The zero_failure data research is a new field in the recent years, but it is required urgently in practical projects, so the work has more theory and practical values. In this paper, for zero_failure data (t i,n i...The zero_failure data research is a new field in the recent years, but it is required urgently in practical projects, so the work has more theory and practical values. In this paper, for zero_failure data (t i,n i) at moment t i , if the prior distribution of the failure probability p i=p{T【t i} is quasi_exponential distribution, the author gives the p i Bayesian estimation and hierarchical Bayesian estimation and the reliability under zero_failure date condition is also obtained.展开更多
Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making,their practical application faces two challenges in the noisy intermediate-scale quantum(NISQ)era.Limited qubit resources...Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making,their practical application faces two challenges in the noisy intermediate-scale quantum(NISQ)era.Limited qubit resources restrict direct application to large-scale inference tasks.Additionally,no quantum methods are currently available for multi-agent collaborative decision-making.To address these,we propose a hybrid quantum–classical multi-agent decision-making framework based on hierarchical Bayesian networks,comprising two novel methods.The first one is a hybrid quantum–classical inference method based on hierarchical Bayesian networks.It decomposes large-scale hierarchical Bayesian networks into modular subnetworks.The inference for each subnetwork can be performed on NISQ devices,and the intermediate results are converted into classical messages for cross-layer transmission.The second one is a multi-agent decision-making method using the variational quantum eigensolver(VQE)in the influence diagram.This method models the collaborative decision-making with the influence diagram and encodes the expected utility of diverse actions into a Hamiltonian and subsequently determines the intra-group optimal action efficiently.Experimental validation on the IonQ quantum simulator demonstrates that the hierarchical method outperforms the non-hierarchical method at the functional inference level,and the VQE method can obtain the optimal strategy exactly at the collaborative decision-making level.Our research not only extends the application of quantum computing to multi-agent decision-making but also provides a practical solution for the NISQ era.展开更多
In traditional sensing,each parameter is treated as a real number in the signal demodulation,whereas the electric field of light is a complex number.The real and imaginary parts obey the Kramers-Kronig relationship,wh...In traditional sensing,each parameter is treated as a real number in the signal demodulation,whereas the electric field of light is a complex number.The real and imaginary parts obey the Kramers-Kronig relationship,which is expected to help further enhance sensing precision.We propose a self-Bayesian estimate of the method,aiming at reducing measurement variance.This method utilizes the intensity and phase of the parameter to be measured,achieving statistical optimization of the estimated value through Bayesian inference,effectively reducing the measurement variance.To demonstrate the effectiveness of this method,we adopted an optical fiber heterodyne interference sensing vibration measurement system.The experimental results show that the signal-to-noise ratio is effectively improved within the frequency range of 200 to 500 kHz.Moreover,it is believed that the self-Bayesian estimation method holds broad application prospects in various types of optical sensing.展开更多
The estimation of quantum phase differences plays an important role in quantum simulation and quantum computation,yet existing quantum phase estimation algorithms face critical limitations in noisy intermediate-scale ...The estimation of quantum phase differences plays an important role in quantum simulation and quantum computation,yet existing quantum phase estimation algorithms face critical limitations in noisy intermediate-scale quantum(NISQ)devices due to their excessive depth and circuit complexity.We demonstrate a high-precision phase difference estimation protocol based on the Bayesian phase difference estimation algorithm and single-photon projective measurement.The iterative framework of the algorithm,combined with the independence from controlled unitary operations,inherently mitigates circuit depth and complexity limitations.Through an experimental realization on the photonic system,we demonstrate high-precision estimation of diverse phase differences,showing root-mean-square errors(RMSE)below the standard quantum limit𝒪(1/√N)and reaching the Heisenberg scaling𝒪(1/N)after a certain number of iterations.Our scheme provides a critical advantage in quantum resource-constrained scenarios,and advances practical implementations of quantum information tasks under realistic hardware constraints.展开更多
