I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
p
u
t
er
Science
Vo
l.
3
8
,
No
.
1
,
A
p
r
il
20
2
5
,
p
p
.
1
82
~
1
92
I
SS
N:
2
502
-
4
7
52
,
DOI
: 1
0
.
1
1
5
9
1
/i
j
ee
cs
.v
3
8
.
i
1
.
pp
1
82
-
1
92
182
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs
.
ia
esco
r
e.
co
m
M
a
chine learning
bas
ed stato
r
-
w
inding
f
a
ult
sev
e
rit
y
det
ec
tion
in induc
tion
m
o
to
rs
P
a
rt
ha
M
is
hra
1
,
Sh
ub
ha
s
i
s
h
Sa
rk
a
r
2
,
Sa
nd
ip Sa
ha
Cho
w
dh
ury
3
,
Sa
nta
nu
Da
s
2
1
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
i
c
a
l
En
g
i
n
e
e
r
i
n
g
,
C
o
l
l
e
g
e
o
f
En
g
i
n
e
e
r
i
n
g
a
n
d
M
a
n
a
g
e
me
n
t
,
K
o
l
a
g
h
a
t
,
I
n
d
i
a
2
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
i
c
a
l
En
g
i
n
e
e
r
i
n
g
,
Jal
p
a
i
g
u
r
i
G
o
v
e
r
n
me
n
t
E
n
g
i
n
e
e
r
i
n
g
C
o
l
l
e
g
e
,
Jal
p
a
i
g
u
r
i
,
I
n
d
i
a
3
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
i
c
a
l
En
g
i
n
e
e
r
i
n
g
,
A
c
a
d
e
my
o
f
Te
c
h
n
o
l
o
g
y
,
H
o
o
g
h
l
y
,
I
n
d
i
a
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
l
20
,
2
0
2
4
R
ev
i
s
ed
Oct
22
,
2
0
2
4
A
cc
ep
ted
Oct
30
,
2
0
2
4
A
p
p
ro
x
ima
tel
y
3
5
%
o
f
a
ll
in
d
u
c
ti
o
n
m
o
to
r
d
e
f
e
c
ts
a
re
c
a
u
se
d
b
y
st
a
to
r
in
ter
-
tu
rn
f
a
u
lt
s.
In
t
h
is
p
a
p
e
r
a
n
o
v
e
l
a
lg
o
rit
h
m
h
a
s
b
e
e
n
p
ro
p
o
se
d
to
a
n
a
ly
z
e
th
e
th
re
e
-
p
h
a
se
sta
to
r
c
u
rre
n
t
sig
n
a
l
s
c
a
p
tu
re
d
f
ro
m
th
e
m
o
to
r
w
h
il
e
it
is
i
n
o
p
e
ra
ti
o
n
.
T
h
e
su
g
g
e
ste
d
m
e
t
h
o
d
se
e
k
s
to
id
e
n
ti
f
y
sta
to
r
in
ter
-
t
u
rn
sh
o
rt
c
ircu
it
f
a
u
lt
s
in
e
a
rly
sta
g
e
a
n
d
tak
e
th
e
a
p
p
ro
p
riate
a
c
ti
o
n
to
p
r
e
v
e
n
t
th
e
m
o
to
r'
s
c
o
n
d
it
i
o
n
f
ro
m
g
e
tt
in
g
wo
rse
.
T
h
re
e
-
p
h
a
se
c
u
rre
n
t
sig
n
a
ls
h
a
v
e
b
e
e
n
c
a
p
tu
re
d
u
n
d
e
r
h
e
a
lt
h
y
a
n
d
f
a
u
lt
y
c
o
n
d
it
i
o
n
s o
f
th
e
m
o
to
r
.
In
v
o
lv
in
g
d
isc
re
te
w
a
v
e
let
tran
s
f
o
r
m
(D
W
T
)
b
a
se
d
d
e
c
o
m
p
o
siti
o
n
f
o
ll
o
w
e
d
b
y
re
c
o
n
stru
c
ti
o
n
u
sin
g
i
n
v
e
rse
D
WT
(ID
WT
),
5
0
Hz
f
u
n
d
a
m
e
n
tal
c
o
m
p
o
n
e
n
t
h
a
s
b
e
e
n
re
m
o
v
e
d
f
ro
m
th
e
c
a
p
tu
re
d
ra
w
c
u
rre
n
t
sig
n
a
ls.
S
u
b
se
q
u
e
n
t
ly
,
fro
m
e
a
c
h
p
h
a
se
c
u
rre
n
t
1
5
sta
ti
stica
l
p
a
ra
m
e
ters
h
a
v
e
b
e
e
n
re
tri
e
v
e
d
.
T
h
e
sta
ti
stica
l
p
a
ra
m
e
ters
in
c
lu
d
e
m
e
a
n
,
sta
n
d
a
rd
d
e
v
iatio
n
,
sk
e
w
n
e
ss
,
k
u
rto
sis,
p
e
a
k
-
to
-
p
e
a
k
,
ro
o
t
m
e
a
n
sq
u
a
re
(
RM
S
)
,
e
n
e
rg
y
,
c
re
st
f
a
c
to
r,
f
o
rm
f
a
c
to
r,
im
p
u
lse
f
a
c
to
r,
a
n
d
m
a
rg
in
f
a
c
to
r.
A
t
th
e
e
n
d
,
a
sta
n
d
a
rd
m
a
c
h
in
e
lea
rn
in
g
a
lg
o
rit
h
m
n
a
m
e
l
y
e
rro
r
c
o
rre
c
ti
n
g
o
u
tp
u
t
c
o
d
e
s
-
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
(ECOC
-
S
V
M
)
h
a
s
b
e
e
n
e
m
p
lo
y
e
d
to
c
las
sify
si
x
d
if
fe
re
n
t
se
v
e
rit
y
o
f
sta
to
r
w
in
d
in
g
f
a
u
lt
s.
T
h
e
p
ro
p
o
se
d
f
a
u
lt
d
iag
n
o
sis m
e
t
h
o
d
is l
o
a
d
a
n
d
m
o
to
r
-
ra
ti
n
g
i
n
d
e
p
e
n
d
e
n
t.
K
ey
w
o
r
d
s
:
Dis
cr
ete
w
av
ele
t
tr
an
s
f
o
r
m
s
E
r
r
o
r
c
o
r
r
ec
tin
g
o
u
tp
u
t c
o
d
es
I
n
d
u
ctio
n
m
o
to
r
s
Statis
t
ical
f
ea
t
u
r
e
Stato
r
w
i
n
d
in
g
f
au
l
ts
Su
p
p
o
r
t v
ec
to
r
m
ac
h
i
n
e
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC
BY
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
San
ta
n
u
Das
E
lectr
ical
E
n
g
i
n
ee
r
i
n
g
Dep
ar
t
m
en
t,
J
alp
aig
u
r
i G
o
v
er
n
m
en
t
E
n
g
i
n
ee
r
i
n
g
C
o
lleg
e
J
alp
aig
u
r
i,
I
n
d
ia
E
m
ail: sa
n
ta
n
u
.
d
d
as@
g
m
a
il.c
o
m
1.
I
NT
RO
D
UCT
I
O
N
I
n
d
u
ctio
n
m
o
to
r
s
h
a
v
e
a
w
id
e
ap
p
licatio
n
in
in
d
u
s
tr
ie
s
a
n
d
th
eir
f
ail
u
r
e
ca
n
lead
to
s
ig
n
i
f
ica
n
t
f
i
n
an
cia
l
lo
s
s
e
s
.
De
g
r
ad
atio
n
an
d
f
ail
u
r
e
in
tu
r
n
-
in
s
u
latio
n
i
n
t
h
e
w
i
n
d
i
n
g
o
f
t
h
e
s
tat
o
r
o
f
a
th
r
ee
-
p
h
a
s
e
in
d
u
ctio
n
m
o
to
r
ar
e
r
ef
er
r
ed
to
as
s
tato
r
w
i
n
d
in
g
f
a
u
lt
s
.
I
f
t
h
ese
f
au
l
ts
at
t
h
eir
ea
r
l
y
s
ta
g
e
s
ar
e
n
o
t
f
i
x
ed
r
ig
h
t
a
w
a
y
,
th
e
y
m
a
y
r
esu
lt
in
p
er
f
o
r
m
a
n
ce
d
ec
lin
e
o
r
p
o
s
s
ib
l
y
co
m
p
lete
m
o
to
r
f
ail
u
r
e
[
1
]
,
[
2
]
.
A
n
i
n
ter
-
tu
r
n
s
h
o
r
t
cir
cu
it
(
I
T
SC
)
is
a
t
y
p
ica
l
s
t
ato
r
w
i
n
d
in
g
f
a
u
lt
th
at
h
ap
p
en
s
w
h
e
n
t
w
o
o
r
m
o
r
e
t
u
r
n
s
o
f
s
a
m
e
p
h
ase
o
r
d
if
f
er
e
n
t
p
h
ases
ar
e
i
n
d
ir
ec
t
e
lectr
ic
al
co
n
tact,
r
esu
lti
n
g
in
a
n
e
x
ce
s
s
iv
e
cu
r
r
e
n
t
f
lo
w
t
h
at
co
n
s
eq
u
e
n
tl
y
h
ar
m
th
e
m
o
to
r
s
e
v
er
el
y
[
3
]
,
[
4
]
.
A
n
o
p
en
cir
cu
it
i
s
a
d
if
f
er
en
t
k
in
d
o
f
f
a
u
lt
t
h
at
r
es
u
lts
w
h
e
n
th
er
e
is
a
b
r
ea
k
o
r
d
is
co
n
ti
n
u
i
t
y
i
n
o
n
e
o
r
m
o
r
e
p
h
ases
o
f
t
h
e
s
tato
r
w
i
n
d
in
g
.
A
s
i
n
g
le
p
h
ase
o
f
th
e
s
tato
r
w
i
n
d
in
g
m
a
y
al
s
o
ex
p
er
ien
ce
i
n
ter
-
tu
r
n
f
a
u
lts
.
T
h
ese
is
s
u
es
en
tail
a
s
h
o
r
t
cir
c
u
it
b
et
w
ee
n
w
ir
e
tu
r
n
s
w
it
h
in
th
e
s
a
m
e
co
il
t
h
a
t
ar
e
ad
j
ac
en
t
to
ea
ch
o
th
er
[
5
]
.
Ov
er
t
h
e
las
t
f
e
w
d
ec
ad
es;
co
n
d
itio
n
m
o
n
ito
r
in
g
g
ain
ed
m
o
r
e
i
m
p
o
r
tan
ce
as
it
p
r
o
v
id
es
u
s
e
f
u
l
in
f
o
r
m
at
io
n
r
eg
ar
d
in
g
t
h
e
m
o
to
r
h
ea
lt
h
.
I
t
m
a
y
b
e
clas
s
i
f
ied
i
n
t
w
o
w
a
y
s
n
a
m
el
y
b
a
s
ic
le
v
el
an
d
ad
v
an
ce
d
le
v
el
co
n
d
itio
n
m
o
n
ito
r
in
g
.
B
asic
le
v
el
c
o
n
d
itio
n
m
o
n
ito
r
i
n
g
h
as
b
ee
n
ca
r
r
ied
o
u
t
b
y
m
ea
s
u
r
in
g
t
h
e
s
tato
r
cu
r
r
en
t
in
d
if
f
er
en
t
f
a
u
lt
a
n
d
lo
ad
c
o
n
d
itio
n
,
v
ib
r
atio
n
lev
el
o
f
r
o
to
r
.
A
d
v
an
ce
d
lev
e
l
m
o
n
ito
r
i
n
g
m
a
in
l
y
b
a
s
ed
o
n
f
o
u
r
ier
tr
an
s
f
o
r
m
,
w
av
ele
t
tr
an
s
f
o
r
m
,
P
ar
k
’
s
v
ec
to
r
m
et
h
o
d
,
s
tatis
t
ical
a
n
al
y
s
i
s
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Ma
ch
in
e
lea
r
n
in
g
b
a
s
ed
s
ta
to
r
-
w
in
d
in
g
fa
u
lt seve
r
ity
d
etec
tio
n
…
(
P
a
r
th
a
Mis
h
r
a
)
183
m
ac
h
in
e
lear
n
in
g
i
n
co
m
b
i
n
at
io
n
w
ith
f
a
u
lt
d
iag
n
o
s
i
s
alg
o
r
ith
m
[
6
]
.
Mo
to
r
cu
r
r
en
t
s
ig
n
a
tu
r
es
[
3
]
,
[
4
]
,
[
6
]
,
v
ib
r
atio
n
[
7
]
,
[
8
]
,
air
g
ap
f
lu
x
[
9
]
,
ac
o
u
s
tic
s
ig
n
al
[
1
0
]
o
f
m
o
to
r
ar
e
th
e
m
o
s
t
s
i
g
n
i
f
ica
n
t
p
a
r
a
m
eter
s
w
h
ic
h
ar
e
w
id
el
y
u
s
ed
f
o
r
s
tato
r
w
i
n
d
in
g
f
a
u
lt
d
ia
g
n
o
s
is
.
Sev
er
al
o
th
er
m
et
h
o
d
s
s
u
c
h
a
s
i
n
s
u
lat
io
n
r
esis
ta
n
ce
test
i
n
g
,
p
o
lar
izatio
n
i
n
d
e
x
tes
tin
g
,
a
n
d
p
ar
tial d
is
ch
ar
g
e
a
n
al
y
s
i
s
,
ca
n
also
b
e
u
s
ed
to
id
e
n
ti
f
y
s
tato
r
w
in
d
i
n
g
p
r
o
b
le
m
s
[
1
1
]
,
[
1
2
]
.
R
ec
en
tl
y
,
A
l
m
o
u
n
ajj
ed
et
a
l.
[
1
3
]
h
av
e
p
r
esen
t
ed
a
co
n
d
itio
n
m
o
n
ito
r
i
n
g
te
ch
n
iq
u
e
w
h
er
e
th
e
ac
cu
r
ac
y
o
f
Mo
to
r
C
u
r
r
en
t
Si
g
n
at
u
r
e
An
al
y
s
is
(
M
C
S
A
)
h
as
b
ee
n
en
h
an
ce
d
b
y
i
n
tr
o
d
u
ci
n
g
d
is
cr
ete
w
a
v
ele
t
tr
an
s
f
o
r
m
(
DW
T
)
.
Statio
n
ar
y
w
a
v
elet
tr
a
n
s
f
o
r
m
s
[
1
]
,
co
n
tin
u
o
u
s
w
av
elet
tr
an
s
f
o
r
m
(
C
W
T
)
[
1
4
]
,
r
eliab
le
f
l
u
x
-
b
ased
d
etec
tio
n
[
1
5
]
-
[
1
9
]
,
m
a
y
e
f
f
ec
ti
v
el
y
b
e
u
s
ed
to
e
x
tr
ac
t
s
ig
n
i
f
ica
n
t
f
ea
t
u
r
es
f
r
o
m
t
h
e
s
i
g
n
al
s
u
n
d
er
an
al
y
s
es.
I
n
m
o
r
e
ad
v
a
n
ce
d
s
ch
e
m
e
s
o
f
f
a
u
lt
d
iag
n
o
s
i
s
th
a
t
ar
e
u
s
ed
to
in
cr
ea
s
e
th
e
p
er
f
o
r
m
a
n
ce
o
f
f
a
u
l
t
d
etec
tio
n
,
m
ac
h
i
n
e
lear
n
i
n
g
m
et
h
o
d
s
ar
e
in
co
r
p
o
r
ated
w
it
h
s
ig
n
al
p
r
o
ce
s
s
i
n
g
to
o
ls
.
