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 & Model predictive control. The organization has 20098 authors who have published 47317 publications receiving 947007 citations. The organization is also known as: Universidad de Sevilla.

Fama: Tooling a Framework for the Automated Analysis of Feature Models

Abstract: The automated analysis of feature models is recognized as one of the key challenges for automated software development in the context of Software Product Lines (SPL). However, after years of research only a few ad-hoc proposals have been presented in such area and the tool support demanded by the SPL community is still insufficient. In previous work we showed how the selection of a logic representation and a solver to handle analysis on feature models can have a remarkable impact in the performance of the analysis process. In this paper we present a first implementation of FAMA (FeAture Model Analyser). FAMA is a framework for the automated analysis of feature models integrating some of the most commonly used logic representations and solvers proposed in the literature. To the best of our knowledge, FAMA is the first tool integrating different solvers for the automated analyses of feature models.

Hints of the existence of Axion-Like-Particles from the gamma-ray spectra of cosmological sources

Abstract: Axionlike particles (ALPs) are predicted to couple with photons in the presence of magnetic fields. This effect may lead to a significant change in the observed spectra of gamma-ray sources such as active galactic nuclei (AGNs). Here we carry out a detailed study that for the first time simultaneously considers in the same framework both the photon/axion mixing that takes place in the gamma-ray source and that one expected to occur in the intergalactic magnetic fields. An efficient photon/axion mixing in the source always means an attenuation in the photon flux, whereas the mixing in the intergalactic medium may result in a decrement and/or enhancement of the photon flux, depending on the distance of the source and the energy considered. Interestingly, we find that decreasing the value of the intergalactic magnetic field strength, which decreases the probability for photon/axion mixing, could result in an increase of the expected photon flux at Earth if the source is far enough. We also find a 30% attenuation in the intensity spectrum of distant sources, which occurs at an energy that only depends on the properties of the ALPs and the intensity of the intergalactic magnetic field, and thus independent of the AGN source being observed. Moreover, we show that this mechanism can easily explain recent puzzles in the spectra of distant gamma-ray sources, like the possible detection of TeV photons from 3C 66A (a source located at $z=0.444$) by MAGIC and VERITAS, which should not happen according to conventional models of photon propagation over cosmological distances. Another puzzle is the recent published lower limit to the extragalactic background light intensity at $3.6\text{ }\text{ }\ensuremath{\mu}\mathrm{m}$ (which is almost twice larger as the previous one), which implies very hard spectra for some detected TeV gamma-ray sources located at $z=0.1--0.2$. The consequences that come from this work are testable with the current generation of gamma-ray instruments, namely Fermi (formerly known as GLAST) and imaging atmospheric Cherenkov telescopes like CANGAROO, HESS, MAGIC, and VERITAS.

Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems

Abstract: This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results.