Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks
Identifieur interne : 000564 ( Pmc/Curation ); précédent : 000563; suivant : 000565Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks
Auteurs : Víctor Martínez-Martínez ; Carlos Baladr N ; Jaime Gomez-Gil ; Gonzalo Ruiz-Ruiz ; Luis M. Navas-Gracia ; Javier M. Aguiar ; Belén CarroSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2012.
Abstract
This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.
Url:
DOI: 10.3390/s121014004
PubMed: 23202032
PubMed Central: 3545603
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks</title><author><name sortKey="Martinez Martinez, Victor" sort="Martinez Martinez, Victor" uniqKey="Martinez Martinez V" first="Víctor" last="Martínez-Martínez">Víctor Martínez-Martínez</name><affiliation><nlm:aff id="af1-sensors-12-14004"> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</nlm:aff></affiliation></author><author><name sortKey="Baladr N, Carlos" sort="Baladr N, Carlos" uniqKey="Baladr N C" first="Carlos" last="Baladr N">Carlos Baladr N</name><affiliation><nlm:aff id="af1-sensors-12-14004"> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</nlm:aff></affiliation></author><author><name sortKey="Gomez Gil, Jaime" sort="Gomez Gil, Jaime" uniqKey="Gomez Gil J" first="Jaime" last="Gomez-Gil">Jaime Gomez-Gil</name><affiliation><nlm:aff id="af1-sensors-12-14004"> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</nlm:aff></affiliation></author><author><name sortKey="Ruiz Ruiz, Gonzalo" sort="Ruiz Ruiz, Gonzalo" uniqKey="Ruiz Ruiz G" first="Gonzalo" last="Ruiz-Ruiz">Gonzalo Ruiz-Ruiz</name><affiliation><nlm:aff id="af2-sensors-12-14004"> Department of Agricultural and Forestry Engineering, University of Valladolid, 34004 Palencia, Spain; E-Mails:<email>gruiz@iaf.uva.es</email> (G.R.-R.);<email>lmnavas@iaf.uva.es</email> (L.M.N.-G.)</nlm:aff></affiliation></author><author><name sortKey="Navas Gracia, Luis M" sort="Navas Gracia, Luis M" uniqKey="Navas Gracia L" first="Luis M." last="Navas-Gracia">Luis M. Navas-Gracia</name><affiliation><nlm:aff id="af2-sensors-12-14004"> Department of Agricultural and Forestry Engineering, University of Valladolid, 34004 Palencia, Spain; E-Mails:<email>gruiz@iaf.uva.es</email> (G.R.-R.);<email>lmnavas@iaf.uva.es</email> (L.M.N.-G.)</nlm:aff></affiliation></author><author><name sortKey="Aguiar, Javier M" sort="Aguiar, Javier M" uniqKey="Aguiar J" first="Javier M." last="Aguiar">Javier M. Aguiar</name><affiliation><nlm:aff id="af1-sensors-12-14004"> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</nlm:aff></affiliation></author><author><name sortKey="Carro, Belen" sort="Carro, Belen" uniqKey="Carro B" first="Belén" last="Carro">Belén Carro</name><affiliation><nlm:aff id="af1-sensors-12-14004"> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23202032</idno><idno type="pmc">3545603</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3545603</idno><idno type="RBID">PMC:3545603</idno><idno type="doi">10.3390/s121014004</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000564</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000564</idno><idno type="wicri:Area/Pmc/Curation">000564</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000564</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks</title><author><name sortKey="Martinez Martinez, Victor" sort="Martinez Martinez, Victor" uniqKey="Martinez Martinez V" first="Víctor" last="Martínez-Martínez">Víctor Martínez-Martínez</name><affiliation><nlm:aff id="af1-sensors-12-14004"> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</nlm:aff></affiliation></author><author><name sortKey="Baladr N, Carlos" sort="Baladr N, Carlos" uniqKey="Baladr N C" first="Carlos" last="Baladr N">Carlos Baladr N</name><affiliation><nlm:aff id="af1-sensors-12-14004"> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</nlm:aff></affiliation></author><author><name sortKey="Gomez Gil, Jaime" sort="Gomez Gil, Jaime" uniqKey="Gomez Gil J" first="Jaime" last="Gomez-Gil">Jaime Gomez-Gil</name><affiliation><nlm:aff id="af1-sensors-12-14004"> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</nlm:aff></affiliation></author><author><name sortKey="Ruiz Ruiz, Gonzalo" sort="Ruiz Ruiz, Gonzalo" uniqKey="Ruiz Ruiz G" first="Gonzalo" last="Ruiz-Ruiz">Gonzalo Ruiz-Ruiz</name><affiliation><nlm:aff id="af2-sensors-12-14004"> Department of Agricultural and Forestry Engineering, University of Valladolid, 34004 Palencia, Spain; E-Mails:<email>gruiz@iaf.uva.es</email> (G.R.