Descripteur
Termes IGN > mathématiques > analyse numérique > optimisation (mathématiques)
optimisation (mathématiques)Synonyme(s)algorithme d'optimisation minimisationVoir aussi |
Documents disponibles dans cette catégorie (412)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Titre : AI based robot safe learning and control Type de document : Monographie Auteurs : Xuefeng Zhou, Auteur ; Shuai Li, Auteur ; et al., Auteur Editeur : Springer Nature Année de publication : 2020 Importance : 127 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-981-1555039-- Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau neuronal récurrent
[Termes IGN] robotique
[Termes IGN] sécurité
[Termes IGN] système de contrôle
[Termes IGN] vitesse angulaireRésumé : (éditeur) This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities. Note de contenu : 1- Adaptive Jacobian based trajectory tracking for redundant manipulators with model uncertainties in repetitive tasks
2- RNN based trajectory control for manipulators with uncertain kinematic parameters
3- RNN based adaptive compliance control for robots with model uncertainties
4- Deep RNN based obstacle avoidance control for redundant manipulators
5- Optimization-based compliant control for manipulators under dynamic obstacle constraints
6- RNN for motion-force control of redundant manipulators with optimal joint torqueNuméro de notice : 28518 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : sans En ligne : https://directory.doabooks.org/handle/20.500.12854/32049 Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97304 Asymptotically exact data augmentation : models and Monte Carlo sampling with applications to Bayesian inference / Maxime Vono (2020)
Titre : Asymptotically exact data augmentation : models and Monte Carlo sampling with applications to Bayesian inference Type de document : Thèse/HDR Auteurs : Maxime Vono, Auteur ; Nicolas Dobigeon, Directeur de thèse ; Pierre Chainais, Auteur Editeur : Toulouse : Université de Toulouse Année de publication : 2020 Importance : 200 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, Signal, Image, Acoustique et OptimisationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] échantillonnage
[Termes IGN] échantillonnage de Gibbs
[Termes IGN] estimation bayesienne
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] optimisation (mathématiques)
[Termes IGN] processus gaussien
[Termes IGN] régression linéaireIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Numerous machine learning and signal/image processing tasks can be formulated as statistical inference problems. As an archetypal example, recommendation systems rely on the completion of partially observed user/item matrix, which can be conducted via the joint estimation of latent factors and activation coefficients. More formally, the object to be inferred is usually defined as the solution of a variational or stochastic optimization problem. In particular, within a Bayesian framework, this solution is defined as the minimizer of a cost function, referred to as the posterior loss. In the simple case when this function is chosen as quadratic, the Bayesian estimator is known to be the posterior mean which minimizes the mean square error and defined as an integral according to the posterior distribution. In most real-world applicative contexts, computing such integrals is not straightforward. One alternative lies in making use of Monte Carlo integration, which consists in approximating any expectation according to the posterior distribution by an empirical average involving samples from the posterior. This so-called Monte Carlo integration requires the availability of efficient algorithmic schemes able to generate samples from a desired posterior distribution. A huge literature dedicated to random variable generation has proposed various Monte Carlo algorithms. For instance, Markov chain Monte Carlo (MCMC) methods, whose particular instances are the famous Gibbs sampler and Metropolis-Hastings algorithm, define a wide class of algorithms which allow a Markov chain to be generated with the desired stationary distribution. Despite their seemingly simplicity and genericity, conventional MCMC algorithms may be computationally inefficient for large-scale, distributed and/or highly structured problems. The main objective of this thesis consists in introducing new models and related MCMC approaches to alleviate these issues. The intractability of the posterior distribution is tackled by proposing a class of approximate but asymptotically exact augmented (AXDA) models. Then, two Gibbs samplers targetting approximate posterior distributions based on the AXDA framework, are proposed and their benefits are illustrated on challenging signal processing, image processing and machine learning problems. A detailed theoretical study of the convergence rates associated to one of these two Gibbs samplers is also conducted and reveals explicit dependences with respect to the dimension, condition number of the negative log-posterior and prescribed precision. In this work, we also pay attention to the feasibility of the sampling steps involved in the proposed Gibbs samplers. Since one of this step requires to sample from a possibly high-dimensional Gaussian distribution, we review and unify existing approaches by introducing a framework which stands for the stochastic counterpart of the celebrated proximal point algorithm. This strong connection between simulation and optimization is not isolated in this thesis. Indeed, we also show that the derived Gibbs samplers share tight links with quadratic penalty methods and that the AXDA framework yields a class of envelope functions related to the Moreau one. Note de contenu : Introduction
