The dramatic rise in the number of people living in cities has made many environmental and social problems worse.The search for a productive method for disposing of solid waste is the most notable of these problems.Ma...The dramatic rise in the number of people living in cities has made many environmental and social problems worse.The search for a productive method for disposing of solid waste is the most notable of these problems.Many scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated ambiguity.The goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site selection.The characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these uncertainties.To tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s method.Following this,an intelligent algorithm is suggested.The steps of the suggested algorithm are explained with an example that explains itself.The compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their viability.This study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,respectively.It uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior literature.It is simpler for the decisionmakers to look at each option separately because the decision is uncertain.Comparing the computed results,it is discovered that they are consistent and dependable because of their preferred properties.展开更多
In this paper,the authors prove that the parameterized area integralμ_(Ω,S)^(ρ)and the parameterized Littlewood-Paley g_(δ)^(*)-functionμ_(Ω,δ)^(*,ρ)are bounded on two-weight grand homogeneous variable Herz-Mo...In this paper,the authors prove that the parameterized area integralμ_(Ω,S)^(ρ)and the parameterized Littlewood-Paley g_(δ)^(*)-functionμ_(Ω,δ)^(*,ρ)are bounded on two-weight grand homogeneous variable Herz-Morrey spaces MK_(p),θ,q(·))^(α(·),λ)(ω_(1),ω_(2)),where θ>0,λ∈(2,∞),q(·)∈B(R^(n)),α(·)∈L^(∞)(R^(n)),ω_(1)∈A_(p_(ω_(1)))for p_(ω_(1))∈[1,∞]and ω_(2) is a weight.Furthermore,the authors prove that the commutators[b,μ_(Ω,S)^(ρ)]which is formed by b∈BMO(R^(n))and the μ_(Ω,S)^(ρ),and the[b,μ_(Ω,δ)^(*,ρ)]generated by b∈BMO(R^(n))and theμ_(Ω,δ)^(*,ρ)are bounded on MK_(p),θ,q(·))^(α(·),λ)(ω_(1),ω_(2)),respectively.展开更多
Parameterized level-set method(PLSM)has been proposed and developed for many years,and is renowned for its efficacy in ad-dressing topology optimization challenges associated with intricate boundaries and nucleation o...Parameterized level-set method(PLSM)has been proposed and developed for many years,and is renowned for its efficacy in ad-dressing topology optimization challenges associated with intricate boundaries and nucleation of new holes.However,most pertinent investigations in the field rely predominantly on fixed background mesh,which is never remeshed.Consequently,the mesh element partitioned by material interface during the optimization process necessitates approximation by using artificial interpolation models to obtain its element stiffness or other properties.This paper introduces a novel approach to topology op-timization by integrating the PLSM with body-fitted adaptive mesh and Helmholtz-type filter.Primarily,combining the PLSM with body-fitted adaptive mesh enables the regeneration of mesh based on the zero level-set interface.This not only precludes the direct traversal of the material interface through the mesh element during the topology optimization process,but also improves the accuracy of calculation.Additionally,the incorporation of a Helmholtz-type partial differential equation filter,relying solely on mesh information essential for finite element discretization,serves to regulate the topological complexity and the minimum feature size of the optimized structure.Leveraging these advantages,the topology optimization program demonstrates its versa-tility by successfully addressing various design problems,encompassing the minimum mean compliance problem and minimum energy dissipation problem.Ultimately,the result of numerical example indicates that the optimized structure exhibits a dis-tinct and smooth boundary,affirming the effective control over both topological complexity and the minimum feature size of the optimized structure.展开更多
Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in c...Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.展开更多
A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that th...A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.展开更多
In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the...In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the identification of these weak influence parameters in the complex systems and propose a identification model based on the parameter recursion.As an application,three parameters of the steam generator are identified,that is,the valve opening,the valve CV value,and the reference water level,in which the valve opening and the reference water level are weak influence parameters under most operating conditions.Numerical simulation results show that,in comparison with the multi-layer perceptron(MLP),the identification error rate is decreased.Actually,the average identification error rate for the valve opening decreases by 0.96%,for the valve CV decreases by 0.002%,and for the reference water level decreases by 12%after one recursion.After two recursions,the average identification error rate for the valve opening decreases by 11.07%,for the valve CV decreases by 2.601%,and for the reference water level decreases by 95.79%.This method can help to improve the control of the steam generator.展开更多
Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and l...To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and low energy consumption was proposed.But the unreasonable process parameters may lead to the inferior consistency of the forming quality of single-channel multilayer in Joule heat additive manufacturing process,and it is difficult to reach the condition for forming thinwalled parts.Orthogonal experiments were designed to fabricate single-channel multilayer samples with varying numbers of layers,and their forming quality was evaluated.The influence of printing current,forming speed,and contact pressure on the forming quality of the single-channel multilayer was analyzed.The optimal process parameters were obtained and the quality characterization of the experiment results was conducted.Results show that the printing current has the most significant influence on the forming quality of the single-channel multilayer.Under the optimal process parameters,the forming section is well fused and the surface is continuously smooth.The surface roughness of a single-channel 3-layer sample is 0.16μm,and the average Vickers hardness of cross section fusion zone is 317 HV,which lays a foundation for the subsequent use of Joule heat additive manufacturing technique to form thinwall parts.展开更多
The lattice parameter,measured with sufficient accuracy,can be utilized to evaluate the quality of single crystals and to determine the equation of state for materials.We propose an iterative method for obtaining more...The lattice parameter,measured with sufficient accuracy,can be utilized to evaluate the quality of single crystals and to determine the equation of state for materials.We propose an iterative method for obtaining more precise lattice parameters using the interaction points for the pseudo-Kossel pattern obtained from laser-induced X-ray diffraction(XRD).This method has been validated by the analysis of an XRD experiment conducted on iron single crystals.Furthermore,the method was used to calculate the compression ratio and rotated angle of an LiF sample under high pressure loading.This technique provides a robust tool for in-situ characterization of structural changes in single crystals under extreme conditions.It has significant implications for studying the equation of state and phase transitions.展开更多
In the field of antenna engineering parameter calibration for indoor communication base stations,traditional methods suffer from issues such as low efficiency,poor accuracy,and limited applicability to indoor scenario...In the field of antenna engineering parameter calibration for indoor communication base stations,traditional methods suffer from issues such as low efficiency,poor accuracy,and limited applicability to indoor scenarios.To address these problems,a high-precision and high-efficiency indoor base station parameter calibration method based on laser measurement is proposed.We use a high-precision laser tracker to measure and determine the coordinate system transformation relationship,and further obtain the coordinates and attitude of the base station.In addition,we propose a simple calibration method based on point cloud fitting for specific scenes.Simulation results show that using common commercial laser trackers,we can achieve a coordinate correction accuracy of 1 cm and an angle correction accuracy of 0.25°,which is sufficient to meet the needs of wireless positioning.展开更多
The high-speed winding spindle employs a flexible support system incorporating rubber O-rings.By precisely configuring the structural parameters and the number of the O-rings,the spindle can stably surpass its critica...The high-speed winding spindle employs a flexible support system incorporating rubber O-rings.By precisely configuring the structural parameters and the number of the O-rings,the spindle can stably surpass its critical speed points and maintain operational stability across the entire working speed range.However,the support stiffness and damping of rubber O-rings exhibit significant nonlinear frequency dependence.Conventional experimental methods for deriving equivalent stiffness and damping,based on the principle of the forced non-resonance method,require fabricating custom setups for each O-ring specification and conducting vibration tests at varying frequencies,resulting in low efficiency and high costs.This study proposes a hybrid simulation-experimental method for dynamic parameter identification.Firstly,the frequency-dependent dynamic parameters of a specific O-ring support system are experimentally obtained.Subsequently,a corresponding parametric finite element model is established to simulate and solve the equivalent elastic modulus and equivalent stiffness-damping coefficient of this O-ring support system.Ultimately,after iterative simulation,the simulated and experimental results achieve a 99.7%agreement.The parametric finite element model developed herein can directly simulate and inversely estimate frequency-dependent dynamic parameters for O-rings of different specifications but identical elastic modulus.展开更多
