The Experts below are selected from a list of 939498 Experts worldwide ranked by ideXlab platform

S Bunjongjit - One of the best experts on this subject based on the ideXlab platform.

  • an application of a discrete wavelet transform and a back propagation neural network algorithm for fault diagnosis on single circuit transmission line
    International Journal of Systems Science, 2013
    Co-Authors: Atthapol Ngaopitakkul, S Bunjongjit
    Abstract:

    This article proposes an application of the discrete wavelet transform DWT and back-propagation neural networks BPNN for fault diagnosis on single-circuit transmission line. ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 db4 is used to decompose the High-Frequency Component of these signals. In addition, characteristics of the fault current at various fault inception angles, fault locations and faulty phases are detailed. The DWT is employed in extracting the high frequency Component contained in the fault currents, and the coefficients of the first scale from the DWT that can detect fault are investigated, and the decision algorithm is constructed based on the BPNN. The results show that the proposed technique provides satisfactory results.

Atthapol Ngaopitakkul - One of the best experts on this subject based on the ideXlab platform.

Jianguo Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks
    'MDPI AG', 2019
    Co-Authors: Jianguo Zhou, Xuejing Huo
    Abstract:

    The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic Components, namely PHC (high frequency Component of wind power), PMC (medium frequency Component of wind power), and PLC (low frequency Component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic Components, called SHC (high frequency Component of wind speed), SMC (medium frequency Component of wind speed), and SLC (low frequency Component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic Components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models

  • predicting the carbon price sequence in the shenzhen emissions exchange using a multiscale ensemble forecasting model based on ensemble empirical mode decomposition
    Energies, 2018
    Co-Authors: Jianguo Zhou, Xiaolei Yuan
    Abstract:

    Accurately predicting the carbon price sequence is important and necessary for promoting the development of China’s national carbon trading market. In this paper, a multiscale ensemble forecasting model that is based on ensemble empirical mode decomposition (EEMD-ADD) is proposed to predict the carbon price sequence. First, the ensemble empirical mode decomposition (EEMD) is applied to decompose a carbon price sequence, SZA2013, into several intrinsic mode functions (IMFs) and one residual. Second, the IMFs and the residual are restructured via a fine-to-coarse reconstruction algorithm to generate three stationary and regular frequency Components that high frequency Component, low frequency Component, and trend Component. The fluctuation of each Component can effectively reveal the factors that influence market operation. Third, extreme learning machine (ELM) is applied to forecast the trend Component, support vector machine (SVM) is applied to forecast the low frequency Component and the high frequency Component is predicted via PSO-ELM, which means extreme learning machine whose input weights and bias threshold were optimized by particle swarm optimization. Then, the predicted values are combined to form a final predicted value. Finally, using the relevant error-type and trend-type performance indexes, the proposed multiscale ensemble forecasting model is shown to be more robust and accurate than the single format models. Three additional emission allowances from the Shenzhen Emissions Exchange are used to validate the model. The empirical results indicate that the established model is effective, efficient, and practical in terms of its statistical measures and prediction performance.

Minoru Fukumi - One of the best experts on this subject based on the ideXlab platform.

  • Invisible calibration pattern based on human visual perception
    2010 11th IEEE International Workshop on Advanced Motion Control (AMC), 2010
    Co-Authors: Hironori Takimoto, Seiki Yoshimori, Yasue Mitsukura, Minoru Fukumi
    Abstract:

    In the print-type steganographic system and watermark, a calibration pattern is arranged around contents where invisible data is embedded, as plural feature points between an original image and the scanned image for normalization of the scanned image. However, it is clear that conventional methods interfere with page layout and artwork of contents. In addition, visible calibration patterns are not suitable for security service. In this paper, we propose an arrangement and detection method of an invisible calibration pattern based on human visual perception. We embed the calibration pattern in an original image by adding high frequency Component to blue intensity in a limited region. Moreover, the proposed calibration pattern protects page layout and artwork.

  • Invisible Calibration Pattern Based on Human Visual Perception Characteristics
    2010 20th International Conference on Pattern Recognition, 2010
    Co-Authors: Hironori Takimoto, Seiki Yoshimori, Yasue Mitsukura, Minoru Fukumi
    Abstract:

    In the print-type steganographic system and watermark, a calibration pattern is arranged around contents where invisible data is embedded, as plural feature points corresponding to between an original image and the scanned image for normalization of the scanned image. However, it is clear that conventional methods interfere with page layout and artwork of contents. In addition, visible calibration patterns are not suitable for security service. In this paper, we propose an arrangement and detection method of an invisible calibration pattern based on characteristics of human visual perception. The calibration pattern is embedded to blue intensity in an original image by adding high frequency Component.

  • invisible print type calibration pattern based on human visual perception
    International Conference on Image Processing, 2010
    Co-Authors: Hironori Takimoto, Yasue Mitsukura, Akira Yoshida, Minoru Fukumi
    Abstract:

    In the print-type steganographic system and watermark, a calibration pattern is arranged around contents where invisible data is embedded, as plural feature points between an original image and the scanned image for normalization of the scanned image. In this paper, we propose an arrangement and detection method of an invisible calibration pattern based on the characteristics of human visual perception. The most important part of human visual perception in the proposed method is the spectral luminous efficiency characteristic and the chromatic spatial frequency characteristic. We embed the calibration pattern in an original image by adding high frequency Component to blue intensity in a limited region. It is suggest that the proposed method protect page layout and artwork of original contents. In order for the detection of embedded calibration pattern to achieve high-speed and accurate processing the detection method composed from brief weak classifiers will be proposed.

D Y Hong - One of the best experts on this subject based on the ideXlab platform.

  • ultra short term forecast of wind speed and wind power based on morphological high frequency filter and double similarity search algorithm
    International Journal of Electrical Power & Energy Systems, 2019
    Co-Authors: D Y Hong
    Abstract:

    Abstract This paper proposes a forecast model for ultra-short-term prediction of wind speed and wind power, which is based on a morphological High-Frequency filter (MHF) and a double similarity search (DSS) algorithm. The MHF is proposed to decompose the time series into two Components: the mean trend, which reveals the non-stationary tendency of the time series, and the high frequency Component, which depicts the fluctuations. The same strategy is employed to forecast the mean trend and the high frequency Component, respectively. The two Components are reconstructed in the phase space, respectively, where a non-uniform embedding strategy is proposed to better reveal their information. To select similar segments to be used for local forecast, the novel DSS algorithm is proposed for high frequency Component, while the Euclidean distance is used for the mean trend. Finally, the least squares-support vector machine (LS-SVM) model is applied to forecast each Component, respectively, and their sum composes the final prediction. Simulation studies are carried out using wind speed and wind power data obtained from four databases, and the results demonstrate that the MHF/DSS model provides more accurate and stable forecast compared to the other methods.