Time-varying Components Research Articles

Active Acoustics with a Phase Cancelling Modal Reverberator

Active acoustics (AA) systems are used to electronically modify the acoustics of a room (e.g., in live music venues). AA systems have an inherent feedback component and can suffer from instability and coloration artifacts resulting from too high feedback gains. State-of-the-art methods can improve system stability and coloration, usually at the cost of complex implementations and long parameter-tuning sessions. They can also cause sound artifacts due to time-varying components, limiting the enhancement at low frequencies. This work proposes a time-invariant feedback attenuation method for low frequencies based on a modal reverberator. The attenuation is achieved through destructive acoustic interference, obtained via phase shifts between the input and output signals. The analyzed frequency range is 0–500 Hz, where the room transfer functions are considered highly invariant over time. The results show a gain-before-instability increase of more than 5 dB for a modal reverberator with high mode density in this frequency range. The improvement is also stable for low-magnitude changes in the room transfer functions over time. The proposed method provides a robust AA system with artificial reverberation for the low-frequency range and can be used alongside other established methods.

BEBOP VI. Enabling the detection of circumbinary planets orbiting double-lined binaries with the DOLBY method of radial–velocity extraction

ABSTRACT Circumbinary planets, orbiting both stars in a binary system, offer the opportunity to study planet formation and orbital migration in an environment different from that around single stars. However, despite the fact that $\gt 90~\% $ of binary systems in the solar neighbourhood are spectrally resolved double-lined binaries, there has been only one detection of a circumbinary planet orbiting a double-lined binary using the radial velocity method so far. Spectrally disentangling binary components is challenging due to blending of spectral lines and inaccuracies in spectral modelling. These inaccuracies add scatter to the measurements, which can hide the weak radial velocity signature of circumbinary exoplanets. We have obtained new high signal-to-noise, high-resolution spectra with the SOPHIE spectrograph, mounted on the 193 cm telescope at Observatoire de Haute-Provence (OHP), for six bright, double-lined binaries for which circumbinary exoplanet detection has been attempted in the past. To extract radial velocities, we use the DOLBY code, a recent method of spectral disentangling using Gaussian processes to model the time-varying components. We analyse the resulting radial velocities with a diffusive nested sampler to seek planets, and compute sensitivity limits. We do not detect any new circumbinary planet. However, we show that the combination of new data, new radial velocity extraction methods, and improved statistical methods to determine a data set’s sensitivity to planets leads to an approximately one order of magnitude improvement compared to previous results. This improvement brings us into the range of known circumbinary exoplanets and paves the way for future observation campaigns targeting double-lined binaries.

An Adaptive Chirp Mode Decomposition-Based Method for Modal Identification of Time-Varying Structures

Modal parameters are inherent characteristics of civil structures. Due to the effect of environmental factors and ambient loads, the physical and modal characteristics of a structure tend to change over time. Therefore, the effective identification of time-varying modal parameters has become an essential topic. In this study, an instantaneous modal identification method based on an adaptive chirp mode decomposition (ACMD) technique was proposed. The ACMD technique is highly adaptable and can accurately estimate the instantaneous frequencies of a structure. However, it is important to highlight that an initial frequency value must be selected beforehand in ACMD. If the initial frequency is set incorrectly, the resulting instantaneous frequencies may lack accuracy. To address the aforementioned problem, the Welch power spectrum was initially developed to extract a high-resolution time–frequency distribution from the measured signals. Subsequently, the time–frequency ridge was identified based on the maximum energy position in the time–frequency distribution plot, with the frequencies associated with the time–frequency ridge serving as the initial frequencies. Based on the initial frequencies, the measured signals with multiple degrees of freedom could be decomposed into individual time-varying components with a single degree of freedom. Following that, the instantaneous frequencies of each time-varying component could be calculated directly. Subsequently, a sliding window principal component analysis (PCA) method was introduced to derive instantaneous mode shapes. Finally, vibration data collected under various operational scenarios were used to validate the proposed method. The results demonstrated the effective identification of time-varying modal parameters in real-world civil structures, without missing modes.

Quantifying the frequency modulation in electrograms during simulated atrial fibrillation in 2D domains

Atrial fibrillation (AF) affects millions of people in the world, causing increased morbidity and mortality. Treatment involves antiarrhythmic drugs and catheter ablation, showing high success for paroxysmal AF but challenges for persistent AF. Experimental evidence suggests reentrant waves and rotors contribute to AF substrates. Ablation procedures rely on electroanatomical maps and electrogram (EGM) signals; however, current methods used in clinical practice lack consideration for time–frequency varying EGM components. The fractional Fourier transform (FrFT) can be adopted to capture time-varying frequency components, thereby enhancing the comprehension of arrhythmogenic substrates during AF for improved ablation strategies. To this end, a FrFT-based algorithm is developed to characterize non-stationary components in EGM signals from simulated AF episodes. The proposed algorithm comprises a pre-processing step to enhance the coarser features of the EGM waveform, a windowing process for dynamic assessment of the EGM, and a FrFT order optimization stage that seeks compact signal representations in fractional Fourier domains. The resulting order is related to the rate of frequency change in the signal, making it a useful indicator for frequency-modulated components. The FrFT-based algorithm is implemented on EGM signals from AF simulations in 2D domains representing a region of the atrial tissue. Consequently, the computed optimum FrFT orders are used to build maps that are spatially correlated to the underlying propagation dynamics of the simulated AF episode. The results evince that the extreme values in the optimum orders map pinpoint the localization of fibrillatory mechanisms, generating EGM activation waveforms with varying frequency content over time.

Specific emitter identification unaffected by time through adversarial domain adaptation and continual learning

Timely identifying the emitter of target signals is crucial for communication security in complex electromagnetic environments. Specific emitter identification (SEI) is a technique to identify emitters using the hardware fingerprints of emitters. In this paper, the impact of changes in hardware fingerprints over time on SEI is solved, which significantly deteriorates identification performance. This issue is addressed from two aspects: one is mitigating the impact of these changes, and the other is tracking and adapting to them. For the aspect of mitigating impact, an alternating adversarial domain adaptation (AADA) method is proposed to eliminate the time-varying component in hardware fingerprints. Subsequently, a feature map calculation method using weighted Euclidean distance is designed, preserving the main parameters of feature maps for each emitter. For the aspect of tracking and adapting to changes, a continual learning method was designed based on feature maps of each emitter. This approach incorporates the selective annotation of unlabeled new data with an iterative optimization training process. To validate the effectiveness of the proposed method, we independently collected comprehensive time-variant datasets as well as simpler datasets with varying receivers and environments. The proposed method was tested on these datasets and compared with existing conventional and advanced methods. The experimental results indicate that the proposed SEI method exhibits superior recognition performance. Compared to existing methods, it achieved an average recognition accuracy improvement of over 8% on the time-variant dataset, and demonstrated enhanced robustness against these three types of variations.

Finite-time H∞ synchronization of semi-Markov jump neural networks with two delay components with stochastic sampled-data control

This article investigates the finite-time H∞ synchronization for semi-Markov jump neural networks with two delay components based on stochastic sampled data control. Additionally, the parametric uncertainties are randomly varying which follows the Bernoulli distributed sequences. In the stochastic sampled data control, the sampling interval ′m′ is supposed to be two different values in the time-varying component with given probability conditions. By constructing triple and quadruple integral term in the Lyapunov-Krasovskii functional (LKF) a new integral inequality technique is addressed to derive the main results. Dissimilar from previous literature, involving the new integral inequality, a delay dependent finite-time H∞ synchronization requirements are acquired with regard to linear matrix inequalities (LMIs). In the end, the effectiveness of the considered stochastic sampled data control finite time synchronization scheme is highlighted by numerical examples.

Longitudinal relations between child emotional difficulties and parent-child closeness: a stability and malleability analysis using the STARTS model