-R.);<email>lmnavas@iaf.uva.es</email> (L.M.N.-G.)</nlm:aff></affiliation></author><author><name sortKey="Navas Gracia, Luis M" sort="Navas Gracia, Luis M" uniqKey="Navas Gracia L" first="Luis M." last="Navas-Gracia">Luis M. Navas-Gracia</name><affiliation><nlm:aff id="af2-sensors-12-14004"> Department of Agricultural and Forestry Engineering, University of Valladolid, 34004 Palencia, Spain; E-Mails:<email>gruiz@iaf.uva.es</email> (G.R.-R.);<email>lmnavas@iaf.uva.es</email> (L.M.N.-G.)</nlm:aff></affiliation></author><author><name sortKey="Aguiar, Javier M" sort="Aguiar, Javier M" uniqKey="Aguiar J" first="Javier M." last="Aguiar">Javier M. Aguiar</name><affiliation><nlm:aff id="af1-sensors-12-14004"> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</nlm:aff></affiliation></author><author><name sortKey="Carro, Belen" sort="Carro, Belen" uniqKey="Carro B" first="Belén" last="Carro">Belén Carro</name><affiliation><nlm:aff id="af1-sensors-12-14004"> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</nlm:aff></affiliation></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Hopkins, D W" uniqKey="Hopkins D">D.W. Hopkins</name></author><author><name sortKey="Webster, E A" uniqKey="Webster E">E.A. Webster</name></author><author><name sortKey="Chudek, J A" uniqKey="Chudek J">J.A. Chudek</name></author><author><name sortKey="Halpin, C" uniqKey="Halpin C">C. Halpin</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Karaivazoglou, N A" uniqKey="Karaivazoglou N">N.A. Karaivazoglou</name></author><author><name sortKey="Tsotsolis, N C" uniqKey="Tsotsolis N">N.C. Tsotsolis</name></author><author><name sortKey="Tsadilas, C D" uniqKey="Tsadilas C">C.D. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Sensors (Basel)</journal-id><journal-id journal-id-type="iso-abbrev">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">23202032</article-id><article-id pub-id-type="pmc">3545603</article-id><article-id pub-id-type="doi">10.3390/s121014004</article-id><article-id pub-id-type="publisher-id">sensors-12-14004</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Martínez-Martínez</surname><given-names>Víctor</given-names></name><xref ref-type="aff" rid="af1-sensors-12-14004"><sup>1</sup></xref><xref ref-type="corresp" rid="c1-sensors-12-14004"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Baladrón</surname><given-names>Carlos</given-names></name><xref ref-type="aff" rid="af1-sensors-12-14004"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Gomez-Gil</surname><given-names>Jaime</given-names></name><xref ref-type="aff" rid="af1-sensors-12-14004"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Ruiz-Ruiz</surname><given-names>Gonzalo</given-names></name><xref ref-type="aff" rid="af2-sensors-12-14004"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Navas-Gracia</surname><given-names>Luis M.</given-names></name><xref ref-type="aff" rid="af2-sensors-12-14004"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Aguiar</surname><given-names>Javier M.</given-names></name><xref ref-type="aff" rid="af1-sensors-12-14004"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Carro</surname><given-names>Belén</given-names></name><xref ref-type="aff" rid="af1-sensors-12-14004"><sup>1</sup></xref></contrib></contrib-group><aff id="af1-sensors-12-14004"><label>1</label> Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails:<email>cbalzor@ribera.tel.uva.es</email> (C.B.);<email>jgomez@tel.uva.es</email> (J.G.-G.);<email>javagu@tel.uva.es</email> (J.M.A.);<email>belcar@tel.uva.es</email> (B.C.)</aff><aff id="af2-sensors-12-14004"><label>2</label> Department of Agricultural and Forestry Engineering, University of Valladolid, 34004 Palencia, Spain; E-Mails:<email>gruiz@iaf.uva.es</email> (G.R.-R.);<email>lmnavas@iaf.uva.es</email> (L.M.N.-G.)</aff><author-notes><corresp id="c1-sensors-12-14004"><label>*</label>Author to whom correspondence should be addressed; E-Mail: <email>vmarmar@ribera.tel.uva.es</email>; Tel.: +34-636-797-528; Fax: +34-983-423-667.