Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin...Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.展开更多
Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predic...Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predictive behavior of dominant frequency responses in slope vibrations remain insufficiently understood and quantified.This study combines time-frequency analysis with machine learning to explore how the dominant frequency(f_(d))evolves in slopes under blasting.Continuous Wavelet Transform(CWT)was employed to characterize the temporal-frequency evolution of vibration signals,revealing that the dominant frequency exhibits strong spatial dependence and nonlinear variability influenced by blasting parameters and rock mass structures.Three machine learning models,namely Back Propagation Neural Network(BP),Support Vector Machine(SVM),and Random Forest(RF),were developed to predict f_(d) based on 1,000 monitoring samples obtained from numerical and field simulations.Among them,the RF model achieved the highest prediction accuracy,with mean absolute percentage errors(MAPE)below 15%,demonstrating strong robustness and generalization capability.Our analysis shows that external excitation factors,especially the loading frequency(f_(d)),mainly control the frequency response,while internal controlling factors,such as spatial position,lithological variation,and mechanical heterogeneity,modulate localized frequency amplification and energy redistribution.The results reveal that f_(d) tends to decrease with elevation and distance from the blasting source,whereas structural planes and weathered zones induce high-frequency amplification due to scattering and modal coupling effects.This study offers a new framework combining time-frequency analysis and machine learning to measure the nonlinear interaction between blasting and rock mass response,offering new insights for dynamic stability evaluation and hazard mitigation in complex rock slope systems.展开更多
The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,sate...The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals.展开更多
A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spa...A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.展开更多
The interrupted sampling repeater jamming（ISRJ） is an effective deception jamming method for coherent radar, especially for the wideband linear frequency modulation（LFM） radar. An electronic counter-countermeasure...The interrupted sampling repeater jamming（ISRJ） is an effective deception jamming method for coherent radar, especially for the wideband linear frequency modulation（LFM） radar. An electronic counter-countermeasure（ECCM） scheme is proposed to remove the ISRJ-based false targets from the pulse compression result of the de-chirping radar. Through the time-frequency（TF） analysis of the radar echo signal, it can be found that the TF characteristics of the ISRJ signal are discontinuous in the pulse duration because the ISRJ jammer needs short durations to receive the radar signal. Based on the discontinuous characteristics a particular band-pass filter can be generated by two alternative approaches to retain the true target signal and suppress the ISRJ signal. The simulation results prove the validity of the proposed ECCM scheme for the ISRJ.展开更多
With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communicat...With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.展开更多
As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security ...As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.展开更多
Powder-Fueled Water Ramjet Engine(PFWRE)is the most promising powerplant in underwater high-speed propulsion.However,the effect of powder injection mode on its performance and the mechanism of this effect are not well...Powder-Fueled Water Ramjet Engine(PFWRE)is the most promising powerplant in underwater high-speed propulsion.However,the effect of powder injection mode on its performance and the mechanism of this effect are not well understood.In this paper,a computational framework for multiphase combustion flow is developed and validated.Further,the effects of different injection schemes on flow combustion characteristics and engine performance are evaluated via simulation.Our findings indicate that the dominant recirculation zone in front of the primary water inlet delivers water vapor to the combustor head,providing the necessary oxidant for the ignition and combustion of Al particles.Changing the injection parameters directly affects the flame zone distribution and the ability of the recirculation zone to deliver water vapor,leading to variations in particle ignition delay.The engine combustion efficiency and specific impulse efficiency exhibit a negative correlation with injection height,peaking before declining with increased injection angle.It is shown that particle mixing degree and particle dispersion degree are closely related to engine performance.Enhanced particle mixing in front of the primary water inlet and particle dispersion behind the secondary water inlet are considered favorable approaches to improve engine performance,which promotes the particle combustion process and improves the heat-work conversion efficiency.展开更多
Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphys...Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.展开更多
In this paper,we present a finite volume trigonometric weighted essentially non-oscillatory(TWENO)scheme to solve nonlinear degenerate parabolic equations that may exhibit non-smooth solutions.The present method is de...In this paper,we present a finite volume trigonometric weighted essentially non-oscillatory(TWENO)scheme to solve nonlinear degenerate parabolic equations that may exhibit non-smooth solutions.The present method is developed using the trigonometric scheme,which is based on zero,first,and second moments,and the direct discontinuous Galerkin(DDG)flux is used to discretize the diffusion term.Moreover,the DDG method directly applies the weak form of the parabolic equation to each computational cell,which can better capture the characteristics of the solution,especially the discontinuous solution.Meanwhile,the third-order TVD-Runge-Kutta method is applied for temporal discretization.Finally,the effectiveness and stability of the method constructed in this paper are evaluated through numerical tests.展开更多
