Search Results (32)

Temperature prediction plays a crucial role across various sectors, including agriculture and climate research. Understanding weather patterns, seasonal shifts, and climate dynamics heavily relies on accurate temperature forecasts. This paper presents an innovative hybrid method, EEMD-LR, that combines ensemble empirical mode decomposition (EEMD) with linear regression (LR) for temperature prediction. EEMD is used to decompose temperature signals into stable sub-signals, enhancing their predictability. LR is then applied to forecast each sub-signal, and the resulting predictions are integrated to obtain the final temperature forecast. The proposed EEMD-LR model achieved RMSE, MAE, and R² values of 0.000027, 0.000021, and 1.000000, respectively, on the sine simulation time-series data used in this study. For actual temperature time-series data, the model achieved RMSE, MAE, and R² values of 0.713150, 0.512700, and 0.994749, respectively. The experimental results on these two datasets indicate that the EEMD-LR model demonstrates superior predictive performance compared to alternative methods. Full article

In the automotive industry, the supply of service parts—such as bumpers, batteries, and aero parts—is required even after the end of vehicle production, as customers need them for maintenance and repairs. To earn customer confidence, manufacturers must ensure timely availability of these parts while managing inventory efficiently. An excess of inventory can increase warehousing costs, while stock shortages can lead to supply delays. Accurate demand forecasting is essential to balance these factors, considering the changing demand characteristics over time, such as long-term trends, seasonal fluctuations, and irregular variations. This paper introduces a novel method for time series forecasting that employs Ensemble Empirical Mode Decomposition (EEMD) and Dynamic Mode Decomposition (DMD) to analyze service part demand. EEMD decomposes historical order data into multiple modes, and DMD is used to predict transitions within these modes. The proposed method demonstrated an approximately 30% reduction in forecasting error compared to comparative methods, showcasing its effectiveness in accurately predicting service parts demand across various patterns. Full article

To address the issues of information redundancy, limited feature representation, and empirically set parameters in rolling bearing fault diagnosis, this paper proposes a Multi-Entropy Screening and Optimization Temporal Convolutional Network (MESO-TCN). The method integrates feature filtering, network modeling, and parameter optimization into a unified diagnostic framework. Specifically, ensemble empirical mode decomposition (EEMD) is combined with a hybrid entropy criterion to preprocess the raw vibration signals and suppress redundant noise. A kernel-extended temporal convolutional network (ETCN) is designed with multi-scale dilated convolution to extract diverse temporal fault patterns. Furthermore, an improved whale optimization algorithm incorporating a firefly-inspired mechanism is introduced to adaptively optimize key hyperparameters. Experimental results on datasets from Xi’an Jiaotong University and Southeast University demonstrate that MESO-TCN achieves average accuracies of 99.78% and 95.82%, respectively, outperforming mainstream baseline methods. These findings indicate the method’s strong generalization ability, feature discriminability, and engineering applicability in intelligent fault diagnosis of rotating machinery. Full article

Sea Surface Temperature (SST) is of great importance to study several major phenomena due to ocean interactions with other earth systems. Previous studies on SST based on statistical inference methods were less accurate for longer prediction lengths. A considerable number of studies in recent years involve machine learning for SST modeling. These models were able to mitigate this problem to some length by modeling SST patterns and trends. Sequence analysis by decomposition is used for SST forecasting in several studies. Ensemble Empirical Mode Decomposition (EEMD) has been proven in previous studies as a useful method for this. The application of EEMD in spatiotemporal modeling has been introduced as Multidimensional EEMD (MEEMD). The aim of this study is to employ fast MEEMD methods to decompose the SST spatiotemporal dataset and apply a Convolutional Long Short-Term Memory (ConvLSTM)-based model to model and forecast SST. The results show that the fast MEEMD method is capable of enhancing spatiotemporal SST modeling compared to the Linear Inverse Model (LIM) and ConvLSTM model without decomposition. The model was further validated by making predictions from April to May 2023 and comparing them to original SST values. There was a high consistency between predicted and real SST values. Full article

