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Diurnal asymmetric warming, a critical feature of climate change, significantly impacts water‐carbon exchange in terrestrial ecosystems. This study analyzes the spatiotemporal characteristics and long‐term trends of the global diurnal temperature range (DTR) from 1961 to 2022 using ensemble empirical mode decomposition (EEMD). Our results reveal a trend reversal in global averaged DTR around 1988, shifting from a decrease to an increase, affecting 47% of global land areas. Subsequent to the reversal, the most pronounced increases were observed in temperate regions. Seasonal analysis shows earlier reversals in spring and summer, with accelerated change rates following the reversal. Additionally, increased surface solar radiation from reduced cloud cover caused daily maximum (Tmax) temperatures to warm faster than the minimum (Tmin), leading to a reversal and intensified DTR. These complex patterns underscore the need for targeted climate policies and adaptation strategies to tackle global warming. Plain Language Summary: Global climate change is causing uneven warming patterns, which significantly affect how ecosystems exchange water and carbon. One important way to understand this is through the diurnal temperature range (DTR), which measures the difference between daytime and nighttime temperatures. In this study, we examined DTR changes globally from 1961 to 2022 using a method called ensemble empirical mode decomposition (EEMD). We discovered that the overall trend in DTR reversed around 1988, changing from a decline to an increase, which affected nearly half of the world's land areas. Subsequent to the reversal, the most pronounced increases were observed in temperate regions, whereas tropical areas exhibited a more subdued rate of rise. Interestingly, we found that the rate of increase in DTR is stronger in southern latitudes compared to northern latitudes. Additionally, increased surface solar radiation from reduced cloud cover caused daily maximum temperatures (Tmax) to rise faster than minimum temperatures (Tmin), resulting in a higher DTR. These findings highlight the need for more effective climate policies and adaptation strategies to tackle the complex challenges posed by global warming. Understanding these changes is crucial for informing decisions related to climate resilience and environmental sustainability. Key Points: Around 1988, the global diurnal temperature range (DTR) reversed from a long‐term decline to an increase, impacting 47% area of landThe reversal in the DTR was most pronounced in spring and summer, with greater changes in temperate regions and southern latitudesDaily maximum temperatures are warming faster than minimum temperatures, driving the DTR reversal and intensification [ABSTRACT FROM AUTHOR]

This research investigates the volatility of carbon prices in Guangdong's emission trading market, a critical element of China's broader climate strategy aimed at reducing greenhouse gas emissions and promoting sustainable development. This study applies ensemble empirical mode decomposition (EEMD) to analyze the complex interactions between carbon price fluctuations and various economic factors, including energy prices and environmental regulations. By decomposing the data, we identify key trends and cycles within the market, providing a clearer understanding of both short-term volatility and long-term market trends. Our findings reveal that regulatory policies play a pivotal role in shaping carbon market dynamics, with shifts in regulations leading to significant price volatility. Additionally, fluctuations in global energy prices, especially oil and coal, are found to have a considerable impact on carbon price movements, further complicating the market's stability. This underscores the interconnected nature of the carbon trading market with broader economic and environmental factors, both domestic and international. The findings provide valuable insights for policymakers and market participants, underscoring the importance of stable carbon markets for promoting the transition to a low-carbon economy and achieving broader sustainability goals. [ABSTRACT FROM AUTHOR]

The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones in the presence of a large amount of external environment noise, noise and drift inevitably occur. The distortion phenomenon makes further signal detection and recognition difficult. In this study, a new method for denoising MEMS vector hydrophones by combining ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) is proposed to improve the utilization of received signals. First, the main frequency of the noise signal is transformed using a Fourier transform. Then, the noise signal is decomposed by EEMD to obtain the intrinsic mode function (IMF) component. The frequency of each IMF component in the center further determines that the IMF component belongs to the noise IMF component, invalid IMF component, or pure IMF component. Then, there are pure IMF reserved components, removing noisy IMF components and invalid IMF components. Finally, the desalinated IMF reconstructs the signal through SSA to obtain the denoised signal, which realizes the denoising processing of the signal, extracting the useful signal and removing the drift. The role of SSA is to effectively separate the trend noise and the periodic vibration noise. Compared to EEMD and SSA separately, the proposed EEMD-SSA algorithm has a better denoising effect and can achieve the removal of drift. Following that, EEMD-SSA is used to process the data measured by Fenhe. The experiment is carried out by the North University of China. The simulation and lake test results show that the proposed EEMD-SSA has certain practical research value. [ABSTRACT FROM AUTHOR]

According to General Relativity Theory (GRT), by comparing the frequencies between two precise clocks at two different stations, the gravity potential (geopotential) difference between the two stations can be determined due to the gravity frequency shift effect. Here, we conduct a clock-transportation experiment for measuring geopotential differences based on frequency comparisons via satellite links between two remote hydrogen atomic clocks. Based on the net frequency shift between the two clocks in two different periods, the geopotential difference between stations of the Beijing 203 Institute Laboratory (BIL) and Luojiashan Time-frequency Station (LTS) is determined. Comparisons show that the experimental result deviated from the reference of Earth gravity model EGM2008 result by (38.5 $ \pm $ ± 45.9) m in Orthometric Height (OH). The results are consistent with the frequency stabilities of the hydrogen clocks (at the level of ${10^{ - 15}}$ 10 − 15 ) used in the experiment. With the rapid development of time and frequency science and technology, the approach discussed in this study for measuring the geopotential is prospective and thus, could have broad applications. [ABSTRACT FROM AUTHOR]

According to General Relativity Theory (GRT), by comparing the frequencies between two precise clocks at two different stations, the gravity potential (geopotential) difference between the two stations can be determined due to the gravity frequency shift effect. Here, we conduct a clock-transportation experiment for measuring geopotential differences based on frequency comparisons via satellite links between two remote hydrogen atomic clocks. Based on the net frequency shift between the two clocks in two different periods, the geopotential difference between stations of the Beijing 203 Institute Laboratory (BIL) and Luojiashan Time-frequency Station (LTS) is determined. Comparisons show that the experimental result deviated from the reference of Earth gravity model EGM2008 result by (38.5[Formula: see text]45.9) m in Orthometric Height (OH). The results are consistent with the frequency stabilities of the hydrogen clocks (at the level of [Formula: see text]) used in the experiment. With the rapid development of time and frequency science and technology, the approach discussed in this study for measuring the geopotential is prospective and thus, could have broad applications.

