The Use of Artificial Intelligence Approaches for Performance Improvement of Low-Cost Integrated Navigation Systems

In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models’ performance has been assessed by means of the root-mean-square error (RMSE) between the target and predicted values of all the navigation parameters

Adsorption of H 2S, HS, S, and H on a stepped Fe(310) surface

Using periodic density functional theory we studied adsorption of H 2S, HS, S and H on the Fe(310) stepped surface, comparing our results with those on Fe(100). H 2S is predicted to weakly adsorb on all high-symmetry sites, with the bridge site at the step edge as preferred one, oriented perpendicularly to the (100) terraces with the two H atoms pointing out of the surface. Adsorption of HS, S, and H is more stable on the bridge, four-fold hollow, and three-fold hollow sites, respectively. The detailed analysis of the computed local density of states show common trends with the behavior of adsorption energies and is able to account for energy differences of all species adsorbed on Fe(100) and Fe(310). Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010