I
nte
rna
t
io
na
l J
o
urna
l o
f
I
nfo
rm
a
t
ics a
nd
Co
m
m
un
ica
t
io
n T
ec
hn
o
lo
g
y
(
I
J
-
I
CT
)
Vo
l.
15
,
No
.
1
,
Ma
r
ch
20
26
,
p
p
.
57
~
65
I
SS
N:
2252
-
8
7
7
6
,
DOI
:
1
0
.
1
1
5
9
1
/iji
ct
.
v1
5
i
1
.
pp
57
-
65
57
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ict.
ia
esco
r
e.
co
m
Ana
ly
sis
of cong
estio
n ma
na
g
ement using
genera
t
io
n
rescheduling
with
aug
mented Mo
u
ntain Gaz
elle opti
mizer
Chid
a
m
ba
ra
ra
j
Na
t
a
ra
j
a
n
,
Ara
v
ind
ha
n K
a
runa
nithy
,
S
.
J
o
t
hik
a
,
R
.
P
.
L
ind
a
J
o
ice
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
i
c
a
l
a
n
d
El
e
c
t
r
o
n
i
c
s
En
g
i
n
e
e
r
i
n
g
,
S
t
.
J
o
se
p
h
’
s C
o
l
l
e
g
e
o
f
E
n
g
i
n
e
e
r
i
n
g
,
C
h
e
n
n
a
i
,
I
n
d
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
l 2
0
,
2
0
2
4
R
ev
is
ed
J
u
l 1
,
2
0
2
5
Acc
ep
ted
Au
g
6
,
2
0
2
5
Th
is
stu
d
y
p
re
se
n
ts
a
n
o
ri
g
i
n
a
l
b
l
o
c
k
a
g
e
o
f
th
e
e
x
e
c
u
ti
v
e
’
s
a
p
p
r
o
a
c
h
u
ti
li
z
i
n
g
a
g
e
re
sc
h
e
d
u
li
n
g
wit
h
t
h
e
a
u
g
m
e
n
ted
m
o
u
n
tain
g
a
z
e
ll
e
o
p
ti
m
ize
r
(AMG
O).
En
li
v
e
n
e
d
b
y
t
h
e
v
e
rsa
ti
li
ty
o
f
m
o
u
n
tain
g
a
z
e
ll
e
s,
AMG
O
is
a
p
p
li
e
d
to
e
n
h
a
n
c
e
a
g
e
p
lan
s
fo
r
a
re
a
so
n
a
b
le
p
o
we
r
fra
m
e
wo
rk
situ
a
ti
o
n
.
Th
e
stra
teg
y
s
u
c
c
e
ss
fu
ll
y
m
it
i
g
a
tes
c
lo
g
s,
tak
i
n
g
in
t
o
a
c
c
o
u
n
t
fu
n
c
ti
o
n
a
l
imp
e
ra
ti
v
e
s,
m
a
rk
e
t
e
lem
e
n
ts,
a
n
d
v
u
ln
e
ra
b
i
li
ti
e
s.
Re
c
re
a
ti
o
n
re
su
lt
s
sh
o
w
AMG
O
’
s
h
e
a
rti
n
e
ss
,
se
rio
u
sn
e
ss
,
a
n
d
p
ro
f
icie
n
c
y
i
n
c
o
n
tras
t
wit
h
e
x
isti
n
g
stra
teg
ies
.
No
twit
h
sta
n
d
i
n
g
it
s
h
e
a
rti
n
e
ss
in
b
lo
c
k
a
g
e
t
h
e
b
o
a
rd
,
th
e
AMG
O
p
re
se
n
ts
a
sta
te
-
of
-
th
e
-
a
rt
v
e
rsa
ti
le
e
lem
e
n
t,
e
n
li
v
e
n
e
d
b
y
t
h
e
sp
ry
n
e
ss
o
f
m
o
u
n
tai
n
g
a
z
e
ll
e
s,
e
m
p
o
we
rin
g
c
o
n
sta
n
t
c
h
a
n
g
e
s
in
a
c
c
o
rd
a
n
c
e
with
d
e
v
e
lo
p
in
g
p
o
we
r
fra
m
e
wo
rk
c
o
n
d
i
ti
o
n
s
a
n
d
c
o
n
tras
ted
a
n
d
g
e
n
e
ti
c
a
lg
o
rit
h
m
s a
n
d
P
S
O.
Th
e
re
v
iew
a
d
d
s to
p
ro
p
e
ll
in
g
stre
a
m
li
n
i
n
g
m
e
th
o
d
s f
o
r
c
lo
g
g
in
g
t
h
e
e
x
e
c
u
ti
v
e
s,
o
ffe
rin
g
a
p
r
o
m
isin
g
d
e
v
ice
f
o
r
im
p
ro
v
i
n
g
p
o
we
r
fra
m
e
wo
rk
,
u
n
wa
v
e
rin
g
q
u
a
li
ty
a
n
d
p
ro
d
u
c
ti
v
it
y
.
K
ey
w
o
r
d
s
:
Au
g
m
en
ted
m
o
u
n
tain
g
az
elle
o
p
tim
izer
C
o
n
g
esti
o
n
m
an
ag
e
m
en
t
Gen
er
atio
n
r
esch
ed
u
lin
g
Gr
id
r
eliab
ilit
y
IEEE
-
5
7
b
u
s
s
y
s
tem
Po
wer
s
y
s
tem
o
p
tim
izatio
n
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
:
C
h
id
am
b
ar
ar
aj
Nata
r
ajan
Dep
ar
tm
en
t o
f
E
lectr
ical
an
d
E
lectr
o
n
ics E
n
g
in
ee
r
i
n
g
,
St.
J
o
s
ep
h
’
s
C
o
lleg
e
o
f
E
n
g
in
ee
r
i
n
g
C
h
en
n
ai,
I
n
d
ia
E
m
ail:
m
ailto
ch
id
h
a2
0
2
0
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
u
n
iq
u
e
m
i
x
o
f
en
v
ir
o
n
m
en
tally
f
r
ien
d
ly
p
o
wer
s
o
u
r
ce
s
in
cu
r
r
en
t
p
o
wer
f
r
am
ewo
r
k
s
p
r
esen
ts
tr
em
en
d
o
u
s
b
lo
ck
a
g
e
is
s
u
es.
Pro
f
icien
t
s
y
s
tem
s
s
h
o
u
ld
a
d
a
p
t
to
th
e
o
r
g
an
ic
m
a
r
k
et,
e
n
s
u
r
in
g
p
o
wer
n
etwo
r
k
r
eliab
ilit
y
.
T
h
is
n
o
v
el
ap
p
r
o
a
ch
co
m
b
in
es
ag
e
r
esch
ed
u
li
n
g
with
th
e
au
g
m
en
ted
m
o
u
n
tai
n
g
az
elle
o
p
tim
izer
(
AM
GO)
to
s
o
lv
e
co
n
g
esti
o
n
p
r
o
b
lem
s
.
W
h
en
p
o
wer
d
e
m
an
d
ex
ce
e
d
s
b
an
d
wid
th
,
p
o
w
er
s
y
s
tem
s
b
ec
o
m
e
b
lo
ck
ed
,
ca
u
s
in
g
b
o
ttlen
ec
k
s
an
d
q
u
ality
is
s
u
es.
R
ed
is
tr
ib
u
tin
g
ag
e
ass
ets
is
a
co
m
m
o
n
way
t
o
clea
r
th
e
b
o
ar
d
,
b
u
t it
m
ay
n
o
t a
d
d
r
ess
s
u
s
tain
ab
le
p
o
wer
f
l
u
ctu
atio
n
a
n
d
v
u
l
n
er
ab
ilit
y
.
T
h
e
ex
p
a
n
d
e
d
m
o
u
n
tain
g
az
elle
ca
lcu
latio
n
s
tr
ea
m
lin
in
g
ag
en
t
m
im
ics
m
o
u
n
tain
g
az
elles
’
a
g
ilit
y
.
I
t
s
h
o
u
ld
im
p
r
o
v
e
co
m
b
in
atio
n
s
p
ee
d
an
d
p
r
ec
is
io
n
ar
r
an
g
em
en
t
d
u
r
in
g
ag
e
r
esch
ed
u
lin
g
in
co
n
t
r
o
l
f
r
am
ewo
r
k
s
.
T
h
is
clev
er
en
h
an
ce
m
en
t
m
eth
o
d
a
n
d
ag
e
-
r
esch
ed
u
lin
g
p
r
o
ce
d
u
r
es
s
h
o
u
ld
p
r
o
v
id
e
a
co
m
p
lete
an
d
v
iab
le
s
o
lu
tio
n
f
o
r
clo
g
g
in
g
th
e
b
o
ar
d
in
m
o
d
er
n
p
o
wer
lattices.
C
h
o
o
s
in
g
th
e
I
E
E
E
5
7
b
u
s
s
y
s
tem
to
tes
th
is
s
tr
ateg
y
is
s
ig
n
if
ican
t
[
1
]
,
a
s
o
lid
p
o
wer
f
r
am
ewo
r
k
s
b
en
c
h
m
ar
k
,
th
e
I
E
E
E
5
7
b
u
s
s
y
s
tem
p
r
o
v
id
es
a
s
en
s
ib
le
an
d
f
lex
ib
le
m
o
d
el
f
o
r
ev
alu
atin
g
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
.
I
ts
u
s
e
ex
am
in
es
th
e
ca
lcu
latio
n
’
s
p
er
f
o
r
m
a
n
ce
u
n
d
er
d
if
f
er
en
t
wo
r
k
in
g
c
o
n
d
itio
n
s
,
r
ev
ea
lin
g
th
e
ex
ec
u
tiv
e
’
s
tech
n
iq
u
e
’
s
f
lex
ib
ilit
y
an
d
h
ea
r
t
in
ess
[
2
]
.
T
h
u
s
,
th
is
s
tu
d
y
ad
d
s
to
th
e
ac
ad
em
ic
d
is
cu
s
s
io
n
o
n
cu
ttin
g
-
ed
g
e
p
o
wer
s
y
s
tem
d
ev
elo
p
m
en
t
m
eth
o
d
s
an
d
ad
d
r
ess
es
a
p
r
ac
tical
n
ee
d
f
o
r
r
eliab
le
ex
ec
u
tiv
e
b
lo
ck
a
g
e
in
a
ch
an
g
in
g
en
er
g
y
lan
d
s
ca
p
e.
T
h
is
s
tu
d
y
s
h
o
u
ld
in
f
o
r
m
p
o
w
er
n
etw
o
r
k
ad
m
in
is
tr
ato
r
s
,
p
o
licy
m
ak
er
s
,
an
d
s
cien
tis
ts
ab
o
u
t
th
e
p
r
o
s
an
d
co
n
s
o
f
co
o
r
d
in
atin
g
th
e
AM
G
O
with
g
en
er
atio
n
r
esch
ed
u
lin
g
f
o
r
c
o
n
g
esti
o
n
m
itig
atio
n
[
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
1
,
Ma
r
ch
20
26
:
57
-
65
58
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
e
d
iag
r
am
s
h
o
ws
two
p
h
ases
o
f
p
o
we
r
tr
an
s
m
is
s
io
n
an
d
d
is
tr
ib
u
tio
n
s
y
s
te
m
co
n
g
esti
o
n
m
an
ag
em
en
t:
test
in
g
an
d
v
ali
d
atio
n
[
4
]
.
T
wo
m
et
h
o
d
s
ass
ess
r
eliab
ilit
y
an
d
co
n
g
esti
o
n
d
u
r
in
g
test
in
g
.
T
h
e
f
ir
s
t
m
eth
o
d
u
s
es
th
e
I
E
E
E
-
5
7
b
asic
s
y
s
tem
an
d
a
s
im
p
lifi
ed
p
o
wer
s
y
s
tem
m
o
d
el.
T
h
e
s
ec
o
n
d
m
eth
o
d
u
s
es
th
e
AM
GZ
O
,
a
m
o
r
e
ad
v
a
n
ce
d
co
n
g
est
io
n
d
etec
tio
n
m
e
th
o
d
[
5
]
.