Ov
er
th
e
y
ea
r
s
,
r
esear
ch
er
s
h
a
v
e
p
r
o
p
o
s
ed
s
ev
er
al
ad
v
a
n
ce
d
s
ig
n
al
p
r
o
ce
s
s
i
n
g
tech
n
iq
u
e
s
s
u
c
h
as
p
r
in
cip
al
co
m
p
o
n
e
n
t
an
al
y
s
is
(
P
C
A
)
[
2
0
]
,
in
d
ep
en
d
en
t
co
m
p
o
n
e
n
t
a
n
al
y
s
i
s
(
I
C
A
)
[
2
1
]
,
an
d
ze
r
o
-
s
eq
u
e
n
ce
c
o
m
p
o
n
en
t
a
n
al
y
s
is
[
2
2
]
.
Statis
tical
m
ea
s
u
r
es
o
f
t
h
e
s
ta
to
r
cu
r
r
en
t
d
ata
s
u
ch
a
s
m
ea
n
,
v
ar
ia
n
ce
,
s
k
e
w
n
es
s
,
an
d
k
u
r
to
s
i
s
,
m
a
y
also
b
e
u
s
ed
w
it
h
s
o
m
e
m
o
d
er
n
class
i
f
ier
to
id
en
ti
f
y
t
h
e
m
o
t
o
r
f
au
lt
s
[
2
3
]
,
[
2
4
]
.
R
ec
en
tl
y
th
er
e
h
a
s
b
ee
n
a
n
in
cr
ea
s
i
n
g
i
n
ter
est
i
n
d
ee
p
lea
r
n
in
g
an
d
m
ac
h
i
n
e
lear
n
i
n
g
te
ch
n
iq
u
es
f
o
r
th
e
d
iag
n
o
s
i
s
o
f
f
au
lts
i
n
in
d
u
ct
io
n
m
o
to
r
s
[
2
5
]
-
[
3
0
]
.
Dee
p
lear
n
in
g
-
b
ased
n
et
w
o
r
k
s
ar
e
m
o
r
e
ef
f
ec
ti
v
e
th
a
n
m
ac
h
i
n
e
lear
n
in
g
as
th
e
y
ca
n
id
en
ti
f
y
in
teg
r
al
f
ea
t
u
r
es
o
f
t
h
e
o
r
ig
i
n
al
d
ata.
R
ec
e
n
tl
y
,
c
o
n
v
o
l
u
tio
n
al
n
e
u
r
al
n
et
w
o
r
k
(
C
NN)
b
ased
d
ee
p
lear
n
in
g
h
as
b
ee
n
e
f
f
ec
tiv
e
l
y
u
s
ed
i
n
f
au
l
t
d
iag
n
o
s
i
s
o
f
ele
ctr
ical
m
ac
h
i
n
es,
b
io
m
e
d
ical
en
g
i
n
ee
r
i
n
g
,
p
atter
n
r
ec
o
g
n
itio
n
o
f
i
m
a
g
es
a
n
d
v
i
d
eo
s
,
id
en
tif
icatio
n
an
d
lo
ca
lizatio
n
o
f
o
b
j
ec
ts
[
3
1
]
-
[
3
7
]
.
H
o
w
e
v
er
,
in
ca
s
e
o
f
d
ec
is
io
n
m
ak
i
n
g
,
f
e
w
o
f
t
h
e
s
e
alg
o
r
ith
m
s
ar
e
in
f
l
u
e
n
ce
d
b
y
m
a
n
y
ex
ter
n
al
co
n
d
itio
n
s
.
P
r
esen
ce
o
f
n
o
is
e
d
u
r
in
g
r
a
w
d
ata
ac
q
u
is
it
io
n
,
d
if
f
er
en
t
i
n
v
er
ter
f
r
eq
u
e
n
cie
s
,
h
ar
m
o
n
ics,
an
d
ef
f
icie
n
c
y
o
f
th
e
d
ata
ac
q
u
is
itio
n
s
y
s
te
m
s
,
m
a
y
lead
to
er
r
o
n
eo
u
s
f
a
u
lt
d
etec
tio
n
.
I
n
m
ac
h
in
e
lear
n
i
n
g
b
ased
r
esear
ch
e
s
,
it
h
as
b
ee
n
f
o
u
n
d
t
h
at
f
ea
t
u
r
e
s
elec
tio
n
,
e
f
f
ec
ti
v
e
f
ea
tu
r
e
ex
tr
ac
tio
n
ar
e
ex
h
au
s
ti
v
e
w
o
r
k
a
n
d
r
eq
u
ir
es
ex
p
er
t
k
n
o
w
led
g
e.
I
n
s
p
ite
o
f
h
av
i
n
g
all
t
h
e
co
n
s
tr
ai
n
ts
a
n
d
li
m
ita
tio
n
s
,
th
ese
tec
h
n
iq
u
e
s
h
elp
to
ac
h
iev
e
b
etter
u
tili
za
ti
o
n
o
f
eq
u
ip
m
e
n
t
i
n
p
er
io
d
ic
m
ai
n
ten
a
n
ce
o
f
t
h
e
m
o
to
r
.
I
t
is
q
u
ite
ev
id
en
t
t
h
at
th
e
r
eg
u
lar
m
ain
te
n
a
n
ce
i
n
c
lu
d
i
n
g
in
s
u
latio
n
test
i
n
g
,
v
ib
r
atio
n
a
n
al
y
s
is
a
n
d
th
er
m
a
l
m
o
n
ito
r
in
g
ca
n
av
o
id
o
r
m
i
n
i
m
ize
t
h
e
p
o
s
s
ib
il
it
y
o
f
m
o
to
r
f
ail
u
r
e.
T
h
is
f
ac
t tr
ad
es th
e
n
ee
d
o
f
a
n
o
n
-
i
n
v
a
s
i
v
e
co
n
d
itio
n
m
o
n
i
to
r
in
g
to
o
l f
o
r
in
d
u
ctio
n
m
o
to
r
in
t
h
e
in
d
u
s
tr
ies.
Af
ter
g
o
in
g
t
h
r
o
u
g
h
a
d
ec
e
n
t
li
ter
atu
r
e
s
u
r
v
e
y
,
it
m
a
y
b
e
f
o
u
n
d
t
h
at
t
h
e
r
esear
c
h
er
s
m
o
s
tl
y
in
v
e
s
ti
g
ated
m
u
ltip
le
s
i
g
n
al
s
s
u
c
h
a
s
c
u
r
r
en
t,
v
ib
r
atio
n
,
th
er
m
al,
a
n
d
ac
o
u
s
tic,
to
d
e
v
el
o
p
a
s
u
i
tab
le
f
au
l
t
d
iag
n
o
s
i
s
m
et
h
o
d
.
Mo
r
eo
v
er
,
m
o
s
t
o
f
th
e
r
ec
en
tl
y
p
r
o
p
o
s
ed
f
a
u
lt
d
iag
n
o
s
i
s
te
c
h
n
iq
u
es
ar
e
b
ased
o
n
s
e
v
er
al
co
m
p
le
x
s
i
g
n
al
p
r
o
ce
s
s
i
n
g
a
n
d
clas
s
i
f
icatio
n
to
o
ls
w
h
ic
h
s
u
b
s
eq
u
e
n
tl
y
r
eq
u
ir
e
h
i
g
h
c
o
m
p
u
tatio
n
ti
m
e
f
o
r
ex
ec
u
t
io
n
p
u
r
p
o
s
e.
B
u
t
t
h
e
i
n
d
u
s
tr
ies
d
e
m
a
n
d
f
ast
r
e
s
p
o
n
d
in
g
m
o
to
r
co
n
d
it
io
n
m
o
n
ito
r
in
g
tec
h
n
iq
u
e
t
h
at
ca
n
d
etec
t
th
e
f
a
u
l
t
w
it
h
i
n
its
l
ea
d
ti
m
e
in
o
r
d
er
to
p
r
o
tec
t
th
e
m
o
to
r
f
r
o
m
p
o
s
s
ib
le
c
atastro
p
h
ic
f
ail
u
r
e.
Hen
ce
f
o
r
th
,
t
h
e
au
t
h
o
r
s
o
f
th
e
p
r
esen
t
w
o
r
k
h
av
e
s
et
t
h
e
o
b
j
ec
tiv
e
o
f
t
h
e
s
t
u
d
y
as
to
o
b
tain
p
r
u
d
en
t
f
au
lt
in
d
icato
r
s
f
o
r
I
T
SC
f
au
lts
i
n
s
tato
r
-
w
i
n
d
in
g
o
f
in
d
u
ctio
n
m
o
to
r
,
w
h
ic
h
ta
k
es
les
s
co
m
p
u
tatio
n
ti
m
e.
I
n
th
e
p
r
o
ce
s
s
,
th
e
au
t
h
o
r
s
p
r
o
p
o
s
ed
a
s
i
m
p
le
y
et
h
i
g
h
l
y
e
f
f
icie
n
t
m
o
to
r
f
a
u
lt
d
ia
g
n
o
s
is
tec
h
n
iq
u
e
t
h
at
i
n
v
o
l
v
es
d
if
f
er
e
n
t
s
tati
s
tical
f
ea
t
u
r
es
ex
tr
ac
tio
n
f
r
o
m
th
e
t
h
r
ee
-
p
h
as
e
s
tato
r
cu
r
r
en
t
s
ig
n
at
u
r
es
o
n
l
y
,
an
d
s
u
b
s
eq
u
en
tl
y
id
en
ti
f
icatio
n
o
f
t
h
e
clas
s
o
f
f
au
lts
u
s
i
n
g
s
u
itab
l
y
s
elec
ted
m
ac
h
in
e
lear
n
i
n
g
m
e
th
o
d
.
T
h
e
p
u
r
p
o
s
e
o
f
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
is
to
p
r
o
v
i
d
e
a
r
eliab
le
a
n
d
ac
cu
r
ate
f
a
u
lt
d
ia
g
n
o
s
is
an
d
d
etec
tio
n
t
ec
h
n
iq
u
e
f
o
r
s
tato
r
w
i
n
d
i
n
g
i
n
ter
-
tu
r
n
f
a
u
lt
s
i
n
in
d
u
cti
o
n
m
o
to
r
s
to
f
ac
ilit
a
te
th
e
co
n
d
itio
n
-
b
a
s
ed
m
ai
n
te
n
an
c
e
(
C
B
M)
s
ch
e
m
e
i
n
o
r
d
er
t
o
im
p
r
o
v
e
r
eliab
ilit
y
o
f
th
e
p
r
o
d
u
ctio
n
p
r
o
ce
s
s
an
d
r
ed
u
ce
m
ai
n
te
n
a
n
ce
co
s
ts
.
2.
SCO
P
E
O
F
T
H
E
WO
RK
I
n
t
h
is
w
o
r
k
,
a
s
i
m
p
ler
b
u
t
r
o
b
u
s
t
a
n
d
n
o
v
el
f
a
u
lt
d
iag
n
o
s
is
tech
n
iq
u
e
b
ased
o
n
a
n
al
y
s
is
o
f
th
r
ee
p
h
ase
s
tato
r
cu
r
r
e
n
ts
h
a
s
b
ee
n
p
r
o
p
o
s
ed
.
T
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
m
a
y
d
etec
t
d
if
f
er
e
n
t
s
ev
er
it
y
o
f
I
T
SC
f
au
lt
s
in
v
o
l
v
i
n
g
v
er
y
f
e
w
n
u
m
b
er
s
o
f
tu
r
n
s
(
m
in
i
m
u
m
o
f
0
.
2
8
%
o
f
to
tal
tu
r
n
s
i
n
a
p
h
ase
w
i
n
d
i
n
g
)
in
s
tato
r
w
i
n
d
i
n
g
o
f
t
h
e
i
n
d
u
ctio
n
m
o
to
r
.
Stati
s
t
ical
f
ea
tu
r
es
w
er
e
u
s
ed
i
n
er
r
o
r
co
r
r
ec
tin
g
o
u
tp
u
t
co
d
es
-
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
(
E
C
OC
-
SVM)
clas
s
if
ier
f
o
r
th
e
ea
r
l
y
d
etec
t
io
n
o
f
t
h
e
I
T
SC
f
a
u
lts
w
it
h
a
h
i
g
h
d
eg
r
ee
o
f
ac
c
u
r
ac
y
.
Hen
ce
f
o
r
th
,
th
e
s
co
p
e
o
f
th
e
wo
r
k
in
cl
u
d
es:
E
x
tr
ac
tin
g
1
5
r
eg
u
lar
s
ta
ti
s
tic
al
f
ea
t
u
r
es
f
r
o
m
m
o
to
r
c
u
r
r
en
t
s
ig
n
als
u
n
d
er
d
if
f
er
en
t
o
p
er
atin
g
co
n
d
itio
n
s
o
f
th
e
m
o
to
r
n
a
m
el
y
h
ea
lt
h
y
a
n
d
6
d
if
f
er
en
t
s
ev
er
i
ties
o
f
I
T
SC
f
au
lt
s
i
n
m
o
to
r
s
tato
r
w
in
d
in
g
.
I
m
p
le
m
e
n
ti
n
g
an
E
C
OC
-
SV
M
m
ac
h
i
n
e
-
lear
n
i
n
g
b
ased
a
lg
o
r
ith
m
to
d
etec
t
f
au
lt
clas
s
es
o
f
v
ar
y
in
g
s
ev
er
it
y
w
it
h
ad
eq
u
ate
ac
cu
r
a
c
y
.
E
n
tire
s
tu
d
y
h
as
b
ee
n
ca
r
r
ied
o
u
t
f
o
llo
w
in
g
th
e
w
o
r
k
-
f
lo
w
d
iag
r
a
m
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
Up
o
n
s
ett
in
g
u
p
a
cu
s
to
m
ized
h
ar
d
w
ar
e
s
et
u
p
,
a
s
er
ies
o
f
ex
p
er
i
m
e
n
t
s
w
e
r
e
p
er
f
o
r
m
ed
u
n
d
er
d
if
f
er
en
t
o
p
er
atin
g
co
n
d
itio
n
s
o
f
th
e
m
o
to
r
.
First,
t
h
r
ee
p
h
as
e
m
o
to
r
cu
r
r
en
t
s
i
g
n
al
s
w
er
e
co
llected
.
T
h
en
,
th
e
ca
p
tu
r
ed
c
u
r
r
en
t
s
i
g
n
als
w
er
e
r
ec
o
n
s
tr
u
cted
b
y
r
e
m
o
v
i
n
g
t
h
e
f
u
n
d
a
m
en
ta
l
f
r
eq
u
en
c
y
(
5
0
Hz)
co
m
p
o
n
e
n
t
w
i
th
th
e
h
elp
o
f
DW
T
an
d
i
n
v
er
s
e
-
DW
T
(
I
DW
T
)
.
Statis
t
ical
f
ea
t
u
r
e
ex
tr
ac
ti
o
n
s
f
o
llo
wed
b
y
f
ee
d
in
g
o
f
th
e
e
x
tr
ac
ted
f
ea
t
u
r
es
to
E
C
O
C
-
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
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
A
p
r
il
20
2
5
:
1
82
-
1
92
184
SVM
m
ac
h
i
n
e
lear
n
i
n
g
clas
s
i
f
ier
w
er
e
s
u
b
s
eq
u
en
t
l
y
p
er
f
o
r
m
ed
to
clas
s
i
f
y
th
e
d
if
f
er
en
t
ca
s
es
u
n
d
er
s
tu
d
y
.
9
4
% c
lass
i
f
icatio
n
ac
c
u
r
ac
y
c
o
u
ld
b
e
ac
h
iev
ed
th
r
o
u
g
h
th
e
p
r
o
p
o
s
ed
f
au
lt d
iag
n
o
s
i
s
m
e
th
o
d
.
Fig
u
r
e
1
.
