Search results for "Neural Networks"

showing 10 items of 599 documents

An Improved Load Flow Method for MV Networks Based on LV Load Measurements and Estimations

2019

A novel measurement approach for power-flow analysis in medium-voltage (MV) networks, based on load power measurements at low-voltage level in each secondary substation (SS) and only one voltage measurement at the MV level at primary substation busbars, was proposed by the authors in previous works. In this paper, the method is improved to cover the case of temporary unavailability of load power measurements in some SSs. In particular, a new load power estimation method based on artificial neural networks (ANNs) is proposed. The method uses historical data to train the ANNs and the real-time available measurements to obtain the load estimations. The load-flow algorithm is applied with the e…

Artificial neural networksBusbarComputer sciencepower system measurement020208 electrical & electronic engineeringArtificial neural networks (ANNs)power system managementpower measurementFlow method02 engineering and technologypower system measurementsload flow (LF)Power (physics)Control theoryload flowsmart grids0202 electrical engineering electronic engineering information engineeringstate estimationElectrical and Electronic Engineeringsmart gridInstrumentationSettore ING-INF/07 - Misure Elettriche E ElettronicheVoltage
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Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

Artificial neural networks; Chaotic oscillators; Granger causality; Multivariate time series analysis; Network physiology; Penalized regression techniques; Remote synchronization; State-space models; Stochastic gradient descent L1; Vector autoregressive modelGeneral Computer ScienceDynamical systems theoryComputer science02 engineering and technologyChaotic oscillatorsPenalized regression techniquesNetwork topologySettore ING-INF/01 - ElettronicaMultivariate time series analysisVector autoregression03 medical and health sciences0302 clinical medicineScientific Computing and Simulation0202 electrical engineering electronic engineering information engineeringRepresentation (mathematics)Optimization Theory and ComputationNetwork physiologyState-space modelsArtificial neural networkArtificial neural networksData ScienceTheory and Formal MethodsQA75.5-76.95Stochastic gradient descent L1Granger causality State-space models Vector autoregressive model Artificial neural networks Stochastic gradient descent L1 Multivariate time series analysis Network physiology Remote synchronization Chaotic oscillators Penalized regression techniquesRemote synchronizationStochastic gradient descentAutoregressive modelAlgorithms and Analysis of AlgorithmsVector autoregressive modelElectronic computers. Computer scienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causality020201 artificial intelligence & image processingGradient descentAlgorithm030217 neurology & neurosurgeryPeerJ Computer Science
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Traitement de données RGB et Lidar à extrêmement haute résolution: retombées de la compétition de fusion de données 2015 de l'IEEE GRSS - Partie A / …

2016

International audience; In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the sci…

Atmospheric Science010504 meteorology & atmospheric sciencesComputer scienceMULTIMODAL-DATA FUSIONGeophysics. Cosmic physics0211 other engineering and technologies02 engineering and technologyCONTESTcomputer.software_genre01 natural sciencesOutcome (game theory)LIDARTraitement des imagesIMAGE ANALYSIS AND DATA FUSION (IADF)DEEP NEURAL NETWORKSDeep neural networksTraitement du signal et de l'imageMULTIRESOLUTION910 Geography & travelMultiresolutionGround truthLANDCOVER CLASSIFICATIONIMAGE AERIENNE1903 Computers in Earth SciencesBenchmarkingVision par ordinateur et reconnaissance de formesOcean engineering10122 Institute of GeographyLidarDeep neural networksData miningExtremely high spatial resolutionMultimodal-data fusionLiDARComputers in Earth Sciences; Atmospheric ScienceImage analysis and data fusion (IADF)EXTREMELY HIGH SPATIAL RESOLUTIONCLASSIFICATIONTRAITEMENT IMAGE1902 Atmospheric ScienceAPPRENTISSAGE STATISTIQUEComputers in Earth SciencesTELEDETECTIONSynthèse d'image et réalité virtuelleTC1501-1800021101 geological & geomatics engineering0105 earth and related environmental sciencesLandcover classificationmultiresolution-[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]QC801-809Intelligence artificielleMULTISOURCESensor fusionRGB color modelcomputerMultisource
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The use of steel angles for the connection of laminated glass beams: Experiments and modelling

