76 resultados para Virtual Probe Vehicle


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We propose the use of the European Geostationary Navigation Overlay Service (EGNOS) data - real time on line data provided by SISNeT - to develop Virtual Reference Stations and, thus, increase the quality of the Position, Velocity an Time (PVT) solution of receivers unable to interface directly with EGNOS. A Virtual Reference Station (VRS) is a concept where the existence of a differential reference station located near a mobile rover is simulated by software in order to increase the accuracy of the PVT solution of the mobile GNSS receiver.

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Scheduling of constrained deadline sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that finding an optimal scheduler is hard, and therefore most studies have focused on developing algorithms with good processor utilization bounds. These algorithms can be broadly classified into two categories: partitioned scheduling in which tasks are statically assigned to individual processors, and global scheduling in which each task is allowed to execute on any processor in the platform. In this paper we consider a third, more general, approach called cluster-based scheduling. In this approach each task is statically assigned to a processor cluster, tasks in each cluster are globally scheduled among themselves, and clusters in turn are scheduled on the multiprocessor platform. We develop techniques to support such cluster-based scheduling algorithms, and also consider properties that minimize total processor utilization of individual clusters. In the last part of this paper, we develop new virtual cluster-based scheduling algorithms. For implicit deadline sporadic task systems, we develop an optimal scheduling algorithm that is neither Pfair nor ERfair. We also show that the processor utilization bound of us-edf{m/(2m−1)} can be improved by using virtual clustering. Since neither partitioned nor global strategies dominate over the other, cluster-based scheduling is a natural direction for research towards achieving improved processor utilization bounds.

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Not just with the emergence but also with the growing of the electronic market, that is, the growth of online suppliers of services and products and Internet users (potential consumers), the necessary conditions to the affirmation of the agile/virtual enterprises (A/VE) as a present and future enterprise organizational model are created. In this context, it is our understanding that the broker may have an important role in its development, namely, if the broker performs functions for the A/VE with better efficacy and efficiency. In this article, we will present first a revision of the broker’s models in a structured form. We present a taxonomy of possible broker’s functions for the broker’s actuation near the A/VE and then the classification of the literature broker’s models. This classification will permit an analysis of a broker’s model and establish a mainframe for our broker’s model according to the BM_Virtual Enterprise Architecture Reference Model (BM_VEARM).

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The process of resources systems selection takes an important part in Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. However, the resources systems selection is still a difficult matter to solve in a D/A/VE, as it is pointed out in this paper. Globally, we can say that the selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project), we intend in this paper to show: a formulation of a kind of resources selection problem and the limitations of the algorithms proposed to solve it. We formulate a particular case of the problem as an integer programming, which is solved using simplex and branch and bound algorithms, and identify their performance limitations (in terms of processing time) based on simulation results. These limitations depend on the number of processing tasks and on the number of pre-selected resources per processing tasks, defining the domain of applicability of the algorithms for the problem studied. The limitations detected open the necessity of the application of other kind of algorithms (approximate solution algorithms) outside the domain of applicability founded for the algorithms simulated. However, for a broker tool it is very important the knowledge of algorithms limitations, in order to, based on problem features, develop and select the most suitable algorithm that guarantees a good performance.

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This paper presents a collaborative virtual learning environment, which includes technologies such as 3D virtual representations, learning and content management systems, remote experiments, and collaborative learning spaces, among others. It intends to facilitate the construction, management and sharing of knowledge among teachers and students, in a global perspective. The environment proposes the use of 3D social representations for accessing learning materials in a dynamic and interactive form, which is regarded to be closer to the physical reality experienced by teachers and students in a learning context. A first implementation of the proposed extended immersive learning environment, in the area of solid mechanics, is also described, including the access to theoretical contents and a remote experiment to determine the elastic modulus of a given object.These instructions give you basic guidelines for preparing camera-ready papers for conference proceedings. Use this document as a template if you are using Microsoft Word 6.0 or later. Otherwise, use this document as an instruction set. The electronic file of your paper will be formatted further. Define all symbols used in the abstract. Do not cite references in the abstract.

