Bioengineering,
Год журнала:
2024,
Номер
11(12), С. 1281 - 1281
Опубликована: Дек. 17, 2024
Multispectral
transmission
imaging
has
emerged
as
a
promising
technique
for
breast
tissue
with
high
resolution.
However,
the
method
encounters
challenges
such
low
grayscale,
noisy
images
weak
signals,
primarily
due
to
strong
absorption
and
scattering
of
light
in
tissue.
A
common
approach
improve
signal-to-noise
ratio
(SNR)
overall
image
quality
is
frame
accumulation.
factors
camera
jitter
respiratory
motion
during
acquisition
can
cause
misalignment,
degrading
accumulated
image.
To
address
these
issues,
this
study
proposes
novel
registration
method.
hybrid
combining
genetic
algorithm
(GA)
constriction
factor-based
particle
swarm
optimization
(CPSO),
referred
GA-CPSO,
applied
before
The
efficiency
enhanced
by
incorporating
squared
factor
(SCF),
which
speeds
up
process
improves
convergence
towards
optimal
solutions.
GA
identifies
potential
solutions,
are
then
refined
CPSO
expedite
convergence.
This
methodology
was
validated
on
sequence
frames
taken
at
600
nm,
620
670
760
nm
wavelength
proved
enhancement
accuracy
various
mathematical
assessments.
It
demonstrated
(99.93%)
reduced
time.
As
result,
GA-CPSO
significantly
effectiveness
accumulation
enhances
quality.
explored
groundwork
precise
multispectral
segmentation
classification.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Авг. 7, 2024
Maximum
power
point
tracking
(MPPT)
is
a
technique
involved
in
photovoltaic
(PV)
systems
for
optimizing
the
output
of
solar
panels.
Traditional
solutions
like
perturb
and
observe
(P&O)
Incremental
Conductance
(IC)
are
commonly
utilized
to
follow
MPP
under
various
environmental
circumstances.
However,
these
algorithms
suffer
from
slow
speed
low
dynamics
fast-changing
environment
conditions.
To
cope
with
demerits,
data-driven
artificial
neural
network
(ANN)
algorithm
MPPT
proposed
this
paper.
By
leveraging
learning
capabilities
ANN,
PV
operating
can
be
adapted
dynamic
changes
irradiation
temperature.
Consequently,
it
offers
promising
environments
as
well
overcoming
limitations
traditional
techniques.
In
paper,
simulations
verification
experimental
validation
ANN-MPPT
presented.
Additionally,
analyzed
compared
methods.
The
numerical
findings
indicate
that,
examined
methods,
approach
achieves
highest
efficiency
at
98.16%
shortest
time
1.3
s.
Energies,
Год журнала:
2024,
Номер
17(7), С. 1760 - 1760
Опубликована: Апрель 7, 2024
Microgrid
optimization
scheduling,
as
a
crucial
part
of
smart
grid
optimization,
plays
significant
role
in
reducing
energy
consumption
and
environmental
pollution.
The
development
goals
microgrids
not
only
aim
to
meet
the
basic
demands
electricity
supply
but
also
enhance
economic
benefits
protection.
In
this
regard,
multi-objective
scheduling
model
for
grid-connected
mode
is
proposed,
which
comprehensively
considers
operational
costs
protection
microgrid
systems.
This
incorporates
improvements
traditional
particle
swarm
(PSO)
algorithm
by
considering
inertia
factors
adaptive
mutation,
it
utilizes
improved
solve
model.
Simulation
results
demonstrate
that
can
effectively
reduce
users
pollution,
promoting
optimized
operation
verifying
superior
performance
PSO
algorithm.
After
algorithmic
improvements,
optimal
total
cost
achieved
was
CNY
836.23,
representing
decrease
from
pre-improvement
value
850.
