Electron energy loss spectroscopy for studies of surface vibrations
High-resolution electron energy loss spectroscopy (ΔE∼ 7 meV) has been applied to study adsorption of gases on Si(111), W(100) and Pt(111) surfaces. In this paper the more recent results for CO adsorption on tungsten and platinum surfaces are reported. The spectra provide evidence for dissociative adsorption of CO on tungsten with an additional molecular phase at high coverages. For platinum CO is adsorbed in two different molecular states. The sequence of the frequencies observed with increasing coverage allows an unequivocal determination of the binding sites involved.
In order to guarantee the security of the network, a lightning search algorithm optimized extreme learning machine(LSA-ELM) method is proposed in this paper, aiming at the problem of parameter optimization in the process of network intrusion detection by extreme learning machine. First, the parameters of extreme learning machine are coded as the discharge projectile position, and the total weighted error is taken as the fitness value. Then the optimal parameters of the extreme learning machine are found by simulating the lightning discharge behavior, and a network intrusion detection classifier is established. Finally, The KDD99 data set is used for simulation experiments on the MATLAB 2015a platform. The results show that LSA-ELM improves the accuracy of network intrusion detection and meets the requirements of online intrusion detection.