Time-domain Analysis Technique Research Articles

Detection of a Transient Quasiperiodic Oscillation in γ-Rays from Blazar PKS 2255-282

We conducted a comprehensive variability analysis of the blazar PKS 2255-282 using Fermi-LAT observations spanning over 4 yr, from MJD 57783.5 to 59358.5. Our analysis revealed a transient quasiperiodic oscillation (QPO) with a period of 93 ± 2.6 days. We employed a variety of Fourier-based methods, including the Lomb–Scargle periodogram (LSP) and weighted wavelet Z-transform (WWZ), as well as time domain analysis techniques such as seasonal and nonseasonal autoregressive Integrated moving average models and the Stochastic modeling with stochastically driven damped harmonic oscillator models. Consistently, the QPO with a period of 93 days was detected across all methods used. The observed peak in LSP and time-averaged WWZ plots has a significance level of 4.06σ and 3.96σ, respectively. To understand the source of flux modulations in the light curve, we explored various physical models. A plausible scenario involves the precession of the jet with a high Lorentz factor or the movement of a plasma blob along a helical trajectory within the relativistic jet.

Research on the construction and verification of evaluation model for borehole collapse in gas extraction

To improve the precision prevention of gas extraction borehole deformation and collapse, it is an important prerequisite to achieve precise monitoring of boreholes. This work is based on distributed optical fiber monitoring and Brillouin Optical Time Domain Analysis Technique (BOTDA). We carried out simulation and validation experiments of borehole plugging with different particle size ratios, constructed a mathematical model for calculating the plugging rate (denoted by ω) of boreholes, and put forward a new methodology for accurate monitoring and evaluation of gas extraction boreholes. The experimental results show that there is a linear relationship between the strain of optical fiber (denoted by ε), the accumulation mass of the simulated coal samples (denoted by m), and the deformation collapse of the borehole. And a mathematical model for calculating the plugging rate of boreholes with more universality has been built by introducing the ratio, volumetric, and strain correction coefficients (denoted by φ, β, and γ). Based on the verification experiment, it was found that the change in the actual and theoretical plugging rates is consistent. With the increase in the ratio of medium coal (2.0–5.0 cm), the plugging rate error decreases first and then increases. The maximum errors are −18.36 % and 19.04 %, but the plugging rate results are still within the middle boundaries of the collapsed boreholes (29.31 % to 75.07 %) and have a neglected effect on the stage classification. Inevitable errors in model construction and actual accumulation errors are two important factors contributing to errors in mathematical models. Taking ω as 0 %, 27.71+40.05·rord.-2%, 74.50+14.13·rord.-2%, and ε limit value 143.77·φ·γ με as the critical value to distinguish the primary, middle, and late periods of borehole collapse. In this work, a new technology of borehole monitoring and evaluation with plugging rate and strain critical value as evaluation indexes is proposed, which can provide a reference for accurate monitoring of drainage boreholes and gas prevention.

The Value and Use of Chinese Traditional Music in Dance Performance in the Internet Era

Abstract In this study, Gaussian mixture model preprocessing and histogram of orientation gradient (HOG) and histogram of optical flow direction (HOF) action feature extraction techniques are used to significantly optimize the accuracy of dance performance action recognition. In addition, the VNect pose estimation algorithm and dual-stream 3D-CNN-based skeleton action recognition are used to improve significantly the accuracy and efficiency of recognition. In terms of music feature recognition, this study utilizes short-time time domain and frequency domain analysis techniques and BP neural networks to effectively achieve accurate recognition and sentiment classification of music features. Analyzing a dataset containing 6000 training videos, 800 validation videos, and 1200 test videos, the study reveals that musical elements, melody, rhythm, emotional tone, and song style significantly impact the stage performance effect. The study not only highlights the importance of traditional Chinese music in dance performance to enhance the artistic effect and deepen the emotional expression, but also opens up new perspectives and methods for disseminating and developing traditional culture.

The Future of Stress Management: Integration of Smartwatches and HRV Technology.

