<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

Due to the relatively closed environment, complex internal structure, and difficult evacuation of personnel, it is more difficult to prevent ship fires than...

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

Online veröffentlicht: 29. Okt. 2022

Seitenbereich: 61 - 68

DOI: https://doi.org/10.2478/pomr-2022-0026

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

© 2022 Zhenghua Xiong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Online veröffentlicht: 29. Okt. 2022

Seitenbereich: 61 - 68

DOI: https://doi.org/10.2478/pomr-2022-0026

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

© 2022 Zhenghua Xiong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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