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Wu D.,Jimei University | Ren F.,Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering
Chinese Control Conference, CCC | Year: 2015

The complex interference from ocean environment and the complicated ship shape and structure result in the model uncertainties of dynamic positioning system (DPS). Due to the inaccuracy and coupling of model, it is difficult to adjust it well for common method. This paper introduces the active disturbance rejection controller (ADRC) to control the ship moving and positioning with DPS because of its independence from accurate model and easy to decouple. In this study, the biogeography-based optimization (BBO) is employed to optimize the parameters of ADRC which are not easy to adjust artificially. Finally, the presented method is verified effectively by computer simulations. © 2015 Technical Committee on Control Theory, Chinese Association of Automation. Source


Wang R.,Jimei University | Wang R.,Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering | Zhan Y.,Sun Yat Sen University | Zhou H.,Jimei University
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | Year: 2015

Aiming at the blind source separation problem of time-varying number of sources, a dynamic source number estimation method based on cross-validation technique is proposed. Then, an adaptive blind source separation algorithm based on natural gradient and Frobenius norm is deduced. The innovative blind separation algorithm does not require the assumption of any restrictions or constraints on source signals; therefore it is suitable for separating the sources obeying super- and sub-Gaussian distributions. At last, the effectiveness of the proposed method was verified in the simulation experiments for time-invariant and time-varying number of sources. ©, 2015, Science Press. All right reserved. Source


Wang R.,Jimei University | Wang R.,Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering | Zhan Y.,Sun Yat Sen University | Zhou H.,Jimei University
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | Year: 2015

A post-nonlinear blind source separation method based on nonlinear principal component analysis and H∞ filtering is proposed. In this method, a cost function of nonlinear principal component analysis is constructed according to the post nonlinear mixture linear time-varying model, which is used to solve the time-varying confusion matrix; then the source signals mixed in the post-nonlinear observation signals are recovered through optimizing the cost function using H∞ filtering algorithm. This method only requires the statistics independent prior information of the source signals. The simulation results show that the proposed method can achieve the blind source separation of the source signals with sub- and super-Gaussian distribution with higher accuracy compared with other traditional algorithms. Moreover, the method can also achieve the post-nonlinear blind source separation of the source signals on-line and dynamically. © 2015, Science Press. All right reserved. Source


Zhu J.,Jimei University | Chen W.,Jimei University | Chen W.,Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering
Applied Thermal Engineering | Year: 2015

Many research results suggested that a good estimation model often played a crucial role in the design, optimization and analysis for the HVAC system, especially during the preliminary design stage. Based on the multivariate linear regression analysis method, this paper presented a simple and high-accuracy prediction model by adding a dynamic correction factor. Newly developed model was not only particularly used to the marine rotary desiccant air-conditioning, but also its veracity and reliability were verified by a series of sample data and three evaluation indicators. Meanwhile, the prediction and optimization schemes of system performance are also introduced in detail. As expected, it was found that the dynamic correction factor can make the fitting value of prediction model close to the real value infinitely, and almost achieved linear fitting perfectly. As the number of correction increased, the residual and the residual standard deviation close to zero rapidly, and the relative error doubled decreased nearly. Besides, the square of multiple coefficient correlation (R2) of the prediction models reached 0.999 after the seventh corrected and the relative error much less than 1%. Furthermore, it was believed that the methodology developed here can be applied to other related fields. © 2015 Elsevier Ltd. All rights reserved. Source


Wang R.,Jimei University | Wang R.,Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering | Zhan Y.,Sun Yat Sen University | Zhou H.,Jimei University
Energies | Year: 2015

The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods. © 2015 by the authors. Source

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