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Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesProceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures : EUROSTRUCT 2021. An automated machine learning-based approach for structural novelty detection based on SHM / Nicolas Manzini (2022)
Titre de série : Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures : EUROSTRUCT 2021 Titre : An automated machine learning-based approach for structural novelty detection based on SHM Type de document : Article/Communication Auteurs : Nicolas Manzini, Auteur ; Ndeye Mar, Auteur ; Franziska Schmidt, Auteur ; Jean-François Bercher, Auteur ; André Orcesi, Auteur ; Pierre Marchand, Auteur ; Julien Gazeaux Editeur : Springer Nature Année de publication : 2022 Collection : Lecture Notes in Civil Engineering num. 200 Projets : 2-Pas d'info accessible - article non ouvert / Importance : pp 1180 - 1189 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] détection d'anomalie
[Termes IGN] ouvrage d'art
[Termes IGN] pont
[Termes IGN] régression multiple
[Termes IGN] réseau de capteurs
[Termes IGN] résidu
[Termes IGN] surveillance d'ouvrageRésumé : (auteur) One major goal of structural health monitoring (SHM) is to detect, and possibly locate, quantify or predict damage on structures. Without detailed knowledge of structural mechanical behavior, data analysis is a complex task and operational monitoring is often limited to the use of more or less arbitrary thresholds. Data-driven techniques, which rely on a statistical analysis of data, have encountered a growing interest over the past two decades. In parallel, SHM is now increasingly considered for several types of structures with the development of low-cost sensors and IoT. In this context, this paper proposes an approach based on multiple automated machine learning-based models for novelty detection and location in monitoring data. This study focuses on the monitoring of large structures with multiple sensors. For each sensor, multiple regression models (based on neural networks) are generated using the same training set, with various input data: internal temperature, environmental conditions, or data from other sensors deployed on the structure. Anomalies are then identified in the dataset based on residuals between model outputs and in situ data. For a given sensor, residuals of all models are then compiled to produce an anomaly indicator. This paper presents some of the results obtained on data acquired from the monitoring of a large concrete bridge. Some anomalies are simulated and added to the dataset to demonstrate the detection performance of the proposed approach. Numéro de notice : C2022-001 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-030-91877-4_134 Date de publication en ligne : 12/12/2021 En ligne : https://doi.org/10.1007/978-3-030-91877-4_134 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99378
Titre : Climate-smart forestry in mountain regions Type de document : Monographie Auteurs : Roberto Tognetti, Éditeur scientifique ; Melanie Smith, Éditeur scientifique ; Pietro Panzacchi, Éditeur scientifique Editeur : Springer Nature Année de publication : 2021 Collection : Managing Forest Ecosystem num. 40 Importance : 587 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-80767-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] croissance des arbres
[Termes IGN] Europe (géographie politique)
[Termes IGN] filière bois - forêt
[Termes IGN] foresterie
[Termes IGN] forêt alpestre
[Termes IGN] gestion forestière durable
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] service écosystémique
[Termes IGN] surveillance forestière
[Termes IGN] sylviculture
[Termes IGN] télédétection
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (éditeur) This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools. Note de contenu : 1- An introduction to climate-smart forestry in mountain regions
2- Defining climate-smart forestry
3- Assessment of indicators for climate smart management in mountain forests
4- National forest inventory data to evaluate climate-smart forestry
5- Effcacy of trans-geographic observational network design for revelation of growth pattern in mountain forests across Europe
6- Changes of tree and stand growth: Review and implications
7- Modelling future growth of mountain forests under changing environments
8- Climate smart-sylviculture in mountain regions
9- Smart harvest operations and timber processing for improved forest management
10- Continuous monitoring of tree responses to climate change for smart forestry: A cybernetic web of trees
11- Remote sensing technologies for assessing climate-smart criteria in mountain forests
12- Economic and social perspective of climate-smart forestry: Incentives for behavioral change to climate-smart practices in the long term
13- Assessing the economic impacts of climate change on mountain forests: A literature review
14- Review of policy instruments for climate-smart mountain forestry
15- The role of forests in climate change mitigation: The EU context
16- Smartforests Canada: A network of monitoring plots for forest management under environmental change
17- Climate smart-forestry in BrazilNuméro de notice : 28498 Affiliation des auteurs : non IGN Thématique : FORET Nature : Recueil / ouvrage collectif DOI : 10.1007/978-3-030-80767-2 En ligne : https://doi.org/10.1007/978-3-030-80767-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99299
Titre : Urban Informatics Type de document : Monographie Auteurs : Wenzhong Shi, Éditeur scientifique ; Michael F. Goodchild, Éditeur scientifique ; Michael Batty, Éditeur scientifique ; et al., Auteur Editeur : Springer Nature Année de publication : 2021 Collection : The Urban Book Series Importance : 941 p. ISBN/ISSN/EAN : 978-981-1589836-- Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Urbanisme
[Termes IGN] données massives
[Termes IGN] infrastructure urbaine de données localisées
[Termes IGN] mobilité urbaine
[Termes IGN] planification urbaine
[Termes IGN] pollution
[Termes IGN] sécurité publique
[Termes IGN] système d'information géographique
[Termes IGN] ville durable
[Termes IGN] ville intelligenteRésumé : (éditeur) This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity. Note de contenu : 1- Introduction
2- Dimensions of Urban Science
3- Urban Systems and ApplicationsNuméro de notice : 28559 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Monographie DOI : 10.1007/978-981-15-8983-6 En ligne : https://link.springer.com/book/10.1007/978-981-15-8983-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97592
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] contrainte d'objet
[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
Titre : Introduction to scientific programming with Python Type de document : Guide/Manuel Auteurs : Joakim Sundnes, Auteur Editeur : Springer Nature Année de publication : 2020 Importance : 157 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-50356-7 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] équation polynomiale
[Termes IGN] interface graphique
[Termes IGN] programmation adaptée à l'objet
[Termes IGN] Python (langage de programmation)Résumé : (éditeur) This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies. Note de contenu : 1- Getting started with Python
2- Computing with formulas
3- Loops and lists
4- Functions and branching
5- User input and error handling
6- Arrays and plotting
7- Dictionaries and strings
8- Classes
9- Object-oriented programmingNuméro de notice : 28555 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Manuel informatique DOI : 10.1007/978-3-030-50356-7 En ligne : https://doi.org/10.1007/978-3-030-50356-7 Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97444 Nonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou · (2020)
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