High-accuracy optical clocks have garnered increasing attention for their potential application in various fields, including geodesy. According to the gravitational red-shift effect, clocks at lower altitudes on the Earth's surface run slower than those at higher altitudes due to the differential gravitational field. Consequently, the geopotential difference can be determined by simultaneously comparing the frequency of two optical clocks at disparate locations. Here, we report geopotential difference measurements conducted using a pair of transportable 40Ca+ optical clocks with uncertainties at the 10 − 17 level. After calibrating the output frequencies of two optical clocks in the horizontal position, frequency comparison is realized by moving Clock 2 to two different positions using a high-precision optical fiber time–frequency transmission link with Clock 1. The elevation difference of the two different positions, as processed by ensemble empirical mode decomposition (EEMD), is measured as −88.4 cm ± 16.7 cm and 104.5 cm ± 20.1 cm, respectively, which is consistent with the geometric measurement results within the error range. This experimental result validates the credibility of the optical clock time–frequency comparison used in determining geopotential differences, thereby providing a novel measurement model for the establishment of a global unified elevation datum. [ABSTRACT FROM AUTHOR]

Cochlear implant (CI) is a neural prosthesis that can restore hearing for patients with severe to profound hearing loss. Observed variability in auditory rehabilitation outcomes following cochlear implantation may be due to cerebral reorganization. Electroencephalography (EEG), favored for its CI compatibility and non-invasiveness, has become a staple in clinical objective assessments of cerebral plasticity post-implantation. However, the electrical activity of CI distorts neural responses, and EEG susceptibility to these artifacts presents significant challenges in obtaining reliable neural responses. Despite the use of various artifact removal techniques in previous studies, the automatic identification and reduction of CI artifacts while minimizing information loss or damage remains a pressing issue in objectively assessing advanced auditory functions in CI recipients. To address this problem, we propose an approach that combines machine learning algorithms—specifically, Support Vector Machines (SVM)—along with Independent Component Analysis (ICA) and Ensemble Empirical Mode Decomposition (EEMD) to automatically detect and minimize electrical artifacts in EEG data. The innovation of this research is the automatic detection of CI artifacts using the temporal properties of EEG signals. By applying EEMD and ICA, we can process and remove the identified CI artifacts from the affected EEG channels, yielding a refined signal. Comparative analysis in the temporal, frequency, and spatial domains suggests that the corrected EEG recordings of CI recipients closely align with those of peers with normal hearing, signifying the restoration of reliable neural responses across the entire scalp while eliminating CI artifacts.

This research investigates the volatility of carbon prices in Guangdong’s emission trading market, a critical element of China’s broader climate strategy aimed at reducing greenhouse gas emissions and promoting sustainable development. This study applies ensemble empirical mode decomposition (EEMD) to analyze the complex interactions between carbon price fluctuations and various economic factors, including energy prices and environmental regulations. By decomposing the data, we identify key trends and cycles within the market, providing a clearer understanding of both short-term volatility and long-term market trends. Our findings reveal that regulatory policies play a pivotal role in shaping carbon market dynamics, with shifts in regulations leading to significant price volatility. Additionally, fluctuations in global energy prices, especially oil and coal, are found to have a considerable impact on carbon price movements, further complicating the market’s stability. This underscores the interconnected nature of the carbon trading market with broader economic and environmental factors, both domestic and international. The findings provide valuable insights for policymakers and market participants, underscoring the importance of stable carbon markets for promoting the transition to a low-carbon economy and achieving broader sustainability goals.

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. [ABSTRACT FROM AUTHOR]

This study examines the asymmetric behaviour of Bitcoin relative to six major African fiat currencies (Egyptian Pound, Cedi, ZAR, Naira, Rupee and Dinar) for the period 10 August 2015 to 31 December 2022. The time and frequency information in the time series of the currencies were captured applying the ensemble empirical mode decomposition. The quantile regression (QR) and quantilein-quantile regression (QQR) were applied on the decomposed series to examine the connections among the currencies at different currency regimes across time. The empirical results show that both QR and QQR can adequately capture the time-varying asymmetric behaviour of the currencies across time. The results range from weak to very strong dependencies albeit both negative and positive across different quantiles. Our findings suggest that except for ZAR, Bitcoin is a viable alternative currency to African reserve currencies from the medium-term since it can hedge depreciation and forex risk of the fiat currencies. Based on the findings of this study, we recommend that forex traders and policymakers in Africa should adopt Bitcoin as an alternative currency to African currencies in the medium-term to mitigate currency crises in the continent. [ABSTRACT FROM AUTHOR]

This research proposes a novel method for enhancing the accuracy of stock price prediction by combining ensemble empirical mode decomposition (EEMD), ensemble convolutional neural network (CNN), and X (Twitter) sentiment scores based on historical stock data. The complexity and volatility of financial markets pose challenges to accurate stock price forecasting. To address this challenge, the presented approach utilizes EEMD to decompose the original stock price time series, X sentiment analysis data, and relative strength index (RSI) technical indicator data obtained from daily stock fluctuations into intrinsic mode functions (IMFs) and a residual component. Subsequently, an ensemble CNN is constructed, comprising parallel subnetworks that learn distinct IMF representations, and their combined predictions result in robust stock price forecasts. This ensemble CNN consists of multiple parallel subnetworks, each learning distinct IMF representations, and combining their predictions yields a robust stock price forecast. X sentiment scores are incorporated through a separate CNN that analyzes sentiment in tweets related to target equities, capturing polarity and intensity. Experiments with actual stock price and X data show that the proposed "EEMD–ensemble CNN" model outperforms baseline methods in accurate stock price forecasting. The incorporation of X sentiment scores improves forecasts by accounting for the influence of public sentiment on stock price fluctuations. This study demonstrates the potential benefits of social media sentiment analysis for financial forecasting and offers practical implications for investors, traders, and financial analysts seeking informed decisions in dynamic stock market environments. [ABSTRACT FROM AUTHOR]

