Sentence examples for properly sample from inspiring English sources

Exact(9)

The supervisor said inspectors had failed to properly sample coal dust or observe work practices other than during the day shift and had not ensured that the maps of the mining activity were kept up to date.

Since modern instrumentation is designed in many ways to self-check to ensure that it is working properly, sample preparation becomes a major source of laboratory deviations.

Recent advances in image sensor technology make it possible nowadays to use high-frame-rate sensors to properly sample fast motion (i.e. as a low-motion scene), which makes a gradient-based approach one of the best options in terms of accuracy and consumption of resources for any real-time implementation.

To properly sample users for studying behavioral trends is important for scientists (especially in social science, cognitive and behavioral science, marketing, etc).

Therefore, if using a single weighting function, a PF would require a large number of particles to properly sample the state space.

Because of the time needed to properly sample the Earth's surface and because of the satellite orbital precession through local time (LT), the CI approach relies on mathematical models describing the sources only on average in space and time.

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Similar(51)

At one of the plants, auditors found that inspectors had not properly sampled some 50 million pounds of ground beef for E. coli over a period of five months.

An exemplary workflow is included, and based on authentic examples, we illustrate a number of sampling principles which can be implemented to obtain properly sampled tissue blocks for various purposes.

(4) Molecules that are too big or too flexible cannot have their conformational space properly sampled.

We calculate these ranges after properly sampling both R and S, due to the fact that this process requires costly sorting of the datasets.

It is based on a "learning phase," where a large number of "known points," with known input and output values, are used to train the neural network system in an "optimal" way, so that it can be later used to predict (unknown) output values for a new set of input values, that should fall in the domain of the hyperspace that is properly sampled by the learning data set.

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