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This study focuses on evaluating several machine learning algorithms for the condition assessment of utility timber poles. An efficient feature extraction technique combining Hilbert Huang transform (HHT) and wavelet packet transform (WPT) is adopted from the authors' previous work and implemented to determine damage-sensitive features from vibration data related to five serviceable and eight unserviceable in-situ timber poles. Then, these features are pre-processed using correlation heat map analysis for feature selection. Principal component analysis (PCA) is adopted as the final step of pre-processing for reducing noise interference and enhancing classification accuracy. Afterwards, a feature matrix is formed, which is fed into the selected classifiers for pattern recognition. In addition, the information gain method is also implemented and compared against PCA to examine the effect of feature selection. Finally, selected classifiers are employed using those dominant features, and their performance is evaluated based on six parameters – accuracy, precision, recall, F1-score, confusion matrix and Receiver Operating Characteristic (ROC) curve. It was found that choosing the best feature set related to the health state helps to improve the performance of the pattern recognition algorithms to a great extent. [ABSTRACT FROM AUTHOR]

Bridge structural health monitoring (BSHM) is extensively employed to assess whether bridge damage exceeds maintenance limits and helps maintenance decision-making. Frequency identification is an essential part of BSHM, which utilizes the first-order modal frequency as an evaluation indicator of bridge health status. This paper proposes a method for bridge damage identification based on extracting the first-order modal frequency of the bridge from dynamic responses of vehicle bogies. Firstly, the feasibility of extracting bridge modal frequency from bogie accelerations is theoretically deduced via a vehicle–bridge interaction model. The result indicates that the bogie accelerations encompass three frequency components, namely vehicle driving frequency, vehicle pitching frequency and bridge first-order modal frequency. Then, a multi-domain conjoint analysis method incorporating the time domain, frequency domain and time–frequency domain is proposed to extract the first-order frequency of damaged bridge from bogie accelerations. By defining a damage sensitivity index of the sliding window, the time-domain sensitive boundaries of bogie acceleration to bridge damage are pinpointed. Leveraging prior knowledge of the bridge modal frequency range, the frequency-domain sensitive boundaries bogie acceleration to bridge frequency are determined. On this basis, the sensitive region of bogie acceleration to bridge frequency can be precisely localized in the Hilbert Huang transform spectrum, ultimately identifying bridge frequency through searching local maximum instantaneous energy points within this region. Finally, comprehensive experiments, considering different vehicle speeds and track spectra scenarios, are carried out to investigate the effectiveness and robustness of the proposed method. The results affirm the performance of the method to accurately identify the first-order modal frequency of the damaged bridge, further underscoring the promising prospect of on-board BSHM. [ABSTRACT FROM AUTHOR]

To address the shortage of damage identification methods for modal bridge expansion joints (MBEJs). Two damage indexes, DI(IPH) and DI(HS) were constructed using the instantaneous phase (IP) and Hilbert spectrum of Hilbert Huang transform (HHT). Three specimens were designed and fabricated to identify the damage to the concrete in the center beam, support bar and anchorage zone of MBEJs. The impact vibration of MBEJs was excited by the hammer impulses and acceleration sensors were used to obtain the vibration response. The damage of the center beam, support bar and concrete in anchorage zone was all classified into five levels (level 1–level 5). DI(IPH) and DI(HS) increase gradually with increasing damage levels. After the center beam was completely broken, the DI(IPH) and DI(HS) improved by 40.31 and 40.61, respectively. The DI(IPH) and DI(HS) increased by 25.25 and 55.91 after the complete breakage of the support bar. The DI(IPH) and DI(HS) of the concrete in the anchorage zone at Level 5 showed more minor numerical changes than the center beam and support bar, with an increase of 7.49 and 35.45, respectively. The experimental results showed that HHT could effectively identify the damage in various parts of MBEJs with high accuracy. There is a clear indication of the degree of damage, which is suitable for the identification of damage in MBEJs. [ABSTRACT FROM AUTHOR]

Pumped storage is an important component of electrified wire netting. The safe and stable operation of pump turbines is extremely important. Among them, pressure pulsation is one of the main causes of pump turbine vibration. The characteristics of pressure pulsation are relatively complex, and it is difficult to directly observe their temporal changes using commonly used FFT methods. The division of frequency characteristics is often vague. Meanwhile, it is difficult to explain some phenomena such as frequency doubling. This article focuses on a certain model of pump turbine and uses SST model to numerically simulate the runaway condition of the pump turbine. And the Hilbert Huang transform method is used to analyze the pressure pulsation in the vaneless region and draft tube. The results show that the main characteristic frequencies of the vaneless region are blade passing frequency 112.5 Hz and rotational frequency 12.5 Hz. The main characteristic frequencies of the draft tube are vortex rope frequency near 3 Hz which energy ratio is up to 50%, rotational frequency, and blade passing frequency. The pressure pulsation characteristics in the vaneless region have changed from a complex composition of double blade passing frequency and rotational frequency to a distribution dominated by blade passing frequency. In the passage of the guide vane, the pressure pulsation is almost only characterized by blade passing frequency. The frequency characteristics of the vaneless region between the runner and the guide vane become complex again. Meanwhile, the results show that the characteristic frequencies of the vaneless region and the draft tube propagate upstream and downstream. [ABSTRACT FROM AUTHOR]

