Deep fisher discriminant analysis

Fisher Discriminant Analysis’ linear nature and the usual eigen-analysis approach to its solution have limited the application of its underlying elegant idea. In this work we will take advantage of some recent partially equivalent formulations based on standard least squares regression to develop a simple Deep Neural Network (DNN) extension of Fisher’s analysis that greatly improves on its ability to cluster sample projections around their class means while keeping these apart. This is shown by the much better accuracies and g scores of class mean classifiers when applied to the features provided by simple DNN architectures than what can be achieved using Fisher’s linear onesWith partial support from Spain's grants TIN2013-42351- P, TIN2016-76406-P, TIN2015-70308-REDT and S2013/ICE-2845 CASI-CAMCM. Work supported also by project FACIL{Ayudas Fundaci on BBVA a Equipos de Investigación Científica 2016, the UAM{ADIC Chair for Data Science and Machine Learning and Instituto de Ingeniería del Conocimiento. The third author is also supported by the FPU{MEC grant AP-2012-5163. We gratefully acknowledge the use of the facilities of Centro de Computacón Científi ca (CCC) at UA

Flexible Smart Textile Coated by PVDF/Graphene Oxide With Excellent Energy Harvesting Toward a Novel Class of Self-Powered Sensors: Fabrication, Characterization and Measurements

Because of some of their diverse benefits, intelligent textiles have attracted a great deal of interest among specialists over the past decade. This paper describes a novel approach to the manufacture of intelligent piezoelectric polymer-based textiles with enhanced piezoelectric responses for applications that extract biomechanical energy. Here we report a highly scalable and ultrafast production of smart textile piezoelectric containing graphene oxide nanosheets (GONS) dispersed in polyvinylidene fluoride (PVDF). In this work, Cotton textiles (CT) were functionalized and by graphene oxide (GO), using PVDF as a binder to obtain a CT-PVDF-GO material. Tetraethyl orthosilicate (TEOS) was further grafted as a coating layer to improve the surface compatibility, resulting in the CT-PVDF-GO-TEOS composite. The research results show that the addition of GONS significantly improves PVDF's overall crystallization rate on CT. More specifically, the piezoelectric β-phase content (100 % higher F[β]) and crystallinity degree on the piezoelectric properties of composite cotton fiber has been improved effectively. Consequently, this fabricated piezo-smart textile has a glorious piezoelectricity even with comparatively low coating content of PVDF-GONS-TEOS. Based on it, the as-fabricated piezoelectric textile device has resulted in the output voltage of up to 13 mV for a given frequency (fm = 8 Hz) at fixed strain amplitude value (0.5 %). It is believed that this research may further reveal the field of energy harvesting for possible applications in the future.. In addition, the set of experimental results that illustrate the smart textile was carried out and discussed, and how it can be used as a wearable device source for this smart textile. Finally, the approach described in this study can also be used to construct other desirable designs, for a wearable low-consumption sensor, etc

