Adaptive signal decomposition is an important signal processing method.The chirp-based signal representation,for example,the Gaussian chirplet decomposition,has been an active research topic in the field of signal pro...Adaptive signal decomposition is an important signal processing method.The chirp-based signal representation,for example,the Gaussian chirplet decomposition,has been an active research topic in the field of signal processing.A main challenge of the Gaussian chirplet decomposition is the numerical implementation of the matching pursuit,which is an adaptive signal decomposition scheme,and the challenge remains an open research topic.In this paper,a new optimal time-frequency atom search method based on the adaptive genetic algorithm is proposed,aiming to the low precision problem of the traditional methods.Firstly,a discrete formula of finite length time-frequency atom sequence is derived.Secondly,an algorithm based on the adaptive genetic algorithm is described in detail.Finally,a simulation is carried out,and the result displays its validity and stability.展开更多
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
A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization ...A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization of Gabor atom and is more delicate for matching most of the signals encountered in practice, especially for those having frequency dispersion characteristics. The time-frequency distribution of this atom concentrates in its time center and frequency center along energy curve, with the curve being oblique to a certain extent along the time axis. A novel parametric adaptive time-frequency distribution based on a set of the derived atoms is then proposed using a adaptive signal subspace decomposition method in frequency domain, which is non-negative time-frequency energy distribution and free of cross-term interference for multicomponent signals. The results of numerical simulation manifest the effectiveness of the approach in time-frequency representation and signal de-noising processing.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
Atomic spin gyroscopes are promising candidates for next-generation inertial navigation due to extremely high theoretical precision,relatively small size among atomic gyroscopes,and promising potential for miniaturiza...Atomic spin gyroscopes are promising candidates for next-generation inertial navigation due to extremely high theoretical precision,relatively small size among atomic gyroscopes,and promising potential for miniaturization.In particular,the spin-exchange relaxation-free(SERF)atomic gyroscope relies on optical pumping to polarize atoms,enabling rotation sensing through the Faraday optical rotation angle(FORA).However,fluctuations in atomic density introduce systematic errors in FORA measurements,limiting long-term stability.We present a data-driven decoupling method that isolates atomic density fluctuations from the FORA signal by modeling spatially resolved light absorption in the vapor cell.The model accounts for the spatial distribution of spin polarization in the pump-light interaction volume,density-dependent relaxation rates,wall-induced relaxation,and polarization diffusion,and is implemented within a finite-element framework.Compared to the conventional Lambert-Beer law,which assumes one-dimensional homogeneity,our approach captures the full threedimensional density and polarization distribution,significantly improving the accuracy of light absorption modeling.The resulting absorption-density maps are used to train a feedforward neural network,yielding a high-precision estimator for atomic density fluctuations.This estimator enables the construction of a decoupling equation that separates the density contribution from the FORA signal.Experimental validation shows that this method improves the bias instability atσ(100 s)of the gyroscope was improved by 73.1%compared to traditional platinum-resistance-based stabilization.The proposed framework is general and can be extended to other optical pumping-based sensors,such as optically pumped magnetometers.展开更多
In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a u...In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a uniform linear array(ULA).We exploit the inherent incomplete data processing capability of atomic norm soft thresholding(AST)to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH signals.The hopping time is obtained by the sudden changes of the spatial information,which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple components.Then,the frequency of multiple signal components can be estimated precisely by AST within each segment.After obtaining the above two parameters of the hopping time and the frequency of signals,the direction of arrival(DOA)can be directly calculated by them,and the network sorting can be realized.Results of simulation show that the proposed method is superior to the existing technology.Even when a large portion of data observations is missing,as the number of array elements increases,the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.展开更多
