This paper introduces the localized Radon transform (LRT) into time-frequency distributions and presents the localized Radon-Wigner transform (LRWT). The definition of LRWT and a fast algorithm is derived, the propert...This paper introduces the localized Radon transform (LRT) into time-frequency distributions and presents the localized Radon-Wigner transform (LRWT). The definition of LRWT and a fast algorithm is derived, the properties of LRWT and its relationship with Radon-Wigner transform, Wigner distribution (WD), ambiguity function (AF), and generalized-marginal time-frequency distributions are analyzed.展开更多
A new quadratic time-frequency distribution (TFD) with a compound kernel is proposed and a comparative study of several popular quadratic TFD is carried out. It is shown that the new TFD with compound kernel has stron...A new quadratic time-frequency distribution (TFD) with a compound kernel is proposed and a comparative study of several popular quadratic TFD is carried out. It is shown that the new TFD with compound kernel has stronger ability than the exponential distribution (ED) and the cone-shaped kernel distribution (CKD) in reducing cross terms, meanwhile almost not decreasing the time-frequency resolution of ED or CKD.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
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
We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensi...We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensional Kardar-Parisi-Zhang(KPZ) growth system within the early growth regime. Through extensive numerical simulations, we find that long-range temporally correlated noise does not significantly impact the distribution form of the interface width. Generally,W^(2)(L, t) approximately obeys a lognormal distribution when the temporal correlation exponentθ ≥0. On the other hand, the effects of long-range spatially correlated noise are evidently different from the temporally correlated case. Our results show that, when the spatial correlation exponent ρ ≤ 0.20, the distribution forms of W^(2)(L, t) approach the lognormal distribution, and when ρ > 0.20, the distribution becomes more asymmetric, steep, and fat-tailed, and tends to an unknown distribution form. As a comparison, probability distributions of the interface height are also provided in the temporally and spatially correlated KPZ system, exhibiting quite different characteristics from each other within the whole correlated strengths. For the temporal correlation, the height distributions follow Tracy-Widom Gaussian orthogonal ensemble(TW-GOE) when θ → 0, and with increasing θ, the height distributions crossover continuously to an unknown distribution. However, for the spatial correlation, the height distributions gradually transition from the TW-GOE distribution to the standard Gaussian form.展开更多
Landslide dams often undergo seepage due to poor particle gradation and loose structure,yet most existing studies focus solely on overtopping-induced breaching mechanisms,neglecting the potential influence of pre-brea...Landslide dams often undergo seepage due to poor particle gradation and loose structure,yet most existing studies focus solely on overtopping-induced breaching mechanisms,neglecting the potential influence of pre-breaching seepage.Seepage may alter the dam's erodibility,structural stability,and material composition,thereby affecting the overtopping breaching process.Through flume experiments,this study investigates the breaching mechanisms of cohesionless landslide dams with different gradations within the same particle size range under coupled seepage-overtopping conditions.The results demonstrate that pre-breaching seepage significantly impacts breaching dynamics.Within a specific particle size range,compared to pure overtopping,seepage reduces downstream slope stability,increases material erodibility,shortens breaching duration,amplifies peak discharge,and advances the timing of peak flow.As the median particle size(D_(50))increases,the amplification effect of seepage on peak discharge initially increases then decreases,the advancement of peak flow timing diminishes,and the breach erosion rate declines.When D_(50)is sufficiently large,seepage has negligible effects on breach development.For smaller D_(50),seepage markedly accelerates breach widening and deepening.Furthermore,coupled seepage-overtopping extends the downstream deposition area and exacerbates channel erosion due to differences in sediment sorting.These findings highlight the critical role of seepage in landslide dam breaching,providing a scientific basis for hazard prevention and mitigation.展开更多
