Data-driven simulation of multivariate nonstationary winds: A hybrid multivariate empirical mode decomposition and spectral representation method

IQ imbalance (IQI) of radio-frequency (RF) Chains is one of the key factors that limits the performance of wireless communications systems, especially in high order modulation modes. Traditional methods usually estimate the IQI parameters with dedicated feedback circuits and compensate the distortion of RF Chains with extra compensation circuits. Moreover, they do not consider the random carrier phase shift between transceivers, which increases the difficulty of estimating IQI parameters. In the paper, we propose a CNN-based auto fully-digital calibration technique with no dedicated feedback and compensation circuits to compensate for IQI and the random phase shift. We train the CNN using the pilot signals to obtain the compensation matrix. Since only the pilot signal is adopted, the algorithm can work with current wireless communications systems automatically. Compared to user equipment (UE) with ideal RF chains, the simulation results show that our algorithm can help UEs with IQI and the random phase shift achieve similar performance even in 1024-QAM modulation mode.

In surface shape measurement with phase-shifting interferometry, the extraction accuracy of initial phase is significant and it will influence the correct of measurement result. A least-squares algorithm is proposed to solve the parameters of interferograms with random and known phase shifts. It can rapidly extract the initial phase, modulation amplitude and background intensity without any iteration. Numerical simulations and optical experiments are implemented to demonstrate the performance of the proposed method. The simulation and experiment results show that the presented method is effective, rapid and accurate.

Envelope modified versions of the empirical mode decomposition (EMD) method such as the B‐spline interpolation‐based EMD (B‐EMD) method and cardinal spline interpolation‐based EMD (C‐EMD) method have been proposed recently for purpose of improving its effectiveness. To shed further light on their performance, the behaviours of these EMD‐type methods in the presence of white Gaussian noises are investigated in this study based on extensive numerical experiments. Similarly to the EMD method, it turns out that the envelope modified EMD methods also act as filter banks essentially. However, the spectra among the first several intrinsic mode functions of the B‐EMD method have fewer overlaps than those of the EMD and C‐EMD methods, which indicate that the B‐EMD method has a better ability to alleviate the mode mixing problem for signals with higher frequencies. On the other hand, the C‐EMD method is shown to perform better than the EMD and B‐EMD methods on separating tones with lower frequencies.

Focusing on feature extraction of non-stationary vibration signals in condition monitoring and fault diagnosis of gear pump, the fault diagnosis approach based on empirical mode decomposition method and kernel principal component analysis (KPCA) and support vector machines (SVM) is proposed. The improved empirical mode decomposition (EMD) method is used to decompose the mechanical equipment output signal into a number of intrinsic mode function (IMF) components and a residue component, and calculated ten dimensionless parameters of each IMF and residue component, then extract result from the original parameters by using KPCA, at last the kernel principal component is classified by inputting the new feature vector to SVM for training and recognizing. The simulation and experiment results show that the advanced method is effective in restraining end effect, and the analysis result of gear pump vibration signals in different conditions validate the method is effective.

This study combined a Convolutional Neural Network (CNN) with the chaos theory and the Empirical Mode Decomposition (EMD) method for the attenuation fault recognition of power capacitors. First, it built six capacitor analysis models, including normal capacitors, failed capacitors, and normal capacitors attenuated by 20–80%. Then a power testing machine was used for an applied voltage test of the capacitor. The EMD method was combined with the chaos synchronisation detection method to chart the discharge signals of the voltage and current that was captured by a high frequency oscilloscope into a 3D chaotic error scatter plot, as the fault diagnosis feature image. Finally, the CNN algorithm was used for the capacitor fault detection. The advantages of the proposed method are that big data are compressed to extract meaningful feature images, the operating state of the power capacitor can be detected effectively, and faults can be diagnosed according to the electrical signal change of the power capacitor. The actual measurement results showed that the accuracy of the proposed method was as high as 97% and has a high efficiency of noise rejection ability, which indicates that the method could be applied to other power‐related fields in the future.

In order to obtain a more accurate topographic model, a noise filtering step must be performed before the unwrapping of phase. In this article, a filter for suppressing speckle in SAR interferogram is proposed. The filter is based on the empirical mode decomposition (EMD) method which can separate the information to parts with different scale, when the parts related to the speckle is subtracted from the original interferogram, the speckle noise is reduced. The result is compared to that of two other methods and shows that EMD method is powerful to interferogram speckle noise reduction, as well as it can preserve fine details in the interferogram that are directly related to the ground topography and maintain phase values distribution.

