I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
15
,
No
.
6
,
Decem
b
er
20
25
,
p
p
.
5
8
3
7
~
5
8
4
6
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
15
i
6
.
pp
5
8
3
7
-
5
8
4
6
5837
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
Ev
a
lua
ting cluste
ring
alg
o
rithms w
ith
in
tegra
ted
elec
tric
v
ehicl
e
cha
rg
ers for dem
a
nd
-
side ma
na
g
ement
Ay
o
ub
Abid
a
,
Redo
ua
ne
M
a
j
do
ul,
M
o
ura
d Z
eg
ra
ri
D
i
g
i
t
a
l
E
n
g
i
n
e
e
r
i
n
g
f
o
r
Le
a
d
i
n
g
T
e
c
h
n
o
l
o
g
y
a
n
d
A
u
t
o
ma
t
i
o
n
L
a
b
o
r
a
t
o
r
y
(
D
ELTA)
,
T
h
e
N
a
t
i
o
n
a
l
H
i
g
h
e
r
S
c
h
o
o
l
o
f
A
r
t
s
a
n
d
C
r
a
f
t
s
(
EN
S
A
M
)
,
H
a
ss
a
n
I
I
U
n
i
v
e
r
si
t
y
C
a
s
a
b
l
a
n
c
a
,
C
a
sa
b
l
a
n
c
a
,
M
o
r
o
c
c
o
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Oct
1
2
,
2
0
2
4
R
ev
is
ed
J
u
l 1
6
,
2
0
2
5
Acc
ep
ted
Sep
1
4
,
2
0
2
5
Th
e
in
teg
ra
ti
o
n
o
f
e
lec
tri
c
v
e
h
icl
e
s
(EVs)
a
n
d
th
e
ir
e
ffe
c
ts
o
n
p
o
we
r
g
rid
s
p
o
se
se
v
e
ra
l
c
h
a
ll
e
n
g
e
s
fo
r
d
istri
b
u
ti
o
n
o
p
e
ra
to
rs.
T
h
e
se
c
h
a
ll
e
n
g
e
s
a
re
d
u
e
to
u
n
c
e
rtain
a
n
d
d
iffi
c
u
lt
-
to
-
p
re
d
ict
lo
a
d
s.
E
v
e
ry
e
lec
tri
c
v
e
h
icle
c
h
a
rg
e
r
(EVC)
h
a
s
it
s
s
p
e
c
ifi
c
p
a
tt
e
r
n
.
Th
is
c
h
a
ll
e
n
g
e
c
a
n
b
e
a
d
d
re
ss
e
d
b
y
c
lu
ste
rin
g
m
e
th
o
d
s
to
d
e
term
in
e
EVC
e
n
e
rg
y
c
o
n
su
m
p
ti
o
n
c
l
u
ste
rs.
De
m
a
n
d
sid
e
m
a
n
a
g
e
m
e
n
t
(DSM
)
is
a
n
e
ffe
c
ti
v
e
so
l
u
ti
o
n
to
m
a
n
a
g
e
th
e
in
c
o
m
i
n
g
lo
a
d
o
f
EVs
a
n
d
th
e
larg
e
n
u
m
b
e
r
o
f
E
VCs
.
Co
n
si
d
e
rin
g
t
h
e
c
h
a
ll
e
n
g
e
s
o
f
p
e
a
k
c
o
n
su
m
p
ti
o
n
s
a
n
d
v
a
ll
e
y
s,
th
e
a
d
o
p
ti
o
n
o
f
v
e
h
icle
-
to
-
g
r
id
(V2
G
)
tec
h
n
o
l
o
g
y
re
q
u
ires
m
a
ste
rin
g
lo
a
d
c
lu
ste
rs
t
o
d
e
v
e
lo
p
e
n
e
rg
y
m
a
n
a
g
e
m
e
n
t
s
y
ste
m
s
fo
r
d
istri
b
u
to
rs
.
T
h
is
w
o
rk
u
se
d
c
lu
ste
rin
g
a
lg
o
rit
h
m
s
(K
-
mean
s,
DBSCAN,
C
-
mean
s,
BIRCH,
M
e
a
n
-
S
h
ift
,
O
P
TICS
)
t
o
i
d
e
n
ti
f
y
lo
a
d
c
u
r
v
e
p
a
t
tern
s,
a
n
d
fo
r
p
e
rfo
rm
a
n
c
e
e
v
a
lu
a
ti
o
n
o
f
a
lg
o
rit
h
m
s,
it
w
o
rk
e
d
o
n
m
e
tri
c
s
li
k
e
th
e
S
il
h
o
u
e
tt
e
c
o
e
fficie
n
t,
Ca
li
n
sk
i
-
Ha
ra
b
a
sz
in
d
e
x
(CHI)
,
a
n
d
Da
v
i
e
s
-
Bo
u
ld
i
n
in
d
e
x
(DBI)
t
o
e
v
a
l
u
a
te
re
su
lt
s.
C
-
mean
s
a
c
h
iev
e
s
th
e
b
e
st
o
v
e
ra
ll
c
lu
s
terin
g
p
e
rfo
rm
a
n
c
e
,
e
v
i
d
e
n
c
e
d
b
y
th
e
h
ig
h
e
st
S
il
h
o
u
e
tt
e
c
o
e
fficie
n
t
(
0
.
3
0
)
a
n
d
a
stro
n
g
Ca
li
n
sk
i
-
Ha
ra
b
a
sz
sc
o
re
(5
4
3
).
M
e
a
n
-
S
h
ift
e
x
c
e
ls
in
t
h
e
Da
v
ies
-
Bo
u
l
d
in
In
d
e
x
(
1
.
1
3
)
b
u
t
u
n
d
e
rp
e
rfo
rm
s
o
n
o
th
e
r
m
e
tri
c
s.
BIRCH p
ro
v
id
e
s
a
b
a
lan
c
e
d
a
p
p
r
o
a
c
h
,
d
e
li
v
e
ri
n
g
m
o
d
e
ra
te res
u
lt
s a
c
ro
ss
e
v
a
lu
a
ted
m
e
tri
c
s.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
C
lu
s
ter
in
g
alg
o
r
ith
m
s
Dem
an
d
e
s
id
e
m
an
a
g
em
en
t
E
lectr
ic
v
eh
icle
E
lectr
ic
v
eh
icle
ch
ar
g
er
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ay
o
u
b
Ab
i
d
a
Dig
ital E
n
g
in
ee
r
in
g
f
o
r
L
ea
d
i
n
g
T
ec
h
n
o
lo
g
y
an
d
Au
to
m
atio
n
L
ab
o
r
ato
r
y
(
DE
L
T
A)
,
T
h
e
Natio
n
al
Hig
h
er
Sch
o
o
l o
f
Ar
ts
an
d
C
r
af
ts
(
E
N
SAM)
,
Hass
an
I
I
Un
iv
er
s
ity
C
asab
lan
ca
1
5
0
Stre
et
Nil,
C
asab
lan
ca
2
0
6
7
0
,
M
o
r
o
cc
o
E
m
ail:
ay
o
u
b
.
a
b
id
a1
-
etu
@
etu
.
u
n
iv
h
2
c.
m
a
/
ay
o
u
b
ab
i
d
a0
8
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
C
o
n
s
id
er
in
g
clim
ate
ch
a
n
g
e
an
d
th
e
ass
o
ciate
d
th
r
ea
ts
o
f
g
lo
b
al
wa
r
m
in
g
,
an
d
th
e
d
an
g
er
s
o
f
g
r
ee
n
h
o
u
s
e
g
as
e
m
is
s
io
n
s
o
n
th
e
p
lan
et,
th
e
r
ed
u
ctio
n
o
f
r
ed
u
cin
g
g
r
ee
n
h
o
u
s
e
g
a
s
(
GHG)
an
d
th
e
d
ec
ar
b
o
n
izatio
n
o
f
r
o
a
d
tr
an
s
p
o
r
tatio
n
ar
e
co
n
s
id
er
e
d
an
i
m
p
o
r
tan
t
s
tep
to
war
d
p
r
eser
v
in
g
th
e
en
v
ir
o
n
m
en
t.
T
o
ac
h
iev
e
th
is
,
g
o
v
er
n
m
en
ts
ar
e
en
c
o
u
r
a
g
in
g
t
h
e
u
s
e
o
f
elec
tr
ic
v
eh
icles
an
d
h
y
b
r
id
elec
tr
ic
v
eh
icles
b
y
o
f
f
er
in
g
tax
in
ce
n
tiv
es
to
co
n
s
u
m
er
s
an
d
r
e
p
lacin
g
g
o
v
e
r
n
m
en
t
f
leets
with
elec
tr
ic
v
eh
icles
(
E
Vs)
an
d
h
y
b
r
id
elec
tr
ic
v
eh
icles
(
HE
Vs
)
[
1
]
.
T
h
ese
m
ea
s
u
r
es
in
clu
d
e
tax
i
n
ce
n
tiv
es
f
o
r
co
n
s
u
m
er
s
an
d
i
n
itiativ
es
to
r
ep
lace
g
o
v
er
n
m
en
t f
leets with
E
Vs a
n
d
HE
Vs,
aim
in
g
to
d
ec
r
ea
s
e
th
e
ca
r
b
o
n
f
o
o
tp
r
in
t o
f
tr
an
s
p
o
r
tatio
n
an
d
m
itig
ate
th
e
en
v
ir
o
n
m
en
tal
im
p
ac
ts
o
f
f
o
s
s
il
f
u
els.
On
o
n
e
h
an
d
,
it
r
em
ain
s
an
ef
f
ec
tiv
e
s
o
lu
tio
n
f
o
r
g
lo
b
al
war
m
in
g
,
b
u
t
o
n
th
e
o
th
e
r
h
a
n
d
,
elec
tr
icity
d
is
tr
ib
u
tio
n
g
r
i
d
s
ar
e
n
o
t y
e
t
p
r
ep
ar
ed
f
o
r
th
e
m
ass
in
teg
r
a
tio
n
o
f
lar
g
e
f
leets
o
f
elec
tr
ic
v
e
h
icles.
Fo
r
elec
tr
icity
d
is
tr
ib
u
tio
n
g
r
id
s
,
it
is
a
d
if
f
icu
lt
ch
allen
g
e
to
r
ec
ei
v
e
an
d
p
r
o
v
id
e
en
o
u
g
h
u
n
p
lan
n
ed
p
o
wer
to
a
h
u
g
e
n
u
m
b
er
o
f
elec
tr
ic
v
eh
icles th
r
o
u
g
h
elec
tr
ic
v
eh
icle
c
h
ar
g
e
r
s
.
Dem
an
d
s
id
e
m
a
n
ag
em
e
n
t
(
D
SM)
is
th
e
p
lan
n
in
g
,
im
p
le
m
e
n
tatio
n
,
an
d
m
o
n
ito
r
in
g
o
f
ele
ctr
ical
g
r
id
u
tili
ty
ac
tiv
ities
to
ef
f
ec
tiv
ely
in
f
lu
en
ce
cu
s
to
m
e
r
u
s
e
o
f
elec
tr
icity
in
way
s
th
at
will
p
r
o
d
u
ce
d
esire
d
ch
an
g
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
8
3
7
-
5
8
4
6
5838
in
th
e
l
o
ad
s
h
a
p
e
[
2
]
.
I
ts
m
ain
g
o
al
is
to
a
m
elio
r
ate
ef
f
icien
cy
o
f
th
e
elec
tr
ical
g
r
i
d
b
y
im
p
lem
en
tin
g
s
tr
ateg
ies
to
m
in
im
ize
en
er
g
y
co
n
s
u
m
p
tio
n
d
u
r
in
g
p
ea
k
d
em
an
d
p
er
io
d
s
an
d
en
co
u
r
ag
e
en
er
g
y
co
n
s
er
v
atio
n
.
B
y
ef
f
ec
tiv
ely
m
an
a
g
in
g
t
h
e
d
em
an
d
f
o
r
elec
tr
icity
,
DSM
h
elp
s
to
m
in
im
ize
lo
s
s
es
in
th
e
elec
tr
ical
p
o
wer
s
y
s
tem
an
d
en
h
a
n
ce
its
o
v
er
all
ef
f
icien
cy
[
3
]
,
[
4
]
.
An
d
c
lu
s
ter
in
g
is
a
m
eth
o
d
o
lo
g
y
i
n
s
id
e
u
n
s
u
p
er
v
is
ed
lear
n
in
g
th
at
ca
teg
o
r
izes
d
ata
in
to
m
u
ltip
le
g
r
o
u
p
s
ac
co
r
d
in
g
to
s
p
ec
if
ic
cr
iter
ia.
I
t
aid
s
u
s
er
s
in
co
m
p
r
eh
e
n
d
in
g
th
e
p
atter
n
s
a
n
d
g
r
o
u
p
in
g
s
with
in
a
d
ataset
[
5
]
.
C
lu
s
ter
in
g
tech
n
iq
u
e
ca
n
b
e
u
s
ed
to
id
en
tif
y
p
atter
n
s
,
s
im
ilar
ities
,
o
r
d
if
f
er
en
ce
s
am
o
n
g
cu
r
v
es,
wh
ich
c
an
b
e
h
elp
f
u
l
f
o
r
v
ar
io
u
s
p
u
r
p
o
s
es
s
u
ch
as
en
er
g
y
co
n
s
u
m
p
tio
n
,
m
ar
k
et
an
al
y
s
is
,
cu
s
to
m
er
s
eg
m
en
tatio
n
,
o
r
tar
g
eted
m
ar
k
etin
g
s
tr
ateg
i
es
[
5
]
.
