Prediction of health of dairy cattle from breath samples using neural network with parametric model of dynamic response of array of semiconducting gas sensors

1999 ◽  
Vol 146 (2) ◽  
pp. 102 ◽  
Author(s):  
J.W. Gardner ◽  
E.L. Hines ◽  
F. Molinier ◽  
P.N. Bartlett ◽  
T.T. Mottram
Keyword(s):  
Neural Network ◽  
Dynamic Response ◽  
Dairy Cattle ◽  
Gas Sensors ◽  
Parametric Model

Dynamic response prediction of water medium explosion container based on convolutional neural network

2020 ◽  
pp. 1-10
Author(s):  
Linna Li ◽  
Chenchen Fang ◽  
Dongwang Zhong ◽  
Li He ◽  
Jianfeng Si
Keyword(s):  
Neural Network ◽  
Dynamic Response ◽  
Convolutional Neural Network ◽  
Bp Neural Network ◽  
Response Prediction ◽  
Feature Analysis ◽  
Water Medium ◽  
Strain Response ◽  
Bp Neural Network Model ◽  
Different Doses

The water medium explosion container is an experimental device that simulates explosion in different water depth environments by loading different hydrostatic pressures and different doses of explosive. To ensure its safety during service, it is necessary to study the dynamic response of water medium explosion container. Because the dynamic response is complicated and the correlation between the response and the load of the container is nonlinear, it is difficult to calculate the dynamic response by analytical and numerical methods. In this paper, a model is built based on convolutional neural network (CNN) to predict the dynamic response of water medium explosion container. The accuracy and usability of the CNN prediction model are verified by comparison with the prediction results of the BP neural network model. The results show that CNN can be effectively used to predict the strain response of the dynamic response of water medium explosion container. and this method will play an important role in the later study of the overall feature analysis of the dynamic response of the water medium explosion vessel.

Engineering Gas Sensors With Aerosol Nanocrystals

2007 ◽  
Author(s):  
Ganhua Lu ◽  
Liying Zhu ◽  
Stephen Hebert ◽  
Edward Jen ◽  
Leonidas Ocola ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Gas Sensors ◽  
Tin Oxide ◽  
Low Cost ◽  
Hydrogen Gas ◽  
Oxide Surface ◽  
Semiconductor Surface ◽  
Oxide Nanoparticles ◽  
Electrostatic Assembly ◽  
Tin Oxide Nanoparticles

Rutile tin oxide (SnO2) is a wide band gap (3.6 eV at 300K [1]) n-type semiconductor material. It is widely used as sensing elements in gas sensors [2]. The sensing mechanism is generally attributed to the significant change in the electrical resistance of the material associated with the adsorption/desorption of oxygen on the semiconductor surface [3]. The formation of oxygen adsorbates (O2− or O−) results in an electron-depletion surface layer due to the electron transfer from the oxide surface to oxygen [4]. Recent studies [5, 6] have shown that use of tin oxide nanocrystals significantly improves the dynamic response and the sensitivity of sensors since the electron depletion may occur in the whole crystallite. Here we report on the fabrication and characterization of a miniaturized gas sensor based on tin oxide nanocrystals. A simple, convenient and low-cost mini-arc plasma source is used to synthesize high-quality tin oxide nanoparticles in aerosol phase at atmospheric pressure. The nanoparticle sensor is then fabricated by electrostatic assembly of product tin oxide nanoparticles onto e-beam lithographically patterned interdigitated electrodes. The microfabricated nanoparticle sensor exhibits good sensitivity and dynamic response to low-concentration ethanol vapor and hydrogen gas diluted in air.

scholarly journals Electronic Nose Technologies in Monitoring Black Tea Manufacturing Process

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Tharaga Sharmilan ◽  
Iresha Premarathne ◽  
Indika Wanniarachchi ◽  
Sandya Kumari ◽  
Dakshika Wanniarachchi
Keyword(s):  
Neural Network ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Black Tea ◽  
Quality Parameters ◽  
Polyphenolic Compounds ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Optimum Level ◽  
Fermentation Stage

“Tea” is a beverage which has a unique taste and aroma. The conventional method of tea manufacturing involves several stages. These are plucking, withering, rolling, fermentation, and finally firing. The quality parameters of tea (color, taste, and aroma) are developed during the fermentation stage where polyphenolic compounds are oxidized when exposed to air. Thus, controlling the fermentation stage will result in more consistent production of quality tea. The level of fermentation is often detected by humans as “first” and “second” noses as two distinct smell peaks appear during fermentation. The detection of the “second” aroma peak at the optimum fermentation is less consistent when decided by humans. Thus, an electronic nose is introduced to find the optimum level of fermentation detecting the variation in the aroma level. In this review, it is found that the systems developed are capable of detecting variation of the aroma level using an array of metal oxide semiconductor (MOS) gas sensors using different statistical and neural network techniques (SVD, 2-NM, MDM, PCA, SVM, RBF, SOM, PNN, and Recurrent Elman) successfully.

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