Polarized light sun position determination artificial neural network
Our previous work has constructed a polarized light orientation determination (PLOD) artificial neural network. Although a PLOD network can determine the solar azimuth angle, it cannot determine the solar elevation angle. Therefore, this paper proposes an artificial neural network for polarized ligh...
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| Veröffentlicht in: | Applied optics (2004) 2022-02, Vol.61 (6), p.1456-1463 |
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| container_title | Applied optics (2004) |
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| creator | Liang, Huaju Bai, Hongyang Li, Zhengmao Cao, Yu |
| description | Our previous work has constructed a polarized light orientation determination (PLOD) artificial neural network. Although a PLOD network can determine the solar azimuth angle, it cannot determine the solar elevation angle. Therefore, this paper proposes an artificial neural network for polarized light solar position determination (PLSPD), which has two branches: the solar azimuth angle determination branch and the solar elevation angle determination branch. Since the solar elevation angle has no cyclic characteristics, and the angle range of the solar elevation angle is different from that of the solar azimuth angle, the solar elevation angle exponential function encoding is redesigned. In addition, compared with the PLOD, the PLSPD deletes a local full connection layer to simplify the network structure. The experimental results show that the PLSPD can determine not only the solar azimuth angle but also the solar elevation angle, and the solar azimuth angle determination accuracy of the PLSPD is higher than that of the PLOD. |
| doi_str_mv | 10.1364/AO.453177 |
| format | Article |
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Although a PLOD network can determine the solar azimuth angle, it cannot determine the solar elevation angle. Therefore, this paper proposes an artificial neural network for polarized light solar position determination (PLSPD), which has two branches: the solar azimuth angle determination branch and the solar elevation angle determination branch. Since the solar elevation angle has no cyclic characteristics, and the angle range of the solar elevation angle is different from that of the solar azimuth angle, the solar elevation angle exponential function encoding is redesigned. In addition, compared with the PLOD, the PLSPD deletes a local full connection layer to simplify the network structure. The experimental results show that the PLSPD can determine not only the solar azimuth angle but also the solar elevation angle, and the solar azimuth angle determination accuracy of the PLSPD is higher than that of the PLOD.</description><identifier>ISSN: 1559-128X</identifier><identifier>EISSN: 2155-3165</identifier><identifier>EISSN: 1539-4522</identifier><identifier>DOI: 10.1364/AO.453177</identifier><identifier>PMID: 35201031</identifier><language>eng</language><publisher>United States: Optical Society of America</publisher><subject>Artificial neural networks ; Azimuth ; Elevation angle ; Exponential functions ; Neural networks ; Neural Networks, Computer ; Polarized light ; Solar position ; Sunlight</subject><ispartof>Applied optics (2004), 2022-02, Vol.61 (6), p.1456-1463</ispartof><rights>Copyright Optical Society of America Feb 20, 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c313t-a763f1571989f39ed954591cf546ad41c735bdbe88791c900ccfa96e1dc51dc83</citedby><cites>FETCH-LOGICAL-c313t-a763f1571989f39ed954591cf546ad41c735bdbe88791c900ccfa96e1dc51dc83</cites><orcidid>0000-0002-4576-2565</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,3244,27903,27904</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35201031$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liang, Huaju</creatorcontrib><creatorcontrib>Bai, Hongyang</creatorcontrib><creatorcontrib>Li, Zhengmao</creatorcontrib><creatorcontrib>Cao, Yu</creatorcontrib><title>Polarized light sun position determination artificial neural network</title><title>Applied optics (2004)</title><addtitle>Appl Opt</addtitle><description>Our previous work has constructed a polarized light orientation determination (PLOD) artificial neural network. Although a PLOD network can determine the solar azimuth angle, it cannot determine the solar elevation angle. Therefore, this paper proposes an artificial neural network for polarized light solar position determination (PLSPD), which has two branches: the solar azimuth angle determination branch and the solar elevation angle determination branch. Since the solar elevation angle has no cyclic characteristics, and the angle range of the solar elevation angle is different from that of the solar azimuth angle, the solar elevation angle exponential function encoding is redesigned. In addition, compared with the PLOD, the PLSPD deletes a local full connection layer to simplify the network structure. The experimental results show that the PLSPD can determine not only the solar azimuth angle but also the solar elevation angle, and the solar azimuth angle determination accuracy of the PLSPD is higher than that of the PLOD.