一种新的数据驱动技术产生多传感器虚拟响应的气体传感器阵列电子鼻

IF 1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Sumit Srivastava, S. N. Chaudhri, N. S. Rajput, A. Mishra
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引用次数: 1

摘要

准确的气体/气味检测需要高选择性的气体传感器。然而,使用部分选择性气体传感器可以实现气体/气味的高性能分类。自20世纪80年代以来,一系列宽调谐(部分选择性)气体传感器已被用于科学和工程的几个领域,由此产生的气体传感系统(GSS)被普遍称为电子鼻(e- nose)。阵列中相似或不同传感器的组合间接补偿了GSS对高选择性的要求。此外,e-Nose的性能不可避免地取决于从气体传感器阵列(GSA)的初始响应中得出的显著特征。因此得到的特征被称为虚拟传感器的响应。在本文中,我们提出了三输入三输出(TITO)技术,以获得有效的虚拟传感器响应(VSRs),优于其已发表的同行技术。由四个要素组成的GSA被用来演示所提出的技术。与同类技术相比,我们提出的技术将VSRs提高了四倍。我们提出的技术的有效性已经使用9个基本分类器进行了测试,即线性支持向量机(100%)、决策树(97.5%)、多层感知器神经网络(100%)、k近邻(85%)、逻辑回归(100%)、径向基函数高斯过程(95%)、线性判别分析(97.5%)、随机森林(100%)和AdaBoost(95%)。十倍交叉验证已被用于最小化类内和类间方差的偏倚影响。结果,四个分类器成功地提供了100%的准确率。因此,我们提出并证明了一种有效的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel data-driven technique to produce multi- sensor virtual responses for gas sensor array-based electronic noses
Abstract Accurate detection of gas/odor requires highly selective gas sensor. However, the high-performance classification of gases/odors can be achieved using partial-selective gas sensors. Since 1980s, an array of broadly tuned (partial-selective) gas sensors have been used in several fields of science and engineering, and the resulting gas sensing systems (GSS) are popularly known as electronic noses (e-Noses). The combination of similar or different sensors in the array indirectly compensates for the requirement of high selectivity in GSS. Further, e-Nose’s performance inevitably depends on the salient features drawn from the initial responses of the gas sensor array (GSA). So obtained features are referred to as the responses of virtual sensors (VS). In this paper, we have proposed the three-input and three-output (TITO) technique to derive efficient virtual sensor responses (VSRs) which outperform its well-published peer technique. A GSA consisting of four elements is used to demonstrate the proposed technique. Our proposed technique augments the VSRs by four times compared to its peer. The efficacy of our proposed technique has been tested using nine fundamental classifiers, viz., linear support vector machine (100%), decision tree (97.5%), multi-layer perceptron neural network (100%), K-nearest neighbor (85%), logistic regression (100%), Gaussian process with radial basis function (95%), linear discriminant analysis (97.5%), random forest (100%), and AdaBoost (95%). Ten-fold cross-validation has been used to minimize the biasing impact of the intra- and inter-class variance. With the result, four classifiers successfully provide an accuracy of 100 percent. Hence, we have proposed and vindicated an efficient technique.
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来源期刊
Journal of Electrical Engineering-elektrotechnicky Casopis
Journal of Electrical Engineering-elektrotechnicky Casopis 工程技术-工程:电子与电气
CiteScore
1.70
自引率
12.50%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The joint publication of the Slovak University of Technology, Faculty of Electrical Engineering and Information Technology, and of the Slovak Academy of Sciences, Institute of Electrical Engineering, is a wide-scope journal published bimonthly and comprising. -Automation and Control- Computer Engineering- Electronics and Microelectronics- Electro-physics and Electromagnetism- Material Science- Measurement and Metrology- Power Engineering and Energy Conversion- Signal Processing and Telecommunications
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