Complete Ensemble Empirical Mode Decomposition With Adaptive Noise Algorithm Research Articles

Hourly photovoltaic power prediction based on signal decomposition and deep learning

Accurate photovoltaic (PV) power prediction is important for the utilization of solar energy resources. However, PV power is non-stationary due to the variable influence of meteorological factors, which poses a challenge for accurate forecasting. In this paper, a hybrid method based on signal decomposition and a deep learning model is proposed. The hybrid model integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the Informer model. The CEEMDAN algorithm is used to separate different modes from the photovoltaic power sequence, enhancing its predictability. The deep learning model, the Informer, is employed to capture the complex relationship between photovoltaic power data and its historical data as well as external meteorological factors, ultimately enabling multi-step forecasting of photovoltaic power data. In hourly PV power forecasting experiments using a public dataset, the model exhibits significant performance improvements when compared to benchmark models such as LSTM, GRU, and Transformer. Specifically, the RMSE reduces by 6.07%-34.74% and the MAE reduces by 7.07%-37.5%. The results demonstrate that the hybrid model exhibits accurate predictive performance in the task of hourly photovoltaic power forecasting.

Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

Due to the complex noise in the ocean environment, the signal-to-noise ratio of the hydrophone receiving signal is often low, making subsequent signal processing difficult. To solve this problem, this paper proposes using CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) decomposition algorithm combined with an improved wavelet threshold algorithm to process the signal, and obtain the reconstructed signal after denoising. In this method, the noise-containing signal is transformed by the function and decomposed into multiple natural mode components with frequencies ranging from high to low using the CEEMDAN algorithm. The correlation component and the non-correlation component are then determined using the cross-correlation function. The non-correlated compinents are denoised using the improved wavelet threshold method and the denoised signal is obtained by reconstructing the signal. Experimental results show that this method can improve the performance of underwater acoustic signal denoising.

Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods

The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction framework; the single-time-step output of these models cannot represent the variation trend in the dam deformation, which may contain important information on dam evolution during the prediction period. Compared with the single value prediction, predicting the tendency of dam deformation in the short term can better interpret the dam’s structural health status. Aiming to capture the short-term variation trends of dam deformation, a multi-step displacement prediction model of concrete dams is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the k-harmonic means (KHM) algorithm, and the error minimized extreme learning machine (EM-ELM) algorithm. The model can be divided into three stages: (1) The CEEMDAN algorithm is adopted to decompose dam displacement series into different signals according to their timing characteristics. Moreover, the sample entropy (SE) method is used to remove the noise contained in the decomposed signals. (2) The KHM clustering algorithm is employed to cluster the denoised data with similar characteristics. Furthermore, the sparrow search algorithm (SSA) is utilized to optimize the KHM algorithm to avoid the local optimal problem. (3) A multi-step prediction model to capture the short-term variation of dam displacement is established based on the clustered data. Engineering examples show that the model has good prediction performance and strong robustness, demonstrating the feasibility of applying the proposed model to the multi-step forecasting of dam displacement.

Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation

Global navigation satellite system (GNSS) technique has irreplaceable advantages in the continuous monitoring of surface deformation. Reducing noise to improve the signal-to-noise ratio (SNR) and extract the concerned signals is of great significance. As an improved algorithm of empirical mode decomposition (EMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm has better signal processing ability. Using the CEEMDAN algorithm, the height time series of 29 GNSS stations in Chinese mainland were analyzed, and good denoising effects and extraction from periodic signals were achieved. The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis, and large differences in the long-term signals were found between the stations at different locations in Chinese mainland. With respect to data denoising, compared with the EMD and wavelet denoising algorithms, the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%, increased the correlation coefficient by 8.67% and 11.96%, and reduced root mean square error (RMSE) by 44.68% and 43.48%, indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms. In addition, the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity. The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm, which indicated that the CEEMDAN algorithm had advantages over frequency aliasing. In conclusion, the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.

