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ay
-
b
ased
p
r
o
tectio
n
s
ch
em
es.
I
n
r
ec
en
t
y
ea
r
s
,
ar
tific
ial
in
tellig
en
ce
(
AI
)
,
p
ar
ticu
lar
ly
m
ac
h
i
n
e
lear
n
in
g
(
ML
)
an
d
d
ee
p
lear
n
in
g
(
DL
)
,
h
as
em
er
g
e
d
as
a
p
o
wer
f
u
l
to
o
l
f
o
r
i
n
tellig
en
t
f
au
lt
an
aly
s
is
.
T
h
ese
m
eth
o
d
s
ca
n
lear
n
co
m
p
lex
n
o
n
lin
e
ar
r
elatio
n
s
h
ip
s
f
r
o
m
lar
g
e
-
sc
ale
d
ata,
en
ab
lin
g
im
p
r
o
v
e
d
f
au
lt c
lass
if
icatio
n
,
d
etec
tio
n
o
f
s
u
b
tle
d
is
tu
r
b
an
ce
s
,
an
d
ad
ap
tiv
e
d
ec
is
io
n
-
m
ak
i
n
g
in
m
o
d
er
n
s
m
ar
t
g
r
id
s
[
1
]
-
[
4
]
.
Dee
p
lea
r
n
in
g
m
o
d
els
h
av
e
d
em
o
n
s
tr
ated
s
tr
o
n
g
ca
p
ab
ilit
ies
in
ex
tr
ac
tin
g
tr
a
n
s
ien
t
f
ea
tu
r
es
an
d
im
p
r
o
v
in
g
class
i
f
icatio
n
p
er
f
o
r
m
an
ce
co
m
p
ar
ed
to
tr
ad
itio
n
a
l a
p
p
r
o
ac
h
es [
5
]
,
[
6
]
.
Desp
ite
s
ig
n
if
ican
t
ad
v
an
ce
m
en
ts
,
s
ev
er
al
p
r
ac
tical
ch
allen
g
es
r
em
ain
u
n
r
eso
lv
e
d
.
C
o
n
v
en
tio
n
al
im
p
ed
an
ce
-
b
ased
an
d
th
r
esh
o
l
d
-
b
ased
p
r
o
tectio
n
tech
n
iq
u
es
o
f
ten
f
ail
u
n
d
er
lo
w
s
ig
n
al
-
to
-
n
o
is
e
r
atio
(
SNR
)
co
n
d
itio
n
s
,
h
ig
h
-
im
p
e
d
an
ce
f
a
u
lts
(
HI
Fs
)
,
an
d
d
y
n
am
ic
o
p
e
r
atin
g
s
ce
n
ar
io
s
[
7
]
.
T
r
ad
itio
n
a
l
ML
m
o
d
els
s
u
ch
as
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
es
(
SVM)
,
r
an
d
o
m
f
o
r
est
(
R
F),
a
n
d
k
-
NN
ar
e
lim
ited
in
ca
p
t
u
r
in
g
d
ee
p
tem
p
o
r
al
–
f
r
eq
u
e
n
cy
ch
a
r
ac
ter
is
tics
o
f
tr
an
s
ien
t
s
ig
n
als
[
8
]
-
[
1
0
]
.
On
th
e
o
th
er
h
an
d
,
d
ee
p
lear
n
i
n
g
m
o
d
els
s
u
ch
as
DNNs,
alth
o
u
g
h
ef
f
ec
tiv
e
i
n
f
ea
tu
r
e
ex
tr
ac
tio
n
,
o
f
ten
s
u
f
f
er
f
r
o
m
p
o
o
r
d
ec
is
io
n
b
o
u
n
d
a
r
y
d
ef
i
n
iti
on
,
o
v
er
f
itti
n
g
,
an
d
r
ed
u
ce
d
r
elia
b
ilit
y
u
n
d
e
r
u
n
ce
r
tain
o
r
am
b
ig
u
o
u
s
f
au
lt
co
n
d
itio
n
s
[
1
1
]
-
[
1
4
]
.
Fu
r
th
er
m
o
r
e,
m
o
s
t
ex
is
tin
g
ap
p
r
o
ac
h
es
f
ail
to
s
im
u
ltan
eo
u
s
ly
ad
d
r
ess
n
o
i
s
e
r
o
b
u
s
tn
ess
,
u
n
ce
r
tain
t
y
h
an
d
lin
g
,
an
d
ac
cu
r
ate
f
au
lt lo
ca
lizatio
n
,
lim
itin
g
th
ei
r
a
p
p
licab
ilit
y
in
p
r
ac
tical
p
o
w
er
d
is
tr
ib
u
tio
n
s
y
s
tem
s
.
R
ec
en
t
r
esear
ch
h
as
ex
p
lo
r
ed
v
ar
io
u
s
AI
-
b
ased
tech
n
iq
u
es
f
o
r
f
au
lt
d
etec
tio
n
an
d
class
if
icatio
n
.
Dee
p
lear
n
in
g
ar
c
h
itectu
r
es
s
u
ch
as
C
NNs,
R
NN
s
,
an
d
DNNs
h
av
e
d
em
o
n
s
tr
ated
s
tr
o
n
g
ca
p
ab
ilit
ies
in
ex
tr
ac
t
in
g
tr
a
n
s
ien
t
f
ea
tu
r
es
f
r
o
m
p
o
wer
s
y
s
tem
s
ig
n
als
[
1
5
]
-
[
1
9
]
.
Hy
b
r
id
ap
p
r
o
ac
h
es
c
o
m
b
in
in
g
wav
elet
tr
an
s
f
o
r
m
s
with
d
ee
p
lear
n
i
n
g
h
a
v
e
s
h
o
wn
im
p
r
o
v
ed
p
er
f
o
r
m
a
n
ce
in
ca
p
tu
r
in
g
b
o
t
h
tim
e
-
d
o
m
ai
n
an
d
f
r
eq
u
e
n
cy
-
d
o
m
ain
ch
ar
ac
ter
is
tics
[
2
0
]
.
