Power Spectrum Density Research Articles

Spatial-temporal characteristics of the transient flow field around high-speed trains transiting the subgrade-cutting transition section under crosswinds

High-speed trains (HSTs) transiting subgrade-cutting transition sections in crosswinds have become a common operating scenario. This paper utilizes the Improved Delayed Detached Eddy Simulation (IDDES) turbulence model, considers air compressibility, and establishes a three-dimensional coupled train-subgrade-wind dynamics model. The aerodynamic load (AL) variation rules of trains and discrepancies in the flow field mechanisms when HSTs transit various types of subgrade-cutting transition sections [half cutting and half flat (HCHF), double side cutting (DC), and half cutting and half embankment (HCHE)] under crosswind conditions are revealed using this model. The results indicate the following. (1) The aerodynamic performance deteriorates when trains transit three subgrade-cutting transition sections under crosswinds. (2) As trains enter the transition section from a flat approach, the head car’s AL power spectrum density (PSD) is the largest when operating in the DC case. (3) After the train drives into the subsequent operating scenario, the average of the AL in the DC case is the lowest. (4) In the DC case, a slope of 1:0.75 should be taken as the basis for the design of the corresponding cutting parameters. (5) After the train enters the cutting, the winding flow speed and number of vortex structures surrounding the trains increase significantly.

A systematic study of the high-frequency bump in the black-hole low-mass X-ray binary GX 339 − 4

ABSTRACT The high-frequency bump, characterized by a frequency exceeding ∼30 Hz, represents a seldom-explored time-variability feature in the power density spectrum (PDS) of black-hole X-ray binaries. In the 2002, 2004, 2007, and 2010 outbursts of GX 339 − 4, the bump has been occasionally observed in conjunction with type-C quasi-periodic oscillations (QPOs). We systematically study the properties of the bump during these four outbursts observed by Rossi X-ray Timing Explorer (RXTE) in the 2–60 keV bands and detect the bump in 39 observations. While the frequencies of the type-C QPOs are in the range of ∼0.1–9 Hz, the root-mean-square (rms) amplitude of the bump shows an evolution in the hardness ratio versus the type-C QPO frequency plot. By comparing the rms amplitude of the bump with the corona temperature and simultaneous radio jet flux of the source, as previously studied in GRS 1915 + 105, we establish that in the hard state of GX 339 − 4, the bump is always strong, with the measurements of the rms amplitude in the range of 4–10 per cent. At the same time, the corona temperature is high and the radio flux is low. These findings indicate that, using the bump as a proxy, the majority of the accretion energy is directed towards the hot corona rather than being channeled into the radio jet. We discuss this phenomenon in terms of an inefficient energy transfer mechanism between the corona and jet in GX 339 − 4.

Mixed-flow pump cavitation characteristics extraction based on power spectrum density through pressure pulsation signal analysis

Mixed-flow pump is an important machine for transporting fluids, which has the advantages of both centrifugal pump and axial flow pump, but it is prone to cavitation, endangering efficiency and safe operation. A 20 m × 8.5 m high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were built for the study of cavitation signals. The detection of pressure pulsation signal is a direct measurement of the cavitation region, and the decay of information is less. In this study, the time domain and frequency domain characteristics of water pressure pulsation signal are analyzed by using fast Fourier transform method. The power spectrum density (PSD) is used to extract the characteristics of each cavitation stage, especially the full cavitation stage with random signal characteristics. The results show that PSD method reflects more information and is more sensitive than traditional time–frequency domain analysis. The characteristic power spectrum (PS) value of the cavitation of the mixed-flow pump machinery can be given through the feature extraction of the PS, which plays a great role in the effective identification and prevention of cavitation.

Dementia classification using a graph neural network on imaging of effective brain connectivity

Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms of neurodegenerative diseases. The literature suggests that effective brain connectivity (EBC) has the potential to track differences between AD, PD and healthy controls (HC). However, how to effectively use EBC estimations for the research of disease diagnosis remains an open problem. To deal with complex brain networks, graph neural network (GNN) has been increasingly popular in very recent years and the effectiveness of combining EBC and GNN techniques has been unexplored in the field of dementia diagnosis. In this study, a novel directed structure learning GNN (DSL-GNN) was developed and performed on the imaging of EBC estimations and power spectrum density (PSD) features. In comparison to the previous studies on GNN, our proposed approach enhanced the functionality for processing directional information, which builds the basis for more efficiently performing GNN on EBC. Another contribution of this study is the creation of a new framework for applying univariate and multivariate features simultaneously in a classification task. The proposed framework and DSL-GNN are validated in four discrimination tasks and our approach exhibited the best performance, against the existing methods, with the highest accuracy of 94.0% (AD vs. HC), 94.2% (PD vs. HC), 97.4% (AD vs. PD) and 93.0% (AD vs. PD vs. HC). In a word, this research provides a robust analytical framework to deal with complex brain networks containing causal directional information and implies promising potential in the diagnosis of two of the most common neurodegenerative conditions.

Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations.

Postoperative delirium (POD) is common and life-threatening, however, with intensive interventions, a potentially preventable clinical syndrome. Although electroencephalography (EEG) is a promising biomarker of delirium, standard 20-leads EEG holds difficulties for screening usage in clinical practice. We aimed to develop an accurate algorithm to predict POD using EEG data obtained from portable device. We recruited 128 patients who underwent scheduled cardiovascular surgery. Cognitive function assessments were conducted, and portable EEG recordings were obtained prior to surgery. Among the patients, 47 (36.7%) patients with POD were identified and they did not significantly differ from patients without POD in sex ratio, age, cognitive function, or treatment duration of intensive care unit. However, significant differences were observed in the preoperative EEG power spectrum densities at various frequencies, especially gamma activity, between patients with and without POD. POD was successfully predicted using preoperative EEG data with a machine learning algorithm, yielding accuracy of 86% and area under the receiver operating characteristic curve of 0.93. This study provides new insights into the objective and biological vulnerability to delirium. The developed algorithm can be applied in general hospitals without advanced equipment and expertise, thereby enabling the reduction of POD occurrences with intensive interventions for high-risk patients.

A computational modeling on two-dimensional laminar flow and thermal characteristics through a strongly bent square channel

In order to have a precise knowledge on how pressure gradients and buoyancy force affect fluid flow and energy distribution in a bending channel, it is important to perform a comprehensive study on flow characteristics and heat transfer mechanisms that trigger out the transition of fluids into a turbulent state, subject to a sustained pressure gradient. The present paper explores a computational modeling on two-dimensional fluid flow and thermal characteristics in a bent square channel of strong curvature. The Newton–Raphson (N-R) iteration method is applied to obtain a bifurcation structure depending on the pressure-driven force, the Dean number (De), covering 0 < De ≤ 5000. As a consequence, four branches of asymmetric steady solutions are identified for each of the cases of the Grashof number, Gn (=1000, 1500, and 2000), where only the first branch is found to exhibit asymmetric two-vortex solutions while the remaining branches encompass two- to four-vortex solutions. The similarity and disparity in the branching structure are also demonstrated. Then, adopting the Adam–Bashforth (A-B) method together with Crank–Nicholson (C-N) formula, the unsteady solutions (US) have been explored, validated by power spectrum density (PSD) and phase space Within the realm of US, two- and three-vortex solutions are found and these solutions exhibit transitions from steady to chaotic behavior profoundly. Effects of the Grashof number with convective heat transfer (CHT) are also compared. By analyzing the Nusselt number (Nu), it is observed that in case of highly chaotic flow, CHT experiences substantial enhancement. This intensified CHT arises from increased turbulence and mixing, facilitating more efficient thermal energy exchange under such chaotic flow conditions.

Micro-flow structure at regime transition from bubbling to turbulent fluidization in a fluidized bed

Gas-solid fluidized beds have found extensive utilization in frontline manufacturing, in particular as low-velocity beds. The fluidization status, the bubbling or turbulent flow regime and the transition in between, determine the system performance in practical applications. Though the convoluted hydrodynamics are quantitively evaluated through numerous data-processing methodologies, none of them alone can reflect all the critical information to identify the transition from the bubbling to the turbulent regime. Accordingly, this study was to exploit a coupling data processing methodology, in the combination of standard deviation, power spectrum density, probability density function, wavelet transform, and wavelet multiresolution method, to jointly explain the micro-flow structure at the regime transition from bubbling to turbulent fluidization. The transient differential pressure fluctuation was measured for the evaluation in a fluidized bed (0.267 m i.d. × 2.5 m height) with FCC catalysts (d¯p=65μm, ρp=1780kg/m3) at different superficial gas velocities (0.02–1.4 m/s). The results show that the onset of turbulent fluidization starts earlier in the top section of the bed than in the bottom section. The wavelet decomposition displays that the fluctuation of differential pressure mainly concentrates on the sub-signals with an intermediate frequency band. These sub-signals could be synthesized into three types of scales (micro-scale, meso-scale, and macro-scale), representing the multi-scale hydrodynamics in the fluidized bed. The micro-scale signal has the characteristic information of bubbling fluidization, and the characteristic information of turbulent fluidization is mainly represented by the meso-scale signal. This work provides a systematic comprehension of fluidization status assessment and serves as an impetus for more coupling analysis in this sector.

