Sentence examples for modelling process can from inspiring English sources

The sinusoidal modelling process can be extended, creating models of sound based on the separation of the audio signal into a combination of sinusoids and noise [22], and further into combinations of sinusoids, noise and transients [23].

The thorough study of available literature suggests that the steps involved in the system dynamics modelling process can be taken as follows: Fig. 4 Model development as an iterative process.

The process illustrates how a modelling process can lead to a fundamental reframing of the issue, resulting in major change for the company.

This paper demonstrates that using such statistical approaches through our modelling process, can overcome a typical issue of modern IT systems capacity planning works: establishing direct and quantified links between business and IT activities.

This modelling process can involve tiers, starting with descriptions that are of relatively low fidelity and leading to a model at the final tier that is a detailed description of the system in service.

It was shown to provide more accurate results than the well-established complex domain method with noticeably lower computation requirements, and as it integrates seamlessly with the sinusoidal modelling process, it can be added to the existing sinusoidal modelling systems at very little cost.

These properties are intended to be consistency checks on the model, which allow the presence of possible human errors to be checked in the modelling process and the model can be guaranteed to behave as expected.

Although recent crop modelling frameworks can technically support model building, the modelling process is not always well documented and difficult to repeat.

In modelling process, the complicated bone scaffolds can be designed with CAD tools, and FEA and CFD are the tools to analyse the mechanical and internal micro circulation properties.

The modelling process of the grey system theory can be summarized as follows: 1. Suppose there is a series of discrete nonnegative data as X^{left( 0 right)} left( m right) = left{ {x^{left( 0 right)} left( 1 right),x^{left( 0 right)} left( 2 right), ldots,x^{left( 0 right)} left( n right)} right}.

Missing covariate data and censored outcomes are unfortunately common occurrences in prognostic modelling studies [ 3], which can complicate the modelling process.