In this work, a surface-mounted circular cylinder with a fixed aspect ratio (ratio of height of the cylinder to its diameter) of 5 is subjected to a non-zero mean oscillating flow with a range of frequencies and amplitudes. Three-dimensional direct numerical simulations are then conducted on this finite-height cylinder. The mass and momentum equations are resolved using the finite volume-based Open Source Field Operation and Manipulation (OpenFOAM). A fixed Reynolds number Re = U o D / ν of 250 is used in this study, which is defined based on mean velocity at the inlet ( U o ) and cylinder diameter (D). Here ν is the kinematic viscosity of the working fluid. Non-dimensional velocity oscillation amplitude ( A ∗ = a / U o ) is varied from 0.1 to 0.3, while the non-dimensional oscillation frequency ( f ∗ = f / f o ) takes the values of 0.33, 0.5, 1, 2, and 3. Here a and f are the dimensional oscillation amplitude and frequency, respectively and f o is the vortex shedding frequency corresponding to a uniform flow at Re = 250. The three-dimensional vortex structures, presented with the help of iso- Q surfaces, show that the oscillating flow changes the size and shape of the hairpin-shaped vortices. Wake is found to be synchronized with the oscillation frequency at f * = 2 for each value of the A * and results in the maximum lift force on the cylinder. Hilbert Huang transformation analysis of the transverse velocity signals at a specific point in the wake reveals that the wake is more complex and aperiodic in nature for f * values of 0.33, 0.5, and 1, whereas it is periodic for f * = 2 and 3. In order to further disclose the nonlinearity associated with the oscillating flow, the degree of stationarity is discussed corresponding to each value of A * and f *. Dynamic mode decomposition is exploited to obtain information about the coherent vortical structures and their spatial and temporal behavior in the wake with a change in the value of f *. Effects of A * and f * on the dynamic characteristics are also investigated. [ABSTRACT FROM AUTHOR]

This research aims to provide the groundwork for smartly categorizing hand movements for use with prosthetic hands. The hand motions are classified using surface electromyography (sEMG) data. In reaction to a predetermined sequence of fibre activation, every single one of our muscles contracts. They could be useful in developing control protocols for bio-control systems, such human-computer interaction and upper limb prostheses. When focusing on hand gestures, data gloves and vision-based approaches are often used. The data glove technique requires tedious and unnatural user engagement, whereas the vision-based solution requires significantly more expensive sensors. This research offered a Deep Neural Network (DNN) automated hand gesticulation recognition system based on electromyography to circumvent these restrictions. This work primarily aims to augment the concert of the hand gesture recognition system via the use of an artificial classifier. To advance the recognition system's classification accuracy, this study explains how to build models of neural networks and how to use signal processing methods. By locating the Hilbert Huang Transform (HHT), one may get the essential properties of the signal. When training a DNN classifier, these characteristics are sent into it. The investigational results reveal that the suggested technique accomplishes a better categorization rate (98.5 % vs. the alternatives).

Pumped storage is an important component of electrified wire netting. The safe and stable operation of pump turbines is extremely important. Among them, pressure pulsation is one of the main causes of pump turbine vibration. The characteristics of pressure pulsation are relatively complex, and it is difficult to directly observe their temporal changes using commonly used FFT methods. The division of frequency characteristics is often vague. Meanwhile, it is difficult to explain some phenomena such as frequency doubling. This article focuses on a certain model of pump turbine and uses SST model to numerically simulate the runaway condition of the pump turbine. And the Hilbert Huang transform method is used to analyze the pressure pulsation in the vaneless region and draft tube. The results show that the main characteristic frequencies of the vaneless region are blade passing frequency 112.5 Hz and rotational frequency 12.5 Hz. The main characteristic frequencies of the draft tube are vortex rope frequency near 3 Hz which energy ratio is up to 50%, rotational frequency, and blade passing frequency. The pressure pulsation characteristics in the vaneless region have changed from a complex composition of double blade passing frequency and rotational frequency to a distribution dominated by blade passing frequency. In the passage of the guide vane, the pressure pulsation is almost only characterized by blade passing frequency. The frequency characteristics of the vaneless region between the runner and the guide vane become complex again. Meanwhile, the results show that the characteristic frequencies of the vaneless region and the draft tube propagate upstream and downstream.