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Most definitions of virtual enterprise (VE) incorporate the idea of extended and collaborative outsourcing to suppliers and subcontractors in order to achieve a competitive response to market demands (Webster, Sugden, & Tayles, 2004). As suggested by several authors (Browne & Zhang, 1999; Byrne, 1993; Camarinha-Matos & Afsarmanesh, 1999; Cunha, Putnik, & Ávila, 2000; Davidow & Malone, 1992; Preiss, Goldman, & Nagel, 1996), a VE consists of a network of independent enterprises (resources providers) with reconfiguration capability in useful time, permanently aligned with the market requirements, created to take profit from a specific market opportunity, and where each participant contributes with its best practices and core competencies to the success and competitiveness of the structure as a whole. Even during the operation phase of the VE, the configuration can change, to assure business alignment with the market demands, traduced by the identification of reconfiguration opportunities and continuous readjustment or reconfiguration of the VE network, to meet unexpected situations or to keep permanent competitiveness and maximum performance (Cunha & Putnik, 2002, 2005a, 2005b).

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The paper proposes a Flexibility Requirements Model and a Factory Templates Framework to support the dynamic Virtual Organization decision-makers in order to reach effective response to the emergent business opportunities ensuring profitability. Through the construction and analysis of the flexibility requirements model, the network managers can achieve and conceive better strategies to model and breed new dynamic VOs. This paper also presents the leagility concept as a new paradigm fit to equip the network management with a hybrid approach that better tackle the performance challenges imposed by the new and competitive business environments.

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THE ninth edition of the International Conference on Remote Engineering and Virtual Instrumentation (REV) [1] was held at the Faculty of Engineering of the University of Deusto, Bilbao (Spain), from the 4th to the 6th of July, 2012. A world-class research community in the subject of remote and virtual laboratories joined the event.

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Learning is not only happening in school or university; it is also an important aspect of the daily life that allows students to remain in their biological and physical environment helping to reshape it, by applying what they have learnt. Today, the higher education sector is a part of important strategies used by countries in order to foster their development. Despite its geographical location, i.e. its closeness to Europe and Asia, the MENA (Middle East and North Africa) region still needs an integrated strategy for the advancement, reform, and update of its higher educational landscape. Although some solutions have been experimented in the region in the field of higher education, they have not been able to raise the quality of education to the level comparable that observed in developed countries. In other words, many MENA higher education systems are facing problems, for which solution ought to be sought. We analyse the situation of higher education systems in the MENA countries and the factors that affect the delay in achieving the level of education existing in other world regions, e.g. Europe, especially in the higher education sector. During the discussion, the impact of new technology-enhanced tools, such as remote laboratories, in the process of development and consolidation of MENA universities, is particularly stressed.

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Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

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Empowered by virtualisation technology, cloud infrastructures enable the construction of flexi- ble and elastic computing environments, providing an opportunity for energy and resource cost optimisation while enhancing system availability and achieving high performance. A crucial re- quirement for effective consolidation is the ability to efficiently utilise system resources for high- availability computing and energy-efficiency optimisation to reduce operational costs and carbon footprints in the environment. Additionally, failures in highly networked computing systems can negatively impact system performance substantially, prohibiting the system from achieving its initial objectives. In this paper, we propose algorithms to dynamically construct and readjust vir- tual clusters to enable the execution of users’ jobs. Allied with an energy optimising mechanism to detect and mitigate energy inefficiencies, our decision-making algorithms leverage virtuali- sation tools to provide proactive fault-tolerance and energy-efficiency to virtual clusters. We conducted simulations by injecting random synthetic jobs and jobs using the latest version of the Google cloud tracelogs. The results indicate that our strategy improves the work per Joule ratio by approximately 12.9% and the working efficiency by almost 15.9% compared with other state-of-the-art algorithms.

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The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.

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Recent changes in the operation and planning of power systems have been motivated by the introduction of Distributed Generation (DG) and Demand Response (DR) in the competitive electricity markets' environment, with deep concerns at the efficiency level. In this context, grid operators, market operators, utilities and consumers must adopt strategies and methods to take full advantage of demand response and distributed generation. This requires that all the involved players consider all the market opportunities, as the case of energy and reserve components of electricity markets. The present paper proposes a methodology which considers the joint dispatch of demand response and distributed generation in the context of a distribution network operated by a virtual power player. The resources' participation can be performed in both energy and reserve contexts. This methodology contemplates the probability of actually using the reserve and the distribution network constraints. Its application is illustrated in this paper using a 32-bus distribution network with 66 DG units and 218 consumers classified into 6 types of consumers.

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Smart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.

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Mestrado em Engenharia Informática - Área de Especialização em Sistemas Gráficos e Multimédia