1,089 research outputs found
Análise quantitativa dos resíduos de construção e demolição (RCD) na cidade de Pau dos Ferros-RN
No full textBased on the analysis of the growth of civil construction in the municipality of Pau
dos Ferros-RN and considering the remarkable increase of waste generated by these activities,
this paper aims to identify the amount of construction and demolition waste generated by the
works in together with its final provisions, since civil construction is directly linked to the
socioeconomic development of the country and as a consequence, it is the largest contributor
to the generation of RCDs. The execution of the methodological procedure was based on
exploratory and descriptive elements followed by the use of a well-designed questionnaire to
aid in the collection of the significant data applied to eight selected works in the municipality.
According to the obtained answers, it was possible to observe how great is the generation of
residues in construction and demolition, where these are responsible for innumerable impacts
to the environment and compromising of the natural resources. In addition, it was found that a
large part of the waste generated is disposed of in inappropriate places without supervision by
both the responsible and the local residents, becoming a common action. Thus, the municipality
of Pau dos Ferros is inefficient in the management of construction and civil demolition waste,
where there is a need to implement more sustainable methods, such as in the state of Minas
Gerais there are refuse recycling plants (Minas Gerais , 2006), to reduce the inappropriate
disposal of these, in order to minimize environmental degradation and help in the
socioeconomic and sustainable development of the municipality.A partir da análise do crescimento da construção civil no município de Pau dos Ferros RN e tendo em vista o notável aumento de resíduos gerados por estas atividades, este presente
trabalho tem por objetivo identificar o quantitativo de resíduos de construção e demolição
gerados pelas obras em execução, juntamente com suas disposições finais, visto que a
construção civil está ligada diretamente com o desenvolvimento socioeconômico do país e em
consequência disto, é a maior contribuinte para a geração dos RCD. A execução do
procedimento metodológico se deu a partir de elementos exploratórios e descritivos
seguidamente da utilização de um questionário bem planejado para o auxílio do recolhimento
dos dados significativos aplicados em oito obras selecionadas no município. De acordo com as
respostas obtidas, pôde-se observar o quão grande é a geração de resíduos em construção e
demolição, onde estes são responsáveis por inúmeros impactos ao meio ambiente e
comprometedores dos recursos naturais. Além disso, verificou-se que grande parte dos resíduos
gerados são dispostos em locais inapropriados sem fiscalização, tanto pelos responsáveis
quanto pelos moradores locais, tornando-se uma ação comum. Assim, o município de Pau dos
Ferros mostra-se ineficiente no gerenciamento dos resíduos de construção e demolição civil,
onde há a necessidade de implantação de métodos mais sustentáveis, como por exemplo no
estado de Minas Gerais existem usinas de reciclagem de entulho (Minas Gerais, 2006), para a
diminuição de disposição inapropriada destes, no intuito de minimizar a degradação ambiental
e auxiliar no desenvolvimento socioeconômico e sustentável do município.Trabalho não financiado por agência de fomento, ou autofinanciad
Functional models and extending strategies for ecological networks
No full textComplex network analysis is rising as an essential tool to understand properties of ecological landscape networks, and as an aid to land management. The most common methods to build graph models of ecological networks are based on representing functional connectivity with respect to a target species. This has provided good results, but the lack of a model able to capture general properties of the network may be seen as a shortcoming when the activity involves the proposal for modifications in land use. Similarity scores, calculated between nature protection areas, may act as a building block for a graph model intended to carry a higher degree of generality. The present work compares several design choices for similarity-based graphs, in order to determine which is most suitable for use in land management
Analysis of compact features for RGB-D visual search
No full textAnticipating the oncoming integration of depth sensing into mobile devices, we experimentally compare different compact features for representing RGB-D images in mobile visual search. Experiments on 3 state-of-the-art datasets, addressing both category and instance recognition, show how Deep Features provided by Convolutional Neural Networks better represent appearance information, whereas shape is more effectively encoded through Kernel Descriptors. Moreover, our evaluation suggests that learning to weight the relative contribution of depth and appearance is key to deploy effectively depth sensing in forthcoming mobile visual search scenarios
On the Reproducibility of Experiments achieved by TinyRCE
No full textTinyRCE is a hyperspherical classifier aimed at Continual Learning On-Tiny-Devices, a challenging task in which a Machine Learning model is required to learn from continuous streams of data while being directly installed on a (tiny) device with limited computational resources. The classifier has so far been applied to several use cases, including Human Activity Recognition, Ball Bearing Anomaly Classification, Keyword Spotting and Image Classification. The proposed work in this paper focuses on the reproducibility of TinyRCE’s experimental results already published on other papers. This to prove that all the published results are quantitatively reproducible. All the experiments have been executed on two independent computing machines to profile the impact on accuracy of the computations. As the outcomes are matching, the experimental reproducibility of TinyRCE’s accuracy over all the use cases has been positively verified