BackgroundPast empirical evidence on the longitudinal relations between emotional mental health symptoms and parent-child close relationships has produced mixed and inconclusive results. Some studies suggest a unidirectional relation, whereas other studies point toward a bidirectional association. Additionally, most of the past research has been carried out with adolescent samples, rather than children. Hence, this study aimed to estimate the longitudinal relations between children’s trait emotional difficulties and trait parent-child closeness, accounting for the time-invariant and time-varying state components of each factor.MethodsParticipants were 7,507 children (ages 3 years, 5 years, 7 years, and 9 years) from the Growing Up in Ireland cohort. Α bivariate stable trait, autoregressive trait, and state (STARTS) model was estimated using Bayesian structural equation modelling.ResultsThe STARTS model revealed that children’s emotional difficulties and parent-child closeness were relatively stable across time, and these overarching traits were strongly negatively correlated. Children’s earlier trait emotional difficulties predicted later trait parent-child closeness and vice versa between 3 years and 5 years, and between 5 years and 7 years, but these effects disappeared between 7 years and 9 years. At all pairs of time points, state emotional difficulties and state parent-child closeness were weakly negatively correlated.ConclusionsOverall, the results suggest that early and middle childhood are critical stages for improving parent-child relationships and reducing children’s emotional difficulties. Developing close parent-child relationships in childhood appears to be a key factor in reducing children’s subsequent emotional difficulties. Children who face greater than usual emotional difficulties tend to be more withdrawn and less receptive to close parent-child relationships and this could serve as an important screening indicator.

Spatiotemporal heterogeneity of the association between short-term exposure to carbon monoxide and COVID-19 incidence: A multistage time-series study in the continental United States

BackgroundPrevious research has established carbon monoxide (CO) as a significant air pollutant contributing to coronavirus disease 2019 (COVID-19) transmission. The spatiotemporal heterogeneity in the relationship between short-duration CO exposure and COVID-19 incidence remain underexplored. Investigating such heterogeneity plays a crucial role in designing region-specific cost-effective public health policies, exploring the reasons for heterogeneity, and understanding the temporal trends in the association between CO and an emerging infectious disease such as COVID-19. MethodsThe 49 states of the continental United States (U.S.) were examined in this study. Initially, we developed time-series generalized additive models (GAMs) for each state to assess the preliminary correlation between daily COVID-19 cases and short-term CO exposure from April 1, 2020, to December 31, 2021. Subsequently, the correlations were compiled utilizing Leroux-prior-based conditional autoregression (LCAR) to achieve a smoothed spatial distribution. Finally, we integrated a time-varying component into the GAM and LCAR to analyze temporal correlations and illuminate the factors contributing to spatiotemporal heterogeneity. ResultsOur analysis revealed that, across the 49 states, a 10-ppb increase in CO concentration was associated with a 1.33 % (95%CI: 0.86%–1.81 %) increase in COVID-19 cases on average. Furthermore, spatial variability was noted, with weaker correlations observed in the central and southeastern regions, stronger associations in the northeastern regions, and negligible associations in the western regions. Temporally, the correlation was not significant from April 2020 to June 2021, but began to increase steadily thereafter until the end of 2021. Additionally, vaccination and temperature were determined to be potential causes contributing to the heterogeneity, indicating stronger positive associations in areas with higher vaccination rates and temperatures. ConclusionThe findings of this study underscore the importance of monitoring CO pollution in the central and northeastern US, especially in the aftermath of the pandemic.

A New Order Tracking Method for Fault Diagnosis of Gearbox under Non-Stationary Working Conditions Based on In Situ Gravity Acceleration Decomposition

Rotational speed measuring is important in order tracking under non-stational working conditions. However, sometimes, encoders or coded discs are not easy to mount due to the limited measurement environment. In this paper, a new in situ gravity acceleration decomposition method (GAD) is proposed for rotational speed estimation, and it is applied in the order tracking scene for fault diagnosis of a gearbox under non-stationary working conditions. In the proposed method, a MEMS accelerometer is locally embedded on the rotating shaft or disc in the tangential direction. The time-varying gravity acceleration component is sensed by the in situ accelerometer during the rotation of the shaft or disc. The GAD method is established to exploit the gravity acceleration component based on the linear-phase finite impulse response (FIR) filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. Then, the phase signal of time-varying gravity acceleration is derived for rotational speed estimations. A motor–shaft–disc experimental setup is established to verify the correctness and effectiveness of the proposed method in comparison to a mounted encoder. The results show that both the estimated average and instantaneous rotational speed agree well with the mounted encoder. Furthermore, both the proposed GAD method and the traditional vibration-based tacholess speed estimation methods are applied in the context of order tracking for fault diagnosis of a gearbox. The results demonstrate the superiority of the proposed method in the detection of tooth spalling faults under non-stationary working conditions.

