I
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37
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1
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J
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u
ar
y
20
25
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p
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90
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I
SS
N:
2
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K
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w
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s
:
C
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n
Gen
etic
alg
o
r
ith
m
HDD
s
ig
n
al
wr
ite
m
ac
h
in
e
Ma
ch
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Po
s
itio
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s
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c
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ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
J
ir
ap
h
o
n
Sriser
tp
o
l
Sch
o
o
l o
f
Me
c
h
an
ical
E
n
g
in
e
er
in
g
,
Su
r
a
n
ar
ee
Un
iv
e
r
s
ity
o
f
T
ec
h
n
o
lo
g
y
Nak
h
o
n
R
atch
asima
,
T
h
ailan
d
E
m
ail:
jira
p
h
o
n
@
s
u
t.a
c.
th
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
p
r
ec
is
e
co
n
tr
o
l
o
f
h
ar
d
d
is
k
d
r
iv
e
(
HDD)
d
em
a
n
d
s
h
ig
h
-
p
r
ec
is
io
n
m
o
v
em
en
t
[
1
]
,
[
2
]
.
T
h
e
HDD
s
ig
n
al
wr
iter
,
w
h
ich
wr
ites
tr
ac
k
s
to
i
d
en
tifie
d
lo
ca
tio
n
s
o
n
th
e
d
is
k
,
e
m
p
lo
y
s
a
p
r
o
p
o
r
tio
n
al
in
teg
r
al
d
er
iv
ativ
e
(
PID
)
ty
p
e
co
n
tr
o
ller
th
at
u
n
d
er
g
o
es
f
in
e
-
t
u
n
i
n
g
b
y
ex
p
er
t
d
esig
n
er
s
u
s
in
g
h
ig
h
-
p
er
f
o
r
m
an
ce
m
ac
h
in
es
to
h
an
d
le
ac
tu
at
o
r
r
eso
n
an
ce
s
,
v
ib
r
atio
n
r
ejec
tio
n
,
an
d
co
m
m
an
d
f
o
llo
win
g
b
e
f
o
r
e
d
ep
lo
y
m
en
t
to
m
an
u
f
ac
tu
r
in
g
[
3
]
.
W
h
ile
PID
co
n
tr
o
ller
s
ar
e
co
m
m
o
n
ly
u
s
ed
,
co
n
tin
u
o
u
s
an
d
p
r
o
lo
n
g
ed
u
s
e
ca
n
lead
to
er
r
o
r
s
in
th
e
c
o
m
p
o
n
en
ts
o
f
t
h
e
s
ig
n
al
wr
iter
.
C
u
r
r
en
tly
,
s
k
ill
ed
ex
p
er
ts
ar
e
e
m
p
lo
y
e
d
to
d
ia
g
n
o
s
e
an
d
i
d
en
tify
th
e
ca
u
s
es
o
f
ab
n
o
r
m
alities
in
th
e
HDD
s
ig
n
al
wr
iter
in
o
r
d
er
to
ca
teg
o
r
ize
th
em
f
o
r
r
e
p
a
ir
.
So
m
e
m
ac
h
in
es
with
im
p
r
o
v
em
en
t p
o
ten
tial
ca
n
r
ed
u
ce
p
o
s
itio
n
al
er
r
o
r
s
b
y
o
p
tim
izin
g
co
n
t
r
o
ller
g
ain
.
Ho
wev
er
,
th
is
p
r
o
ce
s
s
is
tim
e
-
co
n
s
u
m
in
g
in
lar
g
e
-
s
ca
le
m
an
u
f
ac
tu
r
in
g
,
as
m
ac
h
i
n
es
r
eq
u
i
r
in
g
m
ain
ten
a
n
ce
a
n
d
o
p
tim
izatio
n
a
r
e
m
ix
ed
in
with
p
r
o
d
u
ctio
n
.
T
h
er
ef
o
r
e
,
ar
tific
ial
in
tellig
en
c
e
-
b
ased
class
if
icatio
n
s
y
s
tem
s
an
d
o
p
tim
izatio
n
s
y
s
tem
s
m
u
s
t w
o
r
k
to
g
eth
er
to
ad
d
r
ess
th
e
p
r
o
b
lem
ef
f
ec
tiv
e
ly
.
Ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
ANN)
ar
e
em
p
lo
y
e
d
in
v
ar
i
o
u
s
class
if
icatio
n
ap
p
licatio
n
s
,
in
clu
d
in
g
class
if
y
in
g
HDD
s
ig
n
al
wr
it
in
g
m
ac
h
in
e
p
er
f
o
r
m
a
n
ce
.
T
h
is
class
if
icatio
n
u
s
e
s
o
p
er
atin
g
p
ar
am
eter
s
as
f
ea
tu
r
es
an
d
th
e
s
y
m
p
to
m
o
f
p
o
s
itio
n
s
ig
n
al
er
r
o
r
m
o
v
em
e
n
t
as
th
e
lab
el.
Ma
ch
in
es
ar
e
class
if
ied
in
to
f
o
u
r
g
r
o
u
p
s
:
Gr
o
u
p
s
1
an
d
2
ar
e
ea
r
m
ar
k
ed
f
o
r
m
ain
ten
an
ce
d
u
e
to
o
b
v
io
u
s
i
n
ter
n
al
d
am
ag
e,
w
h
ile
Gr
o
u
p
s
0
an
d
3
ar
e
co
n
s
id
er
ed
f
o
r
co
n
tr
o
ller
o
p
tim
izatio
n
[
4
]
.
T
h
ese
f
in
d
i
n
g
s
alig
n
with
p
ast
s
tu
d
ies
o
n
class
if
y
in
g
f
ailu
r
es
in
h
ig
h
-
s
p
ee
d
au
to
co
r
e
a
d
h
esio
n
m
o
u
n
tin
g
m
ac
h
in
es
[
5
]
a
n
d
m
o
u
n
tin
g
h
ea
d
d
eg
r
ad
atio
n
class
if
icatio
n
[
6
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Op
timiz
a
tio
n
s
ig
n
a
l wr
itin
g
w
i
th
ma
ch
in
e
lea
r
n
in
g
a
s
s
is
ted
co
n
tr
o
l
(
C
h
a
w
en
g
S
a
p
a
p
p
o
r
n
)
91
An
o
th
er
im
p
o
r
t
an
t
task
is
PID
co
n
tr
o
ller
tu
n
in
g
,
wh
ic
h
ca
n
r
esto
r
e
p
er
f
o
r
m
an
ce
in
d
r
iv
es
th
at
h
av
e
d
eg
r
ad
ed
in
p
o
s
itio
n
m
o
v
em
en
t.
Sev
er
al
well
-
k
n
o
wn
m
eth
o
d
s
f
o
r
a
s
s
es
s
in
g
th
e
r
o
b
u
s
tn
ess
an
d
p
er
f
o
r
m
a
n
ce
o
f
PID
co
n
tr
o
ller
s
in
b
o
th
th
e
f
r
e
q
u
en
cy
an
d
tim
e
d
o
m
ai
n
s
ar
e
d
is
cu
s
s
ed
an
d
c
o
m
p
ar
e
d
[
7
]
,
[
8
]
.
T
h
e
MA
T
L
AB
GUI
-
b
ased
tu
n
i
n
g
m
eth
o
d
s
,
wh
ich
wo
r
k
with
s
ev
er
al
tech
n
iq
u
es,
h
av
e
d
em
o
n
s
tr
ated
e
f
f
ec
ti
v
e
p
e
r
f
o
r
m
an
ce
in
m
o
to
r
s
p
ee
d
c
o
n
tr
o
l
[
9
]
.
Am
o
n
g
th
ese
m
et
h
o
d
s
ar
e
th
e
p
r
o
ce
s
s
r
ea
ctio
n
cu
r
v
e
an
d
u
ltima
te
s
en
s
itiv
ity
m
eth
o
d
.
T
h
ese
m
eth
o
d
s
ar
e
v
alu
ed
f
o
r
th
eir
s
p
ee
d
,
s
im
p
licity
,
an
d
av
o
i
d
an
ce
o
f
co
m
p
lex
ca
lc
u
latio
n
s
,
m
ak
in
g
t
h
em
ef
f
ec
tiv
e
f
o
r
i
n
itial
PID
tu
n
in
g
an
d
p
r
e
v
en
tin
g
in
s
tab
ilit
y
.
T
h
ey
ar
e
o
f
ten
b
e
n
ch
m
ar
k
s
f
o
r
o
th
er
PID
tu
n
in
g
tech
n
iq
u
es
d
u
e
to
th
eir
r
eliab
ilit
y
[
1
0
]
.
T
h
e
g
r
ad
ien
t
d
escen
t
s
ea
r
ch
m
eth
o
d
is
also
u
tili
ze
d
in
PID
tu
n
in
g
f
o
r
q
u
ad
r
o
to
r
s
.
T
h
is
m
eth
o
d
d
em
o
n
s
tr
ates
n
o
o
v
e
r
s
h
o
o
t,
ze
r
o
s
tead
y
-
s
tate
er
r
o
r
,
a
n
d
s
h
o
r
t
s
ettlin
g
a
n
d
r
is
e
tim
es
in
ev
alu
atio
n
r
esu
lts
.
Ho
wev
er
,
s
in
ce
it
i
n
v
o
lv
es
o
n
lin
e
co
n
tr
o
ller
tu
n
in
g
,
s
ele
ctin
g
th
e
in
itial
co
n
tr
o
ller
g
ain
an
d
lear
n
in
g
r
a
te
is
cr
u
cial
to
av
o
id
l
o
ca
l m
in
im
a
an
d
p
r
ev
en
t in
s
tab
ilit
y
[
1
1
]
.
