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g
b
etter
u
n
d
e
r
s
tan
d
in
g
o
f
th
e
f
ac
to
r
s
in
f
lu
en
cin
g
b
atter
y
d
eg
r
ad
atio
n
p
r
ed
ictio
n
s
[
1
3
]
-
[
1
5
]
.
Nev
er
t
h
eless
,
co
m
p
lex
d
ee
p
-
lear
n
in
g
m
o
d
els
ca
n
r
eq
u
ir
e
lar
g
e
d
atasets
,
ex
ten
s
iv
e
h
y
p
er
p
ar
am
eter
o
p
tim
izatio
n
,
an
d
co
n
s
id
er
ab
le
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
.
T
h
ese
lim
itatio
n
s
m
o
tiv
ate
th
e
d
ev
elo
p
m
e
n
t
o
f
ac
cu
r
ate
an
d
co
m
p
u
tatio
n
ally
ef
f
icien
t le
ar
n
in
g
f
r
am
ewo
r
k
s
s
u
itab
le
f
o
r
p
r
ac
tical
B
MS
im
p
lem
en
tatio
n
[
1
6
]
-
[
2
0
]
.
I
n
th
is
s
tu
d
y
,
a
r
an
d
o
m
f
o
r
e
s
t
-
b
ased
en
s
em
b
le
lea
r
n
in
g
f
r
am
ewo
r
k
is
d
e
v
elo
p
ed
f
o
r
lith
iu
m
-
io
n
b
atter
y
R
UL
p
r
ed
ictio
n
u
s
in
g
th
e
p
u
b
licly
av
ailab
le
Haw
aii
Natu
r
al
E
n
er
g
y
I
n
s
titu
te
(
HNE
I
)
d
ataset
co
n
tain
in
g
1
5
,
0
6
4
ch
ar
g
e
–
d
i
s
ch
ar
g
e
cy
cles.
Sev
en
d
eg
r
ad
atio
n
-
r
elate
d
f
ea
tu
r
es,
n
a
m
ely
cy
cle
in
d
ex
,
d
is
ch
ar
g
e
tim
e,
v
o
ltag
e
d
ec
r
e
m
en
t
b
etwe
en
3
.
6
an
d
3
.
4
V,
m
ax
im
u
m
d
is
ch
ar
g
e
v
o
ltag
e,
m
in
im
u
m
ch
ar
g
in
g
v
o
ltag
e,
tim
e
at
4
.
1
5
V,
an
d
co
n
s
tan
t
-
cu
r
r
e
n
t
ch
ar
g
in
g
tim
e,
ar
e
s
elec
ted
to
ch
ar
ac
ter
ize
b
atter
y
ag
in
g
.
T
h
e
p
r
o
p
o
s
ed
R
F
m
o
d
el
is
co
m
p
ar
ed
with
lin
ea
r
r
eg
r
ess
io
n
(
L
R
)
,
L
STM
,
an
d
a
tten
tio
n
-
L
STM
u
s
in
g
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
,
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
,
m
e
an
ab
s
o
lu
te
p
er
ce
n
tag
e
er
r
o
r
(
MA
PE)
,
an
d
R
²
as
ev
alu
atio
n
m
etr
ics.
Par
ity
p
lo
ts
an
d
r
esid
u
al
an
aly
s
is
ar
e
ad
d
itio
n
ally
em
p
lo
y
e
d
to
ass
es
s
p
r
ed
ictio
n
co
n
s
is
ten
cy
[
2
1
]
-
[
2
3
]
.
T
h
e
s
tu
d
y
d
em
o
n
s
tr
ates
th
at
an
en
s
em
b
le
-
lear
n
in
g
ap
p
r
o
ac
h
ca
n
p
r
o
v
id
e
a
p
r
ac
tical
b
alan
ce
am
o
n
g
p
r
ed
ictio
n
ac
c
u
r
ac
y
,
c
o
m
p
u
tatio
n
al
ef
f
icien
cy
,
an
d
in
ter
p
r
etab
ilit
y
,
s
u
p
p
o
r
tin
g
b
atter
y
h
ea
lth
m
o
n
ito
r
in
g
,
p
r
e
d
ictiv
e
m
ain
ten
an
ce
,
ch
ar
g
in
g
m
a
n
ag
em
en
t,
an
d
tim
ely
b
atter
y
r
e
p
lace
m
en
t
in
E
V
ap
p
licatio
n
s
[
2
4
]
,
[
2
5
]
.
2.
P
RO
P
O
SE
D
DYNA
M
I
C
AP
P
RO
ACH
W
ith
t
h
e
g
r
o
wi
n
g
u
p
ta
k
e
o
f
e
lect
r
i
c
v
eh
icl
es
(
E
Vs
)
,
i
t
h
as
b
e
co
m
e
cr
u
c
ial
to
ac
c
u
r
at
el
y
p
r
e
d
ic
t
th
e
R
UL
i
n
o
r
d
e
r
to
e
n
h
a
n
c
e
b
a
tte
r
y
r
el
ia
b
ili
t
y
a
n
d
s
a
f
et
y
,
as
w
e
ll
as
t
o
o
p
ti
m
i
ze
m
ai
n
te
n
an
ce
p
la
n
n
i
n
g
.
B
at
te
r
ies
wit
h
lit
h
i
u
m
i
o
n
s
e
x
h
i
b
it
n
o
n
lin
ea
r
d
e
g
r
a
d
at
io
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d
u
e
to
v
ar
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i
n
g
o
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n
g
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o
n
d
iti
o
n
s
,
m
ak
i
n
g
co
n
v
e
n
ti
o
n
a
l
p
r
e
d
ic
ti
o
n
m
et
h
o
d
s
l
ess
ef
f
e
ct
iv
e
.
T
h
e
r
e
f
o
r
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t
h
is
w
o
r
k
p
r
o
p
o
s
es
a
n
en
s
em
b
l
e
lea
r
n
i
n
g
f
r
am
e
wo
r
k
f
o
r
R
U
L
p
r
e
d
ic
ti
o
n
u
s
in
g
th
e
H
awa
ii
Nat
u
r
al
E
n
e
r
g
y
I
n
s
ti
tu
te
(
HN
E
I
)
b
a
tte
r
y
d
at
aset
.
F
o
u
r
p
r
ed
icti
o
n
m
o
d
els
l
in
ea
r
r
e
g
r
ess
i
o
n
,
r
a
n
d
o
m
f
o
r
est
,
L
S
T
M,
an
d
L
S
T
M
wi
th
a
tt
en
ti
o
n
a
r
e
d
e
v
e
lo
p
ed
a
n
d
c
o
m
p
ar
ed
to
i
d
e
n
ti
f
y
t
h
e
m
o
s
t
ac
c
u
r
at
e
ap
p
r
o
ac
h
.
R
a
n
d
o
m
f
o
r
est
a
ch
ie
v
e
d
th
e
b
est
p
e
r
f
o
r
m
an
ce
wit
h
MA
E
=
5
.
2
0
,
R
MSE
=
6
.
8
3
,
an
d
MA
P
E
=
1
.
3
3
%
,
o
u
t
p
e
r
f
o
r
m
in
g
t
h
e
d
e
ep
lea
r
n
i
n
g
m
o
d
els
b
y
ef
f
e
cti
v
ely
ca
p
t
u
r
i
n
g
n
o
n
li
n
ea
r
d
e
g
r
a
d
ati
o
n
p
att
er
n
s
[
1
0
]
-
[
1
2
]
.
T
h
e
p
r
o
p
o
s
ed
f
r
a
m
e
wo
r
k
s
u
p
p
o
r
ts
i
n
te
lli
g
e
n
t
b
att
er
y
m
a
n
a
g
e
m
e
n
t
s
y
s
te
m
s
(
B
M
Ss
)
,
e
x
te
n
d
s
b
a
tte
r
y
lif
esp
a
n
,
a
n
d
en
h
an
ce
s
E
V
s
u
s
t
ain
a
b
ili
ty
.
