Search results for "Algorithm"

showing 10 items of 4887 documents

A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem

2017

A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process. peerReviewed

data-driven optimizationPareto optimalityEngineeringBlast furnaceMathematical optimizationOptimization problemmodel managementblast furnaceEvolutionary algorithm02 engineering and technologyMulti-objective optimizationIndustrial and Manufacturing Engineering020501 mining & metallurgyData-drivenironmakingoptimointi0202 electrical engineering electronic engineering information engineeringGeneral Materials Scienceta113business.industrypareto-tehokkuusMechanical EngineeringProcess (computing)metamodelingMetamodeling0205 materials engineeringmulti-objective optimizationMechanics of MaterialsPrincipal component analysis020201 artificial intelligence & image processingbusinessrautateollisuus
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Social Media as an Opinion Formulator: A Study on Implications and Recent Developments

2019

Social media has a great influence on how information reaches us and how we form an opinion based on it. For many users, social media increases the variety of information and ensure its accessibility across different platforms. However, recent years have seen an exponential increase in the power of social media. Most often, the forwarding and sharing of journalistic articles serve the recommendation from the user's point of view, but recently, these platforms have been developed into an echo chamber to transport additional information or even a critical attitude. In addition, the uncontrolled influx of social media in different parts of the world favors phenomena such as fake news and socia…

democratic societiesfake newsFacebookbusiness.industryMediation (Marxist theory and media studies)media_common.quotation_subjectSocial changeInternet privacyconnected audiencesosiaalinen mediaSincerityyleinen mielipideDemocracyVariety (cybernetics)social media.Power (social and political)demokratiavaikuttaminenPolitical scienceThe InternetSocial mediaFacebook algorithmsbusinessvaleuutisetmedia_common2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
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Temporal Denoising of Kinect Depth Data

2012

The release of the Microsoft Kinect has attracted the attention of researchers in a variety of computer science domains. Even though this device is still relatively new, its recent applications have shown some promising results in terms of replacing current conventional methods like the stereo-camera for robotics navigation, multi-camera system for motion detection and laser scanner for 3D reconstruction. While most work around the Kinect is on how to take full advantage of its capabilities, so far only a few studies have been carried out on the limitations of this device and fewer that provide solutions to enhance the precision of its measurements. In this paper, we review and analyse curr…

depth measurement[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingLaser scanning[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceNoise reductionDenoising algorithmComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingtemporal denoising0202 electrical engineering electronic engineering information engineeringComputer visionImage denoisingComputingMilieux_MISCELLANEOUS[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingbusiness.industry3D reconstruction020206 networking & telecommunicationsRoboticsMotion detectionKinect depth dataMicrosoft Kinect020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing2012 Eighth International Conference on Signal Image Technology and Internet Based Systems
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Performance of $b$-Jet Identification in the ATLAS Experiment

