Approximate estimation in generalized linear mixed models with applications to the Rasch model

This article discusses two different approaches to estimate the difficulty parameters (fixed effects parameters) and the variance of latent traits (variance components) in the mixed Rasch model. The first one is the generalized estimating equations (GEE2) which uses an approximation of the marginal likelihood to derive the joint moments whilst the second approach uses the maximum of the approximate likelihood. We illustrate these methods with a simulation study and with an analysis of real data from a quality of life.     

Neuro-Fuzzy Expert System for evaluating the performance of Distributed Software System Architecture

A Neuro-Fuzzy Performance Evaluation Model (NFPEM) proposed in Akinnuwesi, Uzoka, Olabiyisi, and Omidiora (2012) was reviewed in this work with the view of modifying it and thus making it flexible and scalable. The neuro-fuzzy expert system (NFES) reported in this paper is an enhancement to NFPEM with expert system components. NFES can be used to evaluate the performance of Distributed Software System Architecture (DSSA) with user-centric variables as parameters for performance measurement. The algorithm developed for NFES was implemented using Coldfusion programming language and MySQL relational database management system. The prototype of NFES was simulated using some life data and the performance results obtained point to the DSSA responsiveness to the users’ requirements that are defined at the requirements definition phase of the software development process. Thus the performance value is a qualitative value representing DSSA (i.e. system) responsiveness.     

Entropy based region reducing genetic algorithm for reliability redundancy allocation in interval environment

This research paper presents a multi-objective reliability redundancy allocation problem for optimum system reliability and system cost with limitation on entropy of the system which is very essential for effective sustainability. Both crisp and interval-valued system parameters are considered for better realization of the model in more realistic sense. We propose that the system cost of the redundancy allocation problem depends on reliability of the components. A subpopulation and entropy based region reducing genetic algorithm (GA) with Laplace crossover and power mutation is proposed to determine the optimum number of redundant components at each stage of the system. The approach is demonstrated through the case study of a break lining manufacturing plant. A comprehensive study is conducted for comparing the performance of the proposed GA with the single-population based standard GA by evaluating the optimum system reliability and system cost with the optimum number of redundant components. Set of numerical examples are provided to illustrate the effectiveness of the redundancy allocation model based on the proposed optimization technique. We present a brief discussion on change of the system using graphical phenomenon due to the changes of parameters of the system. Comparative performance studies of the proposed GA with the standard GA demonstrate that the proposed GA is promising to solve the reliability redundancy optimization problem providing better optimum system reliability.     

压缩机状态预知检修系统的研究与应用

以煤化工机械压缩机为例,提出了一种压缩机状态预知检修系统的设计方案。该系统通过安装在数据库服务器上的综合统计分析软件评估压缩机重要部件的主要参数,从而提出压缩机的优化运行及检修建议。实际应用表明,该系统提高了压缩机的维修效率,延长了设备使用寿命。     

Uncertainty Analysis in Software Reliability Modeling by Bayesian Analysis with Maximum-Entropy Principle

In software reliability modeling, the parameters of the model are typically estimated from the test data of the corresponding component. However, the widely used point estimators are subject to random variations in the data, resulting in uncertainties in these estimated parameters. Ignoring the parameter uncertainty can result in grossly underestimating the uncertainty in the total system reliability. This paper attempts to study and quantify the uncertainties in the software reliability modeling of a single component with correlated parameters and in a large system with numerous components. Another characteristic challenge in software testing and reliability is the lack of available failure data from a single test, which often makes modeling difficult. This lack of data poses a bigger challenge in the uncertainty analysis of the software reliability modeling. To overcome this challenge, this paper proposes utilizing experts' opinions and historical data from previous projects to complement the small number of observations to quantify the uncertainties. This is done by combining the maximum-entropy principle (MEP) into the Bayesian approach. This paper further considers the uncertainty analysis at the system level, which contains multiple components, each with its respective model/parameter/ uncertainty, by using a Monte Carlo approach. Some examples with different modeling approaches (NHPP, Markov, Graph theory) are illustrated to show the generality and effectiveness of the proposed approach. Furthermore, we illustrate how the proposed approach for considering the uncertainties in various components improves a large-scale system reliability model.     