A deep neural network(DNN)was developed to accurately predict the nuclear charge density distributions for nuclei with proton numbers Z≥8.By incorporating essential nuclear structure features,the model achieved a sig...A deep neural network(DNN)was developed to accurately predict the nuclear charge density distributions for nuclei with proton numbers Z≥8.By incorporating essential nuclear structure features,the model achieved a significant improvement in predictive accuracy over conventional methods.The charge density distributions were analyzed using a Fourier-Bessel(FB)series expansion,and the DNN was trained on a comprehensive dataset derived from relativistic continuum Hartree-Bogoliubov(RCHB)theory calculations.The model demonstrated exceptional performance,with root-mean-square deviations of 0.0123fm and 0.0198 fm for the charge radii on the training and validation sets,respectively,which remarkably surpassed the precision of the original RCHB calculations.In addition to advancing nuclear physics research,this high-precision model provides critical data for applications in atomic physics,nuclear astrophysics,and related fields.展开更多
This work demonstrates the use of the nonlinear time-frequency distribution (NLTFD) of a discrete time energy operator (DTEO) based on amplitude modulation-frequency modulation demodulation techniques as a feature i...This work demonstrates the use of the nonlinear time-frequency distribution (NLTFD) of a discrete time energy operator (DTEO) based on amplitude modulation-frequency modulation demodulation techniques as a feature in speech recognition. The duration distribution based hidden Markov module in a speaker independent large vocabulary mandarin speech recognition system was reconstructed from the feature vectors in the front-end detection stage. The goal was to improve the performance of the existing system by combining new features to the baseline feature vector. This paper also deals with errors associated with using a pre-emphasis filter in the front end processing of the present scheme, which causes an increase in the noise energy at high frequencies above 4 kHz and in some cases degrades the recognition accuracy. The experimental results show that eliminating the pre-emphasis filters from the pre-processing stage and using NLTFD with compensated DTEO combined with Mel frequency cepstrum components give a 21.95% reduction in the relative error rate compared to the conventional technique with 25 candidates used in the test.展开更多
Fast wavelet multi-resolution analysis (wavelet MRA) provides a effective tool for analyzing and canceling disturbing components in original signal. Because of its exponential frequency axis, this method isn't s...Fast wavelet multi-resolution analysis (wavelet MRA) provides a effective tool for analyzing and canceling disturbing components in original signal. Because of its exponential frequency axis, this method isn't suitable for extracting harmonic components. The modified exponential time-frequency distribution ( MED) overcomes the problems of Wigner distribution( WD) ,can suppress cross-terms and cancel noise further more. MED provides high resolution in both time and frequency domains, so it can make out weak period impulse components fmm signal with mighty harmonic components. According to the 'time' behavior, together with 'frequency' behavior in one figure,the essential structure of a signal is revealed clearly. According to the analysis of algorithm and fault diagnosis example, the joint of wavelet MRA and MED is a powerful tool for fault diagnosis.展开更多
The characteristics of summertime raindrop size distribution(DSD) and associated relations in the semi-arid region over the Inner Mongolian Plateau(IMP) were investigated,utilizing five-year continuous observations by...The characteristics of summertime raindrop size distribution(DSD) and associated relations in the semi-arid region over the Inner Mongolian Plateau(IMP) were investigated,utilizing five-year continuous observations by a PARSIVEL2disdrometer in East Ujimqin County(EUC),China.It is found that only 7.94% of the 15 664 one-min precipitation samples meet classification criteria as convective rain(CR),but its contribution to the total rainfall amount is 63.87%.Notably,40.72% of the rainfall comes from large-sized raindrops(D> 3 mm),despite the fact that large-sized raindrops account for only 1.73% of the CR total number concentration.Further results show that the mean value of mass-weighted mean diameters(Dm) is larger(2.43 mm) and generalized intercepts(lgN_(W)) is lower(3.19) in CR,aligning with a "continentallike" cluster,which is mainly influenced by the joint impact of in-cloud ice-based processes and the below-cloud environmental background.Also,the empirical relationships of shape-slope(μ-Λ),radar reflectivity-rain rate(Z-R),and rainfall kinetic energy(KE_(time)-Rand KE_(time)-Z) are localized.To quantitatively analyze the impact of DSD parameters on kinetic energy estimation,power-law KE_(time)-R and KE_(time)-Z relationships are derived based on the normalized gamma distribution.N_(W)takes precedence over μ in affecting variabilities of multiplicative coefficients,especially for KE_(time)-R relationship where the multiplicative coefficient is proportional to N_(W)^(-0.287).It should be noted that although the proportion of CR occurring throughout the summer is small,raindrops with lower N_(W) and larger Dmwill generate higher KE_(time),which will bring a higher potential risk of soil erosion in semi-arid regions over IMP.展开更多
