Forecasting short-term dynamics of shallow cumuli using dynamic mode decomposition

2019 ◽  
Vol 11 (5) ◽  
pp. 053704 ◽  
Author(s):  
Jeff Manning ◽  
Ross Baldick
Keyword(s):  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Short Term ◽  
Mode Decomposition

scholarly journals Streaming dynamic mode decomposition for short‐term forecasting in wind farms

Wind Energy ◽  
2021 ◽  
Author(s):  
Jaime Liew ◽  
Tuhfe Göçmen ◽  
Wai Hou Lio ◽  
Gunner Chr. Larsen
Keyword(s):  
Wind Farms ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Short Term ◽  
Mode Decomposition ◽  
Short Term Forecasting

scholarly journals Unsupervised and Generic Short-Term Anticipation of Human Body Motions

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 976
Author(s):  
Kristina Enes ◽  
Hassan Errami ◽  
Moritz Wolter ◽  
Tim Krake ◽  
Bernhard Eberhardt ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Short Term ◽  
Linear Combinations ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Short Period ◽  
The One

Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use Dynamic Mode Decomposition with delays to represent and anticipate human body motions. Exploring the influence of the number of delays on the reconstruction and prediction of various motion classes, we show that the anticipation errors in our results are comparable to or even better for very short anticipation times (<0.4 s) than a recurrent neural network based method. We perceive our method as a first step towards the interpretability of the results by representing human body motions as linear combinations of previous states and delays. In addition, compared to the neural network based methods large training times are not needed. Actually, our methods do not even regress to any other motions than the one to be anticipated and hence it is of a generic nature.

Experimental Measurement of In-Plane Rolling Nonpneumatic Tire Vibrations Using High-Speed Imaging

2019 ◽  
Vol 47 (3) ◽  
pp. 196-210
Author(s):  
Meghashyam Panyam ◽  
Beshah Ayalew ◽  
Timothy Rhyne ◽  
Steve Cron ◽  
John Adcox
Keyword(s):  
High Speed ◽  
Image Correlation ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
High Speed Imaging ◽  
Correlation Algorithm ◽  
Mode Decomposition ◽  
Series Of Experiments ◽  
Plane Deformations ◽  
Dominant Frequencies

ABSTRACT This article presents a novel experimental technique for measuring in-plane deformations and vibration modes of a rotating nonpneumatic tire subjected to obstacle impacts. The tire was mounted on a modified quarter-car test rig, which was built around one of the drums of a 500-horse power chassis dynamometer at Clemson University's International Center for Automotive Research. A series of experiments were conducted using a high-speed camera to capture the event of the rotating tire coming into contact with a cleat attached to the surface of the drum. The resulting video was processed using a two-dimensional digital image correlation algorithm to obtain in-plane radial and tangential deformation fields of the tire. The dynamic mode decomposition algorithm was implemented on the deformation fields to extract the dominant frequencies that were excited in the tire upon contact with the cleat. It was observed that the deformations and the modal frequencies estimated using this method were within a reasonable range of expected values. In general, the results indicate that the method used in this study can be a useful tool in measuring in-plane deformations of rolling tires without the need for additional sensors and wiring.

Sign in / Sign up

Export Citation Format

Share Document