Ad
v
a
n
ce
d
tech
n
iq
u
es
s
u
ch
as
T
r
an
s
f
o
r
m
er
-
b
ased
m
o
d
els
an
d
g
r
a
p
h
n
eu
r
al
n
etwo
r
k
s
(
GNNs)
h
a
v
e
b
ee
n
in
tr
o
d
u
ce
d
f
o
r
wid
e
-
ar
ea
m
o
n
ito
r
in
g
an
d
d
is
tu
r
b
an
ce
class
if
icatio
n
,
o
f
f
er
in
g
im
p
r
o
v
e
d
s
ca
lab
ilit
y
an
d
s
y
s
tem
-
lev
el
i
n
s
ig
h
ts
[
2
1
]
.
Ma
c
h
in
e
lear
n
in
g
a
p
p
r
o
ac
h
es,
p
ar
ticu
lar
ly
o
p
tim
ized
SVM
s
an
d
en
s
em
b
le
lear
n
in
g
m
eth
o
d
s
,
h
av
e
also
b
ee
n
wid
ely
u
s
ed
d
u
e
to
th
eir
s
tr
o
n
g
g
en
er
aliza
tio
n
ab
ilit
y
[
2
2
]
,
[
2
3
]
.
Fu
zz
y
l
o
g
ic
s
y
s
tem
s
h
av
e
b
ee
n
em
p
lo
y
ed
t
o
h
an
d
le
u
n
ce
r
tain
ty
an
d
im
p
r
ec
is
io
n
in
f
au
lt
d
ia
g
n
o
s
i
s
b
y
in
co
r
p
o
r
atin
g
r
u
le
-
b
ase
d
r
ea
s
o
n
in
g
[
2
4
]
,
[
2
5
]
.
R
ein
f
o
r
ce
m
en
t
lear
n
i
n
g
-
b
ased
ad
ap
tiv
e
p
r
o
tectio
n
s
ch
em
es
h
av
e
f
u
r
th
e
r
d
e
m
o
n
s
tr
at
ed
th
e
p
o
ten
tial
f
o
r
s
elf
-
t
u
n
in
g
u
n
d
er
v
ar
y
i
n
g
g
r
id
co
n
d
itio
n
s
[
2
6
]
.
Ad
d
itio
n
ally
,
AI
-
b
ased
m
et
h
o
d
s
h
a
v
e
b
e
en
ap
p
lied
to
r
e
n
ewa
b
le
-
in
te
g
r
ated
an
d
h
y
b
r
id
AC
/D
C
g
r
id
s
to
ad
d
r
ess
v
ar
ia
b
ilit
y
an
d
u
n
ce
r
tain
ty
i
n
p
o
we
r
s
y
s
tem
s
[
2
7
]
.
A
cr
itical
r
ev
iew
o
f
ex
is
tin
g
s
t
u
d
ies r
ev
ea
ls
s
ev
er
al
k
ey
r
esear
ch
g
ap
s
:
–
L
ac
k
o
f
in
teg
r
ate
d
h
y
b
r
id
f
r
am
ewo
r
k
s
th
at
ef
f
ec
tiv
e
ly
co
m
b
i
n
e
d
ee
p
f
ea
tu
r
e
l
ea
r
n
in
g
,
r
o
b
u
s
t
class
if
icatio
n
,
an
d
u
n
ce
r
tain
t
y
mod
ellin
g
in
a
u
n
if
ie
d
ar
ch
itec
tu
r
e
[
2
8
]
.
–
L
im
ited
r
o
b
u
s
tn
ess
u
n
d
er
n
o
is
y
en
v
ir
o
n
m
en
ts
,
esp
ec
ially
at
l
o
w
SNR
co
n
d
itio
n
s
[
2
9
]
.
–
I
n
ad
eq
u
ate
ca
p
a
b
ilit
y
in
d
et
ec
tin
g
h
ig
h
-
im
p
e
d
an
ce
f
au
lts
(
HI
Fs
)
,
wh
ich
ex
h
ib
it
wea
k
an
d
n
o
n
lin
ea
r
ch
ar
ac
ter
is
tics
[
3
0
]
.
–
I
n
s
u
f
f
icien
t
f
a
u
lt lo
ca
lizatio
n
ac
cu
r
ac
y
u
n
d
er
v
ar
y
i
n
g
s
y
s
tem
p
ar
am
eter
s
an
d
lo
n
g
tr
an
s
m
is
s
io
n
d
is
tan
ce
s
.
–
Po
o
r
m
et
h
o
d
o
lo
g
ical
t
r
an
s
p
a
r
en
cy
,
in
clu
d
i
n
g
in
s
u
f
f
icien
t
d
ataset
d
escr
ip
tio
n
,
p
ar
am
e
ter
tu
n
in
g
,
an
d
v
alid
atio
n
s
tr
ateg
ies in
m
an
y
s
tu
d
ies
.
–
L
im
ited
f
o
cu
s
o
n
r
ea
l
-
ti
m
e
d
ep
lo
y
m
en
t
a
n
d
c
o
m
p
u
tatio
n
al
ef
f
icien
cy
,
wh
ic
h
ar
e
ess
en
tial
f
o
r
p
r
ac
tical
im
p
lem
en
tatio
n
.
T
h
ese
lim
itatio
n
s
h
ig
h
lig
h
t
th
e
n
ee
d
f
o
r
a
c
o
m
p
r
e
h
en
s
iv
e,
tr
an
s
p
ar
en
t,
an
d
d
e
p
lo
y
m
e
n
t
-
o
r
ien
ted
h
y
b
r
i
d
in
tellig
en
t f
r
am
ewo
r
k
f
o
r
f
au
lt
d
iag
n
o
s
is
an
d
lo
ca
lizatio
n
.
T
o
ad
d
r
ess
th
ese
g
ap
s
,
th
is
p
ap
er
p
r
o
p
o
s
es
a
h
y
b
r
id
A
I
-
b
ased
I
n
t
ellig
en
t
f
au
lt
d
ia
g
n
o
s
is
an
d
lo
ca
lizatio
n
f
r
am
ewo
r
k
in
teg
r
atin
g
d
ee
p
n
eu
r
al
n
etwo
r
k
s
(
DNN)
,
SVM,
an
d
f
u
zz
y
lo
g
ic
in
a
s
tr
u
ctu
r
ed
an
d
co
m
p
lem
en
tar
y
m
an
n
er
.