In the modern world, stress has become a pervasive concern that affects individuals' physical and mental well-being. To address this issue, many wearable devices have emerged as potential tools for stress detection and management by measuring heart rate, heart rate variability (HRV), and various metrics related to it. This literature review aims to provide a comprehensive analysis of existing research on HRV tracking and biofeedback using smartwatches pairing with reliable 3rd party mobile apps like Elite HRV, Welltory, and HRV4Training specifically designed for stress detection and management. We apply various algorithms and methodologies employed for HRV analysis and stress detection including time-domain, frequency-domain, and non-linear analysis techniques. Prominent smartwatches, such as Apple Watch, Garmin, Fitbit, Polar, and Samsung Galaxy Watch, are evaluated based on their HRV measurement accuracy, data quality, sensor technology, and integration with stress management features. We describe the efficacy of smartwatches in providing real-time stress feedback, personalized stress management interventions, and promoting overall well-being. To assist researchers, doctors, and developers with using smartwatch technology to address stress and promote holistic well-being, we discuss the data's advantages and limitations, future developments, and the significance of user-centered design and personalized interventions.

Assessment and Therapeutic Modulation of Heart Rate Variability: Potential Implications in Patients with COVID-19.

Cardiac damage has been attributed to SARS-CoV-2-related pathology contributing to increased risk of vascular events. Heart rate variability (HRV) is a parameter of functional neurocardiac integrity with low HRV constituting an independent predictor of cardiovascular mortality. Whether structural cardiac damage translates into neurocardiac dysfunction in patients infected with SARS-CoV-2 remains poorly understood. Hypothesized mechanisms of possible neurocardiac dysfunction in COVID-19 comprise direct systemic neuroinvasion of autonomic control centers, ascending virus propagation along cranial nerves and cardiac autonomic neuropathy. While the relationship between the autonomic nervous system and the cytokine cascade in general has been studied extensively, the interplay between the inflammatory response caused by SARS-CoV-2 and autonomic cardiovascular regulation remains largely unclear. We reviewed the current literature on the potential diagnostic and prognostic value of autonomic neurocardiac function assessment via analysis of HRV including time domain and spectral analysis techniques in patients with COVID-19. Furthermore, we discuss potential therapeutic targets of modulating neurocardiac function in this high-risk population including HRV biofeedback and the impact of long COVID on HRV as well as the approaches of clinical management. These topics might be of particular interest with respect to multimodal pandemic preparedness concepts.

Frequency and Time Domain Analysis of EEG Based Auditory Evoked Potentials to Detect Binaural Hearing in Noise

Hearing loss is a prevalent health issue that affects individuals worldwide. Binaural hearing refers to the ability to integrate information received simultaneously from both ears, allowing individuals to identify, locate, and separate sound sources. Auditory evoked potentials (AEPs) refer to the electrical responses that are generated within any part of the auditory system in response to auditory stimuli presented externally. Electroencephalography (EEG) is a non-invasive technology used for the monitoring of AEPs. This research aims to investigate the use of audiometric EEGs as an objective method to detect specific features of binaural hearing with frequency and time domain analysis techniques. Thirty-five subjects with normal hearing and a mean age of 27.35 participated in the research. The stimuli used in the current study were designed to investigate the impact of binaural phase shifts of the auditory stimuli in the presence of noise. The frequency domain and time domain analyses provided statistically significant and promising novel findings. The study utilized Blackman windowed 18 ms and 48 ms pure tones as stimuli, embedded in noise maskers, of frequencies 125 Hz, 250 Hz, 500 Hz, 750 Hz, 1000 Hz in homophasic (the same phase in both ears) and antiphasic (180-degree phase difference between the two ears) conditions. The study focuses on the effect of phase reversal of auditory stimuli in noise of the middle latency response (MLR) and late latency response (LLR) regions of the AEPs. The frequency domain analysis revealed a significant difference in the frequency bands of 20 to 25 Hz and 25 to 30 Hz when elicited by antiphasic and homophasic stimuli of 500 Hz for MLRs and 500 Hz and 250 Hz for LLRs. The time domain analysis identified the Na peak of the MLR for 500 Hz, the N1 peak of the LLR for 500 Hz stimuli and the P300 peak of the LLR for 250 Hz as significant potential markers in detecting binaural processing in the brain.

