融合贝叶斯和FSRM的相关反馈算法研究

在图像检索领域当中,低层视觉特征和高层语义之间的"语义鸿沟"问题都是许多研究学者面前的一大障碍。相关反馈机制在一定程度上解决了"语义鸿沟"问题,同时相关反馈也存在反馈次数过多,反馈效果不够理想等问题。为解决上述问题,提出一种贝叶斯和模糊语义相关矩阵(FSRM)相结合的反馈算法。实现方法是:用贝叶斯分类器对图像库进行分类,达到压缩图像库的目的,然后用模糊语义相关矩阵对压缩之后的图像库进行检索,并反馈最终结果。研究结果表明,与贝叶斯算法和FSRM相比,本文提出的算法明显地提高了反馈效果,优化了反馈次数。

一种贝叶斯和支持向量机相结合的相关反馈策略

相关反馈技术在提高图像检索性能方面发挥着重要作用,但图像检索过程中的相关反馈存在反馈次数过多,反馈效果不够理想等问题。为解决上述问题,提出一种贝叶斯和支持向量机相结合的反馈算法。实现方法是:用贝叶斯分类器对图像库进行分类,达到压缩图像库的目的,然后用支持向量机分类器对压缩之后的图像库进行分类,并反馈最终结果。研究结果表明,与支持向量机和贝叶斯算法相比,在很少的反馈次数下,该方法明显提高了反馈效果。

Bayesian network model for traffic flow estimation using prior link flows

In order to estimate traffic flow a Bayesian network BN model using prior link flows is proposed.This model sets link flows as parents of the origin-destination OD flows. Under normal distribution assumptions the model considers the level of total traffic flow the variability of link flows and the violation of the conservation law.Using prior link flows the prior distribution of all the variables is determined. By updating some observed link flows the posterior distribution is given.The variances of the posterior distribution normally decrease with the progressive update of the link flows. Based on the posterior distribution point estimations and the corresponding probability intervals are provided. To remove inconsistencies in OD matrices estimation and traffic assignment a combined BN and stochastic user equilibrium model is proposed in which the equilibrium solution is obtained through iterations.Results of the numerical example demonstrate the efficiency of the proposed BN model and the combined method.

Improved seismoacoustic analysis model and its application to source parameter inversion of near-surface small-yield chemical explosions

The seismoacoustic analysis method has broad potential applications to source parameter estimation for near-surface explosion events such as industrial explosions and terrorist attacks.In this study,current models were improved by modifying the acoustic model and adopting the Bayesian Markov-chain-Monte-Carlo inversion method.The source parameters of near-surface small-yield chemical explosions were analyzed via the improved seismoacoustic analysis model and by the estimation accuracy of seismoacoustic joint inversion.Estimation and analysis results showed that the improved seismoacoustic analysis model considered ground shock coupling and the impact of explosion products ejecting from the surface so that the improved acoustic impulse relation was more consistent with the measured data than the Ford impulse relation.It is suitable for deep-burial,shallow-burial,and near-surface aerial explosions.Furthermore,trade-off relationships were declined through the application of the improved model to source parameter inversion for near-surface small-yield chemical explosions,and source parameter estimation accuracy was improved.

Robust SLAM localization method based on improved variational Bayesian filtering

Aimed at the problem that the state estimation in the measurement update of the simultaneous localization and mapping(SLAM)method is incorrect or even not convergent because of the non-Gaussian measurement noise,outliers,or unknown and time-varying noise statistical characteristics,a robust SLAM method based on the improved variational Bayesian adaptive Kalman filtering(IVBAKF)is proposed.First,the measurement noise covariance is estimated using the variable Bayesian adaptive filtering algorithm.Then,the estimated covariance matrix is robustly processed through the weight function constructed in the form of a reweighted average.Finally,the system updates are iterated multiple times to further gradually correct the state estimation error.Furthermore,to observe features at different depths,a feature measurement model containing depth parameters is constructed.Experimental results show that when the measurement noise does not obey the Gaussian distribution and there are outliers in the measurement information,compared with the variational Bayesian adaptive SLAM method,the positioning accuracy of the proposed method is improved by 17.23%,20.46%,and 17.76%,which has better applicability and robustness to environmental disturbance.

