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Autonomous Systems (AS) are increasingly proposed, or used, in Safety Critical (SC) applications. Many such systems make use of sophisticated sensor suites and processing to provide scene understanding which informs the AS' decision-making.…
Time series forecasting is crucial in several sectors, such as meteorology, retail, healthcare, and finance. Accurately forecasting future trends and patterns is crucial for strategic planning and making well-informed decisions. In this…
The development of Automated Driving Systems (ADS) has the potential to revolutionise the transportation industry, but it also presents significant safety challenges. One of the key challenges is ensuring that the ADS is safe in the event…
Machine learning (ML) - based software systems are rapidly gaining adoption across various domains, making it increasingly essential to ensure they perform as intended. This report presents best practices for the Test and Evaluation (T&E)…
The rapid advancement of software development practices has introduced challenges in ensuring quality and efficiency across the software engineering (SE) lifecycle. As SE systems grow in complexity, traditional approaches often fail to…
Worsening global challenges demand solutions grounded in a systems-level understanding of coupled social and environmental dynamics. Existing environmental models encode extensive knowledge of individual systems, yet much of this…
Fault diagnosis of general aviation aircraft faces challenges including scarce real fault data, diverse fault types, and weak fault signatures. This paper proposes an intelligent fault diagnosis framework based on multi-fidelity digital…
Financial risk detection in Enterprise Resource Planning (ERP) systems is an important but underexplored application of machine learning. Published studies in this area tend to suffer from vague dataset descriptions, leakage-prone…
Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets. However, this process requires non-trivial domain-knowledge which involves a time-consuming process.…
For improving flexibility and robustness of the engineering of automated production systems (aPS) in case of extending, reducing or modifying parts, several approaches propose an encapsulation and clustering of related functions, e.g. from…
The increased availability of observation data from engineering systems in operation poses the question of how to incorporate this data into finite element models. To this end, we propose a novel statistical construction of the finite…
Motor bearing fault detection (MBFD) is critical for maintaining the reliability and operational efficiency of industrial machinery. Early detection of bearing faults can prevent system failures, reduce operational downtime, and lower…
Adversarial attacks for machine learning models have become a highly studied topic both in academia and industry. These attacks, along with traditional security threats, can compromise confidentiality, integrity, and availability of…
Even well-designed software systems suffer from chronic performance degradation, also named "software aging", due to internal (e.g. software bugs) and external (e.g. resource exhaustion) impairments. These chronic problems often fly under…
Autonomous driving perception models are typically composed of multiple functional modules that interact through complex relationships to accomplish environment understanding. However, perception models are predominantly optimized as a…
Complex electronic control unit (ECU) architectures, software models and in-vehicle networks are consistently improving safety and comfort functions in modern vehicles. However, the extended functionality and increased connectivity…
Fatigue damages and failure widely exist in engineering structures. However, predicting fatigue life for various structural materials subjected to multiaxial loading paths remains a challenging problem. A novel multi-view deep learning…
With the integration of distributed energy resources and the trend towards low-inertia power grids, the frequency and severity of grid dynamics is expected to increase. Conventional phasor-based signal processing methods are proving to be…
Many agencies have adopted the FHWA-recommended systemic approach to traffic safety, an essential supplement to the traditional hotspot crash analysis which develops region-wide safety projects based on identified risk factors. However,…
Foundation models show great promise for generative tasks in many domains. Here we discuss the use of foundation models to generate structured documents related to critical assets. A Failure Mode and Effects Analysis (FMEA) captures the…