I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
36
,
No
.
3
,
Dec
em
b
er
20
24
,
p
p
.
1
3
5
6
~1
364
I
SS
N:
2
502
-
4
7
52
,
DOI
: 1
0
.
1
1
5
9
1
/ijee
cs
.v
36.
i
3
.
p
p
1
3
5
6
-
1
3
6
4
1356
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs
.
ia
esco
r
e.
co
m
No
n
-
co
ntact
po
w
er sy
stem
fault di
a
g
no
sis
:
a
ma
chine learning
a
ppro
a
ch wit
h
ele
ctroma
g
netic
c
ur
rent
se
nsing
Am
it
L
.
Nehet
e
1,
3
,
Dee
pa
k
S.
B
a
nk
a
r
1
,
Rit
ika
Asa
t
i
2
,
Chet
a
n K
ha
ds
e
3
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
,
B
h
a
r
a
t
i
V
i
d
y
a
p
e
e
t
h
(
D
e
e
m
e
d
t
o
b
e
)
U
n
i
v
e
r
si
t
y
C
o
l
l
e
g
e
o
f
E
n
g
i
n
e
e
r
i
n
g
,
P
u
n
e
,
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
,
C
o
EP
Te
c
h
n
o
l
o
g
i
c
a
l
U
n
i
v
e
r
s
i
t
y
,
P
u
n
e
,
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
a
n
d
El
e
c
t
r
o
n
i
c
s E
n
g
i
n
e
e
r
i
n
g
,
D
r
.
V
i
s
h
w
a
n
a
t
h
K
a
r
a
d
M
I
T
W
o
r
l
d
P
e
a
c
e
U
n
i
v
e
r
s
i
t
y
,
P
u
n
e
,
I
n
d
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
9
,
2
0
2
4
R
ev
is
ed
Au
g
10
,
202
4
Acc
ep
ted
A
ug
26
,
2
0
2
4
M
o
d
e
r
n
p
o
we
r
sy
ste
m
p
ro
tec
ti
o
n
sc
h
e
m
e
s
in
c
o
rp
o
ra
te
a
rti
ficia
l
i
n
telli
g
e
n
c
e
(AI)
tec
h
n
i
q
u
e
s.
Ho
we
v
e
r,
in
a
c
o
n
v
e
n
ti
o
n
a
l
wa
y
,
m
o
st
o
f
t
h
e
se
sc
h
e
m
e
s
re
ly
o
n
t
h
e
d
a
ta
o
f
c
u
rre
n
t
a
n
d
v
o
lt
a
g
e
c
o
ll
e
c
ted
fro
m
c
u
rre
n
t
tr
a
n
sfo
rm
e
r
(CT)
a
n
d
p
o
ten
ti
a
l
tran
sfo
rm
e
r
(P
T)
re
sp
e
c
ti
v
e
l
y
.
CT
s
su
ffe
r
fro
m
th
e
d
ra
wb
a
c
k
o
f
c
o
re
sa
tu
ra
ti
o
n
a
n
d
imp
a
c
t
t
h
e
a
c
c
u
ra
c
y
a
n
d
e
ffe
c
ti
v
e
n
e
ss
o
f
in
telli
g
e
n
t
m
e
th
o
d
s.
Also
,
it
h
a
s
th
e
c
o
n
stra
in
ts
o
f
siz
e
,
sa
fe
ty
,
a
n
d
e
c
o
n
o
m
y
.
Th
e
re
se
a
rc
h
h
e
re
e
x
p
lo
re
s
t
h
e
e
ffe
c
ti
v
e
n
e
ss
o
f
m
a
g
n
e
ti
c
se
n
so
rs
i
n
a
d
v
a
n
c
e
d
p
o
we
r
sy
ste
m
p
ro
tec
ti
o
n
sc
h
e
m
e
s
a
s
a
n
a
lt
e
rn
a
ti
v
e
to
trad
it
io
n
a
l
c
u
rre
n
t
se
n
sin
g
.
I
n
th
e
p
re
se
n
t
e
d
wo
rk
,
a
n
o
v
e
l
d
a
tas
e
t
is
p
r
e
p
a
re
d
b
y
tran
sfo
rm
in
g
tran
sm
issio
n
li
n
e
c
u
rre
n
ts
in
to
m
a
g
n
e
ti
c
field
c
o
m
p
o
n
e
n
ts.
S
e
v
e
ra
l
su
p
e
r
v
ise
d
a
s
we
ll
a
s
u
n
su
p
e
rv
ise
d
m
a
c
h
in
e
lea
rn
in
g
a
lg
o
rit
h
m
s
h
a
v
e
b
e
e
n
a
p
p
li
e
d
to
th
is
d
a
ta
in
s
tea
d
o
f
trad
i
ti
o
n
a
l
c
u
rre
n
ts
a
n
d
v
o
lt
a
g
e
fo
r
fa
u
lt
p
re
d
ictio
n
.
Th
e
p
a
p
e
r
d
isc
u
ss
e
s
th
e
c
o
m
p
a
ra
ti
v
e
e
v
a
lu
a
ti
o
n
o
f
t
h
e
se
a
lg
o
rit
h
m
s
b
a
se
d
o
n
v
a
rio
u
s
p
e
rfo
rm
a
n
c
e
m
e
tri
c
e
s
wh
ich
re
v
e
a
ls
th
a
t
G
a
u
ss
ian
Na
ïv
e
Ba
y
e
s
(
G
NB),
K
-
n
e
a
re
st
n
e
ig
h
b
o
r
(KN
N),
ra
n
d
o
m
fo
re
st
(RF
),
a
n
d
e
x
tr
e
m
e
g
ra
d
ien
t
b
o
o
st
(XG
B)
a
lg
o
rit
h
m
s
e
x
c
e
l
in
fa
u
lt
d
e
tec
ti
o
n
,
wh
il
e
m
u
l
ti
lay
e
r
p
e
rc
e
p
tro
n
(
M
LP
)
a
n
d
KNN
p
e
rfo
rm
s
b
e
tt
e
r
fa
u
lt
c
las
sifica
ti
o
n
.
Th
e
fin
d
in
g
s
p
r
o
m
ise
th
e
p
o
ten
ti
a
l
fo
r
d
e
v
e
lo
p
in
g
c
o
m
p
a
c
t,
sa
fe
,
a
n
d
c
o
st
-
e
ffe
c
ti
v
e
p
r
o
tec
ti
o
n
sc
h
e
m
e
s u
ti
li
z
in
g
m
a
g
n
e
ti
c
fiel
d
se
n
so
rs.
K
ey
w
o
r
d
s
:
C
o
m
p
u
tatio
n
al
ef
f
icien
c
y
E
lectr
o
m
ag
n
etic
s
en
s
in
g
Fau
lt c
lass
if
icat
io
n
Ma
ch
in
e
lear
n
in
g
alg
o
r
ith
m
s
Sm
ar
t g
r
id
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 B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Am
it L
.
Neh
ete
Dep
ar
tm
en
t o
f
E
lectr
ical
E
n
g
i
n
ee
r
in
g
B
h
ar
ati
Vid
y
ap
ee
th
(
Dee
m
ed
to
b
e)
Un
i
v
er
s
ity
C
o
lleg
e
o
f
E
n
g
in
ee
r
in
g
4
1
1
0
4
3
,
Pu
n
e,
I
n
d
ia
E
m
ail: a
m
itn
eh
ete3
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
T
r
ad
itio
n
al
p
o
wer
s
y
s
tem
s
ar
e
r
ap
id
ly
ev
o
lv
in
g
in
to
co
m
p
le
x
,
in
ter
c
o
n
n
ec
te
d
s
m
ar
t
g
r
id
s
,
en
h
an
cin
g
ef
f
icien
cy
,
r
eliab
ilit
y
,
an
d
s
u
s
tain
ab
ilit
y
th
r
o
u
g
h
ad
v
a
n
ce
d
m
o
n
ito
r
in
g
a
n
d
co
n
tr
o
l te
ch
n
o
lo
g
ies [
1
]
.
Ho
wev
er
,
th
is
tr
an
s
itio
n
p
o
s
es
ch
allen
g
e
s
in
ac
h
iev
i
n
g
f
aster
an
d
m
o
r
e
ac
cu
r
ate
f
au
lt
p
r
ed
ictio
n
,
c
r
u
c
ial
f
o
r
m
ain
tain
in
g
g
r
i
d
s
t
a
b
il
i
t
y
a
n
d
r
es
il
i
e
n
c
e
[
2
]
,
n
e
c
e
s
s
i
t
at
i
n
g
t
h
e
i
n
t
e
ll
i
g
e
n
t
t
ec
h
n
i
q
u
e
b
a
s
e
d
d
ia
g
n
o
s
t
i
c
ac
t
i
o
n
s
[
3
]
.
C
o
n
v
e
n
t
i
o
n
a
l
l
y
,
f
a
u
lt
p
r
o
t
e
c
ti
o
n
i
n
s
u
b
s
t
at
i
o
n
s
r
el
i
es
o
n
c
u
r
r
e
n
t
t
r
a
n
s
f
o
r
m
e
r
s
(
C
T
s
)
a
n
d
p
o
t
e
n
t
i
a
l
t
r
a
n
s
f
o
r
m
e
r
s
(
P
T
s
)
,
w
h
i
c
h
c
a
n
b
e
e
c
o
n
o
m
i
ca
l
l
y
i
n
e
f
f
i
ci
e
n
t
a
n
d
p
r
o
n
e
t
o
e
r
r
o
r
s
d
u
r
i
n
g
f
a
u
l
t
t
r
a
n
s
i
e
n
ts
[
4
]
.
C
T
s
a
t
u
r
a
ti
o
n
c
a
n
l
e
a
d
t
o
m
e
a
s
u
r
e
m
e
n
t
i
n
a
c
c
u
r
a
ci
e
s
a
n
d
r
e
l
a
y
m
i
s
o
p
e
r
a
ti
o
n
s
[
5
]
.
T
o
o
v
e
r
c
o
m
e
t
h
e
s
e
l
i
m
i
t
a
ti
o
n
s
,
e
l
e
c
t
r
o
m
a
g
n
e
tic
s
e
n
s
i
n
g
m
a
y
o
f
f
e
r
a
v
i
a
b
l
e
a
l
t
er
n
a
t
i
v
e
,
p
r
o
v
i
d
i
n
g
r
e
a
l
-
t
i
m
e
,
c
o
n
t
a
c
t
l
es
s
m
o
n
i
t
o
r
i
n
g
f
o
r
i
m
p
r
o
v
e
d
f
a
u
l
t
d
e
t
e
c
t
i
o
n
r
e
l
i
a
b
i
li
t
y
a
n
d
a
c
c
u
r
a
c
y
[
6
]
.
O
v
e
r
t
h
e
y
e
a
r
s
,
t
h
e
r
e
s
e
a
r
c
h
e
r
s
h
a
v
e
b
e
e
n
e
f
f
e
ct
i
v
e
l
y
u
s
in
g
s
u
p
e
r
v
i
s
e
d
a
n
d
u
n
s
u
p
e
r
v
i
s
e
d
ML
t
ec
h
n
i
q
u
e
s
,
n
e
u
r
a
l
n
e
t
w
o
r
k
s
,
e
n
s
e
m
b
l
e
m
et
h
o
d
s
t
o
d
e
v
el
o
p
r
o
b
u
s
t
f
a
u
l
t
a
n
a
l
y
s
is
f
r
a
m
ew
o
r
k
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
N
o
n
-
co
n
ta
ct
p
o
w
er sys
tem
fa
u
lt d
ia
g
n
o
s
is
:
a
ma
c
h
in
e
lea
r
n
i
n
g
a
p
p
r
o
a
ch
w
ith
… (
A
mit L.
N
eh
ete
)
1357
d
u
e
t
o
t
h
e
i
r
c
a
p
ac
i
t
y
t
o
a
n
a
l
y
z
e
l
a
r
g
e
d
a
ta
s
et
s
a
n
d
a
b
i
l
it
y
o
f
e
x
t
r
a
c
t
i
n
g
m
e
a
n
i
n
g
f
u
l
p
a
t
t
e
r
n
s
a
n
d
i
n
s
i
g
h
t
s
[
7
]
.
N
e
v
e
r
t
h
e
l
es
s
,
i
n
h
e
r
e
n
t
c
o
m
p
l
ex
i
t
i
es
o
f
s
m
a
r
t
g
r
i
d
e
n
v
i
r
o
n
m
en
t
s
d
e
m
a
n
d
m
o
r
e
a
n
d
m
o
r
e
r
e
s
e
a
r
c
h
.
Uk
wu
o
m
a
et
a
l.
[
8
]
p
r
o
p
o
s
ed
m
u
ltis
ca
le
atten
tio
n
n
etwo
r
k
(
MSA)
f
o
r
f
au
lt
d
iag
n
o
s
is
o
u
tp
er
f
o
r
m
e
d
th
e
XGBo
o
s
t,
m
u
ltil
ay
er
p
er
ce
p
tr
o
n
(
ML
P),
a
n
d
co
n
v
e
n
tio
n
al
g
r
ap
h
n
eu
r
al
n
etwo
r
k
s
.
B
o
u
ch
ib
a
an
d
Kad
d
o
u
r
i
[
9
]
f
o
u
n
d
th
at,
f
o
r
t
h
e
I
E
E
E
1
4
b
u
s
n
etwo
r
k
,
th
e
d
ec
is
io
n
tr
ee
(
DT
)
alg
o
r
ith
m
h
ad
lo
wer
ac
cu
r
ac
y
(
5
3
%)
co
m
p
ar
ed
to
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
(
8
7
%)
in
f
au
lt
class
if
icatio
n
.
Mo
h
an
ty
et
a
l
.
[
1
0
]
h
ig
h
lig
h
ted
th
at
u
s
in
g
DT
b
a
s
ed
o
n
p
o
s
t
-
f
au
lt
cu
r
r
en
t
d
ata
c
an
lead
to
o
v
er
f
itti
n
g
,
s
en
s
itiv
ity
to
n
o
is
e,
an
d
ch
allen
g
es
in
h
an
d
lin
g
c
o
n
ti
n
u
o
u
s
v
a
r
iab
les.
Ab
ed
et
a
l.
[
1
1
]
d
em
o
n
s
tr
ated
th
at
th
e
K
-
n
ea
r
est
n
eig
h
b
o
r
(
KNN)
alg
o
r
ith
m
ac
cu
r
ately
d
iag
n
o
s
es
tr
an
s
m
is
s
io
n
lin
e
f
au
lts
u
s
in
g
p
h
ase
cu
r
r
en
t
an
d
v
o
ltag
e
v
al
u
es.
