Sentence examples for different levels of precision from inspiring English sources

Intuitionistic fuzzy graph theory is finding an increasing number of applications in modeling real time systems where the level of information inherent in the system varies with different levels of precision.

A series of 16 multi-location trials from a corn-breeding program in the north of China were simultaneously analyzed from 2005 to 2008 using a randomized complete block design at each location; the analysis used models with homogeneous residual variances, as well as models with heterogeneous residual variances to take into account that different locations may have different levels of precision.

Depending on their application, the wavelet functions have different types with different levels of precision.

The dates are generally human readable, but equivalent dates are formatted variously (10/4/2015 vs. Oct. 4, 2015 vs. 2015-10-4) are there are different levels of precision (1920s vs. June 1981 vs Spring 1972), and degrees of certainty about the accuracy of the date (circa 1931).

In Section 7, we present our experiments with the WMD model divided into following parts: comparison of the segmentation time with the TAV method using a set of artificial images, comparison of the segmentation time using different levels of precision, performance with real medical images, and the performance gain when executed in a parallel environment.

More generally, different pyramidal estimators, combining different surveillance systems and surveys, have different levels of precision.

This is okay, but for augmented reality, you'll need a completely different level of precision," says Krystian Mikolajczyk, a computer vision expert at Imperial University.

Situations may also arise where it is advantageous to have data collected at a shorter interval or at a different level of precision.

We show that these semantics enjoy different behaviours, and can provide different levels of ranking precision on arguments.

Furthermore, the eventual error variation due to different levels of trial precision across different environments cannot be accounted for in both yield comparison and stability evaluation.

Therefore, in this paper, a new approach is proposed to locate users with different levels of spatial precision, based on his/her spatio-temporal context and a user's group, through fuzzy inference systems.