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Fault diagnosis in electric motors using multi-mode time series and ensemble transformers network. [PDF]

Sci Rep
Xu B, Li H, Ding R, Zhou F.
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Predicting Pose Distribution of Protein Domains Connected by Flexible Linkers Is an Unsolved Problem. [PDF]

Proteins
McBride AC, Yu F, Cheng EH, Mpouli A, Soe AC, Hammel M, Montelione GT, Oas TG, Tsutakawa SE, Donald BR.   +9 more
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A method for degradation features extraction of diesel engine valve clearance based on modified complete ensemble empirical mode decomposition with adaptive noise and discriminant correlation analysis feature fusion

Journal of Vibration and Control, 2021
The health assessment of the valve clearance is a key link to realize the failure prediction and health management of the valve mechanism. To accurately evaluate the state of valve clearance, this article proposes a diesel engine valve clearance degradation feature extraction method based on modified complete ensemble empirical mode decomposition with
Yun Ke, Yihuai Hu, Enzhe Song, Chong Yao, Quan Dong   +4 more
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Accent extraction of emotional speech based on modified ensemble empirical mode decomposition

2010 IEEE Instrumentation & Measurement Technology Conference Proceedings, 2010
Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method to solve the mode mixing problem caused by empirical mode decomposition (EMD), which is a significant step of Hilbert-Huang Transform (HHT). In this paper, a novel fast EEMD preferences algorithm called Quasi-Gradient Search (QGS) is proposed.
Zhiyuan Shen, Qiang Wang, Yi Shen, Jing Jin, Yurong Lin   +4 more
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Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm

Science of The Total Environment, 2020
The accurate prediction of carbon prices poses a tremendous challenge to relevant industry practitioners and governments. This paper proposes a novel hybrid model incorporating modified ensemble empirical mode decomposition (MEEMD) and long short-term memory (LSTM) optimized by the improved whale optimization algorithm (IWOA).
Shaomei Yang, Dongjiu Chen, Shengli Li, Weijun Wang   +3 more
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Modified complementary ensemble empirical mode decomposition and intrinsic mode functions evaluation index for high-speed train gearbox fault diagnosis

Journal of Sound and Vibration, 2018
Abstract Complementary ensemble empirical mode decomposition (CEEMD) has been developed for the mode-mixing problem in Empirical Mode Decomposition (EMD) method. Compared to the ensemble empirical mode decomposition (EEMD), the CEEMD method reduces residue noise in the signal reconstruction.
Dongyue Chen, Jianhui Lin, Yanping Li
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A novel sensor fault diagnosis method based on Modified Ensemble Empirical Mode Decomposition and Probabilistic Neural Network

Measurement, 2015
Abstract A novel fault diagnosis method based on Modified Ensemble Empirical Mode Decomposition (MEEMD) and Probabilistic Neural Network (PNN) is presented in this paper. It aims to achieve more accurate and reliable sensor fault diagnosis in thermal power plant.
Yunluo Yu, Wei Li, Deren Sheng, Jianhong Chen   +3 more
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Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine

Natural Hazards, 2012
In this paper, an M–EEMD–ELM model (modified ensemble empirical mode decomposition (EEMD)-based extreme learning machine (ELM) ensemble learning paradigm) is proposed for landslide displacement prediction. The nonlinear original surface displacement deformation monitoring time series of landslide is first decomposed into a limited number of intrinsic ...
Cheng Lian, Zhigang Zeng, Wei Yao, Huiming Tang   +3 more
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The modified ensemble empirical mode decomposition method and extraction of oceanic internal wave from synthetic aperture radar image

Journal of Shanghai Jiaotong University (Science), 2015
In this paper a modified ensemble empirical mode decomposition (EEMD) method is presented, which is named winning-EEMD (W-EEMD). Two aspects of the EEMD, the amplitude of added white noise and the number of intrinsic mode functions (IMFs), are discussed in this method.
Jing-tao Wang, Xiao-ge Xu, Xiang-hua Meng   +2 more
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