In this paper, we use sample average approximation with adaptive multiple importance sampling to explore moderate deviations for the optimal values. Utilizing the moderate deviation principle for martingale difference...In this paper, we use sample average approximation with adaptive multiple importance sampling to explore moderate deviations for the optimal values. Utilizing the moderate deviation principle for martingale differences and an appropriate Delta method, we establish a moderate deviation principle for the optimal value. Moreover, for a functional form of stochastic programming, we obtain a functional moderate deviation principle for its optimal value.展开更多
To realize dynamic statistical publishing and protection of location-based data privacy,this paper proposes a differential privacy publishing algorithm based on adaptive sampling and grid clustering and adjustment.The...To realize dynamic statistical publishing and protection of location-based data privacy,this paper proposes a differential privacy publishing algorithm based on adaptive sampling and grid clustering and adjustment.The PID control strategy is combined with the difference in data variation to realize the dynamic adjustment of the data publishing intervals.The spatial-temporal correlations of the adjacent snapshots are utilized to design the grid clustering and adjustment algorithm,which facilitates saving the execution time of the publishing process.The budget distribution and budget absorption strategies are improved to form the sliding window-based differential privacy statistical publishing algorithm,which realizes continuous statistical publishing and privacy protection and improves the accuracy of published data.Experiments and analysis on large datasets of actual locations show that the privacy protection algorithm proposed in this paper is superior to other existing algorithms in terms of the accuracy of adaptive sampling time,the availability of published data,and the execution efficiency of data publishing methods.展开更多
This paper develops a residual-based adaptive refinement physics-informed neural networks(RAR-PINNs)method for solving the Gross–Pitaevskii(GP)equation and Hirota equation,two paradigmatic nonlinear partial different...This paper develops a residual-based adaptive refinement physics-informed neural networks(RAR-PINNs)method for solving the Gross–Pitaevskii(GP)equation and Hirota equation,two paradigmatic nonlinear partial differential equations(PDEs)governing quantum condensates and optical rogue waves,respectively.The key innovation lies in the adaptive sampling strategy that dynamically allocates computational resources to regions with large PDE residuals,addressing critical limitations of conventional PINNs in handling:(1)Strong nonlinearities(|u|^(2)u terms)in the GP equation;(2)High-order derivatives(u_(xxx))in the Hirota equation;(3)Multi-scale solution structures.Through rigorous numerical experiments,we demonstrate that RAR-PINNs achieve superior accuracy[relative L^(2)errors of O(10^(−3))]and computational efficiency(faster than standard PINNs)for both equations.The method successfully captures:(1)Bright solitons in the GP equation;(2)First-and second-order rogue waves in the Hirota equation.The RAR adaptive sampling method demonstrates particularly remarkable effectiveness in solving steep gradient problems.Compared with uniform sampling methods,the errors of simulation results are reduced by two orders of magnitude.This study establishes a general framework for data-driven solutions of high-order nonlinear PDEs with complex solution structures.展开更多
A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm o...A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm optimization （PSO）. Firstly, the desired tracking accuracy is set for each target. Secondly, sampling intervals are selected as particles, and then the advantage of the GRG is taken as the measurement function for resource management. Meanwhile, the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy. Finally, it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest. Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar.展开更多
We consider solving the forward and inverse partial differential equations(PDEs)which have sharp solutions with physics-informed neural networks(PINNs)in this work.In particular,to better capture the sharpness of the ...We consider solving the forward and inverse partial differential equations(PDEs)which have sharp solutions with physics-informed neural networks(PINNs)in this work.In particular,to better capture the sharpness of the solution,we propose the adaptive sampling methods(ASMs)based on the residual and the gradient of the solution.We first present a residual only-based ASM denoted by ASMⅠ.In this approach,we first train the neural network using a small number of residual points and divide the computational domain into a certain number of sub-domains,then we add new residual points in the sub-domain which has the largest mean absolute value of the residual,and those points which have the largest absolute values of the residual in this sub-domain as new residual points.We further develop a second type of ASM(denoted by ASMⅡ)based on both the residual and the gradient of the solution due to the fact that only the residual may not be able to efficiently capture the sharpness of the solution.The procedure of ASMⅡis almost the same as that of ASMⅠ,and we add new residual points which