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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment</title><author><name sortKey="Baha, Hakim" sort="Baha, Hakim" uniqKey="Baha H" first="Hakim" last="Baha">Hakim Baha</name></author><author><name sortKey="Dibi, Zohir" sort="Dibi, Zohir" uniqKey="Dibi Z" first="Zohir" last="Dibi">Zohir Dibi</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22291547</idno><idno type="pmc">3260624</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3260624</idno><idno type="RBID">PMC:3260624</idno><idno type="doi">10.3390/s91108944</idno><date when="2009">2009</date><idno type="wicri:Area/Pmc/Corpus">000285</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000285</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment</title><author><name sortKey="Baha, Hakim" sort="Baha, Hakim" uniqKey="Baha H" first="Hakim" last="Baha">Hakim Baha</name></author><author><name sortKey="Dibi, Zohir" sort="Dibi, Zohir" uniqKey="Dibi Z" first="Zohir" last="Dibi">Zohir Dibi</name></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Thanks to their high sensitivity and low-cost, metal oxide gas sensors (MOX) are widely used in gas detection, although they present well-known problems (lack of selectivity and environmental effects…). We present in this paper a novel neural network- based technique to remedy these problems. The idea is to create intelligent models; the first one, called corrector, can automatically linearize a sensor's response characteristics and eliminate its dependency on the environmental parameters. The corrector's responses are processed with the second intelligent model which has the role of discriminating exactly the detected gas (nature and concentration). The gas sensors used are industrial resistive kind (TGS8xx, by Figaro Engineering). The MATLAB environment is used during the design phase and optimization. The sensor models, the corrector, and the selective model were implemented and tested in the PSPICE simulator. The sensor model accurately expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to their gas nature dependency. The corrector linearizes and compensates the sensor's responses. The method discriminates qualitatively and quantitatively between seven gases. The advantage of the method is that it uses a small representative database so we can easily implement the model in an electrical simulator. This method can be extended to other sensors.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Leonard, M" uniqKey="Leonard M">M. Leonard</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Patra, J C" uniqKey="Patra J">J.C. Patra</name></author><author><name sortKey="Van Den Bos, A" uniqKey="Van Den Bos A">A. Van den Bos</name></author><author><name sortKey="Kot, A C" uniqKey="Kot A">A.C. Kot</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jung, Y K" uniqKey="Jung Y">Y.K. Jung</name></author><author><name sortKey="Sung, W K" uniqKey="Sung W">W.K. Sung</name></author><author><name sortKey="Tae, Z S" uniqKey="Tae Z">Z.S. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Sensors (Basel)</journal-id><journal-title-group><journal-title>Sensors (Basel, Switzerland)</journal-title></journal-title-group><issn pub-type="epub">1424-8220</issn><publisher><publisher-name>Molecular Diversity Preservation International (MDPI)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22291547</article-id><article-id pub-id-type="pmc">3260624</article-id><article-id pub-id-type="doi">10.3390/s91108944</article-id><article-id pub-id-type="publisher-id">sensors-09-08944</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>A Novel Neural Network-Based Technique for Smart Gas Sensors Operating in a Dynamic Environment</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Baha</surname><given-names>Hakim</given-names></name><xref ref-type="corresp" rid="c1-sensors-09-08944"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Dibi</surname><given-names>Zohir</given-names></name></contrib><aff id="af1-sensors-09-08944">Laboratoire d'Electronique Avancée, Département d'Electronique, Université de Batna, 05 Avenue Chahid Boukhlouf 05000 Batna, Algeria; E-Mail:<email>zohirdibi@yahoo.fr</email></aff></contrib-group><author-notes><corresp id="c1-sensors-09-08944"><label>*</label>Author to whom correspondence should be addressed; E-Mail: <email>hakim_bahha@yahoo.fr</email>; Tel.: +213 33 80 5494; Fax: +213 33 80 5494.</corresp></author-notes><pub-date pub-type="collection"><year>2009</year></pub-date><pub-date pub-type="epub"><day>11</day><month>11</month><year>2009</year></pub-date><volume>9</volume><issue>11</issue><fpage>8944</fpage><lpage>8960</lpage><history><date date-type="received"><day>9</day><month>9</month><year>2009</year></date><date date-type="rev-recd"><day>27</day><month>10</month><year>2009</year></date><date date-type="accepted"><day>30</day><month>10</month><year>2009</year></date></history><permissions><copyright-statement>© 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.