Time-shift Method Research Articles

Mechanical properties of GFRP bars exposed to natural erosion environment: A case study

Unlike traditional steel bars, the long-term performance of GFRP (glass fiber reinforced plastic) bars in natural erosion environments remains unclear. This study simulated the water environment at the entrance of the Chai River, a tributary to Dianchi Lake in China, and investigated the mechanical properties of GFRP bars under varying corrosion concentrations. Microstructural analysis using SEM (scanning electronic microscopy) was also conducted to uncover the degradation mechanism. The results showed that after 180 days of immersion in natural water and exposure to environmental concentrations of 5 times, 10 times, and 20 times, the tensile strength of GFRP bars exhibited reductions of 2.8 %, 4.2 %, 7.2 %, and 9.5 %, respectively. However, the compressive strength decreased by 38.0 %, 48.6 %, 50.2 %, and 53.2 % under the same condition. As the number of corrosion cycles increased, the rate of strength decline decreased gradually. The combined action of hydroxyl ions generated through bicarbonate hydrolysis and water molecules in the solution led to the breakdown and swelling of the resin matrix, ultimately causing detachment at the fiber-resin interface. An analysis was conducted on the time-shifted regression equation of the accelerated conversion factor (ASF) under different concentrations, based on the correlation between natural erosion and acceleration testing. It was observed that a strong correlation exists between the strength retention rate and the natural erosion environment at 5 times the concentration. Finally, an improved time-shift method based on the Arrhenius theory was proposed to predict the tensile strength of GFRP bars in a natural erosion environment.

The Effects of STRA6 Regulation of the Circadian Rhythm on Choroidal Neovascularization.

This study aims to investigate the relationship among STRA6, circadian rhythm, and choroidal neovascularization (CNV) formation, as well as the regulatory mechanism of STRA6 in CNV under circadian rhythm disturbances. C57BL/6J male mice (aged 6 weeks) were randomly divided into control and jet lag groups (using a time shift method every 4days to disrupt the molecular clock's capacity to synchronize with a stable rhythm). A laser-induced CNV model was established in both the control and the jet lag group after 2 weeks of jet lag. The size of CNV lesions and vascular leakage were detected by morphological and imaging examination on the seventh day post laser. STRA6 was screened by full transcriptome sequencing. Bioinformatics analysis was conducted to assess the variation and association of STRA6 in the GSE29801 dataset. The effects of STRA6 were evaluated both in vivo and in vitro. The pathway mechanism was further elucidated and confirmed through immunofluorescence of paraffin sections and Western blotting. The disturbance of circadian rhythm promotes the formation of CNV. Patients with age-related macular degeneration (AMD) exhibited higher levels of STRA6 expression compared to the control group, and STRA6 was enriched in pathways related to angiogenesis. In addition, CLOCK and BMAL1, which are initiators that drive the circadian cycle, had regulatory effects on STRA6. Knocking down STRA6 reversed the promotion of CNV formation caused by circadian rhythm disturbance in vivo, and it also affected the proliferation, migration, and VEGF secretion of RPE cells without circadian rhythm in vitro, as well as impacting endothelial cells. Through activation of the JAK2/STAT3/VEGFA signaling pathway in unsynchronized RPE cells, STRA6 promotes CNV formation. This study suggests that STRA6 reduces CNV production by inhibiting JAK2/STAT3 phosphorylation after circadian rhythm disturbance. The results suggest that STRA6 may be a new direction for the treatment of AMD.

Circadian disruption reduces MUC4 expression via the clock molecule BMAL1 during dry eye development