In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their ...In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their systems and insufficient consideration of hydration and temperature effects,making it difficult to fully replicate the real flotation environment of chalcopyrite and pyrite.In this study,we employed the self-consistent charge density functional tight-binding(SCC-DFTB)parameterization method to develop a parameter set,CuFeOrg,which includes the interactions between Cu-Fe-C-H-O-N-S-P-Zn elements,to investigate the surface interactions in large-scale flotation systems of chalcopyrite and pyrite.The results of bulk modulus,atomic displacement,band structure,surface relaxation,surface Mulliken charge distribution,and adsorption tests of typical flotation reagents on mineral surfaces demonstrate that CuFeOrg achieves DFT-level accuracy while significantly outperforming DFT in computational efficiency.By constructing large-scale hydration systems of mineral surfaces,as well as large-scale systems incorporating the combined interactions of mineral surfaces,flotation reagents,and hydration,we more realistically reproduce the actual flotation environment.Furthermore,the dynamic analysis results are consistent with mineral surface contact angle experiments.Additionally,CuFeOrg lays the foundation for future studies of more complex and diverse chalcopyrite and pyrite flotation surface systems.展开更多
Crassostrea gigas is distinguished by its robust fertility,adaptability to various environmental conditions,and rich nutritional profile,which render it a cornerstone of the global shellfish market.Tetraploid oysters,...Crassostrea gigas is distinguished by its robust fertility,adaptability to various environmental conditions,and rich nutritional profile,which render it a cornerstone of the global shellfish market.Tetraploid oysters,in particular,is a pivotal role within the oyster industry,however,their growth and survival characteristics need to be improved.This study aimed to assess the viability of enhancing the growth and survival characteristics of tetraploid C.gigas through genetic selection.This was achieved by investigating the heritability and genetic correlation of growth and survival at various stages of C.gigas development.Genetic parameters were assessed based on in-group correlations in 28 full-sib families during the larval stage(days 3,8,13,18,and 23 after fertilization).The heritability of larval shell height and shell length ranged from 0.50 to 0.71 and 0.44 to 0.71,respectively,and varied with age.The heritability of larval survival was 0.40–0.64,indicating that it is viable to enhancelarval survival through selection.The genetic correlation between the growth traits of larvae at different ages was highly positive(0.959–0.999),indicating that the selection of a single growth trait could cause the positive response of other growth traits.However,the genetic correlation between larval survival and growth traits was low,showing a significant negative correlation(-0.198–-0.293)at day 23 of age,suggesting thatselecting for enhanced survival in tetraploid C.gigas may result in a detrimental impact on growth traits.The results are helpful for future researches to evaluate the genetic parameters related to growth and survival traits in juvenile and adult stages of the tetraploid family of C.gigas,which can provide further guidance for the selection of tetraploid C.gigas.展开更多
In the context of the diversity of smart terminals,the unity of the root of trust becomes complicated,which not only affects the efficiency of trust propagation,but also poses a challenge to the security of the whole ...In the context of the diversity of smart terminals,the unity of the root of trust becomes complicated,which not only affects the efficiency of trust propagation,but also poses a challenge to the security of the whole system.In particular,the solidification of the root of trust in non-volatile memory(NVM)restricts the system’s dynamic updating capability,which is an obvious disadvantage in a rapidly changing security environment.To address this issue,this study proposes a novel approach to generate root security parameters using static random access memory(SRAM)physical unclonable functions(PUFs).SRAM PUFs,as a security primitive,show great potential in lightweight security solutions due to their inherent physical properties,low cost and scalability.However,the stability of SRAM PUFs in harsh environments is a key issue.These environmental conditions include extreme temperatures,high humidity,and strong electromagnetic radiation,all of which