International audienceWith the increase of component complexity, protection against single event effects becomes a critical point for the disponibility and reliability of space systems. In this paper, machine learning is investigated to improve the detection of radiation faults. An algorithm named DYD² that meets space application requirements is proposed. In addition, a study to improve the characterisation of single event effects through feature extraction is described. Finally, results of experimentation based on a heavy-ion campaign test are discussed
This study presents a data-driven machine learning approach to predict individual Galactic Cosmic Ra...
This paper proposes a methodology for automatic, accurate, and early detection of amplitude ionosphe...
Space radiation exposure to astronauts will need to be carefully monitored on future missions beyond...
International audienceWith the increase of component complexity, protection against single event eff...
International audienceThe space environment is known to be the seat of radiation of different kinds ...
International audienceAnomaly detection is a crucial aspect of embedded applications. However, limit...
In space, the radiation effects on electronic devices may lead to anomalies referred to as Single-Ev...
Classical approaches for the automatic detection of ionospheric scintillation events in Global Navig...
International audienceMachine learning (ML) algorithms have been regaining momentum thanks to their ...
Nano-satellite MeV telescope is becoming attractive nowadays. The dominant interaction mechanism of ...
International audienceThis paper investigates the tolerance of Artificial Neural Networks with respe...
Spacecraft processors and memory are subjected to high radiation doses and therefore employ radiatio...
Manual inspection of telemetry data in the search for anomalies is a time-consuming threat detection...
This work introduces an embedded approach for the prediction of Solar Particle Events (SPEs) in spac...
The purpose of this work is to perform fault detection and diagnosis regarding the reaction wheels o...
This study presents a data-driven machine learning approach to predict individual Galactic Cosmic Ra...
This paper proposes a methodology for automatic, accurate, and early detection of amplitude ionosphe...
Space radiation exposure to astronauts will need to be carefully monitored on future missions beyond...
International audienceWith the increase of component complexity, protection against single event eff...
International audienceThe space environment is known to be the seat of radiation of different kinds ...
International audienceAnomaly detection is a crucial aspect of embedded applications. However, limit...
In space, the radiation effects on electronic devices may lead to anomalies referred to as Single-Ev...
Classical approaches for the automatic detection of ionospheric scintillation events in Global Navig...
International audienceMachine learning (ML) algorithms have been regaining momentum thanks to their ...
Nano-satellite MeV telescope is becoming attractive nowadays. The dominant interaction mechanism of ...
International audienceThis paper investigates the tolerance of Artificial Neural Networks with respe...
Spacecraft processors and memory are subjected to high radiation doses and therefore employ radiatio...
Manual inspection of telemetry data in the search for anomalies is a time-consuming threat detection...
This work introduces an embedded approach for the prediction of Solar Particle Events (SPEs) in spac...
The purpose of this work is to perform fault detection and diagnosis regarding the reaction wheels o...
This study presents a data-driven machine learning approach to predict individual Galactic Cosmic Ra...
This paper proposes a methodology for automatic, accurate, and early detection of amplitude ionosphe...
Space radiation exposure to astronauts will need to be carefully monitored on future missions beyond...
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