基于注意力- bp神经网络的游轮火灾风险分类研究

IF 2 3区 工程技术 Q2 ENGINEERING, MARINE
Zhenghua Xiong, Bo Xiang, Ye Chen, Bin-bing Chen
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引用次数: 2

摘要

摘要由于船舶环境相对封闭,内部结构复杂,人员疏散困难,船舶火灾比陆地火灾更难预防。本文以大型邮轮为研究对象,通过PyroSim软件建立了邮轮客舱火灾的物理模型,并对烟雾温度、CO浓度、能见度等安全指标进行了数值模拟。为实现客舱火灾的智能识别和风险等级划分,设计了一种Attention-BP神经网络模型,该模型通过自注意机制整合多个神经网络模型的诊断结果,并自适应地分配各个BP神经网络模型的权重。该模型可为后续消防措施和人员疏散提供决策参考。实验结果表明,提出的Attention-BP神经网络模型能够有效地实现火灾危险等级的预警。与其他机器学习算法相比,它具有最高的稳定性和准确性,并减少了早期机舱火灾预警的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Polish Maritime Research
Polish Maritime Research 工程技术-工程:海洋
CiteScore
3.70
自引率
45.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The scope of the journal covers selected issues related to all phases of product lifecycle and corresponding technologies for offshore floating and fixed structures and their components. All researchers are invited to submit their original papers for peer review and publications related to methods of the design; production and manufacturing; maintenance and operational processes of such technical items as: all types of vessels and their equipment, fixed and floating offshore units and their components, autonomous underwater vehicle (AUV) and remotely operated vehicle (ROV). We welcome submissions from these fields in the following technical topics: ship hydrodynamics: buoyancy and stability; ship resistance and propulsion, etc., structural integrity of ship and offshore unit structures: materials; welding; fatigue and fracture, etc., marine equipment: ship and offshore unit power plants: overboarding equipment; etc.
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