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Drought, a critical consequence of water scarcity and climate change, profoundly impacts human life. This study introduces a new W-EEMD-ARIMA hybrid model to forecast drought using Kabul's monthly precipitation data from 1970 to 2019. By integrating Ensemble Empirical Mode Decomposition (EEMD) and wavelet transform, we enhance the ARIMA/SARIMA model. Comparing the accuracy of our proposed method with ARIMA, Wavelet-ARIMA, and EEMD-ARIMA, using a training dataset (1970–2009) and validation data (2010–2019), we observed superior performance in our proposed W-EEMD-ARIMA across both datasets and all Standardized Precipitation Index (SPI) values. For SPI 12 validation, our model achieves an RMSE of 0.0736, MAE of 0.0575, MAPE of 18.9674, and R-squared of 0.9946, surpassing ARIMA (RMSE: 0.2561, MAE: 0.1874, MAPE: 60.0220, R-squared: 0.9361), Wavelet-ARIMA (RMSE: 0.1002, MAE: 0.0691, MAPE: 23.7122, R-squared: 0.9898), and EEMD-ARIMA (RMSE: 0.0858, MAE: 0.0660, MAPE: 24.5411, R-squared: 0.9925). Across SPI 3, 6, and 9, our hybrid model consistently outperforms others in both training and testing datasets, with lower RMSE, MAE, and MAPE, alongside higher R-squared values. These findings illustrate the superiority of our hybrid proposed model in enhancing drought prediction accuracy over the ARIMA, Wavelet-ARIMA, and EEMD-ARIMA approaches. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate electricity load forecasting is crucial for efficient energy management, allowing utility companies to optimize resource allocation, reduce operational costs, and ensure a reliable power supply. Various factors, such as weather conditions, economic activities, and demographic changes, significantly influence electricity load. To address these challenges, we designed a deep learning Bidirectional Long Short-Term Memory (BiLTSM-based model) for electricity load forecasting, leveraging weather data as the primary feature and signal decomposition as an additional feature. To select the best weather parameters, we utilize the Principal Component Analysis (PCA) and Correlation Coefficient (CC) to choose the most correlated weather parameters as inputs for the model. Moreover, we introduce spatial correlation to select the best weather locations. Additionally, we perform a signal decomposition technique using the Ensemble Empirical Mode Decomposition (EEMD) to decompose the historical load signal into Intrinsic Mode Functions (IMFs). We also investigate procedures to choose the best IMF to give the best accuracy. As a case study, we investigate the electricity load data in the Jakarta-Banten region of Indonesia, known for its high consumer and industrial activity. The results demonstrate that combining the BiLSTM model and EEMD achieves high prediction accuracy, with a Correlation Coefficient (CC) of 0.956 and a Mean Absolute Percentage Error (MAPE) of 2.824%. [ABSTRACT FROM AUTHOR]

The mining face under the close coal seam group is affected by the superposition of the concentrated stress of the overlying residual diagonally intersecting coal pillar and the mining stress, which can easily cause the instability and damage of the section coal pillars during the process of mining back to the downward face. Additionally, the traditional methods of monitoring such as numerical simulation, drilling peeping, and acoustic emission fail to realize the real-time and accurate deformation monitoring of the internal deformation of the section coal pillars. The introduction of the drill-hole-implanted fiber-optic grating monitoring method can realize real-time deformation monitoring for the whole area inside the coal pillar, which solves the short board problem of coal pillar deformation monitoring. However, fiber-optic monitoring is easily disturbed by the external environment, which is especially sensitive to the background noise of the complex underground mining environment. Therefore, taking the live chicken and rabbit well of Shaanxi Daliuta Coal Mine as the engineering background, the ensemble empirical modal decomposition (EEMD) is introduced for primary noise reduction and signal reconstruction by the threshold determination (DE) algorithm, and then the singular matrix decomposition (SVD) is introduced for secondary noise reduction. Finally, a machine learning algorithm is combined with the noise reduction algorithm for the prediction of the fiber grating strain signals of coal pillar in a zone, and DBO-LSTM-BP is constructed as the prediction model. The experimental results demonstrate that compared with the other two noise reduction prediction models, the SNR of the EEMD-DE-SVD-DBO-LSTM-BP model is improved by 0.8–2.3 dB on average, and the prediction accuracy is in the range of 88–99%, which realizes the over-advanced prediction of the deformation state of the coal column in the section. [ABSTRACT FROM AUTHOR]

This work combines the Support Vector Machine (SVM) model with Ensemble Empirical Mode Decomposition (EEMD) to present a novel method for drought prediction. The EEMD-SVM model is assessed for drought forecasting, comparing it to conventional Auto Regressive Integrated Moving Average (ARIMA) and SVM models. This study uses monthly precipitation data from Bamyan province in Central Afghanistan, spanning January 1970 to December 2019, including Standardized Precipitation Index (SPI) timelines: SPI 3, SPI 6, SPI 9, and SPI 12. To evaluate predictive effectiveness, statistical measures such as R-squared (R²), mean absolute error (MAE), and root-mean-square error (RMSE) are used. Each SPI series is decomposed into Intrinsic Mode Functions (IMFs) and a residual series using the EEMD approach. The next stage projects each IMF component and residual using the appropriate SVM model. The final step creates an ensemble forecast for the original SPI series by combining the anticipated results of the residual series with the modeled IMFs. Compared to traditional ARIMA and SVM models, results show that the EEMD-SVM technique greatly improves drought forecasting accuracy, especially for mid- and long-term SPI. For example, in the testing period, SPI 9 yields an RMSE of 0.1632, MAE of 0.1208, and R² of 0.9357, while for SPI 12, RMSE is 0.1078, MAE is 0.0745, and R² is 0.9141, indicating the best criteria with the lowest RMSE and MAE and highest R² compared to conventional ARIMA and SVM. This novel technology could enhance the capacity to forecast drought episodes, leading to more efficient water resource management and climate adaptation plans. [ABSTRACT FROM AUTHOR]

