Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Bull, Lawrence A."'
Autor:
Clarkson, Daniel R., Bull, Lawrence A., Wickramarachchi, Chandula T., Cross, Elizabeth J., Rogers, Timothy J., Worden, Keith, Dervilis, Nikolaos, Hughes, Aidan J.
Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used
Externí odkaz:
http://arxiv.org/abs/2409.04328
Autor:
Brealy, Simon M., Hughes, Aidan J., Dardeno, Tina A., Bull, Lawrence A., Mills, Robin S., Dervilis, Nikolaos, Worden, Keith
Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structures. However, benign variation
Externí odkaz:
http://arxiv.org/abs/2408.16527
Autor:
Bull, Lawrence A., Jeon, Chiho, Girolami, Mark, Duncan, Andrew, Schooling, Jennifer, Haro, Miguel Bravo
We suggest a multilevel model, to represent aggregate train-passing events from the Staffordshire bridge monitoring system. We formulate a combined model from simple units, representing strain envelopes (of each train passing) for two types of commut
Externí odkaz:
http://arxiv.org/abs/2403.17820
Autor:
Clarkson, Daniel R., Bull, Lawrence A., Dardeno, Tina A., Wickramarachchi, Chandula T., Cross, Elizabeth J., Rogers, Timothy J., Worden, Keith, Dervilis, Nikolaos, Hughes, Aidan J.
At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental costs. To counter this, the authors propose a Bayesian hierarchical model
Externí odkaz:
http://arxiv.org/abs/2310.05807
Multi-task neural networks learn tasks simultaneously to improve individual task performance. There are three mechanisms of multi-task learning (MTL) which are explored here for the context of structural health monitoring (SHM): (i) the natural occur
Externí odkaz:
http://arxiv.org/abs/2305.09425
Data from populations of systems are prevalent in many industrial applications. Machines and infrastructure are increasingly instrumented with sensing systems, emitting streams of telemetry data with complex interdependencies. In practice, data-centr
Externí odkaz:
http://arxiv.org/abs/2305.08657
Risk-based active learning is an approach to developing statistical classifiers for online decision-support. In this approach, data-label querying is guided according to the expected value of perfect information for incipient data points. For SHM app
Externí odkaz:
http://arxiv.org/abs/2206.12598
Gaining the ability to make informed decisions on operation and maintenance of structures provides motivation for the implementation of structural health monitoring (SHM) systems. However, descriptive labels for measured data corresponding to health-
Externí odkaz:
http://arxiv.org/abs/2206.11616
Reductions in natural frequency are often used as a damage indicator for structural health monitoring (SHM) purposes. However, fluctuations in operational and environmental conditions, changes in boundary conditions, and slight differences among nomi
Externí odkaz:
http://arxiv.org/abs/2206.11683
The practical application of structural health monitoring (SHM) is often limited by the availability of labelled data. Transfer learning - specifically in the form of domain adaptation (DA) - gives rise to the possibility of leveraging information fr
Externí odkaz:
http://arxiv.org/abs/2205.12052