The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Toby P Breckon - One of the best experts on this subject based on the ideXlab platform.
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on the evaluation of prohibited item classification and detection in volumetric 3d computed tomography Baggage security screening imagery
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Neelanjan Bhowmik, Toby P BreckonAbstract:X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on prohibited item detection focuses primarily on 2D X-ray imagery. In this paper, we aim to evaluate the possibility of extending the automatic prohibited item detection from 2D X-ray imagery to volumetric 3D CT Baggage security screening imagery. To these ends, we take advantage of 3D Convolutional Neural Neworks (CNN) and popular object detection frameworks such as RetinaNet and Faster R-CNN in our work. As the first attempt to use 3D CNN for volumetric 3D CT Baggage security screening, we first evaluate different CNN architectures on the classification of isolated prohibited item volumes and compare against traditional methods which use hand-crafted features. Subsequently, we evaluate object detection performance of different architectures on volumetric 3D CT Baggage images. The results of our experiments on Bottle and Handgun datasets demonstrate that 3D CNN models can achieve comparable performance (98% true positive rate and 1.5% false positive rate) to traditional methods but require significantly less time for inference (0.014s per volume). Furthermore, the extended 3D object detection models achieve promising performance in detecting prohibited items within volumetric 3D CT Baggage imagery with 76% mAP for bottles and 88% mAP for handguns, which shows both the challenge and promise of such threat detection within 3D CT X-ray security imagery.
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a reference architecture for plausible threat image projection tip within 3d x ray computed tomography volumes
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Qian Wang, Najla Megherbi, Toby P BreckonAbstract:Threat Image Projection (TIP) is a technique used in X-ray security Baggage screening systems that superimposes a threat object signature onto a benign X-ray Baggage image in a plausible and realistic manner. It has been shown to be highly effective in evaluating the ongoing performance of human operators, improving their vigilance and performance on threat detection. However, with the increasing use of 3D Computed Tomography (CT) in aviation security for both hold and cabin Baggage screening a significant challenge arises in extending TIP to 3D CT volumes due to the difficulty in 3D CT volume segmentation and the proper insertion location determination. In this paper, we present an approach for 3D TIP in CT volumes targeting realistic and plausible threat object insertion within 3D CT Baggage images. The proposed approach consists of dual threat (source) and Baggage (target) volume segmentation, particle swarm optimisation based insertion determination and metal artefact generation. In addition, we propose a TIP quality score metric to evaluate the quality of generated TIP volumes. Qualitative evaluations on real 3D CT Baggage imagery show that our approach is able to generate realistic and plausible TIP which are indiscernible from real CT volumes and the TIP quality scores are consistent with human evaluations.
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a reference architecture for plausible threat image projection tip within 3d x ray computed tomography volumes
Journal of X-ray Science and Technology, 2020Co-Authors: Qian Wang, Najla Megherbi, Toby P BreckonAbstract:BACKGROUND: Threat Image Projection (TIP) is a technique used in X-ray security Baggage screening systems that superimposes a threat object signature onto a benign X-ray Baggage image in a plausible and realistic manner. It has been shown to be highly effective in evaluating the ongoing performance of human operators, improving their vigilance and performance on threat detection. OBJECTIVE: With the increasing use of 3D Computed Tomography (CT) in aviation security for both hold and cabin Baggage screening a significant challenge arises in extending TIP to 3D CT volumes due to the difficulty in 3D CT volume segmentation and the proper insertion location determination. In this paper, we present an approach for 3D TIP in CT volumes targeting realistic and plausible threat object insertion within 3D CT Baggage images. METHOD: The proposed approach consists of dual threat (source) and Baggage (target) volume segmentation, particle swarm optimisation based insertion determination and metal artefact generation. In our experiments, real Baggage data collected from airports are used to generate TIP volumes for evaluation. We also propose a TIP quality score metric to automatically estimate the quality of generated TIP volumes. RESULT: In our experiments with real Baggage CT volumes and varying threat items, 90.25% of the generated TIP volumes are graded as good by human evaluation, 7% of them are of medium quality with minor flaws and 2.75% of them are bad. CONCLUSION: Qualitative evaluations on real 3D CT Baggage imagery show that our approach is able to generate realistic and plausible TIP which are indiscernible from real CT volumes and the TIP quality scores are consistent with human evaluations.