Analysing the variations in hydrological cycle components is essential for water resources planning and management. In this study, the relationship between the streamflow data belonging to five discharge gauging stations in the Eastern Black Sea Basin in Türkiye and the Arctic Oscillation (AO), East Atlantic-Western Russia (EAWR), North Atlantic Oscillation (NAO) and North Sea Caspian Pattern (NCP) was investigated. For this purpose, Spearman’s correlation test, ensemble empirical mode decomposition (EEMD) and relative importance analysis were used. Accordingly, Spearman’s correlation coefficients were calculated between raw streamflow data, decomposed streamflow data via EEMD and atmospheric teleconnections. Then, the relative importance analysis was applied to determine the atmospheric teleconnections’ influences on streamflow data. The findings showed that the relationship between raw streamflow data and atmospheric teleconnections is generally more significant and negative in the winter and spring. Furthermore, it was observed that the linkage between the decomposed streamflow data and atmospheric teleconnections could differentiate. Although no significant correlation between atmospheric teleconnections and raw streamflow data was detected in some months, significant correlations were detected between atmospheric teleconnections and decomposed streamflow data. This reveals the importance of examining the relationship between atmospheric teleconnections and streamflow data for different periods. The relative importance analysis revealed that the influence of atmospheric teleconnections on streamflow data could change from station to station and from component to component. This study showed that investigating the effects of atmospheric teleconnections on streamflow data for different components and periods is important.

This paper assesses quantitatively the impact of COVID-19 on the Capesize as well as the Panamax sectors using the event analysis method based on the ensemble empirical mode decomposition (EEMD). Different components are decomposed from the freight rates time series. The decomposed dominant modes manage to capture the event of COVID-19, accounting for the major movements of the freight market. The event of 2008 financial crisis is utilized as a benchmark to analyze the impact of COVID-19. Compared to the 2008 financial crisis, the dominant modes during COVID-19 rebound in quite a short time. Factors contributing to the decline of freight rates during the COVID-19 pandemic are relatively simpler than during the 2008 financial crisis, indicating that the COVID-19 should not have a long-lasting impact on the dry bulk shipping market. The results not only prove that the method used in this paper is capable of evaluating the impact of future events on the freight market but also provide some practical implications to the maritime industry. [ABSTRACT FROM AUTHOR]

Prediction of grain consumption is crucial for analyzing the changing trend and balancing the grain supply and demand in China. Recently, the use of generative adversarial networks (GAN) to capture the distribution of historical data for generating future data has gained attention in time-series prediction. In order to enhance prediction performance and address model instability, an improved Wasserstein GAN with gradient penalty, referred to as IWGAN-GP, is proposed. The IWGAN-GP utilizes a bidirectional long short-term memory neural network (BiLSTM) as the generator and a convolutional neural network (CNN) as the discriminator, combining the memory capabilities of LSTM with the nonlinear feature extraction capabilities of CNN. Specifically, the loss function of the generator incorporates the mean square error (MSE) between real and generated samples to optimize the LSTM network, while the loss function of the discriminator includes the L1 norm as the gradient penalty term to enhance sparsity and robustness, in contrast to the L2 norm used in existing WGAN-GP models. Experimental results on grain consumption data from 1981 to 2020 demonstrate that the proposed IWGAN-GP improves prediction accuracy compared to BiLSTM, GAN, and WGAN-GP models. [ABSTRACT FROM AUTHOR]

