Using machine learning in on-board data processing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F23%3A00558730" target="_blank" >RIV/60162694:G43__/23:00558730 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/xpl/conhome/1000202/all-proceedings" target="_blank" >https://ieeexplore.ieee.org/xpl/conhome/1000202/all-proceedings</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/DASC55683.2022.9925732" target="_blank" >10.1109/DASC55683.2022.9925732</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using machine learning in on-board data processing

Popis výsledku v původním jazyce

Our department has taken on the task of designing a system that will predict the aircraft structure load based on flight data and the movements of pilot controls in real time. The system must work in highly maneuverable and experimental aircrafts capable of maneuvering at a critical angle of attack. Considering this, along with the fact that no aerodynamic characteristics of the airplane wing or the airplane itself are available from the manufacturer, it is not possible to create a simulation model and perform calculations using it.This paper describes the design of suitable types of neural networks and their testing on real data. The selected solutions are compared and evaluated. It also discusses the possibilities of their implementation in on-board computing units.

Název v anglickém jazyce

Using machine learning in on-board data processing

Popis výsledku anglicky

Our department has taken on the task of designing a system that will predict the aircraft structure load based on flight data and the movements of pilot controls in real time. The system must work in highly maneuverable and experimental aircrafts capable of maneuvering at a critical angle of attack. Considering this, along with the fact that no aerodynamic characteristics of the airplane wing or the airplane itself are available from the manufacturer, it is not possible to create a simulation model and perform calculations using it.This paper describes the design of suitable types of neural networks and their testing on real data. The selected solutions are compared and evaluated. It also discusses the possibilities of their implementation in on-board computing units.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

AIAA/IEEE Digital Avionics Systems Conference - Proceedings

ISBN

978-1-6654-8607-1

ISSN

2155-7195

e-ISSN

2155-7209

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Portsmouth

Místo konání akce

Portsmouth, United States of America

Datum konání akce

18. 9. 2022

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

—