Dominant Intrinsic Mode Functions Research Articles

A new relaying approach for protecting TCSC compensated transmission line connected to DFIG based wind farm

In modern power systems, Thyristor Controlled Series Capacitor (TCSC)-compensated transmission lines are crucial in transporting bulk power from large wind farms. However, the performance of conventional distance relays is adversely impacted by the typical fault characteristics of the Doubly Fed Induction Generator (DFIG) and TCSC. To address this challenge, this paper presents a new relaying algorithm such as Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based Dominant Mode Algorithm-Hilbert Transform (CEEMDAN-DMA-HT) for fault detection and CEEMDAN assisted Enhanced Jaya Optimization-based Random Vector Functional Link Network (EJAYA-RVFL) for fault classification. In proposed fault detection algorithm, the differential current from both ends of the line is subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, leading to the extraction of distinct intrinsic mode functions (IMFs). Employing the Dominant Mode Algorithm, the IMF with the highest Pearson correlation coefficient, referred to as the dominant IMF, is identified. Subsequently, a comparison between this dominant mode IMF and the original input signal is conducted after passing the obtained signal through the Hilbert Transform (HT), enabling effective fault detection. The proposed classifier leverages a straightforward feature set that quantifies the energy of each phase and the ground. These features serve as inputs for the proposed classifier, facilitating the categorization of different fault types. To evaluate the effectiveness of the proposed relaying algorithm, several fault and non-fault scenarios are simulated on a test power system using MATLAB/Simulink. The results demonstrate that; the proposed fault detection and classification algorithm are able to detect and classify faults in less than a half-cycle. The proposed method gives 100% fault accuracy for fault detection and 99.97 % accuracy for fault classification. Furthermore, the proposed classifier achieves the performance metrics like Precision (0.998), Recall (0.997) and F1-Score (0.996), providing quantitative insights into its accuracy and dependability. Finally, a comparative analysis is carried out with existing approaches.

Automatic selection of IMFs to denoise the sEMG signals using EMD

Surface Electromyography (sEMG) signals are muscle activation signals, which has applications in muscle diagnosis, rehabilitation, prosthetics, and speech etc. However, they are known to be affected by noises such as Power Line Interference (PLI), motion artifacts etc. Currently, Empirical Mode Decomposition (EMD) and its modifications such as Ensemble EMD (EEMD), and Complementary EEMD (CEEMD) are used to decompose EMG into a series of Intrinsic Mode Functions (IMFs). The denoised EMG can be obtained from the selected IMFs. Statistical methods are used to select the signal dominant IMFs to reconstruct the denoised signal. In this work, a novel procedure is proposed to automatically separate noisy IMFs from the original sEMG signal. For this purpose, Permutation Entropy (PE) is employed in EEMD sifting process called Partly EEMD (PEEMD), to separate the noisy IMFs from the original sEMG signal according to the preset PE threshold. PEEMD decomposes the original signal into various modes according to a preset PE threshold and the denoised signal is reconstructed from resultant IMFs. The PEEMD denoising procedure is applied on the experimental sEMG data collected from eight subjects, that include six various upper limb movement classes. The proposed denoising procedure achieved an improved denoising performance in comparison with EMD, EEMD, and CEEMD. An alternate measure called Sample Entropy (SE) is also used in place of PE, for the automated sifting process as a comparison. Signal to Noise Ratio (SNR), Root Mean Square Error (RMSE), and Reconstruction Error (RE) parameters are used to evaluate the denoising performance. The results, averaged across eight subjects, demonstrate that the proposed denoising procedure outperforms the state-of-the-art EMD techniques in terms of these performance measures on the experimentally collected sEMG data samples.

