Singapore Institute of Technology
Browse
Hadi_2022_J._Phys.__Conf._Ser._2311_012005.pdf (2.09 MB)

Ship navigation and fuel profiling based on noon report using neural network generative modeling

Download (2.09 MB)
conference contribution
posted on 2023-03-20, 04:07 authored by Januwar HadiJanuwar Hadi, Zhi Yung TayZhi Yung Tay, Dimitrios Konovessis

Harbor craft historical routes contain valuable information on how the experienced crews navigate around the known waters while performing jobs. The noon report logs each job timeframe which can be used to segregate the time-series positional data as routes. Other information from the noon report such as fuel consumption could be associated with a particular job as well. This paper offers a solution to encompass crew navigational experience into neural network models. The variational autoencoder, which is a generative model, can capture the routes into a knowledge base model. The same variational autoencoder is also able to train other neural networks to make predictions of route and fuel consumption based on job metadata (I.e., job duration, activity area, and route classification). The predicted routes could be used as a cost map for pathfinding algorithms such as A* or Dijkstra.

History

Journal/Conference/Book title

Journal of Physics: Conference Series

Publication date

2022

Version

  • Published

Project ID

  • 6524 (R-MOE-A403-C002) Ship's Energy Efficiency via Machine Learning with Big Data Analytics

Usage metrics

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC