Instantaneous Phase Difference Research Articles

Time‐Frequency Analysis of Experimental and Analytical Rotor Thrust during a Rotor Speed Change

A study was conducted examining a small-scale, two‐bladed rotor undergoing a change in rotor speed. The data acquired consists of time histories of nonstationary signals; therefore, a Stockwell transform was employed to analyze the data in lieu of traditional Fourier transform-based methods. The analysis included extraction of time‐varying frequency content of the rotor thrust and hub motion, instantaneous rotor speed, damping ratios, and instantaneous phase difference between thrust and hub motion. This work is divided into two parts. First, an analytical study was conducted to understand how rotor transient response, determined using a time‐marching solution, is affected by the inclusion of a simplified elastic support structure model, and is used to demonstrate the utility of the Stockwell transform. The second part of the study compared the results from an analytical model of the NASA Multirotor Test Bed (MTB) undergoing a rotor speed change to wind tunnel data. The current analytical model of the MTB, including an elastic support structure, properly predicted the measured trends in the frequency content of the thrust; however, it underpredicted the amplitude of these unsteady loads.

A closure mechanism for screech coupling in rectangular twin jets

The twin-jet configuration allows two different scenarios to close the screech feedback. For each jet, there is one loop involving disturbances which originate in that jet and arrive at its own receptivity point in phase (self-excitation). The other loop is associated with free-stream acoustic waves that radiate from the other jet, reinforcing the self-excited screech (cross-excitation). In this work, the role of the free-stream acoustic mode and the guided-jet mode as a closure mechanism for twin rectangular jet screech is explored by identifying eligible points of return for each path, where upstream waves propagating from such a point arrive at the receptivity location with an appropriate phase relation. Screech tones generated by these jets are found to be intermittent with an out-of-phase coupling as a dominant coupling mode. The instantaneous phase difference between the twin jets computed by the Hilbert transform suggests that a competition between out-of-phase and in-phase coupling is responsible for the intermittency. To model wave components of the screech feedback while ensuring perfect phase locking, an ensemble average of leading spectral proper orthogonal decomposition modes is obtained from several segments of large-eddy simulation data that correspond to periods of invariant phase difference between the two jets. Each mode is then extracted by retaining relevant wavenumber components produced via a streamwise Fourier transform. Spatial cross-correlation analysis of the resulting modes shows that most of the identified points of return for the cross-excitation are synchronised with the guided jet mode self-excitation, supporting that it is preferred in closing rectangular twin-jet screech coupling.

Mechanism Analysis of the Effect of Wake Vortex on Cylinder Vibration in Flow Induced Motion

Abstract Unsteady Reynolds-averaged Navier–Stokes (RANS) method combined with Spalart–Allmaras turbulence model and dynamic mesh technology was used to investigate the impact of wake vortex on the vibration response of a cylinder. By analyzing the phase difference between the wake vortex force and the displacement under different mass parameters in flow-induced vibration (FIV), the study reveals that the influence of wake vortex on the cylinder varies significantly in different vibration branches. The wake vortex of the initial branch enhances the cylinder's vibration, whereas the wake vortices of the upper, lower, and desynchronized branches suppress the vibration. At the critical point between the initial branch and the upper branch of vortex-induced vibration (VIV), there is a 90 degree phase jump, and the instantaneous phase difference fluctuation between the wake vortex force and displacement of the VIV branch remains relatively constant. In the galloping branch, there are wake vortices in different directions that affect the cylinder's vibration every quarter of the vibration period, and the phase difference undergoes periodic large fluctuations (either in-phase or out-of-phase), with the result that the wake vortex force periodically promotes or restrains the cylinder's vibration, which can serve as a novel criterion for identifying the occurrence of galloping. Furthermore, when varying the mass parameters at a constant reduced velocity, the impact of the wake vortex in the initial branch is relatively insignificant. However, as the mass ratio increases in other vibration branches, the suppressive effect increases, and the wake vortex force can prevent VIV induced galloping phenomenon by affecting the vibration intensity.

Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer’s disease

Cognitive functions produced by large-scale neural integrations are the most representative ‘emergence phenomena’ in complex systems. A novel approach focusing on the instantaneous phase difference of brain oscillations across brain regions has succeeded in detecting moment-to-moment dynamic functional connectivity. However, it is restricted to pairwise observations of two brain regions, contrary to large-scale spatial neural integration in the whole-brain. In this study, we introduce a microstate analysis to capture whole-brain instantaneous phase distributions instead of pairwise differences. Upon applying this method to electroencephalography signals of Alzheimer’s disease (AD), which is characterised by progressive cognitive decline, the AD-specific state transition among the four states defined as the leading phase location due to the loss of brain regional interactions could be promptly characterised. In conclusion, our synthetic analysis approach, focusing on the microstate and instantaneous phase, enables the capture of the instantaneous spatiotemporal neural dynamics of brain activity and characterises its pathological conditions.

Step-test-based assessment of cardiorespiratory fitness improvement achieved through isovolume maneuver trial

(1) Objective: We performed a step test to investigate whether isovolume maneuver (IVM) trial is effective in improving cardiorespiratory fitness; (2) Methods: The experiment involved two consecutive step tests in which the IVM was employed between the two tests. The heart rate (HR), breathing rate (BR), instantaneous phase difference (IPD) of thoracoabdominal movement, and physical fitness index (PFI) of the participants were analyzed. We also obtained data on the ascending rate of HR (HRasc), the rate relative to the baseline HR during recovery, and the scores for a modified PFI (mPFI) based on the baseline PFI; (3) Results: We recruited 27 sedentary and healthy college students as participants. The results from the second step-test were compared with the first step-test. The participants’ mean HRasc decreased by 30.66 % ± 32.52 %, their mean BR decreased by 13.08 % ± 31.90 %, their mean mPFI increased by 30.78 % ± 38.94 %, and their mean IPD increased by 15.31 % ± 28.44 %; (4) Conclusions: IVM reduced the participants’ BR and HRasc and increased their mPFI and IPD. These findings verify that IVM can effectively improve physical activity performance. Furthermore, an IPD of 39° for implementation of abdominal breathing during exercise is a key threshold that warrants further research.

Determining S-Wave Arrival Times for Microseismic Monitoring in Tunnels Based on Instantaneous Phase Difference of Sensors

Determining S-Wave Arrival Times for Microseismic Monitoring in Tunnels Based on Instantaneous Phase Difference of Sensors

Bridge damage analysis under joint environmental and operational variability

One of the main challenges for bridge damage identification using monitoring data is to acquire sensitive damage features but insensitive to operational and environmental effects as well as noise. Specifically, the temperature as part of environmental variability can mask structural damages in bridges. Principal Component Analysis (PCA) has been applied here as a well-known and robust technique for removing environmental variability and obtain damage-sensitive indices. As a first aim, PCA is used considering only ambient vibrations and the natural frequencies are considered as damage indicators. As a second objective, PCA in conjunction with Hilbert Huang Transform (HHT) and Variational Mode Decomposition (VMD) are applied to eliminate the environmental influence in transient vibrations due to traffic. The combined methodology is applied to the case of a numerical benchmark by using the Instantaneous Phase Difference (IPD) as novel vibration damage feature in the case of non-stationary vibrations. The results show that the proposed strategy to use the non-stationary vibration due to traffic instead of ambient vibration seems a promising tool for damage identification and, therefore, its capabilities in real bridge applications are worth exploring further when experimental data from real bridges will become available.

Functional Connectivity Analysis in Multi-channel EEG for Emotion Detection with Phase Locking Value and 3D CNN.

The noise-assisted multivariate Empirical mode decomposition (NA-MEMD) is applied to multi-channel EEG signals to obtain narrow-band scale-aligned intrinsic mode functions (IMFs) upon which functional connectivity analysis is performed. The connectivity pattern in relation to inherent functional activity of brain is estimated with the phase locking value (PLV). Instantaneous phase difference among different EEG channels gives PLV that is used to build the functional connectivity map. The connectivity map yields spatial-temporal feature representation which is taken as input of the proposed emotion detection system. The spatial-temporal features can be learned with a 3D convolutional neural network for classifying emotion states. The proposed system is evaluated on two publicly available DEAP and SEED dataset for binary and multi-class emotion classification. On detecting low versus high level in the valence and arousal dimensions, the attained accuracy values are 97.37% and 96.26% respectively. Meanwhile, this system yields 94.78% and 99.54% accuracy on multi-class task on DEAP and SEED, which outperform previously reported systems with other deep learning models and conventional EEG features.