The application of explosive blasting for rock breaking works was not possible in densely populated areas, which caused problems in urban tunnel construction. In the application of dry ice powder thermal shock breaking in tunnel excavation, a vibration monitoring system was employed to monitor the vibration response of the tunnel structure during the breaking process, and it was used to extract the signal characteristics by Hilbert-Huang transformation. Experimental results are as follows: 1) The peak vibration speed was below 50 mm/s for concrete structures safe as long as it was farther than 10 m from the burst hole in the support concrete. 2) Aggregate decay of vibration velocities caused by thermal shock breaking of rock with dry ice powder corresponded to the decay law of the power function. The range of influence of the vibration was considerably smaller compared to that of drilling and blasting the rock; 3) The new rock-breaking technology induced damage can be divided into three regions, with radial fractures generated by high-energy fluids dominating. 4) Hilbert Huang transform extracted signal features more objectively and accurately, with excellent reference for the safety monitoring of tunnel structures. [ABSTRACT FROM AUTHOR]

The communication between the human brain and the external devices can be established using Electroencephalograms (EEG)-based Brain–Computer Interface by converting the neural activities of the brain into electric signals. The EEG signals were isolated into an energy–frequency–time spectrum with Hilbert Huang transform that was used by the Deep Learning (DL)-based model to learn discriminative spectro-temporal patterns of the raw EEG signals of ten digits. This paper has two major contributions: first, create a novel dataset known as BrainDigiData of EEG signals of ten digits from (0–9) using a multi-channel EEG device. Second to propose a DL-based one-dimensional Convolutional neural network model BrainDigiCNN to classify the BrainDigiData of EEG signals of digits. The publicly available Mind Big Dataset (MBD) of digits was also used to evaluate the performance of the proposed model. The research done in this paper showed that the band-wise analysis of EEG signals in a complex scenario resulted in improved results as compared to the scenario used in the previously existing work for digit classification using EEG signals. The proposed BrainDigiCNN model achieved the highest average accuracy of 96.99%. The average classification accuracy of 98.27% was achieved for the MBD dataset of 14 channel device EMOTIV EPOC+ and 89.62% on the MBD dataset of 5-channel EMOTIV Insight. The statistical analysis of the proposed model on traditional Machine Learning (ML) classifiers using paired t-test resulted in a p-value less than 0.05 which shows the significant difference between the proposed model and ML classifiers. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the Innovative Trend Methodology (ITM) and their inspired approaches, i.e., Double (D-ITM) and Triple (T-ITM), were combined with Hilbert Huang transform (HHT) time frequency-based method. The new hybrid methods (i.e., ITM-HHT, D-ITM-HHT, and T-ITM-HHT) were proposed and compared to the DWT-based methods in order to recommend the best method. Three total annual rainfall time series from 1920 to 2011 were selected from three hydrological basins in Northern Algeria. The new combined models (ITM-HHT, D-ITM-HHT, and T-ITM-HHT) revealed that the 1950–1975 period has significant wet episodes followed by a long-term drought observed in the western region of Northern Algeria, while Northeastern Algeria presented a wet period since 2001. The proposed approaches successfully detected, in a visible manner, hidden trends presented in the signals, which proves that the removal of some modes of variability from the original rainfall signals can increase the accuracy of the used approaches.

Automatic Speech/Music classification uses different signal processing techniques to categorize multimedia content into different classes. The proposed work explores Hilbert Spectrum (HS) obtained from different AM-FM components of an audio signal, also called Intrinsic Mode Functions (IMFs) to classify an incoming audio signal into speech/music signal. The HS is a twodimensional representation of instantaneous energies (IE) and instantaneous frequencies (IF) obtained using Hilbert Transform of the IMFs. This HS is further processed using Mel-filter bank and Discrete Cosine Transform (DCT) to generate novel IF and Instantaneous Amplitude (IA) based cepstral features. Validations of the results were done using three databases-Slaney Database, GTZAN and MUSAN database. To evaluate the general applicability of the proposed features, extensive experiments were conducted on different combination of audio files from S&S, GTZAN and MUSAN database and promising results are achieved. Finally, performance of the system is compared with performance of existing cepstral features and previous works in this domain.

