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Titre : Real time coupled network failure modelling and visualisation Type de document : Article/Communication Auteurs : Neil Harris, Auteur ; Craig Robson, Auteur ; Stuart Barr, Auteur ; Philip James, Auteur Editeur : Leeds [Royaume-Uni] : University of Leeds Année de publication : 2015 Conférence : GISRUK 2015, 23th GIS Research UK annual conference 15/04/2015 17/04/2015 Leeds Royaume-Uni open access proceedings Importance : pp 246 - 251 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spatio-temporelle
[Termes IGN] logiciel libre
[Termes IGN] modèle de simulation
[Termes IGN] Python (langage de programmation)
[Termes IGN] réseau routier
[Termes IGN] temps réel
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) This paper, presents an approach to real-time spatio-temporal analysis of infrastructure network performance by developing an open source geovisualisation tool coupled with infrastructure network failure models in order to simulate, visualise and analyse how spatial infrastructure networks respond over time to major perturbations and failures. Numéro de notice : C2015-047 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83861 Documents numériques
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Real time coupled network failure modellingAdobe Acrobat PDF
Titre : Regression Modeling Strategies : With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis Type de document : Monographie Auteurs : Frank E. Harrell Jr., Auteur Editeur : Springer International Publishing Année de publication : 2015 Importance : 582 p. Format : 18 x 26 cm ISBN/ISSN/EAN : 978-3-319-19425-7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] Bootstrap (statistique)
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] R (langage)
[Termes IGN] régression linéaire
[Termes IGN] régression logistiqueRésumé : (éditeur) This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty, and its effects on inference, to achieve "safe data mining." It also presents many graphical methods for communicating complex regression models to non-statisticians. Regression Modeling Strategies presents full-scale case studies of non-trivial datasets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation, and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalized least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models, and the Cox semiparametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. Note de contenu : Introduction
1- General Aspects of Fitting Regression Models
2- Missing Data
3- Multivariable Modeling Strategies
4- Describing, Resampling, Validating, and Simplifying the Model
5- R Software
6- Modeling Longitudinal Responses using Generalized Least Squares
7- Case Study in Data Reduction
8- Overview of Maximum Likelihood Estimation
9- Binary Logistic Regression
10- Case Study in Binary Logistic Regression, Model Selection and Approximation: Predicting Cause of Death
11- Logistic Model Case Study 2: Survival of Titanic Passengers
12- Ordinal Logistic Regression
13- Case Study in Ordinal Regression, Data Reduction, and Penalization
14- Regression Models for Continuous Y and Case Study in Ordinal Regression
15- Transform-Both-Sides Regression
16- Introduction to Survival Analysis
17- Parametric Survival Models
18- Case Study in Parametric Survival Modeling and Model Approximation
19- Cox Proportional Hazards Regression Model
20- Case Study in Cox RegressionNuméro de notice : 25813 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-19425-7 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95076 A student's guide to Python for physical modeling / Jesse M. Kinder (2015)
Titre : A student's guide to Python for physical modeling Type de document : Guide/Manuel Auteurs : Jesse M. Kinder, Auteur ; Philip Nelson, Auteur Editeur : Princeton (New Jersey) : Princeton University Press Année de publication : 2015 Importance : 139 p. Format : 21 x 26 cm ISBN/ISSN/EAN : 978-0-691-16958-3 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] distribution de Poisson
[Termes IGN] Python (langage de programmation)
[Termes IGN] traitement d'image
[Termes IGN] visualisation de donnéesIndex. décimale : 26.04 Langages informatiques Résumé : (Editeur) Python is a computer programming language that is rapidly gaining popularity throughout the sciences. A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. This tutorial focuses on fundamentals and introduces a wide range of useful techniques, including: - Basic Python programming and scripting - Numerical arrays - Two- and three-dimensional graphics - Monte Carlo simulations - Numerical methods, including solving ordinary differential equations - Image processing - Animation. Numerous code samples and exercises--with solutions--illustrate new ideas as they are introduced. Web-based resources also accompany this guide and include code samples, data sets, and more. Note de contenu : 1. Getting started with Python
2. Structure and control
3. Data in, results out
4. First computer lab
5. More Python constructions
6. Second computer lab
7. Still more techniques
8. Third computer lab
A. Installing Python
B. Errors and error messages
C. Python 2 versus Python 3
D. Under the hood
E. Answers to "Your turn" questionsNuméro de notice : 22480 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel informatique Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80716 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 22480-01 26.04 Manuel Informatique Centre de documentation Informatique Disponible
Titre : UML @ Classroom : an Introduction to Object-Oriented Modeling Type de document : Guide/Manuel Auteurs : Martina Seidl, Auteur ; Marion Scholz, Auteur ; Christian Huemer, Auteur ; Gerti Kappel, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2015 Collection : Undergraduate Topics in Computer Science UTICS, ISSN 2197-1781 Importance : 206 p. ISBN/ISSN/EAN : 978-3-319-12742-2 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] conception orientée objet
[Termes IGN] diagramme
[Termes IGN] langage de programmation
