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I
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D
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f
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d
am
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ch
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ac
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s
tic
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o
f
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s
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b
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p
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th
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o
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tech
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ies
an
d
th
e
d
ata
wh
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is
b
ein
g
g
en
er
ated
[
1
]
.
T
h
e
C
PS
ar
e
tr
an
s
f
o
r
m
in
g
th
e
elec
tr
ical
in
d
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s
tr
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allo
win
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h
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r
e
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is
o
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as
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ee
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ca
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s
m
ar
t g
r
id
(
SG)
[
2
]
.
T
h
e
SG
s
tar
ted
with
th
e
in
clu
s
io
n
o
f
s
m
ar
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m
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y
s
tem
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(
SMS)
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m
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s
m
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m
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SMS
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b
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ap
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ted
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(
DE
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)
ca
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p
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d
u
ce
elec
tr
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en
er
g
y
t
h
at
h
elp
s
th
em
l
o
wer
t
h
eir
en
er
g
y
co
s
ts
[
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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4
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I
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Sy
s
t
,
Vo
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10
,
No
.
1
,
Ma
r
c
h
202
1
:
11
–
17
12
On
e
o
f
th
e
m
ai
n
tr
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s
in
d
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tr
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co
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f
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an
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E
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E
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I
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T
d
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with
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th
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[
4
]
.
Alth
o
u
g
h
in
r
ec
en
t
y
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s
,
th
e
ca
p
ac
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o
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s
f
o
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I
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T
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as
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e
ased
,
m
ak
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th
em
in
cr
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s
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ly
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f
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an
aly
tics
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d
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in
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m
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I
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T
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d
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e
s
u
ch
a
s
g
r
ap
h
ics
p
r
o
ce
s
s
in
g
u
n
its
(
GPUs
)
an
d
co
m
p
u
tatio
n
al
n
e
u
r
al
s
tick
s
,
am
o
n
g
o
th
er
s
,
h
av
e
b
ee
n
u
s
ed
[
5
]
.
Ma
n
y
s
im
p
le
b
o
ar
d
co
m
p
u
ter
s
(
SB
C
)
ar
e
u
s
in
g
m
u
ltico
r
e
p
r
o
ce
s
s
o
r
s
an
d
ev
e
n
GPUs
,
s
u
ch
as
th
e
ca
s
e
o
f
Go
o
g
le
C
o
r
al
AI
an
d
Nv
id
ia
J
etso
n
Nan
o
,
to
n
am
e
a
f
ew.
Alth
o
u
g
h
th
e
m
o
s
t
cu
r
r
en
t
o
p
er
atin
g
s
y
s
tem
s
an
d
co
m
p
iler
s
ca
n
s
u
p
p
o
r
t th
e
d
ev
elo
p
m
en
t o
f
c
o
n
c
u
r
r
en
t a
n
d
p
ar
allel
p
r
o
ce
s
s
in
g
s
y
s
t
em
s
,
th
e
d
esig
n
o
f
alg
o
r
ith
m
s
th
at
ca
n
b
e
e
x
e
cu
ted
co
n
c
u
r
r
en
tl
y
an
d
i
n
p
ar
allel
is
s
t
ill
r
eq
u
ir
ed
.
T
h
e
p
r
e
s
en
t
wo
r
k
s
h
o
ws
an
SM
th
at
u
s
es
p
ar
allel
ed
g
e
co
m
p
u
tin
g
in
SMS
f
o
r
d
ata
a
n
al
y
tics
ap
p
licatio
n
s
.
T
h
e
r
esu
lts
s
h
o
w
a
s
ig
n
if
ica
n
t
im
p
r
o
v
em
e
n
t
co
m
p
ar
ed
to
th
e
u
s
e
o
f
SM
with
tr
a
d
itio
n
al
p
r
o
ce
s
s
in
g
.
T
h
is
p
ap
er
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws.
Sectio
n
2
.
p
r
esen
ts
a
s
h
o
r
t
r
e
v
iew
o
f
th
e
liter
atu
r
e
r
eg
a
r
d
in
g
d
ata
p
r
o
ce
s
s
in
g
in
p
a
r
allel
in
ed
g
e
c
o
m
p
u
tin
g
,
wh
ile
S
ec
tio
n
3
.
s
h
o
ws
th
e
ar
c
h
itectu
r
e
p
r
o
p
o
s
ed
f
o
r
th
e
d
ev
elo
p
m
en
t
o
f
ed
g
e
co
m
p
u
tin
g
in
SMs.
T
h
e
r
esu
lts
o
b
tain
ed
ar
e
d
is
cu
s
s
ed
in
S
ec
tio
n
4
.
Fin
ally
,
in
S
ec
tio
n
5
.
th
e
co
n
clu
s
io
n
s
an
d
f
u
tu
r
e
w
o
r
k
in
th
is
r
esear
ch
ar
ea
ar
e
p
r
esen
te
d
.
2.
P
ARAL
L
E
L
P
RO
CE
SI
NG
I
N
E
DG
E
CO
M
P
UT
I
NG
Mo
s
t
o
f
th
e
m
u
ltip
r
o
ce
s
s
in
g
r
elate
d
wo
r
k
s
in
SG
h
av
e
f
o
cu
s
ed
o
n
th
e
clo
u
d
o
r
f
o
g
p
a
r
t
:
in
b
o
th
ca
s
es,
th
er
e
ar
e
s
u
f
f
icien
t
co
m
p
u
tin
g
ca
p
ac
ities
to
p
er
f
o
r
m
d
ata
an
aly
tics
an
d
m
ac
h
in
e
l
ea
r
n
in
g
p
r
o
ce
s
s
es
as
in
[
5
-
7
]
.
R
ec
en
tly
m
u
ch
o
f
th
e
m
u
ltip
r
o
ce
s
s
in
g
wo
r
k
in
ed
g
e
co
m
p
u
tin
g
h
as f
o
cu
s
ed
o
n
im
ag
e
p
r
o
ce
s
s
in
g
an
d
co
m
p
u
ter
v
is
io
n
tak
in
g
ad
v
an
tag
e
o
f
th
e
ca
p
a
b
ilit
ies
o
f
th
e
GPUs
th
at
ar
e
b
ei
n
g
in
co
r
p
o
r
ated
in
to
SB
C
s
:
an
ex
am
p
le
o
f
th
ese
ar
e
th
e
[
8
-
1
2
]
r
elate
d
wo
r
k
s
.
T
h
e
u
s
e
o
f
SB
C
with
th
e
in
teg
r
atio
n
o
f
GPU
u
s
e
h
as
b
ee
n
s
tu
d
ied
b
y
v
ar
i
o
u
s
wo
r
k
s
[
1
3
-
1
5
]
in
w
h
ich
t
h
e
J
etso
n
Nan
o
b
o
ar
d
s
tan
d
s
o
u
t f
o
r
its
ex
ce
lle
n
t p
er
f
o
r
m
an
ce
an
d
lo
w
co
s
ts
.