1- Asymptotically exact data augmentation
2- Monte Carlo sampling from AXDA
3- 3A non-asymptotic convergence analysis of the Split Gibbs sampler
4- High-dimensional Gaussian sampling: A unifying approach based on a stochastic proximal point algorithm
5- Back to optimization: The tempered AXDA envelope
ConclusionNuméro de notice : 28575 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : thèse de Doctorat : Signal, Image, Acoustique et Optimisation : Toulouse : 2020 Organisme de stage : Institut de Recherche en Informatique de Toulouse En ligne : https://tel.archives-ouvertes.fr/tel-03143936/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97833 Development of new homogenisation methods for GNSS atmospheric data. Application to the analysis of climate trends and variability / Annarosa Quarello (2020)
Titre : Development of new homogenisation methods for GNSS atmospheric data. Application to the analysis of climate trends and variability Titre original : Développement de nouvelles méthodes d'homogénéisation des données atmosphérique GNSS. Application à l'étude de la variabilité climatique Type de document : Thèse/HDR Auteurs : Annarosa Quarello , Auteur ; Olivier Bock , Directeur de thèse ; Emilie Lebarbier, Directeur de thèse Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2020 Autre Editeur : Paris : Institut de Physique du Globe de Paris IPGP Importance : 156 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] climat terrestre
[Termes IGN] données GNSS
[Termes IGN] données météorologiques
[Termes IGN] erreur systématique
[Termes IGN] estimation statistique
[Termes IGN] homogénéisation
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] méthode robuste
[Termes IGN] programmation dynamique
[Termes IGN] R (langage)
[Termes IGN] régression linéaire
[Termes IGN] segmentation
[Termes IGN] série temporelle
[Termes IGN] teneur intégrée en vapeur d'eau
[Termes IGN] variabilité
[Termes IGN] varianceIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Les séries longues de contenu intégré en vapeur d’eau (CIVE) mesurées par GNSS sont affectées par des inhomogénéités liées aux changements de l’instrumentation, de l’environnement et de la procédure de traitement des données. L’homogénéisation de ces séries est une étape cruciale pour les applications en climatologie. Du fait de la forte variabilité naturelle du CIVE, la segmentation doit être appliquée sur des différences de CIVE entre les observations GNSS et une référence qui dans notre application est actuellement la ré-analyse ERA-Interim. Nous avons développé une méthode de segmentation dédiée à la détection de changements abrupts dans la moyenne qui prend en compte un biais périodique et une variance hétérogène dans ces données. L’algorithme calcule dans un premier temps la variance mensuelle avec un estimateur robuste. Ensuite, il estime à nombre de ruptures fixe de manière itérative (i) le biais périodique et (ii) les positions des points de rupture et les moyennes du signal, pour tous les nombres de ruptures testés. Cette estimation est réalisée au sens du maximum de vraisemblance et s’appuie sur l’algorithme de programmation dynamique qui est le seul à fournir la solution exacte en un temps raisonnable. Finalement, le nombre optimal de ruptures est choisi à l’aide d’une méthode de sélection de modèle pénalisée. La méthode a été testée et optimisée à l’aide de simulations numériques et appliquée aux données de CIVE GNSS pour 120 stations du réseau IGS. Enfin, les informations de segmentation sont incluses dans un algorithme de régression linéaire qui est utilisé pour estimer les tendances. La méthode est implémentée dans le package R GNSSseg disponible sur le CRAN. Numéro de notice : 17618 Affiliation des auteurs : UMR IPGP-Géod (2020- ) Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Thèse française Note de thèse : Thèse : Environnement : IPGP : 2020 Organisme de stage : Equipe Géodésie (IPGP-IGN) nature-HAL : Thèse DOI : sans Date de publication en ligne : 29/01/2021 En ligne : https://hal.science/tel-03771164v2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96984
Titre : Exact optimization algorithms for the aggregation of spatial data Type de document : Thèse/HDR Auteurs : Johannes Oehrlein, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 862 Importance : 184 p. Format : 21 x 30 cm Note générale : bibliographie
Dissertation zur Erlangung des GradesDoktor-Ingenieur (Dr.-Ing.)
