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ev
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[
1
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So
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Had
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Fil
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[
3
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
0
8
8
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8708
I
n
t J
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&
C
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p
E
n
g
,
Vo
l.
10
,
No
.
4
,
A
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g
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s
t 2
0
2
0
:
3
6
2
3
-
3634
3624
Dif
f
er
en
t
d
ata
p
r
ed
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n
an
d
r
ep
lace
m
e
n
t
s
tr
ateg
ies
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
in
r
ec
en
t
li
ter
atu
r
e.
So
m
e
d
ep
en
d
o
n
u
s
in
g
t
h
e
h
i
s
to
r
y
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f
co
llected
d
ata
f
r
o
m
th
e
f
ai
lin
g
n
o
d
e,
lik
e
t
h
o
s
e
i
n
[
4
]
,
o
r
m
o
v
in
g
a
n
ei
g
h
b
o
r
in
g
n
o
d
e
to
d
o
th
e
w
o
r
k
o
f
t
h
i
s
f
ailin
g
n
o
d
e
[
5
]
,
o
r
ch
an
g
i
n
g
t
h
e
r
o
u
te
tr
an
s
m
itted
m
e
s
s
a
g
e
s
u
s
in
g
a
‘
li
f
e
ti
m
e
a
w
ar
e
r
o
u
ti
n
g
p
r
o
to
co
ls
’
[
6
]
.
Oth
er
tech
n
iq
u
e
s
in
v
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lv
e
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s
s
ig
n
i
n
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n
d
er
t
h
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ea
t
o
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f
ail
in
g
[
7
]
.
T
h
is
p
r
o
ce
d
u
r
e,
alth
o
u
g
h
v
e
r
y
e
f
f
icien
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w
o
u
ld
b
e
v
er
y
e
x
p
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s
iv
e.
So
m
e
o
f
th
e
p
r
o
p
o
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d
e
r
ep
lace
m
e
n
t stra
te
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n
liter
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a
n
it
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p
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to
b
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[
8
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esp
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No
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t
al
w
a
y
s
b
e
t
h
e
m
o
s
t
ap
p
licab
le
s
o
lu
tio
n
s
to
t
h
ese
f
aili
n
g
n
o
d
es.
T
h
ese
n
o
d
es
co
u
ld
b
e
ex
p
en
s
i
v
e
t
y
p
es
o
f
s
en
s
o
r
s
,
p
lace
d
in
a
h
ar
d
-
to
-
r
ea
ch
ar
ea
,
tak
e
to
o
m
u
c
h
ti
m
e
to
r
ea
ch
an
d
r
ep
lace
,
o
r
r
eq
u
ir
e
a
c
er
tain
lev
el
o
f
ex
p
er
ien
ce
f
r
o
m
w
o
r
k
er
s
w
h
o
m
ig
h
t
n
o
t
b
e
av
ailab
le
b
y
th
e
ti
m
e
o
f
t
h
e
f
ai
lu
r
e.
T
h
e
m
o
s
t
i
m
p
o
r
tan
t
f
ea
t
u
r
e
in
u
s
i
n
g
h
i
s
to
r
ical
s
tati
s
tics
o
f
n
o
d
es’
r
ea
d
in
g
s
is
t
h
at
est
i
m
a
ti
o
n
er
r
o
r
s
ca
n
b
e
m
i
n
i
m
ized
alo
n
g
t
h
e
ti
m
e
r
ea
d
in
g
s
s
u
b
m
itted
b
y
t
h
ese
n
o
d
es.
I
n
ca
s
e
o
f
a
n
o
d
e
(
o
r
co
n
n
ec
tiv
it
y
)
f
ail
u
r
e,
esti
m
atio
n
o
f
t
h
at
n
o
d
e’
s
d
ata
ca
n
b
e
u
s
ed
u
s
in
g
a
weig
h
ted
p
r
ed
ictio
n
m
ec
h
a
n
is
m
f
r
o
m
th
e
h
is
to
r
y
o
f
p
r
ev
io
u
s
l
y
r
e
g
is
ter
ed
d
ata.
T
h
e
u
s
e
o
f
a
m
icr
o
co
n
tr
o
ller
to
g
o
v
er
n
th
e
s
to
r
ag
e
a
n
d
m
a
n
ag
e
h
is
to
r
ical
d
ata
p
r
esen
ts
a
m
o
r
e
r
eliab
le
an
d
c
o
s
t
ef
f
ec
ti
v
e
tech
n
iq
u
e
f
o
r
s
u
c
h
d
ata
m
a
n
ag
e
m
e
n
t.
I
t
also
p
r
o
v
id
es
an
e
f
f
icien
t
f
ac
ilit
y
to
ca
lc
u
late
er
r
o
r
s
b
et
w
ee
n
s
u
cc
es
s
i
v
e
h
is
to
r
y
f
i
les
’
en
tr
ies.
T
h
ese
ca
n
b
e
ea
s
i
l
y
ac
ce
s
s
ed
an
d
ad
o
p
ted
in
an
ar
ti
f
icial
i
n
tel
lig
e
n
ce
m
o
d
u
le
to
h
elp
in
es
ti
m
atin
g
m
is
s
in
g
d
ata
i
n
a
W
SN.
So
m
e
s
y
s
te
m
s
w
it
h
n
o
d
es
h
a
v
e
a
r
an
g
e
o
f
v
al
u
e
s
t
h
at
t
h
e
y
c
o
llect
d
ata
al
w
a
y
s
f
all
w
it
h
i
n
,
lik
e
s
tea
m
s
ter
ilizer
s
y
s
te
m
s
.
T
h
ese
t
y
p
es
o
f
s
y
s
te
m
f
ac
ilit
a
te
u
s
e
a
n
i
n
t
ellig
e
n
t
tec
h
n
iq
u
e,
lik
e
f
u
zz
y
l
o
g
ic,
ef
f
icie
n
t
an
d
w
o
r
th
y
f
r
o
m
d
if
f
er
en
t
asp
ec
ts
:
f
i
n
an
cia
l,
o
p
er
atio
n
al,
an
d
en
er
g
y
s
a
v
i
n
g
.
F
u
zz
y
lo
g
ic
tech
n
iq
u
e
s
ar
e
b
ec
o
m
i
n
g
w
id
el
y
u
s
ed
in
d
e
cisi
o
n
m
ak
in
g
ap
p
l
icatio
n
es
p
ec
iall
y
th
o
s
e
r
elate
d
to
W
S
N
b
ased
s
y
s
te
m
s
.
