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Fishing capacity evaluation of fishing vessel based on cloud model
In the evaluation problem of fishing vessel fishing capacity, the imperfect evaluation index system and the methods of evaluation indexes are mostly artificial qualitative evaluation methods, which lead to strong subjectivity and fuzziness as well as low accuracy of evaluation results. Therefore, th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148315/ https://www.ncbi.nlm.nih.gov/pubmed/35643858 http://dx.doi.org/10.1038/s41598-022-12852-8 |
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author | Lyu, Chao Zhang, He-xu Liu, Shuang Guo, Yi |
author_facet | Lyu, Chao Zhang, He-xu Liu, Shuang Guo, Yi |
author_sort | Lyu, Chao |
collection | PubMed |
description | In the evaluation problem of fishing vessel fishing capacity, the imperfect evaluation index system and the methods of evaluation indexes are mostly artificial qualitative evaluation methods, which lead to strong subjectivity and fuzziness as well as low accuracy of evaluation results. Therefore, this study introduces cloud model theory on the basis of improving the evaluation index system, converts the artificial qualitative evaluation results into the digital characteristics of clouds, realizes the mutual transformation of qualitative evaluation and quantitative evaluation, and improves the accuracy of evaluation results. Taking the trawler as an example, the cloud model method is used to evaluate the fishing capacity, and the result obtained is (77.1408, 1.6897, 0.0), the result obtained by the fuzzy comprehensive evaluation method is 76.664785, and the result obtained by the cloud center of gravity evaluation method is 0.7919. Compared with the other two methods, the cloud model method uses three numerical characteristics to describe the results, and combining the different numerical characteristics meanings, the evaluation results can be judged to be accurate, and the influence of ambiguity on the results is greatly reduced. Meanwhile, the evaluation results can be presented in the form of pictures, and the results are more intuitive; in addition, the cloud model of the evaluation results is compared with the standard cloud model for similarity, which improves the credibility and authenticity of the results. |
format | Online Article Text |
id | pubmed-9148315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91483152022-05-30 Fishing capacity evaluation of fishing vessel based on cloud model Lyu, Chao Zhang, He-xu Liu, Shuang Guo, Yi Sci Rep Article In the evaluation problem of fishing vessel fishing capacity, the imperfect evaluation index system and the methods of evaluation indexes are mostly artificial qualitative evaluation methods, which lead to strong subjectivity and fuzziness as well as low accuracy of evaluation results. Therefore, this study introduces cloud model theory on the basis of improving the evaluation index system, converts the artificial qualitative evaluation results into the digital characteristics of clouds, realizes the mutual transformation of qualitative evaluation and quantitative evaluation, and improves the accuracy of evaluation results. Taking the trawler as an example, the cloud model method is used to evaluate the fishing capacity, and the result obtained is (77.1408, 1.6897, 0.0), the result obtained by the fuzzy comprehensive evaluation method is 76.664785, and the result obtained by the cloud center of gravity evaluation method is 0.7919. Compared with the other two methods, the cloud model method uses three numerical characteristics to describe the results, and combining the different numerical characteristics meanings, the evaluation results can be judged to be accurate, and the influence of ambiguity on the results is greatly reduced. Meanwhile, the evaluation results can be presented in the form of pictures, and the results are more intuitive; in addition, the cloud model of the evaluation results is compared with the standard cloud model for similarity, which improves the credibility and authenticity of the results. Nature Publishing Group UK 2022-05-28 /pmc/articles/PMC9148315/ /pubmed/35643858 http://dx.doi.org/10.1038/s41598-022-12852-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lyu, Chao Zhang, He-xu Liu, Shuang Guo, Yi Fishing capacity evaluation of fishing vessel based on cloud model |
title | Fishing capacity evaluation of fishing vessel based on cloud model |
title_full | Fishing capacity evaluation of fishing vessel based on cloud model |
title_fullStr | Fishing capacity evaluation of fishing vessel based on cloud model |
title_full_unstemmed | Fishing capacity evaluation of fishing vessel based on cloud model |
title_short | Fishing capacity evaluation of fishing vessel based on cloud model |
title_sort | fishing capacity evaluation of fishing vessel based on cloud model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9148315/ https://www.ncbi.nlm.nih.gov/pubmed/35643858 http://dx.doi.org/10.1038/s41598-022-12852-8 |
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