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Vo
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12
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No
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1
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Octo
b
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2018
:
1
9
3
–
200
194
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o
b
lem
.
P
r
ev
io
u
s
r
esear
c
h
p
ap
er
s
h
a
v
e
ap
p
ea
r
ed
to
s
o
lv
e
DSEP
p
r
o
b
lem
b
y
u
s
i
n
g
s
o
f
t
co
m
p
u
ti
n
g
a
n
d
m
at
h
e
m
a
tical
o
p
ti
m
izatio
n
tec
h
n
iq
u
es.
T
af
r
es
h
i
et
a
l.
[
1
2
]
p
r
o
p
o
s
ed
Gen
etic
A
lg
o
r
it
h
m
(
GA
)
to
ca
lc
u
late
th
e
o
p
tim
u
m
s
y
s
te
m
co
n
f
ig
u
r
atio
n
th
a
t
ca
n
ac
h
ie
v
e
th
e
c
u
s
to
m
er
s
r
eq
u
ir
ed
lo
s
s
o
f
p
o
w
er
s
u
p
p
l
y
p
r
o
b
ab
ilit
y
(
L
P
SP
)
w
it
h
a
m
i
n
i
m
u
m
co
s
t
o
f
en
er
g
y
(
C
OE
)
w
h
ile
Fala
g
h
i
[
1
3
]
u
s
ed
th
e
s
a
m
e
ap
p
r
o
ac
h
to
s
o
lv
e
m
u
lti
s
ta
g
e
DSEP
w
h
ic
h
s
o
lv
ed
in
s
tallat
io
n
o
f
s
u
b
s
tat
io
n
s
,
f
ee
d
er
s
a
n
d
DG
th
a
t
s
u
i
t
to
ca
p
ac
it
y
ex
p
an
s
io
n
.
An
o
t
h
er
ap
p
r
o
ac
h
th
at
co
n
s
i
s
ts
t
h
e
ca
p
ab
ilit
y
o
f
s
o
l
v
i
n
g
DSEP
is
A
r
ti
f
icial
I
m
m
u
n
e
S
y
s
te
m
(
AI
S)
th
at
p
r
o
p
o
s
ed
b
y
So
u
za
et
al.
[
1
4
]
.
A
I
S
p
r
o
d
u
ce
d
a
s
y
s
te
m
atica
l
l
y
s
et
o
f
s
o
lu
tio
n
s
a
n
d
ab
le
to
s
o
lv
e
D
SEP
p
r
o
b
lem
i
n
p
lan
n
i
n
g
s
m
ar
t
g
r
id
a
n
d
o
b
tain
i
n
g
DG
o
p
ti
m
al
s
o
lu
t
io
n
.
Mi
x
ed
I
n
t
eg
er
L
in
ea
r
P
r
o
g
r
a
m
m
in
g
(
M
I
L
P
)
th
at
s
o
lv
ed
b
y
Si
m
u
lated
A
n
n
ea
li
n
g
(
S
A
)
is
p
r
o
p
o
s
ed
b
y
P
o
p
o
v
ic
et
al.
[
1
5
]
w
h
ile
T
ab
u
Sear
ch
p
r
o
p
o
s
ed
b
y
R
a
m
ir
ez
-
R
a
s
ad
o
et
al.
[
1
6
]
to
s
o
lv
e
DSEP
p
r
o
b
le
m
.
T
h
e
MV
A
ap
p
r
o
ac
h
is
u
s
ed
t
o
s
o
lv
e
th
e
p
o
r
tf
o
lio
s
elec
tio
n
p
r
o
b
lem
i
n
th
is
p
ap
er
.
T
h
e
f
o
r
m
u
latio
n
th
at
p
r
o
p
o
s
ed
b
y
Ma
r
k
o
w
itz
i
s
in
t
h
e
f
o
r
m
o
f
m
ix
ed
i
n
teg
er
p
r
o
g
r
am
m
i
n
g
p
r
o
b
le
m
w
h
ic
h
m
in
i
m
is
i
n
g
th
e
r
is
k
w
h
ile
th
e
r
et
u
r
n
h
a
s
b
ee
n
f
i
x
ed
in
to
a
ce
r
tain
co
n
d
itio
n
.
