ISCRAM 2013: A CBR Detection Framework Using Fuzzy Logic- WIP


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Author: Ahmed Nagy
Lusine Mkrtchyan- Klaas van der Meer

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ISCRAM 2013: A CBR Detection Framework Using Fuzzy Logic- WIP

  1. 1. A CBR Detection Framework Using Fuzzy Logic- WIP Ahmed Nagy Lusine Mkrtchyan- Klaas van der Meer 14 May 2013 ISCRAM-2013 Baden- Baden, Germany
  2. 2. Contents  Introduction  Goals  Modeling for Cumulative Belief Degree  Cumulative Belief Degree algorithm  Example  Conclusion
  3. 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.
  4. 4. Fuzzy approach- Cumulative Belief Degrees
  5. 5. Modeling Radiological Dispersion Device Incident
  6. 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. 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. 8. Accumlation of belief Degrees { } ,,,1 ,,,,...,0),,()( 0 ek ekmisIB m i e ik i e ikk e ∀∀≤ ∀∀== ∑= β β  k and e are indices for attribute and sources, respectively, and βe ik is 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. 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. 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. 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. 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. 13. Thank You Ahmed Nagy, Research Engineer Belgian Nuclear Research Center