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20
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K
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CC B
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C
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p
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A
uth
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r
:
Sih
an
a
Dep
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tm
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t o
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Nu
clea
r
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n
g
in
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r
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g
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Ph
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ics,
Facu
lty
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r
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g
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Un
i
v
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s
itas
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alan
Gr
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ik
a
No
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2
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Sen
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o
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d
u
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Mla
ti,
Slem
an
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Dae
r
ah
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s
tim
ewa
Yo
g
y
a
k
ar
ta,
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n
d
o
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esia 5
5
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m
ail: sih
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ac
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id
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I
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RO
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in
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lin
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cr
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[
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[
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[
5
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[
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W
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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C
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m
p
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g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
7
7
-
487
478
R
ec
en
t
ad
v
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ce
s
in
m
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l
ea
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[
1
2
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Su
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f
a
th
er
m
io
n
ic
Pier
ce
-
t
y
p
e
elec
tr
o
n
g
u
n
[
1
9
]
.
Ou
r
s
tu
d
y
ad
d
r
ess
es
th
is
g
ap
b
y
p
r
o
p
o
s
in
g
a
s
u
r
r
o
g
ate
m
o
d
el
d
ed
icate
d
to
p
r
ed
ictin
g
k
ey
p
er
f
o
r
m
a
n
ce
p
ar
a
m
eter
s
b
ea
m
cu
r
r
en
t
a
n
d
p
er
v
ea
n
ce
b
ased
o
n
g
e
o
m
etr
ic
d
esig
n
in
p
u
ts
.
T
h
is
s
p
ec
if
ic
f
o
cu
s
is
cr
itical,
as
th
e
in
itia
l
b
ea
m
p
ar
am
eter
s
s
et
b
y
th
e
g
u
n
f
u
n
d
a
m
en
tally
in
f
lu
en
ce
th
e
en
tire
ac
ce
ler
ato
r
ch
ain
.
B
y
tr
ain
in
g
th
e
m
o
d
el
o
n
a
co
m
p
r
eh
en
s
iv
e
d
ataset
g
en
e
r
ated
f
r
o
m
C
ST
s
im
u
latio
n
s
,
we
en
ab
le
r
ap
id
ev
alu
atio
n
o
f
k
ey
p
er
f
o
r
m
a
n
ce
m
etr
ics
s
u
ch
as
b
ea
m
cu
r
r
en
t
a
n
d
p
er
v
ea
n
ce
.
Ou
r
ap
p
r
o
ac
h
n
o
t
o
n
ly
ac
ce
ler
ates
th
e
d
esig
n
p
r
o
ce
s
s
b
u
t
also
o
p
en
s
p
ath
wa
y
s
to
war
d
m
o
r
e
s
o
p
h
is
ticated
,
r
ea
l
-
tim
e,
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
s
in
f
u
t
u
r
e
lin
ac
d
ev
elo
p
m
en
ts
.
T
h
is
p
ap
er
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
Sectio
n
2
d
escr
ib
es
t
h
e
m
eth
o
d
o
l
o
g
y
in
clu
d
in
g
el
ec
tr
o
n
g
u
n
m
o
d
elin
g
,
d
ataset
g
en
er
atio
n
,
an
d
s
u
r
r
o
g
ate
m
o
d
el
d
ev
elo
p
m
en
t;
Sectio
n
3
p
r
esen
ts
s
i
m
u
latio
n
r
esu
lts
an
d
m
o
d
el
v
alid
atio
n
; a
n
d
Sectio
n
4
d
is
cu
s
s
es th
e
co
n
clu
s
io
n
s
an
d
p
o
ten
tial f
u
tu
r
e
r
esear
c
h
d
ir
e
ctio
n
s
.
2.
M
E
T
H
O
D
T
h
is
s
tu
d
y
em
p
lo
y
e
d
a
two
-
s
t
ag
e
m
eth
o
d
o
lo
g
ical
f
r
am
ewo
r
k
co
m
b
in
i
n
g
p
h
y
s
ics
-
b
ased
s
im
u
latio
n
s
an
d
m
ac
h
in
e
lear
n
in
g
.
I
n
th
e
f
ir
s
t
s
tag
e,
a
Pier
ce
-
ty
p
e
elec
t
r
o
n
g
u
n
was
m
o
d
elled
an
d
s
i
m
u
lated
u
s
in
g
C
ST
Stu
d
io
Su
ite
to
g
en
er
ate
a
co
m
p
r
eh
en
s
iv
e
d
ataset
ac
r
o
s
s
a
wid
e
r
an
g
e
o
f
d
esig
n
p
ar
am
et
er
s
to
b
e
i
n
s
er
ted
in
lear
n
in
g
m
ac
h
in
e.
I
n
th
e
s
ec
o
n
d
s
tag
e,
a
n
e
u
r
al
n
etwo
r
k
-
b
a
s
ed
s
u
r
r
o
g
ate
m
o
d
el
was
d
e
v
elo
p
ed
a
n
d
tr
ain
ed
u
s
in
g
s
im
u
latio
n
d
ata
to
en
ab
l
e
r
ap
id
p
er
f
o
r
m
a
n
ce
p
r
e
d
ictio
n
an
d
d
esig
n
o
p
tim
izatio
n
.
T
h
e
d
etailed
s
tep
s
ar
e
p
r
esen
ted
in
th
e
f
o
llo
win
g
s
u
b
s
ec
tio
n
s
.
2
.
1
.
E
lect
ro
n g
un
des
ig
n a
nd
CST
s
im
ula
t
io
ns
T
h
e
d
e
s
i
g
n
p
h
a
s
e
c
o
m
m
en
ce
d
w
i
th
th
e
cr
e
a
t
io
n
o
f
a
th
r
e
e
-
d
im
e
n
s
i
o
n
a
l
(
3
D)
c
o
m
p
u
t
er
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a
id
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d
d
e
s
i
g
n
(
C
A
D
)
m
o
d
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l
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h
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d
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s
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g
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p
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r
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e
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f
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P
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e
r
ce
-
ty
p
e
el
e
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t
r
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n
g
u
n
a
s
d
e
p
i
ct
e
d
i
n
F
i
g
u
r
e
1
.
T
h
i
s
d
e
s
i
g
n
i
n
c
lu
d
e
s
a
t
h
e
r
m
io
n
ic
c
a
t
h
o
d
e,
a
ca
r
ef
u
l
ly
s
h
ap
ed
P
i
e
r
c
e
e
l
ec
t
r
o
d
e
,
a
n
d
an
an
o
d
e
w
i
t
h
a
f
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cu
s
i
n
g
n
o
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e
.
