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Your search keyword '"Tang, Zhenhao"' showing total 29 results
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29 results on '"Tang, Zhenhao"'

10. Prediction of Wind Power Ramp Events Based on Deep Neural Network

Author

Tang Zhenhao, Meng Qingyu, Wang Gong, and Cao Shengxian

Subjects
Variable (computer science), Electricity generation, Wind power, Artificial neural network, Control theory, Event (computing), Computer science, business.industry, business
Abstract

Wind power ramps seriously threaten grid security due to the increase in the proportion of windpower generation to total power generation. In order to improve the prediction accuracy of wind power ramp events, an Empirical Mode Decomposition Deep Neural Network algorithm (EMDDNN) is proposed. First, the wind power data is preprocessed and the characteristics of the wind ramp event are analyzed. Then, the appropriate time window is selected and the empirical modal decomposition of the time series data is used as the input variable of the prediction model. Finally, a deep neural network is used to construct a wind power ramp prediction model. The experimental results based on actual production data show that the prediction accuracy of the EMDNN algorithm proposed in this paper is higher than other comparison algorithms.

Published
2019

22. A Dynamic Analytics Method Based on Multistage Modeling for a BOF Steelmaking Process.

Author

Liu, Chang, Tang, Lixin, Liu, Jiyin, and Tang, Zhenhao

Subjects
SUPPORT vector machines, LEAST squares, STEELMAKING furnaces, KERNEL functions, DIFFERENTIAL evolution, GAS furnaces, SMELTING furnaces
Abstract

This paper proposes a dynamic analytics method based on the least squares support vector machine with a hybrid kernel to address real-time prediction problems in the converter steelmaking process. The hybrid kernel function is used to enhance the performance of the existing kernels. To improve the model’s accuracy, the internal parameters are optimized by a differential evolution algorithm. In light of the complex mechanisms of the converter steelmaking process, a multistage modeling strategy is designed instead of the traditional single-stage modeling method. Owing to the dynamic nature of the practical production process, great effort has been made to construct a dynamic model that uses the prediction error information based on the static model. The validity of the proposed method is verified through experiments on real-world data collected from a basic oxygen furnace steelmaking process. The results indicate that the proposed method can successfully solve dynamic prediction problems and outperforms other state-of-the-art methods in terms of prediction accuracy. Note to Practitioners—With the development of cyber-physical systems, abundant real-time data have been collected from the converter steelmaking process. These data provide an opportunity to solve product quality prediction problems using data-driven models. This paper proposes a dynamic analytics method based on the least squares support vector machine with a hybrid kernel to address this challenging issue. To improve the model’s performance, a differential evolution algorithm is used to optimize its parameters. Because of the fierce physicochemical reaction in the converter furnace, it is difficult for a single-stage model to achieve accurate predictions. Thus, a multistage modeling strategy is proposed to address this difficulty, and a dynamic model based on feedback error is developed to realize real-time prediction. We verify the effectiveness of the proposed method using real data from a basic oxygen furnace (BOF) steelmaking process. The computational results reveal that the proposed method has a higher prediction accuracy than other methods, making it helpful in guaranteeing the specified product quality and in maintaining stable BOF operation. [ABSTRACT FROM AUTHOR]

Published
2019
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27. A new classic camera calibration method based on coplanar Points

Author

Yang Yinghua, Chen Xiaobo, Qin Shukai, and Tang Zhenhao

Subjects
Pixel, business.industry, Distortion (optics), 3D reconstruction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Astrophysics::Instrumentation and Methods for Astrophysics, Coplanarity, Iterative reconstruction, Nonlinear distortion, Camera auto-calibration, Computer vision, Artificial intelligence, business, Mathematics, Camera resectioning
Abstract

As a necessary step of 3D reconstruction, and a premise and base for computer vision obtaining the spatial information of 3D objects, research on the camera calibration methods has great important significance of theoretical study and practical value. This paper introduces a new simply, flexible and more accurate classic camera calibration method based on coplanar points. This method only requires a coplanar target and without camera motion. We use step-by-step calibration thinking, and first calibrate the principal point, and then neglect the lens distortion and linearly solve all the parameters, when high accuracy is required, we can include Weng's lens distortion model and solve the distortion coefficient by nonlinear algorithm. The experiment of images proves the proposed method is accurate and effective.

Published
2009

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