Intrinsic Modes Research Articles

Integration method of compressed sensing with variational mode decomposition based on gray wolf optimization and its denoising effect in mud pulse signal.

The continuous wave mud pulse transmission holds great promise for the future of downhole data communication. However, significant noise interference during the transmission process poses a formidable challenge for decoding. In particular, effectively eliminating random noise with a substantial amplitude that overlaps with the pulse signal spectrum has long been a complex issue. To address this, an enhanced integration algorithm that merges variational mode decomposition (VMD) and compressed sensing (CS) to suppress high-intensity random noise is proposed in this paper. In response to the inadequacy of manually preset parameters in VMD, which often leads to suboptimal decomposition outcomes, the gray wolf optimization algorithm is designed to obtain the optimal penalty factor and decomposition mode number in VMD. Subsequently, the optimized parameter combination decomposes the signal into a series of intrinsic modes. The mode exhibiting a stronger correlation with the original signal is retained to enhance signal sparsity, thereby fulfilling the prerequisite for compressed sensing. The signal is then observed and reconstructed using the compressed sensing method to yield the final signal. The proposed algorithm has been compared with VMD, CS, and CEEMD; the results demonstrate that the method can enhance the signal-noise ratio by up to ∼20.55 dB. Furthermore, it yields higher correlation coefficients and smaller mean square errors. Moreover, the experimental results using real field data show that the useful pulse waveforms can be recognized effectively, assisting surface workers in acquiring precise downhole information, enhancing drilling efficiency, and significantly reducing the risk of engineering accidents.

Effect of entropy waves on the thermoacoustic instability of choked outlet combustion chambers

Thermoacoustic instability is a major issue in aero-engine and gas turbine combustion chambers, especially for the case of lean premixed combustion. The instability is caused by the interaction of acoustic waves with unsteady heat release. As considering the accelerated entropy waves in the system, the entropy effect on the acoustic waves cannot be ignored, which will further modify the thermoacoustic instability. Recent researches have shown that the entropy waves can be generated by the steady heat sources or temperature gradients, which can exclude the interference of unsteady heat release factors in instability analysis. In this study, the effect of entropy waves on the thermoacoustic instability of the combustion chamber model with choked outlets is investigated by constructing a low-order acoustic network model. New modes introduced by entropy waves, called as the entropy acoustic modes (EA modes), are investigated by a steady heat source. Then the unsteady heat release is considered, and the effect of entropy waves on different modes and the coupling behavior of these modes are analyzed comprehensively. It is found that some modes are jointly affected by the flame, entropy waves as well as the acoustic waves, which are called as the entropy thermoacoustic modes (ETA modes). Moreover, the modal frequencies of the intrinsic thermoacoustic instabilities modes (ITA modes) are changed remarkably due to the introduction of entropy waves. In addition, the entropy wave dispersion and dissipation show a non-negligible effect on the thermoacoustic instability.

Adaptively Optimized Masking EMD for Separating Intrinsic Oscillatory Modes of Nonstationary Signals

Adaptively Optimized Masking EMD for Separating Intrinsic Oscillatory Modes of Nonstationary Signals

High stability and tunable hydrogen evolution material with defect-enhanced photocatalytic properties:penta-BAs5 monolayer

In this work, on the basis of first principles calculations, we theoretically predict a pentagonal structure material called penta-BAs5 and discuss the photocatalytic properties under its intrinsic and defective modes. Formation energy and phonon dispersion curve all proved its high stability. Penta-BAs5 has a 2.38 eV bandgap and has indirect bandgap semiconductor performance. Surprisingly, by analyzing the band edge distribution, the Gibbs free energy changes (ΔG), and applying strain, it is found that penta-BAs5 can spontaneously proceed the hydrogen evolution reaction (HER) can under neutral and acidic conditions. Interestingly, after applying electric field in the range of 0–0.8 eV/Å, the band edge alignment can keep stable. Intriguingly, the hydrogen evolution capacity of penta-BAs5 was greatly improved when the defects were present. Our study predicted a new two-dimensional pentagonal binary compound, broadened the field of the pentagonal structure families, and identified its application prospects in photocatalysis.

