Numerical method for Weibull generalized renewal process and its applications in reliability analysis of NC machine tools

In generalized renewal process (GRP) reliability analysis for repairable systems, Monte Carlo (MC) simulation method instead of numerical method is often used to estimate model parameters because of the complexity and the difficulty of developing a mathematically tractable probabilistic model. In this paper, based on the conditional Weibull distribution for repairable systems, using negative log-likelihood as an objective function and adding inequality constraints to model parameters, a nonlinear programming approach is proposed to estimate restoration factor for the Kijima type GRP model I, as well as the model II. This method minimizes the negative log-likelihood directly, and avoids solving the complex system of equations. Three real and different types of field failure data sets with time truncation for NC machine tools are analyzed by the proposed numerical method. The sampling formulas of failure times for the GRP models I and II are derived and the effectiveness of the proposed method is validated with MC simulation method. The results show that the GRP model is superior to the ordinary renewal process (ORP) and the power law non-homogeneous Poisson process (PL-NHPP) model.     

Statistical inference and prediction for the Weibull process with incomplete observations

In this article, statistical inference and prediction analyses for the Weibull process with incomplete observations via classical approach are studied. Specifically, observations in the early developmental phase of a testing program cannot be observed. We derive the closed-form expressions for the maximum likelihood estimates of the parameters in both the failure- and time-truncated Weibull processes. Confidence interval and hypothesis testing for the parameters of interest are considered. In addition, predictive inferences on future failures and the goodness-of-fit test of the model are developed. Two real examples from an engine system development study and a Boeing air-conditioning system development study are presented to illustrate the proposed methodologies.     

Closed-form solution for reliability of SCI-based multiprocessor systems using Weibull distribution and self-healing rings

This paper introduces a new closed-form solution for the reliability of large-scale multiprocessor systems. The systems are based on SCI rings interconnected in hierarchical structures. Reliability expressions using enumeration technique are derived assuming Weibull failure process. The reliability function derived in this paper is general and valid for any hierarchical ring-based system with arbitrary number of levels. The hierarchical interconnections are constructed from self-healing rings and basic rings. The analysis shows the improvement achieved in reliability when self-healing rings are used. Although we used hierarchical systems based on SCI rings, the technique followed in this work is applied for any type of rings such as slotted or token rings.     

Use of the dirichlet process for reliability analysis

Recently, many researchers apply the non-parametric Bayesian approach to predict the reliability of highly complex electronic systems. The Dirichlet process is the most common model for the non-parametric Bayesian analysis. The Kuo's simulation procedure [6] for Dirichlet process under a variance reduction techniques introduced in Chien and Kuo (1994) [2] is applied for a Weibull-distributed system. Optimal burn-in time is determined given the cost parameters. A model, the percentage of good items in a lot, is used to explain when the Dirichlet process is not a proper choice.     

LRD and SRD traffic: review of results and open issues for the batch renewal process

The batch renewal process is the least-biased choice of process given only the measures of count correlation and interval correlation at all lags.

This paper reviews the batch renewal process, both for LRD (long-range-dependent) traffic and for SRD (short-range-dependent) traffic in the discrete space–discrete time domain, and in the wider context of general traffic in that domain. It shows some applications of the batch renewal process in simple queues and in queueing network models. The paper concludes with open research problems and issues arising from the discussion.     

多源覆盖信息系统下的加权广义多粒度粗糙集模型及其应用

考虑到多源覆盖信息系统中数据的复杂性以及单个信息系统之间的不平等性,引入诱导覆盖粗糙集,并对信息系统的属性赋予权重值,提出了多源覆盖信息系统下的加权广义多粒度粗糙集MCS-WGMRS模型。定义了属性权重的计算方法,给出模型的上、下近似,并获取了相应的决策规则。通过实例分析验证了MCS-WGMRS模型的有效性,结果表明该模型对目标集的分类能力更强,适当调整阈值可进一步提高模型的容错性。     

自治混沌系统普适广义投影同步理论及应用

针对自治混沌系统，基于系统稳定性理论，通过设计合适的非线性反馈控制器，给出了普适的广义投影同步定理．定理中函数的选择可以为系统的线性或非线性函数，更具灵活性和普适性；文中理论还可以通过调整参数提高广义投影同步的速度．数值仿真进一步验证了本文理论的有效性和实用性．     

Identification of an additive nonlinear system and its applications in generalized Hammerstein models

An identification algorithm is developed for a class of nonlinear systems that are multi-input and multi-output in an additive form. The convergence results are achieved and its applications to identification of a generalized Hammerstein system is also discussed.     

