A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
In: 10.3390/s21134535
- Ereignis
-
Veröffentlichung
- (wo)
-
Aachen
- (wer)
-
Universitätsbibliothek der RWTH Aachen
- (wann)
-
2021
- Urheber
-
Mundt, Marion
Johnson, William R.
Potthast, Wolfgang
Markert, Bernd
Mian, Ajmal
Alderson, Jacqueline
- DOI
-
10.18154/RWTH-2021-06372
- URN
-
urn:nbn:de:101:1-2021081107053094298868
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:45 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Mundt, Marion
- Johnson, William R.
- Potthast, Wolfgang
- Markert, Bernd
- Mian, Ajmal
- Alderson, Jacqueline
- Universitätsbibliothek der RWTH Aachen
Entstanden
- 2021
Ähnliche Objekte (12)
Untersuchungen zum Stabilitätsverhalten von Osteosynthesen am Unterkiefer
On the optimal localization of pendulum impacts using a non‐contact acoustic array
Molecular dynamics simulation of ultrasonic metal welding of aluminum alloys
Numerical investigation of the Poisson's ratio of amorphous graphene sheets
Molecular Dynamics Investigation on the Effect of MWCNTs on Thermo-Mechanical Properties of Amorphous Cellulose Reinforced with Silicon Carbide Nanotubes
Auto-correlation-function-based damage index for damage detection and system identification
Modeling the thermomechanical processes and residual stresses in additive manufacturing of metallic components
Mechanical Properties of Nacre-Like Composites: A Bottom-Up Approach
A comparison of two neural network architectures for fast structural response prediction
Anomaly and damage detection for structural health monitoring based on computational intelligence
Deep learning in the finite element method
Physically constrained deep recurrent neural network for stiffness computation of plate structures
Untersuchungen zum Stabilitätsverhalten von Osteosynthesen am Unterkiefer
On the optimal localization of pendulum impacts using a non‐contact acoustic array
Molecular dynamics simulation of ultrasonic metal welding of aluminum alloys
Numerical investigation of the Poisson's ratio of amorphous graphene sheets
Molecular Dynamics Investigation on the Effect of MWCNTs on Thermo-Mechanical Properties of Amorphous Cellulose Reinforced with Silicon Carbide Nanotubes
Auto-correlation-function-based damage index for damage detection and system identification
Modeling the thermomechanical processes and residual stresses in additive manufacturing of metallic components
Mechanical Properties of Nacre-Like Composites: A Bottom-Up Approach
A comparison of two neural network architectures for fast structural response prediction
Anomaly and damage detection for structural health monitoring based on computational intelligence
Deep learning in the finite element method
Physically constrained deep recurrent neural network for stiffness computation of plate structures
Untersuchungen zum Stabilitätsverhalten von Osteosynthesen am Unterkiefer
On the optimal localization of pendulum impacts using a non‐contact acoustic array
Molecular dynamics simulation of ultrasonic metal welding of aluminum alloys
Numerical investigation of the Poisson's ratio of amorphous graphene sheets
Molecular Dynamics Investigation on the Effect of MWCNTs on Thermo-Mechanical Properties of Amorphous Cellulose Reinforced with Silicon Carbide Nanotubes
Auto-correlation-function-based damage index for damage detection and system identification
Modeling the thermomechanical processes and residual stresses in additive manufacturing of metallic components
Mechanical Properties of Nacre-Like Composites: A Bottom-Up Approach
A comparison of two neural network architectures for fast structural response prediction
Anomaly and damage detection for structural health monitoring based on computational intelligence
Deep learning in the finite element method