Time-Frequency Signal Processing for Gas-Liquid Two Phase Flow Through a Horizontal Venturi Based on Adaptive Optimal-Kernel Theory

A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed,and then the AOK theory was used to analyze the dynamic differ-ential pressure signal.The mechanism of two-phase flow was discussed through the time-frequency spectrum.On the condition of steady water flow rate,with the increasing of gas flow rate,the flow pattern changes from bubbly flow to slug flow,then to plug flow,meanwhile,the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band;moreover,when the flow pattern is slug flow,there are two wave peaks showed in the time-frequency spectrum.Finally,a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK.When the characteristic variables were visu-ally analyzed,the relationship between different combination of characteristic variables and flow patterns would be gotten.The results show that,this method can explain the law of flow in different flow patterns.And characteristic variables,defined by this method,can get a clear description of the flow information.This method provides a new way for the flow pattern identification,and the percentage of correct prediction is up to 91.11%.

Discrete Time-Frequency Signal Analysis and Processing Techniques for Non-Stationary Signals

This paper presents the methodology, properties and processing of the time-frequency techniques for non-stationary signals, which are frequently used in biomedical, communication and image processing fields. Two classes of time-frequency analysis techniques are chosen for this study. One is short-time Fourier Transform (STFT) technique from linear time-frequency analysis and the other is the Wigner-Ville Distribution (WVD) from Quadratic time-frequency analysis technique. Algorithms for both these techniques are developed and implemented on non-stationary signals for spectrum analysis. The results of this study revealed that the WVD and its classes are most suitable for time-frequency analysis.

Flexible organic artificial synapse with ultrashort-term plasticity for tunable time-frequency signal processing

A flexible organic artificial synapse(OAS)for tunable time-frequency signal processing was fabricated using a tri-blend film that had been fabricated using a one-step solution method.When combined with a chitosan film,this OAS can achieve an ultrashort-term retention time of only 49 ms for instant electricalcomputing applications;this is the shortest retention time yet achieved by a two-terminal artificial synapse.An array of these flexible OASs can withstand a high bending strain of 5%for 10^(4) cycles;this deformation endurance is a new record.The OAS was also sensitive to the number and frequency of electrical inputs;a tunable cut-off frequency enables dynamic filtering for use in image detail enhancement.This work provides a new resource for development of future neuromorphic computing devices。

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

The W transform and its improved methods for time-frequency analysis of seismic data

The conventional linear time-frequency analysis method cannot achieve high resolution and energy focusing in the time and frequency dimensions at the same time,especially in the low frequency region.In order to improve the resolution of the linear time-frequency analysis method in the low-frequency region,we have proposed a W transform method,in which the instantaneous frequency is introduced as a parameter into the linear transformation,and the analysis time window is constructed which matches the instantaneous frequency of the seismic data.In this paper,the W transform method is compared with the Wigner-Ville distribution(WVD),a typical nonlinear time-frequency analysis method.The WVD method that shows the energy distribution in the time-frequency domain clearly indicates the gravitational center of time and the gravitational center of frequency of a wavelet,while the time-frequency spectrum of the W transform also has a clear gravitational center of energy focusing,because the instantaneous frequency corresponding to any time position is introduced as the transformation parameter.Therefore,the W transform can be benchmarked directly by the WVD method.We summarize the development of the W transform and three improved methods in recent years,and elaborate on the evolution of the standard W transform,the chirp-modulated W transform,the fractional-order W transform,and the linear canonical W transform.Through three application examples of W transform in fluvial sand body identification and reservoir prediction,it is verified that W transform can improve the resolution and energy focusing of time-frequency spectra.