have not only large residuals but also large gradients.To demonstrate the effectiveness of the present methods,we use both ASMⅠand ASMⅡto solve a number of PDEs,including the Burger equation,the compressible Euler equation,the Poisson equation over an Lshape domain as well as the high-dimensional Poisson equation.It has been shown from the numerical results that the sharp solutions can be well approximated by using either ASMⅠor ASMⅡ,and both methods deliver much more accurate solutions than the original PINNs with the same number of residual points.Moreover,the ASMⅡalgorithm has better performance in terms of accuracy,efficiency,and stability compared with the ASMⅠalgorithm.This means that the gradient of the solution improves the stability and efficiency of the adaptive sampling procedure as well as the accuracy of the solution.Furthermore,we also employ the similar adaptive sampling technique for the data points of boundary conditions(BCs)if the sharpness of the solution is near the boundary.The result of the L-shape Poisson problem indicates that the present method can significantly improve the efficiency,stability,and accuracy.展开更多
The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges:small failure probability(typical less than 10-5)and time-demanding mechanical m...The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges:small failure probability(typical less than 10-5)and time-demanding mechanical models.This paper proposes an improved active learning surrogate model method,which combines the advantages of the classical Active Kriging–Monte Carlo Simulation(AK-MCS)procedure and the Adaptive Linked Importance Sampling(ALIS)procedure.The proposed procedure can,on the one hand,adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling(IS)density,on the other hand,adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event.Then,the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models.Compared with the classical AK-MCS and Active Kriging–Importance Sampling(AK-IS)procedure,the proposed method neither need to build very large sample pool even when the failure probability is extremely small,nor need to estimate the Most Probable Points(MPPs),thus it is computationally more efficient and more applicable especially for problems with multiple MPPs.The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.展开更多
Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the los...Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.展开更多
Current measurement method for unknown free-form surface has low efficiency.To acquire given precision, a lot of null points are measured. Based on change surface curvature, anew measurement planning is put forward. S...Current measurement method for unknown free-form surface has low efficiency.To acquire given precision, a lot of null points are measured. Based on change surface curvature, anew measurement planning is put forward. Sample step is evaluated from the change curvature and thelocally-bounded character of extrapolating curve. Two coefficients, maximum error coefficient andlocal camber coefficient, are used to optimize sampling step. The first coefficient is computed toavoid sampling-point exceeding the measurement range and the second control sampling precision.Compared with the other methods, the proposed planning method can reduce the number of themeasuring-point efficiently for the given precision. Measuring point distributes adaptively by thechange surface curvature. The method can be applied to improve measurement efficiency and accuracy.展开更多
A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multi...A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multiple dependent state sampling plan(MDSSP)concepts.Under accelerated conditions,the lifetime of a product follows the Weibull distribution with a known shape parameter,while the scale parameter can be determined using the acceleration factor(AF).The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive.An economic design of the proposed sampling plan was also considered for the ALT.A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number(ASN)and total cost of inspection(TC)under both producer’s and consumer’s risks.Numerical results are presented to support the AMDSSP for the ALT,while performance comparisons between the AMDSSP,the MDSSP and a single sampling plan(SSP)for the ALT are discussed.Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions.The AMDSSP also had a higher operating characteristic(OC)curve than both the existing sampling plans.Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.展开更多
In this work,an adaptive sampling control strategy for distributed predictive control is proposed.According to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the p...In this work,an adaptive sampling control strategy for distributed predictive control is proposed.According to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function.Then,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is repeated.Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object.It can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.展开更多
