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nte
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l J
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o
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lect
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nics
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Driv
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J
P
E
DS
)
Vo
l.
8
,
No
.
1
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r
ch
2
0
1
7
,
p
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.
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05
~
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SS
N:
2088
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8
694
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DOI
: 1
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JP
E
DS
A New
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p
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.
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p
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p
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d
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sis
is
c
a
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u
t
in
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tl
a
b
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k
.
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ey
w
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:
A
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f
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n
e
u
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al
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et
w
o
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k
D
is
cr
ete
w
av
ele
t
t
r
an
s
f
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m
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r
an
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m
is
s
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Co
p
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rig
h
t
©
201
7
In
s
t
it
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
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c
e
.
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l
rig
h
ts
re
se
rv
e
d
.
C
o
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r
e
s
p
o
nd
ing
A
uth
o
r
:
Y.
Srin
i
v
asa
R
ao
,
Dep
ar
t
m
en
t o
f
E
lectr
ical
an
d
C
o
m
p
u
ter
E
n
g
in
ee
r
i
n
g
,
KL
U
n
i
v
er
s
it
y
,
Gr
ee
n
f
ie
ld
s
,
Vad
d
es
w
ar
a
m
,
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n
d
ia
5
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2
5
0
2
.
E
m
ail: l
s
n
t
l@
cc
u
.
ed
u
.
t
w
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
p
o
w
er
s
y
s
te
m
is
f
r
eq
u
en
t
l
y
af
f
ec
ted
b
y
f
a
u
lt
s
,
w
h
ich
g
iv
e
r
is
e
to
d
is
r
u
p
tio
n
i
n
p
o
w
er
f
lo
w
b
y
o
cc
u
r
r
en
ce
o
f
tr
an
s
ie
n
ts
i
n
v
o
lta
g
e
a
n
d
cu
r
r
en
t
s
i
g
n
al
s
.
T
h
e
d
eter
m
in
atio
n
o
f
f
au
lt
s
q
u
ic
k
l
y
w
it
h
a
r
ea
s
o
n
ab
le
ac
cu
r
ac
y
h
elp
s
i
n
f
aster
m
ai
n
te
n
an
ce
an
d
r
esto
r
atio
n
o
f
s
u
p
p
l
y
r
es
u
lti
n
g
in
i
m
p
r
o
v
ed
ec
o
n
o
m
y
,
s
af
et
y
a
n
d
r
eliab
ilit
y
o
f
p
o
w
er
s
y
s
te
m
.
T
h
is
p
a
p
er
is
a
n
e
w
ap
p
r
o
ac
h
b
ased
o
n
w
a
v
elet
m
u
l
ti
-
r
eso
lu
tio
n
a
n
al
y
s
is
a
n
d
ar
tifi
cial
n
e
u
r
al
n
e
t
w
o
r
k
b
ased
o
n
th
e
e
n
er
g
y
o
b
tain
ed
f
r
o
m
wav
elet
co
e
f
f
ic
ien
t
s
.
W
h
en
a
f
a
u
lt
o
cc
u
r
s
i
n
tr
an
s
m
is
s
io
n
li
n
e,
it
i
n
it
iates
a
tr
an
s
itio
n
co
n
d
itio
n
.
T
r
an
s
ie
n
t
s
p
r
o
d
u
ce
o
v
er
cu
r
r
en
t
s
in
t
h
e
p
o
w
er
s
y
s
te
m
,
w
h
ic
h
c
an
d
a
m
a
g
e
t
h
e
p
o
w
er
s
y
s
te
m
d
ep
en
d
in
g
u
p
o
n
its
s
e
v
er
it
y
o
f
o
cc
u
r
r
en
ce
.
T
h
e
y
also
co
n
tai
n
u
s
ef
u
l
i
n
f
o
r
m
atio
n
w
h
ic
h
ca
n
b
e
u
s
ed
f
o
r
an
al
y
zin
g
d
is
t
u
r
b
a
n
ce
s
t
h
at
o
cc
u
r
i
n
tr
an
s
m
i
s
s
io
n
l
in
e
s
.