A
wasth
i
et
a
l.
[
1
2
]
f
u
r
th
er
co
n
f
ir
m
e
d
KNN'
s
ef
f
icac
y
i
n
f
au
lt
class
if
icatio
n
an
d
lo
ca
tio
n
in
co
m
p
lex
d
is
tr
ib
u
tio
n
s
y
s
tem
s
with
m
u
ltip
le
DGs.
C
h
en
et
a
l.
[
1
3
]
f
o
u
n
d
th
at
k
er
n
el
d
en
s
ity
-
b
ased
lo
g
is
tic
r
eg
r
ess
io
n
(
L
o
g
R
eg
)
im
p
r
o
v
es
ac
cu
r
ac
y
co
m
p
ar
ed
to
f
u
zz
y
KN
N,
SV
M
with
a
lin
ea
r
k
e
r
n
el,
a
n
d
s
tan
d
ar
d
m
u
lti
-
class
L
o
g
R
eg
,
th
o
u
g
h
it
is
m
o
r
e
tim
e
-
co
n
s
u
m
in
g
.
Ven
k
ata
et
a
l
.
[
1
4
]
f
o
u
n
d
th
at
u
s
in
g
tim
e
a
n
d
f
r
eq
u
en
cy
s
er
ies
d
ata
f
r
o
m
cu
r
r
en
ts
an
d
v
o
ltag
es
f
o
r
ML
-
b
ased
f
a
u
lt
d
iag
n
o
s
is
,
th
e
Gau
s
s
ian
Naiv
e
B
ay
es
(
GNB)
alg
o
r
ith
m
ac
h
iev
ed
th
e
h
ig
h
est
ac
c
u
r
ac
y
am
o
n
g
class
if
ier
s
.
Asma
n
et
a
l.
[
1
5
]
c
o
m
p
ar
e
d
SVM
a
n
d
KNN
f
o
r
f
au
lt
ca
teg
o
r
izatio
n
f
r
o
m
lig
h
tn
in
g
,
in
s
u
lato
r
f
ailu
r
e
,
an
d
ex
ter
n
al
in
v
asio
n
s
,
f
in
d
in
g
SVM
to
b
e
m
o
r
e
ac
cu
r
ate
(
9
7
.
1
%)
an
d
f
aster
th
an
KNN
(
7
0
.
6
%).
W
u
et
a
l.
[
1
6
]
ap
p
lie
d
a
r
an
d
o
m
f
o
r
est
(
R
F)
alg
o
r
ith
m
-
b
ased
m
eth
o
d
to
HVDC
tr
an
s
m
is
s
io
n
lin
es,
u
s
in
g
S
-
tr
a
n
s
f
o
r
m
v
ar
iatio
n
in
d
ex
a
n
d
e
n
er
g
y
s
u
m
r
atio
f
ea
tu
r
es
to
r
ap
id
ly
id
en
tify
f
a
u
lts
.
Fo
n
s
ec
a
et
a
l.
[
1
7
]
f
o
u
n
d
th
at
a
n
o
tch
f
ilter
-
b
ased
R
F
alg
o
r
ith
m
class
if
ied
tr
an
s
m
is
s
io
n
lin
e
f
au
lts
eig
h
t
tim
es
f
as
ter
th
an
n
eu
r
al
n
etwo
r
k
s
,
th
o
u
g
h
with
s
lig
h
tly
lo
wer
ac
cu
r
ac
y
.
J
iash
u
et
a
l
.
[
1
8
]
u
s
ed
a
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
m
o
d
el
f
o
r
f
a
u
lt
class
if
icatio
n
an
d
lo
ca
tio
n
,
im
p
r
o
v
in
g
ac
c
u
r
ac
y
a
n
d
r
o
b
u
s
tn
ess
b
y
m
in
im
izin
g
d
at
a
p
o
llu
tio
n
,
wh
ile
Ass
ad
i
et
a
l
.
[
1
9
]
d
em
o
n
s
tr
ated
th
at
MLP
o
u
tp
er
f
o
r
m
s
elm
a
n
r
ec
u
r
r
en
t
(
E
R
)
n
eu
r
al
n
etwo
r
k
s
in
s
h
u
n
t f
au
lt c
lass
if
icatio
n
b
ased
o
n
s
tatis
tical
ev
alu
atio
n
p
ar
am
eter
s
.
R
ec
en
t
liter
atu
r
e
h
ig
h
lig
h
ts
ex
ten
s
iv
e
r
esear
ch
o
n
ML
alg
o
r
ith
m
s
f
o
r
p
o
wer
s
y
s
tem
f
au
lt
d
etec
tio
n
an
d
class
if
icatio
n
,
with
p
o
p
u
lar
m
eth
o
d
s
in
clu
d
in
g
SVM,
D
T
,
KNN,
Naiv
e
B
ay
es,
an
d
en
s
em
b
le
tech
n
iq
u
es.
Sev
er
al
k
ey
m
et
r
ics
p
lay
a
cr
u
cial
r
o
le
in
q
u
an
tif
y
in
g
e
f
f
ic
ac
y
an
d
e
v
alu
atio
n
o
f
t
h
ese
alg
o
r
ith
m
s
[
2
0
]
-
[
2
2
]
.
Mo
d
els
o
f
th
ese
alg
o
r
ith
m
s
h
av
e
b
ee
n
u
s
ed
with
t
h
e
d
ata
o
f
th
r
ee
p
h
ase
f
a
u
lt
cu
r
r
en
ts
an
d
v
o
ltag
es
as
a
n
in
p
u
t
co
n
v
en
tio
n
ally
.
Ho
wev
e
r
,
th
ese
r
esear
ch
er
s
h
a
v
e
n
o
t
co
n
s
id
er
ed
th
e
lim
itatio
n
s
o
f
cu
r
r
en
t
a
n
d
v
o
ltag
e
tr
an
s
f
o
r
m
er
s
(
i.e
.
C
T
s
an
d
PTs)
leav
in
g
th
e
s
co
p
e
f
o
r
f
u
r
th
er
en
h
an
ce
m
en
t
o
f
p
r
o
tect
io
n
s
ch
em
es.
T
h
is
r
esear
ch
p
ap
er
u
n
iq
u
ely
f
o
cu
s
es
o
n
a
co
m
p
ar
ativ
e
an
aly
s
is
o
f
ML
alg
o
r
ith
m
s
ap
p
lied
to
m
ag
n
etic
f
ield
d
ata,
r
ath
er
th
an
t
r
ad
itio
n
al
cu
r
r
en
t
an
d
v
o
ltag
e
m
ea
s
u
r
em
en
ts
,
f
o
r
f
au
lt
id
en
tific
atio
n
in
p
o
wer
s
y
s
tem
s
.
T
h
e
s
tu
d
y
aim
s
to
d
ev
elo
p
a
c
u
r
r
e
n
t
-
to
-
m
ag
n
etic
f
ield
tr
an
s
f
o
r
m
atio
n
b
lo
ck
f
o
r
tr
an
s
m
is
s
io
n
lin
e
m
o
d
els,
g
en
e
r
ate
a
n
o
v
el
m
ag
n
etic
f
ield
d
ataset,
a
n
d
ap
p
l
y
b
o
t
h
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
ML
tec
h
n
iq
u
es.
T
h
e
alg
o
r
ith
m
s
ar
e
ev
alu
ated
b
ased
o
n
v
ar
io
u
s
m
etr
ics
to
id
en
tify
th
e
m
o
s
t
ef
f
ec
tiv
e
ap
p
r
o
ac
h
f
o
r
d
ev
elo
p
in
g
a
n
o
n
-
i
n
tr
u
s
iv
e
in
tellig
en
t f
au
lt d
etec
tio
n
a
n
d
class
if
icatio
n
s
y
s
tem
.
O
r
g
a
n
i
z
a
t
i
o
n
o
f
t
h
e
p
a
p
e
r
f
o
l
l
o
w
s
t
h
e
s
t
a
g
e
s
o
f
r
es
e
a
r
c
h
w
o
r
k
:
s
e
c
ti
o
n
1
i
n
c
l
u
d
e
s
i
n
t
r
o
d
u
c
t
i
o
n
,
l
i
t
e
r
at
u
r
e
s
u
r
v
e
y
,
a
n
d
p
r
o
b
l
e
m
f
o
r
m
u
l
a
t
i
o
n
,
s
e
c
t
i
o
n
2
d
e
s
c
r
ib
e
s
t
h
e
p
r
o
p
o
s
e
d
m
e
t
h
o
d
o
l
o
g
y
,
s
y
s
t
e
m
s
i
m
u
l
at
i
o
n
s
t
u
d
i
es
a
r
e
d
is
c
u
s
s
e
d
i
n
s
e
c
t
i
o
n
3
,
s
e
c
t
i
o
n
4
p
r
e
s
e
n
t
s
t
h
e
a
p
p
l
ie
d
M
L
a
p
p
r
o
a
c
h
,
s
e
ct
i
o
n
5
p
r
es
e
n
ts
t
h
e
r
e
s
u
l
t
s
a
n
d
d
i
s
c
u
s
s
i
o
n
o
n
t
h
e
o
b
t
a
i
n
e
d
c
u
r
r
e
n
t
w
a
v
e
f
o
r
m
s
,
m
a
g
n
e
ti
c
f
i
el
d
p
a
t
t
e
r
n
s
,
a
n
d
a
n
al
y
t
i
c
al
c
o
m
p
a
r
i
s
o
n
o
f
v
a
r
i
o
u
s
M
L
m
o
d
e
l
s
a
p
p
li
e
d
f
o
r
f
a
u
l
t
d
et
e
c
t
i
o
n
as
w
el
l
as
f
a
u
l
t
cl
a
s
s
i
f
i
ca
t
i
o
n
,
f
i
n
a
l
l
y
,
s
e
c
ti
o
n
6
c
o
n
c
l
u
d
e
s
t
h
e
r
es
e
a
r
c
h
.
2.
P
RO
P
O
SE
D
M
E
T
H
O
D
T
y
p
i
c
a
l
l
y
,
i
n
a
p
o
w
e
r
s
y
s
te
m
p
r
o
t
e
c
t
i
o
n
s
c
h
e
m
e
,
a
CT
s
e
n
s
es
t
h
e
f
a
u
l
t
c
u
r
r
e
n
t
a
n
d
a
r
e
l
a
y
t
r
a
n
s
m
i
ts
a
t
r
i
p
s
i
g
n
a
l
t
o
t
h
e
c
i
r
c
u
it
b
r
e
a
k
er
(
C
B
)
[
2
3
]
,
[
2
4
]
.
I
n
t
h
e
p
r
o
p
o
s
e
d
m
e
t
h
o
d
o
l
o
g
y
,
a
s
s
h
o
w
n
i
n
F
i
g
u
r
e
1
,
i
n
s
t
ea
d
o
f
t
h
e
C
T
,
m
a
g
n
e
t
i
c
f
i
e
l
d
s
e
n
s
o
r
s
a
r
e
t
o
b
e
u
s
e
d
a
l
o
n
g
w
it
h
an
i
n
t
e
l
li
g
e
n
t
s
y
s
te
m
t
r
a
i
n
e
d
u
s
i
n
g
A
I
t
e
c
h
n
i
q
u
es
.
P
o
w
e
r
s
y
s
t
e
m
s
i
m
u
l
a
te
d
h
e
r
e
i
n
c
o
r
p
o
r
a
t
e
s
c
u
r
r
e
n
t
t
o
m
a
g
n
e
t
ic
f
i
e
l
d
c
o
n
v
e
r
s
i
o
n
b
l
o
c
k
t
h
a
t
i
s
d
e
s
i
g
n
e
d
u
s
i
n
g
i
ts
m
a
t
h
e
m
a
t
i
ca
l
e
q
u
at
i
o
n
s
.
B
o
t
h
i
n
n
o
r
m
a
l
a
n
d
f
a
u
lt
ci
r
c
u
m
s
tan
c
e
s
,
c
u
r
r
e
n
t
is
f
e
d
t
o
t
h
is
b
l
o
c
k
a
s
a
n
i
n
p
u
t
.
T
h
e
o
u
t
p
u
t
o
f
t
h
i
s
b
l
o
c
k
i
s
a
s
e
t
o
f
v
a
l
u
e
s
o
f
m
a
g
n
e
t
i
c
f
i
e
l
d
c
o
m
p
o
n
e
n
t
s
c
o
r
r
e
s
p
o
n
d
i
n
g
t
o
c
u
r
r
e
n
t
s
.
A
n
o
v
e
l
d
a
t
a
s
et
is
g
e
n
e
r
a
t
e
d
u
s
i
n
g
t
h
e
v
a
l
u
es
o
f
m
a
g
n
e
t
i
c
f
i
e
l
d
c
o
m
p
o
n
e
n
ts
c
o
l
le
c
t
e
d
f
r
o
m
t
h
e
w
o
r
k
s
p
a
c
e
.
D
i
f
f
e
r
e
n
t
f
a
u
l
t
t
y
p
e
s
g
e
n
e
r
at
e
d
i
s
t
i
n
ct
p
a
t
t
e
r
n
s
,
o
f
m
a
g
n
e
t
i
c
f
i
e
l
d
s
w
h
i
c
h
c
a
n
b
e
e
f
f
e
c
ti
v
e
l
y
r
e
c
o
g
n
i
z
e
d
a
n
d
c
l
a
s
s
i
f
i
e
d
u
s
i
n
g
a
r
t
i
f
i
ci
a
l
in
t
e
l
l
i
g
e
n
c
e
(
A
I
)
t
e
c
h
n
i
q
u
es
.
T
o
d
e
s
i
g
n
t
h
e
i
n
t
e
l
l
i
g
e
n
t
s
y
s
t
e
m
,
i
t
i
s
i
m
p
e
r
a
t
i
v
e
t
o
a
p
p
l
y
t
h
e
M
L
a
l
g
o
r
i
t
h
m
s
f
o
r
ac
c
u
r
a
t
e
f
a
u
lt
p
r
e
d
i
c
ti
o
n
.