The detection of high-impedance faults(HIFs)in microgrid feeders is a serious issue due to low fault current levels during HIFs.This issue becomes especially problematic when microgrids are operating in an islanded mo...The detection of high-impedance faults(HIFs)in microgrid feeders is a serious issue due to low fault current levels during HIFs.This issue becomes especially problematic when microgrids are operating in an islanded mode integrated with inverter-based distributed generators(IBDGs).This paper proposes an innovative relaying scheme for the protection of microgrid feeders using incremental negative sequence power.After a fault occurs,the negative sequence voltage and currents are collected at the ends of the protected feeder and then its incremental values are measured.After that,incremental negative sequence real power is obtained at the ends of the feeder to obtain the proposed relaying feature,i.e.,a ratio of the sum of incremental negative sequence real power(∆RSNSP).The∆RSNSP is defined as the ratio of the sum of incremental negative sequence real power at the two ends of the feeder to the minimum of the powers among the ends.Simulation studies on a modified IEEE 13-bus system have shown that this scheme can detect HIFs and low-impedance faults(LIFs).It has been rigorously tested under various operating conditions,like variations in fault inception angles,faults during islanding,simultaneous faults,evolving faults,composite faults,capacitors,and load switching.This scheme is not only fast and accurate but also performs well even in noisy conditions,changes in topologies(i.e.,radial or mesh),synchronization errors,and transient faults.A comparative chart comparing its performance with other recent schemes is also included.Finally,the scheme is also validated on a real-time simulator which proves that the proposed scheme can work effectively under various fault conditions.展开更多
The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time...The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results.展开更多
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...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.展开更多
Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyze...Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyzed. Experimental results show that for a spectrum of some density, the joint time-frequency gain-modification based speech enhancement algorithm achieves significant improvement in intelligibility. Additionally, the spectrum region where the estimated spectrum is smaller than the clean spectrum, is the most important region contributing to intelligibility improvement for the enhanced whisper. The spectrum region where the estimated spectrum is larger than twice the size of the clean spectrum is detrimental to speech intelligibility perception within the whisper context.展开更多
In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effect...In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.展开更多
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 o...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%.展开更多
In this study, several advanced analysis methods are applied to understand the relationships between the Nino-3.4 sea surface temperatures （SST） and the SSTs related to the tropical Indian Ocean Dipole （IOD）. By a...In this study, several advanced analysis methods are applied to understand the relationships between the Nino-3.4 sea surface temperatures （SST） and the SSTs related to the tropical Indian Ocean Dipole （IOD）. By analyzing a long data record, the authors focus on the time-frequency characteristics of these relationships, and of the structure of IOD. They also focus on the seasonal dependence of those characteristics in both time and frequency domains. Among the Nino-3.4 SST, IOD, and SSTs over the tropical western Indian Ocean （WIO） and eastern Indian Ocean （EIO）, the WIO SST has the strongest annual and semiannual oscillations. While the Nino-3.4 SST has large inter-annual variability that is only second to its annual variability, the IOD is characterized by the largest semiannual oscillation, which is even stronger than its annual oscillation. The IOD is strongly and stably related to the EIO SST in a wide range of frequency bands and in all seasons. However, it is less significantly related to the WIO SST in the boreal winter and spring. There exists a generally weak and unstable relationship between the WIO and EIO SSTs, especially in the biennial and higher frequency bands. The relationship is especially weak in summer and fall, when IOD is apparent, but appears highly positive in winter and spring, when the IOD is unimportantly weak and even disappears. This feature reflects a caution in the definition and application of IOD. The Nino-3.4 SST has a strong positive relationship with the WIO SST in all seasons, mainly in the biennial and longer frequency bands. However, it shows no significant relationship with the EIO SST in summer and fall, and with IOD in winter and spring.展开更多
The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and th...The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.展开更多
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 eff...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.展开更多
基金supported by the Innovative Human Resource Development for Local Intel-lectualization program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.IITP-2026-2020-0-01741)the research fund of Hanyang University(HY-2025-1110).
