Next Article in Journal
A Multi-Robot Sense-Act Approach to Lead to a Proper Acting in Environmental Incidents
Previous Article in Journal
Feature-Based Laser Scan Matching and Its Application for Indoor Mapping
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(8), 1267; doi:10.3390/s16081267

Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique

Institute of Measurement Science, Electronics and Control, Silesian University of Technology, Gliwice 44-100, Poland
Academic Editor: Tindaro Ioppolo
Received: 22 June 2016 / Revised: 11 July 2016 / Accepted: 8 August 2016 / Published: 10 August 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2003 KB, uploaded 10 August 2016]   |  

Abstract

The paper presents two methods of dynamic error correction applied to transducers used for the measurement of gas concentration. One of them is based on a parametric model of the transducer dynamics, and the second one uses the artificial neural network (ANN) technique. This article describes research of the dynamic properties of the gas concentration measuring transducer with a typical sensor based on tin dioxide. Its response time is about 8 min, which may be not acceptable in many applications. On the basis of these studies, a parametric model of the transducer dynamics and an adequate correction algorithm has been developed. The results obtained in the research of the transducer were also used for learning and testing ANN, which were implemented in the dynamic correction task. Despite the simplicity of the used models, both methods allowed a significant reduction of the transducer’s response time. For the algorithm based on the parametric model the response time was shorter by approximately eight-fold (reduced up to 40–80 s, i.e., about 2–4 sample periods), whereas with the use of an ANN the output signal was practically fixed after a time equal to one sampling period, i.e., 20 s. In addition, the use of ANN has allowed reducing the impact of the transducer dynamic non-linearity on the correction effectiveness. View Full-Text
Keywords: gas sensor; dynamic correction; neural networks; response time; dynamic properties gas sensor; dynamic correction; neural networks; response time; dynamic properties
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Roj, J. Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique. Sensors 2016, 16, 1267.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top