scholarly journals KalmanTune: A Kalman Filter Based Tuning Method to Make Boosted Ensembles Robust to Class-Label Noise

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145887-145897
Author(s):  
Arjun Pakrashi ◽  
Brian Mac Namee
Keyword(s):  
Kalman Filter ◽  
Class Label ◽  
Label Noise ◽  
Tuning Method

scholarly journals Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 108331-108342
Author(s):  
Rui Yang ◽  
Li-Yi Li ◽  
Ming-Yi Wang ◽  
Cheng-Ming Zhang ◽  
Yi-Ming Zeng-Gu
Keyword(s):  
Kalman Filter ◽  
Extended State ◽  
Tuning Method ◽  
Force Ripple

A two-stage ensemble method for the detection of class-label noise

2018 ◽  
Vol 275 ◽  
pp. 2374-2383 ◽  
Author(s):  
Maryam Sabzevari ◽  
Gonzalo Martínez-Muñoz ◽  
Alberto Suárez
Keyword(s):  
Ensemble Method ◽  
Class Label ◽  
Two Stage ◽  
Label Noise

scholarly journals Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series

2017 ◽  
Vol 9 (2) ◽  
pp. 173 ◽  
Author(s):  
Charlotte Pelletier ◽  
Silvia Valero ◽  
Jordi Inglada ◽  
Nicolas Champion ◽  
Claire Marais Sicre ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Satellite Image ◽  
Land Cover Mapping ◽  
Class Label ◽  
Label Noise ◽  
Training Class

scholarly journals Sensorless Control of Permanent Magnet Synchronous Motors and EKF Parameter Tuning Research

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yong Zhang ◽  
Xu-Feng Cheng
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Permanent Magnet ◽  
Extended Kalman Filter ◽  
Simulation Analysis ◽  
Sensorless Control ◽  
Parameter Tuning ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Tuning Method

This paper concerns the parameter tuning and the estimated results postprocessing of the extended Kalman filter for the sensorless control application of permanent magnet synchronous motors. At first an extended Kalman filter parameter tuning method is proposed based on the theoretical and simulation analysis of extended Kalman filter parameters. Furthermore, a sensorless control system is proposed based on the parameter tuning method and the simulation analysis of extended Kalman filter estimation results in different reference speeds and different load torques. The proposed sensorless control system consists of two parts. The first one is a module to self-regulate extended Kalman filter parameters. The second part can correct the estimated speed and the estimated rotation angle based on the reference speed and the electromagnetic torque. Finally, simulation results are presented to verify the feasibility and validity of the proposed sensorless control system.

The Effects of Class Label Noise on Highly-Imbalanced Big Data

2021 ◽  
Author(s):  
Robert K. L. Kennedy ◽  
Justin M. Johnson ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Big Data ◽  
Class Label ◽  
Label Noise
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