Results 121 to 130 of about 34,434 (155)
Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm. [PDF]
Sci RepTang P +6 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Transactions of the Institute of Measurement and Control, 2022
With the continuous development of pipeline transportation industry, pipeline leakage often occurs, posing a great threat to people’s lives and property safety. In order to improve the detection accuracy of natural gas pipeline leakage, a pipeline leakage detection method based on improved variational mode decomposition algorithm and Lempel–Ziv ...
Lijuan Zhu +4 more
openaire +1 more source
With the continuous development of pipeline transportation industry, pipeline leakage often occurs, posing a great threat to people’s lives and property safety. In order to improve the detection accuracy of natural gas pipeline leakage, a pipeline leakage detection method based on improved variational mode decomposition algorithm and Lempel–Ziv ...
Lijuan Zhu +4 more
openaire +1 more source
Complex variational mode decomposition for signal processing applications
Mechanical Systems and Signal Processing, 2017Abstract Complex-valued signals occur in many areas of science and engineering and are thus of fundamental interest. The complex variational mode decomposition (CVMD) is proposed as a natural and a generic extension of the original VMD algorithm for the analysis of complex-valued data in this work.
Yanxue Wang +4 more
openaire +1 more source
Complex Variational Mode Decomposition for Slop-Preserving Denoising
IEEE Transactions on Geoscience and Remote Sensing, 2018We have introduced a new decomposition method for seismic data, termed complex variational mode decomposition (VMD), and we have also designed a new filtering technique for random noise attenuation in seismic data by applying the VMD on constant-frequency slices in the frequency–offset ( $f$ – $x$ ) domain.
Siwei Yu, Jianwei Ma
openaire +1 more source
IEEE/ASME Transactions on Mechatronics, 2018
The vibration signals of faulty rotating machinery are typically nonstationary, nonlinear, and mixed with abundant compounded background noise. To extract the potential excitations from the observed rotating machinery, signal demodulation and time–frequency analysis are indispensable.
Xian-Bo Wang, Zhi-Xin Yang, Xiao-An Yan
openaire +1 more source
The vibration signals of faulty rotating machinery are typically nonstationary, nonlinear, and mixed with abundant compounded background noise. To extract the potential excitations from the observed rotating machinery, signal demodulation and time–frequency analysis are indispensable.
Xian-Bo Wang, Zhi-Xin Yang, Xiao-An Yan
openaire +1 more source
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
Considering the advantages of variational mode decomposition (VMD) in mathematical decomposition and extreme learning machine (ELM) in data modeling, a new regression model named variational mode decomposition unfolded extreme learning machine (VMD-UELM) is introduced for spectral quantitative analysis of complex samples.
Liangliang, Shen +4 more
openaire +2 more sources
Considering the advantages of variational mode decomposition (VMD) in mathematical decomposition and extreme learning machine (ELM) in data modeling, a new regression model named variational mode decomposition unfolded extreme learning machine (VMD-UELM) is introduced for spectral quantitative analysis of complex samples.
Liangliang, Shen +4 more
openaire +2 more sources
Intelligence & Robotics
Due to the strong noise, high dimensionality and time-varying characteristics of industrial process data, data-driven modeling faces challenges in feature extraction and model interpretability. To address these issues, this paper proposes a new prediction model based on adaptive variational empirical mode decomposition-guided (AVEMDG) graph ...
Yujun Chen +4 more
openaire +1 more source
Due to the strong noise, high dimensionality and time-varying characteristics of industrial process data, data-driven modeling faces challenges in feature extraction and model interpretability. To address these issues, this paper proposes a new prediction model based on adaptive variational empirical mode decomposition-guided (AVEMDG) graph ...
Yujun Chen +4 more
openaire +1 more source