Dr. Robert C. Green II

green

Prestige Faculty

Contact Information

EECS Department
The University of Toledo
Toledo,OH 43606
Tel: (419) 530-8174
Office: NI 2050
E-mail: Robert.Green3 @utoledo.edu

Research Interests

High performance computing, cloud computing, metaheuristic and nature-inspired algorithms, software engineering, plug-in hybrid electric vehicles, power system reliability, renewable energy, and the smart grid.

Biography

Robert received his B.S. in Computer Science and Applied Mathematics from Geneva College in 2005 and his M.S. in Computer Science with a focus on Operations Research from Bowling Green State University in 2007. His Ph.D. was awarded by the University of Toledo in 2012 where he was a member of the Sustainable Energy and Renewable Systems Laboratory. His research interests include high performance computing, cloud computing, metaheuristic and nature-inspired algorithms, software engineering, plug-in hybrid electric vehicles, power system reliability, renewable energy, and the smart grid.

NSF Projects

Collaborating on NSF #1248381 EAGER: Collaborative Research: Time Critical Localization in Mobile Networks (Click here for details)

Recent Publications

R. Green, L. Wang, and M.Alam, “Evaluating the Impact of Multiple Neighborhood Topologies on Central Force Optimization,” International Conference on Intelligent Computing, Huangshan, China, July 2012.
R. Green, L. Wang, M. Alam, and C. Singh, “Evaluating the Impact of Low Discrepancy Sequences on the Probabilistic Evaluation of Composite Power System Reliability,” IEEE Power and Energy Society General Meeting 2012, San Diego, CA, July 2012.
R. Green, L. Wang, M. Alam, and C. Singh, “Latin Hypercube Sampling for the Probabilistic Evaluation of Composite Power System Reliability”, Probabilistic Methods Applied to Power Systems, Istanbul, Turkey, June 2012.
R. Green, L. Wang, and M. Alam, Training neural networks using Central Force Optimization and Particle Swarm Optimization: Insights and comparisons, Expert Systems with Applications, vol. 39, no. 1, January 2012, pp. 555-563.
R. Green, L. Wang, M. Alam, and Richard Formato, Central Force Optimization on a GPU: A Case Study in High Performance Metaheuristics, Journal of Supercomputing, pp. 1-21, [Online] Available: http://dx.doi.org/10.1007/s11227-011-0725-y.
R. Green and H. Ledgard, Coding Guidelines: Finding the Art in the Science, Communications of the ACM, vol. 54, no. 12, December 2011, pp. 57-63.
R. Green, L. Wang, M. Alam, and S. S. S. R. Depuru, “An Examination of Artificial Immune System Optimization in Intelligent State Space Pruning,” IEEE North American Power Symposium, Boston, MA, August 2011.
R. Green, L. Wang, M. Alam, and S. S. S. R. Depuru, “Evaluating the Impact of Plug-in Hybrid Electric Vehicles on Composite Power System Reliability,” IEEE North American Power Symposium, Boston, MA, August 2011.
R. Green, L. Wang, and M. Alam, “Composite power system reliability evaluation using support vector machines on a multicore platform,” IEEE International Joint Conference on Neural Networks, San Jose, CA, August 2011.
R. Green, L. Wang, M. Alam, and C. Singh, “High Performance Computing for Electric Power Systems: Applications and Trends,” IEEE Power and Energy Society General Meeting, Detroit, Michigan, July 2011.
R. Green, L. Wang, M. Alam, and C. Singh, “Intelligent State Space Pruning Using Multi-Objective PSO for Reliability Analysis of Composite Power Systems: Observations, Analyses, and Impacts,” IEEE Power and Energy Society General Meeting, Detroit, Michigan, July 2011.
R. Green, L. Wang, M. Alam, R. Formato. “Central Force Optimization on a GPU: A Case Study in High Performance Metaheuristics using Multiple Topologies,” IEEE Congress on Evolutionary Computation, New Orleans, Louisiana, June 2011.
R. Green, L. Wang, M. Alam, and C. Singh, “State space pruning for Reliability Evaluation using Binary Particle Swarm Optimization,” Power Systems Exhibition and Conference, Phoenix, Arizona, March 2011.
R. Green, L. Wang, and M. Alam, The impact of plug-in hybrid electric vehicles on distribution networks: A review and outlook, Renewable and Sustainable Energy Reviews, vol. 15, no. 1, January 2011, pp. 544-553.
R. Green, L. Wang, M. Alam, and C. Singh, Intelligent State Space Pruning for Monte Carlo Simulation with Applications in Composite Power System Reliability. Manuscript Submitted to Engineering Applications of Artificial Intelligence.
R. Green, L. Wang, and M. Alam, Applications and Trends of High Performance Computing for Electric Power Systems: Focusing on Smart Grid. Manuscript Submitted to IEEE Transactions on Smart Grid.
R. Green, L. Wang, and M. Alam, Intelligent Classification based Power System Reliability Assessment Using High-Performance Support Vector Machines. Manuscript Submitted to Parallel Computing.
R. Green, L. Wang, and M. Alam, Power System Reliability Evaluation based on Intelligent and Parallel State Space Pruning Techniques. Manuscript Submitted to Parallel Computing.
R. Green, L. Wang, M. Alam, and C. Singh, “Intelligent and Parallel State Space Pruning for Power System Reliability Analysis Using MPI on a Multicore Platform,” IEEE Conference on Innovative Smart Grid Technologies, Anaheim, California, January 2011.
R. Green, L. Wang, Z. Wang, M. Alam, and C. Singh, “Power System Reliability Assessment Using Intelligent State Space Pruning Techniques: A Comparative Study”, IEEE Conference on Power System Technology, Hangzou, China, October 2010.
R. Green, L. Wang, and C. Singh, “State Space Pruning for Power System Reliability Evaluation using Genetic Algorithms,” in Proceedings of the IEEE PES General Meeting, Minneapolis, MN, July 2010.
R. Green, L. Wang. “The Impact of Plug-in Hybrid Electric Vehicles on Distribution Networks: a Review and Outlook,” in Proceedings of the IEEE PES General Meeting, Minneapolis, MN, July 2010.
R. Green, Z. Wang, L. Wang, M. Alam, and C. Singh, “Evaluation of Loss of Load Probability for Power Systems using Intelligent Search Based State Space Pruning,” Probabilistic Methods Applied to Power Systems, Singapore, June 2010.

Electrical Engineering and Computer Science