文摘Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.
基金supported by the National Natural Science Foundation of China(Grant Nos.52379098,52274075)the Project of Xingliao Talents Program(XLYC2203008)the Science and Technology Program Project of Liaoning Province(2025JH2/101900011).
文摘Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predictive behavior of dominant frequency responses in slope vibrations remain insufficiently understood and quantified.This study combines time-frequency analysis with machine learning to explore how the dominant frequency(f_(d))evolves in slopes under blasting.Continuous Wavelet Transform(CWT)was employed to characterize the temporal-frequency evolution of vibration signals,revealing that the dominant frequency exhibits strong spatial dependence and nonlinear variability influenced by blasting parameters and rock mass structures.Three machine learning models,namely Back Propagation Neural Network(BP),Support Vector Machine(SVM),and Random Forest(RF),were developed to predict f_(d) based on 1,000 monitoring samples obtained from numerical and field simulations.Among them,the RF model achieved the highest prediction accuracy,with mean absolute percentage errors(MAPE)below 15%,demonstrating strong robustness and generalization capability.Our analysis shows that external excitation factors,especially the loading frequency(f_(d)),mainly control the frequency response,while internal controlling factors,such as spatial position,lithological variation,and mechanical heterogeneity,modulate localized frequency amplification and energy redistribution.The results reveal that f_(d) tends to decrease with elevation and distance from the blasting source,whereas structural planes and weathered zones induce high-frequency amplification due to scattering and modal coupling effects.This study offers a new framework combining time-frequency analysis and machine learning to measure the nonlinear interaction between blasting and rock mass response,offering new insights for dynamic stability evaluation and hazard mitigation in complex rock slope systems.
基金National Natural Science Foundation of China(Grant Nos.42388102,42030105,42192535)the Open Fund of State Key Laboratory of Precision Geodesy,Innovation Academy for Precision Measurement Science and Technology,Chinese Academy of Sciences(Grant No.SKLPG2025-1-5)。
文摘The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals.
基金supported in part by the National Natural Science Foundation of China(62372385,62272078,62002337)Chongqing Natural Science Foundation(CSTB2022NSCQ-MSX1486,CSTB2023NSCQ-LZX0069)。
文摘A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.
基金supported by the National Natural Science Foundation of China(61271442)
文摘The interrupted sampling repeater jamming（ISRJ） is an effective deception jamming method for coherent radar, especially for the wideband linear frequency modulation（LFM） radar. An electronic counter-countermeasure（ECCM） scheme is proposed to remove the ISRJ-based false targets from the pulse compression result of the de-chirping radar. Through the time-frequency（TF） analysis of the radar echo signal, it can be found that the TF characteristics of the ISRJ signal are discontinuous in the pulse duration because the ISRJ jammer needs short durations to receive the radar signal. Based on the discontinuous characteristics a particular band-pass filter can be generated by two alternative approaches to retain the true target signal and suppress the ISRJ signal. The simulation results prove the validity of the proposed ECCM scheme for the ISRJ.
基金supported by Natural Science Foundation of China(No.62371231)Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20222001Jiangsu Provincial Key Research and Development Program(No.BE2023027).
