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A framework to manage uncertainty in the computation of waste collection routes after a flood / Arnaud Le Guilcher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2021 (July 2021)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2021 (July 2021) . - pp 61 - 68 Titre : A framework to manage uncertainty in the computation of waste collection routes after a flood Type de document : Article/Communication Auteurs : Arnaud Le Guilcher , Auteur ; Sofiane Martel, Auteur ; Mickaël Brasebin , Auteur ; Yann Méneroux , Auteur Année de publication : 2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 4, 24th ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice on-line France OA Annals Commission 4 Article en page(s) : pp 61 - 68 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] cadre conceptuel [Termes IGN] calcul d'itinéraire [Termes IGN] catastrophe naturelle [Termes IGN] collecte des déchets [Termes IGN] discrétisation spatiale [Termes IGN] incertitude géométrique [Termes IGN] inondation [Termes IGN] programmation stochastique [Termes IGN] variable aléatoire Résumé : (auteur) In this paper, we describe a framework to find a good quality waste collection tour after a flood, without having to solve a complicated optimization problem from scratch in limited time. We model the computation of a waste collection tour as a capacitated routing problem, on the vertices or on the edges of a graph, with uncertain waste quantities and uncertain road availability. Multiple models have been conceived to manage uncertainty in routing problems, and we build on the ideas of discretizing the uncertain parameters and computing master solutions that can be adapted to propose an original method to compute efficient solutions. We first introduce our model for the progressive removal of the uncertainty, then outline our method to compute solutions: our method first considers a low-dimensional set of random variables that govern the behaviour of the problem parameters, discretizes these variables and computes a solution for each discrete point before the flood, and then uses these solutions as a basis to build operational solutions when there are enough information about the parameters of the routing problem. We then give computational tools to implement this method. We give a framework to compute the basis of solutions in an efficient way, by computing all the solutions simultaneously and sharing information (that can lead to good quality solutions) between the different problems based on how close their parameters are, and we also describe how real solutions can be derived from this basis. Our main contributions are our model for the progressive removal of uncertainty, our multi-step method to compute efficient solutions, and our intrusive framework to compute solutions on the discrete grid of parameters. Numéro de notice : A2021-316 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-4-2021-61-2021 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2021-61-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97946 [article]

Stability problems for stochastic models: Theory and applications / Alexander Zeifman (2021)  

Public Titre : Stability problems for stochastic models: Theory and applications Type de document : Monographie Auteurs : Alexander Zeifman, Éditeur scientifique ; Victor Korolev, Éditeur scientifique ; Alexander Sipin, Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 370 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-0365-0453-7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] chaîne de Markov [Termes IGN] intégrale de Laplace [Termes IGN] modèle stochastique [Termes IGN] probabilités [Termes IGN] programmation stochastique [Termes IGN] variable aléatoire Résumé : (éditeur) The aim of this Special Issue of Mathematics is to commemorate the outstanding Russian mathematician Vladimir Zolotarev, whose 90th birthday will be celebrated on February 27th, 2021. The present Special Issue contains a collection of new papers by participants in sessions of the International Seminar on Stability Problems for Stochastic Models founded by Zolotarev. Along with research in probability distributions theory, limit theorems of probability theory, stochastic processes, mathematical statistics, and queuing theory, this collection contains papers dealing with applications of stochastic models in modeling of pension schemes, modeling of extreme precipitation, construction of statistical indicators of scientific publication importance, and other fields. Note de contenu : 1- A Generalized equilibrium transform with application to error boundsin the Renyi theorem with so support constraints 2- Approximations in performance analysis of a controllable queueing system with heterogeneous server 3- Accumulative pension schemes with various decrement factors 4- A priority queue with many customer types, correlated arrivals and changing priorities 5- Highly efficient robust and stable M-estimates of location 6- Local limit theorem for the multiple power series distributions 7- Multivariate scale-mixed stable distributions and related limit theorems 8- On convergence rates of some limits 9- Optimal filtering of Markov jump processes given observations with state-dependent noises: Exact solution and stable numerical schemes 10- On the fractional wave equation 11- Probability models and statistical tests for extreme precipitation based on generalized negative binomial distributions 12- Rates of convergence in Laplace’s integrals and sums and conditional central limit theorems 13- Sensitivity analysis and simulation of a multiserver queueing system with mixed tervice time distribution 14- Statistical indicators of the scientific publications importance: A stochastic model and critical look 15- Second order expansions for high-dimension low-sample-size data statistics in random setting 16- Two approaches to the construction of perturbation bounds for Ccontinuous-time Markov chains 17- The calculation of the density and distribution functions of strictly stable laws 18- Wavelet thresholding risk estimate for the model with random samples and correlated noise Numéro de notice : 28620 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0453-7 En ligne : https://doi.org/10.3390/books978-3-0365-0453-7 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99519

Advances and applications in deep learning / Marco Antonio Aceves-Fernandez (2020)  

