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The landing safety prediction model by integrating pattern recognition and Markov chain with flight data

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
  • Published:
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Abstract

This paper aims to predict the landing state during the landing phase to ensure landing safety and reduce the accidents loss. Some past researches have demonstrated the landing phase is the most dangerous phase in flight cycle and fatal accident. The landing safety problem has become a hot research problem in safety field. This study concentrates more on the prediction and advanced warning for landing safety. Firstly, four landing states are divided by three flight parameter variables including touchdown, vertical acceleration and distance to go; subsequently, pattern recognition based on BP neural network is used to establish the landing state prediction model; the genetic algorithm is used to initialize the model parameter; the Markov chain is proposed to revise and improve the model for higher prediction precision. Finally, in comparison with pattern recognition and the Markov chain revision results, the Markov chain revision method is demonstrated to be practical and effective.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (Grant Nos. 71271009 & 71501007 & 71672006). The study is also sponsored by the Aviation Science Foundation of China (2017ZG51081), the Technical Research Foundation (JSZL2016601A004) and the Graduate Student Education & Development Foundation of Beihang University.

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Authors and Affiliations

  1. School of Reliability and System Engineering, Beihang University, Beijing, China

    Shenghan Zhou, Yuliang Zhou, Zhenzhong Xu & Wenbing Chang

  2. Center for Industrial Production, Aalborg University, Aalborg, Denmark

    Yang Cheng

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  1. Shenghan Zhou
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  2. Yuliang Zhou
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  3. Zhenzhong Xu
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  4. Wenbing Chang
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  5. Yang Cheng
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Correspondence to Wenbing Chang.

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Zhou, S., Zhou, Y., Xu, Z. et al. The landing safety prediction model by integrating pattern recognition and Markov chain with flight data. Neural Comput & Applic 31 (Suppl 1), 147–159 (2019). https://doi.org/10.1007/s00521-018-3669-9

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  • DOI: https://doi.org/10.1007/s00521-018-3669-9

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