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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/18118

Title: A novel approach for pilot error detection using Dynamic Bayesian Networks
Authors: Saada, Mohamad
Meng, Qinggang
Huang, Tingwen
Keywords: Anomaly detection
Pilot error detection
Dynamic Bayesian Networks
Outlier detection
Machine learning
Issue Date: 2014
Publisher: © Springer Science+Business Media
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
Abstract: 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.
Description: 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
Version: Accepted for publication
DOI: 10.1007/s11571-013-9278-5
URI: https://dspace.lboro.ac.uk/2134/18118
Publisher Link: http://dx.doi.org/10.1007/s11571-013-9278-5
ISSN: 1871-4080
Appears in Collections:Published Articles (Computer Science)

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