W
e
h
a
v
e
u
s
e
d
s
e
v
e
r
a
l
s
u
p
e
r
v
is
e
d
a
n
d
u
n
s
u
p
e
r
v
i
s
e
d
M
L
c
l
a
s
s
i
f
ie
r
m
o
d
e
l
s
s
u
c
h
as
G
NB
,
K
NN
,
L
o
g
R
e
g
,
R
F
,
SV
M
,
a
n
d
X
G
B
,
D
T
,
a
n
d
M
L
P
al
g
o
r
i
t
h
m
s
i
n
t
h
e
c
u
r
r
e
n
t
w
o
r
k
.
T
h
e
s
e
a
l
g
o
r
i
t
h
m
s
h
a
v
e
b
e
e
n
a
p
p
l
i
e
d
t
o
t
h
e
g
e
n
e
r
a
t
e
d
m
a
g
n
e
t
i
c
f
i
e
l
d
d
a
t
as
et
a
n
d
e
v
a
l
u
a
t
e
d
s
e
p
a
r
at
e
l
y
:
to
d
e
a
l
w
i
t
h
t
h
e
p
r
o
b
l
e
m
o
f
b
i
n
a
r
y
c
l
a
s
s
i
f
i
c
at
i
o
n
a
n
d
m
u
l
t
i
cl
ass
c
l
as
s
i
f
i
c
at
i
o
n
i
n
c
as
e
o
f
f
a
u
l
t
d
e
t
e
c
t
i
o
n
a
n
d
f
a
u
l
t
t
y
p
e
i
d
e
n
t
i
f
i
c
a
ti
o
n
r
e
s
p
ec
t
i
v
el
y
.
F
u
n
c
t
i
o
n
al
d
es
c
r
i
p
ti
o
n
o
f
t
h
ese
a
l
g
o
r
i
t
h
m
s
i
s
a
v
a
i
la
b
l
e
i
n
t
h
e
l
i
te
r
a
t
u
r
e
[
7
]
-
[
2
2
]
.
F
i
n
a
l
l
y
,
c
o
m
p
a
r
at
i
v
e
a
n
a
l
y
s
is
o
f
a
p
p
l
i
e
d
a
l
g
o
r
i
t
h
m
s
b
a
s
e
d
o
n
v
a
r
i
o
u
s
i
n
d
i
c
es
is
p
e
r
f
o
r
m
e
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
36
,
No
.
3
,
Dec
em
b
er
20
24
:
1
3
5
6
-
1
3
6
4
1358
Fig
u
r
e
1
.
B
lo
ck
d
iag
r
am
o
f
th
e
p
r
o
p
o
s
ed
n
o
n
-
co
n
tact
elec
tr
o
m
ag
n
etic
f
a
u
lt d
iag
n
o
s
is
s
ch
em
e
Ma
g
n
eto
r
esis
tan
ce
(
MR)
ef
f
ec
t
-
b
ased
s
en
s
o
r
s
d
etec
t
th
e
ch
an
g
e
in
r
esis
tan
ce
with
r
esp
ec
t
to
v
ar
iatio
n
s
in
m
ag
n
etic
f
ield
s
t
r
en
g
th
[
2
5
]
.
T
h
er
eb
y
,
m
ag
n
etic
f
ield
s
en
s
o
r
b
ased
o
n
th
is
ef
f
ec
t
ca
n
b
e
a
d
ir
ec
t
an
d
p
r
ec
is
e
to
o
l
t
o
ac
cu
r
ately
m
ea
s
u
r
e
th
e
m
a
g
n
etic
f
ield
p
r
o
d
u
ce
d
ar
o
u
n
d
th
e
c
o
n
d
u
cto
r
d
u
e
to
cu
r
r
e
n
t
f
lo
win
g
in
tr
an
s
m
is
s
io
n
lin
e
[
2
6
]
.
As
th
e
s
en
s
o
r
wo
u
ld
b
e
lo
ca
ted
at
s
o
m
e
d
is
tan
ce
f
r
o
m
th
e
q
u
an
tity
to
b
e
m
ea
s
u
r
ed
,
it o
f
f
er
s
t
h
e
ad
v
an
t
ag
e
o
f
s
af
ety
b
y
av
o
id
in
g
d
ir
e
ct
co
n
tact.
Als
o
,
it
ca
n
b
e
s
wif
tly
tak
en
o
u
t
f
o
r
t
h
e
m
ain
ten
an
ce
.
T
h
ese
f
ea
tu
r
es
m
ak
e
it
s
u
itab
le
to
b
e
u
s
ed
in
s
u
b
s
tatio
n
in
p
lace
o
f
C
T
s
[
2
7
]
.
T
h
e
in
ter
f
e
r
en
ce
d
u
e
to
e
x
ter
n
al
f
ac
to
r
s
is
ce
r
tain
ly
a
to
p
ic
o
f
r
esear
ch
an
d
h
a
v
e
b
ee
n
k
ep
t b
e
y
o
n
d
th
e
s
co
p
e
o
f
th
is
p
ap
e
r
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
ass
u
m
es
a
co
u
p
le
o
f
m
ag
n
etic
f
ield
s
en
s
o
r
s
in
th
e
v
icin
ity
o
f
th
e
co
n
d
u
cto
r
s
at
s
p
ec
if
ic
p
o
s
itio
n
s
s
u
ch
th
at
o
n
e
o
f
th
e
m
will
d
etec
t
th
e
m
ag
n
etic
f
ield
in
h
o
r
izo
n
tal
d
ir
ec
tio
n
an
d
o
th
er
i
n
v
er
tical
d
ir
ec
tio
n
.
T
h
is
s
y
s
te
m
r
eso
lv
es
th
e
m
ag
n
etic
f
ield
to
o
b
tain
th
e
lo
ca
l
2
D
p
atter
n
.
I
n
th
is
p
ap
er
,
we
p
r
esen
t
a
co
m
p
a
r
ativ
e
ass
ess
m
en
t
o
f
s
ev
er
al
ML
al
g
o
r
ith
m
s
ap
p
lied
to
n
o
n
-
in
tr
u
s
iv
e
elec
tr
o
m
ag
n
etic
cu
r
r
e
n
t
m
ea
s
u
r
em
en
ts
f
o
r
p
o
wer
s
y
s
tem
f
au
lt
d
iag
n
o
s
is
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
t
h
ese
alg
o
r
ith
m
s
is
in
v
esti
g
ated
b
ased
o
n
v
ar
io
u
s
ev
alu
atio
n
m
etr
ices
ac
r
o
s
s
d
if
f
er
en
t
f
au
lt
s
ce
n
ar
io
s
.
W
e
aim
to
o
f
f
er
v
alu
ab
l
e
in
s
ig
h
ts
to
war
d
s
o
p
tim
al
s
elec
tio
n
an
d
d
ep
l
o
y
m
en
t
o
f
ML
alg
o
r
ith
m
s
in
o
r
d
er
t
o
i
d
en
tify
an
d
ca
teg
o
r
i
ze
th
e
f
au
lts
in
th
e
co
n
tex
t o
f
s
m
ar
t g
r
id
with
elec
tr
o
m
ag
n
etic
cu
r
r
en
t sen
s
in
g
ca
p
ab
ilit
y
.
3.
SYST
E
M
SI
M
U
L
A
T
I
O
NS
3
.
1
.
M
o
dellin
g
o
f
1
5
4
k
V
t
ra
ns
m
is
s
io
n l
ine
I
n
t
h
i
s
s
t
u
d
y
,
t
h
e
s
y
s
t
e
m
s
p
e
ci
f
i
c
a
ti
o
n
s
l
is
t
e
d
i
n
[
2
8
]
a
r
e
u
s
e
d
t
o
m
o
d
e
l
a
n
a
c
t
u
a
l
2
8
k
m
s
,
1
5
4
k
V
t
r
a
n
s
m
is
s
i
o
n
l
i
n
e
is
s
i
m
u
l
a
te
d
i
n
M
A
T
L
A
B
/
Si
m
u
l
i
n
k
.
A
t
h
r
ee
-
p
h
a
s
e
f
a
u
lt
b
l
o
c
k
i
s
u
s
e
d
t
o
c
r
e
a
t
e
v
a
r
i
o
u
s
f
a
u
lt
c
o
n
d
i
t
i
o
n
s
n
a
m
e
l
y
t
r
i
p
l
e
l
i
n
e
to
g
r
o
u
n
d
,
t
r
i
p
l
e
l
i
n
e
,
d
o
u
b
l
e
lin
e
t
o
g
r
o
u
n
d
,
d
o
u
b
l
e
l
i
n
e
,
s
i
n
g
l
e
l
i
n
e
t
o
g
r
o
u
n
d
.
H
e
r
e
,
t
h
e
p
h
a
s
e
c
o
n
d
u
c
t
o
r
s
i
n
th
r
e
e
p
h
a
s
e
t
r
a
n
s
m
is
s
i
o
n
l
i
n
es
ar
e
t
e
r
m
e
d
a
s
R
,
Y,
a
n
d
B
a
s
g
en
e
r
a
l
l
y
p
r
e
f
e
r
r
e
d
i
n
I
n
d
i
a
.
P
a
r
a
m
e
t
e
r
s
u
s
e
d
i
n
t
h
e
e
q
u
a
t
i
o
n
s
i
n
s
e
c
t
i
o
n
4
h
a
v
e
b
e
e
n
s
y
m
b
o
l
i
z
e
d
r
e
l
a
t
i
n
g
t
o
t
h
e
s
e
p
h
a
s
e
s
.
F
a
u
l
t
s
w
e
r
e
c
r
e
a
t
e
d
a
t
t
h
e
m
i
d
d
l
e
o
f
t
h
e
l
i
n
e
a
t
1
4
k
m
s
f
r
o
m
t
h
e
s
e
n
d
i
n
g
e
n
d
.
S
w
i
t
c
h
i
n
g
t
i
m
e
o
f
t
h
e
f
a
u
l
t
b
l
o
c
k
w
a
s
0
.
0
4
s
t
o
1
s
.
3
.
2
.
Curre
nt
t
o
ma
g
net
ic
f
ie
ld co
nv
er
s
io
n
C
u
r
r
en
ts
f
lo
win
g
th
r
o
u
g
h
th
e
p
h
ase
co
n
d
u
cto
r
s
R
,
Y,
an
d
B
ar
e
s
in
(
2
+
)
,
s
in
(
2
+
)
,
an
d
s
in
(
2
+
)
r
esp
ec
tiv
ely
.
T
h
e
s
ec
ti
o
n
d
is
cu
s
s
es
th
e
m
ath
em
atic
al
eq
u
atio
n
s
b
eh
in
d
th
e
cu
r
r
en
t
to
m
ag
n
etic
f
ield
co
n
v
er
s
io
n
b
lo
ck
.
T
h
e
p
r
o
p
o
s
ed
s
e
t
u
p
co
n
s
is
ts
o
f
two
m
ag
n
etic
f
ield
s
en
s
in
g
co
ils
at
r
ig
h
t
a
n
g
les
to
ea
c
h
o
th
e
r
.
Ou
t
o
f
wh
ic
h
,
o
n
e
c
o
il
s
en
s
es
h
o
r
izo
n
tal
c
o
m
p
o
n
en
t
ℎ
an
d
th
e
o
th
er
s
en
s
es
v
er
ticle
co
m
p
o
n
en
t
o
f
th
e
g
e
n
er
ated
m
a
g
n
etic
f
ield
.
Acc
o
r
d
in
g
to
lab
o
r
ato
r
y
ex
p
e
r
im
en
t
atio
n
in
[
2
9
]
th
at
ex
p
lo
r
ed
th
e
p
o
ten
tial o
f
cu
r
r
e
n
t
to
m
ag
n
etic
f
ield
co
n
v
er
s
io
n
f
o
r
p
o
wer
q
u
ality
a
n
aly
s
is
,
th
e
r
elatio
n
b
etwe
e
n
cu
r
r
en
t a
n
d
co
r
r
esp
o
n
d
in
g
m
a
g
n
etic
f
ield
is
g
iv
en
b
y
(
1
):
[
]
=
[
]
[
]
(
1
)
wh
er
e,
[
]
=
[
ℎ
]
[
]
=
[
]
[
]
=
[
]
(
2
)
D
an
d
Q
in
(
2
)
ar
e
p
o
s
itio
n
a
l
co
ef
f
icien
ts
ass
o
ciate
d
with
h
o
r
iz
o
n
tal
an
d
v
er
tical
m
ag
n
etic
f
ield
r
esp
ec
tiv
ely
.
M
is
ca
lled
as
p
o
s
itio
n
m
atr
ix
.
As
s
h
o
wn
in
Fi
g
u
r
e
2
,
m
ag
n
etic
f
ield
s
en
s
in
g
co
ils
ar
e
lo
ca
ted
at
h
o
r
izo
n
tal
d
is
tan
ce
s
,
,
an
d
,
an
d
h
eig
h
ts
ℎ
,
ℎ
,
ℎ
f
r
o
m
t
h
e
p
h
ase
co
n
d
u
cto
r
s
R
,
Y,
an
d
B
r
esp
ec
tiv
ely
.
Kn
o
win
g
th
is
,
an
g
les
,
,
ar
e
ca
lcu
lated
b
y
(
3
)
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
N
o
n
-
co
n
ta
ct
p
o
w
er sys
tem
fa
u
lt d
ia
g
n
o
s
is
:
a
ma
c
h
in
e
lea
r
n
i
n
g
a
p
p
r
o
a
ch
w
ith
… (
A
mit L.
N
eh
ete
)
1359
=
−
1
ℎ
,
=
−
1
ℎ
,
=
−
1
ℎ
(
3
)
w
ith
th
e
k
n
o
wled
g
e
o
f
t
h
ese
a
n
g
les,
we
ca
n
d
eter
m
in
e
p
o
s
it
io
n
al
co
ef
f
icien
ts
,
,
an
d
,
,
b
y
u
s
in
g
(
4
)
an
d
(
5
)
r
esp
ec
tiv
ely
.
=
2
2
ℎ
,
=
2
2
ℎ
,
=
2
2
ℎ
(
4
)
=
2
ℎ
,
=
2
ℎ
,
=
2
ℎ
(
5
)
Fin
ally
,
th
e
m
atr
ix
o
f
m
ag
n
etic
f
ield
is
co
m
p
u
te
d
b
y
(
6
)
.
[
ℎ
]
=
[
]
[
s
in
(
2
+
)
s
in
(
2
+
)
s
in
(
2
+
)
]
(
6
)
Ma
g
n
etic
f
ield
a
n
aly
s
is
will
v
ar
y
with
lo
ca
tio
n
o
f
th
e
s
en
s
o
r
[
3
0
]
.