Fi
g
u
r
e
1
s
h
o
ws
t
h
e
f
r
am
ew
o
r
k
f
o
r
E
V
b
att
e
r
y
R
U
L
p
r
e
d
ic
ti
o
n
.
Fig
u
r
e
1
.
Pro
p
o
s
ed
f
r
am
ewo
r
k
f
o
r
E
V
b
atter
y
R
UL
p
r
ed
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n
2
.
1
.
P
r
o
po
s
ed
f
ra
m
ewo
r
k
a
nd
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t
t
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deg
ra
da
t
io
n a
na
l
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s
is
T
h
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p
r
o
p
o
s
ed
f
r
am
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k
esti
m
ates
th
e
R
UL
o
f
lith
iu
m
-
i
o
n
b
atter
ies
u
s
in
g
an
en
s
em
b
l
e
lear
n
in
g
ap
p
r
o
ac
h
.
T
h
e
m
eth
o
d
o
lo
g
y
in
v
o
lv
es
HNE
I
d
ataset
ac
q
u
is
itio
n
,
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
f
ea
tu
r
e
ex
tr
ac
tio
n
,
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I
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8
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a
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lith
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n
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n
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itio
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Sev
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ex
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ch
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g
e
tim
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v
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l
tag
e
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ec
r
em
e
n
t
b
etwe
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3
.
6
–
3
.
4
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ax
im
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m
d
is
ch
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g
e
v
o
ltag
e,
m
in
im
u
m
ch
ar
g
in
g
v
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e,
tim
e
at
4
.
1
5
V,
a
n
d
co
n
s
tan
t
-
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r
r
e
n
t
ch
ar
g
in
g
tim
e
ar
e
s
elec
ted
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ep
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t b
atter
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g
in
g
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d
u
p
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e
co
r
d
s
ar
e
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em
o
v
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tlier
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am
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n
d
th
e
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atter
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Fig
u
r
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ates
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h
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u
r
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ec
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n
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ee
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I
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2
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8
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I
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,
Vo
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1
7
,
No
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3
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Sep
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tr
ain
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al
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e
c
o
m
p
l
ex
an
d
n
o
n
li
n
e
ar
p
att
er
n
s
ass
o
cia
t
ed
wit
h
b
a
tte
r
y
d
e
g
r
a
d
at
io
n
.
B
y
o
p
ti
m
iz
in
g
th
e
m
o
d
e
l
p
a
r
a
m
ete
r
s
,
a
b
a
la
n
c
e
w
as
ac
h
ie
v
e
d
b
etw
ee
n
h
i
g
h
p
r
e
d
i
cti
o
n
a
cc
u
r
ac
y
a
n
d
co
m
p
u
tat
i
o
n
al
e
f
f
ici
e
n
c
y
.
T
h
e
m
o
d
e
l
was
ab
le
to
i
d
e
n
t
if
y
im
p
o
r
t
an
t
r
e
lat
io
n
s
h
i
p
s
am
o
n
g
v
ar
io
u
s
b
att
e
r
y
p
a
r
a
m
et
er
s
t
h
a
t i
n
f
l
u
e
n
c
e
t
h
e
d
e
g
r
a
d
a
ti
o
n
p
r
o
ce
s
s
a
n
d
r
e
m
ai
n
i
n
g
u
s
e
f
u
l
li
f
e
(
R
U
L
)
.
As
a
r
esu
lt
,
t
h
e
r
an
d
o
m
f
o
r
est
m
o
d
el
p
r
o
v
id
e
d
m
o
r
e
ac
c
u
r
ate
a
n
d
r
e
lia
b
l
e
R
U
L
p
r
e
d
ic
ti
o
n
s
co
m
p
a
r
e
d
wit
h
t
h
e
t
r
a
d
it
io
n
al
lin
ea
r
r
eg
r
ess
io
n
a
p
p
r
o
ac
h
.