T
h
e
an
al
y
s
i
s
o
f
tr
a
n
s
ie
n
ts
ar
e
d
u
e
to
th
e
p
r
esen
ce
o
f
h
i
g
h
f
r
eq
u
en
c
y
co
m
p
o
n
e
n
ts
i
n
v
o
lta
g
e
an
d
cu
r
r
en
t
f
a
u
lt
s
ig
n
al
s
an
d
th
er
e
ar
e
v
ar
io
u
s
m
et
h
o
d
s
to
ex
tr
ac
t
u
s
e
f
u
l
in
f
o
r
m
atio
n
f
r
o
m
th
e
s
e
h
i
g
h
f
r
eq
u
en
c
y
co
m
p
o
n
e
n
ts
.
Th
ese
m
et
h
o
d
s
ar
e
b
ased
o
n
Fo
u
r
ier
tr
an
s
f
o
r
m
,
w
av
e
let
tr
an
s
f
o
r
m
,
ar
ti
f
icial
n
e
u
r
al
n
et
w
o
r
k
o
r
co
m
b
i
n
atio
n
o
f
th
ese
tec
h
n
iq
u
e
s
[
1
]
.
Fo
u
r
ier
tr
an
s
f
o
r
m
an
d
w
a
v
elet
tr
a
n
s
f
o
r
m
ar
e
th
e
t
w
o
m
aj
o
r
to
o
ls
w
h
i
ch
ar
e
a
g
r
ea
t
h
elp
in
f
r
eq
u
en
c
y
d
o
m
ai
n
a
n
al
y
s
is
o
f
an
y
s
i
g
n
al.
Fo
u
r
ier
tr
a
n
s
f
o
r
m
i
s
u
s
ed
f
o
r
s
ta
tio
n
ar
y
s
i
g
n
al
a
n
d
it
p
r
o
v
id
e
t
w
o
d
i
m
en
s
io
n
al
i
n
f
o
r
m
atio
n
,
it
co
n
v
er
t
s
s
i
g
n
a
l
f
r
o
m
ti
m
e
d
o
m
ai
n
to
f
r
eq
u
en
c
y
d
o
m
ai
n
.
Fo
u
r
ier
tr
an
s
f
o
r
m
h
as
ze
r
o
-
ti
m
e
r
eso
lu
t
io
n
an
d
v
er
y
h
ig
h
f
r
eq
u
e
n
c
y
r
eso
l
u
tio
n
i.e
.
;
it
is
o
n
l
y
lo
ca
lize
d
in
f
r
eq
u
en
c
y
.
W
h
er
ea
s
w
a
v
elet
tr
an
s
f
o
r
m
i
s
u
s
ed
f
o
r
s
tatio
n
ar
y
a
n
d
n
o
n
-
s
tat
io
n
ar
y
s
ig
n
al
an
d
it
g
i
v
es
a
co
m
p
lete
th
r
ee
-
d
i
m
e
n
s
io
n
al
in
f
o
r
m
atio
n
o
f
a
n
y
s
ig
n
al.
W
av
elet
tr
a
n
s
f
o
r
m
h
as
h
i
g
h
t
i
m
e
r
eso
lu
tio
n
a
n
d
h
i
g
h
f
r
eq
u
en
c
y
r
eso
lu
tio
n
i.e
.
,
it i
s
lo
ca
lized
in
b
o
th
tim
e
a
n
d
f
r
eq
u
en
c
y
.
I
t
p
r
o
v
id
es
n
o
n
-
u
n
i
f
o
r
m
d
iv
is
io
n
o
f
f
r
eq
u
e
n
c
y
d
o
m
ai
n
m
ea
n
s
it
u
s
e
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
694
IJ
PEDS
Vo
l.
8
,
No
.
1
,
Ma
r
ch
201
7
:
5
05
–
5
1
2
506
s
h
o
r
t
w
i
n
d
o
w
at
h
i
g
h
f
r
eq
u
e
n
cies
an
d
lo
n
g
w
i
n
d
o
w
at
l
o
w
f
r
eq
u
e
n
cies.
Usi
n
g
w
a
v
el
et
m
u
lti
-
r
eso
l
u
tio
n
an
al
y
s
is
,
a
p
ar
ticu
lar
b
an
d
o
f
f
r
eq
u
en
cies p
r
ese
n
t in
t
h
e
f
au
lt
s
ig
n
al
ca
n
b
e
an
al
y
z
ed
[
2
].
