Cargando…

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...

Descripción completa

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
Descripción
Sumario: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.