Improving purity of blasting vibration signals using advanced Empirical Mode Decomposition and Wavelet packet technique

The study proposes a denoising method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Wavelet packet threshold denoising (WPTD) to reduce the noise of blasting vibration signals. CEEMDAN is first employed to decompose noisy blasting vibration signals into different intrinsic mode functions (IMFs), i.e., different frequencies of subcomponents. By using correlation coefficients, all of IMFs are automatically categorized as the dominant IMFs with higher portions of vibration components and the secondary IMFs with less vibration contents. Then, with further decomposing two groups of IMFs into different scales of Wavelet packet coefficient series, WPTD based on the fixed-threshold technique and the soft threshold function method is applied to minimize the level of noise in two groups of IMFs. Two groups of denoised IMFs are finally reconstructed as the pure blasting vibration waveform. Numerical waveforms with different types and intensities of noise and measured blasting vibration signals with different main frequency bands are used to verify the accuracy and capability of the proposed method. The numerical and field results show that the proposed method can not only effectively reduce noise components, but also well remain components of blasting vibrations.

Parkinson’s disease diagnosis and stage prediction based on gait signal analysis using EMD and CNN–LSTM network

Parkinson’s disease (PD) diagnosis is a complex and challenging task which needs the assessment of various motor and non-motor symptoms. As gait impairment is one of the early and important symptoms of PD, in a clinical setting, physicians generally evaluate the gait abnormality based on visual observations along with other numerous manifestations to assess the severity of PD. As such kind of assessment majorly depends on the experience and expertise of the physicians, there is scope for bias in assessment, leading to misdiagnosis. In this context, to assist the physicians to diagnose PD effectively, this study aims to design and investigate a gait analysis based classifier model using a hybrid convolutional neural network-long short term memory (CNN–LSTM) network to predict the severity rating of PD. For evaluation, we utilize the openly available gait dataset from Physionet that consists of vertical ground reaction force (VGRF) signals from three different walking tests. Firstly, the prominent VGRF signals obtained using the variability analysis are decomposed using the empirical mode decomposition (EMD) technique to extract the significant intrinsic mode functions (IMFs) that contain the vital gait features. Secondly, through the power spectral analysis the dominant IMFs of the selected VGRF signals are extracted to train the CNN–LSTM classifier model. To address the data overfitting problem in the classifier model, the proposed approach employs L2 regularization along with dropout techniques. Moreover, to solve the stochastic cost function, CNN–LSTM network utilizes the Adam optimizer for its minimal memory requirement and tuning. Finally, the experiments conducted using the gait patterns from 93 PD subjects and 73 healthy controls substantiate that the proposed CNN–LSTM​ classifier model can achieve a maximum multi-class classification accuracy of 98.32% and offer superior performance compared to several other similar methods which have used gait pattern to diagnose PD.

SCBSS Signal De-Noising Method of Integrating EEMD and ESMD for Dynamic Deflection of Bridges Using GBSAR

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.

Seizure activity classification based on bimodal Gaussian modeling of the gamma and theta band IMFs of EEG signals

In this manuscript, EEG signals of seizure and non-seizure activities have been discussed and classified into five groups on the basis of seizure onset, seizure action and brain signal recording location. EEG signals consisting of gamma-band (40–80 Hz) and theta-band (4–8 Hz) oscillations have been captured for performing empirical mode decomposition (EMD). Dominant intrinsic mode functions (IMFs) have been selected from the consequences of EMD and a statistical model is employed upon the IMFs to summarize the information on those. Bimodal Gaussian statistical model has been found most effective to prepare feature set taking the modeling parameters of its probability density function (PDF). Plotting together bimodal Gaussian PDF and empirical PDF for pictorial scrutiny; cumulative distribution function (CDF) in probability–probability (p–p) plot and goodness of fit K–S test result justified the effectiveness of proposed bimodal Gaussian statistical model. Hence, aforementioned statistical modeling parameters have been sent to numerous classifiers and rationalization of goodness of features has been shown through inter-class separability and intra-class compactness parameters. Extensive varieties of simulations are performed using a well-established dataset. The suggested strategy reveals the capability of making higher values of sensitivity, specificity and accuracy compared to that made by some cutting-edge methods utilizing the same EEG dataset.

