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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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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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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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

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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

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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

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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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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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Redefining volatility forecasting in the aerospace and defense sector: application of CEEMDAN-GARCH models by Viviane Naimy, Tatiana Abou Chedid, Omar Abou Saleh, Nicolas Bitar

“…Abstract This study pioneers the integration of Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and advanced GARCH models (IGARCH, SGARCH, and GJR-GARCH) to analyze the volatility of aerospace and defense indices across four countries: China, South Korea, France, and the United Kingdom. …”
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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

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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 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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Research on the rapid diagnosis method for hunting of high-speed trains by Wanru Xie, Yixin Zhao, Gang Zhao, Fei Yang, Zilong Wei, Jinzhao Liu

“…Design/methodology/approach – First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. …”
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Condition Monitoring and Predictive Maintenance in Industrial Equipment: An NLP-Assisted Review of Signal Processing, Hybrid Models, and Implementation Challenges by Jose Garcia, Luis Rios-Colque, Alvaro Peña, Luis Rojas

“…It also explores essential signal processing tools (e.g., Fast Fourier Transform (FFT), wavelets, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)) and methods for estimating Remaining Useful Life (RUL) while highlighting major challenges such as the scarcity of labeled data, the need for model explainability, and adaptation to evolving operational conditions. …”
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Enhanced Short-Term PV Power Forecasting via a Hybrid Modified CEEMDAN-Jellyfish Search Optimized BiLSTM Model by Yanhui Liu, Jiulong Wang, Lingyun Song, Yicheng Liu, Liqun Shen

“…This study proposes a novel hybrid forecasting model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the jellyfish search (JS) optimization algorithm, and a bidirectional long short-term memory (BiLSTM) neural network. …”
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Multidimensional Meteorological Variables for Wind Speed Forecasting in Qinghai Region of China: A Novel Approach by He Jiang, Luo Shihua, Yao Dong

“…The objective is to develop a hybrid model with multidimensional meteorological variables for forecasting the wind speed accurately. The complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to handle the nonlinearity of the wind speed. …”
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CEEMDAN-Based Permutation Entropy: A Suitable Feature for the Fault Identification of Spiral-Bevel Gears by Lingli Jiang, Hongchuang Tan, Xuejun Li, Liman Chen, Dalian Yang

“…The vibration signals of spiral-bevel gears are typically quite complicated, as they present both nonlinear and nonstationary characteristics and are interfered with by strong noise. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method has been proven to be an effective method for analyzing this kind of signal. …”
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Long-Term Hourly Ozone Forecasting via Time–Frequency Analysis of ICEEMDAN-Decomposed Components: A 36-Hour Forecast for a Site in Beijing by Taotao Lv, Yulu Yi, Zhuowen Zheng, Jie Yang, Siwei Li

“…However, few studies have been able to reliably provide long-term hourly ozone forecasts due to the complexity of ozone’s diurnal variations. To address this issue, this study constructs a hybrid prediction model integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bi-directional long short-term memory neural network (BiLSTM), and the persistence model to forecast the hourly ozone concentrations for the next continuous 36 h. …”
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