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.

中文翻译：

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基于AIS优化的B样条方法用于基于物联网的二维超压重建

在爆炸波监测中，遇到了传统的行进时间层析成像方法，该方法具有局部最小行进时间和射线覆盖率低的特点。在本文中，提出了一种具有结优化人工免疫系统（AIS）的新型B样条拟合方法，用于二维超压重建。它具有处理大尺寸点集的优点，并且可以灵活地调整结向量。基于行进时间层析成像方法在爆炸过程中的超压值，该方法结合了B样条曲线和结点优化AIS的优点，可以实现传感器的最佳分布，并提高了重建精度。关于线性拟合插值，三次拟合插值，自然邻居拟合插值的比较​​的详细实验结果，还给出了v4拟合插值，Delaunay三角拟合和B样条方法。此外，对于B样条中的结优化问题，与经典的B样条拟合方法相比，提出的具有结优化AIS的自适应拟合方法具有八个结点的均方根（RMS）误差较小。本文旨在为在有限的传感器部署下更精确地重建基于二维物联网（IoT）的爆炸波监控提供新的见解，并进一步提供了一种新的超压重建方案。与经典的B样条拟合方法相比，提出的带有结优化AIS的自适应拟合方法在8个结节点的情况下具有较小的均方根（RMS）误差。本文旨在为在有限的传感器部署下更精确地重建基于二维物联网（IoT）的爆炸波监控提供新的见解，并进一步提供了一种新的超压重建方案。与经典的B样条拟合方法相比，提出的带有结优化AIS的自适应拟合方法在8个结节点的情况下具有较小的均方根（RMS）误差。