Sentence examples for error characterized from inspiring English sources

The error characterized data set was then archived in the developed XML schema.

The sliding surface guarantees finite time convergence of the line-of-sight (LOS) rate and (if specified) the LOS angle error, characterized by a simple function of time-to-go.

Each panel in figure 4 represents a different MSAM and shows the relationship between MSA error, characterized by average MSA distance from the true alignment, and divergence estimate error, taken to be the average sum of errors across individual branches in the phylogenetic tree for amino acid sequences and nucleotide sequences.

This transition manifests itself in increased values of the roughness exponent and the scaling error characterizing the quality of auroral emissions fractal scaling, as well as in the number of simultaneously observed emission events.

k-mer spectrum approaches can measure sequencing error, characterize heterozygosity, strain variation or mixtures of organisms [ 37] within a sample in an annotation-independent, scalable way, and therefore, are applicable to a several sequencing applications, including the interpretation of metagenomic data sets and diagnosing technical aspects of the sequencing procedure.

In the watermarking channel, the errors, characterized by the different attacks, occur in random or burst manner.

When the sensors are subject to perturbations due to errors in calibration or in sensor location, the signal response is corrupted by errors characterized by the vectors { Δ a i } i = 1 K added to the steering vectors { a ( θ i ) } i = 1 K. Theses errors are randomly changing from one observation period to another.

In the MMSES case, all the trajectories tend to reach a zero steady state error whereas the BJ code error is characterized by two different behaviors.

Furthermore, future research on PM2.5-related measurement error should characterize measurement error for regional and local PM2.5 by focusing on PM2.5 composition, which changes both over space and time, suggesting that calibration coefficients will also change over space and time [ 6, 8, 48].

Distributions of all air pollutant measures more closely approximate lognormal distributions than normal distributions ([ 19], see Additional file 1, Table S1); therefore, additive error was characterized and modeled on a log concentration basis so that simulations with error added to a base case time-series would also have lognormal distributions.

The error in each estimate (parameter) used in the model, as described by its standard error, was characterized using an appropriate probability distribution (Table 1), except costs that were assumed known in the context of the evaluation.