6 research outputs found
A Deep Reinforcement Learning-Based Controller for Magnetorheological-Damped Vehicle Suspension
This paper proposes a novel approach to controller design for MR-damped
vehicle suspension system. This approach is predicated on the premise that the
optimal control strategy can be learned through real-world or simulated
experiments utilizing a reinforcement learning algorithm with continuous
states/actions. The sensor data is fed into a Twin Delayed Deep Deterministic
Policy Gradient (TD3) algorithm, which generates the actuation voltage required
for the MR damper. The resulting suspension space (displacement), sprung mass
acceleration, and dynamic tire load are calculated using a quarter vehicle
model incorporating the modified Bouc-Wen MR damper model. Deep RL's reward
function is based on sprung mass acceleration. The proposed approach
outperforms traditional suspension control strategies regarding ride comfort
and stability, as demonstrated by multiple simulated experimentsComment: 19 pages , 9 figures , 5 table
Designing an Improved Deep Learning-based Model for COVID-19 Recognition in Chest X-ray Images: A Knowledge Distillation Approach
COVID-19 has adversely affected humans and societies in different aspects.
Numerous people have perished due to inaccurate COVID-19 identification and,
consequently, a lack of appropriate medical treatment. Numerous solutions based
on manual and automatic feature extraction techniques have been investigated to
address this issue by researchers worldwide. Typically, automatic feature
extraction methods, particularly deep learning models, necessitate a powerful
hardware system to perform the necessary computations. Unfortunately, many
institutions and societies cannot benefit from these advancements due to the
prohibitively high cost of high-quality hardware equipment. As a result, this
study focused on two primary goals: first, lowering the computational costs
associated with running the proposed model on embedded devices, mobile devices,
and conventional computers; and second, improving the model's performance in
comparison to previously published methods (at least performs on par with
state-of-the-art models) in order to ensure its performance and accuracy for
the medical recognition task. This study used two neural networks to improve
feature extraction from our dataset: VGG19 and ResNet50V2. Both of these
networks are capable of providing semantic features from the nominated dataset.
To this end, An alternative network was considered, namely MobileNetV2, which
excels at extracting semantic features while requiring minimal computation on
mobile and embedded devices. Knowledge distillation (KD) was used to transfer
knowledge from the teacher network (concatenated ResNet50V2 and VGG19) to the
student network (MobileNetV2) to improve MobileNetV2 performance and to achieve
a robust and accurate model for the COVID-19 identification task from chest
X-ray images.Comment: 25 pages, 3 figures , 5 table