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47 results on '"Yang, Jiebin"'

16. Rapid quantitative analysis of raw rocks by LIBS coupled with feature-based transfer learning.

Author

Rao, Yu, Ren, Wenxin, Kong, Weiheng, Zeng, Lingwei, Wu, Mengfan, Wang, Xu, Wang, Jie, Fan, Qingwen, Pan, Yi, Yang, Jiebin, and Duan, Yixiang

Subjects
MACHINE learning, ROCK analysis, TRANSFER of training, LASER-induced breakdown spectroscopy, STANDARD deviations, FEATURE selection
Abstract

Quantitative analysis of rock samples using laser-induced breakdown spectroscopy (LIBS) is a challenging task due to the significant differences in spectra between pressed pellet samples and rock samples, with pressed pellet samples usually exhibiting stronger spectral lines. The predictive ability of LIBS quantitative analysis models for rock samples is poorer compared to that for pressed pellet samples, and quantitative analysis models constructed using pressed pellet samples cannot be directly applied to predict the composition of rock samples. To address the issue, a quantitative analysis method for raw rocks based on feature transfer learning using transfer component analysis (TCA) is proposed in this paper. By establishing a feature mapping relationship between pressed pellet samples and rock samples, the differences between data features are reduced, thus enabling the training of a more quantitative analysis model. The transfer learning was introduced into a multivariate regression machine learning model for training using pressed pellet samples, which successfully addresses the problem of prediction accuracy of rock sample element contents. In this model, all data were mapped to a high-dimensional reproducing kernel Hilbert space, and a subset of the most similar features was selected to train the quantitative analysis model. Upon testing the model with independent rock samples, the difference between the predicted and true values was significantly reduced. The model yielded a root mean square error of prediction (RMSEP) of 3.7131, 1.0185, 0.2985, 13.0439, and 1.5450 for Si, Al, Fe, Ca, and Mg in rock samples, respectively. These results indicate that LIBS coupled with the transfer learning algorithm can effectively eliminate the differences between the spectra of the pressed pellet and the raw rock and provide another idea for in situ LIBS detection. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Stability region of hydropower plant with surge tank at HVDC sending terminal.

Author

Yang, Weijia, Huang, Yifan, Zhao, Zhigao, Yang, Jiandong, and Yang, Jiebin

Subjects
HYDROELECTRIC power plants, WATER power, FREQUENCY stability, FREQUENCIES of oscillating systems, STABILITY criterion, TRANSFER functions
Abstract

This paper aims to study the stability region of hydropower plants (HPPs) at high‐voltage direct current (HVDC) sending terminal, to theoretically predict the frequency stability of isolated HPPs under small disturbances. The overall transfer function of the system is derived, and the Hurwitz stability criterion is applied to deduce the stability region. The theoretical analysis of the stability region and time‐domain simulation results of the system show good consistency, and this proves the accuracy of the model. The focus of this paper is mainly on the influence of the hydraulic‐mechanical aspects. Improperly tuned governor parameters and unfavourable design of the surge tank could lead to the frequency instability of HPPs, which appears as divergent oscillations of the frequency of units. A key suggestion of this paper is that the control strategy should consider the factors from not only the electrical side but also hydraulic‐mechanical aspects, for enhancing the stability of the HVDC sending terminal. Besides, the influence mechanism and parameter tuning of frequency limitation controller (FLC) are investigated and the results demonstrate its capability of quick compensation for power disturbances at the HVDC receiving terminal. [ABSTRACT FROM AUTHOR]

Published
2021
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