A de-noising algorithm combining local complementary ensemble empiricalmodedecomposition (LCEEMD) and lifting wavelet transform technology (LWT) is proposed to deal with the crosstalk noise buildup in high-capacity ...
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A de-noising algorithm combining local complementary ensemble empiricalmodedecomposition (LCEEMD) and lifting wavelet transform technology (LWT) is proposed to deal with the crosstalk noise buildup in high-capacity fiber grating multiplexing networks. Complementary ensemble empiricalmodedecomposition (CEEMD) serves for decompose this original spectral signal, and the normalization permutation en-tropy (NPE) is applied for identifying high-frequency nonlinear sequences in low-order intrinsic mode function to suppress the random noise. The filtering accuracy is improved by further decomposing the high-frequency intrinsic mode function with LWT. Finally, reconstruction of de-noised signal. The simulation results show that the LCEEMD-LWT method achieves better quality evaluation indexes than LCEEMD and EEMD-LWT when processing 1dBlow SNR spectral signal. To study the noise elimination capability of this devised method for nonlinear non-stationary semaphores, this de-trended fluctuation analysis (DFA) algorithm served for evaluating the results. It is indicted that this devised way has this highest fractal scaling index (2.114), which is better than other de-noising methods.
Rainfall variability associated with climate change has enormous impacts on ecosystems, agriculture and people in West Africa but few studies have been devoted to it. Monthly rainfall data from 1901 to 2013, provided ...
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Rainfall variability associated with climate change has enormous impacts on ecosystems, agriculture and people in West Africa but few studies have been devoted to it. Monthly rainfall data from 1901 to 2013, provided by the Global Precipitation Climatology Center dataset, were analyzed using segmentation and empirical modal decomposition (EMD) methods to increase our knowledge on past and recent spatio-temporal rainfall trends and their impacts on the West African region. The results obtained showed that the peak of rainfall during the short rainy season is observed in September in Côte d’Ivoire, Ghana and Liberia. The temporal variability of this rainfall is marked by several breakpoints whose durations range from 2 to 70 years. The periods of change in the rainfall regime, characterized by the appearance of breakpoints, vary from one country to another and are of unequal duration. The main breakpoint appears after 1960. Periods of relative or normal increase or decrease in precipitation are observed before and after 1960. The long-term variability of this rainfall is characterized by a decrease in the amount of rainfall over all West African countries. The results of this study can be used as a tool to help raise awareness among populations for sustainable management of water resources in response to climate change and its adverse effects.
This study develop an adaptive stationary target imaging technique based on the empiricalmodedecomposition (EMD) method for the time-division multiple-input multiple-output through-wall radar. By exploiting the disc...
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This study develop an adaptive stationary target imaging technique based on the empiricalmodedecomposition (EMD) method for the time-division multiple-input multiple-output through-wall radar. By exploiting the discrimination between the respiratory rate and the environmental clutters' frequency, the weak echoes of the stationary target are enhanced via EMD processing adaptively. Based on the simulations as well as the experimental data, the authors verify that the stationary target can be positioned accurately via their proposed method.
Accurate short-term forecasting of wind farm power generation is of great significance to economic development and stable operation of power *** order to improve the accuracy of wind power prediction,this paper presen...
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Accurate short-term forecasting of wind farm power generation is of great significance to economic development and stable operation of power *** order to improve the accuracy of wind power prediction,this paper presents a short-term wind power prediction method based on empiricalmodedecomposition and deep *** firstly decompose historical data into two-dimensional tensors by using empiricalmodedecomposition,and then extract the local features of data by using convolutional neural *** features are transmitted to the bidirectional long short-term memory for *** measured wind power data form wind farm in Xinjiang are used for *** use root mean square error and mean absolute error as evaluation indexes to test the *** of the experiment show that the reconstructed two-dimensional tensor is beneficial to the ability of convolutional neural network to extract local features,and the prediction accuracy of our method is higher than that of other traditional prediction algorithms.
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