Search results for "informatique"

showing 10 items of 121 documents

Removing the saturation assumption in Bank-Weiser error estimator analysis in dimension three

2020

International audience; We provide a new argument proving the reliability of the Bank-Weiser estimator for Lagrange piecewise linear finite elements in both dimension two and three. The extension to dimension three constitutes the main novelty of our study. In addition, we present a numerical comparison of the Bank-Weiser and residual estimators for a three-dimensional test case.

010103 numerical & computational mathematicsResidual01 natural sciencesPiecewise linear function: Multidisciplinaire généralités & autres [C99] [Ingénierie informatique & technologie]Dimension (vector space)Bank-Weiser estimatorApplied mathematicsfinite element methodssaturation assumption0101 mathematicsReliability (statistics)Mathematicsresidual estimatorBank-WeiserestimatorApplied Mathematics: Multidisciplinary general & others [C99] [Engineering computing & technology]NoveltyEstimatorExtension (predicate logic)16. Peace & justiceFinite element methoda posteriori error estimation010101 applied mathematics: Mathematics [G03] [Physical chemical mathematical & earth Sciences]: Mathématiques [G03] [Physique chimie mathématiques & sciences de la terre][MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
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Accounting for preferential sampling in species distribution models

2019

D. C., A. L. Q. and F. M. would like to thank the Ministerio de Educación y Ciencia (Spain) for financial support (jointly financed by the European Regional Development Fund) via Research Grants MTM2013‐42323‐P and MTM2016‐77501‐P, and ACOMP/2015/202 from Generalitat Valenciana (Spain). Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a fi…

0106 biological sciencesComputer scienceQH301 BiologySpecies distributionPoint processesStochastic partial differential equation01 natural scienceshttp://aims.fao.org/aos/agrovoc/c_6774EspèceAbundance (ecology)StatisticsPesqueríasQAOriginal Researchhttp://aims.fao.org/aos/agrovoc/c_241990303 health sciencesEcologyU10 - Informatique mathématiques et statistiquesSampling (statistics)Integrated nested Laplace approximationstochastic partial differential equationVariable (computer science)symbolsÉchantillonnageSpecies Distribution Models (SDMs)Modèle mathématiqueBayesian probabilityNDASDistribution des populations010603 evolutionary biologyQH30103 medical and health sciencessymbols.namesakeCovariateQA MathematicsSDG 14 - Life Below WaterCentro Oceanográfico de Murciaspecies distribution modelsRelative species abundanceEcology Evolution Behavior and Systematicspoint processes030304 developmental biologyNature and Landscape Conservationhttp://aims.fao.org/aos/agrovoc/c_6113http://aims.fao.org/aos/agrovoc/c_7280Markov chain Monte Carlointegrated nested Laplace approximationU30 - Méthodes de rechercheBayesian modelling
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Bayesian spatio-temporal approach to identifying fish nurseries by validating persistence areas

2015

Spatial and temporal closures of fish nursery areas to fishing have recently been recognized as useful tools for efficient fisheries management, as they preserve the reproductive potential of populations and increase the recruitment of target species. In order to identify and locate potential nursery areas for spatio-temporal closures, a solid understanding of species− environment relationships is needed, as well as spatial identification of fish nurseries through the application of robust analyses. One way to achieve knowledge of fish nurseries is to analyse the persistence of recruitment hotspots. In this study, we propose the comparison of different spatiotemporal model structures to ass…

