Artificial Intelligence in Flight Safety: Fatigue Monitoring and Risk Mitigation Technologies
DOI:
https://doi.org/10.5281/zenodo.13897958Keywords:
artificial intelligence, physiological fatigue, pilots, flight safetyAbstract
With the improvement of computer, artificial intelligence, information technology and other technical levels, the relationship between man-machine environment systems is more complicated and diversified. The optimization, iteration and development of the new generation of intelligent equipment system and human-computer interaction interface put forward higher requirements for ensuring the safety of personnel, improving the efficiency of human-computer interaction and improving the efficiency of the system. Such as intelligent cabin adaptive cognitive decision aid system, how to adopt intelligent information display and human-computer interaction, optimize information processing, strengthen situational awareness; How to effectively present information and improve the efficiency of human-computer interaction, so that the system has good security, applicability and maximize its effectiveness; How to deal with man-machine matching and man-machine collaboration problems, so as to improve the efficiency of man-machine/unmanned collaborative work. Human factors throughout the life cycle of equipment systems must be fully considered. The human factor is considered in the system design, so that people, machines and the environment can work together and adapt to each other, so as to achieve benign interaction and feedback between people and equipment and interface and complete the full transmission and communication of human-machine intelligent interaction information. The development of new aircraft human-computer interaction systems combined with new technological methods has also gradually changed the role of pilots and staff. From the system operator gradually into the monitor and decision maker, especially with the improvement of the degree of intelligent flight, information technology, advanced complex airborne equipment is increasing, the amount of information that operators need to deal with is also increasing, and the allowed time for judgment and decision is very short, and the mental resources that pilots bear are gradually rising. As the mental load is a key factor affecting the allocation of cognitive tasks, when encountering emergency situations, the mental load overload caused by the increase of information processing tasks often occurs, which seriously affects the task performance of operators, physical and psychological comfort and flight safety, and thus affects the efficiency and safety of the entire aircraft man-machine system. This requires us to conduct real-time analysis of human-computer interaction situational awareness, especially the individual cognitive state as an uncontrollable factor.
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Wang, C., Chen, J., Xie, Z., & Zou, J. (2024). Research on personalized teaching strategies selection based on deep learning. Wang, D. (Ed.). (2016). Information science and electronic engineering: Proceedings of the 3rd International Conference of Electronic Engineering and Information Science (ICEEIS 2016), January 4-5, 2016, Harbin, China. CRC Press.
Wang, H., Li, J., & Li, Z. (2024). AI-generated text detection and classification based on BERT deep learning algorithm. arXiv preprint arXiv:2405.16422.
Wang, J., An, G., Peng, X., Zhong, F., Zhao, K., Qi, L., & Ma, Y. (2024). Effects of three Huanglian-derived polysaccharides on the gut microbiome and fecal metabolome of high-fat diet/streptozocin-induced type 2 diabetes mice. International Journal of Biological Macromolecules, 133060.
Wang, Y., He, Z., Zou, J., Xie, H., & Bao, J. (2024). Energy transition for sustainable economy: What is the role of government governance and public concern?. Finance Research Letters, 106087.
Xie, D., Kuang, Y., Yuan, B., Zhang, Y., Ye, C., Guo, Y., ... & Yang, S. (2025). Convenient and highly efficient adsorption of diosmetin from lemon peel by magnetic surface molecularly imprinted polymers. Journal of Materials Science & Technology, 211, 159-170.
Yang, J., Qin, H., Por, L. Y., Shaikh, Z. A., Alfarraj, O., Tolba, A., ... & Thwin, M. (2024). Optimizing diabetic retinopathy detection with inception-V4 and dynamic version of snow leopard optimization algorithm. Biomedical Signal Processing and Control, 96, 106501.
Yu, C., Jin, Y., Xing, Q., Zhang, Y., Guo, S., & Meng, S. (2024). Advanced user credit risk prediction model using LightGBM, XGBoost and Tabnet with SMOTEENN. arXiv preprint arXiv:2408.03497.
Yu, C., Xu, Y., Cao, J., Zhang, Y., Jin, Y., & Zhu, M. (2024). Credit card fraud detection using advanced transformer model. arXiv preprint arXiv:2406.03733.
Zeng, X., Wang, S., Peng, Z., Wang, M., Zhao, K., Xu, B. B., ... & Wang, J. (2024). Rapid screening and sensing of stearoyl-CoA desaturase 1 (SCD1) inhibitors from ginger and their efficacy in ameliorating non-alcoholic fatty liver disease. Journal of Food Measurement and Characterization, 1-15.