I
n
th
is
p
ar
ticu
la
r
s
tu
d
y
,
th
e
s
en
s
o
r
h
as
b
ee
n
ass
u
m
ed
to
b
e
k
e
p
t
at
r
ig
h
t
-
an
d
lef
t
-
h
a
n
d
s
id
e
o
f
th
e
co
n
d
u
cto
r
s
.
T
h
o
u
g
h
,
o
n
ly
o
n
e
ca
s
e
is
d
is
cu
s
s
ed
h
er
e
as
s
h
o
wn
in
Fig
u
r
e
2
,
as
th
e
r
esu
lt
s
o
b
tain
ed
in
b
o
th
cir
c
u
m
s
tan
ce
s
ar
e
s
am
e
d
u
e
to
s
y
m
m
et
r
y
.
Deta
iled
s
tu
d
y
o
n
d
if
f
er
en
t
lo
ca
tio
n
s
o
f
th
e
s
en
s
in
g
co
ils
c
an
b
e
p
er
f
o
r
m
ed
to
id
en
tif
y
o
p
tim
al
p
o
s
itio
n
th
at
g
iv
es
b
est
p
er
f
o
r
m
an
ce
in
f
u
tu
r
e
wo
r
k
.
B
ased
o
n
th
e
m
ath
em
atica
l
an
aly
s
is
ab
o
v
e,
th
e
v
a
lu
es
o
f
h
o
r
izo
n
tal
as
well
as
v
er
tical
m
ag
n
etic
f
ield
co
m
p
o
n
e
n
ts
ar
e
d
eter
m
in
ed
t
ak
in
g
in
to
ac
co
u
n
t
th
e
lo
ca
tio
n
o
f
s
en
s
o
r
at
b
o
th
lef
t
an
d
r
ig
h
t
p
o
s
itio
n
s
.
T
h
ese
v
alu
es
v
ar
y
with
th
e
ty
p
e
o
f
f
au
lts
an
d
ac
co
r
d
in
g
ly
g
en
e
r
ate
a
d
if
f
er
en
t
p
atter
n
wh
en
p
lo
tted
o
n
g
r
ap
h
.
Fig
u
r
e
2
.
Ma
g
n
etic
f
ield
s
en
s
o
r
p
lace
d
at
lef
t sid
e
o
f
th
e
c
o
n
d
u
cto
r
4.
M
ACH
I
N
E
L
E
AR
NING
AP
P
RO
ACH
4
.
1
.
Da
t
a
g
ener
a
t
io
n a
nd
pre
pro
ce
s
s
ing
I
n
t
h
e
s
i
m
u
l
a
t
e
d
p
o
w
e
r
s
y
s
t
e
m
m
o
d
e
l
,
t
w
e
l
v
e
t
y
p
e
s
o
f
f
a
u
l
t
s
a
n
d
n
o
n
-
f
a
u
l
t
c
o
n
d
i
t
i
o
n
s
w
i
t
h
v
a
r
i
o
u
s
s
p
e
c
i
f
i
c
a
t
i
o
n
s
w
e
r
e
c
r
e
a
t
e
d
.
T
h
e
m
a
g
n
e
t
i
c
f
i
e
l
d
v
a
l
u
e
s
ℎ
a
n
d
c
o
r
r
e
s
p
o
n
d
i
n
g
t
o
t
h
e
s
e
c
o
n
d
i
t
i
o
n
s
w
e
r
e
c
o
l
l
e
c
t
e
d
,
a
n
d
b
i
n
a
r
y
f
a
u
l
t
i
n
d
i
c
a
t
o
r
s
w
e
r
e
a
d
d
e
d
t
o
t
h
e
d
a
t
a
s
e
t
:
0
f
o
r
n
o
r
m
a
l
a
n
d
1
f
o
r
f
a
u
l
t
c
o
n
d
i
t
i
o
n
s
t
o
g
e
n
e
r
a
t
e
t
h
e
n
o
v
e
l
d
a
t
a
s
e
t
s
.
F
o
r
f
a
u
l
t
d
e
t
e
c
t
i
o
n
,
t
h
e
d
a
t
a
s
e
t
i
n
c
l
u
d
e
s
m
a
g
n
e
t
i
c
f
i
e
l
d
c
o
m
p
o
n
e
n
t
s
a
n
d
a
b
i
n
a
r
y
f
a
u
l
t
i
n
d
i
c
a
t
o
r
.
F
o
r
f
a
u
l
t
c
l
a
s
s
i
f
i
c
a
t
i
o
n
,
a
l
o
n
g
w
i
t
h
ℎ
a
n
d
,
t
h
e
d
a
t
a
s
e
t
i
n
c
l
u
d
e
s
f
o
u
r
c
o
l
u
m
n
s
r
e
p
r
e
s
e
n
t
i
n
g
l
i
n
e
s
(
R
,
Y
,
B
,
G
)
w
i
t
h
b
i
n
a
r
y
i
n
d
i
c
a
t
o
r
s
f
o
r
f
a
u
l
t
p
r
e
s
e
n
c
e
i
n
e
a
c
h
l
i
n
e
,
w
i
t
h
t
h
e
d
e
p
e
n
d
e
n
t
v
a
r
i
a
b
l
e
b
e
i
n
g
t
h
e
f
a
u
l
t
t
y
p
e
.
4
.
2
.
P
er
f
o
r
m
a
nce
ev
a
lua
t
io
n c
rit
er
ia
Per
f
o
r
m
an
ce
e
v
alu
atio
n
p
r
o
v
id
e
in
s
ig
h
ts
in
to
th
e
ef
f
e
ctiv
en
ess
an
d
ca
p
ab
ilit
ies
o
f
v
ar
io
u
s
alg
o
r
ith
m
s
.
I
m
p
o
r
tan
t
m
etr
ice
s
u
s
ed
in
th
e
ev
alu
atio
n
o
f
M
L
alg
o
r
ith
m
s
ar
e
ac
c
u
r
ac
y
,
er
r
o
r
r
ate,
p
r
ec
is
io
n
,
r
ec
all,
F1
s
co
r
e
[
3
1
]
,
a
n
d
r
ec
ei
v
er
o
p
er
atin
g
ch
ar
ac
ter
is
tic
-
a
r
ea
u
n
d
er
c
u
r
v
e
(
R
OC
-
AUC)
[
3
2
]
.
Als
o
,
f
it
tim
e
an
d
s
co
r
e
tim
e
ar
e
cr
u
cial
cr
it
er
ia
o
f
ML
m
o
d
el’
s
co
m
p
u
tati
o
n
al
ef
f
icien
c
y
[
3
3
]
.
4
.
3
.
Ste
ps
a
pp
lied in m
a
chin
e
lea
rning
a
pp
ro
a
ch
A
s
y
s
tem
atic
ap
p
r
o
ac
h
f
o
llo
w
ed
to
a
p
p
ly
ML
al
g
o
r
ith
m
s
to
m
ag
n
etic
f
ield
co
m
p
o
n
e
n
t
d
a
taset.
T
h
e
s
cr
ip
t
im
p
o
r
ts
ess
en
tial
lib
r
a
r
ies
f
o
r
m
ac
h
in
e
lear
n
i
n
g
,
i
n
clu
d
in
g
s
cik
it
-
lear
n
,
Nu
m
Py
,
an
d
Pan
d
as,
an
d
d
ef
in
es
a
f
u
n
ctio
n
to
ev
al
u
ate
m
u
ltip
le
m
o
d
els.
I
t
in
itialize
s
v
ar
io
u
s
ML
m
o
d
els
an
d
p
er
f
o
r
m
s
5
-
f
o
ld
c
r
o
s
s
-
v
alid
atio
n
,
f
o
llo
wed
b
y
t
r
ain
i
n
g
an
d
test
in
g
o
n
an
8
0
-
2
0
d
ata
s
p
lit.
Ad
d
itio
n
ally
,
it
u
s
es
b
o
o
ts
tr
ap
s
am
p
lin
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
36
,
No
.
3
,
Dec
em
b
er
20
24
:
1
3
5
6
-
1
3
6
4
1360
f
o
r
s
tab
ilit
y
an
aly
s
is
an
d
o
r
g
an
izes c
r
o
s
s
-
v
alid
atio
n
r
esu
lts
,
test
p
r
ed
ictio
n
s
,
an
d
d
etailed
class
if
icatio
n
r
ep
o
r
ts
f
o
r
ea
ch
m
o
d
el.
5.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
5
.
1
.
M
a
g
net
ic
f
ield pa
t
t
e
rns
Fig
u
r
e
3
s
h
o
ws
th
e
in
s
tan
tan
eo
u
s
cu
r
r
en
t
wa
v
ef
o
r
m
s
an
d
m
ag
n
etic
f
ield
p
atter
n
s
u
n
d
er
v
ar
io
u
s
s
tates
o
f
tr
an
s
m
is
s
io
n
lin
e
.
T
h
ese
p
atter
n
s
ar
e
o
b
tain
e
d
b
y
p
lo
ttin
g
v
alu
es
o
f
ℎ
an
d
v
alu
es
alo
n
g
X
-
a
x
is
an
d
Y
-
ax
is
r
esp
ec
tiv
el
y
co
r
r
esp
o
n
d
in
g
to
cu
r
r
e
n
ts
.
Fig
u
r
e
s
3
(
a)
,
3
(
b
)
,
an
d
3
(
c)
ar
e
t
h
e
wav
ef
o
r
m
s
o
f
cu
r
r
en
ts
at
n
o
r
m
al,
R
YB
G
f
au
lt,
an
d
R
YB
f
au
lt
r
esp
ec
tiv
ely
wh
ile
Fig
u
r
e
s
3
(
d
)
,
3
(
e)
,
3
(
f
)
illu
s
tr
ate
co
r
r
esp
o
n
d
in
g
m
ag
n
etic
f
ield
p
att
er
n
.
Similar
ly
,
Fig
u
r
e
s
3
(
g
)
,
3
(
h
)
,
an
d
3
(
i)
ar
e
th
e
wav
ef
o
r
m
s
o
f
cu
r
r
en
ts
at
R
YG
f
au
lt,
R
Y
f
au
lt,
an
d
R
G
f
au
lt
r
esp
ec
tiv
ely
with
Fig
u
r
e
s
3
(
j)
,
3
(
k
)
,
an
d
3
(
l)
s
h
o
win
g
c
o
r
r
esp
o
n
d
in
g
m
ag
n
etic
f
ield
p
atter
n
.
As
in
Fig
u
r
e
3
(
a)
,
u
n
d
er
n
o
r
m
al
s
tate,
th
e
p
atter
n
o
f
p
lo
t
is
a
p
er
f
ec
t
cir
cle.
H
o
wev
er
,
th
is
s
h
ap
e
g
ets
d
is
tu
r
b
ed
o
n
c
r
ea
tin
g
f
au
lts
.
N
o
tab
ly
,
ea
ch
ty
p
e
o
f
f
au
lt
y
iel
d
s
a
d
is
tin
ct
m
ag
n
etic
f
ield
p
atter
n
wh
ich
lay
s
th
e
f
o
u
n
d
atio
n
o
f
f
au
lt id
e
n
tific
atio
n
an
d
class
if
icatio
n
.
(
a
)
(
b
)
(
c
)
(
d
)
(
e)
(
f
)
(
g
)
(
h
)
(
i)
(
j)
(
k
)
(
l)
Fig
u
r
e
3.
C
u
r
r
e
n
t
wav
ef
o
r
m
s
(
a)
,
(
b
)
,
(
c)
,
(
g
)
,
(
h
)
,
(
i)
a
n
d
c
o
r
r
esp
o
n
d
in
g
m
ag
n
etic
f
ield
p
att
er
n
s
(
d
)
,
(
e)
,
(
f
)
,
(
j)
,
(
k
)
,
(
l)
at
n
o
r
m
al
c
o
n
d
itio
n
,
R
YB
G,
R
YB
,
R
YG,
R
Y,
an
d
R
G
f
au
lts
r
esp
ec
tiv
ely
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
N
o
n
-
co
n
ta
ct
p
o
w
er sys
tem
fa
u
lt d
ia
g
n
o
s
is
:
a
ma
c
h
in
e
lea
r
n
i
n
g
a
p
p
r
o
a
ch
w
ith
… (
A
mit L.
N
eh
ete
)
1361
5
.
2
.
M
L
a
lg
o
rit
hm
s
a
pp
lied f
o
r
f
a
ult
det
ec
t
i
o
n
–
bin
a
ry
cla
s
s
if
ica
t
io
n
Valu
es
o
f
th
e
ac
cu
r
ac
y
an
d
er
r
o
r
r
ate,
ev
alu
atio
n
m
etr
ice
s
,
f
it
tim
e,
an
d
s
co
r
e
tim
e
o
b
tain
ed
f
o
r
ap
p
lied
ML
alg
o
r
ith
m
s
in
r
esu
lts
h
av
e
b
ee
n
d
is
cu
s
s
ed
s
ep
ar
ately
f
o
r
f
au
lt
d
etec
tio
n
in
th
is
s
u
b
s
ec
tio
n
5
.
2
an
d
class
if
icatio
n
in
n
ex
t
s
u
b
s
ec
ti
o
n
5
.
3
.
T
a
b
le
1
p
r
esen
ts
th
e
o
b
tain
ed
v
alu
es
o
f
v
ar
i
o
u
s
p
er
f
o
r
m
an
ce
in
d
icato
r
s
o
f
ML
m
o
d
els
ap
p
lie
d
to
f
au
l
t
d
etec
tio
n
.
T
h
e
X
GB
alg
o
r
ith
m
ac
h
iev
ed
th
e
h
ig
h
est
ac
cu
r
ac
y
o
f
9
9
.
9
8
%
an
d
an
er
r
o
r
o
f
2
2
4
.
4
4
1
7
µ.
T
h
e
G
NB
an
d
KNN
a
lg
o
r
ith
m
s
also
p
er
f
o
r
m
e
d
well,
with
ac
cu
r
ac
ies
o
f
9
9
.
9
7
%
an
d
9
9
.
9
4
%,
a
n
d
e
r
r
o
r
s
o
f
3
3
6
.
6
6
2
6
µ
an
d
5
6
1
.
1
0
4
3
µ,
r
esp
ec
tiv
ely
.
R
F
an
d
SVM
s
h
o
wed
r
es
p
ec
tab
le
ac
cu
r
ac
ies
o
f
8
5
.
9
9
% a
n
d
9
9
.
0
3
%,
wh
ile
L
o
g
R
eg
h
ad
th
e
lo
west a
cc
u
r
a
cy
at
7
8
.
