Text

Algebra och Analys med tillämpningar

Barndom i Antropocen - Utbildning och hållbarhet

Förnybar energi

Komplexa inbyggda system i realtid

Learning, Inclusive education, School transitions – for All (LISA)

Medicinsk teknik

M-TERM - Mälardalen University Team of Educational Researchers in Mathematics

NOMP-gruppen – nya organisations- och managementpraktiker

Produkt- och produktionsutveckling

Heterogena system

Resurseffektivisering

Robotik

Stokastiska processer, statistik och finansmatematik

Säkerhetskritisk teknik

Teknisk matematik

SafeDeep: Dependable Deep Learning for Safety-Critical Airborne Embedded Systems

This project addresses design methods for the use of DNNs in airborne safety-critical systems.

Start

2019-09-01

Planerat avslut

2022-12-31

Huvudfinansiering

Projektansvarig vid MDH

No partial template found

Projektbeskrivning

Deep neural networks (DNNs) have shown to be very successful in several areas, e.g. for object detection in autonomous cars. DNNs may also be successful in airborne systems. One such possible application is guided landing. The enabling of safe landing in adverse weather conditions without full ground support from the instrument landing system, decreases aerospace greenhouse gas emissions as multiple landing attempts and aerospace congestion are mitigated. To land autonomously without support from ground infrastructure requires advanced airborne systems including algorithms for detecting the runway. These systems are safety-critical.

This project addresses design methods for the use of DNNs in airborne safety-critical systems. DNNs cannot rely on traditional design assurance techniques described in documents from certification authorities or standardization bodies. In this project, the research focus is on mitigation techniques for design errors in both hardware and software and for adversarial effects which can lead to system failures. The expected results are design methodologies and fault tolerant architectures for airborne safety-critical applications using neural networks.

 

Forskningen har anknytning till följande globala hållbarhetsmål

Infrastruktur och industri. FN:s globala mål 9.

Bygga upp en motståndskraftig infrastruktur, verka för en inkluderande och hållbar industrialisering och främja innovation.

Läs mer

Hållbara städer och samhällen. FN:s globala mål 11.

Städer och bosättningar ska vara inkluderande, säkra, motståndskraftiga och hållbara.

Läs mer
Till toppen