• Automatic selection of embedding dimension in Singular spectrum analysis is achieved. • Singular spectrum analysis yields the most stable and accurate sea level heights among the methods. • Singular spectrum analysis provides a better concentration of spectral power in the periodogram. In order to obtain reliable GNSS-R derived sea level heights (SLHs), it is crucial to extract accurate reflected signals from signal-to-noise ratio (SNR) data. In this study, Quadratic Fitting, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Singular Spectrum Analysis (SSA) are adopted for extracting the reflected signals from SNR data, which are then analyzed with Lomb Scargle Periodogram (LSP) assisted with tidal harmonic analysis for GNSS-R SLHs. Three continuous GNSS stations including Onsala in Sweden, Friday Harbor in the United States, and Brest in France with tidal ranges of approximately 1 m, 3 m, and 7 m, respectively, were used in the study. The derived SLHs were evaluated against the nearby or co-located tide gauge records. The results reveal that the three proposed algorithms effectively cope with the multi-peaks spectrum problems when using the conventional Quadratic Fitting without a priori reflected height constraint, and SSA can provide the solutions with fewer outliers among the proposed methods with the RMSEs of 6.7 cm, 13.4 cm and 39.6 cm at Onsala, Friday Harbor, and Brest stations, respectively. Without implementing tidal harmonic analysis, SSA is the method capable of acquiring reliable SLHs among the stations, as evidenced by comparing with co-located gauge records, particularly at the Brest station. In summary, SSA not only distinguishes signals across different frequencies, but also orders the signal components according to their eigenvalues, demonstrating the potential to extract the reflected signals from SNR and enhance the stability and accuracy of GNSS-R applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Hazards & Environment (JNHE) / Doğal Afetler ve Çevre Dergisi (DACD) is the property of Artvin Coruh University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the reliability and life of the wind turbine, this paper takes the rolling bearing in the experimental platform of the wind turbine as the research object. In order to obtain the intrinsic mode function (IMF) of each fault type, the original signals of different fault states of the rolling bearing on the experimental platform are decomposed by using the overall average empirical mode decomposition method (EEMD) and the wavelet packet decomposition method (WPD), respectively. Then the energy ratio of the IMF component of the different types of faults to the total energy value is calculated and the eigenvectors of different types of faults are constructed. The extreme learning machine (ELM) and probabilistic neural network (PNN) are used to learn fault types and eigenvector samples to identify the faults of the rolling bearing. It is found that the bearing fault characteristics obtained by the WPD method are more obvious, and the results obtained by the same recognition method are ideal; and the PNN method is obviously superior to the extreme learning machine method in bearing fault recognition rate. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Attributing vegetation changes and assessment of its temporal response patterns can provide valuable information for natural resource management, especially in fragile ecosystems. Hence, this study investigates the dynamics and temporal response patterns of different land use classes based on the Normalized Difference Vegetation Index (NDVI) in Pakistan's Haro River Basin (HRB) as a test study. So, for this purpose, advanced analytical tools such as Breaks for Additive Season and Trend (BFAST), Ensemble Empirical Mode Decomposition (EEMD), Residual Trend Analysis, and correlation coefficients were employed. Based on overall analysis, a significant increasing trend in monthly NDVI changes was observed between 2002 and 2021 and showcasing a positive correlation with climatic factors in diverse land use classes. Spatial analysis revealed distinct variations in the time lag response between climatic parameters and NDVI, with approximately 36.38% and 11.38% of the area exhibiting statistically significant lag time effects of 0-month and 1-month, respectively. The analysis revealed varying rates of relative contributions of climatic change (ranging from 71.88 to 78.26%) and anthropogenic activities (i.e., 21.74–31.25%). Notably, based on individual land use classes, climate change also emerged as the dominant driving factor, accounting for more than 55% of the observed changes to different classes as well. This study breaks new ground by using advanced methods to understand how climate and human activities shape vegetation in different River basins, offering crucial insights for global ecological research and restoration efforts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Amidst the backdrop of global climate change, it is imperative to comprehend the intricate connections among surface water, vegetation, and climatic shifts within watersheds, especially in fragile, arid ecosystems. However, these relationships across various timescales remain unclear. We employed the Ensemble Empirical Mode Decomposition (EEMD) method to analyze the multifaceted dynamics of surfacewater and vegetation in the Bosten Lake Watershed acrossmultiple temporal scales. This analysis has shed light on how these elements interact with climate change, revealing significant insights. From March to October, approximately 14.9-16.8% of the areas with permanent water were susceptible to receding and drying up. Both the annual and monthly values of Bosten Lake's level and area exhibited a trend of initial decline followed by an increase, reaching their lowest point in 2013 (1,045.0 m and 906.6 km2, respectively). Approximately 7.7% of vegetated areas showed a significant increase in the Normalized Difference Vegetation Index (NDVI). NDVI volatility was observed in 23.4% of vegetated areas, primarily concentrated in the southern part of the study area and near Lake Bosten. Regarding the annual components (6 < T < 24 months), temperature, 3-month cumulative NDVI, and 3-month-leading precipitation exhibited the strongest correlation with changes in water level and surface area. For the interannual components (T= 24 months), NDVI, 3-month cumulative precipitation, and 3-month-leading temperature displayed the most robust correlation with alterations in water level and surface area. In both components, NDVI had a negative impact on Bosten Lake's water level and surface area, while temperature and precipitation exerted positive effects. Through comparative analysis, this study reveals the importance of temporal periodicity in developing adaptive strategies for achieving Sustainable Development Goals in dryland watersheds. This study introduces a robust methodology for dissecting trends within scale components of lake level and surface area and links these trends to climate variations and NDVI changes across different temporal scales. The inherent correlations uncovered in this research can serve as valuable guidance for future investigations into surface water dynamics in arid regions. [ABSTRACT FROM AUTHOR]

Analog circuit is an crucial component of electronic equipment, and the ability to diagnose its fault state quickly and accurately is essential for ensuring the safety and reliability of these electronic equipment. This paper addresses the problems of low diagnostic accuracy and the difficulties associated with model parameter selection in traditional fault diagnosis methods, particularly when dealing with nonlinear and non-stationary fault signals. A fault diagnosis method for analog circuits is proposed, which utilizes Ensemble Empirical Mode Decomposition (EEMD) for features extraction, the Maximum Information Coefficient algorithm (MIC) for features selection, and Particle Swarm Optimization (PSO) for optimizing Support Vector Machine (SVM) classification. Firstly, EEMD is used to adaptively decompose the fault signals in the circuit to extract multi-scale fault features. Secondly, the extracted features are quantitatively evaluated using the Pearson correlation coefficient and energy value analysis, leading to the construction of a fault feature vector is constructed. On this basis, the MIC feature selection algorithm is applied to further optimize the feature vector. Finally, an efficient fault classification model is developed by optimizing the hyperparameters of the SVM model using PSO. The simulation results show that the proposed method effectively overcome the problems of model complexity and low classification accuracy caused by improper selection of wavelet basis function. The accuracy of fault diagnosis and the efficiency of model training are significantly superior to those of traditional methods.

Aiming at the problem of waveform distortion of the secondary traveling wave obtained from the primary traveling wave signal transmitted by the voltage traveling wave sensor, an accurate detection method of voltage traveling wave based on ensemble empirical mode decomposition (EEMD) and L1 norm least squares regularization is proposed in this paper. Firstly, the forward transfer function model of the specialized voltage traveling wave sensor is established, and the inversion model is derived to analyze the phase frequency and amplitude frequency characteristics of the secondary traveling wave after transmission. EEMD is used to decompose the secondary traveling wave into the intrinsic modal function (IMF) components of different frequency bands. The L1 norm least square regularization inversion algorithm was used to invert each IMF component. Finally, the IMF' components after inversion are synthesized to obtain the primary traveling wave. Simulation and experimental results demonstrate that the proposed method is not affected by noise, mode aliasing effect and regularization parameter accuracy, and the obtained waveform has high similarity to the simulated waveform, which enables precise identification of fault traveling wave.