</copyright-statement><copyright-year>2009</copyright-year><license><license-p><pmc-comment>CREATIVE COMMONS</pmc-comment>This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">http://creativecommons.org/licenses/by/3.0/</ext-link>).</license-p></license></permissions><abstract><p>Thanks to their high sensitivity and low-cost, metal oxide gas sensors (MOX) are widely used in gas detection, although they present well-known problems (lack of selectivity and environmental effects…). We present in this paper a novel neural network- based technique to remedy these problems. The idea is to create intelligent models; the first one, called corrector, can automatically linearize a sensor's response characteristics and eliminate its dependency on the environmental parameters. The corrector's responses are processed with the second intelligent model which has the role of discriminating exactly the detected gas (nature and concentration). The gas sensors used are industrial resistive kind (TGS8xx, by Figaro Engineering). The MATLAB environment is used during the design phase and optimization. The sensor models, the corrector, and the selective model were implemented and tested in the PSPICE simulator. The sensor model accurately expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to their gas nature dependency. The corrector linearizes and compensates the sensor's responses. The method discriminates qualitatively and quantitatively between seven gases. The advantage of the method is that it uses a small representative database so we can easily implement the model in an electrical simulator. This method can be extended to other sensors.</p></abstract><kwd-group><kwd>gas sensor</kwd><kwd>ANN</kwd><kwd>implementation</kwd><kwd>ABM</kwd><kwd>corrector</kwd></kwd-group></article-meta></front><body><sec><label>1.</label><title>Introduction</title><p>ANNs (artificial neural networks) are used in instrumentation to model complex systems because of their multivariable capability and strong non-linearity. With ANNs the extrapolation errors both inside and outside the calibration range are lower [<xref ref-type="bibr" rid="b1-sensors-09-08944">1</xref>]. ANNs are very efficient for dynamic matter problems, offering the advantages of simple implementation and less computation demands, compared to numerical models [<xref ref-type="bibr" rid="b2-sensors-09-08944">2</xref>].</p><p>Metal oxide sensors (MOX) are one of the most popular technological choices for sensor arrays, due to their high sensitivity [<xref ref-type="bibr" rid="b3-sensors-09-08944">3</xref>]. Their main disadvantage is a lack of selectivity. The working principle of these sensors is based on the variation of their conductivity in the presence of oxidizing and reducing gases. The magnitude of the response depends on the nature and concentration of the gas, and on the type of metal oxide [<xref ref-type="bibr" rid="b4-sensors-09-08944">4</xref>]. Researchers have examined some aspects of the non linearity of the MOX response to certain gases [<xref ref-type="bibr" rid="b5-sensors-09-08944">5</xref>-<xref ref-type="bibr" rid="b7-sensors-09-08944">7</xref>], like its dependence on the temperature [<xref ref-type="bibr" rid="b8-sensors-09-08944">8</xref>,<xref ref-type="bibr" rid="b9-sensors-09-08944">9</xref>], but the dependence on relative humidity and gas nature was not studied and no model of the MOX sensor has been implemented in a simulator.</p><p>The subject of linearization and compensation has been considered in different forms and stages; some cases studied include the use of analog to digital converters [<xref ref-type="bibr" rid="b10-sensors-09-08944">10</xref>,<xref ref-type="bibr" rid="b11-sensors-09-08944">11</xref>], while other work has focused on improving the nonlinear response of specific sensors, like the thermistor [<xref ref-type="bibr" rid="b12-sensors-09-08944">12</xref>] and the Hall effect current sensors [<xref ref-type="bibr" rid="b13-sensors-09-08944">13</xref>]. Numerical methods have been developed using modern technologies capable of computing linearization algorithms [<xref ref-type="bibr" rid="b14-sensors-09-08944">14</xref>,<xref ref-type="bibr" rid="b15-sensors-09-08944">15</xref>]. ROM memories are also used to save data tables and to solve the linearization problem [<xref ref-type="bibr" rid="b16-sensors-09-08944">16</xref>]. They provide a limited solution to the complex problem under the assumptions that the range of variation of environmental parameters is small and that the influence of the environmental parameters on the sensor characteristics is linear.