Single-atom catalysts(SACs)have demonstrated excellent performance in heterogeneous catalytic reactions owing to their maximized atomic efficiency,distinctive geometric,and electronic configurations.However,the effica...Single-atom catalysts(SACs)have demonstrated excellent performance in heterogeneous catalytic reactions owing to their maximized atomic efficiency,distinctive geometric,and electronic configurations.However,the efficacy of SACs remains limited for certain reactions requiring simultaneous activation of multiple reactants over metallic active sites.Herein,we report an atomically dispersed Pt1Ru1 dual-atom pair site anchored on nanodiamond@graphene(ND@G)for CO oxidation.The Pt1Ru1 dual-atom catalyst shows an exceptional turnover frequency(TOF)of 17.6.10^(-2)s^(-1)at significantly lower temperature(30℃),achieving a tenfold increase in TOF compared to singleatom Pt1/ND@G catalyst(1.5.10^(-2)s^(-1))and surpassing to previously reported Pt-based catalysts under similar conditions.Moreover,the catalyst demonstrates excellent stability,maintaining its activity for 40 h at 80℃without significant deactivation.The superior catalytic performance of Pt-Ru dual-atom catalysts is attributed to the synergistic effect between Pt and Ru atoms with enhanced metallicity for improving simultaneous adsorption and activation of CO and O_(2),and the tuning of conventional competitive reactant adsorption into a non-competitive pathway over dual-atom pair sites.The present work manifests the advantages of dual-atom pair sites in heterogeneous catalysis and paves the way for precise design of catalysts at the atomic scale.展开更多
A visible-light-induced synergistic hydrogen atom transfer(HAT)and proton transfer(PT)catalysis was developed for the defluorinative carboxylation of α-CF_(2)R-substituted alkenes.This system affords a variety of γ,...A visible-light-induced synergistic hydrogen atom transfer(HAT)and proton transfer(PT)catalysis was developed for the defluorinative carboxylation of α-CF_(2)R-substituted alkenes.This system affords a variety of γ,γ-difluoro-and γ-monofluoro-vinylacetic acids without stepwise acidification,exhibiting good functional group tolerance,broad scope,and facile scalability.Mechanism studies support that thiol plays the role of the hydrogen relay,which s a hydrogen atom through HAT and then outputs a proton via PT.This strategy also takes full advantage of formate for photocatalytic carboxylation reaction in a step-and atomeconomical way.展开更多
The development of catalysts with highly efficient oxygen evolution performance and low-Ir loading is key to scaling up the application of proton exchange membrane(PEM)water electrolysis technology.Here,an Ir-skin cat...The development of catalysts with highly efficient oxygen evolution performance and low-Ir loading is key to scaling up the application of proton exchange membrane(PEM)water electrolysis technology.Here,an Ir-skin catalyst(Ir@KM)is realized on a potassium-manganese oxide(K_(0.25)MnO_(x)(KM))using an ion-exchange method.The Ir-skin over the prepared Ir@KM has a low Ir-Ir atomic distance,endowing an energetically favorable oxide path mechanism to allow a low theoretical overpotential of 0.13 V.Ir@KM offers a low overpotential of~280 mV at a current density of 10 mA cm^(-2)and provides a high mass activity of up to 18,500 A at a cell voltage of 1.8 V in PEM,which is 17.6 times higher than that of IrO_(2),demonstrating a significant advantage in reducing the cost of the membrane electrode.The presented Ir-skin concept represents a promising strategy to fabricate low-Ir catalyst with high activity and durability for practical applications of PEM.展开更多
The dissolvable polysulfides and sluggish Li_2S conversion kinetics are acknowledged as two significant challenges in the application lithium-sulfur(Li-S)batteries.Herein,we introduce a dual-doping strategy to modulat...The dissolvable polysulfides and sluggish Li_2S conversion kinetics are acknowledged as two significant challenges in the application lithium-sulfur(Li-S)batteries.Herein,we introduce a dual-doping strategy to modulate the electronic structure of MoS_(2),thereby obtaining a multifunctional catalyst that serves as an efficient sulfur host.The W/V dual single-atomdoped MoS_(2)grown on carbon nanofibers(CMWVS)demonstrates a strong adsorption ability for lithium polysulfides,suppressing the shuttle effects.Additionally,the doping process also results in the phase transition from 2H-MoS_(2)to 1T-MoS_(2)and generates sufficient edge sulfur atoms,promoting the charge/electron transfer and enriching the reaction sites.All these merits contribute to the superior conversion reaction kinetics,leading to the outstanding Li-S battery performance.When fabricated as cathodes by compositing with sulfur,the CMWVS/S cathode delivers a high capacity of 1481.7 mAh g^(-1)at 0.1 C(1 C=1672 mAh g^(-1))and maintains 816.3 m Ah g^(-1)after 1000 cycles at 1.0 C,indicating outstanding cycling stability.Even under a high sulfur loading of 7.9 mg cm^(-2)and lean electrolyte conditions(E/S ratio of 9.0μL mg^(-1)),the cathode achieves a high areal capacity of 8.2 m Ah cm^(-2),showing great promise for practical Li-S battery applications.This work broadens the scope of doping strategies in transition-metal dichalcogenides by tailoring their electronic structures,providing insightful direction for the rational development of high-efficiency electrocatalysts for advanced Li-S battery applications.展开更多