W
o
r
k
f
lo
w
d
iag
r
a
m
o
f
th
e
p
r
o
p
o
s
ed
f
au
lt
d
iag
n
o
s
i
s
m
et
h
o
d
3.
M
E
T
H
O
D
3
.
1
.
Arr
a
ng
em
ent
o
f
ex
peri
m
e
nta
l set
up
t
o
ca
pture
t
hree
ph
a
s
e
m
o
t
o
r
curr
ent
da
t
a
T
h
e
w
h
o
le
e
x
p
er
i
m
e
n
t
h
as
b
ee
n
p
er
f
o
r
m
ed
o
n
a
2
h
p
,
3
2
0
V,
3
-
p
h
ase
in
d
u
ct
io
n
m
o
to
r
w
i
th
cu
s
to
m
ized
s
tar
co
n
n
ec
ted
s
ta
to
r
w
in
d
i
n
g
.
T
h
e
m
o
to
r
u
n
d
er
s
tu
d
y
co
n
tai
n
s
6
co
ils
an
d
3
6
0
tu
r
n
s
p
er
p
h
ase
w
i
n
d
i
n
g
.
E
ac
h
o
f
t
h
e
th
r
ee
-
p
h
ase
w
i
n
d
i
n
g
w
as
c
u
s
to
m
ized
to
im
p
le
m
e
n
t
d
if
f
er
en
t
i
n
ter
-
t
u
r
n
f
au
lt
co
n
d
itio
n
s
.
T
a
p
p
i
n
g
s
f
r
o
m
d
if
f
e
r
en
t
tu
r
n
s
o
f
th
e
cu
s
t
o
m
i
z
e
d
w
in
d
in
g
s
w
e
r
e
b
r
o
u
g
h
t
o
u
t
t
o
a
p
a
t
ch
b
o
a
r
d
s
h
o
w
n
i
n
F
ig
u
r
e
2
an
d
th
e
n
w
er
e
f
i
tted
to
d
if
f
er
en
t
ter
m
in
al
s
to
ar
tific
iall
y
i
m
p
le
m
e
n
t
I
T
SC
f
a
u
lt
s
o
f
d
i
f
f
er
en
t
s
e
v
er
it
y
.
T
h
e
3
-
p
h
ase
in
d
u
ctio
n
m
o
to
r
w
as
co
u
p
led
w
i
th
a
D
C
g
e
n
er
ato
r
f
ee
d
in
g
p
o
w
er
to
a
g
r
o
u
p
o
f
la
m
p
lo
ad
s
,
to
o
p
er
ate
th
e
m
o
to
r
at
v
ar
io
u
s
lo
ad
co
n
d
itio
n
s
.
3
s
i
n
g
le
-
p
h
a
s
e
au
to
tr
an
s
f
o
r
m
er
s
ea
c
h
ca
p
ab
le
to
v
a
r
y
v
o
lta
g
e
f
r
o
m
0
%
to
1
2
5
%
w
er
e
u
s
ed
i
n
b
et
w
ee
n
t
h
e
s
u
p
p
l
y
a
n
d
t
h
e
m
o
to
r
f
o
r
k
ee
p
in
g
t
h
e
3
-
p
h
ase
s
u
p
p
l
y
v
o
ltag
es
to
b
ala
n
ce
d
co
n
d
itio
n
ir
r
esp
ec
ti
v
e
o
f
s
u
p
p
ly
v
o
lta
g
e
f
lu
c
tu
at
io
n
s
.
A
Y
OKOG
A
W
A
m
ak
e
3
-
p
h
a
s
e
d
ig
ital
p
o
w
er
m
e
ter
w
a
s
i
n
ter
f
ac
ed
w
it
h
t
h
e
m
o
to
r
an
d
a
P
C
,
f
o
r
ac
q
u
ir
in
g
th
r
ee
p
h
ase
m
o
to
r
cu
r
r
en
t
s
ig
n
al
s
.
A
p
h
o
to
g
r
ap
h
o
f
th
e
ex
p
er
i
m
e
n
tal
s
et
u
p
alo
n
g
w
it
h
th
e
co
m
p
o
n
e
n
t
m
ar
k
er
s
h
as b
ee
n
s
h
o
w
n
i
n
Fi
g
u
r
e
2
.
Fig
u
r
e
2
.
P
h
o
to
g
r
ap
h
o
f
th
e
ex
p
er
im
e
n
tal
s
etu
p
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Ma
ch
in
e
lea
r
n
in
g
b
a
s
ed
s
ta
to
r
-
w
in
d
in
g
fa
u
lt seve
r
ity
d
etec
tio
n
…
(
P
a
r
th
a
Mis
h
r
a
)
185
3
.
2
.
T
heo
re
t
ica
l ba
ck
g
ro
un
d o
f
dis
cr
et
e
w
a
v
elet
t
ra
ns
f
o
r
m
s
T
h
e
w
av
elet
tr
a
n
s
f
o
r
m
s
,
an
ex
ten
s
io
n
o
f
th
e
s
h
o
r
t
-
ti
m
e
f
o
u
r
ier
tr
an
s
f
o
r
m
(
ST
FT
)
is
ca
p
ab
le
o
f
an
al
y
z
in
g
a
n
o
n
-
s
ta
tio
n
ar
y
s
ig
n
al
i
n
b
o
th
t
i
m
e
a
n
d
f
r
eq
u
en
c
y
d
o
m
ai
n
s
i
m
u
lta
n
eo
u
s
l
y
w
it
h
f
le
x
ib
le
m
at
h
e
m
a
tical
s
u
b
s
ta
n
ce
s
.
C
W
T
an
d
DW
T
ar
e
th
e
t
w
o
t
y
p
e
s
o
f
w
av
e
lets
tr
an
s
f
o
r
m
s
w
h
ic
h
ar
e
f
r
eq
u
en
tl
y
u
s
ed
as
s
i
g
n
a
l
p
r
o
ce
s
s
in
g
to
o
l
f
o
r
f
au
lt
d
iag
n
o
s
i
s
o
f
i
n
d
u
c
tio
n
m
o
to
r
s
[
1
4
]
,
[
1
5
]
,
[
1
7
]
,
[
3
8
]
-
[
4
0
]
.
A
b
r
ie
f
th
eo
r
etica
l
b
ac
k
g
r
o
u
n
d
o
f
DW
T
is
d
is
cu
s
s
ed
as f
o
llo
w
i
n
g
:
A
s
ig
n
al
x
(
t)
is
co
n
v
o
lu
ted
w
it
h
a
m
o
t
h
er
w
a
v
elet
f
u
n
ct
io
n
ψ
(
t)
to
p
r
o
d
u
ce
th
e
co
ef
f
ic
ien
t
s
o
f
C
W
T
as:
=
∫
(
)
(
)
=
⟨
(
)
,
(
)
⟩
∞
−
∞
(
1
)
A
ti
m
e
-
s
ca
le
d
ec
o
m
p
o
s
i
tio
n
o
f
th
e
s
ig
n
al
x
(
t)
is
o
b
tain
ed
b
y
a
tr
a
n
s
f
o
r
m
atio
n
p
r
o
ce
s
s
in
w
h
ich
co
n
ce
p
t
o
f
s
ca
le
is
r
ela
ted
to
co
n
ce
p
t
o
f
f
r
eq
u
en
c
y
.
Ho
w
e
v
er
,
th
e
tr
an
s
f
o
r
m
a
tio
n
p
r
o
ce
s
s
i
n
v
o
lv
e
s
t
w
o
p
ar
a
m
eter
s
i.e
.
s
ca
lin
g
p
ar
a
m
eter
“
a”
an
d
s
h
i
f
tin
g
p
ar
a
m
eter
“
b
”
o
f
t
h
e
m
o
t
h
er
w
a
v
elet
f
u
n
ct
io
n
as
(
2
)
.
(
)
=
1
√
|
|
(
)
,
(
)
=
1
√
|
|
(
−
)
(
2
)
T
h
er
ef
o
r
e,
th
e
p
r
o
ce
s
s
d
ef
in
ed
b
y
(
1
)
is
co
n
v
er
ted
to
th
e
p
r
o
ce
s
s
d
ef
i
n
ed
b
y
(
3
)
.
=
∫
(
)
(
)
=
⟨
(
)
,
(
)
⟩
∞
−
∞
,
=
1
√
|
|
∫
(
)
(
−
)
∞
−
∞
(
3
)
T
h
e
DW
T
is
d
er
iv
ed
th
r
o
u
g
h
s
a
m
p
li
n
g
th
e
s
ca
li
n
g
an
d
s
h
i
f
t
in
g
p
ar
a
m
eter
s
o
f
C
W
T
as
s
h
o
w
n
in
(
4
)
,
w
h
ic
h
is
also
k
n
o
w
n
as
d
y
ad
i
c
d
is
cr
etiza
tio
n
m
et
h
o
d
w
h
er
e
th
e
p
ar
am
eter
s
ti
m
e
(
t)
,
s
ca
le
(
a)
an
d
s
h
if
ti
n
g
(
b
)
ar
e
co
n
s
id
er
ed
in
th
e
ir
d
is
cr
ete
v
e
r
s
io
n
s
n
,
j
an
d
k
,
r
esp
ec
ti
v
el
y
.
Ho
w
e
v
er
,
th
e
co
n
ti
n
u
o
u
s
v
ar
iab
les
a
a
n
d
b
ar
e
co
n
v
er
ted
in
to
d
is
cr
ete
v
ar
iab
les in
f
o
r
m
o
f
a=
2
j
an
d
b
=k
2
j
,
w
h
er
e
j
ϵ
N
an
d
k
ϵ
Z
[
1
5
]
.
(
,
)
(
)
=
1
√
2
(
−
2
2
)
(
4
)
T
h
er
ef
o
r
e,
th
e
co
n
tin
u
o
u
s
w
av
elet
p
r
o
ce
s
s
d
escr
ib
ed
in
(
3
)
is
co
n
v
er
ted
in
to
d
is
cr
ete
w
a
v
el
et
p
r
o
ce
s
s
as
(
5
)
.
,
=
∑
(
)
,
(
)
q
,
=
∑
(
)
(
−
2
)
q
(
5
)
T
h
e
DW
T
o
f
a
s
ig
n
al
i
s
i
m
p
l
e
m
en
ted
b
y
f
o
llo
w
i
n
g
th
e
g
u
i
d
elin
es
o
f
Ma
llat
al
g
o
r
ith
m
i
n
w
h
ich
a
b
an
d
p
ass
f
ilter
b
an
k
is
u
s
ed
[
1
7
]
.
A
cc
o
r
d
in
g
to
th
e
p
r
in
cip
le
o
f
Ma
lla
t
al
g
o
r
ith
m
,
i
n
th
e
f
ir
s
t
le
v
el
o
f
d
ec
o
m
p
o
s
itio
n
,
b
an
d
w
id
th
o
f
th
e
o
r
ig
i
n
al
s
i
g
n
al
is
h
al
v
ed
af
ter
p
ass
i
n
g
th
r
o
u
g
h
a
lo
w
p
ass
an
d
a
h
ig
h
p
as
s
f
ilter
.
I
n
t
h
is
p
r
o
ce
s
s
,
t
h
e
o
r
i
g
in
a
l
s
ig
n
al
is
d
ec
o
m
p
o
s
ed
i
n
to
t
w
o
s
i
g
n
al
s
k
n
o
w
n
as
lo
w
p
a
s
s
ap
p
r
o
x
i
m
ate
co
ef
f
icie
n
t
s
(
A
C
1
)
a
n
d
h
ig
h
p
ass
d
etail
co
e
f
f
ic
ien
t
s
(
D
C
1
)
.
T
h
en
,
A
C
1
is
d
ec
o
m
p
o
s
ed
in
to
s
ig
n
al
s
o
f
ap
p
r
o
x
im
a
te
co
ef
f
icie
n
t
s
a
n
d
d
etail
co
ef
f
icie
n
ts
at
lev
el
2
i.
e.
A
C
2
an
d
DC
2
b
y
p
ass
i
n
g
AC
1
t
h
r
o
u
g
h
t
h
e
s
a
m
e
d
ec
o
m
p
o
s
itio
n
p
r
o
ce
s
s
as
d
is
cu
s
s
ed
ab
o
v
e.
Ho
w
e
v
er
,
t
h
e
h
i
g
h
er
-
le
v
el
co
ef
f
icie
n
ts
ar
e
o
b
tain
ed
t
h
r
o
u
g
h
f
u
r
t
h
er
ap
p
licatio
n
o
f
th
e
d
ec
o
m
p
o
s
i
tio
n
p
r
o
ce
s
s
o
n
ap
p
r
o
x
i
m
ate
co
ef
f
icie
n
t
s
ig
n
al
s
o
f
co
r
r
esp
o
n
d
in
g
le
v
el.
B
u
t
th
e
m
a
x
i
m
u
m
le
v
el
o
f
d
e
co
m
p
o
s
i
tio
n
i
s
r
estricte
d
to
t
h
e
s
a
m
p
le
len
g
t
h
o
f
th
e
o
r
i
g
i
n
a
l
s
i
g
n
al
b
ec
au
s
e
in
ea
ch
d
ec
o
m
p
o
s
it
io
n
lev
el,
t
h
e
s
a
m
p
le
len
g
t
h
g
et
s
r
ed
u
ce
d
to
h
al
f
o
f
th
e
in
p
u
t
s
a
m
p
le
s
ize.
Fro
m
(
6
)
,
it
ca
n
b
e
o
b
s
er
v
ed
th
at
o
v
er
all
b
an
d
w
i
d
th
o
f
th
e
s
i
g
n
a
l
u
n
d
er
tr
an
s
f
o
r
m
at
io
n
i
s
d
iv
id
ed
in
e
x
ac
t
p
o
w
er
s
o
f
t
w
o
alo
n
g
ti
m
e.
Ho
w
ev
er
,
as
p
er
N
y
q
u
i
s
t
th
eo
r
e
m
,
b
an
d
w
id
t
h
o
f
th
e
s
ig
n
al
is
le
s
s
t
h
an
o
r
eq
u
al
to
h
alf
t
h
e
s
a
m
p
lin
g
f
r
eq
u
en
c
y
(
f
s
)
.
T
h
er
ef
o
r
e,
b
an
d
w
id
t
h
o
f
t
h
e
ap
p
r
o
x
i
m
ate
an
d
d
etail
co
ef
f
icie
n
ts
at
a
n
a
n
al
y
s
i
s
le
v
el
L
ca
n
b
e
r
elate
d
to
th
e
s
a
m
p
li
n
g
f
r
eq
u
e
n
c
y
(
f
s
)
as
s
h
o
w
n
i
n
(
6
)
.
⇒
[
0
,
2
+
1
]
a
n
d
⇒
[
2
+
1
,
2
]
(
6
)
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
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
A
p
r
il
20
2
5
:
1
82
-
1
92
186
3
.
3
.
F
o
r
m
ula
t
io
n o
f
s
t
a
t
is
t
ica
l f
ea
t
ures
A
s
et
o
f
q
u
an
ti
f
ied
s
ta
tis
tic
al
v
al
u
es
d
ep
ictin
g
th
e
ch
ar
ac
ter
is
tics
o
f
th
e
ti
m
e
s
er
ies
d
ata
w
a
s
ex
tr
ac
ted
f
r
o
m
th
e
r
ec
o
n
s
tr
u
cted
s
i
g
n
als,
a
n
d
later
w
a
s
u
s
ed
as
s
ig
n
i
f
ica
n
t
f
ea
t
u
r
es
f
o
r
t
h
e
f
au
l
t’
s
class
i
f
icatio
n
p
u
r
p
o
s
e.