T
h
ey
p
r
o
v
id
e
th
e
ab
ilit
y
o
f
d
ea
lin
g
w
ith
r
ea
l
-
w
o
r
ld
u
n
ce
r
t
ain
t
y
p
r
o
b
le
m
s
t
h
at
ar
e
n
o
t
b
ased
o
n
s
tatis
tica
l
m
ea
s
u
r
es
[
9
]
.
th
is
tec
h
n
iq
u
e
is
co
n
s
id
er
ed
less
ex
te
n
s
i
v
e
an
d
m
o
r
e
r
eliab
le
th
an
au
to
m
atic
co
n
tr
o
l
o
f
w
ir
ele
s
s
d
ata
m
a
n
ag
e
m
e
n
t
an
d
co
m
m
u
n
ica
tio
n
.
F
u
zz
y
lo
g
ic
d
ea
ls
w
it
h
m
is
s
in
g
in
p
u
t
o
r
u
n
clea
r
in
p
u
t
in
a
lo
g
ical
m
an
n
er
t
h
at
r
ese
m
b
les
h
o
w
a
h
u
m
a
n
m
a
k
e
s
d
ec
is
io
n
s
.
T
h
e
o
b
v
io
u
s
ad
v
an
tag
e
o
f
u
s
i
n
g
f
u
zz
y
lo
g
ic
i
s
th
e
s
p
ee
d
o
f
p
r
o
ce
s
s
in
g
o
f
f
u
zz
y
i
n
p
u
t
a
n
d
p
r
o
d
u
cin
g
d
ec
is
io
n
s
.
F
u
zz
y
lo
g
ic
r
elies
o
n
h
av
in
g
lear
n
i
n
g
r
u
les
[
1
0
]
,
ac
co
r
d
in
g
to
w
h
ich
an
in
f
er
en
ce
e
n
g
i
n
e
m
a
k
es
d
e
cisi
o
n
s
r
eg
ar
d
in
g
th
e
p
r
o
b
le
m
at
h
an
d
.
T
h
e
in
p
u
t
to
th
ese
f
u
zz
y
s
e
ts
is
i
n
n
a
tu
r
al
lan
g
u
ag
e
(
n
o
t
d
is
cr
ete
v
alu
e
s
)
.
T
h
e
o
u
tp
u
t
ca
n
ei
th
er
b
e
c
r
is
p
o
r
in
n
atu
r
al
la
n
g
u
a
g
e
[
11
]
,
alo
n
g
w
it
h
co
n
f
id
e
n
ce
lev
el
s
o
f
h
o
w
“
ap
p
r
o
p
r
iate”
th
e
p
r
o
d
u
ce
d
o
u
tp
u
t
is
to
th
e
s
y
s
te
m
’
s
ap
p
licatio
n
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
te
m
i
n
t
h
i
s
s
t
u
d
y
w
o
r
k
s
o
v
er
t
w
o
m
a
in
s
tag
e
s
.
T
h
e
f
ir
s
t
s
tag
e
is
b
u
ild
in
g
a
h
is
to
r
ical
d
ata
ar
ch
i
v
e
t
h
at
s
to
r
es
ev
er
y
n
o
d
e’
s
r
ea
d
in
g
s
o
v
er
s
ev
er
al
t
i
m
e
s
tep
s
(
T
S)
an
d
th
e
ti
m
e
s
ta
m
p
o
f
th
at
r
ea
d
in
g
.
T
h
e
s
ter
ilizer
w
o
r
k
s
o
n
tr
ea
t
m
e
n
t
o
f
m
ater
ial
b
y
ac
ti
v
ati
n
g
t
h
e
tr
ea
t
m
e
n
t
d
ev
ice
w
h
e
n
tr
ea
t
m
e
n
t
is
r
eq
u
ir
ed
o
n
ti
m
el
y
b
a
s
is
.
E
ac
h
o
f
t
h
ese
tr
ea
t
m
en
t
p
h
a
s
es
is
ca
lled
a
u
n
i
t’
s
tr
ea
t
m
e
n
t
r
u
n
.
T
h
e
s
ec
o
n
d
s
tag
e
in
v
o
l
v
es
p
r
ed
ictio
n
o
f
lo
s
t
d
at
a
w
h
en
a
n
o
d
e
f
ail
s
.
I
t
esti
m
at
es
lo
s
t
d
ata
b
ased
o
n
th
e
o
u
tp
u
t
o
f
t
h
e
f
u
zz
y
lo
g
ic
in
f
er
en
ce
e
n
g
in
e
th
at
is
f
ed
with
t
h
e
h
is
to
r
ical
d
ata
an
d
t
h
e
f
u
zz
y
r
u
les
to
b
u
ild
t
h
e
e
s
ti
m
atio
n
.
A
d
ed
icate
d
m
icr
o
co
n
tr
o
ller
w
ill
b
e
i
n
c
h
ar
g
e
o
f
m
o
n
ito
r
in
g
t
h
e
n
o
d
es
an
d
d
etec
ti
n
g
f
ai
lin
g
n
o
d
es,
an
d
t
h
en
ac
tiv
a
te
s
th
e
d
ata
r
esto
r
atio
n
b
y
p
r
ed
i
ctio
n
tec
h
n
iq
u
e.
T
h
e
m
icr
o
co
n
tr
o
ller
is
al
s
o
r
esp
o
n
s
ib
le
f
o
r
ar
ch
i
v
i
n
g
an
d
lab
elin
g
h
is
to
r
y
d
ata
f
ile
s
.
I
t
w
o
u
ld
f
ac
ilit
ate
f
etch
in
g
th
e
c
o
r
r
ec
t
f
ile
alo
n
g
w
i
th
ca
lc
u
lati
n
g
est
i
m
a
tio
n
er
r
o
r
to
h
elp
t
h
e
s
y
s
te
m
u
s
e
t
h
e
a
p
p
r
o
p
r
iate
r
u
le
b
ase
f
o
r
th
e
f
u
zz
y
lo
g
ic
s
y
s
te
m
to
g
e
n
er
ate
th
e
esti
m
atio
n
.
T
h
is
w
o
u
ld
m
ak
e
th
e
s
y
s
te
m
m
o
r
e
e
f
f
icie
n
t
i
n
ter
m
s
o
f
p
r
o
ce
s
s
i
n
g
a
n
d
est
i
m
a
tio
n
s
p
ee
d
,
f
u
n
ct
io
n
alit
y
,
a
n
d
ad
ap
tab
ilit
y
w
it
h
d
ea
li
n
g
w
it
h
v
ar
io
u
s
f
ai
lin
g
n
o
d
es sce
n
ar
io
s
,
an
d
o
f
co
u
r
s
e
h
a
v
in
g
a
co
s
t
ef
f
ec
tiv
e
tech
n
iq
u
e
f
o
r
n
o
d
es r
ep
lace
m
en
t a
n
d
d
ata
r
ec
o
v
er
y
.