On
th
e
o
th
er
h
an
d
,
co
m
p
u
tin
g
p
r
o
ce
s
s
in
g
is
b
ein
g
ca
r
r
ied
o
u
t
o
n
ed
g
e
with
th
is
ty
p
e
o
f
b
o
ar
d
f
o
r
d
if
f
er
en
t c
o
n
tex
ts
,
s
u
ch
as so
u
n
d
m
an
a
g
em
en
t a
n
d
er
r
o
r
d
etec
tio
n
[
1
6
,
1
7
]
.
O
n
e
o
f
th
e
m
ain
c
h
ar
ac
ter
is
tics
th
at
a
m
u
ltip
r
o
ce
s
s
in
g
s
y
s
tem
s
h
o
u
ld
h
a
v
e
is
th
e
h
an
d
lin
g
o
f
ap
p
r
o
p
r
iate
alg
o
r
ith
m
s
to
b
e
ex
ec
u
ted
i
n
th
is
class
o
f
co
n
cu
r
r
en
t
a
n
d
p
ar
allel
h
ar
d
war
e
p
latf
o
r
m
s
:
f
o
r
th
is
r
ea
s
o
n
,
v
ar
i
o
u
s
au
th
o
r
s
h
av
e
f
o
cu
s
ed
o
n
th
e
p
ar
alleliza
t
io
n
o
f
f
o
r
ec
asti
n
g
a
n
d
class
if
y
in
g
alg
o
r
ith
m
s
th
at
allo
w
im
p
r
o
v
em
e
n
ts
in
th
e
r
esu
lts
o
f
p
r
o
ce
s
s
tim
e
an
d
ef
f
ec
tiv
en
ess
[
1
8
-
2
1
]
.
I
n
th
e
p
ast
y
ea
r
,
th
er
e
was
an
ex
p
o
n
e
n
tially
g
r
o
win
g
in
ter
es
t
in
ed
g
e
c
o
m
p
u
tin
g
a
p
p
licatio
n
s
u
s
in
g
SB
C
lik
e
J
etso
n
Nan
o
.
Fo
r
in
s
tan
ce
,
in
[
2
2
]
a
n
o
v
el
er
asu
r
e
-
c
o
d
e
s
y
s
tem
is
p
r
esen
ted
.
I
n
[
2
3
]
th
e
au
th
o
r
s
p
r
esen
t
d
ee
p
lear
n
i
n
g
tec
h
n
iq
u
es
o
p
tim
ized
f
o
r
e
d
g
e
co
m
p
u
tin
g
.
Oth
er
w
o
r
k
s
ar
e
f
o
cu
s
ed
o
n
en
h
a
n
ce
d
b
io
m
etr
ics
s
ec
u
r
ity
in
ed
g
e
d
ev
ices
[
2
4
]
.
I
n
[
2
5
]
th
e
au
th
o
r
s
p
r
esen
t
a
R
ec
u
r
r
e
n
t
Neu
r
al
Netwo
r
k
(
R
NN)
f
o
r
class
if
icatio
n
p
o
wer
s
y
s
tem
s
co
n
tin
g
en
ce
s
u
s
in
g
ed
g
e
d
ev
ices.
Oth
er
n
o
v
el
d
iv
er
s
e
ap
p
licatio
n
s
u
s
in
g
J
etso
n
Nan
o
f
o
r
e
d
g
e
co
m
p
u
tin
g
a
r
e
p
r
esen
ted
in
wo
r
k
s
[
2
6
-
2
9
]
.
I
n
g
en
e
r
al,
ed
g
e
co
m
p
u
tin
g
ap
p
licatio
n
s
f
o
r
d
ata
a
n
aly
tics
in
th
e
s
m
ar
t
g
r
id
ar
e
o
n
ly
ju
s
t
b
eg
in
n
i
n
g
to
em
er
g
e,
an
d
as
I
o
T
h
a
r
d
wa
r
e
b
ec
o
m
es
m
o
r
e
ca
p
ab
le
o
f
d
ata
an
aly
tics
an
d
m
ac
h
in
e
lea
r
n
in
g
ap
p
licatio
n
s
,
wo
r
k
s
in
th
e
f
ield
o
f
SMS will
ap
p
ea
r
.
3.
F
O
RE
CAS
T
I
NG
IN
S
M
AR
T
M
E
T
E
R
I
NG
S
YST
E
M
USI
NG
A
M
UL
T
I
CO
RE
ARCH
I
T
E
C
T
UR
E
A
SM
wa
s
d
ev
elo
p
ed
u
s
in
g
a
J
etso
n
Nan
o
b
o
ar
d
u
s
in
g
cu
r
r
e
n
t a
n
d
v
o
ltag
e
s
en
s
o
r
s
.
Fig
u
r
e
1
s
h
o
ws a
d
iag
r
am
o
f
th
e
h
ar
d
war
e
ar
c
h
itectu
r
e
u
s
ed
f
o
r
th
e
ac
q
u
is
itio
n
o
f
elec
tr
ical
s
ig
n
al
v
alu
es
u
s
in
g
a
Sm
ar
tPi
en
er
g
y
b
o
ar
d
T
h
e
co
n
n
ec
tio
n
b
etwe
en
th
e
two
b
o
a
r
d
s
is
m
ad
e
th
r
o
u
g
h
th
e
Ge
n
er
al
I
n
p
u
t
an
d
Ou
tp
u
t
Po
r
ts
(
GPI
O)
.
Sm
ar
tPi
ess
en
tially
wo
r
k
s
with
t
h
e
R
asp
b
er
r
y
Pi
b
u
t
ca
n
b
e
ad
a
p
ted
to
th
e
J
etso
n
Nan
o
with
s
o
m
e
m
o
d
if
icatio
n
s
.
Ho
u
s
eh
o
ld
ap
p
lian
ce
s
an
d
Dis
tr
ib
u
ted
E
n
er
g
y
R
eso
u
r
ce
s
(
DE
R
)
s
u
ch
as
p
h
o
to
v
o
ltaic
p
an
els
an
d
win
d
tu
r
b
in
es
a
r
e
co
n
n
ec
ted
th
r
o
u
g
h
ea
ch
o
f
th
e
th
r
ee
p
h
ases
o
f
th
e
t
h
r
ee
-
p
h
ase
m
eter
t
o
its
co
r
r
esp
o
n
d
in
g
v
o
ltag
e
a
n
d
cu
r
r
en
t sen
s
o
r
.
T
h
e
d
ata
v
alu
es
u
s
ed
ar
e
c
u
r
r
e
n
t
in
ten
s
ity
(
A)
,
v
o
ltag
e
(
V)
,
a
ctiv
e
p
o
wer
(
W
)
,
p
o
wer
f
ac
to
r
,
r
ea
ctiv
e
p
o
wer
(
W
)
,
en
er
g
y
c
o
n
s
u
m
e
d
(
k
W
h
)
,
an
d
en
er
g
y
p
r
o
d
u
ce
d
(
k
W
h
)
.
On
e
o
f
t
h
e
m
ain
p
r
o
b
lem
s
in
th
e
SG
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Reco
n
f
ig
u
r
a
b
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4
8
6
4
S
ma
r
t m
eterin
g
s
ystem
d
a
ta
a
n
a
ly
tics
p
la
tfo
r
m
u
s
in
g
mu
ltico
r
e
ed
g
e
co
mp
u
tin
g
(
Ju
a
n
C
.
Oliva
r
es
-
R
o
ja
s
)
13
f
o
r
ec
asti
n
g
th
e
en
er
g
y
c
o
n
s
u
m
p
tio
n
/p
r
o
d
u
ctio
n
o
f
th
e
p
r
o
s
u
m
e
r
s
.
Fo
r
th
is
r
ea
s
o
n
,
we
ch
o
o
s
e
th
e
im
p
lem
en
tatio
n
o
f
a
f
o
r
ec
asti
n
g
alg
o
r
ith
m
f
o
r
test
in
g
th
e
SM
ar
ch
itectu
r
e
u
s
in
g
th
e
tim
e
s
er
ies ap
p
r
o
ac
h
.
Du
e
to
th
e
u
s
e
o
f
lar
g
e
tim
e
s
er
ies
is
v
er
y
c
o
m
p
lex
f
o
r
I
o
T
e
m
b
ed
d
e
d
d
e
v
ices,
it
is
n
ec
es
s
ar
y
to
im
p
r
o
v
e
th
e
f
o
r
ec
asti
n
g
m
o
d
el'
s
p
er
f
o
r
m
an
ce
at
ed
g
e
co
m
p
u
tin
g
.
Fig
u
r
e
1
.
Har
d
war
e
a
r
ch
itectu
r
e
o
f
th
e
m
u
ltico
r
e
s
m
ar
t
m
eter
J
esto
n
n
an
o
is
an
SB
C
with
a
Qu
ad
-
co
r
e
AR
M
m
icr
o
p
r
o
ce
s
s
o
r
with
a
GPU
in
clu
d
ed
with
1
2
8
-
c
o
r
es.
T
h
e
co
r
es
wer
e
u
s
ed
to
d
ev
el
o
p
a
p
a
r
allel
p
o
wer
co
n
s
u
m
p
tio
n
f
o
r
ec
ast
alg
o
r
ith
m
to
d
etec
t p
o
s
s
ib
le
an
o
m
alies
th
at
co
u
ld
b
e
co
n
s
id
er
e
d
f
ai
lu
r
es.
T
h
e
alg
o
r
ith
m
u
s
ed
is
d
esc
r
ib
ed
in
Alg
o
r
ith
m
1
.
A
l
g
o
r
i
t
h
m
1
.
P
a
r
a
l
l
e
l
A
R
I
M
A
I
n
p
u
t
:
A
Ti
m
e
S
e
r
i
e
s (
TS
)
w
i
t
h
e
n
e
r
g
y
v
a
l
u
e
s,
t
h
e
S
i
z
e
(
S
)
o
f
t
h
e
p
a
r
t
i
t
i
o
n
,
t
h
e
T
i
me
(
T
)
,
t
h
e
i
n
i
t
i
a
l
p
,
d
,
q
o
f
t
h
e
A
R
I
M
A
mo
d
e
l
.
1
:
S
p
l
i
t
t
h
e
d
a
t
a
se
t
o
f
s
i
z
e
S
a
c
c
o
r
d
i
n
g
t
o
t
h
e
i
n
d
i
c
a
t
e
d
t
i
m
e
T
2
:
A
ss
i
g
n
e
a
c
h
c
o
r
e
a
n
A
R
I
M
A
(
p
i
,d
i
,q
i
)
p
r
o
c
e
ss
3
:
C
o
mp
u
t
e
t
h
e
A
R
I
M
A
(
p
i
,d
i
,q
i
)
v
a
l
u
e
s
o
f
t
h
e
TS
t
h
a
t
d
o
n
o
t
o
v
e
r
l
a
p
4
:
F
o
r
a
l
l
ser
i
e
s w
h
o
se
v
a
l
u
e
s
o
v
e
r
l
a
p
,
i
n
t
e
g
r
a
t
e
t
h
e
m
i
ss
i
n
g
v
a
l
u
e
s w
i
t
h
t
h
e
d
a
t
a
s
e
t
S
i
+
1
5
:
I
n
t
e
g
r
a
t
e
a
l
l
r
e
s
u
l
t
s
O
u
t
p
u
t
:
c
o
mp
a
r
i
s
o
n
o
f
t
h
e
TS
d
a
t
a
w
i
t
h
i
t
s
e
l
f
u
s
i
n
g
m
e
t
r
i
c
l
i
k
e
R
e
l
a
t
i
v
e
A
b
so
l
u
t
e
Er
r
o
r
(
R
A
E)
I
n
g
en
er
al,
th
e
alg
o
r
ith
m
u
s
es
th
e
AR
I
MA
tech
n
iq
u
e
d
iv
id
i
n
g
th
e
d
ata
b
y
p
er
io
d
s
d
ep
en
d
in
g
o
n
th
e
g
r
an
u
lar
ity
o
f
th
e
s
tu
d
y
tim
e
,
wh
ich
ca
n
b
e
p
er
d
ay
,
wee
k
,
m
o
n
t
h
,
o
r
y
ea
r
.
W
ith
d
ata
s
eg
m
en
tatio
n
,
th
e
ca
lcu
latio
n
s
ar
e
p
r
o
ce
s
s
ed
in
p
ar
allel
ac
co
r
d
in
g
to
th
e
in
d
icate
d
tim
e
win
d
o
w,
ex
ce
p
t
f
o
r
th
e
d
ata
th
at
in
ter
s
ec
ts
b
etwe
en
p
er
io
d
s
th
a
t n
ee
d
to
b
e
c
o
m
p
leted
s
ep
a
r
ately
.
O
n
ce
th
e
co
m
p
u
tatio
n
s
h
a
v
e
b
ee
n
ca
r
r
ied
o
u
t
in
p
ar
allel,
t
h
e
r
esu
lts
ar
e
g
at
h
er
ed
.
Alg
o
r
ith
m
1
.
was
im
p
l
em
en
ted
u
s
in
g
th
e
Py
th
o
n
lan
g
u
ag
e
t
h
r
o
u
g
h
th
e
s
tats
m
o
d
els
l
ib
r
ar
y
u
s
in
g
th
e
Py
C
UDA
f
r
am
ewo
r
k
f
o
r
h
a
n
d
lin
g
p
ar
allel
ca
lcu
latio
n
s
th
r
o
u
g
h
th
e
v
ar
io
u
s
GPU
co
r
es
.
