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[
1
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3
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W
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7
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[
8
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-
[
1
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.
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[
1
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I
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2088
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8
6
9
4
Win
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s
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to
m
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ith
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s
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d
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p
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[
1
1
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[
1
3
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.
Fau
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s
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Fo
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Qu
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d
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o
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s
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Similar
ly
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R
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3
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ag
e
id
en
tific
atio
n
t
o
ad
d
r
ess
n
o
n
lin
ea
r
ities
in
win
d
tu
r
b
in
e
b
lad
es.
W
h
ile
th
ese
s
tu
d
ie
s
p
r
im
ar
ily
f
o
cu
s
e
d
o
n
th
e
m
ec
h
a
n
ical
asp
ec
ts
o
f
win
d
tu
r
b
in
es,
th
ey
h
ig
h
lig
h
t
th
e
g
r
o
win
g
r
o
le
o
f
AI
in
p
r
e
d
ictiv
e
m
ain
ten
an
ce
an
d
f
a
u
lt
d
etec
tio
n
[
1
4
]
-
[
1
9
]
.
B
u
ild
in
g
o
n
th
ese
co
n
ce
p
ts
,
in
teg
r
atin
g
o
b
ject
d
etec
tio
n
al
g
o
r
ith
m
s
in
to
UAV
-
b
ased
win
d
tu
r
b
in
e
in
s
p
ec
tio
n
o
f
f
er
s
th
e
p
o
ten
tial
to
au
to
m
ate
th
e
id
en
tific
atio
n
o
f
s
tr
u
ctu
r
al
d
e
f
ec
ts
,
th
u
s
r
ed
u
cin
g
h
u
m
a
n
in
ter
v
en
tio
n
.
Ob
ject
d
etec
tio
n
m
o
d
els,
s
u
ch
as
th
e
Yo
u
On
ly
L
o
o
k
On
ce
(
YOL
O
)
s
er
ies,
h
av
e
r
ev
o
lu
tio
n
ized
r
ea
l
-
tim
e
o
b
ject
d
etec
tio
n
b
y
p
r
o
v
id
in
g
f
ast
an
d
ac
cu
r
ate
r
esu
lts
.
YOL
Ov
8
,
th
e
latest
iter
atio
n
,
co
m
b
i
n
es
s
p
ee
d
with
im
p
r
o
v
ed
d
etec
tio
n
ac
c
u
r
ac
y
,
m
ak
in
g
it
s
u
itab
le
f
o
r
ap
p
licatio
n
s
lik
e
win
d
tu
r
b
in
e
d
ef
ec
t
d
etec
tio
n
.
Acc
o
r
d
in
g
to
Su
n
et
a
l
.
[
4
]
,
id
en
tify
in
g
d
a
m
ag
e
in
tu
r
b
in
e
b
lad
es
u
s
in
g
ad
v
an
ce
d
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
ca
n
s
ig
n
if
ican
tly
en
h
an
ce
m
ain
ten
a
n
ce
p
r
o
c
ess
es
b
y
p
in
p
o
in
tin
g
is
s
u
es
ea
r
ly
.
L
e
v
er
ag
i
n
g
YOL
Ov
8
in
th
is
co
n
tex
t
co
u
ld
au
to
m
ate
t
h
e
d
etec
tio
n
o
f
cr
itical
d
ef
ec
ts
,
s
u
ch
as
cr
ac
k
s
,
co
r
r
o
s
io
n
,
o
r
b
lad
e
m
is
alig
n
m
en
ts
,
b
ased
o
n
th
e
v
is
u
al
d
ata
ca
p
t
u
r
ed
b
y
U
AVs,
th
u
s
im
p
r
o
v
i
n
g
th
e
ef
f
icien
cy
o
f
tu
r
b
in
e
in
s
p
ec
tio
n
s
[
2
0
]
-
[
2
3
]
.
