Monte Carlo simulations of the classical two-dimensional discrete frustrated ϕ4\phi ^4 model

The classical two-dimensional discrete frustrated $\phi ^4$ model is studied by Monte Carlo simulations. The correlation function is obtained for two values of a parameter $d$ that determines the frustration in the model. The ground state is a ferro-phase for $d=-0.35$ and a commensurate phase with period N=6 for $d=-0.45$. Mean field predicts that at higher temperature the system enters a para-phase via an incommensurate state, in both cases. Monte Carlo data for $d=-0.45$ show two phase transitions with a floating-incommensurate phase between them. The phase transition at higher temperature is of the Kosterlitz-Thouless type. Analysis of the data for $d=-0.35$ shows only a single phase transition between the floating-fluid phase and the ferro-phase within the numerical error.Comment: 5 figures, submitted to the European Physical Journal

Mixed integer nonlinear programming for Joint Coordination of Plug-in Electrical Vehicles Charging and Smart Grid Operations

The problem of joint coordination of plug-in electric vehicles (PEVs) charging and grid power control is to minimize both PEVs charging cost and energy generation cost while meeting both residential and PEVs' power demands and suppressing the potential impact of PEVs integration. A bang-bang PEV charging strategy is adopted to exploit its simple online implementation, which requires computation of a mixed integer nonlinear programming problem (MINP) in binary variables of the PEV charging strategy and continuous variables of the grid voltages. A new solver for this MINP is proposed. Its efficiency is shown by numerical simulations.Comment: arXiv admin note: substantial text overlap with arXiv:1802.0445

Navigation of a UAV Network for Optimal Surveillance of a Group of Ground Targets Moving Along a Road

With the rapid increase of vehicles in recent years, traffic surveillance becomes a crucial issue of traffic management. Since the traditional static sensor-based surveillance system can only passively monitor traffic, this paper considers the usage of unmanned aerial vehicles (UAVs), which can proactively conduct traffic surveillance thanks to the excellent mobility of UAVs. Specifically, we consider the navigation problem of a network of UAVs to effectively monitor a group of ground targets which move along a curvy road. A surveillance optimization problem is stated, and a distributed navigation algorithm for the UAV network is developed. It is proved that the proposed algorithm is locally optimal. Simulations confirm the effectiveness of the proposed navigation algorithm