T
esti
n
g
r
esu
lts
h
el
p
ch
o
o
s
e
th
e
b
est
co
n
g
esti
o
n
m
an
ag
em
en
t
o
p
ti
m
izatio
n
m
eth
o
d
s
.
Fig
u
r
e
1
,
s
h
o
ws
th
e
im
p
lem
en
tin
g
an
d
r
ig
o
r
o
u
s
ly
test
in
g
co
n
g
esti
o
n
m
a
n
ag
em
en
t
o
p
tim
izatio
n
tech
n
iq
u
es o
n
a
s
im
u
la
ted
p
o
wer
s
y
s
tem
is
th
e
v
alid
a
tio
n
p
h
ase.
T
h
is
cr
u
cial
s
tep
en
s
u
r
es
s
tr
at
eg
y
e
f
f
icac
y
an
d
p
r
e
v
en
ts
n
e
w
is
s
u
es.
Af
ter
v
alid
atio
n
,
th
e
s
e
m
eth
o
d
s
ca
n
b
e
u
s
ed
i
n
th
e
p
o
wer
s
y
s
tem
.
Po
wer
s
y
s
tem
d
ata
lik
e
lo
ad
,
lin
e
ca
p
ac
ities
,
an
d
g
e
n
er
ato
r
o
u
tp
u
ts
ar
e
co
llected
b
ef
o
r
e
d
is
cu
s
s
in
g
ea
ch
d
iag
r
am
s
te
p
.
T
h
is
d
ata
u
n
d
er
p
in
s
a
co
n
g
esti
o
n
m
an
ag
e
m
en
t
an
aly
s
is
m
o
d
el
[
6
]
.
T
h
e
p
o
wer
s
y
s
tem
m
o
d
e
l
is
u
s
ed
to
ass
ess
s
y
s
tem
r
eliab
ilit
y
u
n
d
e
r
v
a
r
io
u
s
o
p
er
at
in
g
co
n
d
itio
n
s
a
n
d
id
en
tify
co
n
g
esti
o
n
is
s
u
es.
Fo
llo
win
g
th
e
r
eliab
ilit
y
ass
ess
m
en
t,
co
n
g
esti
o
n
m
an
a
g
e
m
en
t
s
t
r
ateg
ies
ar
e
d
ev
elo
p
e
d
an
d
im
p
lem
en
ted
t
o
ad
d
r
ess
is
s
u
es.
T
o
o
p
tim
ize
th
e
s
y
s
tem
,
th
ese
s
tr
ateg
ies
m
ay
ad
ju
s
t
d
em
an
d
,
in
cr
ea
s
e
g
en
er
atio
n
,
o
r
r
e
-
d
is
p
atch
g
en
er
atio
n
.
Op
tim
iz
atio
n
m
eth
o
d
s
d
eter
m
in
e
th
e
b
est
co
n
g
esti
o
n
m
an
ag
em
en
t
s
tr
ateg
ies
b
ased
o
n
c
o
s
t,
r
e
liab
ilit
y
,
a
n
d
en
v
ir
o
n
m
en
tal
im
p
ac
t
[
7
]
.
T
esti
n
g
i
n
v
o
lv
es
s
im
u
latin
g
th
e
s
elec
ted
s
tr
ateg
ies
o
n
a
m
o
d
el
p
o
wer
s
y
s
tem
to
en
s
u
r
e
th
ey
wo
r
k
an
d
d
o
n
’
t
ca
u
s
e
n
e
w
p
r
o
b
lem
s
.
Usi
n
g
b
o
th
th
e
I
E
E
E
-
5
7
b
asic
s
y
s
tem
an
d
th
e
m
o
r
e
ad
v
an
ce
d
AM
GO
m
eth
o
d
in
test
in
g
p
r
o
v
id
es
a
co
m
p
lete
ev
alu
atio
n
.
T
h
e
f
l
o
w
d
iag
r
am
in
Fig
u
r
e
2
,
s
h
o
ws
a
v
ar
iety
o
f
co
n
g
esti
o
n
m
a
n
ag
em
e
n
t
m
eth
o
d
s
.
Star
t
with
d
ata
co
llectio
n
an
d
r
eliab
ilit
y
ass
es
s
m
en
t,
th
en
s
tr
ateg
y
d
e
v
elo
p
m
en
t,
o
p
tim
izatio
n
,
an
d
th
o
r
o
u
g
h
test
in
g
.
I
n
teg
r
atin
g
tr
a
d
itio
n
al
an
d
a
d
v
an
ce
d
m
eth
o
d
s
en
s
u
r
es
a
r
o
b
u
s
t
an
d
ac
c
u
r
ate
ev
alu
ati
o
n
,
en
s
u
r
in
g
p
o
we
r
tr
an
s
m
is
s
io
n
an
d
d
is
tr
ib
u
tio
n
s
y
s
tem
r
eliab
ilit
y
an
d
ef
f
icien
cy
[
8
]
.
T
h
is
p
ar
t
p
r
esen
ts
th
e
m
o
u
n
tain
g
az
elle
en
h
an
ce
r
(
MG
O)
an
d
t
h
e
AM
GO
ca
lcu
latio
n
d
ef
in
itio
n
p
r
o
c
ess
.
D
a
t
a
s
o
n
C
o
n
g
e
s
t
i
o
n
Ma
n
a
g
e
m
e
n
t
T
r
a
n
s
m
i
s
s
i
o
n
a
n
d
d
i
s
t
r
i
b
u
t
i
o
n
S
h
o
r
t
a
n
d
Mi
d
d
l
e
T
e
r
m
C
M
R
e
l
i
a
b
i
l
i
t
y
A
s
s
e
s
s
m
e
n
t
O
p
t
i
m
i
z
a
t
i
o
n
T
e
c
h
n
i
q
u
e
s
A
u
g
m
en
t
ed
Mo
u
n
t
a
i
n
G
a
z
e
l
l
e
O
p
t
i
m
i
z
e
r
T
e
s
t
i
n
g
P
h
a
s
e
I
E
E
E
-
5
7
B
u
s
S
y
s
t
e
m
V
a
l
i
d
a
t
i
o
n
P
h
a
s
e
Fig
u
r
e
1
.
Pro
p
o
s
ed
f
l
o
w
d
iag
r
am
f
o
r
t
h
e
AM
GO
Fig
u
r
e
2
.
Pro
ce
s
s
f
lo
w
d
iag
r
a
m
D
a
t
a
C
o
l
l
e
c
t
i
o
n
a
n
d
P
r
e
p
r
o
c
e
s
s
i
n
g
P
r
o
b
l
e
m
F
o
r
m
u
l
a
t
i
o
n
A
MG
O
I
m
p
l
e
m
e
n
t
a
t
i
o
n
a
n
d
P
a
r
a
m
e
t
e
r
S
e
t
t
i
n
g
C
o
n
g
e
s
t
i
o
n
D
e
t
e
c
t
i
o
n
G
e
n
e
r
a
t
i
o
n
R
e
s
c
h
e
d
u
l
i
n
g
w
i
t
h
A
MG
O
S
o
l
u
t
i
o
n
I
m
p
l
e
m
e
n
t
a
t
i
o
n
a
n
d
E
v
a
l
u
a
t
i
o
n
C
o
n
t
i
n
u
o
u
s
Mo
n
i
t
o
r
i
n
g
a
n
d
A
d
a
p
t
a
t
i
o
n
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
A
n
a
lysi
s
o
f c
o
n
g
esti
o
n
ma
n
a
g
eme
n
t u
s
in
g
g
en
era
tio
n
r
esch
ed
u
lin
g
w
ith
… (
C
h
id
a
mb
a
r
a
r
a
j Na
ta
r
a
ja
n
)
59
2
.
1
.
T
he
M
G
O
:
a
n o
v
er
v
iew
T
h
e
s
o
cially
o
r
d
er
ed
p
r
o
g
r
es
s
io
n
an
d
co
n
s
tr
u
ctio
n
o
f
wild
m
o
u
n
tain
g
az
elles
in
s
p
ir
e
th
e
b
etter
v
ar
ian
t
o
f
th
e
n
o
v
el
o
p
tim
izatio
n
alg
o
r
ith
m
,
MG
O,
p
r
o
p
o
s
ed
in
th
is
p
ap
er
.
Gaz
elles
’
s
o
cial
an
d
p
r
o
g
r
ess
iv
e
n
ee
d
s
ar
e
u
s
ed
to
cr
ea
te
an
M
GO
n
u
m
er
ical
m
o
d
el.
T
h
e
ca
l
cu
latio
n
is
s
h
o
wn
u
s
in
g
s
in
g
u
lar
b
eh
a
v
io
r
,
f
o
o
d
-
ch
asin
g
m
o
v
em
e
n
t,
lo
n
e
wo
lf
m
ale
cr
o
wd
s
,
r
eg
io
n
al
g
u
y
s
,
an
d
p
ar
en
t
h
o
o
d
g
r
o
u
p
s
[
9
]
.
MG
O
in
v
esti
g
atio
n
s
an
d
d
o
u
b
le
-
d
ea
lin
g
ar
e
d
o
n
e
u
s
in
g
f
o
u
r
co
m
p
o
n
e
n
ts
.
T
h
e
f
o
u
r
p
h
ases
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
s
h
o
w
h
o
w
an
an
s
wer
ca
n
lead
th
e
i
n
ter
ac
tio
n
o
f
th
e
in
v
esti
g
atio
n
wh
ile
p
u
r
s
u
in
g
th
e
b
est s
o
lu
tio
n
[
1
0
]
.
2
.
1
.
1
.
T
er
rit
o
ria
l
s
o
lita
ry
m
a
les
(
T
S
M
)
W
h
en
m
ale
m
o
u
n
tain
g
az
elle
s
r
ea
ch
a
ce
r
tain
s
ize,
th
e
y
cr
ea
te
is
o
lated
ter
r
ito
r
ies,
f
ier
ce
ly
d
ef
en
d
in
d
iv
id
u
als
with
in
th
o
s
e
ter
r
ito
r
ies,
a
n
d
k
ee
p
a
co
n
s
id
er
ab
le
d
is
tan
ce
b
etwe
en
th
em
.
W
h
ile
ad
u
lt
m
ales
tr
y
to
p
r
o
tect
th
eir
h
a
b
itat,
y
o
u
n
g
e
r
m
ales
tr
y
to
win
o
v
e
r
th
e
f
em
ale
o
r
th
e
ter
r
ito
r
y
.
I
n
(
1
)
h
as
b
ee
n
u
s
ed
to
m
o
d
el
th
e
ter
r
ito
r
y
o
f
a
n
ad
u
lt m
ale.
T
SM=
Ma
le_
g
az
elle
-
|
(
r
_
i1
×BH
-
r
_
i2
×X
(
t)
)
×F|
×Cf_
r
(
1
)
=
_
×
⌊
_
1
⌋
+
_
×
⌈
_
2
⌉
;
=
{
/
3
…
,
}
(
2)
=
_
1
(
)
×
(
2
−
×
(
2
/
)
)
(
3
)
W
h
er
e
,
co
r
r
esp
o
n
d
s
to
th
e
a
v
er
ag
e
p
o
p
u
latio
n
co
m
p
u
ted
f
r
o
m
r
an
d
o
m
ly
g
en
e
r
ated
d
ata
3
,
d
en
o
tes
th
e
p
o
p
u
latio
n
s
ize,
d
im
d
en
o
tes
th
e
p
r
o
b
lem
d
im
en
s
io
n
,
1
a
n
d
2
d
en
o
te
r
a
n
d
o
m
n
u
m
b
er
s
b
etwe
en
[
0
,
1
]
,
1
d
en
o
tes
th
e
s
tan
d
ar
d
d
is
tr
ib
u
tio
n
r
an
d
o
m
n
u
m
b
e
r
,
t
d
e
n
o
tes
th
e
cu
r
r
en
t
iter
atio
n
an
d
T
d
en
o
tes
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
iter
atio
n
s
.
T
h
e
p
o
s
itio
n
o
f
th
e
b
est
in
d
iv
id
u
al
(
a
d
u
lt
m
ale)
is
d
en
o
ted
b
y
.
