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Near real time estimation of neighboring actual and imminent wave fields in the vicinity of a floating body

机译:浮体附近的邻近实际和迫近波场的实时估计

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Real time estimation of actual and imminent wave fields in the vicinity of ships and floating structures are important for safe and efficient operations. Historically, three main approaches have been cited in the open literatures with varying degrees of accuracies: the use of wave buoys, ship radars (e.g. X-band and K-band radars) / satellite and ship motions. This paper presents two methods to predict approaching waves using ship motions by employing artificial neural networks and machine learning. Another method based on Short Time Fourier Transform (STFT) of the ship motion data was also used for cross comparison. Both methods were tested in head sea condition only with different ship speeds and sea states. The method based on machine learning estimates the wave elevations as time series, whereas STFT gives its estimates as spectrum. Both methods gave very good results.
机译:对船舶附近的实际和迫在波场的实时估计对于安全和有效的操作是重要的。从历史上看,在开放的文献中引用了三种主要方法,具有不同程度的精度:使用波浮标,船舶雷达(例如X波段和K波段雷达)/卫星和船舶运动。本文通过采用人工神经网络和机器学习,提出了两种预测使用船舶运动接近波的方法。基于船舶运动数据的短时间傅立叶变换(STFT)的另一种方法也用于交叉比较。两种方法在头海状况中仅测试了不同的船舶速度和海洋状态。基于机器学习的方法估计波升升高为时间序列,而STFT将其估计作为频谱。两种方法都产生了很好的效果。

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