Diese Arbeit ist gleichzeitig als elektronische Dissertationbei der Universitäts-und Landesbibliothek Bonn veröffentlichtLangues : Anglais (eng) Descripteur : [Termes IGN] agrégation spatiale
[Termes IGN] cycliste
[Termes IGN] données localisées
[Termes IGN] espace vert
[Termes IGN] généralisation automatique de données
[Termes IGN] programmation linéaire
[Termes IGN] réseau routier
[Termes IGN] trajet (mobilité)
[Termes IGN] zone urbaine
[Vedettes matières IGN] GénéralisationRésumé : (auteur) The aggregation of spatial data is a recurring problem in geoinformation science. Aggregating data means subsuming multiple pieces of information into a less complex representation. It is pursued for various reasons, like having a less complex data structure to apply further processing algorithms or a simpler visual representation as targeted in map generalization. In this thesis, we identify aggregation problems dealing with spatial data and formalize themas optimization problems. That means we set up a function that is capable of evaluating valid solutions to the considered problem, like a cost function for minimization problems. To each problem introduced, we present an algorithm that finds a valid solution that optimizes this objective function. In general, this superiority with respect to the quality of the solution comes at the cost of computation efficiency, a reason why non-exact approaches like heuristics are widely used for optimization. Nevertheless, the higher quality of solutions yielded by exact approaches is undoubtedly important. On the one hand, “good” solutions are sometimes not sufficient. On the other hand, exact approaches yield solutions that maybe used as benchmarks for the evaluation of non-exact approaches. This kind of application is of particular interest since heuristic approaches, for example, give no guarantee on the quality of solutions found. Furthermore, algorithms that provide exact solutions to optimization problems reveal weak spots of underlying models. A result that does not satisfy the user cannot be excused with a mediocre performance of an applied heuristic. With this motivation, we developed several exact approaches for aggregation problems, which we present in this thesis. Since we deal with spatial data, for all problems considered, the aggregation is based on both geometric and semantic aspects although the focus varies. The first problem we discuss is about visualizing a road network in the context of navigation. Given a fixed location in the network, we aim for a clear representation of the surroundings. For this purpose, we introduce an equivalence relation for destinations in the network based on which we perform the aggregation. We succeed in designing an efficient algorithm that aggregates as many equivalent destinations as possible. Furthermore, we tackle a class of similar and frequently discussed problems concerning the aggregation of areal units into larger, connected regions. Since these problems are NP-complete, i.e. extraordinarily complex, we do not aim for an efficient exact algorithm (which is suspected not to exist) but present a strong improvement to existing exact approaches. In another setup, we present an efficient algorithm for the analysis of urban green-space supply. Performing a hypothetical assignment of citizens to available green spaces, it detects local shortages and patterns in the accessibility of green space within a city. Finally, we introduce and demonstrate a tool for detecting route preferences of cyclists based on a selection of given trajectories. Examining a set of criteria forming suitable candidates, we aggregate them efficiently to the best-fitting derivable criterion. Overall, we present exact approaches to various aggregation problems. In particular, the NP-complete problem we deal with firmly underscores, as expected, the need for heuristic approaches. For applications asking for an immediate solution, it may be reasonable to apply a heuristic approach. This holds in particular due to easy and generally applicable meta-heuristics being available. However, with this thesis, we argue for applying exact approaches if possible. The guaranteed superior quality of solutions speaks for itself. Besides, we give additional examples which show that exact approaches can be applied efficiently as well. Numéro de notice : 17681 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : PhD dissertation : : Rheinische Friedrich-Wilhelms-Universität Bonn : 2020 En ligne : https://nbn-resolving.org/urn:nbn:de:hbz:5-60713 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98023