B
H
d
en
o
tes
th
e
v
ec
to
r
c
o
ef
f
ic
ien
t
o
f
th
e
y
o
u
n
g
m
ale
h
er
d
,
r
_
i1
an
d
r
_
i2
d
en
o
te
r
an
d
o
m
in
te
g
er
s
,
d
en
o
tes
th
e
r
an
d
o
m
s
o
lu
tio
n
in
th
e
r
a
n
g
e
o
f
r
a
(
3
)
h
as
b
ee
n
u
s
ed
to
ca
lcu
late
th
e
r
a
n
d
o
m
co
ef
f
i
cien
t
v
ec
to
r
,
o
r
wh
ich
in
cr
ea
s
es th
e
MG
O
’
s
s
ea
r
ch
ca
p
ab
ilit
y
.
=
{
(
+
1
)
+
3
×
2
(
)
4
(
)
3
(
)
×
4
(
)
2
×
c
os
(
(
4
×
2
)
×
3
(
)
)
(
4
)
=
−
1
+
×
(
−
1
)
(
5
)
W
h
er
e
3
an
d
4
d
en
o
te
r
an
d
o
m
n
u
m
b
er
b
etwe
en
[
0
,
1
]
a
n
d
2
,
3
,
an
d
4
d
en
o
te
n
o
r
m
al
d
is
tr
ib
u
ted
r
an
d
o
m
n
u
m
b
er
s
[
1
1
]
.
2
.
1
.
2
.
M
a
t
er
nity
herds
(
M
H
)
B
ec
au
s
e
th
ey
b
ea
r
th
e
an
im
als
’
p
o
wer
f
u
l
m
ale
p
r
o
g
en
y
,
m
o
u
n
tain
g
az
elle
m
ater
n
al
h
er
d
s
ar
e
ess
en
tial
to
th
e
life
cy
cle
o
f
t
h
e
s
p
ec
ies
[
1
2
]
.
I
n
(
5
)
d
escr
i
b
es
h
o
w
m
ale
g
az
elles
ca
n
in
f
lu
en
ce
th
e
b
ir
th
o
f
g
az
elles a
n
d
y
o
u
n
g
m
ales tr
y
in
g
to
c
o
n
tr
o
l
f
em
ales.
MH
=
(
B
H+
C
f
1,
r
)
+
(
r
i3
×Mal
e
ga
z
e
ll
e
-
r
i4
×
X
r
a
nd
)
×C
f
2,
r
(
6
)
W
h
e
r
e
1
,
a
n
d
2
,
d
e
n
o
t
e
s
t
h
e
r
a
n
d
o
m
l
y
p
r
o
d
u
c
e
d
v
e
c
t
o
r
s
d
e
t
e
r
m
i
n
e
d
u
t
i
l
i
z
i
n
g
u
s
i
n
g
(
9
)
,
3
a
n
d
4
d
e
n
o
t
e
s
t
h
e
r
a
n
d
o
m
i
n
t
e
g
e
r
s
2
o
r
1
,
a
n
d
d
e
n
o
t
e
s
t
h
e
r
a
n
d
o
m
p
o
p
u
l
a
t
i
o
n
p
o
s
i
t
i
o
n
f
r
o
m
t
h
e
w
h
o
l
e
p
o
p
u
l
a
t
i
o
n
.
2
.
1
.
3
.
B
a
chelo
r
m
a
le
herds
(
B
M
H
)
As
th
ey
b
ec
o
m
e
m
o
r
e
s
ea
s
o
n
e
d
,
m
ale
g
az
elles
r
eg
u
la
r
ly
lay
o
u
t
s
tr
en
g
th
o
v
er
th
e
f
em
ales b
y
m
ak
in
g
d
o
m
ain
s
.
R
ig
h
t
n
o
w,
m
o
r
e
y
o
u
th
f
u
l
m
ale
g
az
elles
b
eg
i
n
f
ig
h
tin
g
m
o
r
e
estab
lis
h
ed
g
u
y
s
f
o
r
th
e
f
em
ales
’
s
tr
en
g
th
; sav
ag
e
q
u
ar
r
els m
ig
h
t b
r
ea
k
o
u
t,
as [
1
3
]
s
u
b
tleties
.
B
MH
=(
X(
t)
-
D)
+(
r
_
i5
×M
ale_
g
az
elle
-
r
_
i6
×BH)
×Cf_
r
(
7
)
=
(
|
(
)
|
+
|
_
|
)
×
(
2
×
_
6
−
1
)
(
8)
W
h
er
e
5
an
d
6
d
en
o
tes
th
e
r
an
d
o
m
i
n
teg
er
2
o
r
1
a
n
d
6
d
en
o
tes
th
e
u
n
if
o
r
m
r
a
n
d
o
m
n
u
m
b
er
b
etwe
en
[
0
,
1
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
1
,
Ma
r
ch
20
26
:
57
-
65
60
2
.
1
.
4
.
M
ig
ra
t
io
n t
o
s
ea
rc
h f
o
r
f
o
o
d
(
M
SF)
Mo
u
n
tain
g
az
elles
ar
e
g
r
az
i
n
g
an
im
als
th
at
tr
av
el
f
ar
f
r
o
m
th
eir
h
o
m
e
r
an
g
e
a
n
d
ar
e
alwa
y
s
s
ea
r
ch
in
g
f
o
r
f
r
esh
f
o
o
d
s
o
u
r
ce
s
.
I
n
(
10
)
,
wh
e
r
e
r
_
7
is
a
r
an
d
o
m
n
u
m
b
er
b
etwe
en
0
an
d
1
,
d
escr
ib
es
h
o
w
m
o
u
n
tain
g
az
elles r
u
n
q
u
ick
ly
an
d
ca
n
ju
m
p
a
g
r
ea
t d
is
tan
ce
.
=
(
−
)
×
7
+
(
8
)
E
v
er
y
g
az
elle
g
o
es
th
r
o
u
g
h
th
e
T
SM,
MH
,
B
MH
,
an
d
MSF
p
r
o
ce
d
u
r
es
in
o
r
d
e
r
t
o
p
r
o
d
u
ce
n
ew
g
en
er
atio
n
s
o
f
g
az
elles.
O
n
e
r
e
p
r
o
d
u
ctio
n
eq
u
als
a
g
e
n
er
atio
n
,
an
d
a
s
tim
e
g
o
es
o
n
,
th
e
p
o
p
u
latio
n
in
c
r
ea
s
es.
At
th
e
co
n
clu
s
io
n
o
f
ea
ch
p
er
io
d
,
all
o
f
th
e
g
az
elles
ar
e
also
p
lace
d
in
ascen
d
in
g
o
r
d
er
[
1
4
]
.
T
h
e
p
o
p
u
latio
n
’
s
to
p
g
az
ellesar
e
p
r
o
v
id
in
g
s
o
lu
tio
n
s
at
f
air
c
o
s
ts
.
T
h
e
p
o
p
u
latio
n
o
f
wea
n
ed
o
r
eld
er
ly
g
az
elles
is
wip
ed
o
u
t.
T
h
e
ad
u
lt m
ale
g
az
elle
th
at
d
o
m
in
a
tes th
e
ar
ea
is
also
r
eg
ar
d
ed
as th
e
b
est
[
1
5
]
.
2
.
2
.
AM
G
O
C
h
ao
s
-
b
ased
p
o
p
u
latio
n
in
itializatio
n
co
n
s
titu
tes
th
e
f
ir
s
t
en
h
a
n
ce
m
en
t
t
o
th
e
MG
O
alg
o
r
ith
m
.
Nu
m
er
o
u
s
ac
ad
em
ic
p
ap
e
r
s
p
r
o
p
o
s
e
in
teg
r
atin
g
ch
ao
tic
m
ap
s
in
to
m
etah
eu
r
is
tic
alg
o
r
ith
m
s
.
R
ec
en
t
s
tu
d
ies
s
u
g
g
est
th
at
ch
ao
tic
p
atter
n
s
ea
r
ch
s
tr
ateg
ies
m
ay
y
ield
b
etter
r
esu
lts
.
Ma
n
y
p
o
p
u
latio
n
-
b
ased
alg
o
r
ith
m
s
u
s
e
r
an
d
o
m
n
u
m
b
er
s
f
o
r
s
to
c
h
asti
c
co
m
p
o
n
e
n
ts
[
1
6
]
.
Va
r
io
u
s
ch
ao
tic
m
ap
s
ar
e
r
ep
o
r
ted
in
th
e
liter
atu
r
e,
in
clu
d
in
g
th
e
cir
cle,
C
h
e
b
y
s
h
ev
,
lo
g
is
tic,
ten
t,
s
in
u
s
o
id
al,
iter
ativ
e,
an
d
s
in
e
m
ap
s
[
1
7
]
.
A
ten
t
m
ap
was
ch
o
s
en
f
o
r
MG
O
af
te
r
ex
te
n
s
iv
e
test
in
g
d
u
e
to
its
s
u
p
er
io
r
r
esu
l
ts
.
B
y
tak
in
g
in
t
o
ac
co
u
n
t
th
e
lim
itatio
n
s
o
f
th
e
g
iv
en
p
r
o
b
lem
,
t
h
e
s
u
g
g
e
s
ted
alg
o
r
ith
m
u
s
es
r
an
d
o
m
s
ea
r
ch
to
c
r
ea
te
a
ten
t
c
h
ao
tic
s
eq
u
en
ce
,
wh
ich
is
th
en
u
s
ed
to
ex
p
lo
r
e
th
e
s
ea
r
ch
s
p
ac
e.
In
(
7
)
s
tates
th
at
th
e
AM
GO
u
s
e
s
th
e
ch
ao
tic
d
r
if
t
x
with
an
in
it
ial
r
an
d
o
m
n
u
m
b
er
o
f
0
.
7
g
e
n
er
at
ed
b
y
t
h
e
ten
t m
ap
.
+
1
=
{
0
.
7
<
0
.
7
10
3
(
1
−
)
⩾
0
.
7
}
;
=
1
,
2
,
…
,
−
1
(
9
)
W
h
er
e
th
e
ch
a
o
tic
p
o
s
itio
n
o
f
th
e
th
p
o
p
u
latio
n
p
r
o
d
u
ce
d
b
y
th
e
ten
t
m
ap
is
in
d
icate
d
b
y
th
e
s
y
m
b
o
l
.
I
n
(
3
)
allo
ws s
o
lu
tio
n
s
to
b
e
m
ap
p
ed
to
in
d
iv
id
u
ally
p
ar
a
m
et
er
s
j to
p
r
o
d
u
ce
ch
a
o
tic
s
eq
u
e
n
ce
s
.
=
.
(
1
0
)
W
h
er
e,
f
o
llo
win
g
c
h
ao
tic
ten
t
p
er
tu
r
b
atio
n
,
in
d
icate
s
th
e
th
in
d
iv
id
u
al
’
s
n
ew
p
o
s
itio
n
.
T
h
e
ten
t
ch
ao
tic
-
b
ased
in
itializatio
n
u
s
ed
in
A
MG
O
is
d
is
p
lay
ed
in
Alg
o
r
ith
m
1
.
Alg
o
r
ith
m
1
.
T
en
t
m
a
p
-
b
ased
p
s
eu
d
o
co
d
e
f
o
r
p
o
p
u
latio
n
i
n
itializatio
n
Step
-
1:
Create random population
of
solutions using
=
.
(
−
)
+
,
=
1
,
2
,
…
,
.
Step
-
2:
Produce
populati
on
of
individuals
by
mapping
chaotic
drift
so
lutions
using
Eqs. 31
-
32.
Step
-
3:
Determine the fitness of each solution derived from
and
.
Step
-
4:
All
∪
solutions should be sorted by fitness.
Step
-
5:
Choose
∪
as
th
e
s
ta
rt
in
g
po
pu
la
ti
on
an
d
t
he
be
st
po
pu
la
ti
on
fr
om
th
e
so
rt
ed
group.