This study introduces ensemble empirical mode decomposition (EEMD) coupled with the autoregressive integrated moving average (ARIMA) model for drought prediction. In the realm of drought forecasting, we assess the EEMD-ARIMA model against the traditional ARIMA approach, using monthly precipitation data from January 1970 to December 2019 in Herat province, Afghanistan. Our evaluation spans various timescales of standardized precipitation index (SPI) 3, SPI 6, SPI 9, and SPI 12. Statistical indicators like root-mean-square error, mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 are employed. To comprehend data features thoroughly, each SPI series initially computed from the original monthly precipitation time series. Subsequently, each SPI undergoes decomposition using EEMD, resulting in intrinsic mode functions (IMFs) and one residual series. The next step involves forecasting each IMF component and residual using the corresponding ARIMA model. To create an ensemble forecast for the initial SPI series, the predicted outcomes of the modeled IMFs and residual series are finally added. Results indicate that EEMD-ARIMA significantly enhances drought forecasting accuracy compared to conventional ARIMA model. HIGHLIGHTS Improved drought forecasting: Our study introduces the ensemble empirical mode decomposition–autoregressive integrated moving average (ARIMA) model, enhancing drought forecasting accuracy over traditional ARIMA methods.; New drought model for Western Afghanistan: We present a customized model for drought prediction in Western Afghanistan, based on the standardized precipitation index.;

Abstract In this study, we present the EEG-GCN, a novel hybrid model for the prediction of time series data, adept at addressing the inherent challenges posed by the data's complex, non-linear, and periodic nature, as well as the noise that frequently accompanies it. This model synergizes signal decomposition techniques with a graph convolutional neural network (GCN) for enhanced analytical precision. The EEG-GCN approaches time series data as a one-dimensional temporal signal, applying a dual-layered signal decomposition using both Ensemble Empirical Mode Decomposition (EEMD) and GRU. This two-pronged decomposition process effectively eliminates noise interference and distills the complex signal into more tractable sub-signals. These sub-signals facilitate a more straightforward feature analysis and learning process. To capitalize on the decomposed data, a graph convolutional neural network (GCN) is employed to discern the intricate feature interplay within the sub-signals and to map the interdependencies among the data points. The predictive model then synthesizes the weighted outputs of the GCN to yield the final forecast. A key component of our approach is the integration of a Gated Recurrent Unit (GRU) with EEMD within the GCN framework, referred to as EEMD-GRU-GCN. This combination leverages the strengths of GRU in capturing temporal dependencies and the EEMD's capability in handling non-stationary data, thereby enriching the feature set available for the GCN and enhancing the overall predictive accuracy and stability of the model. Empirical evaluations demonstrate that the EEG-GCN model achieves superior performance metrics. Compared to the baseline GCN model, EEG-GCN shows an average R2 improvement of 60% to 90%, outperforming the other methods. These results substantiate the advanced predictive capability of our proposed model, underscoring its potential for robust and accurate time series forecasting.

The mining face under the close coal seam group is affected by the superposition of the concentrated stress of the overlying residual diagonally intersecting coal pillar and the mining stress, which can easily cause the instability and damage of the section coal pillars during the process of mining back to the downward face. Additionally, the traditional methods of monitoring such as numerical simulation, drilling peeping, and acoustic emission fail to realize the real-time and accurate deformation monitoring of the internal deformation of the section coal pillars. The introduction of the drill-hole-implanted fiber-optic grating monitoring method can realize real-time deformation monitoring for the whole area inside the coal pillar, which solves the short board problem of coal pillar deformation monitoring. However, fiber-optic monitoring is easily disturbed by the external environment, which is especially sensitive to the background noise of the complex underground mining environment. Therefore, taking the live chicken and rabbit well of Shaanxi Daliuta Coal Mine as the engineering background, the ensemble empirical modal decomposition (EEMD) is introduced for primary noise reduction and signal reconstruction by the threshold determination (DE) algorithm, and then the singular matrix decomposition (SVD) is introduced for secondary noise reduction. Finally, a machine learning algorithm is combined with the noise reduction algorithm for the prediction of the fiber grating strain signals of coal pillar in a zone, and DBO-LSTM-BP is constructed as the prediction model. The experimental results demonstrate that compared with the other two noise reduction prediction models, the SNR of the EEMD-DE-SVD-DBO-LSTM-BP model is improved by 0.8–2.3 dB on average, and the prediction accuracy is in the range of 88–99%, which realizes the over-advanced prediction of the deformation state of the coal column in the section.

