Related papers: Capturing Lifecycle System Degradation in Digital …
A digital twin (DT), with the components of a physics-based model, a data-driven model, and a machine learning (ML) enabled efficient surrogate, behaves as a virtual twin of the real-world physical process. In terms of Laser Powder Bed…
Data-driven methods for battery lifetime prediction are attracting increasing attention for applications in which the degradation mechanisms are poorly understood and suitable training sets are available. However, while advanced machine…
Digital engineering practices offer significant yet underutilized potential for improving information assurance and system lifecycle management. This paper examines how capabilities like model-based engineering, digital threads, and…
As one of the most widely used metal tube bending methods, the rotary draw bending (RDB) process enables reliable and high-precision metal tube bending forming (MTBF). The forming accuracy is seriously affected by the springback and other…
This article addresses the challenge of adapting data-based models over time. We propose a novel two-fold modelling architecture designed to correct plant-model mismatch caused by two types of uncertainty. Out-of-domain uncertainty arises…
The Digital Twin (DT) offers a novel approach to the management of critical infrastructures, including energy, water, traffic, public health, and communication systems, which are indispensable for the functioning of modern societies. The…
Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation and forecasting. However, the theoretical framework and practical implementations of…
Digital twins (DTs) are improving water distribution systems by using real-time data, analytics, and prediction models to optimize operations. This paper presents a DT platform designed for a Spanish water supply network, utilizing Long…
The challenge of electronic component obsolescence is particularly critical in systems with long life cycles. Various obsolescence management methods are employed to mitigate its impact, with obsolescence forecasting being a highly…
Power system cyber-physical uncertainties, including measurement ambiguities stemming from cyber attacks and data losses, along with system uncertainties introduced by massive renewables and complex dynamics, reduce the likelihood of…
Real world evolves in continuous time but computations are done from finite samples. Therefore, we study algorithms using finite observations in continuous-time linear dynamical systems. We first study the system identification problem, and…
Image deep features extracted by pre-trained networks are known to contain rich and informative representations. In this paper, we present Deep Degradation Response (DDR), a method to quantify changes in image deep features under varying…
Digital transformation often entails small-scale changes to information systems supporting the execution of business processes. These changes may increase the operational frictions in process execution, which decreases the process…
With the continued growth of its core technologies, including the Internet of Things (IoT), artificial intelligence (AI), Big Data and data analytics, and edge computing, digital twin (DT) technology has witnessed a significant increase in…
Although deep learning models owe their remarkable success to deep and complex architectures, this very complexity typically comes at the expense of real-time performance. To address this issue, a variety of model compression techniques…
Generative artificial intelligence (GAI) and digital twin (DT) are advanced data processing and virtualization technologies to revolutionize communication networks. Thanks to the powerful data processing capabilities of GAI, integrating it…
Multi-robot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with significant operational cost savings and productivity improvement, where Unmanned Aerial Vehicles (UAVs) are employed to control and implement…
The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can be computationally intensive and may not fully capture the…
Digital twin is a problem of augmenting real objects with their digital counterparts. It can underpin a wide range of applications in augmented reality (AR), autonomy, and UI/UX. A critical component in a good digital-twin system is…
The concept of Hybrid Twin (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-based models within a model-order reduction framework-to obtain…