T
h
e
r
est
o
f
th
e
p
ap
er
w
ill
p
r
esen
t
a
b
r
ief
r
ev
ie
w
o
f
p
r
ev
io
u
s
s
tu
d
ie
s
r
elate
d
to
r
esto
r
in
g
d
ata
f
r
o
m
f
aili
n
g
n
o
d
es
i
n
W
SNs
,
i
n
Se
ctio
n
2
.
I
n
Sectio
n
3
,
an
e
x
p
l
an
atio
n
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
f
o
r
r
esto
r
in
g
m
is
s
i
n
g
d
ata
f
r
o
m
a
f
ai
lin
g
f
r
o
m
h
i
s
to
r
ical
d
ata
u
s
i
n
g
f
u
zz
y
lo
g
ic
is
p
r
esen
ted
.
Sectio
n
4
illu
s
tr
a
tes
d
if
f
er
e
n
t
r
u
n
s
ce
n
ar
io
s
w
it
h
v
ar
io
u
s
n
o
d
es’
r
ea
d
in
g
s
,
an
d
s
h
o
w
s
a
n
a
n
al
y
s
i
s
o
f
th
e
r
es
u
lts
to
p
r
o
v
e
th
e
e
f
f
icie
n
c
y
a
n
d
ap
p
licab
ilit
y
o
f
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e,
e
s
p
ec
iall
y
i
n
s
tea
m
s
ter
ilizer
s
.
Sectio
n
5
p
r
ese
n
ts
co
n
cl
u
s
io
n
s
an
d
f
u
tu
r
e
asp
ec
ts
o
f
th
e
s
y
s
te
m
.
2.
P
RE
VIOU
S WO
RK
His
to
r
ical
d
ata
ar
e
u
s
ed
to
f
in
d
a
p
atter
n
o
f
co
llected
d
ata
th
at
ca
n
b
e
f
ed
to
a
m
ac
h
in
e
lear
n
in
g
s
y
s
te
m
to
p
r
o
v
id
e
a
p
r
ed
ictio
n
to
f
u
tu
r
e
d
ata,
w
h
ic
h
is
v
er
y
h
elp
f
u
l
i
n
ca
s
es
o
f
n
o
d
e
f
ail
u
r
e.
C
o
m
p
lex
p
atter
n
s
ar
e
r
ec
o
g
n
ized
a
n
d
f
ed
i
n
to
a
m
ac
h
i
n
e
lear
n
in
g
cla
s
s
i
f
ier
i
n
t
h
e
w
o
r
k
o
f
[
1
2
]
to
s
e
n
s
o
r
d
ata
o
f
p
h
o
n
e
-
b
ased
ac
ce
ler
o
m
eter
f
o
r
ac
tiv
it
y
r
e
co
g
n
itio
n
o
f
m
o
b
ile
p
h
o
n
e
u
s
er
s
.
A
p
r
ed
ictiv
e
m
o
d
el
is
b
u
ilt
to
r
ec
o
g
n
ize
ac
tiv
itie
s
o
f
u
s
er
s
a
n
d
b
u
ild
s
a
p
r
ed
ictiv
e
m
o
d
el
o
f
th
e
s
e
ac
ti
v
itie
s
af
ter
w
ar
d
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
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m
p
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g
I
SS
N:
2
0
8
8
-
8708
A
d
a
ta
esti
ma
tio
n
fo
r
fa
ilin
g
n
o
d
es u
s
in
g
fu
z
z
y
lo
g
ic
w
ith
in
t
eg
r
a
ted
micro
co
n
tr
o
ller
in
…
(
S
a
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a
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)
3625
Usi
n
g
p
r
o
b
ab
ilis
tic
co
m
p
u
tati
o
n
th
at
d
ep
en
d
s
o
n
h
i
s
to
r
ical
d
ata
o
f
n
o
d
es
w
a
s
ad
o
p
ted
in
th
e
w
o
r
k
o
f
[
1
3
]
.
T
h
is
u
s
ed
Ma
r
k
o
v
Dec
is
io
n
P
r
o
ce
s
s
es
(
MD
P
)
to
d
ev
elo
p
a
p
r
o
b
a
b
ilis
tic
ap
p
r
o
ac
h
to
s
p
ec
if
y
th
e
n
o
d
es
t
h
at
ar
e
i
n
n
ee
d
to
b
e
r
ep
lace
d
,
an
d
co
llect
s
tatis
tical
d
ata
b
ased
o
n
p
as
t
b
eh
a
v
io
r
o
f
t
h
e
n
o
d
es
i
n
th
e
n
et
w
o
r
k
.
T
h
en
,
th
e
s
e
d
ata
ar
e
u
s
ed
to
d
eter
m
i
n
e
a
s
u
itab
le
p
o
lic
y
f
o
r
n
o
d
e
r
ep
la
ce
m
en
t
b
y
f
i
n
d
in
g
an
est
i
m
ate
o
f
t
h
e
lo
n
g
r
u
n
co
s
t o
f
r
ep
laci
n
g
th
e
n
o
d
e
(
o
r
th
e
en
er
g
y
s
o
u
r
ce
)
.
T
h
is
m
e
t
h
o
d
co
u
ld
b
e
in
e
f
f
icien
t
to
r
ea
ch
n
o
d
e,
an
d
t
h
e
co
m
p
u
tatio
n
s
ar
e
d
o
n
e
r
ep
ea
ted
ly
w
h
ich
co
u
ld
w
a
s
te
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
.
I
n
th
e
w
o
r
k
o
f
[
1
4
]
,
in
tr
u
s
io
n
d
etec
tio
n
m
o
d
el
i
n
a
W
SN
h
as
b
ee
n
d
ev
elo
p
ed
b
ased
o
n
Ma
r
k
o
v
ia
n
I
n
tr
u
s
io
n
Dete
ctio
n
S
y
s
te
m
(
M
-
I
DS)
t
h
at
p
r
ed
icts
w
h
at
n
o
d
es
ar
e
p
r
o
n
e
to
attac
k
s
,
an
d
p
r
ed
ict
a
n
o
m
a
l
y
an
d
m
is
u
s
e
b
eh
av
io
r
o
f
n
o
d
es
b
y
in
te
g
r
at
in
g
g
a
m
e
th
eo
r
y
a
n
d
MD
P
.
T
h
is
w
ill
h
elp
in
d
ec
id
in
g
wh
at
th
e
b
est
d
ef
e
n
s
e
s
tr
ateg
y
to
ap
p
l
y
.
An
o
d
e
f
ai
l
u
r
e
av
o
id
an
ce
s
tr
ate
g
y
r
eq
u
ir
es
co
n
s
tan
t
m
o
n
ito
r
in
g
,
a
n
d
co
u
ld
m
ea
n
lar
g
e
a
m
o
u
n
t o
f
s
to
r
ed
d
ata
to
b
e
k
ep
t f
o
r
v
er
y
lo
n
g
p
er
io
d
s
o
f
ti
m
e.