I
t
also
d
e
m
o
n
s
tr
ate
d
b
et
te
r
o
v
e
r
a
ll
p
e
r
f
o
r
m
a
n
c
e
th
a
n
th
e
L
S
T
M
a
n
d
L
S
T
M
w
it
h
a
tt
en
t
io
n
m
o
d
els in
p
r
ed
ict
in
g
b
a
tte
r
y
h
ea
l
th
a
n
d
d
e
g
r
a
d
at
io
n
tr
en
d
s
.
T
h
e
r
ef
o
r
e
,
t
h
e
p
r
o
p
o
s
e
d
R
a
n
d
o
m
F
o
r
es
t m
o
d
el
is
c
o
n
s
id
er
e
d
a
p
r
o
m
is
i
n
g
a
n
d
co
m
p
u
tat
io
n
all
y
e
f
f
ici
e
n
t
a
p
p
r
o
a
c
h
f
o
r
p
r
ac
tic
al
im
p
le
m
e
n
t
ati
o
n
in
b
at
te
r
y
m
a
n
ag
em
en
t s
y
s
t
em
(
B
MS)
a
p
p
li
c
ati
o
n
s
.
T
ab
le
1
.
R
an
d
o
m
f
o
r
est h
y
p
e
r
p
ar
am
eter
s
P
a
r
a
me
t
e
r
V
a
l
u
e
P
u
r
p
o
se
N
u
mb
e
r
o
f
t
r
e
e
s
2
0
0
I
mp
r
o
v
e
s
p
r
e
d
i
c
t
i
o
n
st
a
b
i
l
i
t
y
M
a
x
i
m
u
m
d
e
p
t
h
20
C
o
n
t
r
o
l
s
mo
d
e
l
c
o
mp
l
e
x
i
t
y
B
o
o
t
s
t
r
a
p
Tr
u
e
En
a
b
l
e
s
b
a
g
g
i
n
g
f
o
r
b
e
t
t
e
r
g
e
n
e
r
a
l
i
z
a
t
i
o
n
M
a
x
i
m
u
m
f
e
a
t
u
r
e
s
√
F
e
a
t
u
r
e
s
R
e
d
u
c
e
s
t
r
e
e
c
o
r
r
e
l
a
t
i
o
n
M
i
n
i
m
u
m
sam
p
l
e
s s
p
l
i
t
2
.
1
A
l
l
o
w
s
e
f
f
e
c
t
i
v
e
n
o
d
e
sp
l
i
t
t
i
n
g
R
a
n
d
o
m s
t
a
t
e
43
En
s
u
r
e
s re
p
r
o
d
u
c
i
b
l
e
r
e
s
u
l
t
s
2
.
3
.
M
o
del
v
a
lid
a
t
io
n
a
nd
perf
o
rma
nce
ev
a
lua
t
io
n
T
h
e
d
ev
elo
p
ed
p
r
ed
ictio
n
m
o
d
els
wer
e
ass
es
s
ed
with
th
e
MA
E
,
R
MSE
,
MA
PE,
an
d
th
e
co
ef
f
icien
t
o
f
d
eter
m
in
atio
n
(
R
2
)
.
L
o
we
r
MA
E
,
R
MSE
,
a
n
d
MA
PE
v
alu
es
to
g
eth
er
with
h
ig
h
er
R
²
in
d
icate
b
etter
p
r
ed
ictio
n
p
er
f
o
r
m
an
ce
.