Sin
ce
th
e
p
r
o
b
lem
is
h
ar
d
to
s
o
lv
e,
th
u
s
s
o
f
t
co
m
p
u
ti
n
g
ap
p
r
o
ac
h
w
il
b
e
u
s
e
b
y
e
m
p
lo
y
in
g
t
h
e
co
m
b
i
n
atio
n
b
et
w
ee
n
Ho
p
f
ield
Net
wo
r
k
an
d
B
o
ltz
m
an
n
Ma
ch
i
n
e
n
a
m
ed
as
Mo
d
if
ied
B
M.
T
h
e
f
u
r
t
h
er
ex
p
la
n
atio
n
w
il
l
b
e
d
is
cu
s
s
ed
in
Sec
tio
n
2
.
Sectio
n
3
p
r
o
v
id
e
d
th
e
d
is
c
u
s
s
io
n
b
a
s
ed
o
n
t
h
e
s
i
m
u
latio
n
r
e
s
u
l
t.
L
astl
y
,
Sect
i
o
n
4
w
ill
b
e
e
x
p
lain
i
n
g
t
h
e
c
o
n
clu
s
io
n
a
n
d
f
u
t
u
r
e
r
ec
o
m
m
e
n
d
atio
n
s
.
2.
M
AT
H
E
M
AT
I
CAL M
O
DE
L
2
.
1
.
M
ea
n
-
Va
ria
nce
Ana
ly
s
is
T
h
e
MV
A
i
s
a
q
u
a
n
tita
tiv
e
to
o
l
t
h
at
allo
w
s
th
e
d
ec
i
s
i
o
n
m
a
k
er
to
m
a
k
e
a
s
s
et
al
l
o
ca
tio
n
b
y
co
n
s
id
er
in
g
t
h
e
i
n
ter
c
h
an
g
e
b
et
w
ee
n
e
x
p
ec
ted
r
etu
r
n
r
ate
a
n
d
t
h
e
m
ea
s
u
r
e
o
f
r
is
k
[
1
7
–
19]
.
T
h
e
p
o
r
tf
o
lio
o
p
tim
izatio
n
e
m
p
lo
y
ed
m
ea
n
a
n
d
v
ar
ia
n
ce
th
at
s
o
l
v
ed
ex
p
ec
ted
r
etu
r
n
r
ate
f
o
r
ea
ch
as
s
et,
s
tan
d
ar
d
d
ev
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n
w
h
ich
is
m
ea
s
u
r
e
o
f
r
is
k
a
n
d
co
v
ar
ian
ce
m
atr
i
x
b
et
w
ee
n
th
e
s
e
ass
et
s
.
T
h
e
o
u
tp
u
t
is
i
n
f
o
r
m
o
f
t
h
e
e
f
f
icien
t
f
r
o
n
tier
w
h
er
e
th
e
ex
p
ec
te
d
r
et
u
r
n
g
r
ea
ter
th
an
a
n
y
o
th
er
w
i
th
t
h
e
s
a
m
e
o
r
less
er
r
i
s
k
[
2
0
]
.
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asicall
y
,
MV
A
i
s
f
o
r
m
u
lated
as
m
i
x
ed
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n
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er
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r
a
m
m
in
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l
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q
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1
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4
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1
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(1
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s
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b
j
e
c
t
t
o
(2
)
n
i
i
x
1
1
(3
)
)
.
.
.
