Multipath aware TCP (MATCP)

On the Internet many different paths exist between each source and destination. When single path routing is used these paths can be under utilized, not used fairly or not used at all. One way to overcome this is to allow multipath routing. But when multiple paths are used TCP congestion control can be negatively affected and cause poor goodput performance due to the reordering of packets. We proposeMATCP (Multipath Aware TCP) which makes modifications to TCP that allows it to monitor and select which path it takes through the network for each flow. MATCP is compared to single path routing and is validated using extensive simulation. MATCP is found to greatly improve fairness between flows while providing equal or better utilization of links than single best path networks

On nonparametric estimation of a reliability function

This article considers the properties of a nonparametric estimator developed for a reliability function which is used in many reliability problems. Properties such as asymptotic unbiasedness and consistency are proven for the estimator and using U-statistics, weak convergence of the estimator to a normal distribution is shown. Finally, numerical examples based on an extensive simulation study are presented to illustrate the theory and compare the estimator developed in this article with another based directly on the ratio of two empirical distributions studied in Zardasht and Asadi (2010)

Use of Markov chain for deterioration modelling and risk management of infrastructure assets

Current annual expenditure for management and renewal of Infrastructure assets around the world is 500 billion US dollars. With an aging stock of infrastructure, innovative methods for management of risk of failure and optimizing of maintenance expenditure becomes extremely important. Whilst different infrastructure assets may have different attributes, governing issues are similar in nature. Prediction of deterioration of some infrastructure is complex since they can constitute of a number of discrete elements with a vast range of influencing factors. A major issue currently faced by local government agencies in Australia is the inability to predict maintenance and replacement expenditure with a reasonable accuracy, which creates situations where emergency repairs would use the funds kept for routine maintenance, which then creates a vicious circle of deterioration

Measuring process capability for bivariate non-normal process using the bivariate burr distribution

As is well known, process capability analysis for more than one quality variables is a complicated and sometimes contentious area with several quality measures vying for recognition. When these variables exhibit non-normal characteristics, the situation becomes even more complex. The aim of this paper is to measure Process Capability Indices (PCIs) for bivariate non-normal process using the bivariate Burr distribution. The univariate Burr distribution has been shown to improve the accuracy of estimates of PCIs for univariate non-normal distributions (see for example, [7] and [16]). Here, we will estimate the PCIs of bivariate non-normal distributions using the bivariate Burr distribution. The process of obtaining these PCIs will be accomplished in a series of steps involving estimating the unknown parameters of the process using maximum likelihood estimation coupled with simulated annealing. Finally, the Proportion of Non-Conformance (PNC) obtained using this method will be compared with those obtained from variables distributed under the bivariate Beta, Weibull, Gamma and Weibull-Gamma distributions

Sample size determination for kernel regression estimation using sequential fixed-width confidence bands

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1&minus;. Here, d(&gt; 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedures together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confi dence bands based on the local linear estimator have the better performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.<br /

Sequential fixed-width confidence bands for kernel regression estimation

We consider a random design model based on independent and identically distributed (iid) pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1&minus;. Here, d(&gt; 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedure together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confidence bands based on the local linear estimator have the best performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.<br /

Process performance evaluation using evolutionary algorithm

Nowadays every business is using different quantitative measures and techniques to assess performance of their products / services. It is well known that different manufacturing processes very often manufacture products with quality characteristics that do not follow normal distribution. In such cases, fitting a known non-normal distribution to these quality characteristics would lead to erroneous results. Furthermore, there is always more than one characteristic Critical to Quality (CTQ) in the process outcomes and very often these quality characteristics are correlated with each other. In this paper, we assess performance of such a bivariate process data which is non-normal as well as correlated. We will use the geometric distance approach to reduce the dimension of the correlated non-normal bivariate data and then fit Burr distribution to the geometric distance variable. The optimal parameters of the fitted Burr distribution are estimated using Evolutionary Algorithm (EA). The results are compared with those using Simulated Annealing (SA) algorithm. The proportion of nonconformance (PNC) for process measurements is then obtained by using the fitted Burr distributions based on the two methods. The results based on both search algorithms are then compared with the exact proportion of nonconformance of the data. Finally, a case study using real data is presented

Estimating process capability index Cpm using a bootstrap sequential sampling procedure

Construction of a confidence interval for process capability index CPM is often based on a normal approximation with fixed sample size. In this article, we describe a different approach in constructing a fixed-width confidence interval for process capability index CPM with a preassigned accuracy by using a combination of bootstrap and sequential sampling schemes. The optimal sample size required to achieve a preassigned confidence level is obtained using both two-stage and modified two-stage sequential procedures. The procedure developed is also validated using an extensive simulation study.<br /

A fuzzy set approach to software reliability modeling

This chapter provides a discussion of a fuzzy set approach which is used to extend the notion of software debugging from a 0-1 (perfect/imperfect) crisp approach to one which incorporates some fuzzy sets ideas. The main objective of this extension is to make current software reliability models more realistic. The theory underlying this approach, and hence its key modeling tool, is the theory of random point processes with fuzzy marks. The relevance of this theory to software debugging arises from the fact that it incorporates the randomness due to the locations of the software faults and the fuzziness bestowed by the imprecision of the debugging effort. Through several examples, we also demonstrates that this theory provides the natural vehicle for an investigation into the properties and efficacy of fuzzy debugging of software programs and is therefore a contribution to computational intelligence