The Dynamics of Fire Activity in the Brazilian Pantanal: A Log-Gaussian Cox Process-Based Structural Decomposition

We present a novel statistical methodology for analyzing shifts in spatio-temporal fire occurrence patterns within the Brazilian Pantanal, utilizing remote sensing data. Our approach employs a Log-Gaussian Cox Process to model the spatiotemporal dynamics of fire occurrence, deconstructing the intensity function into components of trend, seasonality, cycle, covariates, and time-varying spatial effects components. The results indicate a negative correlation between rainfall and fire intensity, with lower precipitation associated with heightened fire intensity. Forest formations exhibit a positive effect on fire intensity, whereas agricultural land use shows no significant impact. Savannas and grasslands, typical fire-dependent ecosystems, demonstrate a positive relationship with fire intensity. Human-induced fires, often used for agricultural purposes, contribute to an increase in both fire frequency and intensity, particularly in grassland areas. Trend analysis reveals fluctuating fire activity over time, with notable peaks in 2018–2021.

Recursive demodulated synchro spline-kernelled chirplet extracting transform: a useful tool for non-linear behavior estimation of non-stationary signal and application to wind turbine fault detection

Non-linear behavior is widespread in many kinds of signals from nature and engineering fields. Although the high energy-concentration level of various advanced time-frequency (TF) analysis (TFA) techniques currently developed ensure a fine representation of non-linear behavior of time-varying component (TVC) of the signal, it is far from sufficient to solely consider the single aspect of energy-concentration level, because the actual signal composition is always more complicated, especially for some thorny difficulties such as strong noise interference and the early weak TVC, etc., these negative factors bring significant challenges to reveal the non-linear behavior of TVC of practical signals. A new TFA method aimed at this issue, called recursive demodulated synchro spline-kernelled chirplet extracting transform (RDSSCET), is proposed in this paper. The proposed RDSSCET is developed on the frame of synchro spline-kernelled chirplet extracting transform (SSCET) and a newly designed external-internal nested double iteration mechanism, which effectively addresses the limitation of SSCET in handling multicomponent signals while also exhibiting superior high energy concentration and noise robustness. As such, the proposed RDSSCET can yield a more favorable outcome when revealing the non-linear behavior of TVC, particularly for weak TVC with strong noise interference. Comparison analysis results in numerical simulations verified the performance of RDSSCET. Its effectiveness in real applications is fully tested via two real-world sound signals and a practical case of wind turbine fault detection.

Identification and estimation of panel semiparametric conditional heteroskedastic frontiers with dynamic inefficiency

We study a semiparametric panel stochastic frontier model with nonseparable unobserved heterogeneity, which allows for time-varying conditional heteroskedastic productivity components. It does not require distributional assumptions on random noise except conditional symmetry. We utilize conditional characteristic functions from Kotlarski’s Lemma to derive new moment conditions that yield the identification of the heteroskedastic variance parameters of inefficiency and random noise. Identification only requires a panel with three periods for serially correlated inefficiency. A nonparametric estimation procedure is also developed for the conditional variance of inefficiency, and its convergence rate is established. Monte Carlo simulation shows that the estimator is robust to misspecification of inefficiency distributions.

Adaptive suboptimal control of some discrete-time plants with nonstochastic bounded disturbances

This paper deals with the adaptive suboptimal control of linear, discrete-time, time-invariant, minimum phase, scalar plants in the presence of nonstochastic bounded unmeasurable disturbances whose upper and lower bounds, which may be asymmetric, are assumed to be unknown a priori. Additional assumption is that an order of the difference equation describing the plant is known a priori. The distinguishing feature of the problem stated in this paper is that neither bounds on the unmeasured disturbances, nor bounds on an allowable region to which the unknown plant parameters belong are assumed to be known a priori. To solve this problem, adaptation procedures for the point and membership set estimation are utilized. The standard recursive procedure with adjustable dead zone is employed in order to derive the point estimates of unknown plant parameters together with the point estimate of time-invariant disturbance component. The size of this dead zone depends on the previous point estimate of the bounds on the time-varying disturbance component and also on a fixed suboptimality index chosen by the designer. The estimates generated by the point estimation procedure are directly exploited to derive the adaptive control law. The main idea advanced in this paper is that, instead of unknown a priori membership set of these parameters, their peculiar hypothetical a posteriori membership sets are designed via the use of the measured system’s signals together with the current point estimate of bounds on the time-varying disturbance component. Contrary to the usual membership set estimation approach, this set is updated if only it is discovered that the unknown parameter vector does not belong in reality to this set. To this end, a remarkable property of the point estimation procedure is utilized. Such an approach makes it possible to reconstruct this set and to update the previous estimate of the bounds on the time-varying disturbance component. The finite convergence of the adaptation procedures and also the ultimate boundedness of system’s signals are established. To demonstrate an efficiency of the adaptive controller and support the theoretical study, simulation results are presented.