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an approach for adaptive automatic threat recognition within 3d computed tomography images for Baggage security screening
Journal of X-ray Science and Technology, 2020Co-Authors: Qian Wang, Khalid N Ismail, Toby P BreckonAbstract:BACKGROUND: The screening of Baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material signatures in contrast to adaptability towards new and emerging threat signatures. To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work. OBJECTIVE: In this paper, we present a solution to AATR based on such X-ray CT Baggage scan imagery. This aims to address the issues of rapidly evolving threat signatures within the screening requirements. Ideally, the detection algorithms deployed within the security scanners should be readily adaptable to different situations with varying requirements of threat characteristics (e.g., threat material, physical properties of objects). METHODS: We tackle this issue using a novel adaptive machine learning methodology with our solution consisting of a multi-scale 3D CT image segmentation algorithm, a multi-class support vector machine (SVM) classifier for object material recognition and a strategy to enable the adaptability of our approach. Experiments are conducted on both open and sequestered 3D CT Baggage image datasets specifically collected for the AATR study. RESULTS: Our proposed approach performs well on both recognition and adaptation. Overall our approach can achieve the probability of detection around 90% with a probability of false alarm below 20%. CONCLUSIONS: Our AATR shows the capabilities of adapting to varying types of materials, even the unknown materials which are not available in the training data, adapting to varying required probability of detection and adapting to varying scales of the threat object.
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an approach for adaptive automatic threat recognition within 3d computed tomography images for Baggage security screening
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Qian Wang, Khalid N Ismail, Toby P BreckonAbstract:The screening of Baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material signatures in contrast to adaptability towards new and emerging threat signatures. To address issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work by \cite{to7}. In this paper, we present a solution to AATR based on such X-ray CT Baggage scan imagery. This aims to address the issues of rapidly evolving threat signatures within the screening requirements. Ideally, the detection algorithms deployed within the security scanners should be readily adaptable to different situations with varying requirements of threat characteristics (e.g., threat material, physical properties of objects). We tackle this issue using a novel adaptive machine learning methodology with our solution consisting of a multi-scale 3D CT image segmentation algorithm, a multi-class support vector machine (SVM) classifier for object material recognition and a strategy to enable the adaptability of our approach. Experiments are conducted on both open and sequestered 3D CT Baggage image datasets specifically collected for the AATR study. Our proposed approach performs well on both recognition and adaptation. Overall our approach can achieve the probability of detection around 90\% with a probability of false alarm below 20\%. Our AATR shows the capabilities of adapting to varying types of materials, even the unknown materials which are not available in the training data, adapting to varying required probability of detection and adapting to varying scales of the threat object.
Andre Mouton - One of the best experts on this subject based on the ideXlab platform.
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On using feature descriptors as visual words for object detection within X-ray Baggage security screening
7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016), 2016Co-Authors: Mikolaj E. Kundegorski, Samet Akcay, Michael Devereux, Andre Mouton, Toby P BreckonAbstract:Here we explore the use of various feature point descriptors as visual word variants within a Bag-of-Visual-Words (BoVW) representation scheme for image classification based threat detection within Baggage security X-ray imagery. Using a classical BoVW model with a range of feature point detectors and descriptors, supported by both Support Vector Machine (SVM) and Random Forest classification, we illustrate the current performance capability of approaches following this image classification paradigm over a large X-ray Baggage imagery data set. An optimal statistical accuracy of 0.94 (true positive: 83%; false positive: 3.3%) is achieved using a FAST-SURF feature detector and descriptor combination for a firearms detection task. Our results indicate comparative levels of performance for BoVW based approaches for this task over extensive variations in feature detector, feature descriptor, vocabulary size and final classification approach. We further demonstrate a by-product of such approaches in using feature point density as a simple measure of image complexity available as an integral part of the overall classification pipeline. The performance achieved characterises the potential for BoVW based approaches for threat object detection within the future automation of X-ray security screening against other contemporary approaches in the field.