Assessment Method of Heavy NC Machine Reliability Based on Bayes Theory

It is difficult to collect the prior information for small-sample machinery products when their reliability is assessed by using Bayes method. In this study, an improved Bayes method with gradient reliability(GR) results as prior information was proposed to solve the problem. A certain type of heavy NC boring and milling machine was considered as the research subject, and its reliability model was established on the basis of its functional and structural characteristics and working principle. According to the stress-intensity interference theory and the reliability model theory, the GR results of the host machine and its key components were obtained. Then the GR results were deemed as prior information to estimate the probabilistic reliability(PR) of the spindle box, the column and the host machine in the present method. The comparative studies demonstrated that the improved Bayes method was applicable in the reliability assessment of heavy NC machine tools.

Random noise suppression for seismic data using a non-local Bayes algorithm

For random noise suppression of seismic data, we present a non-local Bayes （NL- Bayes） filtering algorithm. The NL-Bayes algorithm uses the Gaussian model instead of the weighted average of all similar patches in the NL-means algorithm to reduce the fuzzy of structural details, thereby improving the denoising performance. In the denoising process of seismic data, the size and the number of patches in the Gaussian model are adaptively calculated according to the standard deviation of noise. The NL-Bayes algorithm requires two iterations to complete seismic data denoising, but the second iteration makes use of denoised seismic data from the first iteration to calculate the better mean and covariance of the patch Gaussian model for improving the similarity of patches and achieving the purpose of denoising. Tests with synthetic and real data sets demonstrate that the NL-Bayes algorithm can effectively improve the SNR and preserve the fidelity of seismic data.

Translation-invariant wavelet denoising of full-tensor gravity-gradiometer data

Denoising of full-tensor gravity-gradiometer data involves detailed information from field sources, especially the data mixed with high-frequency random noise. We present a denoising method based on the translation-invariant wavelet with mixed thresholding and adaptive threshold to remove the random noise and retain the data details. The novel mixed thresholding approach is devised to filter the random noise based on the energy distribution of the wavelet coefficients corresponding to the signal and random noise. The translation- invariant wavelet suppresses pseudo-Gibbs phenomena, and the mixed thresholding better separates the wavelet coefficients than traditional thresholding. Adaptive Bayesian threshold is used to process the wavelet coefficients according to the specific characteristics of the wavelet coefficients at each decomposition scale. A two-dimensional discrete wavelet transform is used to denoise gridded data for better computational efficiency. The results of denoising model and real data suggest that compared with Gaussian regional filter, the proposed method suppresses the white Gaussian noise and preserves the high-frequency information in gravity-gradiometer data. Satisfactory denoising is achieved with the translation-invariant wavelet.

A Bayesian Approach to Robot Group Control

This paper describes a Bayesian approach to robot group control applied in industrial applications. The proposed model is based on well-known concepts of Ubiquitous Computing and can enable some degree of contextual perception of the environment. Compared with classical industrial robots, usually preprogrammed for a limited number of operations/actions, the system based on this model can react in uncertain situations and scenarios. The model combines ontology to describe the specific domain of interest and decision-making mechanisms based on Bayesian Networks to enable the work of a single robot without human intervention by learning Behavioral Patterns of other robots in the group. The described model is designed to be expressive enough to provide adequate level of abstractions needed for making timely appropriate actions and respecting the current application.

The Virtue of Open-Mindedness

Bayesianism is a theory of probabilistic reasoning that attempts to capture the logic of confirming and disconfirming hypotheses. I first argue that Bayesianism reveals striking parallels between structures universally held as paradigms of rational belief systems and structures typically considered clear examples of irrational belief systems. I next explain that the crucial difference between these two types of belief systems is found not inside the systems but outside them, in the dynamics, i.e., the attitudes, by which such systems are revised and maintained. The principal attitude that distinguishes these belief systems is ＂open-mindedness.＂ I conclude that rationality and irrationality are primarily properties of attitudes, and derivatively of persons （who exhibit such attitudes） and of beliefs （that are maintained by such attitudes）. It turns out then that, on the one hand, the Bayesian approach reveals important truths about the nature of rationality and irrationality, but, on the other hand, it is inadequate as a theory of rationality, since it leaves some aspects of rationality and irrationality unaccounted for. The Bayesian analysis on the basis of which these conclusions are reached arises from a careful examination of the Duhem problem, which is the problem of determining the disconfirmation impact on the plausibility of hypotheses collectively responsible for a false observational consequence.

