Sentence examples for modeling method such from inspiring English sources

The applications of this method in a simple ring network and a complex mesh network show that the proposed method has significant advantages over the traditional modeling method, such as clear physical concepts, simple calculations, and ease of implementation with conventional computational software.

We chose to use this method because it addresses some of the shortcomings of the modeling method, such as using all information provided in a set of experiment to determine the unchanged population instead of using one experiment at a time.

Typical speech modeling methods such as STP and LTP are good candidates for the pre-processing module.

Fully automated topic modeling methods such as Latent Dirichlet Allocation [123] might be used to create a sustainability vocabulary database.

The proposed method is compared with traditional reduced-order modeling methods such as component mode synthesis, and its advantages are discussed.

Conventional speaker modeling methods such as Gaussian mixture models (GMMs) [1] achieve very high performance for speaker identification and verification tasks on high-quality data when training and testing conditions are well controlled.

Compared with other ab initio modeling methods such as ROSETTA and TOUCHSTONE II, the average performance of I-TASSER is either much better or is similar within a lower computational time.

The benchmark of the FREAD method contains 30 targets for each loop length, (from 4 to 20 residues) and a recent assessment using this benchmark (Choi and Deane, 2010) has shown that template-based methods such as FREAD can achieve better performance compared to the ab-initio loop modeling methods such as MODLOOP, RAPPER and PLOP on this benchmark.

Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability [ 19].

In such cases, modeling methods involving Artificial Intelligence may be employed.

Given that this kind of modeling is both computationally optimal and a natural structural match for many modeling problems, it follows that it is the best modeling method for such problems.