Search results for "Machine"

showing 10 items of 2592 documents

An empirical study on classical and community-aware centrality measures in complex networks

2021

Community structure is a ubiquitous feature in natural and artificial systems. Identifying key nodes is a fundamental task to speed up or mitigate any diffusive processes in these systems. Centrality measures aim to do so by selecting a small set of critical nodes. Classical centrality measures are agnostic to community structure, while community-aware centrality measures exploit this property. Several works study the relationship between classical centrality measures, but the relationship between classical and community-aware centrality measures is almost unexplored. In this work [1], we answer two questions: (1) How do classical and community-aware centrality measures relate? (2) What is …

[INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG][INFO] Computer Science [cs]
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Perspective-n-Learned-Point: Pose Estimation from Relative Depth

2019

International audience; In this paper we present an online camera pose estimation method that combines Content-Based Image Retrieval (CBIR) and pose refinement based on a learned representation of the scene geometry extracted from monocular images. Our pose estimation method is two-step, we first retrieve an initial 6 Degrees of Freedom (DoF) location of an unknown-pose query by retrieving the most similar candidate in a pool of geo-referenced images. In a second time, we refine the query pose with a Perspective-n-Point (PnP) algorithm where the 3D points are obtained thanks to a generated depth map from the retrieved image candidate. We make our method fast and lightweight by using a commo…

[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO]ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO][INFO]Computer Science [cs][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG][INFO] Computer Science [cs]
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Temporal Semantic Centrality for the Analysis of Communication Networks

2012

National audience; De nos jours, la compréhension des communautés en ligne devient un enjeu majeur du Web. Dans cet article nous proposons une nouvelle mesure, la Probabilité de Propagation Sémantique (SPP), qui caractérise la capacité de l'utilisateur à propager un concept sémantique à d'autres utilisateurs, d'une manière rapide et ciblée. La sémantique des messages est analysée selon une ontologie donnée. Nous utilisons cette mesure pour obtenir la Centralité Sémantique Temporelle (TSC) d'un utilisateur dans une communauté. Nous proposons et évaluons une expérimentation de cette mesure, en utilisant une ontologie et des données réelles issues du Web.

[INFO.INFO-WB] Computer Science [cs]/WebComputer scienceSemantic analysis (machine learning)[ INFO.INFO-WB ] Computer Science [cs]/Web02 engineering and technologyOntology (information science)SemanticscommunautéSemantic similarity020204 information systemsSemantic computingcommunication network0202 electrical engineering electronic engineering information engineeringréseau de communicationontologycentralitéontologieanalyse sémantiqueInformation retrieval[INFO.INFO-WB]Computer Science [cs]/WebcentralityTelecommunications networksemantic analysisMetric (mathematics)community020201 artificial intelligence & image processingCentrality
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The Influence of the feedback control of the hexapod platform of the SAAM dynamic driving simulator on neuromuscular dynamics of the drivers

2012

Multi sensorial cues (visual, auditory, haptic, inertial, vestibular, neuromuscular) [Ang2] play important roles to represent a proper sensation (objectively) and so a perception (subjectively as cognition) in driving simulators. Driving simulator aims at giving the sensation of driving as in a real case. For a similar situation, the driver has to react in the same way as in reality in terms of ‘self motion’. To enable this behavior, the driving simulator must enhance the virtual immersion of the subject in the driving situation. The subject has to perceive the motion of his own body in the virtual scene of the virtual car as he will have in a real car. For that reason, restituting the iner…

[PHYS.MECA.VIBR] Physics [physics]/Mechanics [physics]/Vibrations [physics.class-ph]Base de données [Informatique]Motion cueingModélisation et simulation [Informatique]Mécanique: Vibrations [Sciences de l'ingénieur]optimal controlAutomatique [Informatique]Robotique [Informatique][INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]EMG analysisdynamic driving simulatorsLQR[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO]open loop control[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Optimisation et contrôle [Mathématique]motion sicknessInterface homme-machine [Informatique][SPI.MECA.VIBR] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Vibrations [physics.class-ph]motion cue[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation[INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC][INFO.INFO-AU] Computer Science [cs]/Automatic Control EngineeringMotion cueing motion sickness LQR optimal control EMG analysis dynamic driving simulatorsclosed loop control
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Nonlinear sculpturing of optical pulses with normally dispersive fiber-based devices

2018

International audience; We present a general method to determine the parameters of nonlinear pulse shaping systems based on pulse propagation in a normally dispersive fiber that are required to achieve the generation of pulses with various specified temporal properties. The nonlinear shaping process is reduced to a numerical optimization problem over a three-dimensional space, where the intersections of different surfaces provide the means to quickly identify the sets of parameters of interest. We also show that the implementation of a machine-learning strategy can efficiently address the multi-parameter optimization problem being studied.

