Waveform generation and digitization play essential roles in numerous physics experiments.In traditional distributed systems for large-scale experiments,each frontend node contains an FPGA for data preprocessing,which...Waveform generation and digitization play essential roles in numerous physics experiments.In traditional distributed systems for large-scale experiments,each frontend node contains an FPGA for data preprocessing,which interfaces with various data converters and exchanges data with a backend central processor.However,the streaming readout architecture has become a new paradigm for several experiments benefiting from advancements in data transmission and computing technologies.This paper proposes a scalable distributed waveform generation and digitization system that utilizes fiber optical connections for data transmission between frontend nodes and a central processor.By utilizing transparent transmission on top of the data link layer,the clock and data ports of the converters in the frontend nodes are directly mapped to the FPGA firmware at the backend.This streaming readout architecture reduces the complexity of frontend development and maintains the data conversion in proximity to the detector.Each frontend node uses a local clock for waveform digitization.To translate the timing information of events in each channel into the system clock domain within the backend central processing FPGA,a novel method is proposed and evaluated using a demonstrator system.展开更多
A Mixed Numerology OFDM(MN-OFDM)system is essential in 6G and beyond.However,it encounters challenges due to Inter-Numerology Interference(INI).The upcoming 6G technology aims to support innovative applications with h...A Mixed Numerology OFDM(MN-OFDM)system is essential in 6G and beyond.However,it encounters challenges due to Inter-Numerology Interference(INI).The upcoming 6G technology aims to support innovative applications with high data rates,low latency,and reliability.Therefore,effective handling of INI is crucial to meet the diverse requirements of these applications.To address INI in MN-OFDM systems,this paper proposes a User-Based Numerology and Waveform(UBNW)approach that uses various OFDM-based waveforms and their parameters to mitigate INI.By assigning a specific waveform and numerology to each user,UBNW mitigates INI,optimizes service characteristics,and addresses user demands efficiently.The required Guard Bands(GB),expressed as a ratio of user bandwidth,vary significantly across different waveforms at an SIR of 25 dB.For instance,OFDM-FOFDM needs only 2.5%,while OFDM-UFMC,OFDM-WOLA,and conventional OFDM require 7.5%,24%,and 40%,respectively.The time-frequency efficiency also varies between the waveforms.FOFDM achieves 85.6%,UFMC achieves 81.6%,WOLA achieves 70.7%,and conventional OFDM achieves 66.8%.The simulation results demonstrate that the UBNW approach not only effectively mitigates INI but also enhances system flexibility and time-frequency efficiency while simultaneously reducing the required GB.展开更多
Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy...Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.展开更多
Waveform regulator in charge is a method that can realize multi-source detonation wave superposition through a single point detonation.The method does not need to weaken the strength of shell,and relies on the high st...Waveform regulator in charge is a method that can realize multi-source detonation wave superposition through a single point detonation.The method does not need to weaken the strength of shell,and relies on the high stress generated by superposition to cut shell into regular fragments.Additionally,it can be combined with different initiation methods to alter the fragmentation outcomes.In this study,aiming at the fracture strain of metal cylindrical shell driven by explosive charge with waveform regulator,theoretical analysis was first adopted to obtain the prediction model of the fracture strain of cylindrical shell with waveform regulator and the model of the axial distribution of the stress concentration factor.On this basis,both theoretical analysis and numerical models were utilized to investigate the effect of waveform regulator on the initial velocity of fragments.Finally,experiments were conducted to validate the fracture strain prediction model for cylindrical shell with waveform regulator.The research results show that the collision angles of the detonation waves at different axial positions are different,which leads to the stress concentration factor on the shell presenting a trend of gradually decreasing,then sharply increasing,and then rapidly decreasing along the axial direction.Additionally,the changes in the slot spacing and the thickness of outer charge will also affect the stress concentration factor,and the influence of outer charge thickness is relatively large.The smaller the ratio of charge volume to waveform regulator volume,the larger the axial sparse wave intensity and the more the fragment initial velocity decrease.From the initiation end to the non-initiation end,the failure modes of the shell sequentially change from pure shear,to mixed tensile-shear,and finally to pure tensile failure.The experimental results are in good agreement with the calculated results of the fracture strain model,and the maximum relative error is less than 10%,which indicates that the fracture strain prediction model of the cylindrical shell with waveform regulator established in this paper by considering the increase of elastic energy per unit volume caused by stress concentration on the shell is reliable.展开更多
This paper establishes an amplitude modulation heating model, simulating the far-field radiation of ELF/VLF signals generated by modulation heating, as well as the specific location and longitudinal extent of the radi...This paper establishes an amplitude modulation heating model, simulating the far-field radiation of ELF/VLF signals generated by modulation heating, as well as the specific location and longitudinal extent of the radiation source. We consider various modulation waveforms and find that square-wave modulation has the highest excitation efficiency for ELF/VLF signals, and that square-wave modulation with a smaller duty cycle(<50%) exhibits higher excitation efficiency for ELF/VLF signals, while the sin^(2)t waveform modulation yields the lowest proportion of harmonic energy in the generated signals. The amplitude of the second harmonic generated by the sin^(2)t waveform is less than one-tenth that of the fundamental frequency, and the energy of higher-frequency harmonics can be negligibly small compared with those of the fundamental wave. It is a challenging task to achieve a balance between enhancing the excitation efficiency of ELF/VLF signals and also suppressing harmonics generated by the modulated heating process. This is because the harmonics are correspondingly enhanced as the excitation efficiency of the signals is increased. However, we find that under conditions of varying effective radiant power and modulation frequency, as long as the modulation waveform is unchanged, the energy ratio between the fundamental frequency signal generated by modulated heating and each harmonic is relatively fixed, with changes only in signal intensity and the location of the radiation source zone. This implies that one can first select modulation waveforms that make the signal less prone to distortion, then increase the effective radiated power to enhance the signal strength, without concern for harmonic interference of the fundamental signal.展开更多
Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model ...Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model with high accuracy.However,due to inaccurate initial models,the absence of low-frequency data,and incomplete observational data,full waveform inversion(FWI)exhibits pronounced nonlinear characteristics.When the strata are buried deep,the inversion capability of this method is constrained.To enhance the accuracy and precision of FWI,this paper introduces a novel approach to address the aforementioned challenges—namely,a fractional-order anisotropic total p-variation regularization for full waveform inversion(FATpV-FWI).This method incorporates fractional-order total variation(TV)regularization to construct the inversion objective function,building upon TV regularization,and subsequently employs the alternating direction multiplier method for solving.This approach mitigates the step effect stemming from total variation in seismic inversion,thereby facilitating the reconstruction of sharp interfaces of geophysical parameters while smoothing background variations.Simultaneously,replacing integer-order differences with fractional-order differences bolsters the correlation among seismic data and diminishes the scattering effect caused by integer-order differences in seismic inversion.The outcomes of model tests validate the efficacy of this method,highlighting its ability to enhance the overall accuracy of the inversion process.展开更多
Low sidelobe waveform can reduce mutual masking between targets and increase the detection probability of weak targets.A low sidelobe waveform design method based on complementary amplitude coding(CAC)is proposed in t...Low sidelobe waveform can reduce mutual masking between targets and increase the detection probability of weak targets.A low sidelobe waveform design method based on complementary amplitude coding(CAC)is proposed in this paper,which can be used to reduce the sidelobe level of multiple waveforms.First,the CAC model is constructed.Then,the waveform design problem is transformed into a nonlinear optimization problem by constructing an objective function using the two indicators of peak-to-sidelobe ratio(PSLR)and integrated sidelobe ratio(ISLR).Finally,genetic algorithm(GA)is used to solve the optimization problem to get the best CAC waveforms.Simulations and experiments are conducted to verify the effectiveness of the proposed method.展开更多
Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this...Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this process is highly nonlinear and ill-posed,therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging.We propose a multiscale frequency–domain full waveform inversion(FDFWI)framework for ultrasound computed tomography(USCT)imaging of biological tissues,which innovatively incorporates Sobolev space norm regularization for enhancement of prior information.Specifically,we investigate the effect of different types of hyperparameter on the imaging quality,during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term.This strategy reduces reliance on predefined hyperparameters,ensuring robust inversion performance.The inversion results from both numerical and experimental tests(i.e.,numerical breast,thigh,and ex vivo pork-belly tissue)demonstrate the effectiveness of our regularized FWI strategy.These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.展开更多
Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI present...Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.展开更多
Terahertz(THz)wireless communication has been recognized as a powerful technology to meet the everincreasing demand of ultra-high rate services.In order to achieve efficient and reliable wireless communications over T...Terahertz(THz)wireless communication has been recognized as a powerful technology to meet the everincreasing demand of ultra-high rate services.In order to achieve efficient and reliable wireless communications over THz bands,it is extremely necessary to find an appropriate waveform for THz communications.In this paper,performance comparison of various single-carrier and multi-carrier waveforms over THz channels will be provided.Specifically,first,a system model for terahertz communication is briefly described,which includes amplifier nonlinearity,propagation characteristic,phase noise,etc.Then,the transceiver architectures related to both single-carrier and multi-carrier waveforms are presented,as well as their corresponding signal processing techniques.To evaluate the suitability of the waveforms,key performance metrics concerning power efficiency,transmission performance,and computational complexity are provided.Simulation results are provided to compare and validate the performance of different waveforms,which demonstrate the outstanding performance of Discrete-Fourier-Transform spread Orthogonal Frequency Division Multiplexing(DFT-s-OFDM)to THz communications when compared to Cyclic Prefix-OFDM(CP-OFDM)and other single-carrier waveforms.展开更多
The low-wavenumber components in the gradient of full waveform inversion(FWI)play a vital role in the stability of the inversion.However,when FWI is implemented in some high frequencies and current models are not far ...The low-wavenumber components in the gradient of full waveform inversion(FWI)play a vital role in the stability of the inversion.However,when FWI is implemented in some high frequencies and current models are not far away from the real velocity model,an excessive number of low-wavenumber components in the gradient will also reduce the convergence rate and inversion accuracy.To solve this problem,this paper firstly derives a formula of scattering angle weighted gradient in FWI,then proposes a hybrid gradient.The hybrid gradient combines the conventional gradient of FWI with the scattering angle weighted gradient in each inversion frequency band based on an empirical formula derived herein.Using weighted hybrid mode,we can retain some low-wavenumber components in the initial lowfrequency inversion to ensure the stability of the inversion,and use more high-wavenumber components in the high-frequency inversion to improve the convergence rate.The results of synthetic data experiment demonstrate that compared to the conventional FWI,the FWI based on the proposed hybrid gradient can effectively reduce the low-wavenumber components in the gradient under the premise of ensuring inversion stability.It also greatly enhances the convergence rate and inversion accuracy,especially in the deep part of the model.And the field marine seismic data experiment also illustrates that the FWI based on hybrid gradient(HGFWI)has good stability and adaptability.展开更多