Let I be the set of all infinitely divisible random variables with finite second moments,I_(0)={X∈I;Var(X)>0},P_(I)=inf_(x∈I)P{|X-E[X]|≤√Var(X)}and P_(I_(0))=inf P{|X-E[X]|<√Var(X)}.Firstly,we prove that P_...Let I be the set of all infinitely divisible random variables with finite second moments,I_(0)={X∈I;Var(X)>0},P_(I)=inf_(x∈I)P{|X-E[X]|≤√Var(X)}and P_(I_(0))=inf P{|X-E[X]|<√Var(X)}.Firstly,we prove that P_(I)≥P_(I_(0))>0.Secondly,we find_(x∈I_(0))the exact values of inf P{|X-E[X]|≤√Var(X)}and inf P{|X-E[X]|<√Var(X)}for the cases that J is the set of all geometric random variables,symmetric geometric random variables,Poisson random variables and symmetric Poisson random variables,respectively.As a consequence,we obtain that P_(I)≤e^(-1)^(∞)∑_(k=0)1/2^(2k)(k!)^(2)≈0.46576 and P_(I_(0))≤e^(-1)≈0.36788.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
The distributions of light and nitrogen within a plant's canopy reflect the growth adaptation of crops to the environment and are conducive to improving the carbon assimilation ability.So can the yield in crop pro...The distributions of light and nitrogen within a plant's canopy reflect the growth adaptation of crops to the environment and are conducive to improving the carbon assimilation ability.So can the yield in crop production be maximized by improving the light and nitrogen distributions without adding any additional inputs?In this study,the effects of different nitrogen application rates and planting densities on the canopy light and nitrogen distributions of two highyielding maize cultivars(XY335 and DH618)and the regulatory effects of canopy physiological characteristics on radiation use efficiency(RUE)and yield were studied based on high-yield field experiments in Qitai,Xinjiang Uygur Autonomous Region,China,during 2019 and 2020.The results showed that the distribution of photosynthetically active photon flux density(PPFD)in the maize canopy decreased from top to bottom,while the vertical distribution of specific leaf nitrogen(SLN)initially increased and then decreased from top to bottom in the canopy.When SLN began to decrease,the PPDF values of XY335 and DH618 were 0.5 and 0.3,respectively,corresponding to 40.6 and49.3%of the total leaf area index(LAI).Nitrogen extinction coefficient(K_(N))/light extinction coefficient(K_(L))ratio in the middle and lower canopy of XY335(0.32)was 0.08 higher than that of DH618(0.24).The yield and RUE of XY335(17.2 t ha^(-1)and 1.8g MJ^(-1))were 7.0%(1.1 t ha^(-1))and 13.7%(0.2 g MJ^(-1))higher than those of DH618(16.1 t ha^(-1)and 1.6 g MJ^(-1)).Therefore,better light conditions(where the proportion of LAI in the upper and middle canopy was small)improved the light distribution when SLN started to decline,thus helping to mobilize the nitrogen distribution and maintain a high K_(N)and K_(N)/K_(L)ratio.In addition,K_(N)/K_(L)was a key parameter for yield improvement when the maize nutrient requirements were met at 360 kg N ha^(-1).At this level,an appropriately optimized high planting density could promote nitrogen utilization and produce higher yields and greater efficiency.The results of this study will be important for achieving high maize yields and the high efficiency cultivation and breeding of maize in the future.展开更多
To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produce...To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produced by the tandem-accelerator in the China Institute of Atomic Energy was utilized.The proton beam was first transmitted through a 60.5μm aluminum foil and then impinged on a natural LiF target to produce neutron beam via^(7)Li(p,n)7Be reaction.The quasi-Gaussian energy distribution of protons in the LiF target resulted in neutron energy spectra that agreed with a Maxwellian energy distribution at kT=(22±2)keV,which was achieved by integrating neutrons detected within an emission angle of 65.0°±2.6°using a ^(6)Li glass detector positioned at 65°relative to the proton beam direction.The narrow angular spread of the Maxwelliandistributed neutron beam enables direct measurement of neutron capture cross-sections for most s-process nuclides,overcoming previous experimental limitations associated with broad angular distributions.展开更多