T
h
e
k
ey
co
n
tr
ib
u
tio
n
s
ar
e:
–
Hy
b
r
id
m
u
lti
-
s
tag
e
a
r
ch
itectu
r
e
:
I
n
teg
r
atio
n
o
f
DNN
f
o
r
d
ee
p
f
ea
tu
r
e
e
m
b
ed
d
in
g
,
SVM
f
o
r
m
ar
g
in
-
b
ased
class
if
icatio
n
,
an
d
f
u
zz
y
in
f
er
e
n
ce
f
o
r
u
n
ce
r
tain
t
y
-
awa
r
e
d
ec
i
s
io
n
f
u
s
io
n
.
–
R
o
b
u
s
t
f
ea
tu
r
e
en
g
in
ee
r
in
g
:
C
o
m
b
in
atio
n
o
f
wav
elet
-
b
a
s
ed
tim
e
–
f
r
eq
u
en
cy
f
ea
tu
r
es
an
d
s
tatis
tica
l
d
escr
ip
to
r
s
to
ca
p
tu
r
e
tr
an
s
ien
t c
h
ar
ac
ter
is
tics
.
–
E
n
h
an
ce
d
n
o
is
e
r
esil
ien
ce
:
E
v
alu
atio
n
ac
r
o
s
s
a
wid
e
r
an
g
e
o
f
SNR
lev
els
(
3
0
d
B
to
−5
d
B
)
,
d
em
o
n
s
tr
atin
g
im
p
r
o
v
ed
r
o
b
u
s
tn
ess
.
–
I
m
p
r
o
v
ed
H
I
F
d
etec
tio
n
:
E
f
f
ec
tiv
e
id
en
tif
icatio
n
o
f
h
ig
h
-
im
p
ed
an
ce
f
au
lts
u
s
in
g
h
y
b
r
i
d
lear
n
in
g
a
n
d
f
u
zz
y
r
ea
s
o
n
in
g
.
–
A
c
c
u
r
a
t
e
f
a
u
lt
l
o
c
a
li
z
a
ti
o
n
:
I
n
te
g
r
a
t
i
o
n
o
f
i
m
p
e
d
a
n
c
e
-
b
a
s
e
d
es
t
i
m
at
i
o
n
w
it
h
l
e
a
r
n
e
d
c
o
r
r
e
c
tio
n
m
e
c
h
a
n
i
s
m
s
.
–
R
ep
r
o
d
u
cib
le
m
et
h
o
d
o
lo
g
y
:
D
etailed
d
ataset
d
escr
ip
tio
n
,
m
o
d
el
ar
ch
itectu
r
e,
a
n
d
v
alid
atio
n
p
r
o
ce
d
u
r
es.
–
Dep
lo
y
m
en
t
-
o
r
ien
ted
d
e
s
ig
n
:
An
aly
s
is
o
f
in
f
er
e
n
ce
laten
cy
,
p
r
u
n
in
g
,
an
d
q
u
an
tizatio
n
f
o
r
r
ea
l
-
tim
e
ap
p
licatio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
Hyb
r
id
A
I
-
d
r
iven
in
tellig
en
t fa
u
lt d
ia
g
n
o
s
is
a
n
d
lo
ca
liz
a
tio
n
in
mo
d
ern
…
(
Dee
p
a
S
o
ma
s
u
n
d
a
r
a
m
)
2283
T
h
e
p
r
o
p
o
s
ed
h
y
b
r
id
f
r
am
ew
o
r
k
p
r
o
v
id
es
a
r
eliab
le,
s
ca
lab
le,
an
d
r
ea
l
-
tim
e
co
m
p
atib
le
s
o
lu
tio
n
f
o
r
in
t
ellig
en
t
f
au
lt
d
iag
n
o
s
is
in
m
o
d
er
n
p
o
wer
s
y
s
tem
s
.
B
y
co
m
b
in
in
g
d
ee
p
lear
n
in
g
,
m
ac
h
in
e
lear
n
in
g
,
an
d
f
u
zz
y
lo
g
ic,
th
e
m
o
d
el
s
ig
n
if
i
ca
n
tly
im
p
r
o
v
es
class
if
icatio
n
ac
cu
r
ac
y
,
r
o
b
u
s
tn
ess
u
n
d
er
u
n
ce
r
tain
ty
,
a
n
d
f
a
u
lt
lo
ca
lizatio
n
p
r
ec
is
io
n
.
Fro
m
a
p
r
ac
tical
p
er
s
p
ec
tiv
e,
th
e
s
y
s
tem
ca
n
b
e
in
teg
r
ate
d
in
to
d
ig
i
tal
r
elay
s
,
SC
AD
A
s
y
s
tem
s
,
an
d
s
m
ar
t
g
r
id
m
o
n
ito
r
in
g
p
latf
o
r
m
s
,
en
ab
lin
g
f
aster
f
au
lt
d
etec
tio
n
an
d
s
y
s
tem
r
esto
r
atio
n
.
I
m
p
r
o
v
ed
HI
F d
etec
tio
n
en
h
a
n
ce
s
s
af
ety
an
d
r
ed
u
ce
s
f
ir
e
h
az
ar
d
s
,
wh
ile
ac
cu
r
ate
f
au
lt lo
c
aliza
tio
n
m
in
im
izes
m
ain
ten
an
ce
tim
e
an
d
o
p
er
at
io
n
al
co
s
ts
.
Fro
m
a
r
esear
ch
p
er
s
p
ec
tiv
e,
th
is
wo
r
k
a
d
v
a
n
ce
s
th
e
f
ield
b
y
o
f
f
er
in
g
a
well
-
s
tr
u
ctu
r
ed
h
y
b
r
id
f
r
am
ewo
r
k
with
s
tr
o
n
g
m
eth
o
d
o
lo
g
ical
tr
a
n
s
p
ar
en
cy
an
d
im
p
r
o
v
ed
p
er
f
o
r
m
an
ce
,
ad
d
r
ess
in
g
k
ey
lim
itatio
n
s
in
ex
is
tin
g
s
tu
d
ies.