E
a
ch
g
eo
m
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tr
i
c
c
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m
p
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n
t
w
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to
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in
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w
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f
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c
u
r
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th
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n
te
n
d
e
d
6
M
e
V
l
i
n
a
c
ap
p
l
ic
a
t
i
o
n
.
T
h
e
p
r
o
p
o
s
ed
e
l
e
c
tr
o
n
g
u
n
wa
s
d
e
s
i
g
n
e
d
a
cc
o
r
d
in
g
to
t
h
e
f
o
l
l
o
w
in
g
d
e
s
i
g
n
p
ar
a
m
e
t
e
r
s
,
n
am
e
l
y
:
0
.
2
5
A
o
f
c
u
r
r
e
n
t
b
e
am
,
4
.
5
m
m
o
f
b
ea
m
d
ia
m
e
t
er
,
e
m
i
t
ta
n
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e
i
s
l
e
s
s
t
h
a
n
1
0
-
5
m
m
.
r
a
d
an
d
p
e
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v
e
a
n
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e
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s
l
e
s
s
t
h
a
n
1
0
-
7
A/
V
3/2
.
T
h
e
s
ch
e
m
e
o
f
e
l
e
c
t
r
o
n
g
u
n
d
e
s
i
g
n
p
r
o
ce
s
s
i
s
d
ep
i
c
t
ed
in
F
i
g
u
r
e
2
.
T
h
i
s
s
c
h
e
m
e
s
h
o
w
s
s
t
e
p
b
y
s
t
e
p
t
o
d
e
s
i
g
n
th
e
e
l
e
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t
r
o
n
g
u
n
.
T
o
a
c
cu
r
a
t
e
ly
s
i
m
u
l
a
t
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th
e
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l
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c
t
r
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c
f
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el
d
s
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n
d
c
h
ar
g
ed
p
a
r
t
i
c
l
e
d
y
n
a
m
i
c
s
w
i
t
h
in
t
h
e
g
u
n
,
C
S
T
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t
u
d
i
o
Su
i
t
e
(
v
e
r
s
io
n
2
0
2
2
)
w
a
s
e
m
p
l
o
y
ed
[
2
0
]
.
C
S
T
c
o
m
b
in
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s
t
h
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f
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n
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t
m
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d
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tr
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c
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f
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f
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s
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u
l
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t
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p
ar
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n
a
n
d
s
p
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c
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c
h
a
r
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e
f
f
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c
t
s
[
2
1
]
.
T
h
e
E
le
c
t
r
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s
t
a
t
i
c
S
o
lv
er
m
o
d
u
l
e
c
al
c
u
l
a
t
ed
th
e
s
t
a
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c
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c
t
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c
f
i
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ld
d
i
s
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r
i
b
u
t
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o
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r
i
s
in
g
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r
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m
a
p
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c
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u
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e
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ly
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m
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t
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a
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20
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d
el
o
f
p
ier
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-
ty
p
e
elec
tr
o
n
g
u
n
Fig
u
r
e
2
.
Sch
em
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o
f
elec
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o
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g
u
n
d
esig
n
p
r
o
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s
s
I
n
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r
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er
to
s
im
u
latio
n
ca
n
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e
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n
d
u
cte
d
well
an
d
f
ast,
we
u
s
ed
th
e
wo
r
k
s
tatio
n
co
m
p
u
ter
with
tech
n
ical
s
p
ec
if
icatio
n
s
:
Sy
s
tem
m
an
u
f
ac
tu
r
e
r
:
Dell,
I
n
c
.
;
Sy
s
tem
m
o
d
el:
Pre
cisi
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n
5
8
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;
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Mic
r
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in
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ws
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Pro
f
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r
k
s
tatio
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s
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tel
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r
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3
1
.
8
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
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7
0
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I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
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20
2
6
:
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-
487
480
2
.
2
.
Dev
el
o
pm
ent
o
f
t
he
neura
l net
wo
rk
s
urro
g
a
t
e
mo
del
Fo
llo
win
g
d
ata
co
llectio
n
,
a
f
u
lly
co
n
n
ec
ted
f
ee
d
f
o
r
war
d
n
eu
r
al
n
etwo
r
k
(
FNN)
was
co
n
s
tr
u
cted
to
s
er
v
e
as
a
co
m
p
u
tatio
n
ally
ef
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icien
t
s
u
r
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g
ate
m
o
d
el.
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h
e
ar
ch
itectu
r
e
co
n
s
is
ted
o
f
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in
p
u
t
lay
er
co
r
r
esp
o
n
d
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g
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th
e
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o
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r
d
esig
n
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ar
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eter
s
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r
ee
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id
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e
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lay
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with
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4
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n
d
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e
u
r
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s
r
esp
ec
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ely
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d
an
o
u
tp
u
t
lay
er
p
r
o
d
u
cin
g
two
tar
g
et
v
alu
es:
b
ea
m
c
u
r
r
en
t
an
d
p
er
v
ea
n
ce
.
T
h
e
s
c
h
em
e
o
f
s
u
r
r
o
g
ate
m
o
d
el
is
s
h
o
wn
in
Fig
u
r
e
3
.
E
ac
h
h
id
d
en
la
y
er
u
tili
ze
d
th
e
r
ec
tifie
d
lin
ea
r
u
n
it
(
R
eL
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ac
tiv
atio
n
f
u
n
ctio
n
,
wh
ich
p
r
o
m
o
tes
s
p
ar
s
ity
an
d
m
itig
ates
th
e
v
an
is
h
in
g
g
r
ad
ien
t
p
r
o
b
lem
co
m
m
o
n
ly
en
co
u
n
ter
e
d
in
d
ee
p
n
eu
r
al
n
etwo
r
k
s
[
2
2
]
.
Me
an
s
q
u
ar
ed
er
r
o
r
(
MSE
)
was
em
p
lo
y
ed
as
th
e
lo
s
s
f
u
n
ctio
n
to
q
u
an
tif
y
th
e
d
ev
iatio
n
b
etwe
en
p
r
e
d
icted
an
d
tr
u
e
o
u
tp
u
t v
alu
es,
wh
ile
n
etwo
r
k
weig
h
ts
wer
e
u
p
d
ated
u
s
in
g
th
e
Ad
am
o
p
tim
izatio
n
alg
o
r
ith
m
[
2
3
]
.
T
r
ain
in
g
was
co
n
d
u
cted
o
v
er
2
0
,
0
0
0
ep
o
c
h
s
with
a
lear
n
in
g
r
ate
o
f
0
.
0
0
0
2
,
a
ch
o
ice
m
ad
e
b
ased
o
n
p
r
elim
in
ar
y
c
o
n
v
er
g
en
ce
s
tu
d
i
es
to
b
alan
ce
s
p
ee
d
an
d
m
o
d
el
s
tab
ilit
y
.
