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[
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DSR
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[
2
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s
i
n
g
m
u
ltip
le
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
an
d
d
if
f
er
e
n
t
in
te
lli
g
en
t
al
g
o
r
ith
m
s
as
in
r
e
f
er
en
ce
s
[
1
0
-
1
4
]
.
So
m
e
o
f
th
e
s
e
s
tu
d
ie
s
s
u
f
f
er
f
r
o
m
b
u
s
v
o
lta
g
e
a
n
d
ca
p
ac
ito
r
s
ize
co
n
s
tr
ain
t
s
w
er
e
n
o
t
ac
h
ie
v
ed
.
I
n
ad
d
itio
n
,
th
e
lo
ca
tio
n
o
f
ea
ch
ca
p
ac
ito
r
w
a
s
b
ased
o
n
L
o
s
s
Se
n
s
iti
v
it
y
I
n
d
ex
(
L
SI)
in
th
e
s
e
s
tu
d
ie
s
,
w
h
ic
h
m
a
y
t
ak
e
m
o
r
e
ti
m
e
an
d
f
o
r
ce
th
e
o
p
ti
m
ized
al
g
o
r
ith
m
to
s
elec
t th
ese
lo
ca
tio
n
s
.
DSR
alo
n
e
o
r
OC
P
alo
n
e
c
an
n
o
t
co
n
f
ir
m
a
lo
s
s
r
ed
u
c
ti
o
n
an
d
v
o
lta
g
e
e
n
h
an
ce
m
e
n
t
o
f
b
u
s
es
s
ig
n
i
f
ica
n
tl
y
;
th
er
e
f
o
r
e,
a
n
ew
ap
p
r
o
ac
h
h
as
e
m
p
lo
y
ed
t
o
co
m
b
in
e
b
o
th
tech
n
iq
u
e
s
f
o
r
g
etti
n
g
b
etter
p
er
f
o
r
m
a
n
ce
i
n
R
DS.
Ma
n
y
s
t
u
d
ies
f
o
u
n
d
f
o
r
co
m
b
i
n
i
n
g
b
o
th
tec
h
n
iq
u
es
s
eq
u
e
n
tiall
y
,
f
i
r
s
tl
y
r
ec
o
n
f
i
g
u
r
at
io
n
p
r
o
ce
s
s
es
an
d
th
e
n
ca
p
ac
ito
r
p
lace
m
e
n
t
(
DS
R
-
OC
P
)
as
d
o
n
e
in
r
ef
er
e
n
ce
s
[
1
5
-
1
7
]
.
T
h
ese
w
o
r
k
s
co
n
ce
n
tr
ated
o
n
r
ea
l
p
o
w
er
lo
s
s
e
s
w
i
th
an
n
u
al
lo
s
s
e
s
co
s
t
r
ed
u
ctio
n
an
d
n
eg
lecti
n
g
v
o
lta
g
e
i
m
p
r
o
v
e
m
en
t
asp
ec
t.
Fu
r
t
h
er
m
o
r
e,
a
f
e
w
w
o
r
k
s
i
n
t
h
e
liter
at
u
r
e
th
a
t
ap
p
lied
th
e
d
u
al
tec
h
n
iq
u
e
f
o
r
a
lar
g
e
s
ta
n
d
ar
d
s
y
s
te
m
s
u
c
h
as
6
9
b
u
s
R
DS.
I
n
th
is
p
ap
er
,
t
w
o
tec
h
n
iq
u
e
s
ar
e
u
s
ed
,
DS
R
as
a
n
i
n
d
i
v
id
u
a
l
tech
n
iq
u
e
a
n
d
d
u
al
tech
n
iq
u
e
o
f
DS
R
-
OC
P
w
it
h
t
h
r
ee
m
o
d
i
f
ied
v
er
s
io
n
s
o
f
o
p
ti
m
izatio
n
al
g
o
r
ith
m
s
Mo
d
if
ied
B
io
g
eo
g
r
ap
h
y
B
ased
Op
ti
m
izatio
n
(
MB
B
O)
,
B
in
ar
y
T
ea
ch
in
g
L
ea
r
n
i
n
g
B
ased
Op
ti
m
izatio
n
(
B
T
L
B
O)
an
d
Dis
cr
ete
Do
lp
h
in
E
c
h
o
lo
ca
tio
n
(
DDE
)
.
T
h
ese
m
o
d
if
ied
alg
o
r
i
th
m
s
h
a
v
e
b
ee
n
u
s
ed
f
o
r
th
e
f
ir
s
t
ti
m
e
i
n
th
e
i
n
d
iv
id
u
al
an
d
d
u
al
tech
n
iq
u
es
i
n
o
r
d
er
to
en
h
a
n
ce
th
e
q
u
al
it
y
s
ea
r
ch
i
n
g
o
f
t
h
e
o
r
ig
i
n
al
v
er
s
io
n
s
a
n
d
to
p
r
ev
en
t
a
s
lid
e
in
to
th
e
p
r
e
m
at
u
r
e
co
n
v
er
g
e
n
ce
to
lo
ca
l
m
i
n
i
m
a.