Clasificadores con pesos funcionales por puertas

Los conjuntos de máquinas de aprendizaje representan una herramienta poderosa en muchas aplicaciones, porque ofrecen mejores soluciones que una única máquina, proporcionando, además, un diseño más sencillo y comprensible. Entre estos conjuntos cabe destacar, por la solidez de sus fundamentos y sus buenas prestaciones en problemas de regresión, las Mezclas de Expertos. Sin embargo, sus prestaciones para la la toma de decisiones están muy por debajo de las obtenidas por los mejores diseños. En primer lugar, se presenta una modificación de las Mezclas de Expertos con expertos lineales que da como resultado una máquina de clasificación monolítica, a la que se denomina Clasificador con Pesos Funcionales Generados por Puerta (“Gate Generated Functional Weight Classifier”), cuya salida se obtiene como combinación lineal de las entradas multiplicadas por los pesos funcionales proporcionados por una puerta con arquitectura de Red Neuronal de Funciones de Base Radial que se puede entrenar de forma directa mediante un algoritmo Máximo Margen. Así se consigue una máquina con capacidad de aproximación global-local y con un comportamiento localmente lineal, lo que permite entender de forma aproximada su funcionamiento. Los resultados obtenidos indican que la arquitectura propuesta es, en términos generales, ventajosa respecto a métodos que representan el estado del arte en problemas de clasificación, con baja carga computacional en operación, si bien a cambio de un incremento de carga computacional en diseño. En segundo lugar, se propone el uso de la salida de un conjunto como entrada complementaria de un Clasificador con Pesos Funcionales Generados por Puerta, para solventar las limitaciones debidas al uso de agregaciones no entrenables –tales como simple promedio o mayoría de votos– en conjuntos masivos de clasificadores. Se denominará a este procedimiento postagregación. Los resultados obtenidos indican que la postagregación es ventajosa especialmente en escenarios en los que el Clasificador con Pesos Funcionales Generados por Puerta sólo disponen de una parte de las variables observadas del problema, escenarios frecuentes en los sistemas de aprendizaje distribuido, y también cuando se consideran expertos humanos.Machine ensembles represent a powerful tool in many artificial learning applications, because they usually offer better solutions than a single machine, and also provide easy and (somewhat) understandable designs. Among the different ensemble building techniques, Mixtures of Experts have solid foundations and good performance in regression problems. However, their performance in decision making is far below than those obtained by the best designs. Firstly, a modification of Mixtures of Experts with linear experts is presented, resulting in a monolithic classification machine, which will be referred as Gate Generated Functional Weight Classifier, whose output is obtained as a linear combination of the inputs multiplied by the functional weights provided by a gate with a Radial Basis Functions Neural Network architecture. This machine can be trained directly by Maximum Margin algorithms. So, we obtain a machine with global-local approximation capabilities and a locally linear response, allowing an easier interpretation of its functioning. The results of extensive simulation examples indicate that the proposed architecture is, in general, advantageous over methods that represent the state-of-the-art in classification problems, with a lower operation computational load, although paying in, an increase of the design computational effort. Secondly, we propose the use of the output of a classifier ensemble as a complementary input to a Gate Generated FunctionalWeight Classifier, in order to overcome the limitations due to the use of non-trainable aggregations –such as simple average or majority vote– in massive classifier ensembles. This procedure will be referred as post-aggregation. Experimental evidence supports that this provides performance advantage, especially when the Gate Generated Functional Weight Classifier only accesses a part of the features, which is a frequent case for distributed learning systems and also when human experts are making decisions.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: José Ramón Dorronsoro Ibero.- Secretario: Antonio Artés Rodríguez.- Vocal: Francisco Herrer

Deep Neural Networks for Wind and Solar Energy Prediction

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11063-017-9613-7Deep Learning models are recently receiving a large attention because of their very powerful modeling abilities, particularly on inputs that have a intrinsic one- or two-dimensional structure that can be captured and exploited by convolutional layers. In this work we will apply Deep Neural Networks (DNNs) in two problems, wind energy and daily solar radiation prediction, whose inputs, derived from Numerical Weather Prediction systems, have a clear spatial structure. As we shall see, the predictions of single deep models and, more so, of DNN ensembles can improve on those of Support Vector Regression, a Machine Learning method that can be considered the state of the art for regressionWith partial support from Spain’s Grants TIN2013-42351-P (MINECO), S2013/ICE2845 CASI-CAM-CM (Comunidad de Madrid), FACIL (Ayudas Fundación BBVA a Equipos de Investigación Científica 2016) and the UAM–ADIC Chair for Data Science and Machine Learning. The second author is also kindly supported by the FPU-MEC Grant AP-2012-5163. The authors gratefully acknowledge access to the MARS repository granted by the ECMWF, the use of the facilities of Centro de Computación Científica (CCC) at UAM and thank Red Eléctrica de España for kindly supplying wind energy production data and to Sotavento for making their production data publicly available

Improving dielectric properties of composites thin films with polylactic acid and PZT microparticles induced by interfacial polarization

Although polylactic acid (PLA) is widely identified as a biodegradable polymer, its use is limited due to the inherently poor mechanical properties. Therefore, the strengthening of PLA with microscale particles like lead zirconate titanate (PZT) is a promising field of research that has only just begun to be explored. Piezoelectric polymer-PZT films are encouraging materials for modern technological applications in energy harvesting. The PLA/PZT composites were developed using the solvent casting technique. The mechanical characteristics and dielectric properties of the considered films were investigated. X-ray Diffraction (XRD), Fourier Transform Infrared (FTIR), Spectroscopy and Scanning Electron Microscopy (SEM) were used, respectively, to examine the influence of these fillers at the molecular level, crystal structure change and micro charges dispersion inside the polymer matrix. Thermogravimetric Analysis (TGA) was used to examine the stability and thermal degradation of the films. The effect of the content (0.1–1 wt.%) of PZT on these properties has also been studied. The results indicate that the addition of PZT content induces considerable improvement in the β-phase and dielectric constant of microcomposites films compared to that of pure PLA