3. Introduction
Risk of launching CBRN incidents increased- UN report 2012.
Detection of radiological dispersion devices (Covert, Overt) incidents is
not straight forward.
Heterogeneous sources of information and scattered information
Incomplete and conflicting data.
Example of belief degree (Smart Phone Measurement, 5/6, 70%)
The Goal
Take early protective measures in situations where any of the CBR
hazards are present or suspected to be present
Quantify the reliability of a information sources when making the decision
We differentiate between No information and lack of expertise to assess
the situation.
Goal: Early detection and identification the existence of radiological
agent.
6. Preparation Phase
Attribute specification and modeling
Attributes are specified for assessing the category.
Types of indicators are specified (numeric, linguistic, percentages etc...).
Number of linguistic terms is determined, i.e., three level linguistic
values or five etc...
Gathering expert evaluations
Experts investigate the evidence from several sources and make
judgments for relevant RDD attributes.
The expert judgments are represented by belief structures.
7. Calculation of Cumulative Belief Degrees
Cumulative Belief degree Calculation
Specify rules for category activation
Specify Credibility of experts/sources per
category
Aggregate belief structures of the experts
Fulfillment of each activation rule is calculated
Aggregate belief degrees with their reliablity.
8. Accumlation of belief Degrees
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ek
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i
e
ik
i
e
ikk
e
∀∀≤
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k and e are indices for attribute and sources, respectively, and βe
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the belief degree of the data source e for the existence of attribute k
at si level. In our case we take s= {Very Low, Low, Medium, High, Very
High}.
Example : (Dosimeter measurement, 4/6, 90%)
Aggregation by: Category, Attribute, Type
9. Example- Radiological dispersion device with CBD
Aggregation of values from four experts or sources
Experts have different credibility towards the different categories
levels.
Each expert or source used a different approach to describe the
evidence ex: numeric or linguistic
Some experts/sources leave out some attributes.
10. Aggregated Result for the case of existence of RDD
Category Dispersion Measurement Health Signs Material
Value 1.56 3.03 2.46 2.10 2.03
Measurement is pretty high more than 50 % on a 6 scale degree.
Is there an RDD running?-> No it is just rain. Suspicious levels trigger more investigations
11. Next
Currently implementation is a stand alone
Porting the implementation to a web service in the cloud to enable
high volume data processing.
Modeling other incidents chemical and biological
Accounting for variable weights for importance to foresee seasonal
effect
12. Conclusion
We presented a framework that can be fuse data and support decision
The framework should be used to help condense heterogeneous
sources of information with various credibility degrees.
We aim at cascading several stages of that framework that can
improve the accuracy of the evidence fused after data collection
We aim to use the system for computer aided dispatchers in order to
help them ask the proper questions for callers.
13. Thank You
Ahmed Nagy, Research Engineer
Belgian Nuclear Research Center
ahmed.nagy@sckcen.be