In this paper,we formulate the precoding problem of integrated sensing and communication(ISAC)waveform as a non-convex quadratically constrained quadratic programming(QCQP),in which the weighted sum of communication m...In this paper,we formulate the precoding problem of integrated sensing and communication(ISAC)waveform as a non-convex quadratically constrained quadratic programming(QCQP),in which the weighted sum of communication multi-user interference(MUI)and the gap between dual-use waveform and ideal radar waveform is minimized with peak-toaverage power ratio(PAPR)constraints.We propose an efficient algorithm based on alternating direction method of multipliers(ADMM),which is able to decouple multiple variables and provide a closed-form solution for each subproblem.In addition,to improve the sensing performance in both spatial and temporal domains,we propose a new criteria to design the ideal radar waveform,in which the beam pattern is made similar to the ideal one and the integrated sidelobe level of the ambiguity function in each target direction is minimized in the region of interest.The limited memory Broyden-Fletcher-Goldfarb-Shanno(LBFGS)algorithm is applied to the design of the ideal radar waveform which works as a reference in the design of the dual-function waveform.Numerical results indicate that the designed dual-function waveform is capable of offering good communication quality of service(QoS)and sensing performance.展开更多
Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be co...Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be considered for the joint radar communication system.In this paper,a co-sharing waveform(CSW)is proposed to achieve communication and radar sensing simultaneously.To eliminate the co-interference between the communication and sensing signal,signal splitting and processing methods for communication data demodulation and radar signal processing are given respectively.Simulation results show that the bit error rate(BER)of CSW is close to that of the pure communication waveform.Moreover,the proposed CSW can achieve better performance than the existing waveforms in terms of range and velocity estimation.展开更多
Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuit...Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuities.More specifically,seismic migration estimates the reflectivity function(stacked average reflectivity or pre-stack angle-dependent reflectivity)from seismic reflection data.On the other hand,seismic inversion quantitatively estimates the intrinsic rock properties of subsurface formulations.Such seismic inversion methods are applicable to detect hydrocarbon reservoirs that may exhibit lateral variations in the inverted parameters.Although there exist many differences,pre-stack seismic migration is similar with the first iteration of the general linearized seismic inversion.Usually,seismic migration and inversion techniques assume an acoustic or isotropic elastic medium.Unconventional reservoirs such as shale and tight sand formation have notable anisotropic property.We present a linearized waveform inversion(LWI)scheme for weakly anisotropic elastic media with vertical transversely isotropic(VTI)symmetry.It is based on two-way anisotropic elastic wave equation and simultaneously inverts for the localized perturbations(ΔVp_(0)/Vp_(0)/Vs_(0)/Vs_(0)/,Δ∈,Δδ)from the long-wavelength reference model.Our proposed VTI-elastic LWI is an iterative method that requires a forward and an adjoint operator acting on vectors in each iteration.We derive the forward Born approximation operator by perturbation theory and adjoint operator via adjoint-state method.The inversion has improved the quality of the images and reduces the multi-parameter crosstalk comparing with the adjoint-based images.We have observed that the multi-parameter crosstalk problem is more prominent in the inversion images for Thomsen anisotropy parameters.Especially,the Thomsen parameter is the most difficult to resolve.We also analyze the multi-parameter crosstalk using scattering radiation patterns.The linearized waveform inversion for VTI-elastic media presented in this article provides quantitative information of the rock properties that has the potential to help identify hydrocarbon reservoirs.展开更多
In deeply buried tunneling projects,geological conditions are often complex and varied.Microseismic monitoring systems are extensively deployed to enhance construction safety.However,when the current geological condit...In deeply buried tunneling projects,geological conditions are often complex and varied.Microseismic monitoring systems are extensively deployed to enhance construction safety.However,when the current geological conditions differ from those present during the signal collection for model training,recognition accuracy tends to decline significantly.Therefore,improving the applicability and stability of microseismic waveform recognition models across varying geological conditions has emerged as a critical challenge.To address this issue,we first analyze the impact of lithological changes and the development of structural planes on the features of microseismic waveforms.Subsequently,we propose a category-domain-aligned transfer learning method that enables the transfer of recognition capabilities across geological conditions by facilitating similar feature extraction and the recognition of cross-geological fracture waveforms.In this model,feature separation modeling enhances the extraction of category features of waveforms under different geological conditions.A deep transfer learning mechanism distinguishes between unique and common features,allowing for the capture of essential features necessary for model parameter updates.Through comparative experiments and feature distribution alignment and visualization,we demonstrate that the accuracy of microseismic waveform recognition across geological conditions achieves 90%.Additionally,the performance of our method is validated using microseismic signals collected from different sections of the construction site.展开更多
Full-waveform inversion(FWI) uses the full information of seismic data to obtain a quantitative estimation of subsurface physical parameters. Anisotropic FWI has the potential to recover high-resolution velocity and a...Full-waveform inversion(FWI) uses the full information of seismic data to obtain a quantitative estimation of subsurface physical parameters. Anisotropic FWI has the potential to recover high-resolution velocity and anisotropy parameter models, which are critical for imaging the long-offset and wideazimuth data. We develop an acoustic anisotropic FWI method based on a simplified pure quasi P-wave(qP-wave) equation, which can be solved efficiently and is beneficial for the subsequent inversion.Using the inverse Hessian operator to precondition the functional gradients helps to reduce the parameter tradeoff in the multi-parameter inversion. To balance the accuracy and efficiency, we extend the truncated Gauss-Newton(TGN) method into FWI of pure qP-waves in vertical transverse isotropic(VTI) media. The inversion is performed in a nested way: a linear inner loop and a nonlinear outer loop.We derive the formulation of Hessian-vector products for pure qP-waves in VTI media based on the Lagrange multiplier method and compute the model update by solving a Gauss-Newton linear system via a matrix-free conjugate gradient method. A suitable preconditioner and the Eisenstat and Walker stopping criterion for the inner iterations are used to accelerate the convergence and avoid prohibitive computational cost. We test the proposed FWI method on several synthetic data sets. Inversion results reveal that the pure acoustic VTI FWI exhibits greater accuracy than the conventional pseudoacoustic VTI FWI. Additionally, the TGN method proves effective in mitigating the parameter crosstalk and increasing the accuracy of anisotropy parameters.展开更多
Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-vary...Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features.Sound propagation channels in seawater are time-and space-varying convolutional channels.In the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent possible.We propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition.In the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is introduced.This constraint can ensure that the influence of convolutional channels on hash features is minimized.In addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features.Experimental results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.展开更多
The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer(OBS)refraction data.Traditional methods fo...The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer(OBS)refraction data.Traditional methods for first-arrival picking based on sample points are characterized by theoretical errors,especially in low-sampling-frequency OBS data because the travel time of seismic waves is not an integer multiple of the sampling interval.In this paper,a first-arrival picking method that utilizes the spatial waveform variation characteristics of active source OBS data is presented.First,the distribution law of theoretical error is examined;adjacent traces exhibit variation characteristics in their waveforms.Second,a label cross-correlation superposition method for extracting highfrequency signals is presented to enhance the first-arrival picking precision.Results from synthetic and field data verify that the proposed approach is robust,successfully overcomes the limitations of low sampling frequency,and achieves precise outcomes that are comparable with those of high-sampling-frequency data.展开更多
Objective:To observe and compare the clinical effects of different electroacupuncture waveforms on primary dysmenorrhea.Methods: This was a prospective,randomized,three-group,parallel-controlled trial.Participants wit...Objective:To observe and compare the clinical effects of different electroacupuncture waveforms on primary dysmenorrhea.Methods: This was a prospective,randomized,three-group,parallel-controlled trial.Participants with primary dysmenorrhea were randomly divided into dense-sparse wave,continuous wave,and discontinuous wave groups in a 1:1:1 ratio.Two lateral Ciliao(BL 32)points were used.All three groups started treatment 3–5 days before menstruation,once a day for six sessions per course of treatment,one course of treatment per menstrual cycle,and three menstrual cycles.The primary outcome measure was the proportion with an average visual analog scale(VAS)score reduction of≥50%from baseline for dysmenorrhea in the third menstrual cycle during treatment.The secondary outcome measures included changes in dysmenorrhea VAS scores,Cox Menstrual Symptom Scale scores and the proportion of patients taking analgesic drugs.Results: The proportion of cases where the average VAS score for dysmenorrhea decreased by≥50%from baseline in the third menstrual cycle was not statistically significant(P>.05).Precisely 30 min after acupuncture and regarding immediate analgesia on the most severe day of dysmenorrhea,there was a statistically significant difference in the dense-sparse wave group compared with the other two groups during the third menstrual cycle(P<.05).Additionally,there was a statistically significant difference between the dense-sparse wave and discontinuous wave groups 24 h after acupuncture(P<.05).Conclusions: Waveform electroacupuncture can alleviate primary dysmenorrhea and its related symptoms in patients.The three groups showed similar results in terms of short-and long-term analgesic efficacy and a reduction in the number of patients taking analgesic drugs.Regarding achieving immediate analgesia,the dense-sparse wave group was slightly better than the other two groups.展开更多
Traditionally, simplification has been used in scientific modeling practices. However, recent advancements in deep learning techniques have provided a means to represent complex models. As a result, deep neural networ...Traditionally, simplification has been used in scientific modeling practices. However, recent advancements in deep learning techniques have provided a means to represent complex models. As a result, deep neural networks should be able to approximate the complex models, with a high degree of generalization. To achieve generalization, it is necessary to have a diverse range of examples in the training of the neural network, for example in data-driven FWI, training data should cover the expected subsurface models. To meet this requirement, we porposed a method to create geologically meaningful velocity models with complex structures and severe topography. However, it is important to note that generalization comes with its own set of challenges.Because of significant variation in topography of the generated velocity models, we need to include this information as an additional input data in training of the network. Therefore, we have transformed the seismic data to a fixed datum to incorporate geometric information. Additionally, we have enhanced the network's performance by introducing a term in the network loss function. Multiple metrics have been employed to evaluate the performance of the network. The results indicate that by providing the necessary information to the network and employing computational techniques to refine the model's accuracy, deep neural networks are capable of accurately estimating velocity models in complex environments characterized by extreme topography.展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFA1604703)the National Natural Science Foundation of China(No.12375189)the National Key Research and Development Program of China(No.2021YFA1601300)。
文摘Waveform generation and digitization play essential roles in numerous physics experiments.In traditional distributed systems for large-scale experiments,each frontend node contains an FPGA for data preprocessing,which interfaces with various data converters and exchanges data with a backend central processor.However,the streaming readout architecture has become a new paradigm for several experiments benefiting from advancements in data transmission and computing technologies.This paper proposes a scalable distributed waveform generation and digitization system that utilizes fiber optical connections for data transmission between frontend nodes and a central processor.By utilizing transparent transmission on top of the data link layer,the clock and data ports of the converters in the frontend nodes are directly mapped to the FPGA firmware at the backend.This streaming readout architecture reduces the complexity of frontend development and maintains the data conversion in proximity to the detector.Each frontend node uses a local clock for waveform digitization.To translate the timing information of events in each channel into the system clock domain within the backend central processing FPGA,a novel method is proposed and evaluated using a demonstrator system.
