Agile earth observation satellite scheduling over 20 years: Formulations, methods, and future directions
Agile satellites with advanced attitude maneuvering capability are the new generation of
earth observation satellites (EOSs). The continuous improvement in satellite technology and …
earth observation satellites (EOSs). The continuous improvement in satellite technology and …
Scalable inverse reinforcement learning through multifidelity Bayesian optimization
M Imani, SF Ghoreishi - IEEE transactions on neural networks …, 2021 - ieeexplore.ieee.org
Data in many practical problems are acquired according to decisions or actions made by
users or experts to achieve specific goals. For instance, policies in the mind of biologists …
users or experts to achieve specific goals. For instance, policies in the mind of biologists …
Second-order sliding mode control for nonlinear fractional-order systems
K Mathiyalagan, G Sangeetha - Applied Mathematics and Computation, 2020 - Elsevier
Novel results on nonlinear fractional-order systems (FOSs) with second-order sliding mode
control (SMC) is presented. The controller is designed by proposing a fractional switching …
control (SMC) is presented. The controller is designed by proposing a fractional switching …
Bayesian optimization objective-based experimental design
M Imani, SF Ghoreishi - 2020 American control conference …, 2020 - ieeexplore.ieee.org
Design has become a salient part of most of the scientific and engineering tasks, embracing
a wide range of domains including real experimental settings (eg, material discovery or drug …
a wide range of domains including real experimental settings (eg, material discovery or drug …
Bayesian control of large MDPs with unknown dynamics in data-poor environments
M Imani, SF Ghoreishi… - Advances in neural …, 2018 - proceedings.neurips.cc
We propose a Bayesian decision making framework for control of Markov Decision
Processes (MDPs) with unknown dynamics and large, possibly continuous, state, action …
Processes (MDPs) with unknown dynamics and large, possibly continuous, state, action …
An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli
SH Hosseini, M Imani - Information Sciences, 2024 - Elsevier
Interventions in gene regulatory networks (GRNs) aim to restore normal functions of cells
experiencing abnormal behavior, such as uncontrolled cell proliferation. The dynamic …
experiencing abnormal behavior, such as uncontrolled cell proliferation. The dynamic …
Boolean Kalman filter and smoother under model uncertainty
Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear
state-space models that provide a rich framework for modeling many complex dynamical …
state-space models that provide a rich framework for modeling many complex dynamical …
An intelligent classification model for surface defects on cement concrete bridges
J Zhu, J Song - Applied sciences, 2020 - mdpi.com
This paper mainly improves the visual geometry group network-16 (VGG-16), which is a
classic convolutional neural network (CNN), to classify the surface defects on cement …
classic convolutional neural network (CNN), to classify the surface defects on cement …
Control of gene regulatory networks using Bayesian inverse reinforcement learning
M Imani, UM Braga-Neto - IEEE/ACM transactions on …, 2018 - ieeexplore.ieee.org
Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states
to desirable ones has received much attention in recent years. Most of the existing methods …
to desirable ones has received much attention in recent years. Most of the existing methods …
Sequential experimental design for optimal structural intervention in gene regulatory networks based on the mean objective cost of uncertainty
Scientists are attempting to use models of ever-increasing complexity, especially in
medicine, where gene-based diseases such as cancer require better modeling of cell …
medicine, where gene-based diseases such as cancer require better modeling of cell …