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Self-organising Maps for Comparing Flying Performance Using Different Inceptors

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Engineering Psychology and Cognitive Ergonomics (HCII 2024)

Abstract

This paper addresses a new data analysis method which is suitable to cluster flight data and complement current exceedance-based flight data monitoring programmes within an airline. The data used for this study consists of 296 simulated approaches from 4.5 NM to 1 NM to the runway threshold, flown by 74 participants (both pilots and non-pilots) with either a conventional sidestick or a gamepad in the future flight simulator at Cranfield University. It was clustered and analysed with the use of Kohonen’s Self-Organising Maps (SOM) algorithm. The results demonstrate that SOM can be a meaningful indicator for safety analysts to accurately cluster both optimal and less-optimal flying performance. This methodology can therefore complement current deviation-based flight data analyses by highlighting day-to-day as well as exceptionally good performance, bridging the cap of current analyses with safety-II principles.

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Nichanian, A., Li, WC., Korek, W.T., Wang, Y., Chan, W.TK. (2024). Self-organising Maps for Comparing Flying Performance Using Different Inceptors. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. HCII 2024. Lecture Notes in Computer Science(), vol 14693. Springer, Cham. https://doi.org/10.1007/978-3-031-60731-8_8

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  • DOI: https://doi.org/10.1007/978-3-031-60731-8_8

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