0106 biological sciencesMediterranean climatehttp://aims.fao.org/aos/agrovoc/c_28840[SDV]Life Sciences [q-bio]01 natural sciencesMediterranean seaAbundance (ecology)Ecosystem approachEcologybiologyEcologyU10 - Informatique mathématiques et statistiquesinteraction élevage environnementmodèle de distributionMerluccius merlucciushttp://aims.fao.org/aos/agrovoc/c_41529zone de pêcheNursery areasSpatio temporal analysisanalyse bayésienneGeographyGestion des pêchesgestion spatialealevinageFisheries managementFishinganalyse spatiotemporellegestion des ressources naturellesAquatic Science010603 evolutionary biologyhttp://aims.fao.org/aos/agrovoc/c_24026étude comparativeHakeMerluccius merluccius14. Life underwaterhttp://aims.fao.org/aos/agrovoc/c_4699Ecology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_12399Distribution patternapproche ecosystémiqueÉcologie marinehttp://aims.fao.org/aos/agrovoc/c_4609010604 marine biology & hydrobiologybiology.organism_classificationBiologie marineFisheryThéorie bayésiennehttp://aims.fao.org/aos/agrovoc/c_9000115M40 - Écologie aquatiqueBayesian hierarchical modellingMarine protected areaSpatial fisheries managementNursery areas;Distribution pattern;Ecosystem approach;Spatial fisheries management;Spatio temporal analysis;Bayesian hierarchical modelling;Merluccius merluccius
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Bayesian spatio-temporal discard model in a demersal trawl fishery

2014

Spatial management of discards has recently been proposed as a useful tool for the protection of juveniles, by reducing discard rates and can be used as a buffer against management errors and recruitment failure. In this study Bayesian hierarchical spatial models have been used to analyze about 440 trawl fishing operations of two different metiers, sampled between 2009 and 2012, in order to improve our understanding of factors that influence the quantity of discards and to identify their spatio-temporal distribution in the study area. Our analysis showed that the relative importance of each variable was different for each metier, with a few similarities. In particular, the random vessel eff…

0106 biological sciencesPerteSpatial correlationhttp://aims.fao.org/aos/agrovoc/c_28840Computer scienceProcess (engineering)Bayesian probabilitySede Central IEOAquatic ScienceOceanography01 natural sciencesRessource halieutiquehttp://aims.fao.org/aos/agrovoc/c_2173Abundance (ecology)Component (UML)http://aims.fao.org/aos/agrovoc/c_4438Pesquerías14. Life underwaterM11 - Production de la pêchehttp://aims.fao.org/aos/agrovoc/c_7881Ecology Evolution Behavior and SystematicsChalutageU10 - Informatique mathématiques et statistiques010604 marine biology & hydrobiologyhttp://aims.fao.org/aos/agrovoc/c_2801204 agricultural and veterinary sciencesDiscardsFisheryRessource marineVariable (computer science)Théorie bayésienneM40 - Écologie aquatique040102 fisheries0401 agriculture forestry and fisherieshttp://aims.fao.org/aos/agrovoc/c_2942Fisheries managementPêche démersale
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GenExP, un logiciel simulateur de paysages agricoles pour l'étude de la diffusion de transgènes

2007

 ; The software GENEXP allows to simulate 2-dimensional agricultural landscapes by using a traditional algorithmic geometry. Based on real or realistic field-patterns, GENEXP provides multiannual maps of agricultural landscapes, which are used by softwares simulating the dispersal of GM pollen grains and seeds at various scales.; GENEXP est un simulateur de paysages agricoles qui engendre des découpages parcellaires en utilisant une géométrie algorithmique classique. GENEXP fournit, sur la base de parcellaires réels ou réalistes, des cartes pluriannuelles de paysages agricoles utilisables par des logiciels qui simulent la dispersion des pollens et des graines d'OGM à différentes échelles.

0106 biological sciences[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]010603 evolutionary biology01 natural sciencesVORONOÏ TESSELATION[ SDV.EE ] Life Sciences [q-bio]/Ecology environmentAGRICULTURAL LANDSCAPE[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]diagrammes de Voronoi[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]DIAGRAMMES DE VORONOÏpaysage agricole[SDV.EE]Life Sciences [q-bio]/Ecology environmentFIELD PATTERN[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]voronoi tesselationPROCESSUS PONCTUEL MARKOVIEN04 agricultural and veterinary sciencesGeneral Medicineflux de genes15. Life on landsimulationPARCELLAIRE[SDV.EE] Life Sciences [q-bio]/Ecology environmentagricultural landscape field-pattern germs distribution markov point process gene flowpaysage agricole parcellaire simulation diagrammes de Voronoi distribution de germes processus ponctuel markovien flux de genes voronoi tesselation INFORMATIQUEGERMS DISTRIBUTIONINFORMATIQUE040103 agronomy & agricultureMARKOV POINT PROCESS0401 agriculture forestry and fisheriesfield-patterngene flowDISTRIBUTION DE GERMES
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Modelling sensitive elasmobranchs habitat

