Early Warning for Civil Aviation Security Checks Based on Deep Learning
Feng Wengang1,2,3(), Huang Jing2,4
1School of Criminal Investigation and Counter Terrorism, People’s Public Security University of China, Beijing 100038, China 2Research Centre for Civil Aviation Security, People’s Public Security University of China, Beijing 100038, China 3Police Science Postdoctoral Research Station, People’s Public Security University of China, Beijing 100038, China 4Public Security Bureau, Civil Aviation Administration of China, Beijing 100710, China
[Objective]This paper proposes a hierarcical classification screening method, aiming to improve the airport security system and passenger experience. [Methods] We proposed a feature deep learning method based on the civil aviation and public security databases. Then, we trained the deep neural network of three layers: time seriers, space seriers, and environmental features to obtain the joint representing feature of passenger risk factors. Finally, we generated the early warning models for passenger security check. [Results] The proposed early warning models could relieve the pressure of civil aviation security check. [Limitaions] More research is needed to examine the proposed model with data from small airports. [Conclusions] The early-warning model based on deep learning could effectively improve work efficiency of airport security checks and passanger experience.
冯文刚, 黄静. 基于深度学习的民航安检和航班预警研究*[J]. 数据分析与知识发现, 2018, 2(10): 46-53.
Feng Wengang,Huang Jing. Early Warning for Civil Aviation Security Checks Based on Deep Learning. Data Analysis and Knowledge Discovery, 2018, 2(10): 46-53.
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