2.
RE
S
E
ARCH
M
E
T
H
O
D
W
av
elet
m
ea
n
s
„
s
m
al
l
w
av
e
‟
.
So
w
av
e
let
an
al
y
s
is
i
s
ab
o
u
t
an
a
l
y
zi
n
g
s
i
g
n
al
w
it
h
s
h
o
r
t
d
u
r
atio
n
f
i
n
ite
e
n
er
g
y
f
u
n
ctio
n
s
.
T
h
e
y
tr
an
s
f
o
r
m
t
h
e
s
ig
n
al
u
n
d
er
in
v
e
s
ti
g
atio
n
in
to
an
o
t
h
er
r
ep
r
esen
tatio
n
w
h
ic
h
r
ep
r
ese
n
ts
t
h
e
s
i
g
n
al
i
n
a
m
o
r
e
u
s
ef
u
l
f
o
r
m
.
T
h
is
tr
an
s
f
o
r
m
at
io
n
o
f
s
ig
n
al
i
s
ca
lled
w
a
v
elet
tr
a
n
s
f
o
r
m
.
W
av
elet
tr
an
s
f
o
r
m
p
er
f
o
r
m
s
th
e
s
i
g
n
al
tr
an
s
latio
n
a
n
d
s
c
alin
g
.
I
f
th
e
p
r
o
ce
s
s
i
s
d
o
n
e
in
a
s
m
o
o
t
h
an
d
co
n
tin
u
o
u
s
f
as
h
io
n
th
e
n
th
e
t
r
an
s
f
o
r
m
is
ca
lled
co
n
tin
u
o
u
s
w
av
e
let
tr
an
s
f
o
r
m
.
I
f
th
e
s
ca
le
an
d
p
o
s
itio
n
ar
e
ch
an
g
ed
i
n
d
is
cr
ete
s
tep
s
,
t
h
e
tr
an
s
f
o
r
m
i
s
ca
lled
d
is
cr
ete
w
a
v
elet
tr
a
n
s
f
o
r
m
[
3
]
.
T
h
ese
w
a
v
elet
tr
an
s
f
o
r
m
d
ec
o
m
p
o
s
es
t
h
e
an
al
y
zi
n
g
w
a
v
elets
(
m
o
t
h
er
w
av
ele
ts
)
in
t
o
tr
an
s
lated
an
d
d
ilated
v
er
s
io
n
s
ca
lled
d
au
g
h
ter
w
a
v
elet
s
a
n
d
th
e
n
t
h
e
co
e
f
f
i
cien
ts
ar
e
d
er
iv
ed
.
Di
f
f
er
en
t
t
y
p
es
o
f
m
o
th
er
w
a
v
elet
s
ar
e
h
aa
r
,
d
au
b
ec
h
ies,
s
y
m
let
a
n
d
co
if
let.
T
h
e
d
is
cr
ete
w
a
v
elet
tr
an
s
f
o
r
m
(
DW
T
)
is
n
o
r
m
all
y
i
m
p
le
m
e
n
ted
b
y
Ma
llat
‟
s
al
g
o
r
ith
m
it
s
f
o
r
m
u
latio
n
is
r
elate
d
to
Mu
ltire
s
o
l
u
tio
n
a
n
al
y
s
i
s
t
h
eo
r
y
.
Dis
cr
ete
w
av
elet
tr
an
s
f
o
r
m
ca
n
b
e
ef
f
icie
n
tl
y
i
m
p
le
m
en
ted
b
y
u
s
in
g
o
n
l
y
t
w
o
f
il
ter
s
,
o
n
e
h
ig
h
p
ass
(
H
P
)
an
d
o
n
e
lo
w
p
a
s
s
(
L
P
)
at
lev
el
(
k
)
at
w
h
ic
h
f
u
n
d
a
m
en
ta
l
co
m
p
o
n
e
n
ts
g
en
e
r
ate
[
4
]
.