Circadian disruption, as a result of shiftwork, jet lag, and other lifestyle factors, is a common public health problem associated with a wide range of diseases, such as metabolic disorders, neurodegenerative diseases, and cancer. In the present study, we established a chronic jet lag model using a time shift method every 3 days and assessed the effects of circadian disruption on ocular surface homeostasis. Our results indicated that jet lag increased corneal epithelial defects, cell apoptosis, and proinflammatory cytokine expression. However, the volume of tear secretion and the number of conjunctival goblet cells did not significantly change after 30 days of jet lag. Moreover, further analysis of the pathogenic mechanism using RNA sequencing revealed that jet lag caused corneal transmembrane mucin deficiency, specifically MUC4 deficiency. The crucial role of MUC4 in pathogenic progression was demonstrated by the protection of corneal epithelial cells and the inhibition of inflammatory activation following MUC4 replenishment. Unexpectedly, genetic ablation of BMAL1 in mice caused MUC4 deficiency and dry eye disease. The underlying mechanism was revealed in cultured human corneal epithelial cells in vitro, where BMAL1 silencing reduced MUC4 expression, and BMAL1 overexpression increased MUC4 expression. Furthermore, melatonin, a circadian rhythm restorer, had a therapeutic effect on jet lag-induced dry eye by restoring the expression of BMAL1, which upregulated MUC4. Thus, we generated a novel dry eye mouse model induced by circadian disruption, elucidated the underlying mechanism, and identified a potential clinical treatment.

Impacts of water regulation through a reservoir on drought dynamics and propagation in the Pilica River watershed

Study regionThe Pilica River watershed, located in central Poland, is the largest left tributary of the Vistula River basin. Study focusThe study aims to assess the impact of human activities on drought dynamics by considering the specific role of the Sulejów Reservoir. Identifying factors contributing to drought development is essential for decision-makers to implement appropriate management measures. The temporal overlap method is used to distinguish between climate and human-driven droughts. The changes in drought propagation are assessed using the time shift method at various accumulation time scales. To assess the spatial distribution of drought, we applied the Soil and Water Assessment Tool (SWAT) model to simulate soil moisture and streamflow response at a spatial scale. New hydrological insights for the regionThe findings highlight that about 75% of meteorological droughts lead to soil moisture droughts, and about 70% result in hydrological droughts. The results show the contribution of human activities to soil moisture drought (∼25%) and hydrological drought (∼10%), indicating that not all droughts are driven by meteorological factors alone. The key finding suggests a shorter and longer time scale of around 3 and 12 months for soil moisture drought and hydrological drought propagation analysis, respectively. Moreover, the decrease in the duration and severity of drought during 2005–2019 compared to 1997–2004 suggests that the magnitude of the reservoir impact depends on its purpose and operational strategies.

Window size analysis in ultrasonic temperature estimation with timeshifts

In this work, ultrasonic timeshift method for temperature change estimation was investigated for 2D simulated in-silico synthetic ultrasonic signals. Digital phantom tissue was created in MATLAB environment and acoustic simulation was running on k-Wave toolbox for two different temperature conditions. First temperature distribution was assigned to tissue as uniform 37 °C. Second temperature distribution is Gaussian form with peak at tissue center as 45 °C and tails of Gaussian curve is 37 °C. Signal was analyzed with ultrasonic timeshift method for temperature change estimation. This method is based on four steps, calibration with tissue constant, finding timeshift with cross correlation algorithm, find slope of timeshift vector respect to timestep, and multiply tissue constant and slope of local timeshift vector. This multiplication gives temperature change of local point. In this work, window size of smoothing filter of timeshift vector and linear fitting to timeshift—timestep data was analyzed as parametrically with range 3λ to 10λ with a 1λ increment for both windows equally. As a result, window parameters as 5λ to 7λ give best results, maximum absolute error is 0.82 °C, 0.97 °C and 0.92 °C respectively and mean absolute error is ∼0.35 °C. As a verify, different analysis was performed on different temperature distribution with discrete two peak curves.

A Current Noise Cancellation Method Based on Fractional Linear Prediction for Bearing Fault Detection.

The stator current in an induction motor contains a large amount of information, which is unrelated to bearing faults. This information is considered as the noise component for the detection of bearing faults. When there is noise information in the current signal, it can affect the detection of motor bearing faults and lead to the possibility of false alarms. Therefore, to accomplish an effective bearing fault detection, all or some of these noise components must be properly eliminated. This paper proposes the use of fractional linear prediction (FLP) as a noise elimination method in bearing fault diagnosis, which makes these noise components the predictable components and this bearing fault information the unpredictable components. The basis of the FLP is to eliminate noise components in the current signal by predicting predictable components through linear prediction theory and optimal prediction order. Meanwhile, this paper adopts the FLP model with limited memory samples. After determining the optimal number of memories, only the fractional derivative order parameter needs to be optimized, which greatly reduces the computational complexity and difficulty in parameter optimization. In addition, this paper uses spectral analysis of the current signals through experimental simulation to compare the FLP method with the linear prediction (LP) method and the time-shifting (TS) method that have been successfully applied to bearing fault diagnosis. Based on the analysis results, the FLP method can better extract fault features and achieve better bearing fault diagnosis results, verifying the effectiveness and superiority of the FLP method in the field of bearing fault diagnosis. Additionally, the predictive performance of thevFLP and LP was compared based on experimental data, verifying the advantages of the FLP method in predictive performance, indicating that this method has a better noise cancellation effect.