An
o
th
er
ap
p
r
o
ac
h
is
tu
n
in
g
PID
co
n
tr
o
ller
s
with
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
,
u
s
ed
in
an
u
n
m
an
n
ed
ae
r
ial
v
eh
icle
(
U
AV)
ca
m
er
a
p
o
s
itio
n
co
n
tr
o
l.
As
an
ev
o
lu
tio
n
ar
y
al
g
o
r
i
th
m
,
PS
O
r
eq
u
ir
es
ev
alu
atin
g
all
p
o
p
u
latio
n
m
e
m
b
er
s
,
m
ak
in
g
in
itial
p
ar
am
et
er
s
ettin
g
s
cr
u
cial
f
o
r
o
p
tim
izin
g
s
ea
r
ch
tim
e
an
d
tu
n
in
g
ef
f
ec
tiv
en
ess
[
1
2
]
.
An
o
th
er
tu
n
i
n
g
tech
n
i
q
u
e
is
PI
D
tu
n
in
g
with
f
u
zz
y
l
o
g
ic,
u
s
ed
f
o
r
tem
p
er
atu
r
e
co
n
tr
o
l.
W
h
ile
it
r
esu
lts
in
lo
n
g
er
r
is
e
an
d
s
ettlin
g
tim
es
co
m
p
ar
ed
to
t
h
e
Z
ieg
ler
-
Nich
o
ls
m
eth
o
d
,
it
p
r
o
v
i
d
es
a
r
esp
o
n
s
e
with
o
u
t
o
v
e
r
s
h
o
o
t,
wh
ich
is
cr
u
cial
to
av
o
id
m
at
er
ial
d
is
q
u
alif
icatio
n
d
u
e
t
o
o
v
er
s
h
o
o
t
[
1
3
]
.
T
h
is
m
eth
o
d
s
ee
k
s
to
o
p
tim
ize
PID
co
n
tr
o
ller
g
ain
b
y
d
er
i
v
in
g
e
q
u
atio
n
s
f
o
r
o
p
en
-
l
o
o
p
am
p
litu
d
e
r
a
tio
an
d
p
h
ase
s
h
if
t
th
r
o
u
g
h
f
r
eq
u
en
cy
an
aly
s
is
to
m
ee
t
r
o
b
u
s
tn
ess
an
d
p
er
f
o
r
m
an
ce
cr
iter
ia.
Ho
wev
e
r
,
it
is
lim
ited
in
o
n
lin
e
a
p
p
l
i
c
a
t
i
o
n
s
a
n
d
r
e
q
u
i
r
e
s
s
i
g
n
i
f
i
c
a
n
t
e
x
p
e
r
t
i
s
e
t
o
a
d
j
u
s
t
c
o
n
t
r
o
l
l
e
r
g
a
i
n
s
w
h
e
n
s
y
s
t
e
m
c
h
a
r
a
c
t
e
r
i
s
t
i
c
s
c
h
a
n
g
e
[
1
4
]
,
co
n
s
is
ten
t
with
s
tu
d
ies
o
n
f
ir
s
t
-
o
r
d
er
m
o
d
els
[
1
5
]
.
Du
e
to
m
ath
em
atica
l
co
m
p
lex
ity
,
g
e
n
et
ic
alg
o
r
i
th
m
s
(
GA)
ar
e
co
m
m
o
n
ly
u
s
ed
in
c
o
n
tin
u
o
u
s
s
tirr
ed
tan
k
r
ea
cto
r
(
C
STR)
p
r
o
ce
s
s
es.
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
c
o
m
b
in
es
in
teg
r
al
s
q
u
ar
ed
er
r
o
r
(
I
SE)
,
i
n
teg
r
al
ab
s
o
lu
te
er
r
o
r
(
I
AE
)
,
in
teg
r
al
o
f
tim
e
-
weig
h
ted
ab
s
o
lu
te
er
r
o
r
(
I
T
AE
)
,
an
d
th
eir
weig
h
ted
s
u
m
to
ef
f
ec
ti
v
ely
r
ejec
t
d
is
tu
r
b
an
ce
s
q
u
ick
ly
[
1
6
]
.
PID
tu
n
in
g
f
o
r
a
th
ir
d
-
o
r
d
er
s
y
s
tem
u
s
in
g
a
GA
is
d
em
o
n
s
tr
ated
,
wh
er
e
th
e
er
r
o
r
is
s
et
as
th
e
o
b
jectiv
e
f
u
n
ctio
n
.
Hig
h
p
er
f
o
r
m
an
ce
in
ter
m
s
o
f
r
is
e
tim
e,
s
ett
lin
g
tim
e,
an
d
o
v
er
s
h
o
o
t
is
o
b
s
er
v
ed
[
1
7
]
.
T
h
e
wo
r
k
o
n
m
o
t
o
r
s
p
ee
d
co
n
tr
o
l
was
s
h
o
wn
th
r
o
u
g
h
s
im
u
latio
n
s
an
d
a
co
m
p
ar
is
o
n
o
f
th
e
MA
T
L
AB
PID
T
u
n
er
a
p
p
with
GA
[
1
8
]
.
T
h
e
in
tr
o
d
u
ctio
n
o
f
an
im
p
r
o
v
ed
v
er
s
io
n
o
f
th
e
m
u
lti
-
o
b
jecti
v
e
f
u
n
ctio
n
s
ea
r
ch
in
th
e
GA
k
n
o
wn
as
non
-
d
o
m
in
ated
s
o
r
tin
g
g
en
e
tic
alg
o
r
ith
m
(
NSGA)
I
I
is
a
n
o
tewo
r
th
y
co
n
tr
ib
u
tio
n
.
T
h
is
alg
o
r
ith
m
in
co
r
p
o
r
ates
f
ast
n
o
n
-
d
o
m
i
n
a
ted
s
o
r
tin
g
,
c
r
o
wd
in
g
d
is
tan
c
e,
an
d
a
n
elitis
t
m
ec
h
an
is
m
,
co
n
tr
ib
u
tin
g
to
its
ef
f
icien
cy
a
n
d
ef
f
ec
tiv
e
n
ess
in
h
a
n
d
lin
g
co
m
p
lex
o
p
tim
iza
tio
n
p
r
o
b
lem
s
.
T
h
e
NSGA
I
I
m
eth
o
d
h
as
b
ee
n
ex
ten
s
iv
ely
ev
alu
ated
ac
r
o
s
s
a
s
p
ec
tr
u
m
o
f
lin
ea
r
a
n
d
n
o
n
-
lin
ea
r
p
r
o
b
le
m
s
,
co
n
s
is
ten
tly
d
em
o
n
s
tr
atin
g
p
r
o
m
is
in
g
r
esu
lts
.
I
ts
r
o
b
u
s
t
p
er
f
o
r
m
an
ce
a
n
d
a
b
ilit
y
to
a
d
d
r
ess
d
iv
er
s
e
m
u
lti
-
o
b
jectiv
e
s
ce
n
ar
io
s
m
ak
e
it
a
wid
ely
ad
o
p
ted
to
o
l
in
th
e
f
iel
d
o
f
GA
[
1
9
]
,
[
2
0
]
.
NSGA
-
I
I
h
as
b
ee
n
u
s
ed
f
o
r
PID
tu
n
in
g
in
v
ar
io
u
s
s
y
s
tem
s
,
in
clu
d
in
g
an
a
u
to
m
atic
v
o
ltag
e
r
eg
u
lato
r
(
AVR)
s
y
s
tem
,
to
o
p
tim
ize
v
o
ltag
e
a
n
d
c
u
r
r
en
t
r
eg
u
latio
n
.
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
s
co
n
s
is
ted
o
f
s
ev
er
al
co
m
b
in
atio
n
s
to
m
in
im
ize
er
r
o
r
i
n
I
T
AE
,
s
ettlin
g
tim
e
,
o
v
e
r
s
h
o
o
t,
an
d
d
am
p
in
g
r
atio
[
2
1
]
.
Mu
lti
-
o
b
jectiv
e
o
p
tim
izatio
n
with
NSGA
-
I
I
was
also
ap
p
lied
t
o
m
in
im
ize
to
r
q
u
e
m
o
tio
n
an
d
p
o
s
itio
n
d
ev
iatio
n
d
u
r
in
g
r
o
b
o
t m
o
v
em
en
t
[
2
2
]
.
Ad
d
itio
n
ally
,
NSGA
-
I
I
h
as
b
e
en
u
s
ed
in
UAV
n
av
ig
atio
n
s
y
s
tem
s
to
m
in
im
ize
er
r
o
r
,
o
v
er
s
h
o
o
t,
an
d
tu
n
in
g
tim
e
[
2
3
]
.
I
n
HDD
m
an
u
f
ac
tu
r
i
n
g
,
NSGA
-
I
I
o
p
tim
i
ze
s
k
ey
o
b
jectiv
es
s
u
ch
as
p
o
s
itio
n
er
r
o
r
s
ig
n
al
(
PES)
,
g
ain
m
ar
g
in
,
p
h
ase
m
ar
g
in
,
c
r
o
s
s
o
v
er
f
r
eq
u
e
n
cy
,
an
d
p
ea
k
s
en
s
itiv
ity
.
B
u
ilt
o
n
PES
d
ata
f
r
o
m
n
u
m
er
o
u
s
d
r
iv
es,
th
is
m
eth
o
d
ef
f
ec
tiv
ely
h
an
d
les
co
m
p
le
x
,
lar
g
e
-
s
ca
le
o
p
tim
izatio
n
p
r
o
b
lem
s
[
2
4
]
.
T
h
e
co
m
b
in
atio
n
o
f
s
en
s
itiv
ity
lo
o
p
s
h
ap
in
g
an
d
ad
ap
tiv
e
n
o
n
lin
ea
r
co
m
p
en
s
atio
n
.
