University of Seville

About: University of Seville is a education organization based out in Seville, Andalucía, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 20098 authors who have published 47317 publications receiving 947007 citations. The organization is also known as: Universidad de Sevilla.

Speed Control of Five-Phase Induction Motors With Integrated Open-Phase Fault Operation Using Model-Based Predictive Current Control Techniques

Abstract: Fault tolerance is one of the most interesting features in stand-alone electric propulsion systems. Multiphase induction motor drives are presented like a better alternative to their three-phase counterparts because of their capability to withstand faulty situations, ensuring the postfault operation of the drive. Finite-control set model-based predictive control (FCS-MPC) has been introduced in the last decade like an interesting alternative to conventional controllers for the electrical torque and current regulation of multiphase drives. However, FCS-MPC strategies for multiphase drives with the ability to manage pre- and postfault operations have not been addressed at all. This paper proposes a fault-tolerant speed control for five-phase induction motor drives with the ability to run the system before and after an open-phase fault condition using an FCS-MPC strategy. Experimental results are provided in order to validate the functionality of the proposed control method, maintaining rated currents and ensuring fast and ripple-free torque response.

A systematic literature review on entrepreneurial intentions: citation, thematic analyses, and research agenda

Abstract: Entrepreneurial intention is a rapidly evolving field of research. A growing number of studies use entrepreneurial intention as a powerful theoretical framework. However, a substantial part of this research lacks systematization and categorization, and there seems to be a tendency to start anew with every study. Therefore, there is a need to take stock of current knowledge in this field. In this sense, this paper carries out a review of the literature on entrepreneurial intentions. A total of 409 papers addressing entrepreneurial intention, published between 2004 and 2013 (inclusive), have been analyzed. The purpose and contribution of this paper is to offer a clearer picture of the sub-fields in entrepreneurial intention research, by concentrating on two aspects. Firstly, it reviews recent research by means of a citation analysis to categorize the main areas of specialization currently attracting the attention of the academic community. Secondly, a thematic analysis is carried out to identify the specific themes being researched within each category. Despite the large number of publications and their diversity, the present study identifies five main research areas, plus an additional sixth category for a number of new research papers that cannot be easily classified into the five areas. Within those categories, up to twenty-five different themes are recognized. A number of research gaps are singled out within each of these areas of specialization, in order to induce new ways and perspectives in the entrepreneurial intention field of research that may be fruitful in filling these gaps.

Generalized Alpha-Beta Divergences and Their Application to Robust Nonnegative Matrix Factorization

Abstract: We propose a class of multiplicative algorithms for Nonnegative Matrix Factorization (NMF) which are robust with respect to noise and outliers. To achieve this, we formulate a new family generalized divergences referred to as the Alpha-Beta-divergences (AB-divergences), which are parameterized by the two tuning parameters, alpha and beta, and smoothly connect the fundamental Alpha-, Beta- and Gamma-divergences. By adjusting these tuning parameters, we show that a wide range of standard and new divergences can be obtained. The corresponding learning algorithms for NMF are shown to integrate and generalize many existing ones, including the Lee-Seung, ISRA (Image Space Reconstruction Algorithm), EMML (Expectation Maximization Maximum Likelihood), Alpha-NMF, and Beta-NMF. Owing to more degrees of freedom in tuning the parameters, the proposed family of AB-multiplicative NMF algorithms is shown to improve robustness with respect to noise and outliers. The analysis illuminates the links of between AB-divergence and other divergences, especially Gamma- and Itakura-Saito divergences.

Consensus guidelines on screening for hypopituitarism following traumatic brain injury

Abstract: Primary objective: The goal of this consensus statement is to increase awareness among endocrinologists and physicians treating patients with traumatic brain injury (TBI) of the incidence and risks...