Power Spectral Density Analysis Research Articles

Discriminating Landslide Waveforms in Continuous Seismic Data Using Power Spectral Density Analysis

AbstractDiscriminating landslides from other events in seismic records is challenging due to unclear phases and overlapped frequency content. We analyze the seismic waveform power spectral density (PSD) and its skewness to discriminate landslides from earthquakes and background noise. By comparing PSDs of landslides with small‐magnitude earthquakes and noise in the Alaskan region, we find distinct power decay trends in the 0.01–5 Hz frequency range. The method was successfully tested on the seismic waveforms of seven global landslides. Further, the statistical significance of the approach was tested on 835 landslide waveforms using probability density, skewness and crosscorrelation of waveform PSD. This novel integration of seismic waveform PSDs and their skewness analysis is found to be robust and statistically significant for automatic landslide detection in continuous seismic data, with vast potential for early warning through real‐time seismic networks.

Correlation Between Microscopic Current Fluctuations Observed at Ultra-Microelectrodes and Macroscopic Bulk Electrolysis Performance in Redox-Active Microemulsions

Abstract Microemulsions (µEs) have been proposed as redox flow battery (RFB) electrolytes that maximize ionic conductivity and charge capacity by synergizing two immiscible phases. However, charge transfer during electrolysis in µEs is poorly understood. Here, we show that ultramicroelectrode electrolysis of ferrocene-loaded µEs - 20%, 60%, and 90% water - reveals stochastic current fluctuations. These are differentiated in the scanning electrochemical microscopy (SECM) geometry, where power spectral density analysis showed distinct changes in the frequency contributions. SECM in the substrate generation-tip collection mode showed that fluctuations arise under mass-transfer control. Significant differences in the diffusion coefficient of ferrocene species were deducted from SECM approach curves, suggesting phase transfer behavior. Using bulk electrolysis, we calculated the charge accessibility and cycling behavior in the µEs. A decrease in the stochastic behavior of the µEs seems to correlate to a higher accessibility and cycling performance, with the 90% water µE displaying the best reversibility and the 60% the lowest. Altogether, these results suggest that Marangoni-type convection driven by concentration gradients and/or µE restructuring during charge transfer play a role in the electrochemical performance of µEs. This presents opportunities for screening and diagnosing the performance of these emerging RFB electrolytes.

Maximum entropy spectral analysis: an application to gravitational waves data analysis

The maximum entropy spectral analysis (MESA) method, developed by Burg, offers a powerful tool for spectral estimation of a time-series. It relies on Jaynes’ maximum entropy principle, allowing the spectrum of a stochastic process to be inferred using the coefficients of an autoregressive process AR(p) of order p. A closed-form recursive solution provides estimates for both the autoregressive coefficients and the order p of the process. We provide a ready-to-use implementation of this algorithm in a Python package called memspectrum, characterized through power spectral density (PSD) analysis on synthetic data with known PSD and comparisons of different criteria for stopping the recursion. Additionally, we compare the performance of our implementation with the ubiquitous Welch algorithm, using synthetic data generated from the GW150914 strain spectrum released by the LIGO-Virgo-Kagra collaboration. Our findings indicate that Burg’s method provides PSD estimates with systematically lower variance and bias. This is particularly manifest in the case of a small (O(5000)) number of data points, making Burg’s method most suitable to work in this regime. Since this is close to the typical length of analysed gravitational waves data, improving the estimate of the PSD in this regime leads to more reliable posterior profiles for the system under study. We conclude our investigation by utilising MESA, and its particularly easy parametrisation where the only free parameter is the order p of the AR process, to marginalise over the interferometers noise PSD in conjunction with inferring the parameters of GW150914

Extraction of isotropic and anisotropic components for optical surface micro-metrology based on the two-dimensional power spectral density analysis