Most of the existing direction of arrival(DOA)estimation algorithms are applied under the assumption that the array manifold is ideal.In practical engineering applications,the existence of non-ideal conditions such as...Most of the existing direction of arrival(DOA)estimation algorithms are applied under the assumption that the array manifold is ideal.In practical engineering applications,the existence of non-ideal conditions such as mutual coupling between array elements,array amplitude and phase errors,and array element position errors leads to defects in the array manifold,which makes the performance of the algorithm decline rapidly or even fail.In order to solve the problem of DOA estimation in the presence of amplitude and phase errors and array element position errors,this paper introduces the first-order Taylor expansion equivalent model of the received signal under the uniform linear array from the Bayesian point of view.In the solution,the amplitude and phase error parameters and the array element position error parameters are regarded as random variables obeying the Gaussian distribution.At the same time,the expectation-maximization algorithm is used to update the probability distribution parameters,and then the two error parameters are solved alternately to obtain more accurate DOA estimation results.Finally,the effectiveness of the proposed algorithm is verified by simulation and experiment.展开更多
Small area estimation (SAE) tackles the problem of providing reliable estimates for small areas, i.e., subsets of the population for which sample information is not sufficient to warrant the use of a direct estimator....Small area estimation (SAE) tackles the problem of providing reliable estimates for small areas, i.e., subsets of the population for which sample information is not sufficient to warrant the use of a direct estimator. Hierarchical Bayesian approach to SAE problems offers several advantages over traditional SAE models including the ability of appropriately accounting for the type of surveyed variable. In this paper, a number of model specifications for estimating small area counts are discussed and their relative merits are illustrated. We conducted a simulation study by reproducing in a simplified form the Italian Labour Force Survey and taking the Local Labor Markets as target areas. Simulated data were generated by assuming population characteristics of interest as well as survey sampling design as known. In one set of experiments, numbers of employment/unemployment from census data were utilized, in others population characteristics were varied. Results show persistent model failures for some standard Fay-Herriot specifications and for generalized linear Poisson models with (log-)normal sampling stage, whilst either unmatched or nonnormal sampling stage models get the best performance in terms of bias, accuracy and reliability. Though, the study also found that any model noticeably improves on its performance by letting sampling variances be stochastically determined rather than assumed as known as is the general practice. Moreover, we address the issue of model determination to point out limits and possible deceptions of commonly used criteria for model selection and checking in SAE context.展开更多
The side information quality has an immense effect on the compression efficiency of the distributed video coding (DVC) sys- tem. This article, based on the hierarchical motion estimation (HME), proposes a new side inf...The side information quality has an immense effect on the compression efficiency of the distributed video coding (DVC) sys- tem. This article, based on the hierarchical motion estimation (HME), proposes a new side information generation algorithm which is integrated into DVC system. First, forward motion estimation (FME) and bidirectional motion estimation (BME) on the basis of variable block size HME algorithm are used to acquire relatively accurate motion vectors. Second, a motion vector filter (MVF) is i...展开更多
This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades o...This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date,one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective,which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics(e.g., mean and covariance) conditioned on a system's measurement data.This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering(KF)techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter/input estimation.展开更多
Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next gene...Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.展开更多
A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited...A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.展开更多
An efficient despeclding algorithm is proposed based on stationary wavelet transform （SWT） for synthetic aperture radar （SAR） images. The statistical model of wavelet coefficients is analyzed and its performance i...An efficient despeclding algorithm is proposed based on stationary wavelet transform （SWT） for synthetic aperture radar （SAR） images. The statistical model of wavelet coefficients is analyzed and its performance is modeled with a mixture density of two zero-mean Gaussian distributions. A fuzzy shrinkage factor is derived based on the minimum mean square error （MMSE） criteria with Bayesian estimation. In the case above, the ideas of region division and fuzzy shrinkage arc adopted according to the interscale dependencies among wavelet coefficients. The noise-free wavelet coefficients are estimated accurately. Experimental results show that the algorithm proposed is superior to the refined Lee filter, wavelet soft thresbolding shrinkage and SWT shrinkage algorithms in terms of smoothing effects and edges preservation.展开更多
