Is it worth changing pattern recognition methods for structural health monitoring?

The key element of this work is to demonstrate alternative strategies for using pattern recognition algorithms whilst investigating structural health monitoring. This paper looks to determine if it makes any difference in choosing from a range of established classification techniques: from decision trees and support vector machines, to Gaussian processes. Classification algorithms are tested on adjustable synthetic data to establish performance metrics, then all techniques are applied to real SHM data. To aid the selection of training data, an informative chain of artificial intelligence tools is used to explore an active learning interaction between meaningful clusters of data

Towards the probabilistic analysis of small bowel capsule endoscopy features to predict severity of duodenal histology in patients with villous atrophy

Small bowel capsule endoscopy (SBCE) can be complementary to histological assessment of celiac disease (CD) and serology negative villous atrophy (SNVA). Determining the severity of disease on SBCE using statistical machine learning methods can be useful in the follow up of patients. SBCE can play an additional role in differentiating between CD and SNVA. De-identified SBCEs of patients with CD and SNVA were included. Probabilistic analysis of features on SBCE were used to predict severity of duodenal histology and to distinguish between CD and SNVA. Patients with higher Marsh scores were more likely to have a positive SBCE and a continuous distribution of macroscopic features of disease than those with lower Marsh scores. The same pattern was also true for patients with CD when compared to patients with SNVA. The validation accuracy when predicting the severity of Marsh scores and when distinguishing between CD and SNVA was 69.1% in both cases. When the proportions of each SBCE class group within the dataset were included in the classification model, to distinguish between the two pathologies, the validation accuracy increased to 75.3%. The findings of this work suggest that by using features of CD and SNVA on SBCE, predictions can be made of the type of pathology and the severity of disease

Kernelised Bayesian transfer learning for population-based structural health monitoring

Population-based structural health monitoring is the process of utilising information from a group of structures in order to perform and improve inferences that generalise to the complete population. A significant challenge in inferring a general representation for structures is that feature spaces will be inconsistent for a wide variety of populations and datasets. This scenario, where the dimensions of the feature spaces for each structure are different, occurs for a variety of reasons. Firstly, the group of structures themselves may be a heterogeneous population, where differences occur due to topology, leading to inconsistency in modal-based features. Secondly, feature spaces may be inconsistent across the population due to differences in the raw data (i.e. different sample frequencies etc.) and feature extraction processing. In this context, where feature spaces are inconsistent between different structure in a population, a general model that describes their behaviours becomes challenging to infer. This issue is because dimensionality reduction must be performed such that each domain’s feature set projects to a consistent shared latent space where a model can be inferred. This paper introduces a technique, kernelised Bayesian transfer learning, that seeks to learn a projection matrix and kernel embedding that map to a latent space where a discriminative classifier can be inferred in a Bayesian manner, using variational inference. This algorithm allows a general discriminative classifier to be inferred across a population where the feature spaces for each structure are inconsistent. A numerical case study is presented, demonstrating the effectiveness of this approach and for providing a discussion of its implications for population-based structural health monitoring