Energy Science & Engineering,
Journal Year:
2024,
Volume and Issue:
12(11), P. 4932 - 4949
Published: Nov. 1, 2024
Abstract
The
mechanism
of
rock
burst
induced
by
the
superposition
dynamic
and
static
loads
in
multicoal
seam
mining
is
unique.
To
investigate
propagation
attenuation
law
large‐energy
microseismic
events
under
this
condition,
study
employs
FLAC3D's
module
to
simulate
analyze
influence
distance,
overburden
structure
mining,
interlayer
plastic
zone
on
vibration
wave
attenuation.
Results
indicate
that
when
coal
seams
are
mined
at
close
distances,
waves
experience
significant
while
passing
through
between
two
layers
coal.
At
equal
structures
exhibit
greater
effects
Considering
differences
rock‐burst
induction
mechanisms
close‐distance
group
versus
single
a
discriminant
criterion
for
bursts
superimposed
established
along
with
monitoring
early
warning
method
suitable
such
conditions.
Geomechanics and Geophysics for Geo-Energy and Geo-Resources,
Journal Year:
2024,
Volume and Issue:
10(1)
Published: May 29, 2024
Abstract
Rockburst,
coal
bump,
and
mine
earthquake
are
the
most
important
dynamic
disaster
phenomena
in
deep
mining.
This
paper
summarizes
differences
connections
between
rockburst,
bumps
earthquakes
terms
of
definition,
mechanism,
phenomenon,
evaluation
index,
etc.
The
definition
evolution
progress
three
categories
summarized,
as
well
monitoring,
early
warning,
prevention
measures
also
presented.
Firstly,
by
combining
theoretical
research
with
specific
technologies
engineering
field
cases,
main
failure
mechanisms
introduced.
Then,
indexes
bump
a
new
index
rockburst
is
given.
Finally,
characteristics
warning
methods
bumps,
discussed
technology
application.
At
last,
future
directions
put
forward.
International Journal of Mining Science and Technology,
Journal Year:
2024,
Volume and Issue:
34(1), P. 51 - 64
Published: Jan. 1, 2024
The
scientific
community
recognizes
the
seriousness
of
rockbursts
and
need
for
effective
mitigation
measures.
literature
reports
various
successful
applications
machine
learning
(ML)
models
rockburst
assessment;
however,
a
significant
question
remains
unanswered:
How
reliable
are
these
models,
at
what
confidence
level
classifications
made?
Typically,
ML
output
single
grade
even
in
face
intricate
out-of-distribution
samples,
without
any
associated
value.
Given
susceptibility
to
errors,
it
becomes
imperative
quantify
their
uncertainty
prevent
consequential
failures.
To
address
this
issue,
we
propose
conformal
prediction
(CP)
framework
built
on
traditional
(extreme
gradient
boosting
random
forest)
generate
valid
while
producing
measure
its
output.
proposed
guarantees
marginal
coverage
and,
most
cases,
conditional
test
dataset.
CP
was
evaluated
case
Sanshandao
Gold
Mine
China,
where
achieved
high
efficiency
applicable
levels.
Significantly,
identified
several
"confident"
from
model
as
unreliable,
necessitating
expert
verification
informed
decision-making.
improves
reliability
accuracy
assessments,
with
potential
bolster
user
confidence.
Processes,
Journal Year:
2024,
Volume and Issue:
12(5), P. 898 - 898
Published: April 28, 2024
Gas
concentration
monitoring
is
an
effective
method
for
predicting
gas
disasters
in
mines.
In
response
to
the
shortcomings
of
low
efficiency
and
accuracy
conventional
prediction,
a
new
prediction
based
on
Particle
Swarm
Optimization
Long
Short-Term
Memory
Network
(PSO-LSTM)
proposed.
First,
principle
PSO-LSTM
fusion
model
analyzed,
analysis
constructed.
Second,
data
are
normalized
preprocessed.
The
PSO
algorithm
utilized
optimize
training
set
LSTM
model,
facilitating
selection
model.
Finally,
MAE,
RMSE,
coefficient
determination
R2
evaluation
indicators
proposed
verify
analyze
results.
comparison
verification
research
was
conducted
using
measured
mine
as
sample
data.
experimental
results
show
that:
(1)
maximum
RMSE
predicted
0.0029,
minimum
0.0010
when
size
changes.
This
verifies
reliability
effect
(2)
predictive
performance
all
models
ranks
follows:
>
SVR-LSTM
PSO-GRU.
Comparative
with
demonstrates
that
more
concentration,
further
confirming
superiority
this
prediction.
Foods,
Journal Year:
2023,
Volume and Issue:
12(22), P. 4061 - 4061
Published: Nov. 8, 2023
Gastrodia
elata
(G.
elata)
Blume
is
widely
used
as
a
health
product
with
significant
economic,
medicinal,
and
ecological
values.
Due
to
variations
in
the
geographical
origin,
soil
pH,
content
of
organic
matter,
levels
physiologically
active
ingredient
contents
G.
from
different
origins
may
vary.
Therefore,
rapid
methods
for
predicting
origin
these
ingredients
are
important
market.
This
paper
proposes
visible-near-infrared
(Vis-NIR)
spectroscopy
technology
combined
machine
learning.
A
variety
learning
models
were
benchmarked
against
one-dimensional
convolutional
neural
network
(1D-CNN)
terms
accuracy.
In
identification
models,
1D-CNN
demonstrated
excellent
performance,
F1
score
being
1.0000,
correctly
identifying
11
origins.
quantitative
outperformed
other
three
algorithms.
For
prediction
set
eight
ingredients,
namely,
GA,
HA,
PE,
PB,
PC,
PA,
GA
+
total,
RMSEP
values
0.2881,
0.0871,
0.3387,
0.2485,
0.0761,
0.7027,
0.3664,
1.2965,
respectively.
The
Rp2
0.9278,
0.9321,
0.9433,
0.9094,
0.9454,
0.9282,
0.9173,
0.9323,
study
that
showed
highly
accurate
non-linear
descriptive
capability.
proposed
combinations
Vis-NIR
have
potential
quality
evaluation
elata.