文摘A Mixed Numerology OFDM(MN-OFDM)system is essential in 6G and beyond.However,it encounters challenges due to Inter-Numerology Interference(INI).The upcoming 6G technology aims to support innovative applications with high data rates,low latency,and reliability.Therefore,effective handling of INI is crucial to meet the diverse requirements of these applications.To address INI in MN-OFDM systems,this paper proposes a User-Based Numerology and Waveform(UBNW)approach that uses various OFDM-based waveforms and their parameters to mitigate INI.By assigning a specific waveform and numerology to each user,UBNW mitigates INI,optimizes service characteristics,and addresses user demands efficiently.The required Guard Bands(GB),expressed as a ratio of user bandwidth,vary significantly across different waveforms at an SIR of 25 dB.For instance,OFDM-FOFDM needs only 2.5%,while OFDM-UFMC,OFDM-WOLA,and conventional OFDM require 7.5%,24%,and 40%,respectively.The time-frequency efficiency also varies between the waveforms.FOFDM achieves 85.6%,UFMC achieves 81.6%,WOLA achieves 70.7%,and conventional OFDM achieves 66.8%.The simulation results demonstrate that the UBNW approach not only effectively mitigates INI but also enhances system flexibility and time-frequency efficiency while simultaneously reducing the required GB.
基金supported by the National Natural Science Foundation of China(Grant No.82151302)the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-B-113)+1 种基金the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-A-019)the CAMS Innovation Fund for Medical Sciences(Grant No.2021-12M-1-014).
文摘Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.
基金supported by the National Natural Science Foundation of China(Grant No.12302437)Natural Science Foundation of Jiangsu Province(Grant No.SBK2023045424)。
文摘Waveform regulator in charge is a method that can realize multi-source detonation wave superposition through a single point detonation.The method does not need to weaken the strength of shell,and relies on the high stress generated by superposition to cut shell into regular fragments.Additionally,it can be combined with different initiation methods to alter the fragmentation outcomes.In this study,aiming at the fracture strain of metal cylindrical shell driven by explosive charge with waveform regulator,theoretical analysis was first adopted to obtain the prediction model of the fracture strain of cylindrical shell with waveform regulator and the model of the axial distribution of the stress concentration factor.On this basis,both theoretical analysis and numerical models were utilized to investigate the effect of waveform regulator on the initial velocity of fragments.Finally,experiments were conducted to validate the fracture strain prediction model for cylindrical shell with waveform regulator.The research results show that the collision angles of the detonation waves at different axial positions are different,which leads to the stress concentration factor on the shell presenting a trend of gradually decreasing,then sharply increasing,and then rapidly decreasing along the axial direction.Additionally,the changes in the slot spacing and the thickness of outer charge will also affect the stress concentration factor,and the influence of outer charge thickness is relatively large.The smaller the ratio of charge volume to waveform regulator volume,the larger the axial sparse wave intensity and the more the fragment initial velocity decrease.From the initiation end to the non-initiation end,the failure modes of the shell sequentially change from pure shear,to mixed tensile-shear,and finally to pure tensile failure.The experimental results are in good agreement with the calculated results of the fracture strain model,and the maximum relative error is less than 10%,which indicates that the fracture strain prediction model of the cylindrical shell with waveform regulator established in this paper by considering the increase of elastic energy per unit volume caused by stress concentration on the shell is reliable.
基金supported by the National Key R&D Program of China (No. 2022YFE0204100)the National Natural Science Foundation of China (12205067 and 12375199)the Fundamental Research Funds for the Central Universities (Grant No. HIT.OCEF. 2022036)。
文摘This paper establishes an amplitude modulation heating model, simulating the far-field radiation of ELF/VLF signals generated by modulation heating, as well as the specific location and longitudinal extent of the radiation source. We consider various modulation waveforms and find that square-wave modulation has the highest excitation efficiency for ELF/VLF signals, and that square-wave modulation with a smaller duty cycle(<50%) exhibits higher excitation efficiency for ELF/VLF signals, while the sin^(2)t waveform modulation yields the lowest proportion of harmonic energy in the generated signals. The amplitude of the second harmonic generated by the sin^(2)t waveform is less than one-tenth that of the fundamental frequency, and the energy of higher-frequency harmonics can be negligibly small compared with those of the fundamental wave. It is a challenging task to achieve a balance between enhancing the excitation efficiency of ELF/VLF signals and also suppressing harmonics generated by the modulated heating process. This is because the harmonics are correspondingly enhanced as the excitation efficiency of the signals is increased. However, we find that under conditions of varying effective radiant power and modulation frequency, as long as the modulation waveform is unchanged, the energy ratio between the fundamental frequency signal generated by modulated heating and each harmonic is relatively fixed, with changes only in signal intensity and the location of the radiation source zone. This implies that one can first select modulation waveforms that make the signal less prone to distortion, then increase the effective radiated power to enhance the signal strength, without concern for harmonic interference of the fundamental signal.
基金supported by the China Postdoctoral Science Foundation(Grant No.2024MF750281)the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20230326)+1 种基金the Natural Science Foundation Project of Sichuan Province(Grant No.2025ZNSFSC1170)Sichuan Science and Technology Program(Grant No.2023ZYD0158).
