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Vo
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11
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A
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2021
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1
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v
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2820
J
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ttp
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A review
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1.
I
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en
t
l
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i
n
d
u
c
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m
ac
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e
s
[
1
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,
s
u
ch
a
s
i
n
d
u
ctio
n
m
o
to
r
s
(
I
M)
[
2
,
3
]
,
a
r
e
ex
ten
s
iv
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y
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s
ed
i
n
s
ev
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s
tr
ial
p
r
o
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s
s
es
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n
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ap
p
licatio
n
s
[
4
]
,
in
clu
d
i
n
g
m
i
n
in
g
i
n
d
u
s
tr
ies,
ch
e
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d
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co
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d
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s
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5
,
6
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.
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m
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y
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M
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s
,
a
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lo
w
m
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n
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co
s
t
[
7
,
8
]
.
T
h
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p
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f
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r
m
a
n
ce
an
d
ac
cu
r
ac
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u
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s
u
c
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u
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,
a
n
d
en
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m
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tal
f
a
u
lts
[
9
]
.
Ho
w
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v
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,
ea
r
l
y
an
d
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n
ti
n
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s
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M
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d
f
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lt
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is
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k
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d
en
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s
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n
d
f
ail
u
r
es [
1
0
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1
1
]
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ec
en
tl
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to
f
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d
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2
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r
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d
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s
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5
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.
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w
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d
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g
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p
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at
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r
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is
[
1
6
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v
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atio
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a
n
al
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is
[
1
7
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,
n
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an
al
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s
is
[
1
8
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,
in
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is
[
1
9
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,
cu
r
r
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t
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al
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s
i
s
[
2
0
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,
v
o
ltag
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a
n
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[
2
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elec
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[
2
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[
2
3
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,
p
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s
is
[
2
4
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,
u
ltra
s
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d
an
al
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s
[
2
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,
an
d
also
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s
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s
tic
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m
is
s
io
n
an
al
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s
is
[
2
6
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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n
d
i
m
a
g
e
p
r
o
c
e
s
s
i
n
g
f
o
r
t
i
m
e
,
f
r
e
q
u
e
n
c
y
,
a
n
d
t
i
m
e
-
f
r
e
q
u
e
n
c
y
d
o
m
a
i
n
s
[
4
6
,
4
7
]
,
a
n
d
e
x
p
e
r
t
s
y
s
t
em
s
[
4
8
]
.
I
n
r
ec
en
t
liter
at
u
r
e,
th
er
e
h
a
v
e
b
ee
n
m
a
n
y
r
ev
i
e
w
p
ap
er
s
f
o
r
in
te
lli
g
en
ce
C
M
an
d
FD
m
ac
h
i
n
e
lear
n
in
g
m
et
h
o
d
s
o
f
r
o
lli
n
g
el
e
m
en
ts
b
ea
r
in
g
s
o
f
I
M
[
4
9
,
5
0
]
.
Ho
w
e
v
er
,
t
h
er
e
is
a
lack
i
n
t
h
e
liter
at
u
r
e
an
d
th
er
e
ar
e
n
o
t
m
an
y
r
ev
ie
w
p
ap
er
s
f
o
r
b
o
th
S/R
i
n
tell
ig
e
n
t
C
M
an
d
FD.
T
h
e
S/
R
C
M
an
d
FD
f
r
a
m
e
w
o
r
k
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
2
.
T
h
is
s
tu
d
y
ai
m
s
to
p
r
o
p
o
s
e
a
s
y
s
te
m
at
ic
liter
atu
r
e
r
ev
ie
w
f
o
r
C
M
a
n
d
FD
o
f
th
e
I
M,
esp
ec
i
all
y
f
o
r
S/R
b
ased
o
n
ar
tif
icial
i
n
telli
g
e
n
t
(
A
I
)
m
e
th
o
d
s
s
h
o
w
n
i
n
Fi
g
u
r
e
3
.
T
h
e
s
tu
d
y
also
p
o
in
ts
o
u
t
th
e
ad
v
an
ta
g
es
an
d
d
r
a
w
b
ac
k
s
o
f
ea
c
h
m
et
h
o
d
.
Fi
n
all
y
,
ch
alle
n
g
es
a
n
d
p
o
s
s
ib
le
f
u
t
u
r
e
tr
en
d
s
ar
e
al
s
o
ad
d
r
ess
ed
.
I
M
f
au
lt
Me
ch
an
ical
Fa
u
lt
E
lectr
ical
Fau
lt
R
o
llin
g
ele
m
e
n
t
b
ea
r
in
g
s
Un
b
alan
ced
a
n
d
b
o
w
ed
r
o
to
r
C
r
a
w
lin
g
Sh
o
r
t
-
cir
c
u
it (
I
n
ter
-
tu
r
n
)
Ma
s
s
b
alan
ce
Stato
r
w
i
n
d
in
g
R
o
to
r
m
is
ali
g
n
m
en
t
A
ir
-
g
ap
ec
ce
n
tr
icit
y
R
o
to
r
b
ar
(
B
r
o
k
en
)
Stato
r
f
au
l
ts
P
h
ase
u
n
b
alan
ce
an
d
s
h
i
f
ted
p
h
a
s
in
g
Un
b
a
lan
c
e
v
o
lt
a
g
e
a
n
d
c
u
rre
n
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
4
,
A
u
g
u
s
t 2
0
2
1
:
2
8
2
0
-
2829
2822
Fig
u
r
e
2
.
S/R
C
M
a
n
d
FD
f
r
am
e
w
o
r
k
Fig
u
r
e
3
.
T
y
p
es o
f
A
I
m
et
h
o
d
s
in
C
M
an
d
FD [
5
1
,
5
2
]
2.
RE
L
AT
E
D
WO
RK
S
2
.
1
.
Sta
t
o
r
f
a
ults
(
SFs)
dia
g
no
s
is
Stato
r
f
a
u
lt
s
ar
e
co
n
s
id
er
ed
to
b
e
o
n
e
o
f
th
e
m
o
s
t
f
a
u
lts
o
f
t
h
e
I
M
[
5
3
,
5
4
]
.
C
o
n
s
eq
u
e
n
tl
y
,
in
[
5
5
]
,
th
e
f
ea
tu
r
e
e
x
tr
ac
tio
n
m
e
th
o
d
ap
p
lied
to
th
e
th
er
m
al
i
m
ag
e
s
is
u
s
ed
to
d
iag
n
o
s
e
SF
s
o
f
th
e
I
M.
T
h
is
m
et
h
o
d
is
d
ep
en
d
ed
o
n
th
e
s
tates
o
f
th
e
s
elec
ted
ar
ea
.
T
h
e
p
r
im
ar
y
u
s
e
o
f
A
I
alg
o
r
it
h
m
s
in
t
h
i
s
s
tu
d
y
i
s
in
t
h
e
class
i
f
icatio
n
s
ta
g
e.
No
tab
l
y
,
tw
o
t
y
p
e
s
o
f
class
if
ier
s
,
th
e
n
e
ar
est
n
ei
g
h
b
o
r
(
NN)
an
d
th
e
Gau
s
s
ia
n
m
i
x
t
u
r
e
m
o
d
el
s
(
GM
M)
ac
h
iev
ed
t
h
e
o
b
tain
ed
f
ea
t
u
r
e
v
ec
to
r
s
'
clas
s
if
icatio
n
s
ta
g
e.
As
a
r
es
u
lt,
t
h
e
ef
f
ec
ti
v
en
e
s
s
o
f
A
I
r
ec
o
g
n
itio
n
a
n
d
class
i
f
icat
io
n
alg
o
r
ith
m
s
u
s
ed
in
th
is
s
t
u
d
y
r
esear
ch
w
a
s
v
er
y
h
i
g
h
.
I
n
[
5
6
]
,
a
n
eu
r
o
-
f
u
zz
y
c
lass
if
ier
f
o
r
b
o
u
n
d
ar
y
d
etec
tio
n
is
u
s
ed
to
d
iag
n
o
s
e
I
M'
s
SF
u
s
i
n
g
li
n
e
c
u
r
r
en
t
v
ec
to
r
o
b
tain
ed
f
r
o
m
s
tato
r
cu
r
r
en
t.