4
1
%,
s
er
v
in
g
as a
u
s
e
f
u
l b
aselin
e.
I
t
s
h
o
ws
th
at
th
e
GNB,
KNN,
R
F,
an
d
XGB
alg
o
r
ith
m
s
ac
h
iev
ed
h
ig
h
m
ea
n
s
co
r
es
ac
r
o
s
s
all
ev
alu
atio
n
m
etr
ics.
No
ta
b
ly
,
GNB
an
d
KNN
ex
ce
lled
with
m
ea
n
R
OC
AUC
s
co
r
es
o
f
0
.
9
9
9
4
4
an
d
0
.
9
9
9
9
0
7
,
r
esp
ec
tiv
ely
,
alo
n
g
with
h
ig
h
weig
h
ted
r
ec
all,
F1
-
s
co
r
e,
an
d
p
r
ec
is
io
n
v
alu
es,
in
d
icatin
g
t
h
eir
ef
f
ec
tiv
en
ess
in
f
au
lt
d
etec
tio
n
.
W
h
ile
th
e
SVM
alg
o
r
ith
m
p
er
f
o
r
m
ed
w
ell,
L
o
g
R
eg
h
ad
co
m
p
ar
ativ
ely
lo
wer
s
co
r
es,
s
u
g
g
esti
n
g
lim
ited
s
u
itab
ilit
y
f
o
r
th
is
ap
p
licatio
n
.
T
ab
le
1.
C
o
m
p
a
r
ativ
e
o
f
ML
a
lg
o
r
ith
m
’
s
p
e
r
f
o
r
m
an
ce
in
d
icato
r
s
in
f
au
lt d
etec
tio
n
M
L
A
l
g
o
r
i
t
h
m
A
c
c
u
r
a
c
y
Er
r
o
r
P
r
e
c
i
s
i
o
n
R
e
c
a
l
l
F
1
sc
o
r
e
R
O
C
A
U
C
F
i
t
t
i
m
e
S
c
o
r
e
t
i
me
GNB
0
.
9
9
9
6
6
3
3
3
6
.
6
6
2
6
µ
0
.
9
9
9
8
3
4
0
.
9
9
9
8
3
4
0
.
9
9
9
8
3
0
.
9
9
9
4
4
0
.
0
0
5
7
7
8
0
.
0
1
6
0
9
3
K
N
N
0
.
9
9
9
4
3
9
5
6
1
.
1
0
4
3
µ
0
.
9
9
9
6
1
3
0
.
9
9
9
6
1
3
0
.
9
9
9
6
1
0
.
9
9
9
9
0
7
0
.
0
1
0
6
5
3
0
.
2
5
4
4
1
5
Lo
g
R
e
g
0
.
7
8
4
0
8
7
1
0
.
2
1
5
9
1
2
9
0
.
8
3
8
4
8
4
0
.
7
9
0
4
3
7
0
.
7
4
6
1
5
0
.
6
1
7
3
8
5
0
.
0
2
4
4
9
0
0
.
0
2
2
6
1
2
RF
0
.
8
5
9
9
4
8
4
3
3
6
.
6
6
2
6
µ
0
.
9
9
9
9
4
5
0
.
9
9
9
9
4
5
0
.
9
9
9
9
5
0
.
9
9
9
9
0
7
0
.
6
8
3
8
6
8
0
.
0
8
0
3
0
7
S
V
M
0
.
9
9
0
3
4
9
0
.
0
0
9
6
5
0
9
9
3
0
.
9
8
8
5
8
1
0
.
9
8
8
3
9
1
0
.
9
8
8
3
2
0
.
9
9
4
6
4
1
0
.
6
1
9
4
1
6
0
.
1
8
1
2
8
8
XGB
0
.
9
9
9
7
7
6
2
2
4
.
4
4
1
7
µ
0
.
9
9
9
8
3
4
0
.
9
9
9
8
3
4
0
.
9
9
9
8
3
0
.
9
9
9
8
2
1
0
.
0
9
6
1
4
4
0
.
0
2
7
4
1
5
R
eg
ar
d
in
g
f
it
an
d
s
co
r
e
ti
m
es,
it
h
ig
h
lig
h
ts
th
at
GNB
alg
o
r
ith
m
em
er
g
ed
as
th
e
m
o
s
t
co
m
p
u
tatio
n
ally
ef
f
icien
t
alg
o
r
ith
m
.
I
n
c
o
n
tr
ast,
R
F
an
d
S
VM
alg
o
r
ith
m
s
e
x
h
ib
it
lo
n
g
e
r
f
it
tim
es.
R
an
d
o
m
Fo
r
est,
d
esp
ite
it
s
en
s
em
b
le
n
atu
r
e,
r
eq
u
ir
es
r
elativ
ely
h
ig
h
er
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
.
Similar
ly
,
SV
M
alg
o
r
ith
m
d
e
m
an
d
s
co
m
p
u
tati
o
n
al
ef
f
o
r
t
d
u
r
in
g
m
o
d
el
tr
ain
in
g
.
I
n
ter
esti
n
g
l
y
,
th
e
KNN
al
g
o
r
ith
m
s
tan
d
s
o
u
t
with
a
r
elativ
ely
lo
w
f
it
tim
e
b
u
t
a
s
ig
n
if
ican
tly
h
ig
h
er
s
co
r
e
tim
e.
T
h
is
d
is
cr
ep
an
cy
s
u
g
g
ests
th
at
wh
ile
KNN
alg
o
r
ith
m
r
eq
u
ir
es
m
in
im
al
ef
f
o
r
t
d
u
r
in
g
tr
ain
i
n
g
,
it
in
cu
r
s
a
co
m
p
u
tatio
n
al
c
o
s
t
d
u
r
i
n
g
t
h
e
p
r
e
d
ictio
n
p
h
ase
,
esp
ec
ially
f
o
r
lar
g
e
r
d
atasets
.
5
.
3
.
M
L
a
lg
o
rit
hm
s
a
pp
lied f
o
r
f
a
ult
cla
s
s
if
ica
t
io
n
–
m
ulticla
s
s
cla
s
s
if
ica
t
io
n
R
esu
lts
o
b
tain
ed
f
o
r
v
ar
io
u
s
p
er
f
o
r
m
an
ce
in
d
icato
r
s
o
f
M
L
alg
o
r
ith
m
s
in
f
au
lt
class
if
icatio
n
ar
e
tab
u
lated
in
T
ab
le
2
.
I
t
s
h
o
ws
th
at
th
e
ML
P
alg
o
r
ith
m
ac
h
i
ev
ed
th
e
h
ig
h
est
ac
cu
r
ac
y
at
8
9
.
1
6
%
with
a
lo
w
er
r
o
r
r
ate
o
f
2
.
6
8
%,
h
ig
h
lig
h
tin
g
its
ef
f
ec
tiv
en
ess
in
f
au
lt
class
if
icatio
n
f
o
r
tr
a
n
s
m
is
s
io
n
lin
es.
T
h
e
KNN
alg
o
r
ith
m
f
o
llo
wed
with
an
a
cc
u
r
ac
y
o
f
8
7
.
6
2
%
an
d
a
s
lig
h
tly
h
ig
h
e
r
er
r
o
r
r
ate
o
f
3
.
5
1
%.
B
o
th
DT
an
d
R
F
alg
o
r
ith
m
s
p
er
f
o
r
m
ed
well
wi
th
ac
cu
r
ac
ies
o
f
8
5
.
6
9
%
an
d
8
5
.
9
9
%,
r
esp
ec
tiv
el
y
,
b
u
t
h
ad
h
ig
h
er
er
r
o
r
r
ates.
GNB
an
d
SV
M
ac
h
iev
ed
ac
cu
r
ac
ies
o
f
8
1
.
7
9
%
an
d
7
4
.
7
2
%,
with
GN
B
h
av
in
g
a
lo
wer
er
r
o
r
r
ate
b
u
t
s
lig
h
tly
in
f
er
io
r
ac
c
u
r
ac
y
c
o
m
p
ar
ed
to
SVM.
T
h
e
KNN
alg
o
r
ith
m
ac
h
iev
ed
th
e
h
ig
h
est
o
v
er
all
p
er
f
o
r
m
an
ce
,
with
a
m
ea
n
test
R
O
C
AU
C
o
f
0
.
8
8
6
9
an
d
s
tr
o
n
g
weig
h
ted
r
ec
all,
F1
-
s
co
r
e,
an
d
p
r
e
cisi
o
n
v
alu
es
o
f
0
.
8
8
5
8
,
0
.
8
8
6
7
,
an
d
0
.
8
8
9
3
,
r
esp
ec
tiv
ely
,
ef
f
ec
tiv
ely
d
if
f
er
en
tiatin
g
f
au
lt
ty
p
es
in
tr
an
s
m
is
s
io
n
lin
es.
T
h
e
ML
P
alg
o
r
it
h
m
also
p
er
f
o
r
m
ed
well,
with
a
m
ea
n
test
R
O
C
AUC
o
f
0
.
8
8
0
3
,
r
ec
all
o
f
0
.
8
8
4
4
,
F1
-
s
co
r
e
o
f
0
.
8
7
2
0
,
an
d
p
r
ec
is
io
n
o
f
0
.
8
8
0
3
,
in
d
icatin
g
its
ca
p
ab
ilit
y
to
lear
n
co
m
p
lex
c
o
r
r
elatio
n
s
in
th
e
d
ata.
R
F
an
d
DT
alg
o
r
ith
m
s
s
h
o
wed
co
m
p
etitiv
e
r
esu
lts
with
m
ea
n
test
R
OC
AUC
v
alu
es
o
f
0
.
8
7
3
0
a
n
d
0
.
8
6
8
3
,
r
esp
ec
tiv
ely
.
GNB
an
d
SVM
h
ad
lo
wer
p
er
f
o
r
m
an
ce
s
with
R
OC
AUC
v
alu
es
o
f
0
.
8
1
8
7
an
d
0
.
7
0
0
3
,
wh
ile
L
o
g
R
eg
ex
h
ib
ite
d
th
e
l
o
west
p
er
f
o
r
m
an
ce
with
a
m
ea
n
test
R
OC
AU
C
o
f
0
.
6
7
0
5
,
s
u
g
g
esti
n
g
its
lim
ited
s
u
itab
ilit
y
f
o
r
th
is
task
.
DT
an
d
GNB
alg
o
r
ith
m
s
d
em
o
n
s
tr
ated
th
e
f
astes
t
tr
ain
in
g
a
n
d
p
r
ed
ictio
n
tim
es,
m
ak
in
g
th
em
id
ea
l
f
o
r
ap
p
licatio
n
s
n
ee
d
in
g
h
ig
h
co
m
p
u
tatio
n
al
ef
f
icien
c
y
.
K
NN
h
ad
lo
w
f
it
tim
es
b
u
t
h
i
g
h
p
r
e
d
ictio
n
tim
es,
in
d
icatin
g
g
r
ea
ter
co
m
p
u
tatio
n
al
d
em
an
d
d
u
r
in
g
u
s
e.
L
o
g
R
eg
an
d
R
F
s
h
o
wed
m
o
d
er
ate
f
it
an
d
s
co
r
e
tim
es,
b
alan
cin
g
e
f
f
icien
cy
,
an
d
p
e
r
f
o
r
m
a
n
ce
.
W
h
ile
ML
P
an
d
SVM
r
eq
u
ir
e
d
m
o
r
e
tr
ain
i
n
g
r
eso
u
r
ce
s
,
th
e
y
d
eliv
er
ed
co
m
p
etitiv
e
p
er
f
o
r
m
an
ce
,
s
u
itab
le
f
o
r
ap
p
licat
io
n
s
p
r
io
r
itizin
g
ac
cu
r
ac
y
o
v
er
co
m
p
u
tatio
n
al
co
n
s
tr
ain
ts
.
Su
m
m
ar
izin
g
t
h
e
d
is
cu
s
s
io
n
,
s
tu
d
y
r
ev
ea
led
th
at
alg
o
r
ith
m
s
lik
e
GNB,
KNN,
R
F,
an
d
XGB
ex
ce
l
in
f
au
lt
d
etec
tio
n
,
wh
ile
ML
P
an
d
KNN
ar
e
s
u
p
er
io
r
in
f
au
lt
class
if
ica
tio
n
u
s
in
g
m
ag
n
etic
f
ield
d
ata.
Po
s
itio
n
ed
with
in
th
e
b
r
o
ad
e
r
co
n
tex
t,
t
h
is
wo
r
k
co
r
r
o
b
o
r
ate
s
p
r
ev
io
u
s
f
in
d
in
g
s
o
n
t
h
e
lim
itatio
n
s
o
f
C
T
s
an
d
ad
v
an
ce
s
th
e
u
s
e
o
f
elec
tr
o
m
a
g
n
etic
s
en
s
in
g
f
o
r
s
m
ar
t g
r
id
a
p
p
licatio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
36
,
No
.
3
,
Dec
em
b
er
20
24
:
1
3
5
6
-
1
3
6
4
1362
T
ab
le
2
.
C
o
m
p
a
r
ativ
e
o
f
ML
a
lg
o
r
ith
m
’
s
p
e
r
f
o
r
m
an
ce
in
d
icato
r
s
in
f
au
lt c
lass
if
icatio
n
M
L
A
l
g
o
r
i
t
h
m
A
c
c
u
r
a
c
y
Er
r
o
r
P
r
e
c
i
s
i
o
n
R
e
c
a
l
l
F
1
sc
o
r
e
R
O
C
A
U
C
F
i
t
t
i
m
e
S
c
o
r
e
t
i
me
D
-
Tr
e
e
0
.
8
6
8
5
4
.
1
7
3
9
4
2
0
.
8
6
8
3
6
7
0
.
8
6
8
5
0
.
8
6
8
0
0
0
.
8
6
8
3
4
2
0
.
0
2
2
3
4
6
0
.
0
1
0
5
4
4
GNB
0
.
8
2
0
8
3
1
3
.
1
2
3
7
8
0
.
8
1
9
7
0
5
0
.
8
2
0
8
3
0
.
8
1
3
5
4
0
.
8
1
8
7
2
7
7
0
.
0
0
5
8
0
7
0
.
0
1
0
8
3
9
K
N
N
0
.
8
8
5
8
0
2
3
.
5
0
8
3
6
0
.
8
8
9
3
0
1
0
.
8
8
5
8
0
0
.
8
8
6
6
8
0
.
8
8
6
8
9
7
0
.
