Airbus demonstrates A330neo performance during high-altitude test campaign

sábado, 15 de junio de 2024

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NH90 comprehensive upgrade programme launched

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Soft Go-Around: Enhancing Flight Safety



In 2010, an aircraft crashed while attempting to land at Tripoli Airport. The plane was operating in foggy conditions while executing a go-around procedure due to poor visibility and a terrain avoidance alert, the flight team suffered from spatial disorientation.

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Bjorn’s Corner: New engine development. Part 10. Propeller, Rotor or Fan?

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Bjorn’s Corner: New engine development. Part 11. Core cycle.

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V-22 Flights To Remain Restricted Until 2025, Osprey Will Get New Clutch

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Poland Takes Delivery of Its Second Saab 340 AEW Platform

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Southwest Airlines Boeing 737 MAX 8 Comes Within 400 Feet Of The Ocean While Approaching Lihue Airport

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U.S. Air Force F-22 Raptor fleet recently reached 500,000 flight hours

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Russian military plane violates Swedish airspace and is intercepted

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Australia: Defence tests high-energy anti-drone laser

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Machine Learning Application Approval - MLEAP | EASA

https://www.easa.europa.eu/en/research-projects/machine-learning-application-approval 

https://www.youtube.com/watch?v=IHbUl4H-toU

The project deals with the approval of machine learning (ML) technology for systems intended for use in safety-related applications in all domains covered by the EASA Basic Regulation (Regulation (EU) 2018/1139).

Data-driven learning techniques are a major opportunity for the aviation industry but come also with a significant number of challenges with respect to the trustworthiness of ML and deep learning (DL) solutions.

EASA published its Artificial Intelligence Roadmap in February 2020, followed by a first major deliverable, a Concept Paper 'First usable guidance for level 1 machine learning applications' in December 2021. This concept paper lays down the basis of EASA future guidance for ML applications approval thanks to a W-shaped process and identifies a number of areas in which further research is necessary to identify efficient and practicable means of compliance with the defined 'AI trustworthiness' objectives.

MLEAP project is a research project initiated by EASA and funded under the Horizon Europe framework. MLEAP has been tailored to investigate the challenging objectives of the W-shaped process at the core of EASA AI Concept Paper.

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