Mo
r
eo
v
er
,
th
is
s
i
m
p
le
m
et
h
o
d
is
ap
p
lied
a
s
an
FD
o
f
r
o
to
r
f
au
lts
b
ased
o
n
th
e
im
a
g
e
'
s
o
b
tain
ed
p
atter
n
.
I
n
[
5
7
]
,
a
s
t
ato
r
-
w
in
d
i
n
g
-
f
a
u
lt
p
r
ed
ictio
n
ap
p
r
o
ac
h
o
f
I
M
u
s
i
n
g
f
u
zz
y
o
p
tim
izatio
n
an
d
m
u
lti
-
s
ca
le
e
n
tr
o
p
y
is
i
n
tr
o
d
u
c
ed
.
Fu
r
t
h
er
m
o
r
e,
v
ib
r
atio
n
s
i
g
n
a
ls
alo
n
g
w
i
th
th
e
m
o
to
r
'
s
c
u
r
r
en
t
s
ig
n
at
u
r
e
ar
e
u
t
ilized
to
d
iag
n
o
s
e
t
h
e
SF
s
u
n
d
er
d
if
f
er
en
t
o
p
er
atin
g
s
p
ee
d
s
.
T
h
e
w
av
e
let
tr
a
n
s
f
o
r
m
tec
h
n
iq
u
e
i
s
ap
p
lied
i
n
o
r
d
er
to
r
em
o
v
i
n
g
n
o
is
e.
No
tab
l
y
,
n
e
u
r
o
-
f
u
zz
y
is
ap
p
lied
to
m
o
d
el
an
d
p
r
ed
ict
th
e
S
Fs
.
As
a
r
e
s
u
l
t
,
t
h
e
g
r
e
y
-
f
u
z
z
y
i
n
v
e
s
t
i
g
a
t
i
o
n
s
h
o
w
e
d
t
h
e
e
f
f
e
c
t
i
v
e
n
e
s
s
i
n
t
h
e
o
n
-
l
i
n
e
p
r
e
d
i
c
t
i
n
g
o
f
t
h
e
s
t
a
t
o
r
w
i
n
d
i
n
g
f
a
u
l
t
s
.
I
n
[
5
8
]
,
a
s
h
o
r
t c
ir
cu
it st
a
to
r
-
f
au
l
t
an
al
y
s
is
ap
p
r
o
ac
h
o
f
I
M
u
s
in
g
in
f
o
r
m
atio
n
m
ea
s
u
r
es a
n
d
ANN
is
in
tr
o
d
u
ce
d
.
Mo
r
eo
v
er
,
f
ea
tu
r
e
v
ec
to
r
s
ar
e
m
ea
s
u
r
ed
as
a
m
u
tu
al
i
n
f
o
r
m
a
tio
n
tec
h
n
iq
u
e.
I
m
p
o
r
tan
tl
y
,
t
w
o
ANN
to
p
o
lo
g
ies
ar
e
u
s
ed
i
n
th
is
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
.
Mu
ltil
a
y
er
p
er
ce
p
tr
o
n
(
ML
P
)
alo
n
g
w
it
h
r
ad
ial
b
asi
s
f
u
n
ctio
n
(
R
B
F)
ar
e
ap
p
lied
as
p
atter
n
r
ec
o
g
n
itio
n
an
d
cla
s
s
i
f
icatio
n
p
r
o
ce
s
s
es.
As
a
r
e
s
u
l
t,
th
e
er
r
o
r
m
ar
g
i
n
o
f
th
e
M
L
P
n
e
t
w
o
r
k
s
i
s
le
s
s
t
h
an
th
e
m
ar
g
in
o
f
t
h
e
R
B
F.
H
o
w
e
v
er
,
th
e
M
L
P
is
co
n
s
id
er
ed
as
th
e
b
est
ANN
to
p
o
lo
g
y
w
h
er
e
ex
p
er
i
m
en
tal
ac
cu
r
ac
y
is
9
9
%.
A
cc
o
r
d
in
g
to
[
5
9
]
,
th
e
FD
ap
p
r
o
ac
h
b
ased
o
n
A
I
is
p
r
esen
ted
u
s
in
g
b
o
th
v
ib
r
atio
n
an
d
s
tato
r
cu
r
r
en
t a
n
al
y
s
e
s
.
D
is
cr
ete
w
av
ele
t tr
an
s
f
o
r
m
(
DW
T
)
an
d
m
a
tch
i
n
g
p
u
r
s
u
i
t
ar
e
ap
p
lied
in
th
e
f
ea
tu
r
e
ex
tr
ac
tio
n
s
ta
g
e
.
Fo
llo
w
i
n
g
th
at,
f
iv
e
cla
s
s
i
f
ier
s
ar
e
ap
p
lie
d
:
s
u
b
s
p
ac
e,
f
i
n
e
an
d
w
ei
g
h
ted
n
ea
r
est
n
ei
g
h
b
o
r
(
NN)
,
b
ag
g
ed
tr
ee
s
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
(
SVM
)
.
A
s
a
r
esu
lt,
th
e
p
r
o
p
o
s
ed
s
tu
d
y
s
h
o
w
s
h
i
g
h
cl
ass
i
f
icatio
n
ac
cu
r
ac
y
(
ar
o
u
n
d
1
0
0
%).
I
n
[
6
0
]
,
a
s
tato
r
in
ter
-
t
u
r
n
F
D
to
o
l
b
ased
Data
co
llectin
g
an
d
ac
q
u
i
s
itio
n
I
M
(
s
tato
r
an
d
r
o
to
r
)
Data
p
r
e
-
p
r
o
ce
s
s
in
g
Featu
r
e
s
elec
t
io
n
an
d
e
x
tr
ac
tio
n
F
a
u
lt
c
las
sif
ica
ti
o
n
a
n
d
d
e
c
isio
n
-
m
a
k
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
r
ev
iew
o
f in
tellig
en
t m
eth
o
d
s
fo
r
co
n
d
itio
n
mo
n
ito
r
in
g
a
n
d
fa
u
lt d
ia
g
n
o
s
is
o
f sta
to
r
…
(
O
ma
r
A
ls
h
o
r
ma
n
)
2823
o
n
A
NN
is
p
r
ese
n
ted
.
Mo
r
eo
v
er
,
th
e
to
o
l
is
d
ev
elo
p
ed
u
n
d
er
s
ev
er
al
f
a
u
lt
s
izes
an
d
lo
ad
s
.
A
s
tead
y
-
s
tate
elec
tr
o
m
ec
h
a
n
ica
l
to
r
q
u
e
s
i
g
n
atu
r
e
in
t
i
m
e
a
n
d
f
r
eq
u
e
n
c
y
d
o
m
a
in
s
is
ap
p
lied
as
f
ea
tu
r
e
e
x
tr
ac
tio
n
m
et
h
o
d
.
As
a
cla
s
s
i
f
icatio
n
m
et
h
o
d
,
a
n
e
u
r
al
n
e
t
w
o
r
k
i
s
e
m
p
lo
y
ed
.
A
s
a
r
e
s
u
l
t
,
8
8
-
9
6
%
c
l
a
s
s
i
f
i
c
a
t
i
o
n
a
c
c
u
r
a
c
y
i
s
o
b
t
a
i
n
e
d
i
n
t
h
i
s
r
e
s
e
a
r
c
h
s
t
u
d
y
.
T
a
b
l
e
2
s
u
m
m
a
r
i
z
e
s
A
I
s
t
u
d
i
e
s
o
f
C
M
an
d
FD S
Fs
.
T
ab
le
2
.
A
I
s
t
u
d
ies o
f
C
M
an
d
FD f
o
r
SF
s
R
e
f
e
r
e
n
c
e
A
n
a
l
y
si
s t
y
p
e
F
e
a
t
u
r
e
e
x
t
r
a
c
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[
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[
6
3
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C
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[
6
6
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[
6
7
]
,
Evaluation Warning : The document was created with Spire.PDF for Python.
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d
y
,
n
ai
v
e
B
a
y
es
,
K
NN,
b
o
o
ts
tr
ap
ag
g
r
eg
ati
n
g
(
b
ag
g
in
g
)
,
b
o
o
s
tin
g
al
g
o
r
ith
m
s
(
A
d
aB
o
o
s
t)
,
m
u
ltil
a
y
er
p
er
ce
p
tr
o
n
(
ML
P
)
n
eu
r
al
n
et
w
o
r
k
,
a
n
d
SV
M.
