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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >GAS MIXTURE QUANTIFICATION BASED ON HILBERT-HUANG TRANSFORM AND NEURAL NETWORK BY A SINGLE SENSOR
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GAS MIXTURE QUANTIFICATION BASED ON HILBERT-HUANG TRANSFORM AND NEURAL NETWORK BY A SINGLE SENSOR

机译：基于希尔伯特-黄变换和神经网络的单传感器气体混合定量

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摘要

Temperature modulation has been proved to be an efficient technique for improving the selectivity and stability of gas sensors. In this paper, a new signal processing approach is proposed for metal oxide gas sensor signals under the modulation of its operating temperature, which combined a novel global feature extraction method based on the Hilbert—Huang Transform with a pattern recognition method based on neural network. By using the empirical mode decomposition method, the dynamic signals are decomposed into the intrinsic modes that coexist in the sensor system, and a better understanding of the nature of the gas sensing response information contained in the sensor response signals is approached. The method is demonstrated by an application in the identification and quantification of gas mixtures containing three flammable species using a micro gas sensor. The three gas analytes are methane (CH4), ethanol (C2H6O) and carbon monoxide (CO). And the relative average quantification errors for the three gases are about 7%, 8% and 12%, respectively.
机译：事实证明，温度调节是提高气体传感器选择性和稳定性的有效技术。本文提出了一种金属氧化物气体传感器信号在其工作温度调制下的信号处理方法，该方法将基于希尔伯特-黄变换的全局特征提取方法与基于神经网络的模式识别方法相结合。通过使用经验模式分解方法，将动态信号分解为传感器系统中共存的固有模式，从而可以更好地理解传感器响应信号中包含的气体传感响应信息的性质。该方法通过使用微型气体传感器在鉴定和量化包含三种易燃物质的气体混合物中的应用得到证明。三种气体分析物是甲烷（CH4），乙醇（C2H6O）和一氧化碳（CO）。三种气体的相对平均定量误差分别约为7％，8％和12％。

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