Sentence examples for handling variability from inspiring English sources

Therefore, our concept of handling variability is to integrate it around the reference deterministic atlas.

SPL approach provides a structured method for handling variability by using feature models.

Some reports have focused mostly on the difference in the isolation methods, handling, variability of cell yields and mode of administration [37, 38].

Both models are capable of handling variability during the construction process execution and address the complexity of the relations among the production system components, including resource mutual constraints.

Our nearest neighbor-based action classifier adopts dynamic time warping to handle variability in execution rate.

Traditional methods and approaches to Design, Operation, Management and Maintenance come under closer scrutiny, in particular their ability to handle variability and uncertainty.

Instead, it handles variability using component-based configurations that are flexible enough to compensate among others for limited pose changes, if any, and limited occlusion and disguise.

In the practice of implementing SPL, instances of certain design patterns are employed to handle variability, which makes these "variability-aware design patterns" a best practice for SPL design.

These authors first report the benefits of the separation of concerns by comparing their approach with other techniques for handling scenario variability management (Bonifácio et al. 2008), and after that, they describe an approach for use case scenario variability management (Bonifácio and Borba 2009).

The proposed framework consists of five elements: (1) finite element method (FEM) for analyzing wall and ground responses in a braced excavation, (2) fuzzy set modeling of parameter uncertainty, (3) spatial averaging technique for handling spatial variability, (4) vertex method for processing fuzzy input through FEM model, and (5) interpretation of fuzzy output.

Several strategies for handling model variability have been proposed in ensemble forecasting: (i) a single best model (providing the best fit to available data) is chosen, (ii) models are presented individually, and (iii) models are combined into a consensus prediction via model agreement, model central tendency (e.g., mean), or probability density functions [52].