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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.11.016 Byline: Gregory Levitin (a), Min Xie (b), Tieling Zhang (b) Keywords: Performance; Fault-tolerant software; Multiprocessor system; Universal generating function Abstract: The paper considers performance and reliability of fault-tolerant software running on a hardware system that consists of multiple processing units. The software consists of functionally equivalent but independently developed versions that start execution simultaneously. The computational complexity and reliability of different versions are different. The system completes the task execution when the outputs of a pre-specified number of versions coincide. The processing units are characterized by different availability and processing speed. It is assumed that they are able to share the computational burden perfectly and that execution of each version can be fully parallelized. The algorithm based on the universal generating function technique is used for determining the distribution of system task execution time. This algorithm allows analysts to evaluate complex hardware-software reliability and performance indices such as expected task execution time and probability that the task is completed within a given time. Illustrative examples are also presented. Author Affiliation: (a) Reliability Department, Planning, Development and Technology Division, Israel Electric Corporation Ltd., P.O. Box 10, Haifa 31000, Israel (b) Department of Industrial and Systems Engineering, National University of Singapore, Singapore 117576, Singapore Article History: Received 31 December 2004; Accepted 10 November 2005

Inferences from different data sources can often be fused together, a process referred to as "fusion learning," to yield more powerful findings than those from individual data sources alone. Effective fusion learning approaches are in growing demand as increasing number of data sources have become easily available in this big data era. This article proposes a new fusion learning approach, called "iFusion," for drawing efficient individualized inference by fusing learnings from relevant data sources. Specifically, iFusion (i) summarizes inferences from individual data sources as individual confidence distributions (CDs); (ii) forms a clique of individuals that bear relevance to the target individual and then combines the CDs from those relevant individuals; and, finally, (iii) draws inference for the target individual from the combined CD. In essence, iFusion strategically "borrows strength" from relevant individuals to enhance the efficiency of the target individual inference while preserving its validity. This article focuses on the setting where each individual study has a number of observations but its inference can be further improved by incorporating additional information from similar studies that is referred to as its clique. Under the setting, iFusion is shown to achieve oracle property under suitable conditions. It is also shown to be flexible and robust in handling heterogeneity arising from diverse data sources. The development is ideally suited for goal-directed applications. Computationally, iFusion is parallel in nature and scales up easily for big data. An efficient scalable algorithm is provided for implementation. Simulation studies and a real application in financial forecasting are presented. In effect, this article covers methodology, theory, computation, and application for individualized inference by iFusion. [ABSTRACT FROM AUTHOR]

Accelerated life testing (ALT) is commonly used to predict the lifetime of a product at its use stress by subjecting test units to elevated stress conditions that accelerate the occurrence of failures. For new products, the selection of an acceleration model for planning optimal ALT plans is challenging due to the absence of historical lifetime data. The misspecification of an ALT model can lead to considerable errors when it is used to predict the product's life quantiles. This article proposes a two-stage Bayesian approach to constructing ALT plans and predicting lifetime quantiles. At the first stage, the ALT plan is optimized based on the prior information of candidate models under a modified V-optimality criterion that incorporates both asymptotic prediction variance and squared bias. A Bayesian model averaging (BMA) framework is used to derive the posterior model and the posterior distribution for the life quantile of interest under use stress. If the obtained test data cannot provide satisfactory model selection results, an adaptive second-stage test is conducted based on the posterior information from the first stage. A revisited numerical example demonstrates the efficiency and robustness of the resulting Bayesian ALT plans by comparing with the plans derived from previous methods. [ABSTRACT FROM AUTHOR]

