Evolution of the microstructure of MWCNT-modified SBS asphalt under salt-freezing coupling
Construction and Building Materials,
Год журнала:
2025,
Номер
481, С. 141410 - 141410
Опубликована: Май 6, 2025
Язык: Английский
High-precision weld width detection in laser transmission welding via crow and wolf optimized neural networks
Optics & Laser Technology,
Год журнала:
2025,
Номер
190, С. 113211 - 113211
Опубликована: Май 18, 2025
Язык: Английский
Integrated diagnosis optimization design of the electronic equipment based on spatial mapping
Science Progress,
Год журнала:
2024,
Номер
107(4)
Опубликована: Окт. 1, 2024
The
complexity
of
test
and
fault
information
within
electronic
devices
makes
their
integrated
diagnosis
a
challenging
problem
when
designing
equipment
reliability.
Current
is
analyzed
for
optimization
resource
optimization.
However,
this
neglects
the
connection
between
them.
This
paper
proposes
design
strategy
based
on
spatial
mapping
principle
to
quantitatively
describe
constraint
relationship
model
established
by
constructing
logical
space,
optimal
configuration
are
sought
grey
wolf
algorithm.
Seven
high-dimensional
benchmark
functions
an
used
verify
efficiency
algorithm
proposed
in
paper.
compared
with
other
four
terms
algorithm’s
speed
accuracy.
results
indicate
that
after
has
critical
detection,
isolation,
false
alarm
rates
100%,
99.99%,
98.99%,
0.2993%,
respectively.
After
optimization,
number
tests
reduced
88.9%,
cost
saved
89%.
Compared
algorithms,
achieves
best
results,
reduces
42%–55%,
decreases
77.63%–83.91%.
not
only
considers
resources
but
also
dramatically
while
improving
efficiency.
Язык: Английский
A neural network for the prediction of damage to reinforced cylindrical shells subjected to non-contact underwater explosions
Journal of Physics Conference Series,
Год журнала:
2024,
Номер
2891(6), С. 062007 - 062007
Опубликована: Дек. 1, 2024
Abstract
Explosion
tests
and
numerical
simulations
are
of
great
significance
for
the
study
submarine
other
underwater
target
damage
characteristics.
However,
cost
real
ship
test
is
high,
implementation
difficult,
time
simulation
calculation
which
presents
some
difficulties
in
attempting
to
quickly
assess
structures.
Currently,
combination
machine
learning
has
become
a
more
effective
means
addressing
aforementioned
issues.
In
this
paper,
acoustic-structural
arithmetic
employed
realise
non-contact
explosion
reinforced
cylindrical
shell
section.
The
maximum
deflection
reinforcement
bar
extracted
as
output
parameter
from
results.
distance,
explosive
equivalent,
thickness
taken
input
parameter.
prediction
analysis
carried
out
based
on
back-propagation
neural
network
algorithm
learning.
data
generated
by
processed
analysed
error
analysis.
processing
revealed
that
exhibited
superior
effect,
establishing
an
accurate
efficient
model
characteristics
exploding
shells.
Язык: Английский