Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
94 tokens/sec
Gemini 2.5 Pro Premium
55 tokens/sec
GPT-5 Medium
18 tokens/sec
GPT-5 High Premium
24 tokens/sec
GPT-4o
103 tokens/sec
DeepSeek R1 via Azure Premium
93 tokens/sec
GPT OSS 120B via Groq Premium
462 tokens/sec
Kimi K2 via Groq Premium
254 tokens/sec
2000 character limit reached

Estimating dynamic mechanical quantities and their associated uncertainties: application guidance (1808.09652v1)

Published 29 Aug 2018 in cs.SY

Abstract: Recently several European National Measurement Institutes have established traceable calibration methods for dynamic mechanical quantities, e.g., dynamic force, torque and pressure. However, the use in industry and elsewhere of dynamic calibration information provided on certificates is not straightforward. Typically it is necessary to employ deconvolution techniques to obtain estimates of measurands, and the deconvolution method itself and the associated algorithms are sources of uncertainty that must be included in uncertainty budgets. There is a need for practical guidance for end users on how to use the newly-available dynamic calibration information. To this end we set out an approach to the evaluation of uncertainties associated with dynamic measurements that we believe covers the most relevant cases. The methods have been embodied in publicly-available software and we show how they can be used to tackle some example problems. We believe that the methods lead to more reliable estimates of the relevant measurands and their associated uncertainties.

Citations (5)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube