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Research on Fire-Detection Algorithm for Airplane Cargo Compartment Based on Typical Characteristic Parameters

To clarify the reasons for inaccurate fire detection in aircraft cargo holds, this article depicts research from the perspective of a single type of sensor detection. In terms of fire smoke, we select dual-wavelength photoelectric smoke sensors for fire-data collection and a genetic algorithm to opt...

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Detalles Bibliográficos
Autores principales: Wang, Haibin, Ge, Hongjuan, Zhang, Zhihui, Bu, Zonghao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650877/
https://www.ncbi.nlm.nih.gov/pubmed/37960496
http://dx.doi.org/10.3390/s23218797
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author Wang, Haibin
Ge, Hongjuan
Zhang, Zhihui
Bu, Zonghao
author_facet Wang, Haibin
Ge, Hongjuan
Zhang, Zhihui
Bu, Zonghao
author_sort Wang, Haibin
collection PubMed
description To clarify the reasons for inaccurate fire detection in aircraft cargo holds, this article depicts research from the perspective of a single type of sensor detection. In terms of fire smoke, we select dual-wavelength photoelectric smoke sensors for fire-data collection and a genetic algorithm to optimize the classification and detection of random forest fires. From the perspective of fire CO concentration, we use PSO-LSTM to train a CO concentration compensation model to reduce sensor measurement errors. Research is then conducted from the perspective of various types of sensor detection, using the improved BP-AdaBoost algorithm to train a fire-detection model and achieve the high-precision identification of complex environments and fire situations.
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spelling pubmed-106508772023-10-28 Research on Fire-Detection Algorithm for Airplane Cargo Compartment Based on Typical Characteristic Parameters Wang, Haibin Ge, Hongjuan Zhang, Zhihui Bu, Zonghao Sensors (Basel) Article To clarify the reasons for inaccurate fire detection in aircraft cargo holds, this article depicts research from the perspective of a single type of sensor detection. In terms of fire smoke, we select dual-wavelength photoelectric smoke sensors for fire-data collection and a genetic algorithm to optimize the classification and detection of random forest fires. From the perspective of fire CO concentration, we use PSO-LSTM to train a CO concentration compensation model to reduce sensor measurement errors. Research is then conducted from the perspective of various types of sensor detection, using the improved BP-AdaBoost algorithm to train a fire-detection model and achieve the high-precision identification of complex environments and fire situations. MDPI 2023-10-28 /pmc/articles/PMC10650877/ /pubmed/37960496 http://dx.doi.org/10.3390/s23218797 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Haibin
Ge, Hongjuan
Zhang, Zhihui
Bu, Zonghao
Research on Fire-Detection Algorithm for Airplane Cargo Compartment Based on Typical Characteristic Parameters
title Research on Fire-Detection Algorithm for Airplane Cargo Compartment Based on Typical Characteristic Parameters
title_full Research on Fire-Detection Algorithm for Airplane Cargo Compartment Based on Typical Characteristic Parameters
title_fullStr Research on Fire-Detection Algorithm for Airplane Cargo Compartment Based on Typical Characteristic Parameters
title_full_unstemmed Research on Fire-Detection Algorithm for Airplane Cargo Compartment Based on Typical Characteristic Parameters
title_short Research on Fire-Detection Algorithm for Airplane Cargo Compartment Based on Typical Characteristic Parameters
title_sort research on fire-detection algorithm for airplane cargo compartment based on typical characteristic parameters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650877/
https://www.ncbi.nlm.nih.gov/pubmed/37960496
http://dx.doi.org/10.3390/s23218797
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AT gehongjuan researchonfiredetectionalgorithmforairplanecargocompartmentbasedontypicalcharacteristicparameters
AT zhangzhihui researchonfiredetectionalgorithmforairplanecargocompartmentbasedontypicalcharacteristicparameters
AT buzonghao researchonfiredetectionalgorithmforairplanecargocompartmentbasedontypicalcharacteristicparameters