Linking a latent variable trait-state-occasion model of emotion regulation to cognitive control

ABSTRACT Emotion dysregulation (ED) is a vulnerability factor for affective disorders that may originate from deficits in cognitive control (CC). Although measures of ED are often designed to assess trait-like tendencies, the extent to which such measures capture a time-varying (TV) or state-like construct versus a time-invariant (TI) or trait-like personality characteristic is unclear. The link between the TV and TI components of ED and CC is also unclear. In a 6-wave, 5-month longitudinal study, community participants (n = 1281) completed the Difficulties in Emotion Regulation Scale (DERS-16), a commonly used measure of ED and measures of CC. A latent variable (trait-state-occasion) model showed that the proportion of TI factor variance (.80) was greater than the TV factor variance (.19). Although TV factor stability was significant, the coefficients were small in magnitude. Furthermore, regression weights for the ED TI factor (average β = −.62) were significant and larger than those for the TV factor (average β = −.10) in predicting latent CC at each of the six-time points. These findings suggest that ED, as assessed by the DERS-16, is largely TI and this TI component is more strongly linked to CC than the TV component.

A TLOT train gearbox fault diagnosis method based on ridge extraction under variable speed conditions

Owing to the rapidly varying working conditions of urban rail trains, the rotational speed conditions constantly shift in a short time span. As a key component of the running gear, the gearbox generates non-stationary vibration signals, making it challenging to monitor its health status. To address this challenge, a tacholess order tracking method (TLOT) based on ridge extraction method is proposed in this paper. This method determines the optimal search starting point using the time-frequency Gini coefficient and extracts the time-varying gearbox meshing frequency components from the time-frequency representation results. Furthermore, the TLOT method is utilized to process the strongly time-varying gearbox signal. In the order domain, multiple frequency components are extracted to reconstruct the signal. Simulation and experimental results demonstrate that the proposed method achieves a superior ridge extraction effect on gearbox experimental signals. It accurately converts unstable signals into angular domain stable signals, enhancing the energy aggregation of time-varying unstable signals in the order domain. This approach addresses the problem of weak harmonic component extraction from gearbox signals and effectively reduces interference in the resonance band, realizing fault diagnosis of the gearbox under unstable working conditions.

Integrating L1 and weighted L2 regularization for moving force identification from combined response measurements

Existing regularization methods have been proven effective in the ill-posed problem of moving force identification (MFI). However, both the constant and time-varying components of moving forces cannot be accurately identified at the same time, especially under higher measurement noises. To address this issue, a new MFI method is proposed by integrating the L1 and weighted L2 regularization methods in this study, where the presented weighted L2 regularization strategy achieves a more nuanced balance of moving force components through the incorporation of weight coefficients. Furthermore, the inherent solving characteristics of L1 and weighted L2 regularization methods are fully utilized to solve different components in moving forces separately. Numerical simulations and experimental verifications are carried out to assess the effectiveness of the proposed method. The results show that the proposed method can accurately identify moving forces even under 25% higher noise levels and it can effectively evaluate the axle loads and gross vehicle weight of the experimental vehicle. Besides, comparative studies further show that the proposed method outperforms the existing L1 and L2 regularization methods in the identification of both constant and time-varying forces, which provides a potential MFI tool for a higher accuracy and noise robustness to fix the MFI problem in practice.

