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dc.contributor.authorPerera, Lokukaluge Prasad
dc.contributor.authorMo, Brage
dc.date.accessioned2018-06-29T09:10:32Z
dc.date.available2018-06-29T09:10:32Z
dc.date.created2018-06-27T13:03:47Z
dc.date.issued2018-06
dc.identifier.citationJournal of Ocean Engineering and Science. 2018, 3 (2), 133-143.nb_NO
dc.identifier.issn2468-0133
dc.identifier.urihttp://hdl.handle.net/11250/2503746
dc.description.abstractModern vessels are designed to collect, store and communicate large quantities of ship performance and navigation information through complex onboard data handling processes. That data should be transferred to shore based data centers for further analysis and storage. However, the associated transfer cost in large-scale data sets is a major challenge for the shipping industry, today. The same cost relates to the amount of data that are transferring through various communication networks (i.e. satellites and wireless networks), i.e. between vessels and shore based data centers. Hence, this study proposes to use an autoencoder system architecture (i.e. a deep learning approach) to compress ship performance and navigation parameters (i.e. reduce the number of parameters) and transfer through the respective communication networks as reduced data sets. The data compression is done under the linear version of an autoencoder that consists of principal component analysis (PCA), where the respective principal components (PCs) represent the structure of the data set. The compressed data set is expanded by the same data structure (i.e. an autoencoder system architecture) at the respective data center requiring further analyses and storage. A data set of ship performance and navigation parameters in a selected vessel is analyzed (i.e. data compression and expansion) through an autoencoder system architecture and the results are presented in this study. Furthermore, the respective input and output values of the autoencoder are also compared as statistical distributions and sample number series to evaluate its performance.nb_NO
dc.description.sponsorshipThis work has been conducted under the project of “SFI Smart Maritime (237917/O30) – Norwegian Centre for improved energy-efficiency and reduced emissions from the maritime sector” that is partly funded by the Research Council of Norway. An initial version of this paper is presented at the 35th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2016), Busan, Korea, June, 2016, (OMAE2016-54093).nb_NO
dc.language.isoengnb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectAutoencodernb_NO
dc.subjectShip performance and navigation informationnb_NO
dc.subjectShip energy efficiencynb_NO
dc.subjectData compressionnb_NO
dc.subjectData communicationnb_NO
dc.subjectPrincipal component analysisnb_NO
dc.titleShip Performance and Navigation Data Compression and Communication under Autoencoder System Architecturenb_NO
dc.title.alternativeShip Performance and Navigation Data Compression and Communication under Autoencoder System Architecturenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder© 2018 Shanghai Jiaotong University. Published by Elsevier B.V.nb_NO
dc.source.pagenumber133-143nb_NO
dc.source.volume3nb_NO
dc.source.journalJournal of Ocean Engineering and Sciencenb_NO
dc.source.issue2nb_NO
dc.identifier.doi10.1016/j.joes.2018.04.002
dc.identifier.cristin1594197
cristin.unitcode7566,2,0,0
cristin.unitnameFiskeriteknologi
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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