用神经网络预测半潜式平台升沉和纵荡运动
Predicting heave and surge motions of a semi-submersible with neural networks
摘要：
船舶或浮动平台的实时运动预测可以帮助提高运动补偿系统的性能。它还可以为海上作业提供有用的早期预警信息，这对运动至关重要。在这项研究中，开发了一种基于长短期记忆（LSTM）的机器学习模型来预测半潜式平台的升沉和纵荡运动。训练和测试数据来自中国上海交通大学在深水海洋盆地进行的模型测试。运动和测量的波被馈送到LSTM单元中，然后通过几个完全连接（FC）层来获得预测。在实测波浪的帮助下，预测延长了46.5s，平均准确率接近90%。使用噪声扩展数据集，经过训练的模型在噪声水平高达0.8的情况下有效地工作。作为进一步的步骤，模型可以仅基于运动本身来预测运动。基于对模型体系结构的敏感性研究，提出了机器学习模型构建的指导原则。所提出的LSTM模型显示出很强的预测船舶波浪激励运动的能力。
英文摘要：
Real-time motion prediction of a vessel or a floating platform can help to improve the performance of motion compensation systems. It can also provide useful early-warning information for offshore operations that are critical with regard to motion. In this study, a long short-term memory (LSTM) -based machine learning model was developed to predict heave and surge motions of a semi-submersible. The training and test data came from a model test carried out in the deep-water ocean basin, at Shanghai Jiao Tong University, China. The motion and measured waves were fed into LSTM cells and then went through serval fully connected (FC) layers to obtain the prediction. With the help of measured waves, the prediction extended 46.5 s into future with an average accuracy close to 90%. Using a noise-extended dataset, the trained model effectively worked with a noise level up to 0.8. As a further step, the model could predict motions only based on the motion itself. Based on sensitive studies on the architectures of the model, guidelines for the construction of the machine learning model are proposed. The proposed LSTM model shows a strong ability to predict vessel wave-excited motions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