L
e
v
er
ag
in
g
th
e
Kalm
a
n
–
Yak
u
b
o
v
ic
–
Po
p
o
v
(
KYP)
lem
m
a
is
p
r
esen
ted
,
it
aim
s
to
r
ejec
t
d
is
tu
r
b
an
ce
s
with
k
n
o
w
n
f
r
e
q
u
en
cies
w
h
ile
in
tr
o
d
u
cin
g
ad
a
p
tiv
e
n
o
n
lin
ea
r
co
m
p
e
n
s
atio
n
to
a
d
d
r
ess
lo
w
-
f
r
eq
u
e
n
cy
d
is
tu
r
b
an
ce
s
,
u
lt
im
ately
im
p
r
o
v
in
g
p
o
s
itio
n
in
g
ac
cu
r
ac
y
in
th
e
H
DD
co
n
tr
o
l
s
y
s
tem
[
2
5
]
.
An
ad
ap
tiv
e
m
u
ltira
te
co
n
tr
o
l
d
esig
n
is
p
r
o
p
o
s
ed
to
im
p
r
o
v
e
p
er
f
o
r
m
a
n
ce
d
esp
ite
v
ar
iatio
n
s
in
p
lan
t
g
ain
,
en
s
u
r
in
g
g
o
o
d
s
y
s
tem
p
er
f
o
r
m
a
n
ce
with
u
n
ce
r
tain
p
ar
am
eter
s
[
2
6
]
.
Ad
d
itio
n
all
y
,
an
o
p
tim
al
m
u
ltira
te
co
n
t
r
o
l
d
esig
n
is
in
tr
o
d
u
ce
d
,
wh
ich
f
u
r
th
er
b
o
o
s
ts
r
o
b
u
s
tn
ess
an
d
s
tab
ilit
y
[
2
7
]
.
A
n
o
n
lin
ea
r
tim
e
-
v
ar
y
i
n
g
u
n
if
ied
co
n
tr
o
l
s
ch
em
e
(
UC
S)
is
p
r
o
p
o
s
ed
f
o
r
ac
h
iev
in
g
f
ast an
d
s
m
o
o
th
t
r
ac
k
-
s
ee
k
in
g
an
d
tr
ac
k
-
f
o
llo
win
g
in
HDD.
T
h
is
s
ch
em
e
aim
s
to
av
o
id
th
e
p
r
o
b
lem
of
m
o
d
e
s
witch
in
g
an
d
d
em
o
n
s
tr
ates a
s
ig
n
if
ican
t im
p
r
o
v
em
en
t in
p
er
f
o
r
m
a
n
ce
an
d
r
o
b
u
s
tn
ess
[
2
8
]
.
E
v
en
th
o
u
g
h
q
u
an
titativ
e
an
al
y
s
is
o
f
co
n
tr
o
ller
tu
n
in
g
ca
n
c
o
m
p
en
s
ate
f
o
r
p
lan
t
v
ar
iatio
n
s
,
h
an
d
lin
g
m
ac
h
in
es
with
s
ig
n
if
ican
t
d
a
m
ag
e
th
at
r
eq
u
ir
e
m
ain
ten
an
c
e
r
em
ain
s
ch
allen
g
in
g
a
n
d
o
f
ten
lead
s
to
wasted
o
p
tim
izatio
n
ef
f
o
r
ts
.
Ad
d
itio
n
ally
,
s
o
m
e
m
ac
h
i
n
es
with
p
o
ten
tial
f
o
r
im
p
r
o
v
em
e
n
t
ex
h
ib
it
u
n
i
q
u
e
ch
ar
ac
ter
is
tics
,
an
d
a
s
in
g
le
s
et
o
f
c
o
n
tr
o
ller
g
ai
n
s
ca
n
n
o
t
ef
f
ec
tiv
ely
m
in
im
ize
p
o
s
itio
n
er
r
o
r
s
.
T
h
is
p
ap
er
p
r
o
p
o
s
es
a
s
o
lu
tio
n
in
v
o
lv
in
g
g
r
o
u
p
in
g
m
ac
h
in
es
b
ased
o
n
th
eir
b
eh
av
io
r
u
s
in
g
ANN
an
d
em
p
l
o
y
in
g
co
n
tr
o
ller
g
ain
s
o
b
tain
e
d
f
r
o
m
o
f
f
lin
e
GA
o
p
tim
izatio
n
.
T
h
is
m
eth
o
d
aim
s
to
r
ed
u
ce
s
i
g
n
a
l
wr
itin
g
f
ailu
r
es
with
in
tim
e
co
n
s
tr
ain
ts
b
y
u
s
i
n
g
m
ac
h
in
e
lear
n
in
g
(
ML
)
to
s
elec
t
th
e
ap
p
r
o
p
r
iate
co
n
tr
o
l
ler
g
ain
f
r
o
m
GA,
wh
ile
also
class
if
y
in
g
m
ac
h
in
es n
ee
d
in
g
r
e
p
air
an
d
p
r
o
v
id
in
g
g
u
id
elin
es to
e
n
h
an
ce
m
ain
t
en
an
ce
ef
f
icien
c
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
1
,
J
an
u
ar
y
20
25
:
90
-
1
00
92
2.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
in
t
r
o
d
u
ce
d
th
e
m
ac
h
in
es
tar
g
eted
f
o
r
p
er
f
o
r
m
an
ce
im
p
r
o
v
em
e
n
t
a
n
d
o
u
t
lin
ed
th
e
m
eth
o
d
e
m
p
lo
y
e
d
to
d
eter
m
i
n
e
th
e
a
p
p
r
o
p
r
iate
PID
c
o
n
tr
o
ller
g
ain
f
o
r
ea
ch
m
ac
h
in
e
class
.
T
h
e
m
ac
h
in
es
u
n
d
er
co
n
s
id
er
atio
n
v
ar
ie
d
in
th
eir
o
p
er
atio
n
al
p
ar
a
m
eter
s
,
wh
ich
r
eq
u
ir
ed
tail
o
r
e
d
tu
n
i
n
g
s
tr
ateg
ies.
T
h
e
s
elec
ted
m
eth
o
d
en
s
u
r
ed
th
at
th
e
PID
co
n
tr
o
ller
g
ain
was
o
p
tim
ized
to
en
h
an
ce
s
tab
ilit
y
an
d
r
esp
o
n
s
iv
en
ess
ac
r
o
s
s
d
if
f
er
en
t m
ac
h
in
e
ty
p
es.
2
.
1
.
Sig
na
l
w
rit
e
m
a
chine
T
h
e
m
ac
h
in
e
u
s
ed
in
th
is
r
esear
ch
was
an
HDD
s
ig
n
al
wr
ite
r
,
with
th
e
co
n
tr
o
l
d
iag
r
am
illu
s
tr
ated
in
Fig
u
r
e
1
,
I
t
co
n
s
is
ted
o
f
th
e
PA2
0
0
0
p
o
s
itio
n
er
m
o
d
el,
wh
i
ch
in
teg
r
ated
a
v
o
ice
co
il
m
o
t
o
r
(
VC
M)
with
an
ab
s
o
lu
te
en
co
d
er
to
co
n
tr
o
l
t
h
e
HDD
ar
m
m
o
v
em
e
n
t
an
d
ascer
tain
its
p
o
s
itio
n
,
an
d
a
n
SA2
0
0
a
m
p
lifie
r
lin
k
ed
to
r
ec
eiv
e
co
n
tr
o
l
s
ig
n
als
f
r
o
m
a
clo
s
ed
-
lo
o
p
co
n
t
r
o
l
s
y
s
tem
.
T
h
e
f
o
cu
s
o
f
th
is
p
ap
er
was
o
n
two
b
lo
ck
s
in
th
e
p
o
s
itio
n
co
n
tr
o
l:
th
e
f
ir
s
t
b
lo
ck
,
h
ig
h
lig
h
ted
in
o
r
an
g
e
,
in
v
o
lv
ed
o
p
tim
izin
g
t
h
e
co
n
tr
o
ller
g
ain
,
wh
ile
th
e
s
ec
o
n
d
b
l
o
ck
,
in
d
ica
ted
in
b
lu
e,
in
v
o
l
v
ed
u
s
in
g
M
L
to
ass
is
t w
ith
g
ain
s
elec
tio
n
.
T
h
e
m
a
th
em
atica
l
m
o
d
el
o
f
t
h
e
s
ig
n
al
wr
itin
g
m
ac
h
in
e,
r
ep
r
esen
ted
b
y
th
e
r
e
d
b
l
o
ck
,
was
esti
m
ated
u
s
in
g
a
s
y
s
tem
id
en
tific
atio
n
t
ec
h
n
iq
u
e.
T
h
e
m
o
d
el
s
tr
u
ct
u
r
e
co
m
p
r
is
ed
a
s
tan
d
ar
d
s
ec
o
n
d
-
o
r
d
e
r
f
o
r
m
at
lo
w
f
r
eq
u
e
n
cies
an
d
co
m
p
lex
p
o
le
s
an
d
z
er
o
s
at
h
ig
h
f
r
eq
u
e
n
ci
e
s
,
as
r
ep
r
esen
ted
b
y
(
1
)
.
T
h
e
p
ar
am
eter
s
f
o
r
b
o
th
co
n
tin
u
o
u
s
an
d
d
is
cr
ete
m
o
d
el
s
wer
e
p
r
o
v
id
e
d
in
[
4
]
.
Fig
u
r
e
1
.