can affect the performance of SRAM PUFs.In order to ensure the stability of root safety parameters under these conditions,this study proposes an integrated approach that covers not only the acquisition of entropy sources,but also the implementation of algorithms and configuration management.In addition,this study develops a series of reliability-enhancing algorithms,including adaptive parameter selection,data preprocessing,auxiliary data generation,and error correction,which are essential for improving the performance of SRAM PUFs in harsh environments.Based on these techniques,this study establishes six types of secure parameter generation mechanisms,which not only improve the security of the system,but also enhance its adaptability in variable environments.Through a series of experiments,we verify the effectiveness of the proposed method.Under 10 different environmental conditions,our method is able to achieve full recovery of security data with an error rate of less than 25%,which proves the robustness and reliability of our method.These results not only provide strong evidence for the stability of SRAM PUFs in practical applications,but also provide a new direction for future research in the field of smart terminal security.展开更多
AIM:To describe ocular biometric parameters among Han and Uyghur myopic adults in Xinjiang,China.METHODS:A cross-sectional study was conducted.The different ocular biometric parameters collected using Pentacam were an...AIM:To describe ocular biometric parameters among Han and Uyghur myopic adults in Xinjiang,China.METHODS:A cross-sectional study was conducted.The different ocular biometric parameters collected using Pentacam were analyzed,including corneal curvature,corneal astigmatism(CA),horizontal corneal diameter(white-to-white,WTW),corneal volume(CV),pupil diameter(PD),anterior chamber angle(ACA),anterior chamber depth(ACD),anterior chamber volume(ACV)and axial length(AL).RESULTS:In total,2932 participants were included in the final analysis,comprising 2310 Han and 622 Uyghur adults.Adults in the high myopia(HM)group had steeper K2,larger CA,smaller WTW and longer AL in both the Uyghur and Han adults(all P<0.05).The moderate myopia(MM)and HM group had deeper ACV,ACD and wider ACA than the low myopia(all P<0.05)in Han adults,however there were no differences in anterior chamber indices with Uyghur adults.In the Uyghur adults,we noticed that CV,WTW,and ACD were smaller,ACA was narrower,PD was larger,and AL was shorter(all P<0.05).We also noticed sex differences:males had flatter corneas,deeper ACD and ACV,and larger WTW than females(all P<0.05).In the correlation analysis,WTW was positively correlated with ACD and ACV but negatively correlated with K1,K2 and CV(all P<0.05).Narrower ACA was associated with larger PD(rU=-0.25,rH=-0.16,all P<0.01).CONCLUSION:The Han population have different biometric parameters in eyes with HM compared to the Uyghur population,and the Uyghur population may anatomically more susceptible to primary angle closure glaucoma than the Han population.展开更多
Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast res...Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.展开更多
In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In t...In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In the development of MS-GAN,we extend the freeform deformation by incorporating principal component analysis to increase the non-linear deformation ability while maintaining geometric smoothness.The implicit information of multiple baselines is embedded in the feature extraction layers,to enhance the diversity and parameterization of multi-species dataset.Furthermore,Wasserstein GAN with a gradient penalty is used to ensure the stability and convergence of the training networks.Two experiments,ruled surfaces and propeller blade surfaces,are performed to demonstrate the advantages and superiorities of MS-GAN.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
文摘The dramatic rise in the number of people living in cities has made many environmental and social problems worse.The search for a productive method for disposing of solid waste is the most notable of these problems.Many scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated ambiguity.The goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site selection.The characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these uncertainties.To tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s method.Following this,an intelligent algorithm is suggested.The steps of the suggested algorithm are explained with an example that explains itself.The compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their viability.This study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,respectively.It uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior literature.It is simpler for the decisionmakers to look at each option separately because the decision is uncertain.Comparing the computed results,it is discovered that they are consistent and dependable because of their preferred properties.