Extraction of surface characteristics is essential during surface processing and optical inspections. In this work, we propose a new extraction method by dividing the global feature within multiple directions into isotropic and anisotropic components and combining noise filtration, two-dimensional component extraction, and fast reconstruction. The mathematical descriptions of global and local features were derived. The noise by holes, scratches, and finite sampling points was restrained by Bearing Ratio analysis, Hough transform, and Welch window operation. The isotropic and anisotropic surface components were extracted in the two-dimensional power spectral density domain, reconstructed in the two-dimensional Fourier domain, and inversed in Cartesian coordinates. Five surfaces with anisotropic structural characteristics ranging from zero to two dimensions were analyzed. The general applicability was proved according to the consistency between surface processing methods and extracted results. A chemical mechanical polishing experiment was designed and accomplished to verify the sensitivity of the extraction method. The subtle variation in surface morphology was captured on the reconstructed surfaces near the polishing end-point, confirming its detectability on weak anisotropic components. This process-oriented surface extraction method can achieve qualified results without transcendental knowledge of surface conditions and offers openness to various surface evaluation criteria by statistical roughness indicators, which supports surface inspection for multiple surface processing techniques.

On the low-latitude ionospheric irregularities under geomagnetically active and quiet conditions using NavIC observables: A spectral analysis approach

Ionospheric irregularities and associated scintillations under geomagnetically active/quiet conditions have detrimental effects on the reliability and performance of space- and ground-based navigation satellite systems, especially over the low-latitude region. The current work investigates the low-latitude ionospheric irregularities using the phase screen theory and the corresponding temporal Power Spectral Density (PSD) analysis to present an estimate of the outer irregularity scale sizes over these locations. The study uses simultaneous L5 signal C/No observations of NavIC (a set of GEO and GSO navigation satellite systems) near the northern crest of EIA (Indore: 22.52∘N, 75.92∘E, dip: 32.23∘N) and in between the crest and the dip equator (Hyderabad: 17.42∘N, 78.55∘E, dip: 21.69∘N). The study period (2017–2018) covers disturbed and quiet-time conditions in the declining phase of the solar cycle 24. The PSD analysis brings forward the presence of irregularities, of the order of a few hundred meters during weak-to-moderate and quiet-time conditions and up to a few km during the strong event, over both locations. The ROTI values validate the presence of such structures in the Indian region. Furthermore, only for the strong event, a time delay of scintillation occurrence over Indore, with values of 36 min and 50 min for NavIC satellites (PRNs) 5 and 6, respectively, from scintillation occurrence at Hyderabad is observed, suggesting a poleward evolution of irregularity structures. Further observations show a westward propagation of these structures on this day. This study brings forward the advantage of utilizing continuous data from the GEO and GSO satellite systems in understanding the evolution and propagation of the ionospheric irregularities over the low-latitude region.

Resting-state EEG spectral and fractal features in dementia with Lewy bodies with and without visual hallucinations

ObjectiveComplex visual hallucinations (VH) are a core feature of dementia with Lewy bodies (DLB), though they may not occur in all patients. Power spectral density (PSD) analysis of resting-state EEG (rs-EEG) shows associations between some frequency bands (e.g., θ), individual alpha frequency (IAF) and VH. However, new tools that improve early differential diagnosis and symptom-based stratification with higher sensitivity and specificity, even within the DLB population, are desirable. We aimed to assess differences in rs-EEG data between DLB patients with VH (DLB-VH+) and without VH (DLB-VH-), comparing innovative non-linear approaches with more traditional linear ones. MethodsWe retrospectively analyzed rs-EEG recordings of DLB-VH+, DLB-VH-, Alzheimer’s disease patients and age-matched healthy controls. EEG was analyzed using the nonlinear Higuchi’s Fractal Dimension (FD) measure, and the results were compared with those of entropy and standard linear methods based on PSD and IAF. ResultsOnly the FD measure could discriminate between DLB-VH+ and DLB-VH-. ConclusionsIn conclusion, rs-EEG differences between DLB-VH+ and DLB-VH- are better characterized by FD analysis than by a more traditional power spectrum approach. SignificanceThis suggests that the presence of complex VH is associated with less complex brain dynamics at rest, as reflected by the FD measure.

