Search Results (65)

In the light of the worsening of, and the adverse effects produced by, global warming, a study of Shanghai’s transport carbon emissions can provide an advanced model that can be replicated throughout other cities, thus assisting in the management and reduction of carbon emissions. Considering the volatility and nonlinearity of the carbon emission data series of the transport industry, a prediction model combining complementary ensemble empirical modal decomposition (CEEMD), the improved whale optimization algorithm (IWOA), and the Kernel Extreme Learning Machine (KELM) is proposed for a more accurate prediction of the forecasting of carbon emissions from Shanghai’s transport sector. First, nine indicators were screened as the influencing factors of Shanghai’s transport carbon emissions through the STIRPAT model, and the corresponding carbon emissions were calculated with data related to Shanghai’s transport carbon emissions from 1995 to 2019; Secondly, CEEMD was used to decompose the original data into multiple smooth series and one residual term, and KELM was applied to build a prediction model for each decomposition result, and IWOA was used to optimize the model parameters. The experimental results also demonstrate that CEEMD can effectively reduce model errors. Comparative experiments show that the IWOA algorithm can significantly enhance the stability of machine learning models. The outcomes of various experiments indicate that the CEEMD-IWOA-KELM model produces optimal results with the highest accuracy. Additionally, this model exhibits high stability, as it provides a wider range of methods for predicting carbon emissions and contributing to carbon reduction targets. Full article

In order to investigate the analysis and processing methods for nonstationary signals generated in bridge health monitoring systems, this study combines the advantages of complete ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising algorithms to construct the CEEMD–wavelet threshold denoising algorithm. The algorithm follows the following steps: first, add noise to the monitoring data and obtain all the mode components through empirical mode decomposition (EMD), denoise the mode components with noise using the wavelet threshold function to remove the noise components, select the optimal stratification for denoising the monitoring data of the Guozigou Bridge in Xinjiang in January 2023, determine the wavelet type and threshold selection criteria, and reconstruct the denoised intrinsic mode function (IMF) components to achieve accurate extraction of the effective signal. By referencing the deflection, temperature, and strain data of the Guozigou Bridge in Xinjiang in January 2023 and comparing the data cleaned by different mode decomposition and wavelet threshold denoising methods, the results show that compared with empirical mode decomposition (EMD)–wavelet threshold denoising and variational mode decomposition (VMD)–wavelet threshold denoising, the signal-to-noise ratios and root-mean-square errors of the four types of monitoring data obtained by the algorithm proposed in this study are the most ideal. Under the premise of minimizing reconstruction errors when processing a large amount of data, it has better convergence, verifying the practicality and reliability of the algorithm in the field of bridge health monitoring data cleaning and providing a certain reference value for further research in the field of signal processing. The computational method constructed in this study will provide theoretical support for data cleaning and analysis of nonstationary and nonlinear random signals, which is conducive to further promoting the improvement of bridge health monitoring systems. Full article

With the rapid development of China’s civil aviation, the flow of air traffic in terminal areas is also increasing. Short-term air traffic flow prediction is of great significance for the accurate implementation of air traffic flow management. To enhance the accuracy of short-term air traffic flow prediction, this paper proposes a short-term air traffic flow prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and long short-term memory (LSTM) of the Bayesian optimization algorithm and data differential processing. Initially, the model performs CEEMD on the short-term air traffic flow series. Subsequently, to improve prediction accuracy, the data differencing is employed to stabilize the time series. Finally, the smoothed sequences are, respectively, input into the LSTM network model optimized by the Bayesian optimization algorithm for prediction. After data reconstruction, the final short-term flow prediction result is obtained. The model proposed in this paper is verified by using the data from Shanghai Pudong International Airport. The results show that the evaluation indexes of the prediction accuracy and fitting degree of the model, RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R² (Coefficient of Determination), are 0.336, 0.239, and 97.535%, respectively. Compared to other classical time-series prediction models, the prediction accuracy is greatly improved, which can provide a useful reference for short-term air traffic flow prediction. Full article

Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space compression of EEG data using microstate analysis. Following this, we assess the non-randomness of these microstate patterns using information-based similarity analysis. The results suggest that this MSNRI metric is a potential marker for distinguishing between health control (HC) and frontotemporal dementia (FTD) (HC vs. FTD: 6.958 vs. 5.756, p < 0.01), as well as between HC and populations with Alzheimer’s disease (AD) (HC vs. AD: 6.958 vs. 5.462, p < 0.001). Healthy individuals exhibit more complex macroscopic structures and non-random spatiotemporal patterns of microstates, whereas dementia disorders lead to more random spatiotemporal patterns. Additionally, we extend the proposed method by integrating the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to explore spatiotemporal dynamic patterns of microstates at specific frequency scales. Moreover, we assessed the effectiveness of this innovative method in predicting cognitive scores. The results demonstrate that the incorporation of CEEMD-enhanced microstate dynamic indicators significantly improved the prediction accuracy of Mini-Mental State Examination (MMSE) scores (R² = 0.940). The CEEMD-enhanced MSNRI method not only aids in the exploration of large-scale neural changes in populations with dementia but also offers a robust tool for characterizing the dynamics of EEG microstate transitions and their impact on cognitive function. Full article

The welding process itself is a non-linear, multivariable, coupled physical metallurgical process that is easily perturbed. Improper welding parameter selection and welding process conditions will interfere with the welding process and affect the final welding quality. This study aims to identify and locate two types of welding interference, insufficient shielding gas and unremoved oxidation film on the base metal surface, during the Pulse Multi-Control Gas Metal Arc Welding (PMC GMAW) process of aluminum alloy. The Characteristic Intrinsic Mode Function (IMF), which is closely related to the short circuit transition process, was obtained by applying the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to the welding current signal measured during the welding process. Time and frequency domain analysis of the acquired characteristic IMF was then performed. The experimental results demonstrated that for a stable welding process, the frequency of the characteristic IMF is concentrated within a narrow range. The frequency spectrum of the characteristic IMF exhibits distinct variations under different types of welding interference. Based on this, the chronological arrangement of characteristic IMF components’ frequency spectrum allows for locating welding interferences by analyzing their abnormal signals within the reconstructed signal sequence. Full article

The rail is an important factor in railway traffic safety. Surface defects in the rail head comprise a common type of rail damage, and magnetic flux leakage (MFL) technology is applied for its detection. MFL detection is influenced by various factors, resulting in high noise and a low signal-to-noise ratio (SNR) in the collected MFL signal, which influence defect assessment. This article improves the empirical wavelet transform (EWT) to apply it to rail surface-defect MFL signal filtering. A boundary optimization method based on mutual information (MI) is proposed to reduce the boundary redundancy caused by adaptive spectrum division. A method for component selection based on MI and kurtosis is proposed to select the suitable components from the decomposed components for signal reconstruction. The experimental results show that the method can effectively filter out the interference in the MFL signal, and the effectiveness is superior to the traditional methods, such as complementary ensemble empirical mode decomposition (CEEMD) and wavelet transform (WT). Full article

In order to solve the noise reduction problem of acoustic emission signals with cracks, a method combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and wavelet packet (WPT) is proposed and named CEEMD-WPT. Firstly, the single Empirical Mode Decomposition (EMD) used in the traditional CEEMD is improved into the WPT-EMD with a more stable noise reduction effect. Secondly, after decomposition, the threshold value of the correlation coefficient is determined for the Intrinsic Mode Function (IMF), and the low correlation component is further processed by WPT. In addition, in order to solve the problem that it is difficult to quantify the real signal noise reduction effect, a new quantization index “principal interval coefficient (PIC)” is designed in this paper, and its reliability is verified through simulation experiments. Finally, noise reduction experiments are carried out on the real crack acoustic emission dataset consisting of tensile, shear, and mixed signals. The results show that CEEMD-WPT has the highest number of signals with a principal interval coefficient of 0–0.2, which has a better noise reduction effect compared with traditional CEEMD and Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Moreover, the statistical variance of CEEMD-WPT is evidently one order of magnitude smaller than that of CEEMD, so it has stronger stability. Full article

Climate change is harmful to ecosystems and public health, so the concern about climate change has been aroused worldwide. Studies indicated that greenhouse gas emission with CO2 as the main component is an important factor for climate change. Countries worldwide are on the same page that low-carbon development is an effective way to combat climate change. Enhancing public concern about low-carbon development and climate change has a positive effect on universal participation in carbon emission reduction. Therefore, it is significant to study the trend of public concern about low carbon and its relationship with CO2 emissions. Currently, no related studies are available, so this research explores the relationship between the public concern about low carbon and CO2 emissions of China, as well as the respective trends of each. Based on the daily data of Baidu-related keyword searches and CO2 emission, this research proposes the GMM-CEEMD-SGIA-LSTM hybrid model. The GMM is utilized to construct a comprehensive Baidu index (CBI) to reflect public concern about low carbon by clustering keywords search data. CEEMD and SGIA are applied to reconstruct sequences for analyzing the relationship between CBI and CO2 emissions. Then LSTM is utilized to forecast CBI. The reconstructed sequences show that there is a strong correlation between CBI and CO2 emissions. It is also found that CBI affects CO2 emissions, with varying effect lag times for different periods. Compared to LSTM, RF, SVR, and RNN models, the proposed model is reliable for forecasting public concern with a 46.78% decrease in MAPE. The prediction results indicate that public concern about low carbon shows a fluctuating upward trend from January 2023 to January 2025. This research could improve understanding of the relationship between public concern about low carbon and CO2 emissions to better address climate change. Full article

