Multiple instance fuzzy inference.

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of multiple instance learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Fuzzy Inference Systems (MI-FIS). Fuzzy inference is a powerful modeling framework that can handle computing with knowledge uncertainty and measurement imprecision effectively. Fuzzy Inference performs a non-linear mapping from an input space to an output space by deriving conclusions from a set of fuzzy if-then rules and known facts. Rules can be identified from expert knowledge, or learned from data. In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. In this dissertation, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, different multiple instance fuzzy inference styles are proposed. The Multiple Instance Mamdani style fuzzy inference (MI-Mamdani) extends the standard Mamdani style inference to compute with multiple instances. The Multiple Instance Sugeno style fuzzy inference (MI-Sugeno) is an extension of the standard Sugeno style inference to handle reasoning with multiple instances. In addition to the MI-FIS inference styles, one of the main contributions of this work is an adaptive neuro-fuzzy architecture designed to handle bags of instances as input and capable of learning from ambiguously labeled data. The proposed architecture, called Multiple Instance-ANFIS (MI-ANFIS), extends the standard Adaptive Neuro Fuzzy Inference System (ANFIS). We also propose different methods to identify and learn fuzzy if-then rules in the context of MIL. In particular, a novel learning algorithm for MI-ANFIS is derived. The learning is achieved by using the backpropagation algorithm to identify the premise parameters and consequent parameters of the network. The proposed framework is tested and validated using synthetic and benchmark datasets suitable for MIL problems. Additionally, we apply the proposed Multiple Instance Inference to the problem of region-based image categorization as well as to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar

Microwave NDT&E using open-ended waveguide probe for multilayered structures

Ph. D. Thesis.Microwave NDT&E has been proved to be suitable for inspecting of dielectric structures due to low attenuation in dielectric materials and free-space. However, the microwave responses from multilayered structures are complex as an interrogation of scattering electromagnetic waves among the layers and defects. In many practical applications, electromagnetic analysis based on analytic- and forward structural models cannot be generalised since the defect shape and properties are usually unknown and hidden beneath the surface layer. This research proposes the design and implementation of microwave NDT&E system for inspection of multilayered structures. Standard microwave open-ended rectangular waveguides in X, Ku and K bands (frequency range between 8-26.5 GHz) and vector network analyser (VNA) generating sweep frequency of wideband monochromatic waves have been used to obtain reflection coefficient responses over three types of challenging multilayered samples: (1) corrosion progression under coating, (2) woven carbon fibre reinforced polymer (CFRP) with impact damages, and (3) thermal coated glass fibre reinforced polymer (GFRP) pipe with inner flat-bottom holes. The obtained data are analysed by the selected feature extraction method extracting informative features and verify with the sample parameters (defect parameters). In addition, visualisation methods are utilised to improve the presentation of the defects and material structures resulting in a better interpretation for quantitative evaluation. The contributions of this project are summarised as follows: (1) implementation of microwave NDT&E scanning system using open-ended waveguide with the highest resolution of 0.1mm x 0.1 mm, based on the NDT applications for the three aforementioned samples; (2) corrosion stages of steel corrosion under coating have been successfully characterised by the principal component analysis (PCA) method; (3) A frequency selective based PCA feature has been used to visualise the impact damage at different impact energies with elimination of woven texture influences; (4) PCA and SAR (synthetic aperture radar) tomography together with time-offlight extraction, have been used for detection and quantitative evaluation of flat-bottom hole defects (i.e., location, size and depth). The results conclude that the proposed microwave NDT&E system can be used for detection and evaluation of multilayered structures, which its major contributions are follows. (1) The early stages (0-12month) of steel corrosion undercoating has been successfully characterised by mean of spectral responses from microwave opened rectangular waveguide probe and PCA. (2) The detection of low energy impact damages on CFRP as low as 4 Joules has been archived with microwave opened rectangular waveguide probe raster scan together with SAR imaging and PCA for feature extraction methods. (3) The inner flat-bottom holes beneath the thermal coated GFRP up to 11.5 mm depth has been successfully quantitative evaluated by open-ended waveguide raster scan using PCA and 3-D reconstruction based on SAR tomography techniques. The evaluation includes location, sizing and depth. Nevertheless, the major downside of feature quantities extracted from statistically based methods such as PCA, is it intensely relies on the correlation of the input dataset, and thus hardly link them with the physical parameters of the test sample, in particular, the complex composite architectures. Therefore, there are still challenges of feature extraction and quantitative evaluation to accurately determine the essential parameters from the samples. This can be achieved by a future investigation of multiple features fusion and complementary features.Ministry of Science and Technology of Royal Thai Government and Office of Educational Affairs, the Royal Thai Embass

Novel AI-assisted computational solutions for GPR data interpretation and electromagnetic data fusion to detect buried utilities

This research presents a number of novel computational solutions using artificial intelligence (AI) to interpret ground penetrating radar (GPR) data as well as fusing GPR data with data from other sensing modalities, including electromagnetic conductivity (EMC) and electromagnetic locating (EML). The application of the proposed computational solution is predominantly for detecting and locating buried utilities (e.g. pipes and cables) and ground anomalies (e.g. ground disturbances) in the shallow subsurface environment although the work can be extended to detect other buried anomalies. Processing GPR data is usually a subjective and time-consuming practise which involves expert intervention. Thus, the quality of the interpretation of such data depends on user experience and knowledge. Whilst several numerical approaches are available in the literature for post-processing GPR data, they all suffer from various shortcomings including lack of accuracy and/or excessive computational time. The issue is similar (or often worse) for data fusion between GPR and other sensors e.g. EMC and EML. To tackle some of these issues, in this research, four new computational procedures were developed. Three of these computational procedures are based on Kalman Filtering (KF), a less-studied approach to process GPR radargrams despite its great potential in efficient data analysis, and genetic algorithm (GA) as a machine learning based global optimisation tool. The final computational procedure combines finite element modelling and genetic algorithm to infer fused EML-GPR data. For the first two numerical methods, new algorithms were developed to optimise KF parameters using GA to remove noises from GPR radargrams and detect targets. The proposed procedures were validated against data from field and their performance was assessed against additional unseen dataset different to that of the validation to identify their potential limitations. Furthermore, their performances were compared against existing GPR data processing methods and differences were highlighted. The other two computational packages focused on data fusion from GPR and EMC/EML. The first of these two, extended the above KF algorithm to fuse data from GPR and EML as well as GPR and EMC. The results showed that the proposed data fusion algorithm significantly enhanced the quality of locating conductors and conductive regions in the subsurface compared to the individual techniques which were either incapable of defining the material of the buried target or the geometry of conductive anomalies. Finally, a novel inversion algorithm was developed by integrating finite element modelling of a coupled magnetic field and GA for detecting and locating buried live cables using GPR and EML. It was demonstrated that the proposed inversion can successfully detect the location of the buried cables as well as their intensity