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object classification in 3d Baggage security computed tomography imagery using visual codebooks
Pattern Recognition, 2015Co-Authors: Greg T. Flitton, Andre Mouton, Toby P BreckonAbstract:We investigate the performance of a Bag of (Visual) Words (BoW) object classification model as an approach for automated threat object detection within 3D Computed Tomography (CT) imagery from a Baggage security context. This poses a novel and unique challenge for rigid object classification within complex and cluttered volumetric imagery. Within this context it extends the BoW model to 3D transmission imagery (X-ray CT) from its conventional application in 2D reflectance (photographic) imagery. We explore combinations of four 3D feature descriptors (Density Histogram (DH), Density Gradient Histogram (DGH), Scale Invariant Feature Transform (SIFT) and Rotation Invariant Feature Transform (RIFT)), three codebook assignment methodologies (hard, kernel and uncertainty) and seven codebook sizes. Optimal performance is achieved using the DH and DGH descriptors in conjunction with an uncertainty assignment methodology. Successful detection rates in excess of 97% for handguns and 89% for bottles and false-positive rates of approximately 2-3% are achieved. We demonstrate that the underlying imaging modality and the irrelevance of illumination and scale invariance within the transmission imagery context considered here result in the favourable performance of simpler density histogram descriptors (DH, DGH) over 3D extensions of the well-established SIFT and RIFT feature descriptor approaches. HighlightsNovel investigation of BoW model for object classification in 3D Baggage CT scans.Four descriptor types and three codebook assignment methodologies compared.Simple density-based descriptors outperform more complex descriptors.Optimal true and false positive rates for classification of handguns and bottles.Low resolution, noise and artefacts limit performance.
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materials based 3d segmentation of unknown objects from dual energy computed tomography imagery in Baggage security screening
Pattern Recognition, 2015Co-Authors: Andre Mouton, Toby P BreckonAbstract:We present a novel technique for the 3D segmentation of unknown objects from cluttered dual-energy Computed Tomography (CT) data obtained in the Baggage security-screening domain. Initial materials-based coarse segmentations, generated using the Dual-Energy Index (DEI), are refined by partitioning at automatically detected regions. Partitioning is guided by a novel random forest based quality metric, trained to recognise high-quality, single-object segments. A second novel segmentation quality measure is presented for quantifying the quality of full segmentations based on the random forest metric of the constituent parts and the error in the number of objects segmented. In a comparative evaluation between the proposed approach and three state-of-the-art volumetric segmentation techniques designed for single-energy CT data (two region-growing 1,2] and one graph-based 3]) our method is shown to outperform both region-growing methods in terms of segmentation quality and speed. Although the graph-based approach generates more accurate partitions, it is characterised by high processing times and is significantly outperformed by the proposed method in this regard. The observations made in this study indicate that the proposed segmentation technique is well-suited to the Baggage security-screening domain, where the demand for computational efficiency is paramount to maximise throughput. HighlightsNovel dual-energy materials-based 3D segmentation technique.Two novel random forest-based segmentation quality metrics.Novel segmentation refinement procedure.Comparative evaluation using dual-energy Baggage screening CT data.Demonstrate high-quality segmentations at low processing times.
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A review of automated image understanding within 3D Baggage computed tomography security screening
Journal of X-Ray Science and Technology, 2015Co-Authors: Andre Mouton, Toby P BreckonAbstract:Baggage inspection is the principal safeguard against the transportation of prohibited and potentially dangerous materials at airport security checkpoints. Although traditionally performed by 2D X-ray based scanning, increasingly stringent security regulations have led to a growing demand for more advanced imaging technologies. The role of X-ray Computed Tomography is thus rapidly expanding beyond the traditional materials-based detection of explosives. The development of computer vision and image processing techniques for the automated understanding of 3D Baggage-CT imagery is however, complicated by poor image resolutions, image clutter and high levels of noise and artefacts. We discuss the recent and most pertinent advancements and identify topics for future research within the challenging domain of automated image understanding for Baggage security screening CT.
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Fully automatic 3D Threat Image Projection: Application to densely cluttered 3D Computed Tomography Baggage images
2012 3rd International Conference on Image Processing Theory Tools and Applications (IPTA), 2012Co-Authors: Najla Megherbi, Greg T. Flitton, Toby P Breckon, Andre MoutonAbstract:In this paper, we describe a Threat Image Projection (TIP) method designed for 3D Computed Tomography (CT) screening systems. The novel methodology automatically determines a valid 3D location in the passenger 3D CT Baggage image into which a fictional threat 3D image can be inserted without violating the bag content. According to the scan orientation, the passenger bag content and the material of the inserted threat appropriate CT artefacts are generated using a Radon transform in order to make the insertion realistic. Densely cluttered 3D CT Baggage images are used to validate our method. Experimental results confirm that our method is able to reliably insert threat items in challenging 3D images without providing any perceptible visual cue to human screeners.