MAE
=
1
∑
|
j
−
̂
|
j
=
1
(
3
)
R
M
SE
=
√
1
∑
(
−
̂
)
2
j
=
1
(
4
)
M
A
PE
=
100
∑
|
−
̂
|
=
1
(
5
)
2
=
1
−
∑
(
−
̂
)
2
=
1
∑
(
−
‾
)
2
=
1
(
6
)
Mo
d
el
v
alid
atio
n
was
ca
r
r
ie
d
o
u
t
u
s
in
g
p
ar
ity
p
lo
ts
an
d
r
esid
u
al
er
r
o
r
an
aly
s
is
to
e
x
am
in
e
th
e
ac
cu
r
ac
y
an
d
co
n
s
is
ten
cy
o
f
th
e
p
r
e
d
ictio
n
m
o
d
els.
Am
o
n
g
th
e
ev
alu
ated
ap
p
r
o
ac
h
es,
th
e
r
an
d
o
m
f
o
r
est
m
o
d
el
p
r
o
d
u
ce
d
th
e
b
est
p
e
r
f
o
r
m
an
ce
,
with
a
n
MA
E
o
f
5
.
2
0
,
R
MSE
o
f
6
.
8
3
,
MA
PE
o
f
1
.
3
3
%,
an
d
an
R
²
v
alu
e
o
f
0
.
9
9
7
.
T
h
e
p
ar
ity
p
l
o
t
s
h
o
wed
th
at
th
e
p
r
ed
icted
R
UL
v
alu
es
clo
s
ely
m
atch
ed
th
e
co
r
r
esp
o
n
d
in
g
m
ea
s
u
r
ed
v
alu
es,
in
d
icatin
g
a
h
ig
h
lev
el
o
f
p
r
e
d
ictio
n
a
cc
u
r
ac
y
.
I
n
a
d
d
itio
n
,
th
e
r
es
id
u
al
er
r
o
r
s
wer
e
co
n
ce
n
tr
ated
n
ea
r
ze
r
o
with
n
o
n
o
ticea
b
le
s
y
s
tem
atic
p
atter
n
,
d
em
o
n
s
tr
atin
g
s
tab
le
m
o
d
el
p
er
f
o
r
m
a
n
ce
an
d
a
lo
w
lev
el
o
f
p
r
ed
ictio
n
b
ias.
T
h
ese
r
esu
lts
co
n
f
ir
m
th
at
th
e
p
r
o
p
o
s
ed
en
s
em
b
le
lear
n
in
g
f
r
am
ewo
r
k
p
r
o
v
id
es
r
eliab
le
an
d
r
o
b
u
s
t
R
UL
esti
m
atio
n
,
m
ak
i
n
g
it
s
u
itab
le
f
o
r
b
atter
y
h
ea
lth
m
o
n
ito
r
i
n
g
,
p
r
ed
ictiv
e
m
ain
ten
an
ce
,
an
d
p
r
ac
tical
B
MS
ap
p
licatio
n
s
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
p
r
o
p
o
s
ed
b
atter
y
R
UL
p
r
ed
ictio
n
f
r
a
m
ewo
r
k
was
im
p
l
em
en
ted
u
s
in
g
Py
th
o
n
3
.
1
0
w
ith
Scik
it
-
lear
n
,
ten
s
o
r
f
lo
w
/Ker
as
,
an
d
Nu
m
Py
lib
r
ar
ies.
T
h
e
ex
p
er
im
en
ts
wer
e
co
n
d
u
cted
o
n
t
h
e
Haw
aii
Natu
r
al
E
n
er
g
y
I
n
s
titu
te
(
HNE
I
)
lith
iu
m
-
io
n
b
atter
y
d
ataset
,
wh
ic
h
co
n
tain
s
1
5
,
0
6
4
c
h
ar
g
e
–
d
is
ch
ar
g
e
cy
cles
with
s
ev
en
s
ig
n
if
ican
t
d
eg
r
a
d
atio
n
f
ea
tu
r
es.
T
h
e
ex
tr
ac
te
d
f
ea
t
u
r
e
s
in
clu
d
e
c
y
cle
in
d
ex
,
d
is
ch
ar
g
e
tim
e,
d
ec
r
em
e
n
t
(
3
.
6
–
3
.