This paper develops a new method, named E-Bayesian estimation method, to estimate the reliability parameters. The E-Bayesian estimation method of the reliability are derived for the zero-failure data from the product ...This paper develops a new method, named E-Bayesian estimation method, to estimate the reliability parameters. The E-Bayesian estimation method of the reliability are derived for the zero-failure data from the product with Binomial distribution. Firstly, for the product reliability, the definitions of E-Bayesian estimation were given, and on the base, expressions of the E-Bayesian estimation and hierarchical Bayesian estimation of the products reliability was given. Secondly, discuss properties of the E-Bayesian estimation. Finally, the new method is applied to a real zero-failure data set, and as can be seen, it is both efficient and easy to operate.展开更多
The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target thr...The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications.展开更多
Measurement error of unbalance's vibration response plays a crucial role in calibration and on-line updating of influence coefficient(IC).Focusing on the two problems that the moment estimator of data used in cali...Measurement error of unbalance's vibration response plays a crucial role in calibration and on-line updating of influence coefficient(IC).Focusing on the two problems that the moment estimator of data used in calibration process cannot fulfill the accuracy requirement under small sample and the disturbance of measurement error cannot be effectively suppressed in updating process,an IC calibration and on-line updating method based on hierarchical Bayesian method for automatic dynamic balancing machine was proposed.During calibration process,for the repeatedly-measured data obtained from experiments with different trial weights,according to the fact that measurement error of each sensor had the same statistical characteristics,the joint posterior distribution model for the true values of the vibration response under all trial weights and measurement error was established.During the updating process,information obtained from calibration was regarded as prior information,which was utilized to update the posterior distribution of IC combined with the real-time reference information to implement online updating.Moreover,Gibbs sampling method of Markov Chain Monte Carlo(MCMC)was adopted to obtain the maximum posterior estimation of parameters to be estimated.On the independent developed dynamic balancing testbed,prediction was carried out for multiple groups of data through the proposed method and the traditional method respectively,the result indicated that estimator of influence coefficient obtained through the proposed method had higher accuracy;the proposed updating method more effectively guaranteed the measurement accuracy during the whole producing process,and meantime more reasonably compromised between the sensitivity of IC change and suppression of randomness of vibration response.展开更多
In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the...In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.展开更多
This paper considers the Bayesian and expected Bayesian（E-Bayesian） estimations of the parameter and reliability function for competing risk model from Gompertz distribution under Type-I progressively hybrid censori...This paper considers the Bayesian and expected Bayesian（E-Bayesian） estimations of the parameter and reliability function for competing risk model from Gompertz distribution under Type-I progressively hybrid censoring scheme（PHCS）. The estimations are obtained based on Gamma conjugate prior for the parameter under squared error（SE） and Linex loss functions. The simulation results are provided for the comparison purpose and one data set is analyzed.展开更多
In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined...In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.展开更多
This study developed a hierarchical Bayesian(HB)model for local and regional flood frequency analysis in the Dongting Lake Basin,in China.The annual maximum daily flows from 15 streamflow-gauged sites in the study are...This study developed a hierarchical Bayesian(HB)model for local and regional flood frequency analysis in the Dongting Lake Basin,in China.The annual maximum daily flows from 15 streamflow-gauged sites in the study area were analyzed with the HB model.The generalized extreme value(GEV)distribution was selected as the extreme flood distribution,and the GEV distribution location and scale parameters were spatially modeled through a regression approach with the drainage area as a covariate.The Markov chain Monte Carlo(MCMC)method with Gibbs sampling was employed to calculate the posterior distribution in the HB model.The results showed that the proposed HB model provided satisfactory Bayesian credible intervals for flood quantiles,while the traditional delta method