Neural networks and spectra feature selection for retrival of hot gases temperature profiles

Proceeding of: International Conference on Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Vienna, Austria 28-30 Nov. 2005Neural networks appear to be a promising tool to solve the so-called inverse problems focused to obtain a retrieval of certain physical properties related to the radiative transference of energy. In this paper the capability of neural networks to retrieve the temperature profile in a combustion environment is proposed. Temperature profile retrieval will be obtained from the measurement of the spectral distribution of energy radiated by the hot gases (combustion products) at wavelengths corresponding to the infrared region. High spectral resolution is usually needed to gain a certain accuracy in the retrieval process. However, this great amount of information makes mandatory a reduction of the dimensionality of the problem. In this sense a careful selection of wavelengths in the spectrum must be performed. With this purpose principal component analysis technique is used to automatically determine those wavelengths in the spectrum that carry relevant information on temperature distribution. A multilayer perceptron will be trained with the different energies associated to the selected wavelengths. The results presented show that multilayer perceptron combined with principal component analysis is a suitable alternative in this field.Publicad

Selección guiada de características y búsqueda de modelos homogéneos en datos de alta dimensionalidad : un enfoque aplicado a problemas de teledetección

Esta tesis estudia los problemas relacionados con la alta dimensionalidad de los datos en un contexto científico de teledetección, con el fin de estimar perfiles de temperatura en el interior de nubes gaseosas a alta temperatura (como es el caso de una llama). El objetivo principal es identificar los problemas de las técnicas existentes en este contexto práctico y proporcionar soluciones. Para ello se realiza una introducción a los retos presentes en los datos de alta dimensionalidad, y al área de minería de datos que es actualmente la más activa en el estudio y tratamiento de este tipo de datos. La reducción de dimensionalidad aparece como un proceso necesario para solventar algunos de los retos planteados y mejorar el rendimiento de los algoritmos de aprendizaje. El resto del trabajo está dividido principalmente en dos partes. Cada una de estas partes desarrolla un camino alternativo para reducir la dimensionalidad de los datos y solucionar así los problemas relacionados con la alta dimensionalidad en el contexto de teledetección. En el primero de ellos, el trabajo se centra en la selección de características no supervisada para buscar la información relevante a la aplicación. El principal problema en la selección de características es la imposibilidad de realizar una búsqueda exhaustiva debido al gran número de posibles soluciones. Por esto, se propone el uso de conocimiento previo específico de la aplicación física a tratar, para guiar el proceso de selección. Los resultados obtenidos muestran que esta solución mejora los resultados en un entorno de selección no supervisado, o frente a la ausencia de selección. La segunda parte de esta tesis se centra en la reducción de dimensionalidad desde un punto de vista de extracción de características. En ella se trata de abordar uno de los problemas principales relacionados con la alta dimensionalidad, la multicolinealidad, buscando extraer de un modo supervisado los conjuntos de datos que mantienen un comportamiento similar u homogéneo. Esto va a permitir diferenciar diferentes grupos de datos y, lograr con esta división, aplicar modelos de estimación especificos para los diferentes grupos. La aproximación se basa en estructuras de grafos para incluir la información local de los datos, lo cual es muy útil en nuestra aplicación. Esta solución muestra mejoras significativas en los resultados obtenidos, a la vez que permite obtener estimaciones precisas para los nuevos casos. Además, también posee una interpretación física y ayudará a un mejor entendimiento de la aplicación estudiada.---------------------------------------------------------------------------------This thesis studies some of the problems related with high dimensional data in a scientific context, pursuing the estimation of temperature profiles inside a hot gas cloud at high temperature (as it occurs inside a flame). The main objective is to identify the main disadvantages of the actual techniques in this practical context and to provide solutions to them. For that purpose we introduce currently known challenges related to high dimensional data, and to data mining field which is the most active regarding the study and processing of this type of data. The dimensionality reduction appears as an important step to solve some of the established challenges and to improve the performance of machine learning algorithms. The work is mainly divided into two parts. Each one of them develops an alternative to reduce the dimensionality of the data solving some of the problems related to high dimensional data in a remote sensing environment. The first one, focuses on unsupervised feature selection to search for relevant information to the application. The main problem in feature selection is the impossibility to do an exhaustive search due to the huge number of possible solutions. Thus, we propose to use specific physical previous knowledge to guide the selection process. The obtained results show that this solutions improves the results obtained in an unsupervised framework or against non-selection. The second part of the thesis is focused in dimensionality reduction from a feature extraction point of view. In it, we try to solve one of the problems related with high dimensionality data, the multicollinearity. For that purpose we extract, in a supervised mode, subsets of data which have similar behavior or are homogeneous. This allows to find out different groups of data and, with this division, to apply specific estimation models for the different discovered groups. This dimensionality reduction approach is based on graph structures which is useful to include local similarity information about the data, which is extremely useful in our application. This solution shows significant improvements and allows better accuracy for new samples. Furthermore, it also has a physical interpretation and it enables a better understanding of the studied application

Online Teaching Methodologies in Higher Education Credit Mobility Courses: ErasmusX pilot project

