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Advanced Methods for Uncertainty Estimation in Measurement, 2006. AMUEM 2006. Proceedings of the 2006 IEEE International Workshop on

Date 20-21 April 2006

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  • Proceedings of the 2006 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement

    Page(s): i
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  • Copyright

    Page(s): ii
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  • Message from the Chairmen

    Page(s): iii
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  • [Breaker page]

    Page(s): iv
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  • Table of contents

    Page(s): v - vi
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  • Author index

    Page(s): vii
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  • [Breaker page]

    Page(s): viii
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  • Expression of uncertainty I [breaker page]

    Page(s): 1
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  • Modelling and Processing Measurement Uncertainty within the Theory of Evidence

    Page(s): 2 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (158 KB) |  | HTML iconHTML  

    RFVs are variables defined within the theory of evidence, which are suitable for the representation of measurement results together with the associated uncertainty, whichever is its nature. This paper proposes a suitable mathematics for processing RFVs, which considers the different nature of the uncertainty effects. This allows to process measurement algorithms in terms of RFVs, so that the final measurement result (and all associated available information) is directly obtained as a RFV View full abstract»

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  • Type-2 Fuzzy Sets for Modeling Uncertainty in Measurement

    Page(s): 8 - 13
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (432 KB) |  | HTML iconHTML  

    A correct representation of uncertainty in measurement is crucial in many applications. Statistical approach sometimes is not the best choice, especially when the knowledge of the measurement process refers only to the support of the values and does not allow a correct assumption on the probability density function (pdf) of the measured variable. In this paper we present an approach that uses the concept of generalized fuzzy numbers, namely type-2 fuzzy sets, in order to handle the intrinsic dispersion of the possible pdfs associated to a variable. The relation between our representation and the so called random fuzzy variables (RFV) will be also investigated. The use of this representation allows to easily implement the uncertainty propagation, through a functional model, by working directly on the type-2 fuzzy numbers and by evaluating simultaneously the propagation results for the whole set of confidence levels. Anyway, when a statistical analysis can be performed, the results can be embedded in this generalized representation. Moreover, the new approach allows to assign to the final measurement value a reliable confidence level also in this case, by combining the expanded uncertainty evaluated following IEC-ISO guide recommendations with the type-2 fuzzy numbers associated to the output variable. An example of this representation was provided View full abstract»

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  • The Principle of Maximum Entropy Applied in the Evaluation of the Measurement Uncertainty

    Page(s): 14 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (81 KB) |  | HTML iconHTML  

    The maximum entropy approach is a flexible and powerful tool for assigning a probability distribution to a measurable quantity treated as a random variable, subjected to known moment constraints. The aim of this paper is to describe how the principle of maximum entropy may be used to transform information about the value of a quantity into a probability density function reflecting exactly that information and nothing more. The principle will be applied to common cases of metrological interest, where different kinds of information are available. The derivation of the probability density function is given in each case and numerical results are reported to demonstrate the efficiency of the method View full abstract»

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  • Expression of uncertainty II [breaker page]

    Page(s): 18
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  • Possibility Expression of Measurement Uncertainty In a Very Limited Knowledge Context

    Page(s): 19 - 22
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (143 KB) |  | HTML iconHTML  

    At the application level, it is important to be able to define around the measurement result an interval which will contain an important part of the distribution of the measured values, that is, a confidence interval. This practice acknowledged by the ISO guide is a major shift from the probabilistic representation as a confidence interval represents a set of possible values for a parameter associated with a confidence level. It can be viewed as a probability-possibility transformations by viewing possibility distribution as encoding confidence intervals. In this paper, we extend previous works concerning possibility expression of measurement uncertainty to situations where only a very limited knowledge is available: very few measurements, unknown probability density View full abstract»

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  • Propagating Uncertainty Through Discrete Time Dynamic Systems

    Page(s): 23 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (354 KB) |  | HTML iconHTML  

    The Monte Carlo method is used to analyze the propagation of uncertainty through dynamic systems by means of simulation. To this purpose a specifically designed and implemented simulation engine is presented and some results are discussed View full abstract»

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  • Indirect Measurements Via Polynomial Chaos Observer

    Page(s): 27 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (482 KB) |  | HTML iconHTML  

    This paper proposes an innovative approach to the design of algorithms for indirect measurements based on a polynomial chaos observer (PCO). A PCO allows the introduction and management of uncertainty in the process. The structure of this algorithm is based on the standard closed-loop structure of an observer originally introduced by Luenberger. This structure is here extended to include uncertainty in the measurement and in the model parameters in a formal way. Possible applications of this structure are then also discussed View full abstract»

