The effect of superplasticizers on the workability and compressive strength of concrete made with fine recycled concrete aggregates

The reuse of structural concrete elements to produce new concrete aggregates is accepted as an alternative to dumping them and is favourable to the sustainability of natural reserves. Even though the construction sector is familiar with the use of coarse recycled concrete aggregates, the recycled concrete fines are classified as less noble resources. This research sets out to limit the disadvantages associated with the performance of concrete containing fine recycled concrete aggregates through the use of superplasticisers. Two types of latest generation superplasticisers were used that differ in terms of water reduction capacity and robustness, and the workability, density and compressive strength of each of the compositions analysed were then compared: a reference concrete, with no plasticisers, and concrete mixes with the superplasticisers. For each concrete family mixes with 0%, 10%, 30%, 50% and 100% replacement ratios of fine natural aggregates (FNA) by fine recycled concrete aggregates (FRA) were analysed. Concrete with incorporation of recycled aggregates was found to have poorer relative performance. The mechanical performance of concrete with recycled aggregates and superplasticisers was generally superior to that of the reference concrete with no admixtures and of conventional concrete with lower performance superplasticisers.

Global localization with non-quantized local image features

In the field of appearance-based robot localization, the mainstream approach uses a quantized representation of local image features. An alternative strategy is the exploitation of raw feature descriptors, thus avoiding approximations due to quantization. In this work, the quantized and non-quantized representations are compared with respect to their discriminativity, in the context of the robot global localization problem. Having demonstrated the advantages of the non-quantized representation, the paper proposes mechanisms to reduce the computational burden this approach would carry, when applied in its simplest form. This reduction is achieved through a hierarchical strategy which gradually discards candidate locations and by exploring two simplifying assumptions about the training data. The potential of the non-quantized representation is exploited by resorting to the entropy-discriminativity relation. The idea behind this approach is that the non-quantized representation facilitates the assessment of the distinctiveness of features, through the entropy measure. Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure. Experimental results support the effectiveness of this approach, as well as the validity of the proposed computation reduction methods.

Desenvolvimento de modelo de primeiros princípios para simulação dinâmica de uma coluna de destilação contínua

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Química e Biológica

Retificador trifásico controlado com fator de potência quase unitário

Dissertação de Mestrado para obtenção do grau de Mestre em Engenharia Eletrotécnica Ramo Automação e Eletrónica Industrial

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.