This paper therefore proposes using a Hierarchical Task Network (HTN) planning approach to automate and optimize the tasks of designers.
The majority of the existing studies concentrate on optimizing the task scheduling or resource provisioning mechanisms, to improve the data processing rate or data transmission rate of the platform separately, without an overall consideration of both the performance factors.
In the algorithmic side, both use dynamic programming to optimize the computational task, which is also a familiar technique in statistical mechanics (known as the transfer matrix method), and has been used before for similar calculations involving cis-regulatory sequences [59], [60].
From the performance perspective, we optimize the balance between task- and data-parallelism, reporting notable results compared with Intel MKL.
In this paper, we investigate the relationship between accuracy, the number of features and the learning time with the objective to optimize the driver fingerprinting task.
Motion-corrected covariates were included in the model in order to optimize the elimination of task-correlated motion artifacts and maximize sensitivity to true activations [19], and to reduce inter- and intra-subject variability [20].
Each task is modeled using Lock-MS, a method to optimize the WCET of a task, with an added set of constraints to model in the same way the WCEC (worst case energy consumption).
These results suggest there were greater processing costs for trying to interpret the encoded color-object assignments for the baseline color sets than for the optimized color sets, regardless of the merit function used to optimize the color sets or the task.
