Análise de métodos de predição de conforto térmico de habitação em clima quente-úmido com condicionamento passivo

The assessment of building thermal performance is often carried out using HVAC energy consumption data, when available, or thermal comfort variables measurements, for free-running buildings. Both types of data can be determined by monitoring or computer simulation. The assessment based on thermal comfort variables is the most complex because it depends on the determination of the thermal comfort zone. For these reasons, this master thesis explores methods of building thermal performance assessment using variables of thermal comfort simulated by DesignBuilder software. The main objective is to contribute to the development of methods to support architectural decisions during the design process, and energy and sustainable rating systems. The research method consists on selecting thermal comfort methods, modeling them in electronic sheets with output charts developed to optimize the analyses, which are used to assess the simulation results of low cost house configurations. The house models consist in a base case, which are already built, and changes in thermal transmittance, absorptance, and shading. The simulation results are assessed using each thermal comfort method, to identify the sensitivity of them. The final results show the limitations of the methods, the importance of a method that considers thermal radiance and wind speed, and the contribution of the chart proposed

Mapeamento acústico como ferramenta para predição de ruído urbano na área de influência do estádio Arena das Dunas, Natal/ RN

The Noise Pollution causes degradation in the quality of the environment and presents itself as one of the most common environmental problems in the big cities. An Urban environment present scenario and their complex acoustic study need to consider the contribution of various noise sources. Accordingly to computational models through mapping and prediction of acoustic scene become important, because they enable the realization of calculations, analyzes and reports, allowing the interpretation of satisfactory results. The study neighborhood is the neighborhood of Lagoa Nova, a central area of the city of Natal, which will undergo major changes in urban space due to urban mobility projects planned for the area around the stadium and the consequent changes of urban form and traffic. Thus, this study aims to evaluate the noise impact caused by road and morphological changes around the stadium Arena das Dunas in the neighborhood of Lagoa Nova, through on-site measurements and mapping using the computational model SoundPLAN year 2012 and the scenario evolution acoustic for the year 2017. For this analysis was the construction of the first acoustic mapping based on current diagnostic acoustic neighborhood, physical mapping, classified vehicle count and measurement of sound pressure level, and to build the prediction of noise were observed for the area study the modifications provided for traffic, urban form and mobility work. In this study, it is concluded that the sound pressure levels of the year in 2012 and 2017 extrapolate current legislation. For the prediction of noise were numerous changes in the acoustic scene, in which the works of urban mobility provided will improve traffic flow, thus reduce the sound pressure level where interventions are expected

Predição não-linear de curvas de produção de petróleo via redes neurais recursivas

One of the main activities in the petroleum engineering is to estimate the oil production in the existing oil reserves. The calculation of these reserves is crucial to determine the economical feasibility of your explotation. Currently, the petroleum industry is facing problems to analyze production due to the exponentially increasing amount of data provided by the production facilities. Conventional reservoir modeling techniques like numerical reservoir simulation and visualization were well developed and are available. This work proposes intelligent methods, like artificial neural networks, to predict the oil production and compare the results with the ones obtained by the numerical simulation, method quite a lot used in the practice to realization of the oil production prediction behavior. The artificial neural networks will be used due your learning, adaptation and interpolation capabilities

Modelo matemático para predição do VO2máx em cicloergômetro baseado na análise dos gases expirados

Existem diversas equações para predição do VO2máx a partir de variáveis dentro do teste ergométrico em vários ergômetros, no entanto equação semelhante utilizando os limiares ventilatórios na ergoespirometria em teste sub-máximo no cicloergômetro não está disponível. O objetivo do presente estudo foi avaliar a precisão de modelos de predição do VO2máx com base em indicadores de esforço sub-máximo. Neste sentido foram testados em protocolo incremental máximo no cicloergômetro 7.877 voluntários, sendo 4640 indivíduos do sexo feminino e 3147 do sexo masculino, todos saudáveis não atletas, com idades acima de 20 anos, divididos randomicamente em dois grupos: A de estimação e B de validação. A partir das variáveis independentes massa corporal (MC) em kg, carga de trabalho no limiar 2 (WL2) e freqüência cardíaca no limiar 2 (FCL2) foi possível construir um modelo de regressão linear múltipla para predição do VO2máx. Os resultados demonstram que em indivíduos saudáveis não atletas de ambos os sexos é possível predizer o VO2máx com um erro mínimo (EPE = 1,00%) a partir de indicadores submáximos obtidos em teste incremental. O caráter multidisciplinar do trabalho pôde ser caracterizado pelo emprego de técnicas que envolveram pneumologia, educação física, fisiologia e estatística

Predição da maturação puberal de indivíduos do sexo masculino a partir de variáveis antropométricas