文摘Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model with high accuracy.However,due to inaccurate initial models,the absence of low-frequency data,and incomplete observational data,full waveform inversion(FWI)exhibits pronounced nonlinear characteristics.When the strata are buried deep,the inversion capability of this method is constrained.To enhance the accuracy and precision of FWI,this paper introduces a novel approach to address the aforementioned challenges—namely,a fractional-order anisotropic total p-variation regularization for full waveform inversion(FATpV-FWI).This method incorporates fractional-order total variation(TV)regularization to construct the inversion objective function,building upon TV regularization,and subsequently employs the alternating direction multiplier method for solving.This approach mitigates the step effect stemming from total variation in seismic inversion,thereby facilitating the reconstruction of sharp interfaces of geophysical parameters while smoothing background variations.Simultaneously,replacing integer-order differences with fractional-order differences bolsters the correlation among seismic data and diminishes the scattering effect caused by integer-order differences in seismic inversion.The outcomes of model tests validate the efficacy of this method,highlighting its ability to enhance the overall accuracy of the inversion process.
基金supported by the National Natural Science Foundation of China(62001481,61890542)the Natural Science Foundation of Hunan Province(2021JJ40686).
文摘Low sidelobe waveform can reduce mutual masking between targets and increase the detection probability of weak targets.A low sidelobe waveform design method based on complementary amplitude coding(CAC)is proposed in this paper,which can be used to reduce the sidelobe level of multiple waveforms.First,the CAC model is constructed.Then,the waveform design problem is transformed into a nonlinear optimization problem by constructing an objective function using the two indicators of peak-to-sidelobe ratio(PSLR)and integrated sidelobe ratio(ISLR).Finally,genetic algorithm(GA)is used to solve the optimization problem to get the best CAC waveforms.Simulations and experiments are conducted to verify the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(Grant No.12474461)the Basic and Frontier Exploration Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences(Grant No.JCQY202402)the Goal-Oriented Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences(Grant No.MBDX202113).
文摘Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this process is highly nonlinear and ill-posed,therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging.We propose a multiscale frequency–domain full waveform inversion(FDFWI)framework for ultrasound computed tomography(USCT)imaging of biological tissues,which innovatively incorporates Sobolev space norm regularization for enhancement of prior information.Specifically,we investigate the effect of different types of hyperparameter on the imaging quality,during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term.This strategy reduces reliance on predefined hyperparameters,ensuring robust inversion performance.The inversion results from both numerical and experimental tests(i.e.,numerical breast,thigh,and ex vivo pork-belly tissue)demonstrate the effectiveness of our regularized FWI strategy.These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant No.2021QNLM020001)the National Key R&D Program of China(Grant No.2019YFC0605503C)+2 种基金the Major Scientific and Technological Projects of China National Petroleum Corporation(CNPC)(Grant No.ZD2019-183-003)the National Outstanding Youth Science Foundation(Grant No.41922028)the National Innovation Group Project(Grant No.41821002).
文摘Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.
基金supported in part by the National Key R&D Program of China under Grant 2018YFB1801501in part by the National Natural Science Foundation of China under Grant 62101306supported by OPPO Research Fund。
文摘Terahertz(THz)wireless communication has been recognized as a powerful technology to meet the everincreasing demand of ultra-high rate services.In order to achieve efficient and reliable wireless communications over THz bands,it is extremely necessary to find an appropriate waveform for THz communications.In this paper,performance comparison of various single-carrier and multi-carrier waveforms over THz channels will be provided.Specifically,first,a system model for terahertz communication is briefly described,which includes amplifier nonlinearity,propagation characteristic,phase noise,etc.Then,the transceiver architectures related to both single-carrier and multi-carrier waveforms are presented,as well as their corresponding signal processing techniques.To evaluate the suitability of the waveforms,key performance metrics concerning power efficiency,transmission performance,and computational complexity are provided.Simulation results are provided to compare and validate the performance of different waveforms,which demonstrate the outstanding performance of Discrete-Fourier-Transform spread Orthogonal Frequency Division Multiplexing(DFT-s-OFDM)to THz communications when compared to Cyclic Prefix-OFDM(CP-OFDM)and other single-carrier waveforms.
基金jointly supported by Young Scientists Cultivation Fund Project of Harbin Engineering University(79000013/003)the Mount Taishan Industrial Leading Talent Project+1 种基金the Great and Special Project under Grant KJGG-2022-0104 of CNOOC Limitedthe National Natural Science Foundation of China(42006064,42106070,42074138)。
文摘The low-wavenumber components in the gradient of full waveform inversion(FWI)play a vital role in the stability of the inversion.However,when FWI is implemented in some high frequencies and current models are not far away from the real velocity model,an excessive number of low-wavenumber components in the gradient will also reduce the convergence rate and inversion accuracy.To solve this problem,this paper firstly derives a formula of scattering angle weighted gradient in FWI,then proposes a hybrid gradient.The hybrid gradient combines the conventional gradient of FWI with the scattering angle weighted gradient in each inversion frequency band based on an empirical formula derived herein.Using weighted hybrid mode,we can retain some low-wavenumber components in the initial lowfrequency inversion to ensure the stability of the inversion,and use more high-wavenumber components in the high-frequency inversion to improve the convergence rate.The results of synthetic data experiment demonstrate that compared to the conventional FWI,the FWI based on the proposed hybrid gradient can effectively reduce the low-wavenumber components in the gradient under the premise of ensuring inversion stability.It also greatly enhances the convergence rate and inversion accuracy,especially in the deep part of the model.And the field marine seismic data experiment also illustrates that the FWI based on hybrid gradient(HGFWI)has good stability and adaptability.
