基于集成学习模型的飞行学员认知负荷研究  

Study on flight cadets' cognitive load based on ensemble learning model

作　　者：潘亭 汤博凯 司海青[1] 汪海波[1] 张忠哲 PAN Ting;TANG Bokai;SI Haiqing;WANG Haibo;ZHANG Zhongzhe(College of General Aviation and Flight,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学通用航空与飞行学院,南京211106

出　　处：《航空工程进展》2023年第2期81-90,共10页Advances in Aeronautical Science and Engineering

基　　金：国家自然科学基金民航联合基金重点项目(U2033202);教育部2021年第一批产学研合作协同育人项目(202101042005);中央高校基本科研业务费专项资金资助(1027-XAA22008);江苏省高等教育教改研究课题(2021JSJG206);省级大学生创新创业训练计划项目资助(202110287175Y);南京航空航天大学“实验技术研究与开发”项目(SYJS202207Y);南京航空航天大学博士生短期访学项目资助(ZDGB2021024)。

摘　　要：在飞行过程中,飞行员需要在短时间内接收大量信息,并做出正确的判断与决策,而过高的认知负荷会影响其感知、判断、决策等认知过程,进而影响飞行安全。首先通过飞行模拟实验获取飞行学员在执行不同飞行任务时的生理数据;然后通过时域、频域分析等方法提取呼吸和心电信号的特征,并通过统计学方法筛选出能够反映认知负荷水平的指标;最后结合支持向量机、K最邻近、人工神经网络等方法建立集成学习模型,对飞行学员的认知负荷进行评估,并与单一算法进行对比。结果表明:本文建立的集成学习模型具有较高的准确率,能够更好地反映飞行学员的认知负荷水平。During flight,pilots need to receive a large amount of information in a short time and make correct judgments and decisions.The cognitive processes such as perception,judgment and decision-making will be affected by excessive cognitive load and affect flight safety.Firstly,the physiological data of flight cadets during different flight missions are obtained through flight simulation experiments;Then,the characteristics of RESP(respiratory) and ECG(Electrocardiograph) signals are extracted by time-domain and frequency-domain analysis,and the indexes that can reflect the level of cognitive load are selected by statistical methods.Finally,combined with support vector machine,K-nearest neighbor,artificial neural network and other methods,an ensemble learning model is established to evaluate the flight cadets' cognitive load.Furthermore,it is compared with single algorithms.The results show that the flight cadets' cognitive load evaluation model established in this paper has a high accuracy rate and can better reflect the flight cadets' cognitive load level.

关 键 词：生理信号 飞行学员 认知负荷 集成学习 特征筛选 飞行安全 

分 类 号：V328[航空宇航科学与技术—人机与环境工程]