An introductory study on deep neural networks for high resolution aerial images

Barone, Rosamaria Elisa; Salerno, Valerio Mario; Siniscalchi, Sabato Marco

doi:10.1063/1.4825733

Cited by 1 publication

(3 citation statements)

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“…Hence, it becomes more intelligent in predicting the uncertainties. Due to the learning capability of NN, it has a wide range of applications in automation and optimization [20], image processing [21,22], speech recognition [23,24], control [25,26], modelling, and time series prediction [27,28].…”

Section: Neural Network (Nn)mentioning

confidence: 99%

“…Equation (20) and the control structure n f , n g and n h of Equation (21) is used to design a linear controller model to track the reference point. The polynomial B is neglected to make Equation (22) equal to the characteristic equation of the system. Hence, B is replaced with another term, i.e., B q −1 = b 0 B + q −1 .…”

Section: Model Reference Controllermentioning

confidence: 99%

“…Hence, B is replaced with another term, i.e., B q −1 = b 0 B + q −1 . Equation (22) will transform into the following equation:…”

Section: Model Reference Controllermentioning

confidence: 99%

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A Neural Network-Based Model Reference Control Architecture for Oscillation Damping in Interconnected Power System

Uddin

Zeb

et al. 2019

Energies

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In this paper, a model reference controller (MRC) based on a neural network (NN) is proposed for damping oscillations in electric power systems. Variation in reactive load, internal or external perturbation/faults, and asynchronization of the connected machine cause oscillations in power systems. If the oscillation is not damped properly, it will lead to a complete collapse of the power system. An MRC base unified power flow controller (UPFC) is proposed to mitigate the oscillations in 2-area, 4-machine interconnected power systems. The MRC controller is using the NN for training, as well as for plant identification. The proposed NN-based MRC controller is capable of damping power oscillations; hence, the system acquires a stable condition. The response of the proposed MRC is compared with the traditionally used proportional integral (PI) controller to validate its performance. The key performance indicator integral square error (ISE) and integral absolute error (IAE) of both controllers is calculated for single phase, two phase, and three phase faults. MATLAB/Simulink is used to implement and simulate the 2-area, 4-machine power system.

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