A Novel Non-Ferrous Metals Price Forecast Model Based on LSTM and Multivariate Mode Decomposition

Non-ferrous metals are important bulk commodities and play a significant part in the development of society. Their price forecast is of great reference value for investors and policymakers. However, developing a robust price forecast model is tricky due to the price’s drastic fluctuations. In this work, a novel fusion model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Singular Spectrum Analysis (SSA), and Long Short-Term Memory (LSTM) is constructed for non-ferrous metals price forecast. Considering the complexity of their price change, the dual-stage signal preprocessing which combines CEEMDAN and SSA is utilized. Firstly, we use the CEEMDAN algorithm to decompose the original nonlinear price sequence into multiple Intrinsic Mode Functions (IMFs) and a residual. Secondly, the component with maximum sample entropy is decomposed by SSA; this is the so-called Multivariate Mode Decomposition (MMD). A series of experimental results show that the proposed MMD-LSTM method is more stable and robust than the other seven benchmark models, providing a more reasonable scheme for the price forecast of non-ferrous metals.

Short‐term traffic travel time forecasting using ensemble approach based on long short‐term memory networks

AbstractTo enhance the control effect of intelligent transportation system (ITS) of expressway, avoid blind travel causing traffic congestion, improve road capacity, and achieve the purpose of smooth and efficient operation of the network, 3σ criterion and 2σ cycle removal criterion were used to eliminate the abnormal data of travel time. Thereafter, a combined prediction model of Long Short‐Term Memory (LSTM) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and attention mechanism is proposed. Travel time data, collected from the real world, is decomposed into several Intrinsic Mode Functions (IMFs) and a residue with the CEEMDAN. Then, the decomposed sub‐sequence is predicted and the final prediction result is obtained by comprehensive superposition. The results indicate that: the CEEMDAN algorithm has significantly improved the prediction performance. Compared with the AT‐LSTM model, mean absolute percentage error (MAPE) is reduced by 10.75%, 24.84%, 31.80%, 24.98%, 14.29%, and 31.42% when the time window was 3, 6, 9, 12, 15, and 18, respectively, in the dataset of Yaxi expressway. In the dataset of Chengnan expressway, the number of error points with MAPE greater than 40% is significantly reduced after the adoption of the LSTM model. After the introduction of CEEMDAN algorithm, the error at each time point is controlled within 30%. The proposed model controls the error at each time point within 25%. In different datasets, the proposed model has better prediction ability for highly non‐linear travel time, and can better capture the trend abrupt characteristics of rush hour traffic flow.

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid.

A Novel Hybrid Price Prediction Model for Multimodal Carbon Emission Trading Market Based on CEEMDAN Algorithm and Window-Based XGBoost Approach

Accurate prediction of the carbon trading price (CTP) is crucial to the decision-making of relevant stakeholders, and can also provide a reference for policy makers. However, the time interval for the CTP is one day, resulting in a relatively small sample size of data available for predictions. When dealing with small sample data, deep learning algorithms can trade only a small improvement in prediction accuracy at the expense of efficiency and computing time. In contrast, fine-grained configurations of traditional model inputs and parameters often perform no less well than deep learning algorithms. In this context, this paper proposes a novel hybrid CTP prediction model based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a windowed-based XGBoost approach. First, the initial CTP data is decomposed into multiple subsequences with relatively low volatility and randomness based on the CEEMDAN algorithm. Then, the decomposed carbon valence series and covariates are subject to windowed processing to become the inputs of the XGBoost model. Finally, the universality of the proposed model is verified through case studies of four carbon emission trading markets with different modal characteristics, and the superiority of the proposed model is verified by comparing with seven other models. The results show that the prediction error of the proposed XGBoost(W-b) algorithm is reduced by 4.72%~81.47% compared to other prediction algorithms. In addition, the introduction of CEEMDAN further reduces the prediction error by 25.24%~89.28% on the basis of XGBoost(W-b).

Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet

Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient power system operation. A hybrid method that is a combination of the similar day (SD) selection, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and deep neural networks is proposed and explored in this paper. For the SD selection, an extreme gradient boosting (XGB)-based weighted k-means method is chosen and applied to evaluate the similarity between the prediction and historical days. The CEEMDAN algorithm, which is an advanced method of empirical mode decomposition (EMD), is used to decompose original data, to acquire intrinsic mode functions (IMFs) and residuals, and to improve the noise reduction effect. Three popular deep neural networks that have been utilized for load predictions are gated recurrent units (GRUs), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The developed models were assessed on a real-life charging load dataset that was collected from 1000 EVs in nine provinces in Canada from 2017 to 2019. The obtained numerical results of six predictive combination models show that the proposed hybrid SD-CEEMDAN-BiLSTM model outperformed the single and other hybrid models with the smallest forecasting mean absolute percentage error (MAPE) of 2.63% Canada-wide.