Posterior Predictive Checking for Partially Observed Stochastic Epidemic Models

We address the problem of assessing the fit of stochastic epidemic models to data. Two novel model assessment methods are developed, based on disease progression curves, namely the distance method and the position-time method. The methods are illustrated using SIR (susceptible-infective-removed) models. We assume a typical data observation setting in which case-detection times are observed while infection times are not. Both methods involve Bayesian posterior predictive checking, in which the observed data are compared to data generated from the posterior predictive distribution. The distance method does this by calculating distances between disease progression curves, while the position-time method does this pointwise at suitably selected time points. Both methods provide visual and quantitative outputs with meaningful interpretations. The performance of the methods benefits from the development and application of a time-shifting method that accounts for the random time delay until an epidemic takes off. Extensive simulation studies show that both methods can successfully be used to assess the choice of infectious period distribution and the choice of infection rate function.

Time-shift image enhancement method

This paper proposes a time-shift image enhancement method for representing constant images in spacetime, deriving the associated events in a specified time, and using them to enhance the degraded images. We assumed constant images as moving objects on a z-plane with different velocities and adopted the spacetime using the Lorentz factor to represent the movement of objects at the constant moment. The changes between the events and a reference frame are used to create adjacent z-plane events. The events provide statistical and pixel-based relationships in order to reconstruct a single frame for the common perspective of the reference image in time. Then, the gamma correction is applied in the reconstructed image based on the obtained statistics to provide enhancement of images by preserving the brightness and improving the contrast and details. In order to demonstrate the proposed methods’ general applicability, several challenging referenced and non-referenced datasets from different problem domains are considered. A comparative study has been performed, and the proposed method has been compared to state-of-the-art and recent image enhancement techniques. Both qualitative and quantitative results are analyzed for color correction, detail enhancement, and additional noise for each dataset. The results show that the Time-Shift Method could be effectively used for image enhancement for different problem domains without producing over- or under-enhancement.

Gearbox Fault Diagnosis Based on Refined Time-Shift Multiscale Reverse Dispersion Entropy and Optimised Support Vector Machine

The fault diagnosis of a gearbox is crucial to ensure its safe operation. Entropy has become a common tool for measuring the complexity of time series. However, entropy bias may occur when the data are not long enough or the scale becomes larger. This paper proposes a gearbox fault diagnosis method based on Refined Time-Shifted Multiscale Reverse Dispersion Entropy (RTSMRDE), t-distributed Stochastic Neighbour Embedding (t-SNE), and the Sparrow Search Algorithm Support Vector Machine (SSA-SVM). First, the proposed RTSMRDE was used to calculate the multiscale fault features. By incorporating the refined time-shift method into Multiscale Reverse Dispersion Entropy (MRDE), errors that arose during the processing of complex time series could be effectively reduced. Second, the t-SNE algorithm was utilized to extract sensitive features from the multiscale, high-dimensional fault features. Finally, the low-dimensional feature matrix was input into SSA-SVM for fault diagnosis. Two gearbox experiments showed that the diagnostic model proposed in this paper had an accuracy rate of 100%, and the proposed model performed better than other methods in terms of diagnostic performance.