The Chinese tree shrew has gained prominence as a model organism due to its phylogenetic proximity to primates,offering distinct advantages over traditional rodent models in biomedical research.However,the neuroanatom...The Chinese tree shrew has gained prominence as a model organism due to its phylogenetic proximity to primates,offering distinct advantages over traditional rodent models in biomedical research.However,the neuroanatomy of this species remains insufficiently defined,limiting its utility in neurophysiological and neuropathological studies.In this study,immunofluorescence microscopy was employed to comprehensively map the distribution of three calciumbinding proteins,parvalbumin,calbindin D-28k,and calretinin,across the tree shrew cerebrum.Serial brain sections in sagittal,coronal,and horizontal planes from 12 individuals generated a dataset of 3638 cellular-resolution images.This dataset,accessible via Science Data Bank(https://doi.org/10.57760/sciencedb.23471),provides detailed region-and laminar-selective distributions of calcium-binding proteins valuable for the cyto-and chemoarchitectural characterization of the tree shrew cerebrum.This resource will not only advance our understanding of brain organization and facilitate basic and translational neuroscience research in tree shrews but also enhance comparative and evolutionary analyses across species.展开更多
The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-i...The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.展开更多
A double-moment cloud microphysics scheme requires an assumption for cloud droplet size distributions(DSDs).However,since observations of cloud DSDs are limited,default values for shape parameters and cloud condensati...A double-moment cloud microphysics scheme requires an assumption for cloud droplet size distributions(DSDs).However,since observations of cloud DSDs are limited,default values for shape parameters and cloud condensation nuclei activation parameters are often used in numerical simulations.In this study,the effects of cloud DSDs on numerical simulations of warm stratiform precipitation around Tokyo are investigated using the Japan Meteorological Agency's non-hydrostatic model,which incorporates a double-moment cloud microphysics scheme.Simulations using the default cloud DSD showed higher cloud droplet number concentrations and lower radar reflectivity than observed data,suggesting that the default cloud DSD is too narrow.Simulations with a cloud DSD based on in situ cloud observations corrected these errors.In addition,observation-based cloud DSDs affected rainfall amounts through the autoconversion rate of cloud water and improved the threat scores.These results suggest that realistic cloud DSDs should be provided for double-moment cloud microphysics schemes in scientific studies.展开更多
This research delves into controlling the interplay of magnetostatic field and anisotropy field via controlled composition distribution,thereby boosting coercivity in Nd-Ce-Fe-B magnets.As the originally heterogeneous...This research delves into controlling the interplay of magnetostatic field and anisotropy field via controlled composition distribution,thereby boosting coercivity in Nd-Ce-Fe-B magnets.As the originally heterogeneous compositions gradually homogenize within the magnet,an interesting trend in coercivity emerges.Initially,coercivity shows a positive trend,increasing as the components start to blend.However,after reaching an optimal point,it begins to decline.Notably,coercivity peaks once the element Ce permeates to the magnet's surfaces.This phenomenon is closely associated with changes in the H_(crit) distribution.H_(crit) is a measure that reflects the interaction between anisotropy field and magnetostatic field.When the composition becomes more uniform,the distribution of H_(crit) shifts,and its minimum absolute value varies.These changes are driven by the combined influence of anisotropy field and magnetostatic field.Understanding these relationships provides valuable insights.It opens up new avenues for enhancing coercivity in Nd-Ce-Fe-B magnets by adjusting and fine-tuning the interactions between these fields.展开更多
文摘This paper introduces the localized Radon transform (LRT) into time-frequency distributions and presents the localized Radon-Wigner transform (LRWT). The definition of LRWT and a fast algorithm is derived, the properties of LRWT and its relationship with Radon-Wigner transform, Wigner distribution (WD), ambiguity function (AF), and generalized-marginal time-frequency distributions are analyzed.