2016

We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT an…

detector-systems performancePerformance of High Energy Physics Detectorsecondary [vertex]Elementary particle01 natural sciencesPARTONlaw.inventionSubatomär fysikCHANNELcluster findingscattering [p p]impact parameterGeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)протон-протонные столкновенияQBLarge detector-systems performanceHigh energy physics detectorLarge Hadron ColliderLarge detector systems for particle and astroparticle physics; Large detector-systems performance; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics Detectors; Instrumentation; Mathematical Physicstrack data analysisQUARK PAIR PRODUCTIONbottom [jet]CERN LHC CollPattern recognition cluster finding calibration and fitting method7000 GeV-cmscolliding beams [p p]performanceHADRONIC COLLISIONSCiências Naturais::Ciências FísicasLarge detectorFitting methodHigh energy physicATLAS LHC High Energy Physics510 MathematicsmuonDISTRIBUTIONSUncertainty analysis Astroparticle physicHigh Energy Physics010306 general physicsSystematic uncertainties AlgorithmsAstroparticle physicsCalibration and fitting methodsScience & Technology010308 nuclear & particles physicsLarge detector systems for particle and astroparticle physicsParticle acceleratorRangingPerformance of High Energy PhysicsCOLLIDERScorrelationExperimental High Energy PhysicsPerformance of High Energy Physics DetectorshadronATLAS детекторБольшой адронный коллайдерcharm [jet]Elementary particleHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)lawSubatomic Physics[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Detectors and Experimental TechniquesInstrumentationUncertainty analysisMathematical PhysicsPhysicsPattern recognition cluster finding calibration and fitting methods4. EducationATLAS experimentSettore FIS/01 - Fisica SperimentaleDetectorsflavor [jet]calibration and fitting methodsATLASLarge Hadron ColliderLarge detector systems for particle and astroparticle physics; Large; detector-systems performance; Pattern recognition cluster finding; calibration and fitting methods; Performance of High Energy Physics; Detectors; PRODUCTION CROSS-SECTION; QUARK PAIR PRODUCTION; ROOT-S=7 TEV; PARTON; DISTRIBUTIONS; HADRONIC COLLISIONS; MATRIX-ELEMENTS; LHC; COLLIDERS; DETECTOR; CHANNEL8. Economic growthCalibrationparticle identification [bottom]LHCImpact parameterParticle Physics - ExperimentParticle physicsdata analysis method530 Physics:Ciências Físicas [Ciências Naturais]FOS: Physical sciences530MATRIX-ELEMENTSparticle identification [charm]on-line [trigger]Pattern recognition0103 physical sciencesComplementary methodddc:610DETECTORROOT-S=7 TEVCluster findingFísicaLarge detector systems for particle and astroparticle physics; Large detector-systems performance; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics DetectorsPattern recognition systemcalibrationtracksPRODUCTION CROSS-SECTIONefficiencyHadronLarge detector systems for particle and astroparticle physicLargeHigh Energy Physics::ExperimentStatistical correlationstatisticalexperimental results
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Measurement of the spin-dependent structure function g1(x) of the deuteron

1993

We report on the first measurement of the spin-dependent structure function g1d of the deuteron in the deep inelastic scattering of polarised muons off polarised deuterons, in the kinematical range 0.006<x<0.6, 1 GeV2<Q2<30 GeV2. The first moment, Γ1d=sh{phonetic}01 g1d dx=0.023±0.020 (stat.) ± 0.015 (syst.), is smaller than the prediction of the Ellis-Jaffe sum rules. Using earlier measurements of g1p, we infer the first moment of the spin-dependent neutron structure function g1n. The difference Γ1p-Γ1n=0.20 ±0.05 (stat.) ± 0.04 (syst.) agrees with the prediction of the Bjorken sum rule, Γ1p-Γ1n=0.191 ±0.002.

deuteron: polarized targetNuclear and High Energy PhysicsINELASTIC E-P; POLARIZED PROTONS; SUM-RULE; SCATTERING; ELECTROPRODUCTION; ASYMMETRYINELASTIC E-PProtonpolarized target: deuterondeep inelastic scattering: muon deuteronstructure function: spinmuon deuteron: deep inelastic scatteringSUM-RULE530Nuclear physicsINELASTIC E-P; POLARIZED PROTONS; SUM-RULE; SCATTERING; ELECTROPRODUCTION; ASYMMETRY; MODELTheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYSCATTERINGNeutronpolarized beam: muonSpin-½PhysicsQuantum chromodynamicsspin: structure functionMuonScatteringdeuteron: structure functionELECTROPRODUCTIONnucleon: structure functionCERN SPSDeep inelastic scatteringmomentmagnetic spectrometer: experimental resultsPOLARIZED PROTONSapprox. 100 GeVASYMMETRYSum rule in quantum mechanicsmuon: polarized beamParticle Physics - ExperimentPhysics Letters B
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Artificial intelligence in the diagnosis of pediatric allergic diseases.

