While the time-driven layers of the proposed architecture encompass more traditional process monitoring methods based on time series data and analysis, the event-driven layers encompass operation monitoring methods based on discrete event data and analysis.
Many meta-analyses are based on (discrete) count data, where the random-effects model assumptions only hold to an approximation that works well unless event probabilities are very low.
In this paper, a practical approach towards evaluating form deviations of 2D curve contour profiles based on discrete coordinate measurement data is proposed.
While CP(A) and AC(P) are statistically redundant when exact times are used, they are not fully redundant if based on discrete double-classification of data, as often provided in aggregated official statistics.
Unlike the traditional non-parametric methods using the frequency-domain formulations based on discrete Fourier transform (DFT) data, this new approach uses the time-domain measurements directly to estimate the frequency-domain response functions.
It is more realistic and interesting to consider the parameter estimation for diffusion processes based on discrete observation, since the actual data may be obtained discretely.
The approach is based on discrete element method (DEM) and experimental data of fibre diameter distribution.
The existing approaches for topological structure analysis of volumetric data are mainly based on discrete methods, and the results usually need to be simplified and smoothened for further use.
The methodology is based on discrete choice models and was applied to data coming from a survey to 170 residents of Santiago, Chile, who reported their PWN shape by sketching it over a digital map.
The aim of this work is to characterize a silicon strip detector and its associated data acquisition system, based on discrete electronics, to obtain in a near future absorbed dose maps in axial planes for complex radiotherapy treatments, using a novel technique.