</p><p>To enhance the selectivity of metal oxide gas sensors various strategies have been used by different research groups, including the improvement of the sensitive material [<xref ref-type="bibr" rid="b17-sensors-09-08944">17</xref>,<xref ref-type="bibr" rid="b18-sensors-09-08944">18</xref>], the use of sensor arrays together with pattern recognition techniques [<xref ref-type="bibr" rid="b19-sensors-09-08944">19</xref>,<xref ref-type="bibr" rid="b20-sensors-09-08944">20</xref>], and measurements performed in dynamic operation mode [<xref ref-type="bibr" rid="b21-sensors-09-08944">21</xref>,<xref ref-type="bibr" rid="b22-sensors-09-08944">22</xref>]. In a dynamic environment these strategies don't take the influence of the environmental parameters into consideration or they provide a limited solution to the problem under the assumption that the range of variation is small and that influence on the sensor characteristics is linear.</p><p>For those purposes, using artificial neural networks we have designed a model and a corrector for each one of three industrial MOXs (TGS822, TGS821 and TGS813, by Figaro Engineering) and a selective module with the aim of improving the selectivity and eliminating the environmental effects. The diagram of the method used is presented in <xref ref-type="fig" rid="f1-sensors-09-08944">Figure 1</xref>. The MATLAB interface was used during the design phase and optimization; the results (optimal architecture, bias and weights of the network) are used for the implementation of the model, the correctors and the selective module as components in the PSPICE simulator library.</p></sec><sec><label>2.</label><title>Sensor Characteristics</title><p>We illustrate the design of the TGS822 gas sensor model and the same procedure was followed for the TGS821 and the TGS813 units. According to experimental results [<xref ref-type="bibr" rid="b23-sensors-09-08944">23</xref>], the TGS 822 sensor used to detect the gas concentration has a nonlinear sensitivity (<xref ref-type="fig" rid="f2-sensors-09-08944">Figure 2a</xref>; representation is in logarithmic scale) and is dependent on the temperature and humidity of the environment (<xref ref-type="fig" rid="f2-sensors-09-08944">Figure 2b</xref>) where it is placed. R<sub>0</sub> is the sensor's resistance in 300 ppm of ethanol, while R<sub>S</sub> the sensor's resistance to different concentrations of various gases.</p><sec><label>2.1.</label><title>The sensor's use</title><p>In <xref ref-type="fig" rid="f3-sensors-09-08944">Figure 3</xref> a basic gas concentration measurement circuit with the TGS822 sensor is presented. The variation of the TGS sensor's resistance is indirectly measured by the voltage drop appearing on the reading resistance R<sub>L</sub>. If a gas like methane, butane, propane etc. comes into contact with the sensor's surface, it's resistance reduces in correlation with the gas concentration present. To work with the features of the sensor provided, in this case, by Figaro, we have to process this output voltage (V<sub>RL</sub>) and achieve the sensor's resistance R<sub>S</sub> with <xref ref-type="disp-formula" rid="FD1">Equation 1</xref>:
<disp-formula id="FD1"><label>(1)</label><mml:math id="mm1"><mml:mrow><mml:msub><mml:mtext>R</mml:mtext><mml:mtext>S</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mtext>V</mml:mtext><mml:mtext>C</mml:mtext></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mtext>V</mml:mtext><mml:mrow><mml:mtext>RL</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>L</mml:mi></mml:msub></mml:mrow></mml:math></disp-formula>The configuration of the sensor's connected circuits has to ensure the following conditions:
<list list-type="bullet"><list-item><p>V<sub>C</sub> can be 5, 6, 12 or 24 V.</p></list-item><list-item><p>V<sub>H</sub> heating voltage has to be 5 V ± 0.2 V.</p></list-item><list-item><p>The power supply on the sensor maximum 15 mW.</p></list-item></list>If we determine the sensor's sensitivity feature under standard testing conditions, this characteristic has to coincide with the sensitivity feature, provided by the manufacturer, because this feature is determined with a relative representation of the sensor's resistance. Standard testing conditions, indicated by Figaro are:
<list list-type="bullet"><list-item><p>Atmospheric conditions: temperature 20 °C ± 2 °C and relative humidity 65% ± 5%</p></list-item><list-item><p>V<sub>C</sub>: 10 ± 0.1 V, V<sub>H</sub>: 5 ± 0.05 V, R<sub>L</sub>: 10 K ± 1%</p></list-item><list-item><p>Time for the sensor's supply maintenance: seven days or more</p></list-item><list-item><p>Testing gas: ethanol</p></list-item></list></p></sec><sec><label>2.2.