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An Advanced Gas Discrimination Method Utilizing the Periodic Operation of a Semiconductor Gas Sensor

机译:利用半导体气体传感器的周期性操作的先进气体识别方法

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This paper proposes a concentration-independent inflammable gas discrimination method utilizing transient response patterns of a semiconductor gas sensor. In the proposed method, the heater voltage of the sensor is changed periodically for a certain time interval after a gas sample is introduced to obtain accurate and uniform information from the response patterns regardless of the surrounding temperature variations. To realize concentration-independent gas discrimination, the D.C. components of the periodic response patterns which depend largely on the variation of ambient temperature and gas concentration are removed by applying Fourier transform, and only A.C. components which show little change on their shape in spite of the same variations are used for pattern matching. The method is examined in discriminating five kinds of inflammable gases with a three layered back propagation neural network, and average 99% discrimination rate is achieved.
机译:本文提出了一种利用半导体气体传感器瞬态响应模式的浓度无关可燃气体判别方法。在提出的方法中,在引入气体样本后,传感器的加热器电压会在一定时间间隔内定期更改,以从响应模式中获得准确而均匀的信息,而与周围的温度变化无关。为了实现与浓度无关的气体判别,通过应用傅立叶变换消除了周期响应模式中主要依赖于环境温度和气体浓度变化的直流分量,尽管有交流分量,但形状几乎没有变化的交流分量相同的变体用于模式匹配。通过三层反向传播神经网络对五种可燃气体的鉴别方法进行了研究,平均鉴别率达到99%。

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