Compatible matching and synergy operation optimization of hydrogen-electric hybrid energy storage system in DC microgrid by Banghua Du, Yanyu Peng, Yang Li, Changjun Xie, Shihao Zhu, Wenchao Zhu, Yang Yang, Li You, Leiqi Zhang, Bo Zhao

“…Accordingly, this paper proposes a compatible matching and synergy operation optimization for hydrogen-electric hybrid energy storage systems (H-E HESS). Firstly, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is developed to decompose power fluctuation signals into frequency components, and the Hilbert transform calculates the energy value to determine high- and low-frequency dividing points. …”
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Remaining Useful Life Predictionmethod for Lithium-ion Batteries Based on CEEMD-AKF by CHEN Xiang, XIA Fei

“…Firstly, the battery historical capacity was decomposed into a set of intrinsic mode functions (IMFs) and one residue based on the complementary ensemble empirical mode decomposition (CEEMD). …”
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Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM by Taiyong Li, Zijie Qian, Ting He

“…To improve the effectiveness of STLF, this paper proposes a novel approach integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO), and multiple kernel extreme learning machine (MKELM), namely, ICEEMDAN-GWO-MKELM, for STLF, following this framework. …”
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Detection of Low-Flying Target under the Sea Clutter Background Based on Volterra Filter by Hongyan Xing, Yan Yan

“…In order to detect low-flying small targets in complex sea condition effectively, we study the chaotic characteristic of sea clutter, use joint algorithm combined complete ensemble empirical mode decomposition (CEEMD) with wavelet transform to de-noise, and put forward a detection method for low-flying target under the sea clutter background based on Volterra filter. …”
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A Combined Prediction Model for Hog Futures Prices Based on WOA-LightGBM-CEEMDAN by Xiang Wang, Shen Gao, Yibin Guo, Shiyu Zhou, Yonghui Duan, Daqing Wu

“…An integrated hog futures price forecasting model based on whale optimization algorithm (WOA), LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. …”
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Multi-Frequency Information Flows between Global Commodities and Uncertainties: Evidence from COVID-19 Pandemic by Emmanuel Asafo-Adjei, Siaw Frimpong, Peterson Owusu Junior, Anokye Mohammed Adam, Ebenezer Boateng, Robert Ofori Abosompim

“…Consequently, we utilise the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the Rényi effective transfer entropy techniques to establish the dynamic flow of information. …”
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Improved Butterfly Optimizer-Configured Extreme Learning Machine for Fault Diagnosis by Helong Yu, Kang Yuan, Wenshu Li, Nannan Zhao, Weibin Chen, Changcheng Huang, Huiling Chen, Mingjing Wang

“…Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions (IMFs) using the complete ensemble empirical mode decomposition based on adaptive noise (CEEMDAN). …”
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GNSS Precipitable Water Vapor Prediction for Hong Kong Based on ICEEMDAN-SE-LSTM-ARIMA Hybrid Model by Jie Zhao, Xu Lin, Zhengdao Yuan, Nage Du, Xiaolong Cai, Cong Yang, Jun Zhao, Yashi Xu, Lunwei Zhao

“…In the study of GNSS-PWV prediction, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm within a decomposition–integration framework effectively addresses the non-stationarity and complexity of PWV sequences, enhancing prediction accuracy. …”
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Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience by Aastha M. Sathe, Supraja R., Aditya Antony Thomas

“…In this study, we offer a dual-decomposition hybrid time series forecasting model that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and variational mode decomposition (VMD) with gated recurrent unit (GRU) neural networks. …”
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CEEMDAN-MRAL Transformer Vibration Signal Fault Diagnosis Method Based on FBG by Hong Jiang, Zhichao Wang, Lina Cui, Yihan Zhao

“…The vibration signal is decomposed into several intrinsic mode functions (IMFs) by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the wavelet threshold denoising algorithm improves the signal-to-noise ratio (SNR) to 1.6 times. …”
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WEAK FAULT FEATURE EXTRACTION OF ROLLING BEARING BASED ON PARAMETER OPTIMIZED MOMEDA AND CEEMDAN by HAN XueFei, SHI Zhan, HUA YunSong

“…Aiming at the problem that the fault feature information of rolling bearing is weak under the strong background noise environment,and the single use of the complete ensemble empirical mode decomposition with adaptive noise（ CEEMDAN）method is not effective in extracting the fault feature,a method based on parameter optimized multi-point optimal minimum entropy deconvolution adjusted（ POMOMEDA） and CEEMDAN was proposed. …”
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A novel twin time series network for building energy consumption predicting. by Zhixin Sun, Han Cui, Xiangxiang Mei, Hailei Yuan

“…To overcome these issues, the study proposes Twin Time-Series Networks (T2SNET), which incorporates a time-embedding layer and a Temporal Convolutional Network (TCN) to extract patterns from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), along with an adaptive fusion gate to combine energy consumption and meteorological data. …”
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Fault diagnosis technology for three-level inverter based on ICEEMDAN-FE and SVM by CAO Ruijun, GUO Qiyi

“…In order to improve the accuracy to diagnose complex open-circuit faults for three-level inverters, a new fault diagnosis method of three-level inverters was proposed, combining improved complete ensemble empirical mode decomposition with adaptive noise-fuzzy entropy (ICEEMDAN-FE) and support vector machine (SVM). …”
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ICEEMDAN–VMD denoising method for enhanced magnetic memory detection signal of micro-defects by Shouhong Ji, Jie Yan, Yang Liu, Guojun He

“…During the enhanced magnetic memory detection of micro-defects, many interference signals appear in the detection signal, which makes it difficult to accurately extract the characteristics of the micro-defect signals, significantly affecting detection effectiveness. When improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed independently for signal denoising, the noise and feature signals of the transition components are retained or removed. …”
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A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD by Chenguang Huang, Jianhui Lin, Jianming Ding, Yan Huang

“…A novel fault diagnosis method, named CPS, is proposed based on the combination of CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), PSM (periodic segment matrix), and SVD (singular value decomposition). …”
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An Improved CEEMDAN-FE-TCN Model for Highway Traffic Flow Prediction by Heyao Gao, Hongfei Jia, Lili Yang

“…Firstly, an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is applied to decompose the nonlinear time series of highway traffic flow to obtain the intrinsic mode function (IMF). …”
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Dynamic Monitoring of a Bridge from GNSS-RTK Sensor Using an Improved Hybrid Denoising Method by Chunbao Xiong, Zhi Shang, Meng Wang, Sida Lian

“…The improved hybrid denoising method consists of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the detrended fluctuation analysis (DFA), and an improved wavelet threshold denoising method. …”
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An Improved Nonlinear Health Index CRRMS for the Remaining Useful Life Prediction of Rolling Bearings by Yongze Jin, Xubo Yang, Junqi Liu, Yanxi Yang, Xinhong Hei, Anqi Shangguan

“…In the degradation state, a dimensionless prediction index CRRMS is constructed, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold. …”
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A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization by Chunguang He, Dianhai Wang, Yi Yu, Zhengyi Cai

“…This paper presents a hybrid deep learning method that combines the gated recurrent unit (GRU) neural network model with automatic hyperparameter tuning based on Bayesian optimization (BO) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) model. …”
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An improved deep learning model for soybean future price prediction with hybrid data preprocessing strategy by Dingya CHEN, Hui LIU, Yanfei LI, Zhu DUAN

“…In the data preprocessing stage, futures price series are decomposed into subsequences using the ICEEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) method. …”
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