Qianmei Feng - One of the best experts on this subject based on the ideXlab platform.
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a mathematical framework for sequential passenger and Baggage screening to enhance aviation security
Computers & Industrial Engineering, 2009Co-Authors: Hande Sahin, Qianmei FengAbstract:To enhance security at both national and global levels, airport security screening systems must be designed with high efficiency and effectiveness, which are affected by both screening technologies and operational procedures for utilizing those technologies. The operational efficiency and aviation security can be enhanced if an effective passenger prescreening system is integrated into the Baggage screening system. In this paper, passenger information is incorporated into a two-level checked-Baggage screening system to determine the screening strategy for different subsets of passengers. By deploying a passenger prescreening system, this paper considers selectively applying Baggage screening procedures for 100% screening. Since new image-based screening technologies differ widely in cost and accuracy, a comprehensive mathematical framework is developed in this paper for selecting technology or combination of technologies for efficient 100% Baggage screening. The objective is to determine the optimal combination of technologies and the setting of threshold values for these screening technologies as well. Probability and optimization techniques are used to quantify and evaluate the risk and cost-effectiveness of various device deployment configurations, which are captured by using a system life-cycle cost model. Numerical analysis for all possible system arrangements is demonstrated.
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designing airport checked Baggage screening strategies considering system capability and reliability
Reliability Engineering & System Safety, 2009Co-Authors: Qianmei Feng, Hande Sahin, Kailash C KapurAbstract:Emerging image-based technologies are critical components of airport security for screening checked Baggage. Since these new technologies differ widely in cost and accuracy, a comprehensive mathematical framework should be developed for selecting technology or combination of technologies for efficient 100% Baggage screening. This paper addresses the problem of setting threshold values of these screening technologies and determining the optimal combination of technologies in a two-level screening system by considering system capability and human reliability. Probability and optimization techniques are used to quantify and evaluate the cost- and risk-effectiveness of various deployment configurations, which is captured by using a system life-cycle cost model that incorporates the deployment cost, operating cost, and costs associated with system decisions. Two system decision rules are studied for a two-level screening system. For each decision rule, two different optimization approaches are formulated and investigated from practitioner's perspective. Numerical examples for different decision rules, optimization approaches and system arrangements are demonstrated.
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on determining specifications and selections of alternative technologies for airport checked Baggage security screening
Risk Analysis, 2007Co-Authors: Qianmei FengAbstract:: Federal law mandates that every checked bag at all commercial airports be screened by explosive detection systems (EDS), explosive trace detection systems (ETD), or alternative technologies. These technologies serve as critical components of airport security systems that strive to reduce security risks at both national and global levels. To improve the operational efficiency and airport security, emerging image-based technologies have been developed, such as dual-energy X-ray (DX), backscatter X-ray (BX), and multiview tomography (MVT). These technologies differ widely in purchasing cost, maintenance cost, operating cost, processing rate, and accuracy. Based on a mathematical framework that takes into account all these factors, this article investigates two critical issues for operating screening devices: setting specifications for continuous security responses by different technologies; and selecting technology or combination of technologies for efficient 100% Baggage screening. For continuous security responses, specifications or thresholds are used for classifying threat items from nonthreat items. By investigating the setting of specifications on system security responses, this article assesses the risk and cost effectiveness of various technologies for both single-device and two-device systems. The findings provide the best selection of image-based technologies for both single-device and two-device systems. Our study suggests that two-device systems outperform single-device systems in terms of both cost effectiveness and accuracy. The model can be readily extended to evaluate risk and cost effectiveness of multiple-device systems for airport checked-Baggage security screening.
John E. Kobza - One of the best experts on this subject based on the ideXlab platform.
