Prediction of dam deformation using adaptive noise CEEMDAN and BiGRU time series modeling by WANG Zixuan, OU Bin, CHEN Dehui, YANG Shiyong, ZHAO Dingzhu, FU Shuyan

“…【Method】The model uses sample entropy reconstruction and the K-means clustering algorithm to optimize the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) process, generating multiple intrinsic mode functions (IMF). …”
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Accurate earthquake and mining tremor identification via a CEEMDAN-LSTM framework by Chao Wang, Xiufeng Zhang, Weiming Gao, Fangfang Wang, Jianqi Lu, Jianqi Lu, Zhaoyang Yan, Zhaoyang Yan

“…By identifying short-period surface waves in the given data and utilizing an improved complete ensemble empirical mode decomposition method with adaptive noise (CEEMDAN) in combination with long short-term memory (LSTM) networks, we conduct a discriminative analysis of seismic events in Liaoning, China, and Japan. …”
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Vibration Characteristics of Double-Shield TBM Cutterhead Under Rock–Machine Interaction Excitation by Guang Zhang, Qing Song, Qiuming Gong, Dongxing Liu, Dongwei Li, Minghao Sun

“…A denoising method combining Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Multi-scale Permutation Entropy (MPE) was applied for signals reconstruct. …”
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Feature extraction and fault diagnosis of gearbox based on ICEEMDAN, MPE, RF and SVM by DING Xiaofeng, ZHANG Yuhua

“…To solve the challenges related to non-stationary vibration signals in gearboxes, i.e. difficult feature extraction, high redundancy of feature vectors and low fault identification rate, this paper proposed a method of feature extraction and fault diagnosis of gearboxes based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multi-scale permutation entropy (MPE), random forest (RF) feature importance ranking and support vector machine (SVM). …”
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An Integrated CEEMDAN to Optimize Deep Long Short-Term Memory Model for Wind Speed Forecasting by Yingying He, Likai Zhang, Tengda Guan, Zheyu Zhang

“…To address these challenges, this study proposes a novel method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep learning-based Long Short-Term Memory (LSTM) network for wind speed forecasting. …”
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Composite fault feature extraction for gears based on MCKD-EWT adaptive wavelet threshold noise reduction by Yanchang LV, Jingyue Wang, Chengqiang Zhang, Jianming Ding

“…The results of experimental data analysis show that compared with the feature extraction methods such as spatial scale threshold EWT-MCKD and Complete Ensemble Empirical Mode Decomposition (CEEMDAN)-MCKD, the proposed method is more suitable for the diagnosis of gear composite faults in a strong background noise environment, the noise interference is effectively suppressed, and the extraction effect of gear composite fault features is more obvious.…”
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Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model by Liwei Zhang, Lisang Liu, Wenwei Chen, Zhihui Lin, Dongwei He, Jian Chen

“…This paper proposes a learning model named CECSVB-LSTM, which integrates several advanced techniques: a bidirectional long short-term memory (BILSTM) network, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), variational mode decomposition (VMD), and the Sparrow Search Algorithm (CSSSA) incorporating circle chaos mapping and the Sine Cosine Algorithm. …”
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Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening. by Yaoyang Yi

“…First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the carbon price time series once, extract high-frequency and low-frequency components, and denoise the high-frequency components using stacked denoising autoencoder (SDAE). …”
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Research on de-noising method of optical fiber grating sensing signal based on S-G and CEEMDAN technology by HUANG Gang, PAN Like, HE Chengzhang, YANG Chengfeng, LI Zhong, GONG Shaokang

“…Aiming at the problems of low accuracy and poor reliability of existing signal de-noising methods in the signal processing of fiber Bragg grating sensing system, based on the principle of fiber Bragg grating sensing, a signal de-noising method of fiber Bragg grating sensing system combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and S-G filtering method is proposed. …”
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RESEARCH OF EARLY FAULT FEATURE EXTRACTION OF SOLAR WHEEL BASED ON PARAMETRIC ADAPTIVE ICEEMDAN AND MCKD by ZHAO Naizhuo, ZHAO Yumeng, MEN Chengfu

“…In order to solve the problem of difficult to accurately extract early faults of solar wheels under the strong noise background, an improved grey wolf algorithm (newGWO) was proposed to optimize and improve the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the maximum correlated kurtosis deconvolution (MCKD) for early fault feature extraction of solar wheels.NewGWO was used to optimize the selection of parameters of the white noise amplitude weight and noise addition times that affected the decomposition effect.The fault vibration signal was decomposed by newGWO-ICEEMDAN, and the minimum envelope entropy was selected as the fitness function to obtain several related modal components.Then, the envelope spectrum peak factor was selected as the best modal component index.MCKD signals optimized by newGWO were enhanced for the selected optimal intrinsic mode function （IMF） components. …”
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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms. By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and fuzzy entropy (FE) with the new and highly efficient Runge–Kuta optimizer (RUN), adaptive parameter optimization for the support vector machine (SVM) and radial basis function neural network (RBFNN) algorithms was achieved. …”
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BWO–ICEEMDAN–iTransformer: A Short-Term Load Forecasting Model for Power Systems with Parameter Optimization by Danqi Zheng, Jiyun Qin, Zhen Liu, Qinglei Zhang, Jianguo Duan, Ying Zhou

“…First, the parameters of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). …”
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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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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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Research on the volatility characteristics and evolutionary mechanism of the “Asian premium” for natural gas by Jian Chai, Mingxiao Zhao, Xiaokong Zhang, Na Li, Zhefei Zhang, Zenghui Liu

“…First, this paper used the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) model to decompose and reconstruct natural gas “Asian premium.” …”
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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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Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction by Chaoyu Shi, Bohao Chen, Yao Shi, Jun Zha

“…Firstly, the signal was decomposed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm, and the sensitive components were selected based on the correlation coefficient between Intrinsic Mode Functions (IMFs) and the original signal. …”
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A Novel Joint Denoising Strategy for Coherent Doppler Wind Lidar Signals by Yuefeng Zhao, Wenkai Song, Nannan Hu, Xue Zhou, Jiankang Luo, Jinrun Huang, Qianqian Tao

“…This paper proposes a novel joint denoising algorithm based on SVD-ICEEMDAN-SCC-MF to remove noises in CDWL detection. The SVD-ICEEMDAN-SCC-MF consists of singular value decomposition (SVD), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), Spearman correlation coefficient (SCC), and median filtering (MF). …”
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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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