I
n
p
u
t
an
d
o
u
tp
u
t
d
at
a
wer
e
n
o
r
m
alize
d
to
ze
r
o
m
ea
n
a
n
d
u
n
it
v
ar
ian
ce
p
r
io
r
to
tr
ain
i
n
g
to
en
h
an
c
e
lear
n
in
g
d
y
n
am
ics
an
d
ac
ce
ler
ate
co
n
v
er
g
en
ce
[
2
2
]
.
T
h
e
d
ataset
was
s
p
lit
wi
th
an
8
0
/2
0
r
atio
b
etwe
en
tr
ai
n
in
g
a
n
d
v
alid
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n
s
ets
to
as
s
ess
g
en
er
aliza
tio
n
p
er
f
o
r
m
an
ce
an
d
d
etec
t p
o
ten
t
ial
o
v
er
f
itti
n
g
.
Fig
u
r
e
3
.
Sch
em
e
o
f
s
u
r
r
o
g
ate
m
o
d
el
2
.
3
.
Co
m
pu
t
a
t
io
na
l r
eso
urc
es a
nd
cha
lleng
es
T
h
e
h
ig
h
co
m
p
u
tatio
n
al
c
o
s
t
o
f
g
e
n
er
atin
g
th
e
s
im
u
latio
n
d
atab
ase
was
ad
d
r
ess
ed
b
y
le
v
er
ag
in
g
a
h
ig
h
-
p
e
r
f
o
r
m
an
ce
co
m
p
u
tin
g
(
HPC
)
clu
s
ter
eq
u
ip
p
ed
with
lar
g
e
m
em
o
r
y
n
o
d
es
to
h
an
d
le
th
e
m
em
o
r
y
-
in
ten
s
iv
e
FEM
-
PIC si
m
u
latio
n
s
.
Neu
r
al
n
etwo
r
k
tr
ain
in
g
w
as p
er
f
o
r
m
ed
o
n
a
d
e
d
icate
d
wo
r
k
s
tatio
n
eq
u
ip
p
ed
with
GPU
ac
ce
ler
atio
n
(
NVI
DI
A
R
T
X
s
er
ies),
r
ed
u
cin
g
tr
ain
in
g
tim
e
b
y
ap
p
r
o
x
im
ately
an
o
r
d
er
o
f
m
ag
n
itu
d
e
c
o
m
p
ar
e
d
to
C
PU
-
o
n
ly
tr
ain
in
g
.
Desp
ite
th
e
u
p
f
r
o
n
t
in
v
estme
n
t
in
s
im
u
latio
n
tim
e
a
n
d
d
a
ta
s
to
r
ag
e,
th
e
tr
ain
e
d
s
u
r
r
o
g
ate
m
o
d
el
d
r
am
atica
lly
r
ed
u
ce
s
th
e
c
o
m
p
u
tatio
n
al
co
s
t
f
o
r
f
u
tu
r
e
d
esi
g
n
ev
al
u
atio
n
s
.
Pre
d
ictio
n
s
f
o
r
n
ew
co
n
f
ig
u
r
atio
n
s
ca
n
b
e
o
b
tain
ed
in
m
illi
s
ec
o
n
d
s
,
f
ac
ilit
atin
g
r
ea
l
-
tim
e
ex
p
lo
r
atio
n
o
f
th
e
d
esig
n
p
ar
a
m
eter
s
p
ac
e
an
d
e
n
ab
lin
g
r
ap
id
o
p
tim
izatio
n
cy
cles th
at
wer
e
p
r
ev
io
u
s
ly
in
f
ea
s
ib
le
with
tr
ad
itio
n
al
m
et
h
o
d
s
.
2
.
4
.
Co
m
pu
t
a
t
io
na
l r
eso
urc
es a
nd
co
ns
idera
t
io
ns
Du
e
to
th
e
h
ig
h
co
m
p
u
tatio
n
al
d
em
an
d
s
o
f
b
o
th
C
ST
s
im
u
latio
n
an
d
n
eu
r
al
n
etwo
r
k
tr
ain
in
g
,
s
im
u
latio
n
s
wer
e
co
n
d
u
cted
o
n
a
h
ig
h
-
p
er
f
o
r
m
an
ce
co
m
p
u
t
in
g
clu
s
ter
.
T
h
e
lar
g
e
m
em
o
r
y
f
o
o
tp
r
in
t
r
eq
u
ir
e
d
f
o
r
C
ST
p
ar
am
eter
s
wee
p
s
(
a
p
p
r
o
x
im
ately
6
0
0
GB
)
n
ec
ess
itated
ef
f
icien
t
d
ata
m
an
ag
e
m
en
t
p
r
ac
tices.
Fo
r
n
eu
r
al
n
etwo
r
k
tr
ain
in
g
,
tr
ai
n
in
g
tim
e
an
d
co
n
v
er
g
e
n
ce
wer
e
o
p
tim
ized
b
y
tu
n
in
g
th
e
lear
n
in
g
r
ate
a
n
d
n
u
m
b
er
o
f
e
p
o
ch
s
,
u
ltima
tely
ac
h
iev
in
g
an
ac
c
u
r
ate
an
d
ef
f
icien
t
s
u
r
r
o
g
ate
m
o
d
el
c
ap
ab
le
o
f
r
ep
lacin
g
tr
ad
itio
n
al
f
u
ll
-
s
ca
le
C
ST
s
im
u
latio
n
s
f
o
r
s
u
b
s
eq
u
en
t d
esig
n
iter
atio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
Desig
n
o
f a
th
ermio
n
ic
elec
tr
o
n
g
u
n
o
f
6
MeV
lin
a
c
b
y
u
s
in
g
n
eu
r
a
l n
etw
o
r
k
b
a
s
ed
…
(
E
l
in
N
u
r
a
in
i
)
481
3.
RE
SU
L
T
AND
DI
SCUS
SI
O
N
3
.
1
.
E
lect
ric
f
ield a
nd
elec
t
ro
n be
a
m
pa
t
h si
m
ula
t
io
n
E
l
e
c
t
r
i
c
f
i
el
d
a
n
d
e
l
e
c
t
r
o
n
b
e
a
m
p
a
t
h
o
f
e
l
e
c
t
r
o
n
g
u
n
w
e
r
e
s
i
m
u
l
a
t
e
d
u
s
i
n
g
C
S
T
s
o
f
t
w
a
r
e
a
s
d
e
p
i
c
t
e
d
i
n
F
i
g
u
r
e
s
4
a
n
d
5
r
es
p
e
c
t
i
v
el
y
.