文摘With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.
基金supported by the Key Project of Joint Fund of the National Natural Science Foundation of China“Research on Key Technologies and Demonstration Applications for Trusted and Secure Data Circulation and Trading”(U24A20241)the National Natural Science Foundation of China“Research on Trusted Theories and Key Technologies of Data Security Trading Based on Blockchain”(62202118)+4 种基金the Major Scientific and Technological Special Project of Guizhou Province([2024]014)Scientific and Technological Research Projects from the Guizhou Education Department(Qian jiao ji[2023]003)the Hundred-Level Innovative Talent Project of the Guizhou Provincial Science and Technology Department(Qiankehe Platform Talent-GCC[2023]018)the Major Project of Guizhou Province“Research and Application of Key Technologies for Trusted Large Models Oriented to Public Big Data”(Qiankehe Major Project[2024]003)the Guizhou Province Computational Power Network Security Protection Science and Technology Innovation Talent Team(Qiankehe Talent CXTD[2025]029).
文摘As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(No.22305053)the Fundamental Research Funds for the Central Universities,China(No.3072024WD0201)。
文摘Powder-Fueled Water Ramjet Engine(PFWRE)is the most promising powerplant in underwater high-speed propulsion.However,the effect of powder injection mode on its performance and the mechanism of this effect are not well understood.In this paper,a computational framework for multiphase combustion flow is developed and validated.Further,the effects of different injection schemes on flow combustion characteristics and engine performance are evaluated via simulation.Our findings indicate that the dominant recirculation zone in front of the primary water inlet delivers water vapor to the combustor head,providing the necessary oxidant for the ignition and combustion of Al particles.Changing the injection parameters directly affects the flame zone distribution and the ability of the recirculation zone to deliver water vapor,leading to variations in particle ignition delay.The engine combustion efficiency and specific impulse efficiency exhibit a negative correlation with injection height,peaking before declining with increased injection angle.It is shown that particle mixing degree and particle dispersion degree are closely related to engine performance.Enhanced particle mixing in front of the primary water inlet and particle dispersion behind the secondary water inlet are considered favorable approaches to improve engine performance,which promotes the particle combustion process and improves the heat-work conversion efficiency.
基金National Natural Science Foundation of China(42375153,42105153,42205157)Development of Science and Technology at Chinese Academy of Meteorological Sciences(2023KJ038)。
文摘Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.
基金The Natural Science Foundation of Xinjiang Uygur Autonomous Region of China“RBF-Hermite difference scheme for the time-fractional kdv-Burgers equation”(2024D01C43)。
文摘In this paper,we present a finite volume trigonometric weighted essentially non-oscillatory(TWENO)scheme to solve nonlinear degenerate parabolic equations that may exhibit non-smooth solutions.The present method is developed using the trigonometric scheme,which is based on zero,first,and second moments,and the direct discontinuous Galerkin(DDG)flux is used to discretize the diffusion term.Moreover,the DDG method directly applies the weak form of the parabolic equation to each computational cell,which can better capture the characteristics of the solution,especially the discontinuous solution.Meanwhile,the third-order TVD-Runge-Kutta method is applied for temporal discretization.Finally,the effectiveness and stability of the method constructed in this paper are evaluated through numerical tests.