could not provide reliable uncertainty estimations for large flood quantiles,due to the fact that the lower confidence bounds tended to decrease as the return periods increased.Furthermore,the HB model for regional analysis allowed for a reduction in the value of some restrictive assumptions in the traditional index flood method,such as the homogeneity region assumption and the scale invariance assumption.The HB model can also provide an uncertainty band of flood quantile prediction at a poorly gauged or ungauged site,but the index flood method with L-moments does not demonstrate this uncertainty directly.Therefore,the HB model is an effective method of implementing the flexible local and regional frequency analysis scheme,and of quantifying the associated predictive uncertainty.展开更多
In this paper, we consider the problem of determining the order ofINAR（Q） model on the basis of the Bayesian estimation theory. The Bayesian es-timator for the order is given with respect to a squared-error loss fu...In this paper, we consider the problem of determining the order ofINAR（Q） model on the basis of the Bayesian estimation theory. The Bayesian es-timator for the order is given with respect to a squared-error loss function. The consistency of the estimator is discussed. The results of a simulation study for the estimation method are presented.展开更多
Hydrocracking is a catalytic reaction process in the petroleum refineries for converting the higher boiling temperature residue of crude oil into a lighter fraction of hydrocarbons such as gasoline and diesel. In this...Hydrocracking is a catalytic reaction process in the petroleum refineries for converting the higher boiling temperature residue of crude oil into a lighter fraction of hydrocarbons such as gasoline and diesel. In this study, a modified continuous lumping kinetic approach is applied to model the hydro-cracking of vacuum gas oil. The model is modified to take into consideration the reactor temperature on the reaction yield distribution. The model is calibrated by maximizing the likelihood function between the modeled and measured data at four different reactor temperatures. Bayesian approach parameter estimation is also applied to obtain the confidence interval of model parameters by considering the uncertainty associated with the measured errors and the model structural errors. Then Monte Carlo simulation is applied to the posterior range of the model parameters to obtain the 95% confidence interval of the model outputs for each individual fraction of the hydrocracking products. A good agreement is observed between the output of the calibrated model and the measured data points. The Bayesian approach based on the Markov Chain Monte Carlo simulation is shown to be efficient to quantify the uncertainty associated with the parameter values of the continuous lumping model.展开更多
文摘The zero_failure data research is a new field in the recent years, but it is required urgently in practical projects, so the work has more theory and practical values. In this paper, for zero_failure data (t i,n i) at moment t i , if the prior distribution of the failure probability p i=p{T【t i} is quasi_exponential distribution, the author gives the p i Bayesian estimation and hierarchical Bayesian estimation and the reliability under zero_failure date condition is also obtained.
基金supported by the National Natural Science Foundation of China(Grant Nos.62473371 and 61673389)。
文摘Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making,their practical application faces two challenges in the noisy intermediate-scale quantum(NISQ)era.Limited qubit resources restrict direct application to large-scale inference tasks.Additionally,no quantum methods are currently available for multi-agent collaborative decision-making.To address these,we propose a hybrid quantum–classical multi-agent decision-making framework based on hierarchical Bayesian networks,comprising two novel methods.The first one is a hybrid quantum–classical inference method based on hierarchical Bayesian networks.It decomposes large-scale hierarchical Bayesian networks into modular subnetworks.The inference for each subnetwork can be performed on NISQ devices,and the intermediate results are converted into classical messages for cross-layer transmission.The second one is a multi-agent decision-making method using the variational quantum eigensolver(VQE)in the influence diagram.This method models the collaborative decision-making with the influence diagram and encodes the expected utility of diverse actions into a Hamiltonian and subsequently determines the intra-group optimal action efficiently.Experimental validation on the IonQ quantum simulator demonstrates that the hierarchical method outperforms the non-hierarchical method at the functional inference level,and the VQE method can obtain the optimal strategy exactly at the collaborative decision-making level.Our research not only extends the application of quantum computing to multi-agent decision-making but also provides a practical solution for the NISQ era.
基金supported by the National Key Research and Development Plan of China(Grant No.2022YFB3207402)the National Natural Science Foundation of China(Grant Nos.U1833104 and 61735011).