Time-frequency Feature Extraction Method of the Multi-Source Shock Signal Based on Improved VMD and Bilateral Adaptive Laplace Wavelet

Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery.Therefore,it is difficult to extract,analyze,and diagnose mechanical fault features.To accurately extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery,a study on the time-frequency feature extraction method of multi-source shock signals is conducted.Combining the characteristics of reciprocating mechanical vibration signals,a targeted optimization method considering the variational modal decomposition(VMD)mode number and second penalty factor is proposed,which completed the adaptive decomposition of coupled signals.Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals,a new bilateral adaptive Laplace wavelet(BALW)is established.A search strategy for wavelet local parameters of multi-shock signals is proposed using the harmony search(HS)method.A multi-source shock simulation signal is established,and actual data on the valve fault are obtained through diesel engine fault experiments.The fault recognition rate of the intake and exhaust valve clearance is above 90%and the extraction accuracy of the shock start position is improved by 10°.

TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft

Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.

Detection method of forward-scatter signal based on Rényi entropy

The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.

Parameterized time-frequency analysis to separate multi-radar signals

Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The intercepted signal is difficult to separate with conventional parameters because of severe overlapping in both time and frequency domains. On the contrary, time-frequency analysis maps the 1D signal into a 2D time-frequency plane, which provides a better insight into the signal than traditional methods. Particularly, the parameterized time-frequency analysis (PTFA) shows great potential in processing such non stationary signals. Five procedures for the PTFA are proposed to separate the overlapped multi-radar signal, including initiation, instantaneous frequency estimation with PTFA, signal demodulation, signal separation with adaptive filter and signal recovery. The proposed method is verified with both simulated and real signals, which shows good performance in the application on multi-radar signal separation.

Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals

Early detection of sudden cardiac death may be used for surviving the life of cardiac patients. In this paper we have investigated an algorithm to detect and predict sudden cardiac death, by processing of heart rate variability signal through the classical and time-frequency methods. At first, one minute of ECG signals, just before the cardiac death event are extracted and used to compute heart rate variability (HRV) signal. Five features in time domain and four features in frequency domain are extracted from the HRV signal and used as classical linear features. Then the Wigner Ville transform is applied to the HRV signal, and 11 extra features in the time-frequency (TF) domain are obtained. In order to improve the performance of classification, the principal component analysis (PCA) is applied to the obtained features vector. Finally a neural network classifier is applied to the reduced features. The obtained results show that the TF method can classify normal and SCD subjects, more efficiently than the classical methods. A MIT-BIH ECG database was used to evaluate the proposed method. The proposed method was implemented using MLP classifier and had 74.36% and 99.16% correct detection rate (accuracy) for classical features and TF method, respectively. Also, the accuracy of the KNN classifier were 73.87% and 96.04%.

A Recursive Method of Time-Frequency Analysis for the Signal Processing of Flutter Test with Progression Variable Speed

Focused on the non-statlonarity and real-time analysis of signal in flutter test with progression variable speed （FTPVS）, a new method of recursive time-frequency analysis is presented. The time-varying system is tracked on-line by building a time-varying parameter model, and then the relevant parameter spectrum can be obtained. The feasibility and advantages of the method are examined by digital simulation. The results of FTPVS at low-speed wind-tunnel promise the engineering application perspective of the method.

Four-Parameter Signal Decomposition and Related Adaptive Time-Frequency Distribution

We propose a four-parameter adaptive signal decomposition method and related adaptive time-frequency distribution, which is positive, cross-term interference-free energy distribution and yields better time and frequency resolution. The elementary functions are dilations, translations, rotations and modulations of single Gaussian window function, which are obtained by successively matching the signal local natures. So the coefficients and parameters, obtained from the decomposition process, can provide an interpretation of signal structure and can be used for the analysis and synthesis of signals.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.