Ensemble empirical mode decomposition(EEMD)can objectively and truly extract useful information from nonlinear and non-stationary signals, and the waveform signal characterized by water level in seismic observation well is also a typical nonlinear and non-stationary signal, so that EEMD has important application potential in obtaining the co-seismic response information of well water level. Taking well Tangshanin Hebei as an example, the advantages and disadvantages of EEMD for well water level analysis and processing through its co-seismic response of multiple well water levels from 2016 to 2023 were analyzed, the co-seismic response characteristics of well Tangshan were identified to tele seismic and near-seismic well water levels, and the seismic recording ability of well Tangshan was inferred by using the empirical formula of seismic energy density. The results show that after EEMD processing, the reconstruction of appropriate high-frequency components can suppress noise interference, which is more conducive to observing the co-seismic response characteristics of well water level. The oscillating co-seismic response caused by far-field large earthquakes can be identified and extracted objectively and truly. For the study of step-varying co-seismic response caused by near-field earthquakes, it is necessary to combine the original data to identify through high-frequency components. The second value of well water level observation data is more conducive to revealing the change of regional stress field. Due to the improvement of the recording accuracy of second value of observation data, the earthquake with energy density of 1.77×10-7 J?m-3could be record by water level of well Tangshan. The sensitivity of observation wells to earthquakes in different orientations can be used to study the orientation of the fracture in which they are located. For far-field earthquakes over a certain distance, the co-seismic response frequency of well water level recorded by the well-aquifer system can calculate the natural vibration frequency of observation well within a certain range, and the natural vibration frequency of well Tangshan is close to the seismic Rayleigh wave. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mechanical Strength / Jixie Qiangdu is the property of Zhengzhou Research Institute of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing literature primarily examined the impact of unexpected events on the stock market at a single scale, posing the challenge of a lack of multiscale analysis. This paper investigates the impact of COVID-19 on stock markets (China, the U.S., and Hong Kong) from a multiscale perspective using an improved ensemble empirical mode decomposition (EEMD)-based event analysis method. First, the stock price series is decomposed into several independent intrinsic mode functions (IMFs) and a residue. Second, a novel composition method is proposed to reconstruct the IMFs into three components: high-frequency, low-frequency, and long-term trend. We find that the composition of low-frequency and long-term trend components is dominant, which is used to estimate the strength of COVID-19 impact on the stock markets. In addition, the outbreak of COVID-19 significantly increased the intensity of short-term fluctuations in stock prices. Finally, the high-frequency component is analyzed to capture the volatility spillover effects among the three stock markets by the BEKK(Baba-Engle-Kraft-Kroner)-GARCH model. The results show that before the outbreak, there are two-way volatility spillovers between any two of the three markets. After the outbreak, there is no spillover effect between China and Hong Kong, and Hong Kong has no spillover effect on the U.S. However, volatility in the U.S. market still has a significant spillover effect on the other two markets, implying that a mature market can absorb new information more quickly. [ABSTRACT FROM AUTHOR]

The quality of the intermediate point temperature control of a supercritical unit is directly related to the quality of the coal–water ratio and main steam temperature control of the supercritical unit, which is also related to the economy and safety of the unit. In order to improve the accuracy of short-term predictions of the intermediate point temperature, a short-term prediction model of the intermediate point temperature based on the EEMD (Ensemble Empirical Mode Decomposition)-LSTM (Long Short-Term Memory) model is proposed. This model uses the data of a 600 MW thermal power station in 2022 as a sample. The EEMD method is used to decompose the historical data into IMF components and residual components, and the correlation between each component and the original data is calculated. The relevant components are sent to the LSTM neural network, and all the sub-components are superimposed to obtain the final intermediate point temperature prediction results. At the same time, the BP and LSTM models are built to compare the errors with the proposed model. The results show that the single model will produce large errors when predicting the factors of large data fluctuations. The EEMD–LSTM coupling model can fully extract the detailed features and the prediction effect is obvious. The prediction accuracy of the EEMD–LSTM prediction model built in this paper is significantly better than that of the other two models. It has certain application value in the research field of intermediate point temperature prediction and can meet the requirements of short-term predictions of the intermediate point temperature. [ABSTRACT FROM AUTHOR]

As an important factor that directly affects agricultural production, the social economy, and policy implementation, observed changes in dry/wet conditions have become a matter of widespread concern. However, previous research has mainly focused on the long-term linear changes of dry/wet conditions, while the detection and evolution of the non-linear trends related to dry/wet changes have received less attention. The non-linear trends of the annual aridity index, obtained by the Ensemble Empirical Mode Decomposition (EEMD) method, reveal that changes in dry/wet conditions in China are asymmetric and can be characterized by contrasting features in both time and space in China. Spatially, most areas in western China have experienced transitions from drying to wetting, while opposite changes have occurred in most areas of eastern China. Temporally, the transitions occurred earlier in western China compared to eastern China. Research into the asymmetric spatial characteristics of dry/wet conditions compensates for the inadequacies of previous studies, which focused solely on temporal evolution; at the same time, it remedies the inadequacies of traditional research on linear trends over centennial timescales. Analyzing the non-linear trend also provides for a more comprehensive understanding of the drying/wetting changes in China. [ABSTRACT FROM AUTHOR]

Driven by the desire for feasible and convenient healthcare, non-contact heart rate (HR) monitoring based on consumer-grade cameras has gained significant recognition among researchers. However, this technology suffers from performance reliability and consistency in realistic situations of motion artifacts, illumination variations, and skin tones, limiting it to emerge as an alternative to conventional methods. Considering these challenges, this paper suggests an effective technique for HR measurement from facial RGB videos. The face being the region of interest (ROI) is divided into several small sub-ROIs of even size. A group of quality sub-ROIs is formed and weighted based on the fundamental periodicity coefficient to handle spatial non-uniform illumination and facial motions. Five different color spaces are considered, and the most suitable color component from each space is chosen to alleviate the influence of temporal illumination variation and other factors. The resultant color signals are denoised using the ensemble empirical mode decomposition and integrated using the principal component analysis to derive a pulsating component representing the blood volumetric changes for HR computation. Experiments are conducted over three standard datasets, namely PURE, UBFC, and COHFACE. The obtained mean absolute error values are 1.16 beats per minute (bpm), 1.56 bpm, and 2.10 bpm for PURE, UBFC, and COHFACE datasets, respectively, indicating the performance of the technique well above the clinically acceptable threshold. In comparison, the technique showed performance superiority over the state-of-art methods. These outcomes substantiate the potential of alternative color spaces for accurate and reliable HR monitoring from facial videos in challenging scenarios. [ABSTRACT FROM AUTHOR]