T
h
e
o
p
tim
al
s
o
l
u
tio
n
co
n
tai
n
s
th
e
o
p
ti
m
al
s
elec
tio
n
o
f
tie
s
w
itc
h
es,
s
izes
a
n
d
lo
ca
tio
n
s
o
f
ca
p
ac
ito
r
s
b
ased
o
n
m
u
lt
i
-
o
b
j
ec
tiv
e
f
u
n
cti
o
n
s
a
n
d
R
D
S c
o
n
s
tr
ain
ts
.
T
w
o
I
E
E
E
s
tan
d
ar
d
n
e
t
w
o
r
k
s
ar
e
u
s
ed
3
3
b
u
s
a
n
d
6
9
b
u
s
to
c
h
ec
k
t
h
e
e
f
f
ec
tiv
e
n
es
s
o
f
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
.
T
h
e
w
h
o
le
d
e
s
ig
n
o
f
th
e
p
r
o
p
o
s
ed
w
o
r
k
w
ith
t
h
r
ee
-
o
p
ti
m
iza
tio
n
alg
o
r
it
h
m
s
h
a
s
co
m
p
ar
ed
w
i
th
t
h
e
av
ailab
le
l
iter
atu
r
e
w
o
r
k
s
f
o
r
th
e
s
a
m
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
a
n
d
co
n
s
tr
ai
n
t
s
to
tes
t
t
h
e
e
f
f
ic
ien
c
y
an
d
v
alid
it
y
o
f
p
r
o
p
o
s
ed
w
o
r
k
.
T
h
e
r
es
u
lt
s
co
n
f
ir
m
ed
t
h
e
e
f
f
icie
n
c
y
o
f
th
ese
m
o
d
i
f
ied
v
er
s
io
n
s
o
v
er
th
eir
o
r
ig
i
n
al
v
er
s
io
n
s
i
n
te
r
m
s
o
f
i
n
cr
ea
s
i
n
g
th
e
lo
s
s
es
r
ed
u
ctio
n
a
n
d
v
o
ltag
e
p
r
o
f
i
le
i
m
p
r
o
v
e
m
en
t.
A
l
s
o
,
t
h
e
p
r
o
p
o
s
ed
w
o
r
k
c
o
n
f
ir
m
ed
t
h
e
R
DS
co
n
s
tr
ain
ts
a
n
d
n
o
t
co
n
s
id
er
e
d
DG
o
r
P
V
im
p
le
m
en
tatio
n
.
Fin
all
y
,
t
h
e
MB
B
O
alg
o
r
ith
m
is
o
n
e
o
f
b
est
an
d
m
o
d
er
n
ef
f
icie
n
t
e
v
o
lu
t
io
n
a
r
y
a
lg
o
r
it
h
m
s
f
o
r
o
p
ti
m
ize
d
p
r
o
b
lem
s
d
u
e
to
h
ig
h
a
cc
u
r
ac
y
a
n
d
lo
w
er
co
m
p
u
tatio
n
r
eq
u
ir
e
m
e
n
t
s
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
DSR
tec
h
n
iq
u
e
is
a
g
o
o
d
ap
p
r
o
ac
h
to
r
ed
u
ce
p
o
w
er
lo
s
s
an
d
m
ai
n
tai
n
th
e
v
o
ltag
e
p
r
o
f
ile
w
it
h
i
n
p
er
m
i
s
s
ib
le
v
al
u
es.
Fu
r
t
h
er
m
o
r
e,
lo
s
s
ca
n
b
e
m
i
n
i
m
ized
e
x
tr
e
m
el
y
b
y
OC
P
tec
h
n
iq
u
e
s
o
c
o
n
s
ec
u
tiv
e
u
s
ag
e
o
f
b
o
th
tech
n
iq
u
es
ca
n
f
u
r
t
h
er
r
ed
u
ce
lo
s
s
es
an
d
i
m
p
r
o
v
e
v
o
ltag
e
p
r
o
f
ile
th
an
u
s
i
n
g
th
e
m
s
ep
ar
atel
y
.
T
w
o
ap
p
licatio
n
s
ar
e
u
s
ed
h
er
e,
f
ir
s
t o
n
e
DSR
i
n
d
iv
id
u
all
y
ap
p
r
o
a
ch
,
an
d
th
e
s
ec
o
n
d
is
d
u
al
D
S
R
-
OC
P
ap
p
r
o
ac
h
.
2
.
1
.
