Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting with eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on the combination of Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that the prediction accuracy of this method is up to 79.2%, which is substantially higher than that of Binary Logistic Regression, CNN and LSTM (71.3%, 74.6%, and 75.1% respectively). This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation.
Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting to eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that this method, using eye movement data, can provide control forgetting prediction with remarkably high accuracy. This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation.
The phenomenon of change blindness in the process of aircraft flying is easy to cause pilots to miss the key information prompt, resulting in huge safety risks. In order to ensure flight safety by reducing the occurrence of change blindness, this research adopts the forced detection paradigm and eye tracking technology, combining keystroke accuracy, reaction time, average fixation time, fixation point number to explore stimulus presentation time, knob interface layout complexity, and influence of the position of the knob on change blindness. Therefore, it provides theoretical guidance on how to reduce the incidence of change blindness. The results showed that increasing the time of stimulus presentation could improve the efficiency of change detection and reduce the probability of change blindness. The more concise and reasonable the knob interface layout is, the more beneficial it is in reducing the cognitive load of the subjects and improving the change detection ability of the subjects, thereby reducing the incidence of change blindness. The subjects tended to devote more attention to the central knob, and the identification and processing of information were more comprehensive, meaning the success rate of change detection was higher. It is also proved that there is no interaction between these factors.
Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting to eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that this method, using eye movement data, can provide control forgetting prediction with remarkably high accuracy. This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation.
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