Hav
in
g
a
co
m
p
r
eh
e
n
s
iv
e
u
n
d
er
s
tan
d
in
g
o
f
clu
s
ter
in
g
alg
o
r
ith
m
s
allo
ws
en
g
in
ee
r
s
to
g
ain
a
n
u
a
n
ce
d
p
er
s
p
ec
tiv
e
o
n
th
eir
ca
p
ab
ilit
ies,
h
elp
in
g
th
e
m
ch
o
o
s
e
th
e
m
o
s
t
ap
p
r
o
p
r
iat
e
ap
p
r
o
ac
h
f
o
r
v
ar
i
o
u
s
ap
p
lica
tio
n
s
[
6
]
.
C
lu
s
ter
in
g
r
esu
lts
o
f
f
er
b
en
ef
its
f
o
r
e
n
er
g
y
p
r
o
v
id
er
s
b
y
en
ab
lin
g
ef
f
ec
tiv
e
cu
s
to
m
er
s
eg
m
en
tatio
n
,
wh
ich
allo
ws
f
o
r
tailo
r
ed
m
ar
k
eti
n
g
an
d
p
e
r
s
o
n
alize
d
s
er
v
ices.
I
t
en
h
an
ce
s
d
em
an
d
f
o
r
ec
asti
n
g
,
h
elp
in
g
p
r
o
v
id
e
r
s
o
p
tim
ize
en
er
g
y
d
is
tr
ib
u
tio
n
.
C
lu
s
ter
in
g
also
aid
s
in
lo
ad
m
a
n
ag
em
en
t
b
y
id
e
n
tify
in
g
s
im
ilar
cu
s
to
m
er
lo
ad
p
r
o
f
iles
,
f
ac
ilit
atin
g
d
em
an
d
r
esp
o
n
s
e
p
r
o
g
r
a
m
s
th
at
lo
wer
p
ea
k
d
em
an
d
an
d
o
p
e
r
atio
n
al
co
s
ts
.
E
VC
p
lan
n
in
g
b
en
ef
its
f
r
o
m
clu
s
ter
in
g
i
n
s
ig
h
ts
b
y
p
i
n
p
o
in
tin
g
ar
ea
s
with
s
p
ec
if
ic
n
ee
d
s
,
en
s
u
r
i
n
g
s
tr
ateg
ic
in
v
e
s
tm
en
t
an
d
r
eso
u
r
c
e
allo
ca
tio
n
.
Nu
m
er
o
u
s
s
tu
d
ies
h
av
e
ex
p
l
o
r
ed
v
ar
io
u
s
f
ac
ets
o
f
E
Vs
u
s
in
g
clu
s
ter
in
g
tech
n
i
q
u
es
t
o
s
im
p
lify
n
etwo
r
k
co
m
p
u
tatio
n
al
co
m
p
l
ex
ity
d
u
r
in
g
an
aly
s
is
.
Key
f
o
cu
s
ar
ea
s
in
ex
is
tin
g
r
esear
ch
in
clu
d
e
m
o
d
elin
g
E
V
u
s
er
b
eh
av
io
r
[
7
]
,
E
V
d
r
iv
in
g
cy
cles
[
8
]
,
u
s
ed
E
V
b
atter
ies
[
9
]
,
clu
s
ter
in
g
[
1
0
]
,
an
d
E
V
ch
ar
g
in
g
s
tatio
n
s
[
1
1
]
.
Nev
e
r
th
eless
,
ad
d
itio
n
al
E
V
asp
ec
ts
r
eq
u
ir
e
d
ee
p
er
in
v
esti
g
atio
n
th
r
o
u
g
h
clu
s
ter
i
n
g
m
eth
o
d
s
.
T
h
ese
in
clu
d
e
a
n
aly
zin
g
th
e
ef
f
ec
ts
o
f
E
Vs
o
n
d
if
f
er
e
n
t
d
is
tr
ib
u
tio
n
cir
cu
its
[
1
2
]
,
e
x
am
in
in
g
ch
a
r
g
in
g
in
f
r
astru
ct
u
r
e
in
em
er
g
en
cy
s
itu
atio
n
s
[
1
3
]
,
ex
p
lo
r
in
g
eq
u
ity
is
s
u
es
in
r
eb
ate
d
is
tr
ib
u
tio
n
s
[
1
4
]
,
an
d
em
p
lo
y
in
g
b
i
g
d
ata
in
clu
s
ter
an
aly
s
is
to
en
h
an
ce
t
r
an
s
p
o
r
tatio
n
n
etwo
r
k
m
an
a
g
e
m
en
t
[
1
5
]
.
I
n
[
1
6
]
,
a
u
th
o
r
s
s
h
o
w
th
at
K
-
m
ea
n
s
ex
ce
ed
s
th
e
p
e
r
f
o
r
m
an
ce
o
f
o
th
er
alg
o
r
ith
m
s
,
lik
e
DB
SC
AN,
K
-
Me
d
o
id
s
,
Ag
g
l
o
m
er
at
iv
e
clu
s
ter
in
g
,
a
n
d
Gau
s
s
ian
m
ix
tu
r
e
m
o
d
els
(
G
MM
)
,
b
y
ac
h
iev
in
g
a
C
alin
s
k
i
-
Har
ab
asz
in
d
ex
(
C
HI
)
o
f
1
2
0
0
,
a
Sil
h
o
u
ette
s
co
r
e
o
f
0
.
4
5
,
an
d
a
Dav
ies
-
B
o
u
ld
i
n
in
d
e
x
(
DB
I
)
r
ea
ch
ed
0
.
7
4
.
Usi
n
g
th
e
s
am
e
m
eth
o
d
o
l
o
g
y
,
Hasan
et
a
l.
[
1
7
]
wo
r
k
ed
o
n
clu
s
ter
in
g
alg
o
r
ith
m
s
K
-
m
ea
n
s
,
Hier
ar
ch
ical
clu
s
ter
in
g
,
an
d
DB
SC
A
N
f
o
r
d
e
ter
m
in
in
g
th
e
lo
ad
p
atter
n
o
f
d
aily
a
n
d
wee
k
ly
E
V
ch
ar
g
i
n
g
p
r
o
f
ile
clu
s
ter
s
.
I
n
th
is
w
o
r
k
au
th
o
r
s
tr
ied
to
s
elec
t
th
e
o
p
tim
u
m
n
u
m
b
er
o
f
clu
s
ter
s
,
s
o
th
ey
f
o
u
n
d
th
at
b
o
th
K
-
m
ea
n
s
an
d
h
ier
ar
ch
ical
m
eth
o
d
s
f
ea
tu
r
e
two
m
ajo
r
clu
s
ter
s
co
n
tain
in
g
b
etwe
en
3
0
an
d
4
0
%
o
f
cu
s
to
m
e
r
s
an
d
two
s
m
aller
clu
s
ter
s
with
1
0
to
2
0
%
o
f
cu
s
to
m
e
r
s
.
C
o
n
v
er
s
ely
,
DB
SC
AN
p
r
ese
n
ts
o
n
e
m
ajo
r
clu
s
ter
(
in
d
aily
p
r
o
f
ile)
co
m
p
r
is
in
g
ap
p
r
o
x
im
ately
7
0
%
o
f
cu
s
to
m
er
s
.
An
d
f
o
r
th
e
a
n
aly
s
is
o
f
th
e
ef
f
ec
t
o
f
co
r
o
n
a
v
ir
u
s
o
n
E
V
ch
ar
g
in
g
p
atter
n
s
,
Sh
ah
r
iar
an
d
Al
-
Ali
[
1
8
]
e
x
p
lo
r
e
d
th
e
clu
s
ter
in
g
u
s
in
g
th
e
s
am
e
m
etr
ics
(
Sil
h
o
u
ette
s
co
r
e,
DB
I
,
an
d
C
HI
)
to
ev
alu
ate
K
-
m
ea
n
s
,
Hier
ar
ch
ical
clu
s
ter
in
g
,
an
d
GM
M
r
esu
lts
.
I
n
th
i
s
wo
r
k
K
-
m
ea
n
s
r
ev
ea
ls
th
e
h
ig
h
est
Sil
o
u
h
ette
s
co
r
e,
an
d
also
th
e
h
ig
h
est
C
HI
.
I
n
t
h
e
o
th
er
s
id
e,
R
ich
ar
d
et
a
l.
[
1
9
]
p
r
o
p
o
s
ed
a
cl
u
s
ter
in
g
p
r
o
ce
s
s
(
m
u
ltip
le
tem
p
o
r
a
l
g
r
an
u
lar
ities
)
wh
ich
s
er
v
es
f
o
r
th
e
cr
ea
tio
n
o
f
r
elativ
e
r
a
n
k
in
g
s
o
f
s
im
ilar
clu
s
ter
in
g
r
esu
lts
o
v
er
m
u
ltip
le
wee
k
s
.
I
n
th
is
wo
r
k
,
th
e
au
t
h
o
r
s
f
o
c
u
s
ed
o
n
E
V
lo
ad
cu
r
v
e
clu
s
ter
in
g
an
d
th
e
e
x
tr
ac
tio
n
o
f
E
V
u
s
er
s
'
p
o
wer
co
n
s
u
m
p
tio
n
p
atter
n
s
b
y
u
s
in
g
clu
s
ter
in
g
alg
o
r
ith
m
s
.
Star
tin
g
b
y
in
tr
o
d
u
cin
g
elec
tr
ic
v
eh
icle
clu
s
ter
s
,
th
e
au
th
o
r
s
p
r
o
v
i
d
e
in
f
o
r
m
atio
n
ab
o
u
t
clu
s
ter
in
g
tech
n
iq
u
es,
alg
o
r
ith
m
s
u
s
ed
,
a
n
d
m
etr
ics.
Fo
r
elec
tr
icity
d
is
tr
ib
u
to
r
s
,
it
is
im
p
er
ativ
e
to
m
aster
t
h
e
p
o
wer
d
em
an
d
cu
r
v
e
o
f
ev
er
y
c
h
ar
g
in
g
s
tatio
n
to
h
a
v
e
a
clea
r
u
n
d
er
s
tan
d
i
n
g
o
f
its
p
atter
n
(
p
ea
k
lo
ad
,
v
alley
lo
a
d
)
,
w
h
ich
i
s
wh
y
th
e
lo
ad
cu
r
v
es
clu
s
ter
s
o
f
ev
er
y
ch
a
r
g
in
g
s
tatio
n
ar
e
an
al
y
ze
d
.
T
h
e
p
r
o
j
ec
t
in
v
o
lv
es
g
ath
er
in
g
d
ata
an
d
ex
tr
ac
tin
g
lo
a
d
cu
r
v
es,
clu
s
t
er
s
,
an
d
m
etr
ics
to
u
n
d
er
s
tan
d
th
e
b
eh
a
v
io
r
o
f
el
ec
tr
ic
v
eh
icle
c
h
ar
g
e
r
s
o
v
e
r
v
ar
io
u
s
tim
e
u
n
its
(
h
o
u
r
,
d
a
y
,
m
o
n
th
,
y
ea
r
)
.
I
t
is
b
ased
o
n
th
e
p
r
in
cip
le
o
f
m
an
ag
in
g
en
er
g
y
c
o
n
s
u
m
p
tio
n
.
I
n
th
e
co
n
te
x
t
o
f
elec
tr
ic
a
n
d
h
y
b
r
id
elec
tr
ic
m
o
b
ilit
y
,
th
e
an
ticip
ated
f
u
tu
r
e
in
teg
r
atio
n
o
f
elec
tr
ic
v
eh
icles,
co
u
p
led
with
th
e
wid
e
s
p
r
ea
d
ad
d
itio
n
o
f
n
u
m
er
o
u
s
ch
ar
g
in
g
s
tatio
n
s
in
to
d
is
tr
ib
u
tio
n
g
r
id
s
,
is
ex
p
ec
ted
to
s
ig
n
if
ican
tly
im
p
ac
t
elec
tr
ical
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
s
u
b
s
eq
u
en
t
ly
,
th
e
en
er
g
y
d
is
tr
ib
u
tio
n
i
n
f
r
astru
ctu
r
e.
T
h
e
r
esu
lts
s
h
o
wed
th
at
C
-
m
ea
n
s
s
u
r
p
ass
es
o
th
er
m
eth
o
d
s
in
k
e
y
m
etr
ics,
ac
h
iev
in
g
th
e
h
ig
h
est
m
etr
ic
s
co
r
es.
T
h
is
in
d
icate
s
th
at
C
-
m
ea
n
s
is
s
u
p
er
io
r
at
cr
ea
tin
g
clea
r
an
d
d
is
tin
ct
g
r
o
u
p
in
g
s
.
