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1. |
Record Nr. |
UNINA990004088980403321 |
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Autore |
De Armas, Frederick Alfred |
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Titolo |
The return of Astraea : an astral-imperial myth in Calderon / Frederick A. De Armas |
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Pubbl/distr/stampa |
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Lexington (Kentucky) : The University press of Kentucky, 1986 |
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ISBN |
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Descrizione fisica |
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Collana |
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Studies in Romance languages ; 32 |
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Locazione |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910158674503321 |
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Autore |
Obergrießer Mathias |
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Titolo |
Digitale Werkzeuge zur integrierten Infrastrukturbauwerksplanung : Am Beispiel des Schienen- und Straßenbaus / / von Mathias Obergrießer |
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Pubbl/distr/stampa |
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Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Vieweg, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (X, 245 S. 148 Abb., 16 Abb. in Farbe.) |
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Disciplina |
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Soggetti |
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Construction |
Applied mathematics |
Engineering mathematics |
Civil engineering |
Basics of Construction |
Mathematical and Computational Engineering |
Civil Engineering |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Einführung eines modellgestützten Planungsprozesses im Infrastrukturbau -- Beschreibung geometrischer und parametrisch-assoziativer Modellierungsansätze -- Definition eines infrastruktur-spezifischen Modellierungsleitfadens -- Konzepte zur Umsetzung des parametrisch-assoziativen Infrastrukturinformationsmodells. |
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Sommario/riassunto |
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Der Autor entwickelt neue digitale Werkzeuge und Methoden, die eine durchgängige und integrierte Planung einer Infrastrukturmaßnahme anhand eines föderierten Modells ermöglichen. Dabei werden verschiedene Lösungsansätze vorgestellt, die eine Erweiterung der traditionellen Planungsprozesse vorsehen. Mathias Obergrießer fasst diese Methoden und digitalen Werkzeuge zu einem leistungsfähigen Modellierungsleitfaden zusammen, der eine effektive Planung des parametrisch-assoziativen Infrastrukturinformationsmodells erlaubt. Die erfolgreiche Validierung des Leitfadens erfolgt anhand verschiedener Anwendungsbeispiele aus der Praxis. Der Inhalt Einführung eines modellgestützten Planungsprozesses im |
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Infrastrukturbau Beschreibung geometrischer und parametrisch-assoziativer Modellierungsansätze Definition eines infrastruktur-spezifischen Modellierungsleitfadens Konzepte zur Umsetzung des parametrisch-assoziativen Infrastrukturinformationsmodells Die Zielgruppen Dozierende und Studierende des Bauingenieurwesens Praktiker und Praktikerinnen in Ingenieurbüros Der Autor Mathias Obergrießer ist seit 2008 an der Hochschule Regensburg als Lehrbeauftragter tätig. Seine Forschungsschwerpunkte liegen im Bereich der parametrisch-assoziativen 3D-Infrastrukturinformationsmodellierung sowie in der Integration von geotechnischen Planungsprozessen. Seit 2014 ist er hauptverantwortlicher Tragwerksplaner für Infrastrukturbauwerke im einem deutschen Ingenieurbüro. |
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3. |
Record Nr. |
UNINA9910917797503321 |
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Autore |
Yau Stephen S. T |
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Titolo |
Principles of Nonlinear Filtering Theory / / by Stephen S.-T. Yau, Xiuqiong Chen, Xiaopei Jiao, Jiayi Kang, Zeju Sun, Yangtianze Tao |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (477 pages) |
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Collana |
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Algorithms and Computation in Mathematics, , 2512-3254 ; ; 33 |
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Altri autori (Persone) |
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ChenXiuqiong |
JiaoXiaopei |
KangJiayi |
SunZeju |
TaoYangtianze |
YauStephen S. T |
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Disciplina |
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Soggetti |
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Stochastic processes |
Automatic control |
Differential equations |
Equacions diferencials |
Processos estocàstics |
Stochastic Processes |
Control and Systems Theory |
Differential Equations |
Llibres electrònics. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Preface -- I. Preliminary knowledge -- 1. Probability theory -- 2. Stochastic processes -- 3. Stochastic differential equations -- 4. Optimization -- II. Filtering theory -- 5. The filtering equations -- 6. Estimation algebra -- III. Numerical algorithms -- 7. Yau-Yau algorithm -- 8. Direct methods -- 9. Classical filtering methods -- 10. Estimation algorithms based on deep learning. |
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Sommario/riassunto |
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This text presents a comprehensive and unified treatment of nonlinear filtering theory, with a strong emphasis on its mathematical underpinnings. It is tailored to meet the needs of a diverse readership, including mathematically inclined engineers and scientists at both graduate and post-graduate levels. What sets this book apart from other treatments of the topic is twofold. Firstly, it offers a complete treatment of filtering theory, providing readers with a thorough understanding of the subject. Secondly, it introduces updated methodologies and applications that are crucial in today’s landscape. These include finite-dimensional filters, the Yau-Yau algorithm, direct methods, and the integration of deep learning with filtering problems. The book will be an invaluable resource for researchers and practitioners for years to come. With a rich historical backdrop dating back to Gauss and Wiener, the exposition delves into the fundamental principles underpinning the estimation of stochastic processes amidst noisy observations—a critical tool in various applied domains such as aircraft navigation, solar mapping, and orbit determination, to name just a few. Substantive exercises and examples given in each chapter provide the reader with opportunities to appreciate applications and ample ways to test their understanding of the topics covered. An especially nice feature for those studying the subject independent of a traditional course setting is the inclusion of solutions to exercises at the end of the book. The book is structured into three cohesive parts, each designed to build the reader's understanding of nonlinear filtering theory. In the first part, foundational concepts from probability theory, stochastic processes, stochastic differential equations, and optimization are introduced, providing readers with the necessary mathematical background. The second part delves into theoretical aspects of filtering theory, covering topics such as the stochastic partial differential equation governing the posterior density function of the state, and the estimation algebra theory of systems with finite-dimensional filters. Moving forward, the third part of the book explores numerical algorithms for solving filtering problems, including the Yau-Yau algorithm, direct methods, classical filtering algorithms like the particle filter, and the intersection of filtering theory with deep learning. |
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