Analysis of random telegraph noise in resistive memories: The case of unstable filaments

Through Random Telegraph Noise (RTN) analysis, valuable information can be provided about the role of defect traps in fine tuning and reading of the state of a nanoelectronic device. However, time domain analysis techniques exhibit their limitations in case where unstable RTN signals occur. These instabilities are a common issue in Multi-Level Cells (MLC) of resistive memories (ReRAM), when the tunning protocol fails to find a perfectly stable resistance state, which in turn brings fluctuations to the RTN signal especially in long time measurements and cause severe errors in the estimation of the distribution of time constants of the observed telegraphic events, i.e., capture/emission of carriers from traps. In this work, we analyze the case of the unstable filaments in silicon nitride-based ReRAM devices and propose an adaptive filter implementing a moving-average detrending method in order to flatten unstable RTN signals and increase sufficiently the accuracy of the conducted measurements. The τe and τc emission/capture time constants of the traps, respectively, are then calculated and a cross-validation through frequency domain analysis (Lorentzian fitting) was performed proving that the proposed method is accurate.

Comparative analysis of anchor cables in pullout tests using distributed fiber optic sensors

The interaction mechanism between grouted anchor cables and the surrounding rock mass or soil are complicated and difficult to investigate using conventional monitoring technologies. This study focuses on the in-situ pullout behavior of grouted anchor cables using Brillouin Optical Time-Domain Analysis technique. A series of pullout tests were conducted on anchor cables with five different anchor lengths and three grouting methods. Distributed fiber optic sensors were used to measure the strain distribution of cable bolts from tip to top. The in-situ pullout test results show that both ultimate pullout resistance and ultimate displacement at the ultimate pullout resistance increase with anchor length. The bond strength suffers a slight variation with an increase in anchor length. Both ultimate pullout resistance and the ultimate displacement for Type-B grouting (grouting with reaming) and Type-C grouting (secondary grouting) are larger than those for Type-A grouting (one-time grouting). The shear bond strength of Type-C grouting is greater than that of Type-A grouting. Results from Gaussian functions analysis show that it is feasible to use the function to characterize the axial stress of anchor cables. Furthermore, the overall mobilized percentage η was evaluated. The anchor cables with smaller anchor lengths will result in greater η. The η for the anchor cables of 6 m reaches 92% at the last load, whereas the anchor cables of 18 m were mobilized by only 29% at the last load. The grouting method has a negligible effect on the η, increasing from 22% to 29% in process of the pullout test.

Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach.

In this work, we predict the most strongly confined resonant mode of light in strongly disordered systems of dielectric scatterers employing the data-driven approach of machine learning. For training, validation, and test purposes of the proposed regression architecture-based deep neural network (DNN), a dataset containing resonant characteristics of light in 8,400 random arrays of dielectric scatterers is generated employing finite difference time domain (FDTD) analysis technique. To enhance the convergence and accuracy of the overall model, an auto-encoder is utilized as the weight initializer of the regression model, which contains three convolutional layers and three fully connected layers. Given the refractive index profile of the disordered system, the trained model can instantaneously predict the Anderson localized resonant wavelength of light with a minimum error of 0.0037%. A correlation coefficient of 0.95 or higher is obtained between the FDTD simulation results and DNN predictions. Such a high level of accuracy is maintained in inhomogeneous disordered media containing Gaussian distribution of diameter of the scattering particles. Moreover, the prediction scheme is found to be robust against any combination of diameters and fill factors of the disordered medium. The proposed model thereby leverages the benefits of machine learning for predicting the complex behavior of light in strongly disordered systems.

Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis

Rotating machine health monitoring is critical for system safety, cost savings, and increased reliability. The need for a simple and accurate fault diagnosis method has led to the development of various monitoring techniques. They incorporate vibration, motor’s current signature, and acoustic emission signals analysis in condition monitoring. So, based on using vibration signal analysis, a test rig was built for bearing fault identification. The test rig replicates and investigates various bearing problems, such as those found in the inner and outer races. An accelerometer, type ADXL335, was interfaced to a data acquisition device (DAQ USB-6215) for collecting vibration signals under various operating circumstances. In addition, a load cell was embedded with the test rig, interfaced with a digital panel meter, and used for recording the applied load on the bearings. The time-domain signal analysis technique was used after acquiring vibration signals at various bearing health states. Then, the time-domain signal was converted to the frequency domain using the fast Fourier transform, and the result was analyzed to investigate the generated fault frequencies. Finally, the obtained frequencies were compared with the theoretical values extracted from the theoretical equations, and the method proved its effectiveness in detecting the fault generated.

