Search results for "Neural Networks"

showing 10 items of 599 documents

Research and implementation of artificial neural networks models for high velocity oxygen fuel thermal spraying

2020

In the high velocity oxygen fuel (HVOF) spray process, the coating properties are sensitive to the characteristics of in-flight particles, which are mainly determined by the process parameters. Due to the complex chemical and thermodynamic reactions during the deposition procedure, obtaining a comprehensive multi-physical model or analytical analysis of the HVOF process is still a challenging issue. This study proposes to develop a robust methodology via artificial neural networks (ANN) to solve this problem for the HVOF sprayed NiCr-Cr3C2 coatings under different operating parameters.First, 40 sets of HVOF spray experiments were conducted and the coating properties were tested for analysis…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Cr3C2-NiCrArtificial intelligenceArtificial neural networksRéseaux de neurones artificielsHvofIntelligence artificielle[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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Optimisation et implémentation de méthodes bio-inspirées d'extraction de caractéristiques pour la reconnaissance d'objets visuels

2016

Industry has growing needs for so-called “intelligent systems”, capable of not only ac-quire data, but also to analyse it and to make decisions accordingly. Such systems areparticularly useful for video-surveillance, in which case alarms must be raised in case ofan intrusion. For cost saving and power consumption reasons, it is better to perform thatprocess as close to the sensor as possible. To address that issue, a promising approach isto use bio-inspired frameworks, which consist in applying computational biology modelsto industrial applications. The work carried out during that thesis consisted in select-ing bio-inspired feature extraction frameworks, and to optimize them with the aim t…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Bio-inspiréApprentissage automatiqueIntelligence artificielle[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Descripteurs[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]EmbarquéAlgorithm-architecture matching[ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM]Vision par ordinateurMachine learningRéseaux de neuronesComputer vision[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]OptimisationsFPGANeural networks[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
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High third and second order non linearities of chalcogenide glasses and fibers for compact infrared non linear devices.

2008

Due to their intrinsic nature, chalcogenide glasses present attractive nonlinearities from third and second order, with values reaching between 10 and 1000 times those of silica. We present a study of their properties and their shaping with the purpose to reach efficient devices in the near-mid infrared.

[PHYS.PHYS.PHYS-OPTICS] Physics [physics]/Physics [physics]/Optics [physics.optics]Materials scienceOptical fiberOptical glassChalcogenideInfraredPhysics::Optics02 engineering and technologyCondensed Matter::Disordered Systems and Neural Networks01 natural scienceslaw.invention010309 opticschemistry.chemical_compoundOpticslaw0103 physical sciencesComputingMilieux_MISCELLANEOUS[CHIM.MATE] Chemical Sciences/Material chemistry[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics][ PHYS.PHYS.PHYS-OPTICS ] Physics [physics]/Physics [physics]/Optics [physics.optics]business.industrySecond-harmonic generationOrder (ring theory)[CHIM.MATE]Chemical Sciences/Material chemistry021001 nanoscience & nanotechnologyNonlinear systemchemistry[ CHIM.MATE ] Chemical Sciences/Material chemistryOptoelectronics0210 nano-technologybusinessRefractive index
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Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products

2008

International audience; This paper evaluates the performances of a neural network approach to estimate LAI from CYCLOPES and MODIS nadir normalized reflectance and LAI products. A data base was generated from these products over the BELMANIP sites during the 2001-2003 period. Data were aggregated at 3 km x 3 km, resampled at 1/16 days temporal frequency and filtered to reject outliers. VEGETATION and MODIS reflectances show very consistent values in the red, near infrared and short wave infrared bands. Neural networks were trained over part of this data base for each of the 6 MODIS biome classes to retrieve both MODIS and CYCLOPES LAI products. Results show very good performances of neural …

[SPI.OTHER]Engineering Sciences [physics]/OtherMean squared errorBiome0211 other engineering and technologiesSoil Science02 engineering and technologyNEURAL NETWORKSStandard deviationALBEDONadirComputers in Earth SciencesLeaf area indexLEA021101 geological & geomatics engineeringRemote sensingMathematicsCYCLOPESGeology04 agricultural and veterinary sciencesVegetation15. Life on landCONSISTENCY OF PRODUCTSRESEAU DE NEURONESMODISTemporal resolutionOutlier040103 agronomy & agriculture0401 agriculture forestry and fisheriesVEGETATIONLEAF AREA INDEX
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influence of raw data analysis for the use of neural networks for wind farm productivity prediction