Ho
w
e
v
er
,
KNN
s
h
o
w
ed
t
h
e
w
o
r
s
t
r
esu
lts
th
a
n
t
h
e
o
t
h
er
clas
s
i
f
ier
s
j
u
s
t
b
e
f
o
r
e
ML
P
a
n
d
SVM,
w
h
er
ea
s
n
a
iv
e
B
a
y
es
an
d
b
ag
g
i
n
g
cla
s
s
i
f
ier
s
s
h
o
w
ed
th
e
b
est
r
esu
lts
.
I
n
[
6
8
]
,
an
o
n
-
lin
e
m
et
h
o
d
f
o
r
FD
o
f
b
r
o
k
e
n
r
o
to
r
b
ar
s
(
B
R
P
)
u
s
i
n
g
v
ib
r
atio
n
a
n
al
y
s
i
s
b
ased
o
n
en
tr
o
p
y
is
p
r
o
p
o
s
ed
.
Fu
r
t
h
er
m
o
r
e,
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
co
u
ld
d
ea
l
w
it
h
s
e
v
er
al
o
p
er
atio
n
s
.
T
h
e
Sh
an
n
o
n
en
tr
o
p
y
is
ap
p
lied
to
s
ee
k
d
iag
n
o
s
tic
v
ib
r
atio
n
d
ata.
Sig
n
i
f
ica
n
tl
y
,
th
e
-
m
ea
n
s
cl
u
s
te
r
al
g
o
r
ith
m
is
e
m
p
lo
y
ed
.
I
m
p
o
r
tan
tl
y
,
as
a
r
es
u
lt,
in
t
h
i
s
s
tu
d
y
,
-
m
ea
n
s
cl
u
s
ter
-
b
ase
d
Sh
an
n
o
n
en
tr
o
p
y
s
h
o
w
ed
th
e
ab
ilit
y
to
d
etec
t
f
o
u
r
s
e
v
er
itie
s
o
f
r
o
to
r
d
a
m
ag
e,
w
h
ic
h
in
c
lu
d
e
H
L
T
co
n
d
iti
o
n
,
HB
R
B
,
1
B
R
B
,
an
d
2
B
R
B
.
I
n
[
6
9
]
,
an
ea
r
l
y
FD
ap
p
r
o
ac
h
o
f
th
e
r
o
to
r
b
a
s
ed
o
n
e
m
p
ir
ical
m
o
d
e
d
ec
o
m
p
o
s
itio
n
(
E
MD
)
,
A
NN,
an
d
w
a
v
elet
tr
an
s
f
o
r
m
(
W
T
)
u
s
in
g
v
ib
r
atio
n
s
ig
n
al
s
is
p
r
o
p
o
s
ed
.
W
T
is
ap
p
lie
d
to
d
ec
o
m
p
o
s
e
v
ib
r
atio
n
s
i
g
n
al
s
i
n
to
s
ev
er
a
l
b
an
d
w
id
t
h
s
;
t
h
e
n
,
E
MD
is
ap
p
lied
to
o
b
tain
co
r
r
esp
o
n
d
in
g
f
r
eq
u
en
c
y
b
an
d
w
id
t
h
f
r
o
m
i
n
tr
i
n
s
ic
m
o
d
e
f
u
n
ctio
n
s
(
I
MFs).
No
tab
l
y
,
in
th
e
cla
s
s
i
f
icat
io
n
s
tag
e,
th
r
ee
l
a
y
er
s
b
ac
k
p
r
o
p
ag
atio
n
n
e
u
r
al
n
et
w
o
r
k
m
o
d
el
is
e
m
p
lo
y
ed
.
Ho
w
e
v
er
,
th
e
co
m
p
r
e
h
e
n
s
i
v
e
ap
p
r
o
ac
h
o
f
W
P
D,
E
MD
an
d
B
P
NN
s
h
o
wed
g
o
o
d
d
iag
n
o
s
is
,
ex
tr
ac
tio
n
,
a
n
d
class
i
f
ica
tio
n
r
esu
lt
s
les
s
p
o
w
er
s
i
g
n
al.
I
n
[
7
0
]
,
a
d
ata
f
u
s
io
n
tec
h
n
iq
u
e
f
o
r
th
e
r
o
to
r
b
ased
o
n
in
f
o
r
m
atio
n
e
n
tr
o
p
y
an
d
NN
u
s
i
n
g
v
ib
r
atio
n
s
i
g
n
al
s
is
in
tr
o
d
u
ce
d
.
B
y
ap
p
ly
i
n
g
t
h
e
in
f
o
r
m
atio
n
en
tr
o
p
y
m
et
h
o
d
,
th
r
ee
ch
ar
ac
ter
is
tic
s
co
u
ld
ex
tr
ac
t,
n
a
m
el
y
,
p
o
w
er
s
p
ec
tr
u
m
,
s
in
g
u
lar
s
p
ec
tr
u
m
,
an
d
ap
p
r
o
x
im
ate
en
tr
o
p
ies.
A
f
ea
t
u
r
e
f
u
s
io
n
m
o
d
el
b
ased
o
n
P
r
o
b
ab
ilis
tic
(
P
NN)
is
d
ev
elo
p
ed
as
an
FD
an
d
class
i
f
icatio
n
.
Ho
w
e
v
er
,
P
NN
b
ased
i
n
f
o
r
m
a
tio
n
e
n
tr
o
p
y
cla
s
s
i
f
ier
s
h
o
w
ed
s
i
g
n
i
f
ica
n
tl
y
h
i
g
h
er
ac
cu
r
ac
y
.
I
n
[
7
1
]
,
a
C
M
a
n
d
FD a
p
p
r
o
ac
h
f
o
r
cr
ac
k
m
en
to
r
in
g
i
n
t
h
e
r
o
to
r
u
s
in
g
v
ib
r
atio
n
s
ig
n
als i
s
p
r
o
p
o
s
ed
.
Mo
r
eo
v
er
,
in
th
is
a
p
p
r
o
ac
h
,
W
T
an
d
A
NN
ar
e
ap
p
lied
.
T
h
e
W
T
is
ap
p
lied
as
a
f
ea
tu
r
e
ex
tr
ac
tio
n
p
r
o
ce
s
s
.
A
s
a
r
es
u
lt,
th
is
m
eth
o
d
s
h
o
w
s
g
o
o
d
d
iag
n
o
s
i
s
r
esu
lts
.
F
u
r
t
h
er
m
o
r
e,
th
e
s
i
g
n
al
-
to
-
n
o
i
s
e
r
atio
in
cr
ea
s
es
as
a
r
esu
l
t
o
f
s
p
ee
d
's
in
cr
ea
s
i
n
g
;
th
u
s
,
th
e
f
a
u
lt
wo
u
ld
b
e
o
b
v
io
u
s
.
A
cc
o
r
d
in
g
t
o
[
7
2
]
,
a
d
iag
n
o
s
tic
ap
p
r
o
ac
h
f
o
r
s
ev
er
al
lo
ad
s
b
as
ed
o
n
t
h
e
p
s
e
u
d
o
m
et
h
o
d
an
d
cu
r
r
en
t
s
i
g
n
al
is
p
r
o
p
o
s
ed
.
T
h
e
p
s
eu
d
o
-
s
p
ec
tr
u
m
m
et
h
o
d
is
d
e
v
elo
p
ed
to
d
iag
n
o
s
e
f
au
l
t
f
r
eq
u
en
c
y
co
m
p
o
n
en
t
s
.
Ho
w
ev
er
,
d
etec
ti
n
g
f
au
lt
at
li
g
h
t
lo
ad
co
n
d
itio
n
s
i
s
th
e
m
ai
n
ad
v
an
t
ag
e
o
f
th
is
m
eth
o
d
.
T
ab
le
3
(
s
ee
in
A
p
p
en
d
ix
)
s
u
m
m
ar
izes
A
I
s
t
u
d
ies
o
f
C
M
an
d
FD
o
f
R
F
s
.
T
ab
le
4
(
s
ee
i
n
A
p
p
en
d
ix
)
s
u
m
m
ar
ize
s
A
I
a
lg
o
r
ith
m
s
u
s
ed
f
o
r
C
M
an
d
F
D
f
o
r
th
e
r
o
to
r
an
d
th
e
I
M
'
s
s
ta
to
r
.
