On the Number of Maintenance Cycles in Systems with Critical and Non-Critical Components

We present a novel mathematical framework for computing the number of maintenance cycles in a system with critical and non-critical components, where "critical" (CR) means that the component's failure is fatal for the system's operation and renders any more repairs inapplicable, whereas "noncritical" (NC) means that the component can undergo corrective maintenance (replacement or minimal repair) whenever it fails, provided that the CR component is still in operation. Whenever the NC component fails, the CR component can optionally be preventively replaced. We extend traditional renewal theory (whether classical or generalized) for various maintenance scenarios for a system composed of one CR and one NC component in order to compute the average number of renewals of NC under the restriction ("bound") necessitated by CR. We also develop approximations in closed form for the proposed "bounded" renewal functions. We validate our formulas by simulations on a variety of component lifetime distributions, including actual lifetime distributions of wind turbine components.Comment: submitted to IEEE Transactions, in pres

Equitable Shortest Job First: A Preemptive Scheduling Algorithm for Soft Real-Time Systems

The Shortest Job First (SJF) algorithm gives the optimal average turnaround time for a set of processes, but it suffers from starvation for long processes. In this study, the authors developed an algorithm, referred to as Equitable SJF (EQSJF), to reduce the average turnaround time of the long processes without notably affecting the turnaround time of the short processes. Two parameters, the percentage of a process’s burst time to completion and the time spent by a process in the waiting queue, were used to provide the designer with more tradeoff alternatives in keeping the turnaround time of the long processes under control while maintaining the turnaround time of the short processes at low levels, as they are required for soft real-time tasks. Comparisons with previously proposed scheduling algorithms such as the Highest Response Ratio Next (HRRN), Railroad Strategy, Enhanced Shortest Job First (ESJF), and Alpha show that the proposed approach always offers better alternatives

Plurality Voting and the Computation of the Average Duration of Frames of Parallel Mutual Exclusion Accesses

In this paper we first show how to compute the probability that a particular candidate c out of m candidates, 1 ≤ c ≤ m, wins by plurality voting an election conducted by n voters, where each voter either votes for a single candidate i, 1 ≤ i ≤ m, with probability Pi, or abstains with probability P0 = 1−?mi =1 Pi.We then show how this result is involved in the computation of the average execution time of a set (“frame”) of n simultaneous requests, where each request randomly asks for exclusive access to any of m available non-shareable resources with probability Pi,1 ≤ i ≤ m, or for non-exclusive access to a common fully shareable resource with probability P0 = 1−?mi =1 Pi. We also allow that each resource access has a different duration Di,0 ≤ i ≤ m. The formulas that we develop have application in the analysis and evaluation of ensemble classifiers in pattern recognition and classification, and in systems performance evaluation (critical sections in multithreaded programs with barrier synchronization, switch delay in computer networks and interconnection networks)

A Hamming Distance Based Test Pattern Generator with Improved Fault Coverage

This paper proposes a new test pattern generator (TPG) which is an enhancement of GLFSR (Galois LFSR). This design is based on certain non--binary error detecting codes, formulated over an extension field of GF (2 ), #>1.The resulting generator provides a guaranteed Hamming distance between successive test patterns, resulting in shorter test lengths. As an additional advantage, the proposed TPG has the intrinsic ability to detect 1--bit errors in the TPG itself. Detailed design methodology and experimental results are presented. The results presented here also have implications in algebraic coding theory in that they may lead to new coding techniques for test pattern generation

AUCTSP: an improved biomarker gene pair class predictor

Abstract Background The Top Scoring Pair (TSP) classifier, based on the concept of relative ranking reversals in the expressions of pairs of genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. The idea that differences in gene expression ranking are associated with presence or absence of disease is compelling and has strong biological plausibility. Nevertheless, the TSP formulation ignores significant available information which can improve classification accuracy and is vulnerable to selecting genes which do not have differential expression in the two conditions (“pivot" genes). Results We introduce the AUCTSP classifier as an alternative rank-based estimator of the magnitude of the ranking reversals involved in the original TSP. The proposed estimator is based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and as such, takes into account the separation of the entire distribution of gene expression levels in gene pairs under the conditions considered, as opposed to comparing gene rankings within individual subjects as in the original TSP formulation. Through extensive simulations and case studies involving classification in ovarian, leukemia, colon, breast and prostate cancers and diffuse large b-cell lymphoma, we show the superiority of the proposed approach in terms of improving classification accuracy, avoiding overfitting and being less prone to selecting non-informative (pivot) genes. Conclusions The proposed AUCTSP is a simple yet reliable and robust rank-based classifier for gene expression classification. While the AUCTSP works by the same principle as TSP, its ability to determine the top scoring gene pair based on the relative rankings of two marker genes across all subjects as opposed to each individual subject results in significant performance gains in classification accuracy. In addition, the proposed method tends to avoid selection of non-informative (pivot) genes as members of the top-scoring pair