Abstract-Experiments have been at the heart of scientific development and education for centuries. From the outburst of Information and Communication Technologies, virtual and remote labs have added to hands-on labs a new conception of practical experience, especially in Science, Technology, Engineering and Mathematics education. This paper aims at describing the features of a remote lab named Virtual Instruments System in Reality, embedded in a community of practice and forming the spearhead of a federation of remote labs. More particularly, it discusses the advantages and disadvantages of remote labs over virtual labs as regards to scalability constraints and development and maintenance costs. Finally, it describes an actual implementation in an international community of practice of engineering schools forming the embryo of a first world wide federation of Virtual Instruments System in Reality nodes, under the framework of a project funded by the Erasmus+ Program.
Experimenting is fundamental to the training process of all scientists and engineers. While experiments have been traditionally done inside laboratories, the emergence of Information and Communication Technologies added two alternatives accessible anytime, anywhere. These two alternatives are known as virtual and remote labs, and are sometimes indistinguishably referred as online labs. Similarly to other instructional technologies, virtual and remote labs require some effort from teachers in integrating them into curricula, taking into consideration several factors that affect their adoption (i.e. cost) and their educational effectiveness (i.e. benefit). This chapter analyses these two dimensions and sustains the case where only through international cooperation it is possible to serve the large number of teachers and students involved in engineering education. It presents an example in the area of Electrical and Electronics Engineering, based on a remote lab named Virtual Instruments System in Reality, and it then describes how a number
This paper tests and compares two types of modelling to predict the same time series. A time series of electric load was observed and, as a case study, we opted for the metropolitan region of Bahia State. The combination of three exogenous variables were attempted in each model. The exogenous variables are: the number of customers connected to the electricity distribution network, the temperature and the precipitation of rain. The linear model time series forecasting used was a SARIMAX. The modelling of computational intelligence used to predict the time series was a Fuzzy Inference System. According to the evaluation of the attempts, the Fuzzy forecasting system presented the lowest error. But among the smallest errors, the results of the attempts also indicated different exogenous variables for each forecast model.
ResumoEste artigo apresenta um relato da pós-graduação em engenharia no Brasil a partir de marcos importantes na história do ensino superior brasileiro. O trabalho considera a legislação estabelecida em diferentes momentos analisados e o contexto de instituições acadêmicas e científicas na construção da pós-graduação brasileira. São apresentadas, ainda, as principais agências nacionais de fomento para o desenvolvimento da Ciência e Tecnologia, os primeiros anos de pós-graduação em engenharia e o sistema de avaliação.Palavras-chave: Ensino. Engenharia. Pós-graduação. Avaliação. AbstractEngineering postgraduation in Brazil: a historical perspective
ResumoO presente estudo tem como foco analisar a trajetória do Conselho Estadual de Educação do Rio de Janeiro (CEE/RJ), particularmente, as "heranças" e influências dos colegiados que o originaram, e os marcos legais que tiveram maior impacto sobre sua história, em uma demarcação temporal que vai desde o primeiro ordenamento jurídico de sua criação, o Decreto-Lei n° 51/1975, até a Lei n° 6.864/2014, que o torna órgão de Estado. Em um plano mais específico, evidenciam-se as implicações que as mudanças governamentais produziram no colegiado, protagonizadas pelos atores do contexto político em que o CEE/RJ estava inserido. Trata-se de uma pesquisa bibliográfica e essencialmente documental que demonstra, com subsídio das fontes, como o CEE/RJ foi marcado pela descontinuidade na política, tendo como consequência que a condição de órgão de Estado seja quase imperceptível no cotidiano e nas proposições do colegiado que, hoje, normatizam a educação fluminense. Palavras-chave: Conselho Estadual de Educação. Órgão de Estado. Políticas educacionais. Rio de Janeiro.
Public authorities have sought to promote the rational use of energy in various sectors and have formulated national policies to increase energy generation through renewable sources and avoid the risk of an energy shortage. The training of professionals who can contribute to this process becomes essential. Therefore, this work presents an analysis of the current situation of the undergraduate courses in energy engineering. This topic has been the focus of debates in academia and the professional system, since the course represents a recent qualification for both educational assessment and professional assignment. In addition, this area of technical-scientific education is imminently transdisciplinary and the energy sector has been demanding a growing number of professionals qualified to research, develop and act in engineering ventures and services. Therefore, several types of courses with specific characteristics have been found that make the evaluation process, by the educational system, peculiar, as well as the professional system has difficulties in the matter of professional assignment.
This paper presents a fuzzy-based unsupervised segmentation of textured images driven by integrated spectral and spatial features. Spectral information can be obtained directly from pixel values in different frequency-band images, while spatial information can be extracted by mean of texture analysis. A new model, based on a multiplicative autoregressive random field model, was used as texture.
This paper presents a fuzy-based unsupervised segmentation of textured images. We treated texture features and color features separately. Fuzzy-based homogeneity decision is used to measure the similarity of regions by making a soft fusion of texture features and color features. Texture model, the Multiplicative Autoregressive Random Field with Conditional variance (MARC), uses a neighborhood-dependent conditional variance. I. h'TRODUCTIONThere has been a growing interest in the development of automatic classification tecbniques for the analysis remote sensing images. In the remote sensing literature, two main approaches to the classification problem have been proposed the supervised and the unsupervised. The former require the availability of a ground truth in order to derive a suitable training set for the learning process of the classifiers. Although this approach exhibits some advantages over the unsupervised one, the generation of an appropriate ground truth is usually a difficult and expensive task. Consequently, the use of effective unsupervised methods is hdamental in many applications in which a ground truth is not available. Traditional spectral-based image classification has been used for land coverfland use mapping from medium-low resolution remotely sensed data, but with the increase of spatial resolution, between-class spectral confusion and within-class spectral variation were found to increase for land coverfland use studies. Spectral information alone is not enough to map urban land coverfland use from medium-high resolution remotely sensed data. Spatial information needs to be incorporated into the image interpretation. Texture analysis provides one way to incorporate spatial information. It takes into consideration the distribution and variation of neighhorhood pixel values. Studies have demonstrated that texture analysis, which reveals spatial variations, was useful to resolve spectral confusion between land cover classes.This paper presents a fuzzy-based unsupervised segmentation of textured images. We treated texture features and color features separately. Fuzzy-based homogeneity decision is used to measure the similarity of regions by making a soft fusion of texture features and color features.Texture classification is an image processing technique through which different regions of an image are identified based on texture properties, and has been extensively used to classify remotely sensed images. Earlier works utilized statistical and structural methods for texture feature extraction. Gaussian Markov random field (GMRF) and Gibbs distribution texture models were developed and widely used for texture recognition.In this paper, a new algorithm for texture representation based on a multiplicative autoregressive random field model (MAR) is used. MAR model is a natural extension of the Gaussian autoregressive' random model with multiplicative behavior. The multiplicative autoregressive random field model is used in modeling both the spatial correlation structures and the distribution of gray ...
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