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

Radiactive particle biotic and abiotic processes

In ecosystems affected by the presence of radioactive particles, it is imperative to obtain information regarding transformation processes that affect them in order to properly evaluate not only their behaviour, but also the potential transfer of radionuclides between different compartments. A protocol for the abiotic transformation of radioactive particles has been developed within the framework of EC and IAEA research projects and has been applied on a range of particles from various sites. Methods for the characterisation of single radioactive particles pre- and post-leaching, using sub-micron resolution imaging techniques have been developed that enable the quantitative analysis of transformation processes

NO3- selective mini-electrodes as a tool to investigate the NO3- traffic in Chlamydomonas reinhardtii D.

Ion selective NO3- mini-electrodes were used to measure the external NO3- concentration in C. reinhardtii liquid cultures. Electrodes were prepared using glass capillaries (1.5 mm external diameter). Capillaries were cut in 10 cm long pieces, dehydrated for 45 minutes in an oven and silanized by addition of dimethyldichlorosilane in bencene 0.1% (V/V). Once silanized, the capillaries were baked again for 30 minutes. Once cold the capillaries were backfilled with the NO3- ionophore (Fluka: 72549), which contains PVC (5.75% w/w) dissolved in tetrahydrofurane. Then, the NO3- mini-electrodes were stored in dark in a desiccator until tetrahydrofurane gets evaporated. Before use, NO3- selective mini-electrodes were backfilled with 0.1 M NaNO3 and 0.1 M KCl and connected to a high-impedance differential amplifier (WPI FD223). Mini-electrodes were calibrated in N-free Beijerinck medium, which contains 0.1 mM Cl-. In those conditions, electrodes calibration slope was 54 mV/p NO3- in the range 1 - 1000 µM NO3-. The mini-electrodes were used to continuous monitoring of the external NO3- concentration in liquid culture of different C. reinhardtii strains, incubated in N-free Beijerinck medium supplemented with 100 µM NO3Na. Previous to the assays, strains were N starved for 6 days. In the light, wild type strain uptakes NO3- at a rate of 15 nmol NO3-·106 cells-1·h-1, in the dark this rate was one third of this figure. After 5 h, the external NO3- levelled off at 10 µM in the light and around 30 µM in the dark. C. reinhardtii cells cultured in the presence of 2 mM NO3NH4 do not show significant NO3- uptake nor a mutant strain, defective in nitrate transport and having an active nitrate reductase. However, a mutant strain lacking the nitrate reductase shows an enhanced NO3- uptake rate, compared with the value obtained for the wild type in the light.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Observation of radio galaxies with HAWC

The High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory is an extensive air shower array located in Puebla, Mexico. The closest radio galaxy within the HAWC field of view, M87, has been detected in very high energies. In this work we report upper limits on the TeV {\gamma}-ray flux of the radio galaxy M87. At a distance of 16 Mpc, M87 is a supergiant elliptical galaxy located in the Virgo Cluster that has been observed from radio wavelengths to TeV {\gamma}-rays. Although a single-zone synchrotron self-Compton model has been successfully used to explain the spectral energy distribution of this source up to a few GeV, the {\gamma}-ray spectrum at TeV has been interpreted within different theoretical models. We discuss the implications of these upper limits on the photo-hadronic interactions, as well as the number of neutrino events expected in the IceCube neutrino telescope.Comment: 8 pages, 1 figure, ICRC 201

Direct estimation of prediction intervals for solar and wind regional energy forecasting with deep neural networks

Deep neural networks (DNN) are becoming increasingly relevant for probabilistic forecasting because of their ability to estimate prediction intervals (PIs). Two different ways for estimating PIs with neural networks stand out: quantile estimation for posterior PI construction and direct PI estimation. The former first estimates quantiles, which are then used to construct PIs, while the latter directly obtains the lower and upper PI bounds by optimizing some loss functions, with the advantage that PI width is directly considered in the optimization process and thus may result in narrower intervals. In this work, two different DNN-based models are studied for direct PI estimation, and compared with DNN for quantile estimation in the context of solar and wind regional energy forecasting. The first approach is based on the recent quality-driven loss and is formulated to estimate multiple PIs with a single model. The second is a novel approach that employs hypernetworks (HN), where direct PI estimation is formulated as a multi-objective problem, returning a Pareto front of solutions that contains all possible coverage-width optimal trade-offs. This formulation allows HN to obtain optimal PIs for all possible coverages without increasing the number of network outputs or adjusting additional hyperparameters, as opposed to the first direct model. Results show that prediction intervals from direct estimation are narrower (up to 20%) than those of quantile estimation, for target coverages 70%–80% for all regions, and also 85%, 90%, and 95% depending on the region, while HN always achieves the required coverage for the higher target coverages.This publication is part of the I+D+i project PID2019-107455RBC22, funded by MCIN /AEI/10.13039/501100011033. This work was also supported by the Comunidad de Madrid Excellence Program. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022

