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A novel approach for pilot error detection using Dynamic Bayesian Networks

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journal contribution
posted on 02.07.2015 by Mohamad Saada, Qinggang Meng, Tingwen Huang
In the last decade Dynamic Bayesian Networks (DBNs) have become one type of the most attractive probabilistic modelling framework extensions of Bayesian Networks (BNs) for working under uncertainties from a temporal perspective. Despite this popularity not many researchers have attempted to study the use of these networks in anomaly detection or the implications of data anomalies on the outcome of such models. An abnormal change in the modelled environment's data at a given time, will cause a trailing chain effect on data of all related environment variables in current and consecutive time slices. Albeit this effect fades with time, it still can have an ill effect on the outcome of such models. In this paper we propose an algorithm for pilot error detection, using DBNs as the modelling framework for learning and detecting anomalous data. We base our experiments on the actions of an aircraft pilot, and a flight simulator is created for running the experiments. The proposed anomaly detection algorithm has achieved good results in detecting pilot errors and effects on the whole system. © Springer Science+Business Media 2014.

History

School

  • Science

Department

  • Computer Science

Published in

Cognitive Neurodynamics

Volume

8

Issue

3

Pages

227 - 238

Citation

SAADA, M., MENG, Q. and HUANG, T., 2014. A novel approach for pilot error detection using Dynamic Bayesian Networks. Cognitive Neurodynamics, 8 (3), pp. 227 - 238

Publisher

© Springer Science+Business Media

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2014

Notes

This article was accepted for publication in the journal, Cognitive Neurodynamics [© Springer Science+Business Media]. The final publication is available at Springer via http://dx.doi.org/10.1007/s11571-013-9278-5

ISSN

1871-4080

eISSN

1871-4099

Language

en

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