Measurements, Models, Systems and Design

531 s.

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Circuit paradigm in the 21

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Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

Modeling and identification of power electronic converters

Nowadays, many industries are moving towards more electrical systems and components. This is done with the purpose of enhancing the efficiency of their systems while being environmentally friendlier and sustainable. Therefore, the development of power electronic systems is one of the most important points of this transition. Many manufacturers have improved their equipment and processes in order to satisfy the new necessities of the industries (aircraft, automotive, aerospace, telecommunication, etc.). For the particular case of the More Electric Aircraft (MEA), there are several power converters, inverters and filters that are usually acquired from different manufacturers. These are switched mode power converters that feed multiple loads, being a critical element in the transmission systems. In some cases, these manufacturers do not provide the sufficient information regarding the functionality of the devices such as DC/DC power converters, rectifiers, inverters or filters. Consequently, there is the need to model and identify the performance of these components to allow the aforementioned industries to develop models for the design stage, for predictive maintenance, for detecting possible failures modes, and to have a better control over the electrical system. Thus, the main objective of this thesis is to develop models that are able to describe the behavior of power electronic converters, whose parameters and/or topology are unknown. The algorithms must be replicable and they should work in other types of converters that are used in the power electronics field. The thesis is divided in two main cores, which are the parameter identification for white-box models and the black-box modeling of power electronics devices. The proposed approaches are based on optimization algorithms and deep learning techniques that use non-intrusive measurements to obtain a set of parameters or generate a model, respectively. In both cases, the algorithms are trained and tested using real data gathered from converters used in aircrafts and electric vehicles. This thesis also presents how the proposed methodologies can be applied to more complex power systems and for prognostics tasks. Concluding, this thesis aims to provide algorithms that allow industries to obtain realistic and accurate models of the components that they are using in their electrical systems.En la actualidad, el uso de sistemas y componentes eléctricos complejos se extiende a múltiples sectores industriales. Esto se hace con el propósito de mejorar su eficiencia y, en consecuencia, ser más sostenibles y amigables con el medio ambiente. Por tanto, el desarrollo de sistemas electrónicos de potencia es uno de los puntos más importantes de esta transición. Muchos fabricantes han mejorado sus equipos y procesos para satisfacer las nuevas necesidades de las industrias (aeronáutica, automotriz, aeroespacial, telecomunicaciones, etc.). Para el caso particular de los aviones más eléctricos (MEA, por sus siglas en inglés), existen varios convertidores de potencia, inversores y filtros que suelen adquirirse a diferentes fabricantes. Se trata de convertidores de potencia de modo conmutado que alimentan múltiples cargas, siendo un elemento crítico en los sistemas de transmisión. En algunos casos, estos fabricantes no proporcionan la información suficiente sobre la funcionalidad de los dispositivos como convertidores de potencia DC-DC, rectificadores, inversores o filtros. En consecuencia, existe la necesidad de modelar e identificar el desempeño de estos componentes para permitir que las industrias mencionadas desarrollan modelos para la etapa de diseño, para el mantenimiento predictivo, para la detección de posibles modos de fallas y para tener un mejor control del sistema eléctrico. Así, el principal objetivo de esta tesis es desarrollar modelos que sean capaces de describir el comportamiento de un convertidor de potencia, cuyos parámetros y/o topología se desconocen. Los algoritmos deben ser replicables y deben funcionar en otro tipo de convertidores que se utilizan en el campo de la electrónica de potencia. La tesis se divide en dos núcleos principales, que son la identificación de parámetros de los convertidores y el modelado de caja negra (black-box) de dispositivos electrónicos de potencia. Los enfoques propuestos se basan en algoritmos de optimización y técnicas de aprendizaje profundo que utilizan mediciones no intrusivas de las tensiones y corrientes de los convertidores para obtener un conjunto de parámetros o generar un modelo, respectivamente. En ambos casos, los algoritmos se entrenan y prueban utilizando datos reales recopilados de convertidores utilizados en aviones y vehículos eléctricos. Esta tesis también presenta cómo las metodologías propuestas se pueden aplicar a sistemas eléctricos más complejos y para tareas de diagnóstico. En conclusión, esta tesis tiene como objetivo proporcionar algoritmos que permitan a las industrias obtener modelos realistas y precisos de los componentes que están utilizando en sus sistemas eléctricos.Postprint (published version

Applications of discrete wavelet transform for feature extraction to increase the accuracy of monitoring systems of liquid petroleum products

This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction

Performance monitoring of aircraft PMSM based on soft computing technique

Rigorous fault analysis (FA) referring to fault detection its level is important for maintenances to simplify as well as improves performance. The purpose of this study was to present a technique for the electronic diagnosis of switch defects in regular magnetic concurrent motors (PMSM) For Aircraft application. The performance level of the bridges of the thyristor and the device excitation diode is tracked in healthy and defective service processes. Extracted functionality in various procedures that use Decomposition of the Multi-Scale Wavelet (MSWD) to remove useful functionality. The functionalities of the MSWD are utilized to train the Autoregressive of the Nonlinear with Exogenous Model (NARX) that was Sequence of the method controlled to determine the level of the fault in an open circuit forming through a switch. Both models were evaluated and built based on simulated data, where the findings demonstrated a preferable efficacy in the diagnosis of different kinds of fault