2D Bitmapping approach for identification and quantitation of common base flavor adulterants using surface acoustic wave arrays and artificial neural network data analysis

2008 ◽  
Vol 608 (1) ◽  
pp. 79-85 ◽  
Author(s):  
Robert M. Sobel ◽  
David S. Ballantine
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Network Data ◽  
Common Base ◽  
Artificial Neural

scholarly journals Trace Detection of Nerve Agent Simulant in the Fuel Vapour Environment using Metal Oxide Surface Acoustic Wave E Nose

2020 ◽  
Vol 70 (5) ◽  
pp. 520-528
Author(s):  
Jitender Kumar ◽  
Harpreet Singh ◽  
V. Bhasker Raj ◽  
A. T. Nimal ◽  
Vinay Gupta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Principal Component ◽  
Oxide Surface ◽  
Response Patterns ◽  
Trace Detection ◽  
Response Curves ◽  
Artificial Neural

Nerve agents are often used at the military warfront, where diesel is a very common interferant. In the present work, a group of surface acoustic wave (SAW) sensors, called E-Nose with dissimilar sensing layers is developed for the recognition of the mixture of diesel and dimethyl methylphosphonate (DMMP) vapors. The exposure of DMMP and diesel vapors is kept at ppb and ppm levels respectively. Varied response patterns of DMMP and diesel vapors were obtained by SAW E-nose. Principal component analysis (PCA) has been used to extract features from the response curves of SAW sensors. Artificial Neural Network pattern recognition has been implemented to identify the precise detection of DMMP vapors in the binary mixture of DMMP and diesel. The effect of pre-processing (using PCA) the raw data before feeding it to artificial neural network is also studied.

Model of artificial neural network for complex data analysis in slag glass-ceramic

2004 ◽  
Vol 95 (2) ◽  
pp. 97-101 ◽  
Author(s):  
Hongyuan Sun ◽  
Qiye Wen ◽  
Peixin Zhang ◽  
Jianhong Liu ◽  
Qianling Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Glass Ceramic ◽  
Complex Data ◽  
Slag Glass ◽  
Artificial Neural

scholarly journals A New Analyzer Based on Pellistor Sensor with Neural Network Data Postprocessing for Measurement of Hydrocarbons in Lower Explosive Limit Range

2005 ◽  
Vol 2005 (2) ◽  
pp. 55-57
Author(s):  
Radoslaw Bandomir ◽  
Mariusz Krawczyk ◽  
Jacek Namiesnik
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Development Work ◽  
Network Data ◽  
Stage Of Development ◽  
New Type ◽  
Artificial Neural ◽  
Concentration Measurements ◽  
Explosive Limit ◽  
Lower Explosive Limit

We present the results of a first stage of development work on a new type of analyzer for hydrogen and C1–C3hydrocarbons concentration measurements in the lower explosive limit range, based on single pellistor sensor with artificial neural network data postprocessing.

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