Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles
P Saiteja, B Ashok - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
Abstract Hybrid Electric Vehicles (HEVs) are facing difficulties in the aspect of splitting the
demand power between various drivetrain components. To negate this issue an efficient …
demand power between various drivetrain components. To negate this issue an efficient …
Deep computational pathology in breast cancer
A Duggento, A Conti, A Mauriello, M Guerrisi… - Seminars in cancer …, 2021 - Elsevier
Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-
world datasets for cross-domain and cross-discipline prediction and classification tasks. DL …
world datasets for cross-domain and cross-discipline prediction and classification tasks. DL …
An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data products
Y Zhang, J Ma, S Liang, X Li, M Li - Remote sensing, 2020 - mdpi.com
This study provided a comprehensive evaluation of eight machine learning regression
algorithms for forest aboveground biomass (AGB) estimation from satellite data based on …
algorithms for forest aboveground biomass (AGB) estimation from satellite data based on …
Neural adaptive backstepping control of a robotic manipulator with prescribed performance constraint
This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom
manipulator driven by an electrohydraulic actuator. To restrict the system output in a …
manipulator driven by an electrohydraulic actuator. To restrict the system output in a …
Neural adaptive control of single-rod electrohydraulic system with lumped uncertainty
Q Guo, Z Chen - Mechanical Systems and Signal Processing, 2021 - Elsevier
In electro-hydraulic system (EHS), there exist typical lumped uncertainties due to the
uncertain hydraulic parameters and unknown external load, which usually decline the output …
uncertain hydraulic parameters and unknown external load, which usually decline the output …
A comprehensive review of vision-based robotic applications: Current state, components, approaches, barriers, and potential solutions
Being an emerging technology, robotic manipulation has encountered tremendous
advancements due to technological developments starting from using sensors to artificial …
advancements due to technological developments starting from using sensors to artificial …
UAV model-based flight control with artificial neural networks: A survey
Abstract Model-Based Control (MBC) techniques have dominated flight controller designs
for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely …
for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely …
A model-free terminal sliding mode control for robots: Achieving fixed-time prescribed performance and convergence
This paper introduces a new control strategy for robot manipulators, specifically designed to
tackle the challenges associated with traditional model-based sliding mode (SM) controller …
tackle the challenges associated with traditional model-based sliding mode (SM) controller …
Neural network design for manipulator collision detection based only on the joint position sensors
In this paper, a multilayer feedforward neural network (NN) is designed and trained, for
human–robot collisions detection, using only the intrinsic joint position sensors of a …
human–robot collisions detection, using only the intrinsic joint position sensors of a …
Online inverse reinforcement learning for nonlinear systems with adversarial attacks
In the inverse reinforcement learning (RL) problem, there are two agents. A learner agent
seeks to mimic another expert agent's state and control input behavior trajectories by …
seeks to mimic another expert agent's state and control input behavior trajectories by …