T
h
e
r
esu
lts
ar
e
d
o
w
n
-
s
a
m
p
led
b
y
a
f
ac
to
r
t
w
o
a
n
d
th
e
s
a
m
e
t
w
o
f
ilter
s
ar
e
ap
p
lied
to
th
e
o
u
tp
u
t
o
f
th
e
lo
w
p
as
s
f
ilter
f
r
o
m
th
e
p
r
ev
io
u
s
s
ta
g
e
o
f
th
e
s
i
g
n
al.
T
h
e
h
ig
h
p
ass
f
ilter
is
d
er
iv
ed
f
r
o
m
th
e
w
a
v
elet
f
u
n
ctio
n
(
m
o
th
er
w
a
v
elet)
an
d
m
ea
s
u
r
es
t
h
e
d
etails
i
n
a
ce
r
tain
in
p
u
t
h
a
v
i
n
g
lo
w
p
ass
f
ilter
o
n
t
h
e
o
th
er
h
a
n
d
d
eliv
er
s
a
s
m
o
o
t
h
ed
v
er
s
io
n
o
f
th
e
i
n
p
u
t
s
i
g
n
al
an
d
is
d
er
iv
ed
f
r
o
m
a
s
ca
li
n
g
f
u
n
ctio
n
ass
o
ciate
d
to
th
e
m
o
t
h
er
w
a
v
elet.
T
h
e
id
ea
is
illu
s
tr
ated
in
Fig
u
r
e
1
.
T
h
u
s
d
is
cr
ete
w
a
v
elet
tr
an
s
f
o
r
m
d
ec
o
m
p
o
s
es
t
h
e
s
ig
n
al
i
n
to
ap
p
r
o
x
i
m
atio
n
an
d
d
etai
l
co
ef
f
icie
n
t
s
,
ap
p
r
o
x
im
a
tio
n
co
ef
f
icien
ts
ar
e
h
ig
h
f
r
eq
u
en
c
y
co
e
f
f
icie
n
ts
a
n
d
d
etail
co
ef
f
icien
ts
ar
e
lo
w
f
r
eq
u
en
c
y
co
ef
f
icie
n
ts
an
d
t
h
ese
ap
p
r
o
x
i
m
atio
n
co
ef
f
icie
n
t
s
h
av
e
h
ig
h
e
n
er
g
y
an
d
d
etail
co
e
f
f
icien
t
s
h
a
v
e
lo
w
en
er
g
y
at
t
h
e
s
a
m
e
le
v
el
o
f
th
e
d
ec
o
m
p
o
s
itio
n
tr
ee
[
5
].
Fig
u
r
e
1.
DW
T
m
u
ltil
e
v
el
d
ec
o
m
p
o
s
i
tio
n
I
n
th
is
p
ap
er
an
al
y
s
is
ar
e
ca
r
r
ied
o
u
t
b
y
u
s
i
n
g
d
b
4
as
m
o
t
h
er
w
av
ele
t.
T
h
e
f
au
lt
cu
r
r
en
t
s
ig
n
al
s
ar
e
an
al
y
ze
d
w
i
th
d
b
4
at
lev
e
l
6
th
u
s
ap
p
r
o
x
i
m
a
tio
n
a
n
d
d
etailed
co
ef
f
icie
n
t
s
at
le
v
el
6
ar
e
o
b
tain
ed
[
6
]
.
W
e
ca
lcu
late
e
n
er
g
y
o
f
t
h
e
ap
p
r
o
x
i
m
atio
n
co
ef
f
icie
n
ts
b
y
u
s
i
n
g
t
h
e
f
o
r
m
u
la
∑
(
)
C
las
s
i
f
icatio
n
o
f
f
a
u
lt
is
d
o
n
e
f
r
o
m
t
h
e
o
b
tain
ed
e
n
er
g
y
o
f
t
h
e
ap
p
r
o
x
i
m
a
tio
n
co
ef
f
icie
n
ts
,
b
y
u
s
i
n
g
ar
tif
icial
n
eu
r
al
n
et
w
o
r
k
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PEDS
I
SS
N:
2088
-
8
694
A
N
ew A
p
p
r
o
a
ch
fo
r
C
la
s
s
ific
a
tio
n
o
f F
a
u
lt in
Tr
a
n
s
mis
s
o
n
Lin
e
w
ith
C
o
mb
in
a
tio
n
.
