scholarly journals A Micromachined Metal Oxide Composite Dual Gas Sensor System for Principal Component Analysis-Based Multi-Monitoring of Noxious Gas Mixtures

Micromachines ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
In-Hwan Yang ◽  
Joon-Hyung Jin ◽  
Nam Ki Min
Keyword(s):  
Principal Component Analysis ◽  
Metal Oxide ◽  
Gas Sensor ◽  
Microelectromechanical Systems ◽  
Gas Sensing ◽  
Principal Component ◽  
Component Analysis ◽  
Oxide Semiconductor ◽  
Mixed Gas ◽  
Sensor Devices

Microelectronic gas-sensor devices were developed for the detection of carbon monoxide (CO), nitrogen dioxides (NO2), ammonia (NH3) and formaldehyde (HCHO), and their gas-sensing characteristics in six different binary gas systems were examined using pattern-recognition methods. Four nanosized gas-sensing materials for these target gases, i.e., Pd-SnO2 for CO, In2O3 for NOX, Ru-WO3 for NH3, and SnO2-ZnO for HCHO, were synthesized using a sol-gel method, and sensor devices were fabricated using a microsensor platform. Principal component analysis of the experimental data from the microelectromechanical systems gas-sensor arrays under exposure to single gases and their mixtures indicated that identification of each individual gas in the mixture was successful. Additionally, the gas-sensing behavior toward the mixed gas indicated that the traditional adsorption and desorption mechanism of the n-type metal oxide semiconductor (MOS) governs the sensing mechanism of the mixed gas systems.

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

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3264 ◽  
Author(s):  
Yonghui Xu ◽  
Xi Zhao ◽  
Yinsheng Chen ◽  
Wenjie Zhao
Keyword(s):  
Principal Component Analysis ◽  
Olfactory System ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Algorithm ◽  
Oxide Semiconductor ◽  
Kernel Principal Component Analysis ◽  
Mixed Gas ◽  
Gas Identification

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.

Application of principal component analysis to gas sensing characteristics of Cr 0.8 Fe 0.2 NbO 4 thick film array

2015 ◽  
Vol 892 ◽  
pp. 175-182 ◽  
Author(s):  
A. Sree Rama Murthy ◽  
Dhruv Pathak ◽  
Gautam Sharma ◽  
K.I. Gnanasekar ◽  
V. Jayaraman ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Thick Film ◽  
Gas Sensing ◽  
Principal Component ◽  
Component Analysis ◽  
Sensing Characteristics

Gas sensing with zeolite-coated quartz crystal microbalances—principal component analysis approach

2002 ◽  
Vol 86 (1) ◽  
pp. 26-33 ◽  
Author(s):  
Isao Sasaki ◽  
Hiroshi Tsuchiya ◽  
Masateru Nishioka ◽  
Masayoshi Sadakata ◽  
Tatsuya Okubo
Keyword(s):  
Principal Component Analysis ◽  
Quartz Crystal ◽  
Gas Sensing ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Approach ◽  
Quartz Crystal Microbalances
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