R
ec
en
t
ad
v
an
ce
m
e
n
ts
in
AI
,
p
ar
ticu
lar
ly
in
th
e
f
ield
s
o
f
o
b
ject
d
etec
tio
n
a
n
d
a
n
o
m
aly
d
etec
tio
n
,
h
av
e
s
h
o
w
n
p
r
o
m
is
in
g
r
esu
lts
in
win
d
tu
r
b
in
e
m
ain
ten
a
n
ce
.
W
an
g
et
a
l
.
[
5]
p
r
o
p
o
s
ed
a
two
-
s
tag
e
an
o
m
aly
d
etec
tio
n
m
o
d
el
to
en
h
an
ce
f
au
lt
d
etec
tio
n
in
win
d
tu
r
b
in
e
s
,
illu
s
tr
atin
g
th
e
b
en
ef
its
o
f
co
m
b
in
in
g
m
ac
h
in
e
lear
n
in
g
wit
h
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
s
y
s
tem
s
.
B
y
tr
ain
in
g
o
b
ject
d
etec
tio
n
alg
o
r
ith
m
s
lik
e
YOL
Ov
8
o
n
win
d
tu
r
b
in
e
im
ag
es,
it
b
ec
o
m
es
p
o
s
s
ib
le
to
au
to
m
ate
th
e
id
en
tifi
ca
tio
n
o
f
c
o
m
m
o
n
is
s
u
es
s
u
ch
as
b
lad
e
d
am
ag
e
o
r
s
tr
u
ctu
r
al
wea
r
.
T
h
is
ap
p
r
o
ac
h
n
o
t
o
n
ly
r
ed
u
ce
s
th
e
tim
e
a
n
d
ex
p
er
tis
e
r
eq
u
ir
ed
f
o
r
m
an
u
al
in
s
p
ec
tio
n
s
b
u
t
also
in
cr
ea
s
es th
e
o
v
er
all
r
elia
b
ilit
y
o
f
th
e
m
ai
n
ten
an
ce
p
r
o
c
ess
.
Sev
er
al
p
r
ev
io
u
s
s
tu
d
ies
h
av
e
ex
p
lo
r
ed
th
e
in
teg
r
atio
n
o
f
d
r
o
n
e
tech
n
o
lo
g
y
an
d
co
m
p
u
ter
v
is
io
n
f
o
r
tu
r
b
in
e
in
s
p
ec
tio
n
.
Fo
r
e
x
am
p
le,
Fo
s
ter
et
a
l
.
[
1
0
]
d
em
o
n
s
tr
ated
th
e
p
o
ten
tial
o
f
d
r
o
n
e
f
o
o
tag
e
f
o
r
d
etec
tin
g
s
u
r
f
ac
e
d
am
ag
e
o
n
tu
r
b
in
es,
s
h
o
win
g
th
e
ef
f
ec
tiv
en
ess
o
f
UAVs
in
ca
p
tu
r
in
g
r
elev
an
t
d
ata.
Ho
wev
er
,
th
e
ap
p
licatio
n
o
f
cu
ttin
g
-
ed
g
e
o
b
ject
d
etec
tio
n
m
o
d
els
lik
e
YOL
Ov
8
f
o
r
th
is
s
p
ec
if
ic
task
is
s
t
ill
in
it
s
ea
r
ly
s
tag
es,
m
ak
in
g
it
a
p
r
o
m
is
in
g
ar
ea
o
f
r
esear
ch
.
B
y
r
ef
in
i
n
g
th
ese
alg
o
r
ith
m
s
f
o
r
win
d
tu
r
b
in
e
in
s
p
ec
tio
n
,
o
p
er
ato
r
s
ca
n
ac
h
iev
e
h
ig
h
er
p
r
ec
is
io
n
an
d
b
etter
m
ain
ten
a
n
ce
o
u
tco
m
es,
lead
in
g
to
r
ed
u
ce
d
d
o
wn
tim
e
a
n
d
en
h
an
ce
d
en
er
g
y
o
u
t
put
[
2
4
]
-
[
2
5
]
.
