Noise assisted signal decomposition method based on complex empirical mode decomposition

Qu, Jian; Wang, Xiaofei; Gao, Feng; Yu-Ping, None Zhou; Zhang, Xiangyu

doi:10.7498/aps.63.110201

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…As we all know, EMD can be used to decompose several signals for nonlinear, non-stationary signals and natural signals [11]. To improve the prediction accuracy of runoff, a new method named VMD extended by EMD is introduced, and it has the features of adaptive, non-recursive, and quasiorthogonal [12].…”

Section: Vmd Algorithmmentioning

confidence: 99%

Runoff prediction based on a VMD-LSTM model considering the decomposition error

Ma¹,

Yang²,

Li³

et al. 2023

J. Phys.: Conf. Ser.

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For increasing the forecasting accuracy of runoff, a combined prediction model composed of the variational mode decomposition (VMD) and long short-term memory network (LSTM) is investigated in our manuscript. Firstly, data from the runoff is decomposed into three modal components via the VMD algorithm, for reducing the complexity of the original data. In addition, decomposition error is also been considered in this paper, and the hidden information in the series is extracted. Three subsequences and the decomposition error are handled by the LSTM method, respectively. Superimposing the prediction results, the prediction result of the runoff is thus derived. Experimental analysis is carried out, and a comparison of the VMD-LSTM model and other algorithmic indicates that the model constructed in our manuscript is more valid for predicting the runoff.

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Section: Vmd Algorithmmentioning

confidence: 99%

Runoff prediction based on a VMD-LSTM model considering the decomposition error

Ma¹,

Yang²,

Li³

et al. 2023

J. Phys.: Conf. Ser.

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show abstract

“…It is widely known that empirical mode decomposition (EMD) has advantages in dealing with the non-stationary and nonlinear signals [8]. As the extension, variational mode de-composition (VMD) is proposed, while it has the characteristics of adaptive, non-recursive and quasi-orthogonal, and has attracted attentions in the filed of signal processing [9].…”

Section: Vmd Algorithmmentioning

confidence: 99%

Forecasting of the charging load for electric vehicle based on a hybrid model

Li,

Tuo,

et al. 2023

Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023)

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With the fast progress of electric vehicles, the load of charging stations plays an increasingly important impact on the power grid. To guarantee the safe operation of power grid, a prediction model with the Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) is investigated. Firstly, considering that there exist strong randomness and volatility in the data of electric vehicles charging load, we adopt the VMD algorithm to decompose the data into three modal components, for reducing the complexity of original data. In addition, decomposition error is also been decomposed into two modal components, for extracting the hidden information in the error. Thus, five subsequences are respectively predicted by the LSTM method, and summation the prediction results constitutes the forecasting of the charging load. Furthermore, experimental analysis indicates that the model is more appropriate for predicting the charging load.

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“…However, such methods tend to cause group delay and phase distortion and have poor denoising performance. Compared with these traditional algorithms, EMD is more suitable for nonlinear and nonstationary signals (Qu et al , 2014). EMD is a novel adaptive signal time–frequency processing method creatively proposed by N. E. Huang and others in NASA in 1998.…”

Section: Introductionmentioning

confidence: 99%

Improved DOA estimation of MEMS vector hydrophone combined with CEEMDAN and wavelet transform for noise reduction

Chang,

Zhang,

Zhang

et al. 2024

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Purpose Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed. Design/methodology/approach Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference. Findings The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized. Originality/value This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

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Noise assisted signal decomposition method based on complex empirical mode decomposition

Cited by 9 publications

References 23 publications

Runoff prediction based on a VMD-LSTM model considering the decomposition error

Runoff prediction based on a VMD-LSTM model considering the decomposition error

Forecasting of the charging load for electric vehicle based on a hybrid model

Improved DOA estimation of MEMS vector hydrophone combined with CEEMDAN and wavelet transform for noise reduction

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