L
o
a
d f
lo
w
a
nd
o
bje
ct
iv
e
f
un
ct
io
ns
A
D
ir
ec
t
B
ac
k
w
ar
d
Fo
r
w
ar
d
S
w
ee
p
Me
th
o
d
(
DB
FS
M)
lo
a
d
f
lo
w
u
s
ed
h
er
e
to
ca
lc
u
late
b
u
s
v
o
ltag
e,
b
r
an
ch
c
u
r
r
en
t
an
d
to
tal
lo
s
s
es
[
1
8
]
.
C
o
n
v
e
n
tio
n
al
lo
ad
f
lo
w
m
et
h
o
d
s
o
f
tr
an
s
m
is
s
io
n
s
y
s
te
m
s
ar
e
n
o
t s
u
itab
l
e
f
o
r
R
DS
b
ec
a
u
s
e
o
f
h
ig
h
R
/X
r
atio
,
u
n
b
ala
n
ce
d
lo
ad
in
g
,
a
n
d
r
ad
ial
s
tr
u
ctu
r
e.
I
n
ad
d
itio
n
,
th
e
to
ler
an
ce
v
al
u
e
is
s
p
ec
if
ied
eq
u
als
1
×
.
Mu
lti
o
b
j
ec
tiv
e
f
u
n
c
tio
n
s
ar
e
u
s
ed
to
o
b
tain
th
e
lo
w
er
r
ea
l
p
o
w
e
r
lo
s
s
es,
an
d
v
o
ltag
e
en
h
a
n
cin
g
f
o
r
t
h
e
b
u
s
es
u
s
i
n
g
p
r
o
p
o
s
ed
tech
n
iq
u
e
s
an
d
o
p
ti
m
izatio
n
al
g
o
r
ith
m
s
.
T
h
ese
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
(
o
b
j.
f
.
)
ar
e:
R
ea
l p
o
w
er
lo
s
s
e
s
r
ed
u
ctio
n
:
(
1
)
∑
KW
(
2
)
KW
(
3
)
W
h
er
e:
:
T
o
tal
r
ea
l p
o
w
er
lo
s
s
es.
: N
u
m
b
er
o
f
b
r
an
c
h
es.
: Res
i
s
tan
ce
o
f
b
r
an
ch
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
8
0
-
90
82
: Cu
r
r
en
t f
lo
w
i
n
b
r
an
ch
.
Vo
ltag
e
p
r
o
f
ile
i
m
p
r
o
v
e
m
e
n
t (
o
b
j.
f.
2
)
,
h
er
e
v
o
ltag
e
m
u
s
t b
e
w
it
h
i
n
ac
ce
p
tab
le
li
m
i
ts
.
(
4
)
W
h
er
e:
: B
u
s
v
o
lta
g
e
li
m
it
s
.
: B
r
an
ch
cu
r
r
en
t l
i
m
its
.
:
R
etr
ib
u
tio
n
f
ac
to
r
f
o
r
b
u
s
v
o
ltag
e.
T
h
is
f
ac
to
r
eq
u
als
ze
r
o
if
t
h
e
b
u
s
v
o
lta
g
e
w
it
h
in
ac
ce
p
tab
le
li
m
it
s
.
:
R
etr
ib
u
tio
n
f
ac
to
r
f
o
r
b
r
an
ch
cu
r
r
en
t.
T
h
is
eq
u
als
ze
r
o
if
b
r
an
ch
cu
r
r
e
n
t
n
o
t
ex
ce
ed
th
e
th
er
m
al
li
m
it.
T
h
er
ef
o
r
e,
th
e
f
in
a
l o
b
j
ec
tiv
e
f
u
n
ct
io
n
(
)
w
il
l b
e
co
m
b
i
n
atio
n
o
f
ab
o
v
e
in
d
i
v
id
u
a
l o
n
es a
s
:
(
5
)
2
.
2
.
Co
ns
t
ra
ints
T
h
e
co
n
s
tr
ain
ts
t
h
at
co
n
f
ir
m
th
e
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
R
DS
ar
e
class
i
f
ied
to
tech
n
ica
l
an
d
o
p
er
atio
n
al
co
n
s
tr
ain
t
s
as
f
o
ll
o
w
:
2
.
2
.
1
.
T
ec
hn
ica
l c
o
n
s
t
ra
ints
T
h
ese
co
n
s
tr
ain
t
s
ar
e
d
ef
i
n
ed
as a
n
i
n
eq
u
alit
y
li
m
it
s
an
d
d
iv
id
ed
in
to
:
a.