In this article, a novel maintenance model is proposed for single-unit systems with an atypical degradation path, whose pattern is influenced by inspections. After each inspection, the system degradation is assumed to instantaneously decrease by a random value. Meanwhile, the degrading rate is elevated due to the inspection. Considering the double effects of inspections, we develop a parameter estimation procedure for such systems from experimental data obtained via accelerated degradation tests with environmental covariates. Next, the inspection and replacement policy is optimized with the objective to minimize the Expected Long-Run Cost Rate (ELRCR). Inspections are assumed to be non-periodically scheduled. A numerical algorithm that combines analytical and simulation methods is presented to evaluate the ELRCR. We then investigate the robustness of maintenance policies for such systems by taking the parameter uncertainty into account with the aid of large-sample approximation and parametric bootstrapping. The application of the proposed method is illustrated by degradation data from the electricity industry. [ABSTRACT FROM AUTHOR]

Conducting a Reliability Demonstration Test (RDT) is a crucial step in production. Products are tested under certain schemes to demonstrate whether their reliability indices reach pre-specified thresholds. Test schemes for RDT have been studied in different situations, e.g., lifetime testing, degradation testing and accelerated testing. Systems designed with several structures are also investigated in many RDT plans. Despite the availability of a range of test plans for different systems, RDT planning for load-sharing systems hasn’t yet received the attention it deserves. In this paper, we propose a demonstration method for two specific types of load-sharing systems with components subject to two distributions: exponential and Weibull. Based on the assumptions and interpretations made in several previous works on such load-sharing systems, we set the mean time to failure (MTTF) of the total system as the demonstration target. We represent the MTTF as a summation of mean time between successive component failures. Next, we introduce generalized test statistics for both the underlying distributions. Finally, RDT plans for the two types of systems are established on the basis of these test statistics. [ABSTRACT FROM AUTHOR]

The existing statistical process control procedures typically rely on the fundamental assumption of a parametric distribution of the quality characteristic. However, when there is a lack of knowledge about the underlying distribution (as full knowledge is not available in practice), the performance of these parametric charts is very likely to be heavily degraded. Motivated by this problem, a one-sided nonparametric monitoring procedure using the single sample sign statistic is proposed for detecting a shift in the location parameter of a continuous distribution. An economic model of the control chart is developed to optimize the sample size, sampling interval, and control limits. Three data-dependent estimation approaches for the unknown parameter are evaluated and discussed. Simulation results exhibit that our proposed procedure generally performs well under a great variety of continuous distributions and hence it is recommended as an alternative scheme especially when the knowledge of the underlying distribution is imperfect. Furthermore, beneficial recommendations of estimation approach selection are provided for practical implementation of the control chart. [ABSTRACT FROM PUBLISHER]

In this article, an iterative single-point imputation (SPI) algorithm, called quantile-filling algorithm for the analysis of interval-censored data, is studied. This approach combines the simplicity of the SPI and the iterative thoughts of multiple imputation. The virtual complete data are imputed by conditional quantiles on the intervals. The algorithm convergence is based on the convergence of the moment estimation from the virtual complete data. Simulation studies have been carried out and the results are shown for interval-censored data generated from the Weibull distribution. For the Weibull distribution, complete procedures of the algorithm are shown in closed forms. Furthermore, the algorithm is applicable to the parameter inference with other distributions. From simulation studies, it has been found that the algorithm is feasible and stable. The estimation accuracy is also satisfactory. [ABSTRACT FROM AUTHOR]

Motivated by two real-life examples, this article develops a burn-in planning framework with competing risks. Existing approaches to planning burn-in tests are confined to a single failure mode based on the assumption that this failure mode is subject to infant mortality. Considering the prevalence of competing risks and the high reliability of modern products, our framework differentiates between normal and infant mortality failure modes and recommends degradation-based burn-in approaches. This framework is employed to guide the burn-in planning for an electronic device subject to both a degradation-threshold failure, which is an infant mortality mode and can be modeled by a gamma process with random effect, and a catastrophic mode, which is normal and can be represented with a conventional reliability model. Three degradation-based burn-in models are built and the optimal cutoff degradation levels are derived. Their validity is demonstrated by an electronic device example. We also propose three approaches to deal with uncertainty due to parameter estimation. Algorithmic details and proofs are provided in supplementary material online. [ABSTRACT FROM AUTHOR]