The instantaneous phase difference between two parametric-excited cables with distinct parameters: characteristics and origination

The instantaneous phase difference between two parametric-excited cables with distinct parameters: characteristics and origination

Instantaneous phase difference of nonlinear coupled bubbles from nonstationary forcing

The attraction and repulsion regimes of acoustically forced bubbles follow from their phase coupling. The direct evaluation of phase difference has received less attention than the secondary Bjerknes force, though it has important implications for nonlinear, nonstationary forcing regimes. We numerically investigate the response of two nonlinearly coupled bubbles undergoing radial oscillations and translation from chirp forcing. The corresponding scattered acoustic responses are analyzed using the empirical mode decomposition (EMD) and Hilbert transform in terms of respective instantaneous amplitude, frequency, and phase. This allows for determination of the phase differences from the coupling between the two bubbles. The phase coupling results agree with previous analytical theory in the linear acoustic limit. Attraction and repulsion regimes for nonstationary forcing can be classified with respect to the instantaneous phase differences unlike the time averaged secondary Bjerknes force. In the nonlinear regime, the chirp direction (forward, backward) results in significantly different radial and translational responses. This result can be explained with respect to influence of the softening jump discontinuity (backbone curve) on the phase coupling. Notably a backward chirp results in overall greater amplitude response and thus more translation during attraction. Implications for optimal waveforms are highlighted for potential applications.

A new two-sensor non-destructive testing method of grouted rock bolts

Poor anchorage quality of rock bolts is easily caused by inappropriate control of the grouting process, leakage of grouting due to broken rock, and insufficient length of bolts. Wave propagation-based methods were widely used to non-destructive test of rock bolts. Accurate and automatic identification of arrival time of effective reflected waves is significant in the non-destructive test of rock bolts. However, the direct waves and multiple reflections from the start of the bolt body mask weak reflection from the grouted defect and bolt end using only one sensor recording. In this paper, we developed a new stress wave reflection non-destructive test method of rock bolts using a two-sensor acquisition. The sensors are fixed at the free steel bar with clips and the signals are recorded by a two-channel prototype. We define the integrated instantaneous phase difference (IIPD) as an index that characterizes any estimated instantaneous phase difference at the two sensors. The instantaneous phase difference of effective reflected waves between two sensors can be calculated by the two sensors spacing, wave velocity, and dominant frequency of test signals. Higher IIPD values can signify the arrival of a reflection. Thus, IIPD peaks can identify the geometric parameters of rock bolts, the size of the single or multiple grouted defects. Both numerical simulations and experimental model tests were used to demonstrate the effectiveness of the two sensor IIPD method. The two-sensor non-destructive bolt test method and prototype can be widely applied in slope, dam and underground engineering.

Is the asynchronous phase of thoracoabdominal movement a novel feature of successful extubation? A preliminary result.

Mechanical ventilation is necessary to maintain patients' life in intensive care units. However, too early or too late extubation may injure the muscles or lead to respiratory failure. Therefore, the spontaneous breathing trial (SBT) is applied for testing whether the patients can spontaneously breathe or not. However, previous evidence still reported 15%~20% of the rate of extubation fail. The monitor only considers the ventilation variables during SBT. Therefore, this study measures the asynchronization between thoracic and abdomen wall movement (TWM and AWM) by using instantaneous phase difference method (IPD) during SBT for 120 minutes. The respiratory inductive plethysmography were used for TWM and AWM measurement. The preliminary result recruited 31 signals for further analysis. The result showed that in successful extubation group can be classified into two groups, IPD increase group, and IPD decrease group; but in extubation fail group, the IPD value only increase. Therefore, the IPD decrease group can almost perfectly be discriminated with extubation fail group, especially after 70 minutes (Area under curve of operating characteristic curve was 1). These results showed IPD is an important key factor to find whether the patient is suitable for extubation or not. These finding suggest that the asynchronization between TWM and AWM should be considered as a predictor of extubation outcome. In future work, we plan to recruit 150 subjects to validate the result of this preliminary result. In addition, advanced machine learning method is considered to apply for building effective models to discriminate the IPD increase group and extubation fail group.Clinical Relevance- The finding of this study is that the patients whose average IPD of 95 to 100 minutes was smaller than average IPD of first 5 minutes of SBT could be 100% successful extubation. In addition, ability of discrimination of average IPD after 70 minutes presents AUC 1.