A Denoising Method of Micro-Turbine Acoustic Pressure Signal Based on CEEMDAN and Improved Variable Step-Size NLMS Algorithm

The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interference and noise in the measured acoustic pressure signal. Therefore, denoising the acoustic pressure signal is the basis of the subsequent research. In this paper, a denoising method of micro-turbine acoustic pressure signal based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variable step-size Normalized Least Mean Square (VSS-NLMS) algorithms is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original signal into multiple intrinsic mode functions (IMFs), based on the cross-correlation coefficient and continuous mean square error (CMSE) criterion; the obtained IMFs are divided into clear IMFs, noise-dominated IMFs, and noise IMFs. Finally, the improved VSS-NLMS algorithm is adopted to denoise the noise-dominated IMFs and combined with the clear IMF for reconstruction to obtain the final denoised signal. Adopting the above principles, the acoustic pressure signals generated by a micro-turbine with different rotation speeds and different states (normal turbine and fractured turbine) are denoised, respectively, and the results are compared with the axial flow fan test (ideal interference-free signal). The results show that the denoising method proposed in this paper has a good denoising effect, and the denoised signal is smooth and the important features are well preserved, which is conducive to the extraction of acoustic pressure signal characteristics.

State‐of‐Health Prediction for Lithium‐Ion Batteries Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise‐Gate Recurrent Unit Fusion Model

State‐of‐health (SOH) estimation is one of the most critical battery management system (BMS) tasks. A challenge remains for the SOH prediction due to the complicated battery aging mechanism. The most common health indicator is the capacity of the lithium‐ion battery. The fluctuation of capacity caused by the capacity regeneration phenomenon can seriously affect the prediction performance. A new complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and gate recurrent unit (GRU) based fusion prediction model for SOH estimation is proposed to solve the problem effectively. First, the CEEMDAN algorithm decomposes the original SOH into local fluctuations and global degradation trends. Then, the GRU network and autoregressive integrated moving average model are used to predict the above trends, respectively. Next, a sliding window is designed to calculate an average value of the global degradation trend prediction residuals. Then, the second GRU algorithm can be used to correct prediction residuals. Finally, the prediction results of the aforementioned parts are combined to obtain the final SOH estimation. The proposed method is verified by experimental battery data from NASA and CALCE datasets. The results show that the fusion method has both higher estimation accuracy and stronger robustness than other methods.

Series arc fault identification based on complete ensemble empirical mode decomposition with adaptive noise and convolutional neural network

The effective identification of series arc faults is of considerable significance for preventing fires in residential buildings. Series arc fault currents and load currents have a similar waveform, and the fault features and nonfault features are superimposed on the current signal. Fault features are deeply hidden, making it difficult to identify them. This work proposes a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) preprocessing and a one-dimensional convolutional neural network (1DCNN). The CEEMDAN algorithm is used to decompose the collected current signals. Then, the intrinsic mode function (IMF) components with no representational significance are eliminated by calculating the Spearman correlation coefficient before inputting it into the 1DCNN. The experimental results showed that the accuracy of the method for the measured load is 99.3%. Compared with the method that directly uses original current signals as model inputs, the recognition accuracy of the algorithm was significantly improved. Therefore, the proposed algorithm can be used for series arc fault identification in residential building power distribution systems.