3
CH
AL
L
E
N
G
E
S AN
D
F
U
T
URE T
RE
N
DS
Fin
d
i
n
g
an
in
telli
g
en
t
C
M
a
n
d
FD
m
et
h
o
d
f
o
r
t
h
e
r
o
to
r
an
d
I
M'
s
s
tato
r
i
s
co
n
s
id
er
ed
a
ch
a
lle
n
g
i
n
g
task
[
7
6
-
78]
.
T
h
is
s
ec
tio
n
s
u
m
m
ar
izes
t
h
e
c
h
alle
n
g
e
s
an
d
f
u
tu
r
e
tr
e
n
d
s
f
ac
i
n
g
C
M
a
n
d
F
D
o
f
I
M
'
s
s
tato
r
an
d
r
o
to
r
.
-
I
t
is
cr
u
cial
to
d
ev
elo
p
co
s
t
-
ef
f
ec
ti
v
e,
f
as
t,
n
o
n
-
i
n
v
a
s
i
v
e,
n
o
n
-
in
tr
u
s
i
v
e
n
es
s
,
w
ir
eless
,
en
er
g
y
-
e
f
f
icie
n
t,
an
d
h
ig
h
l
y
ac
cu
r
ate
s
en
s
o
r
s
to
s
o
lv
e
co
n
v
en
tio
n
al
s
en
s
o
r
s
p
r
o
b
le
m
s
[
3
6
]
.
-
A
I
alg
o
r
it
h
m
s
h
a
v
e
to
b
e
u
s
ed
to
b
u
ild
a
b
etter
p
er
f
o
r
m
an
ce
,
lo
w
co
s
t,
co
n
ti
n
u
o
u
s
,
an
d
o
n
-
li
n
e
C
M
an
d
FD
m
et
h
o
d
[
7
9
]
.
-
A
I
h
y
b
r
id
s
y
s
te
m
s
s
h
o
u
ld
b
e
d
ev
elo
p
ed
to
d
ea
l w
it
h
m
u
ltip
le
f
au
l
ts
[
8
0
]
.
-
A
I
s
y
s
te
m
t
h
at
ca
n
d
iag
n
o
s
e
al
l I
M
f
au
lt
s
(
b
ea
r
in
g
,
s
tato
r
,
an
d
r
o
to
r
)
s
h
o
u
ld
b
e
d
ev
elo
p
ed
[
8
1
]
.
-
F
au
lt
’
s
s
ize
an
d
s
e
v
er
it
y
b
ased
o
n
A
I
tec
h
n
iq
u
es s
h
o
u
ld
b
e
d
i
s
cu
s
s
ed
m
o
r
e
[
8
2
]
.
-
P
r
o
g
n
o
s
tic
tech
n
iq
u
e
s
s
h
o
u
ld
b
e
d
ev
elo
p
ed
b
ased
o
n
A
I
[
8
3
]
.
-
B
ig
d
ata
an
al
y
tics
,
ex
p
er
t
s
y
s
t
e
m
s
,
ad
v
a
n
ce
d
s
i
g
n
al
p
r
o
ce
s
s
i
n
g
al
g
o
r
ith
m
s
,
a
n
d
d
ata
f
u
s
io
n
s
h
o
u
ld
b
e
u
s
ed
alo
n
g
w
it
h
A
I
to
d
ev
elo
p
C
M
an
d
FD a
lg
o
r
it
h
m
s
[
8
4
-
8
6
]
.
-
Fu
zz
y
-
b
a
s
ed
f
a
u
lt
-
to
ler
an
t
an
d
in
ter
n
e
t
o
f
th
in
g
s
(
I
o
T
)
tech
n
iq
u
e
s
b
ased
o
n
ad
v
a
n
ce
d
s
e
n
s
o
r
s
tech
n
o
lo
g
y
s
h
o
u
ld
b
e
d
ev
elo
p
ed
[
8
7
-
91]
.
4.
CO
NCLU
SI
O
N
R
ed
u
ci
n
g
m
a
in
te
n
a
n
ce
co
s
t
s
a
n
d
i
m
p
r
o
v
i
n
g
t
h
e
a
v
ailab
ilit
y
an
d
r
e
liab
ilit
y
o
f
m
ac
h
i
n
es
ar
e
cr
u
cial
i
n
th
e
m
o
d
er
n
in
d
u
s
tr
ial
w
o
r
ld
.
C
M
an
d
FD
ar
e
b
ein
g
u
s
ed
to
m
o
n
ito
r
th
e
h
ea
lt
h
o
f
m
ac
h
i
n
es.
T
h
u
s
,
th
e
ar
ticle
p
r
esen
ts
a
b
r
ief
r
e
v
ie
w
o
f
A
I
m
et
h
o
d
s
f
o
r
C
M
a
n
d
FD
o
f
S
/R
f
au
lts
o
f
i
n
d
u
ct
io
n
m
ac
h
i
n
es
s
u
ch
as
I
M.
S/
R
f
au
lts
r
ep
r
ese
n
t
ap
p
r
o
x
i
m
ate
l
y
5
0
%
o
f
I
M'
s
to
tal
f
a
u
lt
s
.
Ho
w
e
v
er
,
d
ev
elo
p
i
n
g
n
o
n
-
in
v
asi
v
e,
ea
r
l
y
,
co
n
tin
u
o
u
s
,
an
d
ac
c
u
r
ate
f
a
u
lt
d
iag
n
o
s
t
ic
tech
n
iq
u
es
b
ased
o
n
A
I
m
e
th
o
d
s
i
s
ch
a
llen
g
i
n
g
.
T
h
u
s
,
th
e
p
r
o
p
o
s
ed
s
tu
d
y
d
i
s
cu
s
s
ed
t
h
e
liter
atu
r
e
m
et
h
o
d
s
an
d
h
i
g
h
li
g
h
ted
t
h
e
a
d
v
an
ta
g
es a
n
d
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smal
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d
i
me
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si
o
n
a
l
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y
M
o
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c
o
mp
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t
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t
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o
n
a
l
t
i
me
i
s
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q
u
i
r
e
d
R
e
g
r
e
ssi
o
n
S
i
mp
l
e
a
n
d
d
e
a
l
w
i
t
h
smal
l
d
a
t
a
L
o
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p
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f
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man
c
e
a
n
d
c
l
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ss
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f
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c
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t
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a
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B
a
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e
a
l
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b
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d
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M
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mp
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t
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t
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o
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me
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s
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e
q
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d
.
K
-
me
a
n
s c
l
u
st
e
r
i
n
g
G
o
o
d
p
e
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f
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man
c
e
a
n
d
c
l
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ss
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f
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c
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t
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n
a
c
c
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r
a
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y
D
i
f
f
i
c
u
l
t
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o
i
m
p
l
e
me
n
t
N
a
i
v
e
B
a
y
e
s
D
e
a
l
w
i
t
h
b
i
g
d
a
t
a
L
o
w
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l
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ssi
f
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t
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n
a
c
c
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r
a
c
y
N
e
u
r
o
-
F
u
z
z
y
D
e
a
l
w
i
t
h
b
i
g
d
a
t
a
a
n
d
g
o
o
d
d
i
a
g
n
o
si
s
a
c
c
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r
a
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y
M
o
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e
c
o
mp
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t
a
t
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o
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l
t
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me
i
s
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e
q
u
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d
.
A
N
N
D
e
a
l
w
i
t
h
b
i
g
d
a
t
a
,
g
o
o
d
p
e
r
f
o
r
man
c
e
,
a
n
d
g
o
o
d
d
i
a
g
n
o
si
s
a
c
c
u
r
a
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y
M
o
r
e
t
r
a
i
n
i
n
g
a
n
d
c
o
m
p
u
t
a
t
i
o
n
a
l
t
i
me
i
s
r
e
q
u
i
r
e
d
.
RE
F
E
R
E
NC
E
S
[1
]
R.
P
u
c
h
e
-
P
a
n
a
d
e
ro
,
J.
M
a
rti
n
e
z
-
Ro
m
a
n
,
A
.
S
a
p
e
n
a
-
Ba
n
o
,
J.
Bu
rriel
-
V
a
len
c
ia,
a
n
d
M
.
