Image based real-time ice load prediction tool for ship and offshore platform in managed ice field ...
The increased activities in arctic water warrant modelling of ice properties and ice-structure interaction forces to ensure safe operations of ships and offshore platforms. Several established analytical and numerical ice force estimation models can be found in the literature. Recently, researchers...
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Memorial University of Newfoundland
2023
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Online Access: | https://dx.doi.org/10.48336/9hs3-g532 https://research.library.mun.ca/15936/ |
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ftdatacite:10.48336/9hs3-g532 2023-07-23T04:17:48+02:00 Image based real-time ice load prediction tool for ship and offshore platform in managed ice field ... Akter, Shamima 2023 https://dx.doi.org/10.48336/9hs3-g532 https://research.library.mun.ca/15936/ en eng Memorial University of Newfoundland Text article-journal ScholarlyArticle 2023 ftdatacite https://doi.org/10.48336/9hs3-g532 2023-07-03T19:07:01Z The increased activities in arctic water warrant modelling of ice properties and ice-structure interaction forces to ensure safe operations of ships and offshore platforms. Several established analytical and numerical ice force estimation models can be found in the literature. Recently, researchers have been working on Machine Learning (ML) based, data-driven force predictors trained on experimental data and field measurement. Application of both traditional and ML-based image processing for extracting information from ice floe images has also been reported in recent literature; because extraction of ice features from real-time videos and images can significantly improve ice force prediction. However, there exists room for improvement in those studies. For example, accurate extraction of ice floe information is still challenging because of their complex and varied shapes, colour similarities and reflection of light on them. Besides, real ice floes are often found in groups with overlapped and/or connected ... Text Arctic DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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description |
The increased activities in arctic water warrant modelling of ice properties and ice-structure interaction forces to ensure safe operations of ships and offshore platforms. Several established analytical and numerical ice force estimation models can be found in the literature. Recently, researchers have been working on Machine Learning (ML) based, data-driven force predictors trained on experimental data and field measurement. Application of both traditional and ML-based image processing for extracting information from ice floe images has also been reported in recent literature; because extraction of ice features from real-time videos and images can significantly improve ice force prediction. However, there exists room for improvement in those studies. For example, accurate extraction of ice floe information is still challenging because of their complex and varied shapes, colour similarities and reflection of light on them. Besides, real ice floes are often found in groups with overlapped and/or connected ... |
format |
Text |
author |
Akter, Shamima |
spellingShingle |
Akter, Shamima Image based real-time ice load prediction tool for ship and offshore platform in managed ice field ... |
author_facet |
Akter, Shamima |
author_sort |
Akter, Shamima |
title |
Image based real-time ice load prediction tool for ship and offshore platform in managed ice field ... |
title_short |
Image based real-time ice load prediction tool for ship and offshore platform in managed ice field ... |
title_full |
Image based real-time ice load prediction tool for ship and offshore platform in managed ice field ... |
title_fullStr |
Image based real-time ice load prediction tool for ship and offshore platform in managed ice field ... |
title_full_unstemmed |
Image based real-time ice load prediction tool for ship and offshore platform in managed ice field ... |
title_sort |
image based real-time ice load prediction tool for ship and offshore platform in managed ice field ... |
publisher |
Memorial University of Newfoundland |
publishDate |
2023 |
url |
https://dx.doi.org/10.48336/9hs3-g532 https://research.library.mun.ca/15936/ |
geographic |
Arctic |
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Arctic |
genre |
Arctic |
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Arctic |
op_doi |
https://doi.org/10.48336/9hs3-g532 |
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1772179839209439232 |