A mesh editing framework is presented in this paper, which integrates Free-Form Deformation (FFD) and geometry signal processing. By using simplified model from original mesh, the editing task can be accomplished with...A mesh editing framework is presented in this paper, which integrates Free-Form Deformation (FFD) and geometry signal processing. By using simplified model from original mesh, the editing task can be accomplished with a few operations. We take the deformation of the proxy and the position coordinates of the mesh models as geometry signal. Wavelet analysis is em- ployed to separate local detail information gracefully. The crucial innovation of this paper is a new adaptive regular sampling approach for our signal analysis based editing framework. In our approach, an original mesh is resampled and then refined itera- tively which reflects optimization of our proposed spectrum preserving energy. As an extension of our spectrum editing scheme, the editing principle is applied to geometry details transferring, which brings satisfying results.展开更多
For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering target tracking applications.This paper presents ...For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering target tracking applications.This paper presents a novel interactive multiple model(IMM)algorithm optimized for tracking maneuvering near space hypersonic gliding vehicles(NSHGV)with a fast adaptive sam-pling control logic.The algorithm utilizes the model probabilities to dynamically adjust the revisit time corresponding to NSHGV maneuvers,thus achieving a balance between tracking accuracy and resource consumption.Simulation results on typical NSHGV targets show that the proposed algo-rithm improves tracking accuracy and resource allocation efficiency compared to other conventional multiple model algorithms.展开更多
This is the second part of our series works on failure-informed adaptive sampling for physic-informed neural networks(PINNs).In our previous work(SIAM J.Sci.Comput.45:A1971–A1994),we have presented an adaptive sampli...This is the second part of our series works on failure-informed adaptive sampling for physic-informed neural networks(PINNs).In our previous work(SIAM J.Sci.Comput.45:A1971–A1994),we have presented an adaptive sampling framework by using the failure probability as the posterior error indicator,where the truncated Gaussian model has been adopted for estimating the indicator.Here,we present two extensions of that work.The first extension consists in combining with a re-sampling technique,so that the new algorithm can maintain a constant training size.This is achieved through a cosine-annealing,which gradually transforms the sampling of collocation points from uniform to adaptive via the training progress.The second extension is to present the subset simulation(SS)algorithm as the posterior model(instead of the truncated Gaussian model)for estimating the error indicator,which can more effectively estimate the failure probability and generate new effective training points in the failure region.We investigate the performance of the new approach using several challenging problems,and numerical experiments demonstrate a significant improvement over the original algorithm.展开更多
Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Bas...Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar (PAR) is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix (PCRBM) of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.展开更多
This paper introduces the principle of PPS-based adaptive cluster sampling method and its modified HH estimator and HT estimator calculation method. It compares PPS-based adaptive cluster sampling method with SRS samp...This paper introduces the principle of PPS-based adaptive cluster sampling method and its modified HH estimator and HT estimator calculation method. It compares PPS-based adaptive cluster sampling method with SRS sampling and SRS-based adaptive group. The difference between the group sampling and the advantages and scope of the PPS adaptive cluster sampling method are analyzed. According to the case analysis, the relevant conclusions are drawn: 1) The adaptive cluster sampling method is more accurate than the SRS sampling;2) SRS adaptive The HT estimator of the cluster sampling is more stable than the HH estimator;3) The two estimators of the PPS adaptive cluster sampling method have little difference in the estimation of the population mean, but the HT estimator variance is smaller and more suitable;4) PPS The HH estimator of adaptive cluster sampling is the same as the HH estimator of SRS adaptive cluster sampling, but the variance is larger and unstable.展开更多
In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering...In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.展开更多
An accurate and robust estimation of leaf chlorophyll content(LCC)is very important to better know the process of material and energy exchange between plants and the environment.Compared with traditional remote sensin...An accurate and robust estimation of leaf chlorophyll content(LCC)is very important to better know the process of material and energy exchange between plants and the environment.Compared with traditional remote sensing methods,abundant research has made progress in agronomic parameter retrieval using different CNN frameworks.Nevertheless,limited reports have paid attention to the problems,i.e.,limited measured data,hyperspectral redundancy,and model convergence issues,when concerning CNN models for parameter estimation.Therefore,the present study tried to analyze the effects of synthetic data size expansion employing aGaussian process regression(GPR)model for simulation,input feature optimization using different spectral indices with a competitive