A set of datasets (Excel files since it also contains dynamically generated images) to facilitate the reproducibity of the test performed for the evaluation of the FDS proposal
These data are collected from a 12-bus and a 32-bus DC microgrid. The total operating cost of these two microgrids are optimized by an adaptive Differential Evolution algorithm using real-time pricing.
This is the data of the identified parameters of practical series-series (SS)-compensated wireless power transfer (WPT) systems based on the Genetic Algorithm, Adaptive Differential Evolution, and two-layer Adaptive Differential Evolution. These data strongly validate the accuracy of the Adaptive Differential Evolution to identify the parameters of a two-coil SS-compensated WPT and the two-layer Adaptive Differential Evolution to identify the parameters of a three-coil SS-compensated WPT system. The data can be adopted as a reference for future investigations.
This work quantifies water contamination in jet fuel (Jet A-1), using silica-based Bragg gratings. The optical sensor geometry exposes the evanescent optical field of a guided mode to enable refractometery. Quantitative analysis is made in addition to the observation of spectral features consistent with emulsification of water droplets and Stokes’ settling. Measurements are observed for cooling and heating cycles between ranges of 22oC and -60oC. The maximum spectral sensitivity for water contamination was 2.4 pm/ppm-v with a resolution of < 5 ppm-v.
This work quantifies water contamination in jet fuel (Jet A-1), using silica-based Bragg gratings. The optical sensor geometry exposes the evanescent optical field of a guided mode to enable refractometery. Quantitative analysis is made in addition to the observation of spectral features consistent with emulsification of water droplets and Stokes’ settling. Measurements are observed for cooling and heating cycles between ranges of 22oC and -60oC. The maximum spectral sensitivity for water contamination was 2.4 pm/ppm-v with a resolution of < 5 ppm-v.
Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintain cellular functions. Recently, it has become evident that metabolism is not only responsible for generating the required energy and controlling the abundance of metabolites within a cell, but also has an important role in and influence on cellular fate specification.
To investigate the generalization performance of the evolved scheduling policies(SPs), which are generated by the hyper-heuristic coevolution, the evolutionary SPs extracted from the aggerate Pareto front were applied to 64 testing scenarios to compare with the combinations of 320 existing man-made SPs which include 32 job sequencing rules and 10 machine assignment rules. This dataset provides the simulation performance of the evolved SPs and the 320 existing man-made SPs on the multi-objective dynamic flexible job shop scheduling problem.