I
t
also
o
p
e
n
s
n
ew
d
ir
ec
tio
n
s
f
o
r
ad
ap
tiv
e
p
r
o
tectio
n
s
y
s
tem
s
,
ed
g
e
in
tellig
en
ce
,
an
d
r
en
ewa
b
le
-
in
teg
r
a
ted
g
r
id
r
esil
ien
ce
.
2.
M
E
T
H
O
DO
L
O
G
Y
2
.
1
.
Resea
rc
h desi
g
n/a
pp
ro
a
ch
T
h
is
wo
r
k
ad
o
p
ts
a
s
im
u
latio
n
-
d
r
i
v
en
ex
p
er
im
en
tal
r
esea
r
ch
d
esig
n
c
o
m
b
in
e
d
with
d
ata
-
d
r
iv
en
m
o
d
elin
g
f
o
r
i
n
tellig
en
t
f
au
lt
d
iag
n
o
s
is
an
d
lo
ca
lizatio
n
.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
in
teg
r
ate
s
s
ig
n
al
p
r
o
ce
s
s
in
g
,
d
ee
p
lear
n
i
n
g
,
m
ac
h
in
e
lea
r
n
i
n
g
,
an
d
f
u
zz
y
i
n
f
er
en
ce
in
to
a
u
n
if
ied
h
y
b
r
id
f
r
am
ewo
r
k
.
Fig
u
r
e
1
d
ep
icts
th
e
o
v
er
all
f
lo
w
d
iag
r
am
o
f
th
e
e
n
tire
p
r
o
p
o
s
ed
p
r
o
ce
s
s
.
T
h
e
d
esig
n
is
ch
o
s
en
to
:
i)
ac
cu
r
ate
ly
m
o
d
el
n
o
n
lin
ea
r
an
d
tr
an
s
ien
t
f
au
lt
b
e
h
av
io
r
,
ii)
e
n
s
u
r
e
r
o
b
u
s
tn
ess
u
n
d
er
n
o
is
y
an
d
u
n
ce
r
tain
o
p
er
ati
n
g
co
n
d
itio
n
s
,
a
n
d
iii)
e
n
ab
le
r
ep
r
o
d
u
ci
b
ilit
y
th
r
o
u
g
h
co
n
tr
o
lled
s
im
u
latio
n
en
v
i
r
o
n
m
en
ts
.
Fig
u
r
e
1
.
D
ep
icts
th
e
o
v
er
all
f
l
o
w
d
iag
r
am
o
f
th
e
en
tire
p
r
o
p
o
s
ed
p
r
o
ce
s
s
2
.
2
.
Da
t
a
s
o
urce
s
a
nd
da
t
a
s
et
des
cr
iptio
n
Fau
lt
d
ata
ar
e
g
en
er
ate
d
u
s
in
g
I
E
E
E
3
3
-
b
u
s
an
d
I
E
E
E
6
9
-
b
u
s
r
ad
ial
d
is
tr
ib
u
tio
n
s
y
s
tem
s
m
o
d
eled
in
MA
T
L
AB
/S
im
u
lin
k
to
r
ep
r
es
en
t
r
ea
lis
tic
o
p
er
atin
g
co
n
d
itio
n
s
.
T
h
e
d
ataset
co
n
s
is
ts
o
f
a
p
p
r
o
x
im
ately
1
2
,
0
0
0
f
au
lt
in
s
tan
ce
s
,
in
clu
d
in
g
L
G,
L
L
,
L
L
G,
an
d
L
L
L
f
au
lts
,
with
f
au
lt
r
esis
tan
ce
s
r
an
g
in
g
f
r
o
m
0
.
1
Ω
to
2
0
0
Ω
,
f
au
lt
lo
ca
tio
n
s
f
r
o
m
5
%
to
9
5
%
o
f
lin
e
len
g
th
,
an
d
l
o
ad
in
g
co
n
d
itio
n
s
o
f
5
0
%,
1
0
0
%,
a
n
d
1
5
0
%.
Gau
s
s
ian
n
o
is
e
lev
els
f
r
o
m
3
0
d
B
to
−5
d
B
SNR
ar
e
al
s
o
co
n
s
id
er
ed
.
T
h
r
ee
-
p
h
ase
v
o
ltag
e
,
cu
r
r
en
t,
an
d
f
r
eq
u
e
n
cy
s
ig
n
als
ar
e
s
am
p
led
at
1
0
k
Hz
d
u
r
i
n
g
f
au
lt
e
v
en
ts
,
with
ea
ch
s
am
p
le
co
v
er
in
g
o
n
e
c
y
cle
(
2
0
m
s
)
.
T
h
e
d
ataset
is
d
iv
id
ed
in
to
7
0
%
tr
ain
in
g
,
1
5
%
v
alid
atio
n
,
an
d
1
5
%
test
in
g
s
ets
u
s
in
g
s
tr
atif
ied
s
am
p
lin
g
to
en
s
u
r
e
b
alan
ce
d
class
d
is
tr
ib
u
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
1
7
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
2281
-
2
2
9
0
2284
2
.
3
.
Da
t
a
a
cquis
it
io
n a
nd
pr
epro
ce
s
s
ing
Vo
ltag
e,
cu
r
r
e
n
t,
an
d
f
r
e
q
u
en
cy
m
ea
s
u
r
em
en
ts
ar
e
co
llecte
d
f
r
o
m
s
im
u
lated
I
E
E
E
test
s
y
s
tem
s
an
d
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
d
ev
ice
s
.
R
aw
s
ig
n
als
o
f
ten
co
n
tai
n
n
o
is
e
an
d
ab
r
u
p
t
d
is
co
n
t
in
u
ities
;
th
er
ef
o
r
e,
s
m
o
o
th
in
g
a
n
d
n
o
r
m
aliza
tio
n
ar
e
ap
p
lied
.
–
No
is
e
f
ilter
in
g
—
A
d
is
cr
ete
wav
elet
tr
an
s
f
o
r
m
–
b
ased
d
en
o
is
er
is
u
s
ed
:
(
)
=
−
1
(
ℎ
ℎ
(
(
(
)
)
)
)
(
1
)
–
No
r
m
aliza
tio
n
—
T
o
e
n
s
u
r
e
s
tab
le
tr
ain
in
g
as in
(
2
)
.