0
0
8
8
9
4
0
.
1
9
2
8
9
7
Lo
g
R
e
g
0
.
7
2
6
8
8
4
7
.
1
3
7
3
5
8
0
.
5
9
8
5
4
0
.
7
2
6
8
8
0
.
6
2
9
6
6
0
.
6
7
0
4
9
3
2
0
.
2
5
7
8
8
4
0
.
0
1
0
4
0
8
M
LP
0
.
8
9
1
5
9
4
7
2
.
6
8
0
9
5
6
0
.
8
8
0
2
6
1
0
.
8
8
4
3
9
0
.
8
7
2
0
.
8
8
0
2
6
1
7
9
.
0
3
1
6
7
3
0
.
0
2
0
2
8
6
RF
0
.
8
7
2
3
5
1
4
.
1
6
6
1
9
9
0
.
8
7
4
2
3
8
0
.
8
7
2
3
5
0
.
8
7
2
9
5
0
.
8
7
2
9
7
2
1
.
3
1
9
2
2
8
0
.
0
7
7
8
8
2
S
V
M
0
.
7
4
7
1
6
6
4
6
.
0
7
6
7
5
9
0
.
6
3
6
5
1
3
0
.
7
4
6
7
9
0
.
6
7
1
0
6
0
.
7
0
0
2
9
1
2
7
3
.
0
5
4
6
0
9
0
.
0
1
0
4
2
0
6.
CO
NCLU
SI
O
N
T
h
is
r
esear
ch
a
d
d
r
ess
es
cr
itical
ch
allen
g
es
in
s
m
ar
t
g
r
id
f
au
lt
d
iag
n
o
s
is
.
T
h
e
s
tu
d
y
h
ig
h
lig
h
ts
s
ig
n
if
ican
t
p
o
te
n
tial
o
f
MF
s
en
s
o
r
s
as
a
n
o
n
-
c
o
n
tact,
e
f
f
ec
ti
v
e,
co
m
p
ac
t,
s
af
e,
an
d
ec
o
n
o
m
ical
alter
n
ativ
e
to
tr
ad
itio
n
al
C
T
s
,
wh
ich
ar
e
o
f
ten
lim
ited
b
y
s
ize,
co
s
t,
an
d
in
s
tallatio
n
co
m
p
lex
ities
.
Su
p
er
v
is
ed
a
n
d
u
n
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
h
av
e
b
ee
n
ap
p
lied
to
th
e
m
a
g
n
etic
f
ield
v
alu
e
s
f
o
r
s
u
p
er
io
r
f
au
lt
d
etec
tio
n
an
d
class
if
icatio
n
in
p
o
wer
s
y
s
tem
s
,
an
d
th
eir
ef
f
ec
tiv
en
ess
was
ass
es
s
ed
an
d
co
m
p
ar
ed
an
aly
tically
.
I
n
th
e
f
au
lt
d
ete
ctio
n
s
tu
d
ies,
GNB,
KNN,
R
F,
an
d
XGB
alg
o
r
ith
m
s
p
er
f
o
r
m
ed
ex
ce
p
tio
n
ally
th
at
u
n
d
er
lin
es
th
eir
a
b
ilit
y
to
lev
er
ag
e
f
ea
t
u
r
es
d
er
iv
e
d
f
r
o
m
m
a
g
n
etic
f
ield
co
m
p
o
n
en
ts
to
ef
f
ec
tiv
ely
id
en
tify
f
au
lt
c
o
n
d
itio
n
s
.
Fin
d
in
g
s
o
f
th
e
f
a
u
lt
class
if
icatio
n
s
tu
d
ies
s
u
g
g
est
th
at
o
v
er
a
ll,
ML
P
an
d
KNN
alg
o
r
ith
m
s
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
ed
o
th
er
m
eth
o
d
s
m
ak
in
g
th
em
p
r
o
m
is
in
g
alg
o
r
ith
m
s
f
o
r
th
e
class
if
icatio
n
o
f
tr
an
s
m
is
s
io
n
lin
e
f
au
lts
b
ased
o
n
m
a
g
n
etic
f
ield
co
m
p
o
n
e
n
ts
.
T
h
e
o
v
er
a
r
ch
in
g
ta
k
ea
way
o
f
th
e
r
esear
c
h
is
MF
s
en
s
o
r
s
,
in
teg
r
ated
with
m
ac
h
in
e
lear
n
in
g
,
r
ep
r
esen
t
a
p
r
o
m
is
in
g
d
ir
ec
tio
n
f
o
r
m
o
d
er
n
p
o
wer
s
y
s
tem
p
r
o
tectio
n
tech
n
o
lo
g
ies.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
ex
p
lo
r
e
o
p
tim
ized
alg
o
r
ith
m
co
n
f
ig
u
r
atio
n
s
,
s
en
s
o
r
p
o
s
itio
n
in
g
ef
f
ec
ts
,
an
d
cr
o
s
s
-
d
o
m
ain
in
ter
f
er
en
ce
t
o
f
u
r
th
er
r
ef
in
e
th
is
ap
p
r
o
ac
h
.
RE
F
E
R
E
NC
E
S
[
1
]
Z.
Z
a
n
d
,
M
.
R
.
G
h
a
h
r
i
,
S
.
M
a
j
i
d
i
,
M
.
E
i
d
i
a
n
i
,
M
.
A
.
N
a
sa
b
,
a
n
d
M
.
Za
n
d
,
“
S
m
a
r
t
g
r
i
d
a
n
d
r
e
s
i
l
i
e
n
c
e
,
”
i
n
H
a
n
d
b
o
o
k
o
f
S
m
a
rt
En
e
r
g
y
S
y
st
e
m
s
,
S
p
r
i
n
g
e
r
I
n
t
e
r
n
a
t
i
o
n
a
l
P
u
b
l
i
s
h
i
n
g
,
2
0
2
3
,
p
p
.
3
6
7
–
3
9
0
.
[
2
]
A
.
L.
N
e
h
e
t
e
,
D
.
S
.
B
a
n
k
a
r
,
R
.
A
sat
i
,
a
n
d
C
.
K
h
a
d
se
,
“
F
a
u
l
t
d
i
a
g
n
o
s
i
s
o
f
p
o
w
e
r
sy
s
t
e
m c
o
m
p
o
n
e
n
t
b
y
w
a
v
e
l
e
t
a
n
a
l
y
s
i
s
o
f
c
u
r
r
e
n
t
si
g
n
a
l
s,
”
J
u
l
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
C
C
N
T5
6
9
9
8
.
2
0
2
3
.
1
0
3
0
7
6
1
5
.
[
3
]
F
.
M
.
S
h
a
k
i
b
a
,
S
.
M
.
A
z
i
z
i
,
M
.
Z
h
o
u
,
a
n
d
A
.
A
b
u
s
o
r
r
a
h
,
“
A
p
p
l
i
c
a
t
i
o
n
o
f
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
m
e
t
h
o
d
s
i
n
f
a
u
l
t
d
e
t
e
c
t
i
o
n
a
n
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
p
o
w
e
r
t
r
a
n
sm
i
ss
i
o
n
l
i
n
e
s
:
a
s
u
r
v
e
y
,
”
Ar
t
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
Re
v
i
e
w
,
v
o
l
.
5
6
,
n
o
.
7
,
p
p
.
5
7
9
9
–
5
8
3
6
,
N
o
v
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
0
4
6
2
-
0
2
2
-
1
0
2
9
6
-
0.
[
4
]
C
.
B
.
K
h
a
d
se,
M
.
A
.
C
h
a
u
d
h
a
r
i
,
a
n
d
V
.
B
.
B
o
r
g
h
a
t
e
,
“
C
o
n
j
u
g
a
t
e
g
r
a
d
i
e
n
t
b
a
c
k
-
p
r
o
p
a
g
a
t
i
o
n
b
a
s
e
d
a
r
t
i
f
i
c
i
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
f
o
r
r
e
a
l
t
i
m
e
p
o
w
e
r
q
u
a
l
i
t
y
a
ss
e
ssm
e
n
t
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
t
ri
c
a
l
P
o
w
e
r
a
n
d
E
n
e
r
g
y
S
y
st
e
m
s
,
v
o
l
.
8
2
,
p
p
.
1
9
7
–
2
0
6
,
N
o
v
.
2
0
1
6
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
e
p
e
s.
2
0
1
6
.
0
3
.
0
2
0
.
[
5
]
D
.
M
i
l
l
n
e
r
,
“
H
o
w
m
u
c
h
me
a
su
r
e
me
n
t
e
r
r
o
r
c
a
n
s
o
me
o
n
e
e
x
p
e
c
t
f
r
o
m
v
a
r
i
o
u
s
d
e
g
r
e
e
s
o
f
C
T
sa
t
u
r
a
t
i
o
n
?
,
”
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
C
F
P
R
5
7
8
3
7
.
2
0
2
3
.
1
0
1
2
7
0
5
2
.
[
6
]
P
.
S
h
r
a
w
a
n
e
a
n
d
T.
S
.
S
i
d
h
u
,
“
N
o
n
i
n
v
a
s
i
v
e
me
a
su
r
e
me
n
t
o
f
t
h
r
e
e
-
p
h
a
s
e
c
u
r
r
e
n
t
s,
”
I
EEE
O
p
e
n
J
o
u
r
n
a
l
o
f
I
n
d
u
st
ry
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
5
,
p
p
.
1
4
3
–
1
5
4
,
2
0
2
4
,
d
o
i
:
1
0
.
1
1
0
9
/
O
JI
A
.
2
0
2
4
.
3
3
8
1
8
5
6
.
[
7
]
R
.
V
a
i
sh
,
U
.
D
.
D
w
i
v
e
d
i
,
S
.
Te
w
a
r
i
,
a
n
d
S
.
M
.
Tr
i
p
a
t
h
i
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
l
i
c
a
t
i
o
n
s
i
n
p
o
w
e
r
sy
s
t
e
m
f
a
u
l
t
d
i
a
g
n
o
s
i
s:
R
e
se
a
r
c
h
a
d
v
a
n
c
e
me
n
t
s
a
n
d
p
e
r
s
p
e
c
t
i
v
e
s,”
En
g
i
n
e
e
r
i
n
g
A
p
p
l
i
c
a
t
i
o
n
s
o
f
A
rt
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
,
v
o
l
.
1
0
6
,
p
.
1
0
4
5
0
4
,
N
o
v
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
g
a
p
p
a
i
.
2
0
2
1
.
1
0
4
5
0
4
.
[
8
]
C
.
C
.
U
k
w
u
o
m
a
e
t
a
l
.
,
“
P
o
w
e
r
t
r
a
n
smiss
i
o
n
s
y
st
e
m’
s
f
a
u
l
t
l
o
c
a
t
i
o
n
,
d
e
t
e
c
t
i
o
n
,
a
n
d
c
l
a
ssi
f
i
c
a
t
i
o
n
:
P
a
y
c
l
o
se
a
t
t
e
n
t
i
o
n
t
o
t
r
a
n
s
mi
ss
i
o
n
n
o
d
e
s,
”
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
P
o
w
e
r
a
n
d
En
e
rg
y
S
y
s
t
e
m
s
,
v
o
l
.
1
5
6
,
p
.
1
0
9
7
7
1
,
F
e
b
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
e
p
e
s.
2
0
2
3
.
1
0
9
7
7
1
.
[
9
]
N
.
B
o
u
c
h
i
b
a
a
n
d
A
.
K
a
d
d
o
u
r
i
,
“
F
a
u
l
t
d
e
t
e
c
t
i
o
n
a
n
d
l
o
c
a
l
i
z
a
t
i
o
n
b
a
se
d
o
n
d
e
c
i
s
i
o
n
t
r
e
e
a
n
d
su
p
p
o
r
t
v
e
c
t
o
r
m
a
c
h
i
n
e
a
l
g
o
r
i
t
h
ms
i
n
e
l
e
c
t
r
i
c
a
l
p
o
w
e
r
t
r
a
n
sm
i
ssi
o
n
n
e
t
w
o
r
k
,
”
O
c
t
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
A
EE
5
3
7
7
2
.
2
0
2
2
.
9
9
6
1
9
7
0
.
[
1
0
]
S
.
K
.
M
o
h
a
n
t
y
,
A
.
K
a
r
n
,
a
n
d
S
.
B
a
n
e
r
j
e
e
,
“
D
e
c
i
si
o
n
t
r
e
e
s
u
p
p
o
r
t
e
d
d
i
s
t
a
n
c
e
r
e
l
a
y
f
o
r
f
a
u
l
t
d
e
t
e
c
t
i
o
n
a
n
d
c
l
a
ssi
f
i
c
a
t
i
o
n
i
n
a
s
e
r
i
e
s
c
o
m
p
e
n
sa
t
e
d
l
i
n
e
,
”
J
a
n
.
2
0
2
0
,
d
o
i
:
1
0
.
1
1
0
9
/
P
ESG
R
E
4
5
6
6
4
.
2
0
2
0
.
9
0
7
0
7
2
4
.
[
1
1
]
N
.
K
.
A
b
e
d
,
F
.
T.
A
b
e
d
,
H
.
F
.
A
l
-
Y
a
sr
i
y
,
a
n
d
H
.
T.
S
a
l
i
m
A
l
r
i
k
a
b
i
,
“
D
e
t
e
c
t
i
o
n
o
f
p
o
w
e
r
t
r
a
n
sm
i
ssi
o
n
l
i
n
e
s
f
a
u
l
t
s
b
a
se
d
o
n
v
o
l
t
a
g
e
s
a
n
d
c
u
r
r
e
n
t
s
v
a
l
u
e
s
u
si
n
g
K
-
n
e
a
r
e
st
n
e
i
g
h
b
o
r
s,”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
P
o
w
e
r
E
l
e
c
t
r
o
n
i
c
s
a
n
d
D
ri
v
e
S
y
s
t
e
m
s
,
v
o
l
.
1
4
,
n
o
.
2
,
p
p
.
1
0
33
–
1
0
4
3
,
J
u
n
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
p
e
d
s
.
v
1
4
.
i
2
.
p
p
1
0
3
3
-
1
0
4
3
.
[
1
2
]
S
.
A
w
a
st
h
i
,
G
.
S
i
n
g
h
,
a
n
d
N
.
