Related papers: Uncertainty Quantification and Sensitivity analysi…
This paper argues the need for research to realize uncertainty-aware artificial intelligence and machine learning (AI\&ML) systems for decision support by describing a number of motivating scenarios. Furthermore, the paper defines…
Improving diesel engine efficiency, reducing emissions, and enabling robust health monitoring have been critical research topics in engine modelling. While recent advancements in the use of neural networks for system monitoring have shown…
The Best Estimate plus Uncertainty (BEPU) approach for nuclear systems modeling and simulation requires that the prediction uncertainty must be quantified in order to prove that the investigated design stays within acceptance criteria. A…
Self-adaptive robots adjust their behaviors in response to unpredictable environmental changes. These robots often incorporate deep learning (DL) components into their software to support functionality such as perception, decision-making,…
In existing wireless networks, the control programs have been designed manually and for certain predefined scenarios. This process is complicated and error-prone, and the resulting control programs are not resilient to disruptive changes.…
Advances in Automation and Artificial Intelligence continue to enhance the autonomy of process plants in handling various operational scenarios. However, certain tasks, such as fault handling, remain challenging, as they rely heavily on…
Density Functional Theory (DFT) has been a cornerstone in computational science, providing powerful insights into structure-property relationships for molecules and materials through first-principles quantum-mechanical (QM) calculations.…
Autonomous surface vessels (ASVs) are becoming increasingly significant in enhancing the safety and sustainability of maritime operations. To ensure the reliability of modern control algorithms utilized in these vessels, digital twins (DTs)…
Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles. In this paper, we propose a safe motion planning framework featuring the quantification…
Deep neural network controllers for autonomous driving have recently benefited from significant performance improvements, and have begun deployment in the real world. Prior to their widespread adoption, safety guarantees are needed on the…
Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models, and there is an increasing interest in providing accurate uncertainty estimation on many tasks, such as security…
The concept of Digital Twin (DT) is increasingly applied to systems on different levels of abstraction across domains, to support monitoring, analysis, diagnosis, decision making and automated control. Whilst the interest in applying DT is…
A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually…
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough…
Airborne Base Stations (ABSs) allow for flexible geographical allocation of network resources with dynamically changing load as well as rapid deployment of alternate connectivity solutions during natural disasters. Since the radio…
Accurate prediction of nonstationary multivariate time series remains a critical challenge in complex industrial systems such as iron ore sintering. In practice, pronounced concept drift compounded by significant label verification latency…
Metrology, the science of measurement, plays a key role in Advanced Manufacturing (AM) to ensure quality control, process optimization, and predictive maintenance. However, it has often been overlooked in AM domains due to the current focus…
This paper has proposed an easily replicable and novel approach for developing a Digital Twin (DT) system for industrial robots in intelligent manufacturing applications. Our framework enables effective communication via Robot Web Service…
A permanently increasing number of on-board automotive control systems requires new approaches to their digital mapping that improves functionality in terms of adaptability and robustness as well as enables their easier on-line software…
Accurately capturing the three dimensional power distribution within a reactor core is vital for ensuring the safe and economical operation of the reactor, compliance with Technical Specifications, and fuel cycle planning (safety, control,…