T
h
e
im
p
lem
en
tati
o
n
tr
ies to
u
s
e
th
e
1
2
8
a
v
ailab
l
e
co
r
es.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
o
co
m
p
ar
e
,
a
tr
ad
itio
n
al
s
eq
u
en
tial c
lass
if
icat
io
n
alg
o
r
ith
m
was u
s
ed
,
co
m
p
ar
in
g
it with
th
e
p
ar
allel
alg
o
r
ith
m
d
escr
ib
ed
p
r
ev
io
u
s
ly
in
Alg
o
r
ith
m
1
.
Sin
ce
th
er
e
ar
e
f
ew
m
ea
s
u
r
em
en
t
d
ata
(
1
4
m
o
n
th
s
o
f
r
ea
d
in
g
s
)
,
it
was
d
ec
id
ed
to
u
s
e
th
e
d
ata
s
e
t
s
d
escr
ib
ed
in
I
E
E
E
Data
Po
r
t
[
3
0
]
an
d
UC
I
Ma
ch
in
e
L
ea
r
n
in
g
[
3
1
]
with
t
h
eir
ad
a
p
tatio
n
o
f
d
ata
to
th
e
v
ar
iab
les
o
f
i
n
ter
est
in
th
is
wo
r
k
(
T
im
estam
p
an
d
E
n
er
g
y
C
o
n
s
u
m
p
tio
n
)
g
r
o
u
p
i
n
g
ac
co
r
d
in
g
th
e
p
er
io
d
o
f
ev
al
u
atio
n
.
T
h
e
I
E
E
E
Data
s
et
h
as a
lo
t o
f
v
ar
iab
les an
d
it h
as
d
ata
f
o
r
3
2
4
8
SM
in
o
n
e
y
ea
r
.
T
h
e
r
ea
d
in
g
s
ar
e
in
s
lo
ts
o
f
3
0
m
in
u
tes.
T
h
e
d
ata
m
u
s
t b
e
g
r
o
u
p
ed
ac
c
o
r
d
in
g
o
f
ea
ch
SM.
T
h
e
UC
I
d
ataset
h
as
in
f
o
r
m
ati
o
n
o
f
o
n
ly
o
n
e
m
eter
in
s
lo
ts
o
f
1
5
-
m
in
u
tes.
T
h
e
en
e
r
g
y
h
as e
x
p
r
ess
ed
in
ea
ch
o
f
t
h
e
th
r
ee
p
h
ases
; f
o
r
th
is
r
ea
s
o
n
,
th
e
en
e
r
g
y
c
o
n
s
u
m
p
tio
n
m
u
s
t b
e
s
u
m
m
ar
ized
.
T
h
e
to
tal
n
u
m
b
er
o
f
in
s
tan
ce
s
ar
e
2
,
0
7
5
,
2
5
9
.
T
h
e
r
ea
d
in
g
s
ar
e
f
r
o
m
b
etwe
en
Dec
em
b
er
2
0
0
6
an
d
No
v
em
b
e
r
2
0
1
0
(
4
7
m
o
n
th
s
)
.
T
ab
le
1
s
h
o
ws
th
e
r
esu
lts
o
f
th
e
ex
ec
u
tio
n
o
f
f
o
r
ec
asti
n
g
m
o
d
els
u
s
in
g
lin
ea
r
ar
ch
itect
u
r
e
c
o
m
p
ar
in
g
t
h
em
with
th
eir
p
ar
allel
e
q
u
iv
alen
t,
tak
in
g
i
n
to
ac
c
o
u
n
t
d
if
f
er
en
t
p
ar
titi
o
n
s
izes:
wee
k
,
m
o
n
th
,
d
ay
,
an
d
y
ea
r
f
o
r
th
e
I
E
E
E
d
ataset,
wh
ile
in
T
a
b
le
2
s
h
o
ws
th
e
s
am
e
r
esu
lts
b
u
t
f
o
r
th
e
UC
I
d
ataset.
T
h
e
tim
e
s
ar
e
ex
p
r
ess
ed
in
m
in
u
tes.
B
o
t
h
m
o
d
els h
a
v
e
th
e
s
am
e
ac
cu
r
ac
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
20
89
-
4
8
6
4
I
n
t J
R
ec
o
n
f
ig
u
r
a
b
le
&
E
m
b
ed
d
ed
Sy
s
t
,
Vo
l.
10
,
No
.
1
,
Ma
r
c
h
202
1
:
11
–
17
14
T
h
e
r
esu
lts
s
h
o
w
a
co
n
s
id
er
ab
le
d
ec
r
ea
s
e
in
ex
ec
u
tio
n
tim
es
in
b
o
th
ca
s
es,
al
th
o
u
g
h
th
ey
ar
e
d
ep
en
d
e
n
t
o
n
th
e
s
ize
o
f
th
e
d
ata
an
d
th
e
g
r
an
u
lar
ity
o
f
th
e
in
f
o
r
m
atio
n
a
n
aly
s
is
.
T
h
e
g
r
ea
ter
th
e
am
o
u
n
t
o
f
d
ata,
th
e
lo
n
g
er
t
h
e
p
r
o
ce
s
s
in
g
tim
e.
T
h
e
co
m
p
le
x
ity
o
f
A
R
I
MA
s
eq
u
en
tial
is
eq
u
iv
alen
t
to
O(
n
)
an
d
th
e
p
ar
allel
v
er
s
io
n
is
eq
u
iv
alen
t
to
O(
n
lo
g
n
)
+
C
wh
er
e
C
i
s
th
e
d
elay
tim
e
to
in
teg
r
ate
th
e
n
o
n
-
p
a
r
allel
d
ata.
B
o
th
m
o
d
els
(
p
ar
allel
an
d
n
o
n
-
p
ar
allel)
h
av
e
th
e
s
am
e
ac
cu
r
ac
y
R
2
=8
9
.
9
7
%
with
th
e
I
E
E
E
d
ataset
an
d
R
2
=8
3
.
2
5
% with
th
e
UC
I
d
ata
s
e
t.
T
ab
le
1
.
Par
allel
AR
I
MA
with
I
E
E
E
d
ataset
M
o
d
e
l
P
r
o
c
e
ss
i
n
g
Ti
m
e
s
D
a
y
W
e
e
k
M
o
n
t
h
Y
e
a
r
S
e
q
u
e
n
t
i
a
l
1
4
7
.
7
6
4
3
.
6
4
4
.
9
9
1
.
3
3
P
a
r
a
l
l
e
l
9
.
2
7
5
.
4
7
3
.
0
6
1
.
0
2
T
ab
le
2
.
Par
allel
AR
I
MA
with
UC
I
d
ataset
M
o
d
e
l
P
r
o
c
e
ss
i
n
g
Ti
m
e
s
D
a
y
W
e
e
k
M
o
n
t
h
Y
e
a
r
S
e
q
u
e
n
t
i
a
l
2
4
8
.
5
3
5
7
.
2
5
6
.
3
8
1
.
7
2
P
a
r
a
l
l
e
l
1
2
.