.
.
.
(
Y.
S
r
in
iva
s
a
R
a
o
)
507
2
.
1
.
Neura
l N
et
w
o
rk
A
r
ti
f
icial
Ne
u
r
al
Net
w
o
r
k
(
ANN)
is
a
n
et
w
o
r
k
w
h
ich
is
m
ad
e
o
f
s
e
v
er
al
la
y
er
s
,
ea
ch
co
n
s
i
s
ti
n
g
o
f
n
eu
r
o
n
s
,
w
h
ich
ar
e
co
n
n
ec
te
d
b
y
t
h
e
li
n
k
s
w
it
h
p
r
o
p
er
w
eig
h
ts
.
T
h
e
y
ar
e
tr
ain
ed
u
s
i
n
g
a
s
et
o
f
s
tati
s
tical
lear
n
in
g
al
g
o
r
it
h
m
s
.
A
n
e
u
r
al
n
et
w
o
r
k
h
as
t
h
e
ab
ilit
y
to
lear
n
a
n
d
is
a
co
m
p
le
x
ad
ap
tiv
e
s
y
s
te
m
,
w
h
ic
h
m
ea
n
s
it
ca
n
ch
a
n
g
e
it
s
in
ter
n
al
s
tr
u
ctu
r
e
(
w
ei
g
h
ts
)
b
ased
o
n
th
e
in
f
o
r
m
atio
n
(
er
r
o
r
)
f
lo
w
i
n
g
th
r
o
u
g
h
it.
So
,
th
i
s
m
ak
e
s
it
e
f
f
icie
n
t
i
n
s
o
l
v
i
n
g
t
h
e
co
m
p
le
x
p
r
o
b
lem
s
w
h
er
e
t
h
e
li
n
ea
r
co
m
p
u
t
in
g
f
ails
.
Hen
ce
it
is
e
m
p
lo
y
ed
i
n
ap
p
licatio
n
s
w
h
er
e
f
o
r
m
al
a
n
a
l
y
s
i
s
i
s
d
if
f
ic
u
lt
o
r
i
m
p
o
s
s
ib
le
s
u
c
h
as
p
atter
n
r
ec
o
g
n
itio
n
a
n
d
n
o
n
li
n
ea
r
s
y
s
te
m
id
en
ti
f
icatio
n
a
n
d
co
n
tr
o
l is r
eq
u
ir
ed
.
Fig
u
r
e
2
.
Mo
d
el
o
f
A
NN
Neu
r
al
n
e
t
w
o
r
k
s
ar
e
co
m
p
o
s
e
d
o
f
s
i
m
p
le
ele
m
e
n
t
s
w
h
ich
o
p
er
ate
in
p
ar
allel
w
it
h
i
n
ter
c
o
n
n
ec
tio
n
b
et
w
ee
n
t
h
e
m
.
T
h
e
w
e
ig
h
ts
o
f
co
n
n
ec
tio
n
(
li
n
k
)
d
eter
m
i
n
e
t
h
e
n
e
t
w
o
r
k
f
u
n
ctio
n
.
I
t
i
s
co
n
s
id
er
ed
as
th
e
s
i
m
p
le
s
t
k
in
d
o
f
f
ee
d
f
o
r
w
ar
d
n
et
w
o
r
k
.
A
n
e
u
r
al
n
et
w
o
r
k
wh
en
cr
ea
ted
h
as
to
b
e
tr
ain
ed
w
h
ic
h
is
d
o
n
e
u
s
in
g
tr
ain
i
n
g
f
u
n
ctio
n
.
T
h
e
w
ei
g
h
ts
o
f
t
h
e
li
n
k
i
n
t
h
e
n
et
w
o
r
k
ar
e
ad
j
u
s
ted
au
to
m
atica
ll
y
to
g
et
a
p
ar
ticu
lar
tar
g
et
o
u
tp
u
t
f
o
r
s
p
ec
i
f
ic
in
p
u
t
[
7
]
.
A
n
eu
r
al
n
et
w
o
r
k
ca
n
h
a
v
e
s
e
v
er
al
la
y
er
s
.
