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Guo Zhao,Shulin Li,Wanqing Zuo,Haoran Song,Heping Zhu,Wenjie Hu 전력전자학회 2023 JOURNAL OF POWER ELECTRONICS Vol.23 No.9
To address the problem where the different operating conditions of hydropower units have a large influence on the parameters of the trend prediction model of the operating condition indicators, a support vector regression machine prediction model based on parameter adaptation is proposed in this paper. First, the Aquila optimizer (AO) is improved, and a sine chaotic map is introduced to influence the population initialization process. An improved adaptive weight factor is used to balance the local search and global search capabilities. Second, according to the power and the head, the operating conditions of the unit are refined into several typical sets of operating conditions. On this basis, an SVR model is established using the improved AO search algorithm proposed in this paper, and the prediction parameters under each of the operating condition are optimized to establish the data of the operating conditions and optimal parameters. Then a neural network is used to fit the working condition and the optimal prediction parameters. In addition, the nonlinear function mapping of the complex relationship between the two is constructed. Finally, the constructed mapping relationship is added to the traditional SVR, and an adaptive SVR prediction model suitable for changes in the working conditions of hydropower units is realized. Simulation results show that when compared to the traditional SVR prediction model, the adaptive SVR prediction model designed in this paper can automatically adjust the prediction parameters according to changes in the working conditions and achieve the goal of maintaining optimal prediction performance under different working conditions. In addition, it has the ability to accurately predict the development trend of the unit operating state index within a certain time scale.
The Degree Elevation of UE-spline Curves
Duan Xiaojuan,Wang Guozhao (사)한국CDE학회 2013 한국CAD/CAM학회 국제학술발표 논문집 Vol.2010 No.8
Unified and extended splines(UE-splines), which can unify and extend polynomial, trigonom etric and hyperbolic B-splines, inherit most properties of B-splines and have advantages over B -splines for modeling. This paper mainly studies the degree elevation of UE-splines. First, we construct a new class of basis functions, called bi-order UE-spline basis. The bi-order UE-splines are defined by the integral definition of splines. Then some important properties of bi-order UEsplines are given especially for the transformation formulas of the basis functions before and after inserting a interior knot into the knot vector. We finally proved that the degree elevation of UE-spline curves can be interpreted to be corner cutting on the control polygons.