Accurate forecasting of future electricity consumption is necessary to create a satisfactory design for an electricity distribution system. To enhance forecasting accuracy, autoregressive integrated moving average (ARIMA) was compared with hybrid of ensemble empirical mode decomposition (EEMD) plus autoregressive integrated moving average (ARIMA) denoted by (EEMD+ARIMA), to know which model is better performing a historical US monthly electricity consumption from DEC-2000 to SEP-2022 were used. The data were divided into training set (90%) and testing set (10%) to insure the model accuracy. The mean absolute square error, root mean square error, mean absolute error and mean absolute percentage error measurements were used to test the ARIMA and hybrid EEMD+ARIMA performance, the results show that the hybrid EEMD+ARIMA outperforms ARIMA model with the lowest RMSE, MAE, MPE, MAPE, MASE. For the best model, Akaike Information Criterion and Bayesian Information Criterion were applied to choose the best. The results show that the AIC and BIC of the EEMD+ARIMA were lower than the ARIMA model, which indicates that the EEMD+ARIMA is better than the single ARIMA in forecasting of electricity consumption. The conclusion reveals that the hybrid EEMD+ARIMA provides more accurate forecasting and performs significantly better than the ARIMA in forecasting of electricity. [ABSTRACT FROM AUTHOR]

Osteoarthritis is one of the leading causes of disability around the globe. Up to this date there is no definite cure for cartilage lesions. Only fast and accurate diagnosis enables prolonging joint survivor time. Available diagnostic methods have disadvantages such as high price, radiation, need for experienced radiologists or low availability in some regions. The present study evaluates the use of vibroarthorgraphy as a method of cartilage lesion detection. 47 patients with diagnosed cartilage lesions, and 51 healthy control group patients have been enrolled in this study. The cartilage in the study group was evaluated intraoperatively by experienced orthopaedic surgeon. Signal acquisition was performed in open and closed kinematic chain based on 10 knee joint movements from 0-90 degrees. By using EEMD-DFA algorithms, reducing classifier inputs using ANOVA and then classifying using artificial neural networks (ANN), a classification accuracy of almost 93% was achieved. A sensitivity of 0.93 and a specificity of 0.93 with an AUC of 0.942 were obtained for the multilayer perceptron network. These results allow to apply this testing protocol in a clinical setting in the future.

Abstract Background Hand, foot, and mouth disease (HFMD) is a common infectious disease that poses a serious threat to children all over the world. However, the current prediction models for HFMD still require improvement in accuracy. In this study, we proposed a hybrid model based on autoregressive integrated moving average (ARIMA), ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) to predict the trend of HFMD. Methods The data used in this study was sourced from the National Clinical Research Center for Child Health and Disorders, Chongqing, China. The daily reported incidence of HFMD from 1 January 2015 to 27 July 2023 was collected to develop an ARIMA-EEMD-LSTM hybrid model. ARIMA, LSTM, ARIMA-LSTM and EEMD-LSTM models were developed to compare with the proposed hybrid model. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were adopted to evaluate the performances of the prediction models. Results Overall, ARIMA-EEMD-LSTM model achieved the most accurate prediction for HFMD, with RMSE, MAPE and R2 of 4.37, 2.94 and 0.996, respectively. Performing EEMD on the residual sequence yields 11 intrinsic mode functions. EEMD-LSTM model is the second best, with RMSE, MAPE and R2 of 6.20, 3.98 and 0.996. Conclusion Results showed the advantage of ARIMA-EEMD-LSTM model over the ARIMA model, the LSTM model, the ARIMA-LSTM model and the EEMD-LSTM model. For the prevention and control of epidemics, the proposed hybrid model may provide a more powerful help. Compared with other three models, the two integrated with EEMD method showed significant improvement in predictive capability, offering novel insights for modeling of disease time series.

The quasi-stationary front in Yunnan and Guizhou is one of the most important weather systems in Guizhou in winter， and it is difficult to forecast.This paper analyzes the winter climate characteristics of the quasi-stationary front in Yunnan and Guizhou and frontal rainfall characteristics of different magnitudes in Guizhou by using the daily ground conventional observation station data of five provinces （cities， districts） of Yunnan， Guizhou， Sichuan， Chongqing， Guangxi from 2007 to 2021 and the daily precipitation data of Guizhou from 08:00 to 08:00.The results show that： （1） There are 52.8 days of the quasi-stationary front weather in Guizhou in winter on average.The impact of the quasi-stationary front is most serious in January， followed by February and December.（2） In winter， the front direction of the quasi-stationary front in Yunnan and Guizhou is the most south-north type， averaging near Qujing； The second is the northwest southeast type， which is located in the west of Guizhou on average； There are few east-west types； The moderate intensity quasi-stationary front is the most and tends to occur in the early winter， the strong quasi-stationary front is the second and tends to occur in the late winter， and the weak quasi-stationary front is the least.（3） The frontal precipitation is dominated by rainfall （rain or freezing rain）.The stronger the quasi-stationary front is， the fewer the days of frontal precipitation are， and the proportion of the days of frontal precipitation increases with the weakening of the quasi-stationary front； The frontal rainfall is mainly concentrated below 5.0 mm， and the frontal rainfall probability is inversely proportional to the rainfall and is proportional to the rainfall range.When the frontal rainfall is weak， the frontal rainfall probability is unimodal with the increase of rainfall and bimodal with the expansion of rainfall range.（4） The results of EOF and REOF classification show that there are two types of frontal precipitation with different magnitudes that affect Guizhou： the frontal precipitation in the central and eastern regions is mostly in the early winter and more south-north， with moderate or weak intensity and prone to large-scale rainfall of 0.1~1.0 mm magnitude； The southwest type frontal rainfall is mostly in the late winter， and it is mostly north-south type or north-west southeast type.The intensity is strong， and the small-scale rainfall of 1.1~5.0 mm is easy to occur.（5） The climate tendency rate of the average annual rainfall in the middle-east type is negative， and the cumulative anomaly changes are roughly in the "M" shape； The climate tendency rate of annual average rainfall in the southwest type is positive， with obvious stage characteristics， and the cumulative anomaly changes are roughly "W" shaped； Both of them have quasi 4 years and quasi 8 years periodic oscillations； In recent years， the frontal rainfall in the middle-east has decreased， while that in the southwest has increased.The results can provide an important climate background for the prediction of quasi - stationary front weather in winter in Guizhou.

