Preliminary results on using artificial neural networks for security assessment (of power systems)

1991 ◽  
Vol 6 (2) ◽  
pp. 890-896 ◽  
Author(s):  
M. Aggoune ◽  
M.A. El-Sharkawa ◽  
D.C. Park ◽  
M.J. Damborg ◽  
R.J. Marks
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Systems ◽  
Security Assessment ◽  
Preliminary Results ◽  
Artificial Neural

Correction to 'Preliminary results on using artificial neural networks for security assessment' (May 91 890-896)

1991 ◽  
Vol 6 (3) ◽  
pp. 1324-1325 ◽  
Author(s):  
M. Aggoune ◽  
M.A. El-Sharkawi ◽  
D.C. Park ◽  
M.J. Damborg ◽  
R.J. Marks
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Security Assessment ◽  
Preliminary Results ◽  
Artificial Neural

Applications of Artificial Neural Networks in the Context of Power Systems

2020 ◽  
pp. 345-373
Author(s):  
Jan-Hendrik Menke ◽  
Marcel Dipp ◽  
Zheng Liu ◽  
Chenjie Ma ◽  
Florian Schäfer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Systems ◽  
Artificial Neural

scholarly journals Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter

2019 ◽  
Vol 33 ◽  
pp. 10063-10064
Author(s):  
Frank J. Wouda ◽  
Matteo Giuberti ◽  
Giovanni Bellusci ◽  
Bert-Jan F. Van Beijnum ◽  
Peter H. Veltink
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kalman Filter ◽  
Pose Estimation ◽  
Preliminary Results ◽  
State Prediction ◽  
Artificial Neural ◽  
Body Pose Estimation ◽  
Full Body

Previous research has shown that estimating full-body poses from a minimal sensor set using a trained ANN without explicitly enforcing time coherence has resulted in output pose sequences that occasionally show undesired jitter. To mitigate such effect, we propose to improve the ANN output by combining it with a state prediction using a Kalman Filter. Preliminary results are promising, as the jitter effects are diminished. However, the overall error does not decrease substantially.

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