文摘In traditional sensing,each parameter is treated as a real number in the signal demodulation,whereas the electric field of light is a complex number.The real and imaginary parts obey the Kramers-Kronig relationship,which is expected to help further enhance sensing precision.We propose a self-Bayesian estimate of the method,aiming at reducing measurement variance.This method utilizes the intensity and phase of the parameter to be measured,achieving statistical optimization of the estimated value through Bayesian inference,effectively reducing the measurement variance.To demonstrate the effectiveness of this method,we adopted an optical fiber heterodyne interference sensing vibration measurement system.The experimental results show that the signal-to-noise ratio is effectively improved within the frequency range of 200 to 500 kHz.Moreover,it is believed that the self-Bayesian estimation method holds broad application prospects in various types of optical sensing.
基金Project supported by the Natural Science Foundation of Jiangsu Province(Grant Nos.BK20233001 and BK20243060)the National Natural Science Foundation of China(Grant No.62288101)。
文摘The estimation of quantum phase differences plays an important role in quantum simulation and quantum computation,yet existing quantum phase estimation algorithms face critical limitations in noisy intermediate-scale quantum(NISQ)devices due to their excessive depth and circuit complexity.We demonstrate a high-precision phase difference estimation protocol based on the Bayesian phase difference estimation algorithm and single-photon projective measurement.The iterative framework of the algorithm,combined with the independence from controlled unitary operations,inherently mitigates circuit depth and complexity limitations.Through an experimental realization on the photonic system,we demonstrate high-precision estimation of diverse phase differences,showing root-mean-square errors(RMSE)below the standard quantum limit𝒪(1/√N)and reaching the Heisenberg scaling𝒪(1/N)after a certain number of iterations.Our scheme provides a critical advantage in quantum resource-constrained scenarios,and advances practical implementations of quantum information tasks under realistic hardware constraints.
基金supported by the National Natural Science Foundation of China (62071144)
文摘Most of the existing direction of arrival(DOA)estimation algorithms are applied under the assumption that the array manifold is ideal.In practical engineering applications,the existence of non-ideal conditions such as mutual coupling between array elements,array amplitude and phase errors,and array element position errors leads to defects in the array manifold,which makes the performance of the algorithm decline rapidly or even fail.In order to solve the problem of DOA estimation in the presence of amplitude and phase errors and array element position errors,this paper introduces the first-order Taylor expansion equivalent model of the received signal under the uniform linear array from the Bayesian point of view.In the solution,the amplitude and phase error parameters and the array element position error parameters are regarded as random variables obeying the Gaussian distribution.At the same time,the expectation-maximization algorithm is used to update the probability distribution parameters,and then the two error parameters are solved alternately to obtain more accurate DOA estimation results.Finally,the effectiveness of the proposed algorithm is verified by simulation and experiment.
文摘Small area estimation (SAE) tackles the problem of providing reliable estimates for small areas, i.e., subsets of the population for which sample information is not sufficient to warrant the use of a direct estimator. Hierarchical Bayesian approach to SAE problems offers several advantages over traditional SAE models including the ability of appropriately accounting for the type of surveyed variable. In this paper, a number of model specifications for estimating small area counts are discussed and their relative merits are illustrated. We conducted a simulation study by reproducing in a simplified form the Italian Labour Force Survey and taking the Local Labor Markets as target areas. Simulated data were generated by assuming population characteristics of interest as well as survey sampling design as known. In one set of experiments, numbers of employment/unemployment from census data were utilized, in others population characteristics were varied. Results show persistent model failures for some standard Fay-Herriot specifications and for generalized linear Poisson models with (log-)normal sampling stage, whilst either unmatched or nonnormal sampling stage models get the best performance in terms of bias, accuracy and reliability. Though, the study also found that any model noticeably improves on its performance by letting sampling variances be stochastically determined rather than assumed as known as is the general practice. Moreover, we address the issue of model determination to point out limits and possible deceptions of commonly used criteria for model selection and checking in SAE context.
