Buildings,
Journal Year:
2024,
Volume and Issue:
14(10), P. 3230 - 3230
Published: Oct. 11, 2024
The
You
Only
Look
Once
(YOLO)
series
algorithms
have
been
widely
adopted
in
concrete
crack
detection,
with
attention
mechanisms
frequently
being
incorporated
to
enhance
recognition
accuracy
and
efficiency.
However,
existing
research
is
confronted
by
two
primary
challenges:
the
suboptimal
performance
of
mechanism
modules
lack
explanation
regarding
how
these
influence
model’s
decision-making
process
improve
accuracy.
To
address
issues,
a
novel
Dynamic
Efficient
Channel
Attention
(DECA)
module
proposed
this
study,
which
designed
YOLOv10
model
effectiveness
visually
demonstrated
through
application
interpretable
analysis
algorithms.
In
paper,
dataset
complex
background
used.
Experimental
results
indicate
that
DECA
significantly
improves
localization
detection
discontinuous
cracks,
outperforming
(ECA).
When
compared
similarly
sized
YOLOv10n
model,
YOLOv10-DECA
demonstrates
improvements
4.40%,
3.06%,
4.48%,
5.56%
precision,
recall,
mAP50,
mAP50-95
metrics,
respectively.
Moreover,
even
when
larger
YOLOv10s
indicators
are
increased
2.00%,
0.04%,
2.27%,
1.12%,
terms
speed
evaluation,
owing
lightweight
design
module,
achieves
an
inference
78
frames
per
second,
2.5
times
faster
than
YOLOv10s,
thereby
fully
meeting
requirements
for
real-time
detection.
These
demonstrate
optimized
balance
between
tasks
has
achieved
model.
Consequently,
study
provides
valuable
insights
future
applications
field.
Journal of Computational Design and Engineering,
Journal Year:
2024,
Volume and Issue:
11(2), P. 345 - 364
Published: March 6, 2024
Abstract
Geometric
modeling
has
been
integral
to
the
design
process
with
introduction
of
Computer-Aided
Design.
With
additive
manufacturing
(AM),
freedom
reached
new
heights,
allowing
for
production
complex
lattice
structures
not
feasible
traditional
methods.
However,
there
remains
a
significant
challenge
in
geometric
these
structures,
especially
heterogeneous
strut-based
structures.
Current
methods
show
limitations
accuracy
or
control.
This
paper
presents
Virtual-Trim,
novel
method
that
is
both
efficient
and
robust.
Virtual-Trim
begins
user-defined
wireframe
models
information
create
STL
(STereoLithography)
ready
AM,
eliminating
need
labor-intensive
Boolean
operations.
The
fundamental
principles
steps
involved
are
extensively
described
within.
Additionally,
various
using
designed,
performance
terms
generation
time
model
size
analyzed.
successful
printing
attests
method’s
excellent
manufacturability.
Aerospace Science and Technology,
Journal Year:
2024,
Volume and Issue:
150, P. 109169 - 109169
Published: April 28, 2024
Wing
design
optimization
traditionally
involves
computationally
expensive
high-fidelity
simulations,
limiting
the
exploration
of
spaces.
In
this
study,
we
propose
a
methodology
that
combines
low-fidelity
numerical
models
with
machine
learning
algorithms
to
efficiently
navigate
complex
parameter
space
box-wing
configurations.
Through
utilisation
surrogate
model
trained
on
limited
dataset
derived
from
our
method
strives
predict
results
within
an
acceptable
range,
significantly
curtailing
computational
costs
and
time.
The
effectiveness
is
demonstrated
through
series
case
studies,
involving
Onera
M6
NASA
CRM
wing
as
test
cases
Bionica
application
case.
initial
proposed
successfully
achieved
almost
9.82%
increase
in
overall
aerodynamic
efficiency.
Its
competitive
performance
compared
conventional
methods,
along
its
substantial
reduction
time
resource
requirements,
evident.
This
efficient
holds
promise
for
enhancing
process
aviation
start-ups
by
exploring
spaces
reduced
burden.
Computers,
Journal Year:
2025,
Volume and Issue:
14(2), P. 54 - 54
Published: Feb. 8, 2025
Practice
and
Research
Optimization
Environment
in
Python
(PyPROE)
is
a
GUI-based,
integrated
framework
designed
to
improve
the
user
experience
both
learning
research
on
engineering
design
optimization.
Traditional
optimization
programs
require
either
coding
or
creating
complex
input
files,
often
involve
variety
of
applications
sequence
arrive
at
solution,
which
presents
steep
curve.
PyPROE
addresses
these
challenges
by
providing
an
intuitive,
user-friendly
Graphical
User
Interface
(GUI)
that
integrates
key
steps
into
seamless
workflow
through
single
application.
This
integration
reduces
potential
for
error,
lowers
barriers
entry
learners,
allows
students
researchers
focus
core
concepts
rather
than
software
intricacies.
PyPROE’s
human-centered
simplifies
enhances
productivity
automating
data
transfers
between
function
modules.
automation
users
dedicate
more
time
solving
problems
dealing
with
disjointed
tools.
Benchmarking
surveys
demonstrate
offers
significant
usability
improvements,
making
accessible
broader
audience.
Journal of Physics Conference Series,
Journal Year:
2025,
Volume and Issue:
2951(1), P. 012141 - 012141
Published: Feb. 1, 2025
Abstract
The
effective
utilization
of
materials
and
the
optimization
structural
performance
have
become
important
research
topics.
In
this
context,
technique
topological
emerges
as
a
potent
approach
to
refine
geometrical
configuration
layout
under
specified
limitations,
with
goal
attaining
superior
functionality.
present
study
predominantly
centers
on
optimization,
which
is
enhanced
by
modification
feasible
regions
in
realm
single-phase
materials.
Firstly,
topology
model
for
established,
aiming
minimize
compliance
constraint
volume.
A
sub-model
proposed
based
region
adjustment
scheme.
Subsequently,
leveraging
moving
asymptote
method
approximating
expansion
objective
function,
we
derive
an
approximation
second-order
derivative
function.
This
then
subjected
convexity
treatment
ensure
robustness
process.
By
integrating
primary
quadratic
Taylor
series
function
formulated.
issue
addressed
employing
smooth
dual-solution
approach.
Ultimately,
validation
cases
are
showcased
demonstrate
effectiveness
method.
not
only
effectively
improves
material
but
also
provides
new
engineering
design.