=
(
−
)
/
(
2
)
W
h
er
e
μ
an
d
σ
ar
e
th
e
m
ea
n
a
n
d
s
tan
d
ar
d
d
ev
iatio
n
.
–
Featu
r
e
ex
tr
ac
tio
n
(
wav
elet
+
s
tati
s
tical
d
o
m
ain
)
—
W
av
el
et
co
ef
f
icien
ts
ar
e
ex
tr
ac
ted
to
ca
p
tu
r
e
b
o
th
tr
an
s
ien
t a
n
d
s
tead
y
-
s
tate
ch
ar
ac
ter
is
tics
:
(
,
)
=
∫
−
(
)
(
(
−
)
/
)
(
3
)
E
x
tr
ac
ted
f
ea
tu
r
es:
en
er
g
y
o
f
s
u
b
-
b
a
n
d
s
,
d
etail
co
ef
f
icien
ts
(
D1
–
D5
)
,
a
n
d
a
p
p
r
o
x
im
atio
n
c
o
ef
f
icien
ts
.
–
Statis
t
ical
f
ea
tu
r
es
(
ju
s
tific
atio
n
)
—
T
h
e
f
o
llo
win
g
f
ea
t
u
r
es
ar
e
s
elec
ted
d
u
e
t
o
th
ei
r
p
h
y
s
i
ca
l
r
elev
an
ce
to
f
au
lt
b
eh
av
i
o
u
r
:
i)
R
MS
:
R
ef
lects s
ig
n
al
m
ag
n
itu
d
e
v
ar
iatio
n
d
u
r
i
n
g
f
a
u
lts
ii)
C
r
est f
ac
to
r
: Cap
tu
r
es tr
an
s
ien
t sp
ik
es (
im
p
o
r
tan
t f
o
r
HI
F d
et
ec
tio
n
)
iii)
Var
ian
ce
: I
n
d
ic
ates sig
n
al
f
lu
c
tu
atio
n
an
d
d
is
tu
r
b
an
ce
in
ten
s
ity
.
T
h
ese
f
ea
tu
r
es e
n
h
a
n
ce
d
is
cr
i
m
in
atio
n
b
etwe
en
n
o
r
m
al
an
d
f
au
lt c
o
n
d
itio
n
s
.
–
Dee
p
n
eu
r
al
n
etwo
r
k
f
o
r
f
ea
tu
r
e
em
b
ed
d
in
g
A
DNN
is
em
p
lo
y
ed
to
lear
n
n
o
n
lin
ea
r
f
au
lt
ch
a
r
ac
ter
is
tics
f
r
o
m
th
e
ex
tr
ac
ted
f
ea
tu
r
es.
T
h
e
n
etwo
r
k
co
n
s
is
ts
o
f
an
in
p
u
t
la
y
er
(
6
4
n
eu
r
o
n
s
)
,
th
r
ee
h
id
d
e
n
lay
er
s
(
1
2
8
,
6
4
,
an
d
3
2
n
eu
r
o
n
s
)
with
R
eL
U
ac
tiv
atio
n
,
an
d
an
o
u
tp
u
t e
m
b
ed
d
in
g
la
y
e
r
(
1
6
n
eu
r
o
n
s
)
.
A
d
r
o
p
o
u
t r
ate
o
f
0
.
3
is
ap
p
lied
to
r
ed
u
ce
o
v
e
r
f
itti
n
g
.
T
h
e
DNN
g
en
er
ates d
ee
p
r
ep
r
esen
tatio
n
s
f
o
r
class
if
icatio
n
.
L
et
th
e
in
p
u
t
f
ea
tu
r
e
v
ec
to
r
b
e
f
:
ℎ
=
(
+
)
(
4
)
Mu
ltip
le
h
id
d
en
la
y
er
s
p
r
o
d
u
c
e
th
e
f
in
al
em
b
e
d
d
in
g
:
=
(
)
(
5
)
wh
er
e
z
is
a
lo
w
-
d
im
en
s
io
n
al
d
is
cr
im
in
ativ
e
f
ea
tu
r
e
v
ec
to
r
.
–
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
f
o
r
p
r
im
ar
y
class
if
icatio
n
—
T
h
e
S
VM
p
er
f
o
r
m
s
in
itial
f
au
lt
class
if
icatio
n
u
s
in
g
th
e
DNN
em
b
ed
d
in
g
z.
T
h
e
d
e
cisi
o
n
f
u
n
ctio
n
is
as (
6
)
.
(
)
=
(
+
)
(
6
)
T
h
is
y
ield
s
cr
is
p
class
b
o
u
n
d
ar
ies
an
d
im
p
r
o
v
es
class
if
ic
atio
n
ac
cu
r
ac
y
,
esp
ec
ially
f
o
r
o
v
er
lap
p
in
g
f
a
u
lt
p
atter
n
s
.
Hy
p
er
p
ar
a
m
eter
s
:
R
eg
u
lar
izatio
n
p
a
r
am
eter
C
=
10
C
=
1
0
C
=
10
;
Ker
n
el
p
a
r
am
eter
γ
=
0
.
1
\
g
am
m
a
=
0
.
1
γ
=
0
.
1
.
Hy
p
er
p
ar
a
m
eter
s
ar
e
o
p
tim
ized
u
s
in
g
g
r
id
s
ea
r
ch
with
c
r
o
s
s
-
v
alid
atio
n
.
–
Fu
zz
y
lo
g
ic
f
u
s
io
n
f
o
r
f
in
al
d
e
cisi
o
n
T
h
e
SVM
o
u
tp
u
t
an
d
DNN
co
n
f
id
e
n
ce
s
co
r
e
ar
e
f
ed
i
n
to
a
f
u
zz
y
in
f
er
e
n
ce
s
y
s
tem
.
Fu
zz
y
m
em
b
er
s
h
ip
f
u
n
ctio
n
s
co
n
v
er
t
cr
is
p
in
p
u
ts
in
to
lin
g
u
is
tic
v
a
lu
es
s
u
ch
as
L
o
w
,
Me
d
iu
m
,
a
n
d
Hig
h
.
T
h
e
f
i
n
al
o
u
tp
u
t is o
b
tain
e
d
v
ia
(
7
)
.
y
final
=
(
∑
I
μ
i
⋅
y
i
)
/
∑
i
μ
i
(
7
)
W
h
er
e
μ
i
is
th
e
m
em
b
er
s
h
ip
v
alu
e
an
d
y
i
is
th
e
r
u
le
o
u
t
p
u
t.
T
h
is
s
tep
r
eso
lv
es
am
b
ig
u
ity
an
d
en
h
an
ce
s
r
o
b
u
s
tn
ess
u
n
d
e
r
n
o
is
y
a
n
d
u
n
ce
r
tain
o
p
er
atin
g
co
n
d
itio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
Hyb
r
id
A
I
-
d
r
iven
in
tellig
en
t fa
u
lt d
ia
g
n
o
s
is
a
n
d
lo
ca
liz
a
tio
n
in
mo
d
ern
…
(
Dee
p
a
S
o
ma
s
u
n
d
a
r
a
m
)
2285
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
DNN
–
SVM
–
f
u
zz
y
f
r
am
ewo
r
k
was
ev
alu
ated
u
s
i
n
g
th
e
test
d
ataset
d
escr
ib
e
d
in
Sectio
n
2
.
T
h
e
r
esu
lts
ar
e
p
r
esen
ted
in
ter
m
s
o
f
class
if
icatio
n
ac
cu
r
ac
y
,
r
o
b
u
s
tn
ess
u
n
d
er
n
o
is
e,
f
au
lt
lo
ca
lizatio
n
e
r
r
o
r
,
h
ig
h
-
im
p
ed
an
ce
f
au
lt
(
HI
F)
d
etec
tio
n
ca
p
ab
ilit
y
,
co
m
p
u
tatio
n
al
p
er
f
o
r
m
an
c
e,
an
d
ab
latio
n
an
aly
s
is
.
3
.
1
.
O
v
er
a
ll
cla
s
s
if
ica
t
io
n a
c
cura
cy
co
m
pa
riso
n
T
h
e
h
y
b
r
id
AI
m
o
d
el
in
teg
r
atin
g
DNN,
SVM,
an
d
f
u
zz
y
lo
g
ic
d
em
o
n
s
tr
ated
s
u
p
er
i
o
r
o
v
er
all
ac
cu
r
ac
y
co
m
p
a
r
ed
to
s
tan
d
alo
n
e
m
ac
h
in
e
lear
n
in
g
a
n
d
d
ee
p
lear
n
i
n
g
m
o
d
els.
Fig
u
r
e
2
illu
s
tr
ates
th
e
ac
cu
r
ac
y
co
m
p
a
r
is
o
n
ac
r
o
s
s
all
ev
alu
ated
m
o
d
els,
h
ig
h
lig
h
tin
g
th
e
s
u
p
e
r
io
r
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
AI
ap
p
r
o
ac
h
.
T
r
ad
iti
o
n
al
class
if
ier
s
s
u
ch
as
SV
M,
r
an
d
o
m
f
o
r
est,
an
d
k
-
NN
s
h
o
wed
r
ed
u
ce
d
p
er
f
o
r
m
an
ce
d
u
e
to
th
eir
lim
i
ted
ab
ilit
y
to
ex
tr
ac
t
d
ee
p
te
m
p
o
r
al
–
s
p
atial
r
elatio
n
s
h
ip
s
wi
th
in
p
o
wer
s
y
s
tem
tr
an
s
ien
t
s
ig
n
als.
T
h
e
p
u
r
e
DNN
m
o
d
el
p
e
r
f
o
r
m
ed
b
etter
th
an
class
ical
ML
m
o
d
els
b
u
t
s
till
lag
g
ed
b
eh
in
d
th
e
h
y
b
r
id
s
y
s
tem
b
ec
au
s
e
it
lack
s
an
ex
p
licit
u
n
ce
r
tain
ty
-
h
a
n
d
lin
g
m
ec
h
a
n
is
m
.
T
h
e
ad
d
it
io
n
o
f
f
u
zz
y
lo
g
ic
h
elp
ed
im
p
r
o
v
e
d
ec
is
io
n
in
t
er
p
r
etab
ilit
y
an
d
co
n
f
id
en
ce
,
esp
ec
ially
f
o
r
b
o
r
d
er
lin
e
o
r
am
b
ig
u
o
u
s
f
a
u
lt
ca
s
es.
Ov
er
all,
th
e
h
y
b
r
id
m
o
d
el
co
n
s
is
ten
tly
ac
h
iev
ed
ac
cu
r
ac
y
a
b
o
v
e
9
8
%,
o
u
tp
er
f
o
r
m
i
n
g
th
e
n
ex
t
b
est
m
o
d
el
b
y
m
o
r
e
th
an
5
%.
3
.
2
.
No
is
e
ro
bu
s
t
nes
s
a
na
ly
s
is
(
a
cc
ura
cy
v
s
SNR)
Fig
u
r
e
3
illu
s
tr
ates
th
e
n
o
is
e
r
o
b
u
s
tn
ess
an
aly
s
is
,
s
h
o
win
g
th
e
v
ar
iatio
n
o
f
class
if
icatio
n
ac
cu
r
ac
y
with
r
esp
ec
t
to
d
if
f
e
r
en
t
SNR
lev
els.
I
n
n
o
is
y
m
ea
s
u
r
em
e
n
t
en
v
ir
o
n
m
en
ts
,
s
u
ch
as
d
is
tr
ib
u
tio
n
f
ee
d
er
s
with
h
ar
m
o
n
ics
a
n
d
s
witch
in
g
tr
a
n
s
ien
ts
,
th
e
h
y
b
r
id
m
o
d
el
c
o
n
tin
u
ed
t
o
s
h
o
w
s
ig
n
if
ica
n
tly
h
ig
h
er
r
o
b
u
s
tn
ess
co
m
p
ar
ed
t
o
th
e
s
tan
d
alo
n
e
DNN.