Riera
-
G
u
a
sp
,
“
F
a
u
lt
Dia
g
n
o
sis
in
th
e
S
li
p
-
F
re
q
u
e
n
c
y
P
lan
e
o
f
In
d
u
c
ti
o
n
M
a
c
h
i
n
e
s
W
o
rk
in
g
in
T
i
m
e
-
V
a
r
y
in
g
Co
n
d
it
io
n
s
,
”
S
e
n
so
rs
,
v
o
l.
2
0
,
n
o
.
1
2
,
2
0
2
0
,
A
rt
.
n
o
.
3
3
9
8
.
[2
]
X
.
T
a
n
g
,
L
.
Zh
u
a
n
g
,
J.
Ca
i,
a
n
d
C.
L
i,
“
M
u
lt
i
-
f
a
u
lt
c
la
ss
i
f
ica
ti
o
n
b
a
se
d
o
n
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
train
e
d
b
y
c
h
a
o
s
p
a
rti
c
le sw
a
r
m
o
p
ti
m
iz
a
ti
o
n
,
”
Kn
o
wled
g
e
-
B
a
se
d
S
y
ste
m
s,
v
o
l.
2
3
,
n
o
.
5
,
p
p
.
4
8
6
-
4
9
0
,
2
0
1
0
.
[3
]
A
.
S
in
g
h
,
B.
G
ra
n
t,
R.
De
F
o
u
r,
C.
S
h
a
rm
a
,
a
n
d
S
.
Ba
h
a
d
o
o
rsin
g
h
,
"
A
re
v
ie
w
o
f
in
d
u
c
ti
o
n
m
o
to
r
f
a
u
lt
m
o
d
e
li
n
g
,
"
El
e
c
tric P
o
we
r S
y
ste
ms
Res
e
a
rc
h
,
v
o
l.
1
3
3
,
p
p
.
1
9
1
-
1
9
7
,
2
0
1
6
.
[4
]
N.
G
.
Oz
c
e
li
k
,
U.
E.
Do
g
ru
,
M
.
Im
e
r
y
u
z
,
a
n
d
L
.
T
.
Erg
e
n
e
,
“
S
y
n
c
h
ro
n
o
u
s
re
lu
c
tan
c
e
m
o
to
r
v
s.
In
d
u
c
ti
o
n
m
o
to
r
a
t
lo
w
-
p
o
w
e
r
in
d
u
strial
a
p
p
li
c
a
ti
o
n
s
:
De
sig
n
a
n
d
c
o
m
p
a
riso
n
,
”
En
e
rg
i
e
s
,
v
o
l.
1
2
,
n
o
.
1
1
,
p
p
.
2
1
9
0
-
2
2
1
0
,
2
0
1
9
.
[5
]
A
.
V
a
ld
e
rra
b
a
n
o
-
G
o
n
z
a
lez
,
J.
C.
Ro
sa
s
-
Ca
ro
,
F
.
Be
lt
ra
n
-
Ca
rb
a
jal,
I.
L
o
p
e
z
-
G
a
rc
i
a
,
R.
T
a
p
ia
-
Olv
e
ra
,
a
n
d
H.
A
.
G
a
b
b
a
r,
“
L
a
r
g
e
in
d
u
c
ti
o
n
m
o
to
r
d
riv
e
p
e
r
f
o
r
m
a
n
c
e
c
o
m
p
a
riso
n
,
”
2
0
1
8
IEE
E
I
n
ter
n
a
t
io
n
a
l
Au
tu
mn
M
e
e
ti
n
g
o
n
Po
we
r,
El
e
c
tro
n
ics
a
n
d
C
o
mp
u
ti
n
g
(
ROPE
C)
,
Ix
tap
a
,
M
e
x
ico
,
2
0
1
8
,
p
p
.
1
-
6.
[6
]
R.
G
a
y
a
th
ri
a
n
d
S
.
K.
V
a
su
d
e
v
a
n
,
“
In
tern
e
t
o
f
th
in
g
s
b
a
se
d
sm
a
rt
h
e
a
lt
h
m
o
n
it
o
r
in
g
o
f
in
d
u
strial
st
a
n
d
a
rd
m
o
to
rs,
”
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
I
n
fo
rm
a
ti
c
s (
IJ
EE
I)
,
v
o
l.
6
,
n
o
.
4
,
p
p
.
3
6
1
-
3
6
7
,
2
0
1
8
.
[7
]
K.
Kim
a
n
d
A
.
G
.
P
a
rlo
s,
“
I
n
d
u
c
t
io
n
m
o
to
r
f
a
u
lt
d
iag
n
o
sis
b
a
se
d
o
n
n
e
u
ro
p
re
d
ict
o
rs
a
n
d
w
a
v
e
let
si
g
n
a
l
p
ro
c
e
ss
in
g
,
”
IEE
E/
A
S
M
E
T
ra
n
sa
c
ti
o
n
s
o
n
M
e
c
h
a
tr
o
n
ics
,
v
o
l.
7
,
n
o
.
2
,
p
p
.
2
0
1
-
2
1
9
,
2
0
0
2
.
[8
]
M
.
Ko
rz
o
n
e
k
,
G
.
T
a
rc
h
a
la,
a
n
d
T
.
Orlo
w
s
k
a
-
Ko
w
a
ls
k
a
,
“
A rev
i
e
w
o
n
M
RA
S
-
ty
p
e
sp
e
e
d
e
sti
m
a
to
rs
fo
r
re
li
a
b
le an
d
ef
f
ici
e
n
t
in
d
u
c
ti
o
n
m
o
to
r
d
riv
e
s,”
IS
A
tra
n
sa
c
ti
o
n
s
,
v
o
l.
9
3
,
p
p
.
1
-
1
3
,
2
0
1
9
.
[9
]
S
.
Ka
rm
a
k
a
r,
S
.
Ch
a
tt
o
p
a
d
h
y
a
y
,
M
.
M
it
ra
,
a
n
d
S
.
S
e
n
g
u
p
ta,
“
I
n
d
u
c
t
io
n
m
o
to
r
f
a
u
lt
d
iag
n
o
sis
,”
S
p
ri
n
g
e
r
L
i
n
k
,
v
o
l.
2
5
,
2
0
1
6
.
[1
0
]
C.
L
u
,
Y.
W
a
n
g
,
M
.
Ra
g
u
lsk
is,
a
n
d
Y.
Ch
e
n
g
,
“
F
a
u
lt
d
iag
n
o
sis
f
o
r
ro
tatin
g
m
a
c
h
in
e
ry
:
A
m
e
th
o
d
b
a
se
d
o
n
im
a
g
e
p
ro
c
e
ss
in
g
,
”
Pl
o
S
o
n
e
,
v
o
l
.
1
1
,
n
o
.
1
0
,
2
0
1
6
,
A
rt
.
n
o
.
e
0
1
6
4
1
1
1
.
[1
1
]
J.
P
o
n
s
-
L
li
n
a
re
s,
J.
A
.
A
n
to
n
i
n
o
-
Da
v
iu
,
M
.
Riera
-
G
u
a
sp
,
S
.
B.
L
e
e
,
T
.
-
j.
Ka
n
g
,
a
n
d
C.
Ya
n
g
,
“
A
d
v
a
n
c
e
d
in
d
u
c
ti
o
n
m
o
to
r
ro
to
r
f
a
u
lt
d
iag
n
o
sis
v
ia
c
o
n
ti
n
u
o
u
s
a
n
d
d
isc
re
te
ti
m
e
-
f
re
q
u
e
n
c
y
to
o
ls,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
In
d
u
stria
l
El
e
c
tro
n
ics
,
v
o
l.
6
2
,
n
o
.
3
,
p
p
.
1
7
9
1
-
1
8
0
2
,
2
0
1
4
.
[1
2
]
S
.
L
.
S
o
u
a
d
,
B.
A
z
z
e
d
in
e
,
a
n
d
S
.
M
e
r
a
d
i
,
“
F
a
u
l
t
d
i
a
g
n
o
s
i
s
o
f
r
o
l
l
i
n
g
e
l
e
m
e
n
t
b
e
a
r
i
n
g
s
u
s
i
n
g
a
r
t
i
f
i
c
i
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
E
l
e
c
t
r
i
c
a
l
a
n
d
C
o
m
p
u
t
e
r
E
n
g
i
n
e
e
r
i
n
g
(
I
J
E
C
E
)
,
v
o
l
.