Alth
o
u
g
h
Mean
-
Sh
if
t
h
as
th
e
lo
west
D
B
I
,
s
u
g
g
esti
n
g
less
clu
s
ter
s
im
ilar
ity
,
it
s
lo
wer
s
co
r
es
in
th
e
Sil
h
o
u
ette
co
ef
f
icien
t
an
d
C
HI
s
u
g
g
est
it
m
ay
n
o
t
b
e
as
ad
ep
t
at
cr
ea
tin
g
well
-
d
ef
in
ed
,
s
ep
ar
ate
clu
s
ter
s
.
As
a
co
n
tr
ib
u
tio
n
o
f
th
is
p
ap
er
to
th
e
ex
is
tin
g
liter
atu
r
e,
th
e
p
ap
er
p
r
o
p
o
s
es
to
ex
p
lo
it
th
ese
lo
ad
cu
r
v
e
p
atter
n
s
f
o
r
DSM
to
s
o
l
v
e
th
e
ch
allen
g
e
o
f
m
ass
in
teg
r
atio
n
o
f
E
VC
an
d
th
e
p
r
o
b
lem
s
o
f
lo
a
d
m
an
a
g
e
m
en
t (
p
ea
k
s
h
av
in
g
,
v
alley
f
ill
in
g
,
ef
f
icien
t
u
s
e
o
f
r
e
n
ewa
b
le
en
er
g
y
s
o
u
r
ce
s
)
.
2.
M
E
T
H
O
D
T
h
e
ex
p
er
im
en
tal
p
r
o
ce
d
u
r
e
was
co
n
d
u
cted
in
f
iv
e
s
eq
u
e
n
t
ial
s
tag
es
to
en
s
u
r
e
f
u
ll
r
e
p
r
o
d
u
cib
ilit
y
o
f
th
e
clu
s
ter
in
g
o
f
E
V
ch
ar
g
in
g
p
r
o
f
iles
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
va
lu
a
tin
g
clu
s
teri
n
g
a
lg
o
r
it
h
ms w
ith
in
teg
r
a
ted
elec
tr
ic
v
eh
icle
…
(
A
yo
u
b
A
b
id
a
)
5839
a.
Data
co
llectio
n
an
d
ac
q
u
is
itio
n
:
E
V
ch
ar
g
in
g
d
ata
wer
e
g
at
h
er
ed
f
r
o
m
a
C
S
2
0
1
8
t
o
2
0
2
3
.
T
h
e
d
ataset
o
f
6
,
2
8
2
ch
a
r
g
in
g
s
ess
io
n
,
in
C
SV
f
o
r
m
at,
in
clu
d
es
k
e
y
v
ar
iab
les
s
u
ch
as
t
h
e
s
tar
t
ti
m
e
o
f
ch
ar
g
in
g
,
in
s
tan
tan
eo
u
s
ch
ar
g
in
g
p
o
we
r
,
an
d
to
tal
en
e
r
g
y
co
n
s
u
m
p
tio
n
p
er
s
ess
io
n
.
Mo
n
ito
r
in
g
eq
u
ip
m
en
t
was
ca
lib
r
ated
to
en
s
u
r
e
s
y
n
ch
r
o
n
i
za
tio
n
an
d
ac
c
u
r
ac
y
.
b.
Data
clea
n
in
g
an
d
p
r
e
p
r
o
ce
s
s
in
g
:
T
h
e
r
aw
d
ataset
was
im
p
o
r
ted
in
to
Py
th
o
n
u
s
in
g
lib
r
ar
ies
lik
e
Pan
d
as.
E
r
r
o
n
e
o
u
s
en
tr
ies,
m
is
s
in
g
v
a
lu
es,
an
d
p
er
io
d
s
o
f
in
ac
tiv
it
y
(
d
u
e
t
o
eq
u
ip
m
en
t
f
ailu
r
es,
p
o
wer
o
u
tag
e)
wer
e
s
y
s
tem
atica
lly
r
em
o
v
ed
o
r
r
ep
lace
d
with
ze
r
o
s
to
e
n
s
u
r
e
d
ata
in
teg
r
ity
.
c.
Featu
r
e
ex
tr
ac
tio
n
a
n
d
tr
a
n
s
f
o
r
m
atio
n
:
T
h
e
clea
n
ed
d
ata
wer
e
p
r
o
ce
s
s
ed
to
e
x
tr
ac
t
cr
itical
f
ea
tu
r
es
s
u
ch
as
th
e
ch
ar
g
in
g
s
ess
io
n
s
tar
t
tim
e,
th
e
tim
e
-
s
er
ies
o
f
ch
a
r
g
in
g
p
o
wer
,
s
ess
io
n
d
u
r
atio
n
.
T
h
ese
f
ea
tu
r
es
wer
e
n
o
r
m
alize
d
to
p
r
e
v
en
t scale
im
b
alan
ce
s
d
u
r
i
n
g
clu
s
ter
in
g
.
d.
C
lu
s
ter
in
g
a
n
aly
s
is
:
C
lu
s
ter
i
n
g
alg
o
r
ith
m
s
wer
e
ap
p
lied
to
th
e
p
r
o
ce
s
s
ed
d
ataset
to
ass
ig
n
ea
ch
E
V
ch
ar
g
in
g
p
r
o
f
ile
to
d
is
tin
ct
g
r
o
u
p
s
.
Stan
d
ar
d
alg
o
r
ith
m
s
(
K
-
m
ea
n
s
,
OPTI
C
S,
C
-
m
e
an
s
,
DB
SC
A
N,
B
I
R
C
H,
Mean
-
Sh
if
t
)
wer
e
u
s
ed
to
ca
p
tu
r
e
b
o
t
h
h
ar
d
an
d
s
o
f
t
clu
s
ter
in
g
ch
a
r
ac
ter
is
tics
.
Par
am
eter
s
f
o
r
ea
ch
alg
o
r
ith
m
(
lik
e
th
e
n
u
m
b
er
o
f
clu
s
ter
s
f
o
r
K
-
m
ea
n
s
)
wer
e
in
itially
d
eter
m
in
e
d
b
y
e
x
p
lo
r
ato
r
y
an
aly
s
is
an
d
r
ef
in
e
d
th
r
o
u
g
h
iter
ativ
e
r
u
n
s
u
n
til co
n
v
e
r
g
en
ce
.
e.
E
v
alu
atio
n
an
d
a
n
al
y
s
is
:
T
h
e
q
u
ality
an
d
s
tab
ilit
y
o
f
th
e
r
esu
ltin
g
clu
s
ter
s
wer
e
a
s
s
es
s
e
d
u
s
in
g
in
ter
n
al
v
alid
atio
n
m
etr
ics
s
u
ch
as
th
e
Sil
h
o
u
ette
co
ef
f
icien
t
,
C
HI
,
an
d
DB
I
,
p
r
o
v
i
d
in
g
q
u
an
titativ
e
ju
s
tific
atio
n
f
o
r
th
e
s
elec
ted
m
eth
o
d
s
.
T
h
e
d
is
tin
ct
clu
s
ter
s
r
ev
ea
l
v
ar
y
in
g
ch
ar
g
i
n
g
p
atter
n
s
an
d
p
e
ak
u
s
ag
e
tim
es,
o
f
f
er
in
g
in
s
ig
h
ts
in
to
p
o
wer
d
em
an
d
an
d
g
r
id
s
tab
ilit
y
,
a
n
d
s
u
p
p
o
r
tin
g
tailo
r
e
d
lo
ad
m
an
ag
em
en
t
an
d
p
er
s
o
n
alize
d
m
ar
k
etin
g
s
tr
ateg
ies.
Fig
u
r
e
1
p
r
esen
ts
p
r
o
p
o
s
ed
p
a
p
er
’
s
m
eth
o
d
o
l
o
g
y
f
o
r
cl
u
s
ter
in
g
E
VC
p
r
o
f
iles
.
All
s
tag
es
wer
e
im
p
lem
e
n
ted
i
n
Py
th
o
n
u
s
in
g
s
tan
d
ar
d
lib
r
ar
i
es
(
Pan
d
as,
Nu
m
Py
,
Scik
it
-
le
ar
n
)
,
an
d
d
etailed
ex
p
er
im
e
n
tal
p
ar
am
eter
s
an
d
co
d
e
ar
e
p
r
o
v
id
ed
to
en
s
u
r
e
th
at
th
e
m
eth
o
d
o
lo
g
y
ca
n
b
e
ex
a
ctly
r
ep
licated
b
y
o
th
er
r
esear
ch
er
s
.
Fig
u
r
e
1
.
Pro
p
o
s
ed
m
et
h
o
d
o
lo
g
y
f
o
r
clu
s
ter
in
g
E
VC
p
r
o
f
iles
2
.
1
.
Clus
t
er
ing
a
lg
o
rit
h
m
s
T
h
e
K
-
m
ea
n
s
alg
o
r
ith
m
is
wid
ely
u
s
ed
f
o
r
p
ar
titi
o
n
i
n
g
d
ata
in
v
ar
io
u
s
ap
p
licatio
n
s
.
Ho
w
ev
er
,
it
h
as
s
o
m
e
lim
itatio
n
s
,
s
u
ch
as
th
e
d
if
f
icu
lty
in
d
eter
m
in
i
n
g
th
e
ac
tu
al
n
u
m
b
e
r
o
f
clu
s
ter
s
a
n
d
s
elec
tin
g
in
itial
clu
s
ter
ce
n
tr
o
id
s
.
T
o
ad
d
r
ess
th
ese
is
s
u
es,
ex
ten
s
iv
e
r
esear
ch
h
as
b
ee
n
co
n
d
u
cted
i
n
th
i
s
f
ield
,
r
esu
ltin
g
in
s
ev
er
al
m
o
d
if
icatio
n
s
to
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
.
I
n
o
r
d
er
to
en
h
an
ce
th
e
alg
o
r
ith
m
an
d
o
v
er
c
o
m
e
its
ch
allen
g
es,
it
is
im
p
o
r
tan
t
to
r
ev
iew
th
e
ex
is
tin
g
w
o
r
k
s
an
d
r
esear
ch
in
itiativ
es
in
th
is
ar
e
a.
I
n
t
h
e
f
o
llo
win
g
d
is
cu
s
s
io
n
,
we
will e
x
p
lo
r
e
th
e
m
ajo
r
ad
v
an
ce
m
en
ts
an
d
im
p
r
o
v
em
e
n
ts
m
ad
e
in
t
h
is
f
ield
[
2
0
]
.
Den
s
ity
-
b
ased
s
p
atial
clu
s
ter
i
n
g
o
f
ap
p
licatio
n
s
with
n
o
is
e
alg
o
r
ith
m
DB
SC
AN
is
an
alg
o
r
ith
m
th
at
d
etec
t
clu
s
ter
s
o
f
v
ar
i
o
u
s
s
h
a
p
es.
I
t
id
en
tifie
s
clu
s
ter
s
b
y
an
aly
zin
g
th
e
d
en
s
ity
o
f
p
o
in
ts
,
with
h
ig
h
p
o
in
t
d
en
s
ity
in
d
icatin
g
th
e
p
r
esen
c
e
o
f
clu
s
ter
s
.
T
h
is
alg
o
r
ith
m
is
p
ar
ticu
lar
ly
u
s
ef
u
l
f
o
r
h
a
n
d
lin
g
lar
g
e
d
atasets
th
at
co
n
tain
n
o
is
e.
Ad
d
itio
n
al
ly
,
it
is
ca
p
ab
le
o
f
d
is
tin
g
u
is
h
in
g
clu
s
ter
s
o
f
d
if
f
er
en
t
s
izes
an
d
s
h
ap
es
[
2
1
]
.
T
h
is
alg
o
r
ith
m
is
esp
ec
ially
u
s
ef
u
l
f
o
r
h
a
n
d
lin
g
lar
g
e
d
at
asets
with
n
o
is
e.
I
t
ca
n
also
d
is
tin
g
u
is
h
b
etwe
en
clu
s
ter
s
o
f
d
if
f
er
en
t
s
izes
an
d
s
h
ap
es.
T
h
e
ess
en
tial
co
n
ce
p
t
o
f
th
e
DB
S
C
AN
is
th
at,
in
a
clu
s
ter
,
f
o
r
ea
ch
p
o
in
t
th
e
n
eig
h
b
o
r
h
o
o
d
o
f
a
s
p
ec
if
ic
r
ad
iu
s
s
h
o
u
ld
h
av
e
a
m
in
im
u
m
n
u
m
b
e
r
o
f
p
o
in
ts
,
th
e
d
en
s
ity
in
th
e
n
eig
h
b
o
r
h
o
o
d
m
u
s
t su
r
p
ass
a
s
et
th
r
esh
o
ld
[
2
2
]
,
[
2
3
]
.
C
-
m
ea
n
s
alg
o
r
ith
m
is
o
n
e
o
f
t
h
e
u
n
s
u
p
er
v
is
ed
clu
s
ter
in
g
alg
o
r
ith
m
s
th
at
allo
ws
a
s
in
g
le
d
a
ta
p
o
in
t
to
b
elo
n
g
to
m
u
ltip
le
clu
s
ter
s
.
I
t
ca
n
b
e
u
s
ed
f
o
r
v
ar
io
u
s
f
ea
tu
r
e
an
aly
s
is
,
clu
s
ter
in
g
,
an
d
cla
s
s
if
ier
co
n
s
tr
u
ctio
n
task
s
.
C
-
m
ea
n
s
h
as
b
ee
n
wid
ely
ap
p
lied
in
d
if
f
er
e
n
t
f
ield
s
.
