This manuscript presents a research proposal to investigate how hazardous attitudes among general aviation pilots influence pilot performance in aviation accidents. General aviation pilots train to maintain safe flyin...This manuscript presents a research proposal to investigate how hazardous attitudes among general aviation pilots influence pilot performance in aviation accidents. General aviation pilots train to maintain safe flying conditions, but accidents still occur, and human factors figure prominently among the causes of aviation accidents. The levels of hazardous attitudes among pilots may influence the likelihood of engaging in risky flight behaviors that can lead to accidents. This quantitative study aims to determine whether dangerous attitudes impact risk perception in general aviation pilots. The study will focus on two specific hazardous attitudes, “Anti-Authority” and Macho” behaviors. Among the hazardous attitudes identified by the Federal Aviation Administration (FAA), the two attitudes often stand out in accident investigations and pilot narratives. While all hazardous attitudes have inherent dangers, these two attitudes tend to be more frequently cited in accident reports and investigations. Despite rigorous training in safe flying conditions, general aviation accidents still transpire due to human factors. This research hypothesizes that the five attitudes from the hazardous attitude model, particularly Anti-Authority and Macho, significantly shape pilots’ risk perception. The insights from this study would benefit stakeholders, like the Aircraft Owners and Pilots Association (AOPA), Air Safety Institute, and aviation training programs, in creating training modules tailored to reduce such attitudes.展开更多
训练飞行的首要前提就是保证人机安全,通过分析飞行数据来监控和评估飞行质量成为提高安全性和训练质量的手段之一。基于Energy-Metrics(能量度量)的异常飞行数据分析方法可为分析训练飞行安全分析提供帮助。提出了一种在训练飞行进近...训练飞行的首要前提就是保证人机安全,通过分析飞行数据来监控和评估飞行质量成为提高安全性和训练质量的手段之一。基于Energy-Metrics(能量度量)的异常飞行数据分析方法可为分析训练飞行安全分析提供帮助。提出了一种在训练飞行进近着陆阶段基于能量度量的异常飞行数据识别方法,首先,通过基于能量度量指标方法为飞行数据生成特征向量,然后,借助具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noise,DBSCAN)聚类和单类支持向量机(support vector machine,SVM)方法,对能量度量的指标数据特征向量进行飞行异常检测和识别。通过将模拟异常飞行数据隐藏在实际飞行数据中进行飞行数据的检测和识别,证明该方法的异常数据检测成功率为95%以上,且使用不同能量度量指标识别出的异常飞行数据具有高达98%的一致性,证明了该方法在识别异常飞行数据上具有较强的有效性和鲁棒性,为训练飞行回顾性安全分析提供有力的帮助。展开更多
文摘This manuscript presents a research proposal to investigate how hazardous attitudes among general aviation pilots influence pilot performance in aviation accidents. General aviation pilots train to maintain safe flying conditions, but accidents still occur, and human factors figure prominently among the causes of aviation accidents. The levels of hazardous attitudes among pilots may influence the likelihood of engaging in risky flight behaviors that can lead to accidents. This quantitative study aims to determine whether dangerous attitudes impact risk perception in general aviation pilots. The study will focus on two specific hazardous attitudes, “Anti-Authority” and Macho” behaviors. Among the hazardous attitudes identified by the Federal Aviation Administration (FAA), the two attitudes often stand out in accident investigations and pilot narratives. While all hazardous attitudes have inherent dangers, these two attitudes tend to be more frequently cited in accident reports and investigations. Despite rigorous training in safe flying conditions, general aviation accidents still transpire due to human factors. This research hypothesizes that the five attitudes from the hazardous attitude model, particularly Anti-Authority and Macho, significantly shape pilots’ risk perception. The insights from this study would benefit stakeholders, like the Aircraft Owners and Pilots Association (AOPA), Air Safety Institute, and aviation training programs, in creating training modules tailored to reduce such attitudes.
文摘训练飞行的首要前提就是保证人机安全,通过分析飞行数据来监控和评估飞行质量成为提高安全性和训练质量的手段之一。基于Energy-Metrics(能量度量)的异常飞行数据分析方法可为分析训练飞行安全分析提供帮助。提出了一种在训练飞行进近着陆阶段基于能量度量的异常飞行数据识别方法,首先,通过基于能量度量指标方法为飞行数据生成特征向量,然后,借助具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noise,DBSCAN)聚类和单类支持向量机(support vector machine,SVM)方法,对能量度量的指标数据特征向量进行飞行异常检测和识别。通过将模拟异常飞行数据隐藏在实际飞行数据中进行飞行数据的检测和识别,证明该方法的异常数据检测成功率为95%以上,且使用不同能量度量指标识别出的异常飞行数据具有高达98%的一致性,证明了该方法在识别异常飞行数据上具有较强的有效性和鲁棒性,为训练飞行回顾性安全分析提供有力的帮助。