Software function allocation methodology

One of the challenges of distributed computer systems is the effective allocation of software system functions among the hardware components of the distributed system. Software function allocation methodology (SFAM) provides computer software system designers with a thorough and flexible method to allocate software system functions among the hardware components of a distributed computer system. Software designers select and rank relevant design parameters, analyse how well different distributed computer system components meet the chosen parameters, and allocate the software function accordingly. The paper defines the problem, covers necessary terminology, and discusses the current state of research. The preconditions necessary for an analysis using SFAM are covered along with the environment in which SFAM should be used. Details of SFAM components are discussed. A complete outline of the SFAM methodology is provided, along with discussion of key points and frequent examples.     

Shape optimization for 2-D mixed-mode fracture using Extended FEM (XFEM) and Level Set Method (LSM)

Weight and service life are often the two most important considerations in design of structural components. This research incorporates a novel crack propagation analysis technique into shape optimization framework to support design of 2-D structural components under mixed-mode fracture for: (1) maximum service life, subject to an upper limit on volume, and (2) minimum weight subject to specified minimum service life. In both cases, structural performance measures are selected as constraints and CAD dimensions are employed as shape design variables. Fracture parameters, such as crack growth rate and crack growth direction are computed using extended finite element method (XFEM) and level set method (LSM). XFEM employs special enrichment functions to incorporate the discontinuity of structural responses caused by the crack surfaces and crack tip fields into finite element approximation. The LSM utilizes level set functions to track the crack during the crack propagation analysis. As a result, this method does not require highly refined mesh around the crack tip nor re-mesh to conform to the geometric shape of the crack when it propagates, which makes the method extremely attractive for crack propagation analysis. An accurate and efficient semi-analytical design sensitivity analysis (DSA) method is developed for calculating gradients of fracture parameters. Two different approaches—a batch-mode, gradient-based, nonlinear algorithm and an interactive what-if analysis—are used for optimization. An engine connecting rod example is used to demonstrate the feasibility of the proposed method.     

Bearing fault prognosis based on health state probability estimation

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.     

CAE在同步辐射装置挡光元件设计中的应用

针对第三代同步辐射装置挡光元件承受极高热负载、设计难度大的问题,综述CAE在其设计中的应用情况:借助有限元分析可以获得挡光元件关键部件吸收体的最高温度和最大应力这两个最主要设计参数;通过计算流体力学(Computational Fluid Dynamics,CFD)软件模拟可以获得吸收体中冷却管道的对流换热与流动阻力特性参数;采用热弹塑性有限元分析可以获得用于低周疲劳寿命预估的吸收体热应力应变迟滞循环.下一步工作将围绕当今研究的核心问题——基于低周疲劳的设计准则展开,包括同步辐射高热负载作用下低周疲劳裂纹的起裂寿命和扩展寿命预测,以及吸收体倾角、表面光滑度、冷却管道排布等对寿命的影响.这些研究均需要充分利用CAE的强大分析功能.     

Reliability analysis of complex multi-robotic system using GA and fuzzy methodology

The objective of the study is to compute various reliability parameters for multi-robotic system, using Real Coded Genetic Algorithms (RCGAs) and Fuzzy Lambda-Tau Methodology (FLTM). The paper contains a new idea about the reliability analysis of robotic system. The optimal values of mean time between failures (MTBF) and mean time to repair (MTTR) are obtained using GAs. Petri Net (PN) tool is applied to represent the interactions among the working components of multi-robotic system. To enhance the relevance of the reliability study, triangular fuzzy numbers (TFNs) are developed from the computed data, using possibility theory. The use of fuzzy arithmetic in the PN model increases the flexibility for application to various systems and conditions. Various reliability parameters, namely failure rate, repair time, MTBF, expected number of failures (ENOF), reliability and availability, are computed using FLTM. Sensitivity analysis has also been performed and the effects on system MTBF are addressed. The adopted methodology improves the shortcomings/drawbacks of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation. The analysis presented, may be helpful for the system analyst to analyze and predict the system behavior and to reallocate the required resources.     

