INESCC-DL - Artigos em Livros de Actas
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- A comparison of the vibration characteristics of a rotating machine with a linear and a keyed shaftPublication . Oliveira, F.; Pelaez, G.; Donsion, M. P.This paper presents some results of an ongoing work aimed at studying a number of characteristics of rotating machines and how they are influenced by both constructive features and the way they are driven. The following will present the main results obtained when a simple one-stage inertia turbomachine is built with a linear, cylindrical shaft or, instead, with a shaft that has a small slot such as those used in many mechanical couplings. Experimental results will show some significant differences in the behavior of the machine, both in terms of the orbit and in the resonance frequencies, as well as in the phase and amplitude of mechanical vibrations.
- Data Acquisition and Monitoring System for Legacy Injection MachinesPublication . Silva, Bruno; Sousa, João; Alenya, GuillemNowadays, companies must embrace the concept of Digitalization and Industry 4.0 to remain competitive in the market. The reality is that most of them do not have their industrial devices prepared to access their data on a real-time basis. As most companies do not have the possibility to renew all their legacy devices and because these devices are still very productive, a retrofit solution is of high interest. In this work, we propose an affordable procedure that allows data collection and monitoring of older injection machines, as a contribution towards legacy devices integration. The developed system neither requires additional proprietary modules, nor contractual annual fees for different devices, sharing the same interface across different machine manufacturers and also contributing to uniform data collection. Evaluation was carried out in a real shop floor, monitoring the injection parameters for different machine models, validating the effectiveness of the developed system.
- An Evolutionary Algorithm based on an outranking relation for sorting problemsPublication . Oliveira, Eunice; Antunes, Carlos HenggelerA new approach for using the preferences elicited from a Decision Maker (DM) into the operational framework of an Evolutionary Algorithm (EA) is presented. The preference representation is achieved using the parameters and principles of the ELECTRE TRI method devoted to the sorting problem. The outranking relation is used to replace the non-dominance relation in the usual operators in the EA (crossover, mutation and selection operator). The aim of this approach is to focus the search on the region of interest defined by the DM's preferences and consequently restrict the number of solutions in the Pareto-optimal front to be subject to further screening. This aspect is particularly important when dealing with problems that lead to a large number of non-dominated solutions.
- Influence of a SVC on AC Arc furnaces harmonics, flicker and unbalance. Measurement and analysis.Publication . Donsión, M. P.; Güemes, J. A.; Oliveira, F.An AC arc furnace is an unbalanced, nonlinear and time varying load, which can cause many problems to power system quality. Different studies on arc furnaces harmonics analysis can be found in the bibliography on the topic; however, it is very difficult obtain an exact model that takes into account all the parameters that have influence on the process, therefore it is necessary to take measurements under different conditions. In this paper we'll present the harmonic distortion, flicker and unbalance results and conclusions on three different measurement campaigns in an iron and steel industry (SNL) with an AC arc furnace of 83 MW (170 TM) with a transformer of 120 MVA connected with a dedicated power line of 220 kV (55 km) to the Carregado Substation, where there are another other branches that connect industrial and domestic consumers.
- On the information provided by uncertainty measures in the classification of remote sensing imagesPublication . Gonçalves, Luisa; Fonte, Cidália C.; Júlio, Eduardo N.B.S.; Caetano, MarioThis paper investigates the potential information provided to the user by the uncertainty measures applied to the possibility distributions associated with the spatial units of an IKONOS satellite image, generated by two fuzzy classifiers, based, respectively, on the Nearest Neighbour Classifier and the Minimum Distance to Means Classifier. The deviation of the geographic unit characteristics from the prototype of the class to which the geographic unit is assigned is evaluated with the Un non-specificity uncertainty measures proposed by [1] and the exaggeration uncertainty measure proposed by [2]. The classifications were evaluated using accuracy and uncertainty indexes to determine their compatibility. Both classifications generated medium to high levels of uncertainty for almost all classes, and the global accuracy indexes computed were 70% for the Nearest Neighbour Classifier and 53% for the Minimum Distance to Means Classifier. The results show that similar conclusions can be obtained with accuracy and uncertainty indexes and the latter, along with the analysis of the possibility distributions, may be used as indicators of the classification performance and may therefore be very useful tools. Since the uncertainty indexes may be computed to all spatial units, the spatial distribution of the uncertainty was also analysed. It's visualization shows that regions where less reliability is expected present a great amount of detail that may be potentially useful to the user.
- Using Uncertainty Information to Combine Soft ClassificationsPublication . Gonçalves, Luisa M. S.; Fonte, Cidália C.; Caetano, MarioThe classification of remote sensing images performed with different classifiers usually produces different results. The aim of this paper is to investigate whether the outputs of different soft classifications may be combined to increase the classification accuracy, using the uncertainty information to choose the best class to assign to each pixel. If there is disagreement between the outputs obtained with the several classifiers, the proposed method selects the class to assign to the pixel choosing the one that presents less uncertainty. The proposed approach was applied to an IKONOS image, which was classified using two supervised soft classifiers, the Multi-layer Perceptron neural network classifier and a fuzzy classifier based on the underlying logic of the Minimum-Distance-to-Means. The overall accuracy of the classification obtained with the combination of both classifications with the proposed methodology was higher than the overall accuracy of the original classifications, which shows that the methodology is promising and may be used to increase classification accuracy.