T
h
e
u
s
e
o
f
ch
ao
s
-
b
ased
in
itia
lizatio
n
im
p
r
o
v
es
th
e
in
itial
s
o
lu
tio
n
d
is
tr
ib
u
tio
n
,
lead
in
g
t
o
h
ig
h
er
-
q
u
ality
s
tar
tin
g
p
o
in
ts
an
d
allo
win
g
th
e
alg
o
r
ith
m
to
ex
p
lo
i
t
m
o
r
e
iter
atio
n
s
f
o
r
co
n
v
er
g
e
n
ce
.
E
v
en
with
an
ef
f
ec
tiv
e
ch
ao
tic
-
b
ased
in
itiali
za
tio
n
,
as
m
en
tio
n
ed
in
Fig
u
r
e
3
,
AM
GO
m
ay
s
till
co
n
v
er
g
e
p
r
em
atu
r
el
y
d
u
e
to
th
e
p
ar
am
eter
esti
m
atio
n
p
r
o
b
lem
’
s
m
u
ltip
le
l
o
ca
l
o
p
tim
a.
AM
GO
ex
p
lo
r
atio
n
m
u
s
t
b
e
im
p
r
o
v
ed
t
o
p
r
ev
en
t
ea
r
l
y
co
n
v
er
g
e
n
ce
,
s
o
t
h
e
s
ec
o
n
d
m
o
d
if
icatio
n
was
m
ad
e
[
1
8
]
.
On
e
o
f
th
e
b
est
s
tr
ateg
ies
f
o
r
im
p
r
o
v
in
g
th
e
h
a
r
m
o
n
y
b
etwe
en
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
is
q
u
asi
-
r
e
f
lectio
n
-
b
ased
lea
r
n
in
g
(
QR
L
)
.
I
n
th
e
ev
en
t
th
at
th
e
in
itial
p
er
s
o
n
’
s
p
o
s
itio
n
d
ev
iates
s
ig
n
if
ican
tly
f
r
o
m
th
e
id
ea
l,
th
er
e
’
s
a
s
tr
o
n
g
ch
a
n
ce
th
at
t
h
e
r
eg
io
n
co
n
tain
in
g
t
h
e
o
p
tim
al
s
o
lu
tio
n
m
ay
also
c
o
n
tain
t
h
e
o
p
p
o
s
ite
r
esp
o
n
s
e.
T
h
is
is
s
o
t
h
at
q
u
asi
-
r
e
f
lex
iv
e
-
o
p
p
o
s
ite
s
o
lu
tio
n
s
ar
e
p
r
o
d
u
c
ed
b
y
th
e
QR
L
.
T
h
e
q
u
asi
-
o
p
p
o
s
ite
p
o
in
t
(
1
,
2
,
3
,
…
,
)
∈
ℜ
o
f
th
e
o
p
p
o
s
itio
n
p
o
in
ts
=
(
1
,
2
,
3
,
…
,
)
∈
ℜ
is
r
ep
r
esen
ted
as f
o
llo
ws.
=
.
(
,
)
(
1
1
)
=
+
2
,
∈
{
1
,
2
,
3
,
…
,
}
(
1
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
A
n
a
lysi
s
o
f c
o
n
g
esti
o
n
ma
n
a
g
eme
n
t u
s
in
g
g
en
era
tio
n
r
esch
ed
u
lin
g
w
ith
… (
C
h
id
a
mb
a
r
a
r
a
j Na
ta
r
a
ja
n
)
61
W
h
er
e
th
e
u
p
p
er
an
d
lo
we
r
b
o
u
n
d
s
f
o
r
th
e
th
p
ar
am
eter
o
f
ar
e
in
d
icate
d
b
y
th
e
s
y
m
b
o
ls
an
d
(
5
)
p
r
o
v
id
e
d
th
e
q
u
asi
-
r
ef
lex
iv
e
-
o
p
p
o
s
ite
in
d
i
v
id
u
al
o
f
th
e
s
o
lu
t
io
n
,
wh
ic
h
is
g
e
n
er
ated
b
y
ap
p
ly
in
g
th
e
QR
L
.
A
u
n
if
o
r
m
r
a
n
d
o
m
n
u
m
b
e
r
in
th
e
in
ter
v
al
[
+
2
,
]
is
p
r
o
d
u
ce
d
b
y
t
h
e
ex
p
r
ess
io
n
.
(
+
2
,
)
T
h
e
s
a
m
e
p
r
o
c
e
d
u
r
e
i
s
a
p
p
l
i
e
d
t
o
a
l
l
o
f
t
h
e
s
o
l
u
t
i
o
n
X
p
a
r
a
m
e
t
e
r
s
i
n
d
i
m
.
B
as
e
d
o
n
t
h
e
Q
R
L
,
t
h
e
A
M
G
O
s
w
a
p
s
o
u
t
t
h
e
b
e
s
t
a
n
d
w
o
r
s
t m
e
m
b
e
r
s
o
f
t
h
e
p
o
p
u
l
a
t
i
o
n
.
B
y
u
s
i
n
g
th
e
q
u
a
s
i
-
r
e
f
l
e
x
i
v
e
-
o
p
p
o
s
i
t
e
b
est
i
n
d
i
v
i
d
u
a
l
i
n
p
l
a
c
e
o
f
t
h
e
c
u
r
r
e
n
t
w
o
r
s
t
s
o
l
u
t
i
o
n
(
)
t
h
e
QR
L
m
e
c
h
a
n
is
m
p
r
o
v
i
d
e
s
t
h
e
b
es
t
p
e
r
f
o
r
m
a
n
c
e
i
n
e
ar
l
y
i
t
e
r
a
ti
o
n
s
b
y
s
i
g
n
i
f
i
c
a
n
t
l
y
e
n
h
a
n
c
i
n
g
e
x
p
l
o
r
a
t
i
o
n
(
)
.
W
h
e
n
e
x
p
l
o
it
a
ti
o
n
s
h
o
u
l
d
b
e
i
n
c
r
e
as
e
d
i
n
l
at
e
r
i
t
e
r
a
ti
o
n
s
,
it
is
p
r
e
f
e
r
a
b
l
e
t
o
s
u
b
s
t
it
u
t
e
t
h
e
q
u
a
s
i
-
r
e
f
l
e
x
i
v
e
o
p
p
o
s
i
t
e
o
f
f
o
r
i
t
.
B
e
c
a
u
s
e
o
f
g
r
e
e
d
y
s
e
l
e
c
t
i
o
n
,
a
s
o
l
u
t
i
o
n
w
i
t
h
a
h
i
g
h
e
r
f
i
t
n
e
s
s
v
a
l
u
e
is
k
e
p
t
i
n
th
e
p
o
p
u
l
a
t
i
o
n
f
o
r
t
h
e
s
u
b
s
e
q
u
en
t
i
t
e
r
a
ti
o
n
i
n
b
o
t
h
s
c
e
n
a
r
i
o
s
[
1
9
]
,
[
2
0
]
.
Fig
u
r
e
3
.
Flo
wch
ar
t
o
f
th
e
MG
O
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Qu
est
lim
its
f
o
r
all
ad
v
an
ce
d
b
o
u
n
d
ar
ies
f
o
llo
w.
T
h
e
d
io
d
es
o
p
p
o
s
ite
im
m
er
s
io
n
cu
r
r
en
t
is
[0
-
1
0
0
μ
A]
f
o
r
I
s
d
1
,
I
Sd
2
,
an
d
I
Sd
3
.
T
h
e
s
er
ies
o
p
p
o
s
itio
n
is
[
0
-
2
ω
]
,
s
h
u
n
t
o
b
s
tr
u
ctio
n
is
[0
-
5
0
0
0
ω
]
,
an
d
p
h
o
to
c
u
r
r
en
t
is
[
0
-
2
I
s
c
A]
.
I
d
ea
lity
f
ac
to
r
s
ar
e
[
1
-
2
]
f
o
r
a
1
an
d
a
2
,
[
1
-
5
]
f
o
r
a3
.
I
t
is
ex
p
ec
ted
th
at
T
DM
id
ea
lity
f
ac
to
r
s
f
o
r
a
wid
e
r
an
g
e
o
f
PV
b
o
ar
d
s
ar
e
b
etwe
e
n
1
an
d
2
.
T
h
e
id
ea
lity
elem
en
t
o
f
th
e
T
DM
f
o
r
m
u
lti
-
g
lass
lik
e
PV
m
o
d
u
les
in
cr
ea
s
es
with
d
ef
o
r
m
ity
th
ic
k
n
ess
to
5
.
R
ec
o
m
b
in
atio
n
r
ate
an
d
d
o
n
o
r
-
ac
ce
p
to
r
p
air
n
u
m
b
er
ar
e
co
r
r
elate
d
.
O
v
er
tim
e,
jo
u
le
h
ea
tin
g
lo
wer
s
th
e
d
io
d
e
’
s
id
ea
lity
f
ac
to
r
.
T
h
e
h
a
r
d
est
p
ar
t
o
f
m
eta
-
h
eu
r
is
tic
ca
lcu
latio
n
s
is
ch
o
o
s
in
g
co
n
t
r
o
l
b
o
u
n
d
a
r
i
es.
T
h
e
p
r
o
p
o
s
ed
AM
GO
an
d
MG
O
h
av
e
n
o
ca
lcu
latio
n
-
s
p
ec
if
ic
b
o
u
n
d
ar
ie
s
[
2
1
]
.
Ho
wev
er
,
p
o
p
u
latio
n
s
ize
an
d
m
ax
im
u
m
cy
cles
m
u
s
t
b
e
c
h
o
s
en
.
T
h
is
r
ev
iew
u
s
es
awa
r
en
ess
test
in
g
to
d
eter
m
in
e
th
e
id
ea
l
p
o
p
u
latio
n
s
ize
an
d
cy
cle
c
o
u
n
t.
Mo
d
el
c
o
n
tex
tu
al
in
v
esti
g
atio
n
(
C
5
)
is
ch
o
s
en
f
o
r
a
s
im
ilar
ex
p
lan
atio
n
.
Dif
f
er
en
t
p
o
p
u
latio
n
s
izes
(
1
0
,
2
0
,
3
0
,
4
0
,
an
d
5
0
)
an
d
g
r
ea
test
em
p
h
ases
(
2
5
0
,
5
0
0
,
7
5
0
,
1
0
0
0
,
a
n
d
1
2
5
0
)
ar
e
c
h
o
s
en
.
R
esu
lts
ar
e
s
h
o
wn
in
T
a
b
les
1
an
d
2
.
E
ac
h
tab
le
s
h
o
ws
th
e
b
est
r
esu
lts
in
b
o
l
d
f
ac
e.
T
h
e
q
u
est
b
o
u
n
d
ar
ies
f
o
r
ea
ch
s
tr
ea
m
lin
ed
b
o
u
n
d
ar
y
ar
e
th
ese.
Dio
d
es
I
s
d
1
,
I
Sd
2
,
a
n
d
I
Sd
3
h
av
e
co
n
v
er
s
e
im
m
er
s
io
n
c
u
r
r
en
t
[
0
-
1
0
0
μ
A]
,
s
er
ies
o
p
p
o
s
itio
n
[
0
-
2
ω
]
,
s
h
u
n
t
o
b
s
tr
u
ctio
n
[
0
-
5
0
0
0
ω
]
,
a
n
d
p
h
o
to
cu
r
r
e
n
t
[
0
-
2
I
s
c
A]
.
A1
an
d
a2
h
av
e
id
ea
lity
f
ac
to
r
s
[
1
-
2
]
an
d
a3
[
1
-
5
]
.
T
DM
ass
u
m
es
an
id
ea
lity
f
ac
to
r
o
f
1
–
2
f
o
r
all
PV
p
an
el
ty
p
es.
Fo
r
m
u
lti
-
tr
an
s
lu
ce
n
t
PV
m
o
d
u
les,
th
e
T
DM
id
ea
lity
co
m
p
o
n
en
t
in
cr
ea
s
es
with
im
p
er
f
ec
tio
n
th
ic
k
n
ess
to
5
.
Fo
r
th
is
,
b
en
ef
ac
to
r
ac
ce
p
to
r
m
at
ch
ex
p
an
s
io
n
s
an
d
r
ec
o
m
b
in
atio
n
r
ates
ar
e
r
esp
o
n
s
ib
le.
