97 resultados para Vaz Ferreira, Carlos


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It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.

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One fundamental idea of service-oriented computing is that applications should be developed by composing already available services. Due to the long running nature of service interactions, a main challenge in service composition is ensuring correctness of transaction recovery. In this paper, we use a process calculus suitable for modelling long running transactions with a recovery mechanism based on compensations. Within this setting, we discuss and formally state correctness criteria for compensable processes compositions, assuming that each process is correct with respect to transaction recovery. Under our theory, we formally interpret self-healing compositions, that can detect and recover from faults, as correct compositions of compensable processes. Moreover, we develop an automated verification approach and we apply it to an illustrative case study.

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Trabalho de Projeto para obtenção do grau de Mestre em Engenharia na Área de Especialização em Vias de Comunicação e Transportes

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Trabalho de Projecto para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização em Estruturas

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Diarrhoea is the second leading cause of death in children under five years being responsible for 760.000 deaths, corresponding to 9% of the total deaths in this age group. Africa is the region with more deaths due to diarrhoea (46%), followed by South Asia (38%). Three quarters of the total of deaths occurs within only 15 countries and Angola is in the 15th position with a record of 20,000 annual childhood deaths. Diarrhoeal disease can be caused by bacterial, viral and parasitic infectious agents and can be transmited through contaminated food or drinking water, or directly from person to person. Rotavirus and Escherichia coli were shown the most frequent pathogenic agents in developing countries. This study aims to identify the most frequent pathogenic agents of diarrhoea in children under five atttending the Bengo General Hospital (BGH)..

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Trabalho de Projecto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

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Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Jornalismo.

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This paper presents the project of a mobile cockpit system (MCS) for smartphones, which provides assistance to electric bicycle (EB) cyclists in smart cities' environment. The presented system introduces a mobile application (MCS App) with the goal to provide useful personalized information to the cyclist related to the EB's use, including EB range prediction considering the intended path, management of the cycling effort performed by the cyclist, handling of the battery charging process, and the provisioning of information regarding available public transport. This work also introduces the EB cyclist profile concept, which is based on historical data analysis previously stored in a database and collected from mobile devices' sensors. From the tests performed, the results show the importance of route guidance, taking into account the energy savings. The results also show significant changes on range prediction based on user and route taken. It is important to say that the proposed system can be used for all bicycles in general.

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This paper discusses the results of applied research on the eco-driving domain based on a huge data set produced from a fleet of Lisbon's public transportation buses for a three-year period. This data set is based on events automatically extracted from the control area network bus and enriched with GPS coordinates, weather conditions, and road information. We apply online analytical processing (OLAP) and knowledge discovery (KD) techniques to deal with the high volume of this data set and to determine the major factors that influence the average fuel consumption, and then classify the drivers involved according to their driving efficiency. Consequently, we identify the most appropriate driving practices and styles. Our findings show that introducing simple practices, such as optimal clutch, engine rotation, and engine running in idle, can reduce fuel consumption on average from 3 to 5l/100 km, meaning a saving of 30 l per bus on one day. These findings have been strongly considered in the drivers' training sessions.

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Energy efficiency plays an important role to the CO2 emissions reduction, combating climate change and improving the competitiveness of the economy. The problem presented here is related to the use of stand-alone diesel gen-sets and its high specific fuel consumptions when operates at low loads. The variable speed gen-set concept is explained as an energy-saving solution to improve this system efficiency. This paper details how an optimum fuel consumption trajectory based on experimentally Diesel engine power map is obtained.

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In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.

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Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.

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Relatório de estágio apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.

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Trabalho de projeto apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.

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Trabalho de projeto apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.