Sentence examples for modeling and machine from inspiring English sources

Then the paper summarizes the main aircraft vibration response environment prediction methods and indicates their advantages, disadvantages and applicability scopes, including extrapolation of similar structure, theory analysis and analytical solution of differential dynamical equation, statistical parameter modeling, simulation calculation modeling and machine learning.

According to CEO and co-founder Catherine Havasi, they built data modeling and machine learning so it's all automated, a pretty neat trick if they can pull it off.

We have developed several techniques that synergistically couple web content analysis, user's browsing context, process modeling and machine learning to bridge this divide.

To select an effective forwarding node, SSAR considers both users' willingness to forward and their contact opportunity, and derives a metric with mathematical modeling and machine learning techniques to measure the forwarding capability of the mobile nodes.

Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis.

It uses miRNA expression levels in combination with thermodynamic modeling and machine learning techniques to make combinatorial predictions.

We have introduced and evaluated a new computational approach to represent combinatorial patterns of epigenetic marks as quantitative variables suitable for predictive modeling and supervised machine learning.

The paper discusses application of the algorithm based information processes modeling and Q-machine learning to the task of virtual machines auto configuration.

Furthermore, workpiece with different microstructures and dimensions, as well as cutting tool with different geometries, can be constructed easily through modeling; and the machining parameters such as nominal depth of cut and cutting velocity can be adjusted conveniently for each nanometric cutting simulation.

The tool kits include 1), nonequilibrium statistical-physics techniques for modeling machines and machine-driven processes; and 2), statistical-inference methods for reverse engineering a functional machine from the empirical data.

There may be great potential for using computer-modeling techniques and machine-learning algorithms in clinical decision making, if these can be shown to produce results superior to clinical protocols currently in use.