基金National Natural Science Foundation of China (60702012)
文摘The side information quality has an immense effect on the compression efficiency of the distributed video coding (DVC) sys- tem. This article, based on the hierarchical motion estimation (HME), proposes a new side information generation algorithm which is integrated into DVC system. First, forward motion estimation (FME) and bidirectional motion estimation (BME) on the basis of variable block size HME algorithm are used to acquire relatively accurate motion vectors. Second, a motion vector filter (MVF) is i...
文摘This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date,one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective,which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics(e.g., mean and covariance) conditioned on a system's measurement data.This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering(KF)techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter/input estimation.
基金supported by the Natural Science Foundation of Chongqing(No.cstc2019jcyj-msxmX0017)。
文摘Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.
文摘A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.
基金A Postdoctoral Science Foundation of China (J63104020156) National Defence Foundation of China
文摘An efficient despeclding algorithm is proposed based on stationary wavelet transform （SWT） for synthetic aperture radar （SAR） images. The statistical model of wavelet coefficients is analyzed and its performance is modeled with a mixture density of two zero-mean Gaussian distributions. A fuzzy shrinkage factor is derived based on the minimum mean square error （MMSE） criteria with Bayesian estimation. In the case above, the ideas of region division and fuzzy shrinkage arc adopted according to the interscale dependencies among wavelet coefficients. The noise-free wavelet coefficients are estimated accurately. Experimental results show that the algorithm proposed is superior to the refined Lee filter, wavelet soft thresbolding shrinkage and SWT shrinkage algorithms in terms of smoothing effects and edges preservation.
基金Supported by the Fujian Province NSFC(2009J01001)
文摘This paper develops a new method, named E-Bayesian estimation method, to estimate the reliability parameters. The E-Bayesian estimation method of the reliability are derived for the zero-failure data from the product with Binomial distribution. Firstly, for the product reliability, the definitions of E-Bayesian estimation were given, and on the base, expressions of the E-Bayesian estimation and hierarchical Bayesian estimation of the products reliability was given. Secondly, discuss properties of the E-Bayesian estimation. Finally, the new method is applied to a real zero-failure data set, and as can be seen, it is both efficient and easy to operate.
基金supported by the Fundamental Scientific Research Business Expenses for Central Universities(3072021CFJ0803)the Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Key Laboratory Project(AMCIT21V3).
文摘The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications.
基金supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2008 AA04Z114)
文摘Measurement error of unbalance's vibration response plays a crucial role in calibration and on-line updating of influence coefficient(IC).Focusing on the two problems that the moment estimator of data used in calibration process cannot fulfill the accuracy requirement under small sample and the disturbance of measurement error cannot be effectively suppressed in updating process,an IC calibration and on-line updating method based on hierarchical Bayesian method for automatic dynamic balancing machine was proposed.During calibration process,for the repeatedly-measured data obtained from experiments with different trial weights,according to the fact that measurement error of each sensor had the same statistical characteristics,the joint posterior distribution model for the true values of the vibration response under all trial weights and measurement error was established.During the updating process,information obtained from calibration was regarded as prior information,which was utilized to update the posterior distribution of IC combined with the real-time reference information to implement online updating.Moreover,Gibbs sampling method of Markov Chain Monte Carlo(MCMC)was adopted to obtain the maximum posterior estimation of parameters to be estimated.On the independent developed dynamic balancing testbed,prediction was carried out for multiple groups of data through the proposed method and the traditional method respectively,the result indicated that estimator of influence coefficient obtained through the proposed method had higher accuracy;the proposed updating method more effectively guaranteed the measurement accuracy during the whole producing process,and meantime more reasonably compromised between the sensitivity of IC change and suppression of randomness of vibration response.
基金supported by the National Science Foundation of China under Grant Nos.71361015,71340010,71371074the Jiangxi Provincial Natural Science Foundation under Grant No.20142BAB201013+2 种基金China Postdoctoral Science Foundation under Grant No.2013M540534China Postdoctoral Fund special Project under Grant No.2014T70615Jiangxi Postdoctoral Science Foundation under Grant No.2013KY53
文摘In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.