3
9
6
.
7
5
3
.
8
0
1
.
2
3
T
h
er
e
ar
e
o
th
er
m
et
h
o
d
s
/tech
n
iq
u
es
f
o
r
a
n
o
m
aly
d
etec
tio
n
th
at
co
u
ld
b
e
im
p
lem
en
ted
in
th
i
s
p
ar
allel
f
r
am
ewo
r
k
.
Sti
ll,
n
o
t
all
m
eth
o
d
s
/tech
n
iq
u
es
ar
e
s
u
itab
le
to
b
e
p
r
o
g
r
am
m
ed
in
p
ar
allel
d
u
e
to
r
estrictio
n
s
o
f
an
y
p
ar
allel
alg
o
r
ith
m
.
Par
ticu
lar
ly
,
th
e
s
im
p
lest
im
p
lem
en
ta
tio
n
s
ar
e
b
etter
f
o
r
I
o
T
an
d
em
b
ed
d
ed
d
ev
ices.
5.
CO
NCLU
SI
O
N
AND
F
U
T
U
RE
WO
RK
T
h
e
u
s
e
o
f
ed
g
e
co
m
p
u
tin
g
is
g
r
o
win
g
d
ay
b
y
d
ay
,
a
llo
win
g
I
o
T
d
ev
ices
to
h
av
e
g
r
ea
ter
f
u
n
ctio
n
ality
.
I
n
t
h
e
ca
s
e
o
f
th
e
s
m
ar
t
g
r
id
,
m
a
n
y
I
o
T
d
ev
ices
ar
e
b
e
n
ef
itin
g
f
r
o
m
th
e
m
an
ag
e
m
en
t
o
f
co
m
p
u
tin
g
at
th
e
ed
g
e,
an
d
p
ar
ticu
lar
ly
in
SMS,
it
will
b
e
wid
ely
u
s
ed
to
im
p
r
o
v
e
d
ata
an
aly
tics
p
r
o
ce
s
s
es
th
r
o
u
g
h
v
ar
io
u
s
s
tatis
tical,
p
r
o
b
ab
ilis
tic,
an
d
m
ac
h
in
e
lear
n
in
g
an
d
ar
tific
ial
in
tellig
en
ce
.
T
h
e
in
teg
r
atio
n
o
f
m
u
ltip
r
o
ce
s
s
in
g
ar
c
h
itectu
r
es
in
t
h
e
I
o
T
h
ar
d
war
e
will
m
ak
e
it
p
o
s
s
ib
le
to
im
p
r
o
v
e
ex
ec
u
tio
n
tim
es
i
n
f
o
r
ec
asti
n
g
an
d
d
ata
class
if
icatio
n
m
o
d
els
in
ed
g
e
co
m
p
u
t
in
g
.
Fo
r
th
is
,
it
will
b
e
n
ec
e
s
s
ar
y
to
ad
ap
t
th
e
alg
o
r
ith
m
s
s
o
th
at
th
ey
ca
n
b
e
ex
ec
u
ted
in
p
ar
allel.
T
h
is
wo
r
k
d
em
o
n
s
tr
ates
th
at
it
is
alr
ea
d
y
f
ea
s
ib
le
to
p
er
f
o
r
m
ed
g
e
co
m
p
u
tin
g
u
s
in
g
a
m
u
ltip
r
o
ce
s
s
in
g
ar
c
h
itectu
r
e
in
s
m
ar
t
m
ete
r
in
g
s
y
s
tem
s
.
T
h
e
r
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ed
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ak
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ata
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ata.
As
f
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r
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th
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ce
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esig
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elab
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ate
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ata
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r
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s
s
in
g
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r
ith
m
is
co
n
tem
p
lated
to
tak
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a
d
v
an
t
ag
e
o
f
th
e
co
m
p
u
tin
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ca
p
ab
ilit
ies
o
f
SM.
B
esid
es,
it
is
co
n
tem
p
lated
t
h
e
in
teg
r
atio
n
o
f
t
h
is
ar
ch
itectu
r
e
o
f
m
ea
s
u
r
em
en
t
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d
d
ata
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aly
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u
s
in
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co
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p
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ch
em
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at
th
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lev
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o
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g
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ata
co
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n
tr
ato
r
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t
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clo
u
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th
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f
th
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Me
ter
in
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Dat
ab
ase
Ma
n
ag
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t Sy
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tem
s
(
MD
MS)
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
is
wo
r
k
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p
ar
tially
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u
p
p
o
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ed
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y
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ec
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o
ló
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ico
Nac
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n
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e
Mé
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ts
7
9
4
8
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2
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P
an
d
8
0
0
0
.
2
0
-
P.
RE
F
E
R
E
NC
E
S
[1
]
S
.
Bo
n
il
la
,
e
t
a
l
.
,
“
In
d
u
str
y
4
.
0
a
n
d
su
sta
in
a
b
il
it
y
imp
li
c
a
ti
o
n
s
:
A
sc
e
n
a
rio
-
b
a
se
d
a
n
a
ly
si
s
o
f
t
h
e
imp
a
c
ts
a
n
d
c
h
a
ll
e
n
g
e
s,
”
S
u
sta
i
n
a
b
il
it
y
,
v
o
l.
1
0
,
n
o
.
1
0
,
20
1
8
,
d
o
i:
1
0
.
3
3
9
0
/s
u
1
0
1
0
3
7
4
0
.
[2
]
G
.
Ko
n
sta
n
to
p
o
u
lo
s,
e
t
a
l
.
,
“
To
w
a
rd
s
th
e
in
teg
ra
ti
o
n
o
f
m
o
d
e
rn
p
o
we
r
sy
ste
m
s
in
to
a
c
y
b
e
r
–
p
h
y
si
c
a
l
fra
m
e
wo
rk
,”
En
e
rg
ies
,
v
o
l.
1
3
,
n
o
.
9
,
p
.
2
1
6
9
,
2
0
2
0
,
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o
i
:
1
0
.
3
3
9
0
/e
n
1
3
0
9
2
1
6
9
.
[3
]
M
.
Da
n
e
sh
v
a
r
a
n
d
S
.
As
a
d
i
,
“
CP
S
-
b
a
se
d
tran
sa
c
ti
v
e
e
n
e
rg
y
tec
h
n
o
lo
g
y
fo
r
sm
a
rt
g
ri
d
s
,”
i
n
Cy
b
e
r
-
Ph
y
sic
a
l
S
y
ste
ms
in
t
h
e
Bu
i
lt
E
n
v
iro
n
me
n
t
,
S
p
rin
g
e
r
,
2
0
2
0
,
p
p
.
3
2
3
-
3
3
8
,
d
o
i
:
1
0
.
1
0
0
7
/9
7
8
-
3
-
0
3
0
-
4
1
5
6
0
-
0
_
1
8
.
[4
]
M.
F
o
rc
a
n
a
n
d
M
.
