45 citations · 155 across the 24 of their papers we have counts for
5 papers · 1 filter
Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
Daniel R. Clarkson, Lawrence A. Bull, Tina A. Dardeno +6
At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental cos…
On the hierarchical Bayesian modelling of frequency response functions
T. A. Dardeno, K. Worden, N. Dervilis +2
For situations that may benefit from information sharing among datasets, e.g., population-based SHM of similar structures, the hierarchical Bayesian approach provides a useful mode…
When is an SHM problem a Multi-Task-Learning problem?
Sarah Bee, Lawrence Bull, Nikolas Dervilis +1
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 th…
Meta-models for transfer learning in source localisation
Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross +2
In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent…
Better Together: Using Multi-task Learning to Improve Feature Selection within Structural Datasets
S. C. Bee, E. Papatheou, M Haywood-Alexander +4
There have been recent efforts to move to population-based structural health monitoring (PBSHM) systems. One area of PBSHM which has been recognised for potential development is th…