Correlation-based tracking using tunable training and Kalman prediction

2016 ◽  
Author(s):  
Sergio E. Ontiveros-Gallardo ◽  
Vitaly Kober
Keyword(s):  
Kalman Prediction

Optimization in LQ Control and Kalman Prediction Using Neural Networks

2000 ◽  
Vol 33 (13) ◽  
pp. 531-536
Author(s):  
Dušan Krokavec ◽  
Anna Filasová
Keyword(s):  
Neural Networks ◽  
Kalman Prediction ◽  
Lq Control

scholarly journals A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable Desk

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4699
Author(s):  
Minming Gu ◽  
Yajie Wei ◽  
Haipeng Pan ◽  
Yujia Ying
Keyword(s):  
Pulse Width Modulation ◽  
Detection Algorithm ◽  
Prediction Algorithm ◽  
Actual System ◽  
Model Reference ◽  
Threshold Detection ◽  
System A ◽  
Working Principle ◽  
Current Sampling ◽  
Kalman Prediction

This paper presents a new algorithm based on model reference Kalman torque prediction algorithm combined with the sliding root mean square (SRMS). It is necessary to improve the accuracy and reliability of the pinch detection for avoiding collision with the height adjustable desk and accidents on users. Motors need to regulate their position and speed during the operation using different voltage by PWM (Pulse Width Modulation) to meet the requirement of position synchronization. It causes much noise and coupling information in the current sampling signal. Firstly, to analyze the working principle of an electric height adjustable desk control system, a system model is established with consideration of the DC (Direct Current) motor characteristics and the coupling of the system. Secondly, to precisely identify the load situation, a new model reference Kalman perdition method is proposed. The load torque signal is selected as a pinch state variable of the filter by comparing the current signal. Thirdly, to meet the need of the different loads of the electric table, the sliding root means square (SRMS) of the torque is proposed to be the criterion for threshold detection. Finally, to verify the effectiveness of the algorithm, the experiments are carried out in the actual system. Experimental results show that the algorithm proposed in this paper can detect the pinched state accurately under different load conditions.

Live Migration for Virtual Machine Based on Kalman Prediction of Dirty Pages

2014 ◽  
Vol 668-669 ◽  
pp. 1363-1367 ◽  
Author(s):  
Zhi Hong Sun ◽  
Xian Lang Hu
Keyword(s):  
Virtual Machine ◽  
Migration Time ◽  
Live Migration ◽  
Virtual Machine Migration ◽  
Memory State ◽  
Dynamic Memory ◽  
Virtual Machine Live Migration ◽  
Migration Method ◽  
Kalman Prediction ◽  
Total Migration

The live migration of virtual machine (VM) is an important technology of cloud computing. Down-time, total migration time and network traffic data are the key measures of performance. Through the analysis of dynamic memory state of a virtual machine migration process, we propose a dirty pages algorithm prediction based on pre-copy to avoid dirty pages re transmission. Experimental results show that, compared with the Xen virtual machine live migration method adopted, our method can at least reduce 15.1% of the total amount of data and 12.2% of the total migration time.

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