Improved Attribute Chain Sampling Plan for Darna Distribution

Recently,the Darna distribution has been introduced as a new lifetime distribution.The two-parameter Darna distribution represents is a mixture of two well-known gamma and exponential distributions.A manufacturer or an engineer of products conducts life testing to examine whether the quality level of products meets the customer’s requirements,such as reliability or the minimum lifetime.In this article,an attribute modified chain sampling inspection plan based on the time truncated life test is proposed for items whose lifetime follows the Darna distribution.The plan parameters,including the sample size,the acceptance number,and the past lot result of the proposed sampling plan,are determined with the help of the two-point approach considering the acceptable quality level(AQL)and the limiting quality level(LQL).The plan parameters and the corresponding operating characteristic functions of a new plan are provided in tabular form for various Darna distribution parameters.Also,a few illustrated examples are presented for various distribution parameters.The usefulness of the proposed attribute modified chain sampling plan is investigated using two real failure time datasets.The results indicate that the proposed sampling plan can reduce the sample size when the termination ratio increases for fixed values of the producer’s risk and acceptance number.Hence,the proposed attribute modified chain sampling inspection plan is recommended to practitioners in the field.

Potential-Decomposition Strategy in Markov Chain Monte Carlo Sampling Algorithms

We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in phase space, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner. Furthermore, if the accepted trial samples are insumcient, they can be recycled as initial states to form more unbiased samples. This strategy can greatly improve efficiency when the original potential has multiple metastable states separated by large barriers. We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating in these two representative examples that convergence is accelerated by orders of magnitude.

Discrete-time Markov-based dynamic control approach for compressed sampling

To solve the problem that the signal sparsity level is time-varying and not known as a priori in most cases,a signal sparsity level prediction and optimal sampling rate determination scheme is proposed.The discrete-time Markov chain is used to model the signal sparsity level and analyze the transition between different states.According to the current state,the signal sparsity level state in the next sampling period and its probability are predicted.Furthermore,based on the prediction results,a dynamic control approach is proposed to find out the optimal sampling rate with the aim of maximizing the expected reward which considers both the energy consumption and the recovery accuracy.The proposed approach can balance the tradeoff between the energy consumption and the recovery accuracy.Simulation results show that the proposed dynamic control approach can significantly improve the sampling performance compared with the existing approach.

Active Kriging-Based Adaptive Importance Sampling for Reliability and Sensitivity Analyses of Stator Blade Regulator

The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computing efficiency and accuracy of the current analysismethods.In this case,by fitting the implicit limit state function(LSF)with active Kriging(AK)model and reducing candidate sample poolwith adaptive importance sampling(AIS),a novel AK-AIS method is proposed.Herein,theAKmodel andMarkov chainMonte Carlo(MCMC)are first established to identify the most probable failure region(s)(MPFRs),and the adaptive kernel density estimation(AKDE)importance sampling function is constructed to select the candidate samples.With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF,the failure probability and sensitivity indices are acquired at a lower cost.The proposed method is verified by twomulti-failure numerical examples,and then applied to the reliability and sensitivity analyses of a typical stator blade regulator.Withmethods comparison,the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.

Joint optimization of sampling interval and control for condition-based maintenance using availability maximization criterion

Most of the maintenance optimization models in condition-based maintenance(CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model(HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo(MCMC)simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model(HMM), the age-based replacement policy, Hotelling’s T2, multivariate exponentially weihted moving average(MEWMA) and multivariate cumulative sum(MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method.

Bayesian Inference and Prediction of Burr Type XII Distribution for Progressive First Failure Censored Sampling

This paper deals with Bayesian inference and prediction problems of the Burr type XII distribution based on progressive first failure censored data. We consider the Bayesian inference under a squared error loss function. We propose to apply Gibbs sampling procedure to draw Markov Chain Monte Carlo (MCMC) samples, and they have in turn, been used to compute the Bayes estimates with the help of importance sampling technique. We have performed a simulation study in order to compare the proposed Bayes estimators with the maximum likelihood estimators. We further consider two sample Bayes prediction to predicting future order statistics and upper record values from Burr type XII distribution based on progressive first failure censored data. The predictive densities are obtained and used to determine prediction intervals for unobserved order statistics and upper record values. A real life data set is used to illustrate the results derived.

单变量时间序列DLM的Gibbs Sampling方法

应用Gibbssampler对线性动态模型进行贝叶斯推断 ,与以往不同 ,这种方法是在给定其它变量情况下 ,同时产生出所有的状态向量。本文主要对此方法的滤波过程进行了改进 ,使其更加简洁。

New algorithm of exact sampling with directional threshold

Although it is known that exact sampling algorithm is easy to construct and less sensitive to noise, the samples distri- bution of the algorithm deviates from the target states distribution due to the local dependent coupling problem. A new algorithm, named exact sampling with directional threshold （ES-DT） is intro- duced. The main advantage of the new algorithm, in comparison with the traditional exact sampling algorithm, is that it can control the sampling with a rejection strategy in Markov chain during the path growth, and closely approach the ideal distribution based on maintaining the target density. Simulation experiments show the effectiveness of the proposed algorithm.