Empirical mode decomposition denoising of the electroretinogram to enhance measurement of the photopic negative response

Background and objectiveThe photopic negative response (PhNR) of the electroretinogram (ERG) can be useful in the diagnosis and management of glaucoma. Its utility is limited by its large degree of measurement variability, which can be caused by low frequency oscillations and baseline wander. In this work we show a new denoising strategy using the empirical mode decomposition (EMD) which improves repeatability. MethodsERGs were performed on both eyes of 56 patients using red-on-blue stimulus after 2 min of preadaptation, capturing 10 sweeps per recording. Two recordings were obtained to provide intrasession test–retest estimates. Denoising was performed by removing the residue after the decomposition with the EMD, the Ensemble EMD (EEMD) and the Complete ensemble EMD with adaptive noise (CEEMDAN). The PhNR was measured as the amplitude, relative to the prestimulus baseline, of the first negative going trough after the b wave. The coefficient of repeatability (CoR) was calculated to represent 95% of the test–retest differences for the PhNR between sessions. ResultsDenoising with all of the varieties of the EMD improved the test–retest repeatability of the PhNR. The best results were achieved with the CEEMDAN algorithm, which for the PhNR amplitude improved the coefficient of repeatability relative to the effective dynamic range from 20.2% to 8.3%. ConclusionsThe EMD can be used to remove low frequency baseline drift and oscillation from the ERG and improve the test–retest repeatability of PhNR measurements. This technique could be deployed for a range of visual and other electrophysiological tests.

A method to identify wet ball mill’s load based on CEEMDAN, RCMDE and SRNN classification

Ball mill plays a key role in mineral processing plant, and its load identification for optimal control has great significance for the energy consumption reduction and production efficiency improvement. The vibration signal of ball mill shell contains abundant load information, which can be used to identify ball mill load. However, due to the non-linear and non-stationary characteristics of vibration signals, as well as the heavy background noises, the load identification becomes a challenging task in practice. In this paper, a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), refined composite multi-scale dispersion entropy (RCMDE), and stacked recurrent neural network (SRNN) is proposed. First, CEEMDAN algorithm is used to decompose the ball mill’s vibration signals and obtain the intrinsic mode function (IMF) components. Then, the sensitive IMF components are selected through the correlation coefficient method, and the signal is reconstructed with the sensitive IMF components. Secondly, the RCMDE of the reconstructed signal is calculated to obtain the load feature vector, and the dimension of the feature vector is reduced by principle component analysis (PCA). Thirdly, the SRNN is applied to establish a load recognition model, taking the feature vector as its input and the load state as its output. The experimental results show encouraging accuracy to apply this approach to recognize the wet ball mill’s load, with a recognition rate of 98.67%.

Detection algorithm for magnetic dipole target based on CEEMDAN and pattern recognition

Abstract Due to the physical characteristics that the magnetic dipole target signal (MDTS) decays with the third power of distance and the fact that the measured data contain usually environmental magnetic noise such as diurnal variation noise and cultural noise, it is very difficult to detect long-distance magnetic targets. In this paper, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm which is an improved version of empirical mode decomposition (EMD) algorithm and pattern recognition algorithm are combined to construct a novel magnetic target detection algorithm(CEEMDAN-PR). CEEMDAN algorithm can effectively decompose the measured magnetic signal into multiple intrinsic mode functions (IMFs), and reduce the aliasing effect between modes. Then, the pattern formed by the characteristics of magnetic dipole signal is used to match the signal reconstructed by the sum of several IMFs to obtain the optimal reconstructed signal. Some simulation tests illustrate that this algorithm has good detection performance.

A Novel Deep Learning Approach to Predict the Instantaneous NOₓ Emissions From Diesel Engine

Accurate and stable prediction of NOx emissions from diesel vehicles plays a crucial role in the establishment of virtual NOx sensors and the development and design of diesel engines. This paper presents a method for estimating transient NOx emissions by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a long- and short-term memory neural network (LSTM). First, the CEEMDAN algorithm is used to reduce the non-stationarity and volatility of the transient NOx emission data to obtain multiple subseries with different frequencies. Secondly, a predictive model is developed for each subsequence using an LSTM neural network. Finally, the results of each subsequence prediction are summed to obtain the final prediction. The proposed model uses NOx emission data generated by an EU IV diesel bus during real road driving. The results show that (1) The use of CEEMDAN can effectively improve the smoothness of NOx transient emission data, as well as facilitate more effective extraction of internal characteristics and variations of the raw data. (2) LSTM has better learning and prediction capability for transient changes in NOx emissions. (3) The results of CEEMDAN-LSTM for RMSE, R2, MAE and NRMSE are 46.11,0.98, 29.82 and 2.71, respectively, which are better than the other model with improved prediction performance.