Deep neural networks for the quantile estimation of regional renewable energy production

Wind and solar energy forecasting have become crucial for the inclusion of renewable energy in electrical power systems. Although most works have focused on point prediction, it is currently becoming important to also estimate the forecast uncertainty. With regard to forecasting methods, deep neural networks have shown good performance in many fields. However, the use of these networks for comparative studies of probabilistic forecasts of renewable energies, especially for regional forecasts, has not yet received much attention. The aim of this article is to study the performance of deep networks for estimating multiple conditional quantiles on regional renewable electricity production and compare them with widely used quantile regression methods such as the linear, support vector quantile regression, gradient boosting quantile regression, natural gradient boosting and quantile regression forest methods. A grid of numerical weather prediction variables covers the region of interest. These variables act as the predictors of the regional model. In addition to quantiles, prediction intervals are also constructed, and the models are evaluated using different metrics. These prediction intervals are further improved through an adapted conformalized quantile regression methodology. Overall, the results show that deep networks are the best performing method for both solar and wind energy regions, producing narrow prediction intervals with good coverage

Pareto optimal prediction intervals with hypernetworks

As the relevance of probabilistic forecasting grows, the need of estimating multiple high-quality prediction intervals (PI) also increases. In the current state of the art, most deep neural network gradient descent-based methods take into account interval width and coverage into a single loss function, focusing on a unique nominal coverage target, and adding additional parameters to control the coverage-width trade-off. The Pareto Optimal Prediction Interval Hypernetwork (POPI-HN) approach developed in this work has been derived to treat this coverage-width trade-off as a multi-objective problem, obtaining a complete set of Pareto Optimal solutions (Pareto front). POPI-HN are able to be trained through gradient descent with no need to add extra parameters to control the width-coverage trade-off of PIs. Once the Pareto set has been obtained, users can extract the PI with the required coverage. Comparative results with recently introduced Quality-Driven loss show similar behavior in coverage while improving interval width for the majority of the studied domains, making POPI-HN a competing alternative for estimating uncertainty in regression tasks where PIs with multiple coverages are needed.This publication is part of the I+D+i project PID2019-107455RB-C22, funded by MCIN /AEI/10.13039/501100011033. This work was also supported by the Comunidad de Madrid Excellence Program. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022

THE GEOMETRIAS & GRAPHICA 2015 CONFERENCE

Innovación EducativaNuestro Proyecto de Innovación Docente (PID) ha supuesto un antes y un después en la enseñanza de la Geometría Descriptiva en nuestra Escuela de Arquitectura. Hemos modificado ligeramente el programa para actualizarlo (llevaba más de veinticinco años sin tocar) pero sobre todo, hemos incorporado nuevas metodologías en su enseñanza, tanto para el aula como para casa. El resultado ha sido un alumno más involucrado con la asignatura, que se divierte con ella, le ve sentido y la aprehende y aprende fácilmente. Esta nueva metodología se refiere no sólo al uso de nuevas tecnologías, muy útiles para el pensamiento espacial necesario en la asignatura, y apreciadas, por la ayuda que al alumno prestan a la hora de visualizar y pensarlas; sino en el modo de impartir la clase, convirtiéndola en aulas-talleres donde los alumnos se convierten en protagonistas y profesores.Our Teaching Innovation Project (PID) is a turning point in the teaching of Descriptive Geometry in our School of Architecture. We have slightly modified the program to update it (was over twenty-five years without review) but mostly we have incorporated new methodologies in teaching, both for the classroom and for home. The result has been a student more involved with the course, having fun with it, sees sense and apprehend and learn easily. This new methodology refers not only to the use of new technologies, useful for the necessary spatial thinking on the course, and appreciated, for the assistance provided to the student when viewing and thinking about them; but how to teach the class, making classrooms-workshops where students become actors and teachers.2015-10-0

Adaptive Vectorial Filter for Grid Synchronization of Power Converters Under Unbalanced and/or Distorted Grid Conditions

This paper presents a new synchronization scheme for detecting multiple positive-/negative-sequence frequency harmonics in three-phase systems for grid-connected power converters. The proposed technique is called MAVF-FLL because it is based on the use of multiple adaptive vectorial filters (AVFs) working together inside a harmonic decoupling network, resting on a frequency-locked loop (FLL) which makes the system frequency adaptive. The method uses the vectorial properties of the three-phase input signal in the αβ reference frame in order to obtain the different harmonic components. The MAVF-FLL is fully designed and analyzed, addressing the tuning procedure in order to obtain the desired and predefined performance. The proposed algorithm is evaluated by both simulation and experimental results, demonstrating its ability to perform as required for detecting different harmonic components under a highly unbalanced and distorted input grid voltage