In response to fluctuations in the power levels within the link connecting the direct current transmission system to the upper-level power grid, we propose an optimization approach for determining the ability of a Hybrid Energy Storage System (HESS). Our first step involves calculating the power agreement for the link and the total power requirements for the HESS, taking into account the system’s net load power and the dynamic pricing of electricity based on time-of-use. Subsequently, we utilize the Ensemble Empirical Mode Decomposition (EEMD) technique to break down the total energy generated by the HESS into its constituent components. Incorporating instructions for both powering up and losing power of the HESS across varying filter orders, we solve the capacity allocation model for the HESS using a particle swarm algorithm, with rated power and rated capacity as decision variables. Finally, we identify the filter order that yields the best objective function value and the corresponding energy storage configuration scheme. An illustrative analysis, conducted on an enhanced IEEE-14 node DC distribution network, demonstrates that the proposed HESS configuration effectively mitigates tie-line fluctuations and enhances system stability.

This paper presents the effectiveness of the ensemble empirical mode decomposition-long short-term memory (EEMD-LSTM) model for short term load prediction. The prediction performance of the proposed model is compared to that of three other models (LR, ANN, LSTM). The contribution of this research lay in developing a novel approach that combined the EEMD-LSTM model to enhance the capability of industrial load forecasting. This was a field where there had been limited proposals for improvement, as these hybrid models had primarily been developed for other industries such as solar power, wind power, CO2 emissions, and had not been widely applied in industrial load forecasting before. First, the raw data was preprocessed using the IQR method, serving as the input for all four models. Second, the processed data was then used to train the four models. The performance of each model was evaluated using regression-based metrics such as mean absolute error (MAE) and mean squared error (MSE) to assess their respective output. The effectiveness of the EEMD-LSTM model was evaluated using Seojin industrial load data in Vietnam, and the results showed that it outperformed other models in terms of RMSE, n-RMSE, and MAPE errors for both 1-step and 24-step forecasting. This highlighted the model's capability to capture intricate and nonlinear patterns in electricity load data. The study underscored the significance of selecting a suitable model for electricity load forecasting and concluded that the EEMD-LSTM model was a dependable and precise approach for predicting future electricity assets. The model's robust performance and accurate forecasts showcased its potential in assisting decision-making processes in the energy sector.

The health status of the battery of new energy electric vehicles is related to the quality of vehicle use, so it is of high practical application value to predict the health status of the battery of electric vehicles. In order to predict the health status of lithium battery, this study proposes to optimize the empirical modal decomposition method and obtain the ensemble empirical modal decomposition algorithm, and use this algorithm to collect the vibration signal of the battery, then use wavelet transform to pre-process the collected signal, and finally combine K-mean clustering and particle swarm algorithm to cluster the signal types to complete the prediction of battery State of Health. The experimental results show that the ensemble empirical modal decomposition algorithm proposed in this study can effectively perform signal acquisition for different state types of batteries, and the K-mean clustering-particle swarm algorithm predicts a 63 % decrease in the health state of the battery at 600 cycles, with a prediction error of 2.6 %. Therefore, the algorithm proposed in this study is feasible in predicting the battery health state.

Background: Hand, foot, and mouth disease (HFMD) is a common infectious disease that poses a serious threat to children all over the world. However, the current prediction models for HFMD still require improvement in accuracy. In this study, we proposed a hybrid model based on autoregressive integrated moving average (ARIMA), ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) to predict the trend of HFMD. Methods: The data used in this study was sourced from the National Clinical Research Center for Child Health and Disorders, Chongqing, China. The daily reported incidence of HFMD from 1 January 2015 to 27 July 2023 was collected to develop an ARIMA-EEMD-LSTM hybrid model. ARIMA, LSTM, ARIMA-LSTM and EEMD-LSTM models were developed to compare with the proposed hybrid model. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were adopted to evaluate the performances of the prediction models. Results: Overall, ARIMA-EEMD-LSTM model achieved the most accurate prediction for HFMD, with RMSE, MAPE and R2 of 4.37, 2.94 and 0.996, respectively. Performing EEMD on the residual sequence yields 11 intrinsic mode functions. EEMD-LSTM model is the second best, with RMSE, MAPE and R2 of 6.20, 3.98 and 0.996. Conclusion: Results showed the advantage of ARIMA-EEMD-LSTM model over the ARIMA model, the LSTM model, the ARIMA-LSTM model and the EEMD-LSTM model. For the prevention and control of epidemics, the proposed hybrid model may provide a more powerful help. Compared with other three models, the two integrated with EEMD method showed significant improvement in predictive capability, offering novel insights for modeling of disease time series. [ABSTRACT FROM AUTHOR]