I
n
th
e
c
u
r
r
en
t
s
tu
d
y
,
1
5
co
n
v
e
n
tio
n
al
s
tatis
t
ical
f
ea
t
u
r
es
w
er
e
e
x
tr
ac
ted
f
r
o
m
t
h
e
t
h
r
ee
-
p
h
ase
r
ec
o
n
s
tr
u
cted
cu
r
r
en
t
s
ig
n
a
ls
.
T
h
e
m
at
h
e
m
a
tical
f
o
r
m
u
lae
o
f
th
e
u
s
ed
s
tati
s
tical
f
ea
t
u
r
es
h
a
v
e
b
ee
n
lis
ted
i
n
T
ab
le
1
.
L
et,
x
i
is
th
e
i
th
d
ata
s
a
m
p
le
o
f
a
s
i
n
g
le
-
c
y
cle
-
s
i
n
g
le
-
p
h
ase
c
u
r
r
en
t
v
ec
t
o
r
(
x
)
co
n
s
is
tin
g
N
n
u
m
b
er
o
f
d
ata
s
a
m
p
le
s
,
an
d
i
=1
,
2
,
3
,
…,
N.
T
ab
le
1
.
Ma
th
e
m
atica
l
f
o
r
m
u
l
a
e
o
f
u
s
ed
s
tat
is
tical
f
ea
t
u
r
e
S
l
n
o
S
t
a
t
i
st
i
c
a
l
p
a
r
a
me
t
e
r
M
a
t
h
e
ma
t
i
c
a
l
f
o
r
mu
l
a
e
S
l
n
o
S
t
a
t
i
st
i
c
a
l
p
a
r
a
me
t
e
r
M
a
t
h
e
ma
t
i
c
a
l
f
o
r
mu
l
a
e
1
M
e
a
n
=
1
∑
=
1
9
C
r
e
st
f
a
c
t
o
r
=
2
M
a
x
i
m
u
m
v
a
l
u
e
M
a
x
(
x
)
10
L
a
t
i
t
u
d
e
f
a
c
t
o
r
s
=
3
R
o
o
t
me
a
n
sq
u
a
r
e
(
R
M
S
)
=
√
1
∑
2
=
1
11
I
mp
u
l
se
F
a
c
t
o
r
=
1
∑
|
|
=
1
4
S
q
u
a
r
e
r
o
o
t
me
a
n
(
S
R
M
)
=
1
∑
2
=
1
12
S
k
e
w
n
e
ss
=
(
−
)
3
3
5
S
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
=
√
1
∑
(
−
)
2
=
1
13
K
u
r
t
o
si
s
=
(
−
)
4
4
6
V
a
r
i
a
n
c
e
σ
2
=
1
∑
(
−
)
2
=
1
14
F
i
f
t
h
mo
me
n
t
=
(
−
)
5
5
7
S
h
a
p
e
f
a
c
t
o
r
=
1
∑
|
|
=
1
15
S
i
x
t
h
mo
me
n
t
=
(
−
)
6
6
8
S
R
M
sh
a
p
e
f
a
c
t
o
r
(
S
R
M
S
F
)
=
1
∑
|
|
=
1
3
.
4
.
T
heo
re
t
ica
l
ba
ck
g
ro
un
d o
f
E
CO
C
-
SVM
Fo
r
t
w
o
-
cla
s
s
(
b
i
n
ar
y
)
cla
s
s
if
ica
tio
n
p
r
o
b
lem
s
,
m
ac
h
in
e
lear
n
i
n
g
tec
h
n
iq
u
es
n
a
m
e
l
y
lo
g
is
t
ic
r
eg
r
ess
io
n
an
d
SVM,
ar
e
w
id
e
l
y
u
s
ed
[
4
1
]
.
Ho
w
e
v
er
,
m
o
s
t
o
f
th
e
r
ea
l
-
li
f
e
p
r
o
b
lem
s
ar
e
m
u
lti
-
cla
s
s
p
r
o
b
le
m
s
.
C
u
r
r
en
tl
y
,
a
m
u
lti
-
clas
s
clas
s
if
icatio
n
p
r
o
b
lem
i
s
p
er
f
o
r
m
e
d
b
y
s
e
g
m
en
tin
g
t
h
e
p
r
o
b
le
m
in
to
a
n
u
m
b
er
o
f
b
in
ar
y
p
r
o
b
le
m
s
f
o
llo
w
ed
b
y
i
n
teg
r
at
io
n
o
f
th
e
s
e
b
in
ar
y
p
r
o
b
le
m
s
.
E
C
OC
[
4
2
]
is
o
n
e
o
f
s
u
ch
m
et
h
o
d
s
w
h
ich
ar
e
ex
ten
s
iv
el
y
u
s
ed
f
o
r
m
u
lt
i
-
clas
s
clas
s
i
f
icatio
n
p
r
o
b
le
m
s
.
I
n
th
i
s
ap
p
r
o
ac
h
,
a
k
-
class
cla
s
s
i
f
icatio
n
p
r
o
b
lem
is
co
n
v
er
ted
i
n
to
a
lar
g
er
n
u
m
b
er
(
L
)
o
f
2
-
c
lass
p
r
o
b
le
m
s
.
A
u
n
iq
u
e
co
d
e
w
o
r
d
is
ass
i
g
n
ed
to
ea
c
h
cla
s
s
in
s
tead
o
f
a
clas
s
lab
el
w
h
ich
is
u
s
ed
in
o
t
h
er
co
n
v
en
tio
n
al
m
ac
h
in
e
lear
n
i
n
g
a
lg
o
r
it
h
m
s
.
An
E
C
OC
t
h
at
i
s
L
b
it
lo
n
g
h
as
u
n
iq
u
e
co
d
e
w
o
r
d
s
,
C
an
d
Ha
m
m
in
g
d
is
ta
n
ce
,
d
.
I
n
g
en
er
al,
E
C
O
C
is
a
co
d
in
g
m
atr
ix
w
h
o
s
e
ele
m
e
n
ts
ar
e
0
an
d
1
.
R
o
w
s
o
f
th
e
m
atr
i
x
r
ep
r
esen
t
t
h
e
class
n
u
m
b
er
(
q
)
o
f
th
e
s
a
m
p
les
an
d
co
lu
m
n
s
r
ep
r
esen
t
th
e
n
u
m
b
er
o
f
clas
s
if
ier
s
(
s
)
to
b
e
tr
ain
ed
.
I
n
tr
ain
in
g
p
h
a
s
e
o
f
E
C
O
C
,
an
y
ele
m
en
t
o
f
t
h
e
co
d
in
g
m
atr
i
x
,
M
qs
eq
u
al
s
1
in
d
icate
s
th
at
t
h
e
co
r
r
esp
o
n
d
in
g
s
a
m
p
le
is
p
o
s
itiv
e
f
o
r
q
-
th
cla
s
s
a
n
d
s
-
t
h
class
if
ier
.
An
d
,
M
qs
eq
u
als
0
in
d
icate
s
t
h
at
t
h
e
s
a
m
p
le
i
s
n
e
g
ati
v
e
f
o
r
q
-
th
class
a
n
d
s
-
t
h
clas
s
i
f
ier
.
A
ll
t
h
e
clas
s
if
ier
s
f(
x
)
=
(
f
1
(
x)
,
f
2
(
x
)
,
…,
f
s
(
x
)
)
ar
e
tr
ain
ed
ac
co
r
d
in
g
to
th
is
p
r
in
cip
le.
T
o
class
if
y
a
n
e
w
s
a
m
p
le
X
,
f
ir
s
t,
th
e
d
is
tan
ce
s
b
et
w
ee
n
o
u
tp
u
t a
n
d
class
v
ec
to
r
s
ar
e
m
ea
s
u
r
ed
.
T
h
en
,
clas
s
w
i
th
m
in
i
m
u
m
d
is
tan
ce
is
co
n
s
id
er
ed
to
b
e
th
e
class
i
f
icatio
n
r
es
u
lt
w
h
i
ch
is
o
b
tain
ed
as
(
7
)
.
=
a
r
g
⏟
=
[
1
,
2
,
…
,
]
(
(
,
(
)
)
(
7
)
W
h
er
e,
Z
is
th
e
cla
s
s
o
f
X
an
d
d
is
th
e
Ha
m
m
i
n
g
d
is
ta
n
ce
w
h
ich
is
ca
lc
u
lated
as
(
8
)
.
(
,
(
)
)
=
∑
|
2
−
(
)
−
1
|
2
=
1
(
8
)
T
h
e
len
g
th
,
L
is
d
ec
id
ed
b
y
t
h
e
m
et
h
o
d
u
s
ed
f
o
r
g
en
er
ati
n
g
er
r
o
r
-
co
r
r
ec
tin
g
co
d
es.
Ov
er
th
e
y
ea
r
s
,
v
ar
io
u
s
m
et
h
o
d
s
li
k
e
Had
a
m
ar
d
-
m
atr
i
x
co
d
es,
B
C
H
co
d
es,
r
an
d
o
m
co
d
es,
ex
h
a
u
s
t
iv
e
co
d
es,
co
n
ti
n
u
o
u
s
co
d
i
n
g
,
a
n
d
e
x
p
e
c
ta
t
i
o
n
m
ax
im
iz
a
t
i
o
n
c
o
d
in
g
,
a
r
e
p
r
o
p
o
s
e
d
.
F
o
r
a
k
-
cl
a
s
s
p
r
o
b
l
em
,
L
m
u
s
t
f
o
ll
o
w
2
<
≤
2
−
1
−
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Ma
ch
in
e
lea
r
n
in
g
b
a
s
ed
s
ta
to
r
-
w
in
d
in
g
fa
u
lt seve
r
ity
d
etec
tio
n
…
(
P
a
r
th
a
Mis
h
r
a
)
187
I
n
th
e
p
r
ese
n
t
w
o
r
k
,
t
h
e
m
u
lti
class
cla
s
s
i
f
icatio
n
p
r
o
b
le
m
h
as
b
ee
n
ca
r
r
ied
o
u
t
b
y
t
h
e
E
C
OC
-
SV
M
class
i
f
ier
t
h
at
u
s
es
co
m
b
in
at
io
n
o
f
E
C
OC
a
n
d
m
u
lt
ip
le
b
in
ar
y
SV
M
lear
n
er
s
.
T
h
e
u
p
p
er
li
m
it
o
f
th
e
g
en
er
aliza
tio
n
er
r
o
r
f
o
r
E
C
OC
-
SVM
h
as b
ee
n
r
ep
o
r
ted
as:
130
3
(
l
og
2
(
4
)
l
og
2
(
16
)
+
l
og
2
2
(
2
)
!
(
9
)
w
h
er
e,
N:
n
u
m
b
er
o
f
co
d
es
w
ith
co
d
in
g
l
en
g
t
h
L
an
d
HD
b
et
w
ee
n
co
d
es
D:
∑
1
2
=
1
R:
m
i
n
i
m
u
m
r
ad
iu
s
o
f
en
c
lo
s
u
r
e
b
all
M:
−
−
1
2
C:
Nu
m
b
er
o
f
co
d
e
w
o
r
d
s
o
f
ea
c
h
g
r
o
u
p
W
h
ile
d
er
iv
in
g
th
is
u
p
p
er
li
m
i
t
o
f
er
r
o
r
it
w
as
ass
u
m
ed
th
at
m
s
a
m
p
les
w
o
u
ld
b
e
s
u
itab
l
y
class
i
f
ied
b
y
k
-
cla
s
s
E
C
OC
SVMs
w
i
th
p
r
o
b
ab
ilit
y
at
least
1
-
δ
.
T
h
e
ar
r
an
g
ed
S
VM
class
i
f
icat
io
n
i
n
ter
v
al
s
h
a
v
e
b
ee
n
r
ep
r
esen
ted
b
y
1
,
2
,
…
,
.
I
t
is
to
b
e
n
o
ted
th
at
w
it
h
f
i
x
ed
L
a
n
d
d
,
th
er
e
ex
is
ts
an
o
p
ti
m
al
allo
ca
tio
n
o
r
d
er
f
o
r
co
d
e
w
o
r
d
s
th
a
t p
r
o
m
i
s
es b
est
g
e
n
e
r
aliza
tio
n
ab
ilit
y
o
f
th
e
E
C
OC
-
SVM.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
Da
t
a
a
cquis
it
io
n a
nd
pr
epro
ce
s
s
ing
o
f
t
he
curr
ent
s
i
g
na
ls
Up
o
n
d
ev
elo
p
m
en
t
o
f
t
h
e
c
u
s
to
m
ized
h
ar
d
w
ar
e
s
et
u
p
,
a
s
er
ies
o
f
ex
p
er
i
m
en
ts
w
er
e
co
n
d
u
cted
to
ca
p
tu
r
e
3
-
p
h
ase
li
n
e
cu
r
r
en
t
s
u
n
d
e
r
d
if
f
er
e
n
t
o
p
er
atin
g
co
n
d
itio
n
s
o
f
th
e
m
o
to
r
.
All
th
e
ex
p
er
i
m
en
t
s
w
er
e
ca
r
r
ied
o
u
t
at
b
ala
n
ce
d
3
-
p
h
as
e
s
u
p
p
l
y
v
o
lta
g
e
w
i
th
±
0
.
5
%
t
o
ler
an
ce
li
m
it.
Data
ac
q
u
is
it
io
n
w
er
e
ca
r
r
ied
o
u
t
f
o
r
h
ea
lt
h
y
a
n
d
6
d
if
f
er
e
n
t
af
o
r
em
e
n
tio
n
ed
I
T
SC
f
au
lt
co
n
d
itio
n
s
b
y
co
n
n
ec
t
i
n
g
at
a
ti
m
e
o
n
l
y
o
n
e
s
h
o
r
t
-
cir
cu
iti
n
g
l
in
k
b
et
w
ee
n
t
w
o
tap
s
in
v
o
lv
i
n
g
1
,
2
,
3
,
4
,
5
,
an
d
6
tu
r
n
s
(
T
1
,
T
2
,
…,
T
6
)
in
R
-
p
h
a
s
e
w
in
d
i
n
g
s
o
f
s
tato
r
.
Fi
v
e
d
i
f
f
er
e
n
t
lo
ad
lev
e
ls
i.e
.
n
o
-
lo
ad
,
2
5
%,
5
0
%,
7
5
%
,
an
d
1
0
0
%
o
f
f
u
ll
lo
ad
co
u
ld
b
e
ac
h
ie
v
ed
,
a
n
d
th
e
y
h
a
v
e
b
ee
n
r
ep
r
esen
ted
as
0
L
,
1
L
,
2
L
,
3
L
,
a
n
d
4
L
,
r
esp
ec
tiv
el
y
,
in
t
h
e
s
u
b
s
eq
u
en
t
s
ec
t
io
n
s
o
f
t
h
e
m
an
u
s
cr
ip
t.
A
ll t
h
e
ca
s
e
-
s
tu
d
i
es a
lo
n
g
w
i
th
t
h
eir
id
en
t
if
ier
s
h
av
e
b
ee
n
l
is
ted
i
n
T
ab
le
2
.
T
ab
le
2
.
