DC9: Enhancing the dependability of mission-critical system through an advanced anomaly detection methodology for electromagnetic disturbances

Project: Enhancing the dependability of mission-critical system through an advanced anomaly detection methodology for electromagnetic disturbances (WP3)

Host institution: KU Leuven (Belgium)

Supervisor: Mathias Verbeke (KU Leuven, Belgium)

Co-supervisor(s): Davy Pissoort (KU Leuven, Belgium), Dimitar Nikolov (TUS, Bulgaria), Ronny Deseine (Barco, Belgium)

Objectives:

  1. Create a semi-supervised anomaly-detection methodology tailored to identifying potential EMI.
  2. Improve the accuracy of anomaly detection in the presence of EMI such that false alarms are minimized.
  3. Investigate the integration of the EMI-footprint concept into the anomaly-detection methodology.

Expected Results:

  1. A novel, semi-supervised framework for detecting anomalies in EMI data, which can be applied in real time.
  2. Machine-learning models that exhibit increased accuracy in identifying EMI while minimizing the number of false alarms.

Planned secondment(s):

  1. Academic secondment: TUS, Dimitar Nikolov, M15-M18, 3M, Detecting run time anomalies using digital twins.
  2. Industrial secondment: BARCO, Ronny Deseine, M26-M28, 2M, Applying a run-time anomaly detection method in medical systems

Enrolment in Doctoral degree: KU Leuven (Belgium)

 

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