Exploring charge transfer mechanisms and optical properties in vdW heterostructures of MoS2 and Bi2Se3 at nanoscale regime

We report here the two-dimensional layered material of MoS2/Bi2Se3 vdW heterostructures formed by ultrasonication of different concentrations of exfoliated MoS2 and Bi2Se3 nanosheets. The vdW heterostructures show unique optical characteristics compared to individual exfoliated nanosheets. The enhanced lifetime of heterostructures explained the charge transfer with increasing efficacy for producing optoelectronic devices. Raman spectroscopy confirms the presence of intrinsic Raman active modes of Bi2Se3, MoS2, and respective modes of vdW heterostructures. TEM images show characteristic lattice planes in both materials and clear overlapping regimes, confirming successful heterojunction formation. Further, Zeta potential confirms the enhanced stability of the heterostructures formed while the significant PL quenching, strongly suggests the presence of charge transfer processes within the heterostructures. Ultrafast pump-probe spectroscopy further confirms the non-linear charge transfer and relaxation mechanism of the heterostructures. We employed Surface Xplorer and Glotaran software for model fitting and Glotaran's global fitting revealed unresolved charge carrier dynamics.

Signal Feature Extraction Method of Three-Band Infrared Flame Sensor

A three-band infrared flame sensor is designed and experimentally investigated for the analysis of infrared signals from three radiation sources (Flame, man and ambient). The Fourier transform and empirical mode decomposition (EMD) is applied to the analysis of the spectral characteristics, intrinsic modes and energy spectra. The results show that the received flame infrared signal exhibits significant low-frequency and energy characteristics. Near the zero frequency, the received infrared signal energy of the flame, man and ambient is 3.4 × 104 J, 2.8 × 104 J and 2.1 × 104 J, respectively. In this way the flame signal features can be accurately identified from different radiation sources. It can be believed that the results of the three-band infrared sensor presented here are useful guiding significance and practical application value for fire detection.

Numerical investigation of combustion instability in a liquid rocket engine: Interaction effect between hydrodynamics and acoustic mode

In this paper, we present the numerical evidence of multiple bifurcations in thermoacoustic instability as we decrease the equivalence ratio (ϕ) from fuel-rich (ϕ = 1.5) to fuel-lean (ϕ =0.59) within a single-element coaxial liquid rocket engine burning hydrogen peroxide and methane. An axisymmetric compressible Large Eddy Simulation solver in an open-source platform OpenFOAM combined with a global methane chemical kinetical mechanism was used to model the turbulent mixing and combustion process. After the validation of grid independence and simulation results, the synchronization phenomenon between hydrodynamic instability, acoustics and combustion is the main focus of this paper. The results here first revealed that in a direction of decreasing equivalence ratios, the first transition to limit cycle state from combustion noise state was observed via intermittency. A second bifurcation was also observed, leading to quasi-period thermoacoustic instability from the limit cycle state. Intrinsic vorticity modes and natural acoustic modes could be regarded as oscillators. The competition and cooperation between the two modes led to a phenomenon beyond limit cycle states. The vortex dynamics of thermoacoustic systems are dominated by the interaction of intrinsic vorticity modes and natural acoustic modes and exhibit an adjustment of vortex shedding frequencies, resulting in different stability regions. Herein, the flame index was performed in more detail to distinguish between the premixed flame and diffusion flame. Results indicated that the burning mode was an important driver in promoting instability whilst it also served as a medium for competition and cooperation between the natural acoustic modes and intrinsic vorticity modes.

Dynamic mode characteristics of flow instabilities in a centrifugal compressor impeller

Centrifugal compressors are key parts of medium and small aircraft engines, the flow instabilities of centrifugal compressors have become the bottleneck of the developments of engines. It is vital to investigate the dynamic mode characteristics of flow instabilities in centrifugal compressors for solving the issues. In this study, unsteady full annular simulations of the centrifugal compressor flow are firstly implemented. It is found that the flow instability of the compressor primarily arises in the impeller. To capture the dominant transient modes in process of flow instabilities and accurately describe their temporal evolutions, a modified dynamic mode decomposition method (DMD-TC) is adopted to analyze the dynamic mode characteristics, which was proposed in our previous study. The results show that perturbations in the impeller can be mainly classified into three categories: impeller-diffuser interactions, self-excited perturbations, and vortex-induced perturbations. In detail, the modes induced by the impeller-diffuser interactions and self-excited perturbations are intrinsic modes, and they reflect the inherent flow unsteadiness in the impeller. On the one hand, the vortex-induced perturbations couple with the self-excited ones, which affect the spatial structures of modes induced by self-excited perturbations. The coupling effects enhance with the reduced mass flow rate, and the “resonance” effect might be excited. As a result, the mode of 4 RF (rotor frequency) grows during rotating stall process (RSP), which is a typical characteristic of rotating stall in the impeller. On the other hand, the vortex-induced perturbations emerge as different characteristic modes, due to the variant vortex structures with the mass flow rate. During the RSP, the growth of low-frequency characteristic modes is another typical feature of rotating stall in the impeller.

