37 research outputs found
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery
Common spatial pattern (CSP) is a popular feature extraction method for
electroencephalogram (EEG) motor imagery (MI). This study modifies the
conventional CSP algorithm to improve the multi-class MI classification
accuracy and ensure the computation process is efficient. The EEG MI data is
gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a
bandpass filter and a time-frequency analysis are performed for each experiment
trial. Then, the optimal EEG signals for every experiment trials are selected
based on the signal energy for CSP feature extraction. In the end, the
extracted features are classified by three classifiers, linear discriminant
analysis (LDA), na\"ive Bayes (NVB), and support vector machine (SVM), in
parallel for classification accuracy comparison. The experiment results show
the proposed algorithm average computation time is 37.22% less than the FBCSP
(1st winner in the BCI Competition IV) and 4.98% longer than the conventional
CSP method. For the classification rate, the proposed algorithm kappa value
achieved 2nd highest compared with the top 3 winners in BCI Competition IV.Comment: Accepted by 42nd Annual International Conferences of the IEEE
Engineering in Medicine and Biology Society in conjunction with the 43rd
Annual Conference of the Canadian Medical and Biological Engineering Society,
202
XTENTH-CAR: A Proportionally Scaled Experimental Vehicle Platform for Connected Autonomy and All-Terrain Research
Connected Autonomous Vehicles (CAVs) are key components of the Intelligent
Transportation System (ITS), and all-terrain Autonomous Ground Vehicles (AGVs)
are indispensable tools for a wide range of applications such as disaster
response, automated mining, agriculture, military operations, search and rescue
missions, and planetary exploration. Experimental validation is a requisite for
CAV and AGV research, but requires a large, safe experimental environment when
using full-size vehicles which is time-consuming and expensive. To address
these challenges, we developed XTENTH-CAR (eXperimental one-TENTH scaled
vehicle platform for Connected autonomy and All-terrain Research), an
open-source, cost-effective proportionally one-tenth scaled experimental
vehicle platform governed by the same physics as a full-size on-road vehicle.
XTENTH-CAR is equipped with the best-in-class NVIDIA Jetson AGX Orin System on
Module (SOM), stereo camera, 2D LiDAR and open-source Electronic Speed
Controller (ESC) with drivers written for both versions of the Robot Operating
System (ROS 1 & ROS 2) to facilitate experimental CAV and AGV perception,
motion planning and control research, that incorporate state-of-the-art
computationally expensive algorithms such as Deep Reinforcement Learning (DRL).
XTENTH-CAR is designed for compact experimental environments, and aims to
increase the accessibility of experimental CAV and AGV research with low
upfront costs, and complete Autonomous Vehicle (AV) hardware and software
architectures similar to the full-sized X-CAR experimental vehicle platform,
enabling efficient cross-platform development between small-scale and
full-scale vehicles.Comment: 2023 ASME. This work has been accepted to ASME for
publicatio
Deep Reinforcement Learning for Autonomous Ground Vehicle Exploration Without A-Priori Maps
Autonomous Ground Vehicles (AGVs) are essential tools for a wide range of
applications stemming from their ability to operate in hazardous environments
with minimal human operator input. Effective motion planning is paramount for
successful operation of AGVs. Conventional motion planning algorithms are
dependent on prior knowledge of environment characteristics and offer limited
utility in information poor, dynamically altering environments such as areas
where emergency hazards like fire and earthquake occur, and unexplored
subterranean environments such as tunnels and lava tubes on Mars. We propose a
Deep Reinforcement Learning (DRL) framework for intelligent AGV exploration
without a-priori maps utilizing Actor-Critic DRL algorithms to learn policies
in continuous and high-dimensional action spaces directly from raw sensor data.
The DRL architecture comprises feedforward neural networks for the critic and
actor representations in which the actor network strategizes linear and angular
velocity control actions given current state inputs, that are evaluated by the
critic network which learns and estimates Q-values to maximize an accumulated
reward. Three off-policy DRL algorithms, DDPG, TD3 and SAC, are trained and
compared in two environments of varying complexity, and further evaluated in a
third with no prior training or knowledge of map characteristics. The agent is
shown to learn optimal policies at the end of each training period to chart
quick, collision-free exploration trajectories, and is extensible, capable of
adapting to an unknown environment without changes to network architecture or
hyperparameters. The best algorithm is further evaluated in a realistic 3D
environment.Comment: 2023 the authors. This work has been accepted to
Advances in Artificial Intelligence and Machine Learning for publication
under a Creative Commons License CC BY 4.