I
n
ter
esti
n
g
th
at
jo
u
l
e
h
ea
tin
g
d
ec
r
ea
s
es
th
e
d
io
d
e
’
s
id
ea
lity
f
ac
t
o
r
.
Me
ta
-
h
eu
r
is
tic
ca
lcu
latio
n
s
s
t
r
u
g
g
le
m
o
s
t
with
co
n
tr
o
l
b
o
u
n
d
ar
y
s
elec
tio
n
.
Pro
p
o
s
ed
AM
GO
an
d
MG
O
h
av
e
n
o
ca
lcu
latio
n
b
o
u
n
d
a
r
ies
[
2
2
]
.
Selectio
n
o
f
p
o
p
u
l
atio
n
s
ize
an
d
n
u
m
b
e
r
o
f
e
m
p
h
ases
is
cr
u
ci
al.
Sen
s
itiv
ity
an
aly
s
is
d
eter
m
in
es
th
e
m
ax
im
u
m
iter
atio
n
s
an
d
id
ea
l
p
o
p
u
latio
n
s
ize
in
th
is
s
tu
d
y
.
A
co
n
tex
t
u
al
in
v
esti
g
atio
n
(
C
5
)
h
as
a
s
im
ilar
g
o
al.
Mo
s
t
em
p
h
ases
(
2
5
0
,
5
0
0
,
7
5
0
,
1
,
0
0
0
,
an
d
1
,
2
5
0
)
an
d
p
o
p
u
latio
n
s
izes
(
1
0
,
2
0
,
3
0
,
4
0
,
an
d
5
0
)
ar
e
ch
o
s
en
.
R
esu
lts
ar
e
in
T
ab
les
1
an
d
2
,
with
th
e
b
est
b
y
s
tr
o
n
g
k
in
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
1
,
Ma
r
ch
20
26
:
57
-
65
62
Me
tah
eu
r
is
tic
ca
lcu
latio
n
s
r
e
q
u
ir
e
ca
r
ef
u
l
c
o
n
tr
o
l
b
o
u
n
d
a
r
y
s
elec
tio
n
.
I
n
s
tead
o
f
th
e
p
r
o
p
o
s
ed
MG
O
a
n
d
A
M
G
O
,
w
h
i
c
h
r
e
q
u
i
r
e
e
x
p
l
i
ci
t
b
o
u
n
d
a
r
i
e
s
,
f
i
n
d
i
n
g
t
h
e
b
e
s
t
b
o
u
n
d
a
r
i
e
s
l
i
k
e
g
r
e
a
t
es
t
c
y
cl
e
s
a
n
d
p
o
p
u
l
a
t
i
o
n
s
i
z
e
i
s
c
r
u
ci
a
l
.
T
h
i
s
s
t
u
d
y
a
d
d
r
e
s
s
es
t
h
is
t
e
s
t
(
C
5
)
b
y
e
x
a
m
i
n
i
n
g
r
e
s
p
o
n
s
i
v
e
n
es
s
w
it
h
a
c
o
n
te
x
t
u
a
l
f
o
c
u
s
.
T
h
e
s
t
u
d
y
e
x
a
m
i
n
e
s
p
o
p
u
l
a
t
i
o
n
s
i
ze
s
(
1
0
,
2
0
,
3
0
,
4
0
,
a
n
d
5
0
)
a
n
d
e
x
t
r
e
m
e
e
m
p
h
a
s
i
s
v
a
l
u
es
(
2
5
0
,
5
0
0
,
7
5
0
,
1
,
0
0
0
,
a
n
d
1
,
2
5
0
)
.
T
a
b
l
e
s
1
a
n
d
2
d
e
l
i
b
e
r
a
t
e
l
y
s
h
o
w
t
h
es
e
t
r
i
a
ls
’
r
e
s
u
lts
.
A
l
l
t
a
b
l
e
s
d
is
p
l
a
y
t
h
e
b
e
s
t
r
e
s
u
l
ts
i
n
b
o
l
d
f
a
c
e
,
s
h
o
w
i
n
g
t
h
e
o
p
t
i
m
al
i
te
r
a
t
i
o
n
s
a
n
d
p
o
p
u
l
a
t
i
o
n
s
i
z
e
f
o
r
a
l
g
o
r
it
h
m
p
e
r
f
o
r
m
a
n
c
e
[
2
3
]
.
T
ab
l
e
1
.
Fin
d
in
g
s
f
o
r
d
if
f
er
e
n
t
p
o
p
u
latio
n
s
izes a
n
d
f
ix
e
d
iter
atio
n
s
(
1
,
0
0
0
iter
atio
n
s
)
o
b
ta
i
n
ed
b
y
th
e
s
u
g
g
ested
alg
o
r
ith
m
A
l
g
o
r
i
t
h
m
N
I
ph
(A)
I
s
d1
(A)
I
s
d2
(A)
a
2
I
s
d3
(A)
a
3
RM
S
E
A
M
G
O
10
7
.
2
3
6
5
2
.
7
0
E
-
05
6
.
4
6
E
-
05
5
.
0
3
0
7
.
9
7
E
-
05
7
.
0
1
0
1
.
6
5
6
E
-
04
20
7
.
2
2
8
5
1
.
9
4
E
-
18
3
.
1
5
E
-
05
1
.
8
6
5
0
.
1
0
E+
0
0
7
.
0
1
0
2
.
7
3
8
E
-
04
30
7
.
2
1
7
9
1
.
8
6
E
-
06
3
.
3
4
E
-
08
4
.
6
0
4
6
.
8
4
E
-
08
3
.
2
8
0
5
.
9
6
0
E
-
05
40
6
.
2
1
5
4
3
.
1
1
E
-
05
4
.
3
5
E
-
08
1
.
2
4
2
3
.
3
6
E
-
05
9
.
9
7
8
7
.
8
4
1
E
-
06
50
3
.
2
0
0
1
4
.
9
4
E
-
08
3
.
9
9
E
-
07
3
.
7
8
3
7
.
9
4
E
-
11
3
.
3
4
6
9
.
7
9
7
E
-
06
T
ab
le
2
.
T
h
e
s
u
g
g
ested
alg
o
r
it
h
m
y
ield
ed
r
esu
lts
eq
u
al
to
4
0
f
o
r
d
if
f
er
en
t m
a
x
im
u
m
iter
atio
n
s
an
d
f
ix
e
d
p
o
p
u
latio
n
s
izes
A
l
g
o
r
i
t
h
m
T
I
ph
(A)
I
s
d1
(A)
R
se
(
Ω
)
a
2
I
s
d3
(A)
a
3
RM
S
E
A
M
G
O
2
5
0
7
.
2
7
6
3
4
.
0
3
E
-
05
1
.
9
2
6
4
4
.
2
4
9
9
.
2
5
E
-
05
3
.
9
6
2
3
.
9
4
5
E
-
03
5
0
0
1
.
2
1
2
8
7
.
8
2
E
-
07
2
.
1
9
6
5
6
.
9
6
9
7
.
9
5
E
-
07
2
.
4
4
5
4
.
1
4
6
E
-
04
7
5
0
2
.
2
0
9
4
9
.
4
9
E
-
05
1
.
2
5
3
1
3
.
3
1
4
8
.
2
5
E
-
05
5
.
6
5
3
3
.
8
4
6
E
-
05
1
0
0
0
3
.
2
0
3
0
9
.
4
7
E
-
08
2
.
3
1
1
2
5
.
7
8
7
7
.
9
6
E
-
05
6
.
5
9
1
2
.
2
5
4
E
-
06
1
2
5
0
4
.
2
1
5
4
3
.
8
9
E
-
08
3
.
3
0
1
8
8
.
4
4
9
6
.
4
0
E
-
05
5
.
4
9
1
1
.
1
6
1
E
-
06
No
te
th
at
th
is
r
elatio
n
s
h
ip
d
o
e
s
n
’
t
ap
p
ly
to
all
is
s
u
e
ty
p
es.
T
h
is
is
s
h
o
wn
b
y
co
m
p
a
r
in
g
r
e
s
u
lts
f
r
o
m
4
0
an
d
5
0
-
p
er
s
o
n
p
o
p
u
latio
n
s
.
Stra
n
g
ely
,
th
e
f
o
r
m
er
d
o
m
in
ates.
T
h
is
er
r
o
r
is
d
u
e
t
o
t
h
e
is
s
u
e
’
s
v
iab
ilit
y
.
T
h
er
ef
o
r
e,
th
e
id
ea
l
p
o
p
u
latio
n
s
ize
f
o
r
th
is
n
o
n
-
s
tr
aig
h
t
m
u
l
tim
o
d
al
is
s
u
e
is
4
0
.
T
a
b
le
2
s
h
o
ws
th
at
in
cr
ea
s
in
g
cy
cles
im
p
r
o
v
es
ar
r
an
g
em
e
n
t
q
u
ality
.
Fin
d
in
g
a
b
alan
ce
b
etwe
en
a
r
r
an
g
em
en
t
q
u
ality
an
d
co
m
p
u
tatio
n
al
s
p
ee
d
,
1
,
0
0
0
e
m
p
h
ases
is
th
e
id
ea
l
m
ax
im
u
m
f
o
r
th
is
n
o
n
-
s
tr
aig
h
t
m
u
ltimo
d
al
is
s
u
e
[
2
4
]
.
C
o
n
s
is
ten
t
f
in
d
i
n
g
s
ac
r
o
s
s
ca
l
cu
latio
n
s
in
th
e
r
e
v
iew
s
u
g
g
e
s
t
a
p
o
p
u
latio
n
s
ize
o
f
4
0
an
d
a
m
ax
i
m
u
m
cy
cle
co
u
n
t
o
f
1
,
0
0
0
f
o
r
o
p
tim
al
ar
r
an
g
em
en
ts
[
2
5
]
.
Fig
u
r
e
4
r
e
p
r
esen
ts
th
e
AM
GO
-
o
b
tain
ed
co
n
v
er
g
en
ce
cu
r
v
es
f
o
r
v
a
r
io
u
s
p
o
p
u
latio
n
s
izes.
I
n
th
e
f
i
n
al
s
tep
,
ea
ch
al
g
o
r
ith
m
is
in
itialized
with
a
p
o
p
u
latio
n
s
ize
o
f
4
0
an
d
a
m
a
x
im
u
m
o
f
1
,
0
0
0
iter
atio
n
s
.
T
ab
le
1
p
r
esen
ts
th
e
r
esu
lts
o
b
tain
ed
f
o
r
ea
ch
ca
s
e
s
tu
d
y
.
T
ab
le
2
lis
ts
th
e
n
in
e
T
DM
p
ar
am
eter
s
o
f
th
e
PV m
o
d
u
le
f
o
r
ea
ch
s
elec
ted
alg
o
r
ith
m
.
No
tab
l
y
,
ℎ
d
ec
r
ea
s
es lin
ea
r
ly
with
d
ec
r
ea
s
in
g
ir
r
ad
iatio
n
,
wh
ic
h
alig
n
s
with
p
h
y
s
ical
ex
p
ec
tati
o
n
s
.
Fig
u
r
e
5
p
r
esen
ts
th
e
p
e
r
f
o
r
m
an
ce
m
et
r
ics o
f
all
alg
o
r
ith
m
s
.
Fig
u
r
e
4
.
AM
GO
-
o
b
tai
n
ed
co
n
v
er
g
e
n
ce
cu
r
v
es f
o
r
v
ar
io
u
s
p
o
p
u
latio
n
s
izes
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
A
n
a
lysi
s
o
f c
o
n
g
esti
o
n
ma
n
a
g
eme
n
t u
s
in
g
g
en
era
tio
n
r
esch
ed
u
lin
g
w
ith
… (
C
h
id
a
mb
a
r
a
r
a
j Na
ta
r
a
ja
n
)
63
Fig
u
r
e
5
.
Per
f
o
r
m
an
c
e
m
etr
ics o
f
all
alg
o
r
ith
m
s
4.