A
h
a
ma
d
,
“
C
l
a
ssi
f
y
i
n
g
e
l
e
c
t
r
i
c
a
l
f
a
u
l
t
s
i
n
a
d
i
s
t
r
i
b
u
t
i
o
n
sy
s
t
e
m
u
si
n
g
K
-
N
e
a
r
e
s
t
N
e
i
g
h
b
o
r
(
K
N
N
)
mo
d
e
l
i
n
p
r
e
s
e
n
c
e
o
f
m
u
l
t
i
p
l
e
d
i
st
r
i
b
u
t
e
d
g
e
n
e
r
a
t
o
r
s,
”
J
o
u
r
n
a
l
o
f
T
h
e
I
n
s
t
i
t
u
t
i
o
n
o
f
E
n
g
i
n
e
e
rs
(
I
n
d
i
a
)
:
S
e
ri
e
s
B
,
v
o
l
.
1
0
5
,
n
o
.
3
,
p
p
.
6
2
1
–
6
3
4
,
F
e
b
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
0
7
/
s4
0
0
3
1
-
0
2
4
-
0
0
9
9
4
-
4.
[
1
3
]
J.
C
h
e
n
e
t
a
l
.
,
“
F
a
u
l
t
a
n
a
l
y
si
s
o
f
m
u
l
t
i
p
l
e
t
r
a
n
sm
i
ssi
o
n
l
i
n
e
s b
a
se
d
o
n
d
e
n
si
t
y
-
b
a
se
d
l
o
g
i
s
t
i
c
r
e
g
r
e
ssi
o
n
,
”
i
n
2
0
1
7
4
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
S
y
s
t
e
m
s
a
n
d
I
n
f
o
rm
a
t
i
c
s,
I
C
S
AI
2
0
1
7
,
N
o
v
.
2
0
1
7
,
v
o
l
.
2
0
1
8
-
J
a
n
u
a
r
y
,
p
p
.
1
4
9
8
–
1
5
0
2
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
S
A
I
.
2
0
1
7
.
8
2
4
8
5
2
2
.
[
1
4
]
P
.
V
e
n
k
a
t
a
a
n
d
V
.
P
a
n
d
y
a
,
“
D
a
t
a
m
i
n
i
n
g
m
o
d
e
l
a
n
d
G
a
u
ss
i
a
n
N
a
i
v
e
B
a
y
e
s
b
a
se
d
f
a
u
l
t
d
i
a
g
n
o
s
t
i
c
a
n
a
l
y
si
s
o
f
mo
d
e
r
n
p
o
w
e
r
sy
st
e
m
n
e
t
w
o
r
k
s,”
M
a
t
e
ri
a
l
s To
d
a
y
:
Pro
c
e
e
d
i
n
g
s
,
v
o
l
.
6
2
,
n
o
.
P
1
3
,
p
p
.
7
1
5
6
–
7
1
6
1
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
m
a
t
p
r
.
2
0
2
2
.
0
3
.
0
3
5
.
[
1
5
]
S
.
H
.
A
sma
n
,
N
.
F
.
A
.
A
z
i
z
,
M
.
Z.
A
.
A
.
K
a
d
i
r
,
U
.
A
.
U
.
A
m
i
r
u
l
d
d
i
n
,
N
.
R
o
sl
a
n
,
a
n
d
A
.
El
s
a
n
a
b
a
r
y
,
“
Li
g
h
t
n
i
n
g
f
a
u
l
t
c
l
a
ss
i
f
i
c
a
t
i
o
n
f
o
r
t
r
a
n
smiss
i
o
n
l
i
n
e
u
si
n
g
su
p
p
o
r
t
v
e
c
t
o
r
ma
c
h
i
n
e
,
”
J
u
n
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
A
P
L5
7
3
0
8
.
2
0
2
3
.
1
0
1
8
1
5
2
5
.
[
1
6
]
H
.
W
u
,
Q
.
W
a
n
g
,
K
.
Y
u
,
X
.
H
u
,
a
n
d
M
.
R
a
n
,
“
A
n
o
v
e
l
i
n
t
e
l
l
i
g
e
n
t
f
a
u
l
t
i
d
e
n
t
i
f
i
c
a
t
i
o
n
me
t
h
o
d
b
a
s
e
d
o
n
r
a
n
d
o
m
f
o
r
e
st
s
f
o
r
H
V
D
C
t
r
a
n
s
mi
ss
i
o
n
l
i
n
e
s
,
”
PLo
S
O
N
E
,
v
o
l
.
1
5
,
n
o
.
3
,
p
.
e
0
2
3
0
7
1
7
,
M
a
r
.
2
0
2
0
,
d
o
i
:
1
0
.
1
3
7
1
/
j
o
u
r
n
a
l
.
p
o
n
e
.
0
2
3
0
7
1
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
N
o
n
-
co
n
ta
ct
p
o
w
er sys
tem
fa
u
lt d
ia
g
n
o
s
is
:
a
ma
c
h
in
e
lea
r
n
i
n
g
a
p
p
r
o
a
ch
w
ith
… (
A
mit L.
N
eh
ete
)
1363
[
1
7
]
G
.
A
.
F
o
n
s
e
c
a
,
D
.
D
.
F
e
r
r
e
i
r
a
,
F
.
B
.
C
o
s
t
a
,
a
n
d
A
.
R
.
A
l
m
e
i
d
a
,
“
F
a
u
l
t
c
l
a
s
si
f
i
c
a
t
i
o
n
i
n
t
r
a
n
sm
i
ssi
o
n
l
i
n
e
s
u
s
i
n
g
r
a
n
d
o
m
f
o
r
e
s
t
a
n
d
n
o
t
c
h
f
i
l
t
e
r
,
”
J
o
u
r
n
a
l
o
f
C
o
n
t
r
o
l
,
A
u
t
o
m
a
t
i
o
n
a
n
d
El
e
c
t
ri
c
a
l
S
y
s
t
e
m
s
,
v
o
l
.
3
3
,
n
o
.
2
,
p
p
.
5
9
8
–
6
0
9
,
N
o
v
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
4
0
3
1
3
-
0
2
1
-
0
0
8
4
4
-
4.
[
1
8
]
J.
F
a
n
g
,
K
.
C
h
e
n
,
C
.
Li
u
,
a
n
d
J
.
H
e
,
“
A
n
e
x
p
l
a
i
n
a
b
l
e
a
n
d
r
o
b
u
st
me
t
h
o
d
f
o
r
f
a
u
l
t
c
l
a
ss
i
f
i
c
a
t
i
o
n
a
n
d
l
o
c
a
t
i
o
n
o
n
t
r
a
n
smiss
i
o
n
l
i
n
e
s,
”
I
EEE
T
r
a
n
sa
c
t
i
o
n
s
o
n
I
n
d
u
st
r
i
a
l
I
n
f
o
rm
a
t
i
c
s
,
v
o
l
.
1
9
,
n
o
.
1
0
,
p
p
.
1
0
1
8
2
–
1
0
1
9
1
,
O
c
t
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
TI
I
.
2
0
2
2
.
3
2
2
9
4
9
7
.
[
1
9
]
K
.
A
ssa
d
i
,
J
.
B
e
n
S
l
i
ma
n
e
,
H
.
C
h
a
l
a
n
d
i
,
a
n
d
S
.
S
a
l
h
i
,
“
S
h
u
n
t
f
a
u
l
t
s
d
e
t
e
c
t
i
o
n
a
n
d
c
l
a
ssi
f
i
c
a
t
i
o
n
i
n
e
l
e
c
t
r
i
c
a
l
p
o
w
e
r
t
r
a
n
sm
i
s
si
o
n
l
i
n
e
s
y
st
e
ms
b
a
se
d
o
n
a
r
t
i
f
i
c
i
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
s
,
”
C
O
MP
EL
-
T
h
e
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
f
o
r
C
o
m
p
u
t
a
t
i
o
n
a
n
d
Ma
t
h
e
m
a
t
i
c
s
i
n
El
e
c
t
r
i
c
a
l
a
n
d
E
l
e
c
t
r
o
n
i
c
En
g
i
n
e
e
r
i
n
g
,
v
o
l
.
4
2
,
n
o
.
6
,
p
p
.
1
5
1
8
–
1
5
3
0
,
A
p
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
8
/
C
O
M
P
E
L
-
10
-
2
0
2
2
-
0
3
7
1
.
[
2
0
]
S
.
K
a
n
w
a
l
a
n
d
S
.
Ji
r
i
w
i
b
h
a
k
o
r
n
,
“
A
d
v
a
n
c
e
d
f
a
u
l
t
d
e
t
e
c
t
i
o
n
,
c
l
a
ss
i
f
i
c
a
t
i
o
n
,
a
n
d
l
o
c
a
l
i
z
a
t
i
o
n
i
n
t
r
a
n
sm
i
ssi
o
n
l
i
n
e
s:
a
c
o
m
p
a
r
a
t
i
v
e
st
u
d
y
o
f
A
N
F
I
S
,
n
e
u
r
a
l
n
e
t
w
o
r
k
s,
a
n
d
h
y
b
r
i
d
me
t
h
o
d
s,”
I
E
EE
Ac
c
e
ss
,
v
o
l
.
1
2
,
p
p
.
4
9
0
1
7
–
4
9
0
3
3
,
2
0
2
4
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
4
.
3
3
8
4
7
6
1
.
[
2
1
]
Z.
M
i
a
n
e
t
a
l
.
,
“
A
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
o
f
f
a
u
l
t
d
i
a
g
n
o
s
i
s
b
a
se
d
o
n
e
n
s
e
m
b
l
e
l
e
a
r
n
i
n
g
,
”
En
g
i
n
e
e
r
i
n
g
A
p
p
l
i
c
a
t
i
o
n
s
o
f
Art
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
,
v
o
l
.
1
2
7
,
p
.
1
0
7
3
5
7
,
J
a
n
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
g
a
p
p
a
i
.
2
0
2
3
.
1
0
7
3
5
7
.
[
2
2
]
O
.
A
.
A
l
i
mi
,
K
.
O
u
a
h
a
d
a
,
a
n
d
A
.
M
.
A
b
u
-
M
a
h
f
o
u
z
,
“
A
r
e
v
i
e
w
o
f
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
e
s
t
o
p
o
w
e
r
sy
s
t
e
m
se
c
u
r
i
t
y
a
n
d
st
a
b
i
l
i
t
y
,
”
I
E
EE
A
c
c
e
ss
,
v
o
l
.
8
,
p
p
.
1
1
3
5
1
2
–
1
1
3
5
3
1
,
2
0
2
0
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
0
.
3
0
0
3
5
6
8
.
[
2
3
]
“
I
EEE
g
u
i
d
e
f
o
r
p
r
o
t
e
c
t
i
v
e
r
e
l
a
y
a
p
p
l
i
c
a
t
i
o
n
s
t
o
t
r
a
n
sm
i
ssi
o
n
l
i
n
e
s
,
”
I
EE
E
S
t
d
C
3
7
.
1
1
3
-
2
0
1
5
(
Re
v
i
s
i
o
n
o
f
I
E
EE
S
t
d
C
3
7
.
1
1
3
-
1
9
9
9
)
,
p
p
.
1
–
1
4
1
,
2
0
1
6
.
[
2
4
]
“
I
EEE
d
r
a
f
t
g
u
i
d
e
f
o
r
p
r
o
t
e
c
t
i
v
e
r
e
l
a
y
a
p
p
l
i
c
a
t
i
o
n
s
t
o
d
i
s
t
r
i
b
u
t
i
o
n
l
i
n
e
s,
”
I
E
EE
P
C
3
7
.
2
3
0
/
D
2
.
7
,
p
p
.
1
–
1
1
1
,
2
0
2
0
.
[
2
5
]
P
.
O
.
K
w
a
si
A
n
a
n
e
,
Q
.
H
u
a
n
g
,
O
.
B
a
mi
s
i
l
e
,
a
n
d
P
.
N
.
A
y
i
m
b
i
r
e
,
“
F
a
u
l
t
l
o
c
a
t
i
o
n
i
n
o
v
e
r
h
e
a
d
t
r
a
n
sm
i
ssi
o
n
l
i
n
e
:
A
n
o
v
e
l
n
o
n
-
c
o
n
t
a
c
t
me
a
su
r
e
me
n
t
a
p
p
r
o
a
c
h
f
o
r
t
r
a
v
e
l
i
n
g
w
a
v
e
-
b
a
se
d
s
c
h
e
me
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
E
l
e
c
t
r
i
c
a
l
Po
w
e
r
a
n
d
En
e
r
g
y
S
y
st
e
m
s
,
v
o
l
.
1
3
3
,
p
.
1
0
7
2
3
3
,
D
e
c
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
e
p
e
s
.
2
0
2
1
.
1
0
7
2
3
3
.
[
2
6
]
P
.
S
h
r
a
w
a
n
e
a
n
d
T.
S
.
S
i
d
h
u
,
“
Est
i
m
a
t
i
o
n
o
f
t
h
r
e
e
-
p
h
a
se
c
u
r
r
e
n
t
s
i
n
o
v
e
r
h
e
a
d
p
o
w
e
r
l
i
n
e
c
o
n
d
u
c
t
o
r
s
u
s
i
n
g
n
u
meric
a
l
mo
d
e
l
o
f
mag
n
e
t
i
c
f
i
e
l
d
s,
”
Pr
o
g
ress
I
n
El
e
c
t
r
o
m
a
g
n
e
t
i
c
s R
e
se
a
rc
h
C
,
v
o
l
.
9
7
,
p
p
.
1
2
3
–
1
3
7
,
2
0
1
9
,
d
o
i
:
1
0
.
2
5
2
8
/
P
I
ER
C
1
9
0
8
1
6
0
2
.
[
2
7
]
C
.
A
.
F
.
S
a
r
t
o
r
i
a
n
d
F
.
X
.
S
e
v
e
g
n
a
n
i
,
“
F
a
u
l
t
c
l
a
ssi
f
i
c
a
t
i
o
n
a
n
d
d
e
t
e
c
t
i
o
n
b
y
w
a
v
e
l
e
t
-
b
a
se
d
ma
g
n
e
t
i
c
s
i
g
n
a
t
u
r
e
r
e
c
o
g
n
i
t
i
o
n
,
”
IE
EE
T
ra
n
s
a
c
t
i
o
n
s
o
n
M
a
g
n
e
t
i
c
s
,
v
o
l
.
4
6
,
n
o
.
8
,
p
p
.
2
8
8
0
–
2
8
8
3
,
A
u
g
.
2
0
1
0
,
d
o
i
:
1
0
.
1
1
0
9
/
T
M
A
G
.
2
0
1
0
.
2
0
4
3
9
3
3
.
