ML meets aerospace: challenges of certifying airborne AI

B Luettig, Y Akhiat, Z Daw - Frontiers in Aerospace Engineering, 2024 - frontiersin.org
Artificial Intelligence (AI) technologies can potentially revolutionize the aerospace industry
with applications such as remote sensing data refinement, autonomous landing, and drone …

[HTML][HTML] Enhancing Vertical Trajectory Reconstruction in SASS-C: Advanced Segmentation, Outlier Detection, and Filtering Techniques

D Amigo, D Sánchez Pedroche, J García, JM Molina… - Aerospace, 2024 - mdpi.com
This paper presents significant enhancements to the vertical reconstruction component of
EUROCONTROL's Surveillance Analysis Support System for ATC Centres (SASS-C). We …

Phase of Flight Classification in Aviation Safety Using LSTM, GRU, and BiLSTM: A Case Study with ASN Dataset

A Nanyonga, H Wasswa, G Wild - … International Conference on …, 2023 - ieeexplore.ieee.org
Safety is the main concern in the aviation industry, where even minor operational issues can
lead to serious consequences. This study addresses the need for comprehensive aviation …

Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports

A Nanyonga, H Wasswa, G Wild - 2023 Global Conference on …, 2023 - ieeexplore.ieee.org
Aviation safety is paramount, demanding precise analysis of safety occurrences during
different flight phases. This study employs Natural Language Processing (NLP) and Deep …

GENERAL AVIATION AIRCRAFT FLIGHT STATUS IDENTIFICATION FRAMEWORK

Q Zhang - 2024 - hammer.purdue.edu
The absence or limited availability of operational statistics at general aviation airports
restricts airport managers and operators from assessing comprehensive operational data …

Hidden Markov Models and Flight Phase Identification

R Perrichon, X Gendre, T Klein - 2023 - enac.hal.science
The use of Hidden Markov Models (HMMs) in segmenting flight phases is a compelling
approach with significant implications for aviation and aerospace research. It leverages the …