A novel Fireworks Algorithm with wind inertia dynamics and its application to traffic forecasting

Fireworks Algorithm (FWA) is a recently contributed heuristic optimization method that has shown a promising performance in applications stemming from different domains. Improvements to the original algorithm have been designed and tested in the related literature. Nonetheless, in most of such previous works FWA has been tested with standard test functions, hence its performance when applied to real application cases has been scarcely assessed. In this manuscript a mechanism for accelerating the convergence of this meta-heuristic is proposed based on observed wind inertia dynamics (WID) among fireworks in practice. The resulting enhanced algorithm will be described algorithmically and evaluated in terms of convergence speed by means of test functions. As an additional novel contribution of this work FWA and FWA-WID are used in a practical application where such heuristics are used as wrappers for optimizing the parameters of a road traffic short-term predictive model. The exhaustive performance analysis of the FWA and FWA-ID in this practical setup has revealed that the relatively high computational complexity of this solver with respect to other heuristics makes it critical to speed up their convergence (specially in cases with a costly fitness evaluation as the one tackled in this work), observation that buttresses the utility of the proposed modifications to the naive FWA solver

TESS Data Release Notes: Sector 17, DR24

This release note discusses the science data products produced by the Science Processing Operations Center at Ames Research Center from Sector 17 observations made with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics

A novel fireworks factor and improved elite strategy based on back propagation neural networks for state-of-charge estimation of lithium-ion batteries.

The state of charge (SOC) of Lithium-ion battery is one of the key parameters of the battery management system. In the SOC estimation algorithm, the Back Propagation (BP) neural network algorithm is easy to converge to the local optimal solution, which leads to the problem of low accuracy based on the BP network. It is proposed that the Fireworks Elite Genetic Algorithm (FEG-BP) is used to optimize the BP neural network, which can not only solve the problem of the traditional neural network algorithm that is easy to fall into the local maximum optimal solution but also solve the limitation of the traditional neural network algorithm. The searchability of the improved algorithm has been significantly enhanced, and the error has become smaller and the propagation speed is faster. Combining the experimental data of charging and discharging, the proposed FEG-BP neural network is compared with the traditional genetic neural network algorithm (GA-BP), and the results are analyzed. The results show that the standard BP neural network genetic algorithm predicts error within 7%, while FEG-BP reduces the error to within 3%

TESS Data Release Notes: Sector 16, DR22

This release note discusses the science data products produced by the Science Processing Operations Center at Ames Research Center from Sector 16 observations made with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics

TESS Data Release Notes: Sector 9 DR11

This release note discusses the science data products produced by the Science Processing Operations Center at Ames Research Center from Sector 9 observations made with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics

Lyman Alpha Galaxies: Primitive, Dusty or Evolved Galaxies?

We present stellar population modeling results for 10 newly discovered Lyman alpha emitting galaxies (LAEs), as well as four previously known LAEs at z ~ 4.5 in the Chandra Deep Field - South. We fit stellar population models to these objects in order to learn specifically if there exists more than one class of LAE. Past observational and theoretical evidence has shown that while many LAEs appear to be young, they may be much older, with Lyman alpha EWs enhanced due to resonant scattering of Lyman alpha photons in a clumpy interstellar medium (ISM). Our results show a large range of stellar population age (3 - 500 Myr), stellar mass (1.6 x 10^8 - 5.0 x 10^10 Msol) and dust extinction (A_1200 = 0.3 - 4.5 mag), broadly consistent with previous studies. With such a large number of individually analyzed objects, we have looked at the distribution of stellar population ages in LAEs for the first time, and we find a very interesting bimodality, in that our objects are either very young ( 450 Myr). This bimodality may be caused by dust, and it could explain the Lyman alpha duty cycle which has been proposed in the literature. We find that eight of the young objects are best fit with a clumpy ISM. We find that dust geometry appears to play a large role in shaping the SEDs that we observe, and that it may be a major factor in the observed Lyman alpha equivalent width distribution in high redshift Lyman alpha galaxies, although other factors (i.e. outflows) may be in play. We conclude that 12 out of our 14 LAEs are dusty star-forming galaxies, with the other two LAEs being evolved galaxies.Comment: Replaced with ApJ accepted versionl. 20 pages, 10 figures, four table