Cochlear implant (CI) is a neural prosthesis that can restore hearing for patients with severe to profound hearing loss. Observed variability in auditory rehabilitation outcomes following cochlear implantation may be due to cerebral reorganization. Electroencephalography (EEG), favored for its CI compatibility and non-invasiveness, has become a staple in clinical objective assessments of cerebral plasticity post-implantation. However, the electrical activity of CI distorts neural responses, and EEG susceptibility to these artifacts presents significant challenges in obtaining reliable neural responses. Despite the use of various artifact removal techniques in previous studies, the automatic identification and reduction of CI artifacts while minimizing information loss or damage remains a pressing issue in objectively assessing advanced auditory functions in CI recipients. To address this problem, we propose an approach that combines machine learning algorithms—specifically, Support Vector Machines (SVM)—along with Independent Component Analysis (ICA) and Ensemble Empirical Mode Decomposition (EEMD) to automatically detect and minimize electrical artifacts in EEG data. The innovation of this research is the automatic detection of CI artifacts using the temporal properties of EEG signals. By applying EEMD and ICA, we can process and remove the identified CI artifacts from the affected EEG channels, yielding a refined signal. Comparative analysis in the temporal, frequency, and spatial domains suggests that the corrected EEG recordings of CI recipients closely align with those of peers with normal hearing, signifying the restoration of reliable neural responses across the entire scalp while eliminating CI artifacts. [ABSTRACT FROM AUTHOR]

In order to overcome the low accuracy of traditional Extreme Learning Machine (ELM) network in the performance evaluation of Rotate Vector (RV) reducer, a pattern recognition model of ELM based on Ensemble Empirical Mode Decomposition (EEMD) fusion and Improved artificial Jellyfish Search (IJS) algorithm was proposed for RV reducer fault diagnosis. Firstly, it is theoretically proved that the torque transmission of RV reducer has periodicity during normal operation. The characteristics of data periodicity can be effectively reflected by using the test signal periodicity characteristics of rotating machinery and EEMD. Secondly, the Logistic chaotic mapping of population initialization in JS algorithm is replaced by tent mapping. At the same time, the competition mechanism is introduced to form a new IJS. The simulation results of standard test function show that the new algorithm has the characteristics of faster convergence and higher accuracy. The new algorithm was used to optimize the input layer weight of the ELM, and the pattern recognition model of IJS-ELM was established. The model performance was tested by XJTU-SY bearing experimental data set of Xi'an Jiaotong University. The results show that the new model is superior to JS-ELM and ELM in multi-classification performance. Finally, the new model is applied to the fault diagnosis of RV reducer. The results show that the proposed EEMD-IJS-ELM fault diagnosis model has higher accuracy and stability than other models.

The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones in the presence of a large amount of external environment noise, noise and drift inevitably occur. The distortion phenomenon makes further signal detection and recognition difficult. In this study, a new method for denoising MEMS vector hydrophones by combining ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) is proposed to improve the utilization of received signals. First, the main frequency of the noise signal is transformed using a Fourier transform. Then, the noise signal is decomposed by EEMD to obtain the intrinsic mode function (IMF) component. The frequency of each IMF component in the center further determines that the IMF component belongs to the noise IMF component, invalid IMF component, or pure IMF component. Then, there are pure IMF reserved components, removing noisy IMF components and invalid IMF components. Finally, the desalinated IMF reconstructs the signal through SSA to obtain the denoised signal, which realizes the denoising processing of the signal, extracting the useful signal and removing the drift. The role of SSA is to effectively separate the trend noise and the periodic vibration noise. Compared to EEMD and SSA separately, the proposed EEMD-SSA algorithm has a better denoising effect and can achieve the removal of drift. Following that, EEMD-SSA is used to process the data measured by Fenhe. The experiment is carried out by the North University of China. The simulation and lake test results show that the proposed EEMD-SSA has certain practical research value.

How to effectively extract weak fault features of planetary gears under strong background noise is a difficult problem that needs to be solved in the field of planetary gear fault diagnosis. For the nonlinear and non-stationary vibration signals of planetary gears, in order to improve the accuracy of fault diagnosis, a planetary gear fault diagnosis method optimized by genetic algorithm-optimized long-short-term memory network (GA-LSTM) and ensemble empirical mode decomposition (EEMD) was proposed. First, the vibration signals of four types of planetary gear faults were collected in the experiment, and the original vibration signal of the planetary gear was decomposed into six intrinsic mode function (IMF) components by using EEMD method, which was used as the feature components for further processing. Then, the hyperparameters of the LSTM network were optimized using the genetic algorithm (GA) to improve the accuracy of fault type identification. Finally, the feature components were inputted into the trained GA-LSTM model, the network model was used as the final classifier to diagnose and identify the faults of the planetary gears. By comparing the unoptimized network and artificially adding noise to the original signal to simulate the actual engineering signal, the validity and effectiveness of the method based on EEMD and GA-LSTM algorithms were verified effectiveness. The research results show that the trained network achieves a loss rate of less than 2%, and has good stability. The fault classification accuracy of the GA-LSTM method reaches 94.17%. Comparing with the non-optimized network, the verification accuracy of the GA-LSTM model is found to be higher than that of the LTSM, which shows better timing performance on all components; even when identifying engineering signals with added noise, high fault diagnosis accuracy can also be maintained, which shows its superiority in planetary gear fault diagnosis. This study has certain theoretical guidance and reference value in improving the fault diagnosis ability of mechanical transmission equipment. [ABSTRACT FROM AUTHOR]