A Non-Stationary Cluster-Based Channel Model for Low-Altitude Unmanned-Aerial-Vehicle-to-Vehicle Communications

Under the framework of sixth-generation (6G) wireless communications, the unmanned aerial vehicle (UAV) plays an irreplaceable role in a number of communication systems. In this paper, a novel cluster-based low-altitude UAV-to-vehicle (U2V) non-stationary channel model with uniform planar antenna arrays (UPAs) is proposed. In order to comprehensively model the scattering environment, both single and twin clusters are taken into account. A novel continuous cluster evolution algorithm that integrates time evolution and array evolution is developed to capture channel non-stationarity. In the proposed algorithm, the link between the time evolution of twin clusters and that of single clusters is established to regulate the temporal evolution trend. Moreover, an improved observable radius method is applied to UPAs for the first time to describe array evolution. Based on the combination of cluster evolution and time-variant channel parameters, some vital statistical properties are derived and analyzed, including space–time correlation function (ST-CF), angular power spectrum density (PSD), Doppler PSD, Doppler spread (DS), frequency correlation function (FCF), and delay spread (RS). The non-stationarity in the time, space, and frequency domain is captured. It demonstrates that the airspeed, density of scatterers within clusters, and carrier frequency have an impact on statistical properties. Furthermore, twin clusters have more flexible spatial characteristics with lower power than single clusters. These conclusions can provide assistance and reference for the design and deployment of 6G UAV communication systems.

Effects of non-stationary wind velocity models on buffeting performance of closed-box girder suspension bridges

Non-stationary characteristic in nature wind has a great effect on buffeting performance of long-span bridges. The influence of key parameters in non-stationary wind velocity models on nonlinear buffeting responses of a super long-span suspension bridge was investigated in this paper. Firstly, four non-stationary wind velocity models are established by combing the time-varying average wind velocity with an exponential function and the fluctuating wind velocity with four modulation functions, respectively. These non-stationary wind velocity models have obvious non-stationary characteristics and then are validated by the classical power spectrum densities. Finally, three displacement responses of the bridge deck under four different independent variables of β in the exponential function and four modulation functions were compared, respectively. Results show that the turbulence intensities using two non-uniform modulation functions (NMF) are larger than those using uniform modulation functions (uMF). Moreover, the root mean square (RMS) values of three displacement responses increase with the decrease of β. Besides, the RMS values of three displacement under two NMFs are larger than those under two uMFs, and their RMS values under the second uMF are the smallest.

Whitening method for passive water pipeline assessment using ambient noise

Previous studies have verified the feasibility of using ambient noise in water pipeline networks as natural signals to assess pipeline condition passively. This passive technology uses correlation peaks of the ambient noise as characteristics to identify pipe faults and deterioration, and it requires the noise power spectrum density (PSD) to be adequately whitened. However, conventional noise whitening methods cannot provide sufficient whitening for the ambient noise in water pipelines due to the significant noise PSD variations that are unevenly distributed across the wide relative bandwidth of the noise signals. This paper proposes a tailored whitening process for water pipeline ambient noise by applying logarithmic transforms on both the magnitude and frequency axes to compresses the large dynamic range of the PSD magnitude and bandwidth. Numerical simulations and field experiment results have verified that the proposed method can effectively whiten the noise signals in water pipelines, and accurately restore the amplitude of correlation peaks to be consistent with the theoretical derivation using white noise assumptions. The theoretical derivation also reveals the relation between the amplitudes of correlation peaks and the noise spatial distribution, fault distance and its reflection/transmission coefficients. The feasibility of estimating the reflection coefficient of pipe faults from the correlation results with appropriately whitened noise is also investigated in this paper.

