Applied Mathematics and Nonlinear Sciences,
Journal Year:
2024,
Volume and Issue:
9(1)
Published: Jan. 1, 2024
Abstract
Improving
the
accuracy
and
stability
of
players’
stroke
speed
landing
point
maximizing
energy
conversion
have
become
important
tasks
in
table
tennis
skill
practice.
This
paper
analyzes
forces
during
ball
collision
law
its
motion
state
proposes
to
measure
identify
key
mechanical
parameters
from
aspects
batting
speed,
rotation,
angle.
The
3D
coordinates
are
reconstructed,
measured
translational
rotational
states
ball,
respectively.
UKF
estimator
is
used
construct
system
process
equations
model,
mathematical
expressions
further
derived
obtain
kinematic-based
post-collision
trajectory
prediction
model
ball.
predicting
movement
evaluated
by
distribution
error
points
it
known
through
experiments
that
range
method
this
(x±0.05,
y±0.05)
m,
which
smaller
than
estimated
traditional
physical
model’s
good.
After
analyzing
trajectories
with
different
angular
velocities,
drag
coefficients,
initial
exit
angles,
optimal
hitting
a
curved
circle
obtained
as
V=15m/s.
Sensors,
Journal Year:
2025,
Volume and Issue:
25(3), P. 749 - 749
Published: Jan. 26, 2025
This
paper
presents
a
time-series
point-to-point
generative
adversarial
network
(TS-p2pGAN)
for
synthesizing
realistic
electric
vehicle
(EV)
driving
data.
The
model
accurately
generates
four
critical
operational
parameters—battery
state
of
charge
(SOC),
battery
voltage,
mechanical
acceleration,
and
torque—as
multivariate
Evaluation
on
70
real-world
trips
from
an
open
dataset
reveals
the
model’s
exceptional
accuracy
in
estimating
SOC
values,
particularly
under
complex
stop-and-restart
scenarios
across
diverse
initial
levels.
delivers
high
accuracy,
with
root
mean
square
error
(RMSE),
absolute
(MAE),
dynamic
time
warping
(DTW)
consistently
below
3%,
1.5%,
2.0%,
respectively.
Qualitative
analysis
using
principal
component
(PCA)
t-distributed
stochastic
neighbor
embedding
(t-SNE)
demonstrates
ability
to
preserve
both
feature
distributions
temporal
dynamics
original
data
augmentation
framework
offers
significant
potential
advancing
EV
technology,
digital
energy
management
lithium-ion
batteries
(LIBs),
autonomous
comfort
system
development.
