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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]

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.

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]

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]

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]

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]

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.

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.

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.

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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]

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]

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.

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]

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]

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]

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 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.

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]

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.

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]

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]

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]

A multiple-frequency continuous-wave (MFCW) agile radar system has the ability to cope with various physiological conditions of a human subject. Following the mathematic model, a favorable carrier frequency for detection is pursued by analyzing the amplitude and composition of the baseband signal. In Experiment-1, a CMOS dual-band transceiver prototype emulate searching a subject from a distance. The frequencies of the dual-band system are 4.26–4.95 GHz and 17.06–19.79 GHz. During the experiment, the respiratory signal could be detected up to 12 m, and the heartbeat signal up to 6 m. Even with a wooden or a brick barrier in the path, the vital-sign detection remained satisfactory. Later, a concurrent MFCW detection is performed in Experiment-2 for more precise analyses. From the concurrent results, the breathing status can be inferred, and by cross-correlating the results after the ensemble empirical mode decomposition (EEMD) and principal component analysis (PCA), the nonlinear effects of respiratory harmonics or intermodulation tones are reduced, resulting in an accurate heart rate extraction. The receiver front-end and the signal source are manufactured using a 180-nm CMOS process, both packaged on an RO4003C printed-circuit board (PCB). [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, electrocardiogram (ECG) monitoring has become the most effective method of monitoring cardiac rhythm in critically ill patients. It can detect a variety of arrhythmias, including atrial and ventricular premature beats, myocardial perfusion, etc. Nevertheless, the transmission and storage of large amounts of physiological data is a major challenge. To maintain signal integrity and increase transmission speed, data compression is necessary. Current research is increasingly focused on adaptive compression algorithms. These algorithms adapt coding strategies based on signal characteristics. ECG data compression technique combining empirical mode decomposition (EMD) and discrete wavelet transform (DWT) has been proposed. However, the intrinsic mode functions (IMFs) component generated from EMD decomposition suffers from a mode mixing problem. This paper proposes a scheme for decomposing ECG signals using ensemble empirical mode decomposition (EEMD) and recombining the components with DWT. The scheme compresses and quantizes the ECG signal using a uniform scalar dead-zone quantization method and further compresses the data using run-length coding. Evaluation parameters indicate that the proposed scheme has superior compression performance. Compressed signals can facilitate remote transmission and real-time monitoring, providing patients with more convenient medical services and promoting the development of healthcare. • We propose a novel ECG data compression scheme that utilizes ensemble empirical mode decomposition (EEMD) to process signals and to address the issue of "mode mixing" that is caused by intermittent signals when empirical mode decomposition (EMD) generates intrinsic mode functions (IMFs). • This scheme does not require prior knowledge of signals and is easy to implement. Results demonstrate that the proposed scheme is superior to other ECG data compression schemes based on DWT and EMD. • This scheme demonstrates superior compression performance with low algorithm complexity and high adaptivity. It has broad application prospects for portable devices that detect and transmit human physiological signals. [ABSTRACT FROM AUTHOR]

Soot blowing optimization and health management of coal-fired power plant boiler has received increasing attention in recent years. The ash fouling monitoring and prediction are the basis for achieve this goal. Nowadays, with the development of neural network technology, the new data-driven methodologies are provided for ash fouling monitoring and prediction. This paper presents a comprehensive method based on neural-network for ash fouling prediction. Firstly, the health factor-clearness factor of the heated surface was established from the actual heat transfer coefficient and the theoretical heat transfer coefficient. Wavelet threshold denoising algorithm will be used as data preprocessing method. Secondly, use Ensemble Empirical Mode Decomposition (EEMD) to obtain a series of frequency stable parts. Finally, Encoder-Decoder based Attention (EDA) is used to predict the ash deposit on the heat transfer surface. The EDA model consists of an encoder, a decoder and an attention mechanism. The encoder and decoder are composed of Bidirectional Long Short-Term Memory (BI-LSTM) and Long Short-term Memory Network (LSTM) respectively. The function of the attention mechanism is that the output of each time step of the encoder is given a different degree of attention, and it is sent to the decoder as an attention vector after a weighted average operation. Ash accumulation data on the heat transfer surface of various devices are used to verify the effectiveness of the proposed hybrid model. In addition, the experimental results show that this method has better prediction accuracy than other variant models.