1
0
,
n
o
.
5
,
p
p
.
5
2
8
8
-
5295
,
2
0
2
0
.
[1
3
]
A
.
G
lo
w
a
c
z
,
W
.
G
lo
w
a
c
z
,
Z.
Glo
w
a
c
z
,
a
n
d
J.
Ko
z
ik
,
“
Earl
y
f
a
u
lt
d
iag
n
o
sis
o
f
b
e
a
rin
g
a
n
d
sta
to
r
f
a
u
lt
s
o
f
th
e
sin
g
le
-
p
h
a
se
in
d
u
c
ti
o
n
m
o
to
r
u
sin
g
a
c
o
u
stic sig
n
a
ls,
”
M
e
a
su
re
me
n
t,
v
o
l.
1
1
3
,
p
p
.
1
-
9
,
2
0
1
8
.
[1
4
]
O.
A
lS
h
o
rm
a
n
,
M
.
M
a
sa
d
e
h
,
F
.
A
lk
a
h
tan
i
a
n
d
A
.
A
lS
h
o
rm
a
n
,
"
A
Re
v
ie
w
o
f
Co
n
d
i
ti
o
n
M
o
n
it
o
rin
g
a
n
d
F
a
u
lt
Dia
g
n
o
sis
a
n
d
De
tec
ti
o
n
o
f
Ro
tat
in
g
M
a
c
h
in
e
ry
Ba
se
d
o
n
Im
a
g
e
A
sp
e
c
ts,
"
2
0
2
0
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Da
t
a
An
a
lytics
fo
r
Bu
si
n
e
ss
a
n
d
In
d
u
st
ry
:
W
a
y
T
o
wa
rd
s
a
S
u
st
a
in
a
b
le
Eco
n
o
my
(
ICDABI
),
S
a
k
h
e
e
r,
Ba
h
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2827
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7
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9
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p
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0
.
[3
3
]
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.
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.
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n
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Y.
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g
,
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d
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.
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l
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0
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[3
4
]
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Ja
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.
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r,
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.
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0
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[3
5
]
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.
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e
id
a
n
d
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W
.
P
in
g
,
“
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lt
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[3
6
]
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.
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.
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.
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k
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la,
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lt
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ti
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to
rs:
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ive
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mp
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1
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8
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.
[3
7
]
P
.
G
a
n
g
sa
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n
d
R.
T
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w
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ri,
“
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ig
n
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se
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[3
8
]
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.
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n
,
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.
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.
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.
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S
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lah
,
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str
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h
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s
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&
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l
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0
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2
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[3
9
]
H.
Bo
y
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s,
B.
Ha
ll
a
q
,
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Cu
n
n
in
g
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m
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tern
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T
):
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mp
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ter
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l.
1
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p
p
.
1
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2
,
2
0
1
8
.
[4
0
]
J.
W
u
,
S
.
G
u
o
,
J.
L
i,
a
n
d
D.
Zen
g
,
“
Big
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a
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t
g
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s:
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6
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[4
1
]
J.
W
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,
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.
G
u
o
,
J.
L
i,
a
n
d
D.
Ze
n
g
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“
Big
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ta
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ll
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s:
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ta,”
IEE
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3
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p
.
8
7
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7
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2
0
1
6
.
[4
2
]
R.
A
tat,
L
.
L
iu
,
J.
W
u
,
G
.
L
i,
C.
Ye
,
a
n
d
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Ya
n
g
,
“
Big
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ta
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3
-
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3
6
3
6
,
2
0
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8
.
[4
3
]
J.
W
u
,
S
.
G
u
o
,
H.
Hu
a
n
g
,
W
.
L
i
u
,
a
n
d
Y.
X
ian
g
,
“
In
f
o
rm
a
ti
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a
n
d
c
o
m
m
u
n
ica
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o
n
s
tec
h
n
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o
r
su
sta
in
a
b
le
d
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v
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ls:
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-
a
rt,
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d
s
a
n
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p
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rsp
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c
ti
v
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s,
”
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2
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9
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6
,
2
0
1
8
.
[4
4
]
C.
Ka
n
,
H.
Ya
n
g
,
a
n
d
S
.
Ku
m
a
ra
,
“
P
a
ra
ll
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c
o
m
p
u
ti
n
g
a
n
d
n
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a
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ti
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st
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tern
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ti
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s
sin
g
a
n
d
c
o
n
d
it
io
n
m
o
n
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o
rin
g
,
”
J
o
u
rn
a
l
o
f
m
a
n
u
fa
c
t
u
rin
g
sy
ste
ms
,
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l.
4
6
,
p
p
.
2
8
2
-
2
9
3
,
2
0
1
8
.
[4
5
]
M
.
Ru
n
g
ru
a
n
g
a
n
u
k
u
l
a
n
d
T
.
S
ir
i
b
o
rv
o
r
n
ra
tan
a
k
u
l,
“
De
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L
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a
rn
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g
Ba
se
d
G
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stu
re
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si
f
ica
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f
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r
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n
d
P
h
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l
T
h
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ra
p
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In
tera
c
ti
v
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P
ro
g
ra
m
,
”
in
In
ter
n
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ti
o
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l
Co
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re
n
c
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o
n
Hu
m
a
n
-
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ter
In
ter
a
c
ti
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n
,
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l.
1
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8
,
p
p
.
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,
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[4
6
]
J.
F
a
iz,
A
.
T
a
k
b
a
sh
,
a
n
d
E.
M
a
z
a
h
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ri
-
T
e
h
ra
n
i,
“
A Re
v
ie
w
o
f
A
p
p
li
c
a
ti
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o
f
S
ig
n
a
l
P
ro
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e
ss
in
g
T
e
c
h
n
iq
u
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s f
o
r
F
a
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l
t
Dia
g
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sis o
f
In
d
u
c
ti
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n
M
o
to
rs
-
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a
rt
I,
”
AUT
J
o
u
rn
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l
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4
9
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,
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p
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1
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,
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0
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7
.
[4
7
]
C.
Ke
rd
v
ib
u
lv
e
c
h
,
“
H
y
b
rid
m
o
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n
d
m
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c
s
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p
p
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ti
o
n
,”
2
0
1
4
IEE
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In
ter
n
a
ti
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a
l
Co
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fer
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S
y
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ms
,
M
a
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,
p
p
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2
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6
7
-
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2
.
[4
8
]
M
.
Žark
o
v
ić
a
n
d
Z.
S
to
jk
o
v
ić,
“
A
n
a
l
y
si
s
o
f
a
rti
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telli
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d
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n
d
d
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n
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stics
,
”
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e
c
tric P
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r S
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Res
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v
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.
1
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9
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1
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6
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1
7
.
[4
9
]
C.
M
a
ll
a
a
n
d
I.
P
a
n
ig
ra
h
i,
“
Re
v
iew
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it
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ly
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n
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s,
”
J
o
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l
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ra
ti
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n
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&
T
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l.
7
,
p
p
.
4
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4
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.
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A
.
Ku
m
a
r
a
n
d
R.
Ku
m
a
r,
“
Ro
le
o
f
si
g
n
a
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p
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in
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o
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e
f
e
c
t:
a
re
v
i
e
w
,
”
J
o
u
rn
a
l
o
f
No
n
d
e
stru
c
ti
v
e
Eva
l
u
a
ti
o
n
,
v
o
l.
3
8
,
no
.
5
,
2
0
1
9
.
[5
1
]
P
.
Ku
m
a
r
a
n
d
A
.
S
.
Ha
ti
,
“
Re
v
ie
w
o
n
M
a
c
h
in
e
L
e
a
rn
in
g
A
l
g
o
rit
h
m
B
a
se
d
F
a
u
lt
De
tec
ti
o
n
in
I
n
d
u
c
ti
o
n
M
o
t
o
rs,
”
Arc
h
ive
s o
f
Co
m
p
u
t
a
ti
o
n
a
l
M
e
th
o
d
s in
En
g
in
e
e
rin
g
,
p
p
.
1
-
1
2
,
2
0
2
0
.
[5
2
]
Y.
L
e
i,
B.
Ya
n
g
,
X
.
Jia
n
g
,
F
.
