How to accurately recognize the mental state of pilots is a focus in civil aviation safety.The mental state of pilots is closely related to their cognitive ability in piloting.Whether the cognitive ability meets the s...How to accurately recognize the mental state of pilots is a focus in civil aviation safety.The mental state of pilots is closely related to their cognitive ability in piloting.Whether the cognitive ability meets the standard is related to flight safety.However,the pilot’s working state is unique,which increases the difficulty of analyzing the pilot’s mental state.In this work,we proposed a Convolutional Neural Network(CNN)that merges attention to classify the mental state of pilots through electroencephalography(EEG).Considering the individual differences in EEG,semi-supervised learning based on improvedK-Means is used in themodel training to improve the generalization ability of the model.We collected the EEG data of 12 pilot trainees during the simulated flight and compared the method in this paper with other methods on this data.The method in this paper achieved an accuracy of 86.29%,which is better than 4D-aNN and HCNN etc.Negative emotion will increase the probability of fatigue appearing,and emotion recognition is also meaningful during the flight.Then we conducted experiments on the public dataset SEED,and our method achieved an accuracy of 93.68%.In addition,we combine multiple parameters to evaluate the results of the classification network on a more detailed level and propose a corresponding scoring mechanism to display the mental state of the pilots directly.展开更多
In this work,the primary focus is to identify potential technical risks of Artificial Intel-ligence(AI)-driven operations for the safety monitoring of the air traffic from the perspective of speech communication by st...In this work,the primary focus is to identify potential technical risks of Artificial Intel-ligence(AI)-driven operations for the safety monitoring of the air traffic from the perspective of speech communication by studying the representative case and evaluating user experience.The case study is performed to evaluate the AI-driven techniques and applications using objective metrics,in which several risks and technical facts are obtained to direct future research.Considering the safety–critical specificities of the air traffic control system,a comprehensive subjective evaluation is conducted to collect user experience by a well-designed anonymous questionnaire and a face-to-face interview.In this procedure,the potential risks obtained from the case study are confirmed,and the impacts on human working are considered.Both the case study and the evaluation of user experience provide compatible results and conclusions:(A)the proposed solution is promising to improve the traffic safety and reduce the workload by detecting potential risks in advance;(B)the AI-driven techniques and whole diagram are suggested to be enhanced to eliminate the possible distraction to the attention of air traffic controllers.Finally,a variety of strategies and approaches are discussed to explore their capability to advance the proposed solution to industrial practices.展开更多
基金This research is supported by program of Key Laboratory of Flight Technology and Flight Safety(FZ2020KF07)Ms.ZaijunWang received the grant.This research is also supported by Postgraduate Innovation Project of CAFUC(X2021-37)Mr.Qianlei Wang received the grant.
文摘How to accurately recognize the mental state of pilots is a focus in civil aviation safety.The mental state of pilots is closely related to their cognitive ability in piloting.Whether the cognitive ability meets the standard is related to flight safety.However,the pilot’s working state is unique,which increases the difficulty of analyzing the pilot’s mental state.In this work,we proposed a Convolutional Neural Network(CNN)that merges attention to classify the mental state of pilots through electroencephalography(EEG).Considering the individual differences in EEG,semi-supervised learning based on improvedK-Means is used in themodel training to improve the generalization ability of the model.We collected the EEG data of 12 pilot trainees during the simulated flight and compared the method in this paper with other methods on this data.The method in this paper achieved an accuracy of 86.29%,which is better than 4D-aNN and HCNN etc.Negative emotion will increase the probability of fatigue appearing,and emotion recognition is also meaningful during the flight.Then we conducted experiments on the public dataset SEED,and our method achieved an accuracy of 93.68%.In addition,we combine multiple parameters to evaluate the results of the classification network on a more detailed level and propose a corresponding scoring mechanism to display the mental state of the pilots directly.
基金supported by the National Natural Science Foundation of China(Nos.62001315,71971150,U20A20161)the Open Fund of Key Laboratory of Flight Techniques and Flight Safety,Civil Aviation Administration of China(No.FZ2021KF04)Fundamental Research Funds for the Central Universities of China(No.2021SCU12050).
文摘In this work,the primary focus is to identify potential technical risks of Artificial Intel-ligence(AI)-driven operations for the safety monitoring of the air traffic from the perspective of speech communication by studying the representative case and evaluating user experience.The case study is performed to evaluate the AI-driven techniques and applications using objective metrics,in which several risks and technical facts are obtained to direct future research.Considering the safety–critical specificities of the air traffic control system,a comprehensive subjective evaluation is conducted to collect user experience by a well-designed anonymous questionnaire and a face-to-face interview.In this procedure,the potential risks obtained from the case study are confirmed,and the impacts on human working are considered.Both the case study and the evaluation of user experience provide compatible results and conclusions:(A)the proposed solution is promising to improve the traffic safety and reduce the workload by detecting potential risks in advance;(B)the AI-driven techniques and whole diagram are suggested to be enhanced to eliminate the possible distraction to the attention of air traffic controllers.Finally,a variety of strategies and approaches are discussed to explore their capability to advance the proposed solution to industrial practices.
