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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Main Author: Bing Guo (25387) (author)
Other Authors: Wasim Javed (6105866) (author)
Published: 2024
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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
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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