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Neutral wind takes a significant role in the ionospheric electrodynamics at the middle and low latitudes. The most typical effect is the E region wind dynamo, which is caused by the expansion and creation of horizontal pressure gradients that drive ions and electrons across the magnetic field lines at the ionospheric E region altitude, generate electromotive forces and result in electric currents and fields (e.g., Akasofu & Chapman, 1972;Richmond, 1979;Wagner et al., 1980). During the day, the Pedersen and Hall conductivities peak at altitudes between 100 and 130 km, making the E-region conductive and promoting the development of wind dynamo at this altitude (e.g., Richmond, 1995). During the night, the conductivity of E region decreases, while the F region keeps a relatively higher conductivity. Though the atmospheric tides propagating from lower atmosphere affect mainly the E region neutral wind variations, at F region height there exists also thermospheric winds that drive the charged particles moving across the Earth's magnetic field and further generate electric currents and fields, forming the nighttime F region dynamo (

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In recent years, with the rapid development of Internet of Things (IoT) technology, a large number of Internet of things devices such as network printers, webcams and routers have emerged in the cyberspace. However, the situation of network security is increasingly serious. Large-scale network attacks launched by terminal devices connected to the Internet occur frequently, causing a series of adverse effects such as information leakage and property loss to people. The establishment of a set of fingerprint generation system for Internet of things devices to accurately identify the device type is of great significance for the unified security control of the Internet of things. We proposed a RAFM which is a detection and identification system of IoT. RAFM consists two major module including auto detection and fingerprinting. RAFM collects messages sent by different Internet of things devices by means of passive listening. Based on the differences in the header fields of different devices, it USES a series of multi-class classification algorithms to identify device types. Simulation experiments show that RAFM can achieve an average prediction accuracy of 93.75%.

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Substation inspection is not only one of the most important means of ensuring safe and stable operation of equipment, but also a significant way of timely detecting the dominant and recessive defect of the equipment, and preventing the accidents. This paper presents the virtual video and real-time data demonstration used for smart substation inspection based on the latest wearable services technology (such as Google glass).Google Glass is hands-free and uses augmented reality and voice activation to project useful data into our field of vision.A key use-case of google glass could be to allow a field crew out in the substation to be able to identify all relevant data for the network at the crew's current location. From this interface, they could navigate through all the data for a transformer, such as voltage, current, temperature etc. and identify its location in the GIS and view a single-line diagram, query into its asset history, maintenance history, manufacturer information and catalogue etc.

The firefly algorithm (FA) is one of the swarm intelligence algorithms which can solve global optimization problems accurately. In the traditional FA, the position of each firefly can only be updated by the brightness of other fireflies around it. As a result, it is simple to update the firefly position but easy to fall into local optimum. In this paper, a novel hybrid firefly algorithm based on the vector angle learning mechanism (HFA-VAL) is proposed, which can combine the advantages of both the firefly algorithm (FA) and differential evolution (DE) by the vector angle learning mechanism. HFA-VAL employs vector angle parameters to adaptively adjust the moving step length of firefly in order to avoid falling into local optimum. In the evolutionary process, the difference method is used to update the dominant leader, so as to improve the moving direction of other fireflies and expand the search ability. In order to understand the strengths and weaknesses of HFA-VAL, several experiments are carried out on 25 benchmark functions in CEC2005. Experimental results show that the performance of HFA-VAL algorithm is better than other the-state-of-art algorithms.INDEX TERMS Vector angle learning mechanism, hybrid firefly algorithm, differential evolution, logarithmic spiral parameter.