Po
s
itio
n
co
n
tr
o
l
o
f
s
ig
n
al
wr
iter
m
ac
h
i
n
e
(
)
=
(
−
×
)
×
0
2
2
+
2
0
0
+
0
2
×
×
∏
2
+
2
+
2
2
+
2
+
2
=
1
(
1
)
w
h
er
e
is
th
e
lo
w
f
r
eq
u
e
n
cy
DC
g
ain
is
th
e
h
ig
h
f
r
eq
u
e
n
cy
r
eso
n
an
ce
g
ain
0
is
th
e
n
atu
r
al
f
r
e
q
u
en
c
y
is
th
e
r
eso
n
an
ce
ze
r
o
s
f
r
eq
u
e
n
cy
0
is
th
e
d
am
p
in
g
r
atio
is
th
e
r
eso
n
an
ce
p
o
les f
r
eq
u
e
n
cy
is
th
e
d
elay
tim
e
is
th
e
r
eso
n
an
ce
ze
r
o
s
d
am
p
in
g
r
atio
is
th
e
r
eso
n
an
ce
p
o
le
s
d
am
p
in
g
r
atio
is
th
e
n
u
m
b
e
r
o
f
r
eso
n
an
ce
p
e
ak
s
T
h
e
cu
r
r
en
t
s
y
m
p
t
o
m
s
o
f
m
a
ch
in
e
f
ailu
r
e
r
elate
d
to
p
o
s
itio
n
m
o
v
em
en
t
e
r
r
o
r
s
a
n
d
th
e
p
o
ten
tial
class
if
icatio
n
s
y
s
tem
u
s
in
g
an
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
A
NN)
wer
e
p
r
esen
ted
in
Fig
u
r
e
2
.
T
h
e
an
aly
s
is
o
f
m
ac
h
in
e
c
h
ar
ac
ter
is
tics
in
th
e
f
r
eq
u
en
cy
d
o
m
ain
was
co
n
d
u
cted
b
y
tr
an
s
f
o
r
m
in
g
th
e
PES
f
r
o
m
th
e
tim
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Op
timiz
a
tio
n
s
ig
n
a
l wr
itin
g
w
i
th
ma
ch
in
e
lea
r
n
in
g
a
s
s
is
ted
co
n
tr
o
l
(
C
h
a
w
en
g
S
a
p
a
p
p
o
r
n
)
93
d
o
m
ain
to
th
e
f
r
eq
u
e
n
cy
d
o
m
ain
u
s
in
g
th
e
d
is
cr
ete
f
o
u
r
i
er
tr
an
s
f
o
r
m
(
DFT)
,
as
illu
s
tr
ated
in
Fig
u
r
e
2
(
a)
.
C
las
s
1
,
d
ep
icted
b
y
th
e
o
r
an
g
e
d
o
tted
lin
e,
was
o
b
s
er
v
ed
t
o
ex
h
i
b
it
two
h
i
g
h
p
ea
k
s
at
a
p
p
r
o
x
im
ately
1
,
1
0
0
Hz
an
d
2
,
2
0
0
Hz.
Me
an
wh
ile,
C
las
s
2
,
also
r
ep
r
esen
ted
b
y
a
y
ello
w
d
o
tted
lin
e,
was
o
b
s
er
v
ed
to
d
is
p
lay
a
lo
wer
am
p
litu
d
e
b
etwe
en
6
0
0
an
d
9
0
0
Hz.
T
h
e
m
ain
ten
a
n
ce
b
ac
k
g
r
o
u
n
d
in
d
icate
d
th
at
C
la
s
s
es
1
an
d
2
ex
h
ib
ited
clea
r
d
e
g
r
ad
atio
n
in
m
ac
h
in
e
co
m
p
o
n
e
n
ts
(
v
o
ltag
e
d
r
o
p
in
th
e
en
co
d
er
s
en
s
o
r
f
o
r
C
l
a
s
s
1
a
n
d
l
o
o
s
e
p
u
s
h
p
i
n
f
o
r
C
l
as
s
2
)
,
w
h
i
c
h
n
e
c
e
s
s
i
t
at
e
d
p
r
o
m
p
t
m
ai
n
t
e
n
a
n
ce
.
C
o
n
v
e
r
s
e
l
y
,
C
l
ass
e
s
0
a
n
d
3
,
r
ep
r
esen
ted
b
y
th
e
b
lu
e
lin
e
a
n
d
g
r
ee
n
d
ash
ed
lin
e,
r
esp
ec
tiv
ely
,
we
r
e
s
h
o
wn
to
f
o
llo
w
s
im
ilar
p
atter
n
s
b
u
t
d
i
f
f
er
ed
i
n
am
p
litu
d
e
lev
els
ac
r
o
s
s
f
r
eq
u
en
cies.
C
la
s
s
3
d
em
o
n
s
tr
ated
h
ig
h
er
am
p
litu
d
e
v
alu
es
co
m
p
ar
e
d
to
C
lass
0
,
s
u
g
g
esti
n
g
a
g
r
ea
ter
im
p
ac
t
f
r
o
m
lo
w
-
f
r
eq
u
en
cy
v
ib
r
atio
n
s
.
I
t
was
f
o
u
n
d
th
at
th
e
ex
is
tin
g
co
n
tr
o
ller
g
ain
was
in
s
u
f
f
icien
t
f
o
r
ef
f
ec
tiv
ely
elim
in
atin
g
t
h
e
s
e
er
r
o
r
s
.
ML
,
s
p
ec
if
ically
u
s
i
n
g
an
ANN,
was
em
p
lo
y
e
d
t
o
id
en
tify
m
ac
h
in
e
class
s
y
m
p
to
m
s
,
with
th
e
class
if
icatio
n
r
esu
lts
d
ep
icted
in
Fig
u
r
e
2
(
b
)
.
I
t
was
n
o
ted
th
at
t
wo
d
ata
p
o
in
ts
f
r
o
m
C
las
s
1
m
ig
h
t
h
a
v
e
b
ee
n
m
is
class
if
ied
d
u
r
in
g
t
h
e
o
p
tim
izatio
n
p
r
o
ce
s
s
;
h
o
wev
er
,
th
i
s
d
is
cr
ep
an
cy
was
d
ee
m
ed
to
h
av
e
a
r
elativ
ely
m
in
o
r
im
p
ac
t
o
n
th
e
o
v
er
all
r
esu
lts
.
(
a)
(
b
)
Fig
u
r
e
2
.
Ma
ch
i
n
e
f
ailu
r
e
s
y
m
p
to
m
(
a)
m
ac
h
in
e
a
n
aly
s
is
b
y
DFT
an
d
(
b
)
m
ac
h
in
e
class
if
icatio
n
b
y
ANN
T
ab
le
1
p
r
esen
ted
th
e
d
ef
ec
ts
an
d
f
ailu
r
e
r
ates
r
elate
d
to
p
o
s
itio
n
er
r
o
r
s
f
o
r
ea
c
h
g
r
o
u
p
.
C
lass
0
p
er
f
o
r
m
ed
well
with
ze
r
o
d
ef
e
cts.
I
n
co
n
tr
ast,
C
lass
e
s
1
an
d
2
ex
h
ib
ited
h
ig
h
er
f
ailu
r
e
r
at
es,
ex
ce
ed
in
g
3
0
%.
T
h
e
d
e
f
ec
ts
,
wh
ich
wer
e
d
if
f
i
cu
lt
to
id
en
tify
v
is
u
ally
,
in
clu
d
ed
lo
w
v
o
ltag
e
f
r
o
m
th
e
en
c
o
d
er
s
en
s
o
r
in
C
lass
1
an
d
lo
o
s
e
p
u
s
h
p
in
s
in
C
lass
2
.
T
o
p
r
ev
e
n
t
th
ese
two
class
es
f
r
o
m
g
en
er
atin
g
f
ailu
r
e
r
at
es
ex
ce
ed
in
g
3
0
%,
in
ter
n
al
m
o
n
ito
r
in
g
was
im
p
le
m
en
ted
.
I
f
th
r
ee
o
u
t
o
f
te
n
d
r
i
v
es
f
ailed
,
th
e
test
er
was
d
esig
n
ed
to
s
h
u
t
d
o
wn
au
to
m
atica
lly
an
d
r
eq
u
ir
e
d
m
a
in
ten
an
ce
b
y
s
k
illed
ex
p
e
r
ts
.
Fo
r
C
lass
3
,
n
o
d
ef
ec
ts
wer
e
id
en
tifie
d
,
b
u
t
in
ter
m
itten
t
f
ail
u
r
es
r
elate
d
to
p
o
s
itio
n
m
o
v
e
m
en
t
er
r
o
r
wer
e
o
b
s
er
v
ed
,
with
a
f
ailu
r
e
r
ate
o
f
less
th
an
3
0
%.
T
h
e
s
h
u
td
o
wn
m
o
n
ito
r
i
n
g
s
y
s
tem
was
f
o
u
n
d
to
b
e
in
ad
e
q
u
ate
f
o
r
th
is
g
r
o
u
p
.
I
t
was
in
d
icate
d
b
y
lab
o
r
ato
r
y
test
in
g
th
at
p
er
f
o
r
m
an
ce
co
u
ld
b
e
r
esto
r
ed
b
y
ad
ju
s
tin
g
th
e
co
n
tr
o
ller
g
ain
t
h
r
o
u
g
h
a
d
esig
n
o
f
ex
p
e
r
im
en
ts
ap
p
r
o
ac
h
,
wh
ich
in
v
o
l
v
ed
m
u
ltip
le
ex
p
er
im
en
ts
an
d
th
e
u
s
e
o
f
r
esp
o
n
s
e
s
u
r
f
a
ce
m
eth
o
d
o
lo
g
y
to
f
i
n
d
o
p
tim
al
g
ain
s
.
T
h
e
t
r
en
d
o
f
t
h
e
n
e
w
o
p
tim
al
g
ain
was
f
o
u
n
d
to
in
cr
ea
s
e
Ki.
Alth
o
u
g
h
th
e
im
p
lem
e
n
tatio
n
o
f
t
h
is
tech
n
iq
u
e
was
f
o
u
n
d
to
b
e
ti
m
e
-
co
n
s
u
m
in
g
a
n
d
lab
o
r
-
in
ten
s
iv
e
d
u
e
to
th
e
lar
g
e
s
ca
le
o
f
m
an
u
f
ac
tu
r
in
g
,
it
was
s
u
g
g
ested
th
at
C
las
s
3
s
t
ill
h
ad
p
o
ten
tial
f
o
r
im
p
r
o
v
em
e
n
t in
r
e
d
u
cin
g
p
o
s
i
tio
n
er
r
o
r
.