C
ase
s
tu
d
ies alo
n
g
w
i
th
th
e
ir
id
en
ti
f
ier
s
H
e
a
l
t
h
y
T1
T2
T3
T4
T5
T6
0L
H
_
0
L
T
1
_
0
L
T
2
_
0
L
T
3
_
0
L
T
4
_
0
L
T
5
_
0
L
T
6
_
0
L
1L
H
_
1
L
T
1
_
1
L
T
2
_
1
L
T
3
_
1
L
T
4
_
1
L
T
5
_
1
L
T
6
_
1
L
2L
H
_
2
L
T
1
_
2
L
T
2
_
2
L
T
3
_
2
L
T
4
_
2
L
T
5
_
2
L
T
6
_
2
L
3L
H
_
3
L
T
1
_
3
L
T
2
_
3
L
T
3
_
3
L
T
4
_
3
L
T
5
_
3
L
T
6
_
3
L
4L
H
_
4
L
T
1
_
4
L
T
2
_
4
L
T
3
_
4
L
T
4
_
4
L
T
5
_
4
L
T
6
_
4
L
A
t
t
h
e
in
it
ial
s
ta
g
e
o
f
cu
r
r
en
t
d
ata
co
llectio
n
,
m
u
ltip
le
o
b
s
er
v
atio
n
s
w
er
e
r
ep
ea
ted
c
o
r
r
esp
o
n
d
in
g
to
ea
ch
o
p
er
atin
g
co
n
d
itio
n
i
n
o
r
d
er
to
r
u
le
o
u
t
a
n
y
p
o
s
s
ib
ilit
ies
o
f
m
i
s
lead
i
n
g
th
e
p
r
o
p
o
s
e
d
alg
o
r
ith
m
d
u
e
to
s
u
p
er
f
l
u
o
u
s
e
f
f
ec
t
o
f
n
o
is
e
a
n
d
m
o
m
e
n
tar
y
p
r
o
b
lem
s
i
n
d
ata
ac
q
u
is
itio
n
p
r
o
ce
s
s
.
Mo
to
r
lin
e
c
u
r
r
en
t
s
w
er
e
ca
p
tu
r
ed
at
2
0
k
Hz
s
a
m
p
li
n
g
r
ate,
d
ep
lo
y
in
g
3
-
p
h
a
s
e
d
ig
ita
l
p
o
w
er
m
eter
w
h
ic
h
is
ca
p
ab
le
to
d
is
p
lay
R
M
S
v
alu
e
s
o
f
th
e
s
ig
n
al
s
alo
n
g
w
it
h
t
h
e
p
r
o
v
is
io
n
to
ca
p
tu
r
e
co
r
r
esp
o
n
d
in
g
s
i
g
n
als at
a
g
i
v
en
s
a
m
p
lin
g
f
r
eq
u
e
n
c
y
.
Af
ter
ca
p
tu
r
i
n
g
c
u
r
r
en
t
d
ata
at
all
ex
p
er
i
m
e
n
tal
co
n
d
itio
n
s
,
th
e
cu
r
r
en
t
s
ig
n
al
s
w
er
e
n
o
r
m
alize
d
w
it
h
r
esp
ec
t
to
th
e
p
ea
k
v
al
u
e
o
f
co
r
r
esp
o
n
d
in
g
p
h
ase
c
u
r
r
en
t
s
o
b
tain
e
d
at
H_
0
L
co
n
d
itio
n
.
L
ater
,
o
n
e
co
m
p
lete
c
y
cle
co
m
p
r
is
in
g
ap
p
r
o
x
i
m
atel
y
4
0
0
s
a
m
p
led
d
a
ta
p
o
in
ts
f
o
r
ea
ch
o
f
th
e
n
o
r
m
alize
d
t
h
r
ee
-
p
h
ase
cu
r
r
e
n
t
s
i
g
n
als
w
er
e
s
elec
ted
an
d
w
er
e
co
n
s
id
er
ed
f
o
r
f
u
r
th
er
d
ata
an
al
y
s
is
p
r
o
ce
s
s
.
Fe
w
ex
e
m
p
lar
y
wav
ef
o
r
m
s
o
f
t
h
r
ee
p
h
ase
cu
r
r
en
t
s
h
a
v
e
b
ee
n
s
h
o
w
n
in
F
ig
u
r
e
3
,
w
av
e
f
o
r
m
s
o
b
tain
ed
at
H_
0
L
an
d
6
T
_
0
L
h
a
v
e
b
ee
n
p
r
esen
ted
in
Fig
u
r
e
s
3
(
a)
an
d
3
(
b
)
,
r
esp
ec
tiv
el
y
.
I
t
m
a
y
b
e
o
b
s
er
v
ed
th
a
t
d
u
e
to
ap
p
ea
r
an
ce
o
f
f
a
u
lt
i
n
s
tato
r
w
i
n
d
in
g
s
,
t
h
e
m
o
to
r
cu
r
r
en
t
s
ig
n
at
u
r
es
d
is
to
r
t
f
r
o
m
u
s
u
al
s
i
n
u
s
o
id
al
s
h
ap
e
s
.
Ho
w
ev
er
,
th
e
s
e
ch
a
n
g
e
s
ar
e
d
if
f
ic
u
lt
to
f
ig
u
r
e
o
u
t
i
n
o
p
en
e
y
es
w
h
en
o
p
er
atin
g
co
n
d
itio
n
c
h
an
g
es
f
r
o
m
h
e
alth
y
o
r
s
o
m
e
f
au
lt
le
v
el
to
o
th
er
f
au
lt
co
n
d
itio
n
in
v
o
l
v
i
n
g
n
o
m
in
al
n
u
m
b
er
o
f
t
u
r
n
s
av
ailab
le
i
n
th
e
s
co
p
e
o
f
th
e
p
r
esen
t s
tu
d
y
.
4
.
2
.
Rec
o
ns
t
ruct
io
n o
f
ra
w
3
-
ph
a
s
e
curr
ent
da
t
a
us
ing
D
WT
a
nd
inv
er
s
e
-
DWT
I
t
is
q
u
ite
ev
id
e
n
t
th
at
i
f
f
a
u
lt
o
cc
u
r
s
,
t
h
e
th
r
ee
-
p
h
a
s
e
c
u
r
r
en
ts
b
ec
o
m
e
u
n
b
alan
ce
d
.
Me
r
el
y
b
y
v
is
u
all
y
in
s
p
ec
tin
g
th
e
g
r
ap
h
s
o
f
th
e
cu
r
r
e
n
t
s
ig
n
al
s
,
it
is
d
i
f
f
icu
l
t
to
d
i
f
f
er
en
tia
te
b
et
w
ee
n
f
a
u
lt
y
a
n
d
h
ea
lt
h
y
ca
s
es.
T
h
u
s
,
ad
d
itio
n
al
an
al
y
s
is
o
f
cu
r
r
en
t
s
ig
n
als
i
s
r
eq
u
ir
ed
to
d
etec
t
th
e
f
au
l
ts
ac
cu
r
atel
y
.
First,
th
e
5
0
Hz
f
r
eq
u
en
c
y
(
f
u
n
d
a
m
en
tal
f
r
eq
u
en
c
y
)
co
m
p
o
n
en
t
s
o
f
t
h
e
t
h
r
e
e
p
h
ase
cu
r
r
e
n
t
s
i
g
n
als
w
er
e
eli
m
i
n
ated
b
ec
au
s
e
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
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
A
p
r
il
20
2
5
:
1
82
-
1
92
188
th
e
y
d
o
n
o
t
h
a
v
e
a
n
y
r
o
le
to
p
l
a
y
i
n
d
etec
ti
n
g
f
a
u
lt c
o
n
d
itio
n
o
f
t
h
e
m
o
to
r
[
3
]
.
I
n
th
i
s
p
r
o
ce
s
s
ca
p
t
u
r
ed
s
i
g
n
al
s
s
a
m
p
led
at
2
0
k
Hz
f
r
eq
u
e
n
c
y
h
av
e
b
ee
n
d
ec
o
m
p
o
s
ed
u
p
to
s
e
v
en
lev
e
ls
u
s
in
g
DW
T
w
h
ic
h
is
a
m
u
lt
i
-
r
eso
lu
tio
n
s
ig
n
al
a
n
al
y
s
i
s
to
o
l.
T
h
en
,
r
ec
o
n
s
tr
u
ctio
n
o
f
th
e
s
a
m
e
s
i
g
n
al
w
it
h
o
u
t
5
0
Hz
f
r
e
q
u
en
c
y
co
m
p
o
n
e
n
t
h
as
b
ee
n
i
m
p
le
m
e
n
ted
u
s
in
g
I
DW
T
.
Deb
au
ch
es
w
a
v
elet
-
2
(
Db
2
in
MA
T
L
A
B
)
h
as
b
ee
n
u
s
ed
as
m
o
t
h
er
w
a
v
elet
i
n
DW
T
.
E
x
e
m
p
lar
y
r
ec
o
n
s
tr
u
cted
R
,
Y,
an
d
B
-
p
h
as
e
cu
r
r
en
t
s
ig
n
al
s
h
a
v
e
b
ee
n
p
lo
tted
an
d
s
h
o
w
n
i
n
Fig
u
r
e
4
.
Fig
u
r
e
4
(
a)
r
ep
r
esen
ts
t
h
e
r
ec
o
n
s
tr
u
cted
w
av
e
f
o
r
m
o
f
R
-
p
h
ase
cu
r
r
e
n
ts
o
b
tain
ed
at
H_
0
L
,
2
T
_
0
L
an
d
4
T
_
0
L
co
n
d
itio
n
s
,
w
h
er
e
as
Fi
g
u
r
e
s
4
(
b
)
an
d
4
(
c)
r
ep
r
esen
t
th
e
Y
a
n
d
B
-
p
h
ase
r
ec
o
n
s
tr
u
cted
w
a
v
e
f
o
r
m
s
o
f
th
e
s
a
m
e
co
n
d
itio
n
s
.
Sig
n
if
ica
n
t
ch
a
n
g
es
i
n
m
ag
n
it
u
d
e
an
d
s
h
ap
e
o
f
t
h
e
r
ec
o
n
s
tr
u
c
ted
p
h
ase
cu
r
r
en
t
w
a
v
e
f
o
r
m
s
m
a
y
b
e
n
o
ted
d
u
e
to
ch
an
g
e
in
f
au
l
t
co
n
d
iti
o
n
s
.
T
h
u
s
,
th
e
r
ec
o
n
s
tr
u
cted
s
ig
n
a
ls
m
a
y
ca
r
r
y
p
o
ten
tial in
f
o
r
m
at
io
n
r
elate
d
to
f
au
lt o
r
o
p
er
atin
g
co
n
d
itio
n
o
f
th
e
m
o
to
r
.
(
a)
(
b
)
Fig
u
r
e
3
.
3
-
p
h
ase
cu
r
r
e
n
t
w
av
ef
o
r
m
s
at
(
a)
H_
0
L
co
n
d
itio
n
an
d
(
b
)
6
T
_
0
L
co
n
d
itio
n
(
a)
(
b
)
(
c)
Fig
u
r
e
4
.
R
ec
o
n
s
tr
u
cted
:
(
a)
R
-
p
h
a
s
e,
(
b
)
Y
-
p
h
ase
,
an
d
(
c)
B
-
p
h
a
s
e
cu
r
r
en
t
w
a
v
e
f
o
r
m
s
o
b
t
ain
ed
at
H_
0
L
,
2
T
_
0
L
an
d
4
T
_
0
L
co
n
d
itio
n
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Ma
ch
in
e
lea
r
n
in
g
b
a
s
ed
s
ta
to
r
-
w
in
d
in
g
fa
u
lt seve
r
ity
d
etec
tio
n
…
(
P
a
r
th
a
Mis
h
r
a
)
189
4
.
3
.
St
a
t
is
t
ica
l f
ea
t
ure
ex
t
ra
ct
io
n
I
n
th
e
c
u
r
r
en
t
s
t
u
d
y
,
1
5
co
n
v
e
n
tio
n
al
s
tati
s
tical
f
ea
t
u
r
es
w
er
e
ex
tr
ac
ted
f
r
o
m
ea
ch
o
f
t
h
e
t
h
r
ee
-
p
h
a
s
e
r
ec
o
n
s
tr
u
cted
c
u
r
r
en
t
s
ig
n
al
s
.
I
n
to
tal
4
5
f
ea
t
u
r
es
w
er
e
e
x
tr
ac
ted
.
Mo
s
t
o
f
t
h
ese
f
ea
t
u
r
es
w
er
e
f
o
u
n
d
to
h
a
v
e
r
ea
s
o
n
ab
le
v
ar
ia
n
ce
w
it
h
c
h
a
n
g
e
o
f
th
e
o
p
er
atin
g
co
n
d
itio
n
s
f
r
o
m
h
ea
lt
h
y
to
h
ig
h
es
t
p
o
s
s
i
b
le
f
au
lt
co
n
d
itio
n
u
n
d
er
t
h
e
s
co
p
e
o
f
th
e
p
r
ese
n
t
s
t
u
d
y
.
C
h
a
n
g
e
in
m
o
to
r
lo
ad
h
as
also
i
n
tr
o
d
u
ce
d
s
ig
n
i
f
ican
t
e
f
f
ec
t
o
n
t
h
e
f
ea
t
u
r
e
v
al
u
es.
Var
iatio
n
s
o
f
f
e
w
ex
e
m
p
lar
y
f
ea
t
u
r
es
at
d
if
f
er
e
n
t
m
o
to
r
o
p
er
atin
g
co
n
d
itio
n
s
h
a
v
e
b
ee
n
p
r
esen
ted
in
Fi
g
u
r
e
5
.
F
ig
u
r
e
5
(
a)
s
h
o
w
s
t
h
e
b
ar
p
lo
t
o
f
m
e
an
(
μ
)
v
al
u
es
o
f
R
-
p
h
ase
r
ec
o
n
s
tr
u
cted
cu
r
r
e
n
ts
u
n
d
er
d
if
f
er
en
t
o
p
er
atin
g
co
n
d
itio
n
o
f
th
e
m
o
to
r
,
an
d
it
m
a
y
b
e
o
b
s
er
v
ed
th
at
th
e
v
al
u
e
o
f
th
e
r
esp
ec
tiv
e
μ
d
o
es
n
o
t
co
i
n
cid
e
m
u
c
h
w
it
h
v
ar
y
i
n
g
le
v
el
o
f
f
a
u
lt
u
n
d
er
d
i
f
f
er
en
t
lo
ad
co
n
d
itio
n
s
.
F
ig
u
r
e
5
(
b
)
r
ep
r
esen
ts
b
ar
p
lo
t
o
f
s
h
ap
e
f
ac
to
r
(
sf
)
v
alu
e
s
ex
tr
ac
ted
f
r
o
m
Y
-
p
h
a
s
e
r
ec
o
n
s
tr
u
cted
c
u
r
r
en
t.
Si
g
n
i
f
ica
n
t
v
ar
iatio
n
o
f
t
h
e
sf
v
alu
e
s
co
u
ld
b
e
o
b
s
er
v
ed
co
r
r
esp
o
n
d
in
g
to
v
ar
y
in
g
o
p
er
ati
n
g
co
n
d
itio
n
s
o
f
th
e
m
o
to
r
u
n
d
er
d
if
f
er
en
t
lo
ad
co
n
d
itio
n
s
.
So
,
sf
m
a
y
b
e
co
n
s
id
er
ed
as
a
p
o
ten
tial
f
ea
t
u
r
e
f
o
r
th
e
d
etec
tio
n
o
f
t
h
e
f
a
u
lts
.
H
o
w
e
v
er
,
a
d
ef
i
n
ite
p
atter
n
i
n
c
h
an
g
e
o
f
t
h
e
s
f
v
alu
es
co
u
ld
n
o
t
b
e
d
er
iv
ed
.
Fig
u
r
e
5
(
c)
r
ep
r
esen
ts
b
ar
p
lo
t
o
f
k
u
r
to
s
i
s
(
k
u
r
t
)
v
alu
e
s
ex
tr
ac
ted
f
r
o
m
B
-
p
h
as
e
r
ec
o
n
s
tr
u
cted
cu
r
r
en
t
s
i
g
n
a
l
s
.
T
h
e
f
ea
tu
r
e
,
k
u
r
t
s
ee
m
s
to
b
e
as
g
o
o
d
as
sf
,
an
d
w
a
s
co
n
s
id
er
ed
as
an
i
m
p
o
r
tan
t
f
ea
tu
r
e.