Public Titre : Advances and applications in deep learning Type de document : Monographie Auteurs : Marco Antonio Aceves-Fernandez, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 122 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-1-83962-879-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] constante diélectrique [Termes IGN] méthode fondée sur le noyau [Termes IGN] programmation stochastique [Termes IGN] temps réel [Termes IGN] vision par ordinateur Résumé : (auteur) Artificial Intelligence (AI) has attracted the attention of researchers and users alike and is taking an increasingly crucial role in our modern society. From cars, smartphones, and airplanes to medical equipment, consumer applications, and industrial machines, the impact of AI is notoriously changing the world we live in. In this context, Deep Learning (DL) is one of the techniques that has taken the lead for cognitive processes, pattern recognition, object detection, and machine learning, all of which have played a crucial role in the growth of AI. As such, this book examines DL applications and future trends in the field. It is a useful resource for researchers and students alike. Note de contenu : 1- Advancements in deep learning theory and applications: Perspective in 2020 and beyond 2- Advances in convolutional neural networks 3- Transfer learning and deep domain adaptation 4- Deep learning enabled nanophotonics 5- Explainable artificial intelligence (xAI) approaches and deep meta-learning models 6- Dynamic decision-making for stabilized deep learning software platforms Numéro de notice : 28565 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87786 En ligne : https://doi.org/10.5772/intechopen.87786 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97647

SpiNNaker: A spiking neural network architecture / Steve Furber (2020)  

Public Titre : SpiNNaker: A spiking neural network architecture Type de document : Monographie Auteurs : Steve Furber, Éditeur scientifique ; Petrut Bogdan, Éditeur scientifique Editeur : Boston, Delft : Now publishers Année de publication : 2020 Importance : 352 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-68083-652-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] cerveau [Termes IGN] outil logiciel [Termes IGN] programmation stochastique [Termes IGN] puce [Termes IGN] réseau neuronal convolutif [Termes IGN] système de traitement de l'information [Termes IGN] vision par ordinateur Résumé : (éditeur) 20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come. Note de contenu : 1- Origins 2- The SpiNNaker Chip 3- Building SpiNNaker Machines 4- Stacks of Software Stacks 5- Applications - Doing Stuff on the Machine 6- From Activations to Spikes 7- Learning in Neural Networks 8- Creating the Future Numéro de notice : 25978 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie DOI : 10.1561/9781680836523 En ligne : http://dx.doi.org/10.1561/9781680836523 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96705

Optimization in control applications / Guillermo Valencia-Palomo (2019)  

Public Titre : Optimization in control applications Type de document : Monographie Auteurs : Guillermo Valencia-Palomo, Éditeur scientifique ; Francisco Ronay Lopez-Estrada, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 256 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03897-448-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Mathématique [Termes IGN] modèle mathématique [Termes IGN] optimisation (mathématiques) [Termes IGN] programmation stochastique Résumé : (auteur) Mathematical optimization is the selection of the best element in a set with respect to a given criterion. Optimization has become one of the most-used tools in modern control theory for computing the control law, adjusting the controller parameters (tuning), model fitting, and finding suitable conditions in order to fulfill a given closed-loop property, among others. In the simplest case, optimization consists of maximizing or minimizing a function by systematically choosing input values from a valid input set and computing the function value. Nevertheless, real-world control systems need to comply with several conditions and constraints that have to be taken into account in the problem formulation—these represent challenges in the application of the optimization algorithms.The aim of this Special Issue is to offer the state-of-the-art of the most advanced optimization techniques (online and offline) and their applications in control engineering.] Note de contenu : 1- Rapid solution of optimal control problems by a functional spreadsheet paradigm: A practical method for the non-programme 2- Novel spreadsheet direct method for optimal control problems 3- Time needed to control an epidemic with restricted resources in SIR model with short-term controlled population: A fixed point method for a free isoperimetric optimal control problem 4- Optimal strategies for psoriasis treatment 5- Optimal control analysis of a mathematical model for breast cancer 6- Cost-effective analysis of control strategies to reduce the prevalence of cutaneous leishmaniasis, based on a mathematical model 7- Optimal control and computational method for the resolution of isoperimetric problem in a discrete-time SIRS system 8- Solution of optimal harvesting problem by finite difference approximations of size-structured population model 9- Solution of fuzzy differential equations using fuzzy Sumudu transforms 10- A simple spectral observer 11- Differential evolution algorithm for multilevel assignment problem: A case study in chicken transportation 12- Modeling and simulation of a hydraulic network for leak diagnosis Numéro de notice : 28503 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03897-448-2 En ligne : https://doi.org/10.3390/books978-3-03897-448-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97002

Human mobility semantics analysis : a probabilistic and scalable approach / Xiaohui Guo in Geoinformatica, vol 22 n° 3 (July 2018)  

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Determining the appropriate timing of the next forest inventory: incorporating forest owner risk preferences and the uncertainty of forest data quality / Kyle J. Eyvindson in Annals of Forest Science, vol 74 n° 1 (March 2017)  

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Integrating risk preferences in forest harvest scheduling / Kyle J. Eyvindson in Annals of Forest Science, vol 73 n° 2 (June 2016)  

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Stochastic dynamic programming / Kjetil Kåre Haugen (2016)  

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A stochastic method for the generation of optimized building-layouts respecting urban regulation / Shuang He (oct 2014)    

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LIBRJMCMC: an open-source generic C++ library for stochastic optimization / Mathieu Brédif (2012)  

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Global optimization of core station networks for space geodesy: application to the referencing of the SLR EOP with respect to ITRF / David Coulot in Journal of geodesy, vol 84 n° 1 (January 2010)  

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A geometric stochastic approach based on marked point processes for road mark detection from high resolution aerial images / Olivier Tournaire in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 6 (November - December 2009)  

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Kalman filtering, theory and practice using MATLAB / Mohinder S. Grewal (2008)

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A new computationally efficient stochastic approach for building reconstruction from satellite data / Florent Lafarge (2008)    

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