Abstract

Abstract
Power quality issues have taken a more prominent role in power systems over the last years. These issues are of major concern for energy customers, primarily for customers with a widespread use of electronic devices in their manufacturing processes. Even though the quality of service is increasing, customers are becoming more demanding of the energy provider. This research aims to provide some industrial managers the technical support in deciding of investments in the mitigation of power quality disturbances, such as the use of less sensitive devices or the use of interface devices (UPS, DVR ...) In order to recommend an appropriate solution, the problem is characterized. The technical and economic influences of the PQ disturbances in the manufacturing processes are assessed resorting to power quality audits in the customer facilities. This research covered a significant number of facilities in several industrial activities.

Abstract
Heart sound characteristics are linked to blood pressure, and its interpretation is important for detection of cardiovascular disease. In this study, heart sounds' auscultation, acquired from children patients (27 patients, 10.2 +/- 3.9 years, 35.7 +/- 20.8 kg, 132.3 +/- 25.5 cm), were automatically segmented to extract the two main components: the first sound (S1) and the second sound (S2). Following, a set of time, frequency, and wavelet based features, were extracted from the S2, and analyzed in relation to the noninvasive cuff-based measures of blood pressure (mean blood pressure of 78 +/- 8.8 mmHg). A multivariate regression analysis was performed for each S2 feature set to determine which features better related to the blood pressure measurements. The best results, in the leave-one-out evaluation, were obtained using the frequency features set, with a MAE of 6.08 mmHg, a MAPE of 7.85%, and a ME of 0.31 mmHg, in the estimation of the mean blood pressure.

Abstract
This paper aims at studying the impact of the Electric Vehicles (EV) charging demand and its uncertainty in the adequacy of the transmission grid using the Linearized approach of the Symmetric Fuzzy Power Flow analysis. The fuzzy modelling of the uncertainties caused by the presence of EV in the system is discussed. Two types of charging scenarios are considered: dumb charging and smart charging. Finally, a fuzzy power flow analysis considering the uncertainties associated to the EV load is applied to a test system as well as to the peak load scenario of Portuguese system in 2030, discussing the possibility of congestion occurrence and nodes voltages out of the tolerance limits.

Abstract
The performance of multi-objective evolutionary algorithms (MOEA) is severely deteriorated when applied to many-objective problems. For Pareto dominance based techniques, available information about optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms (NSGA-II, SMPSO and GDE3) when corner solutions are inserted into the population at different evolutionary stages. Corner solutions are found using specific algorithms. Preliminary results are presented concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5).

Abstract
Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign languages are not standard and universal and the grammars differ from country to country. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of features and an accuracy of 99.6% with a second dataset of features. Although the implemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.