Shape and radiative properties of convective systems observed from infrared satellite images

The main purpose of this study is to identify a series of parameters to characterize the shape and internal structure of a convective system (CS). One year of brightness temperatures derived from Meteosat 3 (July 1992-June 1993) (ISCCP-B3 data) was used to develop this work. The identification of convective systems was performed by pixels whose brightness infrared temperature values (T _ir) were below 245 K. The main results obtained are: (a) the shape of a given system which can be categorized in three classes: (i) linear systems, (ii) circular and elliptical systems and (iii) fragmented systems; (b) the restitution of a given CS using only statistical information; (c) the identification of the distribution of the number of coldest tops inside a given system; and (d) the evaluation of the stage of the life cycle through a statistical study of internal structure and radiative parameters.     

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According to the different network monitoring requirements of multiple equipments and servers in Radio Frequency Identification (RFID) information network, the common character and feature of network components are researched. The monitoring parameters are designed and selected including common parameters and special parameters. A kind of Simple Network Management Protocol(SNMP)-based Manager-Agent monitoring model is used. A general monitoring system including MIB node, Agent and monitoring tool for RFID information network is developed. Test results show the system can monitor both RFID readers and code resolution servers.     

基于SNMP的RFID信息网络监控系统

根据射频识别(RFID)信息网络中多种设备与服务器的网络监控需求,研究该信息网络中不同网络组件的共性及特性,选取并设计不同的监控参数,包括各种网络组件的公共参数及特性参数,采用基于SNMP协议的Manager-Agent监控模型,设计MIB节点、监控代理、监控工具,实现一种RFID信息网络通用的监控系统。测试结果表明,该监控系统能够成功地监控RFID读写器、RFID编码解析服务器等RFID信息网络组件。     

Deriving a Fault Architecture to Guide Testing

Defect analysis of software components can be used to guide testing, with the goal of focusing on parts of the software that were fault-prone in earlier releases or earlier life cycle phases, such as development. We replicate a study that adapted a reverse architecting technique using defect reports to derive fault architectures. A fault architecture determines and visualizes components that are fault-prone in their relationships with other components, as well as those that are locally fault-prone. Our case study uses defect data from three releases of a large medical record system to identify relationships among system components, based on whether they are involved in the same defect report.We investigate measures that assess the fault-proneness of components and component relationships. Component relationships are used to derive a fault architecture. The resulting fault architecture indicates what the most fault-prone relationships are in a release. We also apply the technique in a new way. Not only do we derive fault architectures for each release, we derive fault architectures for the development, system test and post release phases within each release. Comparing across releases, makes it possible to see whether some components are repeatedly in fault-prone relationships. Comparing across phases, makes it possible to see whether development fault architectures can be used to identify those parts of the software that need to be tested more. We validate our predictions using system test data from the same release. We also use the development and system test fault architectures to identify fault-prone components after release, and validate our predictions using post release data.     

Fuzzy reliability analysis of repairable industrial systems using soft-computing based hybridized techniques

The purpose of the present study is to analyze the fuzzy reliability of a repairable industrial system utilizing historical vague, imprecise and uncertain data which reflects its components’ failure and repair pattern. Soft-computing based two different hybridized techniques named as Genetic Algorithms Based Lambda–Tau (GABLT) and Neural Network and Genetic Algorithms Based Lambda–Tau (NGABLT) along with a traditional Fuzzy Lambda–Tau (FLT) technique are used to evaluate some important reliability indices of the system in the form of fuzzy membership functions. As a case study, all the three techniques are applied to analyse the fuzzy reliability of the washing system in a paper mill and results are compared. Sensitivity analysis has also been performed to analyze the effect of variation of different reliability parameters on system performance. The analysis can help maintenance personnel to understand and plan suitable maintenance strategy to improve the overall performance of the system. Based on results some important suggestions are given for future course of action in maintenance planning.     

