Design of printing ink spectral collection system and research on ink proportion prediction method
In spectral-based ink content measurement, improving the accuracy of the spectral collection system and reducing the algorithm complexity of the ink content measurement model are important aspects of research. This study designs a spectral collection system with an M-type Czerny–Turner optical path...
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description | In spectral-based ink content measurement, improving the accuracy of the spectral collection system and reducing the algorithm complexity of the ink content measurement model are important aspects of research. This study designs a spectral collection system with an M-type Czerny–Turner optical path structure, uses a third-order polynomial fitting method for calibration analysis, collects the spectral of the standard color card and denoises it through Savitzky–Golay convolution smoothing, and establishes a functional relationship between the spectral and the logarithm of ink content. Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighting Sampling are used to perform comparative analysis on feature wavelength extraction, and a prediction model for printing ink content is established. Experimental results show that the R2 of ink C, ink M, and ink Y are 0.9981, 0.9975, and 0.9892, respectively, and the root mean square error is 0.0134, 0.0153, and 0.0317, respectively, showing good performance in ink prediction accuracy. |
doi_str_mv | 10.1063/5.0186340 |
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This study designs a spectral collection system with an M-type Czerny–Turner optical path structure, uses a third-order polynomial fitting method for calibration analysis, collects the spectral of the standard color card and denoises it through Savitzky–Golay convolution smoothing, and establishes a functional relationship between the spectral and the logarithm of ink content. Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighting Sampling are used to perform comparative analysis on feature wavelength extraction, and a prediction model for printing ink content is established. Experimental results show that the R2 of ink C, ink M, and ink Y are 0.9981, 0.9975, and 0.9892, respectively, and the root mean square error is 0.0134, 0.0153, and 0.0317, respectively, showing good performance in ink prediction accuracy.</description><identifier>ISSN: 2158-3226</identifier><identifier>EISSN: 2158-3226</identifier><identifier>DOI: 10.1063/5.0186340</identifier><identifier>CODEN: AAIDBI</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Adaptive sampling ; Algorithms ; Collection ; Polynomials ; Prediction models</subject><ispartof>AIP advances, 2024-04, Vol.14 (4), p.045216-045216-9</ispartof><rights>Author(s)</rights><rights>2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c353t-5adcb87050625fffcfca85d0f1a02e87bc04d4e2ef2ea9d5bb31e58552c8eece3</cites><orcidid>0009-0008-2267-0685 ; 0000-0002-1370-5371</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,861,2096,27905,27906</link.rule.ids></links><search><creatorcontrib>Zhang, Wenhao</creatorcontrib><creatorcontrib>Liu, Xinru</creatorcontrib><creatorcontrib>Zhang, Rui</creatorcontrib><creatorcontrib>Jiang, Fei</creatorcontrib><creatorcontrib>He, Junjia</creatorcontrib><creatorcontrib>Fang, Shuyang</creatorcontrib><title>Design of printing ink spectral collection system and research on ink proportion prediction method</title><title>AIP advances</title><description>In spectral-based ink content measurement, improving the accuracy of the spectral collection system and reducing the algorithm complexity of the ink content measurement model are important aspects of research. This study designs a spectral collection system with an M-type Czerny–Turner optical path structure, uses a third-order polynomial fitting method for calibration analysis, collects the spectral of the standard color card and denoises it through Savitzky–Golay convolution smoothing, and establishes a functional relationship between the spectral and the logarithm of ink content. Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighting Sampling are used to perform comparative analysis on feature wavelength extraction, and a prediction model for printing ink content is established. Experimental results show that the R2 of ink C, ink M, and ink Y are 0.9981, 0.9975, and 0.9892, respectively, and the root mean square error is 0.0134, 0.0153, and 0.0317, respectively, showing good performance in ink prediction accuracy.