International Journal for Applied Information Management,
Journal Year:
2023,
Volume and Issue:
3(4), P. 154 - 161
Published: Dec. 10, 2023
In
the
context
of
incentivizing
regulation
for
distribution
companies,
utilization
a
reference
network
model
proves
to
be
valuable
tool
evaluating
their
effective
cost.
These
models
play
crucial
role
in
planning
expansive
areas
that
encompass
various
voltage
levels.
This
paper
introduces
green
space
algorithm
designed
optimize
location,
size,
and
power
supply
medium
low
substations
within
Reference
Network
Model
(RNM).
The
aims
enhance
efficiency
environmental
impact
these
substations.
focus
this
study
extends
two
key
aspects:
creation
"environment-friendly"
significance
implementing
"resource-saving"
China.
evaluation
characteristics
is
conducted
through
comprehensive
analysis,
with
results
indicating
notable
features.
Feature
1,
associated
friendliness,
measured
at
0.363,
while
2,
emphasizing
resource-saving
attributes,
achieves
high
score
0.835.
Furthermore,
3,
addressing
importance
eco-friendly
Chinese
context,
attains
commendable
0.824.
findings
underscore
potential
proposed
enhancing
sustainability
incentive
framework
companies.
PLoS ONE,
Journal Year:
2025,
Volume and Issue:
20(4), P. e0319656 - e0319656
Published: April 9, 2025
3D
skeleton-based
human
activity
recognition
has
gained
significant
attention
due
to
its
robustness
against
variations
in
background,
lighting,
and
viewpoints.
However,
challenges
remain
effectively
capturing
spatiotemporal
dynamics
integrating
complementary
information
from
multiple
data
modalities,
such
as
RGB
video
skeletal
data.
To
address
these
challenges,
we
propose
a
multimodal
fusion
framework
that
leverages
optical
flow-based
key
frame
extraction,
augmentation
techniques,
an
innovative
of
streams
using
self-attention
modules.
The
model
employs
late
strategy
combine
features,
allowing
for
more
effective
capture
spatial
temporal
dependencies.
Extensive
experiments
on
benchmark
datasets,
including
NTU
RGB+D,
SYSU,
UTD-MHAD,
demonstrate
our
method
outperforms
existing
models.
This
work
not
only
enhances
action
accuracy
but
also
provides
robust
foundation
future
integration
real-time
applications
diverse
fields
surveillance
healthcare.
Energy Engineering,
Journal Year:
2025,
Volume and Issue:
0(0), P. 1 - 10
Published: Jan. 1, 2025
Harnessing
solar
power
is
essential
for
addressing
the
dual
challenges
of
global
warming
and
depletion
traditional
energy
sources.However,
fluctuations
intermittency
photovoltaic
(PV)
pose
its
extensive
incorporation
into
grids.Thus,
enhancing
precision
PV
prediction
particularly
important.Although
existing
studies
have
made
progress
in
short-term
prediction,
issues
persist,
underutilization
temporal
features
neglect
correlations
between
satellite
cloud
images
data.These
factors
hinder
improvements
performance.To
overcome
these
challenges,
this
paper
proposes
a
novel
method
based
on
multi-stage
feature
learning.First,
improved
LSTM
SA-ConvLSTM
are
employed
to
extract
spatial-temporal
images,
respectively.Subsequently,
hybrid
attention
mechanism
proposed
identify
interplay
two
modalities,
capacity
focus
most
relevant
features.Finally,
Transformer
model
applied
further
capture
patterns
long-term
dependencies
within
multi-modal
information.The
also
compares
with
various
competitive
methods.The
experimental
results
demonstrate
that
outperforms
methods
terms
accuracy
reliability
prediction.
