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Electro-magnetic acoustic transducers (EMATs) are intended as non-contact and non-destructive ultrasound transducers for metallic material. The transmitted intensities from EMATS are modest, particularly at notable lift off distances. Some time ago a concept for a “coil only EMAT” was presented, without static magnetic field. In this contribution, such compact “coil only EMATs” with effective areas of 1–5 cm2 were driven to excessive power levels at MHz frequencies, using pulsed power technologies. RF induction currents of 10 kA and tens of Megawatts are applied. With increasing power the electroacoustic conversion efficiency also increases. The total effect is of second order or quadratic, therefore non-linear and progressive, and yields strong ultrasound signals up to kW/cm2 at MHz frequencies in the metal. Even at considerable lift off distances (cm) the ultrasound can be readily detected. Test materials are aluminum, ferromagnetic steel and stainless steel (non-ferromagnetic). Thereby, most metal types are represented. The technique is compared experimentally with other non-contact methods: laser pulse induced ultrasound and spark induced ultrasound, both damaging to the test object’s surface. At small lift off distances, the intensity from this EMAT concept clearly outperforms the laser pulses or heavy spark impacts.
Background:
Influential actors detection in social media such as twitter or Facebook can play a major role in gathering opinions on particular topics, improving the market
-
ing efficiency, predicting the trends, etc.
Proposed methods:
This work aims to extend our formally defined
T
measure to
present a new measure aiming to recognize the actor’s influence by the strength of
attracting new important actors into a networked community. Therefore, we propose a
model of the actor’s influence based on the attractiveness of the actor in relation to the
number of other attractors with whom he/she has established connections over time.
Results and conclusions:
Using an empirically collected social network for the
underlying graph, we have applied the above-mentioned measure of influence in
order to determine optimal seeds in a simulation of influence maximization. We study
our extended measure in the context of information diffusion because this measure is
based on a model of actors who attract others to be active members in a community.
This corresponds to the idea of the IC simulation model which is used to identify the
most important spreaders in a set of actors.
Keywords: Actor influence, Social media networks, Twitter, IC model, Information
diffusion, Independent cascade model, T measure