Ji
a
,
N.
L
i,
a
n
d
A
.
K.
Na
n
d
i,
“
A
p
p
li
c
a
ti
o
n
s
o
f
m
a
c
h
in
e
lea
rn
in
g
to
m
a
c
h
in
e
fa
u
lt
d
iag
n
o
sis: A
re
v
ie
w
a
n
d
ro
a
d
m
a
p
,
”
M
e
c
h
a
n
ica
l
S
y
ste
ms
a
n
d
S
ig
n
a
l
Pro
c
e
ss
in
g
,
v
o
l.
1
3
8
,
2
0
2
0
,
A
rt
.
n
o
.
1
0
6
5
8
7
.
[5
3
]
G
.
M
irza
e
v
a
,
K.
I.
S
a
a
d
,
a
n
d
M
.
G
.
Ja
h
ro
m
i,
“
Co
m
p
re
h
e
n
siv
e
Dia
g
n
o
stics
o
f
In
d
u
c
ti
o
n
M
o
to
r
F
a
u
lt
s
Ba
se
d
o
n
M
e
a
su
re
m
e
n
t
o
f
S
p
a
c
e
a
n
d
T
i
m
e
De
p
e
n
d
e
n
c
ies
o
f
A
ir
G
a
p
F
lu
x
,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
I
n
d
u
str
y
Ap
p
li
c
a
t
io
n
s
,
v
o
l.
5
3
,
n
o
.
3
,
p
p
.
2
6
5
7
-
2
6
6
6
,
2
0
1
7
.
[5
4
]
C.
D.
T
ra
n
,
P
.
Bra
n
d
ste
tt
e
r
,
M
.
C
.
H.
Ng
u
y
e
n
,
S
.
D.
Ho
,
H
.
D.
Ba
c
h
,
a
n
d
P
.
N.
P
h
a
m
,
“
A
ro
b
u
st
d
i
a
g
n
o
sis
m
e
th
o
d
f
o
r
sp
e
e
d
se
n
so
r
f
a
u
lt
b
a
se
d
o
n
sta
to
r
c
u
rre
n
ts
in
t
h
e
RF
OC
i
n
d
u
c
ti
o
n
m
o
to
r
d
riv
e
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
3
,
p
p
.
3
0
3
5
-
3
0
4
6
,
2
0
2
0
.
[5
5
]
A
.
G
lo
wa
c
z
a
n
d
Z.
G
lo
w
a
c
z
,
“
Dia
g
n
o
stics
o
f
sta
to
r
f
a
u
lt
s
o
f
th
e
sin
g
le
-
p
h
a
se
i
n
d
u
c
ti
o
n
m
o
to
r
u
sin
g
t
h
e
rm
a
l
im
a
g
e
s,
”
M
o
A
S
o
S
a
n
d
se
lec
ted
c
las
sif
i
e
rs,
M
e
a
su
re
me
n
t
,
v
o
l.
9
3
,
p
p
.
8
6
-
9
3
,
2
0
1
6
.
[5
6
]
T
.
Am
a
ra
l,
V
.
P
ires
,
J.
M
a
rti
n
s,
A
.
P
ires
,
a
n
d
M
.
Criso
sto
m
o
,
"
I
m
a
g
e
p
ro
c
e
ss
in
g
to
a
n
e
u
ro
-
f
u
z
z
y
c
la
ss
i
f
ier
f
o
r
d
e
tec
ti
o
n
a
n
d
d
iag
n
o
sis
o
f
in
d
u
c
ti
o
n
m
o
to
r
sta
to
r
f
a
u
lt
,
"
IECO
N
2
0
0
7
-
3
3
rd
An
n
u
a
l
Co
n
fer
e
n
c
e
o
f
th
e
IEE
E
In
d
u
stria
l
El
e
c
tro
n
ics
S
o
c
iety
,
T
a
ip
e
i,
T
a
iw
a
n
,
2
0
0
7
,
p
p
.
2
4
0
8
-
2
4
1
3
.
[5
7
]
A
.
V
e
rm
a
,
S
.
S
a
ra
n
g
i,
a
n
d
M
.
H
.
Ko
lek
a
r,
"
S
tato
r
w
in
d
in
g
f
a
u
lt
p
re
d
ictio
n
o
f
in
d
u
c
ti
o
n
m
o
to
rs
u
sin
g
m
u
l
t
i
-
s
c
a
l
e
e
n
t
r
o
p
y
a
n
d
g
r
e
y
f
u
z
z
y
o
p
t
i
m
i
z
a
t
i
o
n
m
e
t
h
o
d
s
,
"
C
o
m
p
u
t
e
r
s
&
E
l
e
c
t
r
i
c
a
l
E
n
g
i
n
e
e
r
i
n
g
,
v
o
l
.
4
0
,
n
o
.
7
,
p
p
.
2
2
4
6
-
2258
,
2
0
1
4
.
[5
8
]
G
.
H.
Ba
z
a
n
,
P
.
R.
S
c
a
las
sa
ra
,
W.
En
d
o
,
A
.
G
o
e
d
tel,
W
.
F
.
G
o
d
o
y
,
a
n
d
R.
H.
C.
P
a
lác
io
s,
"
S
tato
r
f
a
u
lt
a
n
a
ly
sis
o
f
th
re
e
-
p
h
a
se
in
d
u
c
ti
o
n
m
o
to
rs
u
sin
g
in
f
o
rm
a
ti
o
n
m
e
a
su
re
s
a
n
d
a
rti
f
icia
l
n
e
u
ra
l
n
e
tw
o
rk
s,"
El
e
c
tric
Po
we
r
S
y
ste
ms
Res
e
a
rc
h
,
v
o
l.
1
4
3
,
p
p
.
3
4
7
-
3
5
6
,
2
0
1
7
.
[5
9
]
M
.
Z.
A
li
,
M
.
N.
S
.
K.
S
h
a
b
b
ir,
X.
L
ian
g
,
Y.
Zh
a
n
g
,
a
n
d
T
.
Hu
,
"
M
a
c
h
in
e
lea
rn
in
g
-
b
a
se
d
f
a
u
lt
d
iag
n
o
sis
f
o
r
sin
g
le
-
a
n
d
m
u
lt
i
-
f
a
u
lt
s
in
in
d
u
c
ti
o
n
m
o
to
rs
u
sin
g
m
e
a
su
re
d
sta
to
r
c
u
rre
n
t
s
a
n
d
v
ib
ra
ti
o
n
sig
n
a
ls,"
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
In
d
u
stry
Ap
p
li
c
a
ti
o
n
s
,
v
o
l.
5
5
,
n
o
.
3
,
p
p
.
2
3
7
8
-
2
3
9
1
,
2
0
1
9
.
[6
0
]
L
.
M
a
ra
a
b
a
,
Z.
A
l
-
Ha
m
o
u
z
,
a
n
d
M
.
A
b
id
o
,
"
A
n
e
ff
icie
n
t
sta
to
r
in
t
e
r
-
tu
rn
f
a
u
lt
d
iag
n
o
sis to
o
l
f
o
r
i
n
d
u
c
ti
o
n
m
o
to
rs,"
En
e
rg
ies
,
v
o
l.
1
1
,
n
o
.
3
,
2
0
1
8
,
A
rt
.
n
o
.
6
5
3
.
[6
1
]
H.
Ch
e
rif
,
A
.
M
e
n
a
c
e
r,
R.
Ro
m
a
ry
,
a
n
d
R.
P
u
sc
a
,
"
Disp
e
rsio
n
f
ie
ld
a
n
a
ly
sis
u
sin
g
d
isc
re
te
w
a
v
e
le
t
tran
sf
o
r
m
f
o
r
in
ter
-
tu
r
n
sta
to
r
f
a
u
lt
d
e
tec
ti
o
n
in
in
d
u
c
ti
o
n
m
o
to
rs,"
2
0
1
7
IEE
E
1
1
th
I
n
ter
n
a
ti
o
n
a
l
S
y
mp
o
siu
m
o
n
Dia
g
n
o
stics
f
o
r
El
e
c
trica
l
M
a
c
h
in
e
s,
Po
we
r E
lec
t
ro
n
ics
a
n
d
Dr
ive
s (
S
DEM
PE
D)
,
T
in
o
s,
2
0
1
7
,
p
p
.