</corresp></author-notes><pub-date pub-type="collection"><year>2012</year></pub-date><pub-date pub-type="epub"><day>17</day><month>10</month><year>2012</year></pub-date><volume>12</volume><issue>10</issue><fpage>14004</fpage><lpage>14021</lpage><history><date date-type="received"><day>02</day><month>8</month><year>2012</year></date><date date-type="rev-recd"><day>28</day><month>9</month><year>2012</year></date><date date-type="accepted"><day>06</day><month>10</month><year>2012</year></date></history><permissions><copyright-statement>© 2012 by the authors; licensee MDPI, Basel, Switzerland.</copyright-statement><copyright-year>2012</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>This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.</p></abstract><kwd-group><kwd>estimation</kwd><kwd>prediction</kwd><kwd>Artificial Neural Networks (ANN)</kwd><kwd>tobacco drying process</kwd><kwd>signal processing</kwd></kwd-group></article-meta></front><floats-group><fig id="f1-sensors-12-14004" position="float"><label>Figure 1.</label><caption><p>(<bold>a</bold>) Cross section of the tobacco dryer, where an air fan, a water valve, and two air hatchways are represented in their real locations. (<bold>b</bold>) Spatial distribution of the measurement motes inside the drying chamber: next to the supervision window (green), in the second container in the tobacco mass (red), and next to the dryer roof (purple).</p></caption><graphic xlink:href="sensors-12-14004f1"></graphic></fig><fig id="f2-sensors-12-14004" position="float"><label>Figure 2.</label><caption><p>Target temperature evolution in the analysed drying processes.</p></caption><graphic xlink:href="sensors-12-14004f2"></graphic></fig><fig id="f3-sensors-12-14004" position="float"><label>Figure 3.</label><caption><p>Diagram of the validation trials.</p></caption><graphic xlink:href="sensors-12-14004f3"></graphic></fig><fig id="f4-sensors-12-14004" position="float"><label>Figure 4.</label><caption><p><italic>Data Estimation</italic> results (% error ANN/% error interpolation) for Temperature (T) in Sensor 3 (S3), combining Temperature (T) and Humidity (H) in Sensors 1 and 2 (S1 and S2) as inputs.</p></caption><graphic xlink:href="sensors-12-14004f4"></graphic></fig><fig id="f5-sensors-12-14004" position="float"><label>Figure 5.</label><caption><p>Data Estimation results (% error ANN/% error interpolation) for Humidity (H) in Sensor 3 (S3), combining Temperature (T) and Humidity (H) in Sensors 1 and 2 (S1 and S2) as input.</p></caption><graphic xlink:href="sensors-12-14004f5"></graphic></fig><fig id="f6-sensors-12-14004" position="float"><label>Figure 6.</label><caption><p>Example of ANN estimation of Temperature (T) at Sensor 3 (S3) for one drying process using Sensors 1 and 2 (S1 and S2) as input. (<bold>a</bold>) Input is only Temperature (T). (<bold>b</bold>) Input combines Temperature (T) and Humidity (H).</p></caption><graphic xlink:href="sensors-12-14004f6"></graphic></fig><fig id="f7-sensors-12-14004" position="float"><label>Figure 7.</label><caption><p>Example of ANN estimation of Humidity (H) at Sensor 3 (S3) for one drying process using Sensors 1 and 2 (S1 and S2) as input. (<bold>a</bold>) Input is only Humidity (H). (<bold>b</bold>) Input combines Temperature (T) and Humidity (H).</p></caption><graphic xlink:href="sensors-12-14004f7"></graphic></fig><fig id="f8-sensors-12-14004" position="float"><label>Figure 8.</label><caption><p><italic>Data prediction</italic> results (% error ANN/% error interpolation) for temperature in Sensor 1, using temperature in Sensor 1 as an input.</p></caption><graphic xlink:href="sensors-12-14004f8"></graphic></fig><fig id="f9-sensors-12-14004" position="float"><label>Figure 9.</label><caption><p><italic>Data prediction</italic> results (% error ANN/% error interpolation) for temperature in Sensor 3, using temperature in Sensor 1 as an input.</p></caption><graphic xlink:href="sensors-12-14004f9"></graphic></fig><table-wrap id="t1-sensors-12-14004" position="float"><label>Table 1.</label><caption><p><italic>Data Estimation</italic> results (% error ANN/% error interpolation) for Temperature (T) in Sensor 3 (S3), combining Temperature (T) and Humidity (H) in Sensors 1 and 2 (S1 and S2) as input.