A Microcontroller Based on Multi Sensors Data Fusion and Artificial Intelligent Technique for Gas Identification

Morsi, Iman

doi:10.1109/iecon.2007.4460098

Cited by 10 publications

(18 citation statements)

References 5 publications

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“…All presented sensors have features of long life, low cost, small size, simple electrical circuit, low power consumption and are available in commercial application. The experimental equipment consists basically of the gas bottle mass flow controllers, sensor chamber with volume 475 cm 3 , supply voltage of 5 V and a heating system (Morsi, 2007); .Gases used in the experiment are carbon dioxide, hydrogen, methane, propane, butane and a mixture of propane and butane. All measurements are presented at 45% relative humidity.…”

Section: Methodsmentioning

confidence: 99%

“…Different processing techniques like self calibration, self validation and statistical analysis methods are included. Data averaging standard deviation calculation are used to test and evaluate the performance of the whole measuring system in order to minimize error (Morsi, 2007); .…”

Section: Methodsmentioning

confidence: 99%

“…The feature of each gas is detected, based on the fuzzy system. The input and output surface of the fuzzy inference system is illustrated in figs 41-60 (Morsi, 2007). …”

Section: Wwwintechopencommentioning

confidence: 99%

“…The output variable is the concentration of each gas. There are five identified ranges for each variable (Morsi , 2007) Output volt of microcontroller (V) Linguistic Range V. Low 0.5 < V < 1.5 Low 1 < V < 2 Medium 1. 5 < V < 2.5 High 2 < V < 3 V. High 2.5 < V < 3.5…”

Section: On-line Identification Of Gases Using Artificial Intelligentmentioning

confidence: 99%

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Electronic Nose System and Artificial Intelligent Techniques for Gases Identification

Morsi¹

2010

Data Storage

Self Cite

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The feature of each gas is detected, based on the fuzzy system. The input and output surface of the fuzzy inference system is illustrated in figs 41-60 (Morsi, 2007). …”

Section: Wwwintechopencommentioning

confidence: 99%

Section: On-line Identification Of Gases Using Artificial Intelligentmentioning

confidence: 99%

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Electronic Nose System and Artificial Intelligent Techniques for Gases Identification

Morsi¹

2010

Data Storage

Self Cite

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“…This resembles the system known as electronic noses. In order to correct the nonlinearities, low selectivity and other problems of a single sensor, the multi sensors data fusion are used to combine data to perform inference that may not be possible from a single sensor alone [1]. Thermal conductivity has been the analytical method based on the fact that various gases differ considerably in their ability to conduct heat.…”

Section: Introductionmentioning

confidence: 99%

PIC Microcontroller and PC based Multi Sensors Artificial Intelligent Technique for Gas Identification

Pawar¹

2015

IJCA

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PIC microcontroller & PC based gas sensing system is presented in this project. The analysis presented here depends on thin film metal oxide gas sensors TGS 822, TGS 813, TGS 2600, MQ6 and MQ7. The differences in the steady state performance among their sensors are used for improving their selectivity and sensitivity, while the combination of gas sensors permits success in gas classification problems. In the presented approach the gas sensors are embedded into a chamber with a heating system. Different types of gases are used, such as, Methane, Carbon monoxide and LPG to pass through this chamber with different concentrations, different operating temperatures and different load resistances. Sets of data collected to detect the gas sensitivity for each sensor depending on the output voltage in relation to temperatures, concentration of gases and variable resistances for each sensor. In this project, novel approach, based on the ANN technique, used for the gas identification. The identification is done directly from the data driven from the microcontroller by using ANN trained model. The results of the ANN are shown to provide gas identification according to variation in different parameters, such as gas concentrations, variation in sensor's resistance and output voltage of sensor at different temperatures and to indicate that the selection of different gases is possible, based on microcontroller, which improves sensitivity and selectivity with high accuracy and reliability.

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Detection of Gas Harmful Effect using Fuzzy Logic System

Illahi

Dadios

2019

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environ

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A Microcontroller Based on Multi Sensors Data Fusion and Artificial Intelligent Technique for Gas Identification

Cited by 10 publications

References 5 publications

Electronic Nose System and Artificial Intelligent Techniques for Gases Identification

Electronic Nose System and Artificial Intelligent Techniques for Gases Identification

PIC Microcontroller and PC based Multi Sensors Artificial Intelligent Technique for Gas Identification

Detection of Gas Harmful Effect using Fuzzy Logic System

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