VQ-oriented data hiding based on adjustable error compensation strategy

2021 ◽  
pp. 1-19
Author(s):  
Chin-Chen Chang ◽  
Ji-Hwei Horng ◽  
Chia-Shou Shih ◽  
Xu Wang
Keyword(s):  
Data Hiding ◽  
Error Compensation ◽  
Compensation Strategy

Research on Error Compensation by Semi-Closed Loop Control for a 3-UPS Parallel Machine Tool

2012 ◽  
Vol 461 ◽  
pp. 272-276
Author(s):  
Jian Ye Guo ◽  
Jia Shun Shi ◽  
Liang Zhao
Keyword(s):  
Machine Tool ◽  
Error Compensation ◽  
Closed Loop ◽  
Parallel Machine ◽  
Control Mode ◽  
Closed Loop Control ◽  
Kinematics Analysis ◽  
Compensation Strategy ◽  
Loop Control ◽  
Parallel Machine Tool

This paper took a 3-UPS Parallel Machine Tool (PMT) as the object of research; it mainly introduced the process of establishing the compensation strategy for this PMT. Firstly the kinematics equations on driving chain and constraint chain was established on the basis of kinematics analysis. Then according to the structural characteristics and the results of kinematics analysis, the error compensation strategy of feedback correction type with the semi-closed loop control mode was used in the error compensation for this PMT by the method of installing respectively the encoders on the each joint of parallelogram mechanism, namely the compensation way of “parallel driving and series feedback” was adopted. Finally this paper has also deduced the theoretical model of error compensation. The research results in this paper provided a theoretical basis for realizing error compensation of this PMT, and had important practical significance for improving machining precision of PMT

scholarly journals Commutation Error Compensation Strategy for Sensorless Brushless DC Motors

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 203 ◽  
Author(s):  
Xuliang Yao ◽  
Jicheng Zhao ◽  
Guangxu Lu ◽  
Hao Lin ◽  
Jingfang Wang
Keyword(s):  
Error Compensation ◽  
Compensation Strategy ◽  
Line Voltage ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Conduction Loss ◽  
Voltage Difference ◽  
Dc Motors ◽  
Drive Systems ◽  
The Relationship

Sensorless brushless DC (BLDC) motor drive systems often suffer from inaccurate commutation signals, which result in current fluctuation and high conduction loss. To improve precision of commutation signals, this paper presents a novel commutation error compensation strategy for BLDC motors. First, the relationship between the line voltage difference integral in 60 electrical degree conduction interval and the commutation error is analyzed. Then, in terms of the relationship derived, a feedback compensation strategy based on the line voltage difference integral is proposed to regulate commutation signals by making three-phase back electromotive force (EMF) integral to zero, and the effect of the freewheeling process on the line voltage difference integral is considered. Moreover, an incremental PI controller is designed to achieve closed-loop compensation for the commutation error automatically. Finally, experiment results verify feasibility and effectiveness of the proposed strategy.

scholarly journals Research on the Motion Error Analysis and Compensation Strategy of the Delta Robot

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 411 ◽  
Author(s):  
Deyong Shang ◽  
Yu Li ◽  
Yue Liu ◽  
Shuanwei Cui
Keyword(s):  
Error Compensation ◽  
Kinematic Model ◽  
Transformation Method ◽  
Joint Clearance ◽  
Compensation Strategy ◽  
Motion Error ◽  
Important Indicator ◽  
Before And After ◽  
Robot Performance ◽  
Analysis Of Error

The Delta robots are widely used in packaging, sorting, precision positioning, and other fields. Motion accuracy is an important indicator for evaluating robot performance. However, due to the existence of mechanism errors, the motion accuracy of the robot will be reduced. Therefore, how to reduce motion errors and improve accuracy are important issues for robots. The purpose of the present study is to analyze the motion error and propose an error compensation scheme to improve the motion accuracy of the robot. Firstly, the kinematic model of the robot is established by the D-H matrix transformation method. An error model considering dimension error, the error of revolute joint clearance, driving error, and the error of spherical joint clearance is established. Additionally, the influence of different errors on the motion accuracy is analyzed. Secondly, an error compensation strategy of controlling the driving angle is proposed. The analysis of error compensation is carried out by a numerical example. Comparing the results before and after compensation, it is known that the robot can move along the desired position, so the notion error of the robot is compensated, which proves that this method is effective for improving the motion accuracy of the robot.

Improvement of residual error in hydrodynamic polishing by recursive error compensation strategy

2012 ◽  
Vol 93 ◽  
pp. 27-34 ◽  
Author(s):  
Tu-Chieh Hung ◽  
Shao-Heng Chang ◽  
Chi-Hsien Ding ◽  
Yaw-Terng Su
Keyword(s):  
Error Compensation ◽  
Residual Error ◽  
Compensation Strategy

Multi-step network traffic prediction using echo state network with a selective error compensation strategy

2021 ◽  
pp. 014233122110502
Author(s):  
Ying Han ◽  
Yuanwei Jing ◽  
Georgi M Dimirovski ◽  
Li Zhang
Keyword(s):  
Network Traffic ◽  
Error Compensation ◽  
Cloud Model ◽  
Fruit Fly ◽  
Traffic Prediction ◽  
Local Optimum ◽  
Echo State Network ◽  
Fruit Fly Optimization Algorithm ◽  
Compensation Strategy ◽  
Fruit Fly Optimization

Communication networks grow exponentially in this globalization era; thus, the network traffic modelling and prediction plays a crucial role in network management and security warning. Solely, the multi-step network traffic prediction may involve greater errors hence worsening prediction performance. To overcome this problem, an optimized echo state network model with selective error compensation is proposed. In the optimized echo state network-based multi-step prediction model, an improved fruit–fly optimization algorithm based on cloud model (named LVCMFOA) is used to select optimum values of four key parameters of the model. The proposed LVCMFOA algorithm uses the levy-flight function to redefine the generation of the fruit–fly population, which can randomly change the search radius and help getting out of a possible local optimal solution and prevent local optimum. To reduce the calculation time but improve the prediction accuracy simultaneously, a sophisticated selective error compensation strategy employing the variable sliding window technology is proposed so as to avoid the error accumulation problem in the multi-step prediction. The effectiveness of the proposed method is verified by applying it to Henon mapping chaotic series, Mackey–Glass chaotic series and two public network traffic data sets all known in the literature.

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