American Journal of Traffic and Transportation Engineering

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Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

Received: Mar. 13, 2019    Accepted: Apr. 15, 2019    Published: Jun. 26, 2019
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Abstract

Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.

DOI 10.11648/j.ajtte.20190403.12
Published in American Journal of Traffic and Transportation Engineering ( Volume 4, Issue 3, May 2019 )
Page(s) 82-90
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Genetic Algorithm, Wireless Sensor Network Abnormal Vehicle Load Data, Recognition Method

References
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  • APA Style

    Sorush Niknamian. (2019). Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network. American Journal of Traffic and Transportation Engineering, 4(3), 82-90. https://doi.org/10.11648/j.ajtte.20190403.12

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    ACS Style

    Sorush Niknamian. Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network. Am. J. Traffic Transp. Eng. 2019, 4(3), 82-90. doi: 10.11648/j.ajtte.20190403.12

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    AMA Style

    Sorush Niknamian. Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network. Am J Traffic Transp Eng. 2019;4(3):82-90. doi: 10.11648/j.ajtte.20190403.12

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  • @article{10.11648/j.ajtte.20190403.12,
      author = {Sorush Niknamian},
      title = {Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network},
      journal = {American Journal of Traffic and Transportation Engineering},
      volume = {4},
      number = {3},
      pages = {82-90},
      doi = {10.11648/j.ajtte.20190403.12},
      url = {https://doi.org/10.11648/j.ajtte.20190403.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtte.20190403.12},
      abstract = {Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network
    AU  - Sorush Niknamian
    Y1  - 2019/06/26
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajtte.20190403.12
    DO  - 10.11648/j.ajtte.20190403.12
    T2  - American Journal of Traffic and Transportation Engineering
    JF  - American Journal of Traffic and Transportation Engineering
    JO  - American Journal of Traffic and Transportation Engineering
    SP  - 82
    EP  - 90
    PB  - Science Publishing Group
    SN  - 2578-8604
    UR  - https://doi.org/10.11648/j.ajtte.20190403.12
    AB  - Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.
    VL  - 4
    IS  - 3
    ER  - 

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Author Information
  • Department of Military Medicine, Liberty University, Lynchburg, USA

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