TY - JOUR
T1 - A B-Spline Method with AIS Optimization for 2-D IoT-Based Overpressure Reconstruction
AU - Gao, Shang
AU - Tian, Guiyun
AU - Dai, Xuewu
AU - Jiang, Xuefeng
AU - Kong, Deren
AU - Zong, Yan
AU - Yi, Qiuji
N1 - Funding Information:
This work was supported in part by the Nanjing University of Science and Technology under Research Start-Up Funds under Grant AE89991/032; in part by the Fundamental Research Funds for the Central Universities under Grant 309181A8804 and Grant 30919011263; in part by the Natural Science Foundation of Jiangsu Province, China, under Grant BK20190464; and in part by the National Natural Science Foundation of China under Grant 61527803.61960206010 and Grant 51807094.
PY - 2020/3/12
Y1 - 2020/3/12
N2 - 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.
AB - 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.
KW - Artificial immune system (AIS)
KW - B-spline
KW - Internet of Things (IoT)
KW - overpressure
KW - reconstruction
UR - https://www.scopus.com/pages/publications/85082125850
U2 - 10.1109/JIOT.2019.2960827
DO - 10.1109/JIOT.2019.2960827
M3 - Article
AN - SCOPUS:85082125850
SN - 2327-4662
VL - 7
SP - 2005
EP - 2013
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 3
M1 - 8936851
ER -