A B-Spline Method with AIS Optimization for 2-D IoT-Based Overpressure Reconstruction

Shang Gao*, Guiyun Tian, Xuewu Dai, Xuefeng Jiang, Deren Kong, Yan Zong, Qiuji Yi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In blast wave monitoring, a traditional travel time tomography method is encountered with local minimum travel time and low coverage density of rays. In this article, a novel B-spline fitting method with the knot-optimization artificial immune system (AIS) is proposed for 2-D overpressure reconstruction. It possesses the advantages of handling point sets of large sizes and adjusts the knot vector flexibly. Based on the overpressure value in the explosion from the travel time tomography method, the proposed method combining the advantages of B-splines and knot point optimization AIS is able to achieve the optimal sensor distribution and raise the reconstruction precision. The detailed experimental results about the comparison of linear fitting interpolation, cubic fitting interpolation, natural neighbor fitting interpolation, v4 fitting interpolation, Delaunay triangulation fitting, and B-spline method are also given. Furthermore, for the knot optimization issue in B-spline, the proposed adaptive fitting method with knot-optimization AIS has a smaller root-mean-square (RMS) error with eight knot nodes in comparison with the classic B-spline fitting method. This article is conducted to provide new insights to reconstructing 2-D Internet-of-Things-based (IoT-based) overpressure in blast wave monitoring more precisely under limited sensor deployment and further give a new approach to overpressure reconstruction scenarios.

Original languageEnglish
Article number8936851
Pages (from-to)2005-2013
Number of pages9
JournalIEEE Internet of Things Journal
Volume7
Issue number3
Early online date19 Dec 2019
DOIs
Publication statusPublished - 12 Mar 2020

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