Prediction of VLF propagation signal JJI Japan disturbed by lightning using artificial neural networks: Preliminary results

2011 ◽  
Author(s):  
Suryadi ◽  
Mardina Abdullah ◽  
Hafizah Husain
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Preliminary Results ◽  
Artificial Neural ◽  
Vlf Propagation

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.

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

A Methodology to Estimate Degradation and Reliability Based on Artificial Neural Networks

2005 ◽  
Author(s):  
Enrique A. Susemihl ◽  
Shuzhen Xu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Failure Modes ◽  
Preliminary Results ◽  
Physical Variables ◽  
Artificial Neural ◽  
Paper Insulation

Failure modes associated with degradation or ageing affect most equipment, and the estimates of failure due to them are particularly important in deciding repair or replacement. A methodology is presented here to relate physical variables with a degradation measure by using artificial neural networks to capture data and experience, and to use this degradation measure to estimate probabilities of failure. This methodology has been applied to transformers to estimate probabilities of failure due to the degradation of paper insulation, and some preliminary results are presented. These results show that the method can provide reasonable estimates.

Prescreening entire mammograms for masses with artificial neural networks: Preliminary results

1997 ◽  
Vol 4 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Barry L. Kalman ◽  
William R. Reinus ◽  
Stan C. Kwasny ◽  
Andrew Laine ◽  
Lawrence Kotner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Preliminary Results ◽  
Artificial Neural

scholarly journals EP-1820 Preliminary results of using artificial neural networks for prediction CK planning parameters

2019 ◽  
Vol 133 ◽  
pp. S987
Author(s):  
A. Skrobala ◽  
J. Ginter ◽  
B. Pawalowski ◽  
M. Skowron ◽  
M. Adamczyk ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Preliminary Results ◽  
Artificial Neural
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