Accurate drift-invariant single-molecule force calibration using the Hadamard variance

Single-molecule force spectroscopy (SMFS) techniques play a pivotal role in unraveling the mechanics and conformational transitions of biological macromolecules under external forces. Among these techniques, multiplexed magnetic tweezers (MT) are particularly well suited to probe very small forces, ≤1 pN, critical for studying noncovalent interactions and regulatory conformational changes at the single-molecule level. However, to apply and measure such small forces, a reliable and accurate force-calibration procedure is crucial. Here, we introduce a new approach to calibrate MT based on thermal motion using the Hadamard variance (HV). To test our method, we perform bead-tether Brownian dynamics simulations that mimic our experimental system and compare the performance of the HV method against two established techniques: power spectral density (PSD) and Allan variance (AV) analyses. Our analysis includes an assessment of each method’s ability to mitigate common sources of additive noise, such as white and pink noise, as well as drift, which often complicate experimental data analysis. We find that the HV method exhibits overall similar or higher precision and accuracy, yielding lower force estimation errors across a wide range of signal/noise ratios (SNRs) and drift speeds compared with the PSD and AV methods. Notably, the HV method remains robust against drift, maintaining consistent uncertainty levels across the entire studied SNR and drift speed spectrum. We also explore the HV method using experimental MT data, where we find overall smaller force estimation errors compared with PSD and AV approaches. Overall, the HV method offers a robust method for achieving sub-pN resolution and precision in multiplexed MT measurements. Its potential extends to other SMFS techniques, presenting exciting opportunities for advancing our understanding of mechanosensitivity and force generation in biological systems. To make our methods widely accessible to the research community, we provide a well-documented Python implementation of the HV method as an extension to the Tweezepy package.

Systemic neurophysiological entrainment to behaviorally relevant rhythmic stimuli.

Physiological oscillations, such as those involved in brain activity, heartbeat, and respiration, display inherent rhythmicity across various timescales. However, adaptive behavior arises from the interaction between these intrinsic rhythms and external environmental cues. In this study, we used multimodal neurophysiological recordings, simultaneously capturing signals from the central and autonomic nervous systems (CNS and ANS), to explore the dynamics of brain and body rhythms in response to rhythmic auditory stimulation across three conditions: baseline (no auditory stimulation), passive auditory processing, and active auditory processing (discrimination task). Our findings demonstrate that active engagement with auditory stimulation synchronizes both CNS and ANS rhythms with the external rhythm, unlike passive and baseline conditions, as evidenced by power spectral density (PSD) and coherence analyses. Importantly, phase angle analysis revealed a consistent alignment across participants between their physiological oscillatory phases at stimulus or response onsets. This alignment was associated with reaction times, suggesting that certain phases of physiological oscillations are spontaneously prioritized across individuals due to their adaptive role in sensorimotor behavior. These results highlight the intricate interplay between CNS and ANS rhythms in optimizing sensorimotor responses to environmental demands, suggesting a potential mechanism of embodied predictive processing.

Optimization of stimulus properties for SSVEP-based BMI system with a heads-up display to control in-vehicle features.

Over the years, the driver-vehicle interface has been improved, but interacting with in-vehicle features can still increase distraction and affect road safety. This study aims to introduce brain-machine interface (BMI)- based solution to potentially enhance road safety. To achieve this goal, we evaluated visual stimuli properties (SPs) for a steady state visually evoked potentials (SSVEP)-based BMI system. We used a heads-up display (HUD) as the primary screen to present icons for controlling in-vehicle functions such as music, temperature, settings, and navigation. We investigated the effect of various SPs on SSVEP detection performance including the duty cycle and signal-to-noise ratio of visual stimuli, the size, color, and frequency of the icons, and array configuration and location. The experiments were conducted with 10 volunteers and the signals were analyzed using the canonical correlation analysis (CCA), filter bank CCA (FBCCA), and power spectral density analysis (PSDA). Our experimental results suggest that stimuli with a green color, a duty cycle of 50%, presented at a central location, with a size of 36 cm2 elicit a significantly stronger SSVEP response and enhanced SSVEP detection time. We also observed that lower SNR stimuli significantly affect SSVEP detection performance. There was no statistically significant difference observed in SSVEP response between the use of an LCD monitor and a HUD.