A
s
s
h
o
w
n
i
n
t
h
es
e
f
i
g
u
r
e
s
,
t
h
e
r
e
s
u
l
ts
w
e
r
e
i
n
a
c
c
o
r
d
a
n
c
e
wi
th
d
e
s
i
g
n
p
a
r
a
m
e
t
e
r
s
.
T
h
e
o
p
t
i
m
u
m
r
e
s
u
lt
s
w
e
r
e
o
b
t
ai
n
e
d
a
s
f
o
ll
o
w
s
i
n
T
a
b
l
e
2
.
I
n
o
r
d
e
r
t
o
s
i
m
u
l
at
i
o
n
c
a
n
b
e
c
o
n
d
u
c
t
e
d
w
e
ll
a
n
d
f
a
s
t
,
w
e
u
s
e
d
t
h
e
w
o
r
k
s
t
a
ti
o
n
c
o
m
p
u
t
e
r
w
i
t
h
t
e
c
h
n
i
c
a
l
s
p
ec
i
f
ic
a
t
i
o
n
s
:
S
y
s
te
m
m
a
n
u
f
a
c
t
u
r
e
r
:
D
e
l
l
,
I
n
c
.
;
S
y
s
t
e
m
m
o
d
e
l
:
P
r
e
c
is
i
o
n
5
8
2
0
T
o
w
e
r
;
O
S
M
i
c
r
o
s
o
f
t
W
i
n
d
o
w
s
1
1
P
r
o
f
o
r
W
o
r
k
s
t
a
t
i
o
n
s
;
P
r
o
c
ess
o
r
I
n
t
e
l
(
R
)
X
e
o
n
(
R
)
W
-
2223
C
PU@
3
.
6
0
G
H
z
,
4
C
o
r
e
,
8
L
o
g
i
c
a
l
P
r
o
c
es
s
o
r
s
;
a
n
d
R
A
M
6
4
GB
.
Fig
u
r
e
4
.
E
lectr
ic
f
ield
s
im
u
latio
n
Fig
u
r
e
5
.
E
lectr
o
n
b
ea
m
p
ath
T
ab
le
2
.
O
p
t
i
m
u
m
d
e
s
i
g
n
o
f
e
l
e
c
t
r
o
n
g
u
n
P
a
r
a
me
t
e
r
s
V
a
l
u
e
U
n
i
t
B
e
a
m
c
u
r
r
e
n
t
0
.
5
1
A
P
e
r
v
e
a
n
c
e
9
.
8
0
×
1
0
−8
/
3
/
2
e
mi
t
t
a
n
c
e
(
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
7
7
-
487
482
3
.
2
.
Neura
l net
wo
r
k
t
r
a
ini
ng
perf
o
rma
nce
T
h
e
tr
ain
in
g
p
r
o
ce
s
s
o
f
th
e
s
u
r
r
o
g
ate
m
o
d
el
was
m
o
n
ito
r
e
d
u
s
in
g
b
o
th
tr
ain
in
g
an
d
v
ali
d
atio
n
lo
s
s
cu
r
v
es.
As
s
h
o
wn
in
Fig
u
r
e
6
,
th
e
m
o
d
el
ac
h
iev
e
d
co
n
v
er
g
en
ce
with
in
th
e
f
ir
s
t
f
ew
th
o
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s
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ep
o
ch
s
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d
b
o
th
lo
s
s
v
alu
es
r
em
ain
ed
s
tab
le
with
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o
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ig
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o
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iv
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g
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ce
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r
o
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tire
2
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0
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ep
o
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h
tr
ain
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n
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ch
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le.
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h
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f
in
al
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ain
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g
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ea
ch
ed
2
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1
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h
ile
th
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v
alid
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lo
s
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ettle
d
at
3
.
9
5
6
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×
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⁻⁷
,
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d
icatin
g
ex
ce
llen
t g
en
er
aliza
tio
n
p
er
f
o
r
m
an
ce
.
T
h
e
clo
s
en
e
s
s
o
f
tr
ain
in
g
an
d
v
alid
atio
n
c
u
r
v
es
s
u
g
g
ests
th
at
o
v
er
f
itti
n
g
was su
cc
ess
f
u
lly
m
itig
ated
[
2
4
]
.
T
h
e
lo
w
MSE
v
alu
es
r
ef
lect
t
h
e
m
o
d
el’
s
ab
ilit
y
to
ac
c
u
r
ate
ly
lear
n
th
e
m
a
p
p
in
g
b
etwe
en
g
eo
m
etr
ic
d
esig
n
p
ar
am
eter
s
an
d
o
u
tp
u
t
b
ea
m
m
etr
ics.
T
h
ese
m
et
r
ics
in
clu
d
e
b
ea
m
cu
r
r
en
t
a
n
d
p
er
v
ea
n
ce
,
two
p
ar
am
eter
s
th
at
ar
e
h
ig
h
l
y
s
en
s
itiv
e
to
elec
tr
o
n
g
u
n
g
eo
m
etr
y
an
d
elec
tr
ic
f
ield
d
is
tr
ib
u
tio
n
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
m
o
d
el
co
n
f
ir
m
s
t
h
at
ev
en
r
elativ
ely
s
h
allo
w
n
e
u
r
al
n
etwo
r
k
s
,
wh
en
p
r
o
p
er
l
y
c
o
n
f
i
g
u
r
ed
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d
tr
ain
e
d
o
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h
ig
h
-
f
i
d
elity
s
im
u
latio
n
d
ata,
ca
n
p
r
o
v
id
e
h
i
g
h
ly
r
eliab
le
p
r
e
d
ictio
n
s
f
o
r
c
o
m
p
lex
p
h
y
s
ical
s
y
s
tem
s
[
2
5
]
,
[
2
6
]
.
Fig
u
r
e
6
.
T
r
ain
in
g
p
er
f
o
r
m
an
c
e
o
f
n
e
u
r
al
n
etwo
r
k
s
u
r
r
o
g
ate
m
o
d
el
3
.
3
.
P
re
dict
io
n
a
cc
ura
cy
o
f
bea
m
pa
ra
m
et
er
s
Fig
u
r
e
7
p
r
esen
ts
a
s
ca
tter
p
lo
t
co
m
p
a
r
in
g
th
e
p
r
ed
icte
d
b
ea
m
cu
r
r
en
t
a
g
ain
s
t
th
e
g
r
o
u
n
d
t
r
u
th
v
alu
es
o
b
tain
ed
f
r
o
m
C
ST
s
im
u
lati
o
n
s
.