I
n
s
u
m
m
ar
y
,
UAV
-
b
ased
in
s
p
ec
tio
n
,
co
m
b
in
ed
with
ad
v
an
ce
d
o
b
ject
d
etec
tio
n
alg
o
r
i
th
m
s
lik
e
YOL
Ov
8
,
o
f
f
er
s
a
p
o
wer
f
u
l
s
o
lu
tio
n
to
t
h
e
c
h
allen
g
es
ass
o
ciate
d
with
tr
a
d
itio
n
al
win
d
t
u
r
b
in
e
m
ain
ten
an
ce
.
T
h
is
p
ap
er
s
ee
k
s
to
ex
p
lo
r
e
t
h
e
p
o
ten
tial
o
f
YOL
Ov
8
in
a
u
to
m
atin
g
t
h
e
d
ef
ec
t
d
etec
tio
n
p
r
o
ce
s
s
f
o
r
win
d
tu
r
b
in
es.
B
y
lev
er
ag
i
n
g
r
ec
e
n
t
d
ev
elo
p
m
e
n
ts
in
AI
an
d
d
r
o
n
e
tech
n
o
lo
g
y
,
t
h
is
ap
p
r
o
ac
h
co
u
ld
s
ig
n
if
ican
tly
r
ed
u
ce
th
e
tim
e,
c
o
s
t,
an
d
s
a
f
ety
r
is
k
s
in
v
o
lv
e
d
in
tu
r
b
in
e
in
s
p
ec
tio
n
,
wh
ile
im
p
r
o
v
in
g
th
e
r
eliab
ilit
y
o
f
r
en
ewa
b
le
en
er
g
y
g
en
er
atio
n
.
2.
M
E
T
H
O
DO
L
O
G
Y
T
h
e
o
b
ject
d
etec
tio
n
s
y
s
tem
f
o
r
win
d
tu
r
b
in
es
was
im
p
lem
en
ted
u
s
in
g
th
e
YOL
Ov
8
alg
o
r
ith
m
.
T
h
e
d
ataset
co
n
s
is
ted
o
f
win
d
tu
r
b
in
e
im
ag
es
with
v
ar
io
u
s
d
ef
ec
ts
,
in
clu
d
in
g
cr
ac
k
s
an
d
s
u
r
f
ac
e
co
r
r
o
s
io
n
.
T
h
e
im
ag
es
wer
e
an
n
o
tated
to
d
e
f
in
e
th
e
b
o
u
n
d
in
g
b
o
x
es
ar
o
u
n
d
tu
r
b
in
e
c
o
m
p
o
n
en
ts
an
d
lab
el
ed
f
o
r
d
ef
ec
t
ty
p
es.
T
h
e
d
ataset
was
d
iv
i
d
ed
i
n
to
t
r
ain
in
g
,
v
alid
atio
n
,
an
d
test
in
g
s
ets,
with
a
f
o
cu
s
o
n
tr
ain
i
n
g
YOL
Ov
8
to
d
etec
t
d
ef
ec
ts
ef
f
icien
tly
.
Th
e
YOL
Ov
8
m
o
d
el
was
co
n
f
ig
u
r
ed
u
s
in
g
p
r
e
-
tr
ain
ed
weig
h
ts
an
d
f
in
e
-
tu
n
ed
o
n
t
h
e
win
d
tu
r
b
in
e
d
ataset.
T
h
e
d
ata
p
r
ep
r
o
ce
s
s
in
g
in
clu
d
e
d
r
esizin
g
im
a
g
es
to
a
s
tan
d
ar
d
s
ize
to
en
s
u
r
e
c
o
m
p
atib
ilit
y
with
th
e
YOL
O
ar
ch
itectu
r
e,
wh
ile
m
ain
tain
in
g
t
h
e
asp
ec
t
r
atio
s
o
f
th
e
tu
r
b
in
e
co
m
p
o
n
e
n
ts
.