Timosaponin AⅢ induces drug-metabolizing enzymes by activating constitutive androstane receptor (CAR) via dephosphorylation of the EGFR signaling pathway

The current study aimed to assess the effect of timosaponin AⅢ(T-AⅢ)on drug-metabolizing enzymes during anticancer therapy.The in vivo experiments were conducted on nude and ICR mice.Following a 24-day administration of T-AⅢ,the nude mice exhibited an induction of CYP2B10,MDR1,and CYP3A11 expression in the liver tissues.In the ICR mice,the expression levels of CYP2B10 and MDR1 increased after a three-day T-AⅢ administration.The in vitro assessments with HepG2 cells revealed that T-AⅢ induced the expression of CYP2B6,MDR1,and CYP3A4,along with constitutive androstane receptor(CAR)activation.Treatment with CAR siRNA reversed the T-AⅢ-induced increases in CYP2B6 and CYP3A4 expression.Furthermore,other CAR target genes also showed a significant increase in the expression.The up-regulation of murine CAR was observed in the liver tissues of both nude and ICR mice.Subsequent findings demonstrated that T-AⅢ activated CAR by inhibiting ERK1/2 phosphorylation,with this effect being partially reversed by the ERK activator t-BHQ.Inhibition of the ERK1/2 signaling pathway was also observed in vivo.Additionally,T-AⅢ inhibited the phosphorylation of EGFR at Tyr1173 and Tyr845,and suppressed EGF-induced phosphorylation of EGFR,ERK,and CAR.In the nude mice,T-AⅢ also inhibited EGFR phosphorylation.These results collectively indicate that T-AⅢ is a novel CAR activator through inhibition of the EGFR pathway.

Time-frequency characteristics of blasting vibration signals measured in milliseconds

In order to study the time-frequency characteristics of blasting vibration signals, measured in milliseconds, we carried out site blasting vibration tests at an open pit of the Jinduicheng Mine. Based on recorded field data and applying a combination of RSPWVD and wavelet, we analyzed the time-fre- quency characteristics of recorded field data, summarized the time-frequency characteristics of blasting vibration signals in different frequency bands and present detailed information of blasting vibration sig- nals in milliseconds of high time-frequency resolutions. Because RSPWVD can be seen as of definite physical significance to signal energy distribution in time and frequency domains, we studied the energy distribution of blasting vibration signals for various milliseconds intervals from a perspective of energy distribution. The results indicate that the effect of milliseconds intervals on time-frequency characteris- tics of blasting vibration signals is significant; the length of delay time directly affects the energy distri- bution of blasting vibration signals as well as the duration of energy in ffeauencv bands.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Multisource localization based on angle distribution of time-frequency points using an FOA microphone

Multisource localization occupies an important position in the field of acoustic signal processing and is widely applied in scenarios,such as human‐machine interaction and spatial acoustic parameter acquisition.The direction‐of‐arrival(DOA)of a sound source is convenient to render spatial sound in the audio metaverse.A multisource localization method in a reverberation environment is proposed based on the angle distribution of time-frequency(TF)points using a first‐order ambisonics(FOA)microphone.The method is implemented in three steps.1)By exploring the angle distribution of TF points,a single‐source zone(SSZ)detection method is proposed by using a standard deviation‐based measure,which reveals the degree of convergence of TF point angles in a zone.2)To reduce the effect of outliers on localization,an outlier removal method is designed to remove the TF points whose angles are far from the real DOAs,where the median angle of each detected zone is adopted to construct the outlier set.3)DOA estimates of multiple sources are obtained by postprocessing of the angle histogram.Experimental results in both the simulated and real scenarios verify the effectiveness of the proposed method in a reverberation environment,which also show that the proposed method outperforms reference methods.

Cyclic-Auto-Correlation Based Timing Estimation Algorithm for Time-Frequency Overlapping Multi-Carrier Signals

In recent years,the time-frequency overlapping multi-carrier signal has been a novel and valuable topic in blind signal processing,especially in the non-cooperative receiving field.But there is little related research in public published papers.This paper proposes two timing estimation algorithms,which are non-data-aided and based on the cyclic auto-correlation function.In order to evaluate the performance of the proposed algorithms,the theoretical bound of the timing estimation is derived.According to the analyses and simulation results,the effectiveness of the proposed algorithms has been demonstrated.It shows that MethodⅠhas better performance than MethodⅡ.However,MethodⅡdoes not need prior information,so it has a wider range of applications.

