Dynamic Stress Measurement with Sensor Data Compensation
AbstractApplying 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. View Full-Text
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Gu, J.; Dong, Z.; Zhang, C.; Du, X.; Guizani, M. Dynamic Stress Measurement with Sensor Data Compensation. Electronics 2019, 8, 859.
Gu J, Dong Z, Zhang C, Du X, Guizani M. Dynamic Stress Measurement with Sensor Data Compensation. Electronics. 2019; 8(8):859.Chicago/Turabian Style
Gu, Jingjing; Dong, Zhiteng; Zhang, Cai; Du, Xiaojiang; Guizani, Mohsen. 2019. "Dynamic Stress Measurement with Sensor Data Compensation." Electronics 8, no. 8: 859.
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