The methods of analysis of the selection system sports talent sometimes do not consider the biological age of the athletes, since that the assessment of maturational moment have several limitations The aim of this work is to develop a predictive equation of pubertal assessment in male subjects, based on anthropometric measurements. We evaluated 206 young boys, aged between eight and 18 years, and studing in public and private schools in Natal, Brazil. The sample selection was done randomly, being used the anthropometric measurements and pubertal maturation evaluation according to the Tanner stages. Statistical analysis followed the presentation of central tendency measures and their derivatives. The inferential analysis was performed according to the ANOVA test, multivariate discriminant analysis and weighted Kappa. The advancement of pubertal stages was accompanied by significant changes in anthropometric variables, demonstrating the relationship presented in both. For this purpose, discriminant analysis selected eight variables with the highest prediction of pubertal maturation, and created an equation with a significance level of 75%. and concordance level of 0.840, considered as excellent. This shows that the prediction of pubertal maturation from anthropometric variables presented as a valid method, being used as a practical tool in sports talents selection

Modelagem e medições de ondas de rádio para predição de perda de propagação em ambientes urbanos

In this dissertation new models of propagation path loss predictions are proposed by from techniques of optimization recent and measures of power levels for the urban and suburban areas of Natal, city of Brazilian northeast. These new proposed models are: (i) a statistical model that was implemented based in the addition of second-order statistics for the power and the altimetry of the relief in model of linear losses; (ii) a artificial neural networks model used the training of the algorithm backpropagation, in order to get the equation of propagation losses; (iii) a model based on the technique of the random walker, that considers the random of the absorption and the chaos of the environment and than its unknown parameters for the equation of propagation losses are determined through of a neural network. The digitalization of the relief for the urban and suburban areas of Natal were carried through of the development of specific computational programs and had been used available maps in the Statistics and Geography Brazilian Institute. The validations of the proposed propagation models had been carried through comparisons with measures and propagation classic models, and numerical good agreements were observed. These new considered models could be applied to any urban and suburban scenes with characteristic similar architectural to the city of Natal

Modelo 2,5D de predição de propagação para ambientes interiores utilizando o método do traçado de raios

A 2.5D ray-tracing propagation model is proposed to predict radio loss in indoor environment. Specifically, we opted for the Shooting and Bouncing Rays (SBR) method, together with the Geometrieal Theory of Diffrartion (GTD). Besides the line-of-sight propagation (LOS), we consider that the radio waves may experience reflection, refraction, and diffraction (NLOS). In the Shooting and Bouncing Rays (SBR) method, the transmitter antenna launches a bundle of rays that may or may not reach the receiver. Considering the transmitting antenna as a point, the rays will start to launch from this position and can reach the receiver either directly or after reflections, refractions, diffractions, or even after any combination of the previous effects. To model the environment, a database is built to record geometrical characteristics and information on the constituent materials of the scenario. The database works independently of the simulation program, allowing robustness and flexibility to model other seenarios. Each propagation mechanism is treated separately. In line-of-sight propagation, the main contribution to the received signal comes from the direct ray, while reflected, refracted, and diffracted signal dominate when the line-of-sight is blocked. For this case, the transmitted signal reaches the receiver through more than one path, resulting in a multipath fading. The transmitting channel of a mobile system is simulated by moving either the transmitter or the receiver around the environment. The validity of the method is verified through simulations and measurements. The computed path losses are compared with the measured values at 1.8 GHz ftequency. The results were obtained for the main corridor and room classes adjacent to it. A reasonable agreement is observed. The numerical predictions are also compared with published data at 900 MHz and 2.44 GHz frequencies showing good convergence

Otimização de modelos de predição da perda de propagação aplicáveis em 3,5GHZ utilizando algoritmos genéticos

The telecommunications industry has experienced recent changes, due to increasing quest for access to digital services for data, video and multimedia, especially using the mobile phone networks. Recently in Brazil, mobile operators are upgrading their networks to third generations systems (3G) providing to users broadband services such as video conferencing, Internet, digital TV and more. These new networks that provides mobility and high data rates has allowed the development of new market concepts. Currently the market is focused on the expansion of WiMAX technology, which is gaining increasingly the market for mobile voice and data. In Brazil, the commercial interest for this technology appears to the first award of licenses in the 3.5 GHz band. In February 2003 ANATEL held the 003/2002/SPV-ANATEL bidding, where it offered blocks of frequencies in the range of 3.5 GHz. The enterprises who purchased blocks of frequency were: Embratel, Brazil Telecom (Vant), Grupo Sinos, Neovia and WKVE, each one with operations spread in some regions of Brazil. For this and other wireless communications systems are implemented effectively, many efforts have been invested in attempts to developing simulation methods for coverage prediction that is close to reality as much as possible so that they may become believers and indispensable tools to design wireless communications systems. In this work wasm developed a genetic algorithm (GA's) that is able to optimize the models for predicting propagation loss at applicable frequency range of 3.5 GHz, thus enabling an estimate of the signal closer to reality to avoid significant errors in planning and implementation a system of wireless communication