Vo
ltag
e
co
n
s
r
ai
n
t
s
Vo
ltag
e
v
al
u
e
f
o
r
ea
ch
b
u
s
m
u
s
t b
e
w
it
h
i
n
t
h
eir
ac
ce
p
tab
le
d
o
m
a
in
to
s
u
s
ta
in
p
o
w
er
q
u
alit
y
as:
|
|
|
|
|
|
,
j
є
,
: n
u
m
b
er
o
f
b
u
s
es
(
6
)
T
h
en
,
th
e
s
ta
n
d
ar
d
m
in
i
m
u
m
a
n
d
m
a
x
i
m
u
m
v
o
ltag
e
s
ar
e
0
.
9
5
p
.
u
an
d
1
.
0
5
p
.
u
r
esp
ec
tiv
el
y
[
1
9
]
.
b.
C
u
r
r
en
t c
o
n
s
tr
ai
n
ts
B
r
an
ch
cu
r
r
en
t
m
u
s
t
n
o
t
ex
ce
ed
its
li
m
i
ted
v
alu
e
f
r
o
m
p
r
o
tectio
n
s
id
e
an
d
in
v
e
s
ti
g
ate
p
o
w
er
s
u
p
p
l
y
co
n
ti
n
u
it
y
|
|
|
|
,
є
,
: is n
u
m
b
er
o
f
b
r
an
c
h
e
s
(
7
)
W
h
er
e
th
e
m
a
x
i
m
u
m
b
r
an
c
h
c
ap
ac
it
y
is
s
p
ec
i
f
ied
in
[
2
0
]
.
c.
C
ap
ac
ito
r
s
s
ize
co
n
s
tr
ain
t
T
h
e
to
tal
ca
p
ac
ito
r
s
ize
(
)
in
s
er
ted
m
u
s
t
n
o
t
o
v
er
r
id
e
th
e
to
tal
r
ea
ctiv
e
p
o
w
er
lo
ad
(
)
o
f
th
e
R
D
S a
s
:
(
8
)
2
.
2
.
2
.
O
pera
t
io
na
l c
o
ns
t
ra
ints
T
h
ese
co
n
s
tr
ain
t
s
ar
e
d
ef
i
n
ed
as a
n
eq
u
ali
t
y
li
m
it
s
an
d
d
iv
id
ed
in
to
:
a.
R
ad
ial
co
n
s
tr
ai
n
ts
a
n
d
all
lo
ad
s
ar
e
co
v
er
ed
T
h
is
co
n
d
itio
n
ca
n
co
n
f
ir
m
e
d
b
y
d
eter
m
in
i
n
g
t
h
e
d
eter
m
i
n
an
t
o
f
b
u
s
i
n
cid
en
ce
m
a
tr
ix
[
A
]
a
s
g
iv
e
n
i
n
[
8
]
.
b.
P
o
w
er
b
alan
ce
co
n
s
tr
ai
n
t
(
9
)
W
h
er
e:
is
to
tal
s
u
p
p
lied
p
o
w
e
r
,
is
to
tal
lo
ad
p
o
w
er
.
2
.
3
.
O
pti
m
iza
t
io
n a
lg
o
rit
h
m
s
T
h
r
ee
o
p
tim
iza
tio
n
alg
o
r
it
h
m
s
ar
e
u
s
ed
in
th
is
w
o
r
k
,
MB
B
O,
B
T
L
B
O
an
d
DDE
f
o
r
b
o
th
in
d
iv
id
u
a
l
an
d
d
u
al
tech
n
iq
u
e
s
.
T
h
ese
al
g
o
r
ith
m
s
ar
e
d
escr
ib
ed
in
d
etail
r
esp
ec
tiv
el
y
.
2
.
3
.
1
.
M
B
B
O
Alg
o
rit
h
m
T
h
e
B
B
O
alg
o
r
ith
m
f
ir
s
t
i
n
tr
o
d
u
ce
d
b
y
Da
n
Si
m
o
n
i
n
2
0
0
8
in
s
p
ir
ed
f
r
o
m
n
at
u
r
e
o
f
g
eo
lo
g
ical
d
is
tr
ib
u
tio
n
f
o
r
li
v
i
n
g
b
ein
g
s
[
2
1
]
.
T
h
e
o
r
ig
in
al
a
lg
o
r
it
h
m
s
u
f
f
er
s
f
r
o
m
tr
ap
p
in
g
i
n
lo
ca
l
o
p
tim
a
an
d
w
ea
k
l
y
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:
2
0
8
8
-
8708
Du
a
l te
ch
n
iq
u
e
o
f
r
ec
o
n
fig
u
r
a
tio
n
a
n
d
ca
p
a
cito
r
p
la
ce
men
t f
o
r
d
is
tr
ib
u
tio
n
s
ystem
(
Ha
id
er
F
a
th
i Ka
d
o
m
)
83
b
eh
av
io
r
[
2
2
]
.