Un
lik
e
K
-
m
ea
n
s
,
C
-
m
ea
n
s
ass
ig
n
s
ea
ch
p
atter
n
a
d
eg
r
ee
o
f
m
em
b
er
s
h
ip
to
a
clu
s
ter
,
r
esu
ltin
g
in
a
f
u
zz
y
clu
s
te
r
in
g
[
5
]
.
B
alan
ce
d
iter
ativ
e
r
ed
u
cin
g
an
d
clu
s
ter
in
g
u
s
in
g
h
ier
ar
ch
ies
(
B
I
R
C
H)
is
an
ag
g
lo
m
er
ativ
e
h
ier
ar
ch
ical
clu
s
ter
in
g
alg
o
r
ith
m
d
ev
elo
p
ed
f
o
r
ef
f
icien
tly
clu
s
ter
in
g
lar
g
e
v
o
lu
m
es
o
f
m
etr
ic
d
ata.
I
t
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
8
3
7
-
5
8
4
6
5840
p
ar
ticu
lar
ly
well
-
s
u
ited
f
o
r
s
ce
n
ar
io
s
with
lim
ited
m
ain
m
e
m
o
r
y
an
d
is
ca
p
ab
le
o
f
o
p
e
r
a
tin
g
in
lin
ea
r
tim
e
with
ju
s
t
a
s
in
g
le
s
ca
n
o
f
th
e
d
atab
ase.
B
I
R
C
H
in
tr
o
d
u
ce
s
th
e
co
n
ce
p
ts
o
f
clu
s
ter
in
g
f
e
atu
r
e
an
d
clu
s
ter
in
g
f
ea
tu
r
e
tr
ee
,
wh
ich
s
er
v
e
to
c
o
m
p
ac
tly
s
u
m
m
ar
ize
an
d
r
ep
r
esen
t
clu
s
ter
s
[
2
4
]
.
B
I
R
C
H
u
t
ilizes
an
in
teg
r
ated
h
ier
ar
ch
ical
ap
p
r
o
ac
h
b
y
em
p
lo
y
in
g
cl
u
s
ter
f
ea
tu
r
es
an
d
a
clu
s
ter
f
ea
tu
r
e
tr
ee
.
T
h
e
clu
s
ter
f
ea
tu
r
e
tr
ee
ef
f
icien
tly
s
u
m
m
a
r
izes
clu
s
ter
in
g
in
f
o
r
m
atio
n
wh
ile
u
s
in
g
s
i
g
n
if
ican
tly
less
m
em
o
r
y
th
an
t
h
e
o
r
i
g
in
al
d
ataset.
As
a
r
esu
lt,
B
I
R
C
H
en
h
an
ce
s
th
e
p
er
f
o
r
m
an
ce
o
f
clu
s
ter
in
g
lar
g
e
d
atasets
,
o
f
f
e
r
in
g
b
o
th
h
ig
h
s
p
ee
d
an
d
s
ca
lab
ilit
y
[
2
5
]
.
Mean
-
Sh
if
t
clu
s
ter
in
g
is
a
n
o
n
-
p
ar
am
etr
ic,
d
e
n
s
ity
-
b
ased
alg
o
r
ith
m
d
esig
n
ed
to
id
e
n
tif
y
clu
s
ter
s
with
in
a
d
ataset.
I
t
is
p
ar
ticu
lar
ly
ef
f
ec
tiv
e
f
o
r
d
atasets
co
n
tain
in
g
clu
s
ter
s
o
f
ar
b
itra
r
y
s
h
ap
es
th
at
ar
e
n
o
t
ea
s
ily
s
ep
ar
ated
b
y
lin
ea
r
b
o
u
n
d
ar
ies.
T
h
e
c
o
r
e
id
ea
o
f
M
ea
n
-
Sh
if
t
is
to
iter
ativ
ely
m
o
v
e
ea
ch
d
ata
p
o
in
t
to
war
d
th
e
m
o
d
e,
o
r
t
h
e
r
e
g
io
n
o
f
h
ig
h
est
d
ata
d
en
s
ity
,
with
in
a
s
p
ec
if
ied
r
ad
i
u
s
.
T
h
is
p
r
o
ce
s
s
co
n
tin
u
es
u
n
til
th
e
p
o
in
ts
co
n
v
e
r
g
e
to
lo
ca
l
m
ax
im
a
o
f
th
e
d
en
s
ity
f
u
n
ctio
n
,
wh
ich
co
r
r
esp
o
n
d
t
o
th
e
cl
u
s
ter
s
p
r
esen
t
in
th
e
d
ata.
Or
d
er
in
g
p
o
i
n
ts
to
id
e
n
tify
t
h
e
clu
s
ter
in
g
s
tr
u
ct
u
r
e
(
OPTI
C
S
)
is
a
d
en
s
ity
-
b
ased
clu
s
ter
in
g
alg
o
r
ith
m
d
esig
n
ed
f
o
r
s
p
atial
d
ata.
W
h
ile
s
im
ilar
to
DB
SC
AN,
OPTI
C
S
o
v
er
co
m
es
DB
SC
AN
'
s
li
m
itatio
n
in
d
etec
tin
g
clu
s
ter
s
o
f
v
ar
y
i
n
g
d
en
s
ities
.
I
t
ac
h
iev
es
th
is
b
y
lin
ea
r
ly
o
r
d
er
in
g
th
e
d
ata
p
o
i
n
ts
s
o
th
at
s
p
atially
clo
s
est
p
o
in
ts
ar
e
n
eig
h
b
o
r
s
in
th
e
s
eq
u
en
ce
.
Fo
r
ea
ch
p
o
in
t,
OPTI
C
S
r
ec
o
r
d
s
a
s
p
ec
if
ic
d
is
tan
ce
v
alu
e
th
at
in
d
icate
s
th
e
m
in
im
u
m
d
en
s
ity
r
eq
u
ir
ed
f
o
r
b
o
th
th
e
p
o
in
t a
n
d
its
p
r
e
d
ec
ess
o
r
to
b
e
co
n
s
id
er
e
d
p
a
r
t
o
f
th
e
s
am
e
clu
s
ter
.
2
.
2
.
E
v
a
lua
t
i
o
n m
et
rics
T
h
e
s
ilh
o
u
ette
co
ef
f
icien
t
m
e
tr
ic
m
ea
s
u
r
es
h
o
w
well
ea
ch
d
ata
p
o
in
t
f
its
with
in
its
o
wn
clu
s
ter
co
m
p
ar
ed
t
o
o
th
er
cl
u
s
ter
s
.
I
t
r
an
g
es
f
r
o
m
-
1
to
1
.
wh
er
e
v
alu
es
clo
s
e
to
1
in
d
icate
well
-
s
ep
ar
ated
clu
s
ter
s
.
Valu
es
clo
s
e
to
0
in
d
icate
o
v
er
lap
p
in
g
clu
s
ter
s
.
Neg
ativ
e
v
alu
es
s
u
g
g
est
th
at
d
ata
p
o
in
ts
m
ay
h
av
e
b
ee
n
ass
ig
n
ed
to
th
e
wr
o
n
g
clu
s
ter
[
2
6
]
.
T
h
e
C
alin
s
k
i
-
Har
ab
asz
in
d
ex
,
is
a
m
ea
s
u
r
e
u
s
ed
to
ev
al
u
ate
th
e
q
u
ality
o
f
a
d
ata
p
ar
titi
o
n
in
clu
s
ter
in
g
.
I
t
is
ca
lcu
lated
b
y
co
m
p
ar
in
g
th
e
d
is
p
er
s
io
n
b
et
wee
n
clu
s
ter
s
with
th
e
d
is
p
er
s
io
n
with
in
clu
s
ter
s
.
A
h
ig
h
er
C
alin
s
k
i
-
Har
ab
asz
in
d
ex
in
d
icate
s
a
m
o
r
e
co
h
er
e
n
t
an
d
d
is
tin
ct
d
ata
p
a
r
titi
o
n
.
Als
o
k
n
o
wn
as
th
e
v
ar
ian
ce
r
atio
c
r
iter
io
n
,
th
is
m
etr
ic
q
u
an
tifie
s
th
e
r
atio
o
f
b
et
wee
n
-
clu
s
ter
v
ar
ian
ce
to
with
in
-
clu
s
ter
v
ar
ian
ce
.
Hig
h
er
v
alu
es in
d
icate
m
o
r
e
c
o
m
p
ac
t a
n
d
well
-
s
ep
ar
ated
clu
s
ter
s
[
2
6
]
.
T
h
e
Dav
ies
-
B
o
u
ld
in
in
d
ex
m
e
tr
ic
ca
lcu
lates
th
e
av
er
ag
e
s
im
ilar
ity
b
etwe
en
ea
ch
clu
s
ter
an
d
its
m
o
s
t
s
im
ilar
clu
s
ter
.
T
ak
in
g
in
to
ac
co
u
n
t
b
o
t
h
th
e
with
in
-
clu
s
ter
an
d
b
etwe
en
-
cl
u
s
ter
d
is
tan
ce
s
.
L
o
wer
v
alu
es
in
d
icate
m
o
r
e
co
m
p
ac
t
a
n
d
w
ell
-
s
ep
ar
ated
clu
s
ter
s
.
T
h
e
Da
v
ies
-
B
o
u
ld
in
in
d
e
x
is
b
ased
o
n
th
e
ap
p
r
o
x
im
ately
esti
m
atio
n
o
f
th
e
d
is
tan
ce
s
b
etwe
en
clu
s
ter
s
an
d
th
eir
d
is
p
er
s
io
n
s
to
o
b
tain
a
f
in
al
v
alu
e
th
at
r
ep
r
esen
ts
th
e
q
u
ality
o
f
t
h
e
p
ar
titi
o
n
[
2
7
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
ab
le
1
d
is
p
lay
s
th
e
o
u
tco
m
e
s
d
er
iv
ed
f
r
o
m
th
e
cl
u
s
ter
in
g
alg
o
r
ith
m
s
.
Fig
u
r
e
1
o
n
th
e
o
th
er
h
an
d
,
v
is
u
ally
r
ep
r
esen
ts
th
ese
r
esu
lts
in
th
e
f
o
r
m
o
f
a
cu
r
v
e.
T
h
e
cu
r
v
es
ar
e
p
r
o
v
id
in
g
a
g
r
ap
h
ic
al
in
ter
p
r
etatio
n
o
f
th
e
d
ata
p
r
esen
ted
in
T
ab
le
1
.
T
ab
le
1
.
Me
tr
ics co
m
p
ar
is
o
n
o
f
clu
s
ter
in
g
alg
o
r
ith
m
s
V
a
r
i
a
b
l
e
K
-
me
a
n
s
C
-
mea
n
s
D
B
S
C
A
N
B
I
R
C
H
O
P
TI
C
S
M
e
a
n
-
S
h
i
f
t
S
i
l
h
o
u
e
t
t
e
c
o
e
f
f
i
c
i
e
n
t
0
.
2
3
0
.
3
0
0
.
2
5
0
.
2
5
0
.
2
5
0
.
1
3
D
a
v
i
e
s
-
B
o
u
l
d
i
n
i
n
d
e
x
1
.
9
4
1
.
9
2
2
.
5
6
1
.
4
0
2
.
4
4
1
.
1
3
C
a
l
i
n
sk
i
-
H
a
r
a
b
a
s
z
s
c
o
r
e
4
0
7
5
4
3
2
2
9
1
0
6
2
3
2
1
0
1
As
T
ab
le
1
r
ev
ea
l,
th
e
Sil
h
o
u
ette
co
ef
f
icien
t
m
etr
ic
r
an
g
es
f
r
o
m
-
1
to
1
,
with
1
in
d
icatin
g
th
at
th
e
clu
s
ter
s
ar
e
well
ap
ar
t
f
r
o
m
e
ac
h
o
th
e
r
an
d
-
1
in
d
icatin
g
t
h
at
th
e
clu
s
ter
s
ar
e
to
o
clo
s
e
to
ea
ch
o
th
er
.
Hig
h
er
v
alu
es
ar
e
b
etter
.
Acc
o
r
d
in
g
t
o
th
is
m
etr
ic,
C
-
m
ea
n
s
p
er
f
o
r
m
s
th
e
b
est
with
a
s
co
r
e
o
f
0
.
3
0
,
wh
ile
Me
an
-
Sh
if
t
p
er
f
o
r
m
s
th
e
wo
r
s
t
with
a
s
co
r
e
o
f
0
.
1
3
.
Fo
r
DB
I
,
it
i
n
d
icat
es
th
e
av
er
a
g
e
s
im
ilar
ity
b
etw
ee
n
clu
s
ter
s
,
wh
er
e
s
im
ilar
ity
is
a
m
ea
s
u
r
e
th
at
c
o
m
p
ar
es
th
e
d
is
tan
ce
b
etwe
en
clu
s
ter
s
with
th
e
s
ize
o
f
th
e
clu
s
ter
s
th
em
s
elv
es.
L
o
wer
v
al
u
es
ar
e
b
etter
.
Acc
o
r
d
in
g
to
th
is
m
etr
ic,
Me
an
-
S
h
if
t
p
er
f
o
r
m
s
th
e
b
est
with
a
s
co
r
e
o
f
1
.
