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dc.contributor.authorCheng, Xu
dc.contributor.authorLi, Guoyuan
dc.contributor.authorEllefsen, Andre
dc.contributor.authorChen, Shengyong
dc.contributor.authorHildre, Hans Petter
dc.contributor.authorZhang, Houxiang
dc.date.accessioned2020-05-18T08:01:16Z
dc.date.available2020-05-18T08:01:16Z
dc.date.created2020-01-20T11:03:48Z
dc.date.issued2020
dc.identifier.citationIEEE Transactions on Instrumentation and Measurement. 2020, .en_US
dc.identifier.issn0018-9456
dc.identifier.urihttps://hdl.handle.net/11250/2654721
dc.description.abstractSea state estimation is a fundamental problem in the development of autonomous ships. Traditional methods such as wave buoy, satellites, and wave radars are limited by locations, clouds and costs, respectively. Model-based methods are prone to incorrect estimations due to their high dependency on mathematical models of ships. As previous data-driven studies for sea state estimation only consider wave height and use the motion data from dynamic positioning vessels, this paper introduces a new, deep neural network (SSENET) to estimate sea state in light of both wave height and wave direction, and extends the generality of sensor data from ship motion with forward speed. SSENET is built on the basis of stacked convolutional neural network blocks with dense connections between different blocks, channel attention modules and a feature attention module. The dense connections build short-cut paths between input and all subsequent convolutional blocks, which can make full use of all the hierarchical features from the original time series sensor data. The channel attention modules aim to enhance the features extracted by each convolution block. The feature attention module focuses on combining the feature fusion of hierarchical features in an adaptive manner. Benchmark experiments show the competitive performance against state-ofthe-art approaches. Applying the SSENET on two datasets of zigzag motion for comparative studies shows the effectiveness of the proposed method.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleA Novel Densely Connected Convolutional Neural Network for Sea State Estimation Using Ship Motion Dataen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber10en_US
dc.source.journalIEEE Transactions on Instrumentation and Measurementen_US
dc.identifier.doi10.1109/TIM.2020.2967115
dc.identifier.cristin1777403
dc.relation.projectNorges forskningsråd: 280703en_US
dc.description.localcode© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
cristin.unitcode194,64,93,0
cristin.unitnameInstitutt for havromsoperasjoner og byggteknikk
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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