Welcome

Through incremental integration and independent research and development, build a method library of big data quality control, automatic modeling and analysis, data mining and interactive visualization, form a tool library with high reliability, high scalability, high efficiency and high fault tolerance, realize the integration and sharing of collaborative analysis methods of multi-source heterogeneous, multi-granularity, multi-phase, long-time series big data in three pole environment, as well as high Efficient and online big data analysis and processing.

  • Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)

    Compared with EMD and EEMD, CEEMDAN can further eliminate mode aliasing.

    15 2022-06-15 View Details

  • Empirical Mode Decomposition (EMD)

    EMD processes non-stationary signals of time series, and decomposes the signals according to the time scale characteristics of the data itself, without setting any basis function in advance

    13 2022-06-15 View Details

  • Ensemble Empirical Mode Decomposition (EEMD)

    Compared with EMD, EEMD can eliminate mode aliasing.

    16 2022-06-15 View Details

  • Variational Mode Decomposition (VMD)

    By solving the frequency domain variational optimization problem, each signal component is estimated. Assuming that all components are narrowband signals concentrated near their respective central frequencies, VMD establishes a constrained optimization problem according to the component narrowband conditions, so as to estimate the central frequencies of signal components and reconstruct the corresponding components.

    13 2022-06-15 View Details

  • Empirical Wavelet Transforms (EWT)

    The principle of EWT is to divide the Fourier spectrum of the signal into continuous intervals, then construct wavelet filter banks on each interval for filtering, and finally obtain a group of AM and FM components through signal reconstruction. This method can identify the position of the feature information in the Fourier spectrum of the signal by using the wavelet filter bank with tight support characteristics, and adaptively extract the different frequency components of the signal.

    Installation: matlab

    Operation mode:

    Input variable: time series signal

    Output variable: Constructed signal

    QR code:

    15 2022-06-15 View Details