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Open AccessArticle

Dynamic Stress Measurement with Sensor Data Compensation

1
MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
3
College of Engineering, University of Idaho, Moscow, ID 83844-4264, USA
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(8), 859; https://doi.org/10.3390/electronics8080859
Received: 30 June 2019 / Revised: 27 July 2019 / Accepted: 30 July 2019 / Published: 2 August 2019
(This article belongs to the Special Issue Smart Sensor Networks)
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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. View Full-Text
Keywords: airdropped sensor network; dynamic measuring; data compensation airdropped sensor network; dynamic measuring; data compensation
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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.

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