M
a
k
sim
o
v
ić,
“
Clo
u
d
-
F
o
g
-
b
a
se
d
a
p
p
ro
a
c
h
f
o
r
sm
a
rt
g
rid
m
o
n
it
o
ri
n
g,
”
S
imu
l
a
ti
o
n
M
o
d
e
ll
i
n
g
Pra
c
ti
c
e
a
n
d
T
h
e
o
ry
,
v
o
l.
1
0
1
,
p
p
.
1
-
1
8
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/j
.
sim
p
a
t.
2
0
1
9
.
1
0
1
9
8
8
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Reco
n
f
ig
u
r
a
b
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4
8
6
4
S
ma
r
t m
eterin
g
s
ystem
d
a
ta
a
n
a
ly
tics
p
la
tfo
r
m
u
s
in
g
mu
ltico
r
e
ed
g
e
co
mp
u
tin
g
(
Ju
a
n
C
.
Oliva
r
es
-
R
o
ja
s
)
15
[5
]
Z.
Li
a
n
d
F
.
Ya
n
g
,
“
Ad
v
a
n
c
e
d
m
e
terin
g
in
fra
str
u
c
tu
re
a
n
d
g
r
a
p
h
ics
p
ro
c
e
ss
in
g
u
n
it
tec
h
n
o
l
o
g
ies
in
e
lec
tri
c
d
istri
b
u
ti
o
n
n
e
two
rk
s
,
”
i
n
El
e
c
tri
c
Distrib
u
ti
o
n
Ne
two
rk
M
a
n
a
g
e
me
n
t
a
n
d
Co
n
tro
l
Po
we
r
S
y
ste
ms
,
S
p
ri
n
g
e
r
,
2
0
1
8
,
p
p
.
3
0
9
-
3
4
5
,
d
o
i
:
1
0
.
1
0
0
7
/9
7
8
-
9
8
1
-
10
-
7
0
0
1
-
3
_
1
2
.
[6
]
S
.
G
ru
b
m
ü
ll
e
r,
e
t
a
l
.
,
“
P
re
d
ict
iv
e
e
n
e
rg
y
m
a
n
a
g
e
m
e
n
t
o
n
m
u
lt
i
-
c
o
re
sy
ste
ms
,”
in
Co
mp
re
h
e
n
siv
e
En
e
rg
y
M
a
n
a
g
e
me
n
t
-
S
a
fe
Ad
a
p
ta
ti
o
n
,
Pre
d
ictive
Co
n
tr
o
l
a
n
d
T
h
e
rm
a
l
M
a
n
a
g
e
me
n
,
S
p
ri
n
g
e
r
,
2
0
1
8
,
p
p
.
4
7
-
6
6
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
3
1
9
-
5
7
4
4
5
-
5
_
4
.
[7
]
O
.
De
b
a
u
c
h
e
,
S
.
M
a
h
m
o
u
d
i
,
S
.
A
.
M
a
h
m
o
u
d
i
,
P
.
M
a
n
n
e
b
a
c
k
,
J
.
Bin
d
e
ll
e
,
a
n
d
F
.
Leb
e
a
u
,
“
E
d
g
e
c
o
m
p
u
ti
n
g
a
n
d
a
rti
ficia
l
in
telli
g
e
n
c
e
fo
r
re
a
l
-
ti
m
e
p
o
u
lt
ry
m
o
n
it
o
ri
n
g
,
”
Pro
c
e
d
ia
Co
mp
u
ter
S
c
ien
c
e
,
v
o
l
.
1
7
5
,
p
p
.
5
3
4
-
5
4
1
,
2
0
2
0
,
d
o
i:
1
0
.
1
0
1
6
/j
.
p
ro
c
s.
2
0
2
0
.
0
7
.
0
7
6
.
[8
]
P.
Álv
a
re
z
a
n
d
M
.
L
u
ís,
“
Co
n
fi
g
u
ra
c
ió
n
y
e
jec
u
c
i
ó
n
d
e
a
lg
o
rit
m
o
s
d
e
v
isi
ó
n
a
rti
f
icia
l
e
n
la
tar
jeta
Nv
id
ia
Je
tso
n
TK1
De
v
Kit
,
”
2
0
1
7
,
[On
li
n
e
].
Av
a
il
a
b
le:
h
tt
p
s://
e
b
u
a
h
.
u
a
h
.
e
s/d
s
p
a
c
e
/h
a
n
d
le/1
0
0
1
7
/
3
0
2
0
4
.
[9
]
A.
Ba
su
lt
o
-
Lan
tso
v
a
,
J
.
A.
P
a
d
il
la
-
M
e
d
i
n
a
,
F
.
J.
P
e
re
z
-
P
in
a
l
,
a
n
d
A
.
I.
Ba
rra
n
c
o
-
G
u
ti
e
rre
z
“
P
e
rfo
rm
a
n
c
e
c
o
m
p
a
ra
ti
v
e
o
f
Op
e
n
CV
Te
m
p
lat
e
M
a
tch
in
g
m
e
th
o
d
o
n
Je
tso
n
TX
2
a
n
d
Je
tso
n
Na
n
o
d
e
v
e
lo
p
e
r
k
it
s
,
”
in
2
0
2
0
1
0
t
h
An
n
u
a
l
C
o
mp
u
ti
n
g
a
n
d
Co
mm
u
n
i
c
a
ti
o
n
W
o
rk
sh
o
p
a
n
d
C
o
n
fer
e
n
c
e
(CCW
C)
,
Las
Ve
g
a
s,
NV
,
USA,
2
0
2
0
,
p
p
.
0
8
1
2
-
0
8
1
6
,
d
o
i
:
1
0
.
1
1
0
9
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WC4
7
5
2
4
.
2
0
2
0
.
9
0
3
1
1
6
6
.
[1
0
]
P
.
In
t
h
a
n
o
n
a
n
d
S
.
M
u
n
g
si
n
g
,
“
De
tec
ti
o
n
o
f
d
r
o
ws
in
e
ss
fro
m
fa
c
ial
ima
g
e
s
in
re
a
l
-
ti
m
e
v
id
e
o
m
e
d
ia
u
sin
g
n
v
i
d
ia
Je
tso
n
Na
n
o
,
”
2
0
2
0
1
7
th
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
El
e
c
trica
l
En
g
i
n
e
e
rin
g
/E
lec
tro
n
ics
,
Co
mp
u
ter
,
T
e
lec
o
mm
u
n
ica
ti
o
n
s
a
n
d
In
f
o
r
ma
ti
o
n
T
e
c
h
n
o
l
o
g
y
(ECT
I
-
CO
N)
,
P
h
u
k
e
t,
T
h
a
il
a
n
d
,
2
0
2
0
,
p
p
.
2
4
6
-
2
4
9
,
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o
i:
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0
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1
1
0
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/
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I
-
CON
4
9
2
4
1
.
2
0
2
0
.
9
1
5
8
2
3
5
.
[1
1
]
W.