Data
au
g
m
e
n
tatio
n
tech
n
iq
u
es,
s
u
ch
as
r
an
d
o
m
r
o
tatio
n
,
f
lip
p
in
g
,
an
d
s
ca
lin
g
,
wer
e
ap
p
lied
to
in
c
r
ea
s
e
th
e
v
ar
iab
ilit
y
o
f
th
e
tr
ain
in
g
s
et
an
d
im
p
r
o
v
e
m
o
d
e
l r
o
b
u
s
tn
ess
.
T
h
e
tr
ain
in
g
p
r
o
ce
s
s
u
tili
ze
d
a
b
atch
s
ize
o
f
1
an
d
a
lear
n
in
g
r
ate
o
p
tim
ized
th
r
o
u
g
h
g
r
id
s
ea
r
ch
.
T
h
e
m
o
d
el
was
tr
ain
ed
f
o
r
f
iv
e
ep
o
ch
s
with
a
GPU
ac
ce
ler
ato
r
to
s
p
ee
d
u
p
co
m
p
u
tatio
n
s
.
YOL
Ov
8
'
s
ar
ch
itectu
r
e,
wh
ich
co
n
s
is
ts
o
f
co
n
v
o
l
u
tio
n
al
lay
er
s
an
d
an
ch
o
r
-
b
ased
d
e
tectio
n
,
en
ab
les
it
to
q
u
ick
ly
id
en
tify
o
b
jects
in
r
ea
l
tim
e.
Af
ter
tr
ain
in
g
,
th
e
b
est
-
p
er
f
o
r
m
in
g
m
o
d
el
was
s
a
v
ed
an
d
ev
alu
ated
u
s
in
g
th
e
v
alid
atio
n
d
ataset
to
m
ea
s
u
r
e
its
p
er
f
o
r
m
an
ce
m
etr
i
cs,
in
clu
d
in
g
p
r
ec
is
io
n
,
r
ec
all,
an
d
m
ea
n
av
er
ag
e
p
r
ec
is
io
n
(
m
AP)
.
T
h
e
ev
alu
atio
n
m
etr
ics
p
r
o
v
id
ed
in
s
ig
h
ts
in
to
th
e
m
o
d
el'
s
ab
ilit
y
to
ac
cu
r
ately
d
etec
t
win
d
tu
r
b
in
e
d
ef
ec
ts
.
A
co
n
f
u
s
io
n
m
atr
ix
w
as g
en
er
ated
to
ass
ess
th
e
f
alse p
o
s
itiv
es a
n
d
f
alse n
eg
ativ
es
in
d
ef
ec
t d
etec
tio
n
.
T
h
e
f
in
al
m
o
d
el
was
d
e
p
lo
y
e
d
f
o
r
in
f
er
e
n
ce
o
n
th
e
test
d
a
taset
to
ev
alu
ate
its
r
ea
l
-
wo
r
l
d
ap
p
licab
ilit
y
a
n
d
ef
f
ec
tiv
en
ess
in
d
etec
tin
g
tu
r
b
in
e
d
ef
ec
ts
.
T
h
e
o
b
ject
d
etec
ti
o
n
s
y
s
tem
f
o
r
win
d
tu
r
b
in
e
d
ef
ec
t
id
en
tific
atio
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.
16
,
No
.
2
,
J
u
n
e
20
25
:
1
3
4
8
-
1355
1350
u
s
in
g
YOL
Ov
8
i
n
v
o
lv
es
s
ev
er
al
k
ey
s
tep
s
,
ea
ch
e
n
h
an
ce
d
b
y
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atica
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u
r
e
1
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e
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o
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eth
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u
r
e
1
.
T
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ed
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eth
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Data
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et
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h
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ataset
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ig
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tio
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b
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et
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r
e
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et,
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er
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im
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s
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g
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i
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o
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cial
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en
s
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r
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p
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with
YOL
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t size,
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th
e
tr
an
s
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o
r
m
atio
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n
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1
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atio
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Pre
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en
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g
m
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h
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iq
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es su
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h
as r
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t
h
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ce
s
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o
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el
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u
s
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ess
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y
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iv
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s
e
tr
ain
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g
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am
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les f
r
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m
th
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ig
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Mo
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el
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ain
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bbox
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n
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conf
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h
e
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tal
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iv
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wh
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1
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d
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y
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ch
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-
Mo
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m
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s
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Av
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Pre
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m
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B
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ch
test
im
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I
i.