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  • Uncertainty Estimation I [breaker page]

    Page(s): 33
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  • The evaluation of the uncertainty associated with comparison loss in microwave power meter calibration

    Page(s): 34 - 39
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (130 KB) |  | HTML iconHTML  

    A model of comparison loss in microwave power meter calibration is considered in the case when the standard power meter used for calibration and the signal generator to which this meter and the meter to be calibrated are connected are reflectionless. The uncertainty associated with an estimate of the model output quantity, viz., the ratio of the power absorbed by the meter being calibrated and that absorbed by the standard, is to be evaluated given estimates of the model input quantities, the standard uncertainties associated with those estimates, and a correlation coefficient relating to the estimates. This problem is addressed using three approaches, viz.: (1) the GUM uncertainty framework; (2) the propagation of distributions, implemented using a Monte Carlo method; and (3) analytical or semi-analytical. Circumstances in which approach: (1) can provide invalid solutions, whereas (2) provides sound numerical solutions, are identified View full abstract»

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  • Uncertainty Estimation in a Vision-Based Tracking System

    Page(s): 40 - 45
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (507 KB) |  | HTML iconHTML  

    Vision-based tracking is concerned with the recovery of position and orientation data of moving objects based on visual input provided by one or more cameras. This paper describes a framework to handle geometric parameter uncertainties within a monocular outside-in vision-based tracking application. We present a sensor model - the stochastic camera - that is capable to take parameter calibration uncertainties into consideration even under real-time requirements. The feasibility of the proposed method is shown in closed-loop tracking experiments View full abstract»

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  • Estimating and Controlling the Uncertainty of Learning Machines

    Page(s): 46 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (249 KB) |  | HTML iconHTML  

    The problem of estimating model uncertainty of learning machines (LMs) is becoming a subject of great interest because of the wide application of such kind of methodologies for solving real-world problems. In this work we will provide a general overview on estimating and controlling uncertainity of LMs, by describing the algorithms, the theory and the empirical methods used to obtain a robust estimation. In the end we address the problem of uncertainty estimation when devices with limited resources are considered for the hardware implementation View full abstract»

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  • Uncertainty Estimation II [breaker page]

    Page(s): 51
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  • Peculiarities of Using Uncertainty in Environmental Guides

    Page(s): 52 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (113 KB) |  | HTML iconHTML  

    Significant attention is paid to common questions relative to the methods of the expression of the uncertainty of measurements and the estimates of the results of the definition of the environmental characteristics of global objects. The bases analysis of uncertainty and its main sources are considered. Peculiarities of the expression of uncertainty and the variants of its estimation in international metrological and environmental guides are shown. Basic positions concerning the uncertainty estimates of obtained results, shown in well-known international guides of inventory of greenhouse gases, are considered. The use of international metrological guidance of expression of measurements uncertainty in development of new and reconsideration of old international environmental guides is recommended View full abstract»

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  • Noise Parameter Estimation from Quantized Data

    Page(s): 57 - 61
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    In this paper, the parametric estimation of additive white Gaussian noise is considered, when available data are obtained from a quantized noisy stimulus. The Cramer-Rao lower bound is derived, and the statistically efficiency of a maximum likelihood parametric estimator is discussed, along with the estimation algorithm proposed in IEEE standard IEEE 1241 View full abstract»

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  • Maximum Entropy Analytical Solution for Stochastic Differential Equations Based on the Wiener-Askey Polynomial Chaos

    Page(s): 62 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (132 KB) |  | HTML iconHTML  

    Many measurements models are formalized in terms of a stochastic process relating its solution to some given observables. The expression of the measurement uncertainty for the solution requires the determination of its (joint) pdf evaluated in an assigned time window. Recently, polynomial chaos (PC) theory has been widely recognized as a promising technique in order to address the problem. However, the uncertainty estimation via PC requires the use of a Monte Carlo integration sampling strategy, which is notoriously computationally intensive. In this paper a novel approach was presented in order to achieve the PC uncertainty estimation on the basis of a purely analytical methodology, requiring only an optimization calculus View full abstract»

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  • Uncertainty Estimation - Case studies I [breaker page]

    Page(s): 67
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  • Evaluation of the Uncertainty of Edge Detector Algorithms

    Page(s): 68 - 73
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (274 KB) |  | HTML iconHTML  

    The paper deals with the analytical expression of the uncertainty on edge localization in image analysis applications. The analysis carried out relates analytically the uncertainty affecting the intensity of input image to the output uncertainty of edge detectors based on first and second derivative proprieties. The theoretical results are validated through experimental tests performed on real images acquired in typical conditions of an industrial environment View full abstract»

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