Bayesian Segmentation of Piecewise Linear Regression Models Using Reversible Jump MCMC Algorithm

Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation ofpiecewise linear regression models. The method used to estimate the parameters ofpicewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC （Marcov Chain Monte Carlo） algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters ofpicewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models.

A tour-based analysis of travel mode choice accounting for regional transit service

The aim of this work is to explore the impact of regional transit service on tour-based commuter travel behavior by using the Bayesian hierarchical multinomial logit model, accounting for the spatial heterogeneity of the people living in the same area.With two indicators, accessibility and connectivity measured at the zone level, the regional transit service is captured and then related to the travel mode choice behavior. The sample data are selected from Washington-Baltimore Household Travel Survey in 2007,including all the trips from home to workplace in morning hours in Baltimore city. Traditional multinomial logit model using Bayesian approach is also estimated. A comparison of the two different models shows that ignoring the spatial context can lead to a misspecification of the effects of the regional transit service on travel behavior. The results reveal that improving transit service at regional level can be effective in reducing auto use for commuters after controlling for socio-demographics and travel-related factors.This work provides insights for interpreting tour-based commuter travel behavior by using recently developed methodological approaches. The results of this work will be helpful for engineers, urban planners, and transit operators to decide the needs to improve regional transit service and spatial location efficiently.

Bayesian Method Reliability of Flight Simulator

This paper introduces the basic viewpoints and characteristics of Bayesian statistics. Which provides a theoretical basis for solving the problem of small sample of flight simulator using Bayesian method. A series of formulas were derived to establish the Bayesian reliability modeling and evaluation model for flight simulation equipment. The two key problems of Bayesian method were pointed out as follows： obtaining the prior distribution of WeibuU parameter, calculating the parameter a posterior distribution and parameter estimation without analytic solution, and proposing the corresponding solution scheme.

Bayesian Prediction of Performance in Clinical Trials

This paper presents one of many possible applications of Bayesian inference predictive context of planned tests. We are particularly interested in the use of predictive Bayesian approach in clinical trials or objective is the development of important evidence of an effect of interest We offer the procedure based on the notion of satisfaction index which is a function of the p-value and we look forward, given the available data to calculate a forecast for future satisfaction data as predictive Bayesian hope this index conditional on previous observations. To illustrate the proposed procedure, several models have been studied by choosing the prior distribution justify the reasons of objectivity or neutrality that underlie the analysis of experimental data.

Modelling Distribution of Under-Five Child Diarrhoea across Malawi

Analysis of diarrhoea data in Malawi has been commonly done using classical methods. However, different approaches, such as Bayesian methods, have been introduced in literature. This study aimed at trying out semi-parametric methods in comparison with classical ones, as well as how each isolates risk factors for child diarrhoea. This was done by fitting Logit, Poisson, and Bayesian models to 2006 Malawi Multiple Indicator Cluster Survey data. The comparison between Logit and Poisson models was done via chi-square＇s goodness-of-fit test. Confidence and Credible Intervals were used to compare Logit/Poisson and Bayesian model estimates. Modelling and inference in Bayesian method was done through MCMC techniques. The results showed agreement in significance and direction of estimates from Bayesian and Poisson/Logit models, but Poisson provided better fit than Logit model. Further, all the models identified child＇s age, breastfeeding status, region of stay and toilet-sharing status as significant factors for determining the child＇s risk. The models ruled out effects of mother＇s education, area of residence, and source of drinking water on the risk. Bayesian model separately proved significant closeness to lake/river factor. The findings imply that classical and semi-parametric models are equally helpful when estimating the child＇s risk to diarrhoea.

