Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer charact...Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft.In this paper,the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed,and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed.By modeling the spatial distribution of the disturbance density field,the scattered echo signal images of active light towards the disturbance field at long distance are simulated.On this basis,the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed.The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images.The disturbance signal can be further highlighted by differential processing of the adjacent scattering images.As the distance behind the aircraft increases,the diffusion range of the disturbance signal increases,and the signal intensity and contrast with the background decrease.Under the ground-based observation conditions of the aircraft at a height of 10000 m,a Mach number of1.6,and a detection distance of 100 km,the contrast between the disturbance signal and the back-ground was 30 d B at a distance of one time from the rear of the fuselage,and the diffusion diameter of the disturbance signal was 50 m.At a distance eight times the length of the aircraft,the contrast decreased to 10 dB,and the diameter increased to 290 m.The contrast was reduced to 3 dB at a distance nine times the length of the aircraft,and the diameter was diffused to 310 m.These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.展开更多
针对航空电缆电弧故障引起的微小电流变化难以识别的问题,提出了一种基于Inception模块和双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)的交流串联电弧故障诊断方法。首先通过计算自相关系数的离散平方和(discrete...针对航空电缆电弧故障引起的微小电流变化难以识别的问题,提出了一种基于Inception模块和双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)的交流串联电弧故障诊断方法。首先通过计算自相关系数的离散平方和(discrete sum of squares of the atocorrelation coefficient)、信息熵(Shannon entropy)以及小波能量熵(wavelet energy entropy)提取原始电流数据的特征,将特征合并形成新的特征矩阵,对原始数据实现特征增强。之后Inception-BiLSTM网络利用特征矩阵进行学习,最后完成对电弧故障的诊断。为了验证模型在实际环境中的诊断性能,在充分考虑实际情况下,基于航空电缆电弧模拟实验平台进行了振动试验、应力实验以及潮湿电缆实验,并将实验数据整合作为检测样本。实验结果表明,本文方法对于识别电弧故障有着较高的准确度,可以达到99.69%。展开更多
Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-...Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.展开更多
This paper presents a comprehensive test and systematic evaluation analysis of cabin noise in the Robinson R44 RAVEN II helicopter.Initially,microphones were placed within the cabin to conduct systematic assessments o...This paper presents a comprehensive test and systematic evaluation analysis of cabin noise in the Robinson R44 RAVEN II helicopter.Initially,microphones were placed within the cabin to conduct systematic assessments of noise levels under various flight conditions,including takeoff,climbing,level flight,landing,hovering,etc.Subsequently,time–frequency analysis was conducted on the test data utilizing traditional A-weighted sound pressure levels,which was followed by quantitative comparisons across different flight conditions.Then,detailed evaluation and discussion were conducted,taking into account the subjective perceptions and communication challenges of cabin crew members.This assessment incorporated the use of aviation noise indicators,speech interference levels,and metrics related to sound quality.Finally,potential noise reduction measures and their effects were preliminarily discussed.The results indicate that helicopter cabin noise exhibited variations across different flight states or positions within the same state,ranging from 87.6 d B(A)to 92.6 dB(A).Discrepancies between A-weighted sound pressure level and psychoacoustic parameters were observed,particularly during hovering states,which indicate that there is a necessity for the combination of multiple evaluation indicators.Notably,damping measure can serve as a pivotal factor in mitigating cabin noise.展开更多
The resource-intensive,high-fidelity infrared signature simulations and Radar CrossSection(RCS)calculations limit the integrated optimization of Unmanned Combat Aerial Vehicles(UCAVs)in response to escalating threats ...The resource-intensive,high-fidelity infrared signature simulations and Radar CrossSection(RCS)calculations limit the integrated optimization of Unmanned Combat Aerial Vehicles(UCAVs)in response to escalating threats from joint detection systems.To this end,we present a sample-efficient framework to advance the optimization efficiency of UCAV's exhaust system,focusing on both the stealth characteristics evaluation and the optimization process.A novel multi-fidelity stealth assessment method,powered by multi-fidelity neural network and local perceptive fields,has been developed to fuse different fidelity information from infrared radiation signature and RCS values,respectively.Results demonstrate that the method can achieve relatively high accuracy based on a small set of high-fidelity data.Furthermore,this data fusion method is integrated into a multi-objective Bayesian optimization framework.Employing a Gaussian process regression model and the EHVI acquisition function,the framework effectively explores the stealth objective space,achieving a 15.21%hypervolume indicator increase with fewer optimization iterations compared to NSGA-Ⅱ.Results show that the optimized nozzle significantly reduces both the infrared signature and RCS compared to the baseline configuration.The proposed framework offers a practical and efficient approach for optimizing the integrated stealth performance of UCAVs.展开更多
文摘Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft.In this paper,the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed,and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed.By modeling the spatial distribution of the disturbance density field,the scattered echo signal images of active light towards the disturbance field at long distance are simulated.On this basis,the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed.The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images.The disturbance signal can be further highlighted by differential processing of the adjacent scattering images.As the distance behind the aircraft increases,the diffusion range of the disturbance signal increases,and the signal intensity and contrast with the background decrease.Under the ground-based observation conditions of the aircraft at a height of 10000 m,a Mach number of1.6,and a detection distance of 100 km,the contrast between the disturbance signal and the back-ground was 30 d B at a distance of one time from the rear of the fuselage,and the diffusion diameter of the disturbance signal was 50 m.At a distance eight times the length of the aircraft,the contrast decreased to 10 dB,and the diameter increased to 290 m.The contrast was reduced to 3 dB at a distance nine times the length of the aircraft,and the diameter was diffused to 310 m.These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.
