Search results for "Distributed"

showing 10 items of 1260 documents

Decentralized on-site optimization of a battery storage system using one-way communication

2015

Intermittent renewable energy sources (e.g. wind, solar energy systems) have been providing an exponentially growing share of electricity generation. Due to their highly transient and stochastic nature, they pose substantial challenges for power grid operation. Power dispatched from these sources are uncontrolled and do not necessarily coincide with demand; this in turn affects power quality. Hence, extensive demand side management (DSM) is required. DSM relies on flexible loads as well as energy storage facilities. Furthermore, renewable power generation is by its very nature highly distributed and consists of large numbers of small units. These have a substantial effect on traditional pow…

Battery (electricity)Stand-alone power systemEngineeringElectricity generationbusiness.industryDistributed generationElectricity pricingIntermittent energy sourceElectrical engineeringbusinessEnergy storageRenewable energyReliability engineering
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Power Grid Integration and Use-Case Study of Acid-Base Flow Battery Technology

2021

There are many different types of energy storage systems (ESS) available and the functionality that they can provide is extensive. However, each of these solutions come with their own set of drawbacks. The acid-base flow battery (ABFB) technology aims to provide a route to a cheap, clean and safe ESS by means of providing a new kind of energy storage technology based on reversible dissociation of water via bipolar electrodialysis. First, the main characteristics of the ABFB technology are described briefly to highlight its main advantages and drawbacks and define the most-competitive use-case scenarios in which the technology could be applied, as well as analyze the particular characteristi…

Battery (electricity)distributed energy resourcepower grid integrationProcess (engineering)Computer science020209 energyInterface (computing)Energy storage systempower flow batteriesGeography Planning and Developmentpower convertersTJ807-83002 engineering and technologyManagement Monitoring Policy and LawTD194-1957. Clean energyEnergy storageRenewable energy sourceslaw.inventionacid-base flow batterydistributed energy resourcesSet (abstract data type)law0202 electrical engineering electronic engineering information engineeringpower converterGE1-350Environmental effects of industries and plantsRenewable Energy Sustainability and the Environmentpower flow batterie021001 nanoscience & nanotechnologyFlow batteryPower (physics)Reliability engineeringEnvironmental sciencesElectrical networkenergy storage systems0210 nano-technologySustainability
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Near field improvements of stochastic collaborative beamforming in wireless sensor networks

2020

Wireless sensor networks (WSN) are groups of small devices that contain a microcontroller in which a large number of sensors can be added. They transmit data and communicate to each other in the ISM band, standard IEEE 802.15.4, exchanging packets using a multi-hop routing. These devices are named motes and are nodes of the WSN. They are very simple and easy to program, powered by batteries of 1.5Volts (AA and AAA). The nodes are autonomous elements that can be deployed implementing any type of network. In a typical deployment the nodes communicate with each other and with a master node or Base Station (BS), which in turn transmits the information to an external server, which collects the e…

Beamforming020203 distributed computingNetwork packetbusiness.industryComputer scienceNode (networking)ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS02 engineering and technologySynchronizationBase stationTransmission (telecommunications)0202 electrical engineering electronic engineering information engineeringComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS020201 artificial intelligence & image processingbusinessWireless sensor networkISM bandComputer networkProceedings of the 10th Euro-American Conference on Telematics and Information Systems
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Practical Considerations in the Implementation of Collaborative Beamforming on Wireless Sensor Networks

2017

Wireless Sensor Networks (WSNs) are composed of spatially distributed autonomous sensor devices, named motes. These motes have their own power supply, processing unit, sensors and wireless communications However with many constraints, such as limited energy, bandwidth and computational capabilities. In these networks, at least one mote called a sink, acts as a gateway to connect with other networks. These sensor networks run monitoring applications and then the data gathered by these motes needs to be retrieved by the sink. When this sink is located in the far field, there have been many proposals in the literature based on Collaborative Beamforming (CB), also known as Distributed or Cooper…

BeamformingEngineering02 engineering and technologylcsh:Chemical technologyBiochemistryArticleAnalytical ChemistryDefault gateway0202 electrical engineering electronic engineering information engineeringWirelesslcsh:TP1-1185ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMSElectrical and Electronic Engineeringwireless sensor networksInstrumentationbusiness.industryBandwidth (signal processing)ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKScollaborative beamforming020206 networking & telecommunicationsAtomic and Molecular Physics and OpticsPower (physics)cooperative beamforming020201 artificial intelligence & image processingSink (computing)businessWireless sensor networkdistributed beamformingEnergy (signal processing)Computer networkSensors
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Big Data Processing in the ATLAS Experiment: Use Cases and Experience

2015

Abstract The physics goals of the next Large Hadron Collider run include high precision tests of the Standard Model and searches for new physics. These goals require detailed comparison of data with computational models simulating the expected data behavior. To highlight the role which modeling and simulation plays in future scientific discovery, we report on use cases and experience with a unified system built to process both real and simulated data of growing volume and variety.

Big DataComputational modelLarge Hadron ColliderComputer sciencebusiness.industryPhysics beyond the Standard ModelData managementBig dataATLAS experimentcomputer.software_genreData scienceStandard ModelModeling and simulationParallel and Distributed ComputingGrid-based Simulation and ComputingGrid computingLarge Scale Scientific InstrumentsGeneral Earth and Planetary SciencesUse casebusinesscomputerGeneral Environmental ScienceProcedia Computer Science
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Big Data in metagenomics: Apache Spark vs MPI.