Viji
t
k
u
n
sa
wa
t
a
n
d
P
.
Ch
a
n
tn
g
a
rm
,
“
c
o
m
p
a
riso
n
o
f
m
a
c
h
in
e
l
e
a
rn
in
g
a
lg
o
r
it
h
m
’s
o
n
se
lf
-
d
ri
v
i
n
g
c
a
r
n
a
v
i
g
a
ti
o
n
u
sin
g
Nv
id
ia
Je
tso
n
Na
n
o
,
”
o
n
2
0
2
0
1
7
th
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
El
e
c
tri
c
a
l
E
n
g
i
n
e
e
rin
g
/E
lec
tro
n
ics
,
Co
mp
u
ter
,
T
e
lec
o
mm
u
n
ic
a
ti
o
n
s
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
(E
CT
I
-
CON)
,
P
h
u
k
e
t
,
Th
a
il
a
n
d
,
2
0
2
0
,
p
p
.
2
0
1
-
2
0
4
,
d
o
i:
1
0
.
1
1
0
9
/E
CTI
-
CON
4
9
2
4
1
.
2
0
2
0
.
9
1
5
8
3
1
1
.
[1
2
]
S
.
Ullah
a
n
d
D.
Kim
,
“
Be
n
c
h
m
a
rk
in
g
Je
tso
n
p
latfo
rm
fo
r
3
D
p
o
i
n
t
-
c
lo
u
d
a
n
d
h
y
p
e
r
-
s
p
e
c
tral
ima
g
e
c
las
sifica
ti
o
n
,
”
in
2
0
2
0
IEE
E
In
ter
n
a
ti
o
n
a
l
Co
n
fe
re
n
c
e
o
n
Bi
g
Da
ta
a
n
d
S
m
a
rt
C
o
mp
u
ti
n
g
(Bi
g
Co
mp
)
,
B
u
sa
n
,
Ko
re
a
(S
o
u
th
)
,
2
0
2
0
,
p
p
.
4
7
7
-
4
8
2
,
d
o
i:
1
0
.
1
1
0
9
/Bi
g
Co
m
p
4
8
6
1
8
.
2
0
2
0
.
0
0
-
21.
[1
3
]
S
.
Ca
ss
,
“
Nv
id
ia
m
a
k
e
s
it
e
a
sy
to
e
m
b
e
d
AI:
Th
e
Je
tso
n
n
a
n
o
p
a
c
k
s
a
lo
t
o
f
m
a
c
h
in
e
-
lea
rn
i
n
g
p
o
we
r
in
t
o
DIY
p
ro
jec
ts
,”
IEE
E
S
p
e
c
tru
m
,
v
o
l.
57
,
n
o
.
7
,
p
p
.
1
4
-
1
6
,
2
0
2
0
,
d
o
i
:
1
0
.
1
1
0
9
/M
S
P
EC.
2
0
2
0
.
9
1
2
6
1
0
2
.
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u
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ra
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t
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.
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“
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7
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,
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Ah
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,
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m
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8
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1
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,
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“
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.
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Li
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Q.
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[2
3
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.
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Wan
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Li
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Xu
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4
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,
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,
D.
Hu
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n
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,
C.
C
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Lee
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6
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Aim
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T.
De
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2
0
.
3
0
2
9
7
9
9
.
[3
0
]
Tri
g
u
e
r
o
,
“
IE
EE
-
CIS
tec
h
n
ica
l
c
h
a
ll
e
n
g
e
o
n
e
n
e
r
g
y
p
re
d
icti
o
n
fr
o
m
sm
a
rt
m
e
ter
d
a
ta
,
”
IEE
E
Da
t
a
Po
rt
,
2
0
2
0
,
d
o
i
:
h
tt
p
s:/
/d
x
.
d
o
i.
o
rg
/
1
0
.
2
1
2
2
7
/
2
n
p
g
-
c
2
8
0
.
[3
1
]
G
.
Hé
b
ra
il
.
e
t
a
l
.
,
“
In
d
iv
id
u
a
l
h
o
u
se
h
o
ld
e
lec
tri
c
p
o
we
r
c
o
n
su
m
p
ti
o
n
Da
ta
S
e
t,
”
UCI
M
a
c
h
i
n
e
L
e
a
rn
in
g
,
2
0
1
2
.
[On
li
n
e
].
Av
a
i
lab
le:
h
tt
p
s:/
/arc
h
iv
e
.
ics
.
u
c
i.
e
d
u
/ml/
d
a
tas
e
ts/i
n
d
i
v
i
d
u
a
l+h
o
u
se
h
o
ld
+
e
lec
tri
c
+
p
o
we
r+
c
o
n
su
m
p
ti
o
n
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Ju
a
n
C.
Oliv
a
re
s
-
Ro
jas
re
c
e
iv
e
d
h
is
BEn
g
d
e
g
re
e
in
Co
m
p
u
t
e
r
S
y
ste
m
s
a
t
In
stit
u
to
Tec
n
o
ló
g
ico
d
e
M
o
re
li
a
a
n
d
h
is
M
S
c
d
e
g
re
e
i
n
c
o
m
p
u
ter
sc
ien
c
e
s
a
t
Na
ti
o
n
a
l
Ce
n
ter
fo
r
Re
se
a
rc
h
a
n
d
Tec
h
n
o
lo
g
ica
l
D
e
v
e
lo
p
m
e
n
t
(C
ENIDET
).
He
is
p
u
rs
u
in
g
a
P
h
.
D.
in
e
n
g
in
e
e
rin
g
s
c
ien
c
e
s
fro
m
In
stit
u
to
Tec
n
o
ló
g
ico
d
e
M
o
re
li
a
.
He
is
fu
ll
-
ti
m
e
p
ro
fe
ss
o
r
a
t
De
p
a
rtme
n
t
o
f
sy
ste
m
s
a
n
d
c
o
m
p
u
ti
n
g
,
In
sti
tu
t
o
Tec
n
o
ló
g
ico
d
e
M
o
re
li
a
.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts in
c
lu
d
e
c
y
b
e
r
s
e
c
u
rit
y
,
sm
a
rt
g
r
id
a
n
d
d
istri
b
u
ted
sy
ste
m
s.
En
riq
u
e
Re
y
e
s
-
Arc
h
u
n
d
ia
re
c
e
iv
e
d
h
is
BEn
g
d
e
g
re
e
in
El
e
c
tro
n
ic
s
a
n
d
P
h
.
D
.
i
n
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
a
t
I
n
stit
u
to
Tec
n
o
ló
g
ico
d
e
M
o
re
li
a
a
n
d
h
is
M
S
c
d
e
g
re
e
in
E
lec
tro
n
ics
a
t
Na
ti
o
n
a
l
Ce
n
ter fo
r
Re
se
a
rc
h
a
n
d
Tec
h
n
o
l
o
g
ica
l
De
v
e
lo
p
m
e
n
t
(CE
NID
ET
).