Gut microbiota dysbiosis contributes toα-synuclein-related pathology associated with C/EBPβ/AEP signaling activation in a mouse model of Parkinson’s disease

Parkinson’s disease is a neurodegenerative disease characterized by motor and gastrointestinal dysfunction.Gastrointestinal dysfunction can precede the onset of motor symptoms by several years.Gut microbiota dysbiosis is involved in the pathogenesis of Parkinson’s disease,whether it plays a causal role in motor dysfunction,and the mechanism underlying this potential effect,remain unknown.CCAAT/enhancer binding proteinβ/asparagine endopeptidase(C/EBPβ/AEP)signaling,activated by bacterial endotoxin,can promoteα-synuclein transcription,thereby contributing to Parkinson’s disease pathology.In this study,we aimed to investigate the role of the gut microbiota in C/EBPβ/AEP signaling,α-synuclein-related pathology,and motor symptoms using a rotenone-induced mouse model of Parkinson’s disease combined with antibiotic-induced microbiome depletion and fecal microbiota transplantation.We found that rotenone administration resulted in gut microbiota dysbiosis and perturbation of the intestinal barrier,as well as activation of the C/EBP/AEP pathway,α-synuclein aggregation,and tyrosine hydroxylase-positive neuron loss in the substantia nigra in mice with motor deficits.However,treatment with rotenone did not have any of these adverse effects in mice whose gut microbiota was depleted by pretreatment with antibiotics.Importantly,we found that transplanting gut microbiota derived from mice treated with rotenone induced motor deficits,intestinal inflammation,and endotoxemia.Transplantation of fecal microbiota from healthy control mice alleviated rotenone-induced motor deficits,intestinal inflammation,endotoxemia,and intestinal barrier impairment.These results highlight the vital role that gut microbiota dysbiosis plays in inducing motor deficits,C/EBPβ/AEP signaling activation,andα-synuclein-related pathology in a rotenone-induced mouse model of Parkinson’s disease.Additionally,our findings suggest that supplementing with healthy microbiota may be a safe and effective treatment that could help ameliorate the progression of motor deficits in patients with Parkinson’s disease.

Na^(+)/K^(+)-ATPase:ion pump,signal transducer,or cytoprotective protein,and novel biological functions

Na^(+)/K^(+)-ATPase is a transmembrane protein that has important roles in the maintenance of electrochemical gradients across cell membranes by transporting three Na^(+)out of and two K^(+)into cells.Additionally,Na^(+)/K^(+)-ATPase participates in Ca^(2+)-signaling transduction and neurotransmitter release by coordinating the ion concentration gradient across the cell membrane.Na^(+)/K^(+)-ATPase works synergistically with multiple ion channels in the cell membrane to form a dynamic network of ion homeostatic regulation and affects cellular communication by regulating chemical signals and the ion balance among different types of cells.Therefo re,it is not surprising that Na^(+)/K^(+)-ATPase dysfunction has emerged as a risk factor for a variety of neurological diseases.However,published studies have so far only elucidated the important roles of Na^(+)/K^(+)-ATPase dysfunction in disease development,and we are lacking detailed mechanisms to clarify how Na^(+)/K^(+)-ATPase affects cell function.Our recent studies revealed that membrane loss of Na^(+)/K^(+)-ATPase is a key mechanism in many neurological disorders,particularly stroke and Parkinson's disease.Stabilization of plasma membrane Na^(+)/K^(+)-ATPase with an antibody is a novel strategy to treat these diseases.For this reason,Na^(+)/K^(+)-ATPase acts not only as a simple ion pump but also as a sensor/regulator or cytoprotective protein,participating in signal transduction such as neuronal autophagy and apoptosis,and glial cell migration.Thus,the present review attempts to summarize the novel biological functions of Na^(+)/K^(+)-ATPase and Na^(+)/K^(+)-ATPase-related pathogenesis.The potential for novel strategies to treat Na^(+)/K^(+)-ATPase-related brain diseases will also be discussed.