Af
ter
a
clo
s
e
o
b
s
er
v
atio
n
o
n
al
l
ex
tr
ac
ted
f
ea
tu
r
e
s
,
it
co
u
ld
b
e
n
o
ted
th
at
a
ll t
h
e
f
ea
t
u
r
es
ar
e
p
er
tin
e
n
t a
n
d
i
n
cl
u
d
e
i
n
f
o
r
m
atio
n
ab
o
u
t t
h
e
o
p
er
atin
g
co
n
d
itio
n
o
f
th
e
m
o
to
r
.
B
esid
es,
a
lar
g
e
f
ea
t
u
r
e
s
et
f
ac
ilit
ate
s
m
ac
h
in
e
lear
n
i
n
g
al
g
o
r
ith
m
to
b
e
tr
ain
ed
ef
f
ec
tiv
e
l
y
to
m
ak
e
it
m
o
r
e
r
o
b
u
s
t.
(
a)
(
b
)
(
c)
Fig
u
r
e
5
.
B
ar
p
l
o
ts
o
f
s
tatis
tic
al
f
ea
t
u
r
es o
b
tain
ed
at
d
if
f
er
en
t c
ase
s
tu
d
ie
s
e.
g
.
:
(
a)
μ
v
alu
e
s
o
f
R
-
p
h
ase
r
ec
o
n
s
tr
u
cted
cu
r
r
e
n
ts
,
(
b
)
s
f
v
alu
es o
f
Y
-
p
h
ase
r
ec
o
n
s
tr
u
cte
d
cu
r
r
en
ts
,
an
d
(
c)
k
u
r
t
v
al
u
es
o
f
B
-
p
h
ase
r
ec
o
n
s
tr
u
cted
cu
r
r
e
n
ts
d
i
f
f
er
e
n
t c
ase
s
tu
d
ie
s
4
.
4
.
Cla
s
s
if
ica
t
io
n o
f
f
a
ults
us
ing
E
CO
C
-
SVM
cla
s
s
if
ier
E
x
tr
ac
ted
s
tat
is
tical
f
ea
tu
r
e
s
w
er
e
u
s
ed
to
m
o
d
el
th
e
m
ac
h
in
e
lear
n
i
n
g
al
g
o
r
ith
m
i
n
v
o
lv
i
n
g
E
C
O
C
aid
ed
b
y
SVM,
i
m
p
le
m
e
n
ted
in
M
A
T
L
A
B
p
lat
f
o
r
m
.
7
o
p
er
atin
g
ca
s
es
m
e
n
tio
n
ed
i
n
s
ec
tio
n
4
.
1
,
w
er
e
co
n
s
id
er
ed
in
th
is
s
t
u
d
y
.
E
ac
h
o
p
er
atin
g
ca
s
e
w
a
s
ca
r
r
ied
o
u
t
at
f
iv
e
d
if
f
er
e
n
t
lo
ad
in
g
co
n
d
itio
n
s
.
E
ac
h
ex
p
er
i
m
e
n
t
w
as
r
ep
ea
ted
4
tim
es.
He
n
ce
,
i
n
to
tal
t
h
er
e
w
er
e
(
7
×5
×4
=
1
4
0
)
1
4
0
o
b
s
er
v
atio
n
s
.
A
ll
th
e
1
4
0
o
b
s
er
v
atio
n
s
w
er
e
u
s
ed
f
o
r
t
r
ain
in
g
a
n
d
te
s
ti
n
g
p
h
a
s
es
o
f
E
C
OC
-
SVM
cla
s
s
i
f
ier
.
O
u
t
o
f
t
h
at,
7
0
%
o
f
o
b
s
er
v
atio
n
s
w
e
r
e
tak
e
n
f
o
r
tr
ain
i
n
g
,
a
n
d
3
0
%
o
f
o
b
s
er
v
atio
n
s
w
er
e
u
s
ed
f
o
r
test
in
g
p
u
r
p
o
s
es.
D
u
r
in
g
te
s
ti
n
g
p
h
ase,
th
e
tr
ai
n
ed
m
o
d
el
ex
h
i
b
ited
r
ea
s
o
n
ab
ly
g
o
o
d
f
au
l
t
d
etec
tio
n
ac
cu
r
ac
y
.
T
h
e
r
es
u
lt
o
b
tain
ed
f
r
o
m
th
e
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
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
1
,
A
p
r
il
20
2
5
:
1
82
-
1
92
190
p
r
o
p
o
s
ed
class
i
f
ier
m
o
d
el
h
as
b
ee
n
p
r
esen
ted
i
n
f
o
r
m
o
f
a
c
o
n
f
u
s
io
n
m
atr
ix
[
3
]
,
s
h
o
w
n
i
n
Fig
u
r
e
6
.
Her
e,
in
th
e
co
n
f
u
s
io
n
m
atr
ix
,
th
e
co
r
r
ec
t
class
i
f
ica
tio
n
s
h
a
v
e
b
ee
n
s
h
o
w
n
b
y
b
lu
e
b
o
x
es,
a
n
d
th
e
m
is
s
-
clas
s
i
f
icatio
n
s
ar
e
s
h
o
w
n
b
y
p
i
n
k
b
o
x
e
s
.
I
t
m
a
y
b
e
n
o
ted
th
at
ap
p
r
o
x
i
m
atel
y
9
4
% c
lass
i
f
icatio
n
ac
cu
r
ac
y
co
u
ld
b
e
ac
h
iev
ed
.
Fig
u
r
e
6
.
C
o
n
f
u
s
io
n
m
atr
ix
o
b
tain
ed
f
r
o
m
te
s
ti
n
g
p
h
ase
o
f
E
C
OC
-
S
VM
5.
CO
NCLU
SI
O
N
Dete
ctio
n
o
f
m
in
o
r
s
ta
g
ed
v
ar
y
in
g
s
ev
er
it
y
o
f
I
T
SC
f
a
u
lt
s
a
t
v
ar
y
i
n
g
lo
ad
s
h
as
al
w
a
y
s
b
ee
n
a
to
u
g
h
task
.
Ho
w
e
v
er
,
t
h
e
f
in
d
i
n
g
s
o
f
t
h
e
c
u
r
r
en
t
s
tu
d
y
h
a
v
e
s
u
cc
e
s
s
f
u
l
l
y
e
s
tab
lis
h
ed
a
co
m
p
u
t
atio
n
all
y
s
i
m
p
le
y
et
h
ig
h
l
y
ac
cu
r
ate
f
a
u
lt
-
d
iag
n
o
s
is
m
et
h
o
d
f
o
r
th
e
ea
r
l
y
d
ete
ctio
n
o
f
d
if
f
er
e
n
t
s
e
v
er
it
y
o
f
I
T
SC
f
au
lts
in
a
p
ar
ticu
lar
p
h
ase
o
f
m
o
to
r
s
tat
o
r
w
i
n
d
i
n
g
.
T
h
e
ab
ilit
y
o
f
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
to
ac
cu
r
atel
y
d
etec
t
I
T
SC
f
au
lts
i
n
v
o
lv
i
n
g
v
er
y
f
e
w
n
u
m
b
er
s
o
f
t
u
r
n
s
,
i.e
.
,
m
i
n
i
m
u
m
0
.
2
8
%
o
f
to
tal
tu
r
n
s
i
n
a
p
h
a
s
e
w
in
d
i
n
g
,
m
a
k
es
i
t
u
n
iq
u
e.
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
also
e
s
tab
lis
h
ed
t
h
e
d
etec
tio
n
o
f
I
T
SC
f
a
u
lts
u
n
d
er
v
ar
y
i
n
g
lo
ad
lev
el
s
w
h
ic
h
m
ak
e
s
t
h
e
f
a
u
lt
d
ia
g
n
o
s
is
tec
h
n
iq
u
e
lo
ad
in
d
ep
en
d
en
t.
Mo
r
eo
v
er
,
n
o
r
m
aliza
t
io
n
o
f
t
h
e
3
-
p
h
ase
c
u
r
r
en
t
s
at
t
h
e
in
itial
s
ta
g
e
o
f
t
h
e
a
n
al
y
s
es
m
ak
e
s
t
h
e
e
x
tr
ac
ted
s
tatis
t
ic
al
f
ea
t
u
r
es
m
ac
h
i
n
e
i
n
d
ep
en
d
en
t
w
h
ich
i
n
-
tu
r
n
en
s
u
r
es
t
h
e
ac
ce
p
tab
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
in
c
o
n
d
itio
n
m
o
n
ito
r
in
g
o
f
in
d
u
s
tr
y
-
g
r
ad
ed
3
-
p
h
a
s
e
in
d
u
ctio
n
m
o
to
r
s
.
Mo
s
t
i
m
p
o
r
tan
tl
y
,
t
h
e
ac
h
iev
ed
9
4
%
cla
s
s
if
icatio
n
ac
c
u
r
ac
y
b
y
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
w
h
il
e
class
i
f
y
in
g
d
i
f
f
er
e
n
t
s
ev
er
it
y
o
f
I
T
SC
f
a
u
lts
is
h
i
g
h
l
y
s
a
tis
f
ac
to
r
y
.
Hen
ce
,
al
l
t
h
ese
f
ac
ts
s
tr
e
n
g
t
h
e
n
t
h
e
ac
ce
p
tab
ilit
y
o
f
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
i
n
f
a
u
lt
d
ia
g
n
o
s
is
o
f
i
n
d
u
c
tio
n
m
o
to
r
s
w
it
h
d
i
f
f
er
en
t
r
atin
g
s
ev
e
n
u
n
d
er
d
i
f
f
er
e
n
t
s
tr
ess
e
s
i
n
i
n
d
u
s
tr
ial
e
n
v
ir
o
n
m
e
n
t.
Ho
wev
er
,
th
e
c
u
r
r
en
t
s
t
u
d
y
m
a
y
b
e
ex
p
an
d
ed
b
y
co
n
s
id
er
in
g
v
ar
y
i
n
g
s
e
v
er
it
y
o
f
in
ter
-
t
u
r
n
s
h
o
r
t
cir
cu
it
f
a
u
lts
w
it
h
u
n
b
a
la
n
ce
d
s
u
p
p
l
y
v
o
lta
g
e
an
d
also
f
o
r
th
e
p
ar
tial
in
s
u
la
tio
n
f
a
u
lt
s
w
h
ic
h
p
r
o
v
id
e
id
e
n
tical
m
o
to
r
li
n
e
cu
r
r
en
t
s
o
b
tai
n
ed
i
n
ca
s
e
o
f
I
T
SC
f
a
u
lts
.
T
h
e
p
r
o
p
o
s
ed
r
esear
ch
w
o
r
k
m
a
y
also
b
e
ex
ten
d
ed
to
id
en
ti
f
y
th
e
s
e
v
er
it
y
o
f
m
u
l
tip
le
f
a
u
l
ts
th
at
m
a
y
o
cc
u
r
s
i
m
u
lta
n
eo
u
s
l
y
in
i
n
d
u
ct
io
n
m
o
to
r
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
au
th
o
r
s
w
o
u
ld
lik
e
to
ac
k
n
o
w
led
g
e
DST
-
SERB
(
g
r
an
t
n
u
m
b
er
:
SB
/S3
/EE
C
E
/0
1
7
2
/2
0
1
3
)
an
d
A
I
C
T
E
-
MO
DR
OB
(
g
r
a
n
t
n
u
m
b
er
:
F.NO
:9
-
2
5
/R
I
FD/MO
DR
OB
/P
o
lic
y
-
1
/2
0
1
7
-
1
8
)
f
o
r
f
u
n
d
i
n
g
th
e
c
u
r
r
en
t
s
tu
d
y
.
RE
F
E
R
E
NC
E
S
[
1
]
L
.
W
e
i
,
X
.
R
o
n
g
,
H
.
W
a
n
g
,
S
.
Y
u
,
a
n
d
Y
.
Z
h
a
n
g
,
“
M
e
t
h
o
d
f
o
r
i
d
e
n
t
i
f
y
i
n
g
st
a
t
o
r
a
n
d
r
o
t
o
r
f
a
u
l
t
s
o
f
i
n
d
u
c
t
i
o
n
mo
t
o
r
s
b
a
se
d
o
n
mac
h
i
n
e
v
i
si
o
n
,
”
M
a
t
h
e
m
a
t
i
c
a
l
Pr
o
b
l
e
m
s i
n
E
n
g
i
n
e
e
ri
n
g
,
v
o
l
.
2
0
2
1
,
p
p
.
1
–
1
3
,
Ja
n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
2
1
/
6
6
5
8
6
4
8
.
[
2
]
G
.
S
.
A
y
y
a
p
p
a
n
,
B
.
R
.
B
a
b
u
,
M
.
R
.
R
a
g
h
a
v
a
n
,
a
n
d
R
.
P
o
o
n
t
h
a
l
i
r
,
“
G
e
n
e
t
i
c
a
l
g
o
r
i
t
h
m
&
f
u
z
z
y
l
o
g
i
c
-
b
a
se
d
c
o
n
d
i
t
i
o
n
mo
n
i
t
o
r
i
n
g
o
f
i
n
d
u
c
t
i
o
n
mo
t
o
r
t
h
r
o
u
g
h
e
st
i
ma
t
e
d
mo
t
o
r
l
o
s
s
e
s,”
I
ETE
J
o
u
r
n
a
l
o
f
R
e
s
e
a
r
c
h
,
v
o
l
.
6
9
,
n
o
.
6
,
p
p
.
3
7
5
0
–
3
7
6
1
,
A
u
g
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
8
0
/
0
3
7
7
2
0
6
3
.
2
0
2
1
.
1
9
1
3
0
7
5
.
[
3
]
S
.
S
a
r
k
a
r
,
P
.
P
u
r
k
a
i
t
,
a
n
d
S
.
D
a
s,
“
N
I
C
o
mp
a
c
t
R
I
O
-
b
a
se
d
me
t
h
o
d
o
l
o
g
y
f
o
r
o
n
l
i
n
e
d
e
t
e
c
t
i
o
n
o
f
st
a
t
o
r
w
i
n
d
i
n
g
i
n
t
e
r
-
t
u
r
n
i
n
su
l
a
t
i
o
n
f
a
u
l
t
s
i
n
3
-
p
h
a
se
i
n
d
u
c
t
i
o
n
mo
t
o
r
s,
”
Me
a
su
r
e
m
e
n
t
,
v
o
l
.
1
8
2
,
p
.
1
0
9
6
8
2
,
S
e
p
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
me
a
s
u
r
e
me
n
t
.
2
0
2
1
.
1
0
9
6
8
2
.
[
4
]
S
.
D
a
s,
P
.
P
u
r
k
a
i
t
,
D
.
D
e
y
,
a
n
d
S
.
C
h
a
k
r
a
v
o
r
t
i
,
“
M
o
n
i
t
o
r
i
n
g
o
f
i
n
t
e
r
-
t
u
r
n
i
n
s
u
l
a
t
i
o
n
f
a
i
l
u
r
e
i
n
i
n
d
u
c
t
i
o
n
mo
t
o
r
u
si
n
g
a
d
v
a
n
c
e
d
si
g
n
a
l
a
n
d
d
a
t
a
p
r
o
c
e
ssi
n
g
t
o
o
l
s,”
I
EE
E
T
ra
n
sa
c
t
i
o
n
s o
n
D
i
e
l
e
c
t
ri
c
s
a
n
d
E
l
e
c
t
r
i
c
a
l
I
n
s
u
l
a
t
i
o
n
,
v
o
l
.
1
8
,
n
o
.
5
,
p
p
.
1
5
9
9
–
1
6
0
8
,
O
c
t
.
