Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Prediction of the solar radiation using RBF neural networks and ground-to-sky images

In this study, Artificial Neural Networks are applied to multistep long term solar radiation prediction. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and Auto-regressive with exogenous inputs solar radiationmodels are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output structure of the neural network models is selected using evolutionary computation methods

Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices

In this paper, Artificial Neural Networks are applied for multi-step long term solar radiation prediction. The input-output structure of the neural network models is selected using evolutionary computation methods. The networks are trained as onestep- ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and auto-regressive with exogenous inputs models are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images, captured by a CCD camera

A comparison of four data selection methods for artificial neural networks and support vector machines

The performance of data-driven models such as Artificial Neural Networks and Support Vector Machines relies to a good extent on selecting proper data throughout the design phase. This paper addresses a comparison of four unsupervised data selection methods including random, convex hull based, entropy based and a hybrid data selection method. These methods were evaluated on eight benchmarks in classification and regression problems. For classification, Support Vector Machines were used, while for the regression problems, Multi-Layer Perceptrons were employed. Additionally, for each problem type, a non-dominated set of Radial Basis Functions Neural Networks were designed, benefiting from a Multi Objective Genetic Algorithm. The simulation results showed that the convex hull based method and the hybrid method involving convex hull and entropy, obtain better performance than the other methods, and that MOGA designed RBFNNs always perform better than the other models. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.FCT through IDMEC, under LAETA grant [UID/EMS/50022/2013]info:eu-repo/semantics/publishedVersio

Correction: Ferreira, P.M., et al. A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature. Sensors 2012, 12, 15750–15777

Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature

Deciphering a multi-event in a non-complex set of detrital zircon U–Pb ages from Carboniferous graywackes of SW Iberia

The determination of U–Pb ages from detrital zircons of sedimentary rocks using LA-ICP-MS has been widely used for the purpose of provenance analysis. One problem that frequently arises is finding a population that appears to be non-complex despite several perceptible age peaks in its spectrum. These peaks are qualitatively defined by means of relative probability diagrams, or PDFs, but it is difficult to quantify their statistical significance relative to a zircon forming multi-event. Thus, can a multi-event in a non-complex set of detrital zircon U–Pb ages be deciphered and characterized? The aim of this study is to attempt to provide an answer to this question by means of statistical analysis. Its objectives are: a) to determine the best minimum number of zircon age populations (peaks), BmPs, b) for the characterization of each peak in terms of age and event duration; c) to compare the results obtained from two datasets showing similar zircon ages; and d) to demonstrate the usefulness of deciphering these BmPs. First, cluster analysis is carried out, aimed at grouping zircon ages into a set of consistent clusters. A Gaussian Kernel function is then fitted to each cluster and summed to obtain a theoretical PDFm (modeled probability density function). Finally, the selected modeled PDFm (that built on the BmPs) is that which reports the lowest number of peaks for which the difference as compared with the original gPDF (global probability density function) is equal to or below 5%. Deciphered BmP peaks can be characterized and used for characterizing and providing an understanding of related event(s). A geological interpretation, based on the results obtained, is attempted. This includes a robust measure for maximum age of deposition for both Cabrela and Mértola graywackes

Células de linhagem McCoy como um possível modelo contendo receptores CD4+ para estudos da replicação do HIV

Several studies have recently shown the use of recombinant rabies virus as potential vector-viral vaccine for HIV-1. The sequence homology between gp 120 and rabies virus glycoprotein has been reported. The McCoy cell line has therefore been used to show CD4+ or CD4+ like receptors. Samples of HIV-1 were isolated, when plasma of HIV-1 positive patients was inoculated in the McCoy cell line. The virus infection was then studied during successive virus passages. The proteins released in the extra cellular medium were checked for protein activity, by exposure to SDS Electrophoresis and blotting to nitro-cellulose filter, then reacting with sera of HIV positive and negative patients. Successive passages were performed, and showed viral replication, membrane permeabilization, the syncytium formation, and the cellular lysis (cytopathic effect). Flow cytometry analysis shows clear evidence that CD4+ receptors are present in this cell line, which enhances the likelihood of easy isolation and replication of HIV. The results observed allow the use of this cell line as a possible model for isolating HIV, as well as for carrying out studies of the dynamics of viral infection in several situations, including exposure to drugs in pharmacological studies, and possibly studies and analyses of the immune response in vaccine therapies.Recentes estudos demonstraram o uso do vírus raiva como modelo vetor para produzir vacinas expressando as glicoproteínas do vírus HIV-1. A homologia na seqüência entre gp120 do vírus HIV-1 e a glicoproteína G do vírus rábico já foi previamente relatada. Devido a estes fatos a linhagem de célula McCoy utilizada com sucesso para a replicação do vírus rábico foi utilizada para demonstrar a replicação do HIV-1. Amostra de HIV-1 foi isolada de plasma de um paciente soro positivo e inoculada em células de linhagem McCoy e então a infecção viral foi estudada em passagens sucessivas do vírus nesta célula. As proteínas liberadas no meio extra celular foram analisadas quanto a atividade biológica pela técnica de eletroforese em gel de poliacrilamida e imunotransferência em membrana de nitro-celulose reagindo com soros positivos para HIV-1 e soros de pacientes negativos. As passagens sucessivas do HIV-1 em células demonstraram a replicação viral, o aumento da permeabilidade da membrana citoplasmática, a formação de sinsício e lise celular. Análises com citometria de fluxo mostraram com clara evidência a presença de receptores CD4+ o que possivelmente deve ser a causa que possibilita a facilidade do isolamento e replicação do vírus HIV-1 nesta célula. Concluindo os resultados observados permitem utilizar esta linhagem celular como um possível modelo para isolamentos de HIV, bem como realizar estudos da dinâmica de infecção viral em diversas situações inclusive de exposição a drogas em estudos farmacológicos, e talvez estudos e análises da resposta imune em terapias vacinais