Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis

We propose a novel data-driven approach for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. We demonstrate it through typical examples including polycrystalline BaTiO$_3$, multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Lab. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern by pattern crystal indexing process. This work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning prospective for the development of suitable feature extraction, clustering and labeling algorithms.Comment: 29 pages, 25 figures under the second round of review by Acta Crystallographica

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

Neural Network Models for Nuclear Treaty Monitoring: Enhancing the Seismic Signal Pipeline with Deep Temporal Convolution

Seismic signal processing at the IDC is critical to global security, facilitating the detection and identification of covert nuclear tests in near-real time. This dissertation details three research studies providing substantial enhancements to this pipeline. Study 1 focuses on signal detection, employing a TCN architecture directly against raw real-time data streams and effecting a 4 dB increase in detector sensitivity over the latest operational methods. Study 2 focuses on both event association and source discrimination, utilizing a TCN-based triplet network to extract source-specific features from three-component seismograms, and providing both a complimentary validation measure for event association and a one-shot classifier for template-based source discrimination. Finally, Study 3 focuses on event localization, and employs a TCN architecture against three-component seismograms in order to confidently predict backazimuth angle and provide a three-fold increase in usable picks over traditional polarization analysis

Data-stream driven Fuzzy-granular approaches for system maintenance

Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability

Angular feature extraction and ensemble classification method for 2D, 2.5D and 3D face recognition.

It has been recognised that, within the context of face recognition, angular separation between centred feature vectors is a useful measure of dissimilarity. In this thesis we explore this observation in more detail and compare and contrast angular separation with the Euclidean, Manhattan and Mahalonobis distance metrics. This is applied to 2D, 2.5D and 3D face images and the investigation is done in conjunction with various feature extraction techniques such as local binary patterns (LBP) and linear discriminant analysis (LDA). We also employ error-correcting output code (ECOC) ensembles of support vector machines (SVMs) to project feature vectors non-linearly into a new and more discriminative feature space. It is shown that, for both face verification and face recognition tasks, angular separation is a more discerning dissimilarity measure than the others. It is also shown that the effect of applying the feature extraction algorithms described above is to considerably sharpen and enhance the ability of all metrics, but in particular angular separation, to distinguish inter-personal from extra-personal face image differences. A novel technique, known as angularisation, is introduced by which a data set that is well separated in the angular sense can be mapped into a new feature space in which other metrics are equally discriminative. This operation can be performed separately or it can be incorporated into an SVM kernel. The benefit of angularisation is that it allows strong classification methods to take advantage of angular separation without explicitly incorporating it into their construction. It is shown that the accuracy of ECOC ensembles can be improved in this way. A further aspect of the research is to compare the effectiveness of the ECOC approach to constructing ensembles of SVM base classifiers with that of binary hierarchical classifiers (BHC). Experiments are performed which lead to the conclusion that, for face recognition problems, ECOC yields greater classification accuracy than the BHC method. This is attributed primarily to the fact that the size of the training set decreases along a path from the root node to a leaf node of the BHC tree and this leads to great difficulties in constructing accurate base classifiers at the lower nodes