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Sökning: WFRF:(Zomaya Albert)

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  • Ahammed, Farhan, et al. (författare)
  • Finding lower bounds of localization with noisy measurements using genetic algorithms
  • 2011
  • Ingår i: Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications (DIVANet '11). - Miami, Florida, USA : Association for Computing Machinery (ACM). - 9781450309042 ; , s. 47-54
  • Konferensbidrag (refereegranskat)abstract
    • Vehicular Ad-Hoc Networks (VANETs) are wireless networks with mobile nodes (vehicles) which connect in an ad-hoc manner. Many vehicles use the Global Positioning System (GPS) to provide their locations. However the inaccuracy of GPS devices leads to some vehicles incorrectly assuming they are located at different positions and sometimes on different roads. VANETs can be used to increase the accuracy of each vehicle's computed location by allowing vehicles to share information regarding the measured distances to neighbouring vehicles. This paper looks at finding how much improvement can be made given the erroneous measurements present in the system. An evolutionary algorithm is used to evolve instances of parameters used by the VLOCI2 algorithm, also presented in this paper, to find instances which minimises the inaccuracy in computed locations. Simulation results show a definite improvement in location accuracy and lower bounds on how much improvement is possible is inferred.
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  • Anwar, Adnan, et al. (författare)
  • HPC-Based Intelligent Volt/VAr Control of Unbalanced Distribution Smart Grid in the Presence of Noise
  • 2017
  • Ingår i: IEEE Transactions on Smart Grid. - : IEEE. - 1949-3053 .- 1949-3061. ; 8:3, s. 1446-1459
  • Tidskriftsartikel (refereegranskat)abstract
    • The performance of Volt/VAr optimization has been significantly improved due to the integration of measurement data obtained from the advanced metering infrastructure of a smart grid. However, most of the existing works lack: 1) realistic unbalanced multi-phase distribution system modeling; 2) scalability of the Volt/VAr algorithm for larger test system; and 3) ability to handle gross errors and noise in data processing. In this paper, we consider realistic distribution system models that include unbalanced loadings and multi-phased feeders and the presence of gross errors such as communication errors and device malfunction, as well as random noise. At the core of the optimization process is an intelligent particle swarm optimization-based technique that is parallelized using high performance computing technique to solve Volt/VAr-based power loss minimization problem. Extensive experiments covering the different aspects of the proposed framework show significant improvement over existing Volt/VAr approaches in terms of both the accuracy and scalability on IEEE 123 node and a larger IEEE 8500 node benchmark test systems.
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  • Resultat 1-10 av 87

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