T
h
e
MB
B
O
alg
o
r
ith
m
in
tr
o
d
u
ce
d
in
th
i
s
p
ap
e
r
to
s
o
lv
e
th
e
s
e
d
r
a
w
b
ac
k
s
,
als
o
it
f
ir
s
t
t
i
m
e
u
s
ed
f
o
r
DS
R
a
n
d
OC
P
p
r
o
b
le
m
s
an
d
d
e
m
o
n
s
tr
ated
p
r
o
m
i
s
i
n
g
r
esu
l
ts
o
v
er
t
h
eir
o
r
ig
i
n
al
v
er
s
io
n
i
n
ter
m
s
o
f
r
o
b
u
s
tn
es
s
an
d
g
o
o
d
p
er
f
o
r
m
a
n
ce
.
T
h
e
s
tep
s
o
f
MB
B
O
ar
e
as f
o
llo
w
:
1.
Def
i
n
e
alg
o
r
it
h
m
p
ar
a
m
eter
s
a
s
ex
p
lai
n
in
T
ab
le
1
.
2.
C
alcu
late
Kee
p
n
u
m
b
er
(
K.
NO.
)
o
f
p
o
p
u
latio
n
as:
K
.
N
O.
=
K
ee
p
r
a
te
*
P
o
p
.
N
O
.
(
1
0
)
W
h
er
e,
P
o
p
.
N
O.
is
a
Po
p
u
latio
n
n
u
m
b
er
.
3.
C
alcu
late
Hab
itat n
u
m
b
er
(
Ha
b
.
NO.
)
as:
Ha
b
.
N
O.
= P
o
p
.
N
O.
-
K
.
N
O.
(
1
1
)
4.
C
alcu
late
E
m
i
g
r
atio
n
r
ate
(
EM
)
an
d
th
e
n
I
m
m
i
g
r
atio
n
r
ate
(
IM
)
b
y
:
(
1
2
)
(
1
3
)
5.
C
alcu
late
e
m
i
g
r
atio
n
p
r
o
b
ab
ili
t
y
(
)
u
s
i
n
g
IM
v
al
u
es a
s
:
if ra
n
d
≤
I
M
(
1
4
)
W
h
er
e,
is
r
an
d
o
m
n
u
m
b
er
s
b
e
t
w
ee
n
0
&
1
; T
h
en
,
f
in
al
v
al
u
e
o
f
is
d
eter
m
i
n
e
b
y
[
2
2
]
:
(
)
∑
(
1
5
)
W
h
er
e,
is
s
p
ec
if
ied
s
p
ec
ies
n
u
m
b
er
.
6.
Dete
r
m
i
n
e
u
p
d
ated
Su
i
tab
ilit
y
I
n
d
ex
(
)
f
o
r
n
th
s
o
lu
tio
n
f
r
o
m
p
r
ev
io
u
s
n
th
a
n
d
m
t
h
s
o
l
u
tio
n
[
2
3
]
:
(
)
(
1
6
)
W
h
er
e,
is
a
v
ar
iab
le
r
an
g
e
b
et
w
ee
n
0
&
1
.
7.
C
h
ec
k
m
u
ta
tio
n
p
r
o
b
ab
ilit
y
v
a
lu
e
w
it
h
v
alu
e,
i
f
it b
ig
g
er
th
a
n
o
r
eq
u
al
to
v
alu
e
t
h
en
,
(
1
7
)
W
h
er
e,
is
E
lite n
u
m
b
er
.
8.
R
ep
ea
t step
s
(
2
-
7
)
u
n
til
m
ax
it
er
atio
n
s
ac
h
ie
v
ed
.
9.
E
n
d
.
T
ab
le
1
.
Op
tim
al
v
alu
e
s
o
f
M
B
B
O
alg
o
r
ith
m
P
a
r
a
me
t
e
r
V
a
l
u
e
30
M
u
t
a
t
i
o
n
p
r
o
b
a
b
i
l
i
t
y
0
.
1
2
M
a
x
i
m
u
m
n
u
m
b
e
r
o
f
i
t
e
r
a
t
i
o
n
s
(
R
D
S
)
5
0
(
3
3
b
u
s)
1
0
0
(
6
9
b
u
s)
max
i
mu
m
IM
1
max
i
mu
m
EM
1
0
.
2
0
.
9
2
.
3
.
2
.
B
T
L
B
O
a
lg
o
rit
h
m
T
h
e
T
L
B
O
alg
o
r
ith
m
is
a
n
i
n
tellig
e
n
t
Me
ta
h
eu
r
i
s
tic
t
y
p
e
i
n
tr
o
d
u
ce
d
b
y
R
ao
et
a
l
.
in
2
0
1
2
w
h
ic
h
co
n
s
is
ts
f
r
o
m
t
w
o
p
r
o
ce
s
s
es,
t
ea
ch
in
g
p
r
o
ce
s
s
an
d
lear
n
i
n
g
p
r
o
ce
s
s
[
2
4
]
.