New Method to Identify CT Saturation Based on EMD

Aiming at the problem that Current Transformer (CT) affects differential protection, this paper proposes a new method to identify CT saturation based on Empirical Mode Decomposition (EMD). Differential current can be decomposed into few Intrinsic Mode Functions (IMFs) by EMD. When CT is linear transfer, the current waveform is nearly a sine wave, and it only contains one dominant IMF. However, CT saturation leads to the distortion of secondary current which contains at least two dominant IMFs. From the defined projection area on t-axis of each IMF and the specific gravity coefficient, the number of dominant IMF can be got. Thus CT saturation can be identified. Theoretical analysis and simulation results show that this method can identify CT saturation of different degree. It is convenient to achieve and hardly to be affect by aperiodic component.

Physics-Based Foundation for Empirical Mode Decomposition

We study the correspondence between analytical and empirical slow-flow analyses. Given a sufficiently dense set of sensors, measured time series recorded throughout a mechanical or structural system contains all information regarding the dynamics of that system. Empirical mode decomposition is a useful tool for decomposing the measured time series in terms of intrinsic mode functions, which are oscillatory modes embedded in the data that fully reproduce the time series. The equivalence of responses of the analytical slow-flow models and the dominant intrinsic mode functions derived from empirical mode decomposition provides a physics-based theoretical foundation for empirical mode decomposition, which currently is performed formally in an ad hoc fashion. To demonstrate correspondence between analytical and empirical slow flows, we derive appropriate mathematical expressions governing the empirical slow flows and based on analyticity conditions. Several nonlinear dynamical systems are considered to demonstrate this correspondence, and the agreement between the analytical and empirical slow dynamics proves the assertion.

Forecasting the duration of active and break spells in intrinsic mode functions of Indian monsoon intraseasonal oscillations

Several indices of the observed Indian summer monsoon intraseasonal oscillations (ISO) are decomposed into their respective intrinsic mode functions (IMFs). For each ISO index, it is shown that for most of the dominant IMFs, the peak in the active (break) regime may be used as a predictor for the duration of the subsequent break (active) regime. Similar results are also obtained for a Stochastic Forced Lorenz model proposed earlier as a paradigm model for ISO.

Multi-scaled analysis of the damped dynamics of an elastic rod with an essentially nonlinear end attachment

We study multi-frequency transitions in the transient dynamics of a viscously damped dispersive finite rod with an essentially nonlinear end attachment. The attachment consists of a small mass connected to the rod by means of an essentially nonlinear stiffness in parallel to a viscous damper. First, the periodic orbits of the underlying hamiltonian system with no damping are computed, and depicted in a frequency–energy plot (FEP). This representation enables one to clearly distinguish between the different types of periodic motions, forming back bone curves and subharmonic tongues. Then the damped dynamics of the system is computed; the rod and attachment responses are initially analyzed by the numerical Morlet wavelet transform (WT), and then by the empirical mode decomposition (EMD) or Hilbert–Huang transform (HTT), whereby, the time series are decomposed in terms of intrinsic mode functions (IMFs) at different characteristic time scales (or, equivalently, frequency scales). Comparisons of the evolutions of the instantaneous frequencies of the IMFs to the WT spectra of the time series enables one to identify the dominant IMFs of the signals, as well as, the time scales at which the dominant dynamics evolve at different time windows of the responses; hence, it is possible to reconstruct complex transient responses as superposition of the dominant IMFs involving different time scales of the dynamical response. Moreover, by superimposing the WT spectra and the instantaneous frequencies of the IMFs to the FEPs of the underlying hamiltonian system, one is able to clearly identify the multi-scaled transitions that occur in the transient damped dynamics, and to interpret them as ‘jumps’ between different branches of periodic orbits of the underlying hamiltonian system. As a result, this work develops a physics-based, multi-scaled framework and provides the necessary computational tools for multi-scaled analysis of complex multi-frequency transitions of essentially nonlinear dynamical systems.