Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification

Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Design and validation of structural health monitoring system based on bio-inspired algorithms

The need of ensure the proper performance of the structures in service has made of structural health monitoring (SHM) a priority research area. Researchers all around the world have focused efforts on the development of new ways to continuous monitoring the structures and analyze the data collected from the inspection process in order to provide information about the current state and avoid possible catastrophes. To perform an effective analysis of the data, the development of methodologies is crucial in order to assess the structures with a low computational cost and with a high reliability. These desirable features can be found in biological systems, and these can be emulated by means of computational systems. The use of bio-inspired algorithms is a recent approach that has demonstrated its effectiveness in data analysis in different areas. Since these algorithms are based in the emulation of biological systems that have demonstrated its effectiveness for several generations, it is possible to mimic the evolution process and its adaptability characteristics by using computational algorithms. Specially in pattern recognition, several algorithms have shown good performance. Some widely used examples are the neural networks, the fuzzy systems and the genetic algorithms. This thesis is concerned about the development of bio-inspired methodologies for structural damage detection and classification. This document is organized in five chapters. First, an overview of the problem statement, the objectives, general results, a brief theoretical background and the description of the different experimental setups are included in Chapter 1 (Introduction). Chapters 2 to 4 include the journal papers published by the author of this thesis. The discussion of the results, some conclusions and the future work can be found on Chapter 5. Finally, Appendix A includes other contributions such as a book chapter and some conference papers.La necesidad de asegurar el correcto funcionamiento de las estructuras en servicio ha hecho de la monitorización de la integridad estructural un área de gran interés. Investigadores en todas las partes del mundo centran sus esfuerzos en el desarrollo de nuevas formas de monitorización contínua de estructuras que permitan analizar e interpretar los datos recogidos durante el proceso de inspección con el objetivo de proveer información sobre el estado actual de la estructura y evitar posibles catástrofes. Para desarrollar un análisis efectivo de los datos, es necesario el desarrollo de metodologías para inspeccionar la estructura con un bajo coste computacional y alta fiabilidad. Estas características deseadas pueden ser encontradas en los sistemas biológicos y pueden ser emuladas mediante herramientas computacionales. El uso de algoritmos bio-inspirados es una reciente técnica que ha demostrado su efectividad en el análisis de datos en diferentes áreas. Dado que estos algoritmos se basan en la emulación de sistemas biológicos que han demostrado su efectividad a lo largo de muchas generaciones, es posible imitar el proceso de evolución y sus características de adaptabilidad al medio usando algoritmos computacionales. Esto es así, especialmente, en reconocimiento de patrones, donde muchos de estos algoritmos brindan excelentes resultados. Algunos ejemplos ampliamente usados son las redes neuronales, los sistemas fuzzy y los algoritmos genéticos. Esta tesis involucra el desarrollo de unas metodologías bio-inspiradas para la detección y clasificación de daños estructurales. El documento está organizado en cinco capítulos. En primer lugar, se incluye una descripción general del problema, los objetivos del trabajo, los resultados obtenidos, un breve marco conceptual y la descripción de los diferentes escenarios experimentales en el Capítulo 1 (Introducción). Los Capítulos 2 a 4 incluyen los artículos publicados en diferentes revistas indexadas. La revisión de los resultados, conclusiones y el trabajo futuro se encuentra en el Capítulo 5. Finalmente, el Anexo A incluye otras contribuciones tales como un capítulo de libro y algunos trabajos publicados en conferencias

Analysis of Atrial Electrograms

This work provides methods to measure and analyze features of atrial electrograms - especially complex fractionated atrial electrograms (CFAEs) - mathematically. Automated classification of CFAEs into clinical meaningful classes is applied and the newly gained electrogram information is visualized on patient specific 3D models of the atria. Clinical applications of the presented methods showed that quantitative measures of CFAEs reveal beneficial information about the underlying arrhythmia

Preparation and characterization of magnetite (Fe3O4) nanoparticles By Sol-Gel method

The magnetite (Fe3O4) nanoparticles were successfully synthesized and annealed under vacuum at different temperature. The Fe3O4 nanoparticles prepared via sol-gel assisted method and annealed at 200-400ºC were characterized by Fourier Transformation Infrared Spectroscopy (FTIR), X-ray Diffraction spectra (XRD), Field Emission Scanning Electron Microscope (FESEM) and Atomic Force Microscopy (AFM). The XRD result indicate the presence of Fe3O4 nanoparticles, and the Scherer`s Formula calculated the mean particles size in range of 2-25 nm. The FESEM result shows that the morphologies of the particles annealed at 400ºC are more spherical and partially agglomerated, while the EDS result indicates the presence of Fe3O4 by showing Fe-O group of elements. AFM analyzed the 3D and roughness of the sample; the Fe3O4 nanoparticles have a minimum diameter of 79.04 nm, which is in agreement with FESEM result. In many cases, the synthesis of Fe3O4 nanoparticles using FeCl3 and FeCl2 has not been achieved, according to some literatures, but this research was able to obtained Fe3O4 nanoparticles base on the characterization results

Quantile-Based Fuzzy Clustering of Multivariate Time Series in the Frequency Domain

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] A novel procedure to perform fuzzy clustering of multivariate time series generated from different dependence models is proposed. Different amounts of dissimilarity between the generating models or changes on the dynamic behaviours over time are some arguments justifying a fuzzy approach, where each series is associated to all the clusters with specific membership levels. Our procedure considers quantile-based cross-spectral features and consists of three stages: (i) each element is characterized by a vector of proper estimates of the quantile cross-spectral densities, (ii) principal component analysis is carried out to capture the main differences reducing the effects of the noise, and (iii) the squared Euclidean distance between the first retained principal components is used to perform clustering through the standard fuzzy C-means and fuzzy C-medoids algorithms. The performance of the proposed approach is evaluated in a broad simulation study where several types of generating processes are considered, including linear, nonlinear and dynamic conditional correlation models. Assessment is done in two different ways: by directly measuring the quality of the resulting fuzzy partition and by taking into account the ability of the technique to determine the overlapping nature of series located equidistant from well-defined clusters. The procedure is compared with the few alternatives suggested in the literature, substantially outperforming all of them whatever the underlying process and the evaluation scheme. Two specific applications involving air quality and financial databases illustrate the usefulness of our approach.The authors are grateful to the anonymous referees for their comments and suggestions. The research of Ángel López-Oriona and José A. Vilar has been supported by the Ministerio de Economía y Competitividad (MINECO) grants MTM2017-82724-R and PID2020-113578RB-100, the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14), and the Centro de Investigación del Sistema Universitario de Galicia “CITIC” grant ED431G 2019/01; all of them through the European Regional Development Fund (ERDF). This work has received funding for open access charge by Universidade da Coruña/CISUGXunta de Galicia; ED431C-2020-14Xunta de Galicia; ED431G 2019/0