Guided waves for damage characterization in curved beams
No full textCurved beams are often found as connections of straight elements in structural networks, therefore there is an interest in assessing their integrity. The use of guided waves for damage characterization in structures is well established for straight elements but still requires a full understanding of the phenomena occurring when waves interact with discontinuities of different kinds, such as junctions between straight and curved elements and defects located in curved parts. In this paper, the semi analytical finite element method is used to describe wave propagation in curved beams. Firstly, the dispersion curves in toroidal beams are determined, then, the interaction of a longitudinal guided wave with a change of curvature and a defect is described. The effect of the curvature and damage parameters, that is position, extension and intensity on the propagation is studied using a one-dimensional model for the damaged structure. Notwithstanding its simplicity, this model can provide qualitative guidelines in view of the formulation of a damage characterization procedure
Towards Full Forward On-Tiny-Device Learning: A Guided Search for a Randomly Initialized Neural Network
No full textIn the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger privacy, data safety and robustness to adversarial attacks, higher resilience against concept drift, etc. However, On-Device Learning on resource constrained devices poses severe limitations to computational power and memory. Therefore, deploying Neural Networks on tiny devices appears to be prohibitive, since their backpropagation-based training is too memory demanding for their embedded assets. Using Extreme Learning Machines based on Convolutional Neural Networks might be feasible and very convenient, especially for Feature Extraction tasks. However, it requires searching for a randomly initialized topology that achieves results as good as those achieved by the backpropagated model. This work proposes a novel approach for automatically composing an Extreme Convolutional Feature Extractor, based on Neural Architecture Search and Bayesian Optimization. It was applied to the CIFAR-10 and MNIST datasets for evaluation. Two search spaces have been defined, as well as a search strategy that has been tested with two surrogate models, Gaussian Process and Random Forest. A performance estimation strategy was defined, keeping the feature set computed by the MLCommons-Tiny benchmark ResNet as a reference model. In as few as 1200 search iterations, the proposed strategy was able to achieve a topology whose extracted features scored a mean square error equal to 0.64 compared to the reference set. Further improvements are required, with a target of at least one order of magnitude decrease in mean square error for improved classification accuracy. The code is made available via GitHub to allow for the reproducibility of the results reported in this paper
The contextual database of the generations and gender program in Bulgaria: conceptual framework and an overview of the Bulgarian context concerning the central database topics
No full textThis paper outlines the concept and content of the Contextual Database of the international Generations and Gender Program and gives an overview of the context of demographic behavior in Bulgaria. The Contextual Database provides an instrument that together with the Generations and Gender Survey allows studying how differences in context shape demographic processes. The database offers the opportunity to analyze in a comparative way the interaction between the micro and macro dimension. Bulgaria is among the first countries fielding the Generations and Gender Survey and that is engaged in contextual data collection within this comparative framework. While both micro- and contextual data for Bulgaria will become available in the course of the year 2005, we present in this paper a text contribution that provides an overview of the Bulgarian context and introduces the list of variables that make up the database.Bulgaria, data collection
Damage identification in a parabolic arch by means of natural frequencies, modal shapes and curvatures
No full textThis paper investigates damage identification techniques based on the difference of modal frequencies, shapes and curvatures in the damaged and undamaged states of the structure. The sensitivity of the identification algorithm with respect to damage parameters is discussed and the minimum number of measured quantities to identify the damage is assessed. It is shown that modal curvatures can be effectively used to pre-localise the damage and to add a penalty term in the objective function which weighs the difference between natural frequencies and modal displacements. Such a term improves the local convexity of the objective function and enhances the convergence rate of the minimization algorithm. The procedure is validated against the results of the experiments on a parabolic arch carried out by the authors. The advantages of such an approach compared to techniques solely based on frequencies are that the ill-conditioning of the inverse problem is reduced and a more accurate estimate of the damage parameters is achieved
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