The survey, through the sorting case study, allow us to identify some algorithmic issues and software challenges to master heterogeneous cluster platforms in order to better utilize them: data decomposition techniques, scheduling and load balancing methods.
EMD is a data driven decomposition technique that decomposes a signal from high to low frequencies by generating lower and upper envelops using spline interpolation.
Empirical Mode Decomposition (EMD) is a data-driven decomposition technique that does not assume stationarity.
Next, we will develop a data-driven traffic predictive control scheme using a low rank decomposition technique to learn trends in historical data that are then used to make real-time predictions of traffic flow for control.
Built on the proposed theoretical results and dynamic mode decomposition technique, a distributed data-driven approach is proposed for verification of the structural balance of large-scale multiplex signed networks without relying on the explicit knowledge on network topology.
The other innovation is the use of a predetermined maximum intrinsic time window and cubic spline interpolation technique for the empirical data decomposition method with the aim to decompose a modeless component from nonstationary processes.
In the experiments, the adapted models based on probabilistic 2-mode analysis showed performance improvement over the adapted models based on Tucker decomposition, which is a representative multilinear decomposition technique, for small amounts of adaptation data while maintaining good performance for large amounts of adaptation data.
The rate of uncertainty arising from the choice of RCM, driving GCM, and radiative concentration pathway (RCP4.5 & RCP8.5) is quantified using a variance decomposition technique after reconstruction of missing data in GCM × RCM combinations.
Our decomposition technique accurately captures this information.
The latter is an important attribute of this decomposition technique, and one can therefore consider its application as an exploratory data mining technique in high-throughput X-ray imaging, followed by more physics-based model-dependent analysis.
