Engineering Pattern Recognition
Code to reproduce paper results (or as close as possible, depending on data-availability). Each publication has a Jupyter notebook.
Mostly probabilistic/Bayesian ML for engineering applications, particularly performance and health monitoring. Scripts are provided to test and demonstrate the EPR module.
This is a work-in-progress.
- Multitask Learning
- Hierarchical regression (Stan)
- Domain Adaptation
- Transfer Component Analysis (TCA)
- [Forthcoming]: Domain Adapted Gaussian Mixture Models (GMMs)
- Partially-supervised learning
- Active learning by uncertainty sampling in Gaussian Mixture Models (GMMs)
- Semi-supervised learning of mixture models via (MAP) expectation maximisation
- Hierarchical sampling for active learning (the DH active learner)
Papers and Notebooks
- [Forthcoming]: Knowledge transfer in engineering fleets: Hierarchical
Bayesian modelling for multitask learning (ArXiv, 2022)
- Hierarchical regression models of engineering populations, allowing knowledge transfer between subgroups
- Applications to truck fleet survival analysis and wind farm power prediction.
- Jupyter notebook demo based on truck-fleet survival analysis.
- On the transfer of damage detectors between structures: an experimental
case study (JSV, 2021)
- Domain adaptation to transfer novelty detectors between aircraft tailplane ground-tests.
- The TCA code used in the papers.
- Towards semi-supervised and probabilistic classification in structural health monitoring (MSSP, 2020)
- Probabilistic active learning: An online framework for structural health monitoring (MSSP, 2019)
- Active learning for semi-supervised structural health monitoring (JSV,
- Hierarchical sampling for active learning (the DH active learner) applied to learn a classifier for ground-test vibration data from a Gnat aircraft.
- MATLAB demo.