Related papers: Stream-Based Active Learning for Process Monitorin…
This paper addresses the design of an event-triggered, data-based, and performance-oriented adaption method for model predictive control (MPC). The performance of such a strategy strongly depends on the accuracy of the prediction model,…
Expanding on neural operators, we propose a novel framework for stochastic process learning across arbitrary domains. In particular, we develop operator flow matching (OFM) for learning stochastic process priors on function spaces. OFM…
Condition monitoring is essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the increasing complexity of industrial processes, artificial intelligence (AI) has emerged as a powerful tool for…
In highly distributed environments such as cloud, edge and fog computing, the application of machine learning for automating and optimizing processes is on the rise. Machine learning jobs are frequently applied in streaming conditions,…
Classification predicts classes of objects using the knowledge learned during the training phase. This process requires learning from labeled samples. However, the labeled samples usually limited. Annotation process is annoying, tedious,…
Parameter estimation for discretely observed Markov processes is a challenging problem. However, simulation of Markov processes is straightforward using the Gillespie algorithm. We exploit this ease of simulation to develop an effective…
One of the major challenges in training deep architectures for predictive tasks is the scarcity and cost of labeled training data. Active Learning (AL) is one way of addressing this challenge. In stream-based AL, observations are…
In data-driven SHM, the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are rarely available a priori; furthermore, labelling to…
The present paper introduces stochastic velocity as improvement for moving particle semi-implicit (MPS) method. This improvement is to overcome energy loss caused by numerical dissipation in the basic MPS that brings about rapid decay of…
Data analysts are essential in organizations, transforming raw data into insights that drive decision-making and strategy. This study explores how analysts' productivity evolves on a collaborative platform, focusing on two key learning…
In order to numerically solve high-dimensional nonlinear PDEs and alleviate the curse of dimensionality, a stochastic particle method (SPM) has been proposed to capture the relevant feature of the solution through the adaptive evolution of…
For discrete-time linear systems subject to parametric uncertainty described by random variables, we develop a sampling-based Stochastic Model Predictive Control algorithm. Unlike earlier results employing a scenario approximation, we…
Physical activity is recognized as an essential component of overall health. One measure of physical activity, the step count, is well known as a predictor of long-term morbidity and mortality. Step Counting (SC) is the automated counting…
Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding applications typically involve restrictive assumptions…
The IT Service Management (ITSM) industry has defined processes as best practices in the widely-accepted IT Infrastructure Library (ITIL) framework. However, studies on the measurement of ITSM process improvement are scant. Our research…
Problem solving has been recognized as a central skill that today's students need to thrive and shape their world. As a result, the measurement of problem-solving competency has received much attention in education in recent years. A…
Industrial Control Systems (ICS) are often built from geographically distributed components and often use programmable logic controllers for localized processes. Since verification of such systems is challenging because of both time…
Modern industrial systems face a growing threat from sophisticated cyberattacks that can cause significant operational disruptions. This work presents a novel methodology for identification of the most critical cyberattacks that may disrupt…
The advancing industrial digitalization enables evolved process control schemes that rely on accurate models learned through data-driven approaches. While they provide high control performance and are robust to smaller deviations, a larger…
The field of predictive process monitoring focuses on case-level models to predict a single specific outcome such as a particular objective, (remaining) time, or next activity/remaining sequence. Recently, a longer-horizon, model-wide…