,
,
2
,
1
(
0
n
i
x
(4
)
w
h
er
e
R
is
th
e
lea
s
t
ac
ce
p
tab
le
r
ate
o
f
ex
p
ec
ted
r
etu
r
n
,
is
t
h
e
co
v
ar
ian
ce
b
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w
ee
n
s
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k
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d
j,
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th
e
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p
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r
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r
n
r
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n
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f
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i
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F
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m
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ased
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Fo
r
m
u
lat
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1
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th
e
o
p
ti
m
al
s
o
l
u
tio
n
s
ar
e
o
f
f
er
ed
w
ith
t
h
e
least
r
i
s
k
.
Ho
w
e
v
er
,
th
e
co
n
s
tr
ain
t
f
ac
ed
b
y
th
e
d
ec
is
io
n
m
ak
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is
th
e
g
iv
e
n
v
al
u
e
ca
n
n
o
t
e
x
ce
ed
th
e
ex
p
ec
ted
r
etu
r
n
r
ate.
T
h
e
in
v
est
m
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n
t
r
ate
f
o
r
ea
ch
s
to
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d
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m
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k
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d
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th
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Sin
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th
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k
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co
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f
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r
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f
ex
p
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r
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n
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d
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m
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2
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ap
p
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v
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in
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f
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p
f
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k
a
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d
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M
is
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o
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m
u
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h
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lu
t
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n
b
y
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h
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g
i
n
g
o
v
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th
e
p
o
r
tf
o
lio
in
to
e
n
er
g
y
f
u
n
ct
io
n
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2
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2
.
B
o
lt
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a
n
n M
a
chine
A
B
M
is
an
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n
ter
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n
n
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ted
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u
r
al
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et
w
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k
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p
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s
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b
y
G.
E
.
Hin
to
n
[
2
1
]
.
T
h
is
m
o
d
el
is
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ased
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a
Ho
p
f
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.
()
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(
1
)
]
(
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ates o
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s
1
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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12
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1
,
Octo
b
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2018
:
1
9
3
–
200
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atica
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d
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n
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u
s
th
e
s
e
s
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o
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ld
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th
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h
e
s
i
m
u
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s
tep
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ar
e
lis
ted
as f
o
llo
w
s
:
a.
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h
e
tem
p
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u
r
e
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th
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m
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1
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e
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ted
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0
0
1
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c.
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.
1
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d.
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h
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s
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lated
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r
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3
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5
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0
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7
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d
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0
.
e.
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ltz
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a
n
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e
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eh
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s
p
r
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ilis
ticall
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h
e
r
es
u
lt is
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k
e
n
to
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e
th
e
av
er
ag
e
o
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th
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la
s
t
1
0
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0
0
0
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ials
.
T
h
e
f
o
llo
w
i
n
g
s
tep
s
ar
e
d
escr
ib
ed
as th
e
p
r
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ce
s
s
o
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i
m
p
le
m
e
n
ti
n
g
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e:
Step
1
.
Dete
r
m
ini
ng
the Ri
g
ht
Uncerta
int
y
T
h
e
MV
A
p
r
o
ce
s
s
is
in
itiated
b
y
d
eter
m
in
i
n
g
th
e
r
i
g
h
t
u
n
ce
r
tain
t
y
.
I
n
th
is
ca
s
e,
th
e
u
n
ce
r
tain
t
y
is
t
h
e
av
er
ag
e
i
n
ter
r
u
p
tio
n
d
u
r
atio
n
f
o
r
ea
ch
s
tate
w
ith
in
1
5
y
ea
r
s
i
n
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la
y
s
ia.
Step
2
.
Qua
nti
f
ying
I
nd
ivid
ua
l U
ncerta
int
ies
T
h
e
f
o
llo
w
in
g
d
ata
th
at
h
a
s
b
ee
n
q
u
an
ti
f
ied
as
in
T
ab
le
1
i
s
th
e
av
er
ag
e
i
n
ter
r
u
p
tio
n
d
u
r
atio
n
b
y
t
h
e
s
tate
i
n
Ma
la
y
s
ia.
T
h
e
d
ata
w
as
co
llected
p
er
h
o
u
r
p
er
cu
s
t
o
m
er
p
er
y
ea
r
w
it
h
i
n
1
5
y
ea
r
s
f
r
o
m
2
0
0
1
to
2
0
1
5
.