基金supported by the National Natural Science Foundation of China(7117116471401134+1 种基金71571144)the Natural Science Basic Research Program of Shaanxi Province(2015JM1003)
文摘This paper considers the Bayesian and expected Bayesian（E-Bayesian） estimations of the parameter and reliability function for competing risk model from Gompertz distribution under Type-I progressively hybrid censoring scheme（PHCS）. The estimations are obtained based on Gamma conjugate prior for the parameter under squared error（SE） and Linex loss functions. The simulation results are provided for the comparison purpose and one data set is analyzed.
基金supported by the Ministry of Education,Science,Sports and Culture,Grant-in-Aid for Scientific Research under Grant No.22240021the Grant-in-Aid for Challenging Exploratory Research under Grant No.21650030
文摘In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.
基金supported by the National Natural Science Foundation of China(Grants No.51779074 and 41371052)the Special Fund for the Public Welfare Industry of the Ministry of Water Resources of China(Grant No.201501059)+3 种基金the National Key Research and Development Program of China(Grant No.2017YFC0404304)the Jiangsu Water Conservancy Science and Technology Project(Grant No.2017027)the Program for Outstanding Young Talents in Colleges and Universities of Anhui Province(Grant No.gxyq2018143)the Natural Science Foundation of Wanjiang University of Technology(Grant No.WG18030)
文摘This study developed a hierarchical Bayesian(HB)model for local and regional flood frequency analysis in the Dongting Lake Basin,in China.The annual maximum daily flows from 15 streamflow-gauged sites in the study area were analyzed with the HB model.The generalized extreme value(GEV)distribution was selected as the extreme flood distribution,and the GEV distribution location and scale parameters were spatially modeled through a regression approach with the drainage area as a covariate.The Markov chain Monte Carlo(MCMC)method with Gibbs sampling was employed to calculate the posterior distribution in the HB model.The results showed that the proposed HB model provided satisfactory Bayesian credible intervals for flood quantiles,while the traditional delta method could not provide reliable uncertainty estimations for large flood quantiles,due to the fact that the lower confidence bounds tended to decrease as the return periods increased.Furthermore,the HB model for regional analysis allowed for a reduction in the value of some restrictive assumptions in the traditional index flood method,such as the homogeneity region assumption and the scale invariance assumption.The HB model can also provide an uncertainty band of flood quantile prediction at a poorly gauged or ungauged site,but the index flood method with L-moments does not demonstrate this uncertainty directly.Therefore,the HB model is an effective method of implementing the flexible local and regional frequency analysis scheme,and of quantifying the associated predictive uncertainty.
文摘In this paper, we consider the problem of determining the order ofINAR（Q） model on the basis of the Bayesian estimation theory. The Bayesian es-timator for the order is given with respect to a squared-error loss function. The consistency of the estimator is discussed. The results of a simulation study for the estimation method are presented.
文摘Hydrocracking is a catalytic reaction process in the petroleum refineries for converting the higher boiling temperature residue of crude oil into a lighter fraction of hydrocarbons such as gasoline and diesel. In this study, a modified continuous lumping kinetic approach is applied to model the hydro-cracking of vacuum gas oil. The model is modified to take into consideration the reactor temperature on the reaction yield distribution. The model is calibrated by maximizing the likelihood function between the modeled and measured data at four different reactor temperatures. Bayesian approach parameter estimation is also applied to obtain the confidence interval of model parameters by considering the uncertainty associated with the measured errors and the model structural errors. Then Monte Carlo simulation is applied to the posterior range of the model parameters to obtain the 95% confidence interval of the model outputs for each individual fraction of the hydrocracking products. A good agreement is observed between the output of the calibrated model and the measured data points. The Bayesian approach based on the Markov Chain Monte Carlo simulation is shown to be efficient to quantify the uncertainty associated with the parameter values of the continuous lumping model.
