Surface Acoustic Wave E-nose Sensor Based Pattern Generation and Recognition of Toxic Gases Using Artificial Neural Network Techniques

2017 ◽  
pp. 135-145 ◽  
Author(s):  
M. Sreelatha ◽  
G. M. Nasira
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Pattern Generation ◽  
Toxic Gases ◽  
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.

Textile pattern reconstruction and innovation using artificial neural network

2019 ◽  
Vol 31 (1) ◽  
pp. 103-114
Author(s):  
Chen Tao ◽  
Yafeng Duan ◽  
Xinghua Hong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pattern Generation ◽  
Digital Innovation ◽  
Color Distribution ◽  
Content Type ◽  
Output Layer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sample Set

Purpose The purpose of this paper is to advance a digital technology that is intended to bring about innovations on the existing textile patterns. Design/methodology/approach The pattern is deemed as a relation function between colors and positions which can be learnt by the artificial neural network (ANN). The outputs of the ANN are used for the reconstruction of the pattern and the innovation is performed by interceptors in the input/output layer. The ANN is carried out with one input layer, one output layer and several hidden layers, and the capacity of the architecture is adjusted by the scale of hidden layers to accommodate different function relations of the patterns. The training is conducted repeatedly on a sample set extracted from the pixels of the pattern image to minimize the error, and the chromatic outputs of the architecture are replaced to their origins so as to rebuild the pattern. Then, the interceptors are installed into the input and output layers to modulate the positions and the colors, and consequently the innovations are achieved on the geometric formation and color distribution of the pattern. Findings It has turned out that the precision of reconstruction is concerned with network scale, training epochs and color mode of the sample set. Four primary innovative effects including stripes, twisters, sandification and overprints have been qualified in terms of interceptors. Originality/value This study introduces ANN into textile pattern generation and provides a novel way to perform digital innovation of textile patterns.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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