E
ac
h
la
y
er
co
n
s
i
s
ts
o
f
s
et
o
f
p
r
ed
ef
in
ed
n
e
u
r
o
n
s
f
o
r
w
h
ich
w
ei
g
h
t
m
atr
i
x
,
b
i
as
v
ec
to
r
an
d
an
o
u
tp
u
t
f
u
n
c
tio
n
e
x
is
ts
.
E
ac
h
n
e
u
r
o
n
i
n
o
n
e
la
y
er
h
as
d
ir
ec
t
co
n
n
ec
tio
n
s
w
it
h
t
h
e
n
e
u
r
o
n
s
o
f
t
h
e
n
ei
g
h
b
o
r
in
g
la
y
er
.
T
h
e
lay
er
w
h
i
ch
is
in
b
et
w
ee
n
th
e
in
p
u
t
la
y
er
an
d
o
u
tp
u
t
la
y
er
is
ca
lled
h
id
d
en
la
y
er
[
8
]
.
B
y
i
n
cr
ea
s
i
n
g
th
e
n
u
m
b
er
o
f
h
id
d
en
la
y
er
s
an
d
n
eu
r
o
n
s
t
h
e
n
et
w
o
r
k
is
e
n
ab
l
ed
to
ex
tr
ac
t
h
ig
h
er
o
r
d
er
s
tatis
tics
w
h
ic
h
i
s
ad
v
an
tag
eo
u
s
w
h
e
n
n
u
m
b
er
o
f
in
p
u
t
s
is
lar
g
e
an
d
h
i
g
h
l
y
n
o
n
l
in
ea
r
[
9
].
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
694
IJ
PEDS
Vo
l.
8
,
No
.
1
,
Ma
r
ch
201
7
:
5
05
–
5
1
2
508
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
Fig
u
r
e
3.
Si
m
u
li
n
k
m
o
d
el
o
f
tr
an
s
m
i
s
s
io
n
l
in
e
A
2
2
0
k
V
p
o
w
er
s
y
s
te
m
i
s
s
i
m
u
lated
u
s
i
n
g
M
A
T
L
A
B
Si
m
u
lin
k
,
th
e
tr
an
s
m
is
s
io
n
lin
e
p
ar
am
eter
s
ar
e
R
1
=0
.
0
1
2
7
3
Ω
/k
m
;
R
0
=
0
.
3
8
6
4
Ω
/k
m
;
L
1
=0
.
9
3
3
7
m
H/
k
m
; L
0
=
4
.
1
2
6
4
m
H/
k
m
; C1
=1
2
.
7
4
n
F/k
m
,
C
0
=
7
.
7
5
1
n
F/k
m
an
d
L
o
ad
w
it
h
1
0
0
k
w
ac
tiv
e
p
o
w
er
a
n
d
9
0
0
w
r
ea
ctiv
e
p
o
w
er
ar
e
co
n
s
id
er
ed
.
T
r
an
s
m
is
s
io
n
l
in
e
len
g
th
i
s
3
0
0
k
m
[
1
0
]
.
3
.
1
.
Si
m
ula
t
io
n r
es
ults
(
a)
d
u
r
in
g
n
o
r
m
al
co
n
d
itio
n
0
0
.
0
5
0
.
1
0
.
1
5
0
.
2
0
.
2
5
0
.
3
-
2
0
0
-
1
0
0
0
100
200
300
t
i
m
e
i
n
s
e
c
c
u
r
r
e
n
t
i
n
a
m
p
s
c
u
r
r
e
n
t
s
d
u
r
i
n
g
n
o
r
m
a
l
c
o
n
d
i
t
i
o
n
p
h
a
s
e
A
p
h
a
s
e
B
p
h
a
s
e
C
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PEDS
I
SS
N:
2088
-
8
694
A
N
ew A
p
p
r
o
a
ch
fo
r
C
la
s
s
ific
a
tio
n
o
f F
a
u
lt in
Tr
a
n
s
mis
s
o
n
Lin
e
w
ith
C
o
mb
in
a
tio
n
.
.
.
.
(
Y.