</p></caption><table frame="box" rules="all"><thead><tr content-type="background-color:#D9D9D9"><th align="center" valign="middle" rowspan="1" colspan="1"><bold>Input T</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>No T used</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>T at S1</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>T at S2</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>T at S1 & S2</bold></th></tr><tr content-type="background-color:#D9D9D9"><th align="center" valign="middle" rowspan="1" colspan="1"><bold>Input H</bold></th></tr></thead><tbody><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>No H used</bold></td><td align="center" valign="top" content-type="background-color:#BFBFBF" rowspan="1" colspan="1"></td><td align="center" valign="top" rowspan="1" colspan="1">2.30%/5.87%</td><td align="center" valign="top" rowspan="1" colspan="1">2.19%/3.99%</td><td align="center" valign="top" rowspan="1" colspan="1">2.12%/3.99%</td></tr><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>H at S1</bold></td><td align="center" valign="top" rowspan="1" colspan="1">6.66%/NA</td><td align="center" valign="top" rowspan="1" colspan="1">1.96%/5.87%</td><td align="center" valign="top" rowspan="1" colspan="1">2.01%/3.99%</td><td align="center" valign="top" rowspan="1" colspan="1">1.89%/3.99%</td></tr><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>H at S2</bold></td><td align="center" valign="top" rowspan="1" colspan="1">6.60%/NA</td><td align="center" valign="top" rowspan="1" colspan="1">2.45%/5.87%</td><td align="center" valign="top" rowspan="1" colspan="1">2.10%/3.99%</td><td align="center" valign="top" rowspan="1" colspan="1">2.03%/3.99%</td></tr><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>H at S1 & S2</bold></td><td align="center" valign="top" rowspan="1" colspan="1">8.77%/NA</td><td align="center" valign="top" rowspan="1" colspan="1">2.23%/5.87%</td><td align="center" valign="top" rowspan="1" colspan="1">1.97%/3.99%</td><td align="center" valign="top" rowspan="1" colspan="1">1.80%/3.99%</td></tr></tbody></table></table-wrap><table-wrap id="t2-sensors-12-14004" position="float"><label>Table 2.</label><caption><p>Data Estimation results (% error ANN/% error interpolation) for Humidity (H) in Sensor 3 (S3), combining Temperature (T) and Humidity in Sensors 1 and 2 (S1 and S2) as input.</p></caption><table frame="box" rules="all"><thead><tr content-type="background-color:#D9D9D9"><th align="center" valign="middle" rowspan="1" colspan="1"><bold>Input T</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>No T used</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>T at S1</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>T at S2</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>T at S1 & S2</bold></th></tr><tr content-type="background-color:#D9D9D9"><th align="center" valign="middle" rowspan="1" colspan="1"><bold>Input H</bold></th></tr></thead><tbody><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>No H used</bold></td><td align="center" valign="top" content-type="background-color:#BFBFBF" rowspan="1" colspan="1"></td><td align="center" valign="top" rowspan="1" colspan="1">6.48%/NA</td><td align="center" valign="top" rowspan="1" colspan="1">5.92%/NA</td><td align="center" valign="top" rowspan="1" colspan="1">5.78%/NA</td></tr><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>H at S1</bold></td><td align="center" valign="top" rowspan="1" colspan="1">7.30%/33.43%</td><td align="center" valign="top" rowspan="1" colspan="1">4.95%/33.43%</td><td align="center" valign="top" rowspan="1" colspan="1">5.66%/33.43%</td><td align="center" valign="top" rowspan="1" colspan="1">4.46%/33.43%</td></tr><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>H at S2</bold></td><td align="center" valign="top" rowspan="1" colspan="1">5.36%/28.72%</td><td align="center" valign="top" rowspan="1" colspan="1">5.19%/28.72%</td><td align="center" valign="top" rowspan="1" colspan="1">5.59%/28.72%</td><td align="center" valign="top" rowspan="1" colspan="1">5.32%/28.72%</td></tr><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>H at S1 & S2</bold></td><td align="center" valign="top" rowspan="1" colspan="1">6.97%/28.72%</td><td align="center" valign="top" rowspan="1" colspan="1">4.80%/28.72%</td><td align="center" valign="top" rowspan="1" colspan="1">6.51%/28.72%</td><td align="center" valign="top" rowspan="1" colspan="1">4.98%/28.72%</td></tr></tbody></table></table-wrap><table-wrap id="t3-sensors-12-14004" position="float"><label>Table 3.