Combustible Gas Discrimination by Pattern Recognition Analysis of Responses from Semiconductor Gas Sensor Array

2013 ◽  
Vol 303-306 ◽  
pp. 876-879 ◽  
Author(s):  
Ping Sun ◽  
Zhong Hua Ou ◽  
Xing Feng
Keyword(s):  
Quantitative Analysis ◽  
Gas Sensor ◽  
Sensor Array ◽  
Gas Mixture ◽  
Qualitative And Quantitative ◽  
Semiconductor Gas Sensor ◽  
Gas Sensor Array ◽  
Combustible Gas ◽  
Quantitative Identification ◽  
Combustible Gas Mixture

The qualitative and quantitative identification of combustible gas mixture cannot be realized by a single sensor. Therefore, a semiconductor gas sensor array was built up. The experimental parameters including the dynamic and static information of the sensors were selected. The qualitative and quantitative identification of combustible gas mixture are achieved by the artificial neural network. The results show that this method for the qualitative identification of the combustible gas mixture is completely correct. The highest false rate of the quantitative analysis is 0.38% and the average false rate of the quantitative analysis is 0.079%. Achieve a good qualitative and quantitative identification.

scholarly journals The Effectiveness of Varroa destructor Infestation Classification Using an E-Nose Depending on the Time of Day

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2532
Author(s):  
Andrzej Szczurek ◽  
Monika Maciejewska ◽  
Żaneta Zajiczek ◽  
Beata Bąk ◽  
Jakub Wilk ◽  
...  
Keyword(s):  
Gas Sensor ◽  
Varroa Destructor ◽  
Sensor Array ◽  
Measurement Data ◽  
Time Of Day ◽  
Semiconductor Gas Sensor ◽  
Bee Colony ◽  
Gas Sensor Array ◽  
Array Measurement ◽  
Method Of Assessment

Honey bees are subject to a number of stressors. In recent years, there has been a worldwide decline in the population of these insects. Losses raise a serious concern, because bees have an indispensable role in the food supply of humankind. This work is focused on the method of assessment of honey bee colony infestation by Varroa destructor. The approach allows to detect several categories of infestation: “Low”, “Medium” and “High”. The method of detection consists of two components: (1) the measurements of beehive air using a gas sensor array and (2) classification, which is based on the measurement data. In this work, we indicate the sensitivity of the bee colony infestation assessment to the timing of measurement data collection. It was observed that the semiconductor gas sensor responses to the atmosphere of a defined beehive, collected during 24 h, displayed temporal variation. We demonstrated that the success rate of the bee colony infestation assessment also altered depending on the time of day when the gas sensor array measurement was done. Moreover, it was found that different times of day were the most favorable to detect the particular infestation category. This result could indicate that the representation of the disease in the beehive air may be confounded during the day, due to some interferences. More studies are needed to explain this fact and determine the best measurement periods. The problem addressed in this work is very important for scheduling the beekeeping practices aimed at Varroa destructor infestation assessment, using the proposed method.

A Semiconductor Gas Sensor Array for the Detection of Gas Emissions from Interior Trim Materials in Automobiles

1998 ◽  
Author(s):  
Eva-Lotta Kalman ◽  
Fredrik Winquist ◽  
Ingemar Lundström ◽  
Mona Grönberg ◽  
Anders Löfvendahl
Keyword(s):  
Gas Sensor ◽  
Sensor Array ◽  
Gas Emissions ◽  
Semiconductor Gas Sensor ◽  
Gas Sensor Array

Gas Analysis Based on Piezoelectric Sensor Array

2011 ◽  
Vol 421 ◽  
pp. 674-678
Author(s):  
Min Ming Tong ◽  
Le Jian An ◽  
Shou Feng Tang ◽  
Zi Hui Ren
Keyword(s):  
Bp Neural Network ◽  
Sensor Array ◽  
Safety Monitoring ◽  
Piezoelectric Sensor ◽  
Gas Analysis ◽  
Gas Mixture ◽  
Combustible Gas ◽  
Analysis Error ◽  
Combustible Gas Mixture ◽  
Explosion Risk

A piezoelectric sensor array is introduced for the analysis of gas in mine. This sensor array is made of three different gas-sensitive piezoelectric sensors to detect an explosive gas mixture of methane, butane and hexane. The gas analysis is very important to reliable warning of explosion risk in mine. Because of cross sensing to gas for each sensor of sensor array, we use BP neural network in the artificial neural networks to process the sensing signal to get the concentration of methane, butane and hexane in the combustible gas mixture. Experimental results show that the analysis error is less than 5% and meets the requirements of safety monitoring.

An Odor Monitoring System Based on Differentiated Pattern Recognition Implemented a Semiconductor Gas Sensor Array

2013 ◽  
Vol 19 (10) ◽  
pp. 2901-2904 ◽  
Author(s):  
Eungyeong Kim ◽  
Jung Ho Lee ◽  
Beom Ju Shin ◽  
Seok Lee ◽  
Young Tae Byun ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Gas Sensor ◽  
Monitoring System ◽  
Sensor Array ◽  
Semiconductor Gas Sensor ◽  
Gas Sensor Array ◽  
Odor Monitoring

Evaluation of a multiple gas mixture with a simple MOSFET gas sensor array and pattern recognition

1990 ◽  
Vol 2 (2) ◽  
pp. 115-123 ◽  
Author(s):  
Hans Sundgren ◽  
Ingemar Lundström ◽  
Fredrik Winquist ◽  
Ingrid Lukkari ◽  
Rolf Carlsson ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Gas Sensor ◽  
Sensor Array ◽  
Gas Mixture ◽  
Gas Sensor Array
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