The Relations Among Various Markov Chains

Some basic equations and the relations among various Markov chains are established. These works are the bases in the investigation of the theory of Markov chain in random environment.

A quantitative polymerase chain reaction assay for the enumeration of brown tide algae Aureococcus anophagefferens in coastal waters of Qinhuangdao

Aureococcus anophagefferens, a small pelagophyte algae, has caused brown tide blooms in coastal waters of Qinhuangdao in recent years, presenting significant negative impacts on the shellfish mariculture industry. Under standard light microscopy, it is visually indistinguishable from other small algae in field samples due to its extremely small size. In this study, quantitative polymerase chain reaction（q PCR） based on 18 S r DNA sequences was developed and used to detect and enumerate A. anophagefferens. A linear regression（R2 = 0.91） was generated based on cycle thresholds value（Ct） versus known concentrations of A. anophagefferens. Twenty-two field samples collected in coastal waters of Qinhuangdao were subjected to DNA extraction and then analyzed using q PCR. Results showed that A. anophagefferens had a wide distribution in coastal waters along Qinhuangdao. Elevated A. anophagefferens abundance, category 3 brown tide blooms（〉200 000 cells/m L） occurred at Dongshan Beach and Tiger-stone Beach in August in 2013. In shellfish mariculture areas along coastal waters of Qinhuangdao, 4 stations had category 3 blooms, and 6 stations had category 2 blooms（35 000–200 000 cells/m L） in August and all stations had category 1 blooms（〉0 to ≤35 000 cells/m L） in October. Quantitative PCR allows for detection of A. anophagefferens cells at low levels in filed samples, which is essential to effective management and prediction of brown tide blooms.

A METHOD OF ANALYSIS OF MACHINE TOOL TRANSMISSION CHAIN ERROR

The indexing transmission chain error of gear cutting machines is an obviously periodic timefunction. If data are acquired properly while surveying and analyzing the error with grating typeinstrument attached to a micro-computer, the analysis will be easier and quicker. In this paper,such a system is introduced; a data acquiring interface is designed. Therefore, real-time samplingcan be performed. The results obtained from the indexing tramsmission chain error of several gearhobbing and shaping machines with this system were satisfactory.

Estimations of Weibull-Geometric Distribution under Progressive Type II Censoring Samples

This paper deals with the Bayesian inferences of unknown parameters of the progressively Type II censored Weibull-geometric (WG) distribution. The Bayes estimators cannot be obtained in explicit forms of the unknown parameters under a squared error loss function. The approximate Bayes estimators will be computed using the idea of Markov Chain Monte Carlo (MCMC) method to generate from the posterior distributions. Also the point estimation and confidence intervals based on maximum likelihood and bootstrap technique are also proposed. The approximate Bayes estimators will be obtained under the assumptions of informative and non-informative priors are compared with the maximum likelihood estimators. A numerical example is provided to illustrate the proposed estimation methods here. Maximum likelihood, bootstrap and the different Bayes estimates are compared via a Monte Carlo Simulation

基于最小故障的充电桩检测策略构建方法研究

随机测试是发现和分析充电桩故障的重要手段,针对充电桩系统故障频繁发生,文中提出一种充电桩检测策略生成方法,利用测试过程的马尔可夫性将测试顺序建模为马氏链决定的随机变量。通过对转移顺序进行Gibbs随机采样建立马氏链以得到固有的最小故障,利用公共子串搜索方法来统计最小故障信息,并按照可疑程度将最小故障重新注入到测试策略中,以达到提高测试策略质量和降低测试策略数量的目的。以实测结果对基于最小故障的测试策略有效性进行了验证。所提方法有助于确定共性故障原因,对于减少充电桩故障带来的风险具有重要的指导意义。

短链氯化石蜡的分析方法研究进展

简要介绍了SCCPs的理化特性、全球生产和使用情况以及环境分布,系统综述了不同环境介质中SCCPs的萃取、净化方法和气相色谱—质谱法、二维气相色谱法、超高效液相色谱质谱法等仪器分析技术,为今后全面的评估SCCPs的生态健康暴露风险,进行污染物防治提供科学依据。

