Granger causality identification is a potential approach for the detection of possible interactions in a data driven framework couched in terms of temporal precedence.
Here we introduce a data driven, Bayesian framework to quantify DEM predictions.
SSVS adds a coherent data driven probabilistic framework to search through the fixed effects and identify potentially important associations [ 16– 18, 35].
The concept of building entire image processing workflows with exclusively data-driven components can provide elegant and complimentary solutions for the motion control process and is the basis for defining the data driven motion control framework.
This research received funding from the Defense Threat Reduction Agency, United States Department of Defense for a contract: Data-Driven Framework for Zoonotic Disease Prediction (Contract number: HDTRA1-15-C-0017).
The goal is to illustrate how degradation data can be modelled and analysed to flexibly determine reliability to support preventive maintenance strategy making, based on a general data-driven framework.
Focused on the problems arising from the lack of systematic and comprehensive integration, an information transfer-based novel data-driven framework for fault root cause tracing of complex electromechanical systems in the processing industry was proposed, taking into consideration the experience and qualitative analysis of conventional fault root cause tracing methods.
Fig. 3 The basic framework of experimental data driven modeling methods (Liu et al. 2013).
In particular, control frameworks for the user data driven requirements elicitation process are experimented, and potential control variables are discussed.
This empirically driven framework serves as more of a "how-to" guide than an analytical framework.
A dynamic data driven adaptive multi-scale simulation (DDDAMS) based planning and control framework is proposed for effective and efficient surveillance and crowd control via UAVs and UGVs.