adaptive reweighted sampling(CARS)algorithm,model convergence issue combining transfer learning(TL)method for accurate and robust estimation of plant LCC with a deep learning framework(i.e.,ResNet-18)using the ANGERS data(a public dataset containing foliar biochemical parameters spectral data for various plant types).Results showed that ResNet-18 training using 800 simulated reflectances(400–1000 nm)and partial ANGERS data exhibited better results,with an R^(2)value of 0.89,an RMSE value of 6.98μg/cm^(2),an RPD value of 3.70,for LCC retrieval using remanent ANGERS data,thanmodels that using simulations with different amounts of data.The estimation accuracies obviously increased when nine spectral indexes,selected from the CARS algorithm,were used as model input for running the ResNet-18 model(R^(2)=0.96,RMSE=4.65μg/cm^(2),RPD=4.81).In addition,coupling transfer learning with ResNet-18 improved the model convergence rate,and TL-ResNet-18 exhibited accurate results for LCC estimation(R^(2)=0.94,RMSE=5.14μg/cm^(2),RPD=4.65).These results suggest that adding appropriate synthetic data,input features optimization,and transfer learning techniques could be effectively used for improved LCC retrieval with a ResNet-18 model.展开更多
The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment.However,the inherent limitations of existing datas...The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment.However,the inherent limitations of existing datasets,including significant class imbalances and inadequate sample diversity,pose challenges to the accurate prediction and classification of diabetes.Addressing these issues,this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit(CNN-BiGRU)model for classification with Adaptive Synthetic Sampling(ADASYN)for data augmentation.ADASYN was employed to generate synthetic yet representative data samples,effectively mitigating class imbalance and enhancing the diversity and representativeness of the dataset.This augmentation process is critical for ensuring the robustness and generalizability of the predictive model,particularly in scenarios where minority class samples are underrepresented.The CNN-BiGRU architecture was designed to leverage the complementary strengths of CNN in extracting spatial features and BiGRU in capturing sequential dependencies,making it well-suited for the complex patterns inherent in medical data.The proposed framework demonstrated exceptional performance,achieving a training accuracy of 98.74%and a test accuracy of 97.78%on the augmented dataset.These results validate the efficacy of the integrated approach in addressing the challenges of class imbalance and dataset heterogeneity,while significantly enhancing the diagnostic precision for diabetes prediction.This study provides a scalable and reliable methodology with promising implications for advancing diagnostic accuracy in medical applications,particularly in resource-constrained and data-limited environments.展开更多
In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capabilities.In the early stage of video coding,in-loop filters,such as the d...In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capabilities.In the early stage of video coding,in-loop filters,such as the deblocking filter,sample adaptive offset,and adaptive loop filter,were performed separately for each component.Recently,cross-component filters have been studied to improve chroma fidelity by exploiting correlations between the luma and chroma channels.This paper introduces the cross-component filters used in the state-ofthe-art video coding standards,including the cross-component adaptive loop filter and cross-component sample adaptive offset.Crosscomponent filters aim to reduce compression artifacts based on the correlation between different components and provide more accurate pixel reconstruction values.We present their origin,development,and status in the current video coding standards.Finally,we conduct discussions on the further evolution of cross-component filters.展开更多
A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are...A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model.Each sub-classifier is based on different feature sets.The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability.Secondly,the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal.Emotional data including fidgetiness,happiness and sadness is collected by induction experiments.Finally,the emotion feature extraction method is discussed,including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature.The emotional feature reduction methods are studied, including principle component analysis,sequential forward selection, the Fisher discriminant ratio and maximal information coefficient.The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios.展开更多
基金Supported by the National Natural Science Foundation of China(Grant No.12071175)。
文摘In this paper, we use sample average approximation with adaptive multiple importance sampling to explore moderate deviations for the optimal values. Utilizing the moderate deviation principle for martingale differences and an appropriate Delta method, we establish a moderate deviation principle for the optimal value. Moreover, for a functional form of stochastic programming, we obtain a functional moderate deviation principle for its optimal value.
基金supported by National Nature Science Foundation of China(No.62361036)Nature Science Foundation of Gansu Province(No.22JR5RA279).