Accurate prediction of sea surface wind speed is crucial for marine activities such as marine search and rescue, marine shipping, and marine fishing. Because of the gustiness of sea surface winds, the wind speed data have strong non-stationarity and non-linearity, and it is still challenging to predict sea surface winds accurately and stably in a short time. Most previous studies have only considered the problem of non-smoothness of wind speed in single-point wind speed prediction, and this study extends the method to solve the non-smoothness of data in two dimensions. We proposed a hybrid model based on ensemble empirical mode decomposition (EEMD) and Convolutional long short-term memory network (ConvLSTM). Specifically, this study employs the Pearson correlation coefficient (PCC) to select the most periodic and most predictable subsequences of the EEMD signal decomposition, then fuse them into the spatio-temporal prediction of ConvLSTM. The method eliminates the influence of noise in the two-dimensional wind speed prediction and grasps the wind speed variation pattern more accurately. The proposed model has the best prediction effect based on the experimental findings, and this advantage becomes increasingly evident as time increases. This shows that EEMD signal decomposition is a good way to solve the short-term prediction problem for two-dimensional wind speed. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of sea level change predictions, this study introduced an improved variational mode decomposition and ensemble empirical mode decomposition–long short-term memory hybrid model (VMD–EEMD–LSTM). This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the intrinsic mode functions (IMFs) with various frequencies, along with a residual sequence. EEMD further dissects the residual sequence obtained from VMD into second-order components. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. The experimental results, obtained through a comparative analysis of six sets of Dutch coastal sea surface height data, confirm the excellent accuracy of the hybrid model proposed (root mean square error (RMSE) = 47.2 mm, mean absolute error (MAE) = 33.3 mm, coefficient of determination (R2) = 0.9). Compared to the VMD-LSTM model, the average decrease in RMSE was 58.7%, the average reduction in MAE was 60.0%, and the average increase in R2 was 49.9%. In comparison to the EEMD-LSTM model, the average decrease in RMSE was 27.0%, the average decrease in MAE was 28.0%, and the average increase in R2 was 6.5%. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance. The model proposed in this study demonstrates a notable enhancement in global mean sea lever (GMSL) forecasting accuracy during testing along the Dutch coast. [ABSTRACT FROM AUTHOR]

Atrial fibrillation is a common heart rhythm disorder that is now becoming a significant healthcare challenge as it affects more and more people in developed countries. This paper proposes a novel approach for detecting this disease. For this purpose, we examined the ECG signal by detecting QRS complexes and then selecting 30 successive R-peaks and analyzing the atrial activity segment with a variety of indices, including the entropy change, the variance of the wavelet transform indices, and the distribution of energy in bands determined by the dual-Q tunable Q-factor wavelet transform and coefficients of the Hilbert transform of ensemble empirical mode decomposition. These transformations provided a vector of 21 features that characterized the relevant part of the electrocardiography signal. The MIT-BIH Atrial Fibrillation Database was used to evaluate the proposed method. Then, using the K-fold cross-validation method, the sets of features were fed into the LS-SVM and SVM classifiers and a trilayered neural network classifier. Training and test subsets were set up to avoid sampling from a single participant and to maintain the balance between classes. In addition, individual classification quality scores were analyzed for each signal to determine the dependencies of the classification quality on the subject. The results obtained during the testing procedure showed a sensitivity of 98.86%, a positive predictive value of 99.04%, and a classification accuracy of 98.95%. [ABSTRACT FROM AUTHOR]

The expressway service area's short-term traffic flow forecast is helpful for adjusting expressway traffic and formulating time-sensitive traffic flow adjustment strategies. To solve the problems of strong stochastic fluctuation of traffic flow, insufficient degree of traffic flow data mining and low prediction accuracy in common deep learning prediction models, a hybrid model combining ensemble empirical mode decomposition and bidirectional gated recurrent unit (EEMD+BI_GRU) is proposed for expressway intelligent service area traffic flow prediction. The model decomposes the traffic time series using the EEMD algorithm, with the aim of reducing the non-stationarity of the initial traffic series and providing a potential feature set for prediction. The BI_GRU structure as a prediction model enables the model to process traffic data more deeply and efficiently to improve prediction accuracy. Based on the real traffic flow data of Taishi service area in Gansu Province, several reference models were developed and analyzed in comparison with the suggested model. The results demonstrate that, as compared to traditional mainstream models, the method of applying signal decomposition technique-provided sequences as input characteristics may significantly enhance prediction accuracy. [ABSTRACT FROM AUTHOR]

Time–frequency analysis of EEG data is a key step in exploring the internal activities of the human brain. Studying oscillations is an important part of the analysis, as they are thought to provide the underlying mechanism for communication between neural assemblies. Traditional methods of analysis, such as Short-Time FFT and Wavelet Transforms, are not ideal for this task due to the time–frequency uncertainty principle and their reliance on predefined basis functions. Empirical Mode Decomposition and its variants are more suited to this task as they are able to extract the instantaneous frequency and phase information but are too time consuming for practical use. Our aim was to design and develop a massively parallel and performance-optimized GPU implementation of the Improved Complete Ensemble EMD with the Adaptive Noise (CEEMDAN) algorithm that significantly reduces the computational time (from hours to seconds) of such analysis. The resulting GPU program, which is publicly available, was validated against a MATLAB reference implementation and reached over a 260× speedup for actual EEG measurement data, and provided predicted speedups in the range of 3000–8300× for longer measurements when sufficient memory was available. The significance of our research is that this implementation can enable researchers to perform EMD-based EEG analysis routinely, even for high-density EEG measurements. The program is suitable for execution on desktop, cloud, and supercomputer systems and can be the starting point for future large-scale multi-GPU implementations. [ABSTRACT FROM AUTHOR]

Demand forecasting of auto parts is an essential part of inventory control in the automotive supply chain. Due to non-stationarity, strong randomness, local mutation, and non-linearity in short-term auto parts demand data, and it is difficult to predict accurately. In this regard, this paper proposes a combination prediction model based on EEMD-CNN-BiLSTM-attention. First, the model uses the ensemble empirical mode decomposition method to decompose the original data into a series of eigenmode functions and a residual item to extract more feature information. And then uses the CNN-BiLSTM-attention model to analyze each mode separately. The components are predicted, and the prediction results are summed to obtain the final prediction result. The attention mechanism is introduced to automatically assign corresponding weights to the BiLSTM hidden layer states to distinguish the importance of different time load sequences, which can effectively reduce the loss of historical information and highlight the input of critical historical time points. Finally, the final auto parts demand prediction results are output through the fully connected layer. Then, we conduct an experimental analysis of the collected short-term demand data for auto parts. Finally, the experimental results show that the prediction model proposed in this paper has more minor errors, higher prediction accuracy, and the model prediction performance is better than the other nine comparison models, thus verifying the EEMD-CNN-BiLSTM-attention model for short-term parts demand forecasting effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)