2013

Basic information on the distribution and habitat preferences of ecologically important species is essential for their management and protection. In the Mediterranean Sea there is increasing concern over elasmobranch species because their biological (ecological) characteristics make them highly vulnerable to fishing pressure. Their removal could affect the structure and function of marine ecosystems, inducing changes in trophic interactions at the community level due to the selective elimination of predators or prey species, competitors and species replacement. In this study Bayesian hierarchical spatial models are used to map the sensitive habitats of the three most caught elasmobranch spe…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28840Etmopterus spinaxhabitatAquatic ScienceDistribution des populationshttp://aims.fao.org/aos/agrovoc/c_38371OceanographyGaleus melastomus010603 evolutionary biology01 natural sciencesElasmobranch habitatPredationMediterranean seahttp://aims.fao.org/aos/agrovoc/c_38127http://aims.fao.org/aos/agrovoc/c_3041Scyliorhinus caniculaMediterranean SeaVulnerable speciesMarine ecosystem14. Life underwaterhttp://aims.fao.org/aos/agrovoc/c_4699Ecology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_12399Trophic levelhttp://aims.fao.org/aos/agrovoc/c_6113biologyEcologyU10 - Informatique mathématiques et statistiques010604 marine biology & hydrobiologyScyliorhinus caniculabiology.organism_classificationBiologie marinetechnique de prévisionBayesian hierarchical spatial modelSpecies distribution modelingFisheryHabitatThéorie bayésienneGaleus melastomusM40 - Écologie aquatiquehttp://aims.fao.org/aos/agrovoc/c_10566http://aims.fao.org/aos/agrovoc/c_3456http://aims.fao.org/aos/agrovoc/c_38117Elasmobranchii
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GeneSys-Beet: A model of the effects of cropping systems on gene flow between sugar beet and weed beet

2008

A weedy form of the genus Beta, i.e. Beta vulgaris ssp. vulgaris (hence ''weed beet'') frequently found in sugar beet is impossible to eliminate with herbicides because of its genetic proximity to the crop. It is presumed to be the progeny of accidental hybrids between sugar beet (ssp. vulgaris) and wild beet (ssp. maritima), or of sugar beet varieties sensitive to vernalization and sown early in years with late cold spells. In this context, genetically modified (GM) sugar beet varieties tolerant to non-selective herbicides would be interesting to manage weed beet. However, because of the proximity of the weed to the crop, it is highly probable that the herbicide-tolerance transgene would b…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_890PopulationSoil ScienceContext (language use)H60 - Mauvaises herbes et désherbageFlux de gènesGenetically modified01 natural sciencesF30 - Génétique et amélioration des planteshttp://aims.fao.org/aos/agrovoc/c_9000024Crophttp://aims.fao.org/aos/agrovoc/c_37331http://aims.fao.org/aos/agrovoc/c_34285[SDV.BV]Life Sciences [q-bio]/Vegetal Biologyhttp://aims.fao.org/aos/agrovoc/c_2018Cropping systemeducation2. Zero hungereducation.field_of_studybiologyU10 - Informatique mathématiques et statistiquesModélisation des culturesfungifood and beverages04 agricultural and veterinary sciences15. Life on landbiology.organism_classificationWeed controlGene flowTillagePratique culturalehttp://aims.fao.org/aos/agrovoc/c_8347AgronomyOrganisme génétiquement modifié040103 agronomy & agriculture0401 agriculture forestry and fisheriesSugar beetBeta vulgarisWeedAgronomy and Crop ScienceMauvaise herbeModelCropping system010606 plant biology & botanyField Crops Research
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Feasibility Analysis For Constrained Model Predictive Control Based Motion Cueing Algorithm

