Computationally Efficient Weighted Binary Decision Codes for Gas Identification With Array of Gas Sensors

2017 ◽  
Vol 17 (2) ◽  
pp. 487-497 ◽  
Author(s):  
Muhammad Hassan ◽  
Muhammad Umar ◽  
Amine Bermak
Keyword(s):  
Gas Sensors ◽  
Computationally Efficient ◽  
Binary Decision ◽  
Gas Identification

Efficiency of Linear and Non-Linear Classifiers for Gas Identification from Electrocatalytic Gas Sensor

2013 ◽  
Vol 20 (3) ◽  
pp. 501-512 ◽  
Author(s):  
Paweł Kalinowski ◽  
Łukasz Woźniak ◽  
Anna Strzelczyk ◽  
Piotr Jasinski ◽  
Grzegorz Jasinski
Keyword(s):  
Gas Sensor ◽  
Gas Sensors ◽  
Support Vector ◽  
Voltammetric Sensor ◽  
Linear Classifiers ◽  
Lack Of Information ◽  
The Family ◽  
Gas Identification ◽  
Auto Scaling ◽  
Nonlinear Support

Abstract Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling

scholarly journals Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 685 ◽  
Author(s):  
Han Fan ◽  
Victor Hernandez Bennetts ◽  
Erik Schaffernicht ◽  
Achim Lilienthal
Keyword(s):  
Emergency Response ◽  
Electronic Nose ◽  
Gas Sensing ◽  
Practical Significance ◽  
Computationally Efficient ◽  
Urban Search And Rescue ◽  
Efficient Manner ◽  
Distribution Maps ◽  
Gas Identification ◽  
Sampling Procedures

Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.

Novel array-type gas sensors using conducting polymers, and their performance for gas identification

2002 ◽  
Vol 83 (1-3) ◽  
pp. 270-275 ◽  
Author(s):  
Yoshiaki Sakurai ◽  
Ho-Sup Jung ◽  
Toshinori Shimanouchi ◽  
Takao Inoguchi ◽  
Seiichi Morita ◽  
...  
Keyword(s):  
Conducting Polymers ◽  
Gas Sensors ◽  
Array Type ◽  
Gas Identification
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