Your English writing platform
Discover LudwigExact(60)
The filter data window length and coefficients are adapted according to coarsely estimated signal frequency.
Their compressing abilities are limited by the data window size and the compressing format design.
Both rate and explicit modes abide by the upper and lower data window boundaries which delimit the data range.
This is achieved by matching the data window used for constructing the components with the estimation window.
While this makes PE very robust against outliers and suitable for non-linear time-series, it disregards, by design, all metric information in a given data window.
Generally there are two DC offsets in a CT secondary signal, which are estimated and their effects will be eliminated in first cycle data window.
When compared with other fault location schemes, the proposed scheme requires less information and a smaller time data window to estimate the fault locations.
The linear regression model (similar to ASTM E813-81) results in the least accuracy in the data fitting and is highly sensitive to the data window.
The power law model (similar to ASTM E813-87) gives a better data fitting and is less sensitive to the data window.
The scheme is based on calculating the differential current gradient vector angles in phases A-B-C at all points of the data window.
We propose, for the first time, a hardware co-processor called UWJSP (Uncertain data Window Join Special co-Processor) for implementation.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com