Disorder induced absorption enhancement of light in GaAs nanowire array

Light absorption characteristics of vertically aligned GaAs nanowire (NW) arrays with disordered diameters and heights are investigated in this work using finite difference time domain (FDTD) analysis technique. By varying the random height ranges, an optimum variation range of 1000–2000 nm is obtained that provides the maximum average absorbance at different fill-factors of the arrays. An array having random heights of the NWs within the optimized range is found to have better absorbance for both normal and oblique incidence of light compared to the uniform height structure. Even for 45° incidence angle of light, average absorbance is obtained to be 2% higher for the random-height array, compared to the case of absorbance obtained for the uniform height structure. The proposed arrays having random diameters provide up to 12.8% improvement in short circuit current density whereas the random-height structure enhances the short circuit current density by 1.1% compared to the arrays having uniform diameter and height. The present work also provides an effective medium theory based theoretical model taking into account the random height variation of the arrays. The theoretically calculated values are found to be in good agreement with FDTD simulation results, thereby providing further guidelines for designing random array based high performance photonic devices.

Vibration characteristics of submerged floating tunnels with suspension cables according to wave periods

The tension leg method is typically used for the mooring of submerged floating tunnels (SFTs). These tunnels may have mooring lines and foundations installed very close together, depending on the wave conditions. Sometimes, the construction cost can be increased by the foundation design. To overcome these weaknesses, a suspension SFT using the mechanism of a suspension bridge has been proposed. In this approach, the tunnel body is connected to fixed towers at both ends. Two main cables are installed for controlling the tunnel motion. The main cables are connected to the tunnel using hangers. The main cable is a steel pipe and filled with water to increase its weight. The main cables are arranged in an arch shape for controlling the buoyancy weight ratio (BWR) of the tunnel body. The present study analyzed the vibration characteristics of a suspension SFT using the free vibration analysis and time-domain analysis techniques. The study demonstrates that the SFT has excellent self-vibration control. The suspension SFT affected tuned mass damping via the interaction between the tunnel body and main cables.

Reducing coherent filtering artefacts in time‐domain operational modal analysis

Signals collected for modal analysis are often filtered in order to reduce sensor noise, out-of-band oscillations, or for dealing with closely-spaced modes. This filtering introduces filtering artefacts into the data due to the non-ideal filter response and is well understood for impulse excitation. However, for ambient vibration data filtering, artefacts become superimposed, preventing their visual identification, and will corrupt the correlation function of the data used in time-domain operational modal analysis (OMA) techniques such as covariance-driven stochastic subspace identification and the random decrement technique. This corruption leads to inaccurate, misleading, biased or spurious frequency and damping estimates, with the inaccuracy increasing for systems with higher damping or lower signal-to-noise ratios. Counter-intuitively, the error in damping estimates is as large for modes with natural frequencies far from the filter cutoff frequency as for modes which are close to the cutoff frequency. In this paper, an alternative to filtering for time-domain OMA, trimming of the correlation of noise from unfiltered correlation functions, is introduced and tested using 10,000 numerically generated ambient vibration data sets. It has been shown that this technique reduces the mean absolute error in the frequency estimates by over 200% and the mean absolute error in the damping estimates by over 400%. Additionally, a new technique which incorporates fitting of filtering artefacts as part of the modal analysis is introduced for where filtering of ambient vibration data is unavoidable and is demonstrated using real-world acceleration data collected from a two-way spanning concrete slab subject to footfall excitation.

High-sensitivity dynamic distributed pressure sensing with frequency-scanning φ-OTDR.

