Vor Ort
Merkzettel Warenkorb
Produktbild: Data Science in Engineering, Volume 10
- 13%

Data Science in Engineering, Volume 10 Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023

Aus der Reihe Conference Proceedings of the Society for Experimental Mechanics Series
13% sparen

231,99 € UVP 267,49 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

08.12.2023

Abbildungen

VIII, 188 p. 1 illus.

Herausgeber

Ramin Madarshahian + weitere

Verlag

Springer

Seitenzahl

188

Maße (L/B/H)

28,5/21,5/1,7 cm

Gewicht

755 g

Auflage

2023

Sprache

Englisch

ISBN

978-3-031-34945-4

Beschreibung

Portrait

Ramin Madarshahian–Company: Kount, an Equifax company, Boise, ID, USA ;Francois Hemez–Lawrence Livermore National Laboratory, Livermore, CA, USA

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

08.12.2023

Abbildungen

VIII, 188 p. 1 illus.

Herausgeber

Verlag

Springer

Seitenzahl

188

Maße (L/B/H)

28,5/21,5/1,7 cm

Gewicht

755 g

Auflage

2023

Sprache

Englisch

ISBN

978-3-031-34945-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Data Science in Engineering, Volume 10
  • Chapter 1. A Meta-Learning Approach to Population-Based Modelling of Structure.-, Chapter 2 State Space Reconstruction from Embeddings of Partial Observables in Structural Dynamic Systems for Structure-Preserving Data-Driven Methods.-, Chapter 3 Chapter 2. State Space Reconstruction from Embeddings of Partial Observables in Structural Dynamic Systems for Structure-Preserving Data-Driven Methods.-, Chapter 4 Composite Neural Network Framework for Modeling Impulsive Nonlinear Dynamic Responses.-, Chapter 5 Towards physics-based metrics for transfer learning in dynamics.-, Chapter 6 Principal Component Analysis of Monitoring Data of a High-Rise Building: The Case Study of Palazzo Lombardia.-, Chapter 7 Optimal Contact-Impact Force Model Selection for Damage Detection in Ball Bearings.-, Chapter 8 Simulation Error Influence on Damage Identification Classifiers Trained by Numerical Data.-, Chapter 9 Structural Health Monitoring in the Context ofNon-Equilibrium Phase Transitions.-, Chapter 10 Synthetic Thermal Image Data Generation using Attention-Based Generative Adversarial Network for Concrete Internal Damage Segmentation.-, Chapter 11 Optimal Fiber Optic Sensor Placement Framework for Structural Health Monitoring of an Aircraft’s Wing Spar.-, Chapter 12 Construction Noise Cancellation with Feedback Active Control using Machine Learning.-, Chapter 13 Physics-Informed Data-Driven Reduced-Order Model for Turbomachinery Blisk.-, Chapter 14 High-rate Structural Health Monitoring: Part-II Embedded System Design.-, Chapter 15 Damage Quantification under High-Rate Dynamic Loading and Data Augmentation using Generative Adversarial Network.-, Chapter16 Output-only versus Direct Input-output Structural Condition Monitoring Methods.-, Chapter 17 High-rate Structural Health Monitoring: Part-III Algorithm.-, Chapter 18 A population form via hierarchical Bayesian modelling of the FRF.-, Chapter 19 Lupos: Open-source Scientific Computing in Structural Dynamics.-, Chapter 20 Expert Knowledge-Driven Condition Assessment of Railway Welds from Axle Box Accelerations using Random Forests and Bayesian Logistic Regression.-, Chapter 21 On quantifying data normalisation via cointegration with topological methods.-, Chapter 22 Automatic Selection of Optimal Structures for Population-based Structural Health Monitoring.-, Chapter 23 Online back-propagation of recurrent neural network for forecasting nonstationary structural responses.