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Research on the Risk Classification of Cruise Ship Fires Based on an Attention-Bp Neural Network

Abstract

Due to the relatively closed environment, complex internal structure, and difficult evacuation of personnel, it is more difficult to prevent ship fires than land fires. In this paper, taking the large cruise ship as the research object, the physical model of a cruise cabin fire is established through PyroSim software, and the safety indexes such as smoke temperature, CO concentration, and visibility are numerically simulated. An Attention-BP neural network model is designed for realizing the intelligent identification of a cabin fire and dividing the risk level, which integrates the diagnosis results of multiple neural network models through the self-Attention mechanism and adaptively distributes the weight of each BP neural network model. The proposed model can provide decision-making reference for subsequent fire-fighting measures and personnel evacuation. Experimental results show that the proposed Attention-BP neural network model can effectively realize the early warning of the fire risk level. Compared with other machine learning algorithms, it has the highest stability and accuracy and reduces the uncertainty of early cabin fire warning.

Keywords:

Cruise Fire, Simulation Modeling, Ensemble Learning, BP Neural Network

Details

Issue
Vol. 29 No. 3 (2022)
Section
Latest Articles
Published
25-11-2022
DOI:
https://doi.org/10.2478/pomr-2022-0026
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Open Access License

This journal provides immediate open access to its content under the Creative Commons BY 4.0 license. Authors who publish with this journal retain all copyrights and agree to the terms of the CC BY 4.0 license.

 

Authors

  • Zhenghua Xiong

    Sichuan Communications Vocational and Technical College, Chengdu, China
  • Bo Xiang

    Sichuan Communications Vocational and Technical College, Chengdu, China
  • Ye Chen

    Wuhan University of Technology, China
  • Bin Chen

    Wuhan University of Technology, China

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