文摘The detection of high-impedance faults(HIFs)in microgrid feeders is a serious issue due to low fault current levels during HIFs.This issue becomes especially problematic when microgrids are operating in an islanded mode integrated with inverter-based distributed generators(IBDGs).This paper proposes an innovative relaying scheme for the protection of microgrid feeders using incremental negative sequence power.After a fault occurs,the negative sequence voltage and currents are collected at the ends of the protected feeder and then its incremental values are measured.After that,incremental negative sequence real power is obtained at the ends of the feeder to obtain the proposed relaying feature,i.e.,a ratio of the sum of incremental negative sequence real power(∆RSNSP).The∆RSNSP is defined as the ratio of the sum of incremental negative sequence real power at the two ends of the feeder to the minimum of the powers among the ends.Simulation studies on a modified IEEE 13-bus system have shown that this scheme can detect HIFs and low-impedance faults(LIFs).It has been rigorously tested under various operating conditions,like variations in fault inception angles,faults during islanding,simultaneous faults,evolving faults,composite faults,capacitors,and load switching.This scheme is not only fast and accurate but also performs well even in noisy conditions,changes in topologies(i.e.,radial or mesh),synchronization errors,and transient faults.A comparative chart comparing its performance with other recent schemes is also included.Finally,the scheme is also validated on a real-time simulator which proves that the proposed scheme can work effectively under various fault conditions.
基金funded by the National Basic Research Program of China(973 Program)(No.2011 CB201002)the National Natural Science Foundation of China(No.41374117)the great and special projects(2011ZX05005–005-008HZ and 2011ZX05006-002)
文摘The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results.
基金Aeronautical Science Foundation of China (20071551016)
文摘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.
基金The National Natural Science Foundation of China(No.61301295,61273266,61301219,61201326,61003131)the Natural Science Foundation of Anhui Province(No.1308085QF100,1408085MF113)+2 种基金the Natural Science Foundation of Jiangsu Province(No.BK20130241)the Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.12KJB510021)the Doctoral Fund of Anhui University
文摘Some factors influencing the intelligibility of the enhanced whisper in the joint time-frequency domain are evaluated. Specifically, both the spectrum density and different regions of the enhanced spectrum are analyzed. Experimental results show that for a spectrum of some density, the joint time-frequency gain-modification based speech enhancement algorithm achieves significant improvement in intelligibility. Additionally, the spectrum region where the estimated spectrum is smaller than the clean spectrum, is the most important region contributing to intelligibility improvement for the enhanced whisper. The spectrum region where the estimated spectrum is larger than twice the size of the clean spectrum is detrimental to speech intelligibility perception within the whisper context.
基金This work was funded by National Natural Science Foundation of China-（No. 40474044）.
文摘In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.
基金Supported by the Natural Science Foundation of Zhejiang Province（Y1100842） the Planning Projects of General Administration of Quality Supervision Inspection and Quarantine of the People＇s Republic of China（2006QK23）
文摘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%.
文摘In this study, several advanced analysis methods are applied to understand the relationships between the Nino-3.4 sea surface temperatures （SST） and the SSTs related to the tropical Indian Ocean Dipole （IOD）. By analyzing a long data record, the authors focus on the time-frequency characteristics of these relationships, and of the structure of IOD. They also focus on the seasonal dependence of those characteristics in both time and frequency domains. Among the Nino-3.4 SST, IOD, and SSTs over the tropical western Indian Ocean （WIO） and eastern Indian Ocean （EIO）, the WIO SST has the strongest annual and semiannual oscillations. While the Nino-3.4 SST has large inter-annual variability that is only second to its annual variability, the IOD is characterized by the largest semiannual oscillation, which is even stronger than its annual oscillation. The IOD is strongly and stably related to the EIO SST in a wide range of frequency bands and in all seasons. However, it is less significantly related to the WIO SST in the boreal winter and spring. There exists a generally weak and unstable relationship between the WIO and EIO SSTs, especially in the biennial and higher frequency bands. The relationship is especially weak in summer and fall, when IOD is apparent, but appears highly positive in winter and spring, when the IOD is unimportantly weak and even disappears. This feature reflects a caution in the definition and application of IOD. The Nino-3.4 SST has a strong positive relationship with the WIO SST in all seasons, mainly in the biennial and longer frequency bands. However, it shows no significant relationship with the EIO SST in summer and fall, and with IOD in winter and spring.
基金financially supported by the National 973 Project(No.2014CB239006)the National Natural Science Foundation of China(No.41104069 and 41274124)the Fundamental Research Funds for Central Universities(No.R1401005A)
文摘The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘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.