Fu
zz
y
lo
g
ic
w
as
u
s
ed
to
p
r
e
d
ict
w
h
ic
h
n
o
d
e
is
t
h
e
m
o
s
t
s
u
i
tab
le
n
o
d
e
to
b
e
a
clu
s
t
er
h
ea
d
in
W
SN
[
1
5
,
1
6
]
.
His
to
r
ical
d
ata
o
f
all
n
o
d
es
i
n
cl
u
d
in
g
r
ate
o
f
r
ec
u
r
r
en
t
co
m
m
u
n
icatio
n
,
r
esi
d
u
al
p
o
w
er
,
d
eg
r
ee
o
f
n
ei
g
h
b
o
r
in
g
n
o
d
es,
a
n
d
d
is
t
an
ce
to
b
ase
s
tatio
n
ar
e
tr
ac
k
e
d
an
d
in
p
u
t
i
n
to
t
h
e
f
u
zz
y
lo
g
i
c
d
ec
is
io
n
s
y
s
te
m
.
T
h
e
d
ec
is
io
n
s
y
s
te
m
ca
lc
u
lat
es
th
e
p
r
o
b
ab
ilit
y
o
f
a
n
o
d
e
to
b
e
a
clu
s
ter
h
ea
d
,
b
ased
o
n
th
e
h
is
to
r
y
o
f
co
m
m
u
n
icatio
n
b
et
w
ee
n
th
e
n
o
d
e
an
d
th
e
b
ase
s
tatio
n
.
Yet
th
e
d
ata
th
at
is
co
llecte
d
f
r
o
m
t
h
ese
n
o
d
es
(
clu
s
ter
h
ea
d
s
a
n
d
o
th
er
n
o
d
es)
ar
e
n
o
t
p
u
t
in
to
f
o
cu
s
.
T
h
e
d
ata
its
elf
co
u
ld
b
e
lo
s
t
d
u
r
in
g
t
h
e
clu
s
ter
h
ea
d
ass
i
g
n
in
g
p
r
o
ce
s
s
;
a
n
d
n
o
b
a
ck
u
p
o
f
th
e
d
ata
its
el
f
i
s
p
r
esen
ted
.
T
h
e
u
s
e
o
f
f
u
zz
y
lo
g
i
c
in
es
ti
m
atio
n
o
f
m
is
s
i
n
g
d
ata
b
ased
o
n
h
is
to
r
ic
al
d
ata
h
as
b
ee
n
tac
k
led
a
n
d
p
r
o
v
en
to
b
e
e
f
f
ic
ien
t
[
1
7
,
1
8
]
.
T
h
e
h
is
to
r
ical
d
ata
th
at
w
a
s
co
llected
w
h
e
n
t
h
e
s
y
s
te
m
w
a
s
w
o
r
k
in
g
w
i
th
p
er
f
ec
t
o
p
er
atio
n
w
o
r
k
i
s
t
h
e
f
u
zz
y
i
n
p
u
t
to
th
e
i
n
f
er
en
ce
e
n
g
in
e.
T
h
ese
d
ata
ar
e
w
ei
g
h
ed
a
n
d
p
r
o
ce
s
s
ed
u
s
i
n
g
t
h
e
r
u
les
i
n
t
h
e
r
u
le
b
ase
to
p
r
o
d
u
ce
m
u
ltip
le
esti
m
atio
n
s
,
w
it
h
co
n
f
id
en
ce
le
v
els o
f
h
o
w
e
f
f
icien
t
th
ese
d
ata
w
ill b
e
to
m
a
k
e
th
e
s
y
s
te
m
w
o
r
k
.
Ver
y
li
m
ited
n
u
m
b
er
o
f
r
ec
en
t
r
esear
ch
w
as
f
o
u
n
d
th
at
e
m
p
lo
y
s
A
r
tific
ial
I
n
te
lli
g
e
n
ce
(
A
I
)
in
esti
m
atio
n
o
f
d
ata
in
f
aili
n
g
n
o
d
e.
Ho
w
e
v
er
;
p
r
ed
ictin
g
f
ac
to
r
s
an
d
lev
el
s
o
f
s
o
m
e
n
atu
r
al
ele
m
en
ts
,
lik
e
t
h
e
w
o
r
k
o
f
[
1
9
]
w
a
s
i
m
p
le
m
en
ted
.
I
n
t
h
eir
r
esear
ch
,
th
e
h
is
to
r
y
o
f
w
ea
th
er
d
ata
a
n
d
r
e
m
o
te
s
e
n
s
i
n
g
i
m
a
g
es
w
a
s
f
ed
in
to
a
n
e
n
s
e
m
b
le
est
i
m
at
io
n
s
y
s
te
m
to
p
r
ed
ict
th
e
o
cc
u
r
r
e
n
ce
o
f
f
o
r
est
f
ir
es.
T
h
e
y
d
id
n
o
t
m
en
tio
n
a
r
ec
o
v
er
y
s
tr
ateg
y
f
o
r
f
aili
n
g
d
ata
co
llec
tio
n
m
et
h
o
d
s
,
w
h
ich
is
v
er
y
li
k
el
y
to
h
ap
p
en
i
n
ca
s
e
o
f
f
o
r
est
f
ir
es.
E
n
er
g
y
i
s
a
co
n
c
er
n
i
n
s
y
s
te
m
s
t
h
at
e
m
p
lo
y
W
SN
w
it
h
in
it
s
s
tr
u
ct
u
r
e.
T
h
is
m
ad
e
p
r
eser
v
i
n
g
n
o
d
es’
en
er
g
y
a
n
d
m
in
i
m
izin
g
it
s
co
n
s
u
m
p
t
io
n
in
d
ev
elo
p
i
n
g
d
ata
e
s
ti
m
atio
n
s
tr
ate
g
ie
s
t
h
e
s
u
b
j
ec
t
o
f
m
a
n
y
r
esear
ch
es
[
2
0
]
.
Mo
s
t
o
f
th
e
af
o
r
e
m
e
n
tio
n
ed
s
t
u
d
ies
f
o
cu
s
o
n
o
n
e
asp
ec
t
o
f
d
ata
esti
m
atio
n
in
f
aili
n
g
n
o
d
es.
So
m
eti
m
es
t
h
e
d
ata
its
elf
w
a
s
n
o
t
o
f
i
m
p
o
r
tan
ce
to
s
o
m
e
s
t
u
d
ies.
T
h
e
h
is
to
r
ical
f
ile
s
ar
e
n
o
t
p
r
esen
ted
w
it
h
an
ea
s
y
to
r
etr
iev
e
a
n
d
r
e
-
u
s
e
s
tr
u
ct
u
r
e
in
m
o
s
t
o
f
t
h
e
r
esear
ch
es.
