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|Title:||Optimal deployment of static detectors in a threat area||Authors:||Pang, Jing Chyi||Keywords:||DRNTU::Engineering::Mechanical engineering||Issue Date:||2012||Abstract:||To quote Mohammed M. Hafez, author of the book “Manufacturing Human Bombs: The Making of Palestinian Suicide Bombers”  - Asymmetry in power compels the weak to innovate in order to surprise opponents and circumvent their stronger capabilities. This idea of attack through self sacrifice is centuries old as historical records show, but the technology to achieve such means has slowly evolved down the years and eventually manifested in the recent form of what is known today as the modern suicide bomber. Mostly used by terrorist groups with lack of resources for proper training, the suicide bomber has become the weapon of choice, often becoming the daily fare of major news in the past decade. According to statistics released by the U.S. Department of Defense , out of the 144 recorded suicide bombings since 1968, two thirds occurred between 2001 and 2003; and despite the overall decline of terrorist attacks around the world, the percentage of suicide terrorist attacks have actually increased. Today’s conventional technology has somewhat reached a peak threshold of progression, with the usual arrays of chemical and X-ray detectors, just to name a few. The problem lies in the fact that such suicide bombers often operate on a small scale, making it almost difficult to predict and prevent their attempts, barring a large scale, comprehensive and high maintenance detection system which might put them on full alert as well. Thus, there exists a need to successfully optimize the use of a minimal number of strategically placed and inconspicuous detectors to attempt to neutralize the suicide bomber before detonation, or in the case of failure, trying to minimize the number of casualties inflicted. Developing upon the work by Nie et al (2007) , the author works with the same mathematical model targeted at minimization of expected casualties, but this time subjecting it to different constraints. The author also makes use of a different medium of modeling the aforementioned problem using a high level programming system called GAMS (General Arithmetic Modeling System), which is solved using an online server called NEOS Solver. Lastly, the author also examines the different effects observed when key parameters are changed.||URI:||http://hdl.handle.net/10356/50249||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Student Reports (FYP/IA/PA/PI)|
checked on Sep 23, 2020
checked on Sep 23, 2020
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