Abstract: The detection system for frame embossing surface is discussed in this paper. According to the periodic feature of frame embossing surface, one periodic embossing is inspected once. Then a fast template matching algorithm based on projection and normalized cross correlation measure is proposed in this paper. The experimental results showed that the proposed algorithm was more efficient and faster than conventional image template matching algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, a probability analysis model of offshore fire is presented using the method of converting fault tree (FT) into Bayesian Network (BN) to incorporate the effect of Human and Organizational Factor (HOF). The multi-phase model allows different methods to be applied to different parts. In the first phase, Fault Tree (FT) is used to model the factors how to contribute to the fire scenarios, which guide the construction of BN model. In the second phase, BN is used to extend the causal chain of basic events to potential HOFs and provide a more precise quantitative links between the event nodes. The distinct advantages of BN making them more suitable than FTs are their ability in explicitly representing the dependencies of events, updating probabilities, and coping with uncertainties, which cannot be considered by FT. Finally, the integration algorithm is demonstrated on four offshore fire scenarios. It clearly shows that the model integrates the power of FT for modeling deterministic causal paths with the flexibility of BN for modeling non-deterministic HOF relationships. [Copyright &y& Elsevier]

Recently, there has been a growing interest in applying the design structure matrix (DSM) for planning projects that consist of many interrelated activities. One important objective of planning is to find an activity sequence so as to minimize the sum of superdiagonal numbers in a DSM. It is known that the problem is NP-complete and is difficult to solve. In this study, we prove several structural properties of the problem, and propose a heuristic for obtaining good feasible solutions. We find that based on the fold operation, a block of activities can be treated as a single activity. A novel hybrid algorithm is then presented for solving large activity sequencing problems. Finally, we perform a number of experiments and show that good solutions can be easily obtained by our approach. Moreover, the improvement achieved by the proposed approach is significant. [ABSTRACT FROM PUBLISHER]

There has been quite some research on the development of tools and techniques for grid systems, yet some important issues, e.g., grid service reliability and task scheduling in the grid, have not been sufficiently studied. For some grid services which have large subtasks requiring time-consuming computation, the reliability of grid service could be rather low. To resolve this problem, this paper introduces Local Node Fault Recovery (LNFR) mechanism into grid systems, and presents an in-depth study on grid service reliability modeling and analysis with this kind of fault recovery. To make LNFR mechanism practical, some constraints, i.e. the life times of subtasks, and the numbers of recoveries performed in grid nodes, are introduced; and grid service reliability models under these practical constraints are developed. Based on the proposed grid service reliability model, a multi-objective task scheduling optimization model is presented, and an ant colony optimization (ACO) algorithm is developed to solve it effectively. A numerical example is given to illustrate the influence of fault recovery on grid service reliability, and show a high efficiency of ACO in solving the grid task scheduling problem. [ABSTRACT FROM AUTHOR]

This article develops a control chart system consisting of several individual time-between-events (TBE) charts, each of which is used to monitor the time between successive events at different process stages in the manufacturing of a product in a multistage manufacturing system. The design algorithm considers all the TBE charts within a system in an integrative and optimal manner. Numerical studies show that the proposed design algorithm improves the performance characteristics of the chart system significantly and thus the product quality is further guaranteed. The proposed control chart system is easy to understand and operate; thus, the floor operators can utilize and understand it as easily as for the traditional system. [ABSTRACT FROM AUTHOR]

The power-law process (PLP) is a two-parameter model widely used for modeling repairable system reliability. Results on exact point estimation for both parameters as well as exact interval estimation for the shape parameter are well known. In this paper, we investigate the interval estimation for the scale parameter. Asymptotic confidence intervals are derived using Fisher information matrix and theoretical results by Cocozza-Thivent (1997). The accuracy of the interval estimation for finite samples is studied by simulation methods. [ABSTRACT FROM AUTHOR]

