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.

The alhambra survey: a large area multimedium-band optical and near-infrared photometric survey*

Abstract: Ministerio de Educacion y Ciencia AYA2002-12685-E AYA2003-08729-C02-01 AYA2003-0128 AYA2004-08260-C03-01 AYA2004-20014-E AYA2004-02703 AYA2004-05395 AYA2005-06816 AYA2005-07789 AYA2006-14056

Operational transconductance amplifier-based nonlinear function syntheses

Abstract: It is shown that the operational transconductance amplifier, as the active element in basic building blocks, can be efficiently used for programmable nonlinear continuous-time function synthesis. Two efficient nonlinear function synthesis approaches are presented. The first approach is a rational approximation, and the second is a piecewise-linear approach. Test circuits have been fabricated using a 3- mu m p-well CMOS process. The flexibility of the designed and tested circuits was confirmed. >

Path-based distribution network modeling: application to reconfiguration for loss reduction

Abstract: This paper is devoted to efficiently modeling the connectivity of distribution networks, which are structurally meshed but radially operated. A new approach, based on the "path-to-node" concept, is presented, allowing both topological and electrical constraints to be algebraically formulated before the actual radial configuration is determined. In order to illustrate the possibilities of the proposed framework, the problem of network reconfiguration for power loss reduction is considered. Two different optimization algorithms-one resorting to a genetic algorithm and the other solving a conventional mixed-integer linear problem-are fully developed. The validity and effectiveness of the path-based distribution network modeling are demonstrated on different test systems.

Neural networks to predict earthquakes in Chile

Abstract: A new earthquake prediction system is presented in this work. This method, based on the application of artificial neural networks, has been used to predict earthquakes in Chile, one of the countries with larger seismic activity. The input values are related to the b-value, the Bath's law, and the Omori-Utsu's law, parameters that are strongly correlated with seismicity, as shown in solid previous works. Two kind of prediction are provided in this study: The probability that an earthquake of magnitude larger than a threshold value happens, and the probability that an earthquake of a limited magnitude interval might occur, both during the next five days in the areas analyzed. For the four Chile's seismic regions examined, with epicenters placed on meshes with dimensions varying from 0.5^ox0.5^o to 1^ox1^o, a prototype of neuronal network is presented. The prototypes predict an earthquake every time the probability of an earthquake of magnitude larger than a threshold is sufficiently high. The threshold values have been adjusted with the aim of obtaining as few false positives as possible. The accuracy of the method has been assessed in retrospective experiments by means of statistical tests and compared with well-known machine learning classifiers. The high success rate achieved supports the suitability of applying soft computing in this field and poses new challenges to be addressed.