[Termes IGN] UMLRésumé : (Editeur) This textbook mainly addresses beginners and readers with a basic knowledge of object-oriented programming languages like Java or C#, but with little or no modeling or software engineering experience – thus reflecting the majority of students in introductory courses at universities. Using UML, it introduces basic modeling concepts in a highly precise manner, while refraining from the interpretation of rare special cases. After a brief explanation of why modeling is an indispensable part of software development, the authors introduce the individual diagram types of UML (the class and object diagram, the sequence diagram, the state machine diagram, the activity diagram, and the use case diagram), as well as their interrelationships, in a step-by-step manner. The topics covered include not only the syntax and the semantics of the individual language elements, but also pragmatic aspects, i.e., how to use them wisely at various stages in the software development process. To this end, the work is complemented with examples that were carefully selected for their educational and illustrative value. Overall, the book provides a solid foundation and deeper understanding of the most important object-oriented modeling concepts and their application in software development. An additional website offers a complete set of slides to aid in teaching the contents of the book, exercises and further e-learning material. Note de contenu :
- Introduction
- A Short Tour of UML
- The Use Case Diagram
- The Class Diagram
- The State Machine Diagram
- The Sequence Diagram
- The Activity Diagram
- All Together Now
- Further TopicsNuméro de notice : 26283 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel informatique DOI : 10.1007/978-3-319-12742-2 En ligne : https://doi.org/10.1007/978-3-319-12742-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94937 Publishing deep web geographic data / Helena Piccinini in Geoinformatica, vol 18 n° 4 (October 2014)
[article]
Titre : Publishing deep web geographic data Type de document : Article/Communication Auteurs : Helena Piccinini, Auteur ; Marco A. Casanova, Auteur ; Luiz André P.P. Leme, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 769-792 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données localisées
[Termes IGN] langage naturel (informatique)
[Termes IGN] RDF
[Termes IGN] SPARQL
[Termes IGN] web des donnéesRésumé : (Auteur) This article introduces a design process, called W-RayS, to describe Deep Web geographic data and to publish the descriptions both on the Web of Data and on the Surface Web. The article also outlines a toolkit that supports the process and discusses an experiment in which the toolkit was used to publish data stored in a large map server. Briefly, to describe geographic data in vector format, the designer should first specify views over the underlying geographic database that capture the basic characteristics of the geographic objects and their topological relationships represented in the vector data. The same idea is applied to raster data, but using a gazetteer or any other geographic database that covers the same area as the raster data. Then, the designer should map the view definitions to an RDF schema, following the Linked Data principles. The descriptions of the geographic data are therefore formalized as sets of RDF triples synthesized from the conventional data. To publish geographic data descriptions on the Web of Data, the designer may decide to materialize the RDF triples and store them in a repository or create a SPARQL endpoint to access the triples on demand. To publish geographic data descriptions on the Surface Web, W-RayS offers the designer tools to transform the RDF triples to natural language sentences, organized as static Web pages with embedded RDFa. The inclusion of RDFa preserves the structure of the data and allows more specific queries, processed by engines that analyze Web pages with RDFa. Numéro de notice : A2014-462 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-013-0201-3 Date de publication en ligne : 15/02/2014 En ligne : https://doi.org/10.1007/s10707-013-0201-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74034
in Geoinformatica > vol 18 n° 4 (October 2014) . - pp 769-792[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-2014041 RAB Revue Centre de documentation En réserve L003 Disponible Bases de données NO SQL et SIG : d'un existant restreint à un avenir prometteur / Christian Carolin in Géomatique expert, n° 100 (01/09/2014)PermalinkDéveloppement et exploitation d'un produit de type "image solide". Application à l'analyse géostructurale des ouvrages rocheux de la SNCF / Antoine Gozé in XYZ, n° 140 (septembre - novembre 2014)PermalinkImpact du changement climatique sur les sécheresses en Bretagne. Automatisation d’un bilan hydrique avec ArcGis et Python / Chloé Lamy in Revue internationale de géomatique, vol 24 n° 3 (septembre - novembre 2014)PermalinkPython pour les néophytes (10) : plus de géomatique / Anonyme in Géomatique expert, n° 99 (01/07/2014)PermalinkPython pour les néophytes (9) : traitement d'image (1) / Anonyme in Géomatique expert, n° 98 (01/06/2014)PermalinkSLIDER: Software for LongItudinal Data Exploration with R / Hadrien Commenges in Cybergeo, European journal of geography, n° 2014 ([01/06/2014])PermalinkPython pour les néophytes (8) : bibliothèques (2) / Anonyme in Géomatique expert, n° 97 (01/03/2014)PermalinkAlgorithmique et programmation en Java / V. Granet (2014)PermalinkPermalinkPermalinkPermalinkInitiation à l'algorithmique et à la programmation en C / Rémy Malgouyres (2014)PermalinkMise en place d'un catalogue de données et de services géographiques / Grégory Pain (2014)PermalinkPython pour les néophytes (7) : les entrées-sorties / Anonyme in Géomatique expert, n° 96 (01/01/2014)PermalinkPermalinkPermalinkRetour aux sources / Anonyme in Géomatique expert, n° 96 (01/01/2014)PermalinkThe PyQGIS programmer's guide / Gary Sherman (2014)PermalinkPermalinkUML 2, de l’apprentissage à la pratique [Cours et exercices] / Laurent Audibert (2014)Permalink