Marginal Hilbert spectrum and instantaneous phase difference as total damage indicators in bridges under operational traffic loads

The challenges and future trends in the development of signal processing tools are being widely used for damage identification in bridges. Therefore, it is important to analyse the vibration signals in order to attain effective damage characterization. In this paper, the non-linear and non-stationary dynamic response of bridges under operational loads is studied. First, the signals are decomposed into intrinsic mode functions (IMF) by a novel Improved Completed Ensemble EMD with Adaptive Noise technique (ICEEMDAN). Hilbert-Huang transform is used to obtain their corresponding Hilbert spectra. The marginal Hilbert spectrum (MHS) of each IMF and the instantaneous phase difference (IPD) are proposed as total damage indicators (DI), in the sense that they are able to detect, localize and quantify damage under transient vibration due to traffic. The methodology was tested in two case studies: (i) a numerical model of a two-span steel bridge (ii) a dynamic test conducted on a real steel arch bridge subjected to a series of artificial damages. The experimental and real case results from the damage indices based on the extracted features demonstrate the robustness and more sensitivity of the novel Improved Completed Ensemble EMD with Adaptive Noise technique (ICEEMDAN) in addressing the damage location.

A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids

Fault detection in microgrids is of great significance for power systems’ safety and stability. Due to the high penetration of distributed generations, fault characteristics become different from those of traditional fault detection. Thus, we propose a new fault detection and classification method for microgrids. Only current information is needed for the method. Hilbert–Huang Transform and sliding window strategy are used in fault characteristic extraction. The instantaneous phase difference of current high-frequency component is obtained as the fault characteristic. A self-adaptive threshold is set to increase the detection sensitivity. A fault can be detected by comparing the fault characteristic and the threshold. Furthermore, the fault type is identified by the utilization of zero-sequence current. Simulations for both section and system have been completed. The instantaneous phase difference of the current high-frequency component is an effective fault characteristic for detecting ten kinds of faults. Using the proposed method, the maximum fault detection time is 13.8 ms and the maximum fault type identification time is 14.8 ms. No misjudgement happens under non-fault disturbance conditions. The simulations indicate that the proposed method can achieve fault detection and classification rapidly, accurately, and reliably.

Detection of karst cavity beneath cast-in-place pile using the instantaneous phase difference of two receiver recordings

Karst cavities beneath bored cast in situ piles are hazardous to the stability of infrastructure projects. Therefore, it is important to detect karst cavities during the construction of piles. Downward-looking sonar deployed at the bottom of a pile hole can be used to detect cavities; however, interference of multiple reflected surface waves from the walls of the pile hole masks the weak cavity reflections. We have developed a sonar method that exploits the instantaneous phase difference between signals recorded at two receivers to detect karst cavities beneath piles. The receiver separation is set to half the dominant wavelength of surface waves propagating along the pile hole. We define the instantaneous phase difference intensity (IPDI) as an index that measures the similarity of instantaneous phase between the signals at the two receivers. Higher IPDI values signify that the two signals have a similar instantaneous phase at that time, which implies the arrival of a reflection from a cavity. Reflected surface-wave arrivals exhibit low IPDI by design of the receiver geometry. Thus, the first break of reflected P-waves from the roof and floor of a cavity can be identified. We evaluate the effectiveness of the IPDI-based analysis method using numerical tests simulating the varying depth, azimuth, and size of karst cavities. A prototype using the IPDI analysis demonstrates the application of the new pile hole sonar method at two field investigations. Advance drilling, borehole optical image logs, and crosshole tomography verify the IPDI detection results. We conclude that the two-receiver sonar instrumentation along with the IPDI analysis are effective for detecting cavities beneath piles.

Physiological Synchrony: A New Approach Toward Identifying Unknown Presentation Attacks on Biometric Systems

Presentation attacks are falsified biometric traits presented on biometric systems to deceive them. While biometric systems can be tuned and modified to reliably detect known presentation attacks, their performance significantly degrades when encountering unknown presentation attacks. In this article, we propose a new approach toward detecting unknown presentation attacks based on the measurement and characterization of synchrony between multiple physiological signals obtained from contact and contactless sensors. Synchrony between two physiological signals was captured by analyzing the blood flow dynamics and respiration patterns. The instantaneous phase difference between two physiological signals was represented as a phase vector using the Hilbert transform and the degree of phase coherence defined as the absolute mean of phase vectors over the analysis period was used as a measure of synchrony. A weighted k-nearest neighbors (k-NN) classifier was then designed to detect valid and invalid biometric presentations based on the degree of phase coherence. The proposed method was validated on the detection of synchrony between two respiration patterns obtained through the measurement of chest movements using an ultrawideband (UWB) radar and respiratory sinus arrhythmia (RSA) using a finger photoplethysmogram sensor on data collected from 50 individuals. It achieved a high accuracy of 95.3%, a sensitivity of 96%, and a specificity of 94% in detecting corrupted and nonsynchronous patterns that did not contain valid respiration signatures. The proposed method shows promise toward improving the reliability of biometric systems in the detection of unknown and sophisticated attacks that may spoof one or more of the presented biometrics.

