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It is very difficult to detect these faults during offline testing.
In offline testing low-beta cavities with even higher gradients have already been achieved.
The testing procedure consists of fetching the offline testing dataset entries and running the ANN for one epoch.
Following the offline testing period, in mid-May of 2011, Flowtab put together a landing page.
We bring collective information of fault models, performance parameters and offline testing approaches from the literature where aim is to obtain a near optimal solution by efficiently exploring the entire space.
We combined offline behavioural measures and recordings of electrophysiological responses throughout the learning process and during one online implicit and one offline testing phase.
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Uses offline test cases.
The short-duration offline test is possible only when extensive design-for-test practices are used.
The time penalty caused by the offline test applies only when an error is detected.
Online tests, in turn, are driven onto a platform, and they are more realistic than offline tests.
In all of them, the HR was calculated for each model on the 200 offline tests, observing the arithmetic mean.
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