4
V)
,
m
a
x
im
u
m
d
is
ch
a
r
g
e
v
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ltag
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m
i
n
im
u
m
c
h
ar
g
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n
g
v
o
ltag
e,
tim
e
at
4
.
1
5
V,
a
n
d
co
n
s
tan
t
cu
r
r
e
n
t
ch
ar
g
in
g
tim
e
.
T
h
ese
p
a
r
a
m
eter
s
ef
f
ec
tiv
ely
r
ep
r
esen
t
b
atter
y
ag
in
g
b
e
h
av
io
r
an
d
p
r
o
v
id
e
s
u
f
f
icien
t
in
f
o
r
m
atio
n
f
o
r
ac
cu
r
ate
r
em
ai
n
in
g
u
s
ef
u
l lif
e
p
r
e
d
ictio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
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&
Dr
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s
t
I
SS
N:
2088
-
8
6
9
4
A
cc
u
r
a
te
R
UL p
r
ed
ictio
n
o
f E
V
b
a
tter
ies u
s
in
g
r
a
n
d
o
m
fo
r
e
s
t a
n
d
en
s
emb
le
.
.
.
(
P
o
n
k
u
ma
r
Ga
n
esa
p
a
n
d
iya
n
)
2295
T
ab
le
2
s
u
m
m
ar
izes
th
e
s
tatis
tical
ch
ar
ac
ter
is
tics
o
f
th
e
s
e
lecte
d
b
atter
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eg
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ad
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n
f
ea
tu
r
es.
T
h
e
d
ataset
co
n
s
is
ts
o
f
1
5
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0
6
4
b
att
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y
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with
th
e
cy
cle
i
n
d
ex
r
an
g
in
g
f
r
o
m
1
to
1
1
3
4
cy
cles
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d
an
av
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ag
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5
6
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atin
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ataset
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atter
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s
.
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e
d
is
ch
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g
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tim
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v
ar
ies
co
n
s
id
er
ab
ly
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5
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2
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s
,
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ile
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ar
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g
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r
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im
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4
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r
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en
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ch
ar
g
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g
tim
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it
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ig
n
if
ican
t
v
ar
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s
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u
e
to
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atter
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g
in
g
.
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h
e
m
ax
i
m
u
m
d
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ch
ar
g
e
v
o
lt
ag
e
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as
a
n
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er
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alu
e
o
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3
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9
0
8
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er
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s
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im
u
m
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5
7
8
V
,
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r
ly
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g
th
e
elec
tr
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ch
em
ical
d
e
g
r
ad
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o
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r
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in
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e
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ted
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ar
g
in
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d
is
ch
ar
g
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g
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h
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s
tat
is
tical
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s
er
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atio
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s
co
n
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i
r
m
t
h
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tain
s
s
u
f
f
ic
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ar
iab
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o
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tr
ain
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ac
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in
e
lear
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in
g
m
o
d
els
f
o
r
b
atter
y
R
UL
p
r
ed
ictio
n
.
T
h
e
p
r
ed
ictio
n
p
er
f
o
r
m
an
ce
s
o
f
lin
ea
r
r
eg
r
ess
io
n
,
r
an
d
o
m
f
o
r
est
,
L
STM
,
an
d
L
STM
with
Atten
tio
n
wer
e
ev
alu
ated
u
s
in
g
MA
E
,
R
MSE
,
an
d
MA
PE
.
T
h
e
co
m
p
ar
ativ
e
r
esu
lts
p
r
esen
ted
in
T
ab
le
2
an
d
Fig
u
r
e
3
clea
r
ly
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em
o
n
s
tr
ate
th
e
s
u
p
er
i
o
r
ity
o
f
th
e
r
a
n
d
o
m
f
o
r
est
m
o
d
el
o
v
er
t
h
e
o
th
er
p
r
ed
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n
tech
n
iq
u
es.
T
ab
le
3
p
r
esen
ts
th
e
p
er
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o
r
m
an
ce
co
m
p
ar
is
o
n
o
f
th
e
b
atter
y
R
UL
p
r
ed
ictio
n
m
o
d
els
u
s
in
g
MA
E
,
R
MSE
,
an
d
MA
PE
m
etr
ics.
A
m
o
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2297
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atter
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Fig
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
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6
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4
I
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&
Dr
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3
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Sep
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:
2291
-
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3
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2298
4.
CO
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atter
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ased
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atter
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ataset.
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RE
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1
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p
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DC
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telli
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d
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a
s.
He
c
a
n
b
e
c
o
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ta
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ted
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m
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il
:
p
o
n
k
u
m
a
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c
o
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Evaluation Warning : The document was created with Spire.PDF for Python.
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Dr
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P
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a
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n
k
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iv
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ru
p
a
ti
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n
d
ia.
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h
a
s
m
o
re
th
a
n
1
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y
e
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rs
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x
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s
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m
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5
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tern
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p
ters
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is
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x
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a
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c
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m
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c
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n
d
u
p
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r@
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m
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c
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m
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ja
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d
e
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re
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e
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.
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d
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in
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ly
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s
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icle
s
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h
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c
a
n
b
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ted
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il
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c
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in
.
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.
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o
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ru
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m
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id
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in
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2
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c
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ti
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).
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re
se
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rc
h
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in
c
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re
stru
c
tu
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d
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c
a
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h
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n
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.
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a
v
ith
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ro
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n
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ll
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e
,
with
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p
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ima
tely
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rs
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r
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re
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re
e
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Be
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a
p
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rd
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o
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h
e
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rc
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p
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p
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t
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tern
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l
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re
n
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e
.
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r
re
se
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rc
h
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tere
sts
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o
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tr
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m
a
c
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e
d
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sig
n
,
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n
d
re
n
e
wa
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le
e
n
e
rg
y
sy
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m
s.
S
h
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is
a
m
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m
b
e
r
o
f
IS
OI an
d
I
S
TE
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
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m
a
il
:
p
k
t
.
e
e
e
@rm
k
e
c
.
a
c
.
i
n
.
Dr
.
R.
K
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la
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n
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o
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tai
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e
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r
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d
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re
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g
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g
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m
th
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l
In
stit
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r
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d
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re
e
in
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r
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s
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th
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m
e
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n
in
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0
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.
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h
e
re
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d
h
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r
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h
.
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d
e
g
re
e
fro
m
An
n
a
Un
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y
,
Ch
e
n
n
a
i,
in
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.
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h
e
is
c
u
rre
n
t
ly
a
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e
ss
o
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th
e
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p
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rtme
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o
f
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tri
c
a
l
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n
d
E
lec
tro
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n
g
in
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a
t
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jala
k
sh
m
i
E
n
g
i
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ll
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g
e
.
S
h
e
is
a
m
e
m
b
e
r
o
f
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E,
IEI
,
a
n
d
IS
T
E.
S
h
e
h
a
s
2
3
y
e
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rs
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h
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e
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n
d
h
a
s
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b
li
sh
e
d
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tec
h
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p
a
p
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rs
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n
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a
ti
o
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l
a
n
d
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tern
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l
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rn
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ls
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n
d
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o
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re
n
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ro
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d
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s.
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h
e
h
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s
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lso
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u
t
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o
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h
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tex
t
b
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o
k
s,
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led
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sm
issio
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d
Distrib
u
ti
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n
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o
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y
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d
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o
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h
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h
a
s
fil
e
d
o
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e
p
a
ten
t.
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r
c
u
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n
t
re
se
a
rc
h
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sts
in
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lu
d
e
sm
a
rt
g
rid
s,
r
e
n
e
wa
b
le
e
n
e
rg
y
,
so
ft
c
o
m
p
u
t
i
n
g
tec
h
n
iq
u
e
s,
a
n
d
e
lec
tri
c
v
e
h
icle
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
k
a
laiv
a
n
i.
r@ra
jala
k
sh
m
i.
e
d
u
.
i
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