文摘A new quadratic time-frequency distribution (TFD) with a compound kernel is proposed and a comparative study of several popular quadratic TFD is carried out. It is shown that the new TFD with compound kernel has stronger ability than the exponential distribution (ED) and the cone-shaped kernel distribution (CKD) in reducing cross terms, meanwhile almost not decreasing the time-frequency resolution of ED or CKD.
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.
文摘We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensional Kardar-Parisi-Zhang(KPZ) growth system within the early growth regime. Through extensive numerical simulations, we find that long-range temporally correlated noise does not significantly impact the distribution form of the interface width. Generally,W^(2)(L, t) approximately obeys a lognormal distribution when the temporal correlation exponentθ ≥0. On the other hand, the effects of long-range spatially correlated noise are evidently different from the temporally correlated case. Our results show that, when the spatial correlation exponent ρ ≤ 0.20, the distribution forms of W^(2)(L, t) approach the lognormal distribution, and when ρ > 0.20, the distribution becomes more asymmetric, steep, and fat-tailed, and tends to an unknown distribution form. As a comparison, probability distributions of the interface height are also provided in the temporally and spatially correlated KPZ system, exhibiting quite different characteristics from each other within the whole correlated strengths. For the temporal correlation, the height distributions follow Tracy-Widom Gaussian orthogonal ensemble(TW-GOE) when θ → 0, and with increasing θ, the height distributions crossover continuously to an unknown distribution. However, for the spatial correlation, the height distributions gradually transition from the TW-GOE distribution to the standard Gaussian form.
基金support of the National Natural Science Foundation of China(42107189,U20A20111)。
文摘Landslide dams often undergo seepage due to poor particle gradation and loose structure,yet most existing studies focus solely on overtopping-induced breaching mechanisms,neglecting the potential influence of pre-breaching seepage.Seepage may alter the dam's erodibility,structural stability,and material composition,thereby affecting the overtopping breaching process.Through flume experiments,this study investigates the breaching mechanisms of cohesionless landslide dams with different gradations within the same particle size range under coupled seepage-overtopping conditions.The results demonstrate that pre-breaching seepage significantly impacts breaching dynamics.Within a specific particle size range,compared to pure overtopping,seepage reduces downstream slope stability,increases material erodibility,shortens breaching duration,amplifies peak discharge,and advances the timing of peak flow.As the median particle size(D_(50))increases,the amplification effect of seepage on peak discharge initially increases then decreases,the advancement of peak flow timing diminishes,and the breach erosion rate declines.When D_(50)is sufficiently large,seepage has negligible effects on breach development.For smaller D_(50),seepage markedly accelerates breach widening and deepening.Furthermore,coupled seepage-overtopping extends the downstream deposition area and exacerbates channel erosion due to differences in sediment sorting.These findings highlight the critical role of seepage in landslide dam breaching,providing a scientific basis for hazard prevention and mitigation.
基金the National Natural Science Foundation of China(No.12475119)the Key Laboratory of Nuclear Data Foundation(JCKY2025201C154)the JSPS Grant-in-Aid for Scientific Research(S)(No.20H05648)。
文摘A deep neural network(DNN)was developed to accurately predict the nuclear charge density distributions for nuclei with proton numbers Z≥8.By incorporating essential nuclear structure features,the model achieved a significant improvement in predictive accuracy over conventional methods.The charge density distributions were analyzed using a Fourier-Bessel(FB)series expansion,and the DNN was trained on a comprehensive dataset derived from relativistic continuum Hartree-Bogoliubov(RCHB)theory calculations.The model demonstrated exceptional performance,with root-mean-square deviations of 0.0123fm and 0.0198 fm for the charge radii on the training and validation sets,respectively,which remarkably surpassed the precision of the original RCHB calculations.In addition to advancing nuclear physics research,this high-precision model provides critical data for applications in atomic physics,nuclear astrophysics,and related fields.
