2024-03-28T08:49:30Z
https://zenodo.org/oai2d
oai:zenodo.org:7347559
2022-11-22T14:26:33Z
user-h2020_cleansky_voici
openaire
Radek Baranek, Radek Reznicek, Pavol Malinak, Radu Cioaca
2022-09-21
<p>This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 945583. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.</p>
<p>The results, opinions, conclusions, etc. presented in this work are those of the author(s) only and do not necessarily represent the position of the JU; the JU is not responsible for any use made of the information contained herein.</p>
https://doi.org/10.5281/zenodo.7347559
oai:zenodo.org:7347559
Zenodo
https://zenodo.org/communities/h2020_cleansky_voici
https://doi.org/10.5281/zenodo.7347558
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ION GNSS+ 2022, Denver, CO, USA, 19-23 September 2022
Clean Sky 2 Joint Undertaking
European Union (EU)
Horizon 2020
Flight Testing of Low-Cost Inertial Navigation System with Dual Antenna GPS Heading Functionality in Polar Region
info:eu-repo/semantics/lecture
oai:zenodo.org:2658911
2019-05-03T09:13:14Z
user-h2020_cleansky_voici
user-eu
Reinen, Tor Arne
2019-05-03
<p>Brief information about Clean Sky 2 project VOICI</p>
https://doi.org/10.5281/zenodo.2658911
oai:zenodo.org:2658911
eng
Zenodo
https://zenodo.org/communities/h2020_cleansky_voici
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2658910
info:eu-repo/semantics/openAccess
Creative Commons Attribution Share Alike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/legalcode
Cockpit
Noise
Crew assistant
Clean Sky 2 VOICI project information
info:eu-repo/semantics/other
oai:zenodo.org:2660112
2020-01-24T19:25:30Z
user-h2020_cleansky_voici
openaire_data
user-eu
Reinen, Tor Arne
2019-05-03
<p>Data file: <em>Recording 6_low_freq_boost_remove_speech_2_WoodPk_reinsert_16b.wav<br>
Extended description file: <em>Cockpit noise Falcon 2000LXS TRH-OSL description.pdf</em></em></p>
<p><strong>Scenario</strong>:</p>
<p>Sound recording during flight Trondheim – Oslo by Rely AS 2018-09-13, using a Falcon 2000LXS (2017). The full flight, gate to gate, is included.</p>
<p><strong>Recording and calibration</strong>:</p>
<p>A MicW i436 microphone with windshield was placed just in front of the airplane throttle. The microphone was connected to an Apple iPod, operated by pilots. Signal sample values on the .wav -file can be converted to sound pressure in Pa by multiplying by 13.79.</p>
<p><strong>Post-processing</strong>:</p>
<ol>
<li>The following signal components have been removed from the recording, and replaced by nearby background noise:<br>
- Pilot speech<br>
- ATC communication<br>
- Voice messages automatically generated by the aircraft (e.g., altitude)</li>
<li>The pitch trim confirmation signal has first been removed and then re-inserted at the correct level, but in a version recorded 30 cm from the cockpit loudspeakers.</li>
<li>The recording setup has a high-pass function with cut-off at 150 Hz. A gentle boost has been applied below this frequency.</li>
</ol>
https://doi.org/10.5281/zenodo.2660112
oai:zenodo.org:2660112
eng
Zenodo
https://zenodo.org/communities/h2020_cleansky_voici
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.2660111
info:eu-repo/semantics/openAccess
Creative Commons Attribution Share Alike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/legalcode
Cockpit
Noise
Crew assistant
Cockpit noise Falcon 2000LXS TRH-OSL CleanSky2 VOICI Data 1
info:eu-repo/semantics/other
oai:zenodo.org:4276974
2020-11-17T12:27:18Z
user-h2020_cleansky_voici
Flores, Jon
Garmendia, Iker
Cabanes, Itziar
2020-07-20
<p>In order to improve the capabilities of additive manufacturing, monitoring and control techniques have received increased interest. Monitoring allows to know the conditions during the process by measuring different signals. To develop control algorithms, it is essential to understand the relationship between the recorded signals, the process parameters and the characteristics of the manufactured part.</p>
<p>This paper proposes off-axial thermal monitoring aimed at recording the thermal conditions of the component during manufacture by LMD. In addition, in order to increase the capacities of three-dimensional components, a control system based on the adaptation of the waiting times between layers from the global temperature of the component is proposed. The experimentation described in this study shows that the stability of the thermal conditions throughout the manufacturing process gives homogeneity to the constructions by LMD.</p>
