A Two-Component Gas Analyzer Based on Self-Sustaining Dark Discharge Carbon Nanotube Film Cathode Gas Sensor

2010 ◽  
Vol 121-122 ◽  
pp. 188-191
Author(s):  
Ji Peng Lin ◽  
Jun Hua Liu
Keyword(s):  
Support Vector Machine ◽  
Carbon Nanotube ◽  
Gas Sensor ◽  
Support Vector ◽  
Gas Analyzer ◽  
Mixed Gas ◽  
Accuracy Measurement ◽  
Carbon Nanotube Film ◽  
Two Component ◽  
New Type

A new type of cathode gas sensor based on carbon nanotube film is presented to analyze CO and CH4 mixed gas. The structure and measuring principle of breakdown voltages are proposed to build support vector machine nonlinear model. As a result, the max relative error of CO and CH4 is 3.8% and 6.0%, respectively. This hints the sensor acts an accuracy measurement.

Carbon Nanotube Gas Sensor Fabricated on Anodic Aluminum Oxide

2007 ◽  
Vol 124-126 ◽  
pp. 1309-1312
Author(s):  
Nguyen Duc Hoa ◽  
Nguyen Van Quy ◽  
Gyu Seok Choi ◽  
You Suk Cho ◽  
Se Young Jeong ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Carbon Nanotube ◽  
Gas Sensor ◽  
Chemical Vapor ◽  
Fast Response ◽  
Device Fabrication ◽  
Alumina Oxide ◽  
Response And Recovery ◽  
New Type ◽  
P Type

A new type of gas sensor was realized by directly depositing carbon nanotube on nano channels of the anodic alumina oxide (AAO) fabricated on p-type silicon substrate. The carbon nanotubes were synthesized by thermal chemical vapor deposition at a very high temperature of 1200 oC to improve the crystallinity. The device fabrication process was also developed. The contact of carbon nanotubes and p-type Si substrate showed a Schottky behavior, and the Schottky barrier height increased with exposure to gases while the overall conductivity decreased. The sensors showed fast response and recovery to ammonia gas upon the filling (400 mTorr) and evacuation.

Onion sour skin detection using a gas sensor array and support vector machine

2009 ◽  
Vol 3 (4) ◽  
pp. 193-202 ◽  
Author(s):  
Changying Li ◽  
Ron Gitaitis ◽  
Bill Tollner ◽  
Paul Sumner ◽  
Dan MacLean
Keyword(s):  
Support Vector Machine ◽  
Gas Sensor ◽  
Sensor Array ◽  
Support Vector ◽  
Skin Detection ◽  
Gas Sensor Array ◽  
Sour Skin

scholarly journals Gas sensor array to clasify the chicken meat with E. coli contaminant by using random forest and support vector machine

2021 ◽  
pp. 100083
Author(s):  
Suryani D. Astuti ◽  
Mohammad H. Tamimi ◽  
Anak A.S. Pradhana ◽  
Kartika A. Alamsyah ◽  
Hery Purnobasuki ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Gas Sensor ◽  
Sensor Array ◽  
Chicken Meat ◽  
Support Vector ◽  
E Coli ◽  
Gas Sensor Array

Component Spectrum Recognition for Mixed Gas Based on SVM

2011 ◽  
Vol 128-129 ◽  
pp. 557-560
Author(s):  
Peng Bai ◽  
Juan Zao Ji ◽  
Peng Liu ◽  
Dao Tian Geng
Keyword(s):  
Support Vector Machine ◽  
Natural Gas ◽  
Dimensional Space ◽  
Recognition Rate ◽  
Kernel Functions ◽  
Support Vector ◽  
Mixed Gas ◽  
Characteristic Spectrum ◽  
Component Identification ◽  
Training Samples

As for the problem that component gas characteristic spectrum lines overlaps seriously in the identification of Mixed Gas, Support Vector Machine is introduced for the identification, and an one-by-one identification methods for Mixed Gas classification based on the binary category identification model based on the support vector machine is proposed in this article. One-by-one category identification is carried out for each mixed gas when the characteristic spectrum lines are overlapped seriously and is transformed in high dimensional space into linear by SVM kernel function transformation. In the experiment for gas component identification of a natural gas, we compare the recognition results affected by different kernel functions, data preprocessing, feature extraction, numbers of training samples and other conditions. The results show that the method has the correct recognition rate of over 97% for the natural gas whose concentration is over 1%, and it has a great promotional value both in theory and practical application.

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