Short Term Load Forecasting Based on SBiGRU and CEEMDAN-SBiGRU Combined Model

With the continuous development of global science and technology industry, the demand for power is increasing, so short-term power load forecasting is particularly important. At present, a large number of load forecasting models have been applied to short-term load forecasting, but most of them ignore the error accumulation in the iterative training process. To solve this problem, this article proposes a combined measurement model which combines stacked bidirectional gated recurrent unit (SBiGRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and error correction. In the first stage, SBiGRU model is established to study the time series characteristics of load series under the influence of temperature and holiday types. The error series generated in the prediction process of SBiGRU model reflects the error characteristics of load series; In the second stage, the error sequence is decomposed into several intrinsic mode functions (IMF) components and trend components by CEEMDAN algorithm. The SBiGRU model is established again for each component to learn and predict, and the predicted values of all components are reconstructed to get the error prediction results; Finally, sum the two-stage prediction results to correct the error. The accuracy of SBiGRU-CEEMDAN-SBiGRU combination model is evaluated by two public power load data. The experimental results show that the SBiGRU-CEEMDAN-SBiGRU combination model has better accuracy and stability than the traditional model.

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy.

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

Seismic exploration desert noise suppression based on complete ensemble empirical mode decomposition with adaptive noise

A foundation for the imaging and interpretation of stratigraphic structures is the high quality seismic data. The desert seismic events are contaminated by strong random noise. The random noise in desert seismic records has complex features, including nonlinear, non-Gaussian and low frequency, which is different from the random noise in people's previous cognition. In addition, the effective signal of desert seismic record and noise has the same frequency band. These situations have brought great difficulties to the denoising of conventional methods. While the conventional denoising algorithm for dealing with seismic signals is mainly for the suppression of random Gaussian noise. So the suppression result of random desert noise is not ideal. Based on these problems, we introduce the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm into the noise reduction process of desert seismic records. Compared with other time-frequency analysis methods, CEEMDAN algorithm has significant feature recognition effect, and remains the characteristics of adaptability. At the same time, it overcomes the problems of modal aliasing and false components. The experimental results show that denoising effect is obviously better than those of conventional denoising methods, and the suppression of the surface wave in the real seismic record is relatively thorough.

Adaptive Ultrasound Tissue Harmonic Imaging Based on an Improved Ensemble Empirical Mode Decomposition Algorithm

Complete and accurate separation of harmonic components from the ultrasonic radio frequency (RF) echo signals is essential to improve the quality of harmonic imaging. There are limitations in the existing two commonly used separation methods, that is, the subjectivity for the high-pass filtering (S_HPF) method and motion artifacts for the pulse inversion (S_PI) method. A novel separation method called S_CEEMDAN, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, is proposed to adaptively separate the second harmonic components for ultrasound tissue harmonic imaging. First, the ensemble size of the CEEMDAN algorithm is calculated adaptively according to the standard deviation of the added white noise. A set of intrinsic mode functions (IMFs) is then obtained by the CEEMDAN algorithm from the ultrasonic RF echo signals. According to the IMF spectra, the IMFs that contain both fundamental and harmonic components are further decomposed. The separation process is performed until all the obtained IMFs have been divided into either fundamental or harmonic categories. Finally, the fundamental and harmonic RF echo signals are obtained from the accumulations of signals from these two categories, respectively. In simulation experiments based on CREANUIS, the S_CEEMDAN-based results are similar to the S_HPF-based results, but better than the S_PI-based results. For the dynamic carotid artery measurements, the contrasts, contrast-to-noise ratios (CNRs), and tissue-to-clutter ratios (TCRs) of the harmonic images based on the S_CEEMDAN are averagely increased by 31.43% and 50.82%, 18.96% and 10.83%, as well as 34.23% and 44.18%, respectively, compared with those based on the S_HPF and S_PI methods. In conclusion, the S_CEEMDAN method provides improved harmonic images owing to its good adaptivity and lower motion artifacts, and is thus a potential alternative to the current methods for ultrasonic harmonic imaging.