Titre : Intelligent processing on image and optical information Type de document : Monographie Auteurs : Seakwon Yeom, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 324 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-945-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de lignes
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] détection de changement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] navigation autonome
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau antagoniste génératif
[Termes IGN] segmentation d'imageRésumé : (éditeur) This book focuses on the intelligent processing of images and optical information acquired by various imaging methods. Intelligent image and optical information processing have paved the way for the recent epoch of new intelligence and information era. Certainly, information acquired by various imaging techniques is of tremendous value; thus, an intelligent analysis of them is necessary to make the best use of it. A broad range of research fields is included in this book. Many studies focus on object classification and detection. Registration, segmentation, and fusion are performed between a series of images. Many valuable and up-to-most recent technologies are provided to solve the real problems in selected papers. Note de contenu : 1- Special issue on intelligent processing on image and optical information
2- Change detection of water resources via remote sensing: An L-V-NSCT approach
3- A texture classification approach based on the integrated optimization for parameters and features of gabor filter via hybrid ant lion optimizer
4- Real-time automated segmentation and classification of calcaneal fractures in CT images
5- Automatic zebrafish egg phenotype recognition from bright-field microscopic images using deep convolutional neural network
6- Zebrafish larvae phenotype classification from bright-field microscopic images using a two-tier deep-learning pipeline
7- Unsupervised generation and synthesis of facial images via an auto-encoder-based deep generative adversarial network
8- Detecting green mold pathogens on lemons using hyperspectral images
9- Review on computer aided weld defect detection from radiography images
10- Feature extraction with discrete non-separable shearlet transform and its application to surface inspection of continuous casting slabs
11- A novel extraction method for wildlife monitoring images with wireless multimedia sensor
networks (WMSNs)
12- IMU-aided high-frequency Lidar odometry for autonomous driving
13- Determination of the optimal state of dough fermentation in bread production by using optical sensors and deep learning
14- Multi-sensor face registration based on global and local structures
15- Multifocus image fusion using a sparse and low-rank matrix decomposition for aviator’s night vision Goggle
16- Error resilience for block compressed sensing with multiple-channel transmission
17- Image completion with hybrid interpolation in tensor representation
18- A correction method for heat wave distortion in digital image correlation measurements
based on background-oriented schlieren
19- An effective optimization method for machine learning based on ADAM
20- Boundary matching and interior connectivity-based cluster validity analysisNuméro de notice : 28438 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-945-4 En ligne : https://doi.org/10.3390/books978-3-03936-945-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98875 PermalinkPermalinkA new segmentation method for the homogenisation of GNSS-derived IWV time-series / Annarosa Quarello (2020)PermalinkOn the adjustment, calibration and orientation of drone photogrammetry and laser-scanning / Emmanuel Clédat (2020)PermalinkSimplicial complexes reconstruction and generalisation of 3d lidar data in urban scenes / Stéphane Guinard (2020)PermalinkPermalinkPermalinkIntroducing spatial regularization in SAR tomography reconstruction / Clément Rambour in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkSimulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata / Tingting Xu in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)PermalinkDecomposition of geodetic time series: A combined simulated annealing algorithm and Kalman filter approach / Feng Ming in Advances in space research, vol 64 n°5 (1 September 2019)Permalink