Revealing New Technologies in Ocean Engineering Research using Machine Learning
On par with aerospace engineering, ocean engineering has caught a lot of attention re-cently. In this paper we employ machine learning and natural language processing methods to reveal new technologies and research hotspots in the ocean engineering field. Our data collection includes 14 high-impact...
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ftunivagoraojs:oai:ojs.univagora.ro:article/4101 2023-05-15T15:04:43+02:00 Revealing New Technologies in Ocean Engineering Research using Machine Learning Li, Xin Liang, Yanchun Chen, Biqian He, Baorun Jiang, Yu 2021-03-03 application/pdf http://univagora.ro/jour/index.php/ijccc/article/view/4101 https://doi.org/10.15837/ijccc.2021.2.4101 eng eng Agora University Press http://univagora.ro/jour/index.php/ijccc/article/view/4101/1638 http://univagora.ro/jour/index.php/ijccc/article/view/4101 doi:10.15837/ijccc.2021.2.4101 Copyright (c) 2021 Xin Li, Biqian Chen, Yanchun Liang, Baorun He, Yu Jiang http://creativecommons.org/licenses/by-nc/4.0 CC-BY-NC INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL; Vol 16 No 2 (2021): International Journal of Computers Communications & Control (April) 1841-9844 1841-9836 10.15837/ijccc.2021.2 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2021 ftunivagoraojs https://doi.org/10.15837/ijccc.2021.2.4101 https://doi.org/10.15837/ijccc.2021.2 2021-06-06T09:41:51Z On par with aerospace engineering, ocean engineering has caught a lot of attention re-cently. In this paper we employ machine learning and natural language processing methods to reveal new technologies and research hotspots in the ocean engineering field. Our data collection includes 14 high-impact journals, and the abstracts of almost 30,000 papers pub- lished from 2010 to 2019. We employed two topic models, Latent Dirichlet Allocation (LDA) and PhraseLDA. Used independently, the LDA model may lack interpretability and the PhraseLDA result may lose information in the final topics. We hence combined these two models and discovered the research hotspots for each year using affinity propagation cluster- ing and word-cloud-based visualization. The results reveal that several topics such as "wind power" and "ship structure", areas such as the European and Arctic seas, and some common research methods are increasing in popularity. This work consists of data collection, topic modelling, clustering, and visualization, which can help researchers understand the trends and important topics in ocean engineering as well as other fields. Article in Journal/Newspaper Arctic Agora University Editing House: Journals Arctic INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL 16 2 |
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English |
description |
On par with aerospace engineering, ocean engineering has caught a lot of attention re-cently. In this paper we employ machine learning and natural language processing methods to reveal new technologies and research hotspots in the ocean engineering field. Our data collection includes 14 high-impact journals, and the abstracts of almost 30,000 papers pub- lished from 2010 to 2019. We employed two topic models, Latent Dirichlet Allocation (LDA) and PhraseLDA. Used independently, the LDA model may lack interpretability and the PhraseLDA result may lose information in the final topics. We hence combined these two models and discovered the research hotspots for each year using affinity propagation cluster- ing and word-cloud-based visualization. The results reveal that several topics such as "wind power" and "ship structure", areas such as the European and Arctic seas, and some common research methods are increasing in popularity. This work consists of data collection, topic modelling, clustering, and visualization, which can help researchers understand the trends and important topics in ocean engineering as well as other fields. |
format |
Article in Journal/Newspaper |
author |
Li, Xin Liang, Yanchun Chen, Biqian He, Baorun Jiang, Yu |
spellingShingle |
Li, Xin Liang, Yanchun Chen, Biqian He, Baorun Jiang, Yu Revealing New Technologies in Ocean Engineering Research using Machine Learning |
author_facet |
Li, Xin Liang, Yanchun Chen, Biqian He, Baorun Jiang, Yu |
author_sort |
Li, Xin |
title |
Revealing New Technologies in Ocean Engineering Research using Machine Learning |
title_short |
Revealing New Technologies in Ocean Engineering Research using Machine Learning |
title_full |
Revealing New Technologies in Ocean Engineering Research using Machine Learning |
title_fullStr |
Revealing New Technologies in Ocean Engineering Research using Machine Learning |
title_full_unstemmed |
Revealing New Technologies in Ocean Engineering Research using Machine Learning |
title_sort |
revealing new technologies in ocean engineering research using machine learning |
publisher |
Agora University Press |
publishDate |
2021 |
url |
http://univagora.ro/jour/index.php/ijccc/article/view/4101 https://doi.org/10.15837/ijccc.2021.2.4101 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL; Vol 16 No 2 (2021): International Journal of Computers Communications & Control (April) 1841-9844 1841-9836 10.15837/ijccc.2021.2 |
op_relation |
http://univagora.ro/jour/index.php/ijccc/article/view/4101/1638 http://univagora.ro/jour/index.php/ijccc/article/view/4101 doi:10.15837/ijccc.2021.2.4101 |
op_rights |
Copyright (c) 2021 Xin Li, Biqian Chen, Yanchun Liang, Baorun He, Yu Jiang http://creativecommons.org/licenses/by-nc/4.0 |
op_rightsnorm |
CC-BY-NC |
op_doi |
https://doi.org/10.15837/ijccc.2021.2.4101 https://doi.org/10.15837/ijccc.2021.2 |
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INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL |
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16 |
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2 |
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