Vibration characteristic analysis of three-dimensional sandwich cylindrical shell based on the Spectro-Geometric method

This paper presents the numerical investigations on the free and random vibration properties of thick cylindrical shells under the three-dimensional (3-D) elasticity theory. The analysis of these properties is conducted using the Rayleigh-Ritz method in conjunction with the Spectro-Geometric method (SGM). The cylindrical shell considered in this study is composed of fiber-reinforced composite and functionally graded porous graphene platelet reinforced composites (FGP-GPLRC). To simulate different boundary conditions, an artificial spring technique is utilized. The displacement components, as determined by SGM, are incorporated into the energy expression of the cylindrical shell. By applying the Rayleigh-Ritz method, the intrinsic frequencies and vibration modes of the cylindrical shell are obtained. Additionally, the power spectral density (PSD) function of the cylindrical shell under random excitation is obtained by the Pseudo-Excitation method (PEM). The accuracy and effectiveness of the present method are verified through comparative studies with existing literature and finite element method (FEM). Furthermore, an investigation on the effects of various parameters on the intrinsic frequency and random response of the cylindrical shell is conducted.

Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?

This paper proposes a new forecast method of stock price return and volatility by GAVMD-Carbon-BiLSTM. First, based on the data from eight carbon markets in China, the logarithmic rate of return of carbon emission rights price is constructed. Second, the TVP-VAR model is used to investigate the impact effect of carbon emission rights trading, stock price return, and volatility. It is used as a predictor of return and volatility rationality. Third, the genetic algorithm is used to optimize the parameters of the variational mode decomposition. Fourth, the return and volatility of stock price are decomposed into multiple intrinsic modes, effectively reducing the data’s complexity. Finally, GAVMD is combined with BiLSTM, and the logarithmic return of carbon emission trading price is used as input to predict the stock price return and volatility. The results show that carbon emissions trading impacts the rate of return and volatility at different lead times and time points. Therefore, using it to predict stock price return and volatility can improve prediction accuracy. At the same time, combining GAVMD with BiLSTM, the prediction performance of carbon emission trading price logarithmic return rate as input is much better than other machine learning models.

Birefringence after Tellurium Nanosheets and Copper Intercalation

Symmetry-deficient two-dimensional (2D) layered materials induce a highly anisotropic optical response due to the anisotropy in their crystal structure, facilitating their application in polarized nanodevices. Intercalation is a new way to tune the optoelectronic properties of materials by inserting guest atoms into layered host materials, and 2D layered structures stacked with van der Waals gaps are a prerequisite for this phase of the technique. In this paper, 2D tellurium nanosheets were synthesized with a hydrothermal method, and copper atoms were inserted with a wet chemical method. The widening of the crystal plane spacing proves the introduction of copper atoms, and polarization−related second-harmonic-generation (SHG) studies reveal the intrinsic anisotropic modes of the two samples, and birefringent properties are found with polarizing light microscopy. We further investigated the electrical properties of the samples, and the embedding of the copper atoms caused the samples to exhibit higher currents, but their devices lost the gate control effect.

Unveiling the Core Patterns of High-Latitude Electron Density Distribution at Swarm Altitude

The ionosphere has distinctive characteristics under different solar and geomagnetic conditions, as well as throughout the seasons, and has a direct impact on our technological life in terms of radio communication and satellite navigation systems. In the pursuit of developing highly accurate ionospheric models and/or improving existing ones, understanding the various physical mechanisms that influence electron density dynamics is critical. In this study, we apply the Multivariate Empirical Mode Decomposition (MEMD) method to the electron density distribution in the mid-to-high latitude (above 50° magnetic latitude) regions in order to identify the dominant scales at which these mechanisms operate. The data were collected by the Swarm mission in the Northern Hemisphere. MEMD allows us to separate the main intrinsic modes and assess their relative contributions to the original one, thereby identifying the most important modes and the spatial scales at which they exert influence. Our study spanned the period from 1 January 2016 to 31 December 2021, which was characterized by low solar activity levels. This choice allowed for a more focused investigation of other variables influencing electron density distribution under similar solar activity conditions. We specifically examined the variations of the resulting modes in relation to different seasons and geomagnetic activity conditions, providing valuable insights into the complex behavior of the ionosphere in response to various external factors.

