Image Contrast Soiling Loss Quantification with Multiple Dust Types
<p>The image contrast method is a potentially viable approach for quantifying soiling loss. It involves imaging a surface with intrinsic contrast and applying a mathematical model that correlates the image’s black-to-white ratio with the angle-corrected normal-incidence soiling loss. This meth...
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2024
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| _version_ | 1864513540068474880 |
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| author | Bing Guo (25387) |
| author2 | Wasim Javed (6105866) |
| author2_role | author |
| author_facet | Bing Guo (25387) Wasim Javed (6105866) |
| author_role | author |
| dc.creator.none.fl_str_mv | Bing Guo (25387) Wasim Javed (6105866) |
| dc.date.none.fl_str_mv | 2024-10-11T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.solener.2024.112991 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Image_Contrast_Soiling_Loss_Quantification_with_Multiple_Dust_Types/30094579 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Environmental engineering Environmental sciences Soil sciences Soiling loss Image contrast Soiling sensor Soiling monitoring Performance evaluation Modeling |
| dc.title.none.fl_str_mv | Image Contrast Soiling Loss Quantification with Multiple Dust Types |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The image contrast method is a potentially viable approach for quantifying soiling loss. It involves imaging a surface with intrinsic contrast and applying a mathematical model that correlates the image’s black-to-white ratio with the angle-corrected normal-incidence soiling loss. This method had previously been tested with one type of dust. The objective of this study was to assess the method’s general validity across multiple dust types. Experiments were conducted to measure normal-incidence soiling loss and to capture images of soiling samples over a checker pattern. Histogram data of the images were used to determine the model parameters for each dust type. Using these parameters, the camera-angle-corrected normal-incidence soiling loss could be modeled from the black-to-white ratio, and the model’s predictions agreed closely with the experimental measurements. The findings suggest that the image contrast method can be applied to various dust types and, therefore, can be utilized in different regions around the world.</p><h2>Other Information</h2> <p> Published in: Solar Energy<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.solener.2024.112991" target="_blank">https://dx.doi.org/10.1016/j.solener.2024.112991</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d105bf47b677fd1e89cfe6107b5c152b |
| identifier_str_mv | 10.1016/j.solener.2024.112991 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30094579 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Image Contrast Soiling Loss Quantification with Multiple Dust TypesBing Guo (25387)Wasim Javed (6105866)EngineeringEnvironmental engineeringEnvironmental sciencesSoil sciencesSoiling lossImage contrastSoiling sensorSoiling monitoringPerformance evaluationModeling<p>The image contrast method is a potentially viable approach for quantifying soiling loss. It involves imaging a surface with intrinsic contrast and applying a mathematical model that correlates the image’s black-to-white ratio with the angle-corrected normal-incidence soiling loss. This method had previously been tested with one type of dust. The objective of this study was to assess the method’s general validity across multiple dust types. Experiments were conducted to measure normal-incidence soiling loss and to capture images of soiling samples over a checker pattern. Histogram data of the images were used to determine the model parameters for each dust type. Using these parameters, the camera-angle-corrected normal-incidence soiling loss could be modeled from the black-to-white ratio, and the model’s predictions agreed closely with the experimental measurements. The findings suggest that the image contrast method can be applied to various dust types and, therefore, can be utilized in different regions around the world.</p><h2>Other Information</h2> <p> Published in: Solar Energy<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.solener.2024.112991" target="_blank">https://dx.doi.org/10.1016/j.solener.2024.112991</a></p>2024-10-11T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.solener.2024.112991https://figshare.com/articles/journal_contribution/Image_Contrast_Soiling_Loss_Quantification_with_Multiple_Dust_Types/30094579CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300945792024-10-11T12:00:00Z |
| spellingShingle | Image Contrast Soiling Loss Quantification with Multiple Dust Types Bing Guo (25387) Engineering Environmental engineering Environmental sciences Soil sciences Soiling loss Image contrast Soiling sensor Soiling monitoring Performance evaluation Modeling |
| status_str | publishedVersion |
| title | Image Contrast Soiling Loss Quantification with Multiple Dust Types |
| title_full | Image Contrast Soiling Loss Quantification with Multiple Dust Types |
| title_fullStr | Image Contrast Soiling Loss Quantification with Multiple Dust Types |
| title_full_unstemmed | Image Contrast Soiling Loss Quantification with Multiple Dust Types |
| title_short | Image Contrast Soiling Loss Quantification with Multiple Dust Types |
| title_sort | Image Contrast Soiling Loss Quantification with Multiple Dust Types |
| topic | Engineering Environmental engineering Environmental sciences Soil sciences Soiling loss Image contrast Soiling sensor Soiling monitoring Performance evaluation Modeling |