INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Field-programmable gate array & Control theory. The organization has 932 authors who have published 2618 publications receiving 37658 citations.

A Generic Framework for Replicated Software Transactional Memories

Abstract: Software Transactional Memory (STM) has emerged a powerful abstraction for managing access to shared data. Therefore, it is no surprise that a handful of different STM replication schemes have been proposed in the last recent years. In this context, we propose an architecture that facilitates the integration and execution of multiple replication techniques in a single, coherent, middleware infrastructure. This paves the way towards the development of autonomic mechanisms, able to select in runtime the most appropriate replication technique for the workload at hand.

Function-based modulation control for modular multilevel converters under varying loading and parameters conditions

Abstract: This study presents a new function-based modulation control technique for modular multilevel converters (MMCs). The main contribution of this study is the formulation of two new modulation functions for the required switching signals of the MMC's upper and lower sub-modules, respectively. The output and circulating current equations of the converter are employed to attain the arm's currents which are utilised for the proposed modulation functions, which have two important features: (i) it is much less complex compared to the existing control methods of MMC; and (ii) the proposed controller can be regulated properly to deal with parameter variations in a bid to ensure stable and accurate performance. In this controller, the MMC output current magnitude and phase angle required for special active and reactive power sharing can be easily applied to the modulation functions. Also, the equivalent capacitors of upper and lower sub-modules are discussed based on the proposed modulation functions. Finally, simulations are performed in Matlab/Simulink environment to evaluate the performance of the proposed control technique in both the dynamic conditions of load as well as varying parameters.

Self-consumption and storage as a way to facilitate the integration of renewable energy in low voltage distribution networks

Abstract: Photovoltaic microgeneration (μG) located near the domestic consumers is expected to increase more in the future. Known issues regarding high μG penetration are voltage rise and reverse power flow. The concept of self-consumption and storage is emerging as a way to improve network power supply quality and to facilitate the integration of small renewable energy sources in low voltage networks. This paper intends to give a further contribution by assessing the improvements provided by allowing domestic clients to consume and store the energy they produce. The tool at hand to study the situation is the unbalanced three-phase load flow algorithm, based on the power summation technique, improved with the capability of explicitly compute the neutral voltages. The behaviour of a test radial distribution grid is assessed in different operating conditions, namely winter/summer, μG penetration level and μG operating mode. Voltage profile, active power flow in the service transformer and losses are monitored. The results suggest that self-consumption with storage operating mode is a promising solution. Voltage excursions above normal grid voltage operating limits were not observed, the situation of reverse active power flow does not occur and the power losses are reduced.

Twitter Topic Fuzzy Fingerprints

Abstract: In this paper we propose to approach the subject of Twitter Topic Detection using a new technique called Topic Fuzzy Fingerprints. A comparison is made with two popular text classification techniques, Support Vector Machines (SVM) and fc-Nearest Neighbours (fcNN). Preliminary results show that Twitter Topic Fuzzy Fingerprints outperforms the other two techniques achieving better Precision and Recall, while still being much faster, which is an essential feature when processing large volumes of streaming data.