Comparison of different feature reduction methods in the improvement of gas diagnosis of a temperature modulated resistive gas sensor

Hosseini-Golgoo, Seyed Mohsen; Ebrahimpour, N

doi:10.1088/1757-899x/108/1/012001

Cited by 4 publications

(1 citation statement)

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“…Because the response characteristics of the sensor array are susceptible to temperature and humidity, the selected sensor has the best gas selectivity at 15 °C and relative humidity of 20%. Therefore, signal acquisition must be performed in a room with constant temperature and humidity, where the constant temperature and humidity are ensured by a fan and a humidifier [ 48 ]. The experimental conditions are set to 15 °C and a relative humidity of 20%.…”

Section: Methodsmentioning

confidence: 99%

Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array

Zhao

Chen

et al. 2018

Sensors

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As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH4 as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH4 concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.

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Section: Methodsmentioning

confidence: 99%