Exploring the dynamics of arrivals and prices volatility in onion (Allium cepa) using advanced time series techniques

Modeling the arrivals and prices of agricultural commodities is an essential requirement for farmers, consumers, and governmental organizations to make informed decisions. This is particularly important for perishable commodities such as vegetables, where spoilage can lead to significant losses for farmers and have a ripple effect on supply and demand dynamics. Volatility in the arrivals and prices of vegetables like onion is a serious issue affecting the common person in different ways. The study attempts to employ different time series models like the autoregressive integrated moving average (ARIMA), Artificial neural network (ANN), hybrid, and ensemble empirical mode decomposition (EEMD) techniques to analyze the pattern and trend of onions in Chandigarh and Delhi markets. From the results of the study, the amount of volatility in the data was found to range from medium to high among the markets. Decomposition techniques such as EEMD-ARIMA and EEMD-ANN performed better for the study data with the least mean absolute percentage error (MAPE) values, such as 17.74 and 6.78% for arrivals and 9.76 and 10.24% for prices at Chandigarh and Delhi markets, respectively. The EEMD techniques exceled in handling the non-linearity and non-stationarity by decomposing the data into different intrinsic modes and a residual, providing a better understanding of the fluctuation levels of data.

Time-varying modal identification using multi-channel measurements based on multivariate variational mode decomposition

Structural modal parameters are essential to provide dynamic information for data analysis of structural health monitoring (SHM) system. Because of the variation of the physical properties or the influence of the environment, the structural modal parameters may change with time during the operation, showing time-varying characteristics. Therefore, accurate identification of time-varying modal parameters shows to be an important issue for SHM. In this paper, a time-varying modal identification method is proposed by improving the multivariate variational mode decomposition (MVMD) method with autoregressive power spectrum and windowed principal component analysis (PCA). Firstly, the method for determination of initial center frequency is proposed by autoregressive power spectrum to improve the efficiency of MVMD. Secondly, intrinsic mode function for each mode is extracted using multi-channel responses by MVMD. Subsequently, instantaneous frequencies are identified through detecting the ridgeline of the synchro-squeezed short-time Fourier transform (SSTFT). Moreover, identification method for time-varying mode shapes is proposed by using the windowed PCA of the multi-channel intrinsic modes. Finally, the proposed method is verified by the numerical and practical studies. The results of the numerical study show that the method is effective for continuously varying modal parameters under impulse and random excitations. Through the data analysis of practical bridge, the capability for practical application is demonstrated.

Coherent phonon relaxation in strontium titanate

Ultrafast decays of the intrinsic phonon modes within 260–790 cm−1 range of frequencies, which are symmetry forbidden in the first-order Raman scattering in bulk SrTiO3 crystal, have been traced using time-resolved coherent Raman technique. The effective phonon decay times are within 0.86–1.38 ps and yield corresponding linewidths of 7.7–12.3 cm−1 for optical lattice vibrations. Symmetry breakage mechanisms related to free charged carriers resulting from vacancies do not appear to play a major role.

Experimental study of intrinsic localized mode mobility in a cyclic, balanced, 1D nonlinear transmission line.

Mobile intrinsic localized modes (ILMs) in balanced nonlinear capacitive-inductive cyclic transmission lines are studied by experiment, using a spatiotemporal driver under damped steady-state conditions. Without nonlinear balance, the experimentally observed resonance between the traveling ILM and normal modes of the nonlinear transmission line generates lattice drag via the production of a lattice backwave. In our experimental study of a balanced running ILM in a steady state, it is observed that the fundamental resonance can be removed over extended, well-defined driving frequency intervals and strongly suppressed over the complete ILM driving frequency range. Because both of these nonlinear capacitive and inductive elements display hysteresis our observation demonstrates that the experimental system, which is only partially self-dual, is surprisingly tolerant, regarding the precision necessary to eliminate the ILM backwave. It appears that simply balancing the cell dual nonlinearities makes the ILM envelope shape essentially the same at the two locations in the cell, so that the effective lattice discreteness seen by the ILM nearly vanishes.