2020

The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest in using modern Big Data technologies to process this large amount of information in distributed memory clusters of commodity hardware. Several approaches based on solutions such as Apache Hadoop or Apache Spark, have been proposed. These solutions allow developers to focus on the problem while the need to deal with low level details, such as data distribution schemes or communication patterns among processing nodes, can be ignored. However, performance and scalability are also of high importance when…

Big DataComputer and Information SciencesScienceBig dataMessage Passing InterfaceParallel computingResearch and Analysis MethodsComputing MethodologiesComputing MethodologiesComputer ArchitectureComputer SoftwareDatabase and Informatics MethodsSoftwareSpark (mathematics)GeneticsMammalian GenomicsMultidisciplinarybusiness.industryApplied MathematicsSimulation and ModelingQRBiology and Life SciencesComputational BiologySoftware EngineeringGenomicsDNAGenomic DatabasesGenome AnalysisComputer HardwareSupercomputerBiological DatabasesAnimal GenomicsPhysical SciencesScalabilityEngineering and TechnologyMetagenomeMedicineDistributed memoryMetagenomicsbusinessMathematicsAlgorithmsGenome BacterialSoftwareResearch ArticlePLoS ONE
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Bio-inspired security analysis for IoT scenarios

2020

Computer security has recently become more and more important as the world economy dependency from data has kept growing. The complexity of the systems that need to be kept secure calls for new models capable of abstracting the interdependencies among heterogeneous components that cooperate at providing the desired service. A promising approach is attack graph analysis, however, the manual analysis of attack graphs is tedious and error prone. In this paper we propose to apply the metabolic network model to attack graph analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building…

Bio-inspired techniqueService (systems architecture)Security analysisIoTDependency (UML)Computer scienceNetwork securityDistributed computingmedia_common.quotation_subject0211 other engineering and technologies02 engineering and technologyMetabolic networksAttack graphs; Bio-inspired algorithms; Bio-inspired techniques; IoT; Metabolic networks; Network security; Security analysis; System securityAttack graph03 medical and health sciences0302 clinical medicineUse casemedia_common021110 strategic defence & security studiesSecurity analysisbusiness.industryMetabolic network030208 emergency & critical care medicineBio-inspired techniquesNetwork securitySystem securityFlux balance analysisInterdependenceHardware and ArchitectureBio-inspired algorithmGraph (abstract data type)businessSoftwareAttack graphsBio-inspired algorithms
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Mapreduce in computational biology - A synopsis

2017

In the past 20 years, the Life Sciences have witnessed a paradigm shift in the way research is performed. Indeed, the computational part of biological and clinical studies has become central or is becoming so. Correspondingly, the amount of data that one needs to process, compare and analyze, has experienced an exponential growth. As a consequence, High Performance Computing (HPC, for short) is being used intensively, in particular in terms of multi-core architectures. However, recently and thanks to the advances in the processing of other scientific and commercial data, Distributed Computing is also being considered for Bioinformatics applications. In particular, the MapReduce paradigm, to…

BioinformaticSpark0301 basic medicineSettore INF/01 - InformaticaBioinformaticsProcess (engineering)Computer scienceComputer Science (all)Computational biologybioinformatics; distributed computing; hadoop; MapReduce; spark; computer science (all)Supercomputercomputer.software_genreDistributed computing03 medical and health sciences030104 developmental biologyExponential growthHadoopParadigm shiftMiddleware (distributed applications)Spark (mathematics)MapReducecomputer
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Mapreduce in computational biology via hadoop and spark

2017

Bioinformatics has a long history of software solutions developed on multi-core computing systems for solving computational intensive problems. This option suffer from some issues solvable by shifting to Distributed Systems. In particular, the MapReduce computing paradigm, and its implementations, Hadoop and Spark, is becoming increasingly popular in the Bioinformatics field because it allows for virtual-unlimited horizontal scalability while being easy-to-use. Here we provide a qualitative evaluation of some of the most significant MapReduce bioinformatics applications. We also focus on one of these applications to show the importance of correctly engineering an application to fully exploi…

BioinformaticSparkSettore INF/01 - InformaticaExploitbusiness.industryComputer scienceBioinformaticsDistributed computingScalabilityAlgorithm engineeringField (computer science)Distributed computingSoftwareAlgorithm engineering; Bioinformatics; Distributed computing; Hadoop; MapReduce; Scalability; SparkHadoopSpark (mathematics)ScalabilityData-intensive computingMapReducebusinessImplementationAlgorithm engineering
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Ancillary Services in the Energy Blockchain for Microgrids

2019

The energy blockchain is a distributed Internet protocol for energy transactions between nodes of a power system. Recent applications of the energy blockchain in microgrids only consider the energy transactions between peers without considering the technical issues that can arise, especially when the system is islanded. One contribution of the paper is, thus, to depict a comprehensive framework of the technical and economic management of microgrids in the blockchain era, considering, for the first time, the provision of ancillary services and, in particular, of the voltage regulation service. When more PV nodes are operating in the grid, large reactive power flows may appear in the branches…

BlockchainComputer science020209 energyDistributed computingdis- tributed generation02 engineering and technologyIndustrial and Manufacturing EngineeringElectric power system0202 electrical engineering electronic engineering information engineeringRemunerationElectrical and Electronic Engineeringenergy blockchainGlow- worm Swarm Optimization.Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniP2PSettore ING-INF/03 - Telecomunicazioni020208 electrical & electronic engineeringTransactive energyAC powerGridSettore ING-IND/33 - Sistemi Elettrici Per L'EnergiaControl and Systems EngineeringOptimal reactive power flowMicrogridVoltage regulationDatabase transaction
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