Sentence examples for false alarm level from inspiring English sources

From this numerical experiment, we can find that the false alarm level is comparable with different clutter covariance matrices, so the proposed detector achieves an approximate constant false alarm ratio (CFAR) with respect to different clutter covariance matrices.

At a false alarm level of 0.001 both the longer baseline 28-day and 56-day negative binomial cusums had an overall sensitivity of 80% based on a 3 standard deviation control limit.

At a false alarm level of 0.005 the 28-day negative binomial cusum also had the highest sensitivity during the first 7 days of the outbreak (60%), followed by the C3 algorithm and the 7-day negative binomial cusum (both 40%) (Table 1).

Multiple comparisons at the 0.001 false alarm level were significant for EARS C1 and the 7-day and 28-day negative binomial cusums, and C2 and the 28-day negative binomial cusum following adjustment for the number of comparisons performed (p = 0.006, p = 0.002 and p = 0.006 respectively), and p ≤ 0.04 for all remaining comparisons.

At the 0.01 false alarm level, there was a significant difference in timeliness among the algorithms compared for the larger simulation scenarios only (S1 Friedman χ2 = 5.1, p < 0.41, S2 Friedman χ2 = 9.3, p < 0.10, S3 Friedman χ2 = 20.3, p < 0.001 and S4 Friedman χ2 = 17.1, p < 0.004).

The negative binomial cusum also generally had a greater overall median time to detection at the 0.005 false alarm rate (Table 1), although the overall median time to detection for the negative binomial cusum was less than the EARS algorithms at the 0.001 false alarm level (Table 2).

The system employs online algorithms that optimally aggregate statistical information from raw data and applies a number of pre-specified checks against known fraud scenarios as well as novel clustering-based algorithms for outlier detection which are then fused together to produce alerts with high detection rates at acceptable false alarm levels.

These factors will determine the relative importance of the speed of detection, sensitivity and acceptable false alarm levels.

The 28-day negative binomial cusum was the most timely negative binomial cusum model tested based on all outbreaks detected at the 0.005 and 0.001 false alarm levels.

This investigation indicates that cusum algorithms can produce signals that are consistent with epidemiological opinion through the identification of a high proportion of RRv disease outbreaks at relatively low false alarm levels.

Comparison of the timeliness of the seven cusum algorithms found no significant difference at the 0.005 and 0.001 false alarm levels (Friedman χ2 = 3.8, df = 6, p = 0.71 and Friedman χ2 = 2.1, df = 6, p = 0.91 respectively, Tables 1 and 2).