Aiming at the problem of mutual interference between millimeter-wave frequency-modulation continuous-wave (FMCW) radars, an interference mitigation method based on outlier detection and variational mode decomposition (VMD) is proposed in this paper. Firstly, by differential processing of the raw millimeter-wave FMCW radar data, combined with threshold detection, the interfered sample area is located. Adaptive amplitude limiting is applied to the interfered samples to achieve initial suppression of the interference. Then, based on the VMD algorithm, the processed data are adaptively decomposed to obtain multiple intrinsic mode functions (IMFs). The Pearson correlation coefficient between each IMF and the signal before decomposition is calculated, and the IMF with the maximum Pearson correlation coefficient is extracted as the signal component to achieve the separation of the target signal from the interference and noise. The proposed method was validated based on simulation and experimental data. The results show that the proposed method achieves the best performance in terms of signal-to-interference-plus-noise ratio (SINR), mean square error (MSE), and kurtosis in frequency (KF) compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition (CEEMD). Further comparison was made with two typical methods, and the Range–Doppler (RD) map and SINR results showed that the proposed method exhibited certain performance advantages. Full article

Precipitation prediction is an important technical mean for flood and drought disaster early warning, rational utilization, and the development of water resources. Complementary ensemble empirical mode decomposition (CEEMD) can effectively reduce mode aliasing and white noise interference; extreme learning machines (ELM) can predict non-stationary data quickly and easily; and the fruit fly optimization algorithm (FFOA) has better local optimization ability. According to the multi-scale and non-stationary characteristics of precipitation time series, a new prediction approach based on the combination of complementary ensemble empirical mode decomposition (CEEMD), extreme learning machine (ELM), and the fruit fly optimization algorithm (FFOA) is proposed. The monthly precipitation data measured in Zhengzhou City from 1951 to 2020 was taken as an example to conduct a prediction experiment and compared with three prediction models: ELM, EMD-HHT, and CEEMD-ELM. The research results show that the sum of annual precipitation predicted by the CEEMD-ELM-FFOA model is 577.33 mm, which is higher than the measured value of 572.53 mm with an error of 4.80 mm. The average absolute error is 0.81 and the average relative error is 1.39%. The prediction value of the CEEMD-ELM-FFOA model can closely follow the changing trend of precipitation, which shows a better prediction effect than the other three models and can be used for regional precipitation prediction. Full article

Precipitation is a vital component of the regional water resource circulation system. Accurate and efficient precipitation prediction is especially important in the context of global warming, as it can help explore the regional precipitation pattern and promote comprehensive water resource utilization. However, due to the influence of many factors, the precipitation process exhibits significant stochasticity, uncertainty, and nonlinearity despite having some regularity. In this article, monthly precipitation in Zhoukou City is predicted using a complementary ensemble empirical modal decomposition (CEEMD) method combined with a long short-term memory neural network (LSTM) model and a least squares support vector machine (LSSVM) model. The results demonstrate that the CEEMD-LSTM-LSSVM model exhibits a root mean square error of 15.01 and a mean absolute error of 11.31 in predicting monthly precipitation in Zhoukou City. The model effectively overcomes the problems of modal confounding present in empirical modal decomposition (EMD), the existence of reconstruction errors in ensemble empirical modal decomposition (EEMD), and the lack of accuracy of a single LSTM model in predicting modal components with different frequencies obtained by EEMD decomposition. The model provides an effective approach for predicting future precipitation in the Zhoukou area and predicts monthly precipitation in the study area from 2023 to 2025. The study provides a reference for relevant departments to take effective measures against natural disasters and rationally plan urban water resources. Full article

