Sentence examples for uncertainty processor from inspiring English sources

The BFS has three structural components: the precipitation uncertainty processor, the hydrologic uncertainty processor, and the integrator.

Here, we implement a dedicated uncertainty processor, that can be used within operational flood forecasting systems.

This paper discusses the analogies and the performances of two uncertainty post-processors, the Hydrologic Uncertainty Processor (HUP) introduced by Krzysztofowicz (1999) and the Model Conditional Processor (MCP), which was proposed by Todini (2008) for the assessment of predictive uncertainty, as an alternative to HUP.

The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input).

The paper shows analytically and through a numerical example that the two uncertainty processors are strongly related and explains why MPC results into improved performances with respect to the HUP, when used with the same level of information.

However, the ever-increasing variations cause large uncertainty for delay and power of the processors, resulting in performance parameters of the VFIs also manifesting as statistical distribution.

Additionally, it provides non-preemptive operation which removes the timing uncertainty and overhead involved with interrupt-driven processor-based sensor node implementation, which is required in real-time wireless sensor networks (WSNs).

When the initialization of a landmark is completed, the DSP processor returns the position and the uncertainty of possible new landmarks.

You should also track any challenges and your attempts to resolve them (e.g., difficulty obtaining stakeholder buy-in; lack of technical expertise; uncertainty over whether your business is a data processor or controller – a distinction in compliance obligations).

Finally, the characteristics of farmer-processor relationships considered are trust in commercial partners, uncertainty, and relationship-specific investments.

Driving forces identified for motivating mergers in the dairy industry are: consolidation at the retail and processor levels, potential cost savings from consolidated operations and increased uncertainty over the role of government.