2
0
1
1
,
d
o
i
:
1
0
.
1
1
0
9
/
T
D
E
I
.
2
0
1
1
.
6
0
3
2
8
3
0
.
[
5
]
G
.
S
.
A
y
y
a
p
p
a
n
,
B
.
R
.
B
a
b
u
,
K
.
S
r
i
n
i
v
a
s,
M
.
R
.
R
a
g
h
a
v
a
n
,
a
n
d
R
.
P
o
o
n
t
h
a
l
i
r
,
“
M
a
t
h
e
ma
t
i
c
a
l
mo
d
e
l
l
i
n
g
a
n
d
I
o
T
e
n
a
b
l
e
d
i
n
s
t
r
u
me
n
t
a
t
i
o
n
f
o
r
si
mu
l
a
t
i
o
n
&
e
m
u
l
a
t
i
o
n
o
f
i
n
d
u
c
t
i
o
n
mo
t
o
r
f
a
u
l
t
s,
”
I
ET
E
J
o
u
r
n
a
l
o
f
Re
s
e
a
rc
h
,
v
o
l
.
6
9
,
n
o
.
4
,
p
p
.
1
8
2
9
–
1
8
4
1
,
M
a
y
2
0
2
3
,
d
o
i
:
1
0
.
1
0
8
0
/
0
3
7
7
2
0
6
3
.
2
0
2
1
.
1
8
7
5
2
7
2
.
[
6
]
M
.
Z
.
A
l
i
,
M
.
N
.
S
.
K
.
S
h
a
b
b
i
r
,
X
.
L
i
a
n
g
,
Y
.
Z
h
a
n
g
,
a
n
d
T
.
H
u
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
-
b
a
se
d
f
a
u
l
t
d
i
a
g
n
o
si
s
f
o
r
s
i
n
g
l
e
-
a
n
d
m
u
l
t
i
-
f
a
u
l
t
s
i
n
i
n
d
u
c
t
i
o
n
mo
t
o
r
s
u
s
i
n
g
me
a
su
r
e
d
st
a
t
o
r
c
u
r
r
e
n
t
s
a
n
d
v
i
b
r
a
t
i
o
n
si
g
n
a
l
s,”
I
EEE
T
ra
n
s
a
c
t
i
o
n
s
o
n
I
n
d
u
st
ry
Ap
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
5
5
,
n
o
.
3
,
p
p
.
2
3
7
8
–
2
3
9
1
,
M
a
y
2
0
1
9
,
d
o
i
:
1
0
.
1
1
0
9
/
TI
A
.
2
0
1
9
.
2
8
9
5
7
9
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Ma
ch
in
e
lea
r
n
in
g
b
a
s
ed
s
ta
to
r
-
w
in
d
in
g
fa
u
lt seve
r
ity
d
etec
tio
n
…
(
P
a
r
th
a
Mis
h
r
a
)
191
[
7
]
S
.
Y
a
d
a
v
,
R
.
K
.
P
a
t
e
l
,
a
n
d
V
.
P
.
S
i
n
g
h
,
“
M
u
l
t
i
c
l
a
ss
f
a
u
l
t
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
a
n
i
n
d
u
c
t
i
o
n
mo
t
o
r
b
e
a
r
i
n
g
v
i
b
r
a
t
i
o
n
d
a
t
a
u
si
n
g
w
a
v
e
l
e
t
p
a
c
k
e
t
t
r
a
n
sf
o
r
m
f
e
a
t
u
r
e
s
a
n
d
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
,
”
J
o
u
rn
a
l
o
f
V
i
b
ra
t
i
o
n
E
n
g
i
n
e
e
ri
n
g
&
T
e
c
h
n
o
l
o
g
i
e
s
,
v
o
l
.
1
1
,
n
o
.
7
,
p
p
.
3
0
9
3
–
3
1
0
8
,
O
c
t
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s
4
2
4
1
7
-
0
2
2
-
0
0
7
3
3
-
3.
[
8
]
M
.
O
j
a
g
h
i
,
M
.
S
a
b
o
u
r
i
,
a
n
d
J.
F
a
i
z
,
“
A
n
a
l
y
t
i
c
mo
d
e
l
f
o
r
i
n
d
u
c
t
i
o
n
mo
t
o
r
s
u
n
d
e
r
l
o
c
a
l
i
z
e
d
b
e
a
r
i
n
g
f
a
u
l
t
s,”
I
EEE
T
ra
n
sa
c
t
i
o
n
s
o
n
En
e
r
g
y
C
o
n
v
e
rsi
o
n
,
v
o
l
.
3
3
,
n
o
.
2
,
p
p
.
6
1
7
–
6
2
6
,
Ju
n
.
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
T
E
C
.
2
0
1
7
.
2
7
5
8
3
8
2
.
[
9
]
A
.
G
l
o
w
a
c
z
,
W
.
G
l
o
w
a
c
z
,
Z
.
G
l
o
w
a
c
z
,
a
n
d
J.
K
o
z
i
k
,
“
Ea
r
l
y
f
a
u
l
t
d
i
a
g
n
o
si
s
o
f
b
e
a
r
i
n
g
a
n
d
s
t
a
t
o
r
f
a
u
l
t
s
o
f
t
h
e
si
n
g
l
e
-
p
h
a
se
i
n
d
u
c
t
i
o
n
mo
t
o
r
u
s
i
n
g
a
c
o
u
st
i
c
s
i
g
n
a
l
s,”
Me
a
su
r
e
m
e
n
t
,
v
o
l
.
1
1
3
,
p
p
.
1
–
9
,
J
a
n
.
2
0
1
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
me
a
s
u
r
e
me
n
t
.
2
0
1
7
.
0
8
.
0
3
6
.
[
1
0
]
S
.
M
i
t
r
a
a
n
d
C
.
K
o
l
e
y
,
“
Ea
r
l
y
a
n
d
i
n
t
e
l
l
i
g
e
n
t
b
e
a
r
i
n
g
f
a
u
l
t
d
e
t
e
c
t
i
o
n
u
s
i
n
g
a
d
a
p
t
i
v
e
su
p
e
r
l
e
t
s,”
I
EEE
S
e
n
s
o
rs
J
o
u
r
n
a
l
,
v
o
l
.
2
3
,
n
o
.
7
,
p
p
.
7
9
9
2
–
8
0
0
0
,
A
p
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
EN
.
2
0
2
3
.
3
2
4
5
1
8
6
.
[
1
1
]
S
.
K
.
G
u
n
d
e
w
a
r
a
n
d
P
.
V
.
K
a
n
e
,
“
B
e
a
r
i
n
g
f
a
u
l
t
d
i
a
g
n
o
si
s
u
si
n
g
t
i
me
se
g
m
e
n
t
e
d
F
o
u
r
i
e
r
s
y
n
c
h
r
o
sq
u
e
e
z
e
d
t
r
a
n
sf
o
r
m
i
mag
e
s
a
n
d
c
o
n
v
o
l
u
t
i
o
n
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
Me
a
su
r
e
m
e
n
t
,
v
o
l
.
2
0
3
,
p
.
1
1
1
8
5
5
,
N
o
v
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
me
a
su
r
e
me
n
t
.
2
0
2
2
.
1
1
1
8
5
5
.
[
1
2
]
G
.
H
.
B
a
z
a
n
,
P
.
R
.
S
c
a
l
a
ss
a
r
a
,
W
.
E
n
d
o
,
a
n
d
A
.
G
o
e
d
t
e
l
,
“
I
n
f
o
r
mat
i
o
n
t
h
e
o
r
e
t
i
c
a
l
me
a
su
r
e
me
n
t
s
f
r
o
m
i
n
d
u
c
t
i
o
n
mo
t
o
r
s
u
n
d
e
r
se
v
e
r
a
l
l
o
a
d
a
n
d
v
o
l
t
a
g
e
c
o
n
d
i
t
i
o
n
s
f
o
r
b
e
a
r
i
n
g
f
a
u
l
t
s
c
l
a
ssi
f
i
c
a
t
i
o
n
,
”
I
EEE
T
ra
n
s
a
c
t
i
o
n
s
o
n
I
n
d
u
s
t
ri
a
l
I
n
f
o
rm
a
t
i
c
s
,
v
o
l
.
1
6
,
n
o
.
6
,
p
p
.
3
6
4
0
–
3
6
5
0
,
Ju
n
.
2
0
2
0
,
d
o
i
:
1
0
.
1
1
0
9
/
TI
I
.
2
0
1
9
.
2
9
3
9
6
7
8
.
[
1
3
]
A
.
A
l
mo
u
n
a
j
j
e
d
,
A
.
K
.
S
a
h
o
o
,
a
n
d
M
.
K
.
K
u
mar,
“
D
i
a
g
n
o
si
s
o
f
st
a
t
o
r
f
a
u
l
t
se
v
e
r
i
t
y
i
n
i
n
d
u
c
t
i
o
n
mo
t
o
r
b
a
se
d
o
n
d
i
s
c
r
e
t
e
w
a
v
e
l
e
t
a
n
a
l
y
si
s,”
Me
a
s
u
reme
n
t
,
v
o
l
.
1
8
2
,
p
.
1
0
9
7
8
0
,
S
e
p
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
me
a
su
r
e
me
n
t
.
2
0
2
1
.
1
0
9
7
8
0
.
[
1
4
]
V
.
B
.
B
a
l
’
,
N
.
F
.
K
o
t
e
l
e
n
e
t
s,
a
n
d
M
.
D
e
e
b
,
“
D
i
scre
t
e
w
a
v
e
l
e
t
t
r
a
n
sf
o
r
m
f
o
r
st
a
t
o
r
f
a
u
l
t
d
e
t
e
c
t
i
o
n
i
n
a
n
i
n
d
u
c
t
i
o
n
mo
t
o
r
,
”
P
o
w
e
r
T
e
c
h
n
o
l
o
g
y
a
n
d
E
n
g
i
n
e
e
r
i
n
g
,
v
o
l
.
5
7
,
n
o
.
1
,
p
p
.
1
7
5
–
1
8
5
,
M
a
y
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
0
7
4
9
-
0
2
3
-
0
1
6
3
9
-
0.
[
1
5
]
S
.
c
h
i
k
k
a
m
a
n
d
S
.
S
i
n
g
h
,
“
C
o
n
d
i
t
i
o
n
mo
n
i
t
o
r
i
n
g
a
n
d
f
a
u
l
t
d
i
a
g
n
o
s
i
s
o
f
i
n
d
u
c
t
i
o
n
mo
t
o
r
u
s
i
n
g
D
W
T
a
n
d
A
N
N
,
”
Ara
b
i
a
n
J
o
u
r
n
a
l
f
o
r
S
c
i
e
n
c
e
a
n
d
E
n
g
i
n
e
e
r
i
n
g
,
v
o
l
.
4
8
,
n
o
.
5
,
p
p
.
6
2
3
7
–
6
2
5
2
,
M
a
y
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
3
3
6
9
-
0
2
2
-
0
7
2
9
4
-
3.
[
1
6
]
H
.
T
a
l
h
a
o
u
i
,
T
.
A
m
e
i
d
,
O
.
A
i
ssa,
a
n
d
A
.
K
e
ssal
,
“
W
a
v
e
l
e
t
p
a
c
k
e
t
a
n
d
f
u
z
z
y
l
o
g
i
c
t
h
e
o
r
y
f
o
r
a
u
t
o
ma
t
i
c
f
a
u
l
t
d
e
t
e
c
t
i
o
n
i
n
i
n
d
u
c
t
i
o
n
mo
t
o
r
,
”
S
o
f
t
C
o
m
p
u
t
i
n
g
,
v
o
l
.
2
6
,
n
o
.
2
1
,
p
p
.
1
1
9
3
5
–
1
1
9
4
9
,
N
o
v
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
0
0
5
0
0
-
0
2
2
-
0
7
0
2
8
-
5.
[
1
7
]
G
.
R
.
A
g
a
h
,
A
.
R
a
h
i
d
e
h
,
H
.
K
h
o
d
a
d
a
d
z
a
d
e
h
,
S
.
M
.
K
h
o
sh
n
a
z
a
r
,
a
n
d
S
.
H
e
d
a
y
a
t
i
k
i
a
,
“
B
r
o
k
e
n
r
o
t
o
r
b
a
r
a
n
d
r
o
t
o
r
e
c
c
e
n
t
r
i
c
i
t
y
f
a
u
l
t
d
e
t
e
c
t
i
o
n
i
n
i
n
d
u
c
t
i
o
n
mo
t
o
r
s
u
s
i
n
g
a
c
o
mb
i
n
a
t
i
o
n
o
f
d
i
scre
t
e
w
a
v
e
l
e
t
t
r
a
n
sf
o
r
m
a
n
d
T
e
a
g
e
r
-
K
a
i
se
r
e
n
e
r
g
y
o
p
e
r
a
t
o
r
,
”
I
EE
E
T
ra
n
s
a
c
t
i
o
n
s
o
n
E
n
e
r
g
y
C
o
n
v
e
rs
i
o
n
,
v
o
l
.
3
7
,
n
o
.
3
,
p
p
.
2
1
9
9
–
2
2
0
6
,
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
T
EC
.
2
0
2
2
.
3
1
6
2
3
9
4
.
[
1
8
]
Y
.
P
a
r
k
,
H
.
C
h
o
i
,
S
.
B
.
L
e
e
,
a
n
d
K
.
N
.
G
y
f
t
a
k
i
s,
“
S
e
a
r
c
h
C
o
i
l
-
b
a
se
d
d
e
t
e
c
t
i
o
n
o
f
n
o
n
a
d
j
a
c
e
n
t
r
o
t
o
r
b
a
r
d
a
m
a
g
e
i
n
S
q
u
i
r
r
e
l
C
a
g
e
i
n
d
u
c
t
i
o
n
mo
t
o
r
s,”
I
EE
E
T
r
a
n
s
a
c
t
i
o
n
s
o
n
I
n
d
u
s
t
ry
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
5
6
,
n
o
.
5
,
p
p
.
4
7
4
8
–
4
7
5
7
,
S
e
p
.
2
0
2
0
,
d
o
i
:
1
0
.
1
1
0
9
/
TI
A
.
2
0
2
0
.
3
0
0
0
4
6
1
.
[
1
9
]
S
.
B
.
L
e
e
,
J.
S
h
i
n
,
Y
.
P
a
r
k
,
H
.
K
i
m,
a
n
d
J.
K
i
m,
“
R
e
l
i
a
b
l
e
F
l
u
x
-
b
a
se
d
d
e
t
e
c
t
i
o
n
o
f
i
n
d
u
c
t
i
o
n
mo
t
o
r
r
o
t
o
r
f
a
u
l
t
s fr
o
m t
h
e
f
i
f
t
h
r
o
t
o
r
r
o
t
a
t
i
o
n
a
l
f
r
e
q
u
e
n
c
y
si
d
e
b
a
n
d
,
”
I
EE
E
T
ra
n
sa
c
t
i
o
n
s
o
n
I
n
d
u
s
t
ri
a
l
El
e
c
t
r
o
n
i
c
s
,
v
o
l
.
6
8
,
n
o
.
9
,
p
p
.
7
8
7
4
–
7
8
8
3
,
S
e
p
.
2
0
2
1
,
d
o
i
:
1
0
.
1
1
0
9
/
TI
E.
2
0
2
0
.
3
0
1
6
2
4
1
.
[
2
0
]
S
.
M
a
r
mo
u
c
h
,
T
.
A
r
o
u
i
,
a
n
d
Y
.