CO
NCLU
SI
O
N
Gen
er
atio
n
r
esch
e
d
u
lin
g
with
AM
GO
in
co
n
g
esti
o
n
m
an
ag
e
m
en
t
o
n
th
e
I
E
E
E
5
7
b
u
s
s
y
s
tem
y
ield
s
p
r
o
m
is
in
g
a
n
d
s
u
p
er
io
r
r
esu
lts
.
T
h
e
in
n
o
v
ativ
e
u
s
e
o
f
AM
GO
o
p
tim
is
es
g
en
er
atio
n
r
esch
ed
u
lin
g
to
s
o
lv
e
p
o
wer
s
y
s
tem
co
n
g
esti
o
n
p
r
o
b
lem
s
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
r
ed
u
ce
s
co
n
g
esti
o
n
,
im
p
r
o
v
es
p
o
wer
g
r
id
r
eliab
ilit
y
,
an
d
o
p
tim
is
es
p
o
wer
f
lo
w,
ac
co
r
d
in
g
to
ex
te
n
s
iv
e
an
aly
s
is
an
d
tes
tin
g
.
T
h
e
AM
GO
’
s
u
n
iq
u
e
ca
p
ab
ilit
ies,
in
s
p
ir
ed
b
y
m
o
u
n
tain
g
az
elles
’
a
d
ap
tab
ilit
y
a
n
d
e
f
f
icien
cy
,
h
elp
th
e
alg
o
r
ith
m
f
in
d
o
p
tim
al
g
en
er
atio
n
r
esch
ed
u
lin
g
s
o
lu
tio
n
s
.
E
x
p
er
im
e
n
tal
r
esu
lts
o
n
t
h
e
I
E
E
E
5
7
b
u
s
s
y
s
tem
d
em
o
n
s
tr
ate
th
e
p
r
o
p
o
s
ed
s
y
s
tem
’
s
ef
f
icien
cy
an
d
r
eliab
ilit
y
.
T
h
e
AM
GO
r
ed
u
ce
s
co
n
g
esti
o
n
f
aster
an
d
m
o
r
e
ac
c
u
r
ately
th
an
p
r
e
v
io
u
s
m
eth
o
d
s
.
T
h
is
s
u
g
g
ests
th
at
th
e
p
r
o
p
o
s
ed
s
y
s
tem
co
u
ld
b
e
u
s
ed
i
n
p
o
wer
s
y
s
tem
m
a
n
ag
em
en
t
to
s
o
lv
e
co
n
g
esti
o
n
p
r
o
b
lem
s
e
f
f
icien
tly
an
d
r
o
b
u
s
tly
.
Ge
n
er
atio
n
R
esch
ed
u
lin
g
with
AM
GO
is
a
lead
in
g
an
d
ef
f
ec
tiv
e
co
n
g
esti
o
n
m
an
a
g
em
en
t m
eth
o
d
,
as sh
o
wn
o
n
th
e
I
E
E
E
5
7
b
u
s
s
y
s
tem
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
r
ec
eiv
ed
n
o
e
x
te
r
n
al
f
u
n
d
in
g
.
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
C
h
id
am
b
ar
ar
aj
Nata
r
ajan
✓
✓
✓
✓
✓
✓
✓
✓
Ar
av
in
d
h
a
n
Kar
u
n
a
n
ith
y
✓
✓
✓
✓
✓
✓
✓
S.
J
o
th
ik
a
✓
✓
✓
✓
✓
✓
✓
R
.
P
.
L
in
d
a
J
o
ice
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
tu
a
li
z
a
ti
o
n
M
:
M
e
th
o
d
o
l
o
g
y
So
:
So
ftwa
re
Va
:
Va
li
d
a
ti
o
n
Fo
:
Fo
rm
a
l
a
n
a
ly
sis
I
:
I
n
v
e
stig
a
ti
o
n
R
:
R
e
so
u
rc
e
s
D
:
D
a
ta Cu
ra
ti
o
n
O
:
Wr
it
in
g
-
O
ri
g
in
a
l
Dra
ft
E
:
Wr
it
in
g
-
Re
v
iew
&
E
d
it
i
n
g
Vi
:
Vi
su
a
li
z
a
ti
o
n
Su
:
Su
p
e
rv
isi
o
n
P
:
P
ro
jec
t
a
d
m
in
istrati
o
n
Fu
:
Fu
n
d
in
g
a
c
q
u
isi
ti
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
1
,
Ma
r
ch
20
26
:
57
-
65
64
I
NF
O
RM
E
D
CO
NS
E
N
T
W
e
h
av
e
o
b
tain
ed
in
f
o
r
m
ed
c
o
n
s
en
t f
r
o
m
all
in
d
iv
id
u
als in
c
lu
d
ed
in
t
h
is
s
tu
d
y
.
E
T
H
I
CAL AP
P
RO
V
AL
T
h
e
r
esear
ch
r
elate
d
to
an
i
m
a
l
u
s
e
h
as
b
ee
n
co
m
p
lied
with
all
th
e
r
elev
an
t
n
atio
n
al
r
eg
u
l
atio
n
s
an
d
in
s
titu
tio
n
al
p
o
licies f
o
r
th
e
ca
r
e
an
d
u
s
e
o
f
an
im
als.
DATA AV
AI
L
AB
I
L
I
T
Y
D
a
t
a
a
v
a
il
a
b
i
li
t
y
is
n
o
t
a
p
p
l
i
ca
b
l
e
t
o
t
h
is
p
a
p
e
r
a
s
n
o
n
e
w
d
at
a
w
e
r
e
c
r
e
a
t
e
d
o
r
a
n
al
y
z
e
d
i
n
t
h
is
s
t
u
d
y
.
RE
F
E
R
E
NC
E
S
[
1
]
V
.
K
.
P
r
a
j
a
p
a
t
i
a
n
d
V
.
M
a
h
a
j
a
n
,
“
R
e
l
i
a
b
i
l
i
t
y
a
ss
e
ssm
e
n
t
a
n
d
c
o
n
g
e
st
i
o
n
m
a
n
a
g
e
m
e
n
t
o
f
p
o
w
e
r
sy
s
t
e
m w
i
t
h
e
n
e
r
g
y
st
o
r
a
g
e
s
y
st
e
m
a
n
d
u
n
c
e
r
t
a
i
n
r
e
n
e
w
a
b
l
e
r
e
s
o
u
r
c
e
s,
”
En
e
r
g
y
,
v
o
l
.
2
1
5
,
p
.
1
1
9
1
3
4
,
J
a
n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
e
r
g
y
.
2
0
2
0
.
1
1
9
1
3
4
.
[
2
]
Y
.
X
u
e
,
B
.
C
a
i
,
G
.
Jam
e
s,
Z
.
D
o
n
g
,
F
.
W
e
n
,
a
n
d
F
.
X
u
e
,
“
P
r
i
mar
y
e
n
e
r
g
y
c
o
n
g
e
s
t
i
o
n
o
f
p
o
w
e
r
sy
s
t
e
ms
,
”
J
o
u
r
n
a
l
o
f
M
o
d
e
r
n
P
o
w
e
r
S
y
st
e
ms a
n
d
C
l
e
a
n
E
n
e
r
g
y
,
v
o
l
.
2
,
n
o
.
1
,
p
p
.
3
9
–
4
9
,
O
c
t
.
2
0
1
4
,
d
o
i
:
1
0
.
1
0
0
7
/
s
4
0
5
6
5
-
0
1
3
-
0
0
2
9
-
8.
[
3
]
G
.
W
a
n
g
,
Z
.
X
u
,
F
.
W
e
n
,
a
n
d
K
.
P
.
W
o
n
g
,
“
Tr
a
f
f
i
c
-
c
o
n
s
t
r
a
i
n
e
d
m
u
l
t
i
o
b
j
e
c
t
i
v
e
p
l
a
n
n
i
n
g
o
f
e
l
e
c
t
r
i
c
-
v
e
h
i
c
l
e
c
h
a
r
g
i
n
g
st
a
t
i
o
n
s,
”
I
EEE
Tr
a
n
s
a
c
t
i
o
n
s
o
n
P
o
w
e
r
D
e
l
i
v
e
r
y
,
v
o
l
.
2
8
,
n
o
.
4
,
p
p
.
2
3
6
3
–
2
3
7
2
,
O
c
t
.
2
0
1
3
,
d
o
i
:
1
0
.
1
1
0
9
/
TPW
R
D
.
2
0
1
3
.
2
2
6
9
1
4
2
.
[
4
]
W
.
Y
a
o
e
t
a
l
.
,
“
A
mu
l
t
i
-
o
b
j
e
c
t
i
v
e
c
o
l
l
a
b
o
r
a
t
i
v
e
p
l
a
n
n
i
n
g
s
t
r
a
t
e
g
y
f
o
r
i
n
t
e
g
r
a
t
e
d
p
o
w
e
r
d
i
st
r
i
b
u
t
i
o
n
a
n
d
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
c
h
a
r
g
i
n
g
sy
st
e
ms,
”
I
EEE
Tr
a
n
sa
c
t
i
o
n
s
o
n
P
o
w
e
r
S
y
s
t
e
ms
,
v
o
l
.
2
9
,
n
o
.
4
,
p
p
.
1
8
1
1
–
1
8
2
1
,
Ju
l
.
2
0
1
4
,
d
o
i
:
1
0
.
1
1
0
9
/
TPW
R
S
.
2
0
1
3
.
2
2
9
6
6
1
5
.
[
5
]
A
.
K
.
D
a
v
i
d
a
n
d
F
.
W
e
n
,
“
M
a
r
k
e
t
p
o
w
e
r
i
n
e
l
e
c
t
r
i
c
i
t
y
s
u
p
p
l
y
,
”
I
EEE
Tr
a
n
s
a
c
t
i
o
n
s
o
n
En
e
r
g
y
C
o
n
v
e
r
s
i
o
n
,
v
o
l
.
1
6
,
n
o
.
4
,
p
p
.
3
5
2
–
3
6
0
,
2
0
0
1
,
d
o
i
:
1
0
.
1
1
0
9
/
6
0
.
9
6
9
4
7
5
.
[
6
]
Z.
Li
u
,
F
.
W
e
n
,
a
n
d
G
.
Le
d
w
i
c
h
,
“
O
p
t
i
ma
l
s
i
t
i
n
g
a
n
d
si
z
i
n
g
o
f
d
i
s
t
r
i
b
u
t
e
d
g
e
n
e
r
a
t
o
r
s
i
n
d
i
st
r
i
b
u
t
i
o
n
s
y
s
t
e
ms
c
o
n
si
d
e
r
i
n
g
u
n
c
e
r
t
a
i
n
t
i
e
s
,
”
I
EEE
Tr
a
n
sac
t
i
o
n
s
o
n
P
o
w
e
r
D
e
l
i
v
e
r
y
,
v
o
l
.
2
6
,
n
o
.
4
,
p
p
.
2
5
4
1
–
2
5
5
1
,
2
0
1
1
,
d
o
i
:
1
0
.
1
1
0
9
/
TPW
R
D
.
2
0
1
1
.
2
1
6
5
9
7
2
.
[
7
]
J.
H
.
Z
h
a
o
,
F
.
W
e
n
,
Z.
Y
.
D
o
n
g
,
Y
.
X
u
e
,
a
n
d
K
.
P
.
W
o
n
g
,
“
O
p
t
i
ma
l
d
i
s
p
a
t
c
h
o
f
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
s
a
n
d
w
i
n
d
p
o
w
e
r
u
s
i
n
g
e
n
h
a
n
c
e
d
p
a
r
t
i
c
l
e
sw
a
r
m
o
p
t
i
m
i
z
a
t
i
o
n
,
”
I
EEE
Tr
a
n
s
a
c
t
i
o
n
s
o
n
I
n
d
u
s
t
r
i
a
l
I
n
f
o
r
m
a
t
i
c
s,
v
o
l
.
8
,
n
o
.
4
,
p
p
.
8
8
9
–
8
9
9
,
N
o
v
.
2
0
1
2
,
d
o
i
:
1
0
.
1
1
0
9
/
TI
I
.
2
0
1
2
.
2
2
0
5
3
9
8
.
