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Une généralisation de la méthode de partage des poids dans le cas où la base de sondage est continue / Philippe Brion (2022)
Titre : Une généralisation de la méthode de partage des poids dans le cas où la base de sondage est continue Type de document : Article/Communication Auteurs : Philippe Brion, Auteur ; Olivier Bouriaud , Auteur ; Guillaume Chauvet, Auteur Editeur : Rennes : Université de Rennes 1 Année de publication : 2022 Projets : 1-Pas de projet / Conférence : JMS 2022, 14es Journées de méthodologie statistique de l’Insee Paris France Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes IGN] échantillonnage
[Termes IGN] estimateur
[Termes IGN] estimation statistique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] méthode de partage des poids
[Termes IGN] quantité continue
[Termes IGN] stratification
[Termes IGN] variance
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) La définition de l’unité statistique utilisée dans les enquêtes statistiques est une question difficile : les différents ”univers” enquêtés n’ont pas nécessairement une base de sondage directement utilisable, et il arrive que l’on utilise des unités à échantillonner d’une nature différente de celle des unités observées. La production d’estimations statistiques pose alors des problèmes méthodologiques complexes, qui peuvent être traités en utilisant la méthode dite du partage des poids, formalisée par Deville et Lavallée (2006). Cette méthode est basée sur les liens existant entre les deux populations : population échantillonnée et population observée. Cependant, les deux populations considérées dans cette approche sont des populations discrètes. Pour certains domaines d’étude, en particulier liés à des aspects environnementaux, la population échantillonnée est une population continue : c’est par exemple le cas des inventaires forestiers pour lesquels, fréquemment, les arbres enquêtés sont ceux situés sur des placettes dont les centres sont des points tirés de manière aléatoire dans une zone donnée. La production d’estimations statistiques à partir de l’échantillon d’arbres enquêtés pose alors des difficultés de méthode, ainsi que les calculs de variance associés. L’objet de ce papier est de procéder à une généralisation de la méthode de partage des poids au cas continu (population échantillonnée) – discret (population enquêtée), à partir de la formalisation proposée par Cordy en 1993 sur l’extension de l’estimateur de Horvitz-Thompson au tirage de points réalisé dans un univers continu. Numéro de notice : C2022-001 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : http://www.jms-insee.fr/2022/S15_3_ACTE_BOURIAUD_BRION_JMS2022.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103348 Documents numériques
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Une généralisation de la méthode de partage ... - pdf éditeurAdobe Acrobat PDF Generating GPS decoupled clock products for precise point positioning with ambiguity resolution / Shuai Liu in Journal of geodesy, vol 96 n° 1 (January 2022)
[article]
Titre : Generating GPS decoupled clock products for precise point positioning with ambiguity resolution Type de document : Article/Communication Auteurs : Shuai Liu, Auteur ; Yunbin Yan, Auteur Année de publication : 2022 Article en page(s) : n° 6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] erreur de phase
[Termes IGN] horloge du récepteur
[Termes IGN] modèle stochastique
[Termes IGN] onde porteuse
[Termes IGN] positionnement cinématique
[Termes IGN] positionnement ponctuel précis
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) Carrier-phase integer ambiguity resolution (AR) is the key to improving the positioning accuracy of precise point positioning (PPP). However, in theory, the integer property of ambiguities in PPP are destroyed due to the absorption of phase biases. In this study, we analyzed a set of clock products consisting of a code clock, phase clock and wide-lane (WL) bias based on the decoupled clock (DCK) model, to facilitate PPP AR. The determination of the datum of the receiver clock as well as ambiguity were analyzed in detail to arrive at ways to eliminate rank deficiency. To fix ambiguity at the server end, we propose an approach by rounding directly with “fixing solution” (FS) and “partial ambiguity hold” (PAH) strategies, to increase the fixing rate and avoid the biased solution resulting from ambiguity datum loss. With respect to the International GNSS Service (IGS) legacy clocks, the mean standard deviations (STDs) of the phase clock and code clock were about 0.02 and 1.05 ns respectively, while the WL bias was about 0.12 cycles. Additionally, the convergence speed and stability of the decoupled phase clock are significantly improved compared with the conventional PPP model. Experiments on PPP positioning performance were conducted using 1 week of GPS data from more than 100 stations, considering the IGS weekly solutions as a benchmark. The ambiguity-fixed PPP with decoupled clocks had almost the same accuracy as the integer-recovered clock model, but the average accuracy improvements compared with the conventional PPP model in the east, north, and up components were 59.2, 32.4, and 20.3%, respectively, in the static mode, and approximately 38.0, 26.2, and 19.2% in the kinematic mode. These results demonstrate that users can achieve ambiguity-fixed solutions and obtain high-precision positioning coordinates with our decoupled clock products. Numéro de notice : A2022-093 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01593-7 En ligne : https://doi.org/10.1007/s00190-021-01593-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99517