E
n
er
g
y
a
n
d
co
s
t
ef
f
ec
tiv
e
p
r
o
ce
d
u
r
es
w
er
e
o
n
l
y
ta
k
en
f
r
o
m
t
h
e
f
aili
n
g
n
o
d
e’
s
asp
ec
t o
n
l
y
,
n
o
t t
h
e
en
t
ir
e
n
et
w
o
r
k
’
s
p
er
f
o
r
m
a
n
ce
.
Mo
s
t
o
f
th
e
r
esear
ch
e
s
m
e
n
tio
n
ed
ab
o
v
e
m
ad
e
atte
m
p
ts
to
s
o
lv
e
t
h
e
n
o
d
e
f
ai
lu
r
e
p
r
o
b
le
m
f
r
o
m
o
n
e
asp
ec
t;
eit
h
er
b
y
p
r
ed
ictin
g
d
a
ta
f
r
o
m
lo
s
t
n
o
d
e
(
lik
e
t
h
e
w
o
r
k
in
[
6
]
)
o
r
f
in
d
i
n
g
“
th
e
m
o
s
t
s
u
itab
le
n
o
d
e”
to
r
ep
lace
th
e
f
aili
n
g
n
o
d
e
(
as
in
th
e
w
o
r
k
in
[
2
1
]
).
T
h
e
w
o
r
k
o
f
[
2
2
]
f
o
cu
s
ed
o
n
h
a
n
d
lin
g
n
o
d
es
in
a
W
SN
th
a
t
is
o
p
ted
f
o
r
w
ea
t
h
er
f
o
r
ec
ast.
T
h
ey
p
r
o
p
o
s
ed
a
g
r
ee
d
y
al
g
o
r
i
th
m
to
co
n
s
tr
u
ct
a
b
ar
r
ier
in
h
eter
o
g
en
eo
u
s
W
SN
w
h
er
e
m
o
b
ile
n
o
d
es a
r
e
u
s
ed
t
o
co
n
s
tr
u
ct
a
b
ar
r
ier
w
h
en
th
e
w
ea
t
h
er
c
h
an
g
es
.
I
n
t
h
is
w
o
r
k
,
b
o
th
s
o
lu
tio
n
s
ar
e
co
n
s
id
er
ed
to
m
a
k
e
t
h
e
alg
o
r
it
h
m
ad
ab
tab
le
b
y
al
m
o
s
t
all
ap
p
licatio
n
s
o
f
W
SN
s
.
T
h
is
is
tr
u
e
f
o
r
r
ea
l
-
ti
m
e
d
ata
h
ar
v
e
s
tin
g
an
d
m
o
n
ito
r
i
n
g
(
as
in
tr
af
f
ic
co
n
t
r
o
l)
an
d
f
o
r
d
at
a
b
ased
W
SNs
(
as
in
ap
p
licat
io
n
s
o
f
m
o
n
ito
r
in
g
cli
m
ate
c
h
an
g
e
s
in
s
o
m
e
ar
ea
)
.
3.
E
S
T
I
M
AT
I
N
G
M
I
SS
I
N
G
D
AT
A
O
F
F
AIL
E
D
S
E
N
SO
R
S
T
h
e
tech
n
iq
u
e
p
r
ese
n
ted
i
n
th
is
r
esear
c
h
i
s
b
ased
o
n
g
etti
n
g
r
ea
d
in
g
s
f
r
o
m
s
ets
o
f
s
e
n
s
o
r
s
th
a
t
ar
e
d
is
tr
ib
u
ted
i
n
a
s
tea
m
s
ter
iliz
i
n
g
u
n
it.
T
h
e
co
llected
d
ata
ar
e
s
en
t
w
ir
eles
s
l
y
to
t
h
e
b
ase
s
tatio
n
(
t
h
e
co
n
tr
o
l
s
tatio
n
)
,
m
a
n
a
g
ed
an
d
s
to
r
ed
th
r
o
u
g
h
a
d
ed
i
ca
ted
m
icr
o
co
n
tr
o
ller
.
A
h
is
to
r
ical
d
ata
f
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
4
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A
u
g
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s
t 2
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0
:
3
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-
3634
3626
Fig
u
r
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.
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ical
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eg
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is
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ar
ized
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u
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ith
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ir
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On
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ith
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ata
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ata
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ated
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e
v
alu
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i
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et
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h
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ar
a
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eter
e
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r
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t
h
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atio
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t
k
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cr
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ti
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ta
n
tl
y
.
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h
e
v
alu
e
o
f
p
is
s
et
b
y
t
h
e
h
u
m
a
n
c
o
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ller
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s
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r
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s
m
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n
e
s
s
o
f
t
h
e
alter
atio
n
o
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m
ated
v
al
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es.
I
n
o
th
er
w
o
r
d
s
,
it
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icate
s
h
o
w
f
a
s
t
th
e
tr
an
s
itio
n
d
o
es
f
r
o
m
o
n
e
v
al
u
e
to
t
h
e
o
th
er
c
h
an
g
es.
T
h
e
v
alu
e
o
f
p
co
u
ld
b
e
alter
ed
later
o
n
to
u
s
e
a
n
A
I
m
e
ch
a
n
i
s
m
f
o
r
Input: Sensors Readings
Output: Optimal Table with Estimated Values
Begin
1. store sensors’ data in history file
2.
build meta
-
data file and add run’s readings
3. build optimal table using first run’s data
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
A
d
a
ta
esti
ma
tio
n
fo
r
fa
ilin
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o
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es u
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…
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S
a
a
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l
-
A
z
z
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m
)
3627
au
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ased
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ated
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3
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d
ata
is
u
s
ed
to
u
p
d
ate
it
af
ter
ev
er
y
r
u
n
,
it
is
s
to
r
ed
o
n
t
h
e
m
icr
o
co
n
tr
o
ller
’
s
m
e
m
o
r
y
ca
r
d
to
b
e
u
s
ed
f
o
r
esti
m
atio
n
i
n
ca
s
e
o
f
n
o
d
e
f
ail
u
r
e.
T
h
ese
d
ata
ar
e
r
etr
iev
ed
f
r
o
m
t
h
e
m
e
m
o
r
y
w
h
e
n
a
n
o
d
e
f
ails
.
T
h
e
ap
p
r
o
p
r
iate
d
ata
f
ile
is
f
etc
h
ed
f
r
o
m
t
h
e
m
e
m
o
r
y
b
y
s
ca
n
n
i
n
g
th
e
m
eta
-
d
ata
f
ile
a
n
d
g
et
d
ir
ec
tl
y
to
th
e
f
o
ld
er
w
h
er
e
t
h
e
s
e
n
s
o
r
’
s
d
ata
a
n
d
r
u
n
s
’
d
ata
ar
e
s
to
r
ed
.
