Secure and Safe Multi-Robot Systems

Safety Analysis Concept and Methodology for EDDI development (Initial Version) (2023)
Report
Aslansefat, K., Gerasimou, S., Michalodimi-trakis, E., Papoutsakis, M., Reich, J., Sorokos, I., Walker, M., & Papadopoulos, Y. (2023). Safety Analysis Concept and Methodology for EDDI development (Initial Version). European Comission

Executive Summary:
This deliverable describes the proposed safety analysis concept and accompanying methodology to be defined in the SESAME project. Three overarching challenges to the development of safe and secure multi-robot systems are identifie... Read More about Safety Analysis Concept and Methodology for EDDI development (Initial Version).

Safety-Security Co-Engineering Framework (2023)
Report
Aslansefat, K., Gerasimou, S., Hamibi, H., Matragkas, N., Michalodimitrakis, E., Papadopoulos, Y., Papoutsakis, M., & Walker, M. (2023). Safety-Security Co-Engineering Framework. European Commission

Executive Summary:
The advantages of a model-based approach for safety have been clear for many years now. However, security analysis is typically less formal and more ad-hoc; it may involve systematic processes but these are not generally tied into... Read More about Safety-Security Co-Engineering Framework.

Keep Your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring (2022)
Presentation / Conference Contribution
Farhad, A. H., Sorokos, I., Schmidt, A., Akram, M. N., Aslansefat, K., & Schneider, D. (2022, September). Keep Your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring. Presented at Model-Based Safety and Assessment, 8th International Symposium, IMBSA 2022, Munich, Germany

Machine Learning (ML) has provided promising results in recent years across different applications and domains. However, in many cases, qualities such as reliability or even safety need to be ensured. To this end, one important aspect is to determine... Read More about Keep Your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring.

A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms (2022)
Journal Article
Walker, C., Rothon, C., Aslansefat, K., Papadopoulos, Y., & Dethlefs, N. (2022). A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms. Lecture notes in computer science, 13525 LNCS, 189-203. https://doi.org/10.1007/978-3-031-15842-1_14

With an increasing emphasis on driving down the costs of Operations and Maintenance (O &M) in the Offshore Wind (OSW) sector, comes the requirement to explore new methodology and applications of Deep Learning (DL) to the domain. Condition-based monit... Read More about A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms.

Towards Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems (2021)
Journal Article
Aslansefat, K., Kabir, S., Abdullatif, A., Vasudevan Nair, V., & Papadopoulos, Y. (2021). Towards Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems. Computer, 54(8), 66-76

The application of artificial intelligence (AI) and data-driven decision-making systems in autonomous vehicles is growing rapidly. As autonomous vehicles operate in dynamic environments, the risk that they can face an unknown observation is relativel... Read More about Towards Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems.