moving sources
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2021 ◽  
Vol 14 (1) ◽  
pp. 173
Author(s):  
Mohammed Mahmod Shuaib

Understanding evacuees’ responses to dynamic environmental changes, during an emergency evacuation, is of great importance in determining which aspects are ideal and which aspects should be eliminated or corrected. Evacuees differ in their ability to continually plan escape routes and adapt the routes chosen when they become unsafe owing to moving sources of threat. This is because they have different views and perspectives. The perspectives of evacuees are stochastic and are characterized by a high degree of uncertainty and complexity. To reduce the complexity and control of uncertainty, a model is proposed that can test for variant stochastic representations of evacuees’ perspectives. Two extremely realistic perspectives—the most ideal and the least ideal—are proposed to reasonably limit the range of variance. The success of achieving optimal evacuation is tested when different tendencies towards extreme perspectives are adopted. It is concluded that data toward the most ideal perspectives are capable of demonstrating safer evacuation by reducing the number of simulated burnt agents. This study enables crowd managers and fire safety researchers to test guidance systems as well as configuration of buildings using different perspectives of evacuees.


Author(s):  
He Jingyu ◽  
Bai Baohong ◽  
Zhang Yingzhe ◽  
Lin Dakai ◽  
Liang Wenxin
Keyword(s):  

2021 ◽  
Vol 924 ◽  
Author(s):  
Raphael Ouillon ◽  
Christos Kakoutas ◽  
Eckart Meiburg ◽  
Thomas Peacock

Abstract


Author(s):  
Hao Wang ◽  
Liping Li

AbstractBy utilizing the time difference of arrival (TDOA), the frequency difference of arrival (FDOA), and the differential Doppler rate (DDR) measurements from sensors, this paper proposes an effective moving source localization algorithm with closed solutions. Instead of employing the traditional two-step weighted least squares (WLS) process, the Lagrange multiplier technique is employed in the first step to obtain the initial solution. This initial solution yields a better solution than the existing solution because the dependence among the variables are taken into account. The initial solution is further refined in the second step. The simulation results verify the effectiveness of the proposed algorithm when compared with the relevant existing algorithms.


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