Natural frequencies identification is important for dynamic analysis of subgrade for high-speed railway. The most commonly used method is analyzing the response spectrum feature undergoing ambient excitation directly without any processing. However, this method requires that the ambient excitation should be close to the white signal. If the ambient excitation includes some kinds of non-white signals, this method becomes hard to use. In this paper, based on empirical mode decomposition (EMD) method and random vibration theory, an improved identification method undergoing ambient excitation is proposed. First, the self-correlation function of an acceleration record is decomposed into several intrinsic mode functions (IMFs) via EMD method. Then natural frequencies are determined by Hilbert transform and basic modal analysis theory. The original acceleration records from the Wu-Guang Passenger Dedicated Railway are processed by the proposed method to identify natural frequencies of the subgrade. The identification results are compared with results from the field excitation tests and finite element analysis. Comparisons demonstrate that the proposed method is reliable to identify natural frequencies of subgrade for high-speed railway. The natural frequencies of the test subgrade are at low level.

Comparative study of spectral properties of temperature and CO2 fluxes measured by eddy covariance method at Yucheng (36 57'E, 28 m.a.s.1., in the North China Plain) and at Lhasa (29 41'N, 91 20' E, 3688 m.a.s.1., on the Tibetan Plateau) is described using the empirical mode decomposition (EMD) method. The main results are: (1) The intrinsic oscillation modes or intrinsic mode functions (IMFs) were extracted from data of temperature (T) and CO2 fluxes (F) measured at Yucheng (T1 and F1) and Lhasa (T2 and F2). (2) Hilbert transform was applied to these IMF compontents, then the Hilbert-Huang spectra and the marginal spectra of these data were obtained. (3) Comparison of temperature and CO2 fluxes in North China Plain and on Tibetan Plain illustrated that the characteristic frequencies corresponding to T1, F1, T2 and F2 are 0.05 Hz, 0.03 Hz, 0.014Hz and 0.005Hz, respectively. * Supported by the National Program on Key Basic Research Projects (Grant No. G1999-11708) and National Natural Science Foundation of China (Grant No. 40320081) Keywords: empirical mode decomposition (EMD)intrinsic mode function (IMF)fluxHilbert transformwheatTibet Notes * Supported by the National Program on Key Basic Research Projects (Grant No. G1999-11708) and National Natural Science Foundation of China (Grant No. 40320081)

Modeling heat transfer properties in an ORC direct contact evaporator using RBF neural network combined with EMD

Relationship between flood/drought disasters and ENSO from 1857 to 2003 in the Taihu Lake basin, China

Power grid as an important infrastructure which ensures the healthy development of economy and society and accurate and reasonable prediction of the power grid investment demand has always been the focus problem of the power planning department and the power grid enterprises. In view of the complex nonlinear and nonstationary characteristics of the power grid investment demand sequence, a novel hybrid EMD-GASVM-RBFNN forecasting model based on empirical mode decomposition (EMD) method, support vector machines optimized by genetic algorithm (GA-SVM) model, and radial basis function neural network (RBFNN) model is proposed. Firstly, the EMD method is used to decompose the original power grid investment data sequence into a series of IMF components and a residual component which have stronger regularity compared with the original data. Then, according to the different characteristics of each subsequence, the GA-SVM and RBFNN model will be used to forecast different subsequences, respectively. Next, the prediction results of different subsequences are aggregated to obtain the final prediction results of the power grid investment. Finally, this paper dynamically simulates China’s power grid investment from 2018 to 2020 based on the EMD-GASVM-RBFNN hybrid forecasting model and Monte Carlo method.

The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from the time series data. This paper proposes a combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods, combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.

Separation of closely spaced modes by combining complex envelope displacement analysis with method of generating intrinsic mode functions through filtering algorithm based on wavelet packet decomposition

The Mirror Extending (ME) approach is proposed in this paper for solving the end extending issue in the Empirical Mode Decomposition (EMD) method. By this approach, the data is extended into a closed circuit without end. The derivatives on ends are not necessary any more for Spline fitting. The approach eliminates the possible problems in reliability and uniqueness in the original extending approach of the EMD method. In the ME approach only one extending is necessary before the data analysis. A theoretical criterion is proposed here for checking the extending approach. ME approach has been proved to satisfy the theoretical criterion automatically and permanently. This approach makes the EMD method reliable and easy to follow.

Similarity analysis of DNA sequences based on the EMD method