The base station System based on bidirectional LSTM model

被引:0
|
作者
Kuang, Xuesong [1 ]
Wang, Hongliang [2 ]
机构
[1] Guangdong Ocean Univ, Inst Math & Comp Sci, Zhanjiang, Peoples R China
[2] Inspur Elect Informat Ind Co Ltd, State Key Lab High End Server & Storage Technol, Jinan, Peoples R China
关键词
Base Station Information; Location Information; Bidirectional LSTM;
D O I
10.1109/IWCMC48107.2020.9148473
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
With the development of science and technology, the crime rate through using mobile phone is higher and higher. In order to help the police narrow the scope of the suspects, speed up the efficiency of the police work, and improve the social security. This paper design a base station system, the system collects the base station information for 5G, 4G and 3G, it also collects user's latitude and longitude information, and predicts the user's trajectory based on the bidirectional LSTM (Long Short-Term Memory) model, it can help the public security personnel to grasp the situation better. The police can find the nearby service base station information through the main community base station information, and search the database from base station while using the system. Next, they find out the suspected mobile users who connected into the close-by base station recently and predict their trajectory.
引用
收藏
页码:1410 / 1412
页数:3
相关论文
共 50 条
  • [31] Design of Base Station positioning System Based on RSSI and GPS
    Yang, Mingji
    Zhan, Chenyi
    He, Zhiqiang
    2015 FIFTH INTERNATIONAL CONFERENCE ON INSTRUMENTATION AND MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC), 2015, : 876 - 880
  • [32] Positioning system construction and scheme based on ground base station
    Geng K.
    Huang Z.
    Su Y.
    Shi P.
    Gao Q.
    Xiong H.
    Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, 2019, 45 (10): : 2051 - 2057
  • [33] EEMD-based Wind Speed Forecasting system using Bidirectional LSTM networks
    Jaseena, K. U.
    Kovoor, Binsu C.
    2021 INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND INFORMATICS (ICCCI), 2021,
  • [34] Prediction model of sparse autoencoder-based bidirectional LSTM for wastewater flow rate
    Jianying Huang
    Seunghyeok Yang
    Jinhui Li
    Jeill Oh
    Hoon Kang
    The Journal of Supercomputing, 2023, 79 : 4412 - 4435
  • [35] BIDIRECTIONAL ANALYSIS MODEL OF GREEN INVESTMENT AND CARBON EMISSION BASED ON LSTM NEURAL NETWORK
    Hu, Yiguo
    THERMAL SCIENCE, 2023, 27 (2B): : 1405 - 1415
  • [36] Prediction model of sparse autoencoder-based bidirectional LSTM for wastewater flow rate
    Huang, Jianying
    Yang, Seunghyeok
    Li, Jinhui
    Oh, Jeill
    Kang, Hoon
    JOURNAL OF SUPERCOMPUTING, 2023, 79 (04): : 4412 - 4435
  • [37] Context-based Bidirectional-LSTM Model for Sequence Labeling in Clinical Reports
    Zhu, Henghui
    Paschalidis, Ioannis Ch.
    Tahmasebi, Amir M.
    MEDICAL IMAGING 2019: IMAGING INFORMATICS FOR HEALTHCARE, RESEARCH, AND APPLICATIONS, 2019, 10954
  • [38] Prediction of SSE Shanghai Enterprises index based on bidirectional LSTM model of air pollutants
    Liu, Bingchun
    Yu, Zhecheng
    Wang, Qingshan
    Du, Peng
    Zhang, Xinming
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 204
  • [39] CNN-LSTM Base Station Traffic Prediction Based On Dual Attention Mechanism and Timing Application
    Jia, Hairong
    Wang, Suying
    Ren, Zelong
    COMPUTER JOURNAL, 2024, 67 (06): : 2246 - 2256
  • [40] Deep reinforcement learning for base station switching scheme with federated LSTM-based traffic predictions
    Park, Hyebin
    Yoon, Seung Hyun
    ETRI JOURNAL, 2024, 46 (03) : 379 - 391