基金the National High- Tech Research andDevelopm ent Program of China(No. 2 0 0 1AA114 0 71)
文摘This work demonstrates the use of the nonlinear time-frequency distribution (NLTFD) of a discrete time energy operator (DTEO) based on amplitude modulation-frequency modulation demodulation techniques as a feature in speech recognition. The duration distribution based hidden Markov module in a speaker independent large vocabulary mandarin speech recognition system was reconstructed from the feature vectors in the front-end detection stage. The goal was to improve the performance of the existing system by combining new features to the baseline feature vector. This paper also deals with errors associated with using a pre-emphasis filter in the front end processing of the present scheme, which causes an increase in the noise energy at high frequencies above 4 kHz and in some cases degrades the recognition accuracy. The experimental results show that eliminating the pre-emphasis filters from the pre-processing stage and using NLTFD with compensated DTEO combined with Mel frequency cepstrum components give a 21.95% reduction in the relative error rate compared to the conventional technique with 25 candidates used in the test.
文摘Fast wavelet multi-resolution analysis (wavelet MRA) provides a effective tool for analyzing and canceling disturbing components in original signal. Because of its exponential frequency axis, this method isn't suitable for extracting harmonic components. The modified exponential time-frequency distribution ( MED) overcomes the problems of Wigner distribution( WD) ,can suppress cross-terms and cancel noise further more. MED provides high resolution in both time and frequency domains, so it can make out weak period impulse components fmm signal with mighty harmonic components. According to the 'time' behavior, together with 'frequency' behavior in one figure,the essential structure of a signal is revealed clearly. According to the analysis of algorithm and fault diagnosis example, the joint of wavelet MRA and MED is a powerful tool for fault diagnosis.
基金supported by the National Natural Science Foundation of China(Grant Nos.42325503,42075063,42075066,and 42021004)the Hubei Provincial Natural Science Foundation and the Meteorological Innovation and Development Project of China(Grant No.2023AFD096)the Beijige Foundation of NJIAS(Grant No.BJG202304).
文摘The characteristics of summertime raindrop size distribution(DSD) and associated relations in the semi-arid region over the Inner Mongolian Plateau(IMP) were investigated,utilizing five-year continuous observations by a PARSIVEL2disdrometer in East Ujimqin County(EUC),China.It is found that only 7.94% of the 15 664 one-min precipitation samples meet classification criteria as convective rain(CR),but its contribution to the total rainfall amount is 63.87%.Notably,40.72% of the rainfall comes from large-sized raindrops(D> 3 mm),despite the fact that large-sized raindrops account for only 1.73% of the CR total number concentration.Further results show that the mean value of mass-weighted mean diameters(Dm) is larger(2.43 mm) and generalized intercepts(lgN_(W)) is lower(3.19) in CR,aligning with a "continentallike" cluster,which is mainly influenced by the joint impact of in-cloud ice-based processes and the below-cloud environmental background.Also,the empirical relationships of shape-slope(μ-Λ),radar reflectivity-rain rate(Z-R),and rainfall kinetic energy(KE_(time)-Rand KE_(time)-Z) are localized.To quantitatively analyze the impact of DSD parameters on kinetic energy estimation,power-law KE_(time)-R and KE_(time)-Z relationships are derived based on the normalized gamma distribution.N_(W)takes precedence over μ in affecting variabilities of multiplicative coefficients,especially for KE_(time)-R relationship where the multiplicative coefficient is proportional to N_(W)^(-0.287).It should be noted that although the proportion of CR occurring throughout the summer is small,raindrops with lower N_(W) and larger Dmwill generate higher KE_(time),which will bring a higher potential risk of soil erosion in semi-arid regions over IMP.
基金supported by the National Natural Science Foundation of China(12161029,12171335)the National Natural Science Foundation of Hainan Province(121RC149)+1 种基金the Science Development Project of Sichuan University(2020SCUNL201)the Natural Sciences and Engineering Research Council of Canada(4394-2018).