文摘To realize dynamic statistical publishing and protection of location-based data privacy,this paper proposes a differential privacy publishing algorithm based on adaptive sampling and grid clustering and adjustment.The PID control strategy is combined with the difference in data variation to realize the dynamic adjustment of the data publishing intervals.The spatial-temporal correlations of the adjacent snapshots are utilized to design the grid clustering and adjustment algorithm,which facilitates saving the execution time of the publishing process.The budget distribution and budget absorption strategies are improved to form the sliding window-based differential privacy statistical publishing algorithm,which realizes continuous statistical publishing and privacy protection and improves the accuracy of published data.Experiments and analysis on large datasets of actual locations show that the privacy protection algorithm proposed in this paper is superior to other existing algorithms in terms of the accuracy of adaptive sampling time,the availability of published data,and the execution efficiency of data publishing methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.12575003 and 12235007)the K.C.Wong Magna Fund in Ningbo University。
文摘This paper develops a residual-based adaptive refinement physics-informed neural networks(RAR-PINNs)method for solving the Gross–Pitaevskii(GP)equation and Hirota equation,two paradigmatic nonlinear partial differential equations(PDEs)governing quantum condensates and optical rogue waves,respectively.The key innovation lies in the adaptive sampling strategy that dynamically allocates computational resources to regions with large PDE residuals,addressing critical limitations of conventional PINNs in handling:(1)Strong nonlinearities(|u|^(2)u terms)in the GP equation;(2)High-order derivatives(u_(xxx))in the Hirota equation;(3)Multi-scale solution structures.Through rigorous numerical experiments,we demonstrate that RAR-PINNs achieve superior accuracy[relative L^(2)errors of O(10^(−3))]and computational efficiency(faster than standard PINNs)for both equations.The method successfully captures:(1)Bright solitons in the GP equation;(2)First-and second-order rogue waves in the Hirota equation.The RAR adaptive sampling method demonstrates particularly remarkable effectiveness in solving steep gradient problems.Compared with uniform sampling methods,the errors of simulation results are reduced by two orders of magnitude.This study establishes a general framework for data-driven solutions of high-order nonlinear PDEs with complex solution structures.
基金supported by the Pre-research Fund (N0901-041)the Funding of Jiangsu Innovation Program for Graduate Education(CX09B 081Z CX10B 110Z)
文摘A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm optimization （PSO）. Firstly, the desired tracking accuracy is set for each target. Secondly, sampling intervals are selected as particles, and then the advantage of the GRG is taken as the measurement function for resource management. Meanwhile, the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy. Finally, it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest. Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar.
基金Project supported by the National Key R&D Program of China(No.2022YFA1004504)the National Natural Science Foundation of China(Nos.12171404 and 12201229)the Fundamental Research Funds for Central Universities of China(No.20720210037)。
文摘We consider solving the forward and inverse partial differential equations(PDEs)which have sharp solutions with physics-informed neural networks(PINNs)in this work.In particular,to better capture the sharpness of the solution,we propose the adaptive sampling methods(ASMs)based on the residual and the gradient of the solution.We first present a residual only-based ASM denoted by ASMⅠ.In this approach,we first train the neural network using a small number of residual points and divide the computational domain into a certain number of sub-domains,then we add new residual points in the sub-domain which has the largest mean absolute value of the residual,and those points which have the largest absolute values of the residual in this sub-domain as new residual points.We further develop a second type of ASM(denoted by ASMⅡ)based on both the residual and the gradient of the solution due to the fact that only the residual may not be able to efficiently capture the sharpness of the solution.The procedure of ASMⅡis almost the same as that of ASMⅠ,and we add new residual points which have not only large residuals but also large gradients.To demonstrate the effectiveness of the present methods,we use both ASMⅠand ASMⅡto solve a number of PDEs,including the Burger equation,the compressible Euler equation,the Poisson equation over an Lshape domain as well as the high-dimensional Poisson equation.It has been shown from the numerical results that the sharp solutions can be well approximated by using either ASMⅠor ASMⅡ,and both methods deliver much more accurate solutions than the original PINNs with the same number of residual points.Moreover,the ASMⅡalgorithm has better performance in terms of accuracy,efficiency,and stability compared with the ASMⅠalgorithm.This means that the gradient of the solution improves the stability and efficiency of the adaptive sampling procedure as well as the accuracy of the solution.Furthermore,we also employ the similar adaptive sampling technique for the data points of boundary conditions(BCs)if the sharpness of the solution is near the boundary.The result of the L-shape Poisson problem indicates that the present method can significantly improve the efficiency,stability,and accuracy.