T
h
e
p
o
p
u
latio
n
n
u
m
b
er
is
eq
u
al
to
n
u
m
b
er
o
f
s
t
u
d
en
t
s
w
h
i
le
th
e
n
u
m
b
er
o
f
v
ar
iab
les
is
eq
u
al
to
th
e
l
ec
tu
r
es
i
n
tr
o
d
u
ce
d
to
s
tu
d
en
t
s
.
I
n
th
e
p
r
esen
t
w
o
r
k
b
ec
a
u
s
e
o
f
d
is
cr
ete
n
at
u
r
e
o
f
DSR
a
n
d
OC
P
tech
n
iq
u
e
s
,
th
e
n
th
e
b
in
ar
y
co
d
e
f
o
r
th
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
0
:
8
0
-
90
84
alg
o
r
ith
m
h
a
s
b
ee
n
u
s
ed
at
f
ir
s
t
ti
m
e
to
s
o
l
v
e
th
e
p
r
p
o
s
ed
in
d
iv
id
u
al
a
n
d
d
u
al
tech
n
i
q
u
es.
Fu
r
t
h
e
r
m
o
r
e,
th
e
o
r
ig
i
n
al
v
er
s
io
n
o
f
t
h
i
s
alg
o
r
ith
m
s
u
f
f
e
r
f
r
o
m
lo
w
er
p
er
f
o
r
m
an
ce
b
ec
a
u
s
e
o
f
t
h
e
d
if
f
er
n
at
u
r
e
o
f
th
e
ap
p
lied
alg
o
r
ith
m
w
it
h
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
es
s
u
c
h
th
at
ea
s
il
y
s
l
ip
in
t
h
e
lo
ca
l
o
p
tim
u
m
s
o
l
u
tio
n
.
T
h
e
s
tep
s
o
f
B
T
L
B
O
A
lg
o
r
it
h
m
ar
e:
1.
I
n
itializi
n
g
alg
o
r
it
h
m
p
ar
a
m
et
er
s
as sh
o
w
n
i
n
T
ab
le
2
.
2.
T
ea
ch
in
g
p
r
o
ce
s
s
,
t
h
i
s
s
ta
g
e
b
eg
in
b
y
s
p
ec
if
y
i
n
g
T
ea
ch
er
Facto
r
v
a
lu
e
(
)
an
d
as
g
i
v
e
n
b
y
r
e
f
er
en
ce
[
2
5
]
:
(
)
(
1
8
)
W
h
er
e,
is
an
in
te
g
er
n
u
m
b
er
,
th
en
in
t
h
e
r
an
g
e
is
(
1
-
2
)
.
3.
Dete
r
m
i
n
e
t
h
e
Av
er
ag
e
Di
f
f
er
en
ce
(
A
vg
.
Diff
.
)
i
f
t
h
e
a
v
er
ag
e
d
eg
r
ee
o
f
lect
u
r
e
(
)
f
o
r
th
e
s
tu
d
e
n
t
(
)
at
iter
atio
n
(
)
is
en
h
an
ce
d
f
r
o
m
o
ld
v
alu
e
(
)
to
a
n
e
w
v
al
u
e
(
)
s
u
ch
a
s
:
(
–
)
(
1
9
)
4.
Up
d
ate
s
tu
d
en
t d
eg
r
ee
i
n
lect
u
r
e
u
s
in
g
A
vg
.
Diff
.:
(
2
0
)
5.
L
ea
r
n
i
n
g
p
r
o
ce
s
s
,
i
n
th
i
s
s
ta
g
e
ea
c
h
s
tu
d
e
n
t
i
s
g
ai
n
i
n
f
o
r
m
atio
n
f
r
o
m
t
h
eir
cla
s
s
m
ate
s
t
h
r
o
u
g
h
th
i
s
p
r
o
ce
s
s
:
I
f
is
b
etter
th
a
n
th
e
n
(
)
(
2
1
)
I
f
w
o
r
s
e
t
h
a
n
th
e
n
,
(
)
(
2
2
)
W
h
er
e,
,
ar
e
av
er
ag
e
d
eg
r
ee
o
f
lectu
r
e
f
o
r
th
e
s
t
u
d
en
ts
&
r
esp
ec
tiv
el
y
.
6.
R
ep
ea
t step
s
(
2
-
5
)
u
n
til co
n
v
e
r
g
en
ce
cr
iter
ia
co
n
f
ir
m
ed
,
w
h
i
ch
is
m
ax
i
m
u
m
n
u
m
b
er
o
f
iter
atio
n
s
.
7.
E
n
d
.
T
ab
le
2
.
