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This paper evaluates the effect of data density, and thus data collection duration, in a production system application.
GDD has some similarities with today's most popular clustering algorithms; however, it uses both Gaussian kernel and distances to form clusters according to data density and shape.
The automation, results in a high data density and in a precision which is manually not achievable and represents a qualitative improvement of the analysis of complex patterns.
In summary, flow condition, inverse formulation, grid discretization, observation data density and location, and measurement errors all influence the accuracy of inversion.
Unlike the conventional least-squares error minimization approach which involves fitting of a learning model to data density and then perform a threshold process for error counting, this work directly formulates the required target error count rate in terms of design model parameters with a closed-form solution.
The model was divided into fuzzy functions of the abiotic predictors of growth conditions (Fpred including temperature, precipitation, acid deposition, soil data and relative site insolation) and forest biomass receptors (Frec including remote sensing data, density and volume of aboveground biomass, and surface humus chemical data).
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This allows assessing the power of different data types, data densities and data locations in identifying the best model structure from among a suite of plausible models.
Compiled are volumes containing spectroscopic data, molecular constants, structure data, density, viscosity and vapor pressure data.
Indeed, those data-sets usually show non-homogenous data density, hot and cold zones being generally over represented.
After correcting initial well data (density, compressional and shear velocities logs, etc).
This correlation can be used directly to estimate the static Young's modulus from log data (density, compressional, and shear transit times).
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