1
3
,
wh
ile
DB
S
C
AN
h
as
th
e
wo
r
s
t
s
co
r
e
o
f
2
.
5
6
.
C
HI
:
T
h
is
s
co
r
e
is
u
s
e
d
to
ev
alu
ate
th
e
m
o
d
el
wh
er
e
h
ig
h
e
r
is
b
etter
.
I
t
ca
lcu
lates
th
e
r
atio
o
f
th
e
s
u
m
o
f
b
etwe
en
-
clu
s
ter
d
is
p
er
s
io
n
an
d
o
f
in
te
r
-
clu
s
ter
d
is
p
er
s
io
n
f
o
r
all
clu
s
ter
s
.
Acc
o
r
d
in
g
to
th
is
m
etr
ic,
C
-
m
ea
n
s
p
er
f
o
r
m
s
th
e
b
est
with
a
s
co
r
e
o
f
5
4
3
,
wh
ile
Me
an
-
Sh
if
t
h
as
th
e
lo
west
s
co
r
e
o
f
1
0
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
va
lu
a
tin
g
clu
s
teri
n
g
a
lg
o
r
it
h
ms w
ith
in
teg
r
a
ted
elec
tr
ic
v
eh
icle
…
(
A
yo
u
b
A
b
id
a
)
5841
Ov
er
all,
it
ap
p
ea
r
s
th
at
C
-
m
e
an
s
p
er
f
o
r
m
s
th
e
b
est
wh
en
co
n
s
id
er
in
g
all
th
r
ee
m
etr
ics.
I
t
h
as
t
h
e
h
ig
h
est
s
ilh
o
u
ette
co
e
f
f
icien
t
an
d
C
HI
,
in
d
icatin
g
well
-
s
ep
ar
ated
clu
s
ter
s
an
d
a
g
o
o
d
d
eg
r
ee
o
f
s
ep
ar
atio
n
b
etwe
en
th
em
.
Me
an
wh
ile,
Mean
-
Sh
if
t
h
as
th
e
lo
west
D
B
I
,
in
d
icatin
g
less
s
im
ilar
ity
b
etw
ee
n
clu
s
ter
s
,
b
u
t
its
lo
w
Sil
h
o
u
ette
co
ef
f
icien
t
an
d
C
HI
s
u
g
g
est
th
at
it
m
ay
n
o
t
b
e
as
ef
f
ec
tiv
e
at
cr
ea
tin
g
d
is
t
in
ct,
well
-
s
ep
ar
ated
clu
s
ter
s
.
T
h
er
ef
o
r
e,
co
n
s
id
er
in
g
th
ese
m
etr
ics,
C
-
m
ea
n
s
s
ee
m
s
to
b
e
th
e
m
o
s
t e
f
f
icien
t c
lu
s
ter
in
g
alg
o
r
ith
m
.
−
K
-
m
ea
n
s
:
Fig
u
r
e
2
illu
s
tr
ate
s
th
e
clu
s
ter
in
g
r
esu
lts
u
s
in
g
th
e
K
-
m
ea
n
s
alg
o
r
ith
m
in
t
o
th
r
ee
d
is
tin
ct
clu
s
ter
s
.
C
lu
s
ter
1
s
h
o
ws
m
o
d
er
ate
to
h
i
g
h
e
n
er
g
y
co
n
s
u
m
p
tio
n
with
a
p
ea
k
i
n
th
e
late
m
o
r
n
in
g
(
ar
o
u
n
d
1
0
a.
m
.
)
,
lik
ely
r
ep
r
esen
tin
g
u
s
er
s
wh
o
ch
a
r
g
e
a
f
ter
c
o
m
m
u
t
in
g
to
wo
r
k
.
C
lu
s
ter
s
1
,
2
,
an
d
3
ar
e
d
is
tin
ct
f
r
o
m
ea
c
h
o
th
er
,
wh
ich
in
d
icate
s
th
at
K
-
m
ea
n
s
s
u
cc
ess
f
u
lly
ca
p
tu
r
ed
d
if
f
er
e
n
t u
s
er
b
e
h
av
io
r
s
.
Fig
u
r
e
2
.
C
lu
s
ter
s
o
f
en
e
r
g
y
c
h
ar
g
in
g
u
s
in
g
K
-
m
ea
n
s
alg
o
r
it
h
m
−
C
-
m
ea
n
s
:
Fig
u
r
e
3
r
e
p
r
esen
ts
th
r
ee
clu
s
ter
s
.
I
t
is
clea
r
th
at
c
lu
s
ter
s
2
an
d
3
ar
e
s
im
ilar
,
s
h
o
win
g
m
o
d
er
ate
to
h
ig
h
en
e
r
g
y
c
o
n
s
u
m
p
tio
n
d
u
r
in
g
t
h
e
m
o
r
n
in
g
a
n
d
af
ter
n
o
o
n
with
a
b
r
ea
k
at
m
i
d
d
ay
d
u
r
in
g
t
h
e
lu
n
c
h
p
er
io
d
.
Fo
r
all
o
f
t
h
ese
clu
s
ter
s
,
f
r
o
m
6
p
.
m
.
to
ap
p
r
o
x
im
ately
6
a.
m
.
,
th
e
c
o
n
s
u
m
p
tio
n
is
n
u
ll.
Fig
u
r
e
3
.
C
lu
s
ter
s
o
f
en
e
r
g
y
c
h
ar
g
in
g
u
s
in
g
C
-
m
ea
n
s
alg
o
r
it
h
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
8
3
7
-
5
8
4
6
5842
−
DB
S
C
AN:
Fo
r
th
e
r
esu
lts
o
f
DB
S
C
AN
alg
o
r
ith
m
ar
e
s
h
o
wn
in
Fig
u
r
e
4
.
T
h
e
r
esu
lts
s
h
o
w
two
clu
s
ter
s
s
o
d
if
f
er
en
t
f
r
o
m
ea
ch
o
th
e
r
’
s
.
C
lu
s
ter
1
r
e
v
ea
ls
m
o
d
er
ate
en
er
g
y
c
o
n
s
u
m
p
tio
n
g
e
n
er
ally
with
a
s
m
all
p
ea
k
in
th
e
late
m
o
r
n
in
g
.
Fig
u
r
e
4
.
C
lu
s
ter
s
o
f
en
e
r
g
y
c
h
ar
g
in
g
u
s
in
g
DB
SC
AN
alg
o
r
ith
m
−
B
I
R
C
H:
C
lu
s
ter
in
g
r
esu
lts
f
o
r
th
e
B
I
R
C
H
alg
o
r
ith
m
r
ep
r
ese
n
t
f
o
u
r
clu
s
ter
s
,
as
s
h
o
wn
in
Fig
u
r
e
5
.
C
lu
s
ter
1
s
h
o
ws
a
h
u
g
e
p
ea
k
in
co
n
s
u
m
p
tio
n
j
u
s
t
b
ef
o
r
e
m
id
d
ay
.
C
lu
s
ter
2
r
ev
ea
ls
th
r
ee
co
n
s
u
m
p
t
io
n
p
ea
k
s
in
t
h
e
m
o
r
n
in
g
,
m
id
d
ay
,
a
n
d
at
th
e
e
n
d
o
f
th
e
af
ter
n
o
o
n
,
wh
ich
c
o
r
r
esp
o
n
d
to
h
ig
h
tr
af
f
ic
d
en
s
ity
p
er
io
d
s
.
Fig
u
r
e
5
.
C
lu
s
ter
s
o
f
en
e
r
g
y
c
h
ar
g
in
g
u
s
in
g
B
I
R
C
H
alg
o
r
ith
m
−
OPTI
C
S:
O
PTI
C
S
alg
o
r
ith
m
g
iv
es
co
n
s
u
m
p
tio
n
p
atter
n
s
s
o
m
ewh
at
s
im
ilar
to
DB
S
C
AN.
Fig
u
r
e
6
r
ev
ea
ls
f
o
u
r
clu
s
ter
s
in
wh
ich
th
r
ee
cl
u
s
ter
s
ar
e
v
e
r
y
s
im
ilar
(
clu
s
te
r
s
2
,
3
,
an
d
4
)
,
with
lo
w
en
er
g
y
c
o
n
s
u
m
p
tio
n
th
r
o
u
g
h
o
u
t
t
h
e
d
a
y
ex
ce
p
t
f
o
r
th
e
p
er
io
d
b
etwe
en
1
0
a.
m
.
an
d
1
2
p
.
m
.
C
lu
s
ter
1
r
ep
r
esen
ts
n
o
r
m
al
e
n
er
g
y
co
n
s
u
m
p
tio
n
d
u
r
i
n
g
th
e
d
ay
w
ith
in
ac
tiv
ity
f
r
o
m
th
e
en
d
o
f
t
h
e
d
ay
t
o
th
e
s
tar
t o
f
t
h
e
n
ex
t
d
ay
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
va
lu
a
tin
g
clu
s
teri
n
g
a
lg
o
r
it
h
ms w
ith
in
teg
r
a
ted
elec
tr
ic
v
eh
icle
…
(
A
yo
u
b
A
b
id
a
)
5843
Fig
u
r
e
6
.
C
lu
s
ter
s
o
f
en
e
r
g
y
c
h
ar
g
in
g
u
s
in
g
OPTI
C
S a
lg
o
r
it
h
m
−
Mean
-
Sh
if
t:
Me
an
-
Sh
if
t
alg
o
r
ith
m
’
s
r
esu
lt
is
s
h
o
wn
in
Fi
g
u
r
e
7
.
C
lu
s
ter
s
1
an
d
3
r
ev
ea
l
n
o
r
m
al
a
n
d
co
n
tin
u
o
u
s
co
n
s
u
m
p
tio
n
d
u
r
i
n
g
th
e
d
ay
f
r
o
m
6
a
.
m
.
to
6
p
.
m
.
C
lu
s
ter
2
s
h
o
ws
ac
tiv
ity
a
t
th
e
s
tar
t
o
f
th
e
d
ay
an
d
at
th
e
en
d
o
f
th
e
d
ay
,
an
d
C
lu
s
ter
4
illu
s
tr
ates
m
o
d
er
ate
co
n
s
u
m
p
tio
n
at
th
e
s
tar
t
o
f
th
e
d
ay
an
d
in
ac
tiv
ity
o
u
ts
id
e
th
is
p
er
io
d
.
Fig
u
r
e
7
.
C
lu
s
ter
s
o
f
en
e
r
g
y
c
h
ar
g
in
g
u
s
in
g
Mean
-
Sh
if
t
alg
o
r
ith
m
I
n
co
m
p
ar
is
o
n
with
o
t
h
er
wo
r
k
s
,
in
[
1
6
]
,
K
-
m
ea
n
s
o
u
tp
e
r
f
o
r
m
s
th
e
o
th
e
r
alg
o
r
it
h
m
s
with
th
e
b
est
m
etr
ics
r
esu
lts
,
ac
h
iev
in
g
a
C
HI
o
f
1
,
2
0
0
,
a
s
ilh
o
u
ette
s
co
r
e
0
.
4
5
,
an
d
DB
I
o
f
0
.
7
4
.
K
-
Me
d
o
id
an
d
Ag
g
lo
m
er
ativ
e
clu
s
ter
in
g
als
o
r
ev
ea
l
g
o
o
d
an
d
ap
p
r
o
x
i
m
ately
eq
u
al
r
esu
lts
.
I
n
th
i
s
wo
r
k
,
DB
SC
A
N
alg
o
r
ith
m
s
h
a
v
e
th
e
lo
west
r
esu
lts
,
b
ec
au
s
e
o
f
its
lo
west
C
HI
an
d
s
ilh
o
u
ette
s
co
r
e
,
an
d
th
e
h
ig
h
est
DB
I
o
f
1
.
7
8
.
K
-
m
ea
n
s
also
o
u
tp
er
f
o
r
m
s
th
e
o
th
er
alg
o
r
it
h
m
s
in
[
1
8
]
,
Hier
ar
c
h
ical
clu
s
ter
in
g
alg
o
r
ith
m
r
ev
ea
ls
also
g
o
o
d
r
esu
lts
with
0
.
3
8
in
s
ilh
o
u
ette
s
co
r
e
a
n
d
0
.
7
4
i
n
DB
I
,
a
n
d
2
,
2
7
0
f
o
r
th
e
C
HI
.
GM
M
in
th
is
r
esear
ch
p
ap
e
r
g
iv
es
th
e
lo
west
r
esu
lts
wh
ich
m
ad
e
th
i
s
alg
o
r
ith
m
f
a
r
f
r
o
m
K
-
m
ea
n
s
an
d
Hier
ar
c
h
ical
clu
s
ter
in
g
alg
o
r
ith
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
8
3
7
-
5
8
4
6
5844
4.
CO
NCLU
SI
O
N
AND
P
E
RS
P
E
CT
I
VE
T
h
e
ad
v
en
t
o
f
elec
tr
ic
v
e
h
icles
p
r
esen
ts
s
ig
n
if
ican
t
ch
allen
g
es
f
o
r
p
o
wer
g
r
id
d
is
tr
ib
u
tio
n
an
d
p
r
o
d
u
ctio
n
o
p
er
at
o
r
s
d
u
e
t
o
th
e
u
n
p
r
ed
ictab
le
lo
ad
.