基金supported by National Natural Science Foundation of China(Nos.51905430,51608446)the Fundamental Research Fund for Central Universities(No.3102018zy011)+1 种基金the supports of Alexander von Humboldt Foundation of Germanythe Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University。
文摘The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges:small failure probability(typical less than 10-5)and time-demanding mechanical models.This paper proposes an improved active learning surrogate model method,which combines the advantages of the classical Active Kriging–Monte Carlo Simulation(AK-MCS)procedure and the Adaptive Linked Importance Sampling(ALIS)procedure.The proposed procedure can,on the one hand,adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling(IS)density,on the other hand,adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event.Then,the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models.Compared with the classical AK-MCS and Active Kriging–Importance Sampling(AK-IS)procedure,the proposed method neither need to build very large sample pool even when the failure probability is extremely small,nor need to estimate the Most Probable Points(MPPs),thus it is computationally more efficient and more applicable especially for problems with multiple MPPs.The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.
基金Project supported by the Key National Natural Science Foundation of China(Grant No.62136005)the National Natural Science Foundation of China(Grant Nos.61922087,61906201,and 62006238)。
文摘Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.
基金This project is supported by Provincial Key Project of Science and Technology of Zhejiang (No.2003C21031).
文摘Current measurement method for unknown free-form surface has low efficiency.To acquire given precision, a lot of null points are measured. Based on change surface curvature, anew measurement planning is put forward. Sample step is evaluated from the change curvature and thelocally-bounded character of extrapolating curve. Two coefficients, maximum error coefficient andlocal camber coefficient, are used to optimize sampling step. The first coefficient is computed toavoid sampling-point exceeding the measurement range and the second control sampling precision.Compared with the other methods, the proposed planning method can reduce the number of themeasuring-point efficiently for the given precision. Measuring point distributes adaptively by thechange surface curvature. The method can be applied to improve measurement efficiency and accuracy.
基金This research was supported by The Science,Research and Innovation Promotion Funding(TSRI)(Grant No.FRB650070/0168)This research block grants was managed under Rajamangala University of Technology Thanyaburi(FRB65E0634M.3).
文摘A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multiple dependent state sampling plan(MDSSP)concepts.Under accelerated conditions,the lifetime of a product follows the Weibull distribution with a known shape parameter,while the scale parameter can be determined using the acceleration factor(AF).The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive.An economic design of the proposed sampling plan was also considered for the ALT.A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number(ASN)and total cost of inspection(TC)under both producer’s and consumer’s risks.Numerical results are presented to support the AMDSSP for the ALT,while performance comparisons between the AMDSSP,the MDSSP and a single sampling plan(SSP)for the ALT are discussed.Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions.The AMDSSP also had a higher operating characteristic(OC)curve than both the existing sampling plans.Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.
基金the National Natural Science Foundation of China(61563032,61963025)The Open Foundation of the Key Laboratory of Gansu Advanced Control for Industrial Processes(2019KX01)The Project of Industrial support and guidance of Colleges and Universities in Gansu Province(2019C05).
文摘In this work,an adaptive sampling control strategy for distributed predictive control is proposed.According to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function.Then,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is repeated.Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object.It can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.
基金Project supported by the National Basic Research Program (973) of China (No. 2002CB312102), and the National Natural Science Foun-dation of China (Nos. 60021201, 60333010 and 60505001)
文摘A mesh editing framework is presented in this paper, which integrates Free-Form Deformation (FFD) and geometry signal processing. By using simplified model from original mesh, the editing task can be accomplished with a few operations. We take the deformation of the proxy and the position coordinates of the mesh models as geometry signal. Wavelet analysis is em- ployed to separate local detail information gracefully. The crucial innovation of this paper is a new adaptive regular sampling approach for our signal analysis based editing framework. In our approach, an original mesh is resampled and then refined itera- tively which reflects optimization of our proposed spectrum preserving energy. As an extension of our spectrum editing scheme, the editing principle is applied to geometry details transferring, which brings satisfying results.