2012

Abstract In the present paper the experimental results relative to three-point bending tests on multilayer glass beams and on semi-rigid connections realised with stainless double web angles are presented and discussed. Small and medium size glass beams were tested and load–deflection curves and crack patterns at failure were recorded. The laminated glass specimens, of equal cross-section, were characterised by three different combinations of annealed float and fully thermally tempered glass plies and different interlayers. Steel joints constituted by double web angles to connect two glass beams were tested adopting several geometrical configurations and using stainless steel bolts preloade…

Bearing capacityMaterials scienceStainleConnection (vector bundle)Settore ICAR/10 - Architettura TecnicaToughened glassBuilding and ConstructionBendingPhysics::Classical PhysicsCondensed Matter::Disordered Systems and Neural NetworksFlexural responseSettore ICAR/09 - Tecnica Delle CostruzioniGlass memberBrittlenessFlexural strengthSteel angleMultilayerGlaGeneral Materials ScienceBearing capacityComposite materialLaminated glassCivil and Structural EngineeringStress concentrationConstruction and Building Materials
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Anergy in self-directed B lymphocytes from a statistical mechanics perspective

2012

The ability of the adaptive immune system to discriminate between self and non-self mainly stems from the ontogenic clonal-deletion of lymphocytes expressing strong binding affinity with self-peptides. However, some self-directed lymphocytes may evade selection and still be harmless due to a mechanism called clonal anergy. As for B lymphocytes, two major explanations for anergy developed over three decades: according to "Varela theory", it stems from a proper orchestration of the whole B-repertoire, in such a way that self-reactive clones, due to intensive interactions and feed-back from other clones, display more inertia to mount a response. On the other hand, according to the `two-signal …

Biological Physics (physics.bio-ph)FOS: Biological sciencesCell Behavior (q-bio.CB)FOS: Physical sciencesQuantitative Biology - Cell BehaviorDisordered Systems and Neural Networks (cond-mat.dis-nn)Physics - Biological PhysicsCondensed Matter - Disordered Systems and Neural Networks
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Experimental study of electrical FitzHugh-Nagumo neurons with modified excitability

2006

International audience; We present an electronical circuit modelling a FitzHugh-Nagumo neuron with a modified excitability. To characterize this basic cell, the bifurcation curves between stability with excitation threshold, bistability and oscillations are investigated. An electrical circuit is then proposed to realize a unidirectional coupling between two cells, mimicking an inter-neuron synaptic coupling. In such a master-slave configuration, we show experimentally how the coupling strength controls the dynamics of the slave neuron, leading to frequency locking, chaotic behavior and synchronization. These phenomena are then studied by phase map analysis. The architecture of a possible ne…

BistabilityComputer scienceCognitive NeuroscienceModels Neurological[MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS][ NLIN.NLIN-CD ] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][ MATH.MATH-DS ] Mathematics [math]/Dynamical Systems [math.DS]ChaoticPhase mapAction PotentialsSynchronizationTopologyElectronic neuronsSynaptic Transmission01 natural sciencesSynchronization010305 fluids & plasmaslaw.inventionBiological ClocksArtificial IntelligencelawControl theoryOscillometry0103 physical sciencesmedicineAnimals010306 general physicsElectronic circuitNeuronsArtificial neural networkQuantitative Biology::Neurons and Cognition[SCCO.NEUR]Cognitive science/Neuroscience[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsCoupling (electronics)medicine.anatomical_structureNonlinear DynamicsElectrical network[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][ SCCO.NEUR ] Cognitive science/NeuroscienceChaosBifurcationSynaptic couplingNeural Networks ComputerNeuron
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Deep Neural Networks for Prediction of Exacerbations of Patients with Chronic Obstructive Pulmonary Disease

