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The productivity data from the proposed application, when finally collected, validates the model results generated earlier.
In our work, the traffic data from the proposed system were compared to ground truth measurements to evaluate the accuracy.
Then, the collected data from the proposed system were compared against the manually collected data for accuracy estimation.
Despite these two new important possibilities, some caution is still advised when interpreting data from the proposed partial-trial design.
Solid line is the theoretical data, red dots are the experimental data, and blue triangles show the data from the proposed model for single-gate GNRFET [6].
In this work, we extend that method to the general case where the model parameters cannot be determined completely by the data from the proposed experiments.
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Compared to burst test data from literature, the proposed formula is proved to be reasonable.
When applied to load data from Shanghai the proposed DLC method is shown to be effective in both reducing the forecasting random error and guaranteeing the modelling accuracy, so that a more stable and accurate system load forecasting result can be obtained.
We present two prediction models to evaluate the data collected from the proposed sampling approaches in "Temporal classification model" section.
The data obtained from the proposed non-contact system are consistent and comparable with that of the BVP sensor.
Thus, the data resulting from the proposed methodology are formed by a part of the original signal and by a compressed complementary part in the form of synaptic weights.
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