As wave height is an important parameter in marine climate measurement, its accurate prediction is crucial in ocean engineering. It also plays an important role in marine disaster early warning and ship design, etc. However, challenges in the large demand for computing resources and the improvement of accuracy are currently encountered. To resolve the above mentioned problems, sequence-to-sequence deep learning model (Seq-to-Seq) is applied to intelligently explore the internal law between the continuous wave height data output by the model, so as to realize fast and accurate predictions on wave height data. Simultaneously, ensemble empirical mode decomposition (EEMD) is adopted to reduce the non-stationarity of wave height data and solve the problem of modal aliasing caused by empirical mode decomposition (EMD), and then improves the prediction accuracy. A significant wave height forecast method integrating EEMD with the Seq-to-Seq model (EEMD-Seq-to-Seq) is proposed in this paper, and the prediction models under different time spans are established. Compared with the long short-term memory model, the novel method demonstrates increased continuity for long-term prediction and reduces prediction errors. The experiments of wave height prediction on four buoys show that the EEMD-Seq-to-Seq algorithm effectively improves the prediction accuracy in short-term (3-h, 6-h, 12-h and 24-h forecast horizon) and long-term (48-h and 72-h forecast horizon) predictions. [ABSTRACT FROM AUTHOR]

To improve the accuracy of short-term wind direction forecasting, a hybrid model, named EEMD-CNN-GRU, is proposed based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU）.The EEMD is used to decompose the data into multiple components to address the randomness and unsteadiness of wind direction series, then the local connection and weight sharing of CNN are employed to extract the potential features in each component, and the GRU is adopted to reconstruct the extracted features and superpose the predicted values of each component to obtain the final prediction results.The experimental results show that the proposed method outperforms models of BP neural network and long short-term memory (LSTM). [ABSTRACT FROM AUTHOR]

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In order to overcome the low accuracy of traditional Extreme Learning Machine (ELM) network in the performance evaluation of Rotate Vector (RV) reducer, a pattern recognition model of ELM based on Ensemble Empirical Mode Decomposition (EEMD) fusion and Improved artificial Jellyfish Search (IJS) algorithm was proposed for RV reducer fault diagnosis. Firstly, it is theoretically proved that the torque transmission of RV reducer has periodicity during normal operation. The characteristics of data periodicity can be effectively reflected by using the test signal periodicity characteristics of rotating machinery and EEMD. Secondly, the Logistic chaotic mapping of population initialization in JS algorithm is replaced by tent mapping. At the same time, the competition mechanism is introduced to form a new IJS. The simulation results of standard test function show that the new algorithm has the characteristics of faster convergence and higher accuracy. The new algorithm was used to optimize the input layer weight of the ELM, and the pattern recognition model of IJS-ELM was established. The model performance was tested by XJTU-SY bearing experimental data set of Xi'an Jiaotong University. The results show that the new model is superior to JS-ELM and ELM in multi-classification performance. Finally, the new model is applied to the fault diagnosis of RV reducer. The results show that the proposed EEMD-IJS-ELM fault diagnosis model has higher accuracy and stability than other models. [ABSTRACT FROM AUTHOR]

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE—K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD–WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO–SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue. [ABSTRACT FROM AUTHOR]

Despite numerous studies on the response of vegetation change to climate variation, they have mainly been based on annual mean temperature and annual mean precipitation, and have failed to reveal the impact of climate on the inter-annual variation of vegetation in different geomorphic zones based on non-linear methods and considering daily low and high temperature. In this study, the effect of climate variation on the inter-annual variation of vegetation in different geomorphic zones along the area of t the Yangtze River Basin, China from 1982 to 2015 is revealed using Ensemble Empirical Mode Decomposition method. The results show that the inter-annual variation of vegetation is frequently fluctuating, mainly on a short time scale (3-year time scale), and its contribution gradually increases from 62.35% to 73.57% with increasing relief (from plain to high undulating mountain). The vegetation change of more than 75% of the areas is dominated by inter-annual variations, moreover, the vegetation change of the low relief areas is dominated by the 3-year timescale along with the long-term trend, while that of the other geomorphic zones is dominated by the 3-year timescale only. Inter-annual vegetation variation is positively related to precipitation and maximum temperature, but negatively related to mean temperature and minimum temperature in most areas, with area percentages of 67.20%, 86.55%, 65.38% and 75.02%. Inter-annual variation is mainly controlled by maximum temperature in most areas (54.77%). These results will deepen our understanding of the vegetation-climate relationship, suggesting that the responses of inter-annual variation in vegetation to climate vary not only on different time scales, but also in indifferent geomorphic zones.

Polyethylene pipe finds it hard to avoid aging in outdoor water and gas transmission, and its aging degree is difficult to be evaluated intuitively. Conventional methods for determining whether polyethylene pipes must be replaced or maintained mostly involve destructive testing. Polyethylene aging causes a change of microstructure, resulting in mechanical property changes. Hence, this study presents a nondestructive evaluation technique for evaluating the aging degree of polyethylene pipes by single-transducer single-sided detection. It explored the potential relationship between polyethylene aging degree and ultrasonic nonlinear parameters. Firstly, it denoised ultrasonic signals of polyethylene pipes with different exposure times. Then, it calculated the nonlinear parameter, and the relationship between the nonlinear parameter and the exposure time of polyethylene was established. And the relationship between the tensile strength and the exposure time of polyethylene was also established in combination with the tensile test data. Finally, the nonlinear parameters and tensile properties were found to exhibit opposite trends with similar rate of change. Its findings verified the feasibility of characterizing the aging degree of polyethylene pipe using the nonlinear ultrasonic method. [ABSTRACT FROM AUTHOR]

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert–Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert–Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland–Altman analysis. [ABSTRACT FROM AUTHOR]

Accurate localization of Acoustic Emission (AE) events in rock structures is crucial for investigating rock failure mechanisms. This paper presents a comprehensive analysis of 3D AE localization in basalt-type rock samples, utilizing a novel combination of three powerful techniques: the Simulated Annealing Algorithm (SAA), an innovative AIC-EEMD picking method, and a velocity-free approach to wave propagation. We employed a localized Hsu–Nielsen source to generate elastic waves, which were captured by six AE sensors strategically placed on the rock surface. To address challenges in identifying P-wave arrivals, particularly in noisy or spurious data, we developed an innovative technique that integrates the Akaike Information Criterion (AIC) with Empirical Ensemble Mode Decomposition (EEMD) to enhance signal clarity. The performance of our proposal was validated achieving an average absolute distance error of 3.74 mm. Compared to traditional methods, our technique demonstrates superior accuracy and offers enhanced robustness in the presence of noise. • A new method for Acoustic Emission Source location in rocks based on the Simulated Annealing algorithm. • The geophysical inversion algorithm finds the AE Sources and gets an average of absolute distance error of 3.74 mm. • To enhance P-wave arrival time accuracy, we apply AIC to EEMD modes. • The location accuracy was higher than the method which requires pre-measured velocity. [ABSTRACT FROM AUTHOR]