Investigation of surface textures deterioration on pavement skid-resistance using hysteresis friction models and numerical simulation method

Many rubber friction theories or some method combined theories and field-experiments are employed to evaluate the pavement skid-resistance deterioration due to the evolution of surface textures. However, these methods are difficult to be implemented in the analysis of situations with multi-factor coupling and some extreme conditions. This study developed a framework to evaluate the skid-resistance deterioration of asphalt pavements. In this framework, the portable laser scanning was used to create the digital worn pavement model, and a hydroplaning finite element (FE) model for these digital worn pavements was constructed to evaluate coupling effects of the texture evolution and factors of slip ratio, slip angle, velocity and water film on braking-cornering characteristics of tire. In this study, the deterioration of skid-resistance of five typical asphalt pavements due the surface texture wear was systematically investigated by this framework. Compared with previous works, this study established the rubber friction models for each digital worn pavement considering the energy hysteresis of rubber and the power spectrum density of surface texture. And the rubber friction model was used to define the interaction behaviors between the tire and corresponding wore pavements in the FE hydroplaning model, rather than using an empirical friction model or a fixed friction coefficient.

Ensuring fair assessment of solid-state nanopore sensors with reporting baseline current

In developing solid-state nanopore sensors for single molecule detection, comprehensive evaluation of the nanopore quality is important. Existing studies typically rely on comparing the noise root mean square or power spectrum density values. Nanopores exhibiting lower noise values are generally considered superior. This evaluation is valid when the single molecule signal remains consistent. However, the signal can vary, as it is strongly related to the solid-state nanopore size, which is hard to control during fabrication consistently. This work emphasized the need to report the baseline current for evaluating solid-state nanopore sensors. The baseline current offers insight into several experimental conditions, particularly the nanopore size. Our experiments show that a nanopore sensor with more noise is not necessarily worse when considering the signal-to-noise ratio (SNR), particularly when the pore size is smaller. Our findings suggest that relying only on noise comparisons can lead to inaccurate evaluations of solid-state nanopore sensors, considering the inherent variability in fabrication and testing setups among labs and measurements. We propose that future studies should include reporting baseline current and sensing conditions. Additionally, using SNR as a primary evaluation tool for nanopore sensors could provide a more comprehensive understanding of their performance.

Adaptive Uplink Data Compression in Spectrum Crowdsensing Systems

Understanding spectrum activity is challenging when attempted at scale. The wireless community has recently risen to this challenge in designing spectrum monitoring systems that utilize many low-cost spectrum sensors to gather large volumes of sampled data across space, time, and frequencies. These crowdsensing systems are limited by the uplink bandwidth available to backhaul the raw in-phase and quadrature (IQ) samples and power spectrum density (PSD) data needed to run various applications. This paper presents, a framework based on the Walsh-Hadamard transform to compress spectrum data collected from distributed and low-cost sensors for real-time applications. This transformation allows sensors to significantly save uplink bandwidth thanks to its inherent properties both when it is applied to IQ and PSD data. Additionally, by leveraging a feedback loop between the sensor and the edge device it connects to, carefully adapts the compression ratio over time to changes in the spectrum and different applications, jointly considering data size, application performance, and spectrum variations. We experimentally evaluate in several applications. Our results show that is particularly suitable for IoT transmissions and signals close to the noise floor. Compared with prior work, provides up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\times$</tex-math> </inline-formula> more reduction of uplink data size for signal detection based on PSD data, and reduces up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\times$</tex-math> </inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times$</tex-math> </inline-formula> the number of undecodable messages for IQ sample decoding.

Effect of water jet area on mitigation of vortex rope in a Francis turbine

The rotating vortex rope (RVR) is normally formed at part load operating conditions, which develops flow instability in the draft tube (DT) cone of a Francis turbine. The detrimental effects of RVR reduce the life and performance of the turbine. The present work mitigates RVR through axial water jet injection in the DT cone. This paper extends the author's previous work on water injection with a 50% reduction in the water jet area through two nozzles with a diameter of 12.7 mm. The effect of area reduction is analyzed by spectral analysis like power spectrum density and spatial harmonic decomposition of the pressure field measured at the wall of the cone. The effect of water jet injection on synchronous and asynchronous pressure pulsations is analyzed at both measurement planes of the cone. The improvement in pressure recovery in the cone and turbine efficiency due to water jet injection is also analyzed. The water is injected at 0.5%–2% of the turbine inlet flow at 22.10° guide vane opening.