Integrating Hydrography Observations and Geodetic Data for Enhanced Dynamic Topography Estimation

Dynamic topography (DT) refers to the time-varying component of the sea surface height influenced by factors like ocean currents, temperature, and salinity gradients. Accurate estimation of DT is crucial for comprehending oceanic circulation patterns and their impact on climate. This study introduces two approaches to estimating DT: (1) utilizing satellite altimetry to directly observe sea surface height and (2) considering the steric and non-steric components of sea level anomalies. The steric term is calculated using salinity and temperature data obtained from local buoy data, Argo observations, and the World Ocean Atlas model. The non-steric term is calculated using GRACE Satellite gravimetry data. To estimate the assimilated DT, four methods are utilized, including variance component estimation (VCE), Bayesian theory, Kalman filter, and 3D variational (3DVAR). These methods assimilate the two aforementioned schemes. The validity of the estimated DT is assessed by comparing the calculated sea surface current, derived from the obtained DT, with observations from local current meter stations. The results indicate that the VCE method outperforms other methods in determining the final DT. Furthermore, incorporating the steric and non-steric terms of sea level in determining DT in coastal areas enhances the accuracy of estimating sea surface currents.

Spatiotemporal variation in cave percolation waters: A functional approach

Understanding the mechanisms controlling spatial heterogeneity of drip water percolation into caves is essential for interpreting karst aquifer recharge and speleothem isotopic and geochemical records for paleoclimate analyses. Here we present the first analysis of drip rate variability using a novel time-varying Functional Principal Component Analysis (FPCA), validated against drip water stable isotope composition. Twenty-six drip sites were monitored across Harrie Wood Cave, south-east Australia, over a 2.5 year period. A positive relationship of cave drip water hydrology with precipitation and soil moisture was identified, with soil moisture recording the strongest relationship. FPCA was used to classify drip-water flow (percolation) pathways based on temporal shifts in the drip rate time series. Our results reveal that three percolation classes can be used to explain water movement within the cave: storage baseflow, fracture baseflow and overflow. The successful application of FPCA in this study suggests that this statistical technique will be useful for the analysis and interpretation of other large, discontinuous hydrological datasets.

Problem of Measuring Absorption Using Time-Resolved Photothermal Common-Path Interferometry under Conditions of Developed Heat Diffusion

We present a study of the problem of measuring ultra-low absorption in quartz materials using the time-resolved photothermal common-path interferometry (TPCI) method, which we proposed and elaborated, with allowance for developed heat diffusion in the samples. This task is related to the implementation of one of the ways of increasing the sensitivity of measurements, namely, increasing the energy of the heating pulse by increasing its duration. Analytical formulas for the time dependence of the power of the time-varying component of the probe radiation are obtained in the Gaussian approximation for the laser beams. A correction factor that takes into account the heat diffusion effect is calculated theoretically and used during calibration. The rate at which the power of the time-varying component decreases when the sample is cooled after the end of the heating pulse is calculated. When measuring in crystalline quartz, quartz glass, and also in air, the calculated rate coincided with the experimental one, which is additional evidence for the reliability of the calculations of the correction factor for calibrating the measurements. When the duration of the heating pulse is increased to 5 ms, the calculated sensitivity of the scheme for measuring absorption in quartz glasses is 2 × 10−9 cm−1.

An adaptive cepstrum feature representation method with variable frame length and variable filter banks for acoustic emission signals

Acoustic emission technology is widely employed in defect detection, structural health monitoring, fault diagnosis, and other applications due to its benefits of non-contact, real-time, sensitivity, and flexibility. To implement efficient and timely intelligent monitoring, it is crucial to promptly and precisely gather the time-varying properties in the non-stationary acoustic emission signals. The current work proposes an adaptive cepstrum feature representation method that combines variable frame length and variable filter bank. The variable frame length is implemented based on the spectral distribution correlation, and the optimal frame length and frame rate are selected by iterative operations. The variable filter bank, on the other hand, characterizes the local measure features of variable frame length signals by setting dynamic weight distributions. The proposed adaptive cepstrum algorithm can be used as an auxiliary tool to initially analyze complex non-stationary signals, and thus effectively identify the time-varying and frequency components in acoustic emission signals. The superiority of the proposed method has been confirmed on simulation data. Furthermore, the practical performance of the proposed method is evaluated and illustrated based on the burst signal from laser shock peening processing and the mixed signal from pipeline leakage. The proposed method can be extended to other broadband signals and can be used for feature pre-processing before deep learning network modeling to improve recognition accuracy due to its high time–frequency resolution.