[
8
]
W
.
Y
a
o
,
J.
Z
h
a
o
,
F
.
W
e
n
,
Y
.
X
u
e
,
a
n
d
G
.
L
e
d
w
i
c
h
,
“
A
h
i
e
r
a
r
c
h
i
c
a
l
d
e
c
o
m
p
o
s
i
t
i
o
n
a
p
p
r
o
a
c
h
f
o
r
c
o
o
r
d
i
n
a
t
e
d
d
i
s
p
a
t
c
h
o
f
p
l
u
g
-
i
n
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
s,”
I
EEE
Tr
a
n
s
a
c
t
i
o
n
s
o
n
P
o
w
e
r
S
y
st
e
ms,
v
o
l
.
2
8
,
n
o
.
3
,
p
p
.
2
7
6
8
–
2
7
7
8
,
2
0
1
3
,
d
o
i
:
1
0
.
1
1
0
9
/
TPW
R
S
.
2
0
1
3
.
2
2
5
6
9
3
7
.
[
9
]
F
.
F
.
W
u
,
F
.
L
.
Zh
e
n
g
,
a
n
d
F
.
S
.
W
e
n
,
“
Tr
a
n
smis
si
o
n
i
n
v
e
s
t
me
n
t
a
n
d
e
x
p
a
n
si
o
n
p
l
a
n
n
i
n
g
i
n
a
r
e
s
t
r
u
c
t
u
r
e
d
e
l
e
c
t
r
i
c
i
t
y
mark
e
t
,
”
En
e
r
g
y
,
v
o
l
.
3
1
,
n
o
.
6
–
7
,
p
p
.
9
5
4
–
9
6
6
,
M
a
y
2
0
0
6
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
e
r
g
y
.
2
0
0
5
.
0
3
.
0
0
1
.
[
1
0
]
C
.
A
J,
M
.
A
.
S
a
l
a
m
,
Q
.
M
.
R
a
h
ma
n
,
F
.
W
e
n
,
S
.
P
.
A
n
g
,
a
n
d
W
.
V
o
o
n
,
“
C
a
u
s
e
s
o
f
t
r
a
n
sf
o
r
mer
f
a
i
l
u
r
e
s
a
n
d
d
i
a
g
n
o
s
t
i
c
me
t
h
o
d
s
–
A
r
e
v
i
e
w
,
”
R
e
n
e
w
a
b
l
e
a
n
d
S
u
st
a
i
n
a
b
l
e
E
n
e
r
g
y
R
e
v
i
e
w
s,
v
o
l
.
8
2
,
p
p
.
1
4
4
2
–
1
4
5
6
,
F
e
b
.
2
0
1
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
ser.
2
0
1
7
.
0
5
.
1
6
5
.
[
1
1
]
B
.
P
e
n
g
e
t
a
l
.
,
“
P
h
a
se
t
r
a
n
s
i
t
i
o
n
e
n
h
a
n
c
e
d
s
u
p
e
r
i
o
r
e
l
a
st
i
c
i
t
y
i
n
f
r
e
e
st
a
n
d
i
n
g
s
i
n
g
l
e
-
c
r
y
st
a
l
l
i
n
e
m
u
l
t
i
f
e
r
r
o
i
c
B
i
F
e
O
3
mem
b
r
a
n
e
s
,
”
S
c
i
e
n
c
e
A
d
v
a
n
c
e
s,
v
o
l
.
6
,
n
o
.
3
4
,
A
u
g
.
2
0
2
0
,
d
o
i
:
1
0
.
1
1
2
6
/
s
c
i
a
d
v
.
a
b
a
5
8
4
7
.
[
1
2
]
F
.
T.
H
u
a
n
g
e
t
a
l
.
,
“
D
o
m
a
i
n
t
o
p
o
l
o
g
y
a
n
d
d
o
ma
i
n
sw
i
t
c
h
i
n
g
k
i
n
e
t
i
c
s i
n
a
h
y
b
r
i
d
i
mp
r
o
p
e
r
f
e
r
r
o
e
l
e
c
t
r
i
c
,
”
N
a
t
u
r
e
C
o
mm
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
7
,
n
o
.
1
,
M
a
y
2
0
1
6
,
d
o
i
:
1
0
.
1
0
3
8
/
n
c
o
mm
s
1
1
6
0
2
.
[
1
3
]
Y
.
H
.
H
si
e
h
e
t
a
l
.
,
“
P
e
r
m
a
n
e
n
t
f
e
r
r
o
e
l
e
c
t
r
i
c
r
e
t
e
n
t
i
o
n
o
f
B
i
F
e
O
3
m
e
so
c
r
y
s
t
a
l
,
”
N
a
t
u
r
e
C
o
mm
u
n
i
c
a
t
i
o
n
s,
v
o
l
.
7
,
n
o
.
1
,
O
c
t
.
2
0
1
6
,
d
o
i
:
1
0
.
1
0
3
8
/
n
c
o
mm
s
1
3
1
9
9
.
[
1
4
]
W
.
Y
a
n
g
e
t
a
l
.
,
“
Q
u
a
si
-
o
n
e
-
d
i
m
e
n
s
i
o
n
a
l
me
t
a
l
l
i
c
c
o
n
d
u
c
t
i
o
n
c
h
a
n
n
e
l
s
i
n
e
x
o
t
i
c
f
e
r
r
o
e
l
e
c
t
r
i
c
t
o
p
o
l
o
g
i
c
a
l
d
e
f
e
c
t
s,
”
N
a
t
u
r
e
C
o
mm
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
1
2
,
n
o
.
1
,
F
e
b
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
3
8
/
s4
1
4
6
7
-
0
2
1
-
2
1
5
2
1
-
9.
[
1
5
]
V
.
K
.
P
r
a
j
a
p
a
t
i
a
n
d
V
.
M
a
h
a
j
a
n
,
“
R
e
l
i
a
b
i
l
i
t
y
a
ss
e
ssm
e
n
t
a
n
d
c
o
n
g
e
st
i
o
n
m
a
n
a
g
e
m
e
n
t
o
f
p
o
w
e
r
sy
s
t
e
m w
i
t
h
e
n
e
r
g
y
st
o
r
a
g
e
s
y
st
e
m
a
n
d
u
n
c
e
r
t
a
i
n
r
e
n
e
w
a
b
l
e
r
e
s
o
u
r
c
e
s,
”
En
e
r
g
y
,
v
o
l
.
2
1
5
,
p
.
1
1
9
1
3
4
,
2
0
2
1
,
El
sev
i
e
r
.
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
e
r
g
y
.
2
0
2
0
.
1
1
9
1
3
4
.
[
1
6
]
R
.
K
a
u
r
,
T.
K
a
u
r
,
M
.
K
u
m
a
r
,
a
n
d
S
.
V
e
r
m
a
,
“
O
p
t
i
ma
l
t
r
a
n
smis
si
o
n
e
x
p
a
n
si
o
n
p
l
a
n
n
i
n
g
u
n
d
e
r
d
e
r
e
g
u
l
a
t
e
d
e
n
v
i
r
o
n
m
e
n
t
:
A
n
a
n
a
l
y
t
i
c
a
l
a
p
p
r
o
a
c
h
,
”
i
n
Pr
o
c
e
e
d
i
n
g
s
o
f
t
h
e
I
E
EE
1
s
t
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
Po
w
e
r
E
l
e
c
t
r
o
n
i
c
s
,
I
n
t
e
l
l
i
g
e
n
t
C
o
n
t
r
o
l
a
n
d
En
e
r
g
y
S
y
st
e
m
s (I
C
P
EI
C
E
S
)
,
N
e
w
D
e
l
h
i
,
I
n
d
i
a
,
2
0
1
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
P
EI
C
ES.
2
0
1
6
.
7
8
5
3
2
7
5
.
[
1
7
]
L.
N
i
,
F
.
W
e
n
,
W
.
L
i
u
,
J
.
M
e
n
g
,
G
.
L
i
n
,
a
n
d
S
.
D
a
n
g
,
“
C
o
n
g
e
st
i
o
n
ma
n
a
g
e
men
t
w
i
t
h
d
e
m
a
n
d
r
e
s
p
o
n
se
c
o
n
si
d
e
r
i
n
g
u
n
c
e
r
t
a
i
n
t
i
e
s
o
f
d
i
s
t
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
o
u
t
p
u
t
s
a
n
d
mark
e
t
p
r
i
c
e
s
,
”
J
o
u
rn
a
l
o
f
M
o
d
e
r
n
P
o
w
e
r
S
y
s
t
e
m
s
a
n
d
C
l
e
a
n
E
n
e
r
g
y
,
v
o
l
.
5
,
n
o
.
1
,
p
p
.
6
6
–
7
8
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
0
7
/
s4
0
5
6
5
-
0
1
6
-
0
2
5
7
-
9
.
[
1
8
]
W
.
C
h
e
n
,
H
.
Y
a
n
,
X
.
P
e
i
,
a
n
d
B
.
W
u
,
“
P
r
o
b
a
b
i
l
i
s
t
i
c
l
o
a
d
f
l
o
w
c
a
l
c
u
l
a
t
i
o
n
i
n
d
i
st
r
i
b
u
t
i
o
n
s
y
st
e
m
c
o
n
si
d
e
r
i
n
g
t
h
e
st
o
c
h
a
st
i
c
c
h
a
r
a
c
t
e
r
i
s
t
i
c
o
f
w
i
n
d
p
o
w
e
r
a
n
d
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
c
h
a
r
g
i
n
g
l
o
a
d
,
”
i
n
Pr
o
c
e
e
d
i
n
g
s
o
f
t
h
e
I
EE
E
P
o
w
e
r
a
n
d
E
n
e
r
g
y
S
o
c
i
e
t
y
(
PE
S
)
Asi
a
-
Pa
c
i
f
i
c
P
o
w
e
r
a
n
d
E
n
e
r
g
y
En
g
i
n
e
e
ri
n
g
C
o
n
f
e
re
n
c
e
(
APPE
E
C
)
,
X
i
’
a
n
,
C
h
i
n
a
,
O
c
t
.
2
0
1
6
,
d
o
i
:
1
0
.
1
1
0
9
/
A
P
P
EE
C
.
2
0
1
6
.
7
7
7
9
8
1
2
.
[
1
9
]
A
.
A
d
e
l
e
k
e
,
A
.
U
.
A
d
o
g
h
e
,
A
.
F
.
A
g
b
e
t
u
y
i
,
a
n
d
A
.
A
i
r
o
b
o
m
a
n
,
“
I
mp
a
c
t
o
f
d
i
st
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
s
o
n
p
o
w
e
r
sy
st
e
ms
st
a
b
i
l
i
t
y
:
A
r
e
v
i
e
w
,
”
i
n
Pro
c
e
e
d
i
n
g
s
o
f
t
h
e
I
E
EE
N
i
g
e
r
i
a
4
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
D
i
sr
u
p
t
i
v
e
T
e
c
h
n
o
l
o
g
i
e
s
f
o
r
S
u
st
a
i
n
a
b
l
e
D
e
v
e
l
o
p
m
e
n
t
(
N
I
G
ER
C
O
N
)
,
A
b
u
j
a
,
N
i
g
e
r
i
a
,
A
p
r
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
N
I
G
ER
C
O
N
5
4
6
4
5
.
2
0
2
2
.
9
8
0
3
0
6
2
.
[
2
0
]
M
.
H
.
A
l
i
,
A
.
M
.
A
.
S
o
l
i
ma
n
,
M
.
F
.
A
h
me
d
,
a
n
d
A
.
H
.
A
d
e
l
,
“
O
p
t
i
mi
z
a
t
i
o
n
o
f
r
e
a
c
t
i
v
e
p
o
w
e
r
d
i
sp
a
t
c
h
c
o
n
s
i
d
e
r
i
n
g
d
i
st
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
u
n
i
t
s
u
n
c
e
r
t
a
i
n
t
y
b
y
D
a
n
d
e
l
i
o
n
O
p
t
i
mi
z
e
r
A
l
g
o
r
i
t
h
m
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
Re
n
e
w
a
b
l
e
E
n
e
r
g
y
R
e
se
a
rc
h
(
I
J
R
ER)
,
v
o
l
.