Short-term (e.g., hourly) urban water consumption (or demand) prediction is of great significance for the optimal operation of the intelligent water distribution pump stations. In this study, three single models (autoregressive integrated moving average (ARIMA), back-propagation (BP), support vector machine (SVM)) and three hybrid models (ensemble empirical mode decomposition (EEMD)-ARIMA, EEMD-BP and EEMD-SVM) were developed and compared in terms of prediction accuracy and application convenience. 31-day (1 month) hourly flow series from a water distribution division in Shanghai were used for the demonstration case study, among which 30-day data used for model training and 1-day data used for model verification. Finally, the effects of historical data length on the prediction accuracy of three hybrid models were also analyzed, and the optima of the historical data length for three hybrid models were obtained. Results reveal that (1) the mean absolute percentage errors (MAPE) of EEMD-ARIMA, EEMD-BP, EEMD-SVM, ARIMA, BP and SVM are 5.2036, 1.4460, 1.3424, 5.7891, 4.3857 and 3.8470%, respectively. (2) In terms of prediction accuracy and actual practice convenience, EEMD-SVM performs best among the above six models. (3) The EEMD algorithm is effective for improving the prediction accuracy of six models. (4) The optimal historical data length of EEMD-ARIMA, EEMD-BP and EEMD-SVM are 11, 11 and 10 days, respectively. HIGHLIGHTS Three single models (ARIMA, BP and SVM) and three hybrid models (EEMD-ARIMA, EEMD-BP and EEMD-SVM) were compared for the prediction of hourly water demand.; EEMD-SVM performs best among the six prediction models.; The EEMD algorithm is significant for improving prediction accuracy.; The optimal historical data length for intelligent algorithms should be greater than a week.;

In the process of bearing fault diagnosis of wind turbine, the fault diagnosis method based on deep learning is limited by limited labeled samples, which has problems such as difficulties in model convergence and low recognition accuracy. For this purpose, a parallel convolutional neural network（P-CNN) and feature fusion-based fault diagnosis method for small sample wind turbine bearings was proposed. Firstly, the vibration signal of the bearing was decomposed into several intrinsic mode functions (IMF） components and residual components by ensemble empirical mode decomposition（EEMD). Then, the short time Fourier transform (STFT) was performed on them, and they were respectively converted into time-frequency characteristic maps, and multiple identical convolutional neural network branches were constructed as feature extractors. Finally, the extracted time-frequency domain features were fused in the fusion layer and used as the input of the final classifier to achieve fault identification of wind turbine bearings, the applicability and effectiveness of this method was validated using different size bearing datasets from Case Western Reserve University. The results show that the parallel convolutional neural network (P-CNN) and feature fusion-based fault diagnosis method has an average accuracy of 94.5% when containing only 160 samples, which has higher accuracy and stronger robustness compared to support vector machine (SVM) 、FaultNet and deep convolutional neural networks with wide first-layer kernel (WDCNN). [ABSTRACT FROM AUTHOR]

With global warming, coastal areas are exposed to multiple climate-related hazards. Understanding the facts and attribution of regional climate change in coastal communities is a frontier science challenge. In this study, we focus on fact analysis of multi-scale climate changes in the Guangdong–Hong Kong–Macao Greater Bay area (GBA) and around the Baltic Sea area (BSA). We selected three Asian stations from the GBA in South China (Guangzhou, Hong Kong, and Macao) and five European stations around the Baltic Sea (Stockholm, Haparanda A, Vestervig, Poznan, and Frankfurt) from four countries in the BSA as representative stations, which have more than 100- or 150-year datasets. Based on the ensemble empirical mode decomposition (EEMD) and Mann–Kendall methods, this study focuses on the multi-scale temperature and precipitation fluctuation and mutation analysis in the past. The multi-scale analyses show that there are four time-scale changes in both areas. They are the inter-annual scale, inter-decadal scale, centennial scale, and trend, but the lengths of different timescales vary in both regions, especially the inter-decadal scale and centennial scale. For temperature, the inter-annual scales show the same results, with 2–4 and 7–9 a in both the GBA and BSA. In the GBA, the inter-decadal scales are 10–14, 30–50, and 55–99 a, while in the BSA, they are 13–20, 26–50, and 66–99 a. For centennial scales, there are 143–185 and 200–264 a in the BSA and about 100–135 a in the GBA. Temperature trends in the GBA reveal that the coastal area has experienced an upward trend (Hong Kong and Macao), but in the inland area (Guangzhou), the trend fluctuated. Temperature trends in the BSA have risen since 1756. For precipitation, the inter-annual scales are 2–4 and 6–9 a in both the GBA and BSA. The inter-decadal scales are 11–29 and 50–70 a in the GBA and 11–20, 33–50, and 67–86 a in the BSA. For centennial scales, there are about 100 a in the GBA and 100–136 a in the BSA. In the GBA, the precipitation trends show stronger local characteristics, with three different fluctuation types. In the BSA, most stations had a fluctuating trend except Haparanda A and Vestervig station, which experienced an upward trend throughout the whole time range. Overall, there are no unified trends for precipitation in both areas. Temperature mutation tests show that only Vestervig in the BSA changed abruptly in 1987, while the mutation point of Macao in the GBA was 1991. Precipitation mutation points of Stockholm and Vestervig were 1878 and 1918 in the BSA, while only Macao in the GBA changed abruptly in 1917. The results reveal that the regional climate mutation of both areas is not obvious, but the temperature changes with an upward trend as a whole. [ABSTRACT FROM AUTHOR]

