Identification of Damaged Ship State During Compartments Flooding

DOI: https://doi.org/10.32603/2071-2340-2023-4-41-49
Keywords: Artificial neural network, on-board intelligent system, ship rolling

Abstract

The article is focused on method of ship rolling processing during flooding of compartments. The main goal of such processing is in determination of the time moment, when the type of static stability diagram changes. The ship state corresponding to each of these types requires completely different methods of damage control, which is reflected in the knowledge base of the onboard intelligent system. In navigation conditions, and even more so in extreme situations, direct measurement of the stability characteristics of a marine object is impossible, so their indirect determination is required. The article presents the implementation of the procedural component of an on-board intelligent system based on an artificial neural network.

Author Biographies

Daniil Goncharuk, Saint Petersburg State University, 28 Universitetskiy pr., Stary Peterhof, 198504, Saint Petersburg, Russia

1st year Master’s Degree student, Faculty of Applied Mathematics and Control Processes, St. Petersburg State University, st080521@student.spbu.ru

Aleksandr Degtyarev, Saint Petersburg State University, 28 Universitetskiy pr., Stary Peterhof, 198504, Saint Petersburg, Russia

Doctor of Sciences (Tech.), Docent, Professor of the Department of Computer modeling and multiprocessor systems, St. Petersburg State University, a.degtyarev@spbu.ru

Ilya Bus'ko, Saint Petersburg State University, 35 Universitetskiy pr., Stary Peterhof, 198504, Saint Petersburg, Russia

Candidate of Sciences (Tech.), Research Engineer of the Department of Computer modeling and multiprocessor systems, St. Petersburg State University, i.busko@spbu.ru

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Published
2024-04-08
How to Cite
Goncharuk, D., Degtyarev, A., & Bus’ko, I. (2024). Identification of Damaged Ship State During Compartments Flooding. Computer Tools in Education, (4), 41-49. https://doi.org/10.32603/2071-2340-2023-4-41-49
Issue
No 4 (2023)
Section
Artificial intelligence and machine learning