I
n
DSEP
,
th
e
o
p
ti
m
al
m
ain
ten
an
ce
f
o
r
ea
ch
s
tate
is
d
ec
id
ed
b
y
r
e
f
er
r
in
g
to
av
er
ag
e
i
n
ter
r
u
p
tio
n
d
u
r
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n
.
T
ab
le
1
.
A
v
er
ag
e
I
n
ter
r
u
p
tio
n
D
u
r
atio
n
b
y
State
Step
3
.
P
o
s
t
-
p
r
o
ce
s
s
ing
the U
ncerta
int
ie
s
T
h
e
n
ex
t
s
tep
is
p
o
s
t
-
p
r
o
ce
s
s
in
g
t
h
e
u
n
ce
r
tai
n
ties
.
I
n
t
h
is
s
tep
,
th
e
q
u
an
ti
f
ied
u
n
ce
r
tai
n
t
ies
h
a
v
e
to
u
n
d
er
g
o
th
e
p
r
o
ce
s
s
o
f
m
ea
n
an
d
v
ar
ian
ce
ca
lc
u
latio
n
.
T
ab
le
2
s
h
o
w
s
th
e
co
v
ar
ia
n
ce
m
atr
i
x
f
o
r
th
e
in
ter
r
u
p
tio
n
d
u
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0.72
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2013
2014
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Evaluation Warning : The document was created with Spire.PDF for Python.
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Octo
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2018
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9
3
–
200
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T
h
e
s
i
m
u
lat
io
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lt
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o
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tate
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u
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0
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r
in
g
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al
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3
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er
e
o
n
l
y
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r
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e
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9
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3
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7
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%,
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0
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1
7
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6
0
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d
9
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7
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%
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tiv
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m
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ep
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it
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atio
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n
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ar
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est
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f
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1
0
%
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d
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n
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it
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least
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f
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8
0
%.
Me
a
n
w
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ile
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e
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T
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er
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cc
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r
le
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at
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th
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ce
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k
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N
o
ticed
th
at
t
h
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al
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n
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l
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ce
d
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m
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o
s
e
n
w
h
er
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th
e
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s
tates
ar
e
h
ig
h
as
th
e
v
a
lu
e
o
f
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n
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ea
s
ed
.
A
d
ec
is
io
n
m
ak
er
ca
n
d
eter
m
in
e
th
e
o
p
ti
m
u
m
s
o
lu
tio
n
s
w
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lar
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lu
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f
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lead
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to
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ier
o
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ile
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h
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s
m
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ll
v
alu
e
o
f
K
lead
s
to
co
n
s
er
v
ati
v
e
o
n
e
s
.
Si
n
ce
t
h
is
p
r
o
p
o
s
ed
tech
n
iq
u
e
i
s
f
lex
ib
le,
th
u
s
it
p
r
o
d
u
ce
d
a
s
tr
ateg
ic
p
lan
n
i
n
g
in
v
es
t
m
e
n
t
to
s
o
lv
e
DSEP
p
r
o
b
lem
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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n
d
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J
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C
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p
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N:
2502
-
4752
A
Mo
d
ified
B
o
ltz
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fo
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it
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199
I
n
o
r
d
er
to
d
e
m
o
n
s
tr
ate
t
h
e
e
f
f
icien
c
y
o
f
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e,
a
co
m
p
ar
is
o
n
w
as
m
ad
e
f
o
r
t
h
e
co
m
p
u
tatio
n
al
ti
m
e
b
et
w
ee
n
c
o
n
v
e
n
tio
n
al
B
M
an
d
Mo
d
if
ied
B
M
as
tab
u
lated
in
T
a
b
le
4
.
0
.
B
ased
o
n
th
e
tab
le
,
th
e
co
n
v
e
n
tio
n
al
B
M
co
n
s
u
m
e
d
7
.