Horizon 2020, Clean Sky 2 Joint Undertaking, grant number 831857, European Union (EU)
https://doi.org/10.6036/9379
oai:zenodo.org:4276974
eng
Zenodo
https://zenodo.org/communities/h2020_cleansky_voici
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Laser Metal Deposition
Infrared
Thermal monitoring
THERMAL MONITORING AND CONTROL BY INFRARED CAMERA IN THE MANUFACTURE OF PARTS WITH LASER METAL DEPOSITION
info:eu-repo/semantics/article
oai:zenodo.org:4120808
2020-10-26T07:17:21Z
user-h2020_cleansky_voici
openaire
MAZO, Roberto
GUARINO, Olivia
ROBLIN, Nicolas
2020-02-25
<p>This poster presents an overview of the research work performed in the frame of the Cleansky 2 UBBICK projet. It highlights performance improvements and novel aircraft systems architecture brought by the Utility Management System to Business Jet operations, which is the main goal of the project.</p>
https://doi.org/10.5281/zenodo.4120808
oai:zenodo.org:4120808
Zenodo
https://zenodo.org/communities/h2020_cleansky_voici
https://doi.org/10.5281/zenodo.4120807
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
AEC 2020 - 3AF, Aerospace Conference 2020, Bordeaux, France, 25-28 February 2020
Cleansky 2 UBBICK Project
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7705111
2023-03-08T07:34:53Z
user-h2020_cleansky_voici
Stephane Marche
Dariia Averkova
Hicham Atassi
Jan Ciganek
Tomas Kopriva
Bohdan Blaha
2022-10-19
<p>With the greater emphasis on flight safety, efficiency, cockpit automation and the inevitability of the changing role of a pilot, the aviation industry is becoming more open towards pilot monitoring technologies. If designed and used properly, they can detect, prevent, or mitigate undesirable and/or potentially dangerous states of a pilot, such as fatigue, sleep, loss of consciousness and others. Data-driven decision-making model, harnessing the capabilities of machine learning (as opposed to rule-based algorithms) is the essential element of the proposed pilot state monitoring system. However, leveraging artificial intelligence-based algorithms in aviation domain is a challenging task and the efforts to create unified guidelines for certifying such algorithms by aviation authorities are still in an early stage. Nevertheless, one could state that the machine learning model generalization capability, which is defined not only by the appropriate design of the model itself, but also by the amount of variance represented in the training data, is of the essence for the future certifiability. From the pilot state monitoring perspective, there are several sources of variability: state variability, environmental conditions variability and subject variability. Addressing the state variability means making sure that all the signatures of the state of interest (in this instance, sleep and drowsiness) are captured in the training data set. The signatures admitted into the model will depend on the chosen sensor set. As the ability of the sensors to extract signals may be affected by the properties of the environment, such as light, vibrations, etc., these properties must be considered to cover the environmental variability. Subject variability could be subdivided into the proper subject variability (e.g., sex, age, ethnicity) and situational subject variability (e.g., behaviors, worn garments and accessories). The pilot monitoring model for sleep and drowsiness detection with a camera-based system was trained/tested with the above-mentioned types of variability in mind. Validation of model was performed in the lab as well as in the real environment.</p>
<p>This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 945583. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.</p>
<p>The results, opinions, conclusions, etc. presented in this work are those of the author(s) only and do not necessarily represent the position of the JU; the JU is not responsible for any use made of the information contained herein.</p>
https://doi.org/10.5281/zenodo.7705111
oai:zenodo.org:7705111
Zenodo
https://zenodo.org/communities/h2020_cleansky_voici
https://doi.org/10.5281/zenodo.7705110
