An enhancement method for chloride diffusion coefficient long-term prediction based on Hilbert dynamic probabilistic interpolation and BO-LSTM
Measurement,
Год журнала:
2025,
Номер
unknown, С. 116820 - 116820
Опубликована: Янв. 1, 2025
Язык: Английский
Parameters optimization of PEMFC model based on gazelle optimization algorithm
International Journal of Hydrogen Energy,
Год журнала:
2024,
Номер
87, С. 214 - 226
Опубликована: Сен. 7, 2024
Язык: Английский
Early Warning of College Students’ Ideological and Political Course Performance Using an Optimization Algorithm
Journal of Advanced Computational Intelligence and Intelligent Informatics,
Год журнала:
2025,
Номер
29(2), С. 389 - 395
Опубликована: Март 19, 2025
With
the
reform
of
teaching
methods,
hybrid
online
and
offline
modes
have
been
used
increasingly
in
college
courses.
In
this
setting,
factors
affecting
academic
performance
are
more
complex,
making
it
challenging
to
predict
students’
performance.
Therefore,
there
is
an
urgent
need
for
higher-performance
prediction
algorithms.
This
study
briefly
analyzed
learning
ideological
political
Then,
features
students
courses
were
extracted
using
Super
Star
platform
system.
Feature
selection
was
carried
out
based
on
information
gain
rate,
while
training
set
balanced
synthetic
minority
oversampling
technique
(SMOTE).
Moreover,
seagull
optimization
algorithm
(SOA)
applied
optimize
hyperparameters
eXtreme
Gradient
Boosting
(XGBoost)
develop
SOA-XGBoost
early
warning
Experiments
performed
collected
datasets.
It
found
that
effect
improved
significantly
following
SMOTE
processing.
The
F1-value
reached
0.955
area
under
curve
value
0.976.
SOA
exhibited
superior
hyperparameter
compared
with
other
algorithms
such
as
grid
search.
also
achieved
best
results
These
confirm
effectiveness
proposed
performance,
method
can
be
widely
practice.
Язык: Английский
Advances in membrane-assisted reactors: An integrative review for modeling and experiments
Separation and Purification Technology,
Год журнала:
2025,
Номер
unknown, С. 133095 - 133095
Опубликована: Апрель 1, 2025
Язык: Английский
Synergistic intensification of palladium-based membrane reactors for hydrogen production: A review
Energy Conversion and Management,
Год журнала:
2024,
Номер
325, С. 119424 - 119424
Опубликована: Дек. 24, 2024
Язык: Английский
Mapping flood risk using a workflow including deep learning and MCDM– Application to southern Iran
Urban Climate,
Год журнала:
2024,
Номер
59, С. 102272 - 102272
Опубликована: Дек. 27, 2024
Язык: Английский
Single Well Production Prediction Model of Gas Reservoir Based on CNN-BILSTM-AM
Energies,
Год журнала:
2024,
Номер
17(22), С. 5674 - 5674
Опубликована: Ноя. 13, 2024
In
the
prediction
of
single-well
production
in
gas
reservoirs,
traditional
empirical
formula
reservoirs
generally
shows
poor
accuracy.
process
machine
learning
training
and
prediction,
problems
small
data
volume
dirty
are
often
encountered.
order
to
overcome
above
problems,
a
model
based
on
CNN-BILSTM-AM
is
proposed.
The
built
by
long-term
short-term
memory
neural
networks,
convolutional
networks
attention
modules.
input
includes
previous
period
its
influencing
factors.
At
same
time,
fitting
error
value
reservoir
introduced
predict
future
data.
loss
function
used
evaluate
deviation
between
predicted
real
data,
Bayesian
hyperparameter
optimization
algorithm
optimize
structure
comprehensively
improve
generalization
ability
model.
Three
single
wells
Daniudi
D28
well
area
were
selected
as
database,
was
production.
results
show
that
compared
with
network
(CNN)
model,
long
(LSTM)
bidirectional
(BILSTM)
test
set
three
experimental
reduced
6.2425%,
4.9522%
3.0750%
average.
It
basis
coupling
meets
high-precision
requirements
for
which
great
significance
guide
efficient
development
oil
fields
ensure
safety
China’s
energy
strategy.
Язык: Английский