Selection of Trusted Service Providers by Enforcing Bayesian Analysis in iVCE

The initiative of internet-based virtual computing environment （iVCE） aims to provide the end users and applications with a harmonions, trustworthy and transparent integrated computing environment which will facilitate sharing and collaborating of network resources between applications. Trust management is an elementary component for iVCE. The uncertain and dynamic characteristics of iVCE necessitate the requirement for the trust management to be subjective, historical evidence based and context dependent. This paper presents a Bayesian analysis-based trust model, which aims to secure the active agents for selecting appropriate trusted services in iVCE. Simulations are made to analyze the properties of the trust model which show that the subjective prior information influences trust evaluation a lot and the model stimulates positive interactions.

Dynamic Random Access Channel Allocation Scheme Based on the Pseudo-Bayesian Algorithm in OFDMA System

Random Access Channel （RACH） is an uplink contention-based transport channel usually used for initial channel access, bandwidth request, etc. How to use RACH resources effectively is very important in wireless corrnunication systel In this paper, a dynamical RACH allocation scheme is proposed for Orthogonal Frequency-Division Multiple Access （OFDMA） systen. Based on the PseudoBayesian algorithm, this mechanism predicts the number of RACHs for the next frame according to the current load. A new dynamic RACH assignment algorithm and an adaptive access probability method are adopted by the proposed scheme to irrprove the utilization ratio of RACH resources and increase the successful access rate. Numerical simulation shows that the proposed strategy achieves both improvement in the utilization ratio of RACHs and reduction in the access delay compared with other RACH allocation schemes.

Bayesian texture segmentation based on wavelet domain hidden markov tree and the SMAP rule

According to the sequential maximum a posteriori probability (SMAP) rule, this paper proposes a novel multi-scale Bayesian texture segmentation algorithm based on the wavelet domain Hidden Markov Tree (HMT) model. In the proposed scheme, interscale label transition probability is directly defined and resoled by an EM algorithm. In order to smooth out the variations in the homogeneous regions, intrascale context information is considered. A Gaussian mixture model (GMM) in the redundant wavelet domain is also exploited to formulate the pixel-level statistical features of texture pattern so as to avoid the influence of the variance of pixel brightness. The performance of the proposed method is compared with the state-of-the-art HMTSeg method and evaluated by the experiment results.

Application of Bayesian approach to hydrological frequency analysis

An existing Bayesian flood frequency analysis method is applied to quantile estimation for Pearson type three (P-III) probability distribution. The method couples prior and sample information under the framework of Bayesian formula, and the Markov Chain Monte Carlo (MCMC) sampling approach is used to estimate posterior distributions of parameters. Different from the original sampling algorithm (i.e. the important sampling) used in the existing approach, we use the adaptive metropolis (AM) sampling technique to generate a large number of parameter sets from Bayesian parameter posterior distributions in this paper. Consequently, the sampling distributions for quantiles or the hydrological design values are constructed. The sampling distributions of quantiles are estimated as the Bayesian method can provide not only various kinds of point estimators for quantiles, e.g. the expectation estimator, but also quantitative evaluation on uncertainties of these point estimators. Therefore, the Bayesian method brings more useful information to hydrological frequency analysis. As an example, the flood extreme sample series at a gauge are used to demonstrate the procedure of application.

Current progress in sparse signal processing applied to radar imaging

Sparse signal processing is a signal processing technique that takes advantage of signal’s sparsity,allowing signal to be recovered with a reduced number of samples.Compressive sensing,a new branch of the sparse signal processing,has become a rapidly growing research field.Sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theories,new systems and new methodologies of microwave imaging.This paper first summarizes the latest application of sparse microwave imaging,including Synthetic Aperture Radar(SAR),tomographic SAR and inverse SAR.As sparse signal processing keeps evolving,an avalanche of results have been obtained.We also highlight its recent theoretical advances,including structured sparsity,off-grid,Bayesian approaches,and point out new research directions in sparse microwave imaging.