I
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o
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Co
m
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Science
Vo
l.
3
6
,
No
.
1
,
Octo
b
er
20
24
,
p
p
.
6
3
9
~
6
4
6
I
SS
N:
2
502
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4
7
52
,
DOI
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.v
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6
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i
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639
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Dig
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C
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m
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an
d
civ
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in
g
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cr
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eltin
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o
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n
,
a
n
d
o
th
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r
elate
d
f
ield
s
[
1
]
–
[
3
]
.
As
th
e
c
o
n
v
ey
o
r
b
elts
p
lay
cr
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r
o
l
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tr
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s
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r
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tial
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eg
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lar
ly
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s
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n
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in
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f
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elt
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s
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d
m
ain
tain
t
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s
m
o
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th
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p
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r
atio
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[
4
]
.
Ma
n
u
f
ac
tu
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g
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eq
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ir
es
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d
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tr
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p
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s
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d
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s
tr
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4
.
0
o
n
s
m
al
l
an
d
m
ed
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m
-
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ized
m
an
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f
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SME
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.
T
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izatio
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m
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s
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s
tain
ab
le
p
r
ac
tices
[
5
]
–
[
7
]
.
T
h
e
p
r
ed
ictiv
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m
ain
ten
a
n
ce
m
eth
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is
s
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ap
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v
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s
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v
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tim
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o
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m
ac
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s
in
g
in
teg
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s
en
s
o
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s
.
Pre
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m
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ten
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m
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e
m
p
lo
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s
d
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tr
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s
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tech
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s
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ter
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et
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
1
,
Octo
b
er
20
24
:
639
-
6
4
6
640
th
in
g
s
(
I
o
T
)
,
I
n
d
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s
tr
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4
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ML
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tech
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ith
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e
r
ec
en
t
r
esear
ch
o
n
p
r
ed
ictiv
e
m
ain
te
n
an
ce
m
eth
o
d
s
th
at
em
p
lo
y
ed
in
te
g
r
ated
s
en
s
o
r
s
t
o
m
o
n
ito
r
m
ac
h
in
er
y
h
ea
lth
in
r
ea
l tim
e
u
s
in
g
I
o
T
,
I
n
d
u
s
tr
y
4
.
0
,
ML
,
an
d
clo
u
d
co
m
p
u
tin
g
.
-
R
ev
iew
o
f
ap
p
licatio
n
o
f
d
ig
i
tal
tr
an
s
f
o
r
m
atio
n
tech
n
o
lo
g
ie
s
an
d
th
eir
im
p
ac
t
in
p
r
e
d
ictiv
e
m
ain
ten
an
ce
o
f
co
n
v
ey
o
r
b
elt.
2.
DIGIT
AL
T
RAN
SFO
RM
A
T
I
O
N
T
E
CH
NO
L
O
G
I
E
S F
O
R
P
RE
DIC
T
I
VE
M
AIN
T
E
NANC
E
Dig
ital
tr
an
s
f
o
r
m
atio
n
tech
n
o
lo
g
ies
ar
e
ess
en
tial
f
o
r
I
n
d
u
s
tr
ial
Me
tav
er
s
e,
en
ab
lin
g
i
m
m
er
s
iv
e
ex
p
er
ien
ce
s
an
d
s
h
a
p
in
g
f
u
tu
r
e
p
o
s
s
ib
ilit
ies.
Dig
ita
l
tr
an
s
f
o
r
m
atio
n
s
tr
ea
m
lin
es
b
u
s
in
ess
o
p
er
atio
n
s
th
r
o
u
g
h
s
o
cial
m
ed
ia,
b
ig
d
ata
,
m
o
b
ile
ap
p
licatio
n
s
,
an
d
clo
u
d
co
m
p
u
tin
g
.
T
h
e
ch
allen
g
es
in
clu
d
e
d
ata
s
ec
u
r
ity
an
d
s
y
s
tem
in
t
er
o
p
er
ab
ilit
y
.
Dig
it
al
tr
an
s
f
o
r
m
atio
n
tech
n
o
l
o
g
ies
co
m
p
r
is
e
a
co
m
p
r
eh
e
n
s
iv
e
ar
r
ay
o
f
c
u
ttin
g
-
e
d
g
e
to
o
ls
an
d
tactics
th
at
en
a
b
le
b
u
s
in
ess
es
to
ac
h
iev
e
s
u
cc
ess
in
th
e
d
ig
ital
ag
e.
T
h
ese
r
eso
u
r
ce
s
in
clu
d
e
th
e
I
o
T
,
ar
tific
ial
in
tellig
en
ce
(
AI
)
,
ML
,
clo
u
d
co
m
p
u
tin
g
,
b
ig
d
ata
an
aly
tics
,
ed
g
e
co
m
p
u
tin
g
,
cy
b
er
s
ec
u
r
ity
,
b
lo
ck
ch
ain
,
au
g
m
en
te
d
r
ea
lity
(
AR
)
/v
ir
tu
al
r
ea
lity
(
VR
)
,
an
d
au
to
m
atio
n
/
r
o
b
o
tics
.
T
h
ese
tech
n
o
lo
g
ies
em
p
o
wer
o
r
g
an
izatio
n
s
to
s
tr
ea
m
lin
e
th
eir
o
p
er
atio
n
s
,
im
p
r
o
v
e
cu
s
to
m
er
s
er
v
ice,
an
d
s
u
s
tain
a
co
m
p
etitiv
e
ed
g
e
in
a
d
ig
ital
en
v
ir
o
n
m
e
n
t.
I
n
th
e
I
n
d
u
s
tr
y
4
.
