Cameron Johnson
Missouri University of Science and Technology
7 Papers
35 Citations
Cameron Johnson is an academic researcher from Missouri University of Science and Technology. The author has contributed to research in topics: Artificial neural network & Spiking neural network. The author has an hindex of 6, co-authored 7 publications.
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Papers
2009 Special Issue: Comparison of a spiking neural network and an MLP for robust identification of generator dynamics in a multimachine power system
TL;DR: In this paper, a spiking neural network (SNN) and a multi-layer perceptron (MLP) were used for online identification of generator dynamics in a multimachine power system.
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•Proceedings Article
Comparison of a spiking neural network and an MLP for robust identification of generator dynamics in a multimachine power system
Cameron Johnson,Ganesh K. Venayagamoorthy,Pinaki Mitra +2 more
- 01 Jan 2009
TL;DR: Performances of the SNN and MLP are compared to evaluate robustness on the identification of generator dynamics under small and large disturbances, and to illustrate that SNNs are capable of learning nonlinear dynamics of complex systems.
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Enhanced wide area monitoring system
Bipul Luitel,Ganesh K. Venayagamoorthy,Cameron Johnson +2 more
- 22 Mar 2010
TL;DR: By combining features such as missing sensor fault tolerance, intrusion detection system and integrity check at the receiving end, the proposed method is more reliable and secured and is capable of overcoming communication delays, mitigating attacks and surviving faults.
Encoding real values into polychronous spiking networks
Cameron Johnson,Ganesh K. Venayagamoorthy +1 more
- 18 Jul 2010
TL;DR: An adaptation of Izhikevich's model of a polychronous spiking network and an encoding scheme for real valued data to demonstrate the network's response to different inputs is presented.
Online identification of generator dynamics in a multimachine power system with a spiking neural network
Cameron Johnson,Ganesh K. Venayagamoorthy,Pinaki Mitra +2 more
- 14 Jun 2009
TL;DR: An integrate and fire model of a spiking neuron is used in this paper where the information is communicated through the interspike intervals and speed and terminal voltage deviations of a generator in the IEEE 10-machine 39-bus New England power system are predicted one time step ahead by a spiker network.
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