T
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e
d
etec
tio
n
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J Po
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&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
Win
d
tu
r
b
in
e
d
efec
t d
etec
tio
n
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s
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g
d
ee
p
le
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g
(
Dee
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a
r
a
m
)
1351
co
n
s
id
er
ed
s
u
cc
ess
f
u
l
if
t
h
e
in
ter
s
ec
tio
n
o
v
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n
io
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(
I
o
U)
b
etwe
en
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e
p
r
ed
icted
a
n
d
g
r
o
u
n
d
-
t
r
u
th
b
o
u
n
d
in
g
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x
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e
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s
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th
r
e
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h
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τ
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lated
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(
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.
=
(
∣
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∣
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(
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4
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T
h
e
f
in
al
m
o
d
el’
s
r
ea
l
-
wo
r
l
d
ap
p
licab
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is
ass
ess
ed
b
a
s
ed
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n
th
ese
m
etr
ics.
T
h
e
f
l
o
wch
ar
t
d
is
p
lay
ed
o
u
tlin
es
th
e
co
m
p
lete
p
r
o
ce
s
s
,
f
r
o
m
d
ataset
co
llectio
n
to
in
f
er
en
ce
o
n
test
d
ata,
em
p
h
asizi
n
g
ea
ch
s
tag
e'
s
r
o
le
in
th
e
win
d
t
u
r
b
in
e
d
ef
ec
t
d
et
ec
tio
n
s
y
s
t
em
.
T
h
is
ap
p
r
o
ac
h
i
n
teg
r
ates
m
ath
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atica
l
e
q
u
ati
o
n
s
at
ea
c
h
s
tag
e
t
o
o
p
tim
ize
th
e
m
o
d
el'
s
p
er
f
o
r
m
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ce
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d
e
n
s
u
r
e
ac
c
u
r
ate
d
ef
ec
t
id
en
tific
atio
n
.
Fig
u
r
e
2
s
h
o
ws
th
e
v
is
u
alize
d
s
am
p
le
im
ag
es with
co
r
r
esp
o
n
d
in
g
an
n
o
tatio
n
s
.
Fig
u
r
e
2
.
Vis
u
alizin
g
s
am
p
le
i
m
ag
es with
co
r
r
esp
o
n
d
in
g
an
n
o
tatio
n
s
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
tr
ain
ed
YOL
Ov
8
m
o
d
el
d
em
o
n
s
tr
ated
s
tr
o
n
g
p
er
f
o
r
m
an
ce
in
d
etec
tin
g
d
e
f
ec
ts
o
n
win
d
tu
r
b
in
es
with
h
ig
h
p
r
ec
is
io
n
an
d
r
ec
all.
T
h
e
m
o
d
el
ac
h
iev
ed
a
tr
ain
b
o
x
lo
s
s
o
f
0
.
0
4
,
wh
ich
in
d
icate
s
th
at
t
h
e
p
r
ed
icted
b
o
u
n
d
in
g
b
o
x
es
clo
s
ely
m
atc
h
ed
th
e
ac
tu
al
d
ef
ec
t
lo
ca
tio
n
s
.
Similar
ly
,
th
e
tr
ai
n
class
lo
s
s
was
r
ed
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ce
d
to
0
.
0
2
,
s
u
g
g
esti
n
g
th
at
th
e
m
o
d
el
was
ab
le
to
cla
s
s
if
y
tu
r
b
in
e
d
ef
ec
ts
ac
cu
r
ately
.
T
h
ese
r
esu
lts
in
d
icate
th
e
ef
f
ec
tiv
en
ess
o
f
YOL
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8
in
i
d
en
tify
in
g
win
d
t
u
r
b
in
e
d
e
f
e
cts
f
r
o
m
UAV
-
ca
p
tu
r
ed
im
ag
es.
Fig
u
r
e
3
s
h
o
ws
th
e
tr
ain
in
g
m
et
r
ics an
d
l
o
s
s
.
T
h
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
w
as
f
u
r
th
er
ev
alu
ated
u
s
in
g
th
e
m
AP5
0
m
etr
ic,
wh
ich
m
ea
s
u
r
es
th
e
av
er
ag
e
p
r
ec
is
io
n
ac
r
o
s
s
d
if
f
er
en
t
o
b
ject
ca
teg
o
r
ies
at
a
5
0
%
in
ter
s
ec
tio
n
o
v
er
u
n
io
n
(
I
o
U)
th
r
esh
o
ld
.
T
h
e
m
o
d
el
ac
h
iev
e
d
an
m
AP5
0
s
c
o
r
e
o
f
0
.
8
7
,
d
e
m
o
n
s
tr
atin
g
its
ab
i
lity
to
ac
cu
r
ately
d
etec
t
tu
r
b
in
e
d
ef
ec
ts
ac
r
o
s
s
v
ar
y
in
g
im
a
g
es.
Ad
d
itio
n
ally
,
th
e
m
AP5
0
-
9
5
s
co
r
e
was
r
ec
o
r
d
ed
at
0
.
7
6
,
h
ig
h
lig
h
tin
g
th
e
m
o
d
el’
s
r
o
b
u
s
tn
ess
ac
r
o
s
s
a
wid
e
r
an
g
e
o
f
I
o
U
th
r
esh
o
ld
s
,
th
u
s
co
n
f
i
r
m
in
g
its
ca
p
ab
ilit
y
f
o
r
ac
cu
r
ate
lo
ca
lizati
o
n
o
f
d
ef
ec
ts
.
On
e
o
f
th
e
m
aj
o
r
c
h
allen
g
e
s
in
win
d
t
u
r
b
in
e
d
ef
ec
t
d
et
ec
tio
n
is
d
is
tin
g
u
is
h
in
g
b
et
wee
n
f
alse
p
o
s
itiv
es
an
d
t
r
u
e
p
o
s
itiv
es,
esp
ec
ially
in
c
ases
o
f
s
m
all
o
r
s
u
b
tle
d
ef
ec
ts
.
B
y
an
aly
zi
n
g
th
e
co
n
f
u
s
io
n
m
atr
ix
,
it
was
o
b
s
er
v
ed
th
at
t
h
e
m
o
d
e
l
h
ad
a
f
alse
p
o
s
itiv
e
r
ate
o
f
3
%,
wh
ich
is
ac
ce
p
ta
b
le
g
iv
e
n
th
e
co
m
p
lex
ity
o
f
tu
r
b
in
e
im
ag
es.
T
h
is
lo
w
r
ate
o
f
f
alse
p
o
s
itiv
es
in
d
icate
s
t
h
at
th
e
m
o
d
el
ca
n
r
eliab
ly
d
e
tect
ac
tu
al
d
ef
ec
ts
with
o
u
t o
v
er
-
p
r
e
d
ictin
g
th
em
.
T
h
e
ab
ilit
y
to
d
ep
lo
y
th
is
o
b
j
ec
t
d
etec
tio
n
m
o
d
el
in
r
ea
l
-
w
o
r
ld
s
ce
n
ar
io
s
was
also
ex
p
lo
r
ed
.
UAVs
eq
u
ip
p
e
d
with
h
ig
h
-
r
eso
lu
tio
n
ca
m
er
as
ca
n
ca
p
t
u
r
e
lar
g
e
v
o
lu
m
es
o
f
d
ata
ac
r
o
s
s
win
d
f
ar
m
s
,
a
n
d
th
e
YOL
Ov
8
m
o
d
el
ca
n
p
r
o
ce
s
s
th
ese
im
ag
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in
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ea
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to
id
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tify
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o
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ts
.