基金Supported by the National Natural Science Foundation of China(Grant No.12201500)。
文摘In this paper,the authors prove that the parameterized area integralμ_(Ω,S)^(ρ)and the parameterized Littlewood-Paley g_(δ)^(*)-functionμ_(Ω,δ)^(*,ρ)are bounded on two-weight grand homogeneous variable Herz-Morrey spaces MK_(p),θ,q(·))^(α(·),λ)(ω_(1),ω_(2)),where θ>0,λ∈(2,∞),q(·)∈B(R^(n)),α(·)∈L^(∞)(R^(n)),ω_(1)∈A_(p_(ω_(1)))for p_(ω_(1))∈[1,∞]and ω_(2) is a weight.Furthermore,the authors prove that the commutators[b,μ_(Ω,S)^(ρ)]which is formed by b∈BMO(R^(n))and the μ_(Ω,S)^(ρ),and the[b,μ_(Ω,δ)^(*,ρ)]generated by b∈BMO(R^(n))and theμ_(Ω,δ)^(*,ρ)are bounded on MK_(p),θ,q(·))^(α(·),λ)(ω_(1),ω_(2)),respectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.12372200 and 12072242).
文摘Parameterized level-set method(PLSM)has been proposed and developed for many years,and is renowned for its efficacy in ad-dressing topology optimization challenges associated with intricate boundaries and nucleation of new holes.However,most pertinent investigations in the field rely predominantly on fixed background mesh,which is never remeshed.Consequently,the mesh element partitioned by material interface during the optimization process necessitates approximation by using artificial interpolation models to obtain its element stiffness or other properties.This paper introduces a novel approach to topology op-timization by integrating the PLSM with body-fitted adaptive mesh and Helmholtz-type filter.Primarily,combining the PLSM with body-fitted adaptive mesh enables the regeneration of mesh based on the zero level-set interface.This not only precludes the direct traversal of the material interface through the mesh element during the topology optimization process,but also improves the accuracy of calculation.Additionally,the incorporation of a Helmholtz-type partial differential equation filter,relying solely on mesh information essential for finite element discretization,serves to regulate the topological complexity and the minimum feature size of the optimized structure.Leveraging these advantages,the topology optimization program demonstrates its versa-tility by successfully addressing various design problems,encompassing the minimum mean compliance problem and minimum energy dissipation problem.Ultimately,the result of numerical example indicates that the optimized structure exhibits a dis-tinct and smooth boundary,affirming the effective control over both topological complexity and the minimum feature size of the optimized structure.
基金supported by the National Key R&D Program of China [grant number 2023YFF0805202]the National Natural Science Foun-dation of China [grant number 42175045]the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDB42000000]。
文摘Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.
基金financially supported by the National Natural Science Foundation of China(Grant No.42172292)Taishan Scholars Project Special Funding,and Shandong Energy Group(Grant No.SNKJ 2022A01-R26).
文摘A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.
文摘In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the identification of these weak influence parameters in the complex systems and propose a identification model based on the parameter recursion.As an application,three parameters of the steam generator are identified,that is,the valve opening,the valve CV value,and the reference water level,in which the valve opening and the reference water level are weak influence parameters under most operating conditions.Numerical simulation results show that,in comparison with the multi-layer perceptron(MLP),the identification error rate is decreased.Actually,the average identification error rate for the valve opening decreases by 0.96%,for the valve CV decreases by 0.002%,and for the reference water level decreases by 12%after one recursion.After two recursions,the average identification error rate for the valve opening decreases by 11.07%,for the valve CV decreases by 2.601%,and for the reference water level decreases by 95.79%.This method can help to improve the control of the steam generator.