The electroencephalography (EEG) signal is corrupted with some non-cerebral activities due to patient movement during signal measurement. These non-cerebral activities are termed as artifacts, which may diminish the superiority of acquired EEG signal statistics. The state of the art artifact elimination approaches applied canonical correlation analysis (CCA) for confiscating EEG motion artifacts accompanied by ensemble empirical mode decomposition (EEMD). An improved cascaded approach based on Gaussian elimination CCA (GECCA) and EEMD is applied to suppress EEG artifacts effectively. However, in a highly noisy environment, a novel addition of median filter before the GECCA algorithm is suggested for improving the accuracy of onslaught the EEG signal. The median filter is opted due to its edge preserving nature and speed. This proposed approach is appraised using efficacy grounds for instance Del signal to noise ratio, Lambda (λ), root mean square error and receiver operating characteristic (ROC) parameters and verified contrary to presently obtainable EEG artifacts exclusion methods. The primary concern is to improve the efficacy and precision of the proposed artifact elimination technique. The elapsed time is also calculated to evaluate the computation efficiency. Results show that the proposed algorithm is appropriate to be used as an addition to existing algorithms in use. [ABSTRACT FROM AUTHOR]

In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model is that the only required data are water level data. EEMD is used to decompose water level signals from a tidal river into several intrinsic mode functions (IMFs). These IMFs are then used to reconstruct the ocean and stream components that represent the tide and river flow, respectively. The forecasting model is obtained through stepwise regression on these components. The ocean component at a location 1 h ahead can be forecast using the observed ocean components at the downstream gauging stations, and the corresponding stream component can be forecast using the water stages at the upstream gauging stations. Summing these two forecasted components enables the forecasting of the water level at a location in the tidal river. The proposed model is conceptually simple and highly accurate. Water level data collected from gauging stations in the Tanshui River in Taiwan during typhoons were used to assess the feasibility of the proposed model. The water level forecasting model accurately and reliably predicted the water level at the Taipei Bridge gauging station. [ABSTRACT FROM AUTHOR]

It is of utmost necessity to understand the dynamics of regional active accumulated temperature (AAT) to cope with the negative impacts of global warming on agroforestry development and food security and to provide a real-time and effective reference basis for regional agroforestry planning. The daily temperature data from 30 meteorological stations in Sichuan Province from 1970 to 2020, and sea surface temperature (SST) index data from the Atlantic Multiphase Oscillation (AMO) and Pacific Decadal Oscillation (PDO) were used for the study. Sichuan Province was divided into the western region (WS) and the eastern region (ES), considering 1000 m above sea level as the boundary. The spatiotemporal characteristics of ≥0°C and ≥10°C active accumulated temperature (AAT0, AAT10) in WS and ES were analyzed comprehensively using 5-day average sliding, empirical orthogonal function (EOF), ensemble empirical mode decomposition (EEMD), and multiple mutation tests. The results show that (1) AAT0 and AAT10 of WS ranged from 3034°C to 3586°C and 1971°C to 2636°C, respectively, while the AAT0 and AAT10 of ES ranged from 5863°C to 6513°C and 4847°C to 5875°C, respectively. The period around 1997 was a significant abrupt change, and the AAT in the province generally increased during the subsequent time period (2) AAT in the study area is mainly driven by the fluctuations of AMO, as reflected by the low-to-high variation of AAT coinciding with the jump of the cold-to-warm phase of AMO. Considering different time scale fluctuations in the past 51 years, the major cycle for both AAT0 and AAT10 in WS is 3.40 a, while the major cycles in ES are 3.64 a and 3.19 a, respectively with a sub-cycle of 7.29 a. AAT fluctuation has an insignificant periodic characteristic of 25.50 a on the interdecadal scale (3) The spatial heterogeneity of AAT in WS is prominent and is mainly reflected by the significantly warm conditions in the south of the WS region and relatively slight warm conditions in the north, as well as by the isolated cooling area in the form of "freezing point", i.e., Xiaojin county. In contrast, the spatial variability of AAT in ES is more or less consistent, with the warming areas concentrated in the foothills of the western edge of the basin and a slight increase in AAT observed in the central part of the basin. [ABSTRACT FROM AUTHOR]

In most coastal oceans, the impacts of global warming on season duration and timing of seasonal transitions remain unknown. To mirror the reality of the ongoing climate change, the summer and winter seasons are redefined using the local water temperature thresholds in the Bohai Sea. Then the phase variations of these seasons are quantified using the duration and transition timing indices, including the duration (DUR), onset (ONS), and withdrawal (WIT) indices derived from the OSTIA SST dataset at a very high resolution (0.05°). During the last four decades (1982–2019), secular trends of summer indices extracted by the ensemble empirical mode decomposition (EEMD) method reveal that the summer DUR has an accumulated increase of about 17 days (4.5 days decade-1), which is primarily induced by the phase advance of the summer ONS by about 16 days (4.2 days decade-1). Spatial features of the duration and timing indices demonstrate that the lengthening of summer DUR and the phase advance of summer ONS have significantly enhanced in the shallow regions, due to the limited thermal inertia and the shorter period of the ocean’s memory. In contrast, the secular trend of winter DUR exhibits an accumulated shortening of about 18 days (4.8 days decade-1), which is induced by a moderately delayed winter ONS of 6 days (1.6 days decade-1) and a significantly advanced winter WIT of 12 days (3.2 days decade-1). The potential linkage between the phase variations in the oceanic seasonal cycle and those of the atmospheric forcing was investigated by analyzing both the interannual variability and the secular trend. Over the analysis period, the secular trend of an earlier summer ONS is related to a total reduction of cloud cover by 30% of its climatological mean and an increase of incoming solar radiation of 10 W m-2 month-1 in the late spring. Thus, our results highlight the influence of cloud cover in addition to wind speed on the temporal variations of season transition timing.