文摘For modern phased array radar systems,the adaptive control of the target revisiting time is important for efficient radar resource allocation,especially in maneuvering target tracking applications.This paper presents a novel interactive multiple model(IMM)algorithm optimized for tracking maneuvering near space hypersonic gliding vehicles(NSHGV)with a fast adaptive sam-pling control logic.The algorithm utilizes the model probabilities to dynamically adjust the revisit time corresponding to NSHGV maneuvers,thus achieving a balance between tracking accuracy and resource consumption.Simulation results on typical NSHGV targets show that the proposed algo-rithm improves tracking accuracy and resource allocation efficiency compared to other conventional multiple model algorithms.
基金supported by the NSF of China(No.12171085)This work was supported by the National Key R&D Program of China(2020YFA0712000)+2 种基金the NSF of China(No.12288201)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA25010404)and the Youth Innovation Promotion Association(CAS).
文摘This is the second part of our series works on failure-informed adaptive sampling for physic-informed neural networks(PINNs).In our previous work(SIAM J.Sci.Comput.45:A1971–A1994),we have presented an adaptive sampling framework by using the failure probability as the posterior error indicator,where the truncated Gaussian model has been adopted for estimating the indicator.Here,we present two extensions of that work.The first extension consists in combining with a re-sampling technique,so that the new algorithm can maintain a constant training size.This is achieved through a cosine-annealing,which gradually transforms the sampling of collocation points from uniform to adaptive via the training progress.The second extension is to present the subset simulation(SS)algorithm as the posterior model(instead of the truncated Gaussian model)for estimating the error indicator,which can more effectively estimate the failure probability and generate new effective training points in the failure region.We investigate the performance of the new approach using several challenging problems,and numerical experiments demonstrate a significant improvement over the original algorithm.
文摘Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar (PAR) is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix (PCRBM) of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.
文摘This paper introduces the principle of PPS-based adaptive cluster sampling method and its modified HH estimator and HT estimator calculation method. It compares PPS-based adaptive cluster sampling method with SRS sampling and SRS-based adaptive group. The difference between the group sampling and the advantages and scope of the PPS adaptive cluster sampling method are analyzed. According to the case analysis, the relevant conclusions are drawn: 1) The adaptive cluster sampling method is more accurate than the SRS sampling;2) SRS adaptive The HT estimator of the cluster sampling is more stable than the HH estimator;3) The two estimators of the PPS adaptive cluster sampling method have little difference in the estimation of the population mean, but the HT estimator variance is smaller and more suitable;4) PPS The HH estimator of adaptive cluster sampling is the same as the HH estimator of SRS adaptive cluster sampling, but the variance is larger and unstable.
基金supported by the Science and Technology Development Plan Project of Jilin Provincial Department of Science and Technology (No.20220203112S)the Jilin Provincial Department of Education Science and Technology Research Project (No.JJKH20210039KJ)。
文摘In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.
基金supported by the National Natural Science Foundation of China(Project Nos.41901268 and 42371385)Zhejiang Provincial Natural Science Foundation of China(Grant No.LTGN23D010002).