0
er
a,
c
o
n
v
ey
o
r
b
elt
s
y
s
tem
s
u
n
d
er
wen
t
a
d
ig
ital
r
ev
o
l
u
tio
n
f
u
eled
b
y
tech
n
o
lo
g
ies
s
u
ch
as
I
o
T
,
cy
b
er
-
p
h
y
s
ical
s
y
s
tem
s
(
C
PS
)
,
an
d
th
e
in
ter
n
et
o
f
s
er
v
ices
[
8
]
.
Sch
wer
tn
er
et
a
l
.
[
9
]
ex
am
i
n
ed
th
e
ap
p
licatio
n
o
f
d
i
g
ital
tr
an
s
f
o
r
m
atio
n
tech
n
o
lo
g
ies
in
co
n
v
ey
o
r
b
elt
s
y
s
tem
s
,
f
o
cu
s
in
g
o
n
cr
u
cia
l
tech
n
o
lo
g
ies
s
u
c
h
as
th
e
I
o
T
,
ML
,
d
ee
p
lear
n
in
g
,
clo
u
d
co
m
p
u
tin
g
,
an
d
ed
g
e
co
m
p
u
tin
g
.
Dig
ital tr
an
s
f
o
r
m
a
tio
n
tech
n
o
l
o
g
ies f
o
r
p
r
ed
ictiv
e
m
ain
ten
an
ce
a
r
e
s
h
o
wn
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
Dig
ital
tr
an
s
f
o
r
m
atio
n
tech
n
o
l
o
g
ies
Fed
o
r
k
o
[
1
0
]
e
x
p
lo
r
e
d
th
e
i
n
t
eg
r
atio
n
o
f
d
ig
ital
tr
a
n
s
f
o
r
m
atio
n
tech
n
o
lo
g
ies
a
n
d
c
o
n
v
ey
o
r
b
elt
s
y
s
tem
s
to
em
p
h
asize
b
i
d
ir
ec
tio
n
al
d
ata
co
m
m
u
n
icatio
n
v
ia
C
PS
,
an
d
th
er
e
b
y
en
ab
lin
g
in
tellig
en
t
p
r
o
to
c
o
ls
.
B
ey
o
n
d
h
ea
lth
m
o
n
ito
r
in
g
,
th
i
s
s
y
s
tem
co
o
r
d
in
ates
task
s
wi
th
in
th
e
c
o
n
v
e
y
o
r
an
d
tr
a
n
s
p
o
r
tatio
n
s
y
s
tem
an
d
tr
an
s
f
o
r
m
s
th
e
co
n
v
ey
o
r
in
to
a
s
m
ar
t
en
tity
.
L
ev
er
ag
in
g
th
e
ad
v
an
tag
e
o
f
I
o
T
,
it
co
n
tin
u
o
u
s
ly
co
llects
an
d
s
h
ar
es
r
ea
l
-
tim
e
d
ata,
m
ak
in
g
th
e
co
n
v
e
y
o
r
a
d
ap
tiv
e
a
n
d
r
es
p
o
n
s
iv
e.
T
h
is
d
i
g
ital
in
teg
r
ati
o
n
o
n
th
e
co
n
v
ey
o
r
an
d
tr
an
s
p
o
r
tatio
n
s
p
ar
k
ed
in
t
er
est
in
th
e
in
d
u
s
tr
y
d
is
cu
s
s
io
n
s
o
n
d
ig
ital
tr
an
s
f
o
r
m
atio
n
.
Ulas
[
1
1
]
ad
d
r
ess
ed
h
o
w
d
ig
ital
tech
n
o
lo
g
ies
ar
e
r
esh
ap
in
g
m
an
u
f
ac
tu
r
i
n
g
f
o
r
s
m
all
an
d
m
ed
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u
m
en
ter
p
r
is
es
SME
s
,
s
tr
es
s
in
g
th
e
n
ec
ess
ity
f
o
r
ad
ap
tin
g
s
tr
u
ctu
r
es
an
d
p
r
ac
tices
to
s
tay
co
m
p
etitiv
e.
I
t
also
u
n
d
e
r
s
co
r
e
s
th
e
im
p
o
r
tan
ce
o
f
ass
es
s
in
g
th
e
co
s
ts
an
d
b
en
ef
its
o
f
d
ig
ital
to
o
ls
f
o
r
th
r
iv
in
g
in
to
d
ay
’
s
b
u
s
in
ess
lan
d
s
ca
p
e.
Go
p
al
et
a
l.
[
1
2
]
h
ig
h
lig
h
ted
h
o
w
d
ig
ital
tr
an
s
f
o
r
m
atio
n
,
le
v
er
ag
in
g
I
o
T
,
an
aly
tics
,
ML
,
a
n
d
AI
,
a
d
d
r
ess
es
h
ea
lth
ca
r
e
ch
allen
g
es
an
d
en
h
a
n
ce
s
p
atien
t
o
u
tco
m
es
an
d
co
s
t
m
an
ag
em
en
t.
I
t
also
em
p
h
asizes
b
en
ef
its
,
s
u
ch
as
o
p
tim
ized
ca
r
e,
c
o
s
t
r
ed
u
ctio
n
,
ev
i
d
en
ce
-
b
ased
d
ec
is
io
n
s
,
an
d
o
p
er
atio
n
al
ef
f
icien
c
y
f
o
r
b
o
t
h
p
atien
ts
an
d
p
r
o
v
id
e
r
s
.
Pre
d
ictiv
e
an
aly
ti
cs
m
ax
im
izes
p
er
f
o
r
m
a
n
ce
b
y
u
tili
zin
g
s
en
s
o
r
s
an
d
th
e
I
o
T
f
o
r
ef
f
icien
t
p
r
o
d
u
ctio
n
.