2018

Chronic Obstructive Pulmonary Disease (COPD) patients need help in daily life situations as they are burdened with frequent risks of acute exacerbation and loss of control. An automated monitoring system could lead to timely treatments and avoid unnecessary hospital (re-)admissions and home visits by doctors or nurses. Therefore we present a Deep Artificial Neural Networks for approach prediction of exacerbations, particularly Feed-Forward Neural Networks (FFNN) for classification of COPD patients category and Long Short-Term Memory (LSTM), for early prediction of COPD exacerbations and subsequent triage. The FFNN and LSTM models are trained on data collected from remote monitoring of 94 pa…

COPDmedicine.medical_specialty020205 medical informaticsExacerbationArtificial neural networkbusiness.industryDeep learningHealth conditionPulmonary disease02 engineering and technologymedicine.diseaseTriage03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineeringMedicineDeep neural networks030212 general & internal medicineArtificial intelligencebusinessIntensive care medicine
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Diffusion processes with ultrametric jumps

2007

Abstract In the theory of spin glasses the relaxation processes are modelled by random jumps in ultrametric spaces. One may argue that at the border of glassy and nonglassy phases the processes combining diffusion and jumps may be relevant. Using the Dirichlet form technique we construct a model of diffusion on the real line with jumps on the Cantor set. The jumps preserve the ultrametric feature of a random process on unit ball of 2-adic numbers.

Cantor setUnit sphereDirichlet formStochastic processMathematical analysisStatistical and Nonlinear PhysicsRelaxation (approximation)Diffusion (business)Condensed Matter::Disordered Systems and Neural NetworksReal lineUltrametric spaceMathematical PhysicsMathematicsReports on Mathematical Physics
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Review on Higher-Order Neural Units to Monitor Cardiac Arrhythmia Patterns

2017

An electrocardiogram (ECG) is a non-invasive technique that checks for problems with the electrical activity of a patient’s heart. ECG is economical and extremely versatile. Some of its characteristics make it a very useful tool to detect cardiac pathologies. The ECG records a series of characteristic waves called PQRST; however, the QRS complex analysis enables the detection of a type of arrhythmia in an ECG. Technological developments enable the storage of a large amount of data, from which knowledge extraction is impossible without a powerful data processing tool; in particular, an adequate signal processing tool, whose output provides reliable parameters as a basis to make a precise cli…

Cardiac arrhythmiaspattern detectionhigher-order neural unitsrecurrent neural networks
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New fitting scheme to obtain effective potential from Car-Parrinello molecular dynamics simulations: Application to silica

2008

A fitting scheme is proposed to obtain effective potentials from Car-Parrinello molecular dynamics (CPMD) simulations. It is used to parameterize a new pair potential for silica. MD simulations with this new potential are done to determine structural and dynamic properties and to compare these properties to those obtained from CPMD and a MD simulation using the so-called BKS potential. The new potential reproduces accurately the liquid structure generated by the CPMD trajectories, the experimental activation energies for the self-diffusion constants and the experimental density of amorphous silica. Also lattice parameters and elastic constants of alpha-quartz are well-reproduced, showing th…

Car–Parrinello molecular dynamicsMaterials sciencemolecular dynamics calculations (Car-Parrinello) and other numerical simulationsTransferabilityGeneral Physics and AstronomyFOS: Physical sciences02 engineering and technologyglasses01 natural sciencesMolecular physicsMolecular dynamicsLattice (order)0103 physical sciences[PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn]010306 general physicsdensity functional theoryCondensed Matter - Materials Sciencegradient and other correctionsMaterials Science (cond-mat.mtrl-sci)Disordered Systems and Neural Networks (cond-mat.dis-nn)computer simulation of liquid structureCondensed Matter - Disordered Systems and Neural Networks021001 nanoscience & nanotechnologylocal density approximation[PHYS.COND.CM-MS]Physics [physics]/Condensed Matter [cond-mat]/Materials Science [cond-mat.mtrl-sci]Amorphous silica0210 nano-technologyPair potential
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