文摘针对航空电缆电弧故障引起的微小电流变化难以识别的问题,提出了一种基于Inception模块和双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)的交流串联电弧故障诊断方法。首先通过计算自相关系数的离散平方和(discrete sum of squares of the atocorrelation coefficient)、信息熵(Shannon entropy)以及小波能量熵(wavelet energy entropy)提取原始电流数据的特征,将特征合并形成新的特征矩阵,对原始数据实现特征增强。之后Inception-BiLSTM网络利用特征矩阵进行学习,最后完成对电弧故障的诊断。为了验证模型在实际环境中的诊断性能,在充分考虑实际情况下,基于航空电缆电弧模拟实验平台进行了振动试验、应力实验以及潮湿电缆实验,并将实验数据整合作为检测样本。实验结果表明,本文方法对于识别电弧故障有着较高的准确度,可以达到99.69%。
基金co-supported by the National Key Research and Development Program of China(No. 2021YFB3301504)the National Natural Science Foundation of China (Nos. 62072415, 62036010, 42301526, 62372416 and 62472389)the National Natural Science Foundation of Henan Province, China (No. 242300421215)
文摘Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.
基金the Natural Science Foundation of Sichuan Province,China(Nos.2023NSFSC0902,2024ZYD0083)the Open Project of Key Laboratory of Aerodynamic Noise Control of China Aerodynamics Research and Development Center(No.ANCL20220202)+1 种基金the Open Project of Key Laboratory of Flight Techniques and Flight Safety,CAAC,China(No.FZ2022KF01)the Fundamental Research Funds for the Central Universities,China(No.24CAFUC01007)。
文摘This paper presents a comprehensive test and systematic evaluation analysis of cabin noise in the Robinson R44 RAVEN II helicopter.Initially,microphones were placed within the cabin to conduct systematic assessments of noise levels under various flight conditions,including takeoff,climbing,level flight,landing,hovering,etc.Subsequently,time–frequency analysis was conducted on the test data utilizing traditional A-weighted sound pressure levels,which was followed by quantitative comparisons across different flight conditions.Then,detailed evaluation and discussion were conducted,taking into account the subjective perceptions and communication challenges of cabin crew members.This assessment incorporated the use of aviation noise indicators,speech interference levels,and metrics related to sound quality.Finally,potential noise reduction measures and their effects were preliminarily discussed.The results indicate that helicopter cabin noise exhibited variations across different flight states or positions within the same state,ranging from 87.6 d B(A)to 92.6 dB(A).Discrepancies between A-weighted sound pressure level and psychoacoustic parameters were observed,particularly during hovering states,which indicate that there is a necessity for the combination of multiple evaluation indicators.Notably,damping measure can serve as a pivotal factor in mitigating cabin noise.
基金supported by the National Natural Science Foundation of China(No.12102356)。
文摘The resource-intensive,high-fidelity infrared signature simulations and Radar CrossSection(RCS)calculations limit the integrated optimization of Unmanned Combat Aerial Vehicles(UCAVs)in response to escalating threats from joint detection systems.To this end,we present a sample-efficient framework to advance the optimization efficiency of UCAV's exhaust system,focusing on both the stealth characteristics evaluation and the optimization process.A novel multi-fidelity stealth assessment method,powered by multi-fidelity neural network and local perceptive fields,has been developed to fuse different fidelity information from infrared radiation signature and RCS values,respectively.Results demonstrate that the method can achieve relatively high accuracy based on a small set of high-fidelity data.Furthermore,this data fusion method is integrated into a multi-objective Bayesian optimization framework.Employing a Gaussian process regression model and the EHVI acquisition function,the framework effectively explores the stealth objective space,achieving a 15.21%hypervolume indicator increase with fewer optimization iterations compared to NSGA-Ⅱ.Results show that the optimized nozzle significantly reduces both the infrared signature and RCS compared to the baseline configuration.The proposed framework offers a practical and efficient approach for optimizing the integrated stealth performance of UCAVs.