We propose a high-sensitivity dynamic distributed pressure sensor using frequency-scanning phase-sensitive optical time-domain reflectometry (φ-OTDR) in a single-mode fiber (SMF), where an injection locking laser working as both filter and amplifier is used to generate the multi-frequency signals under a double-sideband modulation. The pressure sensitivity of the SMF is measured to be 702.5 MHz/MPa, which is approximately 1000 times larger than that of the Brillouin optical time-domain analysis technique. Subsequently, a dynamic pressure experiment is carried out in the case of rapid pressure relief from 2 to 0 MPa so that a maximum sampling rate of 33.3 kHz for a 25-m SMF is achieved, and the measurement uncertainty of 0.61 kPa for the proposed scheme is demonstrated.

Flexural Behavior of Natural Hybrid FRP-Strengthened RC Beams and Strain Measurements Using BOTDA.

Experimental and finite element analysis results of reinforced concrete beams under monotonic loading were presented in this study. In the experimental program, one beam was tested in an as-built condition. The other two beams were strengthened using natural hybrid FRP layers in different configurations. The natural hybrid FRP composite was developed by using natural jute FRP and basalt FRP. One of the most appealing advantages of natural fiber is its beneficial impact on the environment, which is necessary for the sustainability recognition as an alternative to synthetic FRP. The hybrid FRP was applied to the bottom concrete surface in one beam, while a U-shaped strengthening pattern was adopted for the other beam. The flexural behavior of each beam was assessed through strain measurements. Each beam was incorporated with conventional strain gages, as well as the Brillouin Optical Time Domain Analysis (BOTDA) technique. BOTDA has its exclusive advantages due to its simple system architecture, easy implementation, measurement speed, and cross-sensitivity. The experimental results revealed that the beam strengthened with the U-shaped hybrid FRP composite pattern had a better flexural response than the other counterpart beams did both in terms of peak loads and maximum bottom longitudinal steel bar strains. Beams B-01 and B-02 exhibited 20.5% and 28.4% higher energy dissipation capacities than the control beam did, respectively. The ultimate failure of the control beam was mainly due to the flexural cracks at very low loads, whereas the ultimate failure mode of FRP composite-strengthened beams was due to the rupture of the hybrid FRP composite. Further, strain measurements using BOTDA exhibited similar patterns as conventional strain gage measurements did. However, it was concluded that BOTDA measurements were substantially influenced by the bottom flexural cracks, ultimately resulting in shorter strain records than those of conventional strain gages. Nonlinear structural analysis of the beams was performed using the computer program ATENA. The analytical results for the control beam specimen showed a close match with the corresponding experimental results mainly in terms of maximum deflection. However, the analytical peak load was slightly higher than the corresponding experimental value.

Emergence and tunability of transmission gap in the strongly disordered regime of a dielectric random scattering medium.

Light transmission characteristics in a strongly disordered medium of dielectric scatterers, having dimensionalities similar to those of self-organized GaN nanowires, is analyzed employing finite difference time domain analysis technique. While photonic bandgap like transmission gaps have already been reported for several quasi-crystalline and weakly disordered media, the results of this work show that in spite of the lack of any form of quasi-crystallinity, distinct transmission gaps can be attained in a strongly disordered medium of dielectric scatterers. In fact, similar to the case of a two-dimensional photonic crystal, transmission gap of a uniform random medium of GaN nanowires can be tuned from ultra-violet to visible regime of the spectrum by varying diameter and fill-factor of the nanowires. Comparison of transmission characteristics of periodic, weakly disordered, correlated strongly disordered and uniform strongly disordered arrays having nanowires of identical diameters and fill factors suggest that in spite of the dominance of multiple scattering process, the underlying Mie and Bragg processes contribute to the emergence and tunability of transmission gaps in a strongly disordered medium. Without any loss of generality, the findings of this work offer significant design latitude for controlling transmission properties in the strong disorder regime, thereby offering the prospect of designing disorder based novel photonic and optoelectronic devices and systems.

