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.

Structural damage detection method based on information fusion technique

Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.

Implementation of ID-based Audit Protocols to Enhance Security and Productivity

Cloud storage has gained increasing popularity,as it helps cloud users arbitrarily store and access the related outsourced data.Numerous public audit buildings have been presented to ensure data transparency.However,modern developments have mostly been constructed on the public key infrastructure.To achieve data integrity,the auditor must first authenticate the legality of the public key certificate,which adds to an immense workload for the auditor,in order to ensure that data integrity is accomplished.The data facilities anticipate that the storage data quality should be regularly tracked to minimize disruption to the saved data in order to maintain the intactness of the stored data on the remote server.One of the main problems for individuals,though,is how to detect data integrity on a term where people have a backup of local files.Meanwhile,a system is often unlikely for a source-limited person to perform a data integrity inspection if the overall data file is retrieved.In this work,a stable and effective ID-based auditing setting that uses machine learning techniques is proposed to improve productivity and enhance the protection of ID-based audit protocols.The study tackles the issue of confidentiality and reliability in the public audit framework focused on identity.The idea has already been proved safe;its safety is very relevant to the traditional presumption of the Computational Diffie-Hellman security assumption.

基于多指标融合的故障诊断理论与方法

运用嵌入约束方法，对故障诊断中的多指标信息融合问题作了统一的数学描述，系统地研究多指标诊断的信息理论、故障的可诊断性检验、诊断指标的筛选以及基于Ｂａｙｅｓ方法的诊断模型等，并将其应用于内燃机活塞缸套磨损故障的诊断，取得了预期的效果。

未知量测噪声分布下的多扩展目标CBMeMBer滤波算法

在实际应用场景中,量测噪声协方差准确模型很难被建立,传统的多扩展目标跟踪算法在量测噪声协方差未知情况下跟踪性能迅速下降。为了解决量测噪声未知对多扩展目标跟踪结果造成的影响,将变分贝叶斯方法引入到CBMeMBer滤波算法中。VB-GM-CBMeMBer算法能在量测噪声未知情况下通过估计噪声协方差进行滤波计算,但该算法存在目标数目估计不准确的问题。针对此问题,提出一种改进的VB-GM-CBMeMBer算法,该算法在滤波算法预测步骤后引入椭球门限,使用保留在门限内的量测来进行下一步计算,以减少杂波量测,降低杂波量测对扩展目标量测的影响,提高对扩展目标状态聚类的精度。实验结果表明,该算法适用于多扩展目标数目未知、量测噪声协方差未知的情况,且其跟踪精度比GM-CBMeMBer和VB-GM-CBMeMBer滤波算法有一定提高。

Trusted dynamic level scheduling based on Bayes trust model

A kind of trust mechanism-based task scheduling model was presented. Referring to the trust relationship models of social persons, trust relationship is built among Grid nodes, and the trustworthiness of nodes is evaluated by utilizing the Bayes method. Integrating the trustworthiness of nodes into a Dynamic Level Scheduling （DLS） algorithm, the Trust-Dynamic Level Scheduling （Trust-DLS） algorithm is proposed. Theoretical analysis and simulations prove that the Trust-DLS algorithm can efficiently meet the requirement of Grid tasks in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way in Grid environment.

Linear Bayes Estimators Applied to the Inverse Gaussian Lifetime Model

For the two-parameter inverse Gaussian distribution denoted by IG（μ,A）, the authors employ a linear Bayes procedure to estimate the parameters μ and A. The superiority of the proposed linear Bayes estimator （LBE） over both the classical UMVUE and the maximum likelihood estimator （MLE） is established in terms of the mean squared error matrix （MSEM） criterion. Compared with the usual Bayes estimator, which is obtained by an MCMC method, the proposed LBE is simple and easy to use. Some numerical results are presented to verify that the LBE performs well.

A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain

We demonstrate a heuristic approach for optimizing the posterior density of the data association tracking algorithm via the random finite set(RFS)theory.Specifically,we propose an adjusted version of the joint probabilistic data association(JPDA)filter,known as the nearest-neighbor set JPDA(NNSJPDA).The target labels in all possible data association events are switched using a novel nearest-neighbor method based on the Kullback-Leibler divergence,with the goal of improving the accuracy of the marginalization.Next,the distribution of the target-label vector is considered.The transition matrix of the target-label vector can be obtained after the switching of the posterior density.This transition matrix varies with time,causing the propagation of the distribution of the target-label vector to follow a non-homogeneous Markov chain.We show that the chain is inherently doubly stochastic and deduce corresponding theorems.Through examples and simulations,the effectiveness of NNSJPDA is verified.The results can be easily generalized to other data association approaches under the same RFS framework.

Phase noise correction for OFDM signal based on DCT approach and variational inference

A novel scheme to joint phase noise （PHN） correcting and channel noise variance estimating for orthogonal frequency division multiplexing （OFDM） signal was proposed, The new scheme was based on the variational Bayes （VB） method and discrete cosine transform （DCT） approximation. Compared with the least squares （LS） based scheme, the proposed scheme could overcome the over-fitting phenomenon and thus lead to an improved performance. Computer simulations showed that the proposed VB based scheme outperforms the existing LS based scheme