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
基金Shaanxi Province Qin Chuangyuan“Scientist+Engineer”Team Construction Project(2022KXJ-071)2022 Qin Chuangyuan Achievement Transformation Incubation Capacity Improvement Project(2022JH-ZHFHTS-0012)+1 种基金Shaanxi Province Key Research and Development Plan-“Two Chains”Integration Key Project-Qin Chuangyuan General Window Industrial Cluster Project(2023QCY-LL-02)Xixian New Area Science and Technology Plan(2022-YXYJ-003,2022-XXCY-010)。
文摘To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and low energy consumption was proposed.But the unreasonable process parameters may lead to the inferior consistency of the forming quality of single-channel multilayer in Joule heat additive manufacturing process,and it is difficult to reach the condition for forming thinwalled parts.Orthogonal experiments were designed to fabricate single-channel multilayer samples with varying numbers of layers,and their forming quality was evaluated.The influence of printing current,forming speed,and contact pressure on the forming quality of the single-channel multilayer was analyzed.The optimal process parameters were obtained and the quality characterization of the experiment results was conducted.Results show that the printing current has the most significant influence on the forming quality of the single-channel multilayer.Under the optimal process parameters,the forming section is well fused and the surface is continuously smooth.The surface roughness of a single-channel 3-layer sample is 0.16μm,and the average Vickers hardness of cross section fusion zone is 317 HV,which lays a foundation for the subsequent use of Joule heat additive manufacturing technique to form thinwall parts.
基金National Natural Science Foundation of China(12102410)Fund of National Key Laboratory of Shock Wave and Detonation Physics(JCKYS2022212005)。
文摘The lattice parameter,measured with sufficient accuracy,can be utilized to evaluate the quality of single crystals and to determine the equation of state for materials.We propose an iterative method for obtaining more precise lattice parameters using the interaction points for the pseudo-Kossel pattern obtained from laser-induced X-ray diffraction(XRD).This method has been validated by the analysis of an XRD experiment conducted on iron single crystals.Furthermore,the method was used to calculate the compression ratio and rotated angle of an LiF sample under high pressure loading.This technique provides a robust tool for in-situ characterization of structural changes in single crystals under extreme conditions.It has significant implications for studying the equation of state and phase transitions.
基金supported by the National Natural Science Foundation of China under Grant No.62471381the ZTE Industry-University-Institute Cooperation Funds.
文摘In the field of antenna engineering parameter calibration for indoor communication base stations,traditional methods suffer from issues such as low efficiency,poor accuracy,and limited applicability to indoor scenarios.To address these problems,a high-precision and high-efficiency indoor base station parameter calibration method based on laser measurement is proposed.We use a high-precision laser tracker to measure and determine the coordinate system transformation relationship,and further obtain the coordinates and attitude of the base station.In addition,we propose a simple calibration method based on point cloud fitting for specific scenes.Simulation results show that using common commercial laser trackers,we can achieve a coordinate correction accuracy of 1 cm and an angle correction accuracy of 0.25°,which is sufficient to meet the needs of wireless positioning.
基金National Key R&D Program of China(No.2017YFB1304000)Fundamental Research Funds for the Central Universities,China(No.2232023G-05-1)。
文摘The high-speed winding spindle employs a flexible support system incorporating rubber O-rings.By precisely configuring the structural parameters and the number of the O-rings,the spindle can stably surpass its critical speed points and maintain operational stability across the entire working speed range.However,the support stiffness and damping of rubber O-rings exhibit significant nonlinear frequency dependence.Conventional experimental methods for deriving equivalent stiffness and damping,based on the principle of the forced non-resonance method,require fabricating custom setups for each O-ring specification and conducting vibration tests at varying frequencies,resulting in low efficiency and high costs.This study proposes a hybrid simulation-experimental method for dynamic parameter identification.Firstly,the frequency-dependent dynamic parameters of a specific O-ring support system are experimentally obtained.Subsequently,a corresponding parametric finite element model is established to simulate and solve the equivalent elastic modulus and equivalent stiffness-damping coefficient of this O-ring support system.Ultimately,after iterative simulation,the simulated and experimental results achieve a 99.7%agreement.The parametric finite element model developed herein can directly simulate and inversely estimate frequency-dependent dynamic parameters for O-rings of different specifications but identical elastic modulus.
