Marine safety and data analytics: vessel crash stop maneuvering performance prediction

Luca Oneto, Andrea Coraddu, Paolo Sanetti, Olena Karpenko, Francesca Cipollini, Toine Cleophas, Davide Anguita

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

3 Citations (Scopus)

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.
LanguageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2017
Subtitle of host publicationLecture Notes in Computer Science
Place of PublicationSwitzerland
PublisherSpringer-Verlag
Pages385-393
Number of pages9
ISBN (Print)9783319686110
DOIs
Publication statusPublished - 2 Dec 2017
Event26th International Conference on Artificial Neural Networks, ICANN 2017 - Alghero, Italy
Duration: 11 Sep 201714 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10614 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Artificial Neural Networks, ICANN 2017
CountryItaly
CityAlghero
Period11/09/1714/09/17

Fingerprint

Performance Prediction
Crash
Vessel
Safety
Shipyards
Shipbuilding
Learning algorithms
Random Forest
Data-driven
Learning Algorithm
Industry
Trivial
Marine safety
Estimate
Design

Keywords

  • crash stop
  • data-driven methods
  • marine safety
  • performance assessment
  • performance estimation
  • random forests
  • vessel maneuvering

Cite this

Oneto, L., Coraddu, A., Sanetti, P., Karpenko, O., Cipollini, F., Cleophas, T., & Anguita, D. (2017). Marine safety and data analytics: vessel crash stop maneuvering performance prediction. In Artificial Neural Networks and Machine Learning – ICANN 2017: Lecture Notes in Computer Science (pp. 385-393). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10614 LNCS). Switzerland: Springer-Verlag. https://doi.org/10.1007/978-3-319-68612-7_44
Oneto, Luca ; Coraddu, Andrea ; Sanetti, Paolo ; Karpenko, Olena ; Cipollini, Francesca ; Cleophas, Toine ; Anguita, Davide. / Marine safety and data analytics : vessel crash stop maneuvering performance prediction. Artificial Neural Networks and Machine Learning – ICANN 2017: Lecture Notes in Computer Science. Switzerland : Springer-Verlag, 2017. pp. 385-393 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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.",
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author = "Luca Oneto and Andrea Coraddu and Paolo Sanetti and Olena Karpenko and Francesca Cipollini and Toine Cleophas and Davide Anguita",
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Oneto, L, Coraddu, A, Sanetti, P, Karpenko, O, Cipollini, F, Cleophas, T & Anguita, D 2017, Marine safety and data analytics: vessel crash stop maneuvering performance prediction. in Artificial Neural Networks and Machine Learning – ICANN 2017: Lecture Notes in Computer Science. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10614 LNCS, Springer-Verlag, Switzerland, pp. 385-393, 26th International Conference on Artificial Neural Networks, ICANN 2017, Alghero, Italy, 11/09/17. https://doi.org/10.1007/978-3-319-68612-7_44

Marine safety and data analytics : vessel crash stop maneuvering performance prediction. / Oneto, Luca; Coraddu, Andrea; Sanetti, Paolo; Karpenko, Olena; Cipollini, Francesca; Cleophas, Toine; Anguita, Davide.

Artificial Neural Networks and Machine Learning – ICANN 2017: Lecture Notes in Computer Science. Switzerland : Springer-Verlag, 2017. p. 385-393 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10614 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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AU - Oneto, Luca

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Oneto L, Coraddu A, Sanetti P, Karpenko O, Cipollini F, Cleophas T et al. Marine safety and data analytics: vessel crash stop maneuvering performance prediction. In Artificial Neural Networks and Machine Learning – ICANN 2017: Lecture Notes in Computer Science. Switzerland: Springer-Verlag. 2017. p. 385-393. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-68612-7_44