Ground-based synthetic aperture radar (GBSAR) technology, as a new measurement technology, has the advantages of noncontact measurements, high precision, and all-weather measurement capability, and it has been widely used for bridge dynamic deflection measurements. In order to reduce the influence of noise in dynamic deflection of bridges obtained using GBSAR, this article proposes a single-channel blind source separation signal (SCBSS) de-noising method to obtain the denoised dynamic deflection of bridges. First, the extreme-point symmetric mode decomposition (ESMD) method and the ensemble empirical mode decomposition (EEMD) method are used to decompose the obtained dynamic deflection—as the original observation signal—into a series of intrinsic mode functions (IMFs) and a residual R. Second, the Spearman's Rho of each IMF with the original observation signal is calculated to remove the dominant IMFs of high-frequency noise. Third, the remaining IMFs and R decomposed by ESMD and EEMD are reconstructed into two sets of new signals, which form a new virtual multichannel data with the original observation signal. Finally, blind source separation is performed on the new virtual multichannel signal to obtain separated signal components. The separate signal components are converted in the frequency domain using the fast Fourier transform algorithm, and the noise signal components are identified using a spectrum analysis, to achieve further removal of noise information. The results of both simulated and on-site experiments show that the SCBSS signal de-noising method has a powerful signal de-noising ability.

Soil temperature plays an important role in agriculture, industry and other fields. Accurate soil temperature prediction can help improve productivity and avoid risks in many fields. At present, many machine learning methods have been applied to soil temperature prediction such as support vector regression (SVR), artificial neural network (ANN), long short-term memory neural network (LSTM) and others. In this article, we propose a machine learning model called convolutional neural network based on ensemble empirical mode decomposition (EEMD-CNN) to predict soil temperature. In this model, ensemble empirical mode decomposition (EEMD) is used to decompose original soil temperature series into several intrinsic mode functions (IMFs). After decomposition, the original series are combined with IMFs to get new two-dimension input data as the input of the convolutional neural network (CNN). By comparing the results which is predicted by the trained model with the original soil temperature series and other four models of persistence forecast (PF), backpropagation neural network (BPNN), LSTM and EEMD-LSTM these, the result shows that EEMD-CNN has the better performance than other four models. EEMD-CNN shows good performance not only on predicting next day’s soil temperature but also on predicting several days delay’s temperature also has good performance. It is concluded that the proposed EEMD-CNN model in this study is a suitable tool for soil temperature prediction.

Prestressed structures have gained popularity in large-span buildings due to their great spanning capacity and light self-weights. This kind of structure is normally subjected to multiple types of loads, such as temperature load, wind load, and construction load. The determination of different load effects not only guides the design of similar structures but also helps reveal the damage-induced variation that would be concealed by the environmental loads. To determine the different load effects, separation of the load effects collected by structural health monitoring (SHM) is needed. This study presents an enhanced approach for load effect separation based on independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD), called EEMD-ICA*. The proposed method is to minimize manual tuning of user-defined parameters, which makes the EEMD-ICA suitable for separating load effects from the different measured data. Specifically, an optimization method is developed to determine the appropriate level of the added white noise in the EEMD using the relative root-mean-square error (RMSE) index. A logarithm form of Bayesian information criterion (BIC) is employed for the robust estimation of the number of load effects in the ICA. Simulated structural responses from a square orthogonal cable-net are used to validate the effectiveness of the EEMD-ICA*. Then, the proposed methodology is employed to extract various load effects from the SHM data of the National Speed Skating Oval (NSSO), which is the largest single-layer cable-net structure in the world. [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.

Pipeline transportation systems play an increasingly essential role in the development of the economy. An accurate location of leakage is important to reduce economic losses and maintain the safe operation of pipelines. In this article, a pipeline leakage detection method based on infrasound and negative pressure waves (NPWs) is adopted. This method can locate the leakage only by collecting the infrasound and pressure signals at the first station of the pipeline, which effectively solves the disadvantages of low positioning accuracy of a signal and easily produces false alarms. In this article, the signal denoising method of cross-spectral analysis combined with ensemble empirical mode decomposition (EEMD) was adopted. First, the leakage signals of sound waves and NPWs are processed by EEMD to obtain a series of intrinsic mode functions (IMFs). The characteristic frequency band is obtained by cross-spectral analysis of the leakage signal. Within this frequency band, the dominant component of the leakage source signal is divided by correlation analysis, and the IMF component is reconstructed to complete the signal denoising. When compared to the single signal pipeline leak detection method, the positioning accuracy of this article showed a 1–3 m improvement on average. [ABSTRACT FROM AUTHOR]