Zhang, X., Xu, L., Li, N., & Zou, J. (2024). Research on credit risk assessment optimization based on machine learning.
Zhang, Y., Qu, T., Yao, T., Gong, Y., & Bian, X. (2024). Research on the application of BIM technology in intelligent building technology. Applied and Computational Engineering, 61, 29-34.
Zhao, K., Qian, C., Qi, L., Li, Q., Zhao, C., Zhang, J., ... & Shi, Z. (2024). Modified acid polysaccharide derived from Salvia przewalskii with excellent wound healing and enhanced bioactivity. International Journal of Biological Macromolecules, 263, 129803.
Zhao, K., Wu, X., Han, G., Sun, L., Zheng, C., Hou, H., ... & Shi, Z. (2024). Phyllostachys nigra (Lodd. ex Lindl.) derived polysaccharide with enhanced glycolipid metabolism regulation and mice gut microbiome. International Journal of Biological Macromolecules, 257, 128588.
Zhao, S., Zhang, T., & Li, N. (2024). Machine learning analysis of key features in household financial decision-making. Academic Journal of Science and Technology, 12(2), 1-6.
Zheng, Q., Yu, C., Cao, J., Xu, Y., Xing, Q., & Jin, Y. (2024). Advanced payment security system: XGBoost, CatBoost and SMOTE Integrated. arXiv preprint arXiv:2406.04658.
Chen, Y., Yan, S., Liu, S., Li, Y., & Xiao, Y. (2024, August). EmotionQueen: A benchmark for evaluating empathy of large language models. In Findings of the Association for Computational Linguistics ACL, pp. 2149-2176.
Chen, Z., Ge, J., Zhan, H., Huang, S., & Wang, D. (2021). Pareto self-supervised training for few-shot learning. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13663-13672.
Feng, Z., Ge, M., & Meng, Q. (2024). Enhancing energy efficiency in green buildings through artificial intelligence.
Feng, Z., Ge, M., Meng, Q., & Chen, Y. (2024). Research on old building renovation strategies by using green building technologies.
Ge, M., Feng, Z., & Meng, Q. (2024). Urban planning and green building technologies based on artificial intelligence: Principles, applications, and global case study analysis.
Huang, D., Liu, Z., & Li, Y. (2024). Research on tumors segmentation based on image enhancement method. arXiv preprint arXiv:2406.05170.
Huang, D., Xu, L., Tao, W., & Li, Y. (2024). Research on genome data recognition and analysis based on louvain algorithm.
Jiang, G., Zhao, S., Yang, H., & Zhang, K. (2024). Research on finance risk management based on combination optimization and reinforcement learning.
Jin, Y., Shimizu, S., Li, Y., Yao, Y., Liu, X., Si, H., ... & Xiao, W. (2023). Proton therapy (PT) combined with concurrent chemotherapy for locally advanced non-small cell lung cancer with negative driver genes. Radiation Oncology, 18(1), 189.
Kumada, H., Li, Y., Yasuoka, K., Naito, F., Kurihara, T., Sugimura, T., ... & Sakae, T. (2022). Current development status of iBNCT001, demonstrator of a LINAC-based neutron source for BNCT. Journal of Neutron Research, 24(3-4), 347-358.
Lai, S., Feng, N., Sui, H., Ma, Z., Wang, H., Song, Z., ... & Yue, Y. (2024). FTS: A framework to find a faithful TimeSieve. arXiv preprint arXiv:2405.19647.
Li, B., Jiang, G., Li, N., & Song, C. (2024). Research on large-scale structured and unstructured data processing based on large language model.
Li, B., Zhang, K., Sun, Y., & Zou, J. (2024). Research on travel route planning optimization based on large language model.
Li, B., Zhang, X., Wang, X. A., Yong, S., Zhang, J., & Huang, J. (2019, April). A feature extraction method for daily-periodic time series based on AETA electromagnetic disturbance data. in Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence, pp. 215-219.
Li, S., & Tajbakhsh, N. (2023). Scigraphqa: A large-scale synthetic multi-turn question-answering dataset for scientific graphs. arXiv preprint arXiv:2308.03349.
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Copyright (c) 2024 Weibo Jin, Haoxing Liu, Fangzhou Shen
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