Avaliação dos modelos de predição de propagação para telefonia móvel celular na cidade do Natal

The development of wireless telecommunication in the last years has been great. It has been taking academics to conceive new ideas and techniques. Their aims are to increase the capacity and the quality of the system s services. Cells that are smaller every time, frequencies that are every time higher and environments that get more and more complex, all those facts deserve more accurate models the propagation prediction techniques are inserted in this context and results with a merger of error that is compatible with the next generations of communication systems. The objective of this Work is to present results of a propagation measurement campaign, aiming at pointing the characteristics of the mobile systems covering in the city of Natal (state of Rio Grande do Norte, Brazil). A mobile laboratory was set up, using the infra-structure available and frequently used by ANATEL. The measures were taken in three different areas: one characterized by high buildings, high relief, presence of trees and towers of different highs. These areas covered the city s central zone, a suburban / rural zone and a section of coast surrounded by sand dunes. It is important to highlight that the analysis was made taking into consideration the actual reality of cellular systems with covering ranges by reduced cells, with the intent of causing greater re-use of frequencies and greater capacity of telephone traffic. The predominance of telephone traffic by cell in the city of Natal occurs within a range inferior to 3 (three) km from the Radio-Base Station. The frequency band used was 800 MHz, corresponding to the control channels of the respective sites, which adopt the FSK modulation technique. This Dissertation starts by presenting a general vision of the models used for predicting propagation. Then, there is a description of the methodology used in the measuring, which were done using the same channels of control of the cellular system. The results obtained were compared with many existing prediction models, and some adaptations were developed by using regression techniques trying to obtain the most optimized solutions. Furthermore, according to regulations from the old Brazilian Holding Telebrás, a minimum covering of 90% of a determined previously area, in 90% of the time, must be obeyed when implanting cellular systems. For such value to be reached, considerations and studies involving the specific environment that is being covered are important. The objective of this work is contribute to this aspect

Predição de promotores de Bacillus subtilis usando técnicas de aprendizado de máquina

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences on world wide database. Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters. In these regions are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying a great number of promoters on a genome is a complex task. Nevertheless, the main drawback is the absence of a large set of promoters to identify conserved patterns among the species. Hence, a in silico method to predict them on any species is a challenge. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this work, we present an empirical comparison of Machine Learning (ML) techniques such as Na¨ýve Bayes, Decision Trees, Support Vector Machines and Neural Networks, Voted Perceptron, PART, k-NN and and ensemble approaches (Bagging and Boosting) to the task of predicting Bacillus subtilis. In order to do so, we first built two data set of promoter and nonpromoter sequences for B. subtilis and a hybrid one. In order to evaluate of ML methods a cross-validation procedure is applied. Good results were obtained with methods of ML like SVM and Naïve Bayes using B. subtilis. However, we have not reached good results on hybrid database

Predição da incrustação em um trocador de calor baseada em redes neurais artificiais

A serious problem that affects an oil refinery s processing units is the deposition of solid particles or the fouling on the equipments. These residues are naturally present on the oil or are by-products of chemical reactions during its transport. A fouled heat exchanger loses its capacity to adequately heat the oil, needing to be shut down periodically for cleaning. Previous knowledge of the best period to shut down the exchanger may improve the energetic and production efficiency of the plant. In this work we develop a system to predict the fouling on a heat exchanger from the Potiguar Clara Camarão Refinery, based on data collected in a partnership with Petrobras. Recurrent Neural Networks are used to predict the heat exchanger s flow in future time. This variable is the main indicator of fouling, because its value decreases gradually as the deposits on the tubes reduce their diameter. The prediction could be used to tell when the flow will have decreased under an acceptable value, indicating when the exchanger shutdown for cleaning will be needed

Neurofisiologia hipocampal durante sessões de sono intercaladas por exploração espacial: Reverberação do passado ou predição do futuro?