Semisupervised semantic segmentation for seismic interpretation

Seismic interpretation plays an essential role in locating subsurface horizons and understanding geologic formations. Traditionally, constructing facies models for the entire reservoir often requires domain experts’ manual interpretations of many seismic images, which is time-consuming. Although labeled horizons are limited, we have many unlabeled seismic images. Therefore, we develop a semisupervised segmentation framework that uses unlabeled seismic data through a reconstruction loss to learn a robust encoder and improve horizon label predictions. To further mitigate the limited data problem, we incorporate data augmentation using the time-shifting method to mimic similar deformations in a reservoir. Finally, we investigate the prediction uncertainty using deep ensembles. Results indicate that unlabeled seismic data help us learn a better latent representation and achieve a higher prediction accuracy and a lower prediction uncertainty than solely using labeled data sets. We also compare semisupervised and supervised semantic segmentation further and understand when semisupervised learning performs better in terms of the number of labels, facies, and locations of predicted sections. We develop an active learning framework to label the most valuable unlabeled sections given the uncertainty estimation. We believe our work helps geophysicists reduce the amount of labeling efforts and achieve a higher facies classification accuracy with the same amount of labeling work.

Spatial distribution characterization of the Temperature-induced gradient viscoelasticity inside asphalt pavement

Under the influence of continuous periodic changes of various meteorological factors, there will be an inhomogeneous temperature field inside the asphalt pavement, which will cause a continuous gradient viscoelasticity of asphalt concrete with depth. In order to perform the structural mechanics analysis accurately, the temperature field gradient effect and the temperature-induced gradient viscoelasticity distribution inside the asphalt pavement should be revealed first. In this research, based on the heat conduction theory, a temperature field finite element analysis (FEA) model of a semi-rigid base asphalt pavement was established to investigate the distribution behavior of the temperature gradient along the depth. Dynamic modulus tests were conducted to survey temperature effect on the viscoelastic parameters of asphalt concrete. Eventually, the intrinsic relationship between temperature, depth, and viscoelastic parameters was integrated, and the relaxation time shift method was proposed to quantitatively characterize the temperature-induced gradient viscoelasticity at different depths. It is found that the temperature varies in a cubic parabolic fashion along the depth under the most significant positive and negative temperature gradient conditions. The relaxation time shift method can be effectively used to characterize the distribution of the relaxation modulus and relaxation time spectrum of asphalt concrete under the influence of temperature. It is easy to be programmed to fully consider the inhomogeneous temperature field, and applicable to both traditional and gradient FEA. The change of parameters of generalized Maxwell model for asphalt concrete with temperature is essentially the result of relaxation spectrum shifting along the relaxation time axis at different temperatures. When the temperature is high, the viscosity decreases, the relaxation spectrum shifts to the direction of decreasing relaxation time, the relaxation modulus of asphalt concrete decreases, and vice versa.

A New Estimation Method for Rotor Size of UAV Based on Peak Time-Shift Effect in Micro-Doppler Lidar

The aim of this study was to solve the problem that the existing identification parameters of rotor unmanned aerial vehicles (UAVs) are few and limited by the detection mode, and an identification method for estimating the rotor blade width based on the peak time-shift effect is proposed for the first time. Taking the width of the rotor blade as the parameter to identify the rotor of UAVs, the time-shift effect and its relationship with rotor blade width are verified by theoretical analysis and simulation. The proposed time-shift method has the characteristics of high-precision extraction of rotor width, and its effectiveness is verified by simulation and experiments. The aspect ratio of the rotor is accurately extracted based on the proposed time-shift method under the condition of an unknown pitch angle. Simulation results show that the estimation accuracy of the width and aspect ratio is up to 98 and 98.4%, respectively. The experimental results show that the relative errors of the width and aspect ratio are less than 7 and 4%, respectively. This study provides the theoretical basis and technical support for the high-accuracy identification of rotorcraft UAVs.

가속센서 데이터 기반 반복 학습 제어 적용 디스플레이 패널 이송 로봇 경로 오차 보상에 대한 연구

Transfer robots for large-sized panels used in the display industry need to compensate for path error and reduce vibration. The iterative learning control (ILC) technique can simply compensate for the uncertainty of a control system in a repetitive motion. This study introduces an ILC compensation system applied with an accelerometer to a display panel transfer robot control system. The ILC technique was used to reduce the path error and vibration induced the flexibility of the large size robot. This method was applied to a robot system without the system model of the mechanical and measurement elements. To improve the iterative learning performance through the accelerometer, the ILC is configured by applying an acceleration element and time shift method to the PD-Offline ILC algorithm. In addition, based on the characteristics of repetitive motion, the ILC derives an acceleration data-based position estimation value. In this study, the ILC system and a large-sized panel transfer robot were implemented in MATLAB-Simulink with RECURDYN. The path errors and vibration level of the robot with a suggested ILC of 20 repeated learnings were reduced by more than 90%.