</label><title>Sensor parameters</title><list list-type="bullet"><list-item><p>Heating resistance: 38.0 Ω ± 3 Ω</p></list-item><list-item><p>Sensor's resistance: 1∼10 KΩ at 300 ppm ethanol</p></list-item><list-item><p>Resistance ratio:</p></list-item></list><disp-formula id="FD2"><label>(2)</label><mml:math id="mm2"><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>S</mml:mi></mml:msub><mml:mspace width="0.2em"></mml:mspace><mml:mtext mathvariant="italic">in</mml:mtext><mml:mspace width="0.2em"></mml:mspace><mml:mtext mathvariant="italic">Ethanol</mml:mtext><mml:mspace width="0.2em"></mml:mspace><mml:mn>300</mml:mn><mml:mspace width="0.2em"></mml:mspace><mml:mtext mathvariant="italic">ppm</mml:mtext></mml:mrow><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>S</mml:mi></mml:msub><mml:mspace width="0.2em"></mml:mspace><mml:mtext mathvariant="italic">in</mml:mtext><mml:mspace width="0.2em"></mml:mspace><mml:mtext mathvariant="italic">Ethanol</mml:mtext><mml:mspace width="0.2em"></mml:mspace><mml:mn>50</mml:mn><mml:mspace width="0.2em"></mml:mspace><mml:mtext mathvariant="italic">ppm</mml:mtext></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mn>0.4</mml:mn><mml:mo>±</mml:mo><mml:mn>0.10</mml:mn></mml:mrow></mml:math></disp-formula><p>Once the value R<sub>0</sub> is measured, the sensor's resistance at different concentrations of various gases and different temperature and relative humidity will be determined as follows: with relation (1) we calculate R<sub>S</sub>, then from <xref ref-type="fig" rid="f2-sensors-09-08944">Figure 2b</xref>, for temperature and relative humidity, we read the value R<sub>S</sub>/R<sub>0</sub> under the given conditions and we obtain a value <italic>x</italic>. We multiply the value <italic>x</italic> by R<sub>0</sub> and we obtain a value <italic>y</italic>. We divide R<sub>S</sub> by <italic>y</italic> and we get R<sub>S</sub>/R<sub>0</sub>, uninfluenced by temperature and relative humidity, with whose help, from <xref ref-type="fig" rid="f2-sensors-09-08944">Figure 2a</xref>, we will determine the appropriate concentration.</p></sec></sec><sec><label>3.</label><title>Neural Network Model</title><p>Using the MATLAB interface and based on experimental results from [<xref ref-type="bibr" rid="b23-sensors-09-08944">23</xref>], according to the previous paragraph a database is created and arranged as (S, T, RH, C) input and (R<sub>S</sub>) as output where:
<list list-type="simple"><list-item><p>S: Selecting the gas</p></list-item><list-item><p>T: Absolute temperature</p></list-item><list-item><p>RH: Relative humidity</p></list-item><list-item><p>C: Gas concentration</p></list-item><list-item><p>R<sub>S</sub>: Sensor resistance</p></list-item><list-item><p>We suppose that Ro = 10 kΩ</p></list-item></list>Most of this database is used mainly in the training phase by using the LP algorithm (back propagation of error), the remaining data are used to test and validate the model. The diagram in <xref ref-type="fig" rid="f4-sensors-09-08944">Figure 4</xref> illustrates the direct modeling of the sensor, where:
<list list-type="simple"><list-item><p>Yd: Desired output</p></list-item><list-item><p>Y: Network output</p></list-item><list-item><p>e: Modeling error</p></list-item></list>To optimize the model architecture an iteration algorithm is used which consists in evaluation of the total error as a function of layer number and the number of neurons per layer and after many tests of different ANN models. The architecture optimized and that produces the smallest error is summarized in <xref ref-type="table" rid="t1-sensors-09-08944">Table 1</xref>.</p><sec><label>3.1.</label><title>Model test</title><p>We designed a neural network-based model by taking into account the dependence on temperature and relative humidity at the measurement point, in addition to the dependence on gas nature when the sensor is placed in a dynamic environment. To illustrate this effect we change concentration, and then we note the variation of the sensor's resistance. <xref ref-type="fig" rid="f5-sensors-09-08944">Figure 5a</xref> shows the difference between the database and the ANN model for the sensor's sensitivity feature. <xref ref-type="fig" rid="f5-sensors-09-08944">Figure 5c</xref> shows the simulation error. The difference between the database and the ANN model for the dependence on temperature and relative humidity is shown in <xref ref-type="fig" rid="f5-sensors-09-08944">Figure 5b</xref>.</p></sec><sec><label>3.2.</label><title>Implementation of TGS822 model</title><p>By using the ABM (Analog Behavioral Modeling) PSPICE Library components and the results (optimal architecture, bias and weights of the network) the sensor model designed previously is implemented as a component in the PSPICE simulator library.</p><p>In order to test the model introduced on PSPICE simulator, it has been implemented in the electrical circuit shown in <xref ref-type="fig" rid="f6-sensors-09-08944">Figure 6</xref>.</p></sec><sec sec-type="results"><label>3.3.</label><title>Simulation results</title><p>Temperature is fixed at 20 °C, relative humidity at 65%, concentration is varied from 300 to 5,000 ppm. A PARAMETRIC DC SWEEP analysis gives the variation of the résistance against the concentration C with different gas. Results are represented in <xref ref-type="fig" rid="f7-sensors-09-08944">Figure 7</xref> where:
