Using remote lab networks to provide support to public secondary school education level

The advantages of networking are widely known in many areas (from business to personal ones). One particular area where networks have also proved their benefits is education. Taking the secondary school education level into account, some successful cases can be found in literature. In this paper we describe a particular remote lab network supporting physical experiments accessible to students of institutions geographically separated. The network architecture and application examples of using some of the available remote experiments are illustrated in detail. ©2008 IEEE.

Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.

Reactive power support pricing of distributed generators with primary energy source uncertainty

In this paper, a novel methodology to price the reactive power support ancillary service of Distributed Generators (DGs) with primary energy source uncertainty is shown. The proposed methodology provides the service pricing based on the Loss of Opportunity Costs (LOC) calculation. An algorithm is proposed to reduce the uncertainty present in these generators using Multiobjective Power Flows (MOPFs) implemented in multiple probabilistic scenarios through Monte Carlo Simulations (MCS), and modeling the time series associated with the generation of active power from DGs through Markov Chains (MC). © 2011 IEEE.

Pricing of reactive power support provided by distributed generators in transmission systems

Distributed Generation, microgrid technologies, two-way communication systems, and demand response programs are issues that are being studied in recent years within the concept of smart grids. At some level of enough penetration, the Distributed Generators (DGs) can provide benefits for sub-transmission and transmission systems through the so-called ancillary services. This work is focused on the ancillary service of reactive power support provided by DGs, specifically Wind Turbine Generators (WTGs), with high level of impact on transmission systems. The main objective of this work is to propose an optimization methodology to price this service by determining the costs in which a DG incurs when it loses sales opportunity of active power, i.e, by determining the Loss of Opportunity Costs (LOC). LOC occur when more reactive power is required than available, and the active power generation has to be reduced in order to increase the reactive power capacity. In the optimization process, three objectives are considered: active power generation costs of DGs, voltage stability margin of the system, and losses in the lines of the network. Uncertainties of WTGs are reduced solving multi-objective optimal power flows in multiple probabilistic scenarios constructed by Monte Carlo simulations, and modeling the time series associated with the active power generation of each WTG via Fuzzy Logic and Markov Chains. The proposed methodology was tested using the IEEE 14 bus test system with two WTGs installed. © 2011 IEEE.

Architecture for peer-to-peer databases with routing queries using ant colony algorithm and semantic support

In a peer-to-peer network, the nodes interact with each other by sharing resources, services and information. Many applications have been developed using such networks, being a class of such applications are peer-to-peer databases. The peer-to-peer databases systems allow the sharing of unstructured data, being able to integrate data from several sources, without the need of large investments, because they are used existing repositories. However, the high flexibility and dynamicity of networks the network, as well as the absence of a centralized management of information, becomes complex the process of locating information among various participants in the network. In this context, this paper presents original contributions by a proposed architecture for a routing system that uses the Ant Colony algorithm to optimize the search for desired information supported by ontologies to add semantics to shared data, enabling integration among heterogeneous databases and the while seeking to reduce the message traffic on the network without causing losses in the amount of responses, confirmed by the improve of 22.5% in this amount. © 2011 IEEE.

Scheduler simulation using iSPD, an iconic-based computer grid simulator

Increased accessibility to high-performance computing resources has created a demand for user support through performance evaluation tools like the iSPD (iconic Simulator for Parallel and Distributed systems), a simulator based on iconic modelling for distributed environments such as computer grids. It was developed to make it easier for general users to create their grid models, including allocation and scheduling algorithms. This paper describes how schedulers are managed by iSPD and how users can easily adopt the scheduling policy that improves the system being simulated. A thorough description of iSPD is given, detailing its scheduler manager. Some comparisons between iSPD and Simgrid simulations, including runs of the simulated environment in a real cluster, are also presented. © 2012 IEEE.

Predicting academic success of engineering students in technical drawing from working memory

Tests on spatial aptitude, in particular Visualization, have been shown to be efficient predictors of the academic performance of Technical Drawing stu-dents. It has recently been found that Spatial Working Memory (a construct defined as the ability to perform tasks with a figurative content that require si-multaneous storage and transformation of information) is strongly associated with Visualization. In the present study we analyze the predictive efficiency of a bat-tery of tests that included tests on Visualization, SpatialWorking Memory, Spatial Short-term Memory and Executive Function on a sample of first year engineering students. The results show that Spatial Working Memory (SWM) is the most important predictor of academic success in Technical Drawing. In our view, SWM tests can be useful for detecting as early as possible those students who will require more attention and support in the teaching-learning process.

Computational support for the process of software requirement specification

The software industry has become more and more concerned with the appropriate application of activities that composes requirement engineering as a way to improve the quality of its products. In order to support these activities, several computational tools have been available in the market, although it is still possible to find a lack of resources related to some activities. In this context, this paper proposes the inclusion of a module to aid in the requirements specification to a tool called Requirements Elicitation Support Tool. This module allows to specify requirements in accordance with IEEE 830 standard, thus contributing to the documentation of the requirements established for a software system, besides supporting the learning of concepts related to the requirements specification, which improves the skills of users of the tool. © 2012 IEEE.

Analysis of the influence of pre-loading in the rating life calculation of tapered roller bearings applied to lightweight front-axle differentials

For intricate automotive systems that enclose several components, such as gearboxes, an important aspect of the design is defining the correct assembly parameters. A proper assembly can ensure optimized operating conditions and therefore the components can achieve a longer life. In the case of the support bearings applied to front-axle lightweight differentials, the assembly preload is a major aspect for an adequate performance of the system. During the design phase it is imperative to define reference values to this preload, so the application would endure its requirements. However, with the assistance of computer simulations, it is possible to determine an optimum condition of operation, i.e. optimum pre-load, which would increase the system reliability. This paper presents a study on the influence of preload on the rating life of tapered roller bearings applied to light-weight front axle differentials, evaluating how preload affects several key parameters such as rating life and displacement of components, taking into account the flexibility of the surrounding differential housing. Copyright © 2012 SAE International.

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.

Metrics to support the evaluation of association rule clustering

Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.

Características da interface dos sites de notícias: um estudo do design e da usabilidade de jornais impressos e eletrônicos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo dos problemas ergonômicos da posição sentada em bancários

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Projeto e construção de um elipsômetro automatizado

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Improving face recognition with multispectral fusion and support vector machines

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)