Desp
ite
n
o
f
ailu
r
es
b
ein
g
p
r
o
d
u
ce
d
b
y
C
lass
0
,
p
o
ten
tial
f
o
r
f
u
r
th
er
r
e
d
u
ctio
n
in
p
o
s
itio
n
er
r
o
r
s
r
em
ain
ed
d
u
e
to
its
h
ig
h
p
er
f
o
r
m
an
ce
.
T
h
er
ef
o
r
e,
n
ew
o
p
tim
al
g
ain
s
,
d
is
tin
ct
f
r
o
m
th
o
s
e
u
s
ed
in
o
th
e
r
class
es,
wer
e
r
ec
o
m
m
en
d
e
d
.
T
o
r
e
d
u
ce
th
e
n
u
m
b
er
o
f
ex
p
er
im
e
n
ts
,
a
GA
was
em
p
lo
y
ed
to
d
eter
m
in
e
th
e
o
p
tim
al
s
o
lu
tio
n
s
f
o
r
b
o
th
class
es.
T
ab
le
1
.
Failu
r
e
r
ate
an
d
d
ef
ec
t
p
ar
ts
M
a
c
h
i
n
e
c
l
a
ss
F
a
i
l
u
r
e
r
a
t
e
(
%)
D
e
f
e
c
t
p
a
r
t
A
c
t
i
o
n
C
l
a
s
s0
0
H
e
a
l
t
h
y
I
mp
r
o
v
e
P
ES
C
l
a
s
s1
>
3
0
Lo
w
e
n
c
o
d
e
r
se
n
so
r
v
o
l
t
a
g
e
R
e
p
a
i
r
C
l
a
s
s2
>
3
0
P
u
sh
p
i
n
l
o
o
s
e
R
e
p
a
i
r
C
l
a
s
s3
<
3
0
,
i
n
t
e
r
m
i
t
t
e
n
t
N
o
d
e
f
e
c
t
f
o
u
n
d
,
Tu
n
a
b
l
e
R
e
q
u
i
r
e
c
o
n
t
r
o
l
l
e
r
t
u
n
i
n
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
1
,
J
an
u
ar
y
20
25
:
90
-
1
00
94
2
.
2
.
G
enet
ic
a
lg
o
rit
h
m
(
G
A)
A
GA
was
an
ev
o
lu
tio
n
ar
y
o
p
tim
izatio
n
m
eth
o
d
in
s
p
ir
ed
b
y
n
atu
r
al
s
elec
tio
n
an
d
g
en
etics.
A
p
o
p
u
latio
n
o
f
p
o
ten
tial
s
o
lu
tio
n
s
was
ev
o
lv
ed
th
r
o
u
g
h
s
elec
tio
n
,
cr
o
s
s
o
v
er
,
an
d
m
u
t
atio
n
to
iter
ativ
ely
ap
p
r
o
ac
h
o
p
tim
al
s
o
lu
tio
n
s
.
T
o
s
o
lv
e
m
u
lti
-
o
b
jectiv
e
p
r
o
b
lem
s
,
s
im
p
le
m
eth
o
d
s
wer
e
u
s
ed
,
wh
ich
in
v
o
lv
e
d
co
m
b
in
in
g
o
b
jectiv
es
u
s
in
g
p
r
ed
ef
in
ed
weig
h
tin
g
v
ec
to
r
s
.
An
o
th
er
s
o
lv
in
g
m
eth
o
d
,
Par
eto
f
r
o
n
ts
,
f
o
r
m
ed
th
e
b
asis
o
f
th
e
No
n
-
Do
m
in
ated
So
r
tin
g
Gen
etic
Alg
o
r
ith
m
I
I
(
NSGA
-
I
I
)
.
NSGA
-
I
I
em
p
lo
y
ed
n
o
n
-
d
o
m
i
n
ated
s
o
r
tin
g
,
cr
o
wd
in
g
d
is
tan
ce
,
an
d
elitis
m
to
m
ain
tain
d
iv
er
s
ity
an
d
m
ee
t
s
p
ec
if
ied
c
r
iter
ia.
NSGA
-
I
I
was
u
s
ed
in
th
is
r
esear
ch
,
im
p
lem
en
te
d
in
MA
T
L
AB
,
to
d
eter
m
in
e
th
e
o
p
tim
al
co
n
tr
o
ller
g
ain
s
f
o
r
C
las
s
0
an
d
C
lass
3
m
ac
h
in
es,
as c
lass
if
ied
b
y
an
ANN.
I
n
m
o
d
el
-
b
ase
d
co
n
tr
o
ller
tu
n
in
g
,
a
p
r
ec
is
e
m
o
d
el
was
r
e
q
u
ir
ed
.
T
h
e
m
ath
e
m
atica
l
m
o
d
els
wer
e
d
er
iv
ed
f
r
o
m
th
e
f
r
eq
u
e
n
cy
r
e
s
p
o
n
s
e
d
ata
s
h
o
wn
in
Fig
u
r
e
3
.
T
h
e
ac
tu
al
r
esp
o
n
s
es
o
f
m
ac
h
in
es
in
class
e
s
0
an
d
3
wer
e
co
llected
b
y
in
jec
tin
g
a
s
in
e
s
wee
p
f
r
o
m
5
0
to
6
,
0
0
0
Hz
at
th
e
in
p
u
t
p
o
in
t
b
ef
o
r
e
b
ein
g
s
er
v
o
am
p
lifie
d
.
Po
s
itio
n
m
o
v
em
en
t
was
co
llected
in
th
e
tim
e
r
es
p
o
n
s
e
at
ea
ch
f
r
eq
u
en
c
y
an
d
tr
an
s
f
o
r
m
ed
in
to
a
f
r
eq
u
e
n
cy
r
esp
o
n
s
e,
as
s
h
o
wn
in
th
e
B
o
d
e
p
lo
t
in
Fig
u
r
e
3
(
a)
.
T
h
e
b
lu
e
lin
e
r
ep
r
esen
ted
C
las
s
0
,
wh
ich
h
ad
th
e
p
o
ten
tial
t
o
f
in
d
a
n
ew
o
p
t
im
al
g
ain
,
w
h
ile
th
e
o
r
an
g
e
li
n
e
in
d
icate
d
C
lass
3
,
wh
ich
r
e
q
u
ir
ed
t
u
n
in
g
.
T
h
e
av
er
ag
e
v
alu
es
at
ea
ch
f
r
e
q
u
e
n
cy
p
o
in
t
wer
e
p
r
esen
ted
in
Fig
u
r
e
3
(
b
)
.
As
ex
p
ec
ted
,
th
e
av
er
ag
e
m
o
d
el
f
o
r
C
las
s
3
clea
r
ly
in
d
icate
d
a
h
ig
h
p
o
ten
tial f
o
r
v
ib
r
atio
n
im
p
ac
t f
r
o
m
th
e
s
p
in
d
le
m
o
to
r
s
p
ee
d
at
7
,
2
0
0
r
p
m
(
1
2
0
Hz)
,
wh
ich
co
u
ld
lead
to
s
ig
n
i
f
ican
t o
s
cillatio
n
s
in
th
e
tim
e
r
esp
o
n
s
e.
(
a)
(
b
)
Fig
u
r
e
3
.
Fre
q
u
en
cy
r
esp
o
n
s
e
f
u
n
ctio
n
(
a)
class
0
an
d
3
d
ata
an
d
(
b
)
av
er
a
g
e
o
f
class
0
an
d
3
2.
3.
G
A
c
o
nfig
ura
t
io
n
T
h
e
o
b
jectiv
e
f
u
n
ctio
n
o
f
th
is
s
tu
d
y
was
a
m
u
lti
-
o
b
jectiv
e
f
u
n
ctio
n
th
at
co
m
b
in
e
d
th
e
g
ain
cr
o
s
s
o
v
er
f
r
eq
u
e
n
cy
at
5
5
0
Hz,
p
h
ase
m
ar
g
in
at
4
0
d
eg
r
ee
s
,
a
n
d
th
e
m
in
im
u
m
PES
DFT
v
al
u
e
at
lo
w
f
r
eq
u
e
n
cies,
as
s
h
o
wn
in
(
2
)
.
=
(
(
−
550
)
)
,
min
(
(
ℎ
−
40
)
)
,
min
(
)
(
2
)
I
n
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
,
s
ettin
g
th
e
in
itial
v
alu
es
was
cr
u
cial.
I
m
p
r
o
p
e
r
in
itializatio
n
,
s
u
ch
as
n
o
t
lim
itin
g
th
e
r
an
g
e
o
f
th
e
co
n
tr
o
ller
g
ain
,
was
f
o
u
n
d
to
lead
t
o
s
y
s
tem
in
s
t
ab
ilit
y
,
as
illu
s
tr
ated
in
Fig
u
r
e
4
.
T
o
ad
d
r
ess
th
is
is
s
u
e,
th
e
r
o
o
t
l
o
cu
s
o
r
R
o
u
t
h
-
Hu
r
witz
m
eth
o
d
was
em
p
lo
y
ed
to
d
eter
m
i
n
e
th
e
g
ai
n
r
an
g
e
n
ec
ess
ar
y
to
m
ain
tain
s
y
s
tem
s
tab
ilit
y
.
Sin
ce
th
e
s
ig
n
al
wr
it
in
g
m
ac
h
i
n
e
alr
ea
d
y
h
a
d
a
c
o
n
v
en
tio
n
ally
s
tab
le
co
n
tr
o
l
ler
g
ain
,
th
is
g
ain
was
u
s
ed
as
th
e
s
tar
tin
g
p
o
in
t
to
p
r
ev
en
t
in
s
tab
ilit
y
an
d
en
s
u
r
e
q
u
ick
co
n
v
e
r
g
en
ce
to
an
ap
p
r
o
p
r
iate
s
o
lu
tio
n
.