K
o
u
b
a
a
,
“
I
n
d
u
c
t
i
o
n
m
a
c
h
i
n
e
f
a
u
l
t
s
d
i
a
g
n
o
si
s
b
y
st
a
t
i
s
t
i
c
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s
w
i
t
h
se
l
e
c
t
i
o
n
v
a
r
i
a
b
l
e
s
b
a
se
d
o
n
p
r
i
n
c
i
p
a
l
c
o
mp
o
n
e
n
t
a
n
a
l
y
si
s,”
i
n
2
0
1
7
1
8
t
h
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
S
c
i
e
n
c
e
s
a
n
d
T
e
c
h
n
i
q
u
e
s
o
f
Au
t
o
m
a
t
i
c
C
o
n
t
ro
l
a
n
d
C
o
m
p
u
t
e
r E
n
g
i
n
e
e
ri
n
g
(
S
T
A)
,
D
e
c
.
2
0
1
7
,
p
p
.
9
9
–
1
0
3
,
d
o
i
:
1
0
.
1
1
0
9
/
S
T
A
.
2
0
1
7
.
8
3
1
4
8
8
7
.
[
2
1
]
B
.
B
r
u
sam
a
r
e
l
l
o
,
J.
C
.
C
.
S
i
l
v
a
,
K
.
M.
S
o
u
sa,
a
n
d
G
.
A
.
G
u
a
r
n
e
r
i
,
“
B
e
a
r
i
n
g
f
a
u
l
t
d
e
t
e
c
t
i
o
n
i
n
t
h
r
e
e
-
p
h
a
se
i
n
d
u
c
t
i
o
n
mo
t
o
r
s
u
si
n
g
su
p
p
o
r
t
v
e
c
t
o
r
m
a
c
h
i
n
e
a
n
d
f
i
b
e
r
b
r
a
g
g
g
r
a
t
i
n
g
,
”
I
EE
E
S
e
n
so
rs
J
o
u
r
n
a
l
,
v
o
l
.
2
3
,
n
o
.
5
,
p
p
.
4
4
1
3
–
4
4
2
1
,
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
EN
.
2
0
2
2
.
3
1
6
7
6
3
2
.
[
2
2
]
A
.
A
b
i
d
,
M
.
T
.
K
h
a
n
,
a
n
d
C
.
W
.
d
e
S
i
l
v
a
,
“
L
a
y
e
r
e
d
a
n
d
r
e
a
l
-
v
a
l
u
e
d
n
e
g
a
t
i
v
e
sel
e
c
t
i
o
n
a
l
g
o
r
i
t
h
m
f
o
r
f
a
u
l
t
d
e
t
e
c
t
i
o
n
,
”
I
EE
E
S
y
s
t
e
m
s
J
o
u
r
n
a
l
,
v
o
l
.
1
2
,
n
o
.
3
,
p
p
.
2
9
6
0
–
2
9
6
9
,
S
e
p
.
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
Y
S
T
.
2
0
1
7
.
2
7
5
3
8
5
1
.
[
2
3
]
H
.
K
h
w
a
j
a
,
S
.
G
u
p
t
a
,
a
n
d
V
.
K
u
mar,
“
A
st
a
t
i
s
t
i
c
a
l
a
p
p
r
o
a
c
h
f
o
r
f
a
u
l
t
d
i
a
g
n
o
si
s
i
n
e
l
e
c
t
r
i
c
a
l
ma
c
h
i
n
e
s,”
I
ETE
J
o
u
r
n
a
l
o
f
Re
se
a
rc
h
,
v
o
l
.
5
6
,
n
o
.
3
,
p
.
1
4
6
,
2
0
1
0
,
d
o
i
:
1
0
.
4
1
0
3
/
0
3
7
7
-
2
0
6
3
.
6
7
0
9
9
.
[
2
4
]
G
.
P
o
n
t
u
a
l
e
,
F
.
A
.
F
a
r
r
e
l
l
y
,
A
.
P
e
t
r
i
,
a
n
d
L
.
P
i
t
o
l
l
i
,
“
A
st
a
t
i
s
t
i
c
a
l
a
n
a
l
y
si
s
o
f
a
c
o
u
st
i
c
e
mi
ssi
o
n
si
g
n
a
l
s
f
o
r
t
o
o
l
c
o
n
d
i
t
i
o
n
mo
n
i
t
o
r
i
n
g
(
T
C
M
)
,
”
A
c
o
u
s
t
i
c
s R
e
se
a
r
c
h
L
e
t
t
e
rs
O
n
l
i
n
e
,
v
o
l
.
4
,
n
o
.
1
,
p
p
.
1
3
–
1
8
,
J
a
n
.
2
0
0
3
,
d
o
i
:
1
0
.
1
1
2
1
/
1
.
1
5
3
2
3
7
0
.
[
2
5
]
M
.
H
.
A
b
i
d
i
,
M
.
K
.
M
o
h
a
mm
e
d
,
a
n
d
H
.
A
l
k
h
a
l
e
f
a
h
,
“
P
r
e
d
i
c
t
i
v
e
ma
i
n
t
e
n
a
n
c
e
p
l
a
n
n
i
n
g
f
o
r
i
n
d
u
s
t
r
y
4
.
0
u
si
n
g
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
f
o
r
su
st
a
i
n
a
b
l
e
man
u
f
a
c
t
u
r
i
n
g
,
”
S
u
st
a
i
n
a
b
i
l
i
t
y
,
v
o
l
.
1
4
,
n
o
.
6
,
p
.
3
3
8
7
,
M
a
r
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
s
u
1
4
0
6
3
3
8
7
.
[
2
6
]
S
.
A
r
e
n
a
,
E.
F
l
o
r
i
a
n
,
I
.
Ze
n
n
a
r
o
,
P
.
F
.
O
r
r
ù
,
a
n
d
F
.
S
g
a
r
b
o
ssa,
“
A
n
o
v
e
l
d
e
c
i
si
o
n
su
p
p
o
r
t
sy
st
e
m
f
o
r
man
a
g
i
n
g
p
r
e
d
i
c
t
i
v
e
mai
n
t
e
n
a
n
c
e
st
r
a
t
e
g
i
e
s
b
a
se
d
o
n
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
e
s,”
S
a
f
e
t
y
S
c
i
e
n
c
e
,
v
o
l
.
1
4
6
,
p
.
1
0
5
5
2
9
,
F
e
b
.
2
0
2
2
,
d
o
i
:
10
.
1
0
1
6
/
j
.
ssc
i
.
2
0
2
1
.
1
0
5
5
2
9
.
[
2
7
]
S
.
A
z
i
z
,
M
.
U
.
K
h
a
n
,
M
.
F
a
r
a
z
,
a
n
d
G
.
A
.
M
o
n
t
e
s,
“
I
n
t
e
l
l
i
g
e
n
t
b
e
a
r
i
n
g
f
a
u
l
t
s
d
i
a
g
n
o
si
s
f
e
a
t
u
r
i
n
g
a
u
t
o
mat
e
d
r
e
l
a
t
i
v
e
e
n
e
r
g
y
b
a
se
d
e
mp
i
r
i
c
a
l
mo
d
e
d
e
c
o
m
p
o
si
t
i
o
n
a
n
d
n
o
v
e
l
c
e
p
st
r
a
l
a
u
t
o
r
e
g
r
e
ssi
v
e
f
e
a
t
u
r
e
s,”
Me
a
su
r
e
m
e
n
t
,
v
o
l
.
2
1
6
,
p
.
1
1
2
8
7
1
,
J
u
l
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
me
a
su
r
e
me
n
t
.
2
0
2
3
.
1
1
2
8
7
1
.
[
2
8
]
M
.
C
a
k
i
r
,
M
.
A
.
G
u
v
e
n
c
,
a
n
d
S
.
M
i
st
i
k
o
g
l
u
,
“
T
h
e
e
x
p
e
r
i
me
n
t
a
l
a
p
p
l
i
c
a
t
i
o
n
o
f
p
o
p
u
l
a
r
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms
o
n
p
r
e
d
i
c
t
i
v
e
mai
n
t
e
n
a
n
c
e
a
n
d
t
h
e
d
e
si
g
n
o
f
I
I
o
T
b
a
se
d
c
o
n
d
i
t
i
o
n
mo
n
i
t
o
r
i
n
g
sy
st
e
m,”
C
o
m
p
u
t
e
rs
&
I
n
d
u
st
r
i
a
l
En
g
i
n
e
e
ri
n
g
,
v
o
l
.
1
5
1
,
p
.
1
0
6
9
4
8
,
Ja
n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
i
e
.
2
0
2
0
.
1
0
6
9
4
8
.
[
2
9
]
P
.
K
u
mar
a
n
d
A
.
S
.
H
a
t
i
,
“
R
e
v
i
e
w
o
n
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
b
a
s
e
d
f
a
u
l
t
d
e
t
e
c
t
i
o
n
i
n
i
n
d
u
c
t
i
o
n
mo
t
o
r
s,”
Arc
h
i
v
e
s
o
f
C
o
m
p
u
t
a
t
i
o
n
a
l
Me
t
h
o
d
s
i
n
En
g
i
n
e
e
r
i
n
g
,
v
o
l
.
2
8
,
n
o
.
3
,
p
p
.
1
9
2
9
–
1
9
4
0
,
M
a
y
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
1
8
3
1
-
0
2
0
-
0
9
4
4
6
-
w.
[
3
0
]
A
.
C
h
o
u
d
h
a
r
y
,
D
.
G
o
y
a
l
,
a
n
d
S
.
S
.
L
e
t
h
a
,
“
I
n
f
r
a
r
e
d
t
h
e
r
mo
g
r
a
p
h
y
-
b
a
sed
f
a
u
l
t
d
i
a
g
n
o
si
s
o
f
i
n
d
u
c
t
i
o
n
mo
t
o
r
b
e
a
r
i
n
g
s
u
si
n
g
mac
h
i
n
e
l
e
a
r
n
i
n
g
,
”
I
EE
E
S
e
n
s
o
rs J
o
u
rn
a
l
,
v
o
l
.
2
1
,
n
o
.
2
,
p
p
.
1
7
2
7
–
1
7
3
4
,
Ja
n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
EN
.
2
0
2
0
.
3
0
1
5
8
6
8
.
[
3
1
]
Z
.
C
h
e
n
,
A
.
M
a
u
r
i
c
i
o
,
W
.
L
i
,
a
n
d
K
.
G
r
y
l
l
i
a
s,
“
A
d
e
e
p
l
e
a
r
n
i
n
g
me
t
h
o
d
f
o
r
b
e
a
r
i
n
g
f
a
u
l
t
d
i
a
g
n
o
si
s
b
a
se
d
o
n
c
y
c
l
i
c
sp
e
c
t
r
a
l
c
o
h
e
r
e
n
c
e
a
n
d
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s,”
Me
c
h
a
n
i
c
a
l
S
y
st
e
m
s
a
n
d
S
i
g
n
a
l
Pro
c
e
ss
i
n
g
,
v
o
l
.
1
4
0
,
p
.
1
0
6
6
8
3
,
Ju
n
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
y
mss
p
.
2
0
2
0
.
1
0
6
6
8
3
.
[
3
2
]
S
.
Q
i
,
J.
Y
a
n
g
,
a
n
d
Z
.
Z
h
o
n
g
,
“
A
r
e
v
i
e
w
o
n
i
n
d
u
s
t
r
i
a
l
su
r
f
a
c
e
d
e
f
e
c
t
d
e
t
e
c
t
i
o
n
b
a
se
d
o
n
d
e
e
p
l
e
a
r
n
i
n
g
t
e
c
h
n
o
l
o
g
y
,
”
i
n
2
0
2
0
T
h
e
3
rd
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
Ma
c
h
i
n
e
L
e
a
r
n
i
n
g
a
n
d
M
a
c
h
i
n
e
I
n
t
e
l
l
i
g
e
n
c
e
,
S
e
p
.
2
0
2
0
,
p
p
.
2
4
–
3
0
,
d
o
i
:
1
0
.
1
1
4
5
/
3
4
2
6
8
2
6
.
3
4
2
6
8
3
2
.
[
3
3
]
J.
J.
S
a
u
c
e
d
o
-
D
o
r
a
n
t
e
s,
A
.
Y
.
J
a
e
n
-
C
u
e
l
l
a
r
,
M
.
D
e
l
g
a
d
o
-
P
r
i
e
t
o
,
R
.
d
e
J.
R
o
me
r
o
-
T
r
o
n
c
o
so
,
a
n
d
R
.
A
.
O
s
o
r
n
i
o
-
R
i
o
s,
“
C
o
n
d
i
t
i
o
n
mo
n
i
t
o
r
i
n
g
st
r
a
t
e
g
y
b
a
se
d
o
n
a
n
o
p
t
i
mi
z
e
d
se
l
e
c
t
i
o
n
o
f
h
i
g
h
-
d
i
me
n
si
o
n
a
l
se
t
o
f
h
y
b
r
i
d
f
e
a
t
u
r
e
s
t
o
d
i
a
g
n
o
se
a
n
d
d
e
t
e
c
t
m
u
l
t
i
p
l
e
a
n
d
c
o
m
b
i
n
e
d
f
a
u
l
t
s
i
n
a
n
i
n
d
u
c
t
i
o
n
mo
t
o
r
,
”
Me
a
s
u
reme
n
t
,
v
o
l
.
1
7
8
,
p
.
1
0
9
4
0
4
,
J
u
n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
me
a
su
r
e
me
n
t
.
2
0
2
1
.
1
0
9
4
0
4
.
[
3
4
]
D
.
D
e
y
,
B
.
C
h
a
t
t
e
r
j
e
e
,
S
.
D
a
l
a
i
,
S
.
M
u
n
s
h
i
,
a
n
d
S
.
C
h
a
k
r
a
v
o
r
t
i
,
“
A
d
e
e
p
l
e
a
r
n
i
n
g
f
r
a
me
w
o
r
k
u
si
n
g
c
o
n
v
o
l
u
t
i
o
n
n
e
u
r
a
l
n
e
t
w
o
r
k
f
o
r
c
l
a
ssi
f
i
c
a
t
i
o
n
o
f
i
mp
u
l
se
f
a
u
l
t
p
a
t
t
e
r
n
s
i
n
t
r
a
n
sf
o
r
me
r
s
w
i
t
h
i
n
c
r
e
a
se
d
a
c
c
u
r
a
c
y
,
”
I
EEE
T
ra
n
s
a
c
t
i
o
n
s
o
n
D
i
e
l
e
c
t
ri
c
s
a
n
d
El
e
c
t
ri
c
a
l
I
n
su
l
a
t
i
o
n
,
v
o
l
.
2
4
,
n
o
.
6
,
p
p
.
3
8
9
4
–
3
8
9
7
,
D
e
c
.
2
0
1
7
,
d
o
i
:
1
0
.
1
1
0
9
/
T
D
E
I
.
2
0
1
7
.
0
0
6
7
9
3
.
[
3
5
]
B
.
G
a
n
g
u
l
y
,
S
.
B
i
sw
a
s,
S
.
G
h
o
sh
,
S
.
M
a
i
t
i
,
a
n
d
S
.
B
o
d
h
a
k
,
“
A
d
e
e
p
l
e
a
r
n
i
n
g
f
r
a
mew
o
r
k
f
o
r
e
y
e
m
e
l
a
n
o
ma
d
e
t
e
c
t
i
o
n
e
mp
l
o
y
i
n
g
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
i
n
2
0
1
9
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
C
o
m
p
u
t
e
r
,
El
e
c
t
ri
c
a
l
&
C
o
m
m
u
n
i
c
a
t
i
o
n
E
n
g
i
n
e
e
r
i
n
g
(
I
C
C
EC
E)
,
J
a
n
.
2
0
1
9
,
p
p
.
1
–
4
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
C
EC
E
4
4
7
2
7
.
2
0
1
9
.
9
0
0
1
8
5
8
.
Evaluation Warning : The document was created with Spire.PDF for Python.