Transformers are key elements in electrical systems. Although they are robust machines, different faults can appear due to their inherent operating conditions, e.g., the presence of different electrical and mechanical stresses. Among the different elements that compound a transformer, the winding is one of the most vulnerable parts, where the damage of turn-to-turn short circuits is one of the most studied faults since low-level damage (i.e., a low number of short-circuited turns—SCTs) can lead to the overall fault of the transformer; therefore, early fault detection has become a fundamental task. In this regard, this paper presents a machine learning-based method to diagnose SCTs in the transformer windings by using their vibrational response. In general, the vibration signals are firstly decomposed by means of the variational mode decomposition method, where a comparison with the empirical mode decomposition (EMD) method and the ensemble empirical mode decomposition (EEMD) method is also carried out. Then, entropy, energy, and kurtosis indices are obtained from each decomposition as fault indicators, where both the combination of features and the dimensionality reduction by using the principal component analysis (PCA) method are analyzed for the global effectiveness improvement and the computational burden reduction. Finally, a pattern recognition algorithm based on artificial neural networks (ANNs) is used for automatic fault detection. The obtained results show 100% effectiveness in detecting seven fault conditions, i.e., 0 (healthy), 5, 10, 15, 20, 25, and 30 SCTs. Full article

Net Ecosystem Productivity (NEP) is an important measure to assess the carbon balance and dynamics of ecosystems, providing a direct measure of carbon source–sink dynamics in terrestrial ecosystems and finding widespread applications in carbon cycle research. However, the nonlinear characteristics of NEP in Xinjiang’s terrestrial ecosystems remain unclear. Additionally, the influence of land use patterns, temperature, and precipitation variations on carbon sink capacity remains unclear. Ensemble Empirical Mode Decomposition (EEMD) is used to investigate the nonlinear variation of NEP in Xinjiang. Landscape pattern analysis of Xinjiang’s land use patterns from 1981 to 2019 is conducted using a 30 km moving window, and the interannual relationships between NEP, land use patterns, and meteorological factors are investigated through EEMD detrending analysis and Pearson correlation. The findings indicate that: (1) NEP exhibits interannual variations, primarily concentrated in the foothills of the Tianshan Mountains, with a three-year cycle. (2) Although NEP changes in most regions are not significant, urban clusters on the northern slopes of the Tianshan Mountains show noteworthy trends, with initial decrease followed by an increase, covering around 34.87% of the total area. Areas at risk of NEP decline constitute approximately 7.32% of the total area. (3) Across Xinjiang, we observe a widespread rise in patch fragmentation and complexity, coupled with a decline in patch connectivity and the size of the dominant patch. Additionally, there is a notable increase in both the diversity and evenness of land use types. However, the correlation between land use patterns and NEP is generally found to be insignificant in the majority of areas, with a percentage exceeding 85%. (4) Approximately 62% of regions in Xinjiang have NEP that is positively correlated with temperature, with significance observed in 33% of these areas. Furthermore, almost 95% of regions demonstrate that NEP is positively correlated with precipitation, with significance noted in 83% of these regions. It appears that precipitation exerts a more pronounced influence on NEP fluctuations in Xinjiang when compared to temperature. Full article

Ventricular fibrillation (VF) is a critical ventricular arrhythmia with severe consequences. Due to the severity of VF, it urgently requires a rapid and accurate detection of abnormal patterns in ECG signals. Here, we present an efficient method to detect abnormal electrocardiogram (ECG) signals associated with VF by measuring orthogonality between intrinsic mode functions (IMFs) derived from a data-driven decomposition method, namely, ensemble empirical mode decomposition (EEMD). The proposed method incorporates the decomposition of the ECG signal into its IMFs using EEMD, followed by the computation of the angles between subsequent IMFs, especially low-order IMFs, as the features to discriminate normal and abnormal ECG patterns. The proposed method was validated through experiments using a public MIT-BIH ECG dataset for its effectiveness in detecting VF ECG signals compared to conventional methods. The proposed method achieves a sensitivity of 99.22%, a specificity of 99.37%, and an accuracy of 99.28% with a 3 s ECG window and a support vector machine (SVM) with a linear kernel, which performs better than existing VF detection methods. The capability of the proposed method can provide a perspective approach for the real-time and practical computer-aided diagnosis of VF. Full article