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a cost benefit analysis of alternative device configurations for aviation checked Baggage security screening
Risk Analysis, 2006Co-Authors: Sheldon H Jacobson, Tamana Karnani, John E. Kobza, Lynsey RitchieAbstract:The terrorist attacks of September 11, 2001 have resulted in dramatic changes in aviation security. As of early 2003, an estimated 1,100 explosive detection systems (EDS) and 6,000 explosive trace detection machines (ETD) have been deployed to ensure 100% checked Baggage screening at all commercial airports throughout the United States. The prohibitive costs associated with deploying and operating such devices is a serious issue for the Transportation Security Administration. This article evaluates the cost effectiveness of the explosive detection technologies currently deployed to screen checked Baggage as well as new technologies that could be used in the future. Both single-device and two-device systems are considered. In particular, the expected annual direct cost of using these devices for 100% checked Baggage screening under various scenarios is obtained and the tradeoffs between using single- and two-device strategies are studied. The expected number of successful threats under the different checked Baggage screening scenarios with 100% checked Baggage screening is also obtained. Lastly, a risk-based screening strategy proposed in the literature is analyzed. The results reported suggest that for the existing security setup, with current device costs and probability parameters, single-device systems are less costly and have fewer expected number of successful threats than two-device systems due to the way the second device affects the alarm or clear decision. The risk-based approach is found to have the potential to significantly improve security. The cost model introduced provides an effective tool for the execution of cost-benefit analyses of alternative device configurations for aviation-checked Baggage security screening.
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modeling and analyzing multiple station Baggage screening security system performance
Naval Research Logistics, 2005Co-Authors: Sheldon H Jacobson, John E. Kobza, Laura A Mclay, Jon M BowmanAbstract:In the aftermath of the tragic events of 11 September 2001, numerous changes have been made to aviation security policy and operations throughout the nation's airports. The allocation and utilization of checked Baggage screening devices is a critical component in aviation security systems. This paper formulates problems that model multiple sets of flights originating from multiple stations (e.g., airports, terminals), where the objective is to optimize a Baggage screening performance measure subject to a finite amount of resources. These measures include uncovered flight segments (UFS) and uncovered passenger segments (UPS). Three types of multiple station security problems are identified and their computational complexity is established. The problems are illustrated on two examples that use data extracted from the Official Airline Guide. The examples indicate that the problems can provide widely varying solutions based on the type of performance measure used and the restrictions imposed by the security device allocations. Moreover, the examples suggest that the allocations based on the UFS measure also provide reasonable solutions with respect to the UPS measure; however, the reverse may not be the case. This suggests that the UFS measure may provide more robust screening device allocations. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005.
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analyzing the cost of screening selectee and non selectee Baggage
Risk Analysis, 2003Co-Authors: Julie L Virta, Sheldon H Jacobson, John E. KobzaAbstract:Determining how to effectively operate security devices is as important to overall system performance as developing more sensitive security devices. In light of recent federal mandates for 100% screening of all checked Baggage, this research studies the trade-offs between screening only selectee checked Baggage and screening both selectee and non-selectee checked Baggage for a single Baggage screening security device deployed at an airport. This trade-off is represented using a cost model that incorporates the cost of the Baggage screening security device, the volume of checked Baggage processed through the device, and the outcomes that occur when the device is used. The cost model captures the cost of deploying, maintaining, and operating a single Baggage screening security device over a one-year period. The study concludes that as excess Baggage screening capacity is used to screen non-selectee checked bags, the expected annual cost increases, the expected annual cost per checked bag screened decreases, and the expected annual cost per expected number of threats detected in the checked bags screened increases. These results indicate that the marginal increase in security per dollar spent is significantly lower when non-selectee checked bags are screened than when only selectee checked bags are screened.
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modeling aviation Baggage screening security systems a case study
Iie Transactions, 2003Co-Authors: Sheldon H Jacobson, John E. Kobza, Julie L Virta, Jon M Bowman, John J NestorAbstract:Aviation security protects vital national interests, as well as passengers and aircraft. Key components of an aviation security system include Baggage and passenger screening devices and operations. Determining how and where to assign (deploy) such devices can be quite challenging. Moreover, even after such systems are in place, it can be difficult to measure their effectiveness. This paper describes how discrete optimization models can be used to address these questions, based on three performance measures that quantify the effectiveness of airport Baggage screening security device systems. These models are used to solve for optimal airport Baggage screening security device deployments considering the number of passengers on a set of flights who have not been cleared using a security risk assessment system in use by the Federal Aviation Administration (i.e., passengers whose Baggage is subjected to screening), the number of flights in this set, and the size of the aircraft for such flights. Several examp...
Sheldon H Jacobson - One of the best experts on this subject based on the ideXlab platform.
