For sparse storage and quick access to projection matrix based on vector type, this paper proposes a method to solve the problems of the repetitive computation of projection coefficient, the large space occupation and...For sparse storage and quick access to projection matrix based on vector type, this paper proposes a method to solve the problems of the repetitive computation of projection coefficient, the large space occupation and low retrieval efficiency of projection matrix in iterative reconstruction algorithms, which calculates only once the projection coefficient and stores the data sparsely in binary format based on the variable size of library vector type. In the iterative reconstruction process, these binary files are accessed iteratively and the vector type is used to quickly obtain projection coefficients of each ray. The results of the experiments show that the method reduces the memory space occupation of the projection matrix and the computation of projection coefficient in iterative process, and accelerates the reconstruction speed.展开更多
As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The ...As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.展开更多
针对目前飞机离地姿态异常的监控依赖单一参数超限探测、缺乏多参数组合异常检测的问题,提出了一种基于近邻搜索空间提取的局部异常因子算法(Isolation-based Data Extracting Local Outlier Factor,IDELOF)的飞机离地姿态异常检测方法...针对目前飞机离地姿态异常的监控依赖单一参数超限探测、缺乏多参数组合异常检测的问题,提出了一种基于近邻搜索空间提取的局部异常因子算法(Isolation-based Data Extracting Local Outlier Factor,IDELOF)的飞机离地姿态异常检测方法。首先,选取空速、俯仰角、滚转角作为飞机离地姿态特征参数,运用基于隔离思想的近邻搜索空间提取方法进行数据降维提取,降低计算复杂度;其次,利用局部异常因子算法对提取后的数据进行异常检测,识别多参综合异常;然后,基于国内某航空公司A319机队297个航班的快速存取记录器(Quick Access Recorder,QAR)数据,验证了模型对单一参数异常和多参综合异常检测结果的有效性;最后,对模型结果的正异常分布特征及可解释性进行分析,分别阐述了八种异常情况出现的主要原因,为飞行安全风险防控提供了深入的数据支持。展开更多
基金National Natural Science Foundation of China(No.6171177)
文摘For sparse storage and quick access to projection matrix based on vector type, this paper proposes a method to solve the problems of the repetitive computation of projection coefficient, the large space occupation and low retrieval efficiency of projection matrix in iterative reconstruction algorithms, which calculates only once the projection coefficient and stores the data sparsely in binary format based on the variable size of library vector type. In the iterative reconstruction process, these binary files are accessed iteratively and the vector type is used to quickly obtain projection coefficients of each ray. The results of the experiments show that the method reduces the memory space occupation of the projection matrix and the computation of projection coefficient in iterative process, and accelerates the reconstruction speed.
基金supported by the Meteorological Soft Science Project(Grant No.2023ZZXM29)the Natural Science Fund Project of Tianjin,China(Grant No.21JCYBJC00740)the Key Research and Development-Social Development Program of Jiangsu Province,China(Grant No.BE2021685).
文摘As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.
文摘针对目前飞机离地姿态异常的监控依赖单一参数超限探测、缺乏多参数组合异常检测的问题,提出了一种基于近邻搜索空间提取的局部异常因子算法(Isolation-based Data Extracting Local Outlier Factor,IDELOF)的飞机离地姿态异常检测方法。首先,选取空速、俯仰角、滚转角作为飞机离地姿态特征参数,运用基于隔离思想的近邻搜索空间提取方法进行数据降维提取,降低计算复杂度;其次,利用局部异常因子算法对提取后的数据进行异常检测,识别多参综合异常;然后,基于国内某航空公司A319机队297个航班的快速存取记录器(Quick Access Recorder,QAR)数据,验证了模型对单一参数异常和多参综合异常检测结果的有效性;最后,对模型结果的正异常分布特征及可解释性进行分析,分别阐述了八种异常情况出现的主要原因,为飞行安全风险防控提供了深入的数据支持。
