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Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning
https://oacis.repo.nii.ac.jp/records/2199
https://oacis.repo.nii.ac.jp/records/21992bf49201-da5b-40b8-9bf1-a40301468cbb
Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2021-09-17 | |||||
タイトル | ||||||
タイトル | Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題 | vessel trajectory prediction, LSTM, cubic spline interpolation, LNG, CO2 emissions | |||||
資源タイプ | ||||||
資源タイプ | journal article | |||||
アクセス権 | ||||||
アクセス権 | metadata only access | |||||
著者 |
Wang, Yongpeng
× Wang, Yongpeng× 渡部, 大輔× Hirata , Enna× Toriumi, Shigeki |
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書誌情報 |
en : Journal of Marine Science and Engineering 巻 9, 号 8, p. 871, 発行日 2021 |
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抄録 | ||||||
内容記述 | In this study, we propose an effective method using deep learning to strengthen real-time vessel carbon dioxide emission management. We propose a method to predict real-time carbon dioxide emissions of the vessel in three steps: (1) convert the trajectory data of the fixed time interval into a spatial–temporal sequence, (2) apply a long short-term memory (LSTM) model to predict the future trajectory and vessel status data of the vessel, and (3) predict the carbon dioxide emissions. Automatic identification system (AIS) database of a liquefied natural gas (LNG) vessel were selected as the sample and we reconstructed the trajectory data with a fixed time interval using cubic spline interpolation. Applying the interpolated AIS data, the carbon dioxide emissions of the vessel were calculated based on the International Towing Tank Conference (ITTC) recommended procedures. The experimental results are twofold. First, it reveals that vessel emissions are currently underestimated. This study clearly indicates that the actual carbon dioxide emissions are higher than those reported. The finding offers insight into how to accurately measure the emissions of vessels, and hence, better execute a greenhouse gases (GHGs) reduction strategy. Second, the LSTM model has a better trajectory prediction performance than the recurrent neural network (RNN) model. The errors of the trajectory endpoint and carbon dioxide emissions were small, which shows that the LSTM model is suitable for spatial–temporal data prediction with excellent performance. Therefore, this study offers insights to strengthen the real-time management and control of vessel greenhouse gas emissions and handle those in a more efficient way. | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 2077-1312 | |||||
DOI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.3390/jmse9080871 | |||||
情報源 | ||||||
関連識別子 | https://doi.org/10.3390/jmse9080871 | |||||
関連名称 | Publisher's Version/PDF(Open Access) | |||||
出版者 | ||||||
出版者 | MDPI |