T
h
is
m
ak
e
s
th
e
f
etc
h
in
g
p
r
o
ce
s
s
f
a
s
ter
an
d
m
o
r
e
ef
f
icie
n
t,
an
d
cu
t
-
o
u
t
th
e
ti
m
e
n
ee
d
ed
b
y
t
h
e
f
u
zz
y
lo
g
ic
m
o
d
u
le
i
n
p
r
ed
ictin
g
m
i
s
s
i
n
g
d
ata.
T
ab
le
1
.
P
ar
t o
f
r
eg
is
ter
ed
d
at
a
f
r
o
m
th
e
o
p
ti
m
al
tab
le
co
n
s
t
r
u
cted
f
o
r
a
s
ter
ilizin
g
r
u
n
o
v
er
d
if
f
er
e
n
t
ti
m
e
s
tep
s
(
T
S)
T
i
me
S
t
e
p
s/
S
e
n
so
r
s
T
S
0
T
S
1
T
S
2
T
S
3
T
e
mp
_
se
n
so
r
_
1
(
i
n
C
e
l
si
u
s
D
e
g
r
e
e
s)
5
0
°
C
5
0
.
2
°
C
5
0
.
3
°
C
5
0
.
6
°
C
H
u
m_
se
n
so
r
_
1
(
i
n
p
e
r
c
e
n
t
a
g
e
)
4
2
.
2
%
4
2
.
1
%
4
2
.
0
%
4
1
.
6
%
T
h
e
d
esig
n
ated
d
ata
ar
e
u
n
if
ied
th
r
o
u
g
h
a
m
e
m
b
er
s
h
ip
f
u
n
ct
io
n
.
T
h
is
is
to
d
ef
in
e
h
o
w
ea
c
h
o
f
th
e
m
e
m
b
er
s
’
v
al
u
e
s
in
t
h
e
i
n
p
u
t
ca
n
b
e
m
ap
p
ed
to
a
d
eg
r
ee
o
f
m
e
m
b
er
s
h
ip
.
T
h
e
m
e
m
b
er
s
h
ip
p
u
t
s
th
e
s
e
v
alu
e
s
i
n
th
e
i
n
ter
v
al
[
0
,
1
]
in
t
h
e
f
u
zz
if
icatio
n
p
r
o
ce
s
s
b
ef
o
r
e
u
s
in
g
th
e
m
b
y
t
h
e
i
n
f
er
en
ce
en
g
i
n
e.
T
h
e
ea
s
iest
an
d
m
o
s
t
d
ir
ec
t
n
o
r
m
aliza
tio
n
p
r
o
ce
s
s
is
to
ass
u
m
e
t
h
at
th
e
l
o
w
est
v
al
u
e
in
th
e
o
p
ti
m
a
l
tab
le
(
p
er
s
en
s
o
r
)
is
0
,
an
d
th
e
h
i
g
h
e
s
t
is
1
,
an
d
m
ap
ea
ch
o
f
th
e
r
e
m
ai
n
i
n
g
v
alu
e
s
to
th
e
s
ca
le
b
et
w
ee
n
t
h
e
m
.
E
q
u
ati
o
n
(
3
)
is
u
s
ed
in
th
e
n
o
r
m
aliza
tio
n
p
r
o
ce
s
s
.
=
(
−
)
×
1
(
ma
x
−
min
)
(
3
)
W
h
er
e
N
is
th
e
n
e
w
n
o
r
m
al
ized
v
alu
e
an
d
x
is
th
e
v
alu
e
to
b
e
n
o
r
m
alize
d
,
m
a
x
an
d
m
i
n
ar
e
th
e
m
a
x
i
m
u
m
an
d
m
i
n
i
m
u
m
v
al
u
es
in
t
h
e
tab
le,
r
esp
ec
ti
v
el
y
.
T
h
e
r
esu
lti
n
g
v
al
u
e
s
ar
e
p
lace
d
in
a
n
e
w
te
m
p
o
r
ar
y
tab
le.
T
h
ese
v
a
lu
e
s
ar
e
th
e
n
f
ed
i
n
to
t
h
e
i
n
f
er
en
c
e
en
g
in
e
to
p
r
o
d
u
ce
p
r
ed
icted
d
ata
f
o
r
th
e
f
ail
in
g
n
o
d
e.
T
h
e
d
ata
in
th
e
o
p
ti
m
a
l
tab
le
an
d
th
e
d
ata
f
r
o
m
t
h
e
last
r
u
n
(
o
r
an
y
r
u
n
d
eter
m
i
n
ed
b
y
t
h
e
h
u
m
a
n
co
n
tr
o
ller
,
s
in
ce
s
o
m
e
r
u
n
s
h
av
e
s
i
m
ilar
co
n
d
itio
n
s
t
h
at
o
th
er
tr
ea
t
m
en
t
r
u
n
s
d
o
n
’
t)
ar
e
u
s
ed
to
p
r
o
d
u
ce
th
e
esti
m
at
io
n
.
T
h
e
d
ata
esti
m
atio
n
p
r
o
ce
s
s
o
f
f
ai
lin
g
n
o
d
esis
s
u
m
m
ar
ized
in
t
h
e
Fi
g
u
r
e
4
.
Input: Sensors Readings, Optimal Tabl
e
Output: Updated Optimal Table, Averaged Error
Condition:
r
> 1
1. add sensors’ data of run
R
to history file
2. update meta
-
data file and add all sensor’s readings
3. update optimal table using (3)
4. Compute Error value for every sensor between R
̥ and
R
r
5. average errors from all sensors’ data in run r
6. store averaged error in meta
-
data file
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
4
,
A
u
g
u
s
t 2
0
2
0
:
3
6
2
3
-
3634
3628
Fig
u
r
e
4
.
Data
esti
m
a
tio
n
p
r
o
ce
s
s
o
f
f
aili
n
g
n
o
d
es
I
t
is
w
o
r
t
h
m
en
tio
n
i
n
g
a
g
ain
th
at
t
h
e
e
s
ti
m
atio
n
er
r
o
r
w
a
s
r
ed
u
ce
d
in
t
h
e
o
p
ti
m
al
tab
le
’
s
b
u
ild
i
n
g
p
r
o
ce
s
s
.
T
h
is
er
r
o
r
is
f
u
r
t
h
er
r
ed
u
ce
d
th
r
o
u
g
h
t
h
e
r
u
les
u
s
e
d
b
y
t
h
e
r
u
le
b
ase
u
s
ed
b
y
t
h
e
in
f
er
en
ce
e
n
g
in
e.
T
h
e
h
ig
h
e
s
t
er
r
o
r
w
ill
ex
i
s
t
i
f
th
e
n
o
d
e
f
ails
at
t
h
e
f
ir
s
t
r
u
n
(
w
h
ic
h
is
,
as
m
en
tio
n
ed
b
ef
o
r
e,
v
er
y
u
n
l
ik
el
y
)
,
s
in
ce
t
h
e
d
ata
in
th
e
o
p
ti
m
al
tab
le
is
th
e
s
a
m
e
as
th
e
d
at
a
in
th
e
cu
r
r
en
t
tr
ea
t
m
en
t
r
u
n
.