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m² in the YRB and increased at rates of 4.64 g C/m²/a¹ (R² = 0.86, p < 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts. Full article

Complex oil and gas two-phase flow exists within an aero-engines bearing cavity scavenge pipe, prone to lubricated self-ignition and coking. Lubricant system designers must be able to accurately identify and understand the flow state of the scavenge pipe. The prediction accuracy of previous models is insufficient to meet the more demanding needs. This paper establishes a visualized flow pattern identification test system for the scavenge pipe, with a test temperature of up to 370 k, using a high-speed camera to photograph four flow patterns, decomposing the pressure signals obtained from high-frequency dynamic pressure sensors using the ensemble empirical mode decomposition (EEMD) method, and then performing Hilbert transform, using the Hilbert spectrum to quantify the changes of amplitude and frequency with time, and establishing the energy and flow pattern correspondence analysis. Then the energy percentage of IMFs is used as the input of feature values, and the random forest algorithm machine learning is used for predictive classification. The experimental results show that the flow pattern recognition rate established in this paper can reach 98%, which can identify the two-phase flow pattern in the scavenge pipe more objectively and accurately. Full article

The aim of this paper was to provide a more scientific and effective analysis of the fluctuation pattern of the Chinese potato market by extracting the characteristics of the price fluctuation cycle to effectively grasp the characteristics of price changes in the potato market, thus promoting the stable and healthy development of the Chinese potato industry, and to expand the application scenarios of the EEMD model to provide a reference for the study of price fluctuation patterns in other agricultural markets. This study used an ensemble empirical modal decomposition (EEMD) model to examine time-series data on Chinese wholesale potato market prices from January 2005 to December 2021. The results showed that (1) Chinese wholesale potato market prices are characterized by some rigidity, with sharp changes in growth rates; (2) Chinese wholesale potato market prices are dominated by short- and medium-term fluctuations, and the decomposed components can better reflect the characteristics of the original series fluctuations; (3) Chinese wholesale potato market monthly prices have long- and short-term fluctuations with a 6- and 19-month cycle, and are dominated by short-term high-frequency fluctuations; (4) monthly price fluctuations in the Chinese wholesale potato market are more intense in high-frequency than low-frequency fluctuations, and there is a strong correlation between high- and low-frequency fluctuations in precipitation, temperature and potato prices. Finally, suggestions were made for creating and improving a national potato price information platform and strengthening the information early warning mechanism; improving the potato production interest linkage mechanism and enhancing potato farmers’ ability to cope with market and natural risks; and improving the potato reserve system and potato storage facilities. 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

Aiming at the problems of nonlinearity and serious confusion of fault characteristics in analog circuits, this paper proposed a fault diagnosis method for an analog circuit based on ensemble empirical pattern decomposition (EEMD) and improved multifractal detrended fluctuations analysis (MF-DFA). This method consists of three steps: preprocessing, feature extraction, and fault classification identification. First, the EEMD decomposition preprocesses (denoises) the original signal; then, the appropriate IMF components are selected by correlation analysis; then, the IMF components are processed by the improved MF-DFA, and the fault feature values are extracted by calculating the multifractal spectrum parameters, and then the feature values are input to a support vector machine (SVM) for classification, which enables the diagnosis of soft faults in analog circuits. The experimental results show that the proposed EEMD-improved MF-DFA method effectively extracts the features of soft faults in nonlinear analog circuits and obtains a high diagnosis rate. Full article