Op
tim
al
p
ar
a
m
eter
s
o
f
B
T
L
B
O
alg
o
r
ith
m
P
a
r
a
me
t
e
r
V
a
l
u
e
20
M
a
x
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m
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m
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f
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(
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5
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(
3
3
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1
0
0
(
6
9
b
u
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2
.
3
.
3
.
DDE a
lg
o
rit
h
m
T
h
is
a
n
e
w
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p
ti
m
izatio
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al
g
o
r
ith
m
d
is
co
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ed
b
y
Kav
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h
a
n
d
Far
h
o
u
d
i
in
2
0
1
3
th
at
m
i
m
i
c
th
e
ec
h
o
o
f
click
s
e
m
it
s
f
r
o
m
d
o
lp
h
in
s
,
a
n
d
u
s
ed
f
o
r
h
u
n
ti
n
g
p
r
o
ce
s
s
[
2
6
,
2
7
]
.
T
h
e
o
r
ig
in
al
v
er
s
io
n
o
f
t
h
i
s
al
g
o
r
ith
m
h
as
b
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n
u
s
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p
r
ev
io
u
s
l
y
to
s
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l
ve
th
e
p
o
w
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y
s
te
m
p
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b
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s
u
c
h
as
th
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o
n
o
m
ic
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p
atch
p
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o
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lem
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d
p
r
o
v
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its
v
a
lid
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y
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d
e
f
f
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en
c
y
w
h
ile
t
h
e
b
i
n
ar
y
v
er
s
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n
o
f
t
h
is
a
lg
o
r
it
h
m
is
u
s
ed
w
it
h
DS
R
o
r
OC
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p
r
o
b
lem
s
f
o
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th
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f
ir
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t ti
m
e
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n
t
h
is
p
ap
er
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h
e
s
tep
s
o
f
th
e
DD
E
alg
o
r
ith
m
ar
e
as f
o
llo
w
s
:
1.
I
n
itializi
n
g
DDE
p
ar
a
m
eter
s
a
s
g
i
v
en
i
n
T
ab
le
3
.
2.
Dete
r
m
i
n
e
P
r
ed
ef
in
ed
P
o
s
s
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b
ilit
y
(
)
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ased
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n
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ce
Facto
r
(
)
f
o
r
cu
r
r
en
t
lo
o
p
(
)
:
(
)
(
2
3
)
3.
Me
asu
r
e
S
u
itab
ilit
y
(
)
f
o
r
ea
ch
lo
ca
tio
n
(
)
w
h
er
e
to
tal
n
u
m
b
e
r
o
f
lo
ca
tio
n
s
is
.
4.
Def
i
n
e
(
)
f
ac
to
r
b
o
u
n
d
s
t
h
at
d
ep
en
d
s
o
n
th
e
ac
ti
v
e
R
ad
i
u
s
(
)
as:
(
2
4
)
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:
2
0
8
8
-
8708
Du
a
l te
ch
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iq
u
e
o
f
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o
n
fig
u
r
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p
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t f
o
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d
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tr
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tio
n
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ystem
(
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id
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th
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d
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85
5.
Dete
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m
i
n
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I
n
cr
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m
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tal
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f
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f
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w
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u
p
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tio
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u
n
d
t
h
en
,
(
2
6
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C
o
m
p
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ab
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f
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y
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(
2
7
)
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h
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,
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b
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d
n
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m
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f
v
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iab
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es r
esp
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y
.
7.
Div
id
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o
r
all
in
cr
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m
e
n
tal
f
o
r
all
v
ar
iab
les i
n
to
:
{
(
)
(
2
8
)
8.
R
ep
eat
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tep
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(
2
-
7
)
u
n
til
m
ax
.
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o
o
p
.
NO.
is
r
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ch
ed
.
9.
E
n
d
.
T
ab
le
3
.
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tim
al
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ar
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f
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ith
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me
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V
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l
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m
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O
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)
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3
3
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0
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(
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9
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u
s)
0
.
1
2
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
T
h
e
ab
o
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th
r
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r
ith
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s
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B
O,
B
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d
DDE
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d
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s
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/MA
T
L
AB
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0
1
8
b
p
r
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g
r
a
m
.
T
h
ese
d
ev
e
lo
p
ed
alg
o
r
ith
m
s
h
a
v
e
b
ee
n
a
p
p
lied
f
o
r
t
w
o
tech
n
iq
u
e
s
(
D
SR
o
n
l
y
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d
DSR
-
OC
P
)
w
it
h
t
w
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s
tan
d
ar
d
I
E
E
E
R
DS
(
3
3
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u
s
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n
d
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9
b
u
s
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.
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h
e
lin
e
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n
d
b
u
s
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ata
f
o
r
t
wo
te
s
t
ca
s
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g
iv
e
n
i
n
r
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er
en
ce
[
2
8
]
an
d
t
h
e
test
d
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n
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f
o
r
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n
e
y
ea
r
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s
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n
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h
e
r
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lt
s
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f
t
h
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tech
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iq
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e
s
ar
e
in
s
er
ted
in
tab
le
s
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f
o
r
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o
r
ith
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s
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.