T
h
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u
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alize
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ated
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Evaluation Warning : The document was created with Spire.PDF for Python.
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4
Win
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4.
CO
NCLU
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co
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clu
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ap
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licatio
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o
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win
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tu
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g
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tio
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to
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ch
allen
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o
f
m
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tain
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g
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d
in
s
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ec
tin
g
win
d
f
ar
m
s
.
T
h
e
m
o
d
el’
s
h
ig
h
ac
cu
r
ac
y
an
d
r
ea
l
-
tim
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d
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p
ab
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c
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n
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ed
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ce
th
e
n
ee
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f
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m
an
u
al
in
s
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s
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im
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s
af
ety
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d
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ed
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cin
g
o
p
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al
c
o
s
ts
.
Fu
r
th
er
m
o
r
e,
th
is
ap
p
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o
ac
h
allo
ws
win
d
f
ar
m
o
p
er
ato
r
s
to
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u
ick
ly
i
d
en
tify
an
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ad
d
r
ess
p
o
ten
tial
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ef
ec
ts
,
en
s
u
r
in
g
c
o
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tin
u
ed
o
p
tim
al
p
er
f
o
r
m
a
n
c
e
o
f
tu
r
b
i
n
es.
W
ith
f
u
r
th
er
im
p
r
o
v
em
e
n
ts
an
d
in
teg
r
atio
n
in
to
UAV
s
y
s
tem
s
,
YOL
Ov
8
ca
n
r
ev
o
lu
tio
n
ize
win
d
tu
r
b
in
e
m
ain
ten
an
ce
,
p
r
o
m
o
tin
g
th
e
wid
er
ad
o
p
tio
n
o
f
r
en
ewa
b
le
en
e
r
g
y
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
e
au
th
o
r
s
co
n
f
ir
m
th
at
th
e
r
esear
ch
was c
ar
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ied
o
u
t i
n
d
ep
e
n
d
en
tly
with
o
u
t f
in
an
cial
i
n
f
lu
en
ce
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
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to
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ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
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ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
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ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
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D
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Vi
Su
P
Fu
Dee
p
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m
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d
ar
a
m
✓
✓
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✓
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✓
M.
Van
ith
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
T
.
Sath
is
h
Ku
m
ar
✓
✓
✓
✓
✓
✓
✓
I
.
Ar
u
l D
o
s
s
Ad
aik
alam
✓
✓
✓
✓
✓
✓
✓
P.
Kav
ith
a
✓
✓
✓
✓
✓
✓
✓
R
.
Kala
iv
an
i
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
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d
o
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g
y
So
:
So
f
t
w
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r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
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s
I
:
I
n
v
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t
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g
a
t
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:
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e
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:
D
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r
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t
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p
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v
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s
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P
:
P
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E
NT
Au
th
o
r
s
s
tate
n
o
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n
f
lict o
f
in
t
er
est.
DATA AV
AI
L
AB
I
L
I
T
Y
R
aw
d
ata
is
n
o
t p
u
b
licly
av
ail
ab
le
d
u
e
to
p
r
iv
ac
y
o
r
in
s
titu
tio
n
al
r
estrictio
n
s
.
RE
F
E
R
E
NC
E
S
[
1
]
J.
L
e
i
,
C
.
L
i
u
,
a
n
d
D
.
J
i
a
n
g
,
“
F
a
u
l
t
d
i
a
g
n
o
si
s
o
f
w
i
n
d
t
u
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se
d
o
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g
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o
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m
m
e
m
o
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y
n
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t
w
o
r
k
s
,
”
R
e
n
e
w
a
b
l
e
E
n
e
r
g
y
,
v
o
l
.
1
3
3
,
p
p
.
4
2
2
–
4
3
2
,
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.
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:
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e
n
e
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e
.
2
0
1
8
.
1
0
.
0
3
1
.
[
2
]
F
.
Q
u
,
J.
Li
u
,
H
.
Z
h
u
,
a
n
d
B
.
