Ship Performance Monitoring using Machine-learning

@article{Gupta2021ShipPM,
  title={Ship Performance Monitoring using Machine-learning},
  author={Prateek Gupta and Adil Rasheed and Sverre Steen},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.03594}
}
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3 Citations
Highly Influential Citations
1
Background Citations
2
Methods Citations
2

3 Citations

Data Processing Framework for Ship Performance Analysis

Ship’s hydrodynamic performance can be assessed using the data from ship-in-service, and a standardized data processing framework for preparing the data is developed.

Towards Improved Prediction of Ship Performance: A Comparative Analysis on In-service Ship Monitoring Data for Modeling the Speed-Power Relation

In-service monitoring data from multiple vessels with different hull shapes is used to compare the accuracy of data-driven machine learning algorithms to traditional methods for assessing ship performance, and shows that a simple neural network outperformed established semi-empirical formulas following first principles.

Damage prediction of stiffened plates subjected to underwater contact explosion using the machine learning-based method

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