Agriculture is one of the dominant industries in the Mun River Basin, but farmlands are frequently affected by floods and droughts due to the water resource management mode of their rainfed crop, especially in the context of climate change. Drought risk assessment plays an important role in the Mun River Basin’s agricultural sustainable development. The objective of this study was to identify the tempo-spatial variation in dryness and wetness patterns; the drought intensity, frequency, and duration; and the potential causes behind drought using the methods of the standardized precipitation evapotranspiration index (SPEI), ensemble empirical mode decomposition (EEMD), correlation analysis, and the Pettitt test over the basin. Results showed that the Mun River Basin underwent a drying climate pattern, which is explained by the significant decreasing trend of SPEI_12M during the study period. In addition, the downstream area of the Mun River Basin was subjected to more intense, extreme dryness and wetness events as the decreased amplitude of SPEI_12M and SPEI_3M was higher than that over the upper and middle reaches. Drought intensity presented a remarkable decadal variation over the past 36 years, and an average 7% increase per decade in the drought intensity was detected. Besides, there have been more mild and moderate droughts frequently appearing over the Mun River Basin in recent decades. For the underlying causes behind the drought condition, on the one hand, the shortened precipitation day over the rainy season accounted more for the intense drought events than the precipitation amount. On the other hand, El Nino Southern Oscillation (ENSO)-brought sea surface temperature anomalies aggravated the potential evapotranspiration (ETr), which might be closely related to the drought intensity and frequency variation. These tempo-spatial maps of dryness and wetness and drought occurrence characteristics can be conducive to local stakeholders and agricultural operators to better understand the agriculture industry risks and vulnerabilities and properly cope with pre-disaster planning and preparedness and post-disaster reconstruction over the Mun River Basin. Full article

Hydroclimatic change across China has received considerable attention due to its vital significance for regional ecosystem stability and economic development, yet the spatiotemporal dynamics of its nonlinear trends and complexity have not been fully understood. Herein, the spatiotemporal evolution of Dai’s self-calibrating Palmer drought severity index (scPDSI) trends in China during the period from 1951 to 2014 is diagnosed using the ensemble empirical mode decomposition (EEMD) method. A persistent and noticeable drying has been identified in North and Northeastern China (NNEC) since the 1950s. Significant wetting in the north of the Tibetan Plateau (TP) and the south of the western parts of Northwestern China (WNWC) started sporadically at first and accelerated until around 1980. A slight wetting trend was found in Southwest China (SC) before 1990, followed by the occurrence of a dramatic drying trend over the following decades. In addition, we have found that the scPDSI variations in WNWC and the TP are more complex than those in NNEC and SC based on our application of Higuchi’s fractal dimension (HFD) analysis, which may be related to complex circulation patterns and diverse geomorphic features. Full article

The accelerated growth of elderly populations in many countries and regions worldwide is creating a major burden to the healthcare system. Intelligent approaches for continuous health monitoring have the potential to promote the transition to more proactive and affordable healthcare. Electrocardiograms (ECGs), collected from portable devices, with noninvasive and cost-effective merits, have been widely used to monitor various health conditions. However, the dynamic and heterogeneous pattern of ECG signals makes relevant feature construction and predictive model development a challenging task. In this study, we aim to develop an integrated approach for one-day-forward wellness prediction in the community-dwelling elderly using single-lead short ECG signal data via multiple-features construction and predictive model implementation. Vital signs data from the elderly were collected via station-based equipment on a daily basis. After data preprocessing, a set of features were constructed from ECG signals based on the integration of various models, including time and frequency domain analysis, a wavelet transform-based model, ensemble empirical mode decomposition (EEMD), and the refined composite multiscale sample entropy (RCMSE) model. Then, a machine learning based predictive model was established to map the l-day lagged features to wellness condition. The results showed that the approach developed in this study achieved the best performance for wellness prediction in the community-dwelling elderly. In practice, the proposed approach could be useful in the timely identification of elderly people who might have health risks, and could facilitating decision-making to take appropriate interventions. Full article