T
h
e
s
tr
o
n
g
lin
ea
r
alig
n
m
en
t
o
f
p
o
in
ts
alo
n
g
th
e
d
i
ag
o
n
al
lin
e
(
y
=
x
)
d
em
o
n
s
tr
ates
a
h
ig
h
d
eg
r
ee
o
f
p
r
ed
ictio
n
ac
cu
r
ac
y
ac
r
o
s
s
t
h
e
f
u
ll
r
a
n
g
e
o
f
in
p
u
t
co
n
d
itio
n
s
[
2
5
]
,
[
2
6
]
.
T
h
is
ca
p
ab
ilit
y
is
p
ar
ticu
lar
ly
v
alu
ab
le
in
p
r
ac
tical
d
esig
n
wo
r
k
f
lo
ws,
wh
er
e
p
r
e
d
ictin
g
h
o
w
s
m
all
ch
an
g
es
in
g
eo
m
etr
y
i
n
f
lu
en
ce
th
e
b
ea
m
cu
r
r
en
t c
a
n
ac
ce
ler
ate
th
e
r
e
f
i
n
em
en
t p
r
o
ce
s
s
.
Similar
ly
,
Fig
u
r
e
8
s
h
o
ws
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
n
e
u
r
al
n
et
wo
r
k
in
p
r
ed
ictin
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b
ea
m
p
er
v
ea
n
ce
.
T
h
e
clo
s
e
ag
r
ee
m
en
t
b
etwe
en
p
r
ed
icted
an
d
ac
tu
al
v
al
u
es
f
u
r
th
e
r
s
u
p
p
o
r
ts
th
e
r
o
b
u
s
tn
ess
o
f
th
e
tr
ain
ed
s
u
r
r
o
g
ate
m
o
d
el
[
2
7
]
.
Acc
u
r
ate
p
r
ed
ict
io
n
o
f
p
e
r
v
ea
n
ce
is
cr
u
cial
i
n
elec
tr
o
n
g
u
n
d
esig
n
b
ec
au
s
e
it
ca
p
tu
r
es
th
e
r
elatio
n
s
h
ip
b
etwe
en
cu
r
r
en
t
an
d
ac
ce
ler
atin
g
v
o
ltag
e,
s
er
v
in
g
as
a
d
iag
n
o
s
tic
in
d
icato
r
f
o
r
s
p
ac
e
ch
ar
g
e
-
lim
ited
em
is
s
io
n
.
T
h
e
b
ea
m
cu
r
r
e
n
t,
em
ittan
ce
,
an
d
p
er
v
ea
n
ce
ar
e
cr
u
cial
p
er
f
o
r
m
an
ce
in
d
icato
r
s
th
at
d
ir
ec
tly
r
ef
lect
th
e
p
h
y
s
ical
b
e
h
av
io
r
o
f
t
h
e
el
ec
tr
o
n
g
u
n
.
B
ea
m
c
u
r
r
en
t
is
p
r
im
ar
ily
in
f
lu
en
c
ed
b
y
th
e
ca
t
h
o
d
e
-
an
o
d
e
v
o
ltag
e
an
d
th
e
em
is
s
io
n
ar
ea
,
wh
ich
ar
e
g
o
v
er
n
ed
b
y
elec
tr
o
d
e
g
eo
m
etr
y
.
A
h
ig
h
er
e
x
tr
ac
tio
n
v
o
ltag
e
an
d
lar
g
er
em
is
s
io
n
ar
ea
lead
to
in
cr
ea
s
ed
b
ea
m
cu
r
r
en
t.
E
m
ittan
ce
,
wh
ich
q
u
an
tifie
s
th
e
s
p
r
ea
d
o
f
th
e
b
ea
m
in
p
h
ase
s
p
ac
e,
is
af
f
ec
te
d
b
y
th
e
f
o
cu
s
in
g
p
r
o
p
e
r
ties
o
f
th
e
g
eo
m
etr
y
;
s
h
ar
p
cu
r
v
atu
r
e
o
r
ab
r
u
p
t
ch
an
g
es
in
f
ield
lin
es
ten
d
to
d
e
g
r
ad
e
em
ittan
ce
.
Me
an
wh
ile,
p
er
v
ea
n
ce
is
a
f
u
n
ctio
n
o
f
b
o
th
th
e
b
ea
m
cu
r
r
en
t
an
d
ex
tr
ac
tio
n
v
o
ltag
e,
s
er
v
in
g
as
a
m
ea
s
u
r
e
o
f
s
p
ac
e
-
c
h
ar
g
e
ef
f
ec
ts
.
B
y
an
aly
zin
g
h
o
w
th
ese
p
ar
am
eter
s
v
ar
y
with
g
eo
m
etr
y
i
n
p
u
ts
s
u
ch
as
el
ec
tr
o
d
e
an
g
le,
g
a
p
d
is
tan
ce
,
an
d
v
o
ltag
e,
we
p
r
o
v
i
d
e
a
d
ee
p
er
p
h
y
s
ical
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
d
esig
n
-
p
er
f
o
r
m
a
n
ce
r
elatio
n
s
h
ip
i
n
th
e
r
m
io
n
ic
elec
tr
o
n
g
u
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
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th
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483
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u
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C
u
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n
t p
r
e
d
ictio
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v
s
tr
u
th
v
alu
es
Fig
u
r
e
8
.
Per
v
ea
n
ce
p
r
ed
ictio
n
v
s
tr
u
e
v
alu
es
An
ad
d
itio
n
al
im
p
o
r
tan
t
o
u
tp
u
t,
b
ea
m
d
iam
eter
at
th
e
g
u
n
ex
it,
was
also
m
o
n
ito
r
ed
ac
r
o
s
s
co
n
f
ig
u
r
atio
n
s
.
Alth
o
u
g
h
n
o
t
u
s
ed
as
a
tr
ain
in
g
tar
g
et,
its
co
r
r
elatio
n
with
p
r
ed
icted
c
u
r
r
en
t
an
d
p
er
v
ea
n
ce
in
d
ir
ec
tly
v
alid
ates
th
e
p
h
y
s
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ca
l
co
n
s
is
ten
cy
o
f
th
e
m
o
d
el.
I
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t
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n
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ig
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,
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em
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m
p
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th
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Pier
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etr
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
7
7
-
487
484
3
.
4
.
So
urce
s
o
f
er
r
o
r
a
nd
mo
del lim
it
a
t
io
ns
Desp
ite
th
e
s
tr
o
n
g
o
v
er
all
p
er
f
o
r
m
a
n
ce
,
m
in
o
r
d
ev
iatio
n
s
wer
e
o
b
s
er
v
ed
in
a
f
ew
h
i
g
h
-
cu
r
r
en
t
co
n
f
ig
u
r
atio
n
s
,
wh
e
r
e
th
e
p
r
e
d
icted
v
alu
es
s
lig
h
tly
u
n
d
er
es
tim
ated
th
e
g
r
o
u
n
d
t
r
u
th
.