T
h
ese
c
h
allen
g
es
s
tem
f
r
o
m
th
e
u
n
iq
u
e
ch
ar
ac
ter
is
tics
o
f
ea
ch
elec
tr
ic
v
eh
icle
ch
ar
g
e
r
,
in
clu
d
in
g
lo
ca
ti
o
n
an
d
e
n
e
r
g
y
co
n
s
u
m
p
tio
n
.
C
lu
s
ter
in
g
m
eth
o
d
s
will
h
elp
id
en
tify
p
atter
n
s
in
en
er
g
y
c
o
n
s
u
m
p
tio
n
,
s
er
v
in
g
to
m
an
ag
e
th
e
in
cr
ea
s
in
g
elec
tr
ical
lo
ad
f
r
o
m
elec
tr
i
c
v
eh
icle
u
s
er
s
an
d
c
h
ar
g
er
s
.
T
h
is
s
tu
d
y
ev
alu
ates
th
e
ef
f
ec
tiv
en
ess
o
f
clu
s
ter
in
g
alg
o
r
ith
m
s
,
in
clu
d
in
g
K
-
m
ea
n
s
,
DB
S
C
AN
,
C
-
m
ea
n
s
,
B
I
R
C
H,
Me
an
-
Sh
if
t,
an
d
OPTI
C
S,
u
s
in
g
p
er
f
o
r
m
an
ce
m
etr
ics
s
u
ch
as
th
e
Si
lh
o
u
ette
co
e
f
f
icien
t,
C
HI
,
an
d
DB
I
.
T
h
e
r
esu
lts
d
if
f
er
in
ter
m
s
o
f
lo
a
d
c
u
r
v
e
clu
s
ter
s
,
clu
s
ter
n
u
m
b
er
s
,
p
ea
k
v
alu
es,
an
d
m
etr
ics.
B
ased
o
n
th
e
clu
s
ter
in
g
p
er
f
o
r
m
an
ce
m
etr
ics,
C
-
m
ea
n
s
d
e
m
o
n
s
tr
ates
th
e
b
est
o
v
er
all
p
er
f
o
r
m
a
n
ce
with
th
e
h
ig
h
est
Sil
h
o
u
ette
c
o
ef
f
icien
t
(
0
.
3
0
)
a
n
d
a
s
tr
o
n
g
C
alin
s
k
i
-
Har
ab
asz
s
co
r
e
(
5
4
3
)
,
wh
ile
Me
an
-
Sh
if
t
s
h
o
ws
th
e
b
est
Dav
ies
-
B
o
u
ld
in
in
d
ex
(
1
.
1
3
)
b
u
t
p
er
f
o
r
m
s
p
o
o
r
ly
o
n
o
th
er
m
etr
ics.
B
I
R
C
H
o
f
f
er
s
a
b
alan
ce
d
p
er
f
o
r
m
an
ce
with
m
o
d
er
ate
s
co
r
es
ac
r
o
s
s
all
m
etr
ics.
T
h
e
r
esu
lts
s
u
g
g
est
th
at
C
-
m
ea
n
s
is
th
e
m
o
s
t
s
u
itab
le
alg
o
r
ith
m
f
o
r
clu
s
ter
in
g
E
V
ch
ar
g
in
g
p
r
o
f
iles
,
p
r
o
v
id
in
g
th
e
b
est
b
alan
ce
b
etwe
en
clu
s
ter
s
ep
ar
atio
n
an
d
co
h
esio
n
.
B
y
m
aster
in
g
th
ese
lo
ad
clu
s
ter
s
,
o
p
er
ato
r
s
ca
n
b
e
tter
ad
o
p
t
v
eh
icle
-
to
-
g
r
i
d
(
V2
G)
tech
n
o
lo
g
y
an
d
d
ev
elo
p
m
o
r
e
ef
f
icien
t
en
er
g
y
m
an
ag
em
e
n
t
s
y
s
tem
s
,
m
itig
at
in
g
th
e
im
p
ac
t
o
f
p
ea
k
c
o
n
s
u
m
p
tio
n
a
n
d
v
alley
s
.
B
u
ild
in
g
u
p
o
n
th
ese
f
in
d
in
g
s
,
we
id
en
tify
a
s
ig
n
if
ican
t
g
ap
in
th
e
f
ield
,
p
ar
ticu
la
r
ly
co
n
ce
r
n
in
g
t
h
e
ef
f
ec
tiv
e
in
teg
r
atio
n
o
f
E
V
ch
ar
g
er
p
att
er
n
s
an
d
o
th
er
in
p
u
ts
to
en
h
an
ce
th
e
m
an
ag
em
en
t
o
f
E
VC
p
o
wer
d
em
an
d
.
T
h
e
d
ev
elo
p
m
e
n
t
o
f
p
r
o
to
co
ls
f
o
r
d
ata
ex
ch
a
n
g
e
b
etwe
en
E
V
s
,
E
VC
s
,
an
d
ce
n
tr
al
s
y
s
tem
m
an
ag
em
e
n
t
is
a
cr
itical
asp
ec
t
th
at
n
ee
d
s
to
b
e
ad
d
r
ess
ed
.
T
h
e
ce
n
t
r
al
s
y
s
tem
,
task
ed
with
t
h
e
m
an
a
g
em
e
n
t
o
f
elec
tr
ic
v
eh
icle
ch
ar
g
er
s
,
co
u
ld
g
r
ea
tly
b
e
n
ef
it f
r
o
m
s
u
c
h
ad
v
a
n
ce
m
en
ts
.
I
n
lig
h
t o
f
th
is
,
o
u
r
p
er
s
p
ec
tiv
es a
im
to
ex
p
lo
r
e
th
e
d
ev
el
o
p
m
en
t o
f
V2
G
p
r
o
to
co
ls
.
T
h
e
d
ev
elo
p
m
en
t
will
b
e
th
r
o
u
g
h
im
p
lem
en
tin
g
an
d
in
tellig
en
t
e
n
er
g
y
m
a
n
ag
em
en
t
al
g
o
r
ith
m
s
with
in
a
ce
n
tr
alize
d
s
m
ar
t
ch
ar
g
in
g
m
a
n
ag
em
en
t
s
y
s
tem
.
T
h
is
d
e
v
elo
p
m
e
n
t
will
en
a
b
le
th
e
e
n
h
an
ci
n
g
g
r
id
s
tab
ilit
y
an
d
o
p
tim
al
e
n
er
g
y
d
is
tr
ib
u
tio
n
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
d
in
g
in
v
o
lv
ed
.
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
iT)
to
r
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
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Ay
o
u
b
Ab
i
d
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
Mo
u
r
ad
Z
e
g
r
ar
i
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
R
ed
o
u
an
e
Ma
jd
o
u
l
✓
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
t
th
e
f
in
d
in
g
s
o
f
th
is
s
tu
d
y
ar
e
av
aila
b
le
o
n
r
eq
u
est
f
r
o
m
th
e
co
r
r
esp
o
n
d
in
g
au
th
o
r
,
AA
.
T
h
e
d
ata,
wh
ich
c
o
n
tain
in
f
o
r
m
atio
n
th
at
co
u
ld
co
m
p
r
o
m
is
e
th
e
p
r
iv
ac
y
o
f
r
es
ea
r
ch
p
ar
ticip
an
ts
,
ar
e
n
o
t p
u
b
licly
av
aila
b
le
d
u
e
to
ce
r
tain
r
estrictio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
va
lu
a
tin
g
clu
s
teri
n
g
a
lg
o
r
it
h
ms w
ith
in
teg
r
a
ted
elec
tr
ic
v
eh
icle
…
(
A
yo
u
b
A
b
id
a
)
5845
RE
F
E
R
E
NC
E
S
[
1
]
A
.
A
b
i
d
a
,
R
.
M
a
j
d
o
u
l
,
a
n
d
M
.
Ze
g
r
a
r
i
,
“
Th
e
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
r
e
q
u
e
st
e
d
e
n
e
r
g
y
p
r
e
d
i
c
t
i
o
n
s
u
si
n
g
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms
f
o
r
t
h
e
d
e
man
d
si
d
e
man
a
g
e
men
t
,
”
I
n
:
El
Fa
d
i
l
,
H
.
,
Z
h
a
n
g
,
W.
(
e
d
s)
Au
t
o
m
a
t
i
c
C
o
n
t
ro
l
a
n
d
Em
e
rg
i
n
g
T
e
c
h
n
o
l
o
g
i
e
s.
AC
ET
2
0
2
3
.
L
e
c
t
u
re
N
o
t
e
s
i
n
El
e
c
t
ri
c
a
l
En
g
i
n
e
e
ri
n
g
,
v
o
l
1
1
4
1
.
S
p
r
i
n
g
e
r
,
S
i
n
g
a
p
o
r
e
.
2
0
2
4
,
p
p
.
6
0
8
–
6
1
7
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
981
-
97
-
0126
-
1_54.
[
2
]
S
.
M
o
h
a
n
t
y
e
t
a
l
.
,
“
D
e
m
a
n
d
si
d
e
ma
n
a
g
e
m
e
n
t
o
f
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
s
i
n
smar
t
g
r
i
d
s:
A
s
u
r
v
e
y
o
n
st
r
a
t
e
g
i
e
s,
c
h
a
l
l
e
n
g
e
s
,
m
o
d
e
l
i
n
g
,
a
n
d
o
p
t
i
m
i
z
a
t
i
o
n
,
”
E
n
e
rg
y
R
e
p
o
rt
s
,
v
o
l
.
8
,
p
p
.
1
2
4
6
6
–
1
2
4
9
0
,
N
o
v
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
g
y
r
.
2
0
2
2
.
0
9
.
0
2
3
.
[
3
]
R
.
Ç
a
k
m
a
k
,
“
D
e
s
i
g
n
a
n
d
i
mp
l
e
me
n
t
a
t
i
o
n
o
f
a
l
o
w
-
c
o
s
t
p
o
w
e
r
l
o
g
g
e
r
d
e
v
i
c
e
f
o
r
s
p
e
c
i
f
i
c
d
e
ma
n
d
p
r
o
f
i
l
e
a
n
a
l
y
si
s
i
n
d
e
ma
n
d
-
si
d
e
man
a
g
e
me
n
t
s
t
u
d
i
e
s
f
o
r
smar
t
g
r
i
d
s,
”
Ex
p
e
r
t
S
y
st
e
m
s
w
i
t
h
A
p
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
2
3
8
,
p
.
1
2
1
8
8
8
,
M
a
r
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
sw
a
.
2
0
2
3
.
1
2
1
8
8
8
.
[
4
]
P
.
P
a
l
e
n
s
k
y
a
n
d
D
.
D
i
e
t
r
i
c
h
,
“
D
e
ma
n
d
s
i
d
e
ma
n
a
g
e
m
e
n
t
:
d
e
m
a
n
d
r
e
s
p
o
n
se,
i
n
t
e
l
l
i
g
e
n
t
e
n
e
r
g
y
s
y
s
t
e
ms
,
a
n
d
s
mart
l
o
a
d
s,
”
I
E
E
E
T
ra
n
s
a
c
t
i
o
n
s
o
n
I
n
d
u
st
r
i
a
l
I
n
f
o
rm
a
t
i
c
s
,
v
o
l
.
7
,
n
o
.
3
,
p
p
.
3
8
1
–
3
8
8
,
A
u
g
.
2
0
1
1
,
d
o
i
:
1
0
.
1
1
0
9
/
TI
I
.
2
0
1
1
.
2
1
5
8
8
4
1
.
[
5
]
M
.
N
a
z
a
r
i
,
A
.
H
u
ssa
i
n
,
a
n
d
P
.
M
u
si
l
e
k
,
“
A
p
p
l
i
c
a
t
i
o
n
s
o
f
c
l
u
st
e
r
i
n
g
met
h
o
d
s
f
o
r
d
i
f
f
e
r
e
n
t
a
s
p
e
c
t
s
o
f
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
s,
”
El
e
c
t
r
o
n
i
c
s
,
v
o
l
.
1
2
,
n
o
.
4
,
p
.
7
9
0
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
e
l
e
c
t
r
o
n
i
c
s1
2
0
4
0
7
9
0
.
[
6
]
H
.
M
.
Za
n
g
a
n
a
a
n
d
A
.
M
.
A
b
d
u
l
a
z
e
e
z
,
“
D
e
v
e
l
o
p
e
d
c
l
u
s
t
e
r
i
n
g
a
l
g
o
r
i
t
h
ms
f
o
r
e
n
g
i
n
e
e
r
i
n
g
a
p
p
l
i
c
a
t
i
o
n
s:
a
r
e
v
i
e
w
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
I
n
f
o
rm
a
t
i
c
s
,
I
n
f
o
rm
a
t
i
o
n
S
y
st
e
m
a
n
d
C
o
m
p
u
t
e
r
E
n
g
i
n
e
e
r
i
n
g
(
I
N
J
I
I
S
C
O
M)
,
v
o
l
.
4
,
n
o
.
2
,
p
p
.
1
4
7
–
1
6
9
,
D
e
c
.
2
0
2
3
,
d
o
i
:
1
0
.
3
4
0
1
0
/
i
n
j
i
i
sc
o
m
.
v
4
i
2
.