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Clean Sky 2 Joint Undertaking
European Union (EU)
Horizon 2020
Data Representativeness in the Context of Pilot State Monitoring: A Case Study on Sleep and Drowsiness Detection and Certification Challenges
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4067853
2020-11-17T12:27:18Z
user-h2020_cleansky_voici
Madarieta-Churruca, Mikel
Leunda-Arrizabalaga, Josu
Garmendia-Saez, Iker
Soriano-Reyes, Carlos
2020-07-20
<p>The aeronautical sector is considering additive manufacturing as an alternative for the manufacture of titanium components. In this sense, the Laser Metal Deposition (LMD) is presented as a direct deposition technology of great potential, mainly for the manufacture of large structural components. The deposition of material in powder form is the most used method, however, the heads of coaxial deposition of wire developed in the last years allow, a priori, to manufacture structures with a better efficiency and feeding rate. In this work a comparative study is carried out between the processes of laser deposited coaxial wire and powder in order to verify the highest deposition rates and efficiencies of the wire feed. For this purpose, several walls have been deposited with the highest deposition rate achieved in a parametric search for both wire and powder format and the difference between the material fed and the material deposited has been calculated. The two formats have also been analysed in terms of microstructural and geometric quality. It is observed that wire exceeds powder in terms of deposition rates and efficiency. However, geometric limitations and more unfavourable microstructural structures can be seen.</p>
Horizon 2020, Clean Sky 2 Joint Undertaking, grant number 831857, European Union (EU)
https://doi.org/10.6036/9378
oai:zenodo.org:4067853
eng
Zenodo
https://zenodo.org/communities/h2020_cleansky_voici
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Laser Metal Deposition (LMD)
Ti-6Al-4V
additive manufacturing
wire
powder
coaxial
COMPARATIVE STUDY OF LASER METAL DEPOSITION (LMD) OF COAXIAL WIRE AND POWDER IN THE MANUFACTURE OF TI-6AL-4V STRUCTURES
info:eu-repo/semantics/article
oai:zenodo.org:4495328
2021-02-03T12:27:15Z
user-h2020_cleansky_voici
Zdenek Kana
Pavol Malinak
Tomas Vaispacher
Milos Sotak
Libor Slabak
Jan Zlebek
Guillaume Bourely
Alain Guillet
Fabien Garrido
2021-02-02
<p>This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 945583. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.</p>
<p>The results, opinions, conclusions, etc. presented in this work are those of the author(s) only and do not necessarily represent the position of the JU; the JU is not responsible for any use made of the information contained herein.</p>
<p>Prototype of the new Honeywell GPS-aided MEMS based navigation system (GPAHRS) has been validated through a large flight-test campaign performed under the Clean Sky 2 Large Passenger Aircraft (LPA) Platform 3 project. The campaign itself (more than 200 flight hours in total) was focused on the business jets (Dassault Falcon 900EX), commercial jets (Airbus A330), and regional aircrafts and various flight test scenarios. This paper provides an overview of the GPAHRS system itself, its integration within the Flight Test Bed (FTB) equipment, and most importantly, performance (in terms of accuracy and integrity) validation of results obtained during the campaign.</p>
https://doi.org/10.5281/zenodo.4495328
oai:zenodo.org:4495328
eng
Zenodo
https://www.cleansky.eu
https://www.cleansky.eu/large-passengeraircraft.
https://hdl.handle.net/: https://www.cleansky.eu/news/honeywelltests-new-technologies-in-flight-for-clean-sky2
https://hdl.handle.net/: https://www.cleansky.eu/european-aviation-inthe-driving-seat-clean-skys-disruptive-cockpitfor-large-passenger-aircraft
https://aerospace.honeywell.com/ en/ learn/ products/ navigation-and-radios/ laseref-vi-micro-inertial-reference-systems.
https://aerospace.honeywell.com/ en/ learn/ products/ sensors/ hg4930-mems-inertialmeasurement-unit.
https://zenodo.org/communities/h2020_cleansky_voici
https://doi.org/10.5281/zenodo.4494592
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Clean Sky 2 Joint Undertaking
European Union (EU)
Horizon 2020
GPAHRS – Navigation Enabler for More Autonomous Aircraft
info:eu-repo/semantics/conferencePaper