T
h
e
s
e
er
r
o
r
s
ar
e
lik
ely
d
u
e
to
s
p
ar
s
e
r
e
p
r
esen
tatio
n
o
f
s
u
ch
co
n
f
ig
u
r
atio
n
s
in
th
e
tr
ain
in
g
d
ataset.
I
n
c
r
ea
s
in
g
th
e
d
en
s
ity
o
f
s
am
p
les
in
h
ig
h
-
c
u
r
r
e
n
t r
eg
im
es c
o
u
ld
im
p
r
o
v
e
p
r
ed
ictio
n
ac
cu
r
ac
y
i
n
f
u
tu
r
e
s
tu
d
ies
[
2
8
]
.
Mo
r
eo
v
er
,
th
e
s
u
r
r
o
g
ate
m
o
d
e
l
is
lim
i
ted
to
th
e
p
ar
am
eter
r
an
g
es
s
ee
n
d
u
r
in
g
tr
ain
in
g
.
E
x
t
r
ap
o
latio
n
to
u
n
s
ee
n
r
e
g
io
n
s
o
f
th
e
d
esig
n
s
p
ac
e
m
ay
y
ield
u
n
r
elia
b
le
r
esu
lts
,
wh
ich
h
ig
h
lig
h
ts
th
e
im
p
o
r
tan
ce
o
f
th
o
u
g
h
t
f
u
l
d
ataset
co
n
s
tr
u
cti
o
n
.
I
n
te
g
r
atio
n
o
f
u
n
ce
r
tain
t
y
q
u
an
tific
atio
n
tec
h
n
iq
u
es
o
r
ac
tiv
e
lear
n
in
g
s
tr
ateg
ies co
u
ld
f
u
r
th
e
r
en
h
an
ce
m
o
d
el
r
eliab
ilit
y
,
p
ar
ticu
lar
ly
f
o
r
h
ig
h
-
r
is
k
o
p
er
ati
n
g
p
o
in
ts
.
3
.
5
.
Co
m
pu
t
a
t
io
na
l e
f
f
iciency
a
nd
pra
ct
ica
l im
pli
ca
t
io
ns
Fro
m
a
c
o
m
p
u
tatio
n
al
p
er
s
p
e
ctiv
e,
th
e
s
u
r
r
o
g
ate
m
o
d
el
s
ig
n
if
ican
tly
o
u
tp
er
f
o
r
m
s
tr
a
d
itio
n
al
FEM
-
PIC
s
im
u
latio
n
s
in
ter
m
s
o
f
ev
alu
atio
n
tim
e.
On
ce
tr
ain
ed
,
th
e
m
o
d
el
p
r
ed
icts
b
ea
m
c
h
ar
ac
ter
is
tics
f
o
r
n
ew
co
n
f
ig
u
r
atio
n
s
in
u
n
d
er
1
0
m
illi
s
ec
o
n
d
s
o
n
a
s
tan
d
ar
d
GPU,
co
m
p
ar
ed
t
o
h
o
u
r
s
o
f
p
r
o
ce
s
s
in
g
r
eq
u
ir
ed
b
y
C
ST
s
im
u
latio
n
s
.
T
h
is
s
p
ee
d
en
ab
les
r
ap
i
d
iter
ativ
e
d
esig
n
,
g
lo
b
al
s
en
s
itiv
ity
an
al
y
s
is
,
an
d
r
ea
l
-
tim
e
m
u
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
th
at
wo
u
ld
o
th
er
wis
e
b
e
i
n
f
ea
s
ib
le.
T
h
ese
r
esu
lts
u
n
d
er
s
co
r
e
th
e
v
alu
e
o
f
co
m
b
in
in
g
h
ig
h
-
f
i
d
elity
s
im
u
latio
n
to
o
ls
with
d
ata
-
d
r
iv
e
n
s
u
r
r
o
g
ate
m
o
d
els
in
ac
ce
ler
at
o
r
d
esig
n
.
W
h
ile
th
e
s
im
u
latio
n
p
r
o
ce
s
s
r
em
ain
s
ess
en
tial
f
o
r
in
itial
d
ataset
g
en
er
atio
n
,
th
e
tr
ain
ed
s
u
r
r
o
g
a
te
allo
ws f
o
r
f
ast d
esig
n
ex
p
lo
r
atio
n
an
d
d
ee
p
er
p
h
y
s
ical
in
s
i
g
h
t.
Similar
m
u
lti
-
o
b
jectiv
e
f
r
a
m
ewo
r
k
s
h
a
v
e
b
e
en
im
p
lem
en
te
d
s
u
cc
ess
f
u
lly
i
n
r
ec
en
t
s
u
r
r
o
g
ate
-
ass
is
ted
in
jecto
r
d
esig
n
s
[
2
9
]
,
an
d
th
eir
ef
f
icien
cy
h
as
b
ee
n
b
en
c
h
m
ar
k
e
d
in
r
ev
iew
s
t
u
d
ies
o
f
n
eu
r
al
-
b
ased
o
p
tim
izatio
n
in
b
ea
m
lin
e
co
m
p
o
n
en
ts
[
3
0
]
,
[
3
1
]
.
T
h
ese
f
i
n
d
i
n
g
s
a
r
e
b
u
ilt
u
p
o
n
p
r
e
v
i
o
u
s
r
ese
ar
ch
i
n
t
h
e
f
ie
ld
.
Fo
r
i
n
s
t
an
ce
,
A
h
m
a
d
i
an
n
a
m
i
n
et
a
l
.
[
3
2
]
d
e
m
o
n
s
tr
ate
d
t
h
e
c
h
al
le
n
g
es
o
f
b
al
a
n
ci
n
g
b
e
am
e
m
it
ta
n
c
e
a
n
d
c
u
r
r
e
n
t
in
t
h
e
r
m
i
o
n
ic
s
o
u
r
ce
s
u
s
i
n
g
s
em
i
-
an
al
y
ti
ca
l
m
et
h
o
d
s
s
u
c
h
as
t
h
e
Va
u
g
h
an
a
p
p
r
o
a
ch
,
w
h
i
ch
o
u
r
m
o
d
el
a
d
d
r
ess
es
w
it
h
a
u
t
o
m
ate
d
,
d
at
a
-
d
r
i
v
e
n
p
r
e
d
ic
ti
o
n
s
.
L
i
u
et
a
l.