T
h
e
Kp
,
Ki,
an
d
Kd
g
ain
s
wer
e
o
f
f
s
et
b
y
±
2
0
%,
±
1
0
0
%,
an
d
±
3
0
%,
r
esp
ec
tiv
ely
,
f
r
o
m
th
eir
o
r
ig
i
n
al
v
alu
es.
T
h
e
o
r
ig
in
al
v
al
u
es
f
o
r
K
p
,
Ki,
an
d
Kd
wer
e
6
0
0
0
,
2
0
0
,
an
d
2
8
0
0
,
r
esp
ec
tiv
ely
.
T
h
e
in
itial v
alu
es a
n
d
o
b
jectiv
e
f
u
n
ctio
n
wer
e
p
er
f
o
r
m
e
d
as
f
o
llo
win
g
d
etails.
Kp
lo
wer
/ u
p
p
er
lim
it:
4
8
0
0
t
o
7
2
0
0
DAC
Par
eto
f
r
o
n
t:
1
6
s
o
lu
tio
n
s
Ki
lo
wer
/ u
p
p
er
lim
it:
1
0
0
to
4
0
0
DAC
C
r
o
s
s
o
v
er
r
ate:
100%
Kd
lo
wer
/ u
p
p
er
lim
it:
2
2
4
0
t
o
3
6
4
0
DAC
Mu
tatio
n
r
ate:
1
0
%
Nu
m
b
er
o
f
b
it:
1
6
b
its
Gen
er
atio
n
:
1
0
0
0
Ge
n
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Op
timiz
a
tio
n
s
ig
n
a
l wr
itin
g
w
i
th
ma
ch
in
e
lea
r
n
in
g
a
s
s
is
ted
co
n
tr
o
l
(
C
h
a
w
en
g
S
a
p
a
p
p
o
r
n
)
95
Fig
u
r
e
4
.
Un
s
tab
le
s
y
s
tem
f
r
o
m
im
p
r
o
p
er
g
ai
n
T
h
e
GA
s
ea
r
ch
r
esu
lts
wer
e
s
h
o
wn
in
Fig
u
r
e
5
.
T
h
e
1
6
s
o
l
u
tio
n
s
f
r
o
m
th
e
s
ea
r
ch
r
esu
lts
f
o
r
C
lass
3
wer
e
p
r
esen
ted
in
Fig
u
r
e
5
(
a
)
.
T
h
e
s
elec
tio
n
o
f
th
e
o
p
tim
al
g
ain
s
o
lu
tio
n
was
b
ased
o
n
th
e
s
o
lu
tio
n
th
at
p
r
o
v
id
e
d
th
e
m
in
im
u
m
PES
DFT
r
esu
lt
wh
ile
m
ain
tain
in
g
th
e
clo
s
est
g
ain
cr
o
s
s
o
v
er
f
r
eq
u
en
cy
a
n
d
p
h
ase
m
ar
g
in
.
T
h
e
c
o
m
p
ar
is
o
n
o
f
th
e
o
p
en
-
lo
o
p
f
r
e
q
u
en
c
y
r
esp
o
n
s
e
b
etwe
en
th
e
co
n
v
en
tio
n
al
g
ain
an
d
th
e
n
ewly
o
p
tim
ized
g
ain
s
was
s
h
o
wn
i
n
Fig
u
r
e
5
(
b
)
.
I
t
was
ev
id
en
t
th
at
b
o
th
n
ew
g
ain
s
in
c
r
ea
s
ed
th
e
m
ag
n
itu
d
e
to
m
ee
t
th
e
g
ain
cr
o
s
s
o
v
er
f
r
eq
u
en
cy
an
d
p
h
ase
m
ar
g
i
n
,
w
h
ich
wer
e
o
b
jectiv
e
s
in
th
e
f
r
eq
u
e
n
cy
d
o
m
ain
.
Sp
ec
if
ically
,
th
e
lo
w
-
f
r
e
q
u
en
c
y
r
esp
o
n
s
e
was
in
cr
ea
s
ed
d
u
e
to
Ki,
r
esu
ltin
g
in
th
e
elim
in
at
io
n
o
f
e
r
r
o
r
s
at
th
e
clo
s
ed
-
lo
o
p
s
tead
y
s
tate
in
th
e
tim
e
d
o
m
ain
,
th
is
r
esu
lt wa
s
s
tr
o
n
g
ly
c
o
r
r
elate
d
with
th
e
d
es
ig
n
o
f
ex
p
er
im
en
ts
ap
p
r
o
ac
h
,
wh
ich
u
tili
ze
d
th
e
r
esp
o
n
s
e
s
u
r
f
ac
e
m
eth
o
d
o
lo
g
y
.
T
h
e
f
in
al
r
esu
lts
o
f
th
e
g
ain
s
o
b
tain
ed
d
u
r
in
g
th
e
s
ea
r
c
h
,
wh
ich
m
et
th
e
o
b
jectiv
e
f
u
n
ctio
n
o
f
m
an
u
f
ac
tu
r
in
g
cr
iter
ia
f
o
r
b
o
t
h
tim
e
an
d
f
r
eq
u
e
n
cy
r
esp
o
n
s
e,
wer
e
p
r
esen
ted
in
T
a
b
le
2
.
T
h
ey
ex
h
ib
ited
a
tr
en
d
o
f
o
v
e
r
all
in
cr
ea
s
e,
s
u
g
g
esti
n
g
th
at
th
e
m
ac
h
i
n
e
co
u
ld
o
p
er
ate
f
aster
th
a
n
n
o
r
m
al
f
o
r
b
o
th
m
ac
h
in
e
class
es.
T
h
e
Kp
g
ain
f
o
r
C
la
s
s
0
was
f
o
u
n
d
to
b
e
s
lig
h
tly
h
ig
h
er
th
an
th
at
f
o
r
C
lass
3
,
s
tr
o
n
g
ly
in
d
icatin
g
th
at
C
las
s
0
was
ab
le
to
p
r
o
d
u
ce
a
lo
w
p
o
s
iti
o
n
er
r
o
r
wh
ile
m
ain
t
ain
in
g
f
r
e
q
u
en
c
y
r
esp
o
n
s
e
cr
it
er
ia.
Nev
er
th
eless
,
th
er
e
was sti
ll r
o
o
m
f
o
r
C
lass
3
to
r
ed
u
ce
p
o
s
itio
n
er
r
o
r
b
y
in
cr
ea
s
in
g
th
e
Kp
g
ain
to
a
s
u
itab
le
lev
el.
(
a)
(
b
)
Fig
u
r
e
5
.
GA
s
ea
r
ch
r
esu
lt (
a)
3
o
b
jectiv
es in
class
3
an
d
(
b
)
o
p
en
lo
o
p
r
esp
o
n
s
e
o
f
class
0
a
n
d
3
T
ab
le
2
.
Su
m
m
a
r
y
o
f
PID
g
ai
n
s
ea
r
ch
r
esu
lt
C
o
n
t
r
o
l
l
e
r
K
p
(
D
A
C
)
K
i
(
D
A
C
)
K
d
(
D
A
C
)
C
o
n
v
e
n
t
i
o
n
a
l
6
0
0
0
2
0
0
2
8
0
0
O
p
t
.
C
l
a
ss
0
6
5
9
7
3
3
1
3
1
4
7
O
p
t
.
C
l
a
ss
3
6
4
5
0
3
4
0
3
0
8
8
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
1
,
J
an
u
ar
y
20
25
:
90
-
1
00
96
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
W
h
en
GA
-
d
er
iv
ed
g
ain
s
wer
e
ap
p
lied
t
o
C
lass
0
an
d
C
l
ass
3
m
ac
h
in
es,
th
eir
p
er
f
o
r
m
an
ce
was
ev
alu
ated
th
r
o
u
g
h
f
r
eq
u
e
n
cy
a
n
d
tim
e
r
esp
o
n
s
e
an
al
y
s
es.
T
h
is
ev
alu
atio
n
h
i
g
h
lig
h
ted
th
e
i
m
p
ac
t
o
f
o
p
tim
ized
co
n
tr
o
ller
s
ettin
g
s
o
n
th
e
b
eh
av
io
r
o
f
th
e
s
ig
n
al
wr
iter
m
ac
h
in
e.
T
h
e
o
p
e
n
-
lo
o
p
f
r
eq
u
e
n
c
y
r
esp
o
n
s
e
a
n
aly
s
is
ex
am
in
ed
th
e
g
ain
cr
o
s
s
o
v
er
f
r
eq
u
e
n
cy
an
d
p
h
ase
m
ar
g
in
,
wh
ile
th
e
tim
e
r
esp
o
n
s
e
an
aly
s
is
f
o
cu
s
ed
o
n
th
e
clo
s
ed
-
lo
o
p
PES.
3
.
1
.
O
pen lo
o
p
f
re
qu
ency
re
s
po
ns
e
Fig
u
r
e
6
illu
s
tr
ated
two
k
ey
f
r
eq
u
e
n
cy
r
esp
o
n
s
e
p
ar
a
m
eter
s
:
g
ain
cr
o
s
s
o
v
er
f
r
e
q
u
en
c
y
an
d
p
h
ase
m
ar
g
in
.
T
h
e
g
ain
c
r
o
s
s
o
v
er
f
r
eq
u
en
cy
in
d
icate
d
wh
er
e
th
e
s
y
s
tem
’
s
o
p
en
-
lo
o
p
tr
a
n
s
f
er
f
u
n
ctio
n
m
ag
n
itu
d
e
eq
u
aled
1
(
0
d
B
)
,
wh
ile
t
h
e
p
h
ase
m
ar
g
in
was
th
e
d
if
f
e
r
en
c
e
b
etwe
en
th
e
p
h
ase
an
g
le
at
th
is
f
r
e
q
u
en
c
y
an
d
n
eg
ativ
e
1
8
0
d
e
g
r
ee
s
.