Spectral analysis of bipolaron dynamics in nondegenerate conjugated polymers

Spectral characteristics of the bipolaron dynamics in nondegenerate polymers have been investigated based on the Su-Schrieffer-Heeger model, by using nonadiabatic molecular dynamics and power-spectrum density methods. The results show that the fundamental frequencies of bond-current density are proportional to the saturation velocities of the bipolaron. Further, the dependence of the fundamental frequencies of bond-current density on electric field and temperature can be divided into two cases. For the case of the field higher than a critical value, the thermal effects enhance the fundamental frequency of bond-current density, whereas at field lower than the threshold, the fundamental frequency is inhibited by thermal effects. It reflects the competition between electric field and temperature in bipolaron dynamics. Moreover, in the spectrum of optic mode, the weight of characteristic peak corresponding to breather first rapidly increases and then increases slowly with increasing electric field, which corresponds to the variation of the bipolaron velocity.

Harvesting vibration energy by branch structures and amplified inertial force acting on piezoelectric sheets

To improve the efficiency of ambient vibration energy harvesting, a novel vibration energy harvester with branch structures is proposed, which primarily generates electric output through dynamic force rather than deflection. When excited, the branch can amplify the inertial force and acts on piezoelectric sheets perpendicularly. This capability allows the harvester to generate a substantial output without undergoing excessive deflection. Theoretical models are established, and corresponding dynamic equations are derived using the Lagrange equation. The simulation results show that there exists super-harmonic resonance in the response. The validation experiments were carried out, in which the excitation types were chosen as the sweeping-frequency, harmonic and random types sequentially. The experiment results for sweeping-frequency and harmonic excitations demonstrate the occurrence of super-harmonic vibration and resonance in the response. These phenomena lead to a significant increase in output voltages. The experiments involving stochastic excitations demonstrate the harvester's capability to generate large outputs under weak random excitations, with a piezoelectric material with dimensions of 10 × 10 × 0.2 mm3. The root mean square of output power could reach 46.24 μW under the excitation of power spectrum density of 0.06 g2/Hz, about 80 % higher than the classical inverted-beam harvester.

Performance and Quality Comparison of Movie Alignment Software for Cryogenic Electron Microscopy.

Cryogenic electron microscopy (Cryo-EM) has been established as one of the key players in structural biology. It can reconstruct a 3D model of a sample at a near-atomic resolution. With the increasing number of facilities, faster microscopes, and new imaging techniques, there is a growing demand for algorithms and programs able to process the so-called movie data produced by the microscopes in real time while preserving a high resolution and maximal information. In this article, we conduct a comparative analysis of the quality and performance of the most commonly used software for movie alignment. More precisely, we compare the most recent versions of FlexAlign (Xmipp v3.23.03), MotionCor2 (v1.6.4), Relion MotionCor (v4.0-beta), Warp (v1.0.9), and CryoSPARC (v4.0.3). We tested the quality of the alignment using generated phantom data, as well as real datasets, comparing the alignment precision, power spectra density, and performance scaling of each program.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

Validity of Single Fluorescent Molecule to Report Local Dynamics of a Polymer Matrix: The Effect of Molecular Architecture

AbstractThe influence of van der Waals volume as well as molecular configuration of the probe on its rotation is investigated by defocused single molecule fluorescence microscopy. The results show that the probe with a larger van der Waals volume has lower integrated power spectra density and the fraction of rotational molecules at each temperature. However, the asymmetric configuration of probe can make the integrated power spectra density and the fraction of rotational molecules larger. Therefore, the van der Waals volume of probe is the most dominating factor to determine the length scales and frequency of reported local dynamics while the asymmetric configuration of probe can bring about additional effect.

Filter bank property of direct fast iterative filtering and its applications

The direct fast iterative filtering (dFIF) is a novel time–frequency signal processing technique for non-stationary and non-linear signals, which can be considered the improved method of fast iterative filtering (FIF). Firstly, the equivalent impulse response is analyzed based on extensive numerical experiments to reveal the performances of dFIF. And then, fractional Gaussian noise (fGn) with different Hurst exponents is constructed to study the power spectrum density (PSD) and the frequency band distribution of the equivalent filter banks of the dFIF. The corresponding comparison analysis results are also illustrated, including empirical mode decomposition (EMD), variational mode decomposition (VMD) and db4-wavelet transform. Furthermore, the zero-crossings detection and estimate of empirical variance analysis of IMFs are performed, which indicates that dFIF does not follow a dyadic filter bank. Based on these findings, two application cases are presented in extracting time-varying oscillations and trends using dFIF, EMD and VMD. The results show that with appropriate parameter selection, dFIF performs better than EMD and VMD in terms of decomposition orthogonality, accuracy and anti-mode mixing.