Chlorinated antibiotics pose great challenges in efficient removal,while for the first time,this work greatly enhanced their electrocatalytic dechlorination performance by construction of non-noble metal Co_(3)O_(4)/g...Chlorinated antibiotics pose great challenges in efficient removal,while for the first time,this work greatly enhanced their electrocatalytic dechlorination performance by construction of non-noble metal Co_(3)O_(4)/g-C_(3)N_(4) heterojunctions to improve process cost-effectiveness.The Co_(3)O_(4)/g-C_(3)N_(4) heterojunction demonstrated an effective removal of 93.6%thiamphenicol(TAP)within 45 min,with the rate constant(0.0584 min^(-1))that was 2.4 and 2.8 times that of Co_(3)O_(4) and g-C_(3)N_(4) alone,respectively.The formation of heterojunctions facilitated electron transfer,enriched the electron density on Co_(3)O_(4),and enhanced the adsorption of pollutants as well as the desorption of degradation intermediates.The enhanced production of atomic hydrogen(H*)of Co_(3)O_(4)/g-C_(3)N_(4),which increased by 13.6-28.2 times,contributed more to pollutant removal(64.0%),much higher than that of Co_(3)O_(4)(37.3%)and g-C_(3)N_(4)(6.1%).The energy barrier for H_(2) formation on Co_(3)O_(4)/g-C_(3)N_(4)(0.75 eV)was higher than that on Co_(3)O_(4)(-1.84 eV),supporting that it could stabilize H*and inhibit the formation of H_(2).The Co_(3)O_(4)/g-C_(3)N_(4) heterojunction exhibited stable performance with less impact by pH and co-existing ions,and posed effectiveness for the dechlorination of typical chlorinated antibiotics.This study offers an efficient and sustainable strategy for constructing heterojunctions to enhance the performance of non-noble metal catalysts in electrocatalytic dechlorination.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
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
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 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
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Sponsored by National Nature Science Foundation of China (60575013)
文摘Adaptive signal decomposition is an important signal processing method.The chirp-based signal representation,for example,the Gaussian chirplet decomposition,has been an active research topic in the field of signal processing.A main challenge of the Gaussian chirplet decomposition is the numerical implementation of the matching pursuit,which is an adaptive signal decomposition scheme,and the challenge remains an open research topic.In this paper,a new optimal time-frequency atom search method based on the adaptive genetic algorithm is proposed,aiming to the low precision problem of the traditional methods.Firstly,a discrete formula of finite length time-frequency atom sequence is derived.Secondly,an algorithm based on the adaptive genetic algorithm is described in detail.Finally,a simulation is carried out,and the result displays its validity and stability.
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This project was supported by the National Natural Science Foundation of China (60472102)Shanghai Leading Academic Discipline Project (T0103).
文摘A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization of Gabor atom and is more delicate for matching most of the signals encountered in practice, especially for those having frequency dispersion characteristics. The time-frequency distribution of this atom concentrates in its time center and frequency center along energy curve, with the curve being oblique to a certain extent along the time axis. A novel parametric adaptive time-frequency distribution based on a set of the derived atoms is then proposed using a adaptive signal subspace decomposition method in frequency domain, which is non-negative time-frequency energy distribution and free of cross-term interference for multicomponent signals. The results of numerical simulation manifest the effectiveness of the approach in time-frequency representation and signal de-noising processing.
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 Beijing Natural Science Foundation(Grant No.3252013)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0300402)+1 种基金the National Natural Science Foundation of China(Grant No.61673041)Key Area Research and Development Program of Guangdong Province(Grant No.2021B0101410005)。
文摘Atomic spin gyroscopes are promising candidates for next-generation inertial navigation due to extremely high theoretical precision,relatively small size among atomic gyroscopes,and promising potential for miniaturization.In particular,the spin-exchange relaxation-free(SERF)atomic gyroscope relies on optical pumping to polarize atoms,enabling rotation sensing through the Faraday optical rotation angle(FORA).However,fluctuations in atomic density introduce systematic errors in FORA measurements,limiting long-term stability.We present a data-driven decoupling method that isolates atomic density fluctuations from the FORA signal by modeling spatially resolved light absorption in the vapor cell.The model accounts for the spatial distribution of spin polarization in the pump-light interaction volume,density-dependent relaxation rates,wall-induced relaxation,and polarization diffusion,and is implemented within a finite-element framework.Compared to the conventional Lambert-Beer law,which assumes one-dimensional homogeneity,our approach captures the full threedimensional density and polarization distribution,significantly improving the accuracy of light absorption modeling.The resulting absorption-density maps are used to train a feedforward neural network,yielding a high-precision estimator for atomic density fluctuations.This estimator enables the construction of a decoupling equation that separates the density contribution from the FORA signal.Experimental validation shows that this method improves the bias instability atσ(100 s)of the gyroscope was improved by 73.1%compared to traditional platinum-resistance-based stabilization.The proposed framework is general and can be extended to other optical pumping-based sensors,such as optically pumped magnetometers.
