Advanced Mathematical And Computational Tools in Metrology by P. Ciarlini, E Filipe, A B Forbes, F Pavese, C Perruchet, B

By P. Ciarlini, E Filipe, A B Forbes, F Pavese, C Perruchet, B R L Siebert

This quantity collects the refereed contributions in accordance with the shows made on the 7th Workshop on complicated Mathematical and Computational instruments in Metrology, a discussion board for metrologists, mathematicians and software program engineers that would motivate a more desirable synthesis of abilities, functions and assets. the quantity includes articles by means of international well known metrologists and mathematicians fascinated about dimension technology and, including the six past volumes during this sequence, constitutes an authoritative resource of the mathematical, statistical and software program instruments helpful in sleek metrology.

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Velocity level dynamic water area lateral pressure sensor s ^z\ f r static water area Water area Pitot tube^) ont pressure sensor Figure 3. Example of a physical system to model. A Pitot tube into an open water channel. The mereological representation of such system is made by the definition of the proper_part_of relation, for example the lateral pressure sensor is a proper part of Pitot tube. The dynamic water area is a part of water area. PP(lateralSensor, PitotTube) (10) PPifrontSensor, PitotTube) (11) P(staticWaterArea,waterArea) (12) PP(dynamicWaterArea,waterArea) (13) Note that when the water does not move, the static water area is the water area.

Information about the robustness of the traditional Shewhart and Cusum control charts, in the presence of non-normal distributions, has been obtained in Balakrishnan and Kocherlakota [1], Borror et al. [2], Chan and Heng [5], Chang and Bai [6], Figueiredo and Gomes [9], Lucas and Croisier [13], Ryan and Faddy [16], Stoumbos and Reynolds [18], among others. In Sec. 2 we introduce some background about the control statistics to be herewith considered, and present a simulation study on their robustness and efficiency.

8. O. Figueiredo, Proc. 13th European Young Statisticians Meeting, 53 (2003b). 9. O. I. Gomes, Applied Stochastic Models in Business and Industry 20, 339 (2004). 10. M. H. Olwell, Springer-Verlag. (1998). 11. P. Langenberg and B. Iglewicz, J. Quality Technology 18 3, 152 (1986). 12. D. A. Lax, J. Amer. Statist. Assoc. 80, 736 (1985). 13. J. M. Lucas and R. B. Crosier, Commun. : Theory and Methods 11 23, 2669 (1982). 14. D. M. Rocke, Technometrics 31 2, 173 (1989). 15. D. M. Rocke, The Statistician 4 1 , 97 (1992).

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