Based on the ensemble empirical mode decomposition (EEMD) method, a DAF method for signal construction is proposed that repeatedly decomposes (D) the signal, amplifies (A) the local signal characteristics, and then filters (F) the signal. This method is used to decompose and reconstruct the electric field waveform (called a sferic) of an energetic in-cloud pulse (EIP) with a 247-kA peak current detected by a fast antenna (FA). Based on synchronous sub-microsecond very high-frequency (VHF, 14–88 MHz) radio interferometer (INTF) observations and observed downward fast positive and upward fast negative breakdowns, which occurred simultaneously with the EIP, the EIP sferic is decomposed by the DAF method in 11 steps into two independent sferics: a smoother filtered EIP sferic and an embedded narrow bipolar-like event (NBE). It is verified that strong VHF radiation is generated by the NBE-like event, rather than being associated with the smooth EIP sferic. The analysis, decomposition, and reconstruction of the correlated signals by the EEMD-based DAF method proposed in this article support the idea that the large-amplitude EIP sferic was generated by relativistic discharge responsible for an accompanying terrestrial gamma-ray flash (TGF) rather than by streamer or leader activity. [ABSTRACT FROM AUTHOR]

Objective: Wearable systems that enable continuous non-invasive monitoring of hemodynamic parameters can aid in cardiac health evaluation in non-hospital settings. The seismocardiogram (SCG) is a non-invasively acquired cardiovascular biosignal for which timings of fiducial points, like aortic valve opening (AO) and aortic valve closing (AC), can enable estimation of key hemodynamic parameters. However, SCG is susceptible to motion artifacts, making accurate estimation of these points difficult when corrupted by high-g or in-band vibration artifacts. In this paper, a novel denoising pipeline is proposed that removes vehicle-vibration artifacts from corrupted SCG beats for accurate fiducial point detection. Methods: The noisy SCG signal is decomposed with ensemble empirical mode decomposition (EEMD). Corrupted segments of the decomposed signal are then identified and removed using the quasi-periodicity of the SCG. Signal quality assessment of the reconstructed SCG beats then removes unreliable beats before feature extraction. The overall approach is validated on simulated vehicle-corrupted SCG generated by adding real subway collected vibration signals onto clean SCG. Results: SNR increased by 8.1dB in the AO complex and 11.5dB in the AC complex of the SCG signal. Hemodynamic timing estimation errors reduced by 16.5% for pre-ejection period (PEP), 67.2% for left ventricular ejection time (LVET), and 57.7% for PEP/LVET—a feature previously determined in prior work to be of great importance for assessing blood volume status during hemorrhage. Conclusion: These findings suggest that usable SCG signals can be recovered from vehicle-corrupted SCG signals using the presented denoising framework, allowing for accurate hemodynamic timing estimation. Significance: Reliable hemodynamic estimates from vehicle-corrupted SCG signals will enable the adoption of the SCG in outside-of-hospital settings. [ABSTRACT FROM AUTHOR]

Electrooculogram (EOG) is one of common artifacts in recorded electroencephalogram (EEG) signals. Many existing methods including independent component analysis (ICA) and wavelet transform were applied to eliminate EOG artifacts but ignored the possible impact of the nature of EEG signal. Therefore, the removal of EOG artifacts still faces a major challenge in EEG research. In this paper, the ensemble empirical mode decomposition (EEMD) and ICA algorithms were combined to propose a novel EEMD-based ICA method (EICA) for removing EOG artifacts from multichannel EEG signals. First, the ICA method was used to decompose original EEG signals into multiple independent components (ICs), and the EOG-related ICs were automatically identified through the kurtosis method. Then, by performing the EEMD algorithm on EOG-related ICs, the intrinsic mode functions (IMFs) linked to EOG were discriminated and eliminated. Finally, artifact-free IMFs were projected to obtain the ICs without EOG artifacts, and the clean EEG signals were ultimately reconstructed by the inversion of ICA. Both EOGs correction from simulated EEG signals and real EEG data were studied, which verified that the proposed method could achieve an improved performance in EOG artifacts rejection. By comparing with other existing approaches, the EICA obtained the optimal performance with the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal, which demonstrated that the proposed method could more effectively eliminate blink artifacts from multichannel EEG signals with less error influence. This study provided a novel promising method to eliminate EOG artifacts with high performance, which is of great importance for EEG signals processing and analysis.

