{"title":"基于注意力- bp神经网络的游轮火灾风险分类研究","authors":"Zhenghua Xiong, Bo Xiang, Ye Chen, Bin-bing Chen","doi":"10.2478/pomr-2022-0026","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":49681,"journal":{"name":"Polish Maritime Research","volume":"29 1","pages":"61 - 68"},"PeriodicalIF":2.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Research on the Risk Classification of Cruise Ship Fires Based on an Attention-Bp Neural Network\",\"authors\":\"Zhenghua Xiong, Bo Xiang, Ye Chen, Bin-bing Chen\",\"doi\":\"10.2478/pomr-2022-0026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":49681,\"journal\":{\"name\":\"Polish Maritime Research\",\"volume\":\"29 1\",\"pages\":\"61 - 68\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2022-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Polish Maritime Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.2478/pomr-2022-0026\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Polish Maritime Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.2478/pomr-2022-0026","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
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.
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.