基金supported in part by the National Natural Science Foundation of China under Grant 62271142in part by the Key Research and Development Program of Jiangsu Province BE2023021+2 种基金in part by the Jiangsu Key Research and Development Program Project under Grant BE2023011-2in part by the Young Scholar Funding of Southeast Universityin part by the Fundamental Research Funds for the Central Universities 2242022k60001。
文摘In this paper,we formulate the precoding problem of integrated sensing and communication(ISAC)waveform as a non-convex quadratically constrained quadratic programming(QCQP),in which the weighted sum of communication multi-user interference(MUI)and the gap between dual-use waveform and ideal radar waveform is minimized with peak-toaverage power ratio(PAPR)constraints.We propose an efficient algorithm based on alternating direction method of multipliers(ADMM),which is able to decouple multiple variables and provide a closed-form solution for each subproblem.In addition,to improve the sensing performance in both spatial and temporal domains,we propose a new criteria to design the ideal radar waveform,in which the beam pattern is made similar to the ideal one and the integrated sidelobe level of the ambiguity function in each target direction is minimized in the region of interest.The limited memory Broyden-Fletcher-Goldfarb-Shanno(LBFGS)algorithm is applied to the design of the ideal radar waveform which works as a reference in the design of the dual-function waveform.Numerical results indicate that the designed dual-function waveform is capable of offering good communication quality of service(QoS)and sensing performance.
基金supported by the National Natural Science Foundation of China(No.62171052 and No.61971054)the Fundamental Research Funds for the Central Universities(No.24820232023YQTD01).
文摘Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be considered for the joint radar communication system.In this paper,a co-sharing waveform(CSW)is proposed to achieve communication and radar sensing simultaneously.To eliminate the co-interference between the communication and sensing signal,signal splitting and processing methods for communication data demodulation and radar signal processing are given respectively.Simulation results show that the bit error rate(BER)of CSW is close to that of the pure communication waveform.Moreover,the proposed CSW can achieve better performance than the existing waveforms in terms of range and velocity estimation.
文摘Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuities.More specifically,seismic migration estimates the reflectivity function(stacked average reflectivity or pre-stack angle-dependent reflectivity)from seismic reflection data.On the other hand,seismic inversion quantitatively estimates the intrinsic rock properties of subsurface formulations.Such seismic inversion methods are applicable to detect hydrocarbon reservoirs that may exhibit lateral variations in the inverted parameters.Although there exist many differences,pre-stack seismic migration is similar with the first iteration of the general linearized seismic inversion.Usually,seismic migration and inversion techniques assume an acoustic or isotropic elastic medium.Unconventional reservoirs such as shale and tight sand formation have notable anisotropic property.We present a linearized waveform inversion(LWI)scheme for weakly anisotropic elastic media with vertical transversely isotropic(VTI)symmetry.It is based on two-way anisotropic elastic wave equation and simultaneously inverts for the localized perturbations(ΔVp_(0)/Vp_(0)/Vs_(0)/Vs_(0)/,Δ∈,Δδ)from the long-wavelength reference model.Our proposed VTI-elastic LWI is an iterative method that requires a forward and an adjoint operator acting on vectors in each iteration.We derive the forward Born approximation operator by perturbation theory and adjoint operator via adjoint-state method.The inversion has improved the quality of the images and reduces the multi-parameter crosstalk comparing with the adjoint-based images.We have observed that the multi-parameter crosstalk problem is more prominent in the inversion images for Thomsen anisotropy parameters.Especially,the Thomsen parameter is the most difficult to resolve.We also analyze the multi-parameter crosstalk using scattering radiation patterns.The linearized waveform inversion for VTI-elastic media presented in this article provides quantitative information of the rock properties that has the potential to help identify hydrocarbon reservoirs.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20297,52222810,52309126 and 52109116).
文摘In deeply buried tunneling projects,geological conditions are often complex and varied.Microseismic monitoring systems are extensively deployed to enhance construction safety.However,when the current geological conditions differ from those present during the signal collection for model training,recognition accuracy tends to decline significantly.Therefore,improving the applicability and stability of microseismic waveform recognition models across varying geological conditions has emerged as a critical challenge.To address this issue,we first analyze the impact of lithological changes and the development of structural planes on the features of microseismic waveforms.Subsequently,we propose a category-domain-aligned transfer learning method that enables the transfer of recognition capabilities across geological conditions by facilitating similar feature extraction and the recognition of cross-geological fracture waveforms.In this model,feature separation modeling enhances the extraction of category features of waveforms under different geological conditions.A deep transfer learning mechanism distinguishes between unique and common features,allowing for the capture of essential features necessary for model parameter updates.Through comparative experiments and feature distribution alignment and visualization,we demonstrate that the accuracy of microseismic waveform recognition across geological conditions achieves 90%.Additionally,the performance of our method is validated using microseismic signals collected from different sections of the construction site.