基金supported by the National Natural Science Foundation of China(No.52374264)the National Key Technologies Research and Development Program of China(No.2024YFC2909600)the Major Science and Technology Projects in Yunnan Province(No.202402AB080010).
文摘In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their systems and insufficient consideration of hydration and temperature effects,making it difficult to fully replicate the real flotation environment of chalcopyrite and pyrite.In this study,we employed the self-consistent charge density functional tight-binding(SCC-DFTB)parameterization method to develop a parameter set,CuFeOrg,which includes the interactions between Cu-Fe-C-H-O-N-S-P-Zn elements,to investigate the surface interactions in large-scale flotation systems of chalcopyrite and pyrite.The results of bulk modulus,atomic displacement,band structure,surface relaxation,surface Mulliken charge distribution,and adsorption tests of typical flotation reagents on mineral surfaces demonstrate that CuFeOrg achieves DFT-level accuracy while significantly outperforming DFT in computational efficiency.By constructing large-scale hydration systems of mineral surfaces,as well as large-scale systems incorporating the combined interactions of mineral surfaces,flotation reagents,and hydration,we more realistically reproduce the actual flotation environment.Furthermore,the dynamic analysis results are consistent with mineral surface contact angle experiments.Additionally,CuFeOrg lays the foundation for future studies of more complex and diverse chalcopyrite and pyrite flotation surface systems.
基金supported by grants from the National Natural Science Foundation of China(No.32373115)the Earmarked Fund for Agriculture Seed Improvement Project of Shandong Province(Nos.2022LZGCQY010,2021ZLGX03 and 2021TSGC1240)the China Agriculture Research System Project(No.CARS-49)。
文摘Crassostrea gigas is distinguished by its robust fertility,adaptability to various environmental conditions,and rich nutritional profile,which render it a cornerstone of the global shellfish market.Tetraploid oysters,in particular,is a pivotal role within the oyster industry,however,their growth and survival characteristics need to be improved.This study aimed to assess the viability of enhancing the growth and survival characteristics of tetraploid C.gigas through genetic selection.This was achieved by investigating the heritability and genetic correlation of growth and survival at various stages of C.gigas development.Genetic parameters were assessed based on in-group correlations in 28 full-sib families during the larval stage(days 3,8,13,18,and 23 after fertilization).The heritability of larval shell height and shell length ranged from 0.50 to 0.71 and 0.44 to 0.71,respectively,and varied with age.The heritability of larval survival was 0.40–0.64,indicating that it is viable to enhancelarval survival through selection.The genetic correlation between the growth traits of larvae at different ages was highly positive(0.959–0.999),indicating that the selection of a single growth trait could cause the positive response of other growth traits.However,the genetic correlation between larval survival and growth traits was low,showing a significant negative correlation(-0.198–-0.293)at day 23 of age,suggesting thatselecting for enhanced survival in tetraploid C.gigas may result in a detrimental impact on growth traits.The results are helpful for future researches to evaluate the genetic parameters related to growth and survival traits in juvenile and adult stages of the tetraploid family of C.gigas,which can provide further guidance for the selection of tetraploid C.gigas.
基金supported by National key Research and Development Program“Security Protection Technology for Critical Information Infrastructure of Distribution Network”(2022YFB3105100).
