Rok Hribar

Journal articlesshow recent

Advanced computing to support urban climate neutrality, Energy Sustain. Soc. (2025)
Models for forecasting the traffic flow within the city of Ljubljana, ETRE (2023)
Four algorithms to solve symmetric multi-type non-negative matrix tri-factorization problem, J GLOBAL OPTIM (2022)
A comparison of models for forecasting the residential natural gas demand of an urban area, EGY (2019)

Conference papersshow all

Books & Chaptersshow recent

A Framework for Applying Data-Driven AI/ML Models in Reliability, Recent Advances in Microelectronics Reliability, Springer (2024)
Reliability Improvements for In-Wheel Motor, Recent Advances in Microelectronics Reliability, Springer (2024)
Parameter Control in Evolutionary Optimisation, Optimization Under Uncertainty with Applications to Aerospace Engineering, Springer (2021)
Memetic Algorithm and Evolutionary Operators for Multi-Objective Matrix Tri-Factorization Problem, Heuristics for Optimization and Learning, Springer (2020)

A Framework for Applying Data-Driven AI/ML Models in Reliability

R. Hribar, M. Antoniou, G. Papa
DOI: 10.1007/978-3-031-59361-1_12

BibTex:
@Inbook{Hribar2024, author="Hribar, Rok and Antoniou, Margarita and Papa, Gregor", editor="van Driel, Willem Dirk and Pressel, Klaus and Soyturk, Mujdat", title="A Framework for Applying Data-Driven AI/ML Models in Reliability", bookTitle="Recent Advances in Microelectronics Reliability: Contributions from the European ECSEL JU project iRel40", year="2024", publisher="Springer International Publishing", address="Cham", pages="323--337", isbn="978-3-031-59361-1", doi="10.1007/978-3-031-59361-1_12", url="https://doi.org/10.1007/978-3-031-59361-1_12" }

abstract:
In this chapter, we present a framework for applying artificial intelligence (AI)/machine learning (ML) in reliability, in the context of the iRel40 project. Data-driven models are becoming an increasingly fruitful tool for detecting patterns in complex data and identifying the circumstances in which they occur. Using only data, gathered along the value chain, data-driven methods are now being used to detect indications of potential early failures, signs of wear out or degradation, and other unwanted events within the development, fabrication, or service phases of the electronic components and systems. We present general considerations that were found to be important during the iRel40 project, when designing pipelines that combine data processing with the AI/ML models for predicting or detecting reliability issues. This chapter serves as an introduction to the definitions and concepts used within the specific use cases that rely on the AI/ML methodology within the iRel40 project.