scholarly journals Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3917 ◽  
Author(s):  
Shurui Fan ◽  
Zirui Li ◽  
Kewen Xia ◽  
Dongxia Hao
Keyword(s):  
Gas Sensor ◽  
Sensor Array ◽  
Recognition Rate ◽  
Principal Component ◽  
High Sensitivity ◽  
Pso Algorithm ◽  
Support Vector ◽  
Gas Sensor Array ◽  
Multicomponent Gas ◽  
Difficult Challenge

The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters C and γ to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

Study of the Gas Sensor Array Based on Micro-Machined Ceramic Hotplate

2013 ◽  
Vol 631-632 ◽  
pp. 1117-1122 ◽  
Author(s):  
Yan Bing Xue ◽  
Zhe Nan Tang
Keyword(s):  
Gas Sensor ◽  
Sensor Array ◽  
Thermal Response ◽  
High Sensitivity ◽  
Quantitative Detection ◽  
Average Temperature ◽  
Gas Sensor Array ◽  
Pd Doping ◽  
Constant Heating ◽  
Mixed Gases

In order to improve the thermal stability of silicon micro hotplate, a ceramic hotplate with structure of suspending bridge was designed. The steady-state thermal response of the hotplate and the structure of the micro-heater were simulated by using the finite element method. By using conventional microelectronics technology and laser micro processing technology, the microstructures with thickness of 100 μm and bridge width of 2 mm were produced. The test results show that the ceramic hotplate has higher working temperature than traditional silicon hotplate, and it can be worked steadily at the average temperature of 630 °C on 1.5W heating power. Taking ceramic hotplate as heating platform and nano-SnO2 materials with Pd doping concentration of 0.2 at.% and 10 at.% as sensitive materials respectively, the array with two gas sensors was designed and fabricated. The gas sensor array can be used to detect single gas of CO or CH4 with high sensitivity and good selectivity when it works with constant heating voltage. When the sensor array works with periodic voltage heating pulse, it can realize quantitative detection for mixed gases of CO and CH4.

Onion sour skin detection using a gas sensor array and support vector machine

2009 ◽  
Vol 3 (4) ◽  
pp. 193-202 ◽  
Author(s):  
Changying Li ◽  
Ron Gitaitis ◽  
Bill Tollner ◽  
Paul Sumner ◽  
Dan MacLean
Keyword(s):  
Support Vector Machine ◽  
Gas Sensor ◽  
Sensor Array ◽  
Support Vector ◽  
Skin Detection ◽  
Gas Sensor Array ◽  
Sour Skin

scholarly journals Decay detection of constructional softwoods using machine olfaction

2021 ◽  
Vol 67 (1) ◽  
Author(s):  
Masaki Suzuki ◽  
Teruhisa Miyauchi ◽  
Shinichi Isaji ◽  
Yasushi Hirabayashi ◽  
Ryuichi Naganawa
Keyword(s):  
Gas Sensor ◽  
Sensor Array ◽  
Wood Decay ◽  
Principal Component ◽  
Cost Effective ◽  
Fungal Species ◽  
Gas Sensor Array ◽  
Test Pieces ◽  
Timber Constructions ◽  
Machine Olfaction

AbstractFungal decomposition of wood severely affects the soundness of timber constructions. The diagnosis of wood decay requires direct observations or sampling by skilled experts. Wood decay often occurs in obscure spaces, including the enclosed inner spaces of walls or under the floor. In this study, we examined the ability of machine olfaction to detect odors of fungi grown on common construction softwoods to provide a novel diagnostic method for wood construction soundness. The combination of a simple device equipped with semiconductor gas sensors (gas sensor array) and multivariate analysis discriminated a fungi-related odor from control odor without instrumental analysis (e.g., gas chromatography). This method is often referred to as machine olfaction or electronic nose. We measured the odor of wood test pieces that were infected with Fomitopsis palustris or Trametes versicolor and sound test pieces using a gas sensor array. The sensor responses of the specimens showed different patterns between the inoculated and control samples. Each specimen class formed independent groups in a principal component score plot, almost regardless of wood species, fungal species, or cultivation period. This method provides a new decay diagnosis method that is cost-effective and easy to operate.

scholarly journals Gas sensor array to clasify the chicken meat with E. coli contaminant by using random forest and support vector machine

2021 ◽  
pp. 100083
Author(s):  
Suryani D. Astuti ◽  
Mohammad H. Tamimi ◽  
Anak A.S. Pradhana ◽  
Kartika A. Alamsyah ◽  
Hery Purnobasuki ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Gas Sensor ◽  
Sensor Array ◽  
Chicken Meat ◽  
Support Vector ◽  
E Coli ◽  
Gas Sensor Array
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