Dee
p
lear
n
in
g
en
h
an
ce
s
m
an
u
f
ac
tu
r
i
n
g
p
er
f
o
r
m
an
ce
s
u
p
p
o
r
te
d
b
y
AI
an
d
I
o
T
f
o
r
p
r
ed
ictiv
e
an
aly
tics
[
1
3
]
.
A
n
in
v
esti
g
atio
n
was
co
n
d
u
cte
d
b
y
B
ar
n
ewo
l
d
an
d
L
o
tter
m
o
s
er
[
1
4
]
t
o
d
eter
m
in
e
k
ey
d
ig
ital
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Dig
ita
l
tr
a
n
s
fo
r
ma
tio
n
tech
n
o
l
o
g
ies fo
r
co
n
ve
yo
r
b
elts
p
r
ed
i
ctive
…
(
P
ed
ir
ed
la
V
en
i S
a
n
to
s
h
i A
n
u
s
h
a
)
641
tr
an
s
f
o
r
m
atio
n
tech
n
o
lo
g
ies
i
n
th
e
m
in
in
g
s
ec
to
r
.
T
h
e
r
esu
lts
in
d
icate
th
at
ess
en
tial
te
ch
n
o
lo
g
ies
in
clu
d
e
au
to
m
atio
n
,
r
o
b
o
tics
,
th
e
I
o
T
,
b
ig
d
ata,
r
ea
l
-
tim
e
d
ata
an
aly
s
is
,
ML
,
AI
,
an
d
t
h
r
ee
-
d
i
m
en
s
io
n
al
p
r
in
tin
g
.
An
o
th
er
s
tu
d
y
[
1
5
]
e
x
p
lo
r
ed
ar
tific
ial
in
tellig
en
ce
in
d
r
illi
n
g
an
d
p
r
o
d
u
ctio
n
,
a
d
d
r
ess
in
g
ch
allen
g
es
s
u
ch
as
s
tu
ck
p
ip
es
a
n
d
h
y
d
r
ate
f
o
r
m
a
tio
n
.
T
h
is
u
n
d
e
r
s
co
r
es
h
o
w
d
i
g
ital
tr
an
s
f
o
r
m
atio
n
e
n
h
an
ce
s
o
il
well
o
p
er
atio
n
s
,
f
o
s
ter
in
g
co
llab
o
r
atio
n
b
etwe
en
in
d
u
s
tr
ies an
d
r
esear
ch
ce
n
te
r
s
.
3.
AP
P
L
I
CA
T
I
O
N
S
T
ab
le
1
s
u
m
m
ar
izes
th
e
d
ig
ital
tr
an
s
f
o
r
m
atio
n
tec
h
n
o
lo
g
ies
em
p
lo
y
e
d
f
o
r
th
e
p
r
ed
ictiv
e
m
ain
ten
an
ce
o
f
co
n
v
ey
o
r
b
elt
s
y
s
tem
s
.
T
h
e
I
o
T
is
th
e
m
o
s
t
co
m
m
o
n
l
y
u
tili
ze
d
tech
n
o
lo
g
y
,
wh
er
e
as
ML
is
u
s
ed
to
a
less
er
ex
ten
t,
an
d
clo
u
d
an
d
ed
g
e
c
o
m
p
u
tin
g
ar
e
r
ar
ely
em
p
lo
y
e
d
b
y
r
esear
ch
er
s
i
n
th
is
f
ield
.
T
ab
le
1
.
T
h
e
s
u
m
m
ar
y
o
f
s
tu
d
ies ap
p
ly
in
g
d
i
g
ital tr
an
s
f
o
r
m
a
tio
n
tech
n
o
l
o
g
ies in
co
n
v
ey
o
r
b
elt
m
ain
ten
an
ce
Ref
A
p
p
l
i
c
a
t
i
o
n
I
o
T
ML
C
l
o
u
d
[
1
6
]
R
e
a
l
-
t
i
me
mai
n
t
e
n
a
n
c
e
man
a
g
e
me
n
t
A
p
r
i
l
U
S
B
B
e
a
c
o
n
3
0
6
,
TC
R
T
5
0
0
0
i
n
f
r
a
r
e
d
s
e
n
so
r
,
a
r
d
u
i
n
o
n
a
n
o
3
3
B
LE
,
a
n
d
R
a
s
p
b
e
r
r
y
P
i
D
a
t
a
a
n
a
l
y
s
i
s
Th
i
n
g
s
p
e
a
k
[
1
7
]
P
r
e
d
i
c
t
i
v
e
m
a
i
n
t
e
n
a
n
c
e
o
f
mi
n
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mac
h
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s
Te
mp
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r
a
t
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r
e
,
v
i
b
r
a
t
i
o
n
,
c
u
r
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e
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t
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a
n
d
p
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ss
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r
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m
e
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t
C
l
o
u
d
f
o
r
st
o
r
a
g
e
a
n
d
a
n
a
l
y
si
s
[
1
8
]
D
e
t
e
c
t
i
o
n
o
f
p
o
t
a
t
o
h
a
r
v
e
st
e
r
d
r
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v
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b
e
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t
a
b
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o
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A
n
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s
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o
p
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52
In
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d
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tr
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in
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h
iev
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an
ac
cu
r
ac
y
o
f
9
6
.
7
%.
Hasn
ita
an
d
Her
r
i
[
3
9
]
,
d
ev
elo
p
ed
a
wir
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v
ib
r
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n
d
etec
to
r
u
s
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m
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MPU6
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at
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o
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p
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x
im
ity
in
cr
ea
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e
d
etec
tio
n
.
T
h
e
ab
o
v
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s
tu
d
ie
s
h
ig
h
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v
ar
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eth
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d
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a
n
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ad
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m
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ts
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f
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lts
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,
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cl
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d
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d
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m
ai
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ten
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c
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v
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ased
m
o
n
ito
r
in
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,
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d
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tellig
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t
a
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io
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aly
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is
.