In this paper, the Innovative Trend Methodology (ITM) and their inspired approaches, i.e., Double (D-ITM) and Triple (T-ITM), were combined with Hilbert Huang transform (HHT) time frequency-based method. The new hybrid methods (i.e., ITM-HHT, D-ITM-HHT, and T-ITM-HHT) were proposed and compared to the DWT-based methods in order to recommend the best method. Three total annual rainfall time series from 1920 to 2011 were selected from three hydrological basins in Northern Algeria. The new combined models (ITM-HHT, D-ITM-HHT, and T-ITM-HHT) revealed that the 1950–1975 period has significant wet episodes followed by a long-term drought observed in the western region of Northern Algeria, while Northeastern Algeria presented a wet period since 2001. The proposed approaches successfully detected, in a visible manner, hidden trends presented in the signals, which proves that the removal of some modes of variability from the original rainfall signals can increase the accuracy of the used approaches. [ABSTRACT FROM AUTHOR]

Psychotic disorders are mental disorders that negatively affect human life. Diagnosis of psychotic patients is usually done in consultation with the patient, and this is a time-consuming process. In this study, a Computer Aided Diagnosis (CAD) system that will support expert opinion with automatic diagnosis of schizophrenia (SZ) disease, which is the leading psychotic disorder, is presented. In this study, Hilbert Huang Transform (HHT) method was used to analyze the non-stationary and non-periodic structure of EEG (Electroencephalograph) signals in the best way. The data set we used in our study includes 19-channel EEG signals from 28 (14 SZ and 14 healthy controls) participants, and the second data set includes 16-channel EEG signals from 84 (45 SZ and 39 healthy controls) participants. First of all, HS (Hilbert Spectrum) images of the first four Intrinsic Mode Functions (IMF) components obtained by applying Empirical Mode Decomposition (EMD) to EEG signals were created. These images were then classified with the VGG16 pre-trained Convolutional Neural Network (CNN) network. With our proposed method, the highest classification performance was obtained as 98.2% for Dataset I and 96.02% for Dataset II, respectively, by training the HS images obtained from the IMF 1 component with the VGG16 pre-trained CNN network. In the next step, classification performances were tested with VGG16, XCeption, DenseNet121, ResNet152 and Inception V3 pre-trained CNN networks. The high classification success achieved by the proposed method in our study demonstrates the accuracy of the model in distinguishing between SZ and healthy control. [ABSTRACT FROM AUTHOR]

The occurrence probability of plasma irregularities has long been used to quantify that of ionospheric scintillations. We use the Hilbert-Huang transform to examine the occurrence probability and the instantaneous total amplitude of plasma irregularities in the low-latitude nighttime ionosphere observed by the low-inclination satellites, ROCSAT-1 during 1999–2004 and FORMOSAT-7/COSMIC-2 (F7C2) in 2020, as well as a high-inclination satellite, DEMETER, during 2006–2010. Generally, the longitudinal distribution of the occurrence probability is similar to that of the total amplitude, except that the probability of ion density irregularity observed by F7C2 and DEMETER yields nonphysical anomalous enhancements over the South American sector during May–August of 2007–2010 and 2020, respectively. S4 scintillations observed by F7C2 show that the anomalous enhancement results from the low ambient density in calculating the occurrence probability during the low solar activity year of 2020. We suggest the instantaneous total amplitude as a better indicator for describing ionospheric plasma irregularities. [ABSTRACT FROM AUTHOR]

Disabilities due to neurodegenerative disease are rapidly increasing in number. The need for rehabilitative devices to achieve a normal and comfortable life in the absence of biochannels has also increased. The activities of biochannels can be easily replaced by implementing rehabilitative devices. The electrooculography (EOG)-based human–computer interface (HCI) is one of the most important techniques for enabling disabled persons to enjoy a normal life. The technique of measuring the cornea-retina potential difference is called EOG. The technology for converting thoughts to different control patterns to activate external devices is called the HCI. In this paper, we carried out a study on ten subjects aged 20–30 years using a five-electrode signal acquisition system (AD T26). The subject performances were experimentally verified by implementing periodogram features with a feedforward neural network trained on a nature-inspired algorithm. The analysis was conducted offline and online to evaluate the achievement of the developed HCI. The study showed an average classification accuracy of 93.93% for four tasks, with 95% accuracy for the offline mode and 90.12% for the online mode. The participating subjects drove the mobile robot in all directions frequently and quickly with a recognition accuracy of 98.12%. The study confirmed that the four tasks (related to driving the external device) performed by the subjects were convenient to perform in real time. [ABSTRACT FROM AUTHOR]