A Time-Shifting Algorithm for Alleviating Convergence Difficulties at Interior Acoustic Resonance Frequencies

This paper proposes a time-shifting boundary element method in the time domain to calculate the radiating pressures of an arbitrary object pulsating at eigenfrequencies of the interior (i.e., interior resonance frequencies). In this paper, the frequency shifting is time-step-dependent and could be viewed as an iterative, or relaxation, technique for the solution of the problem. The proposed method avoids numerical problems due to the internal resonance frequency by initializing the iteration with each scaled frequency. The scaled frequency is approximately equal to the true frequency at the last iterating time step. A sphere pulsating at the eigenfrequency in an infinite acoustic domain was calculated first; the result was compared with the analytical solution, and they were in good agreement. Moreover, two arbitrary-shaped radiators were taken as study cases to predict the radiating pressures at the interior resonance frequencies, and robustly convergent results were obtained. Finally, the accuracy of the proposed method was tested using a problem with a known solution. A point source was placed inside the object to compute the surface velocities; the computed surface pressures were identical to the pressures computed using the point source.

A Detection Method of Dense Harmonics/Inter-harmonics Based on Improved All-phase Time-shifting Method and ZoomFFT

Aiming at the complex situation of intensive harmonics/inter-harmonics in power signals, this paper proposes an analysis method based on improved all phase time shift phase difference method and ZoomFFT. Firstly, the Hankel matrix of the input signal is constructed, and the TLS-ESPRIT algorithm is used to obtain the frequency components in the signal. The relationship between the peak spectral line and the frequency offset in the all phase time shift method is derived. The amplitude information of the input signal is calculated. The phase information of the input signal is obtained by analyzing the “phase invariant” characteristic of the all phase FFT spectrum. The frequency interval of adjacent frequencies is used to judge whether there is density. If there are intensive harmonics/inter-harmonics, TLS-ESPRIT method is used to calculate the center frequency of the dense spectrum and construct a complex analytic band-pass filter to improve the ZoomFFT method to extract the frequency and amplitude information of the signal at the dense spectrum; If there is no intensive harmonic/inter-harmonic, the improved all phase time shift method is used to extract the parameters of the input signal. Simulation results show that the proposed algorithm can effectively and accurately extract the frequency, amplitude and phase parameters of dense harmonics/inter-harmonics under the conditions of multiple dense spectrum, fluctuation of fundamental frequency and noise interference.

Effect of Solid Particles on Droplet Size Applying the Time-Shift Method for Spray Investigation

This study investigated the influence of solid particles on primary breakup and resulting droplet size for different process parameters. Two sets of Newtonian fluids (each consisting of one pure liquid and one suspension at the same respective viscosity) were used, for isolated investigation of solid particles on spray formation independent of liquid viscosity. The spray was recorded by a high-speed camera and a SpraySpy® system based on the time-shift effect, while a commonly used Spraytec® laser diffraction analyzer was employed for validation. An external-mixing twin-fluid atomizer was operated at different gas velocities and corresponding GLR at constant liquid mass flow. For the investigated suspensions an increased Sauter mean diameter was detected, compared to the pure liquids with identical dynamic viscosity. This effect was explained by the tensile strength stabilizing the suspension droplets.

Force-velocity profiling in ice hockey skating: reliability and validity of a simple, low-cost field method

ABSTRACT In recent years, a simple method for force–velocity (F-v) profiling, based on split times, has emerged as a potential tool to examine mechanical variables underlying running sprint performance in field conditions. In this study, the reliability and concurrent validity of F-v profiling based on split times were examined when used for ice hockey skating. It was also tested how a modification of the method, in which the start instant of the sprint is estimated based on optimisation (time shift method), affects the reliability and validity of the method. Both intra- and inter-rater reliability were markedly improved when using the time shift method (approximately 50% decrease in the standard error of measurement). Moreover, the results calculated using the time shift method highly correlated (r > 0.83 for all variables) with the results calculated from a continuously tracked movement of the athlete, which was considered here as the reference method. This study shows that a modification to the previously published simple method for F-v profiling improves intra- and inter-rater reliability of the method in ice hockey skating. The time shift method tested here can be used as a reliable tool to test a player’s physical performance characteristic underlying sprint performance in ice hockey skating.