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h
ey
ar
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cr
u
cial
f
o
r
m
ai
n
tain
in
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t
h
e
o
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ety
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elt
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e
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ap
p
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h
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th
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v
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m
en
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o
wca
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T
tech
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co
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p
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iv
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ito
r
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g
an
d
m
ain
ten
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ce
o
f
co
n
v
ey
o
r
b
elts
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I
t
en
ab
les
r
ea
l
-
tim
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ata
c
o
llec
tio
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,
f
ac
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ac
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lear
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o
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ith
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tco
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an
d
e
n
h
an
ce
s
in
d
u
s
tr
ial
p
r
o
ce
s
s
es
[
4
0
]
,
[
4
1
]
.
H
am
id
an
d
Aln
ea
m
y
[
4
2
]
c
o
m
p
ar
e
d
I
o
T
p
r
o
t
o
co
ls
f
o
r
th
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m
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t
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o
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p
r
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cts
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d
f
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th
at
MQ
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is
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tab
le
in
I
o
T
,
C
o
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co
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ec
ts
q
u
eu
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an
d
XM
PP
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its
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ltit
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ea
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h
er
ef
o
r
e,
I
o
T
p
lay
s
a
p
iv
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ta
l
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o
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en
h
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cin
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f
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lt
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iag
n
o
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is
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p
r
ed
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m
a
in
ten
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ce
,
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d
h
ea
lth
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o
n
ito
r
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g
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co
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r
b
elt
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tem
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s
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iv
er
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e
d
ata
s
o
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ce
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u
ch
as im
ag
e,
v
ib
r
ati
o
n
,
au
d
io
,
a
n
d
s
en
s
o
r
d
ata.
3
.
2
.
AI
a
nd
ML
Ger
ik
e
et
a
l.
[
4
3
]
h
ig
h
lig
h
te
d
co
m
m
o
n
d
ia
g
n
o
s
tic
cr
iter
ia
f
o
r
m
in
in
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ac
h
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ed
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ce
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s
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f
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t
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ter
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p
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ictio
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b
y
m
o
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ito
r
in
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v
a
r
iatio
n
s
r
elate
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to
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ad
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d
s
p
ee
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.
Ad
d
itio
n
ally
,
th
e
s
tu
d
y
en
h
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ce
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th
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ev
alu
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s
o
f
g
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its
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d
p
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m
o
d
els f
o
r
b
elt
c
o
n
v
ey
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y
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tem
s
.
An
o
th
er
s
tu
d
y
b
y
Kian
g
ala
an
d
[
2
0
]
in
tr
o
d
u
ce
d
a
p
r
ac
tical
p
r
ed
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e
m
ain
ten
a
n
ce
f
r
am
ew
o
r
k
f
o
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r
m
o
to
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s
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s
m
all
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an
u
f
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tu
r
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n
its
,
alig
n
ed
with
I
n
d
u
s
tr
y
4
.
0
with
th
e
h
elp
o
f
ML
an
d
co
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v
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tio
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r
al
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r
k
(
C
NN)
with
tim
e
-
s
er
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es
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ag
in
g
,
a
n
d
ac
h
iev
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ac
cu
r
ate
f
a
u
lt
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if
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.
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h
e
ad
ap
ta
b
ilit
y
o
f
th
e
m
o
d
el
h
an
d
les
v
ar
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s
d
ata
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p
es,
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p
lo
y
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g
p
r
in
ci
p
al
co
m
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o
n
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t a
n
aly
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is
(
PC
A)
f
o
r
d
im
en
s
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ality
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ed
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ctio
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d
g
r
am
ian
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g
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lar
f
ield
(
GAF)
f
o
r
im
ag
e
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o
n
v
e
r
s
io
n
.
B
y
ac
h
iev
in
g
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p
p
r
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x
im
ately
1
0
0
%
ac
cu
r
ac
y
,
th
e
C
NN
m
o
d
el
r
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ce
s
th
e
r
is
k
o
f
m
is
s
ed
cr
itical
f
au
lts
o
r
u
n
n
ec
ess
ar
y
m
ain
ten
an
ce
.
L
iu
et
a
l.
[
2
7
]
in
tr
o
d
u
ce
d
a
n
e
w
m
eth
o
d
f
o
r
b
elt
co
n
v
ey
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ler
s
,
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len
d
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o
p
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al
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ata
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d
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
to
im
p
r
o
v
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r
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liab
ilit
y
.
I
t
ad
ju
s
ts
m
o
n
ito
r
in
g
th
r
esh
o
ld
s
b
ased
o
n
liv
e
co
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d
itio
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s
an
d
o
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tp
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f
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r
m
s
tr
ad
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m
eth
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d
s
.
T
h
is
d
y
n
am
ic
ap
p
r
o
ac
h
p
r
o
m
is
es
im
p
r
o
v
e
d
co
n
v
ey
o
r
m
ain
ten
an
ce
s
tr
ateg
ies.
Kian
g
ala
et
a
l.
[
4
4
]
co
n
d
u
cted
a
s
tu
d
y
in
a
s
m
all
b
o
ttli
n
g
p
lan
t
to
d
em
o
n
s
tr
ate
th
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p
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ten
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I
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d
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s
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4
.
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p
r
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s
m
all
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SME
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.
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t
f
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etec
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f
f
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o
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tem
s
an
d
im
p
lem
e
n
ts
a
p
r
ed
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e
m
ain
ten
an
ce
s
tr
ateg
y
.
T
h
e
p
r
o
p
o
s
ed
p
r
ed
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e
m
ain
ten
an
ce
s
tr
ateg
y
ac
h
iev
ed
b
y
co
llectin
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d
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aly
z
in
g
r
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l
-
tim
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v
ib
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d
ata
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te
m
ain
te
n
an
ce
s
ch
e
d
u
les
f
o
r
m
o
to
r
s
.