文摘In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a uniform linear array(ULA).We exploit the inherent incomplete data processing capability of atomic norm soft thresholding(AST)to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH signals.The hopping time is obtained by the sudden changes of the spatial information,which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple components.Then,the frequency of multiple signal components can be estimated precisely by AST within each segment.After obtaining the above two parameters of the hopping time and the frequency of signals,the direction of arrival(DOA)can be directly calculated by them,and the network sorting can be realized.Results of simulation show that the proposed method is superior to the existing technology.Even when a large portion of data observations is missing,as the number of array elements increases,the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.
基金supported by the National Key R&D Program of China(2021YFA1502802)the National Natural Science Foundation of China(U21B2092,22202213,22402210,22502215,22502214,22572200,and 22579171)+4 种基金the International Partnership Program of Chinese Academy of Sciences(172GJHZ2022028MI)the Shenyang Bureau of Science and Technology(24-213-3-25)the Natural Science Foundation of Liaoning Province(2025BS0153)Zhongke Technology Achievement Transfer and Transformation Center of Henan Province 2025119The XAS experiments were conducted in Beijing Synchrotron Radiation Facility(BSRF)and Shanghai Synchrotron Radiation Facility(SSRF).
文摘Single-atom catalysts(SACs)have demonstrated excellent performance in heterogeneous catalytic reactions owing to their maximized atomic efficiency,distinctive geometric,and electronic configurations.However,the efficacy of SACs remains limited for certain reactions requiring simultaneous activation of multiple reactants over metallic active sites.Herein,we report an atomically dispersed Pt1Ru1 dual-atom pair site anchored on nanodiamond@graphene(ND@G)for CO oxidation.The Pt1Ru1 dual-atom catalyst shows an exceptional turnover frequency(TOF)of 17.6.10^(-2)s^(-1)at significantly lower temperature(30℃),achieving a tenfold increase in TOF compared to singleatom Pt1/ND@G catalyst(1.5.10^(-2)s^(-1))and surpassing to previously reported Pt-based catalysts under similar conditions.Moreover,the catalyst demonstrates excellent stability,maintaining its activity for 40 h at 80℃without significant deactivation.The superior catalytic performance of Pt-Ru dual-atom catalysts is attributed to the synergistic effect between Pt and Ru atoms with enhanced metallicity for improving simultaneous adsorption and activation of CO and O_(2),and the tuning of conventional competitive reactant adsorption into a non-competitive pathway over dual-atom pair sites.The present work manifests the advantages of dual-atom pair sites in heterogeneous catalysis and paves the way for precise design of catalysts at the atomic scale.
基金supported by the National Natural Science Foundation of China (22472031,U24A20567,22032002)the 111 Project。
文摘A visible-light-induced synergistic hydrogen atom transfer(HAT)and proton transfer(PT)catalysis was developed for the defluorinative carboxylation of α-CF_(2)R-substituted alkenes.This system affords a variety of γ,γ-difluoro-and γ-monofluoro-vinylacetic acids without stepwise acidification,exhibiting good functional group tolerance,broad scope,and facile scalability.Mechanism studies support that thiol plays the role of the hydrogen relay,which s a hydrogen atom through HAT and then outputs a proton via PT.This strategy also takes full advantage of formate for photocatalytic carboxylation reaction in a step-and atomeconomical way.
基金supported by the Hainan Province Science and Technology Special Fund(ZDYF2023GXJS165)the National Natural Science Foundation of China(52164028,22109035,52274297)+2 种基金the Foundation of State Key Laboratory of Marine Resource Utilization in South China Sea(Hainan University,MRUKF2021029)the Start-up Research Foundation of Hainan University(KYQD(ZR)-20008,20084,21170)the Specific Research Fund of the Innovation Platform for Academicians of Hainan Province。
文摘The development of catalysts with highly efficient oxygen evolution performance and low-Ir loading is key to scaling up the application of proton exchange membrane(PEM)water electrolysis technology.Here,an Ir-skin catalyst(Ir@KM)is realized on a potassium-manganese oxide(K_(0.25)MnO_(x)(KM))using an ion-exchange method.The Ir-skin over the prepared Ir@KM has a low Ir-Ir atomic distance,endowing an energetically favorable oxide path mechanism to allow a low theoretical overpotential of 0.13 V.Ir@KM offers a low overpotential of~280 mV at a current density of 10 mA cm^(-2)and provides a high mass activity of up to 18,500 A at a cell voltage of 1.8 V in PEM,which is 17.6 times higher than that of IrO_(2),demonstrating a significant advantage in reducing the cost of the membrane electrode.The presented Ir-skin concept represents a promising strategy to fabricate low-Ir catalyst with high activity and durability for practical applications of PEM.