S
r
in
iva
s
a
R
a
o
)
509
(
b
)
d
u
r
in
g
A
-
G
f
a
u
lt
(
c)
d
u
r
in
g
A
B
-
G
f
a
u
lt
(
d
)
d
u
r
in
g
A
B
C
-
G
f
a
u
lt
Fig
u
r
e
4
.
Si
m
u
latio
n
R
es
u
lt
s
0
0
.
0
5
0
.
1
0
.
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RO
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elet
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1
1
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.
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ased
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n
6
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al
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et
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ased
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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PEDS
I
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N:
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8
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A
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.
(
Y.
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511
5.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
p
r
esen
ts
th
e
ap
p
licatio
n
o
f
w
av
e
let
m
u
lti
r
e
s
o
lu
tio
n
an
al
y
s
i
s
in
co
m
b
i
n
atio
n
w
ith
ar
tif
icial
n
e
u
r
al
n
et
w
o
r
k
f
o
r
ac
cu
r
ate
class
if
icatio
n
.
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h
e
m
et
h
o
d
u
s
es e
n
er
g
y
o
f
ap
p
r
o
x
i
m
ati
o
n
co
ef
f
icien
ts
f
o
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f
au
lt
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s
i
f
icatio
n
.
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a
v
elet
t
r
an
s
f
o
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m
is
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s
ed
to
g
et
ap
p
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o
x
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m
atio
n
co
e
f
f
icien
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f
o
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t
s
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d
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h
u
s
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f
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o
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f
a
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lt
s
w
as
e
x
ac
t.
T
h
is
w
o
r
k
d
ea
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it
h
f
a
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lt
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f
icatio
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u
t
th
e
p
r
o
p
o
s
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alg
o
r
ith
m
a
n
d
s
ch
e
m
e
ca
n
b
e
ex
ten
d
ed
to
lo
ca
te
th
e
f
a
u
lt
d
is
tan
ce
f
r
o
m
s
e
n
d
in
g
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d
an
d
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ec
eiv
i
n
g
e
n
d
.
RE
F
E
R
E
NC
E
S
[1
]
P
.
S
o
m
a
n
,
e
t
a
l.
,
“
I
n
sig
h
t
i
n
t
o
w
a
v
e
lets
,
”
Ne
w
D
e
lh
i,
P
HI L
e
a
rn
in
g
P
riv
a
te L
i
m
it
e
d
,
2
0
1
0
.
[2
]
A
.
M
.
G
a
o
u
d
a
,
e
t
a
l.
,
“
P
o
w
e
r
Qu
a
li
ty
De
te
c
ti
o
n
a
n
d
Clas
sif
ica
ti
o
n
u
sin
g
Wav
e
l
e
t
-
M
u
lt
ir
e
so
lu
ti
o
n
S
ig
n
a
l
De
c
o
m
p
o
siti
o
n
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
P
o
we
r De
li
v
e
ry
,
v
ol
/i
ss
u
e
:
14
(
4
)
,
p
p
.
1
4
6
9
-
1
4
7
6
,
1
9
9
9
.
[3
]
D.
Ch
a
n
d
a
,
e
t
a
l
.
,
“
A
wa
v
e
l
e
t
m
u
lt
ires
o
lu
t
io
n
a
n
a
ly
sis
f
o
r
lo
c
a
ti
o
n
o
f
fa
u
lt
o
n
tran
sm
issio
n
li
n
e
s
,
”
El
e
c
trica
l
p
o
we
r
a
n
d
En
e
rg
y
sy
ste
ms
,
p
p
.
59
-
6
9
,
2
0
0
3
.
[4
]
S.
A.
S
h
a
a
b
a
n
a
n
d
T
.
Hiy
a
m
a
,
“
T
ra
n
sm
issio
n
L
i
n
e
Fa
u
lt
s
Cl
a
ss
if
ica
ti
o
n
Us
in
g
W
a
v
e
let
T
ra
n
sf
o
rm
,
”
P
ro
c
e
e
d
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n
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s
o
f
th
e
1
4
th
I
n
tern
a
ti
o
n
a
l
M
id
d
le
Eas
t
P
o
w
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r
S
y
ste
m
s Co
n
f
e
r
e
n
c
e
,
Ca
iro
Un
iv
e
rsity
,
Eg
y
p
t,
p
p
.
