Maximum likelihood-based adaptive differential evolution identification algorithm for multivariable systems in the state-space form

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.     

安全管理平台中基于云计算的日志分析系统设计

安全管理平台(SMP)是实现安全管理工作常态化运行的技术支撑平台,在实际应用中需要实时处理来自安全设备所产生的海量日志信息。为解决现有SMP中海量日志查询效率低下的问题,设计基于云计算的SMP日志存储分析系统。基于Hive的任务转化模式,利用Hadoop架构的分布式文件系统和MapReduce并行编程模型,实现海量SMP日志的有效存储与查询。实验结果表明,与基于关系数据的多表关联查询方法相比,该系统使得SMP日志的平均查询效率提高约90%,并能加快SMP集中管控的整体响应速度。     

A pseudo-likelihood approach for estimating diagnostic accuracy of multiple binary medical tests

Latent class models with crossed subject-specific and test(rater)-specific random effects have been proposed to estimate the diagnostic accuracy (sensitivity and specificity) of a group of binary tests or binary ratings. However, the computation of these models are hindered by their complicated Monte Carlo Expectation–Maximization (MCEM) algorithm. In this article, a class of pseudo-likelihood functions is developed for conducting statistical inference with crossed random-effects latent class models in diagnostic medicine. Theoretically, the maximum pseudo-likelihood estimation is still consistent and has asymptotic normality. Numerically, our results show that not only the pseudo-likelihood approach significantly reduces the computational time, but it has comparable efficiency relative to the MCEM algorithm. In addition, dimension-wise likelihood, one of the proposed pseudo-likelihoods, demonstrates its superior performance in estimating sensitivity and specificity.     

基于关联规则的网络行为分析

网络用户访问网站的过程中,产生了大量的用户浏览网页的相关记录,隐含着用户在上网过程中的行为习惯。但其中潜在的用户信息难以发现。因此,急需有效的方法提取这些数据中的信息,数据挖掘应用而生。其中,关联规则技术是应用广泛的技术之一。文中利用Apriori算法对Web结构数据进行关联规则挖掘,所得到的规则反映出页面之间的链接关系。分析挖掘结果可得到用户访问的行为规律,为相关网站的安全性和优化改进提供有效的决策依据。     

An E-Business Event Stream Mechanism for Improving User Tracing Processes

With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record infinite user transactions. This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file. Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations. The experimental results are conducted by applying clustering-based classification algorithms on two different datasets; namely, Online Shoppers Purchasing Intention and Instacart Market Basket Analysis. The clustering process is used to group related objects into the same cluster, then the classification process measures the predicted classes of clustered objects. The experimental results record provable accuracy in predicting user preferences on both datasets.     

A side-sensitive synthetic coefficient of variation chart

Control charts for monitoring the coefficient of variation (γ) are useful for processes with an inconsistent mean (μ) and a standard deviation (σ) which changes with μ, by monitoring the consistency in the ratio σ over μ. The synthetic-γ chart is one of the charts proposed to monitor γ, and its attractiveness lie in waiting until a second point to fall outside the control limits before a decision is made. However, existing synthetic-γ charts do not differentiate between the points falling outside the upper control limit (UCL) and lower control limit (LCL). Hence, this paper proposes a side-sensitive synthetic-γ chart, where successive nonconforming samples must either fall above the UCL or below the LCL. Formulae to compute the average run length (ARL), the standard deviation of the run length (SDRL) and expected average run length (EARL) are derived using the Markov chain approach, and the algorithms to obtain the optimal charting parameters are proposed. Subsequently, the optimal charting parameters, ARL, SDRL and EARL values for various numerical examples are shown. Comparisons show that the side-sensitive synthetic-γ chart consistently outperforms the existing synthetic-γ chart, especially for small shifts. The proposed chart also consistently outperforms the Shewhart-γ chart, while showing comparable or better performance than the Exponentially Weighted Moving Average (EWMA) chart for most shift sizes, except for very small shifts. Finally, this paper shows the implementation of the proposed chart on an industrial example.     

Exploring behavioural patterns during complex problem-solving

In this explorative study, we investigate how sequences of behaviour are related to success or failure in complex problem-solving (CPS). To this end, we analysed log data from two different tasks of the problem-solving assessment of the Programme for International Student Assessment 2012 study (n = 30,098 students). We first coded every interaction of students as (initial or repeated) exploration, (initial or repeated) goal-directed behaviour, or resetting the task. We then split the data according to task successes and failures. We used full-path sequence analysis to identify groups of students with similar behavioural patterns in the respective tasks. Double-checking and minimalistic behaviour was associated with success in CPS, while guessing and exploring task-irrelevant content was associated with failure. Our findings held for both tasks investigated, from two different CPS measurement frameworks. We thus gained detailed insight into the behavioural processes that are related to success and failure in CPS.     

A hidden Markov model to detect regime changes in cryptoasset markets

The objective of this work is to understand the dynamics of cryptocurrency prices. Specifically, how prices switch between different regimes, going from “bull” to “stable” and “bear” times. For this purpose, we propose a hidden Markov model that aims at explaining the evolution of Bitcoin prices through different, unobserved states. The implementation of the proposed model includes a likelihood ratio test that allows to compare models with different states and with different covariance structures. Our empirical findings show that the time movements of Bitcoin prices across different exchange markets are well-described by the proposed model. In particular, a parsimonious model with a diagonal covariance matrix leads to better predictions, compared with a model with a full covariance matrix.     

基于改进的最大似然法识别风力发电机转子的载荷参数

风机不对中故障是多载荷参数耦合而成,工程师难以凭经验了解载荷参数的大致范围。针对因转子结构参数和测试条件等不确定性参数的存在及载荷参数先验信息未知,最大似然法识别转子不对中故障的载荷参数时采用的搜索计算面临计算量大,且迭代过程中灵敏度会带来一些数值问题。引入搜索区间进退法到敏感矩阵法与最大似然法中,对最大似然法进行改进。改进后的最大似然法中的迭代适于处理复杂工程优化问题,避免传统迭代数值法对搜索空间的苛刻要求。以输入尺寸和输出测试响应具有随机性测量误差的风力发电机转子系统为例,在三种测量误差下的识别结果表明,改进的最大似然法提高了最大似然法的辨识效果,可以减少不确定性因素对识别结果的影响,鲁棒性好。     

带缓冲区且产品保修的设备预防性维护建模

论文考虑的是一个完美的生产系统,即生产系统所生产的产品均没有质量问题。为提高设备的可靠性,调整市场需求,提出带缓冲区的设备预防性维护优化建模方法。已经出售的产品,顾客在使用过程中可能会出现故障,基于这个情况,在保修期内会予以免费维修。找出生产系统的运行时间(T)和缓冲区库存量(S),以此为决策变量建立期望成本模型。在此基础上,构造求解最优策略的算法。通过数值算例演示了该模型,通过灵敏度分析确定了最优策略和相关参数的关系。