基金supported by the National Natural Science Foundation of China (grant No. 42174156)the Young Science and Technology Star Project of Shaanxi Province (grant No. 2023KJXX-021)the Fundamental Research Funds for the Central Universities, CHD (grant Nos. 300102263401 and 300102264204)。
文摘Full-waveform inversion(FWI) uses the full information of seismic data to obtain a quantitative estimation of subsurface physical parameters. Anisotropic FWI has the potential to recover high-resolution velocity and anisotropy parameter models, which are critical for imaging the long-offset and wideazimuth data. We develop an acoustic anisotropic FWI method based on a simplified pure quasi P-wave(qP-wave) equation, which can be solved efficiently and is beneficial for the subsequent inversion.Using the inverse Hessian operator to precondition the functional gradients helps to reduce the parameter tradeoff in the multi-parameter inversion. To balance the accuracy and efficiency, we extend the truncated Gauss-Newton(TGN) method into FWI of pure qP-waves in vertical transverse isotropic(VTI) media. The inversion is performed in a nested way: a linear inner loop and a nonlinear outer loop.We derive the formulation of Hessian-vector products for pure qP-waves in VTI media based on the Lagrange multiplier method and compute the model update by solving a Gauss-Newton linear system via a matrix-free conjugate gradient method. A suitable preconditioner and the Eisenstat and Walker stopping criterion for the inner iterations are used to accelerate the convergence and avoid prohibitive computational cost. We test the proposed FWI method on several synthetic data sets. Inversion results reveal that the pure acoustic VTI FWI exhibits greater accuracy than the conventional pseudoacoustic VTI FWI. Additionally, the TGN method proves effective in mitigating the parameter crosstalk and increasing the accuracy of anisotropy parameters.
基金partially supported by the National Key Research and Development Program of China(No.2018 AAA0100400)the Natural Science Foundation of Shandong Province(Nos.ZR2020MF131 and ZR2021ZD19)the Science and Technology Program of Qingdao(No.21-1-4-ny-19-nsh).
文摘Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features.Sound propagation channels in seawater are time-and space-varying convolutional channels.In the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent possible.We propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition.In the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is introduced.This constraint can ensure that the influence of convolutional channels on hash features is minimized.In addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features.Experimental results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.
基金supported by the Major Research Plan on West-Pacific Earth System Multispheric Interactions (Nos.91858215,91958206)the National Natural Science Foundation of China (NSFC)Shiptime Sharing Project (No.41949581)the Key Research and Development Program of Shandong Province (No.2019GHY112019)。
文摘The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer(OBS)refraction data.Traditional methods for first-arrival picking based on sample points are characterized by theoretical errors,especially in low-sampling-frequency OBS data because the travel time of seismic waves is not an integer multiple of the sampling interval.In this paper,a first-arrival picking method that utilizes the spatial waveform variation characteristics of active source OBS data is presented.First,the distribution law of theoretical error is examined;adjacent traces exhibit variation characteristics in their waveforms.Second,a label cross-correlation superposition method for extracting highfrequency signals is presented to enhance the first-arrival picking precision.Results from synthetic and field data verify that the proposed approach is robust,successfully overcomes the limitations of low sampling frequency,and achieves precise outcomes that are comparable with those of high-sampling-frequency data.
基金supported by Technology Innovation Special Project of Dongzhimen Hospital affiliated to Beijing University of Chinese Medicine.
文摘Objective:To observe and compare the clinical effects of different electroacupuncture waveforms on primary dysmenorrhea.Methods: This was a prospective,randomized,three-group,parallel-controlled trial.Participants with primary dysmenorrhea were randomly divided into dense-sparse wave,continuous wave,and discontinuous wave groups in a 1:1:1 ratio.Two lateral Ciliao(BL 32)points were used.All three groups started treatment 3–5 days before menstruation,once a day for six sessions per course of treatment,one course of treatment per menstrual cycle,and three menstrual cycles.The primary outcome measure was the proportion with an average visual analog scale(VAS)score reduction of≥50%from baseline for dysmenorrhea in the third menstrual cycle during treatment.The secondary outcome measures included changes in dysmenorrhea VAS scores,Cox Menstrual Symptom Scale scores and the proportion of patients taking analgesic drugs.Results: The proportion of cases where the average VAS score for dysmenorrhea decreased by≥50%from baseline in the third menstrual cycle was not statistically significant(P>.05).Precisely 30 min after acupuncture and regarding immediate analgesia on the most severe day of dysmenorrhea,there was a statistically significant difference in the dense-sparse wave group compared with the other two groups during the third menstrual cycle(P<.05).Additionally,there was a statistically significant difference between the dense-sparse wave and discontinuous wave groups 24 h after acupuncture(P<.05).Conclusions: Waveform electroacupuncture can alleviate primary dysmenorrhea and its related symptoms in patients.The three groups showed similar results in terms of short-and long-term analgesic efficacy and a reduction in the number of patients taking analgesic drugs.Regarding achieving immediate analgesia,the dense-sparse wave group was slightly better than the other two groups.
文摘Traditionally, simplification has been used in scientific modeling practices. However, recent advancements in deep learning techniques have provided a means to represent complex models. As a result, deep neural networks should be able to approximate the complex models, with a high degree of generalization. To achieve generalization, it is necessary to have a diverse range of examples in the training of the neural network, for example in data-driven FWI, training data should cover the expected subsurface models. To meet this requirement, we porposed a method to create geologically meaningful velocity models with complex structures and severe topography. However, it is important to note that generalization comes with its own set of challenges.Because of significant variation in topography of the generated velocity models, we need to include this information as an additional input data in training of the network. Therefore, we have transformed the seismic data to a fixed datum to incorporate geometric information. Additionally, we have enhanced the network's performance by introducing a term in the network loss function. Multiple metrics have been employed to evaluate the performance of the network. The results indicate that by providing the necessary information to the network and employing computational techniques to refine the model's accuracy, deep neural networks are capable of accurately estimating velocity models in complex environments characterized by extreme topography.