2011

In the last decade wind energy had a strong growth because of cost effectiveness of the technology and the high remunerative of investments. The increase of wind power penetration in power grids, however, makes necessary the development of instruments for prediction of productivity of a wind farm. This paper presents a study dealing with the capability of neural network to forecast short term production of a wind farm by the correlation of wind and energy production data. Available measures of wind parameters were related to productivity data of a real wind farm. Also wind data not strictly related to the site have been used in order to assess their possible influence on the production. Aft…

artificial neural networks multi layer perceptron wind data wind energy production
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Domain‐specific neural networks improve automated bird sound recognition already with small amount of local data

2022

1. An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades, progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. 2. In this article, we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentatio…

bio-monitoringeläinten äänetEcological ModelingMODELSautonomous recording unitsdeep learningsyväoppiminenneuroverkotbird sound recognitionRECORDERSddc:bioacousticshavainnotkoneoppiminen1181 Ecology evolutionary biologyconvolutional neural networksmodel fine-tuninglinnutddc:630tunnistaminenEcology Evolution Behavior and Systematics
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Computer Programming Aptitude Test as a Tool for Reducing Student Attrition

2015

Submitted to the VTR conference to be held in Rezekne, June 2015

business.industryComputer sciencemedia_common.quotation_subjectdata analysisComputer programmingaptitude test; attrition rate; computer science education; data analysisaptitude testmedicine.diseaseField (computer science)Test (assessment)attrition rateAction planComputingMilieux_COMPUTERSANDEDUCATIONmedicineMathematics educationcomputer science educationAttritionAptitudebusinessDropout (neural networks)media_commonEnvironment. Technology. Resources. Proceedings of the International Scientific and Practical Conference
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Monte Carlo simulation of the glass transition in polymeric systems: Recent developments

1995

Abstract The bond fluctuation model on square and s.c. lattices is used as a coarse-grained model for flexible polymers in dense melts. Using an energy that favours long bonds, a conflict is created between the tendency of the bonds to stretch at low temperatures and packing constraints. This simple concept of ‘geometric frustration’ leads to glass transition. Both static and dynamic properties of this model are investigated by Monte Carlo simulations, paying attention to effects found by varying the cooling rate and the chain length N of the polymers. In two and three spatial dimensions an effective (cooling-rate dependent) glass transition temperature T g can be defined, where the system …

chemistry.chemical_classificationChemistryGeneral Chemical Engineeringmedia_common.quotation_subjectMonte Carlo methodGeneral Physics and AstronomyThermodynamicsFrustrationPolymerCondensed Matter::Disordered Systems and Neural NetworksSquare (algebra)Chain lengthCooling rateDiffusion (business)Glass transitionmedia_commonPhilosophical Magazine B
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Polymer Films in the Normal-Liquid and Supercooled State: A Review of Recent Monte Carlo Simulation Results

2000

This paper reviews recent Monte Carlo simulation studies of the glassy behavior in thin polymer films. The simulations employ a version of the bond-fluctuation lattice model, in which the glass transition is driven by the competition between a stiffening of the polymers and their dense packing in the melt. The melt is geometrically confined between two impenetrable walls separated by distances ranging from once to about fifteen times the bulk radius of gyration. The confinement influences static and dynamic properties of the films: Chains close to the wall preferentially orient parallel to it. This orientation tendency propagates through the film and leads to a layer structure at low temper…

chemistry.chemical_classificationLattice model (finance)Materials scienceCondensed matter physicsMonte Carlo methodRelaxation (NMR)FOS: Physical sciencesGyration tensorSurfaces and InterfacesPolymerDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Soft Condensed MatterCondensed Matter - Disordered Systems and Neural NetworksCondensed Matter::Soft Condensed MatterColloid and Surface ChemistrychemistryRadius of gyrationSoft Condensed Matter (cond-mat.soft)Physical and Theoretical ChemistryGlass transitionSupercooling
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Disorder Classification of the Vibrational Spectra of Modern Glasses

2021

Using the coherent-potential approximation in heterogeneous-elasticity theory with a log-normal distribution of elastic constants for the description of the Raman spectrum and the temperature dependence of the specifi?c heat, we are able to reconstruct the vibrational density of states and characteristic descriptors of the elastic heterogeneity of a wide range of glassy materials. These descriptors are the non-affi?ne contribution to the shear modulus, the mean-square fluctuation of the local elasticity, and its correlation length. They enable a physical classification scheme for disorder in modern, industrially relevant glass materials. We apply our procedure to a broad range of real-world…

chemistry.chemical_classificationMaterials scienceCondensed matter physicsChalcogenidePolymerElasticity (physics)Condensed Matter::Disordered Systems and Neural NetworksPoisson's ratioShear modulussymbols.namesakechemistry.chemical_compoundFragilitychemistryPosition (vector)symbolsRaman spectroscopy
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