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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. (in press). Towards Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition Systems. Computer,

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.

Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050 (2020)
Journal Article
Golizadeh Akhlaghi, Y., Aslansefat, K., Zhao, X., Sadati, S., Badiei, A., Xiao, X., …Ma, X. (2021). Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050. Applied energy, 281, https://doi.org/10.1016/j.apenergy.2020.116062

The empirical success of the Artificial Intelligence (AI), has enhanced importance of the transparency in black box Machine Learning (ML) models. This study pioneers in developing an explainable and interpretable Deep Neural Network (DNN) model for a... Read More about Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050.

SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures (2020)
Journal Article
Aslansefat, K., Sorokos, I., Whiting, D., Tavakoli Kolagari, R., & Papadopoulos, Y. (2020). SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures. Lecture notes in computer science, 12297, 197-211. https://doi.org/10.1007/978-3-030-58920-2_13

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with... Read More about SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures.

A Hybrid Modular Approach for Dynamic Fault Tree Analysis (2020)
Journal Article
Kabir, S., Aslansefat, K., Sorokos, I., Papadopoulos, Y., & Konur, S. (2020). A Hybrid Modular Approach for Dynamic Fault Tree Analysis. IEEE access : practical innovations, open solutions, 8, 97175-97188. https://doi.org/10.1109/ACCESS.2020.2996643

Over the years, several approaches have been developed for the quantitative analysis of dynamic fault trees (DFTs). These approaches have strong theoretical and mathematical foundations; however, they appear to suffer from the state-space explosion a... Read More about A Hybrid Modular Approach for Dynamic Fault Tree Analysis.

A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins (2020)
Journal Article
Golizadeh Akhlaghi, Y., Badiei, A., Zhao, X., Aslansefat, K., Xiao, X., Shittu, S., & Ma, X. (2020). A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins. Energy Conversion and Management, 211, https://doi.org/10.1016/j.enconman.2020.112772

This study is pioneered in developing digital twins using Feed-forward Neural Network (FFNN) and multi objective evolutionary optimization (MOEO) using Genetic Algorithm (GA) for a counter-flow Dew Point Cooler with a novel Guideless Irregular Heat a... Read More about A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins.

A runtime safety analysis concept for open adaptive systems (2019)
Journal Article
Kabir, S., Sorokos, I., Aslansefat, K., Papadopoulos, Y., Gheraibia, Y., Reich, J., …Wei, R. (2019). A runtime safety analysis concept for open adaptive systems. Lecture notes in computer science, 11842, 332-346. https://doi.org/10.1007/978-3-030-32872-6_22

© Springer Nature Switzerland AG 2019. In the automotive industry, modern cyber-physical systems feature cooperation and autonomy. Such systems share information to enable collaborative functions, allowing dynamic component integration and architectu... Read More about A runtime safety analysis concept for open adaptive systems.

A conceptual framework to incorporate complex basic events in HiP-HOPS (2019)
Book Chapter
Kabir, S., Aslansefat, K., Sorokos, I., Papadopoulos, Y., & Gheraibia, Y. (2019). A conceptual framework to incorporate complex basic events in HiP-HOPS. In Y. Papadopoulos, K. Aslansefat, P. Katsaros, & M. Bozzano (Eds.), Model-Based Safety and Assessment. IMBSA 2019 (109-124). Cham: Springer Verlag. https://doi.org/10.1007/978-3-030-32872-6_8

Reliability evaluation for ensuring the uninterrupted system operation is an integral part of dependable system development. Model-based safety analysis (MBSA) techniques such as Hierarchically Performed Hazard Origin and Propagation Studies (HiP-HOP... Read More about A conceptual framework to incorporate complex basic events in HiP-HOPS.

Safety + ai: A novel approach to update safety models using artificial intelligence (2019)
Journal Article
Gheraibia, Y., Kabir, S., Aslansefat, K., Sorokos, I., & Papadopoulos, Y. (2019). Safety + ai: A novel approach to update safety models using artificial intelligence. IEEE access : practical innovations, open solutions, 7, 135855-135869. https://doi.org/10.1109/ACCESS.2019.2941566

Safety-critical systems are becoming larger and more complex to obtain a higher level of functionality. Hence, modeling and evaluation of these systems can be a difficult and error-prone task. Among existing safety models, Fault Tree Analysis (FTA) i... Read More about Safety + ai: A novel approach to update safety models using artificial intelligence.

Performance evaluation and design for variable threshold alarm systems through semi-Markov process (2019)
Journal Article
Aslansefat, K., Bahar Gogani, M., Kabir, S., Shoorehdeli, M. A., & Yari, M. (in press). Performance evaluation and design for variable threshold alarm systems through semi-Markov process. ISA Transactions, https://doi.org/10.1016/j.isatra.2019.08.015

In large industrial systems, alarm management is one of the most important issues to improve the safety and efficiency of systems in practice. Operators of such systems often have to deal with a numerous number of simultaneous alarms. Different kinds... Read More about Performance evaluation and design for variable threshold alarm systems through semi-Markov process.

A Hierarchical Approach for Dynamic Fault Trees Solution Through Semi-Markov Process (2019)
Journal Article
Aslansefat, K., & Latif-Shabgahi, G. (2020). A Hierarchical Approach for Dynamic Fault Trees Solution Through Semi-Markov Process. IEEE Transactions on Reliability, 69(3), 986-1003. https://doi.org/10.1109/tr.2019.2923893

Dynamic fault tree (DFT) is a top-down deductive technique extended to model systems with complex failure behaviors and interactions. In two last decades, different methods have been applied to improve its capabilities, such as computational complexi... Read More about A Hierarchical Approach for Dynamic Fault Trees Solution Through Semi-Markov Process.

Resilience Supported System for Innovative Water Monitoring Technology (2018)
Book Chapter
Aslansefat, K., Ghodsirad, M. H., Barata, J., & Jassbi, J. (2018). Resilience Supported System for Innovative Water Monitoring Technology. In L. M. Camarinha-Matos, K. O. Adu-Kankam, & M. Julashokri (Eds.), Technological Innovation for Resilient Systems (73-80). Cham: Springer. https://doi.org/10.1007/978-3-319-78574-5_7

The level of intelligence in monitoring & controlling systems are increasing dramatically. The critical issue for an autonomous resilient system is detecting the anomalous behavior through standard patterns to react properly and on time. In cyber-phy... Read More about Resilience Supported System for Innovative Water Monitoring Technology.