2019

International audience; This paper deals with motion control for an 8-degree-of-freedom (DOF) high performance driving simulator. We formulate a constrained optimal control that defines the dynamical behavior of the system. Furthermore, the paper brings together various methodologies for addressing feasibility issues arising in implicit model predictive control-based motion cueing algorithms.The implementation of different techniques is described and discussed subsequently. Several simulations are carried out in the simulator platform. It is observed that the only technique that can provide ensured closed-loop stability by assuring feasibility over all prediction horizons is a braking law t…

0209 industrial biotechnology021103 operations researchComputer scienceDriving simulationControl (management)0211 other engineering and technologiesStability (learning theory)Driving simulator02 engineering and technologyModélisation et simulation [Informatique]Motion controlOptimal control[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationAutomatique / Robotique [Sciences de l'ingénieur]Motion (physics)[SPI.AUTO]Engineering Sciences [physics]/AutomaticModel predictive controlAcceleration020901 industrial engineering & automationMotion Cueing AlgorithmAlgorithmModel Predictive Control
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Adaptive-gain extended Kalman filter: Extension to the continuous-discrete case

2009

In the present article we propose a nonlinear observer that merges the behaviors 1) of an extended Kalman filter, mainly designed to smooth off noise , and 2) of high-gain observers devoted to handle large perturbations in the state estimation. We specifically aim at continuous-discrete systems. The strategy consists in letting the high-gain self adapt according to the innovation. We define innovation computed over a time window and justify its usage via an important lemma. We prove the general convergence of the resulting observer.

0209 industrial biotechnology: Multidisciplinary general & others [C99] [Engineering computing & technology]020208 electrical & electronic engineering02 engineering and technologyKalman filterInvariant extended Kalman filter[SPI.AUTO]Engineering Sciences [physics]/Automatic: Multidisciplinaire généralités & autres [C99] [Ingénierie informatique & technologie]Extended Kalman filterNoise020901 industrial engineering & automation[SPI.AUTO] Engineering Sciences [physics]/AutomaticControl theory[ SPI.AUTO ] Engineering Sciences [physics]/AutomaticConvergence (routing)0202 electrical engineering electronic engineering information engineeringFast Kalman filterObservabilityAlpha beta filterComputingMilieux_MISCELLANEOUSMathematics
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Enhancement of Anticipatory Postural Adjustments by Virtual Reality in Older Adults with Cognitive and Motor Deficits: A Randomised Trial.

2021

Background: Postural activities involved in balance control integrate the anticipatory postural adjustments (APA) that stabilize balance and posture, facilitating arm movements and walking initiation and allowing an optimal coordination between posture and movement. Several studies reported the significant benefits of virtual reality (VR) exercises in frail older adults to decrease the anxiety of falling and to induce improvements in behavioural and cognitive abilities in rehabilitation processes. The aim of this study was thus to test the efficiency of a VR system on the enhancement of the APA period, compared to the use of a Nintendo Wii system. Methods: Frail older adults (n = 37) were i…

030506 rehabilitationAgingmedicine.medical_specialtyHealth (social science)Synthèse d'image et réalité virtuelle [Informatique]medicine.medical_treatmenteducation[INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR]Context (language use)Virtual realitypostural controlArticleHealth(social science)03 medical and health sciences0302 clinical medicinePhysical medicine and rehabilitationmedicineBalance (ability)Geriatrics[SDV.IB] Life Sciences [q-bio]/BioengineeringRehabilitationbusiness.industryRC952-954.6ingénierie bio-médicale [Sciences du vivant]Cognition[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]Test (assessment)Ageingcognitive and motor deficitsGeriatricsrehabilitation exerciseAnxietyvirtual reality[SDV.IB]Life Sciences [q-bio]/BioengineeringGeriatrics and Gerontologymedicine.symptom0305 other medical sciencebusinessGerontology030217 neurology & neurosurgeryGeriatrics (Basel, Switzerland)
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