2
1
s
co
m
p
ar
ed
to
6
.
4
2
s
f
o
r
th
e
M
o
d
if
ied
B
M
w
h
e
n
th
e
n
u
m
b
er
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ta
te
eq
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al
to
1
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A
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th
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m
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er
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w
a
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6
0
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th
e
Mo
d
if
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B
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co
n
s
u
m
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o
n
l
y
w
h
ile
co
n
v
en
t
io
n
al
B
M
n
ee
d
ed
4
3
.
4
1
s
to
co
m
p
lete
t
h
e
tas
k
.
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h
e
n
t
h
e
n
u
m
b
er
o
f
s
tate
w
as
6
4
0
,
th
e
co
n
v
en
t
i
o
n
al
B
M
co
n
s
u
m
ed
2
2
3
.
0
1
s
m
e
an
w
h
i
le
Mo
d
if
ied
B
M
co
n
s
u
m
ed
les
s
t
h
a
n
a
m
i
n
u
te
w
h
ic
h
i
s
3
9
.
9
6
s
o
n
l
y
.
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th
a
t,
t
h
e
co
n
v
e
n
tio
n
al
B
M
r
eq
u
ir
ed
ti
m
e
-
co
n
s
u
m
i
n
g
co
m
p
ar
ed
to
Mo
d
if
ied
B
M.
T
h
is
is
d
u
e
t
o
th
e
Mo
d
if
ied
B
M
d
elete
d
u
s
ele
s
s
u
n
it
s
d
u
r
i
n
g
t
h
e
r
estru
ct
u
r
i
n
g
s
tep
.
B
y
co
n
tr
a
s
t,
a
co
n
v
e
n
tio
n
a
l
B
M
co
m
p
u
t
es
all
u
n
it
s
u
n
t
il
t
h
e
ter
m
i
n
atio
n
co
n
d
itio
n
is
r
ea
c
h
ed
.
C
o
m
p
ar
i
n
g
co
m
p
u
ti
n
g
ef
f
icien
c
y
,
th
e
Mo
d
if
ied
B
M
is
m
o
r
e
e
f
f
icien
t,
esp
ec
iall
y
w
h
en
t
h
e
in
itial
n
u
m
b
er
o
f
u
n
it
is
lar
g
e.
T
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
p
r
o
v
id
es
a
m
o
r
e
ef
f
ec
ti
v
e
s
elec
tio
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by
u
s
in
g
t
h
e
Ho
p
f
ield
n
et
w
o
r
k
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t
h
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u
p
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y
er
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h
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o
s
e
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li
m
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ted
n
u
m
b
er
o
f
u
n
it,
a
n
d
th
e
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M
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n
t
h
e
lo
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er
to
d
ec
id
e
th
e
o
p
tim
a
l so
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u
tio
n
f
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m
t
h
e
li
m
ited
n
u
m
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er
o
f
u
n
it selecte
d
b
y
t
h
e
u
p
p
er
la
y
er
.
4.
CO
NCLU
SI
O
N
AND
F
U
T
U
RE
RE
CO
M
M
E
NDATI
O
N
I
n
t
h
is
p
ap
er
,
th
e
Mo
d
i
f
i
ed
B
M
ca
n
d
ea
l
w
ell
with
DSEP
p
r
o
b
lem
t
h
at
c
o
n
s
is
ts
o
f
m
i
x
ed
-
i
n
te
g
er
p
r
o
g
r
a
m
m
in
g
p
r
o
b
lem
.
B
y
f
o
llo
w
in
g
t
h
e
i
n
v
est
m
e
n
t
e
x
p
en
s
es
r
ate
f
o
r
ea
c
h
s
tate
a
s
p
r
o
p
o
s
ed
,
th
e
co
s
t
s
av
in
g
ca
n
b
e
in
cr
ea
s
ed
.