<list list-type="bullet"><list-item><p><inline-formula><mml:math id="mm3"><mml:mrow><mml:mi mathvariant="bold-italic">R</mml:mi><mml:mi mathvariant="bold-italic">s</mml:mi><mml:mo>=</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mfrac><mml:mrow><mml:mtext mathvariant="bold">V</mml:mtext><mml:mn mathvariant="bold">1</mml:mn></mml:mrow><mml:mrow><mml:mi mathvariant="bold-italic">V</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mtext mathvariant="bold-italic">A</mml:mtext><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mn mathvariant="bold">1</mml:mn><mml:mo stretchy="false">)</mml:mo><mml:mtext mathvariant="bold">R</mml:mtext><mml:mn mathvariant="bold">1</mml:mn></mml:mrow></mml:math></inline-formula></p></list-item><list-item><p>R1 = Ro = 10 kΩ</p></list-item></list>By analogy to the first test, we carried out a second test. With a PARAMETRIC DC SWEEP analysis, we fixed the gas as ethanol, the concentration to 300 ppm and the relative humidity RH as a parameter then we varied the temperature from −10 to 40 °C. <xref ref-type="fig" rid="f8-sensors-09-08944">Figure 8</xref> illustrates the obtained results.</p></sec></sec><sec><label>4.</label><title>Corrector</title><p>Using the circuit in <xref ref-type="fig" rid="f3-sensors-09-08944">Figure 3</xref> and by analogy to the model we design the corrector. The database is arranged as (T, RH, V<sub>RL</sub>, V<sub>S</sub>) where:
<list list-type="simple"><list-item><p>T: Absolute temperature</p></list-item><list-item><p>RH: Relative humidity</p></list-item><list-item><p>V<sub>RL</sub>: Sensor's output voltage</p></list-item><list-item><p>Vs: Corrector's output voltage</p></list-item></list>The generation of the training base and test base is similar to that of the model's one. However, in the corrector, the temperature T, relative humidity RH and the sensor's output voltage V<sub>RL</sub> are taken as inputs, and the corrector's output voltage Vs is taken as output. The diagram of <xref ref-type="fig" rid="f9-sensors-09-08944">Figure 9</xref> illustrates the methodology used in the corrector design.</p><p>The corrector was trained in a similar manner as in the case of the model. After many tests, the optimized architecture which produces the smallest error is summarized in <xref ref-type="table" rid="t2-sensors-09-08944">Table 2</xref>.</p><sec><label>4.1.</label><title>Corrector test</title><p>We designed an ANN-based corrector for the TGS822. To illustrate the effect of this corrector we change concentration, and then we note the variation of the corrector's output. <xref ref-type="fig" rid="f10-sensors-09-08944">Figure 10a</xref> shows that the corrector correctly linearizes the sensor's sensitivity feature (representation is in linear scale). The corrector's effect in environmental parameters dependency is shown in <xref ref-type="fig" rid="f10-sensors-09-08944">Figure 10b</xref>.</p></sec><sec><label>4.2.</label><title>Implementation of the TGS822 corrector</title><p>In order to test the effect of the corrector on electrical environment, the latter is implemented on the PSPICE simulator. The corrector and the TGS822 model have been implemented in the circuit shown in <xref ref-type="fig" rid="f11-sensors-09-08944">Figure 11</xref>.</p></sec><sec sec-type="results"><label>4.3.</label><title>Simulation results</title><p>Temperature is fixed at 20 °C, relative humidity at 65% and concentration is varied from 300 to 5,000 ppm. A PARAMETRIC DC SWEEP analysis gives the results represented in <xref ref-type="fig" rid="f12-sensors-09-08944">Figure 12</xref> with different gases.</p><p>The parametric simulation shows that the corrector linearizes the sensor's response whatever the gas. By analogy to the first test, we carried out a second test with a PARAMETRIC DC SWEEP analysis, we fixed the gas as ethanol, the concentration to 300 ppm and the relative humidity RH as a parameter then we varied the temperature from −10 to 40 °C. <xref ref-type="fig" rid="f13-sensors-09-08944">Figure 13</xref> illustrates the obtained results.</p><p>The response of the parametric simulation shows that the corrector eliminates the environmental parameters' effect.</p></sec></sec><sec><label>5.</label><title>The Selective Module</title><p>By analogy to the model and the corrector we design the selective module. The database is arranged as (Ve1, Ve2, Ve3, Vsn, Vsc), where Ve1, Ve2, Ve3 are taken as input of the selective model and they are the output of the correctors, Vsn is taken as output and it defines the gas nature, Vsc is taken as output and it defines the gas concentration. Note that:
<list list-type="bullet"><list-item><p>Vcon is a voltage varing linearly from 0.4 to 5 V corresponding to a concentration variation from 400 to 5,000 ppm.</p></list-item><list-item><p>Vsc is a voltage varing from 1 V to 10 V, the <xref ref-type="table" rid="t3-sensors-09-08944">table 3</xref> indicates their matched gases.</p></list-item></list>The selective module was trained in a similar manner as in the case of direct model and corrector. The optimized architecture characteristics are summarized in <xref ref-type="table" rid="t4-sensors-09-08944">Table 4</xref>.</p><sec><label>5.1.</label><title>Implementation of the selective module</title><p>The selective module was implemented in the same manner as the sensor model and the corrector. The schematic shown in <xref ref-type="fig" rid="f14-sensors-09-08944">Figure 14</xref> represents the electrical circuit used for testing the technique used in electrical environment.