Fig
u
r
e
5
s
h
o
w
s
th
e
er
r
o
r
v
alu
es
i
n
t
h
e
f
ir
s
t
r
u
n
f
o
r
o
n
e
o
f
t
h
e
te
m
p
er
at
u
r
e
s
en
s
o
r
s
,
an
d
Fig
u
r
e
6
s
h
o
w
s
t
h
e
er
r
o
r
af
ter
f
o
u
r
r
u
n
s
f
o
r
th
e
s
a
m
e
s
en
s
o
r
.
Fig
u
r
e
5
.
E
r
r
o
r
v
alu
es i
n
te
m
p
er
atu
r
e
in
1
s
tr
u
n
Fig
u
r
e
6
.
E
r
r
o
r
v
alu
es i
n
te
m
p
er
atu
r
e
in
4
th
r
u
n
Fig
u
r
e
5
s
h
o
w
s
th
a
t
th
e
esti
m
ated
er
r
o
r
is
0
b
ef
o
r
e
m
i
n
u
te
5
(
b
ef
o
r
e
th
e
f
ailu
r
e)
,
b
ec
au
s
e
th
er
e
is
n
o
d
if
f
er
e
n
ce
b
et
w
ee
n
r
ea
d
d
ata
an
d
d
ata
i
n
t
h
e
o
p
ti
m
al
tab
le.
T
h
e
o
p
tim
a
l
tab
le
i
s
b
ei
n
g
f
i
ll
ed
w
h
ile
th
e
s
y
s
te
m
is
r
u
n
n
i
n
g
f
o
r
th
e
f
ir
s
t
ti
m
e
i
n
th
i
s
tr
ea
t
m
en
t.
W
h
e
n
f
ail
u
r
e
h
ap
p
en
ed
at
m
i
n
u
te
5
,
th
e
e
r
r
o
r
v
alu
e
s
p
ik
ed
to
ar
o
u
n
d
4
5
°C
,
s
i
n
ce
t
h
e
r
ea
d
v
alu
e
i
n
la
s
t
r
ea
d
i
n
g
f
r
o
m
t
h
at
s
e
n
s
o
r
w
a
s
4
5
°C
an
d
th
er
e’
s
n
o
n
e
w
d
ata
to
u
p
d
ate
th
e
o
p
ti
m
al
tab
le
f
r
o
m
.
T
h
e
er
r
o
r
co
n
tin
u
es
to
r
is
e
a
s
th
e
s
y
s
te
m
co
n
ti
n
u
e
s
r
u
n
n
i
n
g
w
it
h
o
u
t
n
e
w
d
ata
co
m
in
g
f
r
o
m
t
h
at
n
o
d
e
to
u
p
d
ate
th
e
o
p
ti
m
al
tab
le
p
r
o
p
er
l
y
.
T
h
e
er
r
o
r
d
r
o
p
p
ed
ag
ain
a
f
te
r
m
i
n
u
te
2
5
,
s
i
n
ce
th
e
te
m
p
er
at
u
r
e
v
al
u
e
d
r
o
p
p
ed
w
it
h
i
n
th
is
tr
ea
t
m
en
t.
T
h
e
d
ata
r
e
p
r
esen
ted
in
Fi
g
u
r
e
6
s
h
o
w
s
th
a
t
th
e
er
r
o
r
v
alu
e
s
ar
e
al
m
o
s
t
s
tead
y
.
Si
n
ce
th
e
s
y
s
te
m
h
a
s
s
to
r
ed
d
ata
ab
o
u
t
p
r
ev
io
u
s
tr
e
at
m
e
n
t
r
u
n
s
,
t
h
e
h
is
to
r
ical
esti
m
atio
n
p
r
o
d
u
ce
s
an
er
r
o
r
v
al
u
e
o
f
0
f
o
r
th
e
f
ir
s
t
m
i
n
u
te
o
f
tr
ea
t
m
e
n
t.
Si
n
ce
d
ata
w
a
s
co
llected
f
r
o
m
p
r
ev
i
o
u
s
r
u
n
s
,
t
h
e
ca
lc
u
lated
e
s
ti
m
ated
er
r
o
r
r
an
g
es
b
et
w
ee
n
-
4
°
C
an
d
+5
°C
,
w
h
ic
h
is
a
v
er
y
g
o
o
d
r
an
g
e.
Failu
r
e
h
ap
p
en
ed
at
m
i
n
u
te
5
o
f
th
e
r
u
n
,
y
et
t
h
e
er
r
o
r
v
alu
e
s
r
e
m
ai
n
ed
s
tab
le,
an
d
t
h
e
s
y
s
te
m
’
s
p
er
f
o
r
m
an
ce
w
a
s
n
o
t a
f
f
ec
ted
.
Af
ter
esti
m
ated
er
r
o
r
s
ar
e
ca
l
cu
lated
,
th
e
y
ar
e
f
ed
to
th
e
in
f
er
en
ce
en
g
i
n
e
i
n
th
e
f
u
zz
y
lo
g
ic
m
o
d
u
le
to
b
e
u
s
ed
as
w
ei
g
h
t
to
t
h
e
o
p
tim
a
l
tab
le
’
v
alu
e
s
.
An
es
t
i
m
ate
o
f
t
h
e
ac
t
u
al
v
al
u
e
t
h
a
t
co
u
ld
h
a
v
e
b
ee
n
co
llected
f
r
o
m
t
h
at
f
ailed
s
e
n
s
o
r
i
s
p
r
o
d
u
ce
d
.
T
h
e
r
u
le
e
v
alu
a
tio
n
p
r
o
ce
d
u
r
e
in
th
e
f
u
zz
y
lo
g
ic
m
o
d
u
le
Input: optimal table, average of errors
Output: error, new
-
error
1. Generate an estimation using Fuzzy Logic
1.1. input optimal table & average of errors
1.2. normalize each data in the optimal table, add error as a weight
for each value
1.3. compu
te estimated value for failing node.
2. compute error between optimal table and estimated value:
3.If (error <error_threshold)
Continue treatment with estimated value
Else
Compute new error between estimated and previous runs of the failing
node, get least error value.
3.1. if (new_error<error_threshold)
Continue treatment with data from history file
Else
Shutdown system and discard treated material
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
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Hig
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#
1
2
3
4
5
6
7
8
9
H
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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8
8
-
8708
I
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&
C
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10
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4
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A
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0
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0
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3
6
2
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-
3634
3630
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ata.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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p
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I
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N:
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8
8
-
8708
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in
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Ma
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ab
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ig
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1
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w
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tab
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cc
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if
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–
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m
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Fig
u
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s
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t
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ail
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v
er
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clo
s
e
to
th
e
esti
m
ated
d
ata.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
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m
p
E
n
g
,
Vo
l.
