International Journal of Coal Preparation and Utilization,
Год журнала:
2024,
Номер
unknown, С. 1 - 26
Опубликована: Дек. 10, 2024
To
increase
the
accuracy
of
clean
coal
ash
content
prediction
during
dense
medium
separation
process
and
address
time
lag
issue
encountered
when
measuring
content,
a
model
based
on
WaOA-VMD-SGMD-WaOA-LSTM
was
proposed.
The
adopts
dual
decomposition
techniques
optimized
Variational
Mode
Decomposition
(VMD)
Symplectic
Geometric
(SGMD),
which
can
completely
decompose
original
data,
uses
Walrus
optimization
algorithm
(WaOA)
to
optimize
hyperparameters
Long
Short-Term
Memory
(LSTM)
model.
In
construction,
characteristic
data
ore
(𝑍2),
raw
(𝑍3),
heavy
mesoporous
cyclone
pressure
(𝑍4),
suspension
density
(𝑍5),
magnetic
(𝑍6)
were
combined
with
decomposed
cleaned
grouping
S-IMF0~S-IMFn,
CO-IMF1,
CO-IMF2
as
input
variables
construct
multiple
LSTM
models.
Finally,
value
is
superimposed
realize
content.
Based
industrial
preparation
plant
in
Shanxi,
China,
results
show
that
coefficient
determination
(R2)
0.9974.
After
adding
secondary
technology,
average
absolute
error
reduced
by
60.99%
compared
single
strategy.
Measurement Science and Technology,
Год журнала:
2024,
Номер
35(12), С. 126112 - 126112
Опубликована: Авг. 2, 2024
Abstract
Ship-radiated
noise
(SRN)
contains
abundant
ship
characteristic
information.
The
detection
and
analysis
of
SRN
is
very
important
for
target
recognition,
positioning
tracking.
However,
complex
ocean
easily
interferes
with
the
propagation
in
water.
To
achieve
a
preferable
denoising
effect,
new
method
proposed.
First,
decomposed
by
an
improved
variational
mode
decomposition
(DVMD)
dung
beetle
optimizer,
complexity
each
intrinsic
function
after
measured
fractional
order
refined
composite
multiscale
fluctuation
dispersion
entropy
(FRCMFDE).
Second,
distribution
characteristics
are
analyzed,
different
adaptive
division
methods
used
to
determine
modes,
i.e.
it
divides
all
modes
into
clean
mildly
noisy
moderately
highly
modes.
Then,
locally
weighted
scatterplot
smoothing
dual-tree
wavelet
transform
(IDTCWT)
denoise
respectively.
Finally,
denoised
obtained
reconstructing
two
groups
proposed
Rossler,
Chen
Lorenz
signals,
signal-to-noise
ratio
(SNR)
13.0785,
11.9390
12.3775
dB,
Compared
DVMD-FRCMFDE,
DVMD-FRCMFDE-wavelet
soft
threshold
(
WSTD)
DVMD-FRCMFDE-IDTCWT,
SNR
increased
48%,
45.93%
38.76%,
respectively,
root
mean
square
error
46.55%,
42.76%
30.04%,
applied
four
types
SRN.
Based
on
these
findings,
enhances
clarity
smoothness
phase
space
attractor,
effectively
suppresses
marine
environmental
SRN,
which
provides
solid
groundwork
subsequent
processing
