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121
U-model Based Adaptive Tracking Scheme for Unknown MIMO Bilinear Systems
Published 2006“…U-model is a control oriented model used to represent a wide range of non-linear discrete time dynamic plants. …”
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122
U-Model Based Adaptive Tracking Scheme For Unknown MIMO Bilinear Systems
Published 2020“…U-Model is a control oriented model used to represent a wide range of non-linear discrete time dynamic plants. …”
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article -
123
U-Model Based Adaptive Tracking Scheme For Unknown MIMO Bilinear Systems
Published 2020“…U-Model is a control oriented model used to represent a wide range of non-linear discrete time dynamic plants. …”
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article -
124
U-Model Based Adaptive Tracking Scheme For Unknown MIMO Bilinear Systems
Published 2020“…U-Model is a control oriented model used to represent a wide range of non linear discrete time dynamic plants. …”
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125
An Extension Of Rahim And Banerjee'S Model For A Process With Upper And Lower Specification Limits
Published 2020“…In this paper, we consider the model of Rahim and Banerjee (1988) for a process with random linear drift. …”
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126
Searching for Heavy-Tailed Probability Distributions for Modeling Real-World Complex Networks
Published 2022“…We introduce a new family of generalized Lomax models (GLM) to capture the non-linearity of these heavy-tailed networks. …”
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127
Efficient Time-Domain Beam-Propagation Method for Modeling Integrated Optical Devices
Published 2001“…A new efficient technique that models the behavior of pulsed optical beams in homogenous medium, metallic and dielectric waveguides, is introduced and verified using both linear nondispersive and dispersive examples that have analytical predictions. …”
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128
A Comparative Analysis of Numerical Methods for Solving the Leaky Fire and Integrate Model
Published 2023“…Given the fact that the model’s equation is a linear ordinary differential equation, the purpose of this research is to compare which numerical analysis method gives the best results for the simplified version of this model. …”
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129
Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data
Published 2022“…The dynamic approach is adopted to perform the analysis on 3-layered rigid and flexible pavements. The AC layer is modeled as an LVE/material while the PCC and the unbound layers are modeled as linear/elastic materials with damping. …”
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masterThesis -
130
A finite element approach to model thin films in circular EHD contacts
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conferenceObject -
131
Three-tier offloading model for energy-efficient mobile computation. (c2018)
Published 2018“…Second, we provide a mobile offloading model that makes use of the multiple wireless interfaces of mobile devices to transfer computation tasks to edge servers only when energy savings are expected and delay requirements can be met. …”
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masterThesis -
132
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133
AI-based remaining useful life prediction and modelling of seawater desalination membranes
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doctoralThesis -
134
Integrated Material Lot Sizing and Multi-Resource Leveling Models with Activity Splitting
Published 2015Get full text
doctoralThesis -
135
A model-based approach for jet aircraft lateral motion control with constraints satisfaction
Published 2021“…In a block diagram framework as a function of elementary tuning parameters, all strategies are implemented on a linearized state space model which is furnished by the set of fundamental equations of motion. …”
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136
Effect of Concrete Type on Flexural Behavior of Concrete Beams Reinforced with HSS Bars
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doctoralThesis -
137
New nonlinear estimators of the gravity equation
Published 2021“…<p dir="ltr">The gravity model of international trade is often applied by economists to explain bilateral trade between countries. …”
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138
Wind Turbine Signal Fault Diagnosis using Deep Neural Networks-Inspired Model
Published 2021“…A 1D convolution deep neural network architecture is proposed, constructed and validated. The proposed model was constructed of 1D signal for the input layer, 10 different learned kernels as signal features, convolution layer, activation layer using rectified linear unit function, max-pooling layer, flatten layer and traditional multi-perceptron neural network for classification with soft-max class assignment. …”
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139
Wind turbine signal fault diagnosis using deep neural networks-inspired model
Published 2023“…A 1D convolution deep neural network architecture is proposed, constructed and validated. The proposed model was constructed of 1D signal for the input layer, ten different learned kernels as signal features, convolution layer, activation layer using rectified linear unit function, max-pooling layer, flatten layer and traditional multi-perceptron neural network for classification with soft-max class assignment. …”
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