1
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E
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A
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5
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3
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r
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d
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tain
s
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ch
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2
clo
s
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d
5
o
p
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s
w
itc
h
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u
r
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1
ex
p
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th
e
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li
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r
a
m
f
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y
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te
m
.
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h
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lts
o
f
u
s
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n
g
t
w
o
tec
h
n
iq
u
es
w
it
h
t
h
r
ee
o
p
ti
m
izati
o
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alg
o
r
ith
m
s
ar
e
g
iv
e
n
in
T
ab
le
4
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d
T
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le
5
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esp
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tiv
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y
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g
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r
e
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to
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r
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s
h
o
w
th
e
g
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h
s
o
f
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ile
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n
d
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n
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ce
u
s
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t
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o
tech
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th
t
h
r
ee
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p
ti
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iz
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h
m
s
r
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el
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l
s
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d
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co
n
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m
p
ar
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o
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it
h
Ha
r
m
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n
y
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ch
A
l
g
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r
ith
m
(
H
S
A
)
[
2
9
]
,
C
u
c
k
o
o
Sear
ch
A
l
g
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r
ith
m
(
C
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A
)
[
3
0
]
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d
B
in
ar
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ar
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w
ar
m
Op
ti
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izatio
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(
B
-
P
SO)
alg
o
r
ith
m
[
3
1
]
u
n
d
er
th
e
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a
m
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co
n
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tr
ai
n
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s
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d
o
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j
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n
ad
d
itio
n
,
t
h
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tab
le
s
also
e
x
p
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n
b
etter
lo
s
s
r
ed
u
ctio
n
a
n
d
co
n
f
ir
m
i
n
g
v
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a
n
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p
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ito
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s
ize
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n
s
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ain
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(
2
3
0
0
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r
)
o
f
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
w
h
ile
th
e
liter
at
u
r
e
w
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r
k
d
id
n
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t
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e
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e
co
n
s
tr
ai
n
ts
.
MB
B
O
alg
o
r
ith
m
p
er
f
o
r
m
b
etter
lo
s
s
r
ed
u
ctio
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d
f
ast
co
n
v
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g
e
n
c
e
th
an
o
t
h
e
r
t
w
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p
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o
p
o
s
ed
alg
o
r
ith
m
s
a
n
d
liter
atu
r
e
w
o
r
k
s
f
o
r
t
w
o
tech
n
iq
u
es
an
d
ac
h
iev
e
lo
w
er
ca
p
ac
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to
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h
is
r
ed
u
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A
ls
o
,
th
e
p
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f
o
r
m
a
n
ce
o
f
o
t
h
er
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
s
u
r
p
as
s
t
h
e
p
r
ev
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u
s
w
o
r
k
s
i
n
lo
s
s
r
ed
u
ctio
n
a
n
d
k
ee
p
th
e
R
DS c
o
n
s
tr
ai
n
ts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
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1
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Feb
r
u
ar
y
2
0
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0
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Fig
u
r
e
1
.
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le
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m
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s
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u
s
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h
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d
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l
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tec
h
n
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e
A
l
g
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t
h
ms
R
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a
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r
l
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2
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s
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h
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u
al
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SR
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tech
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e
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l
g
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m
R
e
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0
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[1
]
S
.
M
.
Ba
n
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Re
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ld
,
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Distrib
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]
H.
K.
V
e
rm
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D.
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[5
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.
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ri,
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t
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l
.
,
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s f
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1
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p
.
4
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5
.
[7
]
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.
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m
a
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d
S
o
m
v
ir,
"
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t
w
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re
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o
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ter
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ter
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(
IJ
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(8
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p
.
1
7
3
6
9
-
1
7
3
7
4
,
2
0
1
6
.
[8
]
M
.
S
e
d
ig
h
iza
d
e
h
,
e
t
a
l
.
,
"
Re
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ial
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istri
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),
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p
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6
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2
0
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4
.
[9
]
M
.
E.
Ba
ra
n
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n
d
F
.
F
.
W
u
,
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t
im
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ial
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li
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ry
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l.
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,
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8
9
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0
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p
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[1
1
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R.
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M
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.
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.
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3
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1
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.
[1
4
]
D.
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L
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1
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5
]
Ra
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IEE
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ter
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(
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DES
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,
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2
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1
4
.
[1
6
]
L
e
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ter
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&
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.
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7
]
N.
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.
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.
[1
8
]
J.
T
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"
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s,"
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ra
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sa
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ry
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l.
1
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(3
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,
p
p
.
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7
,
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0
0
3
.
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