The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety.The current airborne fire detection technology mostly relies on single-parameter s...The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety.The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light.This often results in a high false alarm rate in complex air transportation envi-ronments.The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information.This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model.Dual-wavelength optical sensors,flue gas analyzers,and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy.The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective,which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN(recurrent neural network)and CNN(convolutional neural network).Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information,respectively,which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism.Finally,the output results of the two models are fused through the gate mechanism.The research results show that,compared with the traditional single-feature detection technology,the multi-technology collaborative fire detection method can better capture fire information.Compared with the traditional deep learning model,the multivariate fire pre-diction model constructed by the improved Transformer can better detect fires,and the accuracy rate is 0.995.展开更多
This paper presents a high-speed ground effect vehicle(HS-GEV)used specifically for maritime transportation.Given the limitations of current vessels,including various types of watercraft and high-speed boats,in fulfil...This paper presents a high-speed ground effect vehicle(HS-GEV)used specifically for maritime transportation.Given the limitations of current vessels,including various types of watercraft and high-speed boats,in fulfilling of needs in different maritime transportation scenarios,the HS-GEV emerges as a promising solution to address unmet requirements.To efficiently accomplish maritime transportation missions with quickness and safety,several critical features are emphasized,including short take-off on water,flight maneuverability and flight stability.The key techniques required to achieve these features,as well as recent progress highlights,are introduced.Following and promoting these crucial techniques is also suggested as a future step to improve HS-GEV performance.With its predominant features,the HS-GEV holds immense application value in enhancing a high-speed maritime transportation system that aligns with the evolving needs of the real world.展开更多
基金This work was funded by the National Science Foundation of China(Grant No.U2033206)the Project of Civil Aircraft Fire Science and Safety Engineering Key Laboratory of Sichuan Province(Grant No.MZ2022KF05,Grant No.MZ2022JB01)+3 种基金the project of Key Laboratory of Civil Aviation Emergency Science&Technology,CAAC(Grant No.NJ2022022,Grant No.NJ2023025)the project of Postgraduate Project of Civil Aviation Flight University of China(Grant No X2023-1)the project of the undergraduate innovation and entrepreneurship training program(Grant No 202210624024)the project of General Programs of the Civil Aviation Flight University of China(Grant No J2020-072).
文摘The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety.The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light.This often results in a high false alarm rate in complex air transportation envi-ronments.The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information.This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model.Dual-wavelength optical sensors,flue gas analyzers,and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy.The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective,which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN(recurrent neural network)and CNN(convolutional neural network).Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information,respectively,which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism.Finally,the output results of the two models are fused through the gate mechanism.The research results show that,compared with the traditional single-feature detection technology,the multi-technology collaborative fire detection method can better capture fire information.Compared with the traditional deep learning model,the multivariate fire pre-diction model constructed by the improved Transformer can better detect fires,and the accuracy rate is 0.995.
基金supported by the Fundamental Research Funds for the Central Universities[No.ILA 22012]CARDC Fundamental and Frontier Technology Research Found[No.PJD20200210].
文摘This paper presents a high-speed ground effect vehicle(HS-GEV)used specifically for maritime transportation.Given the limitations of current vessels,including various types of watercraft and high-speed boats,in fulfilling of needs in different maritime transportation scenarios,the HS-GEV emerges as a promising solution to address unmet requirements.To efficiently accomplish maritime transportation missions with quickness and safety,several critical features are emphasized,including short take-off on water,flight maneuverability and flight stability.The key techniques required to achieve these features,as well as recent progress highlights,are introduced.Following and promoting these crucial techniques is also suggested as a future step to improve HS-GEV performance.With its predominant features,the HS-GEV holds immense application value in enhancing a high-speed maritime transportation system that aligns with the evolving needs of the real world.