</description><subject>Adaptive sampling</subject><subject>Algorithms</subject><subject>Collection</subject><subject>Polynomials</subject><subject>Prediction models</subject><issn>2158-3226</issn><issn>2158-3226</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9kUtLAzEQx4MoWGoPfoOAJ4VqHpvd7FHqqyB40XPIY9Ju3W7WZHvotzd1i3hyLjMMv_nPC6FLSm4pKfmduCVUlrwgJ2jCqJBzzlh5-ic-R7OUNiRbUVMiiwkyD5CaVYeDx31suqHpVrjpPnHqwQ5Rt9iGts1hEzqc9mmALdadwxES6GjXOKcPeB9DH-IP1UdwzViwhWEd3AU687pNMDv6Kfp4enxfvMxf356Xi_vXueWCD3OhnTWyIoKUTHjvrbdaCkc81YSBrIwlhSuAgWegayeM4RSEFIJZCWCBT9Fy1HVBb1TeZqvjXgXdqJ9EiCul84i2BUVqlkXAUEdtUTmihQGua15SUwkjRNa6GrXyYl87SIPahF3s8viKE15IVtX54FN0PVI2hpQi-N-ulKjDR5RQx49k9mZkk20GfTjPP_A3LZmMYw</recordid><startdate>20240401</startdate><enddate>20240401</enddate><creator>Zhang, Wenhao</creator><creator>Liu, Xinru</creator><creator>Zhang, Rui</creator><creator>Jiang, Fei</creator><creator>He, Junjia</creator><creator>Fang, Shuyang</creator><general>American Institute of Physics</general><general>AIP Publishing LLC</general><scope>AJDQP</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0008-2267-0685</orcidid><orcidid>https://orcid.org/0000-0002-1370-5371</orcidid></search><sort><creationdate>20240401</creationdate><title>Design of printing ink spectral collection system and research on ink proportion prediction method</title><author>Zhang, Wenhao ; Liu, Xinru ; Zhang, Rui ; Jiang, Fei ; He, Junjia ; Fang, Shuyang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c353t-5adcb87050625fffcfca85d0f1a02e87bc04d4e2ef2ea9d5bb31e58552c8eece3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adaptive sampling</topic><topic>Algorithms</topic><topic>Collection</topic><topic>Polynomials</topic><topic>Prediction models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Wenhao</creatorcontrib><creatorcontrib>Liu, Xinru</creatorcontrib><creatorcontrib>Zhang, Rui</creatorcontrib><creatorcontrib>Jiang, Fei</creatorcontrib><creatorcontrib>He, Junjia</creatorcontrib><creatorcontrib>Fang, Shuyang</creatorcontrib><collection>AIP Open Access Journals</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Directory of Open Access Journals</collection><jtitle>AIP advances</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Wenhao</au><au>Liu, Xinru</au><au>Zhang, Rui</au><au>Jiang, Fei</au><au>He, Junjia</au><au>Fang, Shuyang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Design of printing ink spectral collection system and research on ink proportion prediction method</atitle><jtitle>AIP advances</jtitle><date>2024-04-01</date><risdate>2024</risdate><volume>14</volume><issue>4</issue><spage>045216</spage><epage>045216-9</epage><pages>045216-045216-9</pages><issn>2158-3226</issn><eissn>2158-3226</eissn><coden>AAIDBI</coden><abstract>In spectral-based ink content measurement, improving the accuracy of the spectral collection system and reducing the algorithm complexity of the ink content measurement model are important aspects of research. This study designs a spectral collection system with an M-type Czerny–Turner optical path structure, uses a third-order polynomial fitting method for calibration analysis, collects the spectral of the standard color card and denoises it through Savitzky–Golay convolution smoothing, and establishes a functional relationship between the spectral and the logarithm of ink content. Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighting Sampling are used to perform comparative analysis on feature wavelength extraction, and a prediction model for printing ink content is established. Experimental results show that the R2 of ink C, ink M, and ink Y are 0.9981, 0.9975, and 0.9892, respectively, and the root mean square error is 0.0134, 0.0153, and 0.0317, respectively, showing good performance in ink prediction accuracy.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0186340</doi><tpages>9</tpages><orcidid>https://orcid.org/0009-0008-2267-0685</orcidid><orcidid>https://orcid.org/0000-0002-1370-5371</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adaptive sampling Algorithms Collection Polynomials Prediction models |
title | Design of printing ink spectral collection system and research on ink proportion prediction method |
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