T
h
e
r
esu
lts
al
s
o
s
h
o
w
th
at
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
f
o
r
i
n
c
o
r
p
o
r
atin
g
s
tr
u
ct
u
r
al
lear
n
in
g
i
n
to
B
M
is
ef
f
ec
ti
v
e
a
n
d
ca
n
en
h
a
n
ce
th
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
.
Oth
er
t
h
an
t
h
at,
t
h
e
ap
p
r
o
p
r
iat
en
es
s
o
f
Mo
d
if
ied
B
M
h
as b
ee
n
v
er
i
f
ied
as t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
ab
le
to
s
o
lv
e
th
e
p
r
o
b
lem
o
f
c
h
o
o
s
in
g
t
h
e
m
o
s
t
p
o
te
n
tial
s
tat
es
to
r
ec
eiv
e
t
h
e
i
n
v
e
s
t
m
en
t.
A
s
t
h
e
r
e
s
u
l
t,
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
ca
n
s
u
cc
ess
f
u
l
l
y
d
eter
m
i
n
e
t
h
e
o
p
ti
m
al
s
o
lu
tio
n
s
f
o
r
ea
ch
s
tated
at
d
if
f
er
e
n
t
K
.
T
h
e
s
i
m
u
lat
io
n
r
es
u
lts
al
s
o
s
h
o
w
t
h
at
th
er
e
i
s
s
i
g
n
if
ica
n
t d
ec
r
ea
s
e
in
co
m
p
u
ta
tio
n
al
ti
m
e
co
m
p
ar
ed
to
o
r
ig
in
al
B
M.
I
n
f
u
t
u
r
e,
s
e
v
er
al
f
u
r
t
h
er
w
o
r
k
s
co
u
ld
b
e
e
x
p
lo
r
ed
to
en
h
an
ce
th
e
e
f
f
ec
ti
v
en
e
s
s
o
f
t
h
e
p
r
o
p
o
s
e
d
tech
n
iq
u
e.
R
ec
en
tl
y
,
d
ata
is
b
ein
g
g
e
n
er
ated
i
n
lar
g
e
a
m
o
u
n
t
w
i
th
v
ar
y
i
n
g
n
u
m
b
er
o
f
q
u
alit
y
,
h
e
n
ce
t
h
e
ter
m
o
f
b
ig
d
ata
w
a
s
u
s
ed
.
No
w
ad
a
y
s
,
b
ig
d
ata
h
a
s
s
tar
ted
to
af
f
ec
t
t
h
e
liv
e
s
o
f
m
o
d
er
n
d
a
y
i
n
al
m
o
s
t
ev
er
y
ar
ea
,
w
h
et
h
er
en
g
in
ee
r
i
n
g
,
i
n
v
e
s
t
m
en
t,
b
u
s
i
n
es
s
,
ed
u
ca
tio
n
o
r
h
e
alth
ca
r
e.
Sin
ce
t
h
e
p
r
o
p
o
s
ed
t
ec
h
n
iq
u
e
ca
n
y
ield
th
e
o
p
ti
m
u
m
s
o
l
u
tio
n
f
o
r
lar
g
e
u
n
it
n
u
m
b
er
w
i
th
f
as
t
co
m
p
u
tat
io
n
a
l
ti
m
e,
t
h
u
s
t
h
e
M
o
d
if
ied
B
M
is
h
i
g
h
l
y
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[1
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G
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o
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M
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e
t
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Re
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o
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[2
]
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ö
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R
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irez
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o
IJ.
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v
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ti
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lan
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m
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[4
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[5
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ire
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:
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I
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J
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n
g
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o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2018
:
1
9
3
–
200
200
[1
3
]
F
a
lag
h
i
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h
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t
J
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tr
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0
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1
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4
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7
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[1
4
]
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o
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rra
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0
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1
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[1
5
]
P
o
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rleta
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Ra
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[1
7
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a
ss
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m
m
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[1
9
]
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1
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2
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[2
3
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0
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5
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–
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6
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.
Evaluation Warning : The document was created with Spire.PDF for Python.