I
t
s
h
o
wca
s
ed
th
e
b
en
ef
its
o
f
I
n
d
u
s
tr
y
4
.
0
an
d
p
r
o
v
id
ed
an
ef
f
ec
tiv
e
ap
p
r
o
ac
h
t
o
p
r
e
d
ictiv
e
m
ain
ten
an
ce
f
o
r
SME
s
.
T
h
e
au
th
o
r
s
o
f
t
h
e
f
o
llo
win
g
s
tu
d
y
,
Al
-
Kah
wati
et
a
l.
[
1
9
]
s
u
g
g
ested
a
s
o
lu
tio
n
f
o
r
th
e
p
r
ed
ictiv
e
m
ain
ten
an
ce
o
f
b
elt
co
n
v
ey
o
r
s
y
s
tem
s
,
wh
ich
is
cr
u
cial
f
o
r
ac
h
iev
in
g
p
r
o
d
u
ctio
n
o
b
jectiv
es.
T
h
is
s
o
lu
tio
n
i
n
teg
r
ates
a
d
ig
ital
twin
,
d
e
g
r
ad
atio
n
m
o
d
el,
an
d
v
is
io
n
-
b
ased
h
az
ar
d
d
etec
tio
n
.
T
h
e
v
alid
atio
n
r
esu
lts
s
h
o
wed
th
e
ef
f
ica
cy
o
f
th
e
s
o
lu
tio
n
in
p
r
ed
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n
g
th
e
co
m
p
o
n
en
t
s
tatu
s
an
d
d
etec
tin
g
h
az
ar
d
s
.
AI
an
d
ML
r
esear
ch
in
v
o
lv
es
alg
o
r
ith
m
s
im
itatin
g
h
u
m
an
in
tellig
en
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f
o
r
task
s
,
r
e
co
g
n
izin
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p
atter
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s
,
an
d
m
a
k
in
g
d
ec
is
io
n
s
b
ased
o
n
d
ata
with
o
u
t
ex
p
li
cit
p
r
o
g
r
am
m
in
g
.
Ma
n
y
s
tu
d
i
es
h
av
e
u
s
ed
d
ee
p
lear
n
in
g
f
o
r
im
ag
e
class
if
icatio
n
in
co
n
v
e
y
o
r
-
b
elt
s
y
s
tem
s
.
Gu
o
et
a
l.
[
4
]
in
tr
o
d
u
ce
d
an
im
p
r
o
v
ed
m
o
b
ile
clo
u
d
c
o
m
p
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tin
g
(
MCC
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-
c
y
cle
GAN
m
o
d
el,
wh
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d
em
o
n
s
tr
ated
f
ast
d
etec
tio
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,
h
ig
h
p
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f
o
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a
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n
d
s
am
p
le
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f
o
r
en
h
an
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d
n
eu
r
al
n
e
two
r
k
tr
ain
i
n
g
.
I
t
ef
f
ec
tiv
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id
en
tifie
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d
am
ag
ed
co
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v
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y
o
r
b
elt
s
u
r
f
ac
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im
ag
es
d
esp
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r
e
q
u
ir
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g
e
x
ten
s
iv
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tr
ain
in
g
tim
e.
F
u
r
th
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m
o
r
e,
t
h
is
ar
ticle
h
i
g
h
li
g
h
ts
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e
a
p
p
licatio
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o
f
R
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-
3
4
,
R
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5
0
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VGG
1
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,
I
n
ce
p
tio
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v
3
,
an
d
Alex
Net
alg
o
r
ith
m
s
in
im
ag
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cl
ass
if
icatio
n
.
San
to
s
et
a
l.
[
4
5
]
,
p
r
esen
ted
a
m
eth
o
d
f
o
r
id
en
tify
in
g
s
o
il
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co
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v
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r
b
elts
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s
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Den
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t1
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1
f
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tim
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cu
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ac
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a
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alan
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all.
R
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-
c
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tr
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s
itu
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s
,
in
Evaluation Warning : The document was created with Spire.PDF for Python.
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d
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&
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2
5
0
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-
4
7
52
Dig
ita
l
tr
a
n
s
fo
r
ma
tio
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tech
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g
ies fo
r
co
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b
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p
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(
P
ed
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la
V
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i S
a
n
to
s
h
i A
n
u
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h
a
)
643
wh
ich
p
er
f
o
r
m
an
ce
is
d
esire
d
.
Din
g
r
an
Qu
et
a
l.
[
4
6
]
u
tili
ze
d
an
ad
ap
tiv
e
d
ee
p
c
o
n
v
o
lu
tio
n
al
n
etwo
r
k
(
ADCN
)
to
d
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n
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s
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a
n
d
m
o
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ito
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co
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v
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r
b
elt
im
p
air
m
en
t
s
in
r
ea
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tim
e.
T
h
is
m
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d
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tr
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SVM
-
b
ased
m
eth
o
d
s
.
A
h
o
lis
tically
n
ested
ed
g
e
d
e
tectio
n
(
HE
D)
m
o
d
el
em
p
lo
y
in
g
d
ee
p
co
n
v
o
lu
tio
n
n
etwo
r
k
s
ex
h
ib
its
p
r
ec
is
e
co
n
v
ey
o
r
b
elt
d
ev
iatio
n
d
ete
ctio
n
,
with
ed
g
e
d
etec
tio
n
e
r
r
o
r
s
b
elo
w
1
6
m
m
an
d
r
esil
ien
t
an
ti
-
jam
m
in
g
f
ea
tu
r
es,
en
s
u
r
i
n
g
s
wif
t
an
d
ac
cu
r
ate
r
eso
lu
tio
n
o
f
co
n
v
e
y
o
r
b
elt
is
s
u
es
[
4
7
]
.