文摘Let I be the set of all infinitely divisible random variables with finite second moments,I_(0)={X∈I;Var(X)>0},P_(I)=inf_(x∈I)P{|X-E[X]|≤√Var(X)}and P_(I_(0))=inf P{|X-E[X]|<√Var(X)}.Firstly,we prove that P_(I)≥P_(I_(0))>0.Secondly,we find_(x∈I_(0))the exact values of inf P{|X-E[X]|≤√Var(X)}and inf P{|X-E[X]|<√Var(X)}for the cases that J is the set of all geometric random variables,symmetric geometric random variables,Poisson random variables and symmetric Poisson random variables,respectively.As a consequence,we obtain that P_(I)≤e^(-1)^(∞)∑_(k=0)1/2^(2k)(k!)^(2)≈0.46576 and P_(I_(0))≤e^(-1)≈0.36788.
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 National Natural Science Foundation of China(32172118)the National Key Research and Development Program of China(2016YFD0300110 and 2016YFD0300101)+1 种基金the Basic Scientific Research Fund of Chinese Academy of Agricultural Sciences,China(S2022ZD05)the Agricultural Science and Technology Innovation Program,China(CAAS-ZDRW202004)。
文摘The distributions of light and nitrogen within a plant's canopy reflect the growth adaptation of crops to the environment and are conducive to improving the carbon assimilation ability.So can the yield in crop production be maximized by improving the light and nitrogen distributions without adding any additional inputs?In this study,the effects of different nitrogen application rates and planting densities on the canopy light and nitrogen distributions of two highyielding maize cultivars(XY335 and DH618)and the regulatory effects of canopy physiological characteristics on radiation use efficiency(RUE)and yield were studied based on high-yield field experiments in Qitai,Xinjiang Uygur Autonomous Region,China,during 2019 and 2020.The results showed that the distribution of photosynthetically active photon flux density(PPFD)in the maize canopy decreased from top to bottom,while the vertical distribution of specific leaf nitrogen(SLN)initially increased and then decreased from top to bottom in the canopy.When SLN began to decrease,the PPDF values of XY335 and DH618 were 0.5 and 0.3,respectively,corresponding to 40.6 and49.3%of the total leaf area index(LAI).Nitrogen extinction coefficient(K_(N))/light extinction coefficient(K_(L))ratio in the middle and lower canopy of XY335(0.32)was 0.08 higher than that of DH618(0.24).The yield and RUE of XY335(17.2 t ha^(-1)and 1.8g MJ^(-1))were 7.0%(1.1 t ha^(-1))and 13.7%(0.2 g MJ^(-1))higher than those of DH618(16.1 t ha^(-1)and 1.6 g MJ^(-1)).Therefore,better light conditions(where the proportion of LAI in the upper and middle canopy was small)improved the light distribution when SLN started to decline,thus helping to mobilize the nitrogen distribution and maintain a high K_(N)and K_(N)/K_(L)ratio.In addition,K_(N)/K_(L)was a key parameter for yield improvement when the maize nutrient requirements were met at 360 kg N ha^(-1).At this level,an appropriately optimized high planting density could promote nitrogen utilization and produce higher yields and greater efficiency.The results of this study will be important for achieving high maize yields and the high efficiency cultivation and breeding of maize in the future.
基金National Natural Science Foundation of China(12125509,11961141003,12275361,U2267205,12175152,12175121)National Key Research and Development Project(2022YFA1602301)Continuous-support Basic Scientific Research Project。
文摘To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produced by the tandem-accelerator in the China Institute of Atomic Energy was utilized.The proton beam was first transmitted through a 60.5μm aluminum foil and then impinged on a natural LiF target to produce neutron beam via^(7)Li(p,n)7Be reaction.The quasi-Gaussian energy distribution of protons in the LiF target resulted in neutron energy spectra that agreed with a Maxwellian energy distribution at kT=(22±2)keV,which was achieved by integrating neutrons detected within an emission angle of 65.0°±2.6°using a ^(6)Li glass detector positioned at 65°relative to the proton beam direction.The narrow angular spread of the Maxwelliandistributed neutron beam enables direct measurement of neutron capture cross-sections for most s-process nuclides,overcoming previous experimental limitations associated with broad angular distributions.