Generally, radar echo is analysed in the assumptions that echo comes from stationary processes. Signal processing algorithms of atmospheric radar echoes are based on stationarity assumptions. In this paper, we propose a technique for processing the radar echoes by still preserving their non-stationary nature. The proposed algorithm is based on the complex empirical mode decomposition (CEMD) of non-stationary complex atmospheric radar signals. The well-established technique Hilbert Huang transform has been applied for the practical radar echoes collected from the Mesosphere-Stratosphere-Troposphere radar located at Gadanki Andhra Pradesh. The data is subjected to proposed CEMD algorithm and results are obtained and Stationarity is tested using the wavelet transform. The inference from the obtained results is of two-fold: Hilbert spectrum is very well localized and can be used for stationarity test like Wavelet spectrum, the frequency components in Hilbert spectrum lies in the signal region of Fourier spectrum thereby resembling it. For better interpretation, the algorithm is also applied for simulated signals under both stationary and non-stationary environments. The results motivate us for better exploitation of HHT for the spectral analysis of atmospheric radar echoes. [ABSTRACT FROM AUTHOR]

A common issue in water infrastructure is that it suffers from leakage. The hydroinformatics technique for recognizing the presence of leaks in the pipeline system by means of pressure transient analysis was briefly explored in this study. Various studies have been done of improvised leak detection methods, and Hilbert Huang Transform has the potential to overcome the concern. The HHT processing algorithm has been successfully proven through simulation and experimentally tested to evaluate the ability of pressure transient analysis to predict and locate the leakage in the pipeline system. However, HHT relies on the selection of the suitable IMF in the pre-processing phase which will determine the precision of the estimated leak location. This paper introduces a NIKAZ filter technique for automatic selector of Intrinsic Mode Function (IMF). A laboratory-scale experimental test platform was constructed with a 68-metre long Medium Polyethylene (MDPE) pipe with 63 mm in diameter used for this study and equipped with a circular orifice as an artificial leak in varying sizes with a system of 2 bar to 4 bar water pressure. The results showed that, although with a low ratio of signal-to-noise, the proposed method could be used as an automatic selector for Intrinsic Mode Function (IMF). Experimental tests showed the efficiency, and the work method was successful as an automatic selector of IMF. The proposed mathematical algorithm was then finally evaluated on field measurement tested on-site of a real pipeline system. The results recommended NIKAZ as an automatic selector of IMF to increase the degree of automation of HHT technique, subsequently enhancing the detection and identification of water pipeline leakage. [ABSTRACT FROM AUTHOR]

The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based cross-spectrum is one of the most widely implemented methods, but it is limited by the spectral leakage caused by the finite length of the basic function that impacts the time and frequency resolutions. This paper proposes a new time-frequency brain functional connectivity analysis framework to track the non-stationary association of two EEG signals based on a Revised Hilbert-Huang Transform (RHHT). The framework can estimate the cross-spectrum of decomposed components of EEG, followed by a surrogate significance test. The results of two simulation examples demonstrate that, within a certain statistical confidence level, the proposed framework outperforms the wavelet-based method in terms of accuracy and time-frequency resolution. A case study on classifying epileptic patients and healthy controls using interictal seizure-free EEG data is also presented. The result suggests that the proposed method has the potential to better differentiate these two groups benefiting from the enhanced measure of dynamic time-frequency association. [ABSTRACT FROM AUTHOR]

Effective water resource management requires accurate estimation of the volume of water withdrawn through anthropogenic groundwater pumping. However, insufficient pumping information makes accurate estimation difficult. To address this issue, this study proposes a novel method for estimating pumping volumes that does not rely on pumping information. The proposed method uses high-resolution groundwater level (GWL) data from densely monitored wells and integrates Hilbert–Huang transform and empirical orthogonal function analysis to extract pump-associated signals, which can then be used to estimate total pumping. Our method was validated by analyzing GWL data from monitoring stations on the Choshui River alluvial fan in Taiwan. The results indicate the estimated pumping amount of 1. 71 × 1 0 9 – 2. 05 × 1 0 9 m 3 /year consistent with previous estimates obtained using survey and physics-based methods ranging from 9 × 1 0 8 m 3 /year to 2. 2 × 1 0 9 m 3 /year. In the areas with abundant groundwater head observations, the proposed method can efficiently estimate pumping rates with a high space–time resolution, and may serve as a useful reference for groundwater management. • This study proposes a method utilizing groundwater data to estimate spatiotemporal pumping rate. • Multi-year high space–time resolution of pumping rate distribution estimation was efficiently derived from hourly groundwater data. • The pumping rate distribution aligns with geological and agricultural water usage patterns. [ABSTRACT FROM AUTHOR]