A continuous and complete spring discharge record is critical in understanding the hydrodynamic behavior of karst aquifers and the variability of freshwater resources. However, due to equipment errors, failure of observation and other reasons, missing data is a common problem for spring discharge monitoring and further hydrological investigations and data analysis. In this study, a novel approach that integrates deep learning algorithms and ensemble empirical mode decomposition (EEMD) is proposed to reconstruct the missing spring discharge data with a given local precipitation record. Using EEMD, the local precipitation data is decomposed into several intrinsic mode functions (IMFs) from high to low frequencies and a residual function, which are served as the input of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM models to reconstruct the missing discharge data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency coefficient (NSE), are calculated to evaluate the reconstruction performance. The monthly spring discharge and precipitation data from March 1978 to October 2021 collected at Barton Springs in Texas are used for the validation and evaluation of newly proposed deep learning models. The results indicate that deep learning models coupled with EEMD overperform the models without EEMD and significantly improve the reconstruction results. The LSTM-EEMD model obtains the best reconstruction results among three deep learning algorithms. For models with monthly data, the missing rate affects the reconstruction performance because of the number of data samples: the best reconstruction results are achieved when the missing rate was low. If the missing rate was 50%, the reconstruction results become notably poorer. However, when the daily precipitation and discharge data are used, the models can obtain satisfactory reconstruction results with missing rate ranged from 10 to 50%. [ABSTRACT FROM AUTHOR]

The stiffness index (SI) is used to estimate cardiovascular risk in humans. In this study, we developed a refined SI for determining arterial stiffness based on the decomposed radial pulse and digital volume pulse (DVP) waveforms. In total, 40 mature asymptomatic subjects (20 male and 20 female, 42 to 76 years of age) and 40 subjects with type 2 diabetes mellitus (T2DM) (23 male and 17 female, 35 to 78 years of age) were enrolled in this study. We measured subjects' radial pulse at the wrist and their DVP at the fingertip, and then implemented ensemble empirical mode decomposition (EEMD) to derive the orthogonal intrinsic mode functions (IMFs). An improved SI (SInew) was calculated by dividing the body height by the mean transit time between the first IMF5 peak and the IMF6 trough. Another traditional index, pulse wave velocity (PWVfinger), was also included for comparison. For the PWVfinger index, the subjects with T2DM presented significantly higher SInew values measured according to the radial pulse (SInew-RP) and DVP signals (SInew-DVP). Using a one-way analysis of variance, we found no statistically significant difference between SInew-RP and PWVfinger when applied to the same test subjects. Binary logistic regression analysis showed that a high SInew-RP value was the most significant risk factor for developing T2DM (SInew-RP odds ratio 3.17, 95% CI 1.53–6.57; SInew-DVP odds ratio 2.85, 95% CI 1.27–6.40). Our refined stiffness index could provide significant information regarding the decomposed radial pulse and digital volume pulse signals in assessments of arterial stiffness. [ABSTRACT FROM AUTHOR]

The radiation pressure signals generated by the bubble oscillation are often utilized to recognize the characteristics of the target objects in many fields. However, these signals are easily contaminated by complex background noises. In order to accurately extract the effective components of the radiation pressure signal generated by the bubble oscillation, this paper proposes a de-noising procedure for the radiation pressure signal, based on the ensemble empirical mode decomposition (EEMD), the autocorrelation function and the modified wavelet soft-threshold de-noising method. In order to verify the effectiveness of the procedure, the typical radiation pressure signal generated based on the Keller-Miksis model under the acoustic excitation is employed for the subsequent de-noising analysis. The results of the qualitative analysis show that the amplitude and the period of the bubble oscillation can be clearly observed in the time-domain diagram of the de-noised signal based on the EEMD. In the quantitative analysis, the de-noised signal based on the EEMD has better performance with higher signal-to-noise ratio (SNR), smaller root-mean-square error, and larger correlation coefficient than that based on the wavelet transform (WT) and the empirical mode decomposition (EMD). Furthermore, with the increase of the complexity of the radiation pressure signal (e.g., the increase of the dimensionless pressure amplitude of the acoustic wave and the decrease of the SNR of the input signal), the above three evaluation indexes of the de-noised signal based on the EEMD are all better than those based on the other two methods. When the signal is more complex, the de-noising capabilities of the WT, the EMD are greatly reduced, but the EEMD can still maintain the good de-noising capability, which shows the superiority of the signal de-noising procedure proposed in the present paper. [ABSTRACT FROM AUTHOR]

For the fault diagnosis needs of the full life cycle (light degradation, moderate degradation, and severe degradation) of rolling bearing under the environment of high background noise, a genetic algorithm-output input hidden feedback (GA-OIHF ) Elman neural network model is proposed to achieve precise diagnosis of the degradation faults of rolling bearing. Ensemble empirical mode decomposition (EEMD) is selected to effectively reduce the noise and extract fault features of the vibration signal. An OIHF Elman neural network is designed by increasing the feedbacks from the output layer to the hidden layer and the input layer based on the Elman neural network, thus further improves its ability to process full life cycle data of rolling bearing. Then, a novel GA-OIHF Elman neural network model is developed by combining the genetic algorithm (GA) and the OIHF Elman neural network. The novel GA-OIHF Elman neural network model combines the global optimization of GA and the local optimization ability of the OIHF Elman neural network to achieve an accurate fault diagnosis of the entire life cycle of rolling bearing. The experimental results show that the GA-OIHF Elman algorithm model can not only accurately diagnose the fault in the full life cycle of rolling bearing, but also ensure the stability of the diagnosis model for different faults including different fault components and stages.

In recent years, with the frequent occurrence of severe drought events, climate change in arid regions has become one of the research hotspots. However, previous studies mainly focused on a specific arid region, and the correlations and differences of drought among various arid regions have not been clearly understood. In this study, based on the latest monthly gridded dataset of the CRU, we compare the characteristics of climate change and its relationship with large-scale oceanic oscillation indexes in the three typical arid regions of Pan-Central-Asia (PCA), North America (NAm) and North Africa (NAf) in multiple perspectives. The results show that the precipitation in the PCA and NAm has increased obviously over the past 80 years, while the NAf precipitation has decreased. After the 1980s, the climate in the PCA and NAm show warm-wet types. This type of the former continues to the present, but the latter’s has changed to a warm-dry type since the 21st century. The NAf climate remains the warm-dry type since the 1990s. Nonetheless, the arid and semi-arid climate patterns in the three typical arid regions remain unchanged. The NAm precipitation has an anti-phase variability pattern compared with the NAf precipitation on both interdecadal and multi-decadal time scales. The Pacific Decadal Oscillation (PDO) has a great influence on the precipitation of the PCA and NAm. The temperature of three arid regions is significantly related to the variations in the Arctic oscillation (AO). In the inland arid region, the contribution of strong warming effect during cold season to the whole year is much greater than that during warm season, while the contribution of the coastal arid regions in warm season is greater. The precipitation in the mid-latitude arid regions is dominated by cold-season precipitation regardless of whether these regions are near the sea or not. The precipitation in the low-latitude arid regions has little difference between cold and warm seasons.

