Real Time Dynamic Simulator for a Multi-Phase Pipeline System to Increase Production and Reduce Operational Costs

2016 ◽  
Author(s):  
G. Pagliuca ◽  
R. la Riviere ◽  
M. Le Clercq ◽  
S. van de Lisdonk ◽  
C. Macnair
Keyword(s):  
Real Time ◽  
Pipeline System ◽  
Time Dynamic ◽  
Operational Costs ◽  
Dynamic Simulator ◽  
Multi Phase

The structure of a real time dynamic simulator for training (siden)

1987 ◽  
Vol 8 (1) ◽  
pp. 49-63 ◽  
Author(s):  
B. Drozdowicz ◽  
R. Quirós ◽  
A. Schiliuk ◽  
R. Cerro
Keyword(s):  
Real Time ◽  
Time Dynamic ◽  
Dynamic Simulator

Remarks on the software for a real time dynamic simulator (SIDEN)

1987 ◽  
Vol 8 (4) ◽  
pp. 325-337 ◽  
Author(s):  
B. Drozdowicz ◽  
R. Quirós ◽  
A. Schiliuk ◽  
R. Cerro
Keyword(s):  
Real Time ◽  
Time Dynamic ◽  
Dynamic Simulator

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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