This paper presents clustering of automotive spring fatigue life for failure classification based on K-means approach. For safety promotion of buses, fatigue life prediction of the spring is needed to be classified for maintenance. In this analysis, the strain signals of a heavy vehicle leaf spring were collected from two common roads and analyzed using Hilbert Huang transform. The strain amplitude was used to obtain fatigue life of the leaf spring. Subsequently, the instantaneous frequencies, energies and fatigue lives were clustered into three groups according to the K-means approach. Numerous classification trees were trained with the clustered group as target while the instantaneous frequencies, energies and fatigue lives datasets as input. The trained classification trees were evaluated using receiver operating characteristic curve which shown an acceptable prediction of classes. This classification tree serves as a tool to evaluation automotive leaf spring design for fatigue failure prevention without destroying the component. [ABSTRACT FROM AUTHOR]

All welded parts undergo aging once operated at elevated temperatures. Exposures of structures on elevated temperatures adversely affect the material performance and deteriorate the structure's toughness. The welded part also develops the high risk of brittle fracture. In order to establish the fracture toughness, it is not always possible to remove the specimen from the service. Therefore, it is desirable to develop an in situ non-destructive testing (NDT) based on ultrasonic testing (UT) method along with advanced signal processing techniques. Ultrasonic testing is an extensively used Non-destructive testing technique that offers improved damage detection capability. The objective of the study is to found a quantitative relation between UT and mechanical properties of welded joint heat treated specimens. The data was acquired from the testing of weld specimens at different levels of temperatures through ultrasonic testing. There is a trend observed between the variation in the UT signal's characteristics i.e. attenuation and the variation in the mechanical properties. The possible categorization of UT signal in terms of different thermal aging levels has also been explored using Hilbert Huang transform (HHT) on acquired UT signals. The experimental relationships will enable welded specimen toughness prediction solely through in situ ultrasonic testing while the specimen will remain in service. [ABSTRACT FROM AUTHOR]

The use of renewable power generation brings new challenges related to power quality issues. Furthermore, with the changing power system nature due to the presence of new components such as power electronics in large numbers and distributed generation systems, the tools used for more than a century to analyze signals in this type of systems are no longer providing accurate information with a good resolution in time and frequency domain. To contribute with a new view of the problem, this paper presents a hybrid technique for the analysis of oscillations in low voltage distribution systems considering photovoltaic generation. The aim is to characterize the behavior of the system in a time–frequency domain and get the different instantaneous frequencies that appear. The results obtained with this technique are compared with three well-known methods of analysis. The validation of the methodology is carried out in a real-time digital simulator of a distributed system with photovoltaic generation. [ABSTRACT FROM AUTHOR]

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Groundwater plays an important role in mitigating drought. It is necessary to analyze the spatiotemporal variation characteristics of groundwater and establish an appropriate assessment for the risk of drought for disaster prevention. This study developed a novel approach for the drought risk assessment system based on the Hilbert Huang Transform (HHT) method to understand the spatial distribution of the risk of drought in terms of groundwater. We analyzed drought vulnerability applying the HHT to analyze the variation characteristics of groundwater level spatiotemporally and the physical mechanism of groundwater factors to quantify groundwater sensitivity to the environment, and present the resilience of each region. Furthermore, the drought hazard was determined using the standardized precipitation index and the dynamic drought intensity gave the durability characteristics of each region. The drought exposure was also investigated, which quantifies the water demand to satisfy people's livelihoods for a certain population density. Based on the framework proposed in this study, an overall risk map of droughts in the Pingtung Plain was obtained. This study effectively classified the main time–frequency characteristics, the availability of groundwater, and the risk of drought in each region. A total of 11 of the 45 groundwater monitoring stations are located at the highest risk of level 5, most of which are located in the coastal area of Gaoping River and Linbian River. Over-pumping should be avoided in these areas. On the contrary, the alluvial fan area showed the lowest groundwater risk of drought. The results can be adopted for water management, drought resilience, sustainable development of groundwater resources, and decision making in determining drought risk. [ABSTRACT FROM AUTHOR]

This research aims to provide the groundwork for smartly categorizing hand movements for use with prosthetic hands. The hand motions are classified using surface electromyography (sEMG) data. In reaction to a predetermined sequence of fibre activation, every single one of our muscles contracts. They could be useful in developing control protocols for bio-control systems, such human-computer interaction and upper limb prostheses. When focusing on hand gestures, data gloves and vision-based approaches are often used. The data glove technique requires tedious and unnatural user engagement, whereas the vision-based solution requires significantly more expensive sensors. This research offered a Deep Neural Network (DNN) automated hand gesticulation recognition system based on electromyography to circumvent these restrictions. This work primarily aims to augment the concert of the hand gesture recognition system via the use of an artificial classifier. To advance the recognition system's classification accuracy, this study explains how to build models of neural networks and how to use signal processing methods. By locating the Hilbert Huang Transform (HHT), one may get the essential properties of the signal. When training a DNN classifier, these characteristics are sent into it. The investigational results reveal that the suggested technique accomplishes a better categorization rate (98.5 % vs. the alternatives)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

This paper investigates an underwater noise target classification algorithm in order to identify vessels in shallow water. To this aim the Hilbert Huang transform has been used to extract features in order to be used in a classifier. The Support Vector Machine has been considered to identify targets. The proposed method based on Hilbert Huang Transform shows considerable gain against similar approach based on Wavelet Packet Transform.