• The selective EEMD-TFT model is introduced for predicting multi-step discharge. • Decomposing nonlinear and nonstationary data improves prediction performance. • The selective EEMD-TFT outperforms other seq-to-seq models in comparison. • It provides a more robust prediction and is less sensitive to forecast horizons. Karst groundwater is a critical freshwater resource for numerous regions worldwide. Monitoring and predicting karst spring discharge is essential for effective groundwater management and the preservation of karst ecosystems. However, the high heterogeneity and karstification pose significant challenges to physics-based models in providing robust predictions of karst spring discharge. In this study, an interpretable multi-step hybrid deep learning model called selective EEMD-TFT is proposed, which adaptively integrates temporal fusion transformers (TFT) with ensemble empirical mode decomposition (EEMD) for predicting karst spring discharge. The selective EEMD-TFT hybrid model leverages the strengths of both EEMD and TFT techniques to learn inherent patterns and temporal dynamics from nonlinear and nonstationary signals, eliminate redundant components, and emphasize useful characteristics of input variables, leading to the improvement of prediction performance and efficiency. It consists of two stages: in the first stage, the daily precipitation data is decomposed into multiple intrinsic mode functions using EEMD to extract valuable information from nonlinear and nonstationary signals. All decomposed components, temperature and categorical date features are then fed into the TFT model, which is an attention-based deep learning model that combines high-performance multi-horizon prediction and interpretable insights into temporal dynamics. The importance of input variables will be quantified and ranked. In the second stage, the decomposed precipitation components with high importance are selected to serve as the TFT model's input features along with temperature and categorical date variables for the final prediction. Results indicate that the selective EEMD-TFT model outperforms other sequence-to-sequence deep learning models, such as LSTM and single TFT models, delivering reliable and robust prediction performance. Notably, it maintains more consistent prediction performance at longer forecast horizons compared to other sequence-to-sequence models, highlighting its capacity to learn complex patterns from the input data and efficiently extract valuable information for karst spring prediction. An interpretable analysis of the selective EEMD-TFT model is conducted to gain insights into relationships among various hydrological processes and analyze temporal patterns. [ABSTRACT FROM AUTHOR]

Low-frequency errors are a type of testing errors commonly existing in pyroshock experiments due to the harsh mechanical environment that lead to inaccuracy and contamination of pyroshock data. It is necessary to eliminate such low-frequency errors from pyroshock data via data analysis. Two methods based on discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) are proposed, respectively, in this article. Firstly, the principle and operating process of these two methods are introduced, where four correlation coefficients are defined to determine the decomposition level for quantitative correction. Then, the two methods are applied to remove the integral zero shift from a set of pyroshock test data, respectively, and the capability and effectiveness of the two methods are compared. It is revealed from the comparison studies that both methods are capable of removing integral zero shift effectively to correct the shock response spectrum and improve the accuracy of pyroshock data. The correlation coefficients constructed here can help to realize quantitative selection of key parameters for the two methods. The DWT method shows better correction performance for extracting accurate parameters, whereas the EEMD method is more concise and convenient for application. [ABSTRACT FROM AUTHOR]

Non-contact vital sign detection technology has brought a more comfortable experience to the detection process of human respiratory and heartbeat signals. Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method which can be used to decompose the echo data of frequency modulated continuous wave (FMCW) radar and extract the heartbeat and respiratory signals. The key of EEMD is to add Gaussian white noise into the signal to overcome the mode aliasing problem caused by original empirical mode decomposition (EMD). Based on the characteristics of clutter and noise distribution in public places, this paper proposed a static clutter filtering method for eliminating ambient clutter and an improved EEMD method based on stable alpha noise distribution. The symmetrical alpha stable distribution is used to replace Gaussian distribution, and the improved EEMD is used for the separation of respiratory and heartbeat signals. The experimental results show that the static clutter filtering technology can effectively filter the surrounding static clutter and highlight the periodic moving targets. Within the detection range of 0.5 m~2.5 m, the improved EEMD method can better distinguish the heartbeat, respiration, and their harmonics, and accurately estimate the heart rate. [ABSTRACT FROM AUTHOR]

A method to estimate modal frequency based on empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) is proposed. This method can decrease the difficulties in identifying modal frequency of combine harvesters. First, we used 16 acceleration sensors installed at different test points to collect vibration signals of a corn combine harvester under operating conditions (mass time-varying conditions). Second, we calculated mean value, variance and root mean square (RMS) value of the vibration signals, and analyzed its stationarity of vibration signals. Third, the main frequencies of the 16 points were extracted using the EMD and EEMD methods. Finally, we considered modal frequencies identified by the SSI algorithm as standard, and calculated the fitting degrees of the EMD and EEMD methods. The results show that in different time periods (0~60 s and 60~120 s), the maximum differences of the mean value, variance and RMS value of signals were 0.8633, 171.1629 and 11.3767, and the vibration signal under the operating condition of field harvesting can be regarded as a typical non-stationary random vibration signal. The EMD method had more modal aliasing than EEMD, and when we obtained the fitting equations of EMD, EEMD and SSI methods, the value of the Euler distance between the EMD fitting equation and the SSI fitting equation was 446.7883, while that for EEMD and SSI was 417.2845. The vibration frequencies calculated by the EEMD method is closer to the modal frequencies identified by SSI algorithm. The proposed method provides a reference for modal frequency identification and vibration control in a complex working environment. [ABSTRACT FROM AUTHOR]