1
1
6
3
6
.
[
7
]
D
.
H
u
,
K
.
Z
h
o
u
,
F
.
L
i
,
a
n
d
D
.
M
a
,
“
El
e
c
t
r
i
c
v
e
h
i
c
l
e
u
s
e
r
c
l
a
ss
i
f
i
c
a
t
i
o
n
a
n
d
v
a
l
u
e
d
i
sc
o
v
e
r
y
b
a
se
d
o
n
c
h
a
r
g
i
n
g
b
i
g
d
a
t
a
,
”
E
n
e
r
g
y
,
v
o
l
.
2
4
9
,
p
.
1
2
3
6
9
8
,
J
u
n
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
e
r
g
y
.
2
0
2
2
.
1
2
3
6
9
8
.
[
8
]
L.
B
e
r
z
i
,
M
.
D
e
l
o
g
u
,
a
n
d
M
.
P
i
e
r
i
n
i
,
“
D
e
v
e
l
o
p
me
n
t
o
f
d
r
i
v
i
n
g
c
y
c
l
e
s
f
o
r
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
s
i
n
t
h
e
c
o
n
t
e
x
t
o
f
t
h
e
c
i
t
y
o
f
F
l
o
r
e
n
c
e
,
”
T
ra
n
s
p
o
rt
a
t
i
o
n
Re
s
e
a
r
c
h
P
a
rt
D
:
T
ra
n
sp
o
r
t
a
n
d
En
v
i
r
o
n
m
e
n
t
,
v
o
l
.
4
7
,
p
p
.
2
9
9
–
3
2
2
,
2
0
1
6
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
t
r
d
.
2
0
1
6
.
0
5
.
0
1
0
.
[
9
]
A
.
H
u
ssa
i
n
a
n
d
P
.
M
u
s
i
l
e
k
,
“
R
e
l
i
a
b
i
l
i
t
y
-
as
-
a
-
serv
i
c
e
u
s
a
g
e
o
f
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
s
:
su
i
t
a
b
i
l
i
t
y
a
n
a
l
y
si
s
f
o
r
d
i
f
f
e
r
e
n
t
t
y
p
e
s
o
f
b
u
i
l
d
i
n
g
s,
”
E
n
e
r
g
i
e
s
,
v
o
l
.
1
5
,
n
o
.
2
,
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
e
n
1
5
0
2
0
6
6
5
.
[
1
0
]
X
.
H
u
a
n
d
F
.
S
u
n
,
“
F
u
z
z
y
c
l
u
s
t
e
r
i
n
g
b
a
s
e
d
m
u
l
t
i
-
m
o
d
e
l
su
p
p
o
r
t
v
e
c
t
o
r
r
e
g
r
e
ssi
o
n
s
t
a
t
e
o
f
c
h
a
r
g
e
e
s
t
i
m
a
t
o
r
f
o
r
l
i
t
h
i
u
m
-
i
o
n
b
a
t
t
e
r
y
o
f
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
,
”
i
n
2
0
0
9
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
I
n
t
e
l
l
i
g
e
n
t
H
u
m
a
n
-
Ma
c
h
i
n
e
S
y
s
t
e
m
s
a
n
d
C
y
b
e
r
n
e
t
i
c
s
,
2
0
0
9
,
p
p
.
3
9
2
–
3
9
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
H
M
S
C
.
2
0
0
9
.
1
0
6
.
[
1
1
]
D
.
G
.
S
á
n
c
h
e
z
,
A
.
Ta
b
a
r
e
s,
L.
T.
F
a
r
i
a
,
J
.
C
.
R
i
v
e
r
a
,
a
n
d
J.
F
.
F
r
a
n
c
o
,
“
A
c
l
u
s
t
e
r
i
n
g
a
p
p
r
o
a
c
h
f
o
r
t
h
e
o
p
t
i
mal
si
t
i
n
g
o
f
r
e
c
h
a
r
g
i
n
g
st
a
t
i
o
n
s
i
n
t
h
e
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
r
o
u
t
i
n
g
p
r
o
b
l
e
m w
i
t
h
t
i
me
w
i
n
d
o
w
s,
”
E
n
e
r
g
i
e
s
,
v
o
l
.
1
5
,
n
o
.
7
,
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
e
n
1
5
0
7
2
3
7
2
.
[
1
2
]
A
.
H
u
ssai
n
a
n
d
P
.
M
u
si
l
e
k
,
“
U
t
i
l
i
t
y
-
s
c
a
l
e
e
n
e
r
g
y
st
o
r
a
g
e
s
y
st
e
m
f
o
r
l
o
a
d
m
a
n
a
g
e
me
n
t
u
n
d
e
r
h
i
g
h
p
e
n
e
t
r
a
t
i
o
n
o
f
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
s
:
A
marg
i
n
a
l
c
a
p
a
c
i
t
y
v
a
l
u
e
-
b
a
s
e
d
si
z
i
n
g
a
p
p
r
o
a
c
h
,
”
J
o
u
rn
a
l
o
f
E
n
e
rg
y
S
t
o
ra
g
e
,
v
o
l
.
5
6
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
s
t
.
2
0
2
2
.
1
0
5
9
2
2
.
[
1
3
]
A
.
H
u
ssai
n
a
n
d
P
.
M
u
si
l
e
k
,
“
F
a
i
r
n
e
ss
a
n
d
u
t
i
l
i
t
a
r
i
a
n
i
sm
i
n
a
l
l
o
c
a
t
i
n
g
e
n
e
r
g
y
t
o
EV
s
d
u
r
i
n
g
p
o
w
e
r
c
o
n
t
i
n
g
e
n
c
i
e
s
u
si
n
g
m
o
d
i
f
i
e
d
d
i
v
i
s
i
o
n
r
u
l
e
s
,
”
I
EE
E
T
ra
n
sa
c
t
i
o
n
s
o
n
S
u
s
t
a
i
n
a
b
l
e
En
e
r
g
y
,
v
o
l
.
1
3
,
n
o
.
3
,
p
p
.
1
4
4
4
–
1
4
5
6
,
Ju
l
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
TST
E.
2
0
2
2
.
3
1
6
1
8
9
7
.
[
1
4
]
S
.
G
u
o
a
n
d
E.
K
o
n
t
o
u
,
“
D
i
s
p
a
r
i
t
i
e
s
a
n
d
e
q
u
i
t
y
i
ss
u
e
s
i
n
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
s
r
e
b
a
t
e
a
l
l
o
c
a
t
i
o
n
,
”
E
n
e
r
g
y
P
o
l
i
c
y
,
v
o
l
.
1
5
4
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
p
o
l
.
2
0
2
1
.
1
1
2
2
9
1
.
[
1
5
]
Z.
L
v
,
L.
Q
i
a
o
,
K
.
C
a
i
,
a
n
d
Q
.
W
a
n
g
,
“
B
i
g
d
a
t
a
a
n
a
l
y
s
i
s
t
e
c
h
n
o
l
o
g
y
f
o
r
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
n
e
t
w
o
r
k
s
i
n
sm
a
r
t
c
i
t
i
e
s
,
”
I
EEE
T
ra
n
s
a
c
t
i
o
n
s
o
n
I
n
t
e
l
l
i
g
e
n
t
T
r
a
n
s
p
o
rt
a
t
i
o
n
S
y
s
t
e
m
s
,
v
o
l
.
2
2
,
n
o
.
3
,
p
p
.
1
8
0
7
–
1
8
1
6
,
M
a
r
.
2
0
2
1
,
d
o
i
:
1
0
.
1
1
0
9
/
TI
TS.2
0
2
0
.
3
0
0
8
8
8
4
.
[
1
6
]
P
.
C
B
,
K
.
P
.
R
,
A
.
S
.
P
i
l
l
a
i
,
A
.
S
.
K
h
w
a
j
a
,
a
n
d
A
.
A
n
p
a
l
a
g
a
n
,
“
E
n
h
a
n
c
i
n
g
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
c
h
a
r
g
i
n
g
i
n
f
r
a
st
r
u
c
t
u
r
e
:
A
f
r
a
mew
o
r
k
f
o
r
e
f
f
i
c
i
e
n
t
c
h
a
r
g
i
n
g
p
o
i
n
t
ma
n
a
g
e
men
t
,
”
e
-
Pr
i
m
e
-
Ad
v
a
n
c
e
s
i
n
El
e
c
t
ri
c
a
l
En
g
i
n
e
e
ri
n
g
,
El
e
c
t
r
o
n
i
c
s
a
n
d
En
e
r
g
y
,
v
o
l
.
1
1
,
p
.
1
0
0
9
2
6
,
M
a
r
.
2
0
2
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
p
r
i
me
.
2
0
2
5
.
1
0
0
9
2
6
.
[
1
7
]
K
.
N
.
H
a
s
a
n
e
t
a
l
.
,
“
A
f
r
a
m
e
w
o
r
k
t
o
i
n
v
e
st
i
g
a
t
e
c
h
a
r
g
e
r
c
a
p
a
c
i
t
y
u
t
i
l
i
z
a
t
i
o
n
a
n
d
n
e
t
w
o
r
k
v
o
l
t
a
g
e
p
r
o
f
i
l
e
t
h
r
o
u
g
h
r
e
si
d
e
n
t
i
a
l
EV
c
h
a
r
g
i
n
g
d
a
t
a
c
l
u
st
e
r
i
n
g
,
”
S
u
s
t
a
i
n
a
b
l
e
E
n
e
r
g
y
T
e
c
h
n
o
l
o
g
i
e
s
a
n
d
A
ssessm
e
n
t
s
,
v
o
l
.
7
4
,
p
.
1
0
4
1
4
1
,
F
e
b
.
2
0
2
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
se
t
a
.
2
0
2
4
.
1
0
4
1
4
1
.
[
1
8
]
S
.
S
h
a
h
r
i
a
r
a
n
d
A
.
R
.
A
l
-
A
l
i
,
“
I
mp
a
c
t
s
o
f
C
O
V
I
D
-
1
9
o
n
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
c
h
a
r
g
i
n
g
b
e
h
a
v
i
o
r
:
d
a
t
a
a
n
a
l
y
t
i
c
s,
v
i
su
a
l
i
z
a
t
i
o
n
,
a
n
d
c
l
u
st
e
r
i
n
g
,
”
A
p
p
l
i
e
d
S
y
s
t
e
m
I
n
n
o
v
a
t
i
o
n
,
v
o
l
.
5
,
n
o
.
1
,
p
.
1
2
,
Ja
n
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
a
s
i
5
0
1
0
0
1
2
.
[
1
9
]
R
.
R
i
c
h
a
r
d
,
H
.
C
a
o
,
a
n
d
M
.
W
a
c
h
o
w
i
c
z
,
“
A
n
a
u
t
o
ma
t
e
d
c
l
u
st
e
r
i
n
g
p
r
o
c
e
ss
f
o
r
h
e
l
p
i
n
g
p
r
a
c
t
i
t
i
o
n
e
r
s
t
o
i
d
e
n
t
i
f
y
s
i
mi
l
a
r
EV
c
h
a
r
g
i
n
g
p
a
t
t
e
r
n
s
a
c
r
o
s
s
m
u
l
t
i
p
l
e
t
e
mp
o
r
a
l
g
r
a
n
u
l
a
r
i
t
i
e
s,”
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
S
m
a
rt
C
i
t
i
e
s
a
n
d
G
r
e
e
n
I
C
T
S
y
s
t
e
m
s,
S
MA
RTG
REE
N
S
-
Pr
o
c
e
e
d
i
n
g
s
,
v
o
l
.
2
0
2
1
-
A
p
r
i
l
,
p
p
.
6
7
–
7
7
,
2
0
2
1
,
d
o
i
:
1
0
.
5
2
2
0
/
0
0
1
0
4
8
5
0
0
0
6
7
0
0
7
7
.
[
2
0
]
A
.
A
.
A
b
d
u
l
n
a
ssar
a
n
d
L.
R
.
N
a
i
r
,
“
P
e
r
f
o
r
ma
n
c
e
a
n
a
l
y
s
i
s
o
f
K
m
e
a
n
s
w
i
t
h
mo
d
i
f
i
e
d
i
n
i
t
i
a
l
c
e
n
t
r
o
i
d
sel
e
c
t
i
o
n
a
l
g
o
r
i
t
h
ms
a
n
d
d
e
v
e
l
o
p
e
d
K
m
e
a
n
s
9
+
m
o
d
e
l
,
”
M
e
a
s
u
remen
t
:
S
e
n
so
r
s
,
v
o
l
.
2
5
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
me
a
se
n
.
2
0
2
3
.
1
0
0
6
6
6
.
[
2
1
]
G
.
G
a
n
,
C
.
M
a
,
a
n
d
J
.
W
u
,
D
a
t
a
C
l
u
s
t
e
r
i
n
g
:
t
h
e
o
ry,
a
l
g
o
ri
t
h
m
s,
a
n
d
a
p
p
l
i
c
a
t
i
o
n
s
.
S
o
c
i
e
t
y
f
o
r
I
n
d
u
st
r
i
a
l
a
n
d
A
p
p
l
i
e
d
M
a
t
h
e
ma
t
i
c
s,
2
0
0
7
.
[
2
2
]
M
.
F
r
a
j
,
M
.
A
.
