.png){kind=icon}
返回
Response predictions using the observed autocorrelation function
.png){kind=icon}
OA
AI
DOI：10.1016/j.marstruc.2017.10.012.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article studies a procedure that facilitates short-time, deterministic predictions of the wave induced motion of a marine vessel, where it is understood that the future motion of the vessel is calculated ahead of time. Such predictions are valuable to assist in the execution of many marine operations (crane lifts, helicopter landings, etc.), as a specific prediction can be used to inform whether it is safe, or not, to carry out the particular operation within the nearest time horizon. The examined prediction procedure relies on observations of the correlation structure of the wave-induced response in study. Thus, predicted (future) values ahead of time for a given time history recording are computed through a mathematical combination of the sample auto correlation function and previous measurements recorded just prior to the moment of action. Importantly, the procedure does not need input about the exciting wave system, and neither does it rely on off-line training. In the article, the prediction procedure is applied to experimental data obtained through model-scale tests, and the procedure's predictive performance is investigated for various irregular wave scenarios. The presented results show that predictions can be successfully made in a time horizon corresponding to about 8-9 wave periods ahead of current time (the moment of action).
Keyword:
Determinstic motion prediction
Real-time
Measurements
Stationary process
Sample autocorrelation function
Conditional process
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.1
论文数:
2.0K
被引数:
6.7K
机构
引用论文
分享收藏分享收藏Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification噪声标记医学图像的鲁棒学习: 应用于皮肤病变分类OAAI
OAAI分享收藏
Highly dense cellulose acetate specimens with superior mechanical properties produced by fused filament fabrication.png)
Polymer
PolymerIF0
PREAI
分享收藏
查看更多内容