[
2
8
]
M
.
Y
u
m
u
r
t
a
c
i
,
G
.
G
ǒ
k
me
n
,
Č
.
K
o
c
a
man
,
S
.
Er
g
i
n
,
a
n
d
O
.
K
i
l
i
ç
,
“
C
l
a
ssi
f
i
c
a
t
i
o
n
o
f
s
h
o
r
t
-
c
i
r
c
u
i
t
f
a
u
l
t
s
i
n
h
i
g
h
-
v
o
l
t
a
g
e
e
n
e
r
g
y
t
r
a
n
s
mi
ss
i
o
n
l
i
n
e
u
si
n
g
e
n
e
r
g
y
o
f
i
n
s
t
a
n
t
a
n
e
o
u
s
a
c
t
i
v
e
p
o
w
e
r
c
o
m
p
o
n
e
n
t
s
-
b
a
se
d
c
o
mm
o
n
v
e
c
t
o
r
a
p
p
r
o
a
c
h
,
”
T
u
rk
i
sh
J
o
u
rn
a
l
o
f
El
e
c
t
r
i
c
a
l
En
g
i
n
e
e
r
i
n
g
a
n
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
s
,
v
o
l
.
2
4
,
n
o
.
3
,
p
p
.
1
9
0
1
–
1
9
1
5
,
2
0
1
6
,
d
o
i
:
1
0
.
3
9
0
6
/
e
l
k
-
1
3
1
2
-
1
3
1
.
[
2
9
]
D
.
J.
P
i
l
e
g
g
i
a
n
d
E.
M
.
G
u
l
a
c
h
e
n
s
k
i
,
“
A
n
o
n
-
c
o
n
t
a
c
t
t
e
c
h
n
i
q
u
e
f
o
r
d
e
t
e
r
m
i
n
i
n
g
h
a
r
m
o
n
i
c
c
u
r
r
e
n
t
s
p
r
e
se
n
t
i
n
i
n
d
i
v
i
d
u
a
l
c
o
n
d
u
c
t
o
r
s
o
f
o
v
e
r
h
e
a
d
l
i
n
e
s,
”
I
EEE
T
ra
n
s
a
c
t
i
o
n
s
o
n
Po
w
e
r
Ap
p
a
ra
t
u
s
a
n
d
S
y
s
t
e
m
s
,
v
o
l
.
P
A
S
-
1
0
2
,
n
o
.
3
,
p
p
.
5
9
6
–
6
0
3
,
M
a
r
.
1
9
8
3
,
d
o
i
:
1
0
.
1
1
0
9
/
TPA
S
.
1
9
8
3
.
3
1
7
9
8
0
.
[
3
0
]
K
.
J
.
F
e
r
r
e
i
r
a
a
n
d
A
.
E.
Ema
n
u
e
l
,
“
A
n
o
n
i
n
v
a
s
i
v
e
t
e
c
h
n
i
q
u
e
f
o
r
f
a
u
l
t
d
e
t
e
c
t
i
o
n
a
n
d
l
o
c
a
t
i
o
n
,
”
I
E
EE
T
r
a
n
sa
c
t
i
o
n
s
o
n
Po
w
e
r
D
e
l
i
v
e
ry
,
v
o
l
.
2
5
,
n
o
.
4
,
p
p
.
3
0
2
4
–
3
0
3
4
,
O
c
t
.
2
0
1
0
,
d
o
i
:
1
0
.
1
1
0
9
/
TPW
R
D
.
2
0
1
0
.
2
0
5
7
4
5
5
.
[
3
1
]
O
.
R
a
i
n
i
o
,
J.
Te
u
h
o
,
a
n
d
R
.
K
l
é
n
,
“
E
v
a
l
u
a
t
i
o
n
me
t
r
i
c
s
a
n
d
s
t
a
t
i
st
i
c
a
l
t
e
st
s
f
o
r
mac
h
i
n
e
l
e
a
r
n
i
n
g
,
”
S
c
i
e
n
t
i
f
i
c
R
e
p
o
rt
s
,
v
o
l
.
1
4
,
n
o
.
1
,
M
a
r
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
3
8
/
s
4
1
5
9
8
-
0
2
4
-
5
6
7
0
6
-
x.
[
3
2
]
J.
H
u
a
n
g
a
n
d
C
.
X
.
L
i
n
g
,
“
U
si
n
g
A
U
C
a
n
d
a
c
c
u
r
a
c
y
i
n
e
v
a
l
u
a
t
i
n
g
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms,
”
I
EEE
T
r
a
n
sa
c
t
i
o
n
s
o
n
K
n
o
w
l
e
d
g
e
a
n
d
D
a
t
a
E
n
g
i
n
e
e
ri
n
g
,
v
o
l
.
1
7
,
n
o
.
3
,
p
p
.
2
9
9
–
3
1
0
,
M
a
r
.
2
0
0
5
,
d
o
i
:
1
0
.
1
1
0
9
/
T
K
D
E.
2
0
0
5
.
5
0
.
[
3
3
]
M
.
G
h
a
r
i
b
,
T.
Z
o
p
p
i
,
a
n
d
A
.
B
o
n
d
a
v
a
l
l
i
,
“
O
n
t
h
e
p
r
o
p
e
r
n
e
ss
o
f
i
n
c
o
r
p
o
r
a
t
i
n
g
b
i
n
a
r
y
c
l
a
ssi
f
i
c
a
t
i
o
n
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms
i
n
t
o
s
a
f
e
t
y
-
c
r
i
t
i
c
a
l
sy
st
e
ms,”
I
E
EE
T
r
a
n
s
a
c
t
i
o
n
s o
n
Em
e
rg
i
n
g
T
o
p
i
c
s
i
n
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
0
,
n
o
.
4
,
p
p
.
1
6
7
1
–
1
6
8
6
,
O
c
t
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
TE
TC
.
2
0
2
2
.
3
1
7
8
6
3
1
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Am
it
L.
Ne
h
e
te
re
c
e
iv
e
d
th
e
B.
E.
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
fro
m
No
r
t
h
M
a
h
a
ra
sh
tra
Un
iv
e
rsity
,
Ja
lg
a
o
n
,
In
d
ia,
M
.
Tec
h
.
d
e
g
re
e
in
e
lec
tri
c
a
l
p
o
we
r
sy
ste
m
s
fro
m
Bh
a
rti
Vid
y
a
p
e
e
th
(De
e
m
e
d
to
b
e
Un
iv
e
rsity
)
,
Co
ll
e
g
e
o
f
En
g
in
e
e
rin
g
,
P
u
n
e
,
In
d
ia,
a
n
d
p
u
rsu
i
n
g
P
h
.
D.
d
e
g
re
e
i
n
En
g
in
e
e
rin
g
fr
o
m
Bh
a
rti
Vid
y
a
p
e
e
th
(
De
e
m
e
d
to
b
e
Un
iv
e
rsit
y
),
P
u
n
e
,
In
d
ia.
Wi
t
h
th
e
a
c
a
d
e
m
ic
e
x
p
e
rien
c
e
o
f
o
v
e
r
1
3
y
e
a
rs,
p
re
se
n
tl
y
,
h
e
is
w
o
rk
i
n
g
a
t
th
e
De
p
a
rtme
n
t
o
f
th
e
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
ri
n
g
,
Dr.
Vis
h
wa
n
a
th
Ka
ra
d
M
IT
W
o
rl
d
P
e
a
c
e
Un
iv
e
rsity
,
P
u
n
e
,
I
n
d
ia
a
n
As
sista
n
t
P
ro
fe
ss
o
r
.
He
is
a
re
se
a
rc
h
stu
d
e
n
t
m
e
m
b
e
r
o
f
IE
EE
a
n
d
th
e
li
fe
m
e
m
b
e
r
o
f
In
d
ian
S
o
c
iety
fo
r
Tec
h
n
ica
l
Ed
u
c
a
ti
o
n
(IS
TE
).
H
e
h
a
s
a
u
th
o
re
d
se
v
e
ra
l
re
se
a
rc
h
p
u
b
li
c
a
ti
o
n
s
a
n
d
tex
tb
o
o
k
s
i
n
h
is
a
re
a
s
o
f
i
n
tere
st:
p
o
we
r
sy
ste
m
p
r
o
tec
ti
o
n
,
e
lec
tri
c
v
e
h
icle
s,
a
n
d
e
n
e
r
g
y
a
u
d
it
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
m
it
.
n
e
h
e
te@
m
it
wp
u
.
e
d
u
.
in
.
Dr
.
De
e
p
a
k
S
.
Ba
n
k
a
r
re
c
e
iv
e
d
B.
E.
d
e
g
re
e
i
n
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fr
o
m
P
u
n
e
Un
iv
e
rsity
,
P
u
n
e
,
In
d
ia,
M
.
E
.
d
e
g
re
e
in
e
lec
tri
c
a
l
p
o
we
r
sy
ste
m
s
fr
o
m
G
o
v
e
rn
m
e
n
t
C
o
ll
e
g
e
o
f
En
g
i
n
e
e
rin
g
,
P
u
n
e
,
In
d
ia,
a
n
d
P
h
.
D.
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
fro
m
B
h
a
ra
ti
Vid
y
a
p
e
e
t
h
(De
e
m
e
d
to
b
e
)
Un
iv
e
rsity
(BVD
UCo
E),
P
u
n
e
,
In
d
ia.
Ha
v
in
g
a
c
a
d
e
m
ic
e
x
p
e
rien
c
e
o
f
o
v
e
r
two
d
e
c
a
d
e
s,
c
u
rre
n
tl
y
,
h
e
is
a
P
ro
fe
ss
o
r
a
n
d
He
a
d
o
f
t
h
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
Co
m
p
u
ter
E
n
g
in
e
e
rin
g
a
t
BVD
UCo
E
P
u
n
e
.
He
h
a
s
g
u
i
d
e
d
m
o
re
t
h
a
n
2
0
st
u
d
e
n
ts
f
o
r
M
.
Tec
h
a
n
d
0
3
re
se
a
rc
h
c
a
n
d
id
a
tes
h
a
v
e
c
o
m
p
lete
d
P
h
.
D.
His
re
se
a
rc
h
a
re
a
s
a
r
e
re
n
e
wa
b
le
e
n
e
rg
y
e
y
ste
m
s,
a
n
d
p
o
we
r
q
u
a
li
t
y
.
He
h
a
s
a
u
th
o
re
d
m
o
re
t
h
a
n
4
2
re
se
a
rc
h
p
u
b
li
c
a
ti
o
n
s
a
n
d
2
2
b
o
o
k
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
d
sb
a
n
k
a
r@b
v
u
c
o
e
p
.
e
d
u
.
in
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
36
,
No
.
3
,
Dec
em
b
er
20
24
:
1
3
5
6
-
1
3
6
4
1364
Ritik
a
As
a
t
i
re
c
e
iv
e
d
th
e
B.
E
.
d
e
g
re
e
i
n
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
in
2
0
0
7
a
n
d
M
.
E
d
e
g
re
e
in
p
o
we
r
e
lec
tro
n
ics
in
2
0
0
9
wit
h
t
h
e
g
o
l
d
m
e
d
a
l
fr
o
m
Ra
ji
v
G
a
n
d
h
i
Tec
h
n
ica
l
Un
iv
e
rsity
,
Bh
o
p
a
l,
In
d
ia.
S
h
e
is
p
u
rsu
i
n
g
P
h
.
D.
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fro
m
Bh
a
ra
ti
Vid
y
a
p
e
e
th
(De
e
m
e
d
to
b
e
)
Un
iv
e
rsity
,
P
u
n
e
,
I
n
d
ia.
Wi
th
th
e
tea
c
h
in
g
e
x
p
e
rien
c
e
o
f
o
v
e
r
1
4
y
e
a
rs,
c
u
rre
n
tl
y
,
sh
e
is
w
o
rk
i
n
g
a
t
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
E
n
g
in
e
e
rin
g
,
Co
EP
Tec
h
n
o
l
o
g
ica
l
Un
iv
e
rsity
,
P
u
n
e
,
In
d
ia.
He
r
re
se
a
rc
h
in
tere
st
in
c
lu
d
e
s
p
o
we
r
e
lec
tro
n
ics
a
n
d
sw
it
c
h
e
d
re
lu
c
tan
c
e
m
o
to
r
d
riv
e
s
y
ste
m
.
Sh
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
rit
ik
a
a
sa
ti
8
6
@g
m
a
il
.
c
o
m
.
Cheta
n
K
h
a
d
se
is
a
n
a
c
c
o
m
p
li
sh
e
d
E
lec
tri
c
a
l
En
g
in
e
e
r
wit
h
p
r
o
v
e
n
re
se
a
r
c
h
b
a
c
k
g
ro
u
n
d
.
He
re
c
e
iv
e
d
t
h
e
B.
E.
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
fr
o
m
S
h
ri
S
a
n
t
G
a
jan
a
n
M
a
h
a
ra
j
Co
ll
e
g
e
o
f
En
g
in
e
e
rin
g
S
h
e
g
a
o
,
Am
a
ra
v
a
ti
Un
i
v
e
rsity
,
Am
a
ra
v
a
ti
,
In
d
ia,
i
n
2
0
1
0
a
n
d
th
e
M
.
Tec
h
.
d
e
g
re
e
in
p
o
we
r
sy
ste
m
s
fro
m
Walc
h
a
n
d
Co
ll
e
g
e
o
f
En
g
i
n
e
e
rin
g
,
S
h
i
v
a
ji
Un
iv
e
rsity
,
S
a
n
g
li
,
In
d
ia,
in
2
0
1
3
.
H
e
h
a
s
c
o
m
p
lete
d
P
h
D
in
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
fro
m
Visv
e
sv
a
ra
y
a
Na
ti
o
n
a
l
In
stit
u
te
o
f
Tec
h
n
o
lo
g
y
,
Na
g
p
u
r,
In
d
ia.
H
is
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
th
e
a
re
a
s
o
f
p
o
we
r
q
u
a
li
t
y
m
o
n
it
o
rin
g
,
n
e
u
ra
l
n
e
two
r
k
s,
a
n
d
a
rt
ifi
c
ial
in
telli
g
e
n
c
e
.
He
h
a
s
p
u
b
li
sh
e
d
se
v
e
ra
l
re
se
a
rc
h
p
a
p
e
rs
in
re
p
u
te
d
jo
u
rn
a
ls.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
c
h
e
tan
.
k
h
a
d
se
@m
it
wp
u
.
e
d
u
.
i
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.