Application of superconducting bulks and stacks of tapes in electrical machines

The present dissertation focuses on the research of the recent approach of innovative high-temperature superconducting stacked tapes in electrical ma-chines applications, taking into account their potential benefits as an alternative for the massive superconducting bulks, mainly related with geometric and me-chanical flexibility. This work was developed in collaboration with Institut de Ciència de Ma-terials de Barcelona (ICMAB), and is related with evaluation of electrical and magnetic properties of the mentioned superconducting materials, namely: analysis of magnetization of a bulk sample through simulations carried out in the finite elements COMSOL software; measurement of superconducting tape resistivity at liquid nitrogen and room temperatures; and, finally, development and testing of a frequency controlled superconducting motor with rotor built by superconducting tapes. In the superconducting state, results showed a critical current density of 140.3 MA/m2 (or current of 51.15 A) on the tape and a 1 N∙m developed motor torque, independent from the rotor position angle, typical in hysteresis motors.

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.