B
e
n
H
a
j
K
a
c
e
m,
a
n
d
N
.
Ess
o
u
ssi
,
“
A
n
o
v
e
r
v
i
e
w
o
f
m
u
l
t
i
-
v
i
e
w
m
e
t
h
o
d
s
f
o
r
t
e
x
t
c
l
u
st
e
r
i
n
g
,
”
I
n
:
Al
y
o
u
b
i
,
B
.
,
Be
n
N
c
i
r
,
C
E.
,
Al
h
a
r
b
i
,
I
.
,
J
a
rb
o
u
i
,
A
.
(
e
d
s)
M
a
c
h
i
n
e
L
e
a
rn
i
n
g
a
n
d
D
a
t
a
An
a
l
y
t
i
c
s
f
o
r
S
o
l
v
i
n
g
Bu
s
i
n
e
ss
Pro
b
l
e
m
s.
U
n
su
p
e
rvi
s
e
d
a
n
d
S
e
m
i
-
S
u
p
e
rv
i
se
d
L
e
a
r
n
i
n
g
.
S
p
ri
n
g
e
r,
C
h
a
m
,
2
0
2
2
,
p
p
.
1
4
1
–
1
6
4
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
0
3
1
-
1
8
4
8
3
-
3
_
8
.
[
2
3
]
T.
S
.
M
a
d
h
u
l
a
t
h
a
,
“
A
n
o
v
e
r
v
i
e
w
o
n
c
l
u
s
t
e
r
i
n
g
met
h
o
d
s
,
”
I
O
S
R
J
o
u
r
n
a
l
o
f
En
g
i
n
e
e
r
i
n
g
,
v
o
l
.
0
2
,
n
o
.
0
4
,
p
p
.
7
1
9
–
7
2
5
,
2
0
1
2
,
d
o
i
:
1
0
.
9
7
9
0
/
3
0
2
1
-
0
2
0
4
7
1
9
7
2
5
.
[
2
4
]
Y
.
R
a
n
i
,
M
.
-
,
a
n
d
H
.
R
o
h
i
l
,
“
C
o
m
p
a
r
a
t
i
v
e
a
n
a
l
y
s
i
s
o
f
B
I
R
C
H
a
n
d
C
U
R
E
h
i
e
r
a
r
c
h
i
c
a
l
c
l
u
s
t
e
r
i
n
g
a
l
g
o
r
i
t
h
m
u
s
i
n
g
W
E
K
A
3
.
6
.
9
,
”
T
h
e
S
I
J
T
r
a
n
s
a
c
t
i
o
n
s
o
n
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
E
n
g
i
n
e
e
r
i
n
g
&
i
t
s
A
p
p
l
i
c
a
t
i
o
n
s
(
C
S
E
A)
,
v
o
l
.
0
2
,
n
o
.
0
1
,
p
p
.
2
5
–
2
9
,
2
0
1
4
,
d
o
i
:
1
0
.
9
7
5
6
/
s
i
j
c
se
a
/
v
2
i
1
/
0
2
0
1
0
8
0
2
0
1
.
[
2
5
]
T.
W
a
h
y
u
n
i
n
g
r
u
m
,
S
.
K
h
o
ms
a
h
,
S
.
S
u
y
a
n
t
o
,
S
.
M
e
l
i
a
n
a
,
P
.
E.
Y
u
n
a
n
t
o
,
a
n
d
W
.
F
.
A
l
M
a
k
i
,
“
I
mp
r
o
v
i
n
g
c
l
u
st
e
r
i
n
g
m
e
t
h
o
d
p
e
r
f
o
r
m
a
n
c
e
u
s
i
n
g
k
-
m
e
a
n
s,
mi
n
i
b
a
t
c
h
k
-
m
e
a
n
s,
B
I
R
C
H
a
n
d
sp
e
c
t
r
a
l
,
”
i
n
2
0
2
1
4
t
h
I
n
t
e
rn
a
t
i
o
n
a
l
S
e
m
i
n
a
r
o
n
Re
s
e
a
r
c
h
o
f
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
a
n
d
I
n
t
e
l
l
i
g
e
n
t
S
y
st
e
m
s
(
I
S
RI
T
I
)
,
D
e
c
.
2
0
2
1
,
p
p
.
2
0
6
–
2
1
0
,
d
o
i
:
1
0
.
1
1
0
9
/
I
S
R
I
T
I
5
4
0
4
3
.
2
0
2
1
.
9
7
0
2
8
2
3
.
[
2
6
]
X
.
W
a
n
g
a
n
d
Y
.
X
u
,
“
A
n
i
m
p
r
o
v
e
d
i
n
d
e
x
f
o
r
c
l
u
st
e
r
i
n
g
v
a
l
i
d
a
t
i
o
n
b
a
se
d
o
n
S
i
l
h
o
u
e
t
t
e
i
n
d
e
x
a
n
d
C
a
l
i
n
s
k
i
-
H
a
r
a
b
a
s
z
i
n
d
e
x
,
”
I
O
P
C
o
n
f
e
re
n
c
e
S
e
r
i
e
s:
M
a
t
e
ri
a
l
s
S
c
i
e
n
c
e
a
n
d
E
n
g
i
n
e
e
r
i
n
g
,
v
o
l
.
5
6
9
,
n
o
.
5
,
p
.
0
5
2
0
2
4
,
Ju
l
.
2
0
1
9
,
d
o
i
:
1
0
.
1
0
8
8
/
1
7
5
7
-
8
9
9
X
/
5
6
9
/
5
/
0
5
2
0
2
4
.
[
2
7
]
J.
C
.
R
.
Th
o
m
a
s,
M
.
S
.
P
e
ñ
a
s,
a
n
d
M
.
M
o
r
a
,
“
N
e
w
v
e
r
si
o
n
o
f
D
a
v
i
e
s
-
B
o
u
l
d
i
n
i
n
d
e
x
f
o
r
c
l
u
st
e
r
i
n
g
v
a
l
i
d
a
t
i
o
n
b
a
s
e
d
o
n
c
y
l
i
n
d
r
i
c
a
l
d
i
s
t
a
n
c
e
,
”
Pro
c
e
e
d
i
n
g
s
-
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
f
t
h
e
C
h
i
l
e
a
n
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
S
o
c
i
e
t
y
,
S
C
C
C
,
v
o
l
.
0
,
p
p
.
4
9
–
5
3
,
2
0
1
3
,
d
o
i
:
1
0
.
1
1
0
9
/
S
C
C
C
.
2
0
1
3
.
2
9
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
8
3
7
-
5
8
4
6
5846
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Ay
o
u
b
Ab
id
a
a
M
o
ro
c
c
a
n
sc
h
o
lar,
c
o
m
p
lete
d
h
is
e
n
g
in
e
e
ri
n
g
d
e
g
re
e
in
th
e
m
a
n
a
g
e
m
e
n
t
o
f
sm
a
rt
e
lec
tri
c
a
l
s
y
ste
m
s
in
2
0
2
1
fro
m
T
h
e
Na
ti
o
n
a
l
Hig
h
e
r
S
c
h
o
o
l
o
f
Arts
a
n
d
Cra
fts
(ENS
AM)
a
t
Ha
ss
a
n
II
U
n
iv
e
rsit
y
in
Ca
sa
b
lan
c
a
,
M
o
r
o
c
c
o
.
F
o
ll
o
win
g
h
is
g
ra
d
u
a
ti
o
n
,
h
e
e
m
b
a
rk
e
d
o
n
h
is
d
o
c
to
ra
l
j
o
u
r
n
e
y
t
h
e
su
b
se
q
u
e
n
t
y
e
a
r
a
t
th
e
La
b
o
ra
to
r
y
o
f
Co
m
p
lex
Cy
b
e
r
P
h
y
sic
a
l
S
y
ste
m
s.
His
P
h
D
re
se
a
rc
h
is
fo
c
u
se
d
o
n
t
h
e
d
e
v
e
lo
p
m
e
n
t
o
f
v
e
h
icle
-
to
-
g
rid
p
ro
t
o
c
o
ls
with
i
n
sm
a
rt
g
ri
d
s,
u
ti
l
i
z
in
g
a
rti
ficia
l
i
n
telli
g
e
n
c
e
.
T
h
is
wo
rk
is
p
iv
o
tal
in
th
e
re
a
lm
o
f
sm
a
rt
g
rid
tec
h
n
o
lo
g
y
a
n
d
e
lec
tri
c
v
e
h
icle
in
teg
ra
ti
o
n
,
a
imin
g
to
o
p
ti
m
ize
th
e
two
-
wa
y
e
n
e
rg
y
e
x
c
h
a
n
g
e
b
e
twe
e
n
e
lec
tri
c
v
e
h
icle
s
a
n
d
th
e
p
o
we
r
g
ri
d
.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
y
o
u
b
.
a
b
i
d
a
1
-
e
tu
@e
t
u
.
u
n
i
v
h
2
c
.
m
a
/
a
y
o
u
b
a
b
id
a
0
8
@
g
m
a
il
.
c
o
m
.
Re
d
o
u
a
n
e
M
a
jd
o
u
l
is
a
p
r
o
fe
ss
o
r
a
t
th
e
Na
ti
o
n
a
l
S
c
h
o
o
l
o
f
Arts
a
n
d
Cra
fts
(ENS
AM)
in
Ca
sa
b
lan
c
a
.
As
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
,
h
e
b
rin
g
s
a
we
a
lt
h
o
f
k
n
o
wle
d
g
e
a
n
d
e
x
p
e
rti
se
to
h
is
ro
le.
He
o
b
tain
e
d
h
is
d
o
c
to
ra
te
fro
m
th
e
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s
a
n
d
Tec
h
n
i
q
u
e
s
in
2
0
1
7
a
n
d
h
a
s
sin
c
e
m
a
d
e
sig
n
ifi
c
a
n
t
stri
d
e
s
i
n
h
is
f
ield
.
His
re
se
a
rc
h
c
o
n
tri
b
u
t
io
n
s
a
re
e
x
ten
siv
e
a
n
d
c
o
v
e
r
a
wid
e
a
rra
y
o
f
to
p
i
c
s
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
,
in
c
lu
d
in
g
f
u
n
d
a
m
e
n
tal
fre
q
u
e
n
c
y
,
m
o
d
u
lar
m
u
lt
il
e
v
e
l
c
o
n
v
e
rter,
m
u
lt
il
e
v
e
l
in
v
e
rters
,
p
o
we
r
e
lec
tro
n
ics
,
a
n
d
p
o
we
r
g
rid
.
His
d
u
a
l
ro
le
a
s
a
n
e
d
u
c
a
to
r
a
n
d
a
c
ti
v
e
re
se
a
rc
h
e
r
e
n
a
b
les
h
im
to
c
o
n
ti
n
u
a
ll
y
a
d
v
a
n
c
e
t
h
e
u
n
d
e
rst
a
n
d
in
g
o
f
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
a
n
d
c
o
n
tro
l
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
r.
m
a
jd
o
u
l@
g
m
a
il
.
c
o
m
.
Mo
u
r
a
d
Ze
g
r
a
r
i
is
a
g
ra
d
u
a
t
e
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
fr
o
m
t
h
e
Hig
h
e
r
N
o
rm
a
l
S
c
h
o
o
l
o
f
Tec
h
n
ica
l
Ed
u
c
a
ti
o
n
(ENS
ET
)
in
Ra
b
a
t.
He
o
b
tai
n
e
d
a
Dip
l
o
m
a
o
f
Ad
v
a
n
c
e
d
S
tu
d
ies
(DES
A),
th
e
n
d
e
fe
n
d
e
d
h
is
Na
ti
o
n
a
l
Do
c
to
ra
te
t
h
e
sis
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
a
t
Ha
ss
a
n
II
Un
iv
e
rsity
M
o
h
a
m
m
e
d
ia
in
2
0
1
2
.
S
in
c
e
2
0
1
3
,
h
e
h
a
s
b
e
e
n
th
e
h
e
a
d
o
f
t
h
e
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
d
e
p
a
rtme
n
t
a
t
th
e
Na
ti
o
n
a
l
S
c
h
o
o
l
o
f
Arts
a
n
d
Cra
ft
s
(ENS
AM)
i
n
Ca
sa
b
lan
c
a
,
wh
e
re
h
e
tea
c
h
e
s
p
o
we
r
e
lec
tro
n
ics
a
n
d
m
a
c
h
in
e
-
c
o
n
v
e
rter
a
ss
o
c
iatio
n
.
C
u
rre
n
tl
y
,
h
e
is
a
m
e
m
b
e
r
o
f
th
e
Lab
o
ra
to
ry
o
f
El
e
c
tro
n
ics
,
El
e
c
tro
tec
h
n
ics
,
Au
to
m
a
ti
o
n
a
n
d
I
n
fo
rm
a
ti
o
n
P
ro
c
e
ss
in
g
(LE
EA
-
TI)
,
o
f
R
EUNET
a
n
d
a
u
th
o
r
o
f
se
v
e
ra
l
re
se
a
rc
h
wo
r
k
s
o
n
t
h
e
m
o
d
e
li
n
g
a
n
d
c
o
n
tro
l
o
f
re
n
e
wa
b
le en
e
rg
y
s
y
ste
m
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t:
z
e
g
ra
ri.
e
n
sa
m
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.