基于邻居采样和图注意力机制的产业链风险评估模型

风险评估是提高产业链韧性的重要途径,也是降低产业链不稳定性的有效手段。然而,现有风险评估的研究基于供应链结构,忽略了其他因素,无法准确地刻画产业链上下游各节点的关联关系,导致评估效果存在偏差。针对上述问题,考虑到产业链内部各节点相互关联、风险状况多样、存在风险传递的特性,提出了基于邻居采样和图注意力机制的产业链风险评估模型GANS。首先,构建了产业链的异质图,利用“产品-公司”“产品-产品”刻画了产业链节点之间的关联关系,并从产业链中提取财务数据等作为节点的数据特征;其次,提出了基于元路径和公司投融资关联规则的公司关系图生成模块,实现对产业链中公司节点关系的高效转化和结构特征的高效学习;接着,针对生成的多种公司关系图,设计了结合邻居采样和图注意力机制的产业链风险评估模块,对节点邻域特征进行随机采样和聚合,同时结合注意力机制对基于多种公司图的节点特征进行自适应聚合,并通过分类器实现节点级风险评估;最后,依据节点风险等级与节点的结构特征对产业链进行风险评估。实验表明,在真实产业链数据集上,GANS在准确性、F1分数等方面均优于现有的模型,证明了GANS实现产业链风险评估的准确性和有效性。

基于频次与极值外推综合的载荷外推总体方法

载荷外推作为载荷谱编制的重要技术手段,当前研究缺乏对于载荷外推总体方法的全面梳理、马尔可夫稳态分布的求解方法适应性不够、缺乏不同非参频次外推方法的比较与选用原则,导致不便生成高精度载荷谱以支撑装备性能设计。围绕坦克在高机动和极限工况下的载荷谱编制问题,基于某坦克行进间身管位移数据样本,分别使用基于雨流矩阵及核密度估计的非参数外推法、基于马尔可夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)的信号重构法以及Metropolis-Hastings(简称MH)直接采样法进行了载荷频次外推,并针对MCMC的信号重构法提出了一种改良马尔可夫稳态分布的求解方法。应用所提出的频次-极值相结合的载荷外推总体方法对坦克身管位移进行了频次扩充与极值预测,并结合实车试验结果验证了方法的准确性。研究结果表明:改良的马尔可夫稳态分布求解方法是有效的;在样本长度足够、外推精度要求不甚高的情况下,MH直接采样法可作为一种新的频次外推方法;运用频次-极值相结合的载荷外推总体方法所得结果精度较高;形成的频次外推法选用原则对于载荷谱编制过程中的方法选择具有一定的指导意义。研究工作为装备载荷谱的高质量编制提供了成熟的技术路线和参考。

纺织服装供应链中颜色标准样品的使用

纺织服装产品涉及到颜色、面料和款式3个要素,其中颜色是决定消费者是否购买服装产品的重要因素之一,如何让设计师精心选择的颜色以最快最准的方式生产出来,是每个企业关注的重点。纺织服装供应链中的颜色管理是一门科学,其中颜色标准样品的正确使用发挥着巨大作用,文章对颜色标准样品的定义、特征和使用方法等进行分析和讨论,以期为企业使用颜色标准样品提供参考。

离子色谱法检测短链脂肪酸的研究进展

离子色谱法是近年快速发展的检测技术,具有高灵敏度、高选择性、样品用量少等优点.短链脂肪酸作为肠道菌群的代谢产物,对维持机体生理健康具有重要作用.以“离子色谱法”“短链脂肪酸”“样品前处理”等为检索词,检索中国知网、维普文献数据库和PubMed数据库,归纳、分析从2007年到2023年的文献,对以离子色谱法为主的短链脂肪酸前处理方式和检测方法进行总结和归纳.结果发现,短链脂肪酸的前处理方法主要有萃取法、离心法、柱前衍生化法等,而离子色谱法中大多采用离心法或简单萃取法.在流动相中加入适量盐酸酸化有助于增加短链脂肪酸的保留率,加入有机改性试剂能够改善短链脂肪酸的峰形和分离度.离子色谱法结合在线富集或复杂基质消除方法,能够实现痕量短链脂肪酸的准确定量分析.离子色谱法与质谱联用能够满足复杂基质样品的检测要求,是未来发展的趋势.

A Bayesian Mixture Model Approach to Disparity Testing

The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.

基于粪便样本检测幽门螺杆菌感染及耐药情况的研究进展

幽门螺杆菌(Helicobacter pylori,H.pylori)是一种革兰氏阴性细菌,特异性定植于人类的胃上皮,特别是胃窦上皮中。它是慢性胃炎、消化性溃疡、胃癌、胃黏膜相关淋巴组织淋巴瘤及一些胃外疾病的主要病因。由于H.pylori抗生素耐药菌株的迅速出现,其根治率正逐年下降。对抗生素耐药性进行检测,指导临床医师选择治疗方案是目前提高H.pylori根治率的重要手段。目前,H.pylori抗生素耐药性的检测主要是患者基于接受内镜检查的侵入性检测。而针对非侵入性手段获得粪便样本的检测相比于侵入性检测具有无创便捷、高效、依从性好等优点。近年来针对粪便样本进行抗原检测、分子检测等多种方式的研究均有报道。本文就基于粪便样本诊断H.pylori感染及检测其耐药情况的研究进展进行综述和分析,为探索更为便捷、高效的诊断方式和指导治疗手段提供思考。