10
,
No
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4
,
A
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3632
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e
–
E
s
tima
ted
va
lu
e.
I
n
th
i
s
ex
a
m
p
le,
th
e
er
r
o
r
w
o
u
ld
b
e:
4
0
–
4
5
=
-
5
° C
.
Af
ter
s
e
v
er
al
r
u
n
s
,
t
h
e
s
y
s
te
m
s
tab
ilized
,
an
d
co
n
ti
n
u
ed
o
p
er
atin
g
w
it
h
th
e
f
ai
lin
g
n
o
d
es,
b
u
t
u
s
in
g
esti
m
ated
v
alu
e
s
.
T
h
e
er
r
o
r
,
i
n
ca
s
e
o
f
te
m
p
er
at
u
r
e
b
y
t
h
e
en
d
o
f
th
e
tr
ea
t
m
e
n
t
ti
m
e
d
r
o
p
p
ed
t
o
-
2
5
(
s
in
ce
th
e
u
n
it
s
tar
ts
lo
w
er
i
n
g
t
h
e
te
m
p
er
at
u
r
e
v
al
u
es
a
f
ter
2
5
m
i
n
u
te
s
o
f
t
h
e
tr
ea
t
m
en
t)
.
A
ls
o
,
th
e
h
u
m
id
it
y
er
r
o
r
d
r
o
p
p
ed
to
2
0
%
(
s
in
ce
th
e
t
e
m
p
er
atu
r
e
d
r
o
p
p
e
d
)
.
I
t’
s
w
o
r
th
m
e
n
tio
n
i
n
g
th
at
t
h
er
e
ar
e
s
o
m
e
e
s
tab
lis
h
ed
r
elatio
n
s
h
ip
s
b
et
w
ee
n
t
h
e
n
o
d
es
in
th
e
s
ter
ilizer
.
Fo
r
ex
a
m
p
l
e,
as
th
e
te
m
p
er
at
u
r
e
in
cr
ea
s
e
s
,
th
e
h
u
m
id
it
y
al
s
o
in
cr
ea
s
es
an
d
t
h
e
e
n
er
g
y
c
o
n
s
u
m
p
tio
n
i
n
cr
ea
s
es.
T
h
is
p
iece
o
f
in
f
o
r
m
a
tio
n
ca
n
b
e
u
s
ed
i
n
b
u
ild
in
g
th
e
o
p
ti
m
al
tab
le
if
a
n
o
d
e
f
ai
l
s
at
t
h
e
f
ir
s
t
r
u
n
,
a
n
d
ca
n
h
elp
th
e
f
u
zz
y
lo
g
ic
esti
m
at
io
n
m
o
d
u
le
p
r
ed
ict
v
al
u
e
s
th
at
ar
e
clo
s
e
to
th
e
r
ea
l v
al
u
e
s
.
R
u
n
s
t
h
at
f
o
llo
w
a
f
ir
s
t
“su
cc
ess
f
u
l”
r
u
n
w
ill
h
av
e
m
o
r
e
d
ata
to
w
o
r
k
w
i
th
an
d
ca
lc
u
late
a
s
m
a
ller
er
r
o
r
v
alu
e
.
T
h
is
w
o
u
ld
u
lti
m
atel
y
p
r
o
d
u
ce
clo
s
er
esti
m
ated
v
alu
e
s
to
r
ea
l
d
ata.
A
t
th
e
f
o
u
r
th
r
u
n
o
f
th
e
s
y
s
te
m
,
f
o
r
e
x
a
m
p
le,
t
h
e
o
p
tim
a
l
tab
le
w
il
l
h
a
v
e
r
ea
l
d
ata
th
at
t
h
e
m
icr
o
co
n
tr
o
ller
w
il
l
u
s
e
i
n
f
i
n
d
in
g
th
e
er
r
o
r
m
ar
g
i
n
b
e
f
o
r
e
u
s
i
n
g
th
e
f
u
zz
y
lo
g
ic
e
s
ti
m
atio
n
m
o
d
u
le.
T
h
ese
d
ata
w
ill
b
e
clo
s
er
to
r
ea
l
co
llected
d
ata
if
t
h
e
n
o
d
e
d
id
n
o
t
f
ail.
T
h
e
s
y
s
te
m
w
ill
h
a
v
e
lo
w
er
r
is
k
o
f
o
v
er
all
f
a
ilu
r
e,
a
n
d
t
h
e
n
ee
d
f
o
r
a
u
n
i
t
s
h
u
td
o
w
n
ca
n
b
e
av
o
id
ed
alt
o
g
eth
er
a
n
d
n
o
d
e
r
ep
lace
m
e
n
t
co
u
ld
b
e
p
o
s
tp
o
n
ed
w
it
h
o
u
t
cr
itical
d
a
n
g
er
o
n
th
e
s
y
s
te
m
.
Fig
u
r
es
1
3
,
1
4
,
an
d
1
5
s
h
o
w
t
h
e
es
ti
m
ated
v
al
u
e
s
p
r
o
d
u
ce
d
b
y
t
h
e
s
y
s
te
m
ag
ai
n
s
t
t
h
e
r
ea
l
v
al
u
es
th
at
w
er
e
ac
tu
a
ll
y
r
ea
d
b
y
t
h
e
s
en
s
o
r
s
.
M
A
T
L
A
B
allo
w
s
d
e
v
elo
p
er
s
“
p
ea
k
”
a
t
r
ea
l
-
d
ata,
a
n
d
h
id
e
th
e
m
f
r
o
m
th
e
f
u
zz
y
lo
g
ic’
s
m
o
d
u
le.
T
h
e
d
ata
p
r
esen
ted
in
t
h
e
g
r
ap
h
s
ar
e
f
o
r
th
e
s
a
m
e
s
en
s
o
r
s
at
d
if
f
er
en
t
ti
m
e
s
tep
s
i
n
th
e
f
o
u
r
th
r
u
n
o
f
th
e
s
ter
ilizi
n
g
u
n
it.
Fig
u
r
e
13.
A
ctu
a
l te
m
p
er
at
u
r
e
(
b
lu
e)
v
s
.
esti
m
ated
te
m
p
er
atu
r
e
(
r
ed
)
in
4
th
r
u
n
Fig
u
r
e
1
4
.
A
ctu
a
l h
u
m
id
it
y
(
b
l
u
e)
v
s
.
es
ti
m
ated
h
u
m
id
it
y
(
r
ed
)
in
4
th
r
u
n
Fig
u
r
e
1
5
.
A
ctu
a
l lu
x
(
r
ed
)
v
s
.
esti
m
a
ted
lu
x
(
y
ello
w
)
in
4
t
h
r
u
n
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