20 resultados para diffusion process, wavelet estimator, non-parametric rate of convergence, Markov chain, estimation of unknown signal


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Distributed Brillouin sensing of strain and temperature works by making spatially resolved measurements of the position of the measurand-dependent extremum of the resonance curve associated with the scattering process in the weakly nonlinear regime. Typically, measurements of backscattered Stokes intensity (the dependent variable) are made at a number of predetermined fixed frequencies covering the design measurand range of the apparatus and combined to yield an estimate of the position of the extremum. The measurand can then be found because its relationship to the position of the extremum is assumed known. We present analytical expressions relating the relative error in the extremum position to experimental errors in the dependent variable. This is done for two cases: (i) a simple non-parametric estimate of the mean based on moments and (ii) the case in which a least squares technique is used to fit a Lorentzian to the data. The question of statistical bias in the estimates is discussed and in the second case we go further and present for the first time a general method by which the probability density function (PDF) of errors in the fitted parameters can be obtained in closed form in terms of the PDFs of the errors in the noisy data.

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Topic management by non-native speakers (NNSs) during informal conversations has received comparatively little attention from researchers, and receives surprisingly little attention in second language learning and teaching. This article reports on one of the topic management strategies employed by international students during informal, social interactions with native-speaker peers, exploring the process of maintaining topic continuity following temporary suspensions of topics. The concept of side sequences is employed to illustrate the nature of different types of topic suspension, as well as the process of jointly negotiating a return to the topic. Extracts from the conversations show that such sequences were not exclusively occasioned by language difficulties, and that the non-native speaker participants were able to effect successful returns to the main topic of the conversations.

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The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios. © 2011 Elsevier Inc.

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Distributed Brillouin sensing of strain and temperature works by making spatially resolved measurements of the position of the measurand-dependent extremum of the resonance curve associated with the scattering process in the weakly nonlinear regime. Typically, measurements of backscattered Stokes intensity (the dependent variable) are made at a number of predetermined fixed frequencies covering the design measurand range of the apparatus and combined to yield an estimate of the position of the extremum. The measurand can then be found because its relationship to the position of the extremum is assumed known. We present analytical expressions relating the relative error in the extremum position to experimental errors in the dependent variable. This is done for two cases: (i) a simple non-parametric estimate of the mean based on moments and (ii) the case in which a least squares technique is used to fit a Lorentzian to the data. The question of statistical bias in the estimates is discussed and in the second case we go further and present for the first time a general method by which the probability density function (PDF) of errors in the fitted parameters can be obtained in closed form in terms of the PDFs of the errors in the noisy data.

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Time, cost and quality are the prime objectives of any project. Unfortunately, today’s project management does not always ensure the realisation of these objectives. The main reasons of project non-achievement are changes in scope and design, changes in Government policies and regulations, unforeseen inflation, under-estimation and mis-estimation. An overall organisational approach with the application of appropriate management philosophies, tools and techniques can only solve the problem. The present study establishes a methodology for achieving success in implementing projects using a business process re-engineering (BPR) framework. Internal performance characteristics are introspected through condition diagnosis that identifies and prioritises areas of concern requiring attention. Process re-engineering emerges as a most critical area for immediate attention. Project process re-engineering is carried out by eliminating non-value added activities, taking up activities concurrently by applying information systems rigorously and applying risk management techniques throughout the project life cycle. The overall methodology is demonstrated through applications to cross country petroleum pipeline project organisation in an Indian scenario.