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RISS 인기검색어
- 검색키워드
- 저자 : Luo Chengming (검색결과 2 건)
- 무료
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- 유료
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Polarization Error Analysis of an All-Optical Fibre Small Current Sensor for Partial Discharge
Gaifang Xin,Jun Zhu,Chengming Luo,Jing Tang,Wei Li,Yuxin Cao,Haiyan Xu 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.5
In view of the characteristics of the small amplitude of a current signal, an all-optical fi bre small-current sensor for partial discharge is devised. A novel type of sensor head with electric winding light is proposed. A complete optical model of the optical fi bre current sensor is developed under the eff ect of polarization error factors. In addition, the eff ect of polarization error factors on the optical fi bre sensing system is simulated. The simulation results show that as the extinction ratio increases, the output relative error of the optical fi bre small-current sensor decreases. The eff ect of the fast and slow axis misalignment errors of a polarizing beam splitter is similar to the eff ect of the alignment angle error of an optical fi bre polarizer. It is observed that the relative error curve follows a sine function. Meanwhile, the output relative error increases fi rst and then oscillates with an increase in the linear birefringence. We conclude that the linear birefringence of the sensing optical fi bre is the main error source for the measurement accuracy of the sensing system. Finally, an experimental system for the optical fi bre small-current sensor is implemented. The experimental results show that the eff ect of linear birefringence on the fi bre current sensing system can be suppressed by introducing a large amount of circular birefringence into the sensing optical fi bre. The theoretical mechanism of these errors is analysed, which can be applied to implement corresponding measures to reduce the eff ect of error factors on the sensing system and further improve the measurement accuracy of the optical fi bre small-current sensor.
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Trajectory Learning and Reproduction for Tracked Robot Based on Bagging-GMM/HSMM
Lu En,Zhao Zhan,Yin Jianjun,Luo Chengming,Tian Zhongming 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.6
Learning and reproducing the tracked robot’s demonstration trajectory is a promising intelligent path planning solution that can summarize and extract the relevant characteristics of the tracked robot’s desired trajectory. However, the existing methods are difficult to ensure the robustness when there are deviations in the demonstration trajectory data or when the task constraints are added. In order to address the problem, this paper proposes a novel trajectory learning and reproduction method for tracked robots that is based on Bagging algorithm and GMM/HSMM. The Bagging algorithm is used to randomly resample the demonstration trajectory data and construct the sub trajectory data sets. Then, GMM/HSMM is used to train and learn these sub trajectory data sets, and the output probability density function of the hidden state and the Gaussian component with the largest average position probability are selected as the motion elements. According to the mean and variance of these motion elements, the least square method is used to reconstruct the tracked robot’s trajectory. On the basis of using the IPSO algorithm to optimize the position of constraint points in each sub trajectory data sets, combined with the weight method, the learning results are integrated to realize the trajectory reproduction of a tracked robot under task constraints. The final results and analysis show that the proposed method can successfully realize the trajectory learning and reproduction of tracked robot, as well as ensure that the reproduced trajectory can pass through the required task constraint points without increasing the algorithm’s complexity.
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