Andesite rock samples were subjected to uniaxial compression tests monitored by Acoustic Emission (AE) for the assessment of the brittle fracture process. The identification and evolution of different fracture regimes were based on the application of methods suited for non-stationary and nonlinear time series: Wavelet Transform (WT); Empirical Mode Decomposition (EMD); Hilbert-Huang Transform (HHT) and Shannon Entropy (SE). The jumps of accumulated AE energy, which indicate where the greatest damage due to macro fractures occurs, were preceded by minimum entropy values of the EMD components, which would indicate that they act as precursors to catastrophic damage. [ABSTRACT FROM AUTHOR]

A common issue in water infrastructure is that it suffers from leakage. The hydroinformatics technique for recognizing the presence of leaks in the pipeline system by means of pressure transient analysis was briefly explored in this study. Various studies have been done of improvised leak detection methods, and Hilbert Huang Transform has the potential to overcome the concern. The HHT processing algorithm has been successfully proven through simulation and experimentally tested to evaluate the ability of pressure transient analysis to predict and locate the leakage in the pipeline system. However, HHT relies on the selection of the suitable IMF in the pre-processing phase which will determine the precision of the estimated leak location. This paper introduces a NIKAZ filter technique for automatic selector of Intrinsic Mode Function (IMF). A laboratory-scale experimental test platform was constructed with a 68-metre long Medium Polyethylene (MDPE) pipe with 63 mm in diameter used for this study and equipped with a circular orifice as an artificial leak in varying sizes with a system of 2 bar to 4 bar water pressure. The results showed that, although with a low ratio of signal-to-noise, the proposed method could be used as an automatic selector for Intrinsic Mode Function (IMF). Experimental tests showed the efficiency, and the work method was successful as an automatic selector of IMF. The proposed mathematical algorithm was then finally evaluated on field measurement tested on-site of a real pipeline system. The results recommended NIKAZ as an automatic selector of IMF to increase the degree of automation of HHT technique, subsequently enhancing the detection and identification of water pipeline leakage.

Mesoscale wind speed fluctuations influence the characteristics of offshore wind energy. These recurring wind speed changes on time scales between tens of minutes and six hours lead to power output fluctuations. In order to investigate the meteorological conditions associated with mesoscale wind speed fluctuations, a measure is needed to detect these situations in wind speed time series. Previous studies used the empirical Hilbert-Huang Transform to determine the energy in the mesoscale frequency range or calculated the standard deviation of a band-pass filtered wind speed time series. The aim of this paper is to introduce newly developed empirical mesoscale fluctuation measures and to compare them with existing measures in regard to their sensitivity to recurring wind speed changes. One of the methods is based on the Hilbert-Huang Transform, two on the Fast Fourier Transform and one on wind speed increments. It is found that despite various complexity of the methods, all methods can identify days with highly variable mesoscale wind speeds equally well.

In this paper, Hilbert Huang transform based technique is introduced to detect and classify double line to ground faults in a 138 kV, 60 Hz, 68 km long six phase transmission line. Hilbert Huang transform is used to extract the hidden features contained in fault current signal in the form of Hilbert coefficients. The proposed technique does not requires the communication link between the two terminals of six phase transmission line since the fault current signals are recorded at the first terminal only i.e. at bus-1. For the justification of the proposed technique, wide variety of fault test studies were carried out in MATLAB/ Simscape power system toolbox for different types of three phase to ground faults by varying fault type, fault location, fault inception time, fault resistance and ground resistance. Test results shows that the Hilbert Huang transform based proposed technique effectively detects/ classifies the double line to ground faults.