19
-
2
1
,
2
0
1
0
.
[5
]
Ka
le
V
.
S.,
e
t
a
l
.,
“
Fa
u
lt
e
d
p
h
a
se
se
lec
ti
o
n
o
n
d
o
u
b
le
c
irc
u
it
tra
n
s
miss
io
n
li
n
e
u
sin
g
wa
v
e
let
tra
n
sf
o
rm
a
n
d
n
e
u
r
a
l
n
e
two
rk
,
”
T
h
ird
In
ter
n
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
P
o
w
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S
y
st
e
m
,
Kh
a
ra
g
p
u
r,
I
n
d
ia,
2
0
0
9
.
[6
]
G
a
f
o
o
r
S
.
A.
a
n
d
Ra
m
a
n
a
R
.
P.
V
.
,
“
W
a
v
e
let
b
a
se
d
fa
u
lt
d
e
tec
ti
o
n
,
c
la
ss
if
ica
ti
o
n
a
n
d
l
o
c
a
ti
o
n
i
n
tra
n
s
miss
io
n
li
n
e
,
”
P
o
w
e
r
a
n
d
En
e
rg
y
Co
n
f
e
r
e
n
c
e
,
P
ECo
n
‟
0
6
,
I
EE
E
I
n
tern
a
ti
o
n
a
l
,
2
0
0
6
.
[7
]
D
.
Ku
m
a
r
a
n
d
S
.
R
.
S
a
g
a
r,
“
Disc
ri
m
in
a
ti
o
n
o
f
F
a
u
lt
s
a
n
d
T
h
e
ir
L
o
c
a
ti
o
n
Id
e
n
t
if
ica
ti
o
n
o
n
a
Hig
h
V
o
lt
a
g
e
T
ra
n
s
m
issio
n
L
in
e
s Us
in
g
th
e
Disc
re
te W
a
v
e
let
T
ra
n
s
f
o
r
m
,”
IJ
EA
R
,
v
ol
/i
ss
u
e
:
4
(
1
)
,
p
p
.
1
0
7
-
1
1
1
,
2
0
1
4
.
[8
]
Oth
m
a
n
M
.
F
.
a
n
d
A
m
a
ri
H.
A
.
,
“
On
li
n
e
fa
u
lt
d
e
tec
ti
o
n
f
o
r
p
o
we
r
sy
ste
m
u
sin
g
wa
v
e
let
a
n
d
P
NN
,”
2
n
d
I
EE
E
In
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
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e
o
n
P
o
w
e
r
a
n
d
E
n
e
rg
y
(P
EC
o
n
0
8
)
,
Jo
h
o
r
Ba
h
a
ru
,
M
a
lay
sia
,
2
0
0
8
.
[9
]
Co
sta
F
.
B.
,
e
t
a
l.
,
“
A
me
th
o
d
f
o
r
fa
u
lt
c
l
a
ss
if
ica
ti
o
n
i
n
T
ra
n
sm
issio
n
L
i
n
e
s
b
a
se
d
o
n
ANN
a
n
d
w
a
v
e
let
Co
e
ff
icie
n
t
En
e
rg
y
,
”
I
n
tern
a
ti
o
n
a
l
J
o
in
t
Co
n
f
e
re
n
c
e
o
n
Ne
u
ra
l
Ne
tw
o
rk
s V
a
n
c
o
u
v
e
r,
BC,
Ca
n
a
d
a
,
2
0
0
6
.
[1
0
]
M
.
P
a
tel
a
n
d
R.
N.
P
a
tel,
“
F
a
u
lt
De
tec
ti
o
n
a
n
d
Clas
sif
ica
ti
o
n
o
n
a
T
ra
n
sm
issio
n
L
in
e
u
sin
g
W
a
v
e
l
e
t
M
u
lt
i
Re
so
lu
ti
o
n
A
n
a
l
y
sis
a
n
d
Ne
u
ra
l
Ne
t
w
o
rk
,”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Co
m
p
u
ter
Ap
p
li
c
a
t
io
n
s
(
0
9
7
5
–
8
8
8
7
),
v
ol
/i
ss
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