As
SNR
v
alu
es
d
ec
r
ea
s
ed
f
r
o
m
3
0
d
B
to
–
5
d
B
,
all
m
o
d
els
ex
p
er
ien
ce
d
s
o
m
e
p
er
f
o
r
m
a
n
ce
d
eg
r
a
d
atio
n
;
h
o
we
v
er
,
th
e
h
y
b
r
id
class
i
f
ier
m
ain
tain
e
d
s
m
o
o
th
er
ac
c
u
r
ac
y
d
ec
ay
d
u
e
to
co
m
b
in
ed
wav
elet
-
b
ased
f
ea
tu
r
e
r
ep
r
esen
tatio
n
an
d
th
e
f
u
zz
y
co
n
f
id
e
n
ce
r
ef
in
em
e
n
t
lay
er
.
T
h
is
d
em
o
n
s
tr
ates
its
ab
ilit
y
to
p
r
eser
v
e
d
is
cr
im
in
ativ
e
f
au
lt
s
ig
n
atu
r
es
ev
en
u
n
d
er
s
ev
er
e
n
o
is
e
o
r
s
en
s
o
r
d
i
s
tu
r
b
an
ce
s
.
Fig
u
r
e
3
illu
s
tr
ates
th
e
n
o
is
e
r
o
b
u
s
tn
ess
an
aly
s
i
s
,
s
h
o
win
g
th
e
v
ar
iatio
n
o
f
class
if
icatio
n
ac
cu
r
ac
y
with
r
esp
ec
t
to
d
if
f
er
en
t SNR
lev
els.
Fig
u
r
e
2
.
T
h
e
ac
cu
r
ac
y
co
m
p
a
r
is
o
n
ac
r
o
s
s
all
ev
alu
ated
m
o
d
els,
h
ig
h
lig
h
tin
g
th
e
s
u
p
er
i
o
r
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
AI
a
p
p
r
o
ac
h
Fig
u
r
e
3
.
T
h
e
n
o
is
e
r
o
b
u
s
tn
ess
an
aly
s
is
,
s
h
o
win
g
th
e
v
ar
iatio
n
o
f
class
if
icatio
n
ac
cu
r
ac
y
with
r
esp
ec
t
to
d
if
f
er
e
n
t SNR
lev
els
3
.
3
.
F
a
ult
lo
ca
liza
t
i
o
n per
f
o
rm
a
nce
Fig
u
r
e
4
illu
s
tr
ates
th
e
f
au
lt
l
o
ca
lizatio
n
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
i
d
m
o
d
el
co
m
p
ar
ed
with
co
n
v
en
tio
n
al
m
eth
o
d
s
.
Acc
u
r
ate
f
au
lt
d
is
tan
ce
esti
m
atio
n
is
ess
en
tial
f
o
r
q
u
ick
r
esto
r
atio
n
an
d
r
e
d
u
cin
g
d
o
wn
tim
e.
T
h
e
h
y
b
r
id
m
o
d
el
ac
h
iev
ed
lo
wer
m
ea
n
ab
s
o
l
u
te
er
r
o
r
(
MA
E
)
co
m
p
ar
ed
to
tr
ad
itio
n
al
SVM
r
eg
r
ess
io
n
,
esp
ec
ially
f
o
r
lo
n
g
er
tr
an
s
m
is
s
io
n
lin
es
wh
er
e
im
p
ed
an
ce
an
d
lo
ad
v
ar
iatio
n
s
in
tr
o
d
u
ce
g
r
ea
ter
u
n
ce
r
tain
ty
.
AI
-
ass
is
ted
wav
elet
f
ea
tu
r
es
h
elp
ed
to
r
etain
th
e
tr
an
s
ien
t
f
au
lt
c
h
ar
ac
ter
is
tics
n
ec
ess
ar
y
f
o
r
p
r
ec
is
e
d
is
tan
ce
id
en
tific
atio
n
.
E
v
en
at
5
0
k
m
lin
e
len
g
t
h
,
th
e
h
y
b
r
id
m
o
d
el
m
ain
tain
ed
an
MA
E
with
in
0
.
7
k
m
,
wh
er
ea
s
SVM
ex
h
ib
i
ted
er
r
o
r
s
u
p
to
2
k
m
.
T
h
is
a
cc
u
r
ac
y
im
p
r
o
v
em
e
n
t
d
ir
ec
tly
tr
an
s
lates
to
f
aster
r
ep
air
o
p
er
atio
n
s
an
d
r
ed
u
ce
d
eq
u
ip
m
en
t
d
am
ag
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
1
7
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
2281
-
2
2
9
0
2286
3.
4
.
H
I
F
det
ec
t
i
o
n per
f
o
rma
nce
Hig
h
-
im
p
ed
a
n
ce
f
au
lts
ar
e
ty
p
ically
h
ar
d
to
d
etec
t
d
u
e
to
lo
w
cu
r
r
en
t
m
ag
n
itu
d
es
an
d
n
o
n
lin
ea
r
ar
cin
g
b
eh
a
v
io
r
,
wh
ic
h
o
f
ten
lead
s
to
m
is
s
ed
d
etec
tio
n
s
.
Fig
u
r
e
5
s
h
o
ws
th
e
h
ig
h
-
im
p
ed
an
ce
f
au
lt
(
HI
F)
d
etec
tio
n
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
m
o
d
el
co
m
p
ar
ed
with
b
aselin
e
class
if
ier
s
.
T
h
e
h
y
b
r
id
s
y
s
tem
s
ig
n
if
ican
tly
en
h
an
ce
d
th
e
d
etec
tio
n
m
etr
ics,
ac
h
iev
in
g
a
p
r
ec
is
io
n
o
f
0
.
9
2
a
n
d
r
e
ca
ll
o
f
0
.
9
0
.
T
h
is
im
p
r
o
v
em
e
n
t
s
tem
s
f
r
o
m
t
h
e
DNN’
s
ab
ilit
y
to
ex
tr
ac
t
s
u
b
tle
ar
c
-
in
d
u
ce
d
h
ar
m
o
n
ic
f
ea
tu
r
es
an
d
th
e
f
u
zz
y
lay
er
’
s
s
tr
en
g
th
in
h
an
d
lin
g
n
o
n
-
i
d
ea
l,
u
n
ce
r
tain
d
ec
i
s
io
n
b
o
u
n
d
a
r
ies.
