Measurement Uncertainty
This reprint focuses on a very important topic in metrology, which is represent by measurement uncertainty. Any good metrologist or scientist in engineering knows that no measurement makes sense without an associated uncertainty value: without an uncertainty value, no decision can be taken; no compa...
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| Materialtyp: | Online |
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| Språk: | engelska |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Länkar: | ONIX_20230623_9783036566085_127 |
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| _version_ | 1869530047873810432 |
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| collection | Directory of Open Access Books |
| description | This reprint focuses on a very important topic in metrology, which is represent by measurement uncertainty. Any good metrologist or scientist in engineering knows that no measurement makes sense without an associated uncertainty value: without an uncertainty value, no decision can be taken; no comparisons can be made; no conformity can be assessed. Any decision, comparison or conformity assessment made without considering the measurement uncertainty affecting the measurement value is completely useless and meaningless. Stated that, it becomes very clear that uncertainty in measurement plays indeed a very important rule in our everyday life. This is the reason why there is a great need to have a fruitful academic and scientific discussion on this topic. We have been speaking about measurement uncertainty for less than 30 years, since the concept of “measurement uncertainty” has been introduced in 1995 by the “Guide to the expression of uncertainty in measurement” (GUM). Thirty years seems to be many, but still the concept of measurement uncertainty has not been spread worldwide and the GUM is a document that is not known everywhere. On the other hand, this document should be considered not only in academic scenario, but also in any technical and industrial scenario, where it is pivotal to know the meaning of measurement uncertainty, identify the uncertainty contributions and know how these contributions affect the final measurement result. |
| format | Online |
| id | doab-20.500.12854ir-100895 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1008952024-04-11T15:10:15Z Measurement Uncertainty Salicone, Simona random-fuzzy variables Kalman filter systematic uncertainty contributions styling measurement uncertainty random contribution systematic contribution probability density functions possibility distributions t-norms measuring bridge calibration non-conventional instrument transformer sampled values digital output synchronization digitalization metrological traceability key comparison digital calibration certificate uncertain number metrology nuclear data data evaluation systematic distortion factor unrecognized source of uncertainties DCC machine-readable data communication uncertainty Monte Carlo method MCM guide to the expression of uncertainty in measurement measurement modelling uncertainty propagation information fusion possibility theory information fusion system design digital signal processing spectral resolution frequency domain analysis frequency–domain interpolation frequency uncertainty uncertainty assessment three-dimensional point clouds ISO 15530 data-driven metrology model-based definition virtual twin bayesian modeling Hamiltonian Monte Carlo diagnostic uncertainty expert opinion data verbal probability n/a Tsallis q-Gaussian distribution characteristic function numerical inversion linear measurement model thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology This reprint focuses on a very important topic in metrology, which is represent by measurement uncertainty. Any good metrologist or scientist in engineering knows that no measurement makes sense without an associated uncertainty value: without an uncertainty value, no decision can be taken; no comparisons can be made; no conformity can be assessed. Any decision, comparison or conformity assessment made without considering the measurement uncertainty affecting the measurement value is completely useless and meaningless. Stated that, it becomes very clear that uncertainty in measurement plays indeed a very important rule in our everyday life. This is the reason why there is a great need to have a fruitful academic and scientific discussion on this topic. We have been speaking about measurement uncertainty for less than 30 years, since the concept of “measurement uncertainty” has been introduced in 1995 by the “Guide to the expression of uncertainty in measurement” (GUM). Thirty years seems to be many, but still the concept of measurement uncertainty has not been spread worldwide and the GUM is a document that is not known everywhere. On the other hand, this document should be considered not only in academic scenario, but also in any technical and industrial scenario, where it is pivotal to know the meaning of measurement uncertainty, identify the uncertainty contributions and know how these contributions affect the final measurement result. 2023-06-23T09:51:30Z 2023-06-23T09:51:30Z 2023 book ONIX_20230623_9783036566085_127 9783036566085 9783036566092 https://directory.doabooks.org/handle/20.500.12854/100895 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7363 https://mdpi.com/books/pdfview/book/7363 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-6609-2 10.3390/books978-3-0365-6609-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036566085 9783036566092 202 Basel open access |
| spellingShingle | random-fuzzy variables Kalman filter systematic uncertainty contributions styling measurement uncertainty random contribution systematic contribution probability density functions possibility distributions t-norms measuring bridge calibration non-conventional instrument transformer sampled values digital output synchronization digitalization metrological traceability key comparison digital calibration certificate uncertain number metrology nuclear data data evaluation systematic distortion factor unrecognized source of uncertainties DCC machine-readable data communication uncertainty Monte Carlo method MCM guide to the expression of uncertainty in measurement measurement modelling uncertainty propagation information fusion possibility theory information fusion system design digital signal processing spectral resolution frequency domain analysis frequency–domain interpolation frequency uncertainty uncertainty assessment three-dimensional point clouds ISO 15530 data-driven metrology model-based definition virtual twin bayesian modeling Hamiltonian Monte Carlo diagnostic uncertainty expert opinion data verbal probability n/a Tsallis q-Gaussian distribution characteristic function numerical inversion linear measurement model thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Measurement Uncertainty |
| title | Measurement Uncertainty |
| title_full | Measurement Uncertainty |
| title_fullStr | Measurement Uncertainty |
| title_full_unstemmed | Measurement Uncertainty |
| title_short | Measurement Uncertainty |
| title_sort | measurement uncertainty |
| topic | random-fuzzy variables Kalman filter systematic uncertainty contributions styling measurement uncertainty random contribution systematic contribution probability density functions possibility distributions t-norms measuring bridge calibration non-conventional instrument transformer sampled values digital output synchronization digitalization metrological traceability key comparison digital calibration certificate uncertain number metrology nuclear data data evaluation systematic distortion factor unrecognized source of uncertainties DCC machine-readable data communication uncertainty Monte Carlo method MCM guide to the expression of uncertainty in measurement measurement modelling uncertainty propagation information fusion possibility theory information fusion system design digital signal processing spectral resolution frequency domain analysis frequency–domain interpolation frequency uncertainty uncertainty assessment three-dimensional point clouds ISO 15530 data-driven metrology model-based definition virtual twin bayesian modeling Hamiltonian Monte Carlo diagnostic uncertainty expert opinion data verbal probability n/a Tsallis q-Gaussian distribution characteristic function numerical inversion linear measurement model thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | random-fuzzy variables Kalman filter systematic uncertainty contributions styling measurement uncertainty random contribution systematic contribution probability density functions possibility distributions t-norms measuring bridge calibration non-conventional instrument transformer sampled values digital output synchronization digitalization metrological traceability key comparison digital calibration certificate uncertain number metrology nuclear data data evaluation systematic distortion factor unrecognized source of uncertainties DCC machine-readable data communication uncertainty Monte Carlo method MCM guide to the expression of uncertainty in measurement measurement modelling uncertainty propagation information fusion possibility theory information fusion system design digital signal processing spectral resolution frequency domain analysis frequency–domain interpolation frequency uncertainty uncertainty assessment three-dimensional point clouds ISO 15530 data-driven metrology model-based definition virtual twin bayesian modeling Hamiltonian Monte Carlo diagnostic uncertainty expert opinion data verbal probability n/a Tsallis q-Gaussian distribution characteristic function numerical inversion linear measurement model thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20230623_9783036566085_127 |