Este documento se considera que es un capítulo de libro en lugar de un artículoSixth International Conference on e-Learning (econf)Based on Erasmus credit mobility programs, ErasmusX is a project envisaged to offer students the possibility to add value to international exchange periods by combining online certified studies with further professional or academic experiences abroad. However, differences between national higher education systems and teaching styles can lead to issues concerning the recognition of credits, which makes necessary to establish common basic procedures. For this purpose, five different European Higher Education Institutions which have traditionally based their academic organization in the face to face credit system, have joined to develop on-line certified courses on several pre-defined areas following common practices. Bearing in mind this purpose, this communication focuses on the proposal of key pedagogical models, approaches and strategies to consider when designing online courses for mobility students. The revision of existing practices underline that online teaching requires not only an adjustment of the role of professors and students, but also a different structure of the courses based on a flexible online instructional design. The methods and models revised in this preliminary study point to the need of incorporating a collaborative online teaching approach with processed educational technology in English mediated instruction

Multilayer perceptron as inverse model in a ground-based remote sensing temperature retrieval problem

In this paper, a combustion temperature retrieval approximation for high-resolution infrared ground-based measurements has been developed based on a multilayer perceptron (MLP) technique. The introduction of a selection subset of features is mandatory due to the problems related to the high dimensionality data and the worse performance of MLPs with this high input dimensionality. Principal component analysis is used to reduce the input data dimensionality, selecting the physically important features in order to improve MLP performance. The use of a priori physical information over other methods in the chosen feature’s phase has been tested and has appeared jointly with the MLP technique as a good alternative for this problem.Publicad

A combination of supervised dimensionality reduction and learning methods to forecast solar radiation

Machine learning is routinely used to forecast solar radiation from inputs, which are forecasts of meteorological variables provided by numerical weather prediction (NWP) models, on a spatially distributed grid. However, the number of features resulting from these grids is usually large, especially if several vertical levels are included. Principal Components Analysis (PCA) is one of the simplest and most widely-used methods to extract features and reduce dimensionality in renewable energy forecasting, although this method has some limitations. First, it performs a global linear analysis, and second it is an unsupervised method. Locality Preserving Projection (LPP) overcomes the locality problem, and recently the Linear Optimal Low-Rank (LOL) method has extended Linear Discriminant Analysis (LDA) to be applicable when the number of features is larger than the number of samples. Supervised Nonnegative Matrix Factorization (SNMF) also achieves this goal extending the Nonnegative Matrix Factorization (NMF) framework to integrate the logistic regression loss function. In this article we try to overcome all these issues together by proposing a Supervised Local Maximum Variance Preserving (SLMVP) method, a supervised non-linear method for feature extraction and dimensionality reduction. PCA, LPP, LOL, SNMF and SLMVP have been compared on Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) radiation data at two different Iberian locations: Seville and Lisbon. Results show that for both kinds of radiation (GHI and DNI) and the two locations, SLMVP produces smaller MAE errors than PCA, LPP, LOL, and SNMF, around 4.92% better for Seville and 3.12% for Lisbon. It has also been shown that, although SLMVP, PCA, and LPP benefit from using a non-linear regression method (Gradient Boosting in this work), this benefit is larger for PCA and LPP because SMLVP is able to perform non-linear transformations of inputs.This work has been made possible by projects funded by Agencia Estatal de Investigación (PID2019-107455RB-C22 / AEI / 10.13039/501100011033). This work was also supported by the Comunidad de Madrid Excellence Program and Comunidad de Madrid-Universidad Politécnica de Madrid young investigators initiative

Seasonal variations of the humoral immune parameters of European sea bass (Dicentrarchus labrax L.).

Seasonal cycles, mainly due to great variations in the light duration and temperature, are important and modulate several aspects of the animal behavior. In the case of poikilotherms animals such as fish this is very relevant. Thus, temperature changes fish immunity and affects disease resistance. We evaluate in this work the season variations of the European sea bass (Dicentrarchus labrax) humoral innate parameters focusing on winter months, at which the culture of this specie is more difficult. Our results showed that not all the innate immune parameters are depressed by low temperatures. Moreover, some of them are more dependent than others to the season and both temperature and photoperiod are operating together.Postprint

Antimicrobial response is increased in the testis of European sea bass but not in gilthead seabream upon nodavirus infection

Antimicrobial peptides (AMPs) have a crucial role in the fish innate immune response, being considered a fundamental component of the first line of defence against pathogens. Moreover, AMPs have not been studied in the fish gonad since this is used by some pathogens as a vehicle or a reservoir to be transmitted to the progeny, as occurs with nodavirus (VNNV), which shows vertical transmission through the gonad, and/or gonadal fluids but no study has looked into the gonad of infected fish. In this framework, we have characterized the antimicrobial response triggered by VNNV in the testis of European sea bass, a very susceptible species of the virus, and in the gilthead seabream, which acts as a reservoir, both in vivo and in vitro, and compared with that present in the serum and brain (target tissue of VNNV). First, our data show a great antiviral response in the brain of gilthead seabream and in the gonad of European sea bass. In addition, for the first time, our results demonstrate that the antimicrobial activities (complement, lysozyme and bactericidal) and the expression of AMP genes such as complement factor 3 (c3), lysozyme (lyz), hepcidin (hamp), dicentracin (dic), piscidin (pis) or β-defensin (bdef) in the gonad of both species are very different, but generally activated in the European sea bass, probably related with the differences of susceptibility upon VNNV infection, and even differs to the brain response. Furthermore, the in vitro data suggest that some AMPs are locally regulated playing a local immune response in the gonad, while others are more dependent of the systemic immune system. Data are discussed in the light to ascertain their potential role in viral clearance by the gonad to avoid vertical transmission.Postprin