[
3
3
]
f
o
c
u
s
e
d
o
n
b
ea
m
t
r
a
n
s
p
o
r
t t
u
n
in
g
u
s
i
n
g
s
u
r
r
o
g
ate
-
a
u
g
m
e
n
te
d
o
p
t
im
i
za
ti
o
n
(
AS
T
R
A
co
m
b
i
n
e
d
w
it
h
NSG
A
-
I
I
)
,
b
u
t
d
i
d
n
o
t
i
n
c
o
r
p
o
r
at
e
i
n
j
ec
t
o
r
-
le
v
el
s
u
r
r
o
g
ate
d
esi
g
n
f
o
r
ca
th
o
d
e
g
eo
m
e
tr
y
,
a
g
a
p
o
u
r
w
o
r
k
f
i
lls
.
Si
m
il
a
r
l
y
,
Ka
n
e
et
a
l.
[
1
1
]
ap
p
l
ie
d
n
e
u
r
al
n
et
w
o
r
k
s
t
o
p
r
e
d
ic
t
o
u
t
p
u
t
b
e
am
p
r
o
p
e
r
ti
es
f
r
o
m
l
ase
r
-
p
las
m
a
i
n
t
er
ac
ti
o
n
s
b
u
t
d
i
d
n
o
t
a
d
d
r
ess
in
itia
l
b
ea
m
q
u
al
it
y
p
ar
am
ete
r
s
s
u
ch
as
p
e
r
v
e
an
ce
,
wh
i
ch
a
r
e
ce
n
t
r
al
t
o
o
u
r
s
t
u
d
y
.
B
y
f
o
c
u
s
i
n
g
o
n
g
e
o
m
et
r
y
-
to
-
b
e
am
o
u
tp
u
t
m
a
p
p
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n
g
at
t
h
e
g
u
n
s
t
ag
e,
o
u
r
m
et
h
o
d
e
x
te
n
d
s
s
u
r
r
o
g
ate
m
o
d
eli
n
g
t
o
e
ar
l
ie
r
a
n
d
m
o
r
e
c
r
iti
ca
l
d
es
ig
n
s
ta
g
es
i
n
t
h
e
a
cc
e
ler
at
o
r
c
h
ai
n
.
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
s
u
cc
ess
f
u
lly
ap
p
li
ed
a
n
eu
r
al
n
etwo
r
k
s
u
r
r
o
g
ate
m
o
d
el
to
p
r
ed
ict
th
e
p
er
f
o
r
m
an
ce
o
f
a
Pier
ce
-
ty
p
e
elec
tr
o
n
g
u
n
u
s
in
g
d
ata
g
en
e
r
ated
f
r
o
m
C
ST
Stu
d
io
Su
ite
s
im
u
latio
n
s
.
T
h
e
s
u
r
r
o
g
ate
m
o
d
el
ac
cu
r
ately
p
r
ed
icted
k
e
y
b
ea
m
p
ar
am
eter
s
n
am
ely
b
ea
m
cu
r
r
en
t
an
d
p
er
v
ea
n
ce
a
n
d
d
em
o
n
s
tr
ated
r
ap
id
co
n
v
er
g
en
ce
d
u
r
in
g
tr
ain
in
g
.
T
h
ese
r
esu
lts
in
d
icate
s
tr
o
n
g
g
en
er
aliza
tio
n
p
er
f
o
r
m
an
ce
a
n
d
co
n
f
i
r
m
th
at
d
ata
-
d
r
iv
en
m
o
d
els
ca
n
ca
p
tu
r
e
th
e
ess
en
tial
p
h
y
s
ics
o
f
elec
tr
o
n
g
u
n
b
eh
av
io
r
.
T
h
e
ap
p
r
o
ac
h
s
ig
n
if
ican
tly
r
ed
u
ce
d
th
e
s
im
u
latio
n
tim
e
r
eq
u
ir
e
d
f
o
r
ea
ch
d
esig
n
ev
alu
atio
n
,
en
ab
lin
g
r
ea
l
-
tim
e
p
ar
am
etr
ic
ex
p
lo
r
atio
n
a
n
d
ac
ce
ler
atin
g
th
e
d
esig
n
iter
at
io
n
p
r
o
ce
s
s
.
T
h
is
ca
p
ab
ilit
y
is
p
ar
ticu
lar
ly
v
alu
ab
le
f
o
r
c
o
m
p
lex
ac
ce
ler
ato
r
s
y
s
tem
s
,
wh
er
e
co
m
p
u
tatio
n
a
l
co
s
t
o
f
ten
b
ec
o
m
es
a
lim
itin
g
f
ac
to
r
.
Fu
tu
r
e
d
ir
ec
tio
n
s
o
f
th
is
wo
r
k
in
cl
u
d
e
ex
p
an
d
i
n
g
t
h
e
s
u
r
r
o
g
ate
m
o
d
el
to
war
d
m
u
lti
-
o
b
jectiv
e
o
p
t
im
izatio
n
,
in
teg
r
atin
g
u
n
ce
r
ta
in
ty
q
u
an
tific
atio
n
,
an
d
in
co
r
p
o
r
atin
g
th
er
m
al
a
n
d
m
ec
h
an
ical
ef
f
ec
ts
to
in
cr
ea
s
e
r
o
b
u
s
tn
ess
.
T
h
ese
en
h
an
ce
m
en
ts
wo
u
ld
f
u
r
th
e
r
s
o
lid
if
y
th
e
r
o
le
o
f
m
ac
h
in
e
l
ea
r
n
in
g
-
b
ased
s
u
r
r
o
g
ate
m
o
d
e
lin
g
as
a
g
en
er
aliza
b
le
an
d
ef
f
icien
t
s
tr
ateg
y
f
o
r
d
esig
n
in
g
h
ig
h
-
p
er
f
o
r
m
a
n
ce
c
o
m
p
o
n
en
ts
in
m
o
d
er
n
p
ar
ticle
ac
ce
ler
ato
r
s
.
Ov
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all,
th
is
m
e
th
o
d
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,
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.
[
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Me
t
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3
0
]
I
.
K
a
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t
e
,
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M
a
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h
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f
o
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e
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ml
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,
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a
rXi
v
p
re
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ri
n
t
a
rXi
v
:
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3
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1
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0
7
5
1
9
,
p
p
.
1
–
9
,
2
0
2
3
.
[
3
1
]
J.
W
a
n
,
P
.
C
h
u
,
a
n
d
Y
.
J
i
a
o
,
“
N
e
u
r
a
l
n
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t
w
o
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k
-
b
a
se
d
m
u
l
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o
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j
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m
f
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n
l
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a
r
b
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a
m
d
y
n
a
m
i
c
s,
”
Ph
y
s
i
c
a
l
Re
v
i
e
w
Ac
c
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l
e
r
a
t
o
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d
Be
a
m
s
,
v
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l
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3
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p
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2
3
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0
8
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6
0
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.