</p></sec><sec sec-type="results"><label>5.2.</label><title>Simulation results</title><sec><label>5.2.1.</label><title>Concentration effect</title><p>To study the concentration effect we fixed the temperature at 20 °C, relative humidity at 5% and concentration is varied from 300 to 5,000 ppm. A DC SWEEP analysis gives the results represented in <xref ref-type="fig" rid="f15-sensors-09-08944">Figure 15</xref> for the choice of the methane and <xref ref-type="fig" rid="f16-sensors-09-08944">Figure 16</xref> for the choice of the CO, where Ve1, Ve2 and Ve3 are the corrector output, Vsng is the selective module output for the gas nature and Vsc is the selective module output for the gas concentration.</p><p><xref ref-type="fig" rid="f15-sensors-09-08944">Figure 15a</xref> shows that the method identifies the gas as methane (<bold>V<sub>(G)</sub></bold> = 2V, <xref ref-type="table" rid="t3-sensors-09-08944">Table 3</xref>), and the quantity of this gas (linear variation of <bold>V<sub>(H)</sub></bold> against C). <xref ref-type="fig" rid="f15-sensors-09-08944">Figure 15b</xref> shows that the method discriminates the gas as CO (<bold>V<sub>(G)</sub></bold> = 3V, <xref ref-type="table" rid="t3-sensors-09-08944">Table 3</xref>), and the quantity of this gas (linear variation of <bold>V<sub>(H)</sub></bold> against C).</p></sec><sec><label>5.2.2.</label><title>Temperature effect</title><p>The effect of the temperature is analyzed with a DC SWEEP analysis. 2,500 ppm of hydrogen is used for the test. We fixed the relative humidity to 65% then we varied the temperature from −10 to 40 °C. <xref ref-type="fig" rid="f16-sensors-09-08944">Figure 16</xref> illustrates the results of this simulation.</p><p><xref ref-type="fig" rid="f16-sensors-09-08944">Figure 16</xref> shows that the method discriminates 2,500 ppm of hydrogen (<bold>V<sub>(H)</sub></bold> = 2.5 V, <bold>V<sub>(G)</sub></bold> = 9 V), and stay unchanged vs. the temperature.</p></sec><sec><label>5.2.3.</label><title>Relative humidity effect</title><p>We introduce the humidity effect with a DC SWEEP analysis. <xref ref-type="fig" rid="f17-sensors-09-08944">Figure 17</xref> illustrates the relative humidity influence on the corrector and the selective module outputs.</p><p><xref ref-type="fig" rid="f17-sensors-09-08944">Figure 17</xref> shows that the method discriminates 1,300 ppm of benzene (<bold>V<sub>(H)</sub></bold> = 1.3 V, <bold>V<sub>(G)</sub></bold> = 6 V), and stay unchanging vs. the humidity.</p></sec></sec></sec><sec><label>6.</label><title>Conclusions</title><p>In this paper we modeled three TGS8xx sensors using an artificial neural network sensor. It accurately reproduces the behavior of the gas sensors in a dynamic environment by taking into account the nonlinearity of their responses, the dependence on temperature and relative humidity at the measurement point, in addition to the dependence on the gas nature. The proposed ANN models were tested and implemented as components of the PSPICE simulator library. We used the ANN capability to correct the nonlinear response and to compensate gor temperature and relative humidity influences on the target sensor variations which is tested by Matlab and PSPICE simulation results. We wire all the components with the selective module with the aim of increasing the selectivity. 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B</source><year>2005</year><volume>111-112</volume><fpage>271</fpage><lpage>280</lpage></element-citation></ref><ref id="b22-sensors-09-08944"><label>22.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ionescu</surname><given-names>R.</given-names></name><name><surname>Hoel</surname><given-names>A.</given-names></name><name><surname>Granqvist</surname><given-names>C.G.</given-names></name><name><surname>Llobet</surname><given-names>E.</given-names></name><name><surname>Heszler</surname><given-names>P.</given-names></name></person-group><article-title>Low level detection of ethanol and H2S with temperature modulated WO<sub>3</sub> nanoparticle gas sensors</article-title><source>Sens. Actuat. B</source><year>2005</year><volume>104</volume><fpage>132</fpage><lpage>139</lpage></element-citation></ref><ref id="b23-sensors-09-08944"><label>23.</label><element-citation publication-type="book"><person-group person-group-type="author"><collab>Figaro Gas Sensor Company</collab></person-group><source>Technical Information on Usage of TGS Sensors for Toxic and Explosive Gas Leak Detectors</source><publisher-name>Figaro Gas Sensor Company</publisher-name><publisher-loc>Osaka, Japan</publisher-loc><year>2000</year></element-citation></ref></ref-list></back><floats-group><fig id="f1-sensors-09-08944" position="float"><label>Figure 1.</label><caption><p>Multisensory system used.</p></caption><graphic xlink:href="sensors-09-08944f1"></graphic></fig><fig id="f2-sensors-09-08944" position="float"><label>Figure 2.