Zh
o
u
,
“
W
i
n
d
t
u
r
b
i
n
e
f
a
u
l
t
d
e
t
e
c
t
i
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n
b
a
se
d
o
n
e
x
p
a
n
d
e
d
l
i
n
g
u
i
st
i
c
t
e
r
ms
a
n
d
r
u
l
e
s
u
s
i
n
g
n
o
n
-
si
n
g
l
e
t
o
n
f
u
z
z
y
l
o
g
i
c
,
”
A
p
p
l
i
e
d
E
n
e
r
g
y
,
v
o
l
.
2
6
2
,
p
.
1
1
4
4
6
9
,
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r
.
2
0
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0
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:
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0
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.
a
p
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r
g
y
.
2
0
1
9
.
1
1
4
4
6
9
.
[
3
]
M
.
M
.
R
e
z
a
e
i
,
M
.
B
e
h
z
a
d
,
H
.
M
o
r
a
d
i
,
a
n
d
H
.
H
a
d
d
a
d
p
o
u
r
,
“
M
o
d
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l
-
b
a
s
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d
d
a
ma
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n
t
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e
n
o
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l
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a
r
mo
d
e
l
o
f
mo
d
e
r
n
w
i
n
d
t
u
r
b
i
n
e
b
l
a
d
e
,
”
R
e
n
e
w
a
b
l
e
E
n
e
r
g
y
,
v
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l
.
9
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–
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.
2
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6
.
0
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.
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4
.
[
4
]
S
.
S
u
n
,
T
.
W
a
n
g
,
H
.
Y
a
n
g
,
a
n
d
F
.
C
h
u
,
“
D
a
m
a
g
e
i
d
e
n
t
i
f
i
c
a
t
i
o
n
o
f
w
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n
d
t
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r
b
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n
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b
l
a
d
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s
u
s
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n
g
a
n
a
d
a
p
t
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v
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m
e
t
h
o
d
f
o
r
c
o
m
p
r
e
ss
i
v
e
b
e
a
mf
o
r
mi
n
g
b
a
se
d
o
n
t
h
e
g
e
n
e
r
a
l
i
z
e
d
mi
n
i
m
a
x
-
c
o
n
c
a
v
e
p
e
n
a
l
t
y
f
u
n
c
t
i
o
n
,
”
R
e
n
e
w
a
b
l
e
En
e
r
g
y
,
v
o
l
.
1
8
1
,
p
p
.
5
9
–
7
0
,
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n
.
2
0
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,
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o
i
:
1
0
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1
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6
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j
.
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e
n
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e
.
2
0
2
1
.
0
9
.
0
2
4
.
[
5
]
A
.
W
a
n
g
,
Y
.
P
e
i
,
Z
.
Q
i
a
n
,
H
.
Za
r
e
i
p
o
u
r
,
B
.
Ji
n
g
,
a
n
d
J.
A
n
,
“
A
t
w
o
-
s
t
a
g
e
a
n
o
m
a
l
y
d
e
c
o
m
p
o
s
i
t
i
o
n
sc
h
e
m
e
b
a
s
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d
o
n
m
u
l
t
i
-
v
a
r
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a
b
l
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c
o
r
r
e
l
a
t
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e
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t
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o
n
f
o
r
w
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t
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t
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,
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Ap
p
l
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En
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g
y
,
v
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l
.
3
2
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p
.
1
1
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,
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0
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:
1
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a
p
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g
y
.
2
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.
1
1
9
3
7
3
.
[
6
]
S
.
A
.
B
o
y
e
r
,
S
C
AD
A
:
S
u
p
e
rv
i
so
r
y
c
o
n
t
r
o
l
a
n
d
d
a
t
a
a
c
q
u
i
s
i
t
i
o
n
.
R
e
se
a
r
c
h
Tr
i
a
n
g
l
e
P
a
r
k
,
N
C
,
U
n
i
t
e
d
S
t
a
t
e
s
:
I
n
t
e
r
n
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t
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o
n
a
l
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