Aplicación de algoritmos SSA a la predicción de demanda en centros de transformación

Este TFG pretende explorar una solución a la problemática existente en los sistemas de distribución de energía eléctrica, que por motivos de planificación, ajuste de oferta-demanda, etc., necesitan de una predicción del consumo. Esta predicción se puede realizar a largo, medio o corto plazo. En este TFG se trata el problema de la predicción de corto plazo, por lo que se realiza con una antelación de 24 horas de horizonte, con el objeto de ajustar la oferta y demanda de energía. Para realizar la predicción se propone un sistema mixto que aúna dos herramientas: el Análisis de Espectro Singular y Redes Neuronales.This TFG aims to explore a solution to an existing problem in the distribution systems of electric energy, which for reasons of planning, supply-demand adjustment, etc., need a prediction of consumption. This prediction can be made long, medium or short term. This TFG treats the problem of short-term prediction, so it is done with a 24-hour horizon ahead, in order to adjust supply and demand for energy. To perform the prediction, we propose a mixed system that combines two tools: Singular Spectrum Analysis(SSA) and Artificial Neural Networks(ANN).Grado en Ingeniería en Electrónica y Automática Industria

Definición de una arquitectura configurable para la implementación de redes neuronales en dispositivos FPGA

En los últimos años, en el mundo de la robótica cada vez es más importante el campo de la Inteligencia Artificial y el Machine Learning, donde, además, cada vez cobran más importancia las redes neuronales. El propósito de este trabajo es el diseño de una arquitectura flexible y eficiente para la implementación de redes neuronales en un dispositivo FPGA; para ello, se atenderá a la maximización en la eficiencia de los recursos, sin reducir la frecuencia de actualización de la red neuronal y la latencia de ésta. Finalmente se mostrarán ejemplos de cómo generar algunas estructuras basadas en la arquitectura propuesta.In recent years, in the field of Robotics, Artificial Intelligence and Machine Learning has become increasingly important, and neural networks are even more interesting. The purpose of this work is the design of a flexible and efficient architecture for the implementation of neural networks in an FPGA device; for that , we will focus on maximizing the efficiency of resources, without reducing the update frequency of the neural network and its latency. Finally, some examples will be shown about how to generate certain structures based on the proposed architecture.Máster Universitario en Ingeniería Industrial (M141

Copper (II) Metallodendrimers Combined with Pro-Apoptotic siRNAs as a Promising Strategy Against Breast Cancer Cells

Cancer treatment with small interfering RNA (siRNA) is one of the most promising new strategies; however, transfection systems that increase its bioavailability and ensure its delivery to the target cell are necessary. Transfection systems may be just vehicular or could contain fragments with anticancer activity that achieves a synergistic effect with siRNA. Cationic carbosilane dendrimers have proved to be powerful tools as non-viral vectors for siRNA in cancer treatment, and their activity might be potentiated by the inclusion of metallic complexes in its dendritic structure. We have herein explored the interaction between Schiff-base carbosilane copper (II) metallodendrimers, and pro-apoptotic siRNAs. The nanocomplexes formed by metallodendrimers and different siRNA have been examined for their zeta potential and size, and by transmission electron microscopy, fluorescence polarisation, circular dichroism, and electrophoresis. The internalisation of dendriplexes has been estimated by flow cytometry and confocal microscopy in a human breast cancer cell line (MCF-7), following the ability of these metallodendrimers to deliver the siRNA into the cell. Finally, in vitro cell viability experiments have indicated effective interactions between Cu (II) dendrimers and pro-apoptotic siRNAs: Mcl-1 and Bcl-2 in breast cancer cells. Combination of the first-generation derivatives with chloride counterions and with siRNA increases the anticancer activity of the dendriplex constructs and makes them a promising non-viral vector.Polish National Agency for Academic Exchange (NAWA)European CommissionMinisterio de Economía y CompetitividadComunidad de MadridJunta de Comunidades de Castilla-La Manch