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p(temp|ABNORMAL)p(M C TEMP|temp)
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24.5 Prototype Decision-Making Support Assistant Design
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Figure 24.17. Averaging of decision making: the belief (the output of decision-making block) is
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varied during the time of surveillance while the observation conditions change. p(AB TEMP|ABNORMAL)p(M C TEMP|Ab TEMP) + p(C TEMP|ABNORMAL)p(M C TEMP|C TEMP)} = 0.2 {0.1 0 + 0.6 0.1 + 0.3 0.75} = 0.057 = 0.429
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Note that = 7.519 is computed from the equality p(ABNORMAL|M C p(NORMAL|M C TEMP)= 1.
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Data from the preprocessing block of a decision support assistant is varied during the observation of a pre-screening individual (Figure 24.17). The data variation is caused by pose, lighting, and other conditions of observation. For example, measuring the temperature of a pre-screened individual may be delayed for several minutes because of the critical angle of the individual s position with respect to the cameras. However, the equipment utilizes this time interval for processing of the other available zones for instance, the ear. This may result in the production the supporting decision that is not reliable and cannot supply any recommendations to the of cer. The processing is then continued during all pre-screening time intervals using adaptive weighted averaging. Note that two classes of averaging procedures are distinguished: numerical and linguistic averaging. The adaptive weighted averaging is a numerical procedure resulting in the belief probability.2 This probability is converted into linguistic form. At this
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statistical estimations are used at this phase, including con dence, tolerance, and prediction intervals and the quality of the point estimate (errors).
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phase, linguistic averaging is performed using linguistic constructions to represent the numerical measures.
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Training of personnel naturally lags behind changes in technologies for the PASS. It is assumed that the of cer obtains his/her skills through training with an instructor. Traditionally, training is implemented on a speci cally designed training system. For example, training methodologies are well-developed for pilots, astronauts, surgeons [50], and the military. These are expensive professional simulation systems, which are dif cult to modify or extend, since they are unique in architecture and functions. In our approach, the design of an expensive training system is replaced by an inexpensive extension of the PASS, already deployed at the place of application [11, 12]. In this way, an important effect is achieved: a simulated environment is replaced with real-world conditions. Furthermore, long-term training is replaced by periodically repeated, short-term, intensive and computer-aided training. This means in practice that the PASS as a mobile system can be deployed in a new place (mobile border checkpoints, important public events, etc.), and the security personnel can be adapted to the new conditions by intensive and short-term training. We propose a training paradigm utilizing a combination of various biometrics, including visual-band, IR, and acoustic acquisition data for identi cation of both physical appearance (including natural factors such as aging, and intentional (surgical) changes) and physiological characteristics (temperature, blood ow rate, etc.). Other biometrics can be used in pre-screening and at check-points: gait biometrics [17] and near distance noncontact and contact biometrics at the checkpoint. The biometric-based PASS is a complex semiautomatic system. The question is, What kind of skills do secure personnel need to explore this system The skilled PASS user possesses an ability to manipulate and ef ciently utilize various sources of information in decision-making. The typical premise about the training component is that the necessary skills to employ a new system can be obtained through instruction. The PASS can be easily recon gured into a training system. In the training system, decisions are generated by special tools according to various scenarios. In T-PASS prototyping, we utilized the Silicon Graphics facilities and monitoring equipment of the Virtual Reality Room of the University of Calgary (Figure 24.18). We also used software tools for synthetic biometrics, such as SFinGe, the package for generation of synthetic ngerprints developed at the University of Bologna, Cesena, Italy, the FaceGen package for face modeling, and the Comnetix Life-Scan station for ngerprint acquisition and identi cation. The PASS and T-PASS implement the concept of multitarget platforms; that is, the PASS can be easily recon gured into the T-PASS, and vice versa. Using the possibilities of recon guration and minimal additional tools, the PASS can implement functions of the training system, T-PASS. The skills of the personnel contribute to decisionmaking. The skills can be gained by training (short-term) and experience (long-term). Note that traditionally, training is implemented on a unique training system.
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