Regression Methods Applied to Flight Variables for Situational Awareness Estimation Using Dynamic Bayesian Networks

Carlos Morales, Serafín Moral
Proceedings of the Eighth International Conference on Probabilistic Graphical Models, PMLR 52:356-367, 2016.

Abstract

Situational awareness can be a valuable indicator of the performance of flight crews and the way pilots manage navigation information can be relevant to its estimation. In this research, dynamic Bayesian networks are applied to a dataset of variables both collected in real time during simulated flights and added with expert knowledge. This paper compares different approaches to the discretization of continuous variables and to the estimation of pilot actions based on variable regression, in order to optimize the model performance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v52-morales16, title = {Regression Methods Applied to Flight Variables for Situational Awareness Estimation Using Dynamic {B}ayesian Networks}, author = {Morales, Carlos and Moral, Serafín}, booktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models}, pages = {356--367}, year = {2016}, editor = {Antonucci, Alessandro and Corani, Giorgio and Campos}, Cassio Polpo}, volume = {52}, series = {Proceedings of Machine Learning Research}, address = {Lugano, Switzerland}, month = {06--09 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v52/morales16.pdf}, url = {https://proceedings.mlr.press/v52/morales16.html}, abstract = {Situational awareness can be a valuable indicator of the performance of flight crews and the way pilots manage navigation information can be relevant to its estimation. In this research, dynamic Bayesian networks are applied to a dataset of variables both collected in real time during simulated flights and added with expert knowledge. This paper compares different approaches to the discretization of continuous variables and to the estimation of pilot actions based on variable regression, in order to optimize the model performance.} }
Endnote
%0 Conference Paper %T Regression Methods Applied to Flight Variables for Situational Awareness Estimation Using Dynamic Bayesian Networks %A Carlos Morales %A Serafín Moral %B Proceedings of the Eighth International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2016 %E Alessandro Antonucci %E Giorgio Corani %E Cassio Polpo Campos} %F pmlr-v52-morales16 %I PMLR %P 356--367 %U https://proceedings.mlr.press/v52/morales16.html %V 52 %X Situational awareness can be a valuable indicator of the performance of flight crews and the way pilots manage navigation information can be relevant to its estimation. In this research, dynamic Bayesian networks are applied to a dataset of variables both collected in real time during simulated flights and added with expert knowledge. This paper compares different approaches to the discretization of continuous variables and to the estimation of pilot actions based on variable regression, in order to optimize the model performance.
RIS
TY - CPAPER TI - Regression Methods Applied to Flight Variables for Situational Awareness Estimation Using Dynamic Bayesian Networks AU - Carlos Morales AU - Serafín Moral BT - Proceedings of the Eighth International Conference on Probabilistic Graphical Models DA - 2016/08/15 ED - Alessandro Antonucci ED - Giorgio Corani ED - Cassio Polpo Campos} ID - pmlr-v52-morales16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 52 SP - 356 EP - 367 L1 - http://proceedings.mlr.press/v52/morales16.pdf UR - https://proceedings.mlr.press/v52/morales16.html AB - Situational awareness can be a valuable indicator of the performance of flight crews and the way pilots manage navigation information can be relevant to its estimation. In this research, dynamic Bayesian networks are applied to a dataset of variables both collected in real time during simulated flights and added with expert knowledge. This paper compares different approaches to the discretization of continuous variables and to the estimation of pilot actions based on variable regression, in order to optimize the model performance. ER -
APA
Morales, C. & Moral, S.. (2016). Regression Methods Applied to Flight Variables for Situational Awareness Estimation Using Dynamic Bayesian Networks. Proceedings of the Eighth International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 52:356-367 Available from https://proceedings.mlr.press/v52/morales16.html.

Related Material