</label><caption><p>(a) TGS822 sensor's sensitivity feature [<xref ref-type="bibr" rid="b23-sensors-09-08944">23</xref>]. (b) TGS822 dependence on temperature and relative humidity [<xref ref-type="bibr" rid="b23-sensors-09-08944">23</xref>].</p></caption><graphic xlink:href="sensors-09-08944f2"></graphic></fig><fig id="f3-sensors-09-08944" position="float"><label>Figure 3.</label><caption><p>Measurement circuit with TGS sensor.</p></caption><graphic xlink:href="sensors-09-08944f3"></graphic></fig><fig id="f4-sensors-09-08944" position="float"><label>Figure 4.</label><caption><p>Modeling of the TGS822.</p></caption><graphic xlink:href="sensors-09-08944f4"></graphic></fig><fig id="f5-sensors-09-08944" position="float"><label>Figure 5.</label><caption><p>(a) Model and database TGS822 sensor's sensitivity feature. (b) Model and database TGS822 dependence on temperature and relative humidity. (c) Simulation error vs. concentration.</p></caption><graphic xlink:href="sensors-09-08944f5"></graphic></fig><fig id="f6-sensors-09-08944" position="float"><label>Figure 6.</label><caption><p>Simulation circuit.</p></caption><graphic xlink:href="sensors-09-08944f6"></graphic></fig><fig id="f7-sensors-09-08944" position="float"><label>Figure 7.</label><caption><p>Variation of the résistance ratio (Rs/Ro) vs. gas concentration C at 20 °C and 65% RH.</p></caption><graphic xlink:href="sensors-09-08944f7"></graphic></fig><fig id="f8-sensors-09-08944" position="float"><label>Figure 8.</label><caption><p>Variation of the résistance ratio (Rs/Ro) vs. temperature T at 300 ppm of ethanol.</p></caption><graphic xlink:href="sensors-09-08944f8"></graphic></fig><fig id="f9-sensors-09-08944" position="float"><label>Figure 9.</label><caption><p>Modeling of the TGS822 corrector.</p></caption><graphic xlink:href="sensors-09-08944f9"></graphic></fig><fig id="f10-sensors-09-08944" position="float"><label>Figure 10.</label><caption><p>(a) TGS822 feature effect (b) TGS822 corrector temperature and relative humidity dependency effect (c) Corrector sensitivity simulation error.</p></caption><graphic xlink:href="sensors-09-08944f10"></graphic></fig><fig id="f11-sensors-09-08944" position="float"><label>Figure 11.</label><caption><p>Electrical circuit of the corrector test.</p></caption><graphic xlink:href="sensors-09-08944f11"></graphic></fig><fig id="f12-sensors-09-08944" position="float"><label>Figure 12.</label><caption><p>Variation of the corrector's input and output vs. concentration C at 20 °C and 65% RH.</p></caption><graphic xlink:href="sensors-09-08944f12"></graphic></fig><fig id="f13-sensors-09-08944" position="float"><label>Figure 13.</label><caption><p>Variation of the corrector's input and output vs. temperature T at 300 ppm of ethanol.</p></caption><graphic xlink:href="sensors-09-08944f13"></graphic></fig><fig id="f14-sensors-09-08944" position="float"><label>Figure 14.</label><caption><p>Electrical circuit of the method test.</p></caption><graphic xlink:href="sensors-09-08944f14"></graphic></fig><fig id="f15-sensors-09-08944" position="float"><label>Figure 15.</label><caption><p>Variation of the inputs and the outputs the selective module vs. concentration C at 20 °C and 65%RH for the detection of (a) methane (b) CO.</p></caption><graphic xlink:href="sensors-09-08944f15"></graphic></fig><fig id="f16-sensors-09-08944" position="float"><label>Figure 16.</label><caption><p>Variation of the corrector and the selective module's inputs and outputs vs. the temperature T at 65% RH for 2,500 ppm of hydrogen.</p></caption><graphic xlink:href="sensors-09-08944f16"></graphic></fig><fig id="f17-sensors-09-08944" position="float"><label>Figure 17.</label><caption><p>Variation of the inputs and the outputs of the correctors and the selective module vs. the relative humidity RH at 20 °C for the detection of 1,300 ppm of benzene.</p></caption><graphic xlink:href="sensors-09-08944f17"></graphic></fig><table-wrap id="t1-sensors-09-08944" position="float"><label>Table 1.</label><caption><p>Summary the model's optimized parameters.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="middle" rowspan="1" colspan="1"><bold>Property</bold></th><th colspan="2" align="left" valign="middle" rowspan="1"><bold>Characteristic</bold></th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="2" colspan="1">Database</td><td align="left" valign="top" rowspan="1" colspan="1">Training base</td><td align="left" valign="top" rowspan="1" colspan="1">3,800</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Test base</td><td align="left" valign="top" rowspan="1" colspan="1">504</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Architecture</td><td colspan="2" align="left" valign="top" rowspan="1">9-5-1 Feed-forward MLP</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Activation functions</td><td colspan="2" align="left" valign="top" rowspan="1">Logsig-Logsig- linear</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Training rule</td><td colspan="2" align="left" valign="top" rowspan="1">Retropropagation error</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Training MSE</td><td colspan="2" align="left" valign="top" rowspan="1"><0.0001</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Iterations number</td><td colspan="2" align="left" valign="top" rowspan="1">3,000</td></tr></tbody></table></table-wrap><table-wrap id="t2-sensors-09-08944" position="float"><label>Table 2.