An
o
th
e
r
ar
ticle
p
r
o
p
o
s
ed
a
m
eth
o
d
wh
er
e
it
in
teg
r
ates
I
o
T
s
en
s
o
r
in
f
o
r
m
atio
n
with
ML
alg
o
r
ith
m
s
to
en
h
an
ce
f
au
l
t
d
etec
tio
n
in
co
al
p
r
o
d
u
ctio
n
co
n
v
ey
o
r
b
elts
.
T
h
e
s
y
s
tem
aim
s
to
ac
cu
r
ately
id
en
t
if
y
d
e
v
iatio
n
s
,
s
lip
p
ag
e,
an
d
tear
in
g
f
a
u
lts
,
th
er
eb
y
en
a
b
lin
g
r
em
o
te
m
o
n
ito
r
in
g
an
d
r
ea
l
-
tim
e
f
au
lt
d
iag
n
o
s
is
.
T
h
e
f
au
lt
id
en
tific
atio
n
an
d
p
r
o
ac
tiv
e
m
ain
ten
an
ce
s
y
s
tem
ar
e
b
ase
d
o
n
a
n
o
m
aly
d
etec
tio
n
u
s
in
g
a
r
u
le
-
b
ased
b
o
tto
m
-
u
p
ap
p
r
o
ac
h
[
3
6
]
,
[
4
8
]
.
T
h
e
MCC
-
C
y
cleG
AN,
R
esN
et
-
3
4
,
R
esNet
-
5
0
,
VGG
1
6
,
I
n
ce
p
tio
n
v
3
,
Alex
Net,
Den
s
e
Net1
6
1
,
R
esNet1
8
,
ADCN
,
HE
D
m
o
d
el,
an
d
L
GB
M
alg
o
r
ith
m
s
h
av
e
b
ee
n
em
p
lo
y
ed
in
v
a
r
io
u
s
s
tu
d
ies
to
ad
d
r
ess
d
if
f
e
r
en
t
asp
ec
ts
o
f
co
n
v
ey
o
r
b
elt
s
y
s
tem
s
,
in
clu
d
in
g
im
ag
e
class
if
icatio
n
,
f
au
l
t
d
etec
tio
n
,
r
ea
l
-
ti
m
e
m
o
n
ito
r
in
g
,
an
d
p
r
o
ac
tiv
e
m
ain
te
n
an
ce
.
3
.
3
.
Clo
ud
co
m
pu
t
ing
,
edg
e
co
m
pu
t
ing
a
nd
t
iny
ML
C
lo
u
d
co
m
p
u
tin
g
en
h
a
n
ce
s
co
n
v
ey
o
r
-
b
elt
s
y
s
tem
s
b
y
o
f
f
er
in
g
im
p
r
o
v
ed
d
ata
s
to
r
a
g
e,
a
n
aly
s
is
,
an
d
m
an
ag
em
en
t.
I
o
T
s
en
s
o
r
s
an
d
ML
alg
o
r
ith
m
s
en
ab
le
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
a
n
d
p
r
e
d
i
ctiv
e
m
ain
ten
an
ce
,
en
s
u
r
in
g
s
ca
lab
le
a
n
d
r
eliab
le
o
p
er
atio
n
.
I
n
ad
d
itio
n
,
clo
u
d
c
o
m
p
u
tin
g
p
r
o
v
i
d
es
co
s
t
-
ef
f
ec
tiv
e
an
d
f
lex
ib
le
r
eso
u
r
ce
s
o
v
er
t
h
e
i
n
ter
n
et.
S
alh
ao
u
i
et
a
l.
[
4
9
]
in
t
r
o
d
u
ce
s
a
d
r
o
n
e
-
b
ased
m
o
n
ito
r
i
n
g
m
o
d
el
f
o
r
c
o
n
v
e
y
o
r
b
elts
in
co
n
cr
ete
p
la
n
ts
an
d
p
r
o
ce
s
s
in
g
im
ag
es
in
t
h
e
cl
o
u
d
f
o
r
r
ea
l
-
tim
e
an
aly
s
is
.
I
n
ter
co
m
m
u
n
icatio
n
ch
allen
g
es
ar
e
ad
d
r
ess
ed
th
r
o
u
g
h
n
o
d
e
-
R
E
D
co
m
p
u
tatio
n
an
d
f
o
g
o
n
th
e
I
o
T
g
atew
ay
.
W
an
g
et
a
l.
[
5
0
]
p
r
esen
ted
a
clo
u
d
-
b
ased
f
r
am
e
wo
r
k
th
at
f
ac
ilit
ates
r
o
b
o
t
c
o
m
m
u
n
icatio
n
an
d
n
e
g
o
tiatio
n
in
s
m
ar
t f
ac
to
r
ies
b
y
co
n
n
ec
tin
g
clo
u
d
,
r
o
b
o
t,
a
n
d
clien
t
ter
m
in
al
lay
e
r
s
.
T
h
r
o
u
g
h
d
y
n
am
ic
r
ec
o
n
f
ig
u
r
atio
n
,
it
en
ab
les
s
ea
m
less
co
m
m
u
n
icatio
n
ac
r
o
s
s
lay
er
s
an
d
n
e
g
o
tiatio
n
f
o
r
co
m
p
an
io
n
r
o
b
o
ts
,
s
h
o
wca
s
ed
in
a
ca
n
d
y
p
ac
k
in
g
ap
p
licatio
n
,
th
er
e
b
y
en
h
an
cin
g
f
ac
to
r
y
o
p
er
atio
n
s
.