In Situ Detection of Interfacial Ions at the Single-Bond Level.

Detecting the ionic state at the solid-liquid interface is essential to reveal the various chemical and physical processes that occur at the interface. In this study, the adsorption states of the highly electronegative ions F- and OH- at the solid-liquid interface are detected by using the scanning tunneling microscopy break junction technique. With the active hydrogen atom of the amino group as a probe, the formed ionic hydrogen bonds are successfully detected, thereby enabling in situ monitoring of the ionic state at the solid-liquid interface. Through noise power spectral density analysis and theoretical simulations, we reveal the mechanism by which ionic hydrogen bonds at the interface affect the charge transport properties. In addition, we discover that the ionic state at the solid-liquid interface can be effectively manipulated by electric fields. Under high electric fields, the concentration of the anion near the electrode is higher, and the proportion of hydrogen bonds formed is greater than that under low electric fields. This study of the interfacial ionic state at the single-bond level provides guidance for the design of high-performance materials for energy conversion and environmental purification.

Aperiodic Component Analysis in Quantification of Steady-State Visually Evoked Potentials.

In this study, we aimed to investigate whether and how the aperiodic component in electroencephalograms affects different quantitative processes of steady-state visually evoked potentials and the performance of corresponding brain-computer interfaces. We applied the Fitting Oscillations & One-Over-F method to parameterize power spectra as a combination of periodic oscillations and an aperiodic component. Electroencephalographic responses and system performance were measured and compared using four prevailing methods: power spectral density analysis, canonical correlation analysis, filter bank canonical correlation analysis and the state-of-the-art method, task discriminant component analysis. We found that controlling for the aperiodic component prominently downgraded the performance of brain-computer interfaces measured by canonical correlation analysis (94.9% to 82.8%), filter bank canonical correlation analysis (94.1% to 87.6%), and task discriminant component analysis (96.5% to 70.3%). However, it had almost no effect on that measured by power spectral density analysis (80.4% to 78.7%). This was accompanied by a differential aperiodic impact between power spectral density analysis and the other three methods on the differentiation of the target and non-target stimuli. The aperiodic component distinctly impacts the quantification of steady-state visually evoked potentials and the performance of corresponding brain-computer interfaces. Our work underscores the significance of taking into account the dynamic nature of aperiodic activities in research related to the quantification of steady-state visually evoked potentials. The source code for our approach is available at https://github.com/didi226/scut_ssvep_aperiod.

Adaptive variable sampling T-SSD algorithm

Addressing the challenges of non-unique decomposition outcomes and prolonged decomposition durations in the fault feature adaptive extraction algorithm based on tensor decomposition, this paper presents a novel algorithm called the adaptive variable sampling tensor singular spectrum decomposition (T-SSD) algorithm. The proposed approach centers on decomposing multichannel time series with adaptive sampling frequency, leveraging tensor singular value decomposition. Initially, the embedding dimension and the number of resampling points were optimized by power spectral density analysis and adaptive sampling algorithm. Subsequently, a third-order tensor is constructed based on the principle of tensor-tensor-preserving order multiplication, combining trajectory tensor construction and embedding dimension. Finally, the signal decomposition and reconstruction of multichannel component signals are achieved through adaptive sampling, interpolation, and complementary back steps. Experimental signal analysis indicates that this algorithm can better extract fault characteristics from signals compared to common signal processing algorithms. In comparison with the traditional T-SSD algorithm, this method significantly improves decomposition efficiency, particularly for low-frequency components. It effectively tackles the efficiency challenge caused by data redundancy, enabling the organic fusion and adaptive decomposition of multichannel signals.

