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.
With the ultimate goal of developing models that involve the use of environmental variables, a GIS-based application is being developed that is circumscribed to the region of Galicia (Spain). ...Ten-minute data of six meteorological variables were collected from 150 stations of the MeteoGalicia network over a period of 18 years, but the time series data are not complete. In order to estimate missing rainfall data, four imputation methods were evaluated in this study: missForest, MICE, Amelia II, and inverse distance weighting (IDW). Crossvalidation results show that the precipitation is out of phase in the different stations due to their geographical locations, and the imputation can be improved with a displacement of the time series; on the other hand, the missForest method provided better results in the imputation of this meteorological variable than the MICE, Amelia, or IDW.
Este trabajo describe el potencial energético de cierto tipo de biomasa residual (restos de comida) que proviene de los hogares urbanos y que pueden ser utilizados como un recurso energético ...alternativo de energía limpia diferente a la actual dependencia de la energía fósil (gas GLP doméstico). El objetivo del estudio fue demostrar que el nivel de producción de biogás está determinado por la valorización energética del sustrato orgánico (restos de comida), el cual es viable para alimentar un dispositivo basado en fermentación metánica que sustituya a los actuales cilindros de gas doméstico de 15 kg. La aportación científica del artículo es el resultado de la investigación llevada a cabo mediante el diseño experimental al azar con 5 muestras de 196 hogares y 2 muestras de dos hogares independiente y su posterior análisis. Los parámetros que se midieron fueron: análisis físico, punto de fusión de ceniza, análisis elemental y contenido energético cuyas variables inciden en el proceso de conversión de biomasa a energía basados en métodos bioquímicos. En el contraste de hipótesis con otros tipos de RSU (resto de comida) no se encontraron diferencias significativas (p > 0.05). Finalmente, los resultados del poder calórico superior e inferior promedio son de: 3742.21 kcal/kg y 3309.68 kcal/kg mostrando un potencial energético adecuado para utilizar la biomasa residual como una alternativa válida al uso de nuevas fuentes de energía renovables, en proyectos de valorización o generación de combustible con tratamiento de fermentación metánica. El uso de biomasa orgánica urbana o de sus derivados puede considerarse nulo en términos de emisiones netas que no afectarán al medio ambiente o a la atmósfera.
The present research work focuses on overcoming cybersecurity problems in the Smart Grid. Smart Grids must have feasible data capture and communications infrastructure to be able to manage the huge ...amounts of data coming from sensors. To ensure the proper operation of next-generation electricity grids, the captured data must be reliable and protected against vulnerabilities and possible attacks. The contribution of this paper to the state of the art lies in the identification of cyberattacks that produce anomalous behaviour in network management protocols. A novel neural projectionist technique (Beta Hebbian Learning, BHL) has been employed to get a general visual representation of the traffic of a network, making it possible to identify any abnormal behaviours and patterns, indicative of a cyberattack. This novel approach has been validated on 3 different datasets, demonstrating the ability of BHL to detect different types of attacks, more effectively than other state-of-the-art methods.
Abstract
Nowadays, the quality standards of higher education institutions pay special attention to the performance and evaluation of the students. Then, having a complete academic record of each ...student, such as number of attempts, average grade and so on, plays a key role. In this context, the existence of missing data, which can happen for different reasons, leads to affect adversely interesting future analysis. Therefore, the use of imputation techniques is presented as a helpful tool to estimate the value of missing data. This work deals with the academic records of engineering students, in which imputation techniques are applied. More specifically, it is assessed and compared to the performance of the multivariate imputation by chained equations methodology, the adaptive assignation algorithm (AAA) based on multivariate adaptive regression splines and a hybridization based on self-organisation maps with Mahalanobis distances and AAA algorithm. The results show that proposed methods obtain successfully results regardless the number of missing values, in general terms.
► The quality of the global irradiation measurements in Galicia was checked. ► Data obtained after filtering can be further used. ► Monthly global irradiation followed a normal distribution in 35 out ...of 36months. ► Irradiation phenomena are more stable in summer and winter than in spring and autumn. ► Very high correlation between monthly global irradiation measured in certain stations.