E
d
g
e
co
m
p
u
tin
g
b
o
o
s
ts
r
ea
l
-
tim
e
p
r
o
c
ess
in
g
f
o
r
I
o
T
wit
h
a
SiP
E
E
D
b
o
ar
d
d
ep
lo
y
in
g
d
e
ep
n
eu
r
al
n
etwo
r
k
s
o
n
co
n
v
ey
o
r
b
elt
im
a
g
es
u
s
in
g
Mo
b
ileNet
0
.
7
5
f
o
r
r
ip
d
etec
tio
n
in
ir
o
n
p
r
o
ce
s
s
in
g
p
lan
ts
,
en
h
an
cin
g
ef
f
icien
cy
d
esp
ite
r
eso
u
r
ce
co
n
s
tr
ain
ts
[
5
1
]
.
L
iu
et
a
l.
[
5
2
]
in
tr
o
d
u
ce
d
a
h
ar
d
war
e
s
y
s
tem
lev
er
ag
i
n
g
e
d
g
e
d
ev
ices
f
o
r
im
p
r
o
v
ed
b
o
ttle
ca
p
d
etec
tio
n
,
u
tili
zin
g
FP
GA
-
b
ased
p
r
o
ce
s
s
in
g
f
o
r
f
aster
alg
o
r
ith
m
ex
ec
u
tio
n
,
c
o
r
r
ec
tin
g
b
ac
k
ca
p
s
,
o
p
tim
iz
in
g
d
etec
tio
n
,
an
d
r
eso
u
r
ce
ef
f
icien
c
y
.
T
in
y
ML
em
p
o
wer
s
th
e
I
o
T
b
y
en
ab
lin
g
lo
ca
l
d
ata
a
n
aly
s
is
an
d
d
ec
is
io
n
-
m
ak
i
n
g
.
R
es
ea
r
ch
ar
ticles
o
n
T
in
y
ML
d
em
o
n
s
tr
ated
its
ef
f
ec
tiv
en
ess
in
p
o
wer
to
o
l
m
o
n
ito
r
in
g
d
u
r
in
g
c
o
n
s
tr
u
ctio
n
,
ac
h
iev
in
g
9
0
.
6
%
ac
cu
r
ac
y
wh
ile
co
n
s
er
v
in
g
p
o
wer
.
Ad
d
itio
n
ally
,
T
in
y
ML
ex
ce
l
s
in
s
tr
u
ctu
r
al
h
ea
lth
m
o
n
ito
r
in
g
(
SHM)
,
ac
h
iev
in
g
h
ig
h
ac
cu
r
ac
y
in
i
m
p
ac
t
lo
ca
lizatio
n
an
d
r
ea
l
-
ti
m
e
an
aly
s
is
o
n
lo
w
-
p
o
wer
d
ev
ices,
with
f
u
t
u
r
e
s
tr
ateg
ies
f
o
r
d
ataset
ex
p
an
s
io
n
an
d
s
ig
n
al
p
r
o
ce
s
s
in
g
r
ef
in
e
m
en
t
[
5
3
]
.
E
d
g
e
co
m
p
u
tin
g
a
n
d
d
e
v
ices,
s
u
ch
as
SiP
E
E
D
b
o
ar
d
s
a
n
d
FP
GA
-
b
ased
m
o
d
u
les,
h
el
p
im
p
r
o
v
e
p
r
ed
ictiv
e
m
ain
ten
an
ce
b
y
o
v
er
c
o
m
in
g
n
etwo
r
k
co
n
s
tr
ain
ts
,
en
h
an
cin
g
s
ec
u
r
ity
,
an
d
in
c
r
ea
s
in
g
s
p
ee
d
.
T
h
is
en
ab
les s
wif
t lo
ca
l a
n
aly
s
is
u
s
in
g
T
in
y
ML
f
o
r
to
o
l
an
d
s
tr
u
ctu
r
al
h
ea
lth
m
o
n
ito
r
in
g
.
T
h
ese
ad
v
an
ce
m
en
ts
p
r
o
m
is
e
s
m
ar
ter
s
o
lu
tio
n
s
to
i
n
d
u
s
tr
y
ch
allen
g
es,
in
clu
d
in
g
c
o
n
v
e
y
o
r
s
y
s
tem
s
a
n
d
s
tr
u
ctu
r
al
h
ea
lth
m
o
n
ito
r
in
g
ap
p
licatio
n
s
[
5
4
]
–
[
5
6
]
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
tu
d
y
ex
ten
s
iv
ely
r
ev
iew
ed
f
au
lt
d
iag
n
o
s
tics
,
p
r
e
d
ictio
n
,
f
ea
tu
r
e
ex
tr
ac
tio
n
,
f
a
u
lt
cla
s
s
if
icatio
n
,
an
d
d
ec
is
io
n
-
m
ak
in
g
,
s
ig
n
if
ic
an
tly
en
h
an
cin
g
o
u
r
u
n
d
er
s
tan
d
in
g
o
f
p
r
ed
ictiv
e
m
ain
ten
a
n
c
e
an
d
f
o
r
tify
in
g
th
e
r
eliab
ilit
y
o
f
in
d
u
s
tr
ial
m
ac
h
in
er
y
.
L
ik
ewise,
th
e
in
teg
r
atio
n
o
f
d
ig
ital
tr
an
s
f
o
r
m
atio
n
te
ch
n
o
lo
g
ies
s
u
ch
as
I
o
T
,
I
n
d
u
s
tr
y
4
.
0
,
m
ac
h
in
e
lear
n
in
g
,
clo
u
d
co
m
p
u
tin
g
,
an
d
ed
g
e
co
m
p
u
tin
g
o
p
tim
izes
tr
ad
itio
n
al
in
d
u
s
tr
ial
sy
s
tem
s
,
p
av
in
g
th
e
way
f
o
r
m
o
r
e
ef
f
icien
t
m
ain
ten
an
c
e
s
tr
ateg
ies.
