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Open AccessArticle

Dynamic Stress Measurement with Sensor Data Compensation

MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
College of Engineering, University of Idaho, Moscow, ID 83844-4264, USA
Author to whom correspondence should be addressed.
Electronics 2019, 8(8), 859;
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)
PDF [2800 KB, uploaded 14 August 2019]


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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