Reconfigurable Control of Morphing Air Vehicle Based on Real-time Dynamic Modeling

2014 ◽  
Vol 11 (17) ◽  
pp. 6151-6166
Author(s):  
Chao Zhang
Keyword(s):  
Real Time ◽  
Dynamic Modeling ◽  
Reconfigurable Control ◽  
Time Dynamic ◽  
Air Vehicle

Real-time dynamic modeling of hydrogen PEMFCs

2008 ◽  
Vol 345 (2) ◽  
pp. 182-203 ◽  
Author(s):  
Y.H. Hung ◽  
P.H. Lin ◽  
C.H. Wu ◽  
C.W. Hong
Keyword(s):  
Real Time ◽  
Dynamic Modeling ◽  
Time Dynamic

Dynamic Modeling of a Flexible Cantilever Beam: An Experimental Technique

2003 ◽  
Author(s):  
S. M. Megahed ◽  
A. A. Nada
Keyword(s):  
Real Time ◽  
Cantilever Beam ◽  
Dynamic Modeling ◽  
Measurement Techniques ◽  
Beam Theory ◽  
Time Estimation ◽  
Beam Deflection ◽  
Strain Gauges ◽  
Time Dynamic ◽  
Polynomial Curve

This paper describes an investigation of real time dynamic modeling method for a flexible cantilever beam, as an approximation of a robot flexible link, using experimental measurement techniques. The beam is equipped with a number of strain gauges to measure the corresponding nodal flexibilities and using them to obtain its real-time dynamic model. Two methods are proposed; the 1st method is based on approximating the beam deflection by a 4th order polynomial curve while the 2nd one is based on the solution of its Eulerbernoulli equation. The polynomial constants are obtained by real-time estimation using the readings of the strain gauges. These readings are used to estimate the beam temporal solution in the 2nd method. The error in the beam tip deflection is reduced to 0.01% by relocating the last strain gauge at 12.5% instead of 30% from its tip. Both methods show good agreement with the general beam theory and results.

Hierarchical Bayesian continuous time dynamic modeling.

2018 ◽  
Vol 23 (4) ◽  
pp. 774-799 ◽  
Author(s):  
Charles C. Driver ◽  
Manuel C. Voelkle
Keyword(s):  
Dynamic Modeling ◽  
Continuous Time ◽  
Hierarchical Bayesian ◽  
Time Dynamic

Control Model Constructing on Real-Time Dynamic Reversible Lane in Condition of Connected Vehicle

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Lina Mao ◽  
Wenquan Li ◽  
Pengsen Hu ◽  
Guiliang Zhou ◽  
Huiting Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Control Model ◽  
Connected Vehicle ◽  
Time Dynamic

scholarly journals Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

2021 ◽  
Vol 11 (4) ◽  
pp. 1933
Author(s):  
Hiroomi Hikawa ◽  
Yuta Ichikawa ◽  
Hidetaka Ito ◽  
Yutaka Maeda
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Weight Vector ◽  
Recognition System ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Dynamic ◽  
System Input ◽  
Self Organizing ◽  
Gesture Spotting

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.

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