Routine measurements of irradiance are valuable for many research fields such as energy applications. However, ground data of solar global radiation can present questionable values. In this study, a set of check procedures was used to test the quality of solar global radiation measurements taken at 75 observatories in Galicia (NW Spain) during 2005–2007. In this short period, the number of radiometric stations in the region increased from 30 to 75. A simple reliability index was defined to characterize the ability of a given station to supply quality data during the study period. Most of the data fulfilled the control procedures; moreover, some data could be recalculated from daily and 10-min records. However, data from certain stations were removed. Then, the stability of the solar radiation was assessed through statistical analyses. Monthly global radiation followed normal distributions in 35 out of 36months, December 2007 being the only exception. Irradiation was more stable in summer and winter months than in spring and autumn. Records from certain stations showed a high correlation. Solar radiation from two station networks taken from the dataset was interpolated in order to exemplify the improvement obtained from using a higher quality dataset when mapping this variable. The obtained database can be used in further research.
The physical processes taking place in the ground are influenced by solar radiation. This climatic variable presents a strong local behavior; therefore local models to estimate irradiation are ...usually more adequate than others which are more global. The aim of this study was to develop models for global, diffuse and direct radiation in A Coruna (Northwest of Spain), and to apply Multifractal Detrending Fluctuation Analysis (MFDFA) as tool for the assessment of model quality, complementing traditional validation parameters. Autoregressive Integrated Moving Average (ARIMA) methodology was used to obtain daily radiation models. The global irradiation series model explained over 55% of the variance. The model for diffuse radiation showed the lowest prediction errors, and the direct radiation model offered the worst outcomes with the highest errors and the lowest R2. MFDFA allowed us to check that the model for global irradiation reproduced the main statistical characteristics of the data series: scale exponent values and points of slope change, and, in general, the multifractal behavior. The diffuse model presented a similar behavior of the series for short-term large fluctuations, whereas the model for direct irradiation was not capable to reflect the multifractality of the data series. MFDFA can be used as a complement for model assessment, since it offers an analysis of model behavior at different timescales.
The determination of 90Sr in milk samples is the main objective of radiation
monitoring laboratories because of its environmental importance. In this
paper the concentration of activity of 39 milk ...samples was obtained through
radiochemical separation based on selective retention of Sr in a cationic
resin (Dowex 50WX8, 50-100 mesh) and subsequent determination by a low-level
proportional gas counter. The results were checked by performing the
measurement of the Sr concentration by using the flame atomic absorption
spectroscopy technique, to finally obtain the mass of 90Sr. From the data
obtained a statistical treatment was performed using linear regressions. A
reliable estimate of the mass of 90Sr was obtained based on the gravimetric
technique, and secondly, the counts per minute of the third measurement in
the 90Sr and 90Y equilibrium, without having to perform the analysis. These
estimates have been verified with 19 milk samples, obtaining overlapping
results. The novelty of the manuscript is the possibility of determining the
concentration of 90Sr in milk samples, without the need to perform the third
measurement in the equilibrium.
nema
In the search for new and more efficient ways to administer drugs, clinicians are turning to engineering tools. The availability of these models to predict physiological variables are a significant ...factor. A model is set out in this research to predict the EMG (electromyogram) signal during surgery, in patients under general anaesthesia. This prediction hinges on the Bispectral Index™ (BIS) and the infusion rate of the drug propofol. The results of the research are very satisfactory, with error values of less than 0.67 (for a Normalized Mean Squared Error). A hybrid intelligent model is used which combines both clustering and regression algorithms. The resulting model is validated and trained using real data.
This study is based on a mixed research with a bibliographic-documentary and experimental approach to identify the
main professional skills with the STEM model according to industry 4.0, which must ...be developed by students of the
Applied Physics course in the industrial engineering career. To determine STEM skills based on industry 4.0, we rely on
a scientific, pedagogical and didactic foundation in which a qualitative development technique is applied: “motivational
support of learning skills”. The results show that skills must be developed in the field of communication, collaborative
work, emotional intelligence, problem solving, technical knowledge related to the work area in order to interact in
professional environments and be able to perform correctly in a certain work activity at future as required by Industry
4.0.
Este estudio se basa en una investigación mixta con enfoque bibliográfico-documental y experimental para identificar las
principales habilidades profesionales con el modelo STEM según la industria 4.0, que deben desarrollar los estudiantes
del curso de Física aplicada en la carrera de ingeniería industrial. Para determinar las habilidades STEM basado en la
industria 4.0 nos apoyamos en una cimentación científica, pedagógica y didáctica en la cual se aplica una técnica
cualitativa de desarrollo: “apoyo motivacional de destrezas de aprendizaje”. Los resultados muestran que se deben
desarrollar habilidades en el campo de la comunicación, trabajo colaborativo, inteligencia emocional, resolución de
problemas, conocimientos técnicos relacionados con área de trabajo con el propósito de interactuar en los ambientes
profesionales y pueda desempeñarse correctamente en determinada actividad laboral a futuro según lo requiere la
industria 4.0
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