</label><caption><p><italic>Data prediction</italic> results (% error ANN/% error interpolation) for temperature in Sensor 1, using temperature in Sensor 1 as an input.</p></caption><table frame="box" rules="all"><thead><tr content-type="background-color:#D9D9D9"><th align="center" valign="middle" rowspan="1" colspan="1"><bold>Horizon</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>15 minutes</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>30 minutes</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>150 minutes</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>300 minutes</bold></th></tr><tr content-type="background-color:#D9D9D9"><th align="center" valign="middle" rowspan="1" colspan="1"><bold>Length of Input Sequence</bold></th></tr></thead><tbody><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>5 samples</bold></td><td align="center" valign="top" rowspan="1" colspan="1">0.77%/0.44%</td><td align="center" valign="top" rowspan="1" colspan="1">1.30%/0.76%</td><td align="center" valign="top" rowspan="1" colspan="1">1.92%/3.03%</td><td align="center" valign="top" rowspan="1" colspan="1">2.81%/5.33%</td></tr><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>10 samples</bold></td><td align="center" valign="top" rowspan="1" colspan="1">0.94%/0.43%</td><td align="center" valign="top" rowspan="1" colspan="1">0.93%/0.76%</td><td align="center" valign="top" rowspan="1" colspan="1">1.97%/3.00%</td><td align="center" valign="top" rowspan="1" colspan="1">2.87%/5.26%</td></tr><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>20 samples</bold></td><td align="center" valign="top" rowspan="1" colspan="1">0.85%/0.43%</td><td align="center" valign="top" rowspan="1" colspan="1">1.38%/0.74%</td><td align="center" valign="top" rowspan="1" colspan="1">2.01%/2.90%</td><td align="center" valign="top" rowspan="1" colspan="1">3.40%/5.10%</td></tr></tbody></table></table-wrap><table-wrap id="t4-sensors-12-14004" position="float"><label>Table 4.</label><caption><p><italic>Data prediction</italic> results (% error ANN/% error interpolation) for temperature in Sensor 3, using temperature in Sensor 1 as an input.</p></caption><table frame="box" rules="all"><thead><tr content-type="background-color:#D9D9D9"><th align="center" valign="middle" rowspan="1" colspan="1"><bold>Horizon</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>5 samples</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>10 samples</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>50 samples</bold></th><th align="center" valign="middle" rowspan="2" colspan="1"><bold>100 samples</bold></th></tr><tr><th align="center" valign="middle" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>Length of input sequence</bold></th></tr></thead><tbody><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>5 samples</bold></td><td align="center" valign="top" rowspan="1" colspan="1">2.29%/5.70%</td><td align="center" valign="top" rowspan="1" colspan="1">3.30%/5.69%</td><td align="center" valign="top" rowspan="1" colspan="1">3.02%/6.44%</td><td align="center" valign="top" rowspan="1" colspan="1">4.10%/7.27%</td></tr><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>10 samples</bold></td><td align="center" valign="top" rowspan="1" colspan="1">2.74%/5.66%</td><td align="center" valign="top" rowspan="1" colspan="1">2.74%/5.66%</td><td align="center" valign="top" rowspan="1" colspan="1">3.02%/6.41%</td><td align="center" valign="top" rowspan="1" colspan="1">4.21%/7.20%</td></tr><tr><td align="center" valign="top" content-type="background-color:#D9D9D9" rowspan="1" colspan="1"><bold>20 samples</bold></td><td align="center" valign="top" rowspan="1" colspan="1">2.41%/5.64%</td><td align="center" valign="top" rowspan="1" colspan="1">2.70%/5.68%</td><td align="center" valign="top" rowspan="1" colspan="1">3.36%/6.34%</td><td align="center" valign="top" rowspan="1" colspan="1">5.21%/7.06%</td></tr></tbody></table></table-wrap></floats-group></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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