Dynamic stress measurement with sensor data compensation

Jingjing Gu, Zhiteng Dong, Cai Zhang, Xiaojiang Du, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Applying parachutes-deployed Wireless Sensor Network (WSN) in monitoring the high-altitude space is a promising solution for its effectiveness and cost. However, both the high deviation of data and the rapid change of various environment factors (air pressure, temperature, wind speed, etc.) pose a great challenge. To this end, we solve this challenge with data compensation in dynamic stress measurements of parachutes during the working stage. Specifically, we construct a data compensation model to correct the deviation based on neural network by taking into account a variety of environmental parameters, and name it as Data Compensation based on Back Propagation Neural Network (DC-BPNN). Then, for improving the speed and accuracy of training the DC-BPNN, we propose a novel Adaptive Artificial Bee Colony (AABC) algorithm. We also address its stability of solution by deriving a stability bound. Finally, to verify the real performance, we conduct a set of real implemented experiments of airdropped WSN.

Original languageEnglish
Article number859
JournalElectronics (Switzerland)
Volume8
Issue number8
DOIs
StatePublished - 2019

Keywords

  • Airdropped sensor network
  • Data compensation
  • Dynamic measuring

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