The phrase "accurate reliability" is not commonly used in written English and may be considered redundant.
It can be used when discussing the precision of a measurement or assessment that is expected to be dependable, but it may be clearer to use one term instead of both.
Example: "The accurate reliability of the test results ensures that we can trust the findings."
Alternatives: "dependable accuracy" or "reliable precision."
The results show that multi-scale analysis can provide more accurate reliability estimates.
Accurate reliability assessment for these problems usually requires very time-consuming computation, and sometimes it is unacceptable.
While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed.
Unfortunately, a great deal of effort needed to produce accurate reliability predictions is involved in developing believable component and commercial power failure rates.
This work also serves the purpose of helping the engineering and design departments to carry out an accurate reliability analysis on the behavior of impacted composite structures.
This suggests that to get more accurate reliability estimates, one should be able to utilize all sorts of information available for assessing the reliability.
Results show that the displacement-based approach comes up with more convergent and accurate reliabilities for selected models, which can be applied to other hazards.
Classification of the six call types was highly accurate (reliabilities: 96% between day 1 and 2; 96% between day 1 and 3, and 98% between day 2 and 3), demonstrating that humans can discriminate these six call types very reliably.
This method can be extended to establish accurate CCS reliability curves that illustrate CCS reliability characteristics across a wide range of VM allocations.
An accurate product reliability prediction model can not only learn and track the product's reliability and operational performance, but also offer useful information for managers to take follow-up actions to improve the product' quality and cost.
Due to low proximity difference between predicted results and actual results with each other, based on confirmation tests, the predictive model is sufficient accurate at reliability of 95%.