Enhancement of Synchronization between Physiological Signals during Exercise: A Preliminary Investigation.

During running, interactions were considered between three physiological oscillators - the heart, breaths, and steps. During intense exercise, the oscillations of all three systems are close to regular, producing good conditions to observe and characterise synchronization. The origin, as well as any physiological significance, of synchronization between these systems during running is not fully accepted or understood. Furthermore, the impact on synchronization of controlling both breathing and step rate has not been previously reported in detail. This study aims to measure cardiolocomotor, cardiorespiratory and respiratory- locomotor synchronization during different running protocols. Breathing was controlled by taking a fixed number of steps per breath (ratios of 5:1 and 3:1). Step rate was then guided at rates close to active heart rate, to instigate 1:1 phase-locking. Instantaneous phase difference quantified synchronization episodes. We have successfully observed all three forms of synchronization during all running protocols. Furthermore, coupling between heartbeats and steps was more pronounced when step rate was guided, and both cardiorespiratory and respiratory-locomotor coupling were extended when breathing rate was fixed to steps. These are exciting initial results from a novel experimental design, highlighting the complex interconnection that exists between these three systems during running, and the conditions to best observe the phenomena.

DBSCAN‐based coherency identification of generators using complex wavelet transform

The coherency of generators in a multi-machine power system is identified by determining the instantaneous phase difference between their rotor angles using a complex wavelet transform. A new phase difference matrix is proposed that represents the relative phase difference between the dominant frequency components of each pair of oscillating rotor angles and maps it into a higher dimensional space where data points corresponding to coherent generators lie in proximity. Next, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for automated grouping of coherent generators. A new objective function is proposed for the determination of control parameters of the clustering algorithm. The proposed approach is tested on the New England (NE) 39-bus test system and IEEE 145-bus test system.

A Chair-Based Unconstrained/Nonintrusive Cuffless Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram

Hypertension is a well-known chronic disease that causes complications such as cardiovascular diseases or stroke, and thus needs to be continuously managed by using a simple system for measuring blood pressure. The existing method for measuring blood pressure uses a wrapping cuff, which makes measuring difficult for patients. To address this problem, cuffless blood pressure measurement methods that detect the peak pressure via signals measured using photoplethysmogram (PPG) and electrocardiogram (ECG) sensors and use it to calculate the pulse transit time (PTT) or pulse wave velocity (PWV) have been studied. However, a drawback of these methods is that a user must be able to recognize and establish contact with the sensor. Furthermore, the peak of the PPG or ECG cannot be detected if the signal quality drops, leading to a decrease in accuracy. In this study, a chair-type system that can monitor blood pressure using polyvinylidene fluoride (PVDF) films in a nonintrusive manner to users was developed. The proposed method also uses instantaneous phase difference (IPD) instead of PTT as the feature value for estimating blood pressure. Experiments were conducted using a blood pressure estimation model created via an artificial neural network (ANN), which showed that IPD could estimate more accurate readings of blood pressure compared to PTT, thus demonstrating the possibility of a nonintrusive blood pressure monitoring system.

Time-resolved quantum spin transport through an Aharonov–Casher ring

After obtaining an exact analytical time-varying solution for the Aharonov–Casher conducting ring embedded in a textured static/dynamic electric field, we investigate the spin-resolved quantum transport in the structure. It is shown that the interference patterns are governed by not only the Aharonov–Casher geometry phase but also the instantaneous phase difference of spin precession through different traveling paths. This dynamic phase is determined by the strength of the applied electric field and can have substantial effects on the charge/spin conductances, especially in the weak field regime as the period of spin precession comparable to that of the orbital motion. Our studies suggest that a low-frequency normal electric field with moderate strength possesses more degrees of freedom for manipulating the spin interference of incident electrons.