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a cost benefit analysis of alternative device configurations for aviation checked Baggage security screening
Risk Analysis, 2006Co-Authors: Sheldon H Jacobson, Tamana Karnani, John E. Kobza, Lynsey RitchieAbstract:The terrorist attacks of September 11, 2001 have resulted in dramatic changes in aviation security. As of early 2003, an estimated 1,100 explosive detection systems (EDS) and 6,000 explosive trace detection machines (ETD) have been deployed to ensure 100% checked Baggage screening at all commercial airports throughout the United States. The prohibitive costs associated with deploying and operating such devices is a serious issue for the Transportation Security Administration. This article evaluates the cost effectiveness of the explosive detection technologies currently deployed to screen checked Baggage as well as new technologies that could be used in the future. Both single-device and two-device systems are considered. In particular, the expected annual direct cost of using these devices for 100% checked Baggage screening under various scenarios is obtained and the tradeoffs between using single- and two-device strategies are studied. The expected number of successful threats under the different checked Baggage screening scenarios with 100% checked Baggage screening is also obtained. Lastly, a risk-based screening strategy proposed in the literature is analyzed. The results reported suggest that for the existing security setup, with current device costs and probability parameters, single-device systems are less costly and have fewer expected number of successful threats than two-device systems due to the way the second device affects the alarm or clear decision. The risk-based approach is found to have the potential to significantly improve security. The cost model introduced provides an effective tool for the execution of cost-benefit analyses of alternative device configurations for aviation-checked Baggage security screening.
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modeling and analyzing multiple station Baggage screening security system performance
Naval Research Logistics, 2005Co-Authors: Sheldon H Jacobson, John E. Kobza, Laura A Mclay, Jon M BowmanAbstract:In the aftermath of the tragic events of 11 September 2001, numerous changes have been made to aviation security policy and operations throughout the nation's airports. The allocation and utilization of checked Baggage screening devices is a critical component in aviation security systems. This paper formulates problems that model multiple sets of flights originating from multiple stations (e.g., airports, terminals), where the objective is to optimize a Baggage screening performance measure subject to a finite amount of resources. These measures include uncovered flight segments (UFS) and uncovered passenger segments (UPS). Three types of multiple station security problems are identified and their computational complexity is established. The problems are illustrated on two examples that use data extracted from the Official Airline Guide. The examples indicate that the problems can provide widely varying solutions based on the type of performance measure used and the restrictions imposed by the security device allocations. Moreover, the examples suggest that the allocations based on the UFS measure also provide reasonable solutions with respect to the UPS measure; however, the reverse may not be the case. This suggests that the UFS measure may provide more robust screening device allocations. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005.
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analyzing the cost of screening selectee and non selectee Baggage
Risk Analysis, 2003Co-Authors: Julie L Virta, Sheldon H Jacobson, John E. KobzaAbstract:Determining how to effectively operate security devices is as important to overall system performance as developing more sensitive security devices. In light of recent federal mandates for 100% screening of all checked Baggage, this research studies the trade-offs between screening only selectee checked Baggage and screening both selectee and non-selectee checked Baggage for a single Baggage screening security device deployed at an airport. This trade-off is represented using a cost model that incorporates the cost of the Baggage screening security device, the volume of checked Baggage processed through the device, and the outcomes that occur when the device is used. The cost model captures the cost of deploying, maintaining, and operating a single Baggage screening security device over a one-year period. The study concludes that as excess Baggage screening capacity is used to screen non-selectee checked bags, the expected annual cost increases, the expected annual cost per checked bag screened decreases, and the expected annual cost per expected number of threats detected in the checked bags screened increases. These results indicate that the marginal increase in security per dollar spent is significantly lower when non-selectee checked bags are screened than when only selectee checked bags are screened.
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modeling aviation Baggage screening security systems a case study
Iie Transactions, 2003Co-Authors: Sheldon H Jacobson, John E. Kobza, Julie L Virta, Jon M Bowman, John J NestorAbstract:Aviation security protects vital national interests, as well as passengers and aircraft. Key components of an aviation security system include Baggage and passenger screening devices and operations. Determining how and where to assign (deploy) such devices can be quite challenging. Moreover, even after such systems are in place, it can be difficult to measure their effectiveness. This paper describes how discrete optimization models can be used to address these questions, based on three performance measures that quantify the effectiveness of airport Baggage screening security device systems. These models are used to solve for optimal airport Baggage screening security device deployments considering the number of passengers on a set of flights who have not been cleared using a security risk assessment system in use by the Federal Aviation Administration (i.e., passengers whose Baggage is subjected to screening), the number of flights in this set, and the size of the aircraft for such flights. Several examp...