The performance of Tiangong-2 InIRA in gravity anomaly recovery from fused conventional nadir-observing altimeter data

Satellite altimetry has emerged as the primary data source for deriving marine gravity, owing to advancements in satellite altimetry technologies. The Tiangong-2 interferometric imaging radar altimeter (TG2 InIRA) is a new generation wide-swath altimeter (WSA) launched by China on 15 September 2016. TG2 InIRA employs a short-baseline interferometry measurement method at a small incidence angle, thereby enabling the precise measurement of wide-swath sea surface heights (SSHs). The present study demonstrates the efficacy of utilizing data from the TG2 InIRA in conjunction with data from the CryoSat-2 (CS2) satellite to recover marine gravity anomalies. In the Northwest Pacific region (15°N ∼ 25°N, 140°E ∼ 150°E), the TG2 InIRA data were filtered to a 2 km × 2 km grid and any anomalous values were excluded using a mean sea surface model. Deflections of the vertical (DOVs) were calculated using the remove-restore technique, and the inverse Vening-Meinesz formula was applied to invert the gravity anomaly. A power spectral density analysis was conducted to assess variations in gravity anomalies at ship locations in the frequency domain. The results, based on the SIO V31.1 (DOV) model, indicate that the gridded vertical deflections derived from CS2 and TG2 InIRA data achieve more balanced accuracy in the east–west and north–south directions. The integration of TG2 InIRA and CS2 data yielded gravity results exhibiting a root mean square (RMS) of 0.519 mGal lower than those derived from TG2 InIRA data alone, and 0.034 mGal lower than those derived from CS2 data. These findings were derived from shipborne gravity data. Furthermore, the inversion of gravity anomalies from TG2 InIRA demonstrated an enhanced capability in the reduction of high-frequency gravity noise.

Research on signal denoising algorithm based on ICEEMDAN eddy current detection

This study addresses the challenges of non-stationarity and significant background noise interference in eddy current detection signals by proposing a noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise (ICEEMDAN). The process commences with the signal being decomposed using Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise into a finite number of Intrinsic Mode Functions (IMFs). Each Intrinsic Mode Function is then evaluated for the presence of high-frequency noise using a Power Spectral Density (PSD) analysis. The high-frequency noise present in the Intrinsic Mode Functions is then reduced using Normalized Least Mean Squares (NLMS) before being reconstructed with the remaining Intrinsic Mode Functions. Subsequently, the reconstructed signals are subjected to another round of decomposition using Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise. The Pearson Product Moment Correlation Coefficient (PPMCC) is utilised to calculate the correlation between the Intrinsic Mode Functions within each layer, retaining those with a strong correlation to further attenuate noise. Ultimately, the local maxima judgement method selectively amplifies defect signals by assessing changes in peak and valley degrees, thereby improving the signal-to-noise ratio of the eddy current detection signal. The experimental results demonstrate that, in comparison to the use of only the conventional Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise and Normalized Least Mean Squares denoising methods, the proposed method increases the Signal-to-Noise Ratio (SNR) by 1.08 dB and 2.31 dB, respectively, and decreases the Mean Square Error (MSE) by 106.9 and 223.9, respectively. The false alarm rate for stainless steel welded tubes with defects is 1.4%, while the false alarm rate for stainless steel welded tubes without defects is 0.4%.