文摘In the context of the diversity of smart terminals,the unity of the root of trust becomes complicated,which not only affects the efficiency of trust propagation,but also poses a challenge to the security of the whole system.In particular,the solidification of the root of trust in non-volatile memory(NVM)restricts the system’s dynamic updating capability,which is an obvious disadvantage in a rapidly changing security environment.To address this issue,this study proposes a novel approach to generate root security parameters using static random access memory(SRAM)physical unclonable functions(PUFs).SRAM PUFs,as a security primitive,show great potential in lightweight security solutions due to their inherent physical properties,low cost and scalability.However,the stability of SRAM PUFs in harsh environments is a key issue.These environmental conditions include extreme temperatures,high humidity,and strong electromagnetic radiation,all of which can affect the performance of SRAM PUFs.In order to ensure the stability of root safety parameters under these conditions,this study proposes an integrated approach that covers not only the acquisition of entropy sources,but also the implementation of algorithms and configuration management.In addition,this study develops a series of reliability-enhancing algorithms,including adaptive parameter selection,data preprocessing,auxiliary data generation,and error correction,which are essential for improving the performance of SRAM PUFs in harsh environments.Based on these techniques,this study establishes six types of secure parameter generation mechanisms,which not only improve the security of the system,but also enhance its adaptability in variable environments.Through a series of experiments,we verify the effectiveness of the proposed method.Under 10 different environmental conditions,our method is able to achieve full recovery of security data with an error rate of less than 25%,which proves the robustness and reliability of our method.These results not only provide strong evidence for the stability of SRAM PUFs in practical applications,but also provide a new direction for future research in the field of smart terminal security.
基金Supported by the National Natural Science Foundation of China(No.81960181).
文摘AIM:To describe ocular biometric parameters among Han and Uyghur myopic adults in Xinjiang,China.METHODS:A cross-sectional study was conducted.The different ocular biometric parameters collected using Pentacam were analyzed,including corneal curvature,corneal astigmatism(CA),horizontal corneal diameter(white-to-white,WTW),corneal volume(CV),pupil diameter(PD),anterior chamber angle(ACA),anterior chamber depth(ACD),anterior chamber volume(ACV)and axial length(AL).RESULTS:In total,2932 participants were included in the final analysis,comprising 2310 Han and 622 Uyghur adults.Adults in the high myopia(HM)group had steeper K2,larger CA,smaller WTW and longer AL in both the Uyghur and Han adults(all P<0.05).The moderate myopia(MM)and HM group had deeper ACV,ACD and wider ACA than the low myopia(all P<0.05)in Han adults,however there were no differences in anterior chamber indices with Uyghur adults.In the Uyghur adults,we noticed that CV,WTW,and ACD were smaller,ACA was narrower,PD was larger,and AL was shorter(all P<0.05).We also noticed sex differences:males had flatter corneas,deeper ACD and ACV,and larger WTW than females(all P<0.05).In the correlation analysis,WTW was positively correlated with ACD and ACV but negatively correlated with K1,K2 and CV(all P<0.05).Narrower ACA was associated with larger PD(rU=-0.25,rH=-0.16,all P<0.01).CONCLUSION:The Han population have different biometric parameters in eyes with HM compared to the Uyghur population,and the Uyghur population may anatomically more susceptible to primary angle closure glaucoma than the Han population.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130608 and 42075142)the National Key Research and Development Program of China(Grant No.2020YFA0608000)the CUIT Science and Technology Innovation Capacity Enhancement Program Project(Grant No.KYTD202330)。
文摘Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.
基金support of the National Natural Science Foundation of China(No.12372221)is acknowledged.
文摘In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In the development of MS-GAN,we extend the freeform deformation by incorporating principal component analysis to increase the non-linear deformation ability while maintaining geometric smoothness.The implicit information of multiple baselines is embedded in the feature extraction layers,to enhance the diversity and parameterization of multi-species dataset.Furthermore,Wasserstein GAN with a gradient penalty is used to ensure the stability and convergence of the training networks.Two experiments,ruled surfaces and propeller blade surfaces,are performed to demonstrate the advantages and superiorities of MS-GAN.
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.