基金supported by the Science and Technology Innovation(STI)2030-Major Projects(2022ZD0205000 to L.L.)CAS“Light of West China”Program(xbzg-zdsys-202404 to L.L.)+1 种基金Yunnan Revitalization Talent Support Program Yunling Scholar Project(to L.L.)Yunnan Fundamental Research Projects(202305AH340006,202301AS070060 to L.L.,202401AT070206 to X.C.)。
文摘The Chinese tree shrew has gained prominence as a model organism due to its phylogenetic proximity to primates,offering distinct advantages over traditional rodent models in biomedical research.However,the neuroanatomy of this species remains insufficiently defined,limiting its utility in neurophysiological and neuropathological studies.In this study,immunofluorescence microscopy was employed to comprehensively map the distribution of three calciumbinding proteins,parvalbumin,calbindin D-28k,and calretinin,across the tree shrew cerebrum.Serial brain sections in sagittal,coronal,and horizontal planes from 12 individuals generated a dataset of 3638 cellular-resolution images.This dataset,accessible via Science Data Bank(https://doi.org/10.57760/sciencedb.23471),provides detailed region-and laminar-selective distributions of calcium-binding proteins valuable for the cyto-and chemoarchitectural characterization of the tree shrew cerebrum.This resource will not only advance our understanding of brain organization and facilitate basic and translational neuroscience research in tree shrews but also enhance comparative and evolutionary analyses across species.
基金This paper is supported by National Natural Science Foundation of China under Grant No.50675209 InnovationFund for Outstanding Scholar of Henan Province under Grant No. 0621000500
文摘The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.
基金supported by Grants in Aid from the Japan Society for the Promotion of Science(JSPS)KAKENHI[grant numbers JP21H01163 and JP23H00149].
文摘A double-moment cloud microphysics scheme requires an assumption for cloud droplet size distributions(DSDs).However,since observations of cloud DSDs are limited,default values for shape parameters and cloud condensation nuclei activation parameters are often used in numerical simulations.In this study,the effects of cloud DSDs on numerical simulations of warm stratiform precipitation around Tokyo are investigated using the Japan Meteorological Agency's non-hydrostatic model,which incorporates a double-moment cloud microphysics scheme.Simulations using the default cloud DSD showed higher cloud droplet number concentrations and lower radar reflectivity than observed data,suggesting that the default cloud DSD is too narrow.Simulations with a cloud DSD based on in situ cloud observations corrected these errors.In addition,observation-based cloud DSDs affected rainfall amounts through the autoconversion rate of cloud water and improved the threat scores.These results suggest that realistic cloud DSDs should be provided for double-moment cloud microphysics schemes in scientific studies.
基金financially supported by the National Key Research and Development Program of China(Nos.2022YFB3505200 and 2023YFB3507000)Inner Mongolia Northern Rare Earth Advanced Materials Technology Innovation Co.,Ltd.Project(No.CXZX-B-202304-0004)
文摘This research delves into controlling the interplay of magnetostatic field and anisotropy field via controlled composition distribution,thereby boosting coercivity in Nd-Ce-Fe-B magnets.As the originally heterogeneous compositions gradually homogenize within the magnet,an interesting trend in coercivity emerges.Initially,coercivity shows a positive trend,increasing as the components start to blend.However,after reaching an optimal point,it begins to decline.Notably,coercivity peaks once the element Ce permeates to the magnet's surfaces.This phenomenon is closely associated with changes in the H_(crit) distribution.H_(crit) is a measure that reflects the interaction between anisotropy field and magnetostatic field.When the composition becomes more uniform,the distribution of H_(crit) shifts,and its minimum absolute value varies.These changes are driven by the combined influence of anisotropy field and magnetostatic field.Understanding these relationships provides valuable insights.It opens up new avenues for enhancing coercivity in Nd-Ce-Fe-B magnets by adjusting and fine-tuning the interactions between these fields.