Discrete wavelet transform based branched deep hybrid network for environmental noise classification

With ever growing urbanization, the environmental noise is becoming hazardous. Vehicular traffic, locomotives, heavy machinery in industry, and construction processes are the major sources of noise pollution. It has adverse effects on the health of humans as well as that of the wild life. World Health Organization (WHO) puts noise pollution as the second major cause of illness due to environmental reasons. The effects of noise pollution on the quality of life are usually ignored. Due to this reason it is common, even in the first world countries, to have the WHO's peak noise standards violated in residential areas. Therefore, there is a need to have a real time, portable and easy to replicate, mechanism to monitor the noise sources. In this work, we propose a novel architecture of a deep neural network to classify a 10-class environmental noise data called URBANSOUND8K. This network is comprised of three components, (1) one dimensional two level Discrete Wavelet Transform (DWT) component, (2) branched component for feature extraction through auto-encoders, and (3) LSTM and fully-connected layers based classification component. With all components combined, we call this network DWTNet. By embedding the DWT component as a part of network, we eliminate the need of prior data conversion into spectral and/or spectro-temporal domains. The efficiency of DWTNet is comparable to the state of the art networks with significantly lower number of trainable parameters. We analyze the contribution of classification accuracy. We further study some of the classification results individually and show that some of the mis-classifications are actually multi-class classifications with distributed decision confidence.     

Synthesis from component libraries

Synthesis is the automated construction of a system from its specification. In the classical temporal synthesis algorithms, it is always assumed the system is “constructed from scratch” rather than “composed” from reusable components. This, of course, rarely happens in real life. In real life, almost every non-trivial commercial system, either in hardware or in software system, relies heavily on using libraries of reusable components. Furthermore, other contexts, such as web-service orchestration, can be modeled as synthesis of a system from a library of components. In this work, we define and study the problem of LTL synthesis from libraries of reusable components. We define two notions of composition: data-flow composition, for which we prove the problem is undecidable, and control-flow composition, for which we prove the problem is 2EXPTIME-complete. As a side benefit, we derive an explicit characterization of the information needed by the synthesizer on the underlying components. This characterization can be used as a specification formalism between component providers and integrators.     

Reliability analysis for k-out-of-n systems with shared load and dependent components

Many structural systems require a minimal number of components to be operational, and predicting the reliability of such systems is a challenge because surviving components share the original system workload with higher component loads after the failure of some components. The states of all the components are also dependent. Such dependence, however, is generally neglected in many existing methods. In this study, we develop a new reliability method for systems with dependent components that share the system load equally before and after other components have failed. The components are also subjected to other loads, such as a preload. The new method is based on limit-state functions that predict the states of components, and the First Order Reliability Method is used. The advantage of the proposed method is that it can directly link the system reliability with design variables and random parameters because of the use of a physics-based approach. High accuracy is maintained with the consideration of dependent component states. Two examples are used to demonstrate the good accuracy and efficiency of the proposed method.     

Variable-structure coherent systems†

The notion of coherent systems plays an essential role in conventional reliability theory. A system is said to be coherent if all of its components are relevant and the system reliability is improved as the component reliabilities are improved. However, in many complex systems or networks, not all the components are unconditionally relevant. As a result, in this paper we introduce the notion of variable-structure coherent systems to describe those systems that extensively exist and demonstrate essentially distinct features not observed in conventional coherent systems. A variable-structure coherent system consists of a number of substructures that are each a coherent system in conventional sense themselves. We then analyze the structural properties of variable-structure coherent systems; define the system operational profile, the system reliability, and the system structural profile. We study the system life distribution, the substructure importance, and the component importance. Finally, we deal with phase-cyclic systems in the context of variable-structure coherent systems.