Heart rate differences between symptomatic and asymptomatic Brugada syndrome patients at night

Peer ReviewedPostprint (author's final draft

Multi-GPU Development of a Neural Networks Based Reconstructor for Adaptive Optics

Aberrations introduced by the atmospheric turbulence in large telescopes are compensated using adaptive optics systems, where the use of deformable mirrors and multiple sensors relies on complex control systems. Recently, the development of larger scales of telescopes as the E-ELT or TMT has created a computational challenge due to the increasing complexity of the new adaptive optics systems. The Complex Atmospheric Reconstructor based on Machine Learning (CARMEN) is an algorithm based on artificial neural networks, designed to compensate the atmospheric turbulence. During recent years, the use of GPUs has been proved to be a great solution to speed up the learning process of neural networks, and different frameworks have been created to ease their development. The implementation of CARMEN in different Multi-GPU frameworks is presented in this paper, along with its development in a language originally developed for GPU, like CUDA. This implementation offers the best response for all the presented cases, although its advantage of using more than one GPU occurs only in large networks

A new missing data imputation algorithm applied to electrical data loggers

Spanish Economics and Competitiveness Ministry [AYA2014-57648-P]; Government of the Principality of Asturias (Consejeria de Economia y Empleo) [FC-15-GRUPIN14-017

A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers

Nowadays, data collection is a key process in the study of electrical power networks when searching for harmonics and a lack of balance among phases. In this context, the lack of data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, and current in each phase and power factor) adversely affects any time series study performed. When this occurs, a data imputation process must be accomplished in order to substitute the data that is missing for estimated values. This paper presents a novel missing data imputation method based on multivariate adaptive regression splines (MARS) and compares it with the well-known technique called multivariate imputation by chained equations (MICE). The results obtained demonstrate how the proposed method outperforms the MICE algorithm.Ministerio de Economía y Competitividad; AYA2014-57648-PAsturias (Comunidad Autónoma). Consejería de Economía y Empleo; FC-15-GRUPIN14-01

Bio-inspired model of ground temperature behavior on the horizontal geothermal exchanger of an installation based on a heat pump

[Abstract] Nowadays the Heat Pump is one of the best systems to warm a building with a good performance. Usually, with the aim to increase the efficiency, a geothermal heat exchanger is added to the installation. This component shows a disturbing effect on the ground where it is placed. On this research a bio-inspired system was developed to test the ground temperature behavior where there is a heat exchanger. The novel approach has been implemented and tested under a real dataset. One year temperature measurements were recorded. The final approach is based on clustering and regression techniques. Then, the model was validated and tested with a dataset from a real installation with a good performance

Radon Mitigation Approach in a Laboratory Measurement Room

[Abstract] Radon gas is the second leading cause of lung cancer, causing thousands of deaths annually. It can be a problem for people or animals in houses, workplaces, schools or any building. Therefore, its mitigation has become essential to avoid health problems and to prevent radon from interfering in radioactive measurements. This study describes the implementation of radon mitigation systems at a radioactivity laboratory in order to reduce interferences in the different works carried out. A large set of radon concentration samples is obtained from measurements at the laboratory. While several mitigation methods were taken into account, the final applied solution is explained in detail, obtaining thus very good results by reducing the radon concentration by 76%.Ministerio de Economía y Competitividad; AYA2014-57648-PAsturias (Comunidad Autónoma). Consejería de Economía y Empleo; FC-15-GRUPIN14-01

Hybrid algorithm for missing data imputation and its application to electrical data loggers

The storage of data is a key process in the study of electrical power networks related to the search for harmonics and the finding of a lack of balance among phases. The presence of missing data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, current in each phase and power factor) affects any time series study in a negative way that has to be addressed. When this occurs, missing data imputation algorithms are required. These algorithms are able to substitute the data that are missing for estimated values. This research presents a new algorithm for the missing data imputation method based on Self-Organized Maps Neural Networks and Mahalanobis distances and compares it not only with a well-known technique called Multivariate Imputation by Chained Equations (MICE) but also with an algorithm previously proposed by the authors called Adaptive Assignation Algorithm (AAA). The results obtained demonstrate how the proposed method outperforms both algorithm

Missing data imputation of solar radiation data under different atmospheric conditions

[Abstract] Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW.Ministerio de Economía y Competitividad; AYA2010-1851

A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers

The storage of data is a key process in the study of electrical power networks related to the search for harmonics and the finding of a lack of balance among phases. The presence of missing data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, current in each phase and power factor) affects any time series study in a negative way that has to be addressed. When this occurs, missing data imputation algorithms are required. These algorithms are able to substitute the data that are missing for estimated values. This research presents a new algorithm for the missing data imputation method based on Self-Organized Maps Neural Networks and Mahalanobis distances and compares it not only with a well-known technique called Multivariate Imputation by Chained Equations (MICE) but also with an algorithm previously proposed by the authors called Adaptive Assignation Algorithm (AAA). The results obtained demonstrate how the proposed method outperforms both algorithms.Ministerio de Economía y Competitividad, AYA2014-57648-PAsturias (Comunidad Autónoma). Consejería de Economía y Empleo, FC-15-GRUPIN14-01