文摘An accurate and robust estimation of leaf chlorophyll content(LCC)is very important to better know the process of material and energy exchange between plants and the environment.Compared with traditional remote sensing methods,abundant research has made progress in agronomic parameter retrieval using different CNN frameworks.Nevertheless,limited reports have paid attention to the problems,i.e.,limited measured data,hyperspectral redundancy,and model convergence issues,when concerning CNN models for parameter estimation.Therefore,the present study tried to analyze the effects of synthetic data size expansion employing aGaussian process regression(GPR)model for simulation,input feature optimization using different spectral indices with a competitive adaptive reweighted sampling(CARS)algorithm,model convergence issue combining transfer learning(TL)method for accurate and robust estimation of plant LCC with a deep learning framework(i.e.,ResNet-18)using the ANGERS data(a public dataset containing foliar biochemical parameters spectral data for various plant types).Results showed that ResNet-18 training using 800 simulated reflectances(400–1000 nm)and partial ANGERS data exhibited better results,with an R^(2)value of 0.89,an RMSE value of 6.98μg/cm^(2),an RPD value of 3.70,for LCC retrieval using remanent ANGERS data,thanmodels that using simulations with different amounts of data.The estimation accuracies obviously increased when nine spectral indexes,selected from the CARS algorithm,were used as model input for running the ResNet-18 model(R^(2)=0.96,RMSE=4.65μg/cm^(2),RPD=4.81).In addition,coupling transfer learning with ResNet-18 improved the model convergence rate,and TL-ResNet-18 exhibited accurate results for LCC estimation(R^(2)=0.94,RMSE=5.14μg/cm^(2),RPD=4.65).These results suggest that adding appropriate synthetic data,input features optimization,and transfer learning techniques could be effectively used for improved LCC retrieval with a ResNet-18 model.
基金supported by the National Natural Science Foundation of China(No.42075129)Hebei ProvinceNatural Science Foundation(No.E2021202179)Key Research andDevelopment Project fromHebei Province(No.21351803D).
文摘The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment.However,the inherent limitations of existing datasets,including significant class imbalances and inadequate sample diversity,pose challenges to the accurate prediction and classification of diabetes.Addressing these issues,this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit(CNN-BiGRU)model for classification with Adaptive Synthetic Sampling(ADASYN)for data augmentation.ADASYN was employed to generate synthetic yet representative data samples,effectively mitigating class imbalance and enhancing the diversity and representativeness of the dataset.This augmentation process is critical for ensuring the robustness and generalizability of the predictive model,particularly in scenarios where minority class samples are underrepresented.The CNN-BiGRU architecture was designed to leverage the complementary strengths of CNN in extracting spatial features and BiGRU in capturing sequential dependencies,making it well-suited for the complex patterns inherent in medical data.The proposed framework demonstrated exceptional performance,achieving a training accuracy of 98.74%and a test accuracy of 97.78%on the augmented dataset.These results validate the efficacy of the integrated approach in addressing the challenges of class imbalance and dataset heterogeneity,while significantly enhancing the diagnostic precision for diabetes prediction.This study provides a scalable and reliable methodology with promising implications for advancing diagnostic accuracy in medical applications,particularly in resource-constrained and data-limited environments.
基金supported in part by National Science Foundation of China under Grant No.62031013PCL-CMCC Foundation for Science and Innovation under Grant No.2024ZY1C0040+1 种基金New Cornerstone Science Foundation for the Xplorer PrizeHigh performance Computing Platform of Peking University。
文摘In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capabilities.In the early stage of video coding,in-loop filters,such as the deblocking filter,sample adaptive offset,and adaptive loop filter,were performed separately for each component.Recently,cross-component filters have been studied to improve chroma fidelity by exploiting correlations between the luma and chroma channels.This paper introduces the cross-component filters used in the state-ofthe-art video coding standards,including the cross-component adaptive loop filter and cross-component sample adaptive offset.Crosscomponent filters aim to reduce compression artifacts based on the correlation between different components and provide more accurate pixel reconstruction values.We present their origin,development,and status in the current video coding standards.Finally,we conduct discussions on the further evolution of cross-component filters.
基金The National Natural Science Foundation of China(No.61231002,61273266,51075068,61271359)Doctoral Fund of Ministry of Education of China(No.20110092130004)
文摘A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model.Each sub-classifier is based on different feature sets.The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability.Secondly,the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal.Emotional data including fidgetiness,happiness and sadness is collected by induction experiments.Finally,the emotion feature extraction method is discussed,including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature.The emotional feature reduction methods are studied, including principle component analysis,sequential forward selection, the Fisher discriminant ratio and maximal information coefficient.The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios.
