Microsoft Word - -2012-Vol 3 No 3Journal of Taiwan Maritime Safety
and Security Studies Vol 3, No 3
19
***
Abstract
Recently, maritime accidents emerge in an endless stream, and the
cause of the
maritime accidents result largely from human factors. Thus, a
comprehensive risk
* Shuen-Tai UngE-mail:
[email protected]
** Yi-San LinE-mail:
[email protected]
(June 2012) ISSN 2077-8759
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assessment needs to be developed in advance in order to reduce the
risks of
disasters. It is, however, difficult to acquire sufficient and
historical data in the
maritime industry, hence expert judgment plays crucial roles. Fuzzy
set theory is
one of the methods that is often applied to convert the expert
judgment into
numerical values. Some studies adopt Mass Assignment theory as a
conversion
mechanism. However, Mass Assignment theory confines the membership
functions
of linguistic terms to a discrete nature and this violates the
logic of human thoughts.
In order to solve the drawbacks of Mass Assignment theory, a new
risk
assessment method capable of transforming expert judgment into
probability values
is proposed by combining curve fitting methods with Bayesian
network. After the
validation it is concluded that the framework proposed has the
capability of solving
the shortcomings aforementioned.
Bayesian Network
Mass Assignment
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22
(Preliminary Hazard Analysis, PHA) (What-If
Analysis)
2.4
(Emergency, Escape, Evacuation, and Rescue
System, EEER) 2008 Eleye-Datubo (Human
Performance) EEER Mass Assignment
2008 Eleye-Datubo
(Numerical
(Functional Membership Function)
(Continuous Membership Function)
(Membership Grade)
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199620032008)
2-1
0 1 1
0.1
45 1
2-2 0 1
45 1 45
45 90 0 90
90
65 0.6 65 0.6
1
0.6
0.8
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
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2-3 HP
Very poorPoorBelow AverageAverage
GoodVery Good Excellent
2-3
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0 20 40 60 80 100
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Mass Assignment
3-1 A
0
0.2
0.4
0.6
0.8
1
12345
26
3.3
3-3
MATLAB
0 0.2 0.4 0.6 0.8
1
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3.4
3.6
Netica
Expert
28
EEER
11
(
)
if-then
IFextraANDminimalAND
very goodANDslightANDfantastic
ANDinconsequentialANDhighAND
fairANDacceptableAND
certified very healthyANDnormalTHEN
excellent
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EEER
EEER
2008 Eleye-Datubo EEER
Very PoorPoorBelow AverageAverageGoodVery Good Excellent
HP1HP2HP3HP4HP5HP6 HP7 HP
2-3
30
HP 00010.70.5 0.1
4-2 4-3 Hp
HP1HP2
HP3
4.2.3
HP6 HP7 HP
MATLAB
4-4 MATLAB polyfit
4-5
0
0.2
0.4
0.6
0.8
1
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HP4HP5HP6 HP7 000
10.70.5 0.1
4.2.4
for k = 1:4
32
3.1
HP4 HP
HP1=0HP2=0HP3=0HP4=1HP5=0.7HP6=0.5 HP7=0.1 3.1
HP4 0.435
4.2.5
teP 1 t
25 λ
λ 8.93×10-38.43×10-33.77×10-3
4-1
0.8 0.81 0.91
0.2 0.19 0.09
Eleye-Datubo et al. (2006)
EEER (Fire Alarm)(Flooding Alarm)
4-2 EEER
0.87 0.001 0.89 0.001 0.13 0.999 0.11 0.999
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EEER (Evacuation)
4-3
4-3 EEER
Eleye-Datubo et al. (2006)
EEER
4-4 EEER
0 0 0 0.435 0.304 0.217 0.043
EEER
28 2006 Eleye-Datubo
4-5 4-6
4-5 EEER
0.92 0.35 0.9 0.3 0.85 0.25 0.8 0.2 0.75 0.15 0.7 0.1 0.65
0.04
0.08 0.65 0.1 0.7 0.15 0.75 0.2 0.8 0.25 0.85 0.3 0.9 0.35
0.96
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0.95 0.45 0.9 0.4 0.85 0.35 0.8 0.3 0.75 0.25 0.7 0.2 0.65
0.15
0.05 0.55 0.1 0.6 0.15 0.65 0.2 0.7 0.25 0.75 0.3 0.8 0.35
0.85
4.2.6
4-7
4-7 EEER
2006 Eleye-Datubo
60.76% 73.70% 39.24% 26.30%
59.83% 63.48% 40.17% 36.52%
EEER
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1. 2008 FUZZY 3
2. Steven C. Chapra 2005 MATLAB
3. William J. Palm III 2010MATLAB7
6. 2011
9. 2003
10. 1997
11. Lindfield, G. and Penny, J.1997 MATLAB
14. 1996
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