Global navigation satellite system (GNSS) positions include various useful signals and some unmodeled errors. In order to enhance the accuracy and extract the features of the GNSS daily time sequence, an improved method of complete ensemble empirical mode decomposition (CEEMD) and multi-PCA (MPCA) based on correlation coefficients and block spatial filtering was proposed. The results showed that the mean standard deviations of the raw residual time sequence were 1.09, 1.20 and 4.79 mm, while those of the newly proposed method were 0.15, 0.20 and 2.86 mm in north, east and up directions, respectively. The proposed method outperforms wavelet decomposition (WD)-PCA and empirical mode decomposition (EMD)-PCA in effectively eliminating low- and high-frequency noise, and is suitable for denoising nonlinear and nonstationary GNSS position sequences. Furthermore, feature extraction of the denoised GNSS daily time series was based on CEEMD, which is superior to WD and EMD. Results of noise analysis suggested that the noise components in the original and denoised GNSS time sequence are complex. The advantages of the proposed method are the following: (i) it fully exploits the merits of CEEMD and WD, where CEEMD is first used to obtain the limited intrinsic modal functions (IMFs) and then to extract seasonal and trend features; (ii) it has good adaptive processing ability via WD for noise-dominant IMFs; and (iii) it fully considers the correlation between the different components of each station and the non-uniform behavior of common mode error on a spatial scale. Full article

Noises in the GPS vertical coordinate time series, mainly including the white and flicker noise, have been proven to impair the accuracy and reliability of GPS products. Various methods were adopted to weaken the white and flicker noises in the GPS time series, such as the complementary ensemble empirical mode decomposition (CEEMD), wavelet denoising (WD), and variational mode decomposition (VMD). However, a single method only works at a limited frequency band of the time series, and the corresponding denoising ability is insufficient, especially for the flicker noise. Hence, in this study, we try to build two combined methods: CEEMD & WD and VMD & WD, to weaken the flicker noise in the GPS positioning time series from the Crustal Movement Observation Network of China. First, we handled the original signal using CEEMD or VMD with the appropriate parameters. Then, the processed signal was further denoised by WD. The results show that the average flicker noise in the time series was reduced from 19.90 mm/year^0.25 to 2.8 mm/year^0.25. This relates to a reduction of 86% after applying the two methods to process the GPS data, which indicates our solutions outperform CEEMD by 6.84% and VMD by 16.88% in weakening the flicker noise, respectively. Those apparent decreases in the flicker noises for the two combined methods are attributed to the differences in the frequencies between the WD and the other two methods, which were verified by analyzing the power spectrum density (PSD). With the help of WD, CEEMD & WD and VMD & WD can identify more flicker noise hidden in the low-frequency signals obtained by CEEMD and VMD. Finally, we found that the two combined methods have almost identical effects on removing the flicker noise in the time series for 226 GPS stations in China, testified by the Wilcoxon rank sum test. Full article

Sudden cardiac death (SCD) is a global health problem, which represents 15–20% of global deaths. This type of death can be due to different heart conditions, where ventricular fibrillation has been reported as the main one. These cardiac alterations can be seen in an electrocardiogram (ECG) record, where the heart’s electrical activity is altered. The present research uses these variations to be able to predict 30 min in advance when the SCD event will occur. In this regard, a methodology based on the complete ensemble empirical mode decomposition (CEEMD) method to decompose the cardiac signal into its intrinsic mode functions (IMFs) and a convolutional neural network (CNN) for automatic diagnosis is proposed. Results for the ensemble empirical mode decomposition (EEMD) method and the empirical mode decomposition (EMD) method are also compared. Results demonstrate that the combination of the CEEMD and the CNN is a potential solution for SCD prediction since 97.5% of accuracy is achieved up to 30 min in advance of the SCD event. Full article

Time series classification (TSC) is always a very important research topic in many real-world application domains. MultiRocket has been shown to be an efficient approach for TSC, by adding multiple pooling operators and a first-order difference transformation. To classify time series with higher accuracy, this study proposes a hybrid ensemble learning algorithm combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) with improved MultiRocket, namely CEEMD-MultiRocket. Firstly, we utilize the decomposition method CEEMD to decompose raw time series into three sub-series: two Intrinsic Mode Functions (IMFs) and one residue. Then, the selection of these decomposed sub-series is executed on the known training set by comparing the classification accuracy of each IMF with that of raw time series using a given threshold. Finally, we optimize convolution kernels and pooling operators, and apply our improved MultiRocket to the raw time series, the selected decomposed sub-series and the first-order difference of the raw time series to generate the final classification results. Experiments were conducted on 109 datasets from the UCR time series repository to assess the classification performance of our CEEMD-MultiRocket. The extensive experimental results demonstrate that our CEEMD-MultiRocket has the second-best average rank on classification accuracy against a spread of the state-of-the-art (SOTA) TSC models. Specifically, CEEMD-MultiRocket is significantly more accurate than MultiRocket even though it requires a relatively long time, and is competitive with the currently most accurate model, HIVE-COTE 2.0, only with 1.4% of the computing load of the latter. Full article