Sentence examples for the algorithm is studied using from inspiring English sources

The performance of the algorithm is studied using proton-proton collisions recorded during 2016 at root s = 13 TeV, corresponding to an integrated luminosity of 35.9 fb -1).

The performance of the algorithm is studied using benchmark test cases with data sets corresponding to real rock masses.

Parallel performance of the algorithm is studied using different numerical experiments and the scalability results show striking improvement over its previous version.

Two different control algorithms were studied using dynamic simulation and motion capture experiments with healthy subjects.

Additionally, the effect of overlap between biclusters and noise on the performance of the algorithm can be studied using the synthetic data.

The stability and the performance of the proposed FD estimation algorithm can be studied using classical error detector open-loop analysis tools.

The response of the algorithm to photomultiplier signals was studied using a specially designed experimental system based on fast plastic scintillators.

Three design parameters of the algorithm are studied: set of STFT windows used in the rough estimation stage, width of the moving average filter and downsampling factor from the refinement stage.

The applicability and effectiveness of the proposed ACO algorithm, namely ACOFRS, have been studied using a challenging set of thermodynamic benchmark problems with both liquid liquid and vapor liquid equilibrium.

In experiments, where random bipartite BNs generated using the BPART algorithm are studied, we systematically increase the out-degree of the root nodes in bipartite Bayesian networks, by increasing the number of leaf nodes.

The algorithm was used to study the Janus Kinase and Signal Transducers and Activators of Transcription (JAK/STAT) pathway, correctly reconstructing the core topology of the pathway along with model variants.