Objectivity in molecular dynamics
In classical Continuum Mechanics, Principle of Objectivity requires that balance laws and constitutive equations must be form-invariant with respect to rigid motions of the spatial frame of reference. Any tensorial quantity is said to be objective if it obeys the appropriate tensor transformation law. Quantities such as temperature and stress tensor are known to be objective. In Molecular Dynamics (MD) simulation, which is a prevalent numerical method in nanoscience on atomistic basis, Principle of Objectivity was rarely discussed. This research explores the objectivity issue in the classical MD by examining the governing equation and constitutive equation. It can be shown that the interatomic potential and the corresponding interatomic force are objective because they are determined by relative atomic positions, which are objective. On the other hand, velocity and relative velocity are not objective. As a consequence, quantities such as temperature and Virial stress that are calculated based on apparent atomic velocities are not objective. Therefore, multiphysics body forces generated by these nonobjective quantities are not objective either. This becomes problematic when the system or subsystem is described in a noninertial reference frame, i.e., the reference frame undergoes acceleration or rotation. To resolve this deficiency, this research proposes the theory of Objectivity Incorporated MD. With the adoption of an objective form of velocity, the objectivity of temperature and Virial stress are restored. The theory also requires all kinds of body forces to be objective so that the constitutive equation well satisfies the Principle of Objectivity. The theory further supplements the governing equation with fictitious force, which accounts for the motion of reference frame, so that MD simulation can be extended to noninertial reference frame. It is considered that the application of Principle of Objectivity on MD will provide more power and credibility to the simulations of complex systems
EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals
A lack of driver's vigilance is the main cause of most vehicle crashes.
Electroencephalography(EEG) has been reliable and efficient tool for drivers'
drowsiness estimation. Even though previous studies have developed accurate and
robust driver's vigilance detection algorithms, these methods are still facing
challenges on following areas: (a) small sample size training, (b) anomaly
signal detection, and (c) subject-independent classification. In this paper, we
propose a generalized few-shot model, namely EEG-Fest, to improve
aforementioned drawbacks. The EEG-Fest model can (a) classify the query
sample's drowsiness with a few samples, (b) identify whether a query sample is
anomaly signals or not, and (c) achieve subject independent classification. The
proposed algorithm achieves state-of-the-art results on the SEED-VIG dataset
and the SADT dataset. The accuracy of the drowsy class achieves 92% and 94% for
1-shot and 5-shot support samples in the SEED-VIG dataset, and 62% and 78% for
1-shot and 5-shot support samples in the SADT dataset.Comment: Submitted to peer review journal for revie
AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for Sim-to-Real Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle
(AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which
is a necessity in hazardous, information poor environments such as regions
where natural disasters occur, and extraterrestrial planets. The substantial
training time required to learn an optimal DRL policy, which can be days or
weeks for complex tasks, is a major hurdle to real-world implementation in AGV
applications. Training entails repeated collisions with the surrounding
environment over an extended time period, dependent on the complexity of the
task, to reinforce positive exploratory, application specific behavior that is
expensive, and time consuming in the real-world. Effectively bridging the
simulation to real-world gap is a requisite for successful implementation of
DRL in complex AGV applications, enabling learning of cost-effective policies.
We present AutoVRL, an open-source high fidelity simulator built upon the
Bullet physics engine utilizing OpenAI Gym and Stable Baselines3 in PyTorch to
train AGV DRL agents for sim-to-real policy transfer. AutoVRL is equipped with
sensor implementations of GPS, IMU, LiDAR and camera, actuators for AGV
control, and realistic environments, with extensibility for new environments
and AGV models. The simulator provides access to state-of-the-art DRL
algorithms, utilizing a python interface for simple algorithm and environment
customization, and simulation execution.Comment: 2023 the authors. This work has been accepted to IFAC
for publication under a Creative Commons License CC-BY-NC-N