Verification of evolving systems
The ITEA project IVVES (Industrial-grade Verification and Validation of Evolving Systems) has developed verification and validation (V&V) tools for evolving systems (ES), allowing artificial intelligence (AI) and machine learning (ML) to be introduced to mission-critical environments. Today’s enormous software growth has fuelled the evolution of evolving systems, including machine learning components that (semi-)automatically adapt to […]
Predictive Failure Data Analysis
Abstract This report uses event activations within the AddTrack system in order to predict train failures. The results show a difficulty in performing such failure predictions with any sufficient precision, since the best precision lies in the range of 70% to 75%. It is concluded that more data, as well as a better data quality, […]
Exploring AI Challenges
1. Abstract In this report, we delve into a range of significant challenges and their corresponding solutions, which are pertinent to tensors, training accelerators, and quality assurance. Our intended audience comprises decision-makers who are tasked with implementing such systems in professional applications. The combination of AI’s dual capabilities, offering both quantitative estimations and narrative depth, […]