He
is f
u
ll
-
ti
me
p
ro
fe
ss
o
r
a
t
e
lec
tro
n
ics
g
ra
d
u
a
ted
p
r
o
g
ra
m
,
I
n
stit
u
to
Tec
n
o
ló
g
ico
d
e
M
o
re
li
a
.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts in
c
lu
d
e
c
o
n
tr
o
l
sy
ste
m
s fo
r
sm
a
rt
g
rid
a
n
d
si
g
n
a
l
p
ro
c
e
ss
in
g
.
Jo
sé
A.
G
u
ti
é
rre
z
-
G
n
e
c
c
h
i
re
c
e
iv
e
d
h
is
BE
n
g
d
e
g
re
e
i
n
In
d
u
strial
El
e
c
tro
n
ics
a
t
I
n
stit
u
t
o
Tec
n
o
ló
g
ico
d
e
S
a
n
Lu
is
P
o
t
o
si,
a
n
d
h
is
M
S
c
d
e
g
re
e
i
n
I
n
stru
m
e
n
tatio
n
a
n
d
An
a
l
y
ti
c
a
l
S
c
ien
c
e
a
n
d
h
is
P
h
.
D.
in
El
e
c
t
rica
l
a
n
d
El
e
c
tro
n
ics
E
n
g
i
n
e
e
rin
g
a
t
th
e
Un
i
v
e
rsity
o
f
M
a
n
c
h
e
ste
r.
He
is
fu
ll
-
ti
m
e
p
ro
fe
ss
o
r
a
t
e
lec
tro
n
ics
g
ra
d
u
a
ted
p
r
o
g
ra
m
,
In
stit
u
t
o
Tec
n
o
ló
g
ico
d
e
M
o
re
li
a
.
His
c
u
rr
e
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
S
m
a
rt
M
e
terin
g
S
y
ste
m
s
in
d
iv
e
rse
field
s s
u
c
h
a
s b
i
o
m
e
d
ica
l
a
n
d
a
g
ric
u
lt
u
ra
l
.
Ism
a
e
l
M
o
li
n
a
-
M
o
re
n
o
re
c
e
iv
e
d
h
is
BEn
g
d
e
g
re
e
i
n
I
n
d
u
strial
El
e
c
tro
n
ics
a
t
In
stit
u
t
o
Tec
n
o
ló
g
ico
d
e
S
a
n
L
u
is
P
o
t
o
si,
h
is
M
S
c
d
e
g
re
e
i
n
El
e
c
tro
n
ics
a
t
Tec
h
n
o
lo
g
ica
l
I
n
stit
u
te
o
f
M
o
re
li
a
,
a
n
d
h
is
P
h
.
D.
a
t
Un
iv
e
rsid
a
d
M
ich
o
a
c
a
n
a
.
He
is
fu
ll
-
ti
m
e
p
ro
fe
ss
o
r
a
t
e
lec
tro
n
ics
g
ra
d
u
a
ted
p
r
o
g
ra
m
,
In
stit
u
t
o
Tec
n
o
ló
g
ico
d
e
M
o
re
li
a
.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
p
o
we
r
q
u
a
li
t
y
sta
te
e
stim
a
ti
o
n
,
p
o
we
rs
e
lec
t
ro
n
ics
,
a
n
d
e
m
b
e
d
d
e
d
sy
ste
m
s.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Reco
n
f
ig
u
r
a
b
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4
8
6
4
S
ma
r
t m
eterin
g
s
ystem
d
a
ta
a
n
a
ly
tics
p
la
tfo
r
m
u
s
in
g
mu
ltico
r
e
ed
g
e
co
mp
u
tin
g
(
Ju
a
n
C
.
Oliva
r
es
-
R
o
ja
s
)
17
Ad
rian
a
Téllez
-
An
g
u
ia
n
o
re
c
e
iv
e
d
h
is
BRn
g
d
e
g
re
e
in
El
e
c
tro
n
ics
a
t
In
stit
u
t
o
Tec
n
o
l
ó
g
ic
o
d
e
M
o
re
li
a
,
a
n
d
h
e
r
M
S
c
.
in
El
e
c
tro
n
ics
a
n
d
P
h
.
D.
a
t
Ce
n
tro
Na
c
io
n
a
l
d
e
In
v
e
sti
g
a
c
ió
n
y
De
sa
rro
ll
o
Tec
n
o
ló
g
ico
(CENIDET
).
S
h
e
is
fu
ll
-
ti
m
e
p
r
o
fe
ss
o
r
a
t
e
lec
tro
n
ics
g
ra
d
u
a
ted
p
ro
g
ra
m
,
In
stit
u
to
Tec
n
o
l
ó
g
ico
d
e
M
o
re
li
a
.
He
r
re
se
a
rc
h
li
n
e
s
a
re
S
ig
n
a
l
P
ro
c
e
ss
in
g
,
Au
to
m
a
ti
c
Co
n
tro
l
a
n
d
Re
n
e
wa
b
l
e
s E
n
e
rg
ies
.
Ja
ime
Ce
rd
a
-
Ja
c
o
b
o
re
c
e
iv
e
d
t
h
e
El
e
c
tri
c
a
l
En
g
in
e
e
r
d
e
g
re
e
fr
o
m
Un
iv
e
rsid
a
d
M
ich
o
a
c
a
n
a
d
e
S
a
n
Nic
o
las
d
e
Hi
d
a
lg
o
,
M
o
re
li
a
,
M
e
x
ico
,
i
n
1
9
9
0
,
th
e
M
.
S
c
.
in
c
o
m
p
u
ter
sc
ien
c
e
fr
o
m
th
e
Re
se
a
rc
h
a
n
d
Ad
v
a
n
c
e
d
S
tu
d
ies
Ce
n
ter
a
t
IP
N,
M
e
x
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Cit
y
,
M
e
x
ico
i
n
2
0
0
0
a
n
d
th
e
P
h
D
d
e
g
re
e
in
e
lec
tri
c
a
l
a
n
d
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lec
tro
n
ic
e
n
g
in
e
e
rin
g
fro
m
Un
iv
e
rsi
ty
o
f
S
o
u
t
h
a
m
p
to
n
,
UK
in
2
0
1
0
.
He
is
with
th
e
Un
i
v
e
rsid
a
d
M
ich
o
a
c
a
n
a
a
s
As
so
c
iat
e
P
ro
fe
ss
o
r.
His
a
c
tu
a
l
re
se
a
rc
h
a
c
ti
v
it
ies
c
o
n
c
e
n
trate
i
n
t
h
e
a
re
a
s
o
f
m
a
c
h
in
e
lea
rn
i
n
g
,
o
p
t
imiz
a
ti
o
n
a
n
d
it
s
d
e
c
e
n
traliza
ti
o
n
b
y
g
ra
p
h
m
a
n
i
p
u
latio
n
s.
Evaluation Warning : The document was created with Spire.PDF for Python.