Kärnenergiteknik

Ämnesområden: Kärnenergiteknik: allmänt
Kommittébeteckning: SIS/TK 405 (Kärnenergi)
Källa: ISO
Svarsdatum: den 2 feb 2018
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This part of ISO 20785 is intended for the validation of codes used for the calculation of doses received by individuals on board aircraft. It gives guidance to radiation protection authorities and code developers on the basic functional requirements which shall be fulfilled by the codes.

Depending on any formal approval by a radiation protection authority, additional requirements concerning the software testing may apply.

Ämnesområden: Klyvbara material
Kommittébeteckning: SIS/TK 405 (Kärnenergi)
Källa: ISO
Svarsdatum: den 23 feb 2018
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This method applies to the measurement of the isotopic composition and the concentration of uranium and plutonium in input solutions of irradiated Magnox and light water reactor fuels (boiling water reactor or pressurized water reactor), in final products at spent-fuel reprocessing plants, and in feed and products of MOX and uranium fuel fabrication. The method is applicable to other fuels, but the chemical separation and spike solution are, if necessary, adapted to suit each type of fuel.

Ämnesområden: Klyvbara material
Kommittébeteckning: SIS/TK 405 (Kärnenergi)
Källa: ISO
Svarsdatum: den 28 feb 2018
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This International Standard provides the chemical analyzers with a method for evaluation of the measurement uncertainty arising when an impurity content of uranium solution is determined by a regression line that has been fitted by the "method of least squares".

Simple linear regression, hereinafter called "basic regression", is defined as a model with a single independent variable that is applied to fit a regression line through n different data points (xi, yi) (i = 1,…, n) in such a way that makes the sum of squared errors, i.e. the squared vertical distances between the data points and the fitted line, as small as possible. For the linear calibration, "classical regression" or "inverse regression" is usually used; however, they are inconvenient to use. Instead, "reversed inverse regression" is used in this International Standard[1].

Reversed inverse regression treats y (the reference solutions) as the response and x (the observed measurements) as the inputs; these values are used to fit a regression line of y on x by the method of least squares. This regression is distinguished from basic regression in that the xi’s (i = 1,…, n) vary according to normal distributions but the yi’s (i = 1,…, n) are fixed; in basic regression, the yi’s vary but the xi’s are fixed.

The regression line fitting, calculation of combined uncertainty, calculation of effective degrees of freedom, calculation of expanded uncertainty, reflection of reference solutions’ uncertainties in the evaluation result, and bias correction are explained in order of mention. Annex A presents a practical example of uncertainty evaluation. Annex B provides a flowchart showing the steps for uncertainty evaluation. In addition, Annex C explains the use of weighting factors for handling non-uniform variances in reversed inverse regression.

NOTE 1 In the case of classical regression, the fitted regression line must be inverted prior to actual sample measurement[2]. In the case of inverse regression, the roles of x and y are not consistent with the convention that the variable x represents the inputs, whereas the variable y represents the response. For these reasons, the two regressions are excluded from this International Standard.

NOTE 2 The term "reversed inverse regression" was suggested taking into account the history of regression analysis theory. Instead, it may be desirable to use some other term, e.g. "pseudo-basic regression".