AIR application for reliability analysis considering debugging process and network traffic in mobile clouds

Mobile clouds are used by many people via a network service to share the computing resources, because of the unification management of data and low cost. In case of the mobile device, the network access devices are frequently used by many software installed via the installer software. Considering the effect of the debugging process on mobile software in the development of a method of reliability assessment for the mobile clouds, it is necessary to grasp the situation of installer software, the network traffic, the installed software, etc.In this paper, we develop an integrated method of reliability assessment considering the software failure and network traffic based on a hazard rate model and neural network for the mobile clouds. In particular, we develop the AIR application for reliability analysis based on the proposed method. Then, we show the performance examples of the developed AIR application to analyze the method of software reliability assessment for the mobile clouds.     

空间故障树与因素空间融合的智能可靠性分析方法

现有系统可靠性分析方法一般具有针对性,缺乏广泛的适应性和扩展性。由于智能科学、信息科学和大数据技术的涌现和发展,使得传统可靠性分析技术难以适应新的需要。因此作者提出了空间故障树理论,目的是分析多因素影响下的系统可靠性变化特征。将空间故障树理论与因素空间理论、云模型、模糊数学及系统稳定性等相结合,使其具有智能分析和故障大数据处理能力,以满足未来技术环境下的分析要求。本文论述了空间故障树和因素空间的发展史及主要理论与功能;以及两种理论结合,描述和分析系统演化过程的可行性。研究表明,空间故障树理论具有良好的扩展性和适应性,可适应未来技术环境,也可作为系统演化过程分析的普适框架。     

Multivariable process identification for mpc: the asymptotic method and its applications

In this work we will introduce the asymptotic method (ASYM) of identification and provide two case studies. The ASYM was developed for multivariable process identification for model based control. The method calculates time domain parametric models using frequency domain criterion. Fundamental problems, such as test signal design for control, model order/structure selection, parameter estimation and model error quantification, are solved in a systematic manner. The method can supply not only input/output model and unmeasured disturbance model which are asymptotic maximum likelihood estimates, but also the upper bound matrix for the model errors that can be used for model validation and robustness analysis. To demonstrate the use of the method for model predictive control (MPC), the identification of a Shell benchmark process (a simulated distillation column) and an industrial application to a crude unit atmospheric tower will be presented.     

Application of the homotopy analysis method to determine the analytical limit state functions and reliability index for large deflection of a cantilever beam subjected to static co-planar loading

In this paper, the Homotopy Analysis Method (HAM) is applied to obtain the limit state function, probability of failure and reliability index based on all stochastic and deterministic variables for a cantilever beam subjected to co-planar loading for the first time. First, it is established that a few iterations in the series expansion are sufficient to obtain highly accurate results and a substantial convergence region. After showing the effectiveness of HAM, two limit state functions are introduced as the maximum deflection in the y direction and maximum allowable stress, respectively. Then the first order reliability method (FORM) is employed to obtain reliability index, and omission sensitivity factor analytically. It is shown that HAM is a promising tool to obtain limit state function, probability of failure and reliability index analytically for nonlinear problems. Finally, a sensitivity analysis is done to show that which parameters could be considered deterministic or stochastic variables.     

A hybrid method based on genetic algorithm and dynamic programming for solving a bi-objective cell formation problem considering alternative process routings and machine duplication

Cell formation is one of the first and most important steps in designing a cellular manufacturing system. It consist of grouping parts with similar design features or processing requirements into part families and associated machines into machine cells. In this study, a bi-objective cell formation problem considering alternative process routings and machine duplication is presented. Manufacturing factors such as part demands, processing times and machine capacities are incorporated in the problem. The objectives of the problem include the minimization of the total dissimilarity between the parts and the minimization of the total investment needed for the acquisition of machines. A normalized weighted sum method is applied to unify the objective functions. Due to the computational complexity of the problem, a hybrid method combining genetic algorithm and dynamic programming is developed to solve it. In the proposed method, the dynamic programming is implemented to evaluate the fitness value of chromosomes in the genetic algorithm. Computational experiments are conducted to examine the performance of the hybrid method. The computations showed promising results in terms of both solution quality and computation time.     

