The Effectiveness of a Digital Twin Learning System in Assisting Engineering Education Courses: A Case of Landscape Architecture
Applied Sciences,
Год журнала:
2024,
Номер
14(15), С. 6484 - 6484
Опубликована: Июль 25, 2024
In
conventional
engineering
education,
issues
such
as
the
discrepancy
between
virtual
and
real
environments,
rigid
practical
operations,
lack
of
reflective
support,
a
disconnect
online
offline
learning
prevail.
Digital
twin
technology,
with
its
high
fidelity
real-time
interaction
features,
presents
an
innovative
instructional
aid
for
education.
This
study
developed
digital
system
to
assist
instructors
in
implementing
project-based
teaching
models
landscaping
technology
courses.
To
assess
effectiveness
this
system,
quasi-experiment
was
designed.
Seventy
students
from
vocational
school
majoring
China
were
recruited
participants.
These
divided
into
two
groups,
each
consisting
35
students,
same
pace.
The
experimental
group
utilized
supplement
instructor’s
courses,
while
control
received
instruction
through
traditional
methods.
experiment
lasted
eight
weeks,
comprising
total
16
classes.
Ultimately,
results
indicated
that
significantly
outperformed
those
critical
thinking,
cognitive
load,
experience,
academic
performance.
Additionally,
research
examined
acceptance
learners
toward
using
influencing
factors
based
on
Technology
Acceptance
Model,
aiming
provide
insights
enhancing
education
courses
targeted
technological
development.
Язык: Английский
Real-Time Analysis of Industrial Data Using the Unsupervised Hierarchical Density-Based Spatial Clustering of Applications with Noise Method in Monitoring the Welding Process in a Robotic Cell
Information,
Год журнала:
2025,
Номер
16(2), С. 79 - 79
Опубликована: Янв. 22, 2025
The
application
of
modern
machine
learning
methods
in
industrial
settings
is
a
relatively
new
challenge
and
remains
the
early
stages
development.
Current
computational
power
enables
processing
vast
numbers
production
parameters
real
time.
This
article
presents
practical
analysis
welding
process
robotic
cell
using
unsupervised
HDBSCAN
algorithm,
highlighting
its
advantages
over
classical
k-means
algorithm.
paper
also
addresses
problem
predicting
monitoring
undesirable
situations
proposes
use
real-time
graphical
representation
noisy
data
as
particularly
effective
solution
for
managing
such
issues.
Язык: Английский
Human-Machine Dialogue: Chabots Revolutionizing Maintenance in Industry 5.0
Опубликована: Янв. 1, 2025
Язык: Английский
Opportunities and Barriers for Implementing Human-Centric Manufacturing in SMEs: Results from Focus Group Workshops in Argentina
Procedia Computer Science,
Год журнала:
2025,
Номер
253, С. 1452 - 1461
Опубликована: Янв. 1, 2025
Язык: Английский
Factories of the future in industry 5.0—Softwarization, Servitization, and Industrialization
Internet of Things,
Год журнала:
2024,
Номер
28, С. 101431 - 101431
Опубликована: Ноя. 21, 2024
Язык: Английский
Gemelos Digitales en la Industria 5.0 – una Revisión Sistemática de Literatura
European Public & Social Innovation Review,
Год журнала:
2024,
Номер
9, С. 1 - 21
Опубликована: Авг. 30, 2024
Introducción:
La
Industria
5.0
integra
tecnologías
avanzadas
con
enfoques
centrados
en
el
ser
humano
para
mejorar
la
seguridad
fabricación,
colaboración
humano-robot
y
eficiencia.
Los
gemelos
digitales,
réplicas
virtuales
de
sistemas
físicos,
son
centrales
esta
iniciativa
laboral
eficiencia
operativa.
Metodología:
Esta
SLR
utilizó
una
estrategia
búsqueda
exhaustiva
cinco
bibliotecas
digitales:
IEEE
Explore,
Scopus,
Taylor
&
Francis
Online,
ACM
Digital
Library
Web
of
Science.
Resultados:
hallazgos
destacan
las
contribuciones
los
digitales
a
trabajadores
mediante
monitoreo
tiempo
real,
detección
inteligente
gestión
proactiva
riesgos.
se
logra
través
integración
datos
real.
también
mejoran
fabricación
al
permitir
producción
más
inteligentes
adaptativos.
Discusión:
A
pesar
su
potencial,
deben
abordar
desafíos
como
calidad
datos,
complejidad
computacional,
ciberseguridad,
factores
humanos
impactos
socioeconómicos.
Conclusiones:
subraya
papel
avance
5.0,
promoviendo
entornos
industriales
seguros,
eficientes
humano.
AI-Powered Obstacle Detection for Safer Human-Machine Collaboration
Acta Electrotechnica et Informatica,
Год журнала:
2024,
Номер
24(3), С. 23 - 27
Опубликована: Сен. 1, 2024
Abstract
This
article
deals
with
ensuring
and
increasing
the
safety
of
mobile
robotic
systems
in
human-machine
collaboration.
The
goal
research
was
to
design
implement
an
artificial
intelligence
application
that
recognizes
obstacles,
including
humans,
increases
safety.
resulting
Android
uses
a
MiDaS
model
generate
depth
map
environment
from
drone’s
camera
approximate
distance
all
obstacles
avoid
collision.
Besides,
this
work
introduced
us
DJI
Mobile
SDK
neural
network
optimizations
for
their
use
on
smartphones.
Язык: Английский