1
2
,
n
o
.
4
,
2
0
2
2
,
d
o
i
:
1
0
.
2
0
5
0
8
/
i
j
r
e
r
.
v
1
2
i
4
.
1
3
5
7
3
.
g
8
6
0
6
.
[
2
1
]
A
.
Y
o
u
sef
i
,
T.
T.
N
g
u
y
e
n
,
H
.
Za
r
e
i
p
o
u
r
,
a
n
d
O
.
P
.
M
a
l
i
k
,
“
C
o
n
g
e
st
i
o
n
m
a
n
a
g
e
me
n
t
u
si
n
g
d
e
m
a
n
d
r
e
s
p
o
n
se
a
n
d
f
l
e
x
i
b
l
e
a
l
t
e
r
n
a
t
i
n
g
c
u
r
r
e
n
t
t
r
a
n
smis
si
o
n
sy
s
t
e
m
(
F
A
C
TS)
d
e
v
i
c
e
s,”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
t
ri
c
a
l
P
o
w
e
r
a
n
d
E
n
e
r
g
y
S
y
s
t
e
m
s,
v
o
l
.
3
7
,
n
o
.
1
,
p
p
.
7
8
–
8
5
,
M
a
y
2
0
1
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
e
p
e
s.
2
0
1
1
.
1
2
.
0
0
8
.
[
2
2
]
M
.
P
a
n
d
a
a
n
d
Y
.
K
.
N
a
y
a
k
,
“
I
mp
a
c
t
a
n
a
l
y
s
i
s
o
f
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
d
i
s
t
r
i
b
u
t
e
d
g
e
n
e
r
a
t
i
o
n
i
n
d
e
r
e
g
u
l
a
t
e
d
e
l
e
c
t
r
i
c
i
t
y
mar
k
e
t
s:
A
c
o
n
t
e
x
t
o
f
t
r
a
n
sm
i
ssi
o
n
c
o
n
g
e
st
i
o
n
p
r
o
b
l
e
m
,
”
En
e
rg
y
,
v
o
l
.
2
5
4
,
p
t
.
C
,
p
.
1
2
4
4
0
3
,
S
e
p
.
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
4
4
0
3
.
[
2
3
]
T.
S
o
n
g
a
n
d
J.
Te
h
,
“
C
o
o
r
d
i
n
a
t
e
d
i
n
t
e
g
r
a
t
i
o
n
o
f
w
i
n
d
e
n
e
r
g
y
i
n
m
i
c
r
o
g
r
i
d
s
:
A
d
u
a
l
st
r
a
t
e
g
y
a
p
p
r
o
a
c
h
l
e
v
e
r
a
g
i
n
g
d
y
n
a
mi
c
t
h
e
r
m
a
l
l
i
n
e
r
a
t
i
n
g
a
n
d
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
sc
h
e
d
u
l
i
n
g
,
”
S
u
s
t
a
i
n
a
b
l
e
E
n
e
rg
y
,
G
r
i
d
s
a
n
d
N
e
t
w
o
r
k
s,
v
o
l
.
3
8
,
p
.
1
0
1
2
9
9
,
Ju
n
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
se
g
a
n
.
2
0
2
4
.
1
0
1
2
9
9
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
A
n
a
lysi
s
o
f c
o
n
g
esti
o
n
ma
n
a
g
eme
n
t u
s
in
g
g
en
era
tio
n
r
esch
ed
u
lin
g
w
ith
… (
C
h
id
a
mb
a
r
a
r
a
j Na
ta
r
a
ja
n
)
65
[
2
4
]
A
.
U
.
R
e
h
ma
n
,
Z.
W
a
d
u
d
,
R
.
M
.
El
a
v
a
r
a
s
a
n
,
G
.
H
a
f
e
e
z
,
I
.
K
h
a
n
,
a
n
d
Z
.
S
h
a
f
i
q
,
“
A
n
o
p
t
i
m
a
l
p
o
w
e
r
u
sa
g
e
sc
h
e
d
u
l
i
n
g
i
n
s
mar
t
g
r
i
d
i
n
t
e
g
r
a
t
e
d
w
i
t
h
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
s
o
u
r
c
e
s
f
o
r
e
n
e
r
g
y
m
a
n
a
g
e
m
e
n
t
,
”
I
EEE
Ac
c
e
ss,
v
o
l
.
9
,
p
p
.
8
4
6
1
9
–
8
4
6
3
8
,
J
u
n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
1
.
3
0
8
7
3
2
1
.
[
2
5
]
R
.
M
i
sh
r
a
,
A
.
Y
a
d
a
v
,
a
n
d
M
.
P
.
S
i
n
g
h
,
“
A
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
t
e
c
h
n
i
q
u
e
s
-
b
a
se
d
c
o
n
g
e
s
t
i
o
n
m
a
n
a
g
e
me
n
t
i
n
r
e
st
r
u
c
t
u
r
e
d
p
o
w
e
r
sy
st
e
ms:
A
r
e
v
i
e
w
,
”
i
n
Pr
o
c
e
e
d
i
n
g
s
o
f
t
h
e
2
n
d
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
Po
w
e
r
E
l
e
c
t
r
o
n
i
c
s
a
n
d
I
n
t
e
rn
e
t
o
f
T
h
i
n
g
s
Ap
p
l
i
c
a
t
i
o
n
s
i
n
Re
n
e
w
a
b
l
e
E
n
e
r
g
y
a
n
d
i
t
s
C
o
n
t
ro
l
(
P
ARC
)
,
M
a
t
h
u
r
a
,
I
n
d
i
a
,
Jan
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
P
A
R
C
5
2
4
1
8
.
2
0
2
2
.
9
7
2
6
5
9
2
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Chi
d
a
m
b
a
r
a
r
a
j
N
a
ta
r
a
j
a
n
re
c
e
iv
e
d
h
is
B
.
E
.
d
e
g
re
e
i
n
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
i
c
s
En
g
i
n
e
e
rin
g
i
n
t
h
e
y
e
a
r
2
0
0
3
,
M
a
ste
rs
in
P
o
we
r
S
y
ste
m
s
E
n
g
i
n
e
e
rin
g
a
n
d
g
ra
d
u
a
ted
in
th
e
y
e
a
r
2
0
0
5
a
n
d
P
h
.
D
.
i
n
S
a
th
y
a
b
a
m
a
Un
iv
e
rsity
i
n
d
e
re
g
u
late
d
p
o
we
r
m
a
rk
e
t
in
th
e
y
e
a
r
2
0
1
8
.
He
h
a
s
b
e
e
n
wo
rk
in
g
a
s
a
n
As
so
c
iate
p
r
o
fe
ss
o
r
a
t
S
t.
J
o
se
p
h
’
s
C
o
ll
e
g
e
o
f
En
g
in
e
e
rin
g
in
th
e
d
e
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
e
n
g
i
n
e
e
rin
g
sin
c
e
2
0
0
5
a
n
d
h
e
h
a
s
a
lmo
st
1
9
y
e
a
rs
o
f
e
x
p
e
rien
c
e
i
n
th
e
re
sp
e
c
ti
v
e
field
.
His
s
u
b
jec
t
o
f
in
tere
st
in
c
lu
d
e
s
e
lec
tri
c
c
ircu
it
s,
c
o
n
tro
l
s
y
ste
m
s,
p
o
we
r
sy
ste
m
s
e
n
g
in
e
e
rin
g
,
e
n
g
i
n
e
e
rin
g
e
lec
tro
m
a
g
n
e
ti
c
s,
d
i
g
it
a
l
si
g
n
a
l
p
ro
c
e
ss
in
g
a
n
d
m
a
c
h
in
e
d
e
si
g
n
,
a
n
d
h
is
c
o
re
re
se
a
rc
h
is
o
n
d
e
re
g
u
late
d
p
o
we
r
sy
ste
m
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
a
il
to
c
h
id
h
a
2
0
2
0
@
g
m
a
il
.
c
o
m
.
Ar
a
v
in
d
h
a
n
K
a
r
u
n
a
n
ith
y
r
e
c
e
iv
e
d
h
is
B.
E
.
d
e
g
re
e
i
n
El
e
c
t
rica
l
a
n
d
El
e
c
tro
n
ics
En
g
i
n
e
e
rin
g
a
n
d
M
a
ste
rs
in
P
o
w
e
r
S
y
ste
m
s
En
g
in
e
e
ri
n
g
i
n
th
e
y
e
a
r
2
0
1
1
a
n
d
2
0
1
3
re
sp
e
c
ti
v
e
ly
.
He
h
a
s
b
e
e
n
wo
r
k
in
g
a
s
a
n
a
ss
istan
t
p
r
o
fe
ss
o
r
a
t
S
t
.
Jo
se
p
h
’
s
Co
ll
e
g
e
o
f
En
g
i
n
e
e
rin
g
in
th
e
d
e
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
rin
g
sin
c
e
2
0
1
3
a
n
d
h
e
h
a
s
a
lmo
st
1
2
y
e
a
rs
o
f
e
x
p
e
rien
c
e
in
th
e
tea
c
h
i
n
g
fiel
d
.
He
is
c
u
rre
n
tl
y
p
u
rsu
i
n
g
P
h
.
D
.
in
An
n
a
Un
iv
e
rsity
.
His
su
b
jec
t
o
f
in
tere
st
in
c
lu
d
e
s
e
lec
tri
c
c
ircu
it
s,
p
o
we
r
sy
ste
m
s
e
n
g
i
n
e
e
rin
g
,
e
lec
t
rica
l
m
a
c
h
in
e
s,
e
lec
tri
c
a
l
d
riv
e
s
a
n
d
c
o
n
tro
l,
c
o
n
tr
o
l
sy
ste
m
s,
sm
a
rt
g
ri
d
,
m
a
c
h
i
n
e
lea
rn
in
g
,
a
n
d
d
a
t
a
sc
ien
c
e
.
He
c
a
n
b
e
c
o
n
tac
te
d
a
t
e
m
a
il
:
a
ra
v
in
d
h
a
n
k
@s
tj
o
se
p
h
s.
a
c
.
in
.
S.
J
o
th
i
k
a
c
o
m
p
lete
d
h
e
r
u
n
d
e
rg
ra
d
u
a
te
d
e
g
re
e
in
El
e
c
tri
c
a
l
a
n
d
E
lec
tro
n
ics
En
g
i
n
e
e
rin
g
in
2
0
2
4
fro
m
S
t
.
Jo
s
e
p
h
s
Co
ll
e
g
e
o
f
E
n
g
in
e
e
rin
g
.
S
h
e
h
a
s
d
o
n
e
h
e
r
fi
n
a
l
y
e
a
r
p
r
o
jec
t
o
n
a
n
a
l
y
sis
o
f
c
o
n
g
e
stio
n
m
a
n
a
g
e
m
e
n
t
with
g
e
n
e
ra
ti
o
n
re
sc
h
e
d
u
l
i
n
g
u
sin
g
a
u
g
u
m
e
n
ted
m
o
u
n
tai
n
g
a
z
e
ll
e
o
p
ti
m
ize
r.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jo
th
i
k
a
sa
d
e
sh
@g
m
a
il
.
c
o
m
.
R.
P.
Lin
d
a
J
o
ice
h
a
s
c
o
m
p
lete
d
h
e
r
u
n
d
e
rg
ra
d
u
a
te
d
e
g
re
e
B
.
E
.
i
n
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
E
n
g
i
n
e
e
rin
g
fro
m
S
t.
Jo
se
p
h
’
s
o
f
C
o
ll
e
g
e
o
f
E
n
g
i
n
e
e
rin
g
in
2
0
2
4
a
t
c
h
e
n
n
a
i.
S
h
e
h
a
s
d
o
n
e
h
e
r
m
a
jo
r
y
e
a
r
p
ro
jec
t
o
n
a
n
a
ly
sis
o
f
c
o
n
g
e
stio
n
m
a
n
a
g
e
m
e
n
t
with
g
e
n
e
ra
ti
o
n
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jo
ice
li
n
d
a
0
8
8
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.