Diversity of quantum optimizations for training adaptive support vector regression and its prediction applications

Three kinds of quantum optimizations are introduced in this paper as follows: quantum minimization (QM), neuromorphic quantum-based optimization (NQO), and logarithmic search with quantum existence testing (LSQET). In order to compare their optimization ability for training adaptive support vector regression, the performance evaluation is accomplished in the basis of forecasting the complex time series through two real world experiments. The model used for this complex time series prediction comprises both BPNN-Weighted Grey-C3LSP (BWGC) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) that is tuned perfectly by quantum-optimized adaptive support vector regression. Finally, according to the predictive accuracy of time series forecast and the cost of the computational complexity, the concluding remark will be made to illustrate and discuss these quantum optimizations.     

数据挖掘算法及其在股市技术分析中的应用

提出了一种基于动态规划和动态时间弯折函数的数据挖掘算法,并应用该算法对股市进行技术分析。基于某股市实际数据的实验结果表明该算法是有效的。     

半监督极限学习机及其在近红外光谱数据分析中的应用

当数据集中包含的训练信息不充分时,监督的极限学习机较难应用,因此将半监督学习应用到极限学习机,提出一种半监督极限学习机分类模型;但其模型是非凸、非光滑的,很难直接求其全局最优解。为此利用组合优化方法,将提出的半监督极限学习机化为线性混合整数规划,可直接得到其全局最优解。进一步,利用近红外光谱技术,将半监督极限学习机应用于药品和杂交种子的近红外光谱数据的模式分类。与传统方法相比,在不同的光谱区域的数值实验结果显示:当数据集中包含训练信息不充分时,提出的半监督极限学习机提高了模型的推广能力,验证了所提出方法的可行性和有效性。     

独立成分分析和支持向量机混合方法在过程监控中的应用

为克服传统过程监控方法需假设过程特征信号服从多元正态分布的缺陷,本文提出了一种将独立成分分析(ICA)与支持向量机结合的故障诊断方法。通过建立独立成分模型确定相应的统计量界限,筛选出需进一步检测的故障数据,再由支持向量机进行故障识别。将该方法用于化工聚合反应的过程监控与故障诊断中,仿真结果表明,这种混合故障诊断方法通过适当地调节统计量控制界限,不仅能够正确识别故障,而且能够纠正由误检数据引起的误报,提高故障诊断的准确率。     

Cognitive mechanism related to line drawings and its applications in intelligent process of visual media: a survey

Line drawings, as a concise form, can be recognized by infants and even chimpanzees. Recently, how the visual system processes line-drawings attracts more and more attention from psychology, cognitive science and computer science. The neuroscientific studies revealed that line drawings generate similar neural actions as color photographs, which give insights on how to efficiently process big media data. In this paper, we present a comprehensive survey on line drawing studies, including cognitive mechanism of visual perception, computational models in computer vision and intelligent process in diverse media applications. Major debates, challenges and solutions that have been addressed over the years are discussed. Finally some of the ensuing challenges in line drawing studies are outlined.     

面向钢铁企业能源流程的逐步形式化建模方法

针对钢铁企业能源流程的特点,以及以往能源建模存在的不足,提出一种从UML到Petri网模型逐步形式化的建模方法。所建模型可以通过仿真平台进行验证评估,对能源流程进一步定性和定量分析,深入理解、优化,并可以根据反馈结果对UML模型进行修改,有助于提高系统编码实现的准确性,保证工程技术人员高效建模。以炼铁系统的能源流程系统建模为例,对方法进行说明。     

油田过程信息管理系统身份认证与权限设定方法

本文使用USB加密狗技术,设计实现了油田采油厂生产过程信息管理系统中的身份认证和权限设定模块。通过该模块,合法用户可以安全登录系统,不同级别用户可进行相应的操作,从而提高油田生产过程的信息安全性。