Presentation on theme: "Nathalie Japkowicz, Colin Bellinger, Shiven Sharma, Rodney Berg, Kurt Ungar University of Ottawa, Northern Illinois University Radiation Protection Bureau,"— Presentation transcript:
Nathalie Japkowicz, Colin Bellinger, Shiven Sharma, Rodney Berg, Kurt Ungar University of Ottawa, Northern Illinois University Radiation Protection Bureau, Health Canada
Goal and Methodology Goal: To identify people concealing radioactive material that may represent a threat to attendees at public gatherings. Methodology: Analysis of Gamma-Ray spectra produced by spectrometer s at short intervals of time and decision on the fly of whether a threat is present. General idea: to place spectrometers in strategic locations (e.g., the entry points to the event) and try to detect whether the new spectra coming in are similar or different from a normal spectrum for this particular location.
Gamma-Ray Spectroscopy (Wikipedia) The gamma-ray spectrum of natural uranium, showing about a dozen discrete lines superimposed on a smooth continuum, allows the identification the nuclides 226 Ra, 214 Pb, and 214 Bi of the uranium decay chain.uraniumnuclides Ra Pb Bidecay chain The quantitative study of the Energy spectra of gamma-ray Sources. Most radioactive sources produce gamma rays of various energy levels and intensities
The data I= Iodine, Tc=Technicium, Th= Thallium, Cs=Cesium, Co=Cobalt
Approach To apply Machine Learning/Pattern recognition techniques to the data. Issue 1: There is a lot of background data, but very few alarms. E.g., for one station: 24,712/6 Data was augmented with simulated Cobalt entries (though we only used that data for testing) We used one-class learning/anomaly detection algorithms to deal with this extreme class imbalance Issue 2: We discovered that rain was a problem as it masked the presence of isotopes in the spectra. Since we had labelled data of both the rain and non-rain classes, we used binary classification on this problem.
The effect of rain
Hypothesis Separating rain from non-rain data in a first phase and applying an anomaly detection system on each group of data separately in a second phase could help us improve the results.
Experiments (Contd) We experimented with different classifiers in both phases. Phase 1: Classifiers tried: SVM, J48, NB, MLP and IBL. Winner: NB Phase 2: Classifiers tried: oc-SVM, AA, Mahalanobis Distance Winner: Mahalanobis Distance