</label><caption><p>Summary of the corrector's optimized parameters.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="top" rowspan="1" colspan="1"><bold>Property</bold></th><th colspan="2" align="left" valign="top" rowspan="1"><bold>Characteristic</bold></th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="2" colspan="1">Database</td><td align="left" valign="top" rowspan="1" colspan="1">Training base</td><td align="left" valign="top" rowspan="1" colspan="1">3,800</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Test base</td><td align="left" valign="top" rowspan="1" colspan="1">504</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Architecture</td><td colspan="2" align="left" valign="top" rowspan="1">9-8-1 Feed-forward MLP</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Activation functions</td><td colspan="2" align="left" valign="top" rowspan="1">Logsig-Logsig- linear</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Training rule</td><td colspan="2" align="left" valign="top" rowspan="1">Retropropagation error</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Training MSE</td><td colspan="2" align="left" valign="top" rowspan="1"><0.00001</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Iterations number</td><td colspan="2" align="left" valign="top" rowspan="1">5,000</td></tr></tbody></table></table-wrap><table-wrap id="t3-sensors-09-08944" position="float"><label>Table 3.</label><caption><p>Voltages and their matched gases.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" valign="bottom" rowspan="1" colspan="1"><bold>Gas</bold></th><th align="center" valign="bottom" rowspan="1" colspan="1"><bold>Voltage [V]</bold></th></tr></thead><tbody><tr><td align="center" valign="top" rowspan="1" colspan="1">Air</td><td align="center" valign="top" rowspan="1" colspan="1">1</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Methane</td><td align="center" valign="top" rowspan="1" colspan="1">2</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">CO</td><td align="center" valign="top" rowspan="1" colspan="1">3</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Isobutane</td><td align="center" valign="top" rowspan="1" colspan="1">4</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Ethanol</td><td align="center" valign="top" rowspan="1" colspan="1">5</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Benzene</td><td align="center" valign="top" rowspan="1" colspan="1">6</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1"><italic>n</italic>-Hexane</td><td align="center" valign="top" rowspan="1" colspan="1">7</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Acetone</td><td align="center" valign="top" rowspan="1" colspan="1">8</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Hydrogen</td><td align="center" valign="top" rowspan="1" colspan="1">9</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Propane</td><td align="center" valign="top" rowspan="1" colspan="1">10</td></tr></tbody></table></table-wrap><table-wrap id="t4-sensors-09-08944" position="float"><label>Table 4.</label><caption><p>Summarizing of the optimized parameters of selective module.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" valign="bottom" rowspan="1" colspan="1"><bold>Property</bold></th><th align="left" valign="bottom" rowspan="1" colspan="1"><bold>Characteristic</bold></th><th align="center" valign="top" rowspan="1" colspan="1"></th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="2" colspan="1">Database</td><td align="left" valign="top" rowspan="1" colspan="1">Training base</td><td align="center" valign="top" rowspan="1" colspan="1">250</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Test base</td><td align="center" valign="top" rowspan="1" colspan="1">50</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Architecture</td><td colspan="2" align="left" valign="top" rowspan="1">5-2-2 Feed-forward MLP</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Activation functions</td><td colspan="2" align="left" valign="top" rowspan="1">Logsig-Logsig- linear</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Training rule</td><td colspan="2" align="left" valign="top" rowspan="1">Retropropagation error</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Training MSE</td><td colspan="2" align="left" valign="top" rowspan="1"><10<sup>−6</sup></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Iterations number</td><td colspan="2" align="left" valign="top" rowspan="1">1,000</td></tr></tbody></table></table-wrap></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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