@inproceedings{5c028b0fb3d048b6bc3af61e3b81c974,
title = "Marine safety and data analytics: vessel crash stop maneuvering performance prediction",
abstract = "Crash stop maneuvering performance is one of the key indicators of the vessel safety properties for a shipbuilding company. Many different factors affect these performances, from the vessel design to the environmental conditions, hence it is not trivial to assess them accurately during the preliminary design stages. Several first principal equation methods are available to estimate the crash stop maneuvering performance, but unfortunately, these methods usually are either too costly or not accurate enough. To overcome these limitations, the authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance. Results on real-world data provided by the DAMEN Shipyards show the effectiveness of the proposal.",
keywords = "crash stop, data-driven methods, marine safety, performance assessment, performance estimation, random forests, vessel maneuvering",
author = "Luca Oneto and Andrea Coraddu and Paolo Sanetti and Olena Karpenko and Francesca Cipollini and Toine Cleophas and Davide Anguita",
note = "Part of the Lecture Notes in Computer Science book series (LNCS, volume 10614).; 26th International Conference on Artificial Neural Networks, ICANN 2017 ; Conference date: 11-09-2017 Through 14-09-2017",
year = "2017",
month = dec,
day = "2",
doi = "10.1007/978-3-319-68612-7_44",
language = "English",
isbn = "9783319686110",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "385--393",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2017",
}