An attention-based Bayesian sequence to sequence model for short-term solar power generation prediction within decomposition-ensemble strategy

The utilization of renewable energy has attracted much attention with the deterioration of the environment. Solar energy is widely distributed and has huge reserves. But, solar energy is also volatile and intermittent. This brings difficulties to the large-scale grid connection of solar energy and the distribution of power resources. The key to solve this problem is to provide accurate and stable short-term prediction for solar power generation. For this purpose, we present an attention-based Bayesian sequence to sequence (Seq2Seq) model within decomposition-ensemble strategy. Firstly, an influential factors analysis is performed on the data about meteorological conditions, operation status of the photovoltaic panels and historical solar power generation sequences to obtain the optimal combination of the influential factors. Secondly, this paper proposes a novel decomposition-ensemble framework based on complete ensemble empirical mode decomposition with adaptive noise and independent component analysis to mine the intrinsic modes of the solar power generation time series. Thirdly, this paper presents an attention-based Bayesian Seq2Seq method for modeling the relationships between solar power generation and influential factors. Using a real-world dataset from State Administration of Electricity Investment Science and Technology of China, a case study, comparative analysis and robustness checks are conducted, through which the contributions of the methods in the proposed model and the superior performance of the model are demonstrated.

Degradation Kinetics of Organic-Inorganic Hybrid Materials from Micro-Raman Spectroscopy and Density-Functional Theory: The Case of β-ZnTe(en)0.5.

Organic-inorganic hybrid materials often face a stability challenge. β-ZnTe(en)0.5 , which uniquely has over 15-year real-time degradation data, is taken as a prototype structure to demonstrate an accelerated thermal aging method for assessing the intrinsic and ambient-condition long-term stability of hybrid materials. Micro-Raman spectroscopy is used to investigate the thermal degradation of β-ZnTe(en)0.5 in a protected condition and in air by monitoring the temperature dependences of the intrinsic and degradation-product Raman modes. First, to understand the intrinsic degradation mechanism, the transition state of the degradation is identified, then using a density functional theory, the intrinsic energy barrier between the transition state and ground state is calculated to be 1.70eV, in excellent agreement with the measured thermal degradation barrier of 1.62eV in N2 environment. Second, for the ambient-condition degradation, a reduced thermal activation barrier of 0.92eV is obtained due to oxidation, corresponding to a projected ambient half-life of 40 years at room temperature, in general agreement with the experimental observation of no apparent degradation over 15 years. Furthermore, the study reveals a mechanism, conformation distortion enhanced stability, which plays a pivotal role in forming the high kinetic barrier, contributing greatly to the impressive long-term stability of β-ZnTe(en)0.5 .

Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex

Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.

Streamflow prediction using a hybrid methodology based on variational mode decomposition (VMD) and machine learning approaches

The optimal management of water resources depends on accurate and reliable streamflow prediction. Therefore, researchers have become interested in the development of hybrid approaches in recent years to enhance the performance of modeling techniques for predicting hydrological variables. In this study, hybrid models based on variational mode decomposition (VMD) and machine learning models such as random forest (RF) and K-star algorithm (KS) were developed to improve the accuracy of streamflow forecasting. The monthly data obtained between 1956 and 2017 at the Iranian Bibijan Abad station on the Zohreh River were used for this purpose. The streamflow data were initially decomposed into intrinsic modes functions (IMFs) using the VMD approach up to level eight to develop the hybrid models. The following step models the IMFs obtained by the VMD approach using the RF and KS methods. The ensemble forecasting result is then accomplished by adding the IMFs’ forecasting outputs. Other hybrid models, such as EDM-RF, EMD-KS, CEEMD-RF, and CEEMD-KS, were also developed in this research in order to assess the performance of VMD-RF and VMD-KS hybrid models. The findings demonstrated that data preprocessing enhanced standalone models’ performance, and those hybrid models developed based on VMD performed best in terms of increasing the accuracy of monthly streamflow predictions. The VMD-RF model is proposed as a superior method based on root mean square error (RMSE = 13.79), mean absolute error (MAE = 8.35), and Kling–Gupta (KGE = 0.89) indices.