The ability to predict future rewards or threats is crucial for survival. Recent studies have addressed future event prediction by the hippocampus. Hippocampal neurons exhibit robust selectivity for spatial location. Thus, the activity of hippocampal neurons represents a cognitive map of space during navigation as well as during planning and recall. Spatial selectivity allows the hippocampus to be involved in the formation of spatial and episodic memories, including the sequential ordering of events. On the other hand, the discovery of reverberatory activity in multiple forebrain areas during slow wave and REM sleep underscored the role of sleep on the consolidation of recently acquired memory traces. To this date, there are no studies addressing whether neuronal activity in the hippocampus during sleep can predict regular environmental shifts. The aim of the present study was to investigate the activity of neuronal populations in the hippocampus during sleep sessions intercalated by spatial exploration periods, in which the location of reward changed in a predictable way. To this end, we performed the chronic implantation of 32-channel multielectrode arrays in the CA1 regions of the hippocampus in three male rats of the Wistar strain. In order to activate different neuronal subgroups at each cycle of the task, we exposed the animals to four spatial exploration sessions in a 4-arm elevated maze in which reward was delivered in a single arm per session. Reward location changed regularly at every session in a clockwise manner, traversing all the arms at the end of the daily recordings. Animals were recorded from 2-12 consecutive days. During spatial exploration of the 4-arm elevated maze, 67,5% of the recorded neurons showed firing rate differences across the maze arms. Furthermore, an average of 42% of the neurons showed increased correlation (R>0.3) between neuronal pairs in each arm. This allowed us to sort representative neuronal subgroups for each maze arm, and to analyze the activity of these subgroups across sleep sessions. We found that neuronal subgroups sorted by firing rate differences during spatial exploration sustained these differences across sleep sessions. This was not the case with neuronal subgroups sorted according to synchrony (correlation). In addition, the correlation levels between sleep sessions and waking patterns sampled in each arm were larger for the entire population of neurons than for the rate or synchrony subgroups. Neuronal activity during sleep of the entire neuronal population or subgroups did not show different correlations among the four arm mazes. On the other hand, we verified that neuronal activity during pre-exploration sleep sessions was significantly more similar to the activity patterns of the target arm than neuronal activity during pre-exploration sleep sessions. In other words, neuronal activity during sleep that precedes the task reflects more strongly the location of reward than neuronal activity during sleep that follows the task. Our results suggest that neuronal activity during sleep can predict regular environmental changes

Predição em modelos de tempo de falha acelerado com efeito aleatório para avaliação de riscos de falha em poços petrolíferos

We considered prediction techniques based on models of accelerated failure time with random e ects for correlated survival data. Besides the bayesian approach through empirical Bayes estimator, we also discussed about the use of a classical predictor, the Empirical Best Linear Unbiased Predictor (EBLUP). In order to illustrate the use of these predictors, we considered applications on a real data set coming from the oil industry. More speci - cally, the data set involves the mean time between failure of petroleum-well equipments of the Bacia Potiguar. The goal of this study is to predict the risk/probability of failure in order to help a preventive maintenance program. The results show that both methods are suitable to predict future failures, providing good decisions in relation to employment and economy of resources for preventive maintenance.

Utilizando comitês de classificadores para predição de rendimento escolar

Educational Data Mining is an application domain in artificial intelligence area that has been extensively explored nowadays. Technological advances and in particular, the increasing use of virtual learning environments have allowed the generation of considerable amounts of data to be investigated. Among the activities to be treated in this context exists the prediction of school performance of the students, which can be accomplished through the use of machine learning techniques. Such techniques may be used for student’s classification in predefined labels. One of the strategies to apply these techniques consists in their combination to design multi-classifier systems, which efficiency can be proven by results achieved in other studies conducted in several areas, such as medicine, commerce and biometrics. The data used in the experiments were obtained from the interactions between students in one of the most used virtual learning environments called Moodle. In this context, this paper presents the results of several experiments that include the use of specific multi-classifier systems systems, called ensembles, aiming to reach better results in school performance prediction that is, searching for highest accuracy percentage in the student’s classification. Therefore, this paper presents a significant exploration of educational data and it shows analyzes of relevant results about these experiments.

Predição de séries temporais de parâmetros de redes HSPA - WCDMA

With the growing demand of data traffic in the networks of third generation (3G), the mobile operators have attempted to focus resources on infrastructure in places where it identifies a greater need. The channeling investments aim to maintain the quality of service especially in dense urban areas. WCDMA - HSPA parameters Rx Power, RSCP (Received Signal Code Power), Ec/Io (Energy per chip/Interference) and transmission rate (throughput) at the physical layer are analyzed. In this work the prediction of time series on HSPA network is performed. The collection of values of the parameters was performed on a fully operational network through a drive test in Natal - RN, a capital city of Brazil northeastern. The models used for prediction of time series were the Simple Exponential Smoothing, Holt, Holt Winters Additive and Holt Winters Multiplicative. The objective of the predictions of the series is to check which model will generate the best predictions of network parameters WCDMA - HSPA.