The Simulation Algorithms Based on RL and SFM in Emergency Evacuation

Author(s):  
Zhenpeng An ◽  
Fang Xu ◽  
Di Zhang ◽  
Ao Xing ◽  
Shufeng Yang ◽  
...  
2018 ◽  
Vol 30 (3) ◽  
pp. 524
Author(s):  
Chengqiu Gou ◽  
Hanyou Yu ◽  
Zian Xu ◽  
Xiaofeng Li ◽  
Yanci Zhang

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 47
Author(s):  
Wattana Chanthakhot ◽  
Kasin Ransikarbum

Emergency events in the industrial sector have been increasingly reported during the past decade. However, studies that focus on emergency evacuation to improve industrial safety are still scarce. Existing evacuation-related studies also lack a perspective of fire assembly point’s analysis. In this research, location of assembly points is analyzed using the multi-criteria decision analysis (MCDA) technique based on the integrated information entropy weight (IEW) and techniques for order preference by similarity to ideal solution (TOPSIS) to support the fire evacuation plan. Next, we propose a novel simulation model that integrates fire dynamics simulation coupled with agent-based evacuation simulation to evaluate the impact of smoke and visibility from fire on evacuee behavior. Factors related to agent and building characteristics are examined for fire perception of evacuees, evacuees with physical disabilities, escape door width, fire location, and occupancy density. Then, the proposed model is applied to a case study of a home appliance factory in Chachoengsao, Thailand. Finally, results for the total evacuation time and the number of remaining occupants are statistically examined to suggest proper evacuation planning.


2021 ◽  
Vol 30 (1) ◽  
pp. 893-902
Author(s):  
Ke Xu

Abstract A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall to collect and monitor the portrait and establish a data set. The results showed that the missing detection rate and false detection rate of the SSD algorithm were 0.78 and 2.89%, respectively, which were lower than those of the AdaBoost algorithm. Comparisons with PCA, LDA, and PCA + LDA algorithms demonstrated that the recognition rate of the improved PCA + LDA algorithm was the highest, which was 95.8%, the area under the receiver operating characteristic curve was the largest, and the recognition time was the shortest, which was 465 ms. The experimental results show that the improved PCA + LDA algorithm is reliable in portrait recognition and can be used for emergency evacuation in mass emergencies.


Sign in / Sign up

Export Citation Format

Share Document