Pre
d
ictiv
e
m
ain
ten
an
ce
r
elies
o
n
in
teg
r
ated
s
en
s
o
r
s
em
p
lo
y
in
g
I
o
T
,
I
n
d
u
s
tr
y
4
.
0
,
m
ac
h
i
n
e
lear
n
in
g
,
clo
u
d
co
m
p
u
tin
g
,
an
d
e
d
g
e
co
m
p
u
tin
g
f
o
r
th
e
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
o
f
m
ac
h
in
er
y
h
ea
lth
.
T
h
is
r
e
v
iew
u
n
d
er
s
co
r
es
th
e
p
iv
o
tal
r
o
le
o
f
d
ig
ital
tr
an
s
f
o
r
m
atio
n
tech
n
o
lo
g
ies
t
h
at
ar
e
s
p
ec
if
ic
to
p
r
e
d
ictiv
e
m
ain
ten
an
ce
in
b
elt
co
n
v
ey
e
r
s
y
s
tem
s
.
Ou
t
o
f
all
d
ig
ital
tr
an
s
f
o
r
m
atio
n
tec
h
n
o
lo
g
ies,
I
o
T
tech
n
o
lo
g
y
h
as
e
m
er
g
ed
as
a
p
iv
o
tal
f
ac
ilit
ato
r
,
e
n
h
an
cin
g
f
au
lt
d
iag
n
o
s
is
,
p
r
e
d
ictiv
e
m
ai
n
ten
a
n
ce
,
an
d
h
ea
lth
m
o
n
ito
r
i
n
g
i
n
co
n
v
ey
o
r
b
elt
s
y
s
tem
s
b
y
lev
e
r
ag
in
g
d
iv
er
s
e
d
ata
s
o
u
r
ce
s
,
s
u
ch
as
im
ag
es,
v
ib
r
atio
n
s
,
au
d
io
,
an
d
s
en
s
o
r
d
ata.
B
esid
es
th
at,
m
u
ltip
le
s
tu
d
ies
h
av
e
s
u
cc
ess
f
u
lly
ap
p
lied
d
iv
e
r
s
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
s
u
ch
as
MCC
-
C
y
cleG
AN,
R
esNet
-
3
4
,
R
e
s
Net
-
5
0
,
VGG
1
6
,
I
n
ce
p
tio
n
v
3
,
Alex
Net,
Den
s
eNe
t1
6
1
,
R
esNet1
8
,
ADCN,
HE
D
m
o
d
el,
an
d
L
GB
M
t
o
ad
d
r
ess
d
if
f
er
en
t
asp
ec
ts
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
6
,
No
.
1
,
Octo
b
er
20
24
:
639
-
6
4
6
644
T
h
e
p
r
esen
ted
r
e
v
iew
co
n
f
ir
m
s
th
e
p
o
ten
tial
o
f
d
ig
ital
tr
an
s
f
o
r
m
atio
n
tech
n
o
lo
g
ies
to
im
p
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o
v
e
th
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c
y
o
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h
ea
lth
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m
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ito
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in
g
a
p
p
licatio
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s
.
5.
CO
NCLU
SI
O
N
T
h
is
r
ev
iew
ex
p
lo
r
ed
h
o
w
s
en
s
o
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s
an
d
d
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s
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tech
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l
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ies
im
p
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v
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t
h
e
p
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ed
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e
m
ain
ten
an
ce
o
f
co
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v
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r
b
elt
s
y
s
tem
s
.
I
t
en
co
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ag
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esear
ch
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s
to
in
co
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p
o
r
ate
in
n
o
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ativ
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m
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els
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ased
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n
d
ig
ital
in
f
o
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m
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tech
n
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l
o
g
ies
in
th
e
m
ain
te
n
an
ce
m
e
th
o
d
s
an
d
s
tr
ateg
ies
o
f
b
elt
co
n
v
ey
er
s
y
s
tem
s
.
I
t
em
p
h
asizes
th
e
tr
an
s
f
o
r
m
in
g
p
o
we
r
o
f
I
o
T
,
AI
,
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d
clo
u
d
co
m
p
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tin
g
in
th
e
in
d
u
s
tr
ial
ap
p
licatio
n
s
.
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h
ey
en
s
u
r
e
m
ac
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lth
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s
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ts
to
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d
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s
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.
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h
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au
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p
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is
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ticle
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ar
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te
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m
eth
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d
o
l
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g
ies.
RE
F
E
R
E
NC
E
S
[
1
]
T.
K
o
z
ł
o
w
sk
i
,
J.
W
o
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c
k
i
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R
.
Z
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.
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M
.
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.
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[
6
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J.
B
u
t
t
,
“
A
c
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c
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t
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m
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p
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man
a
g
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me
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p
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D
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n
s
,
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.
4
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.
[
7
]
M
.
K
o
u
m
a
s,
P
.
-
E
.
D
o
ss
o
u
,
a
n
d
J.
-
Y
.
D
i
d
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e
r
,
“
D
i
g
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me
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p
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p
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d
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c
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i
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n
man
u
f
a
c
t
u
r
i
n
g
,
”
J
o
u
r
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a
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S
o
f
t
w
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r
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En
g
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A
p
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[
8
]
P
.
C
h
o
i
,
K
.
-
R
.
K
w
o
n
,
S
.
G
e
u
n
K
w
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,
M
.
M
.
R
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s
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,
S
.
-
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.
K
w
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n
,
a
n
d
S
.
-
H
.
Le
e
,
“
Em
e
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f
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m
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se
:
h
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,
A
I
,
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[
9
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K
.
S
c
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,
“
D
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.
F
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.
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