</description><subject>Artificial neural networks</subject><subject>Azimuth</subject><subject>Elevation angle</subject><subject>Exponential functions</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Polarized light</subject><subject>Solar position</subject><subject>Sunlight</subject><issn>1559-128X</issn><issn>2155-3165</issn><issn>1539-4522</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkEtPwzAQhC0EoqVw4A-gSFzgkOKNYzs-VuUpVSoHkLhFrh_gksTFToTg15M-4MBhNburT6PRIHQKeAyE5VeT-TinBDjfQ8MMKE0JMLqPhv0qUsiKlwE6inGJMaG54IdoQGiGARMYoutHX8ngvo1OKvf61iaxa5KVj651vkm0aU2oXSM3lwyts045WSWN6cJG2k8f3o_RgZVVNCc7HaHn25un6X06m989TCezVBEgbSo5IxYoB1EIS4TRguZUgLI0Z1LnoDihC70wRcH7r8BYKSsFM6AV7acgI3Sx9V0F_9GZ2Ja1i8pUlWyM72KZMZIVmIIQPXr-D136LjR9ujXFGcMcWE9dbikVfIzB2HIVXC3DVwm4XFdbTublttqePds5dova6D_yt0vyAw-9cuw</recordid><startdate>20220220</startdate><enddate>20220220</enddate><creator>Liang, Huaju</creator><creator>Bai, Hongyang</creator><creator>Li, Zhengmao</creator><creator>Cao, Yu</creator><general>Optical Society of America</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4576-2565</orcidid></search><sort><creationdate>20220220</creationdate><title>Polarized light sun position determination artificial neural network</title><author>Liang, Huaju ; Bai, Hongyang ; Li, Zhengmao ; Cao, Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c313t-a763f1571989f39ed954591cf546ad41c735bdbe88791c900ccfa96e1dc51dc83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial neural networks</topic><topic>Azimuth</topic><topic>Elevation angle</topic><topic>Exponential functions</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Polarized light</topic><topic>Solar position</topic><topic>Sunlight</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liang, Huaju</creatorcontrib><creatorcontrib>Bai, Hongyang</creatorcontrib><creatorcontrib>Li, Zhengmao</creatorcontrib><creatorcontrib>Cao, Yu</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Applied optics (2004)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liang, Huaju</au><au>Bai, Hongyang</au><au>Li, Zhengmao</au><au>Cao, Yu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Polarized light sun position determination artificial neural network</atitle><jtitle>Applied optics (2004)</jtitle><addtitle>Appl Opt</addtitle><date>2022-02-20</date><risdate>2022</risdate><volume>61</volume><issue>6</issue><spage>1456</spage><epage>1463</epage><pages>1456-1463</pages><issn>1559-128X</issn><eissn>2155-3165</eissn><eissn>1539-4522</eissn><abstract>Our previous work has constructed a polarized light orientation determination (PLOD) artificial neural network. Although a PLOD network can determine the solar azimuth angle, it cannot determine the solar elevation angle. Therefore, this paper proposes an artificial neural network for polarized light solar position determination (PLSPD), which has two branches: the solar azimuth angle determination branch and the solar elevation angle determination branch. Since the solar elevation angle has no cyclic characteristics, and the angle range of the solar elevation angle is different from that of the solar azimuth angle, the solar elevation angle exponential function encoding is redesigned. In addition, compared with the PLOD, the PLSPD deletes a local full connection layer to simplify the network structure. The experimental results show that the PLSPD can determine not only the solar azimuth angle but also the solar elevation angle, and the solar azimuth angle determination accuracy of the PLSPD is higher than that of the PLOD.</abstract><cop>United States</cop><pub>Optical Society of America</pub><pmid>35201031</pmid><doi>10.1364/AO.453177</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-4576-2565</orcidid></addata></record> |
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| ispartof | Applied optics (2004), 2022-02, Vol.61 (6), p.1456-1463 |
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| source | MEDLINE; Alma/SFX Local Collection; Optica Publishing Group Journals |
| subjects | Artificial neural networks Azimuth Elevation angle Exponential functions Neural networks Neural Networks, Computer Polarized light Solar position Sunlight |
| title | Polarized light sun position determination artificial neural network |
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