[
3
2
]
S
.
A
h
ma
d
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mi
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.
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.
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.
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h
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.
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,
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.
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R
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.
Za
r
e
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,
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,
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I
PAC
2
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:
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p
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0
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.
[
3
3
]
L.
Li
u
e
t
a
l
.
,
“
S
i
m
u
l
a
t
i
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g
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.
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p
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j
.
n
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t
.
2
0
2
5
.
1
0
3
5
3
1
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Eli
n
Nura
in
i
g
ra
d
u
a
ted
f
o
r
u
n
d
e
rg
ra
d
u
a
te
p
ro
g
ra
m
in
P
h
y
sic
fro
m
1
0
No
v
e
m
b
e
r
In
sti
tu
te
o
f
Tec
h
n
o
l
o
g
y
,
I
n
d
o
n
e
sia
,
i
n
1
9
9
0
.
Cu
rre
n
tl
y
,
sh
e
is
a
S
e
n
i
o
r
re
se
a
rc
h
e
r
a
t
th
e
Re
se
a
rc
h
Ce
n
ter
fo
r
Ac
c
e
lera
to
r
Tec
h
n
o
l
o
g
y
,
Re
se
a
rc
h
Org
a
n
iza
ti
o
n
fo
r
N
u
c
lea
r
Tec
h
n
o
l
o
g
y
,
Na
ti
o
n
a
l
Re
se
a
rc
h
a
n
d
In
n
o
v
a
ti
o
n
Ag
e
n
c
y
(BRIN).
S
h
e
is
a
lso
stu
d
y
in
g
a
t
De
p
a
rtme
n
t
o
f
Nu
c
lea
r
En
g
i
n
e
e
rin
g
a
n
d
E
n
g
in
e
e
rin
g
P
h
y
sic
s,
F
a
c
u
lt
y
o
f
E
n
g
i
n
e
e
rin
g
,
Un
iv
e
rsitas
G
a
d
jah
M
a
d
a
,
Yo
g
y
a
k
a
rta,
In
d
o
n
e
sia
.
He
r
re
se
a
rc
h
in
t
e
re
sts
in
c
lu
d
e
a
c
c
e
lera
to
r
tec
h
n
o
l
o
g
y
a
n
d
a
p
p
li
c
a
ti
o
n
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
e
li
n
0
0
1
@b
rin
.
g
o
.
id
.
S
ih
a
n
a
g
ra
d
u
a
ted
f
o
r
u
n
d
e
rg
ra
d
u
a
te
p
ro
g
ra
m
i
n
n
u
c
lea
r
e
n
g
i
n
e
e
rin
g
fro
m
Un
iv
e
rsitas
G
a
d
jah
M
a
d
a
,
Yo
g
y
a
k
a
rta,
In
d
o
n
e
sia
,
i
n
1
9
9
0
.
He
h
a
s
g
ra
d
u
a
ted
f
o
r
G
ra
d
u
a
te
p
ro
g
ra
m
a
t
In
stit
u
t
o
f
En
e
rg
y
E
n
g
in
e
e
rin
g
,
Tec
h
n
ica
l
Un
iv
e
rsit
y
,
Be
rli
n
,
G
e
rm
a
n
y
in
1
9
9
3
.
He
re
c
e
iv
e
d
th
e
P
h
.
D.
d
e
g
re
e
in
F
a
k
u
lt
a
e
t
f
u
e
r
P
ro
z
e
ss
wiss
e
n
sc
h
a
ften
(F
a
c
u
lt
y
o
f
P
r
o
c
e
ss
S
c
ien
c
e
),
In
stit
u
e
t
f
u
e
r
E
n
e
rg
iete
c
h
n
ik
(In
stit
u
t
o
f
En
e
rg
y
En
g
in
e
e
rin
g
),
Tec
h
n
ica
l
Un
iv
e
rsity
Be
rli
n
,
G
e
rm
a
n
y
i
n
2
0
0
0
.
Cu
rre
n
tl
y
,
h
e
is
a
Lec
tu
r
e
r
a
t
t
h
e
De
p
a
rtme
n
t
o
f
Nu
c
lea
r
En
g
in
e
e
rin
g
a
n
d
En
g
in
e
e
rin
g
P
h
y
sic
s,
F
a
c
u
l
ty
o
f
E
n
g
i
n
e
e
rin
g
,
Un
i
v
e
rsitas
G
a
d
jah
M
a
d
a
,
Yo
g
y
a
k
a
rta,
In
d
o
n
e
sia
.
Hi
s
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
a
n
a
ly
sis
o
f
sa
fe
ty
,
se
c
u
rit
y
a
n
d
sa
fe
g
u
a
rd
fo
r
n
u
c
lea
r
sy
ste
m
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sih
a
n
a
@u
g
m
.
a
c
.
id
.
Ta
u
fik
g
ra
d
u
a
ted
f
o
r
u
n
d
e
rg
ra
d
u
a
te
p
r
o
g
ra
m
in
P
h
y
sic
s
fr
o
m
Un
iv
e
rsitas
P
a
d
jaja
ra
n
,
Ba
n
d
u
n
g
,
I
n
d
o
n
e
sia
,
in
2
0
0
4
.
He
h
a
s
g
ra
d
u
a
ted
f
o
r
G
ra
d
u
a
te
P
ro
g
ra
m
a
t
De
p
a
rtme
n
t
o
f
P
h
y
sic
s,
Un
i
v
e
r
sitas
G
a
d
jah
M
a
d
a
,
Yo
g
y
a
k
a
rta
,
In
d
o
n
e
sia
in
2
0
1
3
a
n
d
re
c
e
iv
e
d
th
e
P
h
.
D.
d
e
g
re
e
in
S
o
k
e
n
d
a
i,
Tsu
k
u
b
a
,
Ja
p
a
n
i
n
2
0
1
9
.
Cu
rre
n
t
ly
,
h
e
is
a
s
e
n
i
o
r
re
se
a
rc
h
e
r
a
t
th
e
Re
se
a
r
c
h
Ce
n
t
e
r
fo
r
Ac
c
e
lera
to
r
Tec
h
n
o
l
o
g
y
,
Re
se
a
rc
h
Org
a
n
iza
ti
o
n
f
o
r
Nu
c
lea
r
Tec
h
n
o
lo
g
y
,
Na
ti
o
n
a
l
Re
se
a
rc
h
a
n
d
In
n
o
v
a
ti
o
n
Ag
e
n
c
y
(BRIN).
His
re
se
a
r
c
h
in
tere
sts in
c
lu
d
e
a
c
c
e
lera
to
r
sc
ien
c
e
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
tau
f0
0
9
@b
ri
n
.
g
o
.
id
.
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