[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics]Optimization problemGeneral methodComputer scienceFiber (mathematics)AcousticsProcess (computing)02 engineering and technologynonlinear fiber opticsSpace (mathematics)01 natural sciencesPulse shapingAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic MaterialsPulse propagation010309 opticsNonlinear system020210 optoelectronics & photonicsmachine learningControl and Systems Engineering0103 physical sciences0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringInstrumentationNonlinear shaping
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Structuration spatiale des principaux poissons démersaux autour de l’île de la Réunion à partir de la formeexterne de leurs otolithes

2022

L’identification et la connaissance de la structuration spatiale de stocks sont essentielles pour étudier la dynamique des populations de poissons et ainsi gérer les pêcheries. Dans cette étude, la forme des otolithes a été employée pour comprendre la structuration des stocks des populations des principales espèces commerciales capturées à l’île de La Réunion. Un total de 1091 individus, appartenant à 9 espèces de poissons osseux bentho-pélagiques de différents compartiments d’habitats coralliens et profonds (Aphareus rutilans, Cephalopholis aurantia, Epinephelus fasciatus, Etelis carbunculus, Lutjanus kasmira, Lutjanus notatus, Pristipomoides argyrogrammicus, Pristipomoides filamentosus, V…

[SDE.BE] Environmental Sciences/Biodiversity and EcologyElliptiques de FourierLa réunionOtolithesStructures des stocksClassification[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]
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Systèmes imageurs 3D pour des applications agricoles : caractérisation de cultures et phénotypage de racines

2016

The development of the concepts of precision agriculture and viticulture since the last three decades has shown the need to use first 2D image acquisition techniques and dedicated image processing. More and more needs concern now 3D images and information. The main ideas of this chapter is thus to present some innovations of the 3D tools and methods in the agronomic domain. This chapter will particularly focus on two main subjects such as the 3D characterization of crop using Shape from Focus or Structure from Motion techniques and the 3D use for root phenotyping using rhizotron system. Results presented show that 3D information allows to better characterize crucial crop morphometric parame…

[SDE] Environmental Sciences0106 biological sciences2. Zero hungerRoot (linguistics)Focus (computing)SHAPE FROM FOCUSComputer scienceMachine vision3D reconstructionImage processing04 agricultural and veterinary sciencesPHENOTYPAGE15. Life on land01 natural sciencesData scienceDomain (software engineering)Agricultural science[SDE]Environmental Sciences040103 agronomy & agriculture0401 agriculture forestry and fisheriesStructure from motionSTRUCTURE FROM MOTIONPrecision agriculture010606 plant biology & botany
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Estimation de l’indice foliaire et de la biomasse du blé et des adventices par imagerie visible et machine learning : vers un nouvel indicateur non d…

2019

National audience; Cette étude propose d’estimer précocement par imagerie deux variables clés dans la gestion des cultures et dans la compétition culture-adventices : l’indice foliaire (LAI) et la biomasse aérienne sèche (BM). Une expérimentation a été conduite au champ pendant la phase végétative d’une culture de blé. Pour chaque peuplement (culture de blé, adventices), les taux de couverture du sol par la végétation (TCc, TCw) ont été déduits du traitement d’image basé sur une technique de machine learning. LAI et BM ont été mesurés de façon destructive. Puis, une calibration a été réalisée entre TC et LAI d’une part et entre TC et BM d’autre part. Ce travail pourrait, à terme, faciliter …

[SDE] Environmental Sciencesnuisibilitéharmfulnessbiomass[SDV]Life Sciences [q-bio][SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/AgronomyAdventiceImagerie visibleimage visible[SDV] Life Sciences [q-bio]Indice foliairemachine learning[SDE]Environmental Sciencesbiomasse[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologyadventicesvisible image[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingweed
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Analyse des réseaux trophiques et quantification des interactions

2017

Prod 2017-344e SPE équipe EA GESTAD INRA; National audience; L’importante littérature consacrée au sujet suggère une relation positive entre la biodiversité en milieu agricole et la fourniture de services écosystémiques, notamment le service de contrôle des ravageurs par leurs ennemis naturels. Cependant, cette relation n’est que statistique et de nombreux contre-exemples peuvent être trouvés. L’une des raisons principales de l’absence d’additivité des effets des ennemis naturels réside dans la complexité des réseaux d’interactions qui se mettent en place dans les communautés diversifiées. Ainsi, par exemple, des phénomènes de compétition, voire de prédation intra-guilde peuvent conduire à …

[SDV] Life Sciences [q-bio][SDE] Environmental SciencesRéseaux trophiquesdynamique des populationsmachine learninganalyses moléculaires[SDV]Life Sciences [q-bio][SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal Biology[SDV.BV] Life Sciences [q-bio]/Vegetal Biologycontrôle biologique
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Robotics for weed control: I-Weed Robot for a specific spraying

2012

International audience; To preserve environment for a sustainable agriculture, we explore the development of a new autonomous robot, called I-Weed Robot (Intelligent Weed Robot), which aims at reducing herbicides in crop fields (maize, sunflower...). Using a high precision positioning signal (RTK) to locate the robot in the field, a Kaman filter and a proportional-integral-derivative controller (PID controller) allow adjusting the orientation of the robot depending on a predefined trajectory. As for the spraying system, a camera in front of the mobile platform detects weed plants thanks to an image processing based on a crop/weed discrimination algorithm (Hough Transform). At the back a spr…

[SDV] Life Sciences [q-bio]weed controlherbicidesPrecision agriculturespraying[SDV]Life Sciences [q-bio]robotsmachine visionWeedsalgorithms[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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