基金supported by the National Natural Science Foundation of China(52402166)the Science and Technology Development Fund+2 种基金Macao SAR(0065/2023/AFJ,0116/2022/A3)the Australian Research Council(DE220100154)the Natural Science Foundation of Guangdong Province(2025A1515011120)。
文摘The dissolvable polysulfides and sluggish Li_2S conversion kinetics are acknowledged as two significant challenges in the application lithium-sulfur(Li-S)batteries.Herein,we introduce a dual-doping strategy to modulate the electronic structure of MoS_(2),thereby obtaining a multifunctional catalyst that serves as an efficient sulfur host.The W/V dual single-atomdoped MoS_(2)grown on carbon nanofibers(CMWVS)demonstrates a strong adsorption ability for lithium polysulfides,suppressing the shuttle effects.Additionally,the doping process also results in the phase transition from 2H-MoS_(2)to 1T-MoS_(2)and generates sufficient edge sulfur atoms,promoting the charge/electron transfer and enriching the reaction sites.All these merits contribute to the superior conversion reaction kinetics,leading to the outstanding Li-S battery performance.When fabricated as cathodes by compositing with sulfur,the CMWVS/S cathode delivers a high capacity of 1481.7 mAh g^(-1)at 0.1 C(1 C=1672 mAh g^(-1))and maintains 816.3 m Ah g^(-1)after 1000 cycles at 1.0 C,indicating outstanding cycling stability.Even under a high sulfur loading of 7.9 mg cm^(-2)and lean electrolyte conditions(E/S ratio of 9.0μL mg^(-1)),the cathode achieves a high areal capacity of 8.2 m Ah cm^(-2),showing great promise for practical Li-S battery applications.This work broadens the scope of doping strategies in transition-metal dichalcogenides by tailoring their electronic structures,providing insightful direction for the rational development of high-efficiency electrocatalysts for advanced Li-S battery applications.
基金supported by Natural Science Foundation of China(Nos.U23B20165 and 52170085)National Key R&D Program International Cooperation Project(No.2023YFE0108100)+1 种基金Key Project of Natural Science Foundation of Tianjin(No.21JCZDJC00320)Fundamental Research Funds for the Central Universities,Nankai University.
文摘Chlorinated antibiotics pose great challenges in efficient removal,while for the first time,this work greatly enhanced their electrocatalytic dechlorination performance by construction of non-noble metal Co_(3)O_(4)/g-C_(3)N_(4) heterojunctions to improve process cost-effectiveness.The Co_(3)O_(4)/g-C_(3)N_(4) heterojunction demonstrated an effective removal of 93.6%thiamphenicol(TAP)within 45 min,with the rate constant(0.0584 min^(-1))that was 2.4 and 2.8 times that of Co_(3)O_(4) and g-C_(3)N_(4) alone,respectively.The formation of heterojunctions facilitated electron transfer,enriched the electron density on Co_(3)O_(4),and enhanced the adsorption of pollutants as well as the desorption of degradation intermediates.The enhanced production of atomic hydrogen(H*)of Co_(3)O_(4)/g-C_(3)N_(4),which increased by 13.6-28.2 times,contributed more to pollutant removal(64.0%),much higher than that of Co_(3)O_(4)(37.3%)and g-C_(3)N_(4)(6.1%).The energy barrier for H_(2) formation on Co_(3)O_(4)/g-C_(3)N_(4)(0.75 eV)was higher than that on Co_(3)O_(4)(-1.84 eV),supporting that it could stabilize H*and inhibit the formation of H_(2).The Co_(3)O_(4)/g-C_(3)N_(4) heterojunction exhibited stable performance with less impact by pH and co-existing ions,and posed effectiveness for the dechlorination of typical chlorinated antibiotics.This study offers an efficient and sustainable strategy for constructing heterojunctions to enhance the performance of non-noble metal catalysts in electrocatalytic dechlorination.
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%.
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）.
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.
