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.

scholarly journals PDG Pressure Estimation in Offshore Oil Well: Extended Kalman Filter vs. Artificial Neural Networks

2019 ◽  
Vol 52 (1) ◽  
pp. 508-513
Author(s):  
Andressa Apio ◽  
Jônathan W.V. Dambros ◽  
Fabio C. Diehl ◽  
Marcelo Farenzena ◽  
Jorge O. Trierweiler
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Oil Well ◽  
Artificial Neural ◽  
Pressure Estimation ◽  
Offshore Oil

System identification of a robot arm with extended Kalman filter and artificial neural networks

2019 ◽  
Vol 13 (2) ◽  
pp. 135-150 ◽  
Author(s):  
Sabine Horvath ◽  
Hans Neuner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Industrial Robot ◽  
Parametric Models ◽  
Limiting Factor ◽  
General Description ◽  
Robot Arm ◽  
Artificial Neural

Abstract The development of an algorithm to describe a dynamic system and to predict its future behaviour in further consequence is the aim of the present study. Non parametric models provide a general description of object dynamics and artificial neural networks (ANN), which are a very flexible and universal learning method, belong to it. However, the standard estimation procedures for ANN like Levenberg-Marquardt (LM) do not consider that data is observed and consequently is uncertain. The combination with the extended Kalman filter (EKF) enables the consideration of the uncertainty in the estimation process. The analogies between EKF and LM are discussed and thereon the advantages of the EKF are outlined. The integration of ANN into EKF will be evaluated on an industrial robot arm. At first, a simplified model is determined; the ANN describes the robot position deviations as a function of the joint encoder values. The robot reference positions are measured by a laser tracker. In order to compare it with the robot outputs, the observations need to be transformed to the robot frame and the offset between the end-effector and the robot flange has to be determined. A method to estimate both parameters simultaneously is developed and the results are verified on basis of simulated data. This paper comprises two novel approaches. First, uncertainty is considered in the ANN estimation on basis of the combination with the EKF. Considering the full covariance matrix of the robot deviations leads to a better prediction of the robot’s behaviour. Second, an integrated transformation and lever arm determination is introduced and the robot’s repeatability presents the limiting factor of the achievable parameter uncertainty.

Estimating photovoltaic power generation: Performance analysis of artificial neural networks, Support Vector Machine and Kalman filter

2017 ◽  
Vol 143 ◽  
pp. 643-656 ◽  
Author(s):  
Raul V.A. Monteiro ◽  
Geraldo C. Guimarães ◽  
Fabricio A.M. Moura ◽  
Madeleine R.M.C. Albertini ◽  
Marcelo K. Albertini
Keyword(s):  
Neural Networks ◽  
Power Generation ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Kalman Filter ◽  
Performance Analysis ◽  
Support Vector ◽  
Photovoltaic Power ◽  
Artificial Neural ◽  
Power Generation Performance

EXTENDED KALMAN FILTER (EKF) AND ARTIFICIAL NEURAL NETWORKS (ANN) FOR ESTIMATE OF CONCENTRATION IN A NON ISOTHERMIC CSTR OF PRODUCTION OF PROPYLENE GLYCOL

2017 ◽  
Author(s):  
Marcelo da Silva Pedro ◽  
Walter Yanko de Aragão Brandão ◽  
Paulo Romero de Araujo Mariz ◽  
Emanuella Francisca de Lacerda Vieira ◽  
Jéssica Oliveira de Brito Lira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kalman Filter ◽  
Propylene Glycol ◽  
Extended Kalman Filter ◽  
Artificial Neural

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
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