Electrooculogram (EOG) is one of common artifacts in recorded electroencephalogram (EEG) signals. Many existing methods including independent component analysis (ICA) and wavelet transform were applied to eliminate EOG artifacts but ignored the possible impact of the nature of EEG signal. Therefore, the removal of EOG artifacts still faces a major challenge in EEG research. In this paper, the ensemble empirical mode decomposition (EEMD) and ICA algorithms were combined to propose a novel EEMD-based ICA method (EICA) for removing EOG artifacts from multichannel EEG signals. First, the ICA method was used to decompose original EEG signals into multiple independent components (ICs), and the EOG-related ICs were automatically identified through the kurtosis method. Then, by performing the EEMD algorithm on EOG-related ICs, the intrinsic mode functions (IMFs) linked to EOG were discriminated and eliminated. Finally, artifact-free IMFs were projected to obtain the ICs without EOG artifacts, and the clean EEG signals were ultimately reconstructed by the inversion of ICA. Both EOGs correction from simulated EEG signals and real EEG data were studied, which verified that the proposed method could achieve an improved performance in EOG artifacts rejection. By comparing with other existing approaches, the EICA obtained the optimal performance with the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal, which demonstrated that the proposed method could more effectively eliminate blink artifacts from multichannel EEG signals with less error influence. This study provided a novel promising method to eliminate EOG artifacts with high performance, which is of great importance for EEG signals processing and analysis. [ABSTRACT FROM AUTHOR]

Technology prediction has been the subject of many prior studies, which have the issues of the long-term effectiveness, the high uncertainty, and the low predictive accuracy. To address these problems, this study developed a model based on a mixed neural network that combines the Ensemble Empirical Mode Decomposition (EEMD) signal decomposition method with the Gated Recurrent Unit (GRU) deep learning model. In this study, the literature data is first preprocessed using Latent Dirichlet Allocation (LDA) topic modeling, and clusters of key technology topics are obtained accordingly. Secondly, within the identified technology topics, the EEMD signal processing method is employed to decompose complex time-series data into simpler subsequences, and GRU prediction models are established. Thirdly, the ultimate technological prediction results are obtained by integrating each subsequence's prediction results. In addition, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used to evaluate the prediction results. Finally, the field of aircraft assembly technology is analyzed as a case study. The results show that the EEMD-GRU hybrid model excels in prediction accuracy, and brings a new perspective and method to the field of technological prediction. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) have fine spectral information, and rich spatial information, of which the feature quality is one of the key factors that affect the classification performance. Therefore, how to extract essential features, and eliminate redundant features from hyperspectral data are the main research focus of this article. Here, we propose a spectral-spatial feature extraction method based on ensemble empirical mode decomposition for HSI classification, which contains several steps as follows: First, the dimension reduction for HSI is performed by using the principal component analysis method. Second, in order to decrease the sensitivity to noise, and extract rough outline features, the adaptive total variation filtering (ATVF) is conducted on the selected principal components. Furthermore, by using the ensemble empirical mode decomposition (EEMD) to resolve each spectral band into sequence components, the features of HSIs can be better coalesced into the transform domain. Finally, the first K principal components of the input image, and the outputs of the ATVF, and EEMD are integrated into a stacking system to obtain the final feature image, which is then classified by a pixel-wise classifier. The experimental results of three authentic hyperspectral datasets show that the proposed algorithm obtains a superior classification performance compared with other methods.

Fetal phonocardiography (fPCG) is a non-invasive technique for detection of fetal heart sounds (fHSs), murmurs and vibrations. This acoustic recording is passive and provides an alternative low-cost method to ultrasonographic cardiotocography (CTG). Unfortunately, the fPCG signal is often disturbed by the wide range of artifacts that make it difficult to obtain significant diagnostic information from this signal. The study focuses on the filtering of an fPCG signal containing three types of noise (ambient noise, Gaussian noise, and movement artifacts of the mother and the fetus) having different amplitudes. Three advanced signal processing methods: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and adaptive wavelet transform (AWT) were tested and compared. The evaluation of the extraction was performed by determining the accuracy of S1 sounds detection and by determining the fetal heart rate (fHR). The evaluation of the effectiveness of the method was performed using signal-to-noise ratio (SNR), mean error of heart interval measurement (|ΔTi| ), and the statistical parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). Using the EMD method, ACC > 95$ % was achieved in 7 out of 12 types and levels of interference with average values of ACC = 88.73 %, SE = 91.57 %, PPV = 94.80 % and $\text {F1} = 93.12$ %. Using the EEMD method, ACC > 95$ % was achieved in 9 out of 12 types and levels of interference with average values of ACC = 97.49 %, SE = 97.89 %, PV = 99.53 % and F1 = 98.69 %. In this study, the best results were achieved using the AWT method, which provided ACC > 95 % in all 12 types and levels of interference with average values of ACC = 99.34 %, SE = 99.49 %, PPV = 99.85 % a F1 = 99.67 %.

