A bayesian artificial neural network method to characterise laminar defects using dynamic measurements

This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.

Matching of catalogues by probabilistic pattern classification

We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HI Parkes All Sky Survey radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.

mecA locus diversity in methicillin-resistant Staphylococcus aureus isolates in Brisbane, Australia, and the development of a novel diagnostic procedure for the Western Samoan phage pattern clone

An emerging public health phenomenon is the increasing incidence of methicillin-resistant Staphylococcus aureus (MRSA) infections that are acquired outside of health care facilities. One lineage of community-acquired MRSA (CA-MRSA) is known as the Western Samoan phage pattern (WSPP) clone. The central aim of this study was to develop an efficient genotyping procedure for the identification of WSPP isolates. The approach taken was to make use of the highly variable region downstream of mecA in combination with a single nucleotide polymorphism (SNP) defined by the S. aureus multilocus sequence typing (MLST) database. The premise was that a combinatorial genotyping method that interrogated both a highly variable region and the genomic backbone would deliver a high degree of informative power relative to the number of genetic polymorphisms-interrogated. Thirty-five MRSA isolates were used for this study, and their gene contents and order downstream of mecA were determined. The CA-MRSA isolates were found to contain a truncated mecA downstream region consisting of mecA-HVR-IS431 mec-dcs-Ins117, and a PCR-based method for identifying this structure was developed. The hospital-acquired isolates were found to contain eight different mecA downstream regions, three of which were novel. The Minimum SNPs computer software program was used to mine the S. aureus MLST database, and the arcC 2726 polymorph was identified as 82% discriminatory for ST-30. A real-time PCR assay was developed to interrogate this SNP. We found that the assay for the truncated mecA downstream region in combination with the interrogation of arcC position 272 provided an unambiguous identification of WSPP isolates.