Integrated Study of X-Ray Spectrum and Time Lags for HBL Mrk 421 within the Framework of the Multiple-zone Leptonic Model

We present the timing analysis of 10 archived XMM-Newton observations with an exposure of >40 ks of Markarian 421. Mrk 421 is the brightest high-frequency-peaked BL Lac object emitting in X-rays produced by electrons accelerated in the innermost regions of a relativistic jet pointing toward us. For each observation, we construct averaged X-ray spectra in 0.5–10 keV band, as well as 100 s binned light curves (LCs) in various subbands. During these observations, the source exhibited various intensity states differing by close to an order of magnitude in flux, with the fractional variability amplitude increasing with energy through the X-ray band. Bayesian power spectral density analysis reveals that the X-ray variability can be characterized by a colored noise, with an index ranging from ∼ −1.9 to −3.0. Moreover, both the standard cross-correlation function and cross-spectral methods indicate that the amount of time lags increases with the energy difference between two compared LCs. A time-dependent two-zone jet model is developed to extract physical information from the X-ray emission of Mrk 421. In the model, we assume that the jet emission mostly comprises a quasi-stationary component and a highly variable one. Our results show that the two-zone model can simultaneously provide a satisfactory description for both the X-ray spectra and time lags observed in different epochs, with the model parameters constrained in a fully acceptable interval. We suggest that shocks within the jets may be the primary energy dissipation process responsible for triggering the rapid variability, although magnetic reconnection cannot be excluded.

Enhancing triboelectrification via multiscale roughness dependent thermal dissipation

Polymer-based triboelectric nanogenerators (TENGs) have held promise due to their excellent interfacial conformity and ease of fabrication. However, the role of surface roughness in triboelectricity requires further study. In this study, we have manipulated the nano-/micro-scale roughness configuration in polydimethylsiloxane (PDMS) over a wide range of extents using various sandpaper-based templates. According to the power spectral density analysis, the spatial frequency of template-free PDMS exhibits several distinct bandwidth regions each with different fractal dimensions significantly higher than 2, despite having the lowest roughness value. In contrast, most template-based PDMS shows an entire spatial frequency region that scales nearly with a single power factor corresponding to a fractal dimension as low as 2, despite slight increases in roughness values. Consequently, the surface temperature gradient and output performance of TENG increased, following the trend of fractal dimension and roughness, but the surface potentials have remained almost invariant. However, excessive increases in the surface roughness cause the spatial frequency to be divided once again into several different bandwidth regions with different cutoffs and higher fractal dimensions. These results suggest that the performance of TENG can be controlled by tuning both surface roughness and self-affine properties over multiple scales. Specifically, adhesive interaction becomes dominant on surfaces with lower fractality, enhancing TENG performance due to the expanded contact area. This study sheds light on the relationship among triboelectricity, thermal dissipation, and topography.

Flow field reconstruction of trash rack based on generative adversarial networks

ABSTRACT A new model - super-resolution Wasserstein Generative Adversarial Network with Gradient Penalty (SRWgan-GP) - is developed with resolution of 512×512 to reconstruct the sliced 2D high-resolution flow field from low-resolution data. To train the SRWgan-GP model, flow field data obtained from Large Eddy Simulation (LES) behind the trash racks is utilized. A sub-pixel convolution layer is incorporated in the framework to generate higher-resolution feature maps (512 × 512), which significantly reduces the network's memory requirements under the same output resolution .The performance of the proposed model is compared with that of other commonly used generative models including u-shaped architecture model (Unet) and Convolutional Neural Network (CNN). The results reveal that the SRWgan-GP model excels in reconstructing the flow field along both the x with and y axes, demonstrating the most accurate performance with minimal error achieving an MSE of 0.001, PSNR of 46.557, and SSIM of 0.994 in depicting turbulent structures and the Kįrmįn vortex street. Power Spectral Density (PSD) analysis shows that the primary shedding frequency of the vortex street is consistent with LES at approximately 10Hz for SRWgan-GP. Additionally, the SRWgan-GP exhibits proficient accuracy in computing second-order statistics of the flow field, achieving minimal error in instantaneous Reynolds shear stresses.