Abstract This study introduces a more recent data analysis method, Hilbert Huang Transform method (HHT), to describe contaminant concentration data of a non-stationary and non-linear nature. In order to improve the modeling of the contaminant concentrations, it is proposed to first process the data using the Empirical mode decomposition (EMD) method from HHT to obtain a collection of intrinsic mode functions (IMFs) which can then be modeled separately using either autoregressive moving average (ARMA) models expanded with a seasonal term, or linear regression analysis, depending on the nature of the IMF. Three priority contaminants measured at Niagara-on-the-Lakes are selected for this study. It is found that the trend of fluoranthene concentrations from April of 1986 to March of 1997 is decreasing and then beginning to increase; the 1,2,4-trichlorobenzene concentrations are decreasing; while the dieldrin concentrations are decreasing. With HHT, appropriate time series models can be identified and constructed for the studied contaminant concentrations to better illustrate the variability of each IMF (and thus the contaminant concentrations) for the studied period. For all data sets modeled in this study, pre-processing the data with HHT allowed for higher R2 values, correlation coefficients and lower sum of squared errors when compared to modeling without HHT. It is thus confirmed that pre-processing the data with HHT and modeling with time series analysis will provide a more effective means of the studied data sets when identifying and analyzing the trends and variability of studied contaminant concentrations in the Niagara River. Highlights • To illustrate the contaminant concentration data of a non-stationary and non-linear nature via Hilbert-Huang Transform (HHT) method. • Three priority contaminants measured at Niagara-on-the-Lakes are selected, fluoranthene, 1,2,4-trichlorobenzene and dieldrin. • It can be suggested pre-processing the data with HHT would enhance the effectiveness and accuracy of the data interpretation and model predictability. [ABSTRACT FROM AUTHOR]

Nonconvulsive status epilepticus is a condition where the patient is exposed to abnormally prolonged epileptic seizures without evident physical symptoms. Since these continuous seizures may cause permanent brain damage, it constitutes a medical emergency. This paper proposes a method to detect nonconvulsive seizures for a further nonconvulsive status epilepticus diagnosis. To differentiate between the normal and seizure electroencephalogram (EEG), a K-Nearest Neighbor, a Radial Basis Support Vector Machine, and a Linear Discriminant Analysis classifier are used. The classifier features are obtained from the Canonical Polyadic Decomposition (CPD) and Block Term Decomposition of the EEG data represented as third order tensor. To expand the EEG into a tensor, Wavelet or Hilbert-Huang transform are used. The algorithm is tested on a scalp EEG database of 139 seizures of different duration. The experimental results suggest that a Hilbert-Huang tensor representation and the CPD analysis provide the most suitable framework for nonconvulsive seizure detection. The Radial Basis Support Vector Machine classifier shows the best performance with sensitivity, specificity, and accuracy values over 98%. A rough comparison with other methods proposed in the literature shows the superior performance of the proposed method for nonconvulsive epileptic seizure detection. [ABSTRACT FROM AUTHOR]

In this study, a fault detection technique and faulty phase identification technique using Hilbert Huang Transform (HHT) are presented for single line to ground boundary faults in six phase transmission line. Hilbert Huang transform is used for processing of the six phase fault current signals, which are recorded by the transducers located at bus-1 of the six phase transmission line. The possibility of the HHT-based fault detection technique is tested under the variation of fault type, fault location, fault inception time, fault and ground resistances. [ABSTRACT FROM AUTHOR]

Extreme weather events are occurring more frequently as a result of climate change. Recently dengue fever has become a serious issue in southern Taiwan. It may have characteristic temporal scales that can be identified. Some researchers have hypothesized that dengue fever incidences are related to climate change. This study applies time-frequency analysis to time series data concerning dengue fever and hydrologic and meteorological variables. Results of two time-frequency analytical methods - the Hilbert Huang transform (HHT) and the Wavelet Transform (WT) are compared and discussed. As a result, a more effective time-frequency analysis method will be identified to analyze relevant time series data. The most influential time scales of hydrologic and meteorological variables that are associated with dengue fever are determined. Finally, the linkage between hydrologic/meteorological factors and dengue fever incidences can be established using time-dependent intrinsic correlation (TDIC) approaches. [ABSTRACT FROM AUTHOR]

This study applies a novel concept to decompose water stages to understand the factors that affect an estuary. The estuary water stages vary due to different complex, often nonlinear and non-stationary factors. Therefore, it is very difficult for researchers to break down water stages into contributing factors with single integrated methods. The Hilbert Huang transform (HHT) is an easy to use, efficient and powerful method of processing non-stationary, non-linear signals to optimize a complicated data process. The HHT is composed of empirical mode decomposition (EMD) and Hilbert transform. EMD decomposes the water stages into several intrinsic mode functions (IMFs). Through the Hilbert transform, IMFs could obtain amplitude and instantaneous frequency with time. Those IMFs with amplitude and frequency can be used to represent those factors that affect the water stages in an estuary. However, the physical meanings represented by IMFs should be inferred in conjunction with the other hydrological data. This study uses Tanshui River estuary water stages to show HHT applied to determining the factors that affect the water stages. HHT application provides a methodology for others to follow to identify the water stage components in an estuary. [ABSTRACT FROM AUTHOR]