A review of different deep learning methods in processing the CT scan images of the COVID-19 patients' lungs
H Zhang - Multiscale and Multidisciplinary Modeling, Experiments …, 2024 - Springer
The Coronavirus disease started in January 2020 in Wuhan, China, and the World Health
Organization (WHO) introduced it as a public disease and an international threat. In …
Organization (WHO) introduced it as a public disease and an international threat. In …
Deep Lyapunov-based physics-informed neural networks (DeLb-PINN) for adaptive control design
Physics-informed learning is an emerging machine learning technique driven by the desire
to leverage known physical principles in machine learning algorithms. Recent developments …
to leverage known physical principles in machine learning algorithms. Recent developments …
Composite Adaptive Lyapunov-Based Deep Neural Network (Lb-DNN) Controller
Recent advancements in adaptive control have equipped deep neural network (DNN)-
based controllers with Lyapunov-based adaptation laws that work across a range of DNN …
based controllers with Lyapunov-based adaptation laws that work across a range of DNN …
Accelerated Gradient Approach For Deep Neural Network-Based Adaptive Control of Unknown Nonlinear Systems
Recent connections in the adaptive control literature to continuous-time analogs of
Nesterov's accelerated gradient method have led to the development of new real-time …
Nesterov's accelerated gradient method have led to the development of new real-time …
Lyapunov-Based Physics-Informed Long Short-Term Memory (LSTM) Neural Network-Based Adaptive Control
Deep neural networks (DNNs) and long short-term memory networks (LSTMs) have grown
in recent popularity due to their function approximation performance when compared to …
in recent popularity due to their function approximation performance when compared to …
Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control
Deep Neural Network (DNN)-based controllers have emerged as a tool to compensate for
unstructured uncertainties in nonlinear dynamical systems. A recent breakthrough in the …
unstructured uncertainties in nonlinear dynamical systems. A recent breakthrough in the …
Adaptive Deep Neural Network-Based Control Barrier Functions
Safety constraints of nonlinear control systems are commonly enforced through the use of
control barrier functions (CBFs). Uncertainties in the dynamic model can disrupt forward …
control barrier functions (CBFs). Uncertainties in the dynamic model can disrupt forward …
Reinforcement Learning-based Control of Nonlinear Systems using Carleman Approximation: Structured and Unstructured Designs
J Kar, H Bai, A Chakrabortty - arXiv preprint arXiv:2302.10864, 2023 - arxiv.org
We develop data-driven reinforcement learning (RL) control designs for input-affine
nonlinear systems. We use Carleman linearization to express the state-space representation …
nonlinear systems. We use Carleman linearization to express the state-space representation …
Lyapunov-Based Long Short-Term Memory (Lb-LSTM) Neural Network-Based Adaptive Observer
Long short-term memory (LSTM) neural networks excel at capturing short-and long-term
dependencies, making them powerful tools for system identification and state estimation …
dependencies, making them powerful tools for system identification and state estimation …
Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller
Deep neural network (DNN)-based adaptive controllers can be used to compensate for
unstructured uncertainties in nonlinear dynamic systems. However, DNNs are also very …
unstructured uncertainties in nonlinear dynamic systems. However, DNNs are also very …