Engineering Aviation Security Environments—Reduction of False Alarms in Computed Tomography-Based Screening of Checked Baggage (2013) / Chapter Skim
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B--Quantifying the Risk of False Alarms with Airport Screening of Checked Baggage
B--Quantifying the Risk of False Alarms with Airport Screening of Checked Baggage
Pages 64-72
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From page 64... ...
will provide value-added insights for taking corrective actions to reduce the frequency of false alarms. This appendix outlines a systematic process based on QRA principles for a rigorous analysis of the causes of false alarms.
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From page 65... ...
In a machine-driven false alarm, the screening algorithm signals an alarm when there is no threat. Common causes of machine driven false alarms are non-threat substances mistaken for a threat substance or items that aggregate several non-threat items into single items that meet the screening criteria for a threat.
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From page 66... ...
In this illustration the committee focuses on the risk of false alarms from EDSs, and in the process exposes their causes to guide corrective actions for their reduction. The parameter of the model is the frequency of false alarms and, more particularly, the frequency with which alarms lead to different action states such as extra screening or even the need for an ODT.
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From page 67... ...
A comprehensive risk assessment of false alarms would most likely involve segregating the causes and developing separate event trees for each cause set. Candidate cause sets for baggage inspection include cosmetics, foodstuffs, metals/electronics, paper (including books)
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From page 68... ...
FAULT TREE ANALYSIS To develop the split fraction distributions so as to reveal false alarm causes, the committee introduces another risk assessment tool known as the "fault tree." Whereas the event tree is basically an application of inductive logic and thus the framework for structuring event sequences or scenarios, the fault tree is based on deductive logic and is useful for quantifying split fractions. The fault tree starts with the undesired event -- for example, a false alarm -- and works backwards decomposing the logic to basic causes or events.
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From page 69... ...
TABLE B-1 Boolean Expressions for Scenarios 1 Through 6 Scenario Event Description Frequency Scenario Cost S1 = I ABCD Bag cleared by EDS Φ(S1) C1 𝐶̅ D S2 = I ĀBCD Bag cleared by OSR Φ(S2)
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From page 70... ...
. illustrates the process whereby the probability arithmetic is usually performed using Monte Carlo S6 = I𝐴̅ � C𝐷 𝐵 � FIGURE B-5 Bayesian convolution of split fraction uncertainties.
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From page 71... ...
One approach is to compute a 90 percent probability interval such that the to read this result is we are 90 percent confident that the false alarm rate is between 𝜑1 and 𝜑2. FIGURE B-7 Probability density function.
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From page 72... ...
And in particular, understanding the root causes of risk behind the split fraction analyses illustrated earlier using the fault tree methodology can provide insight to help determine the parts of the system that can be modified to produce important improvements -- for example, a modification that will reduce the probability of a false positive without reducing the probability of detecting explosives. INTERPRETING THE RESULTS As indicated earlier, the link between the proposed model and the actual reduction of false alarms is the quantification of the total screening process in such a manner that part of the output is the exposure of detailed causes of the risks -- in this case, the causes of false alarms.
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