Remote techniques for measuring human vital signs have attracted great interests due to the benefits shown in medical monitoring and military applications. Compared with continuous-wave Doppler radar, frequency-modulated continuous-wave (FMCW) radar which can discriminate vital signs from different distances, shows potential for reducing the interferences from other targets and the environment. However, in the state-of-the-art algorithms, only one chirp per frame is utilized for FMCW-based vital sign monitoring. Moreover, the vital signal is extracted from only one range bin of the fast Fourier transform. Which does not make full utilization of the long system idle time, and loses the power distributed on other range bins. By exploiting the relationship between respiration and heartbeat vibrations, an adaptive identification embedded ensemble empirical mode decomposition (EEMD) method for joint-range spectral estimation is proposed to measure the heart rate. First, A multi-chirp processing is presented for a 2-dimensional phase accumulation. Then the phase signals from a sequence of range bins are decomposed with a fast adaptive identification process. Finally, with the identified heartbeat components, we solve a multiple measurement vectors problem to estimate the heart rate. Experimental results showed that, at the detection range from 1 m~ 2.5 m, the proposed method can robustly distinguish the heartbeat from respiration and its harmonics and accurately estimate the heart rate with a root mean square error less than 6 bpm.

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.

Noninvasive monitoring of respiratory activity is an emerging research area in biomedical health monitoring. This article describes a neural network-based model, intelligent Photoplethysmography derived Respiration signal Extraction, and Tracking (${i}$ -PRExT). Here, an ensemble empirical mode decomposition (EEMD) is used to select the appropriate intrinsic mode functions (IMFs) through filtering in the respiration band and reconstruct by a linear weighted sum to obtain the photoplethysmography derived respiration (PDR) signal. The weight factors are derived by a multilayer perceptron neural network (MLPNN) fed with respiratory induced amplitude variation (RIAV) features extracted by a deep autoencoder (DAE). The tracking of respiration rate (RR) is done by an adaptive filter-based predictor. ${i}$ -PRExT was tested and validated with BIDMC data set under PhysioNet and 30 volunteers’ data collected under resting condition. The PDRs achieved over 90% correlation and low error (NRMSE~0.2) with reference respiration signal, while RRs have almost 100% correlation even under motion artifact (MA) corrupted photoplethysmography (PPG). The PDR shows improved performance, while RR tracking outperforms the published research on respiration signal extraction based on PPG. [ABSTRACT FROM AUTHOR]

Spasticity is a common disabling complication caused by the upper motor neurons dysfunction following neurological diseases such as stroke. Currently, the assessment of the spastic hypertonia triggered by stretch reflexes is manually performed by clinicians using perception-based clinical scales, however, their reliability is still questionable due to the inter-rater and intra-rater variability. In order to objectively quantify the complex spasticity phenomenon in post-stroke patients, this study proposed a multi-layer assessment system based on a novel measurement device. The exoskeletal device was developed to synchronously record the kinematic, biomechanical and electrophysiological information in sixteen spastic patients and ten age-matched healthy subjects, while the spastic limb was stretched at low, moderate and high velocities. The mechanical impedance of the elbow joint was identified using a modified genetic algorithm to quantify the alterations in viscoelastic properties underlying pathological resistance. Simultaneously, the time-frequency features were extracted from the surface electromyography (sEMG) signals to reveal the neurophysiological mechanisms of the spastic muscles. By concatenating these single-layer decisions, a support vector regression (SVR)-based fusion model was developed to generate a more comprehensive quantification of spasticity severity. Experimental results demonstrated that the stiffness and damping components of the spastic arm significantly deviated from the nonspastic baseline, and strong correlations were observed between the proposed spasticity assessment and the severity level measured by clinical scales (${R = {0.86},\;{P} = {1.67}{e} - {5}}$), as well as the tonic stretch reflex threshold (TSRT) value (${R = - {0.89},\;{P} = {3.54}{e} - {6}}$). These promising results suggest that the proposed assessment system holds great potential to support the clinical diagnosis of motor abnormalities in spastic patients, and ultimately enables optimal adjustment of treatment protocols. [ABSTRACT FROM AUTHOR]