A novel time-shifting method to find popular blog post topics

Blog search engines and general search engines automatically crawl Web pages from the Internet and generate search results to users. One difference between the two is that blog search engines focus on blog posts and filter general pages. This difference allows bloggers to focus only on page results for posts rather than other types of page results. Another difference is that posts involve more time-related issues than general pages. For general pages, the general search engine can only show the last update time for the page. However, for posts, the blog search engine can show all possible time clues for the post. For some frequently updated posts, time clues help bloggers find information more efficiently. In this paper, we first use some well-known semantic analysis models to analyze the performance of Google Blog Search. Next, we apply a hybrid strategy that considers the document link and time clue relationships between posts to further improve its retrieval performance. Finally, we present several experiments to simulate various possible scenarios to confirm the effectiveness of our strategy.

Automated and antidispersive coherent and incoherent noise reduction of waveforms that contain a reference pulse

Fully automated and antidispersive noise reduction of a waveform containing a reference pulse is often necessary for more efficient, accurate, and reliable interpretation of signals – such as highly attenuated waves in ultrasonic NDE. We propose two methods, pulse specification (PS) and coherent enhanced pulse specification (CEPS), for improving the signal-to-noise ratio (SNR) of coherent pulse amplitudes against both incoherent and coherent noise, as well as apply complementary antidispersive pulse generation (APG) to localise the pulse upon reception. We have demonstrated PS and CEPS effectiveness, with and without APG, using two examples of ultrasonic inspection, by comparison with established methods and demonstrated improvements in SNR up to a factor of 2 (6.02 dB) and never less than 1 (0 dB) for favoured pulse specifications. This has benefits in applications such as NDE, medical ultrasonics, seismology, and acoustics.We deconvolve waveforms using a reference pulse time windowed from said waveforms. These waveforms are convolved with pulses that have centre frequencies and bandwidths that are optimally and automatically specifiable for that unprocessed waveform. We combine independent measurements of the waveforms using PS and CEPS to maximise the coherent pulse amplitude of known scatterers and minimise the noise content. CEPS also enhances the coherent scatterer amplitude to significantly greater than achieved using an established filtered time shift method – where the time shift is subjective. Unlike such time shift methods, PS and CEPS do not reduce the amplitude of the coherent pulse signals when reducing noise.Automated and antidispersive coherent and incoherent noise reduction by pulse specification has been demonstrated to be effective in ultrasonic NDE of both damping and scattering media. Suggestions for further applications of this method have already included application to ultrasonic NDE of composite media, application to ultrasonic guided wave NDE, ultrasonic NDE imaging that can include ultrasonic B-scans, and more. This is greatly encouraging regarding the potential immediate applicability of noise reduction by pulse specification.

Simple methods for quick determination of aquifer parameters using slug tests

The slug test is still one of the simplest and cost-effective methods to interpret the hydraulic parameters for aquifer analysis. This study introduces two new estimation approaches for the slug test, the time shift method (TSM) and arc-length matching method (AMM), to identify aquifer parameters in a reliable and accurate manner, which was established on the idea that any change in the normalized drawdown or arc-length measurements of the data curve at the predefined drawdown levels is linked with the variation of storativity. These approaches remove the need for superimposition of the type curves and the field data. The proposed methods are straightforward to apply and automatize the parameter estimation process. TSM and AMM were tested with a number of numerical experiments including synthetically generated data augmented with random noise, hypothetical slug tests conducted in a heterogeneous rock-fracture system, and well-known real field data. The skin effect was also implemented to evaluate its impact on the estimation performance of the suggested approaches. The results verified that both proposed methods are able to produce estimates of hydraulic parameters more accurately than existing methods. The proposed methods could serve as a viable supplementary interpretation tool for slug test analysis.