B
o
th
p
a
r
am
eter
s
wer
e
cr
itical
f
o
r
e
v
al
u
atin
g
th
e
tr
a
n
s
ien
t r
esp
o
n
s
e
a
n
d
s
tab
ilit
y
.
Fig
u
r
e
6
(
a)
d
em
o
n
s
tr
ated
th
at
GA
-
d
er
iv
ed
g
ai
n
s
(
in
o
r
a
n
g
e
an
d
y
ello
w)
s
ig
n
i
f
ican
tly
in
c
r
ea
s
ed
th
e
g
ain
cr
o
s
s
o
v
er
f
r
eq
u
e
n
cy
to
w
ar
d
s
th
e
tar
g
e
t
o
f
5
5
0
Hz.
Fo
r
C
las
s
0
,
it
r
o
s
e
f
r
o
m
5
2
7
to
5
6
7
Hz,
an
d
f
o
r
C
lass
3
,
it
in
cr
ea
s
ed
f
r
o
m
5
3
4
to
5
6
4
Hz.
T
h
e
in
cr
ea
s
e
in
g
ain
cr
o
s
s
o
v
er
f
r
eq
u
e
n
cy
,
o
b
s
er
v
ed
with
th
e
n
ew
g
ain
s
,
was c
o
r
r
elate
d
with
a
h
ig
h
er
c
lo
s
ed
-
lo
o
p
b
an
d
wid
th
an
d
a
f
a
s
ter
r
esp
o
n
s
e
co
m
p
ar
ed
to
c
o
n
v
en
tio
n
al
g
ai
n
s
.
Fig
u
r
e
6
(
b
)
s
h
o
wed
th
at
GA
-
d
er
iv
ed
g
ai
n
s
s
lig
h
tly
d
ec
r
ea
s
ed
th
e
p
h
ase
m
ar
g
in
to
t
h
e
tar
g
et
o
f
4
0
d
eg
r
ee
s
b
u
t
d
id
n
o
t
s
ig
n
if
ican
t
ly
d
if
f
er
f
r
o
m
co
n
v
en
tio
n
al
g
a
in
s
,
in
d
icatin
g
s
im
ilar
lev
els
o
f
s
tab
ilit
y
f
o
r
ea
ch
m
ac
h
in
e
g
r
o
u
p
.
T
h
e
c
o
m
b
in
a
tio
n
o
f
an
in
cr
ea
s
ed
g
ain
cr
o
s
s
o
v
er
f
r
eq
u
en
c
y
with
a
s
lig
h
t
ch
an
g
e
in
p
h
ase
m
ar
g
in
s
u
g
g
ested
th
at
th
e
m
ac
h
in
e
co
u
ld
ac
h
iev
e
a
f
aster
tr
an
s
ien
t
r
esp
o
n
s
e
wh
ile
m
ain
tain
in
g
th
e
s
am
e
s
tab
ilit
y
lev
el.
Ad
d
itio
n
al
test
in
g
r
ev
ea
led
t
h
at
s
wap
p
in
g
c
o
n
tr
o
ller
s
b
etwe
en
C
lass
0
an
d
C
lass
3
r
esu
lted
in
f
r
eq
u
e
n
cies th
at
r
em
ain
ed
h
ig
h
er
th
an
with
co
n
v
en
tio
n
al
g
ain
s
.
C
las
s
0
h
ad
a
g
ain
cr
o
s
s
o
v
er
f
r
eq
u
en
c
y
o
f
5
6
7
Hz,
wh
ile
s
o
m
e
C
la
s
s
3
m
ac
h
in
es
ex
ce
ed
ed
6
0
1
Hz.
I
m
p
l
em
en
tin
g
C
lass
0
g
ain
s
in
C
l
ass
3
m
ac
h
in
es
was
ad
v
is
ed
ag
ain
s
t d
u
e
to
in
cr
ea
s
ed
s
en
s
itiv
ity
to
s
p
in
d
le
m
o
to
r
v
ib
r
atio
n
s
an
d
h
ig
h
-
f
r
e
q
u
en
c
y
r
eso
n
an
ce
.
(
a)
(
b
)
Fig
u
r
e
6
.
Fre
q
u
en
cy
r
esp
o
n
s
e
(
a)
g
ain
c
r
o
s
s
o
v
er
f
r
eq
u
e
n
cy
a
n
d
(
b
)
p
h
ase
m
a
r
g
in
3
.
2
.
Clo
s
ed
lo
o
p
t
i
m
e
re
s
po
ns
e
Fig
u
r
e
7
illu
s
tr
ated
th
e
m
o
v
e
m
en
t e
r
r
o
r
s
d
u
r
i
n
g
th
e
s
ig
n
al
wr
itin
g
p
r
o
ce
s
s
f
r
o
m
th
e
o
u
ter
to
th
e
in
n
er
p
ar
t
o
f
th
e
d
is
k
.
I
t
was
s
h
o
w
n
in
Fig
u
r
e
7
(
a
)
th
at
th
e
s
p
ir
al
PES,
in
f
lu
en
ce
d
b
y
th
e
n
ewly
a
p
p
lied
GA
-
d
e
r
iv
e
d
g
ain
s
,
ex
h
ib
ited
a
r
ed
u
ce
d
o
v
e
r
s
h
o
o
t o
f
p
o
s
itio
n
er
r
o
r
d
u
r
in
g
in
itial m
o
v
em
en
ts
.
T
h
e
2
0
0
d
ata
p
o
in
ts
u
s
ed
to
ass
ess
m
o
v
em
en
t
q
u
ality
,
in
d
ica
ted
b
y
th
e
v
er
tical
d
ash
ed
lin
e
,
wer
e
n
o
t
clea
r
in
th
e
tim
e
d
o
m
ain
.
T
h
e
r
ef
o
r
e,
t
h
e
s
p
ir
al
PES
was
tr
an
s
f
o
r
m
ed
f
r
o
m
t
h
e
tim
e
d
o
m
a
in
to
th
e
f
r
e
q
u
en
c
y
d
o
m
ain
u
s
in
g
th
e
DFT
,
(
)
,
as
d
escr
ib
ed
in
(
3
)
.
On
ly
th
e
am
p
litu
d
e
co
m
p
o
n
en
t
d
ef
i
n
ed
b
y
(
4
)
,
was
co
n
s
id
er
ed
(
ig
n
o
r
in
g
th
e
p
h
ase
co
m
p
o
n
e
n
t)
.
T
h
e
o
v
er
all
a
m
p
litu
d
e
o
f
th
e
h
ar
m
o
n
ic
at
ea
ch
f
r
eq
u
e
n
cy
was
ca
lcu
lated
,
with
th
e
r
esu
lts
s
h
o
wn
in
Fig
u
r
e
7
(
b
)
.
No
tab
le
i
m
p
r
o
v
e
m
en
ts
wer
e
clea
r
ly
s
h
o
wn
,
esp
ec
ially
at
lo
wer
f
r
eq
u
en
cies star
tin
g
f
r
o
m
4
0
0
Hz.
I
t
was
in
f
er
r
e
d
th
at
th
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s
y
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er
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ed
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ain
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atin
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T
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u
r
th
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p
p
o
r
ted
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y
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e
g
ain
c
r
o
s
s
o
v
er
f
r
eq
u
en
cy
o
b
jectiv
e
f
u
n
ctio
n
,
wh
er
e
th
e
h
ig
h
g
ain
cr
o
s
s
o
v
er
f
r
e
q
u
e
n
cy
in
d
icate
d
th
e
p
o
te
n
tial
to
in
cr
ea
s
e
s
y
s
tem
b
an
d
wid
th
,
r
esu
ltin
g
in
a
f
aster
s
y
s
tem
r
esp
o
n
s
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Op
timiz
a
tio
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ig
n
a
l wr
itin
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97
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in
(
2
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f
s
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les?
(
)
=
√
(
)
2
+
(
)
2
(
4
)
(
a)
(
b
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Fig
u
r
e
7
.
Po
s
itio
n
m
o
v
em
e
n
t
er
r
o
r
(
a)
s
p
ir
al
m
o
v
em
en
t e
r
r
o
r
in
t
im
e
an
d
(
b
)
DFT
o
f
s
p
ir
al
PES
3
.
3
.
H
y
po
t
hes
is
t
est
W
h
en
ad
d
itio
n
al
DFT
d
ata
we
r
e
co
llected
f
r
o
m
m
ac
h
i
n
es
in
b
o
th
C
lass
0
an
d
C
la
s
s
3
u
s
in
g
th
e
n
ew
PID
g
ain
s
f
r
o
m
GA,
th
e
r
ep
r
e
s
en
tativ
e
PES
in
th
e
6
0
0
to
1
,
5
0
0
Hz
r
an
g
e
was
ca
lcu
lated
u
s
in
g
th
e
E
u
clid
ea
n
d
is
tan
c
e,
as
s
h
o
wn
in
(
5
)
,
Fig
u
r
e
8
co
m
p
ar
ed
th
e
p
e
r
f
o
r
m
an
ce
o
f
th
e
GA
g
ain
s
,
with
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lu
e
r
ep
r
esen
tin
g
co
n
v
en
tio
n
al
g
ain
s
,
o
r
an
g
e
r
ep
r
esen
tin
g
PID
g
ain
s
f
o
r
C
lass
0
,
an
d
y
ello
w
r
ep
r
esen
tin
g
PI
D
g
ain
s
f
o
r
C
lass
3
.