1
0
4
-
1
0
9
.
[6
2
]
R.
Ke
c
h
id
a
,
A
.
M
e
n
a
c
e
r,
H.
T
a
lh
a
o
u
i,
a
n
d
H.
C
h
e
rif
,
"
Disc
r
e
te
wa
v
e
let
tr
a
n
sf
o
r
m
f
o
r
sta
to
r
fa
u
lt
d
e
tec
ti
o
n
i
n
in
d
u
c
ti
o
n
m
o
to
rs,"
2
0
1
5
IEE
E
1
0
t
h
In
ter
n
a
ti
o
n
a
l
S
y
mp
o
siu
m
o
n
Dia
g
n
o
stics
fo
r
El
e
c
trica
l
M
a
c
h
in
e
s,
Po
we
r
El
e
c
tro
n
ics
a
n
d
Dr
ive
s (
S
DEM
PE
D
)
,
G
u
a
rd
a
,
P
o
rt
u
g
a
l,
2
0
1
5
,
p
p
.
1
0
4
-
1
0
9
.
[6
3
]
S
.
L
ip
in
g
,
T
.
Jia
sh
e
n
g
,
W
.
P
a
n
p
a
n
,
H.
L
i,
a
n
d
Z.
X
ia
o
lei,
"
S
tato
r
f
a
u
lt
d
iag
n
o
sis
o
f
in
d
u
c
ti
o
n
m
o
to
rs
u
si
n
g
th
e
o
p
ti
m
a
l
w
a
v
e
let
tree
a
n
d
im
p
ro
v
e
d
BP
n
e
u
ra
l
n
e
tw
o
rk
,
"
T
ra
n
sa
c
ti
o
n
s
o
f
C
h
in
a
El
e
c
tro
tec
h
n
ica
l
S
o
c
iety
,
v
o
l.
3
0
,
n
o
.
2
4
,
p
p
.
3
8
-
4
5
,
2
0
1
5
.
[6
4
]
M
.
S
a
b
o
u
ri,
M
.
O
j
a
g
h
i
,
J
.
F
a
i
z
,
a
n
d
A
.
J
.
M
.
C
a
r
d
o
s
o
,
"
M
o
d
e
l
-
b
a
s
e
d
u
n
i
f
i
e
d
t
e
c
h
n
i
q
u
e
f
o
r
i
d
e
n
t
i
f
y
i
n
g
s
e
v
e
r
i
t
i
e
s
o
f
s
t
a
t
o
r
i
n
t
e
r
-
t
u
r
n
a
n
d
r
o
t
o
r
b
r
o
k
e
n
b
a
r
f
a
u
l
t
s
i
n
S
C
I
M
s
,
"
I
E
T
E
l
e
c
t
r
i
c
P
o
w
e
r
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
1
4
,
n
o
.
2
,
pp.
204
-
2
1
1
,
2
0
2
0
.
[6
5
]
P
.
L
u
o
n
g
a
n
d
W
.
W
a
n
g
,
"
S
m
a
rt
S
e
n
so
r
-
b
a
se
d
S
y
n
e
rg
isti
c
A
n
a
l
y
sis
f
o
r
Ro
to
r
Ba
r
F
a
u
lt
De
tec
ti
o
n
o
f
In
d
u
c
ti
o
n
M
o
to
rs
,
"
IEE
E/
A
S
M
E
T
ra
n
sa
c
ti
o
n
s o
n
M
e
c
h
a
tro
n
ics
,
v
o
l.
2
5
,
n
o
.
2
,
p
p
.
1
0
6
7
-
1
0
7
5
,
2
0
2
0
.
[6
6
]
A
.
G
lo
w
a
c
z
,
"
A
c
o
u
stic
b
a
se
d
fa
u
lt
d
iag
n
o
sis
o
f
t
h
re
e
-
p
h
a
se
in
d
u
c
t
io
n
m
o
to
r,
"
Ap
p
l
ied
Aco
u
stics
,
v
o
l.
1
3
7
,
p
p
.
8
2
-
8
9
,
2
0
1
8
.
[6
7
]
I.
M
a
rti
n
-
Dia
z
,
D.
M
o
rin
ig
o
-
S
o
te
lo
,
O.
Du
q
u
e
-
P
e
re
z
,
a
n
d
R.
J.
Ro
m
e
ro
-
T
ro
n
c
o
so
,
"
A
n
E
x
p
e
ri
m
e
n
tal
Co
m
p
a
ra
ti
v
e
Ev
a
lu
a
ti
o
n
o
f
M
a
c
h
in
e
L
e
a
rn
in
g
T
e
c
h
n
iq
u
e
s
f
o
r
M
o
to
r
F
a
u
l
t
Dia
g
n
o
sis
Un
d
e
r
V
a
ri
o
u
s
Op
e
ra
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9
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0
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1
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.
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2
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3
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4
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9
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L
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rti
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4
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[8
5
]
O.
M
.
A
l
-
S
h
o
rm
a
n
,
"
L
o
ss
y
Dig
i
tal
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m
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g
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p
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Tec
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iq
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e
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th
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c
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e
n
Ba
sis,"
Ya
r
m
o
u
k
Un
iv
e
rsit
y
,
2
0
1
2
.
[8
6
]
M
.
A
l
-
k
h
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a
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O
.
A
lS
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g
a
n
d
In
f
o
rm
a
ti
c
s
,
2
0
2
0
.
[8
7
]
A
lsh
o
r
m
a
n
,
A
.
M
.
,
A
lsh
o
rm
a
n
,
O.,
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a
n
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M
.
,
G
lo
w
a
c
z
,
A
.
,
M
u
h
a
m
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a
d
,
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.
,
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e
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ra
,
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.
,
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u
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s
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a
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lt
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o
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t
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tr
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f
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id
irec
ti
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n
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l
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o
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il
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b
o
t
,
”
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a
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,
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8
,
n
o
.
3
,
2
0
2
0
,
A
rt
.
n
o
.
5
5
.
[8
8
]
O.
A
lS
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o
rm
a
n
,
B.
A
lS
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o
rm
a
n
,
M
.
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lk
h
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ss
a
w
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.
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lk
a
h
tan
i,
"
A
Re
v
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w
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te
rn
e
t
o
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M
e
d
ica
l
T
h
in
g
s
(Io
M
T
)
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m
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te
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a
lt
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o
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it
o
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th
ro
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W
e
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ra
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le
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e
n
so
rs:
A
Ca
se
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tu
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Dia
b
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P
a
ti
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n
ts
,
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n
d
o
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si
a
n
J
o
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rn
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l
o
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trica
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g
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m
p
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ter
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c
ien
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e
(
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)
,
v
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l.
2
0
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n
o
.
1
,
p
p
.
4
1
4
-
4
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2
,
2
0
2
0
.
[8
9
]
O.
A
lS
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o
rm
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n
,
B.
A
lsh
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rm
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n
,
F
.
A
lk
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re
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ie
w
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f
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ra
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le
se
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rs
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b
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se
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w
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a
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ti
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to
m
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d
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tes
,
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ter
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(
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),
v
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o
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1
,
p
p
.
6
4
6
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3
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2
0
2
1
.
[9
0
]
O.
A
lS
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rm
a
n
,
B.
A
lsh
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rm
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n
,
a
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d
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a
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,
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m
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c
ti
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Re
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a
in
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in
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so
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d
o
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o
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rn
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trica
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g
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g
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f
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ma
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s (
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,
v
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8
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o
.
3
,
p
p
.
5
6
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-
5
7
3
,
2
0
2
0
.
[9
1
]
A
lS
h
o
rm
a
n
,
O.,
Ir
f
a
n
,
M
.
,
S
a
a
d
,
N.,
Zh
e
n
,
D.,
Ha
id
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N.,
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lo
w
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A
.
a
n
d
A
lS
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rm
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,
A
.
,
“
A Re
v
iew
o
f
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icia
l
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telli
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e
M
e
th
o
d
s
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g
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d
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a
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lt
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g
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sis
o
f
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ll
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g
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m
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t
Be
a
rin
g
s
f
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r
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d
u
c
ti
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n
M
o
to
r
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S
h
o
c
k
a
n
d
Vi
b
r
a
ti
o
n
,
v
o
l
.
2
0
2
0
,
2
0
2
0
.
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