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Once into each matrix function, all pointed data are saved into local variables (matrices), converting them to float variables without scale.
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Beyond that, it means creating a huge amount of new data gathering points – data coming in from virtually everywhere.
To solve the floating point data limitations.
The experimental cloud point data were determined using titration method.
Fig. 1 Workflow for extraction of melting point data.
Most of this research was conducted based on point data.
The freeze point data are provided in Table 1.
The measured point data sets are registered automatically and the quality of point data is evaluated by checking the difference between the CAD model and the measured data.
Data optimization includes filtering bad ranging points data and GNSS data.
The reduction of the number of point data is important while generating STL file directly from the measured point data with maintaining their precision.
The prediction models were calibrated with legacy point data, that were updated for peat thickness before being used, in addition to a set of newly acquired point data.
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