T
h
e
r
esu
ltin
g
h
ig
h
F1
-
s
co
r
e
d
em
o
n
s
tr
ates th
at
th
e
h
y
b
r
id
s
y
s
tem
r
ed
u
ce
s
f
alse p
o
s
itiv
es wh
ile
m
ain
tain
in
g
s
tr
o
n
g
d
ete
ctio
n
r
eliab
ilit
y
.
Fig
u
r
e
4
.
T
h
e
f
au
lt lo
ca
lizatio
n
p
er
f
o
r
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r
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re
a
o
f
in
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lu
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o
c
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in
g
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m
o
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d
a
tab
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rc
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s
in
st
it
u
t
io
n
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
p
r
iy
a
r6
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rm
i
st.ed
u
.
i
n
.
Dr
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a
n
d
i
p
D.
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rm
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y
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t
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y
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wa
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trao
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ll
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g
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rin
g
,
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d
a
p
sa
r,
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u
n
e
,
M
a
h
a
ra
sh
tra.
He
g
ra
d
u
a
ted
i
n
E&TC
a
t
S
VPM
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ll
e
g
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o
f
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n
g
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r
in
g
,
P
u
n
e
.
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se
c
u
re
d
a
n
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.
i
n
c
o
m
p
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ter
sc
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g
/
i
n
fo
rm
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ti
o
n
tec
h
n
o
l
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g
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t
Vish
wa
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a
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stit
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te
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c
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y
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u
n
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se
c
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re
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a
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h
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in
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m
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ter
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g
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n
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rin
g
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t
M
UIT,
Lu
c
k
n
o
w,
In
d
ia.
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is
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n
t
h
e
fi
el
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f
n
e
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k
se
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m
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rk
s
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ima
g
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ro
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g
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d
a
ta
sc
ien
c
e
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n
d
b
i
g
d
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ta
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n
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ly
ti
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s
.
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h
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s
b
e
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n
in
t
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g
p
ro
fe
ss
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o
r
m
o
re
t
h
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n
2
2
y
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a
rs.
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h
a
s
p
re
se
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m
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th
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1
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n
n
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ti
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ter
n
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ti
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jo
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rn
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ls,
c
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re
n
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s,
a
n
d
sy
m
p
o
si
u
m
s
.
His
m
a
in
a
re
a
s
o
f
in
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st
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n
c
lu
d
e
n
e
two
rk
se
c
u
rit
y
,
wire
les
s
se
c
u
rit
y
,
a
n
d
AR
&
VR.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sa
n
d
isa
tav
5
9
3
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m
a
il
.
c
o
m
.
P.
Ar
th
i
De
v
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r
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n
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re
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iv
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d
h
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r
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E.
d
e
g
re
e
i
n
El
e
c
tr
o
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
En
g
i
n
e
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rin
g
a
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d
h
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r
M
.
E.
d
e
g
re
e
in
Co
m
m
u
n
ica
ti
o
n
S
y
ste
m
s
fro
m
An
n
a
Un
i
v
e
rsity
,
I
n
d
ia
.
S
h
e
a
lso
h
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ld
s
a
n
M
.
Tec
h
.
d
e
g
re
e
in
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c
ial
In
telli
g
e
n
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e
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h
e
h
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se
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rtme
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telli
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Tec
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g
y
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n
d
ia.
S
h
e
is
p
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rsu
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n
g
h
e
r
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h
.
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i
n
Art
ifi
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tell
ig
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n
c
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a
t
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rsit
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h
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m
b
le
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p
lea
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f
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l
ima
g
e
c
las
sifica
ti
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a
n
d
d
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tec
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n
.
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r
a
c
a
d
e
m
ic
in
tere
sts
in
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l
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d
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Io
T
,
m
a
c
h
in
e
-
to
-
m
a
c
h
in
e
(
M
2
M
)
c
o
m
m
u
n
ica
ti
o
n
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rti
ficia
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in
telli
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m
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c
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lea
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P
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th
o
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fo
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lu
b
s.
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r
re
se
a
r
c
h
in
tere
sts
in
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lu
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e
d
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p
lea
rn
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g
fo
r
m
e
d
ica
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in
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a
n
d
AI
-
d
ri
v
e
n
h
e
a
lt
h
c
a
re
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p
p
li
c
a
ti
o
n
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
rt
h
id
e
v
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ra
n
ia
d
s@
rm
k
c
e
t.
a
c
.
in
.
J
a
y
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shre
e
K
a
th
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p
let
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h
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d
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re
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)
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n
a
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l
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f
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g
y
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a
lem
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p
lete
d
h
e
r
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a
ste
r'
s
d
e
g
re
e
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0
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)
in
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d
ian
I
n
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
,
M
a
d
ra
s.
S
h
e
is
c
u
rr
e
n
tl
y
w
o
rk
i
n
g
a
s
a
n
a
ss
istan
t
p
ro
f
e
ss
o
r
(S
e
n
io
r
G
ra
d
e
),
in
th
e
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
i
n
e
e
rin
g
D
e
p
a
rtme
n
t
a
t
Ra
jala
k
s
h
m
i
En
g
in
e
e
rin
g
Co
ll
e
g
e
,
Ch
e
n
n
a
i.
He
r
c
u
rre
n
t
re
se
a
rc
h
a
re
a
in
c
lu
d
e
s
h
y
b
rid
re
n
e
wa
b
le en
e
rg
y
sy
ste
m
s,
i
n
v
e
stig
a
t
io
n
o
n
c
o
n
v
e
rter
to
p
o
lo
g
ies
fo
r
e
lec
tri
c
v
e
h
icle
s
,
F
ACTS
d
e
v
ice
s
,
a
n
d
p
o
we
r
sy
ste
m
sta
b
il
it
y
a
n
d
c
o
n
tro
l
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jay
a
sre
e
.
k
@ra
jala
k
sh
m
i.
e
d
u
.
in
.
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