实时气体分类是必不可少的问题和挑战。近年来,即使在电子鼻(E-Nose)领域,深度学习(DL)模型也显示出了在各种问题中分类和预测数据的巨大潜力。在这项工作中,使用支持向量机(SVM)算法和三种不同的DL模型来验证各种测量条件下的快速检测方法(基于处理原始信号的早期部分和上升窗口协议)。我们对五个不同的E-Nose数据库进行了一组实验,包括与这些算法一起使用的十五个数据集。根据获得的结果,我们得出的结论是,提出的方法具有很高的潜力,并且可以减少做出电子鼻预测的响应时间。因为在超过60%的情况下,它仅使用前30%或更少的测量数据(在注气开始后计算)即可获得可靠的估计。研究结果表明,快速检测方法使用不同的分类方法可以生成可靠的预测模型。而且,SVM似乎可以实现最佳的准确性和更好的培训时间。

更新日期:2020-10-02
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