in Journal of geodesy > vol 96 n° 1 (January 2022) . - n° 6[article]Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)
[article]
Titre : Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles Type de document : Article/Communication Auteurs : Nico Lang, Auteur ; Nicolai Kalischek, Auteur ; John Armston, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n* 112760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] biomasse aérienne
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation bayesienne
[Termes IGN] forme d'onde
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Numéro de notice : A2022-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112760 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112760 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99495
in Remote sensing of environment > vol 268 (January 2022) . - n* 112760[article]
Titre : Metalearning : Applications to automated machine learning and data mining Type de document : Monographie Auteurs : Pavel Brazdil, Auteur ; Jan N. van Rijn, Auteur ; Carlos Soares, Auteur ; Joaquin Vanschoren, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2022 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-67024-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] chaîne de traitement
[Termes IGN] échantillonnage
[Termes IGN] modèle stochastique
[Termes IGN] ontologie
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression
[Termes IGN] science des données
[Termes IGN] série temporelleRésumé : (éditeur) This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence. Note de contenu : 1- Basic concepts and architecture
2- Advanced techniques and methods
3- Organizing and Exploiting MetadataNuméro de notice : 28698 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-67024-5 En ligne : https://doi.org/10.1007/978-3-030-67024-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100469 A method for precisely predicting satellite clock bias based on robust fitting of ARMA models / Guochao Zhang in GPS solutions, vol 26 n° 1 (January 2022)
[article]
Titre : A method for precisely predicting satellite clock bias based on robust fitting of ARMA models Type de document : Article/Communication Auteurs : Guochao Zhang, Auteur ; Songhui Han, Auteur ; Jun Ye, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] décalage d'horloge
[Termes IGN] erreur systématique interfréquence d'horloge
[Termes IGN] estimation bayesienne
[Termes IGN] international GPS service for geodynamics
[Termes IGN] série temporelle
[Termes IGN] statistique mathématique
[Termes IGN] valeur aberranteRésumé : (auteur) The precise satellite clock bias prediction is critical in improving the positioning, navigation and timing (PNT) service capabilities of the global navigation satellite system (GNSS). Due to the influence of satellite signal path and the observation environment, the satellite clock bias data usually contain outliers that heavily affect the accuracy of satellite clock bias prediction. Based on the time series ARMA model and Bayes statistical theory, we propose a method to precisely predict satellite clock bias and detect outliers in the historical sequence of satellite clock bias. At first, considering the effects of an additive outlier (AO) and innovative outlier (IO), a labeling model for robustly fitting the time series ARMA model and detecting AOs and IOs simultaneously is constructed based on the labeling method of classification variables. Second, the Bayes method for robustly fitting time series ARMA model is proposed based on the Bayes statistical theory. Furthermore, it develops an algorithm to precisely predict satellite clock bias using the Bayes method for robustly fitting the time series ARMA model mentioned above. Finally, in order to illustrate the performance of the method for precisely predicting satellite clock bias that we presented, three examples are designed based on the real GPS data come from the IGS official website, and the prediction results of the method are compared with that of original ARMA model (oARMA), quadratic polynomial model (QP) and gray model (GM). It is found that the method can precisely predict the satellite clock bias as well as accurately detect the outliers in the historical sequence. Numéro de notice : A2022-002 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01182-3 Date de publication en ligne : 20/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01182-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98827
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