WiSigPro: Transformer for elevating CSI-based human activity recognition through attention mechanisms

The utilization of Channel State Information (CSI) in Wi-Fi-based passive sensing has become popular due to its cost-effectiveness and broad applicability. This technology is highly valued for its ability to gather data without requiring active user interaction, making it versatile in various contexts. However, existing methods face significant challenges, including low sensing accuracy, high processing requirements, and system instability. To address these issues, we introduce the WiSigPro Transformer, an advanced CSI-based Wi-Fi passive sensing model specifically designed for Human Activity Recognition. This model employs multi-head attention mechanisms and positional encoding to effectively capture complex spatiotemporal patterns, thereby enhancing both robustness and accuracy. Our approach integrates advanced signal processing techniques to improve signal quality and feature extraction. These techniques include wavelet denoising to reduce noise, median filtering to smooth the signal, and Power Spectral Density analysis using Welch’s method to capture frequency domain features. Additionally, we use normalization to standardize amplitude and phase data, and feature engineering methods to extract comprehensive signal characteristics, such as skewness and kurtosis. To address data imbalance, we apply the Synthetic Minority Over-sampling Technique and data augmentation strategies, ensuring balanced representation and improved model generalization. Through comprehensive simulations, the WiSigPro Transformer demonstrates superior performance across key metrics, including recognition accuracy, precision, recall, and F1-score. Achieving an impressive 98% accuracy, it outperforms conventional neural networks such as CNN, RNN, BiLSTM, LSTM, and ABLSTM. These results underscore the transformative potential of the WiSigPro Transformer in Wi-Fi-based passive sensing and activity recognition applications, making it a powerful tool for accurately capturing and analyzing spatiotemporal data.

Autonomous ground vehicle gravity anomaly measurement and dynamic error compensation

Abstract To address the issue that dynamic gravity anomaly measurement is overly dependent on GNSS and can’t be measured autonomously at this stage, this paper proposes an autonomous ground vehicle dynamic gravity anomaly measurement method based on a strapdown inertial navigation system (SINS), odometer (OD), barometer and platform gravimeter. The SINS/OD /barometer integrated navigation solution delivers high-precision navigation parameters, completes the calculation of correction terms, and performs the autonomous dynamic gravity anomaly measurement combined with the primary measurement results of the platform gravimeter. Numerical calculations provide the requirements for the application of the proposed method, and the cut-off frequency for extracting gravity anomalies is 0.02 Hz, as determined by power spectral density analysis. In order to further improve the measurement accuracy and account for dynamic errors caused by vehicle maneuvering, a long-short-term memory (LSTM) model of recurrent neural network (RNN) is introduced. A series of experiments under multiple circumstances with repeated lines were conducted in Tianjin, China, and the static measurements along the line were taken using CG-5 to provide true values of gravity anomalies. The results demonstrate that the autonomous measurement scheme can achieve accuracy comparable to GNSS-assisted, and that dynamic error compensation algorithm based on LSTM improves the dynamic gravity measurements accuracy significantly without sacrificing the spatial resolution of gravity anomalies.

Unsteady measurement in a transonic axial compressor with optimized axial slot casing treatment

Casing treatment has been widely adopted to enhance the operating range of compressors by extending the stall margin. In this article, a high-precision experimental investigation was conducted in a transonic compressor. Unsteady pressure is measured in the casing wall using a cluster of time-resolved transducers with axial and circumferential spatial resolution. The experimental data show that the optimized axial slot casing treatment improves the stall margin without peak efficiency penalty for all measured speedlines. A comprehensive flow field analysis indicates that the surge boundary of transonic compressor is sensitive to the position of shock wave and tip leakage flow. After the application of optimized axial slot casing treatment, the flow structure can be significantly modified, which specifically manifests as both the mitigation in shock wave detachment and redistribution of tip leakage flow trajectory toward the trailing edge of the blade. Furthermore, power spectral density analysis is conducted to examine the unsteady effect. The amplitude of characteristic frequency band induced by the unsteady fluctuation of tip leakage flow and shock wave is considerably suppressed, which can also be regard as the stability enhancement mechanism of casing treatment.