2020

Abstract: Artificial intelligence (AI) is a field of data science pertaining to advanced computing machines capable of learning from data and interacting with the human world. Early diagnosis and diagnostics, self-care, prevention and wellness, clinical decision support, care delivery, and chronic care management have been identified within the healthcare areas that could benefit from introducing AI. In pediatric allergy research, the recent developments in AI approach provided new perspectives for characterizing the heterogeneity of allergic diseases among patients. Moreover, the increasing use of electronic health records and personal healthcare records highlighted the relevance of AI in …

diagnosisChronic care managementImmunologyClinical decision support systemField (computer science)03 medical and health sciencesSettore MED/38 - Pediatria Generale E Specialistica0302 clinical medicinechildrenArtificial IntelligenceHealth careHypersensitivityrespiratory allergyImmunology and AllergyMedicineElectronic Health RecordsHumansRelevance (information retrieval)030212 general & internal medicineChildfood allergybusiness.industryRespiratory allergyallergydiagnosi030228 respiratory systemData qualityPediatrics Perinatology and Child HealtheczemaArtificial intelligencePediatric allergybusinessDelivery of Health CareAlgorithmsPediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and ImmunologyREFERENCES
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Simultaneous Noise and Impedance Fitting to Transition-Edge Sensor Data Using Differential Evolution

2020

We discuss a robust method to simultaneously fit a complex multi-body model both to the complex impedance and the noise data for transition-edge sensors. It is based on a differential evolution (DE) algorithm, providing accurate and repeatable results with only a small increase in computational cost compared to the Levenberg–Marquardt (LM) algorithm. Test fits are made using both DE and LM methods, and the results compared with previously determined best fits, with varying initial value deviations and limit ranges for the parameters. The robustness of DE is demonstrated with successful fits even when parameter limits up to a factor of 10 from the known values were used. It is shown that the…

differential evolutiondifferentiaalievoluutiosignaalinkäsittelygeneettiset algoritmittutkimuslaitteetgenetic algorithmthermal modelanturittransition-edge sensor
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Introduction: Exploring Nordic Game Research

2014

Nordic DiGRA 2012 Conference was held at the University of Tampere on June 6-8, 2012. In this Special Issue of the Transactions of DiGRA journal, we present a selection of the best papers of that conference.

digital gamesMedia studiesLibrary scienceSociologyGame researchSelection (genetic algorithm)digitaaliset pelit
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Decentralized classification in societies of autonomous and heterogenous robots

2011

This paper addresses the classification problem for a set of autonomous robots that interact with each other. The objective is to classify agents that “behave” in “different way”, due to their own physical dynamics or to the interaction protocol they are obeying to, as belonging to different “species”. This paper describes a technique that allows a decentralized classification system to be built in a systematic way, once the hybrid models describing the behavior of the different species are given. This technique is based on a decentralized identification mechanism, by which every agent classifies its neighbors using only local information. By endowing every agent with such a local classifie…

distributed algorithm0106 biological sciencesSpecies classification0209 industrial biotechnologyEngineeringbusiness.industrymulti-robot systemInteraction protocolRoboticsMobile robot02 engineering and technologyAutonomous robotconsensus protocols010603 evolutionary biology01 natural sciencesComputer Science::Multiagent SystemsIdentification (information)020901 industrial engineering & automationSettore ING-INF/04 - AutomaticaRobotArtificial intelligenceSet (psychology)businessClassifier (UML)2011 IEEE International Conference on Robotics and Automation
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Decentralized Deployment of Mobile Sensors for Optimal Connected Sensing Coverage

2008

In this paper, we address the optimal connected sensing coverage problem, i.e., how mobile sensors with limited sensing capabilities can cooperatively adjust their locations so as to maximize the extension of the covered area while avoiding any internal “holes”, areas that are not covered by any sensor. Our solution consists in a distributed motion algorithm that is based on an original extension of the Voronoi tessellation.

distributed algorithmsSettore ING-INF/04 - AutomaticaComputer scienceSoftware deploymentDistributed computingMobile sensorMotion (geometry)Extension (predicate logic)Motion strategysensing coverageVoronoi diagramComputingMethodologies_COMPUTERGRAPHICS
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