This paper considers software systems consisting of fault-tolerant components built from functionally equivalent but independently developed modules (versions) characterized by different reliability and execution times. The components are designed using either the N-version programming method or the recovery block scheme. In our general model, we also allow the number of versions that can run simultaneously to be limited because of hardware or computation time constraints. An analytical algorithm and a numerical procedure to evaluate system execution-time distributions are presented. This algorithm takes into account the positive correlation among failures in different versions by introducing common-cause failures (mutually exclusive and independent common causes are considered). Illustrative examples are presented. [ABSTRACT FROM AUTHOR]

A goal attainment approach to optimize multiresponse systems is presented. This approach aims to identify the settings of control factors to minimize the overall weighted maximal distance measure with respect to individual response targets. Based on a nonlinear programming technique, a sequential quadratic programming algorithm, the method is proved to be robust and can achieve good performance for multiresponse optimization problems with multiple conflicting goals. Moreover, the optimization formulation may include some prior work as special cases by assigning proper response targets and weights. Fewer assumptions are needed when using the approach as compared to other techniques. Furthermore, the decision-maker's preference and the model's predictive ability can easily be incorporated into the weights' adjustment schemes with explicit physical interpretation. The proposed approach is investigated and compared with other techniques through various classical examples in the literature. [ABSTRACT FROM AUTHOR]

A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable. [ABSTRACT FROM AUTHOR]

In this paper we consider vulnerable systems which can have different states corresponding to different combinations of available elements composing the system. Each state can be characterized by a performance rate, which is the quantitative measure of a system''s ability to perform its task. Both the impact of external factors (stress) and internal causes (failures) affect system survivability, which is determined as probability of meeting a given demand.In order to increase the survivability of the system, a multi-level protection is applied to its subsystems. This means that a subsystem and its inner level of protection are in their turn protected by the protection of an outer level. This double-protected subsystem has its outer protection and so forth. In such systems, the protected subsystems can be destroyed only if all of the levels of their protection are destroyed. Each level of protection can be destroyed only if all of the outer levels of protection are destroyed.We formulate the problem of finding the structure of series–parallel multi-state system (including choice of system elements, choice of structure of multi-level protection and choice of protection methods) in order to achieve a desired level of system survivability by the minimal cost. An algorithm based on the universal generating function method is used for determination of the system survivability. A multi-processor version of genetic algorithm is used as optimization tool in order to solve the structure optimization problem. An application example is presented to illustrate the procedure presented in this paper. [Copyright &y& Elsevier]

Although China is a big carbon dioxide (CO 2 ) emitter, in situ measurements of atmospheric CO 2 are sparse in urban China. The mixing ratio of carbon dioxide (CO 2 ) and its influencing factors in urban Nanjing were investigated in this study, from the 18th of January to the 31st of December 2011. The annual average mixing ratio of CO 2 was 406.5 ± 20.0 ppmv over the study period. The signal analysis using the fast Fourier transform (FFT) algorithm showed that CO 2 had different cycles as a result of multiple controlling factors. The seasonal and intra-seasonal fluctuations of CO 2 were mainly caused by the terrestrial biospheric uptake and emission and atmospheric oscillation. The weekly variation of CO 2 was largely influenced by traffic volume. The diurnal cycle of CO 2 presented a bimodal pattern in winter (DJF) probably due to the rush hour emissions. The seasonal mean CO 2 /CO correlation slope varied from 0.024 ppmv/ppbv to 0.029 ppmv/ppbv, comparable to the fossil fuel combustion emission ratio. The diurnal pattern of CO 2 /CO was irregular, indicating random anthropogenic emissions in an urban area. Firework setting was a large source of CO 2 during the Spring Festival holiday. The backward trajectories by the HYSPLIT model showed that the local anthropogenic emissions contributed the most to the high CO 2 mixing ratio in the urban area. [ABSTRACT FROM AUTHOR]