Bearing Fault Diagnosis Using Motor Current Signature Analysis and the Artificial Neural Network

Bearings are critical components in rotating machinery. The need for easy and effective bearings fault diagnosis techniques has led to developing different monitoring approaches. In this research, however, a fault diagnosis system for bearings is developed based on the motor current signature analysis (MCSA) technique. Firstly, a test rig was built, and then different bearing faults were simulated and investigated in the test rig. Three current sensors, type SCT013, were interfaced to Arduino MEGA 2560 microcontroller and utilized together for data acquisition. The time-domain signals analysis technique was utilized to extract some characteristic features that are related to the simulated faults. It was noticed that the simulated bearing faults have led to generating vibrations in the induction motors, which in turn cause a change in its magnetic field. For classification (identification) of the extracted features, the artificial neural network (ANN) was employed. An ANN model was developed using the Matlab ANN toolbox to detect the simulated faults and give an indication about the machine health state. The obtained features from the captured motor current signals were utilized for training the ANN model. The results showed the effectiveness of using MCSA based on the time-domain signal analysis in combination with ANN in diagnosis different bearings faults.

Sensitivity-Enhanced Distributed Hydrostatic Pressure Sensor Based on BOTDA in Single-Mode Fiber With Double-Layer Polymer Coatings

A sensitivity-enhanced distributed hydrostatic pressure sensor based on Brillouin optical time domain analysis (BOTDA) technique is theoretically proposed and experimentally demonstrated, where double-layer polymer coatings are used on the single-mode fibers (SMF) to enhance the hydrostatic pressure sensitivity. The pressure-induced strain model of multilayer coated fibers based on Lame formula is established to analyze the enhancement mechanism of pressure sensitivity of Brillouin frequency shift (BFS). Simulation results show that BFS pressure sensitivity can be enhanced by increasing the outer coating radius or by decreasing the outer coating Young's modulus and Poisson's ratio. Four types of optical fibers are designed including single-coated and double-coated fibers to verify theoretical results, where the outer coating radius of double-coated fibers are 450, 1000, and 1500 μm, respectively. Experimental results show that the BFS pressure sensitivity are −0.75 MHz/MPa, −1.65 MHz/MPa, −2.66 MHz/MPa and −3.61 MHz/MPa in the range of 0–30 MPa after temperature compensation for above four kinds of fibers, respectively. The maximum pressure sensitivity is measured about five times higher than single-coated SMF, and the maximum measurement error of pressure sensing system is less than 0.09 MPa with a spatial resolution of 1.5 m.

Horizontal Loading Performance of Offshore Wind Turbine Pile Foundation Based on DPP-BOTDA

Offshore wind power is becoming attractive in the wind-power field. With the rapid development of wind-power technology, high-power wind turbines have been implemented in practice. However, the increase in the length of the wind turbine blade causes the pile foundation to withstand a prone overturning moment. For overcoming the problems of traditional sensing technology and meeting the monitoring requirements of pile foundations, a 20 cm spatial resolution differential pulse pair Brillouin optical time-domain analysis (DPP-BOTDA) technique is used to measure a 69 m long offshore wind turbine pile under horizontal loading. From the distributed strain data collected in the test, the maximum stress location of the long pile under the horizontal load can be obtained. By analyzing the load and maximum strain (F-εmax) curve, the horizontal bearing capacity of the pile foundation can exceed 900 kN, which is the maximum horizontal load of the design. The distributed displacement calculation method based on distributed strain data is proposed, according to the force characteristics of steel pipe piles. By comparing the calculated displacement data with the measured data by the dial indicators, the mean absolute percentage error (MAPE) value is only 0.03548. Results show that the 20 cm spatial resolution DPP-BOTDA technology is very suitable for the bearing capacity test of offshore wind turbine steel pipe pile foundations.

Identifcation and Location of Partial Discharge Defects in Medium Voltage AC Cables

The presence of partial discharge (PD) in electricalequipment is the confirmation or indication of an ongoingdegradation of its dielectric insulation. PD study is a widely usedtool for condition monitoring of medium voltage (MV) cables.The location of the defects can be considered as one of the mostimportant tasks of diagnosis in underground installations of MVcables. A lot of references can be found on the location of singlePD sources along the cable. However, several PD defects can beactive simultaneously along a cable section. This paper presentsan experimental study on a MV cable having two PD sourcesand noise. Adopting one end measurement technique, a highfrequency current transformer (HFCT) is used to capture the PDcurrent pulses. A simple approach of identification of the PDsignals originated from different PD sources is presented basedon wave-shape study along with a time domain reflectometryanalysis. The presented measurement methodology and timedomain analysis technique provides a convenient way fordetection and location of more than one PD sources along a cablesection.