The Application of Epsilon-SVR in Infrared Temperature Demarcating
A epsiv-SVR (epsiv-Support Vector Regression) based modeling method is introduced to process data acquired from infrared temperature demarcating experiment. In the process of the temperature of black body ranging from 30degC to 72degC, 22 groups of samples are acquired, which include 17 groups of tr...
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| creator | Sun, Jian Chen, Liang Fu, Yaqiong Wu, Juan Chen, Le |
| description | A epsiv-SVR (epsiv-Support Vector Regression) based modeling method is introduced to process data acquired from infrared temperature demarcating experiment. In the process of the temperature of black body ranging from 30degC to 72degC, 22 groups of samples are acquired, which include 17 groups of training samples and 5 groups of forecasting samples. The fitting curves are got through training samples and forecasting samples under MATLAB. Compared with traditional method of least square, the precision of this method is far higher. In conclusion, the method of epsiv-SVR can become a method of data processing to infrared temperature demarcating. |
| doi_str_mv | 10.1109/APCIP.2009.14 |
| format | Conference Proceeding |
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In the process of the temperature of black body ranging from 30degC to 72degC, 22 groups of samples are acquired, which include 17 groups of training samples and 5 groups of forecasting samples. The fitting curves are got through training samples and forecasting samples under MATLAB. Compared with traditional method of least square, the precision of this method is far higher. In conclusion, the method of epsiv-SVR can become a method of data processing to infrared temperature demarcating.</description><identifier>ISBN: 9780769536996</identifier><identifier>ISBN: 0769536999</identifier><identifier>DOI: 10.1109/APCIP.2009.14</identifier><identifier>LCCN: 2009903120</identifier><language>eng</language><publisher>IEEE</publisher><subject>Curve fitting ; Information processing ; Infrared detectors ; Infrared imaging ; infrared temperature demarcating ; Least squares methods ; Mathematical model ; Power system reliability ; Support vector machine classification ; Support vector machines ; Temperature measurement ; ε -SVR</subject><ispartof>2009 Asia-Pacific Conference on Information Processing, 2009, Vol.1, p.25-27</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5196986$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27903,54897</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5196986$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Sun, Jian</creatorcontrib><creatorcontrib>Chen, Liang</creatorcontrib><creatorcontrib>Fu, Yaqiong</creatorcontrib><creatorcontrib>Wu, Juan</creatorcontrib><creatorcontrib>Chen, Le</creatorcontrib><title>The Application of Epsilon-SVR in Infrared Temperature Demarcating</title><title>2009 Asia-Pacific Conference on Information Processing</title><addtitle>APCIP</addtitle><description>A epsiv-SVR (epsiv-Support Vector Regression) based modeling method is introduced to process data acquired from infrared temperature demarcating experiment. 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In conclusion, the method of epsiv-SVR can become a method of data processing to infrared temperature demarcating.</description><subject>Curve fitting</subject><subject>Information processing</subject><subject>Infrared detectors</subject><subject>Infrared imaging</subject><subject>infrared temperature demarcating</subject><subject>Least squares methods</subject><subject>Mathematical model</subject><subject>Power system reliability</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>Temperature measurement</subject><subject>ε -SVR</subject><isbn>9780769536996</isbn><isbn>0769536999</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8FOhDAURZuYSdSRpSs3_QHG10Jf6RJxVJJJnCi6nRR41RoGSMGFfy8TXd3Fyb25h7FrARshwNzm-6LcbySA2Yj0jEVGZ6DRqASNwRW7PBEDiZBwzqJp-gIAYVBLkVywu-qTeD6OnW_s7IeeD45vx8l3Qx-_vr9w3_Oyd8EGanlFx5GCnb8D8Xs62nCq9B9XbOVsN1H0n2v29rCtiqd49_xYFvku9kKrOXaI2EgwGoRGKShBdBZcuhiIRimZta6WEqnOKF2oAZlZTAmMgjaVbZ2s2c3frieiwxj88uDnoBYVk2HyC_TMSOI</recordid><startdate>200907</startdate><enddate>200907</enddate><creator>Sun, Jian</creator><creator>Chen, Liang</creator><creator>Fu, Yaqiong</creator><creator>Wu, Juan</creator><creator>Chen, Le</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200907</creationdate><title>The Application of Epsilon-SVR in Infrared Temperature Demarcating</title><author>Sun, Jian ; Chen, Liang ; Fu, Yaqiong ; Wu, Juan ; Chen, Le</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-f666c2097017621e366fa0f41091c5528dfb226eb8e41e39028a64e0950d42db3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Curve fitting</topic><topic>Information processing</topic><topic>Infrared detectors</topic><topic>Infrared imaging</topic><topic>infrared temperature demarcating</topic><topic>Least squares methods</topic><topic>Mathematical model</topic><topic>Power system reliability</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>Temperature measurement</topic><topic>ε -SVR</topic><toplevel>online_resources</toplevel><creatorcontrib>Sun, Jian</creatorcontrib><creatorcontrib>Chen, Liang</creatorcontrib><creatorcontrib>Fu, Yaqiong</creatorcontrib><creatorcontrib>Wu, Juan</creatorcontrib><creatorcontrib>Chen, Le</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sun, Jian</au><au>Chen, Liang</au><au>Fu, Yaqiong</au><au>Wu, Juan</au><au>Chen, Le</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The Application of Epsilon-SVR in Infrared Temperature Demarcating</atitle><btitle>2009 Asia-Pacific Conference on Information Processing</btitle><stitle>APCIP</stitle><date>2009-07</date><risdate>2009</risdate><volume>1</volume><spage>25</spage><epage>27</epage><pages>25-27</pages><isbn>9780769536996</isbn><isbn>0769536999</isbn><abstract>A epsiv-SVR (epsiv-Support Vector Regression) based modeling method is introduced to process data acquired from infrared temperature demarcating experiment. In the process of the temperature of black body ranging from 30degC to 72degC, 22 groups of samples are acquired, which include 17 groups of training samples and 5 groups of forecasting samples. The fitting curves are got through training samples and forecasting samples under MATLAB. Compared with traditional method of least square, the precision of this method is far higher. In conclusion, the method of epsiv-SVR can become a method of data processing to infrared temperature demarcating.</abstract><pub>IEEE</pub><doi>10.1109/APCIP.2009.14</doi><tpages>3</tpages></addata></record> |
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| subjects | Curve fitting Information processing Infrared detectors Infrared imaging infrared temperature demarcating Least squares methods Mathematical model Power system reliability Support vector machine classification Support vector machines Temperature measurement ε -SVR |
| title | The Application of Epsilon-SVR in Infrared Temperature Demarcating |
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