4 research outputs found
Aircraft Dynamic Rerouting Support
In the frame of Clean Sky 2 JU, the HARVIS (Human Aircraft Roadmap for Virtual Intelligent
System) project introduces a cockpit assistant committed to help the pilot to reroute the aircraft
in single-pilot operations. A relevant scenario for this AI assistant is that in which diversion to
alternate airfield is required after an emergency. Another interesting scenario is the anticipation
of radar vectors in the arrivals with time enough to safely configure the aircraft for the descent.
A demonstrator is being developed for this second scenario in the context of Project HARVIS
(www.harvis-project.eu).
Diversion is often required after system failure, medical emergency, or just for weather phenomena
(dense fog, storms, etc.) in the approaching. During regular operation if a diversion is needed the
pilot in command and first officer discuss on the multiple options they have and try to find out
the one they think is the best. The AI assistant will take into account characteristics of nearby
airports, METAR at destination, and facilities to take care of passengers, among other factors. It
may then consider several options, assess the risks and benefits of each one, and finally inform
the pilot accordingly. In this scenario, the digital assistant takes care of the Options and Risks in a
FORDEC procedure
Toward a Non Stabilized Approach assistant based on human expertise
97% of Non-Stabilized Approach (NSA) are continued until landing going against Standard Operational
Procedures (SOP). For some of these approaches, the reason is a lack of situation awareness
for others it is because of operational constraints that standard SOP do not take into account like
ATC, remaining fuel on board, weather… Most of the time everything goes well but pilots often
admit afterwards that they should have go-around and that safety margins were greatly reduced
A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents
This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permissionThis work focuses on finding the most discriminatory or representative features that
allow to classify commercials according to negative, neutral and positive effectiveness
based on the Ace Score index. For this purpose, an experiment involving forty-seven
participants was carried out. In this experiment electroencephalography (EEG),
electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were
acquired while subjects were watching a 30-min audiovisual content. This content was
composed by a submarine documentary and nine commercials (one of themthe ad under
evaluation). After the signal pre-processing, four sets of features were extracted from the
physiological signals using different state-of-the-art metrics. These features computed in
time and frequency domains are the inputs to several basic and advanced classifiers. An
average of 89.76% of the instances was correctly classified according to the Ace Score
index. The best results were obtained by a classifier consisting of a combination between
AdaBoost and RandomForest with automatic selection of features. The selected features
were those extracted from GSR and HRV signals. These results are promising in the
audiovisual content evaluation field by means of physiological signal processing.This work has been supported by the Heineken Endowed Chair in Neuromarketing at the Universitat Politecnica de Valencia in order to research and apply new technologies and neuroscience in communication, distribution and consumption fields.Colomer Granero, A.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Guixeres Provinciale, J.; Ausin-Azofra, JM.; Alcañiz Raya, ML. (2016). A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Frontiers in Computational Neuroscience. 10(74):1-16. doi:10.3389/fncom.2016.00074S116107