=
√
600
2
+
⋯
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1500
2
(
5
)
F
o
r
m
a
c
h
i
n
e
s
i
n
C
l
as
s
0
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Fi
g
u
r
e
8
(
a
)
s
h
o
w
s
t
h
a
t
b
o
t
h
n
e
w
PI
D
g
a
i
n
s
p
e
r
f
o
r
m
w
e
l
l
,
wi
t
h
DF
T
v
a
l
u
es
b
e
l
o
w
1
0
c
o
u
n
t
s
,
e
x
c
e
p
t
f
o
r
m
a
c
h
i
n
e
1
8
7
3
C
3
,
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h
i
c
h
e
x
h
i
b
i
t
s
a
n
u
p
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t
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D
g
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n
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r
o
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l
a
s
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i
g
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r
e
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b
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r
e
v
e
a
l
s
t
h
a
t
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m
a
l
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n
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FT
i
s
4
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3
8
6
c
o
u
n
t
s
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it
h
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l
o
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n
d
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r
d
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f
1
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2
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o
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n
t
s
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h
i
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u
p
p
o
r
t
s
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e
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v
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t
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o
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i
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s
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m
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n
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t
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r
r
o
r
s
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h
e
G
A
-
d
e
r
i
v
e
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g
a
i
n
f
o
r
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l
a
s
s
0
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e
l
d
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t
h
e
b
es
t
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u
l
ts
f
o
r
t
h
is
m
a
c
h
i
n
e
g
r
o
u
p
.
Fo
r
m
ac
h
in
es in
C
lass
3
,
Fig
u
r
e
8
(
a)
s
h
o
ws s
ev
er
al
s
ce
n
ar
io
s
with
d
etailed
ex
p
lan
atio
n
s
:
Ma
ch
in
e
1
9
2
2
D2
:
T
h
is
m
ac
h
in
e
s
h
o
wed
s
ig
n
if
ican
t
im
p
r
o
v
em
en
t
in
f
ailu
r
e
r
ates.
C
o
n
v
en
tio
n
al
g
ain
r
esu
lted
in
DFT
m
ed
ia
n
v
alu
es
ex
ce
ed
in
g
3
0
.
6
6
c
o
u
n
ts
.
B
y
u
s
in
g
t
h
e
n
ew
g
ai
n
s
,
th
e
m
ed
ian
v
al
u
e
was
r
ed
u
ce
d
t
o
2
7
.
9
2
c
o
u
n
ts
with
Op
t
0
g
ain
an
d
to
2
5
.
2
7
co
u
n
ts
w
ith
Op
t
3
g
ain
,
th
u
s
r
e
d
u
c
in
g
m
an
u
f
ac
tu
r
in
g
f
ailu
r
e
r
ates.
Ma
ch
in
e
1
9
2
8
C
3
,
1
9
2
8
C
2
,
1
9
2
2
D
1
,
1
0
8
1
B
3
:
T
h
ese
m
ac
h
in
es
also
p
er
f
o
r
m
ed
well
with
b
o
th
g
ain
s
,
with
th
e
Op
t
3
g
ain
p
r
o
v
id
in
g
th
e
b
est
p
er
f
o
r
m
a
n
c
e.
Ma
ch
in
e
2
3
2
3
B
3
,
1
0
8
1
D
4
:
T
h
ese
m
ac
h
in
es
ex
h
ib
ited
o
p
ti
m
al
p
e
r
f
o
r
m
an
c
e
o
n
ly
with
th
e
C
lass
3
g
ain
.
T
h
e
u
s
e
o
f
th
e
C
lass
0
g
ain
r
esu
lted
in
in
cr
ea
s
ed
er
r
o
r
s
,
lik
el
y
d
u
e
t
o
th
e
h
ig
h
er
g
ain
cr
o
s
s
o
v
e
r
f
r
eq
u
e
n
cy
o
b
s
er
v
ed
in
t
h
e
o
p
en
-
l
o
o
p
f
r
eq
u
en
cy
r
esp
o
n
s
e,
wh
ich
am
p
lifie
d
n
o
is
e
an
d
v
i
b
r
atio
n
s
f
r
o
m
th
e
s
p
in
d
le
m
o
t
o
r
.
C
o
n
s
eq
u
en
tly
,
th
e
C
lass
0
g
ain
s
wer
e
av
o
id
ed
f
o
r
th
ese
m
ac
h
i
n
es.
Fig
u
r
e
8
(
b
)
d
em
o
n
s
tr
ated
th
at
th
e
PID
g
ain
f
o
r
m
ac
h
in
es
in
C
las
s
3
p
er
f
o
r
m
ed
as
ex
p
ec
t
ed
,
with
a
DFT
o
f
1
2
.
1
0
co
u
n
ts
an
d
a
l
o
w
s
tan
d
ar
d
d
ev
iatio
n
o
f
8
.
0
2
2
co
u
n
ts
.
T
h
e
GA
-
d
er
iv
ed
g
ain
f
o
r
C
lass
3
y
ield
ed
th
e
b
est r
esu
lts
f
o
r
th
is
m
ac
h
in
e
g
r
o
u
p
.
Du
e
to
th
e
s
m
all
s
am
p
le
s
ize
in
th
e
ev
alu
atio
n
,
a
two
-
s
am
p
le
T
-
test
wa
s
co
n
d
u
cted
to
v
alid
ate
th
e
im
p
r
o
v
em
e
n
ts
.
T
ab
le
3
d
em
o
n
s
tr
ated
th
at
th
e
T
-
test
r
esu
lts
y
ield
ed
T
-
v
alu
es o
f
-
2
.
6
2
an
d
-
2
.
0
1
,
with
P
-
v
al
u
es
o
f
0
.
0
0
5
an
d
0
.
0
2
3
f
o
r
C
lass
0
an
d
C
lass
3
,
r
esp
ec
tiv
ely
,
b
o
t
h
o
f
wh
ich
wer
e
b
elo
w
0
.
0
5
.
T
h
e
n
u
ll
h
y
p
o
th
eses
f
o
r
b
o
th
class
es
wer
e
r
ejec
te
d
,
an
d
t
h
e
alter
n
ativ
e
h
y
p
o
t
h
eses
wer
e
ac
ce
p
ted
.
T
h
is
in
d
icate
d
th
at
th
e
n
ew
co
n
tr
o
ller
g
ain
s
s
ig
n
if
ican
tly
r
e
d
u
ce
d
th
e
m
ea
n
DFT
co
m
p
ar
ed
to
th
e
c
o
n
v
e
n
tio
n
al
g
ain
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
37
,
No
.
1
,
J
an
u
ar
y
20
25
:
90
-
1
00
98
(
a)
(
b
)
Fig
u
r
e
8
.
PES D
FT
o
b
jectiv
e
(
a)
d
ata
b
y
m
ac
h
in
e
a
n
d
(
b
)
d
is
tr
ib
u
tio
n
b
y
m
ac
h
i
n
e
class
T
ab
le
3
.
T
wo
-
Sam
p
le
T
-
T
est
C
l
a
s
s
0
C
l
a
s
s
3
μ1
:
me
a
n
o
f
D
F
T,
O
p
t
0
G
a
i
n
μ1
:
me
a
n
o
f
D
F
T,
O
p
t
3
G
a
i
n
μ2
:
me
a
n
o
f
D
F
T,
C
o
n
v
e
n
t
i
o
n
a
l
G
a
i
n
μ2
:
me
a
n
o
f
D
F
T,
C
o
n
v
e
n
t
i
o
n
a
l
G
a
i
n
N
u
l
l
h
y
p
o
t
h
e
s
i
s
H
0
:
μ1
–
μ2
=
0
N
u
l
l
h
y
p
o
t
h
e
s
i
s
H
0
:
μ1
–
μ2
=
0
A
l
t
e
r
n
a
t
i
v
e
h
y
p
o
t
h
e
si
s
H
1
:
μ1
–
μ2
<
0
A
l
t
e
r
n
a
t
i
v
e
h
y
p
o
t
h
e
si
s
H
1
:
μ1
–
μ2
<
0
T
-
V
a
l
u
e
=
-
2
.
6
2
D
F
=
1
2
9
P
-
V
a
l
u
e
=
0
.
0
0
5
T
-
V
a
l
u
e
=
-
2
.
0
1
D
F
=
1
7
8
P
-
V
a
l
u
e
=
0
.
0
2
3
T
ab
les
4
an
d
5
s
u
m
m
ar
ized
t
h
e
ex
p
e
r
im
en
tal
r
esu
lts
f
o
r
t
h
e
th
r
ee
o
b
jectiv
es.
T
h
e
g
ain
cr
o
s
s
o
v
er
f
r
eq
u
e
n
cy
was
in
cr
ea
s
ed
b
y
ap
p
r
o
x
im
ately
3
0
Hz,
wh
ich
ex
p
an
d
e
d
th
e
clo
s
ed
-
lo
o
p
b
a
n
d
wid
th
an
d
led
to
im
p
r
o
v
e
d
tr
ac
k
in
g
an
d
a
q
u
ic
k
er
r
esp
o
n
s
e
to
r
ef
er
e
n
ce
s
ig
n
als,
th
er
eb
y
r
ed
u
cin
g
p
o
s
itio
n
m
o
v
em
en
t
er
r
o
r
s
.
T
h
e
p
h
ase
m
ar
g
in
r
em
ai
n
ed
ar
o
u
n
d
4
1
d
eg
r
ee
s
,
in
d
icatin
g
co
n
s
is
ten
t
s
tab
ilit
y
,
tr
an
s
ien
t
r
esp
o
n
s
e,
an
d
d
is
tu
r
b
an
ce
r
ejec
tio
n
.
T
h
e
s
ig
n
al
wr
ite
q
u
ali
ty
,
m
ea
s
u
r
ed
u
s
in
g
DFT
o
f
th
e
p
o
s
itio
n
er
r
o
r
s
ig
n
al,
im
p
r
o
v
e
d
b
y
1
4
.
6
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