Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study

We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a multimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs), Artificial Neural Networks (ANNs), Fuzzy Expert Systems (FESs), and Support Vector Machines (SVMs). The fusion of biometrics leads to security systems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised learning was carried out using a number of patterns from a well-known benchmark biometrics database, and the validation/testing took place with patterns from the same database which were not included in the training dataset. The comparison of the algorithms reveals that the biometrics fusion system is superior to the original unimodal systems and also other fusion schemes found in the literature

Dynamic Wireless Charging for More Efficient FEVs: The Fabric Project Concept

This paper describes the technology avenues that will be explored in FABRIC, an FP7 Integrated Project, for accomplishing and evaluating the dynamic, wireless charging of future fully electric vehicles. Initially, the charging modes supported by FABRIC are specified in detail. The preliminary high-level conceptual system architecture follows, and afterwards the wireless charging technologies that will be developed and tested are presented. Finally there is a discussion on the challenges that are anticipated during the design, prototyping and testing phases

A decision support system tool to manage the flexibility in renewable energy-based power systems

Renewable Energy Sources (RES) have taken on an increasingly important role in the energy mix in the last few years, and it has been forecasted that this trend will continue in the future. The energy production from these sources is not dispatchable, and the increasing penetration of RES in energy mixes may therefore lead to a progressive loss of generation control and predictability. It has become clear that, to reach higher RES penetration levels, it is essential to increase power system flexibility in order to ensure stable operations are maintained. An ICT (Information and Communication Technology) tool that may be used to manage and optimize the flexibility offered by energy storage and conversion systems is described in this paper with specific reference to the Decision Support System (DSS) developed within the H2020 PLANET (PLAnning and operational tools for optimizing energy flows and synergies between energy NETworks) project. The paper focuses on how the PLANET DSS tool evaluates, manages, and dispatches the flexibility of Power to Gas/Heat (P2X) technologies. Moreover, the tool has been used to analyze a realistic case in order to show how the PLANET DSS tool could be used to evaluate the energy and economic benefits of taking advantage of the flexibility of P2X technologies