Gary L. Lebby

TitleProfessor

DepartmentElectrical and Computer Engineering

Phone336-285-3714

Fax336-334-7716

Emaillebby@ncat.edu

OfficeMcNair Hall
Room: 562

1601 East Market Street
Greensboro, NC 27411

Gary L. Lebby

Education

Ph D: Electrical and Electronics Engineering, Clemson University, 1985

MS: Optics and Computational Vision, University of South Carolina, 1982

BS: University of South Carolina, 1980

BS: University of South Carolina, 1980


Research Interests

Power Systems Neural Networks


Recent Publications

Koch, Camille  Murphy, Joshua  Winley, Jr, Charles  Osareh, Ali  Lebby, Gary  (2015).  Evolving Artificial Neural Systems for Short-Term Power System Load Forecasting. 

Oleka, Emmanuel  Khanal, Anil  Osareh, Ali  Lebby, Gary  (2015).  Exploring the Challenging Issues with Synchrophasor Technology Deployments in Electric Power Grids.  (9,  9,  pp. 957–960).  World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering.

Khanal, Anil  Osareh, Ali  Lebby, Gary  (2015).  Impact of Wind Energy on Cost and Balancing Reserves. 

Lebby, Gary  Koch, Camille  Murphy, Joshua  Osareh, Ali  (2014).  Evolving Artificial Neural Systems for Short-Term Power System Load Forecasting Network Observability Using Python-Gurobi .  In Mark Rajai (Editor-in-Chief)   International Journal of Engineering Research Innovation .

Lebby, Gary  Khanal, Anil   Oleka, Emmanuel  Osareh, Ali  (2014).  Optimal Placement of Phasor Measurement Units for Maximum Network Observability Using Python-Gurobi .  In Mark Rajai (Editor-in-Chief)   International Journal of Engineering Research Innovation .

Workineh, Abrham  Dugda, Mulugeta  Homaifar, Abdollah  Lebby, Gary  (2012).  GMDH and RBFGRNN Networks for Multi-Class Data Classification.  (1,  pp. 216–221).  Proceedings of the 2012 International Conference on Artificial Intelligence.

Song, YD  Weng, Liguo  Lebby, Gary  (2010).  Human memory/learning inspired control method for flapping-wing micro air vehicles.  (2,  7,  pp. 127–133).  Journal of Bionic Engineering.

Miller, Shonique  Lebby, Gary  Osareh, Ali  (2010).  Improving the performance of the truncated fourier series least squares (TFSLS) power system load model using an artificial neural network paradigm.  Intelligent Data Engineering and Automated Learning–IDEAL 2010   Springer.