Industrial & Engineering Chemistry Research,
Год журнала:
2024,
Номер
63(44), С. 19051 - 19062
Опубликована: Окт. 22, 2024
In
the
field
of
industrial
production,
precise
and
timely
implementation
fault
diagnosis
methods
is
crucial
for
improving
product
quality,
enhancing
operational
safety,
reducing
downtime,
minimizing
losses.
Recent
studies
have
shown
that
most
CNN-based
models
are
more
suitable
handling
Euclidean
data
such
as
images
or
videos
but
not
dealing
with
non-Euclidean
sensor
data.
practical
scenarios,
chemical
process
imbalanced
patterns
may
lead
data-driven
to
assign
different
attentions
patterns.
The
SMOTE
algorithm
commonly
used
generate
new
data,
it
often
tends
overfit
when
there
very
few
nearest
neighbor
samples.
To
address
these
issues,
we
designed
an
efficient
model
named
KRGAT.
fully
utilize
spatial
structural
information
on
employed
graph
attention
networks
(GATs),
which
well-suited
Additionally,
introduced
top-k
loss
method
select
hard
samples,
thereby
increasing
weight
Furthermore,
improved
DropMessage
enhance
model's
accuracy
robustness.
Experimental
results
demonstrate
our
outperforms
baseline
under
both
balanced
conditions.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Дек. 30, 2024
Incremental
broad
learning
system
(IBLS)
is
an
effective
and
efficient
incremental
method
based
on
paradigm.
Owing
to
its
streamlined
network
architecture
flexible
dynamic
update
scheme,
IBLS
can
achieve
rapid
reconstruction
the
basis
of
previous
model
without
entire
retraining
from
scratch,
which
enables
it
adept
at
handling
streaming
data.
However,
two
prominent
deficiencies
still
persist
in
constrain
further
promotion
large-scale
data
stream
scenarios.
Firstly,
needs
retain
all
historical
perform
associated
calculations
process,
causes
computational
overhead
storage
burden
increase
over
time
as
such
puts
efficacy
algorithm
risk
for
massive
or
unlimited
streams.
Additionally,
due
random
generation
rule
hidden
nodes,
generally
necessitates
a
large
size
guarantee
approximation
accuracy,
resulting
high-dimensional
matrix
calculation
poses
greater
challenge
updating
efficiency
model.
To
address
these
issues,
we
propose
novel
bidimensionally
partitioned
online
sequential
(BPOSBLS)
this
paper.
The
core
idea
BPOSBLS
partition
feature
aspects
instance
dimension
dimension,
consequently
decompose
least
squares
problem
into
multiple
smaller
ones,
then
be
solved
individually.
By
doing
so,
scale
complexity
original
high-order
are
substantially
diminished,
thus
significantly
improving
usability
complex
tasks.
Meanwhile,
ingenious
recursive
computation
called
devised
solve
BPOSBLS.
This
exclusively
utilizes
current
samples
iterative
updating,
while
disregarding
samples,
thereby
rendering
lightweight
with
consistently
low
costs
requirements.
Theoretical
analyses
simulation
experiments
demonstrate
effectiveness
superiority
proposed
algorithm.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Июнь 4, 2024
Abstract
To
deal
with
the
highly
nonlinear
and
time-varying
characteristics
of
Batch
Process,
a
model
named
adaptive
stacking
approximate
kernel
based
broad
learning
system
is
proposed
in
this
paper.
This
innovatively
introduces
(AKBLS)
algorithm
Adaptive
Stacking
framework,
giving
it
strong
fitting
ability,
excellent
generalization
ability.
The
Broad
Learning
System
(BLS)
known
for
its
shorter
training
time
effective
processing,
but
uncertainty
brought
by
double
random
mapping
results
poor
resistance
to
noisy
data
unpredictable
impact
on
performance.
address
issue,
paper
proposes
an
AKBLS
that
reduces
uncertainty,
eliminates
redundant
features,
improves
prediction
accuracy
projecting
feature
nodes
into
space.
It
also
significantly
computation
matrix
searching
kernels
enhance
ability
industrial
online
applications.
Extensive
comparative
experiments
various
public
datasets
different
sizes
validate
this.
framework
utilizes
ensemble
method,
which
integrates
predictions
from
multiple
models
using
meta-learner
improve
generalization.
Additionally,
employing
moving
window
method—where
fixed-length
slides
through
database
over
time—the
gains
allowing
better
respond
gradual
changes
Process.
Experiments
substantial
dataset
penicillin
simulations
demonstrate
predictive
compared
other
common
algorithms.
Industrial & Engineering Chemistry Research,
Год журнала:
2024,
Номер
63(44), С. 19051 - 19062
Опубликована: Окт. 22, 2024
In
the
field
of
industrial
production,
precise
and
timely
implementation
fault
diagnosis
methods
is
crucial
for
improving
product
quality,
enhancing
operational
safety,
reducing
downtime,
minimizing
losses.
Recent
studies
have
shown
that
most
CNN-based
models
are
more
suitable
handling
Euclidean
data
such
as
images
or
videos
but
not
dealing
with
non-Euclidean
sensor
data.
practical
scenarios,
chemical
process
imbalanced
patterns
may
lead
data-driven
to
assign
different
attentions
patterns.
The
SMOTE
algorithm
commonly
used
generate
new
data,
it
often
tends
overfit
when
there
very
few
nearest
neighbor
samples.
To
address
these
issues,
we
designed
an
efficient
model
named
KRGAT.
fully
utilize
spatial
structural
information
on
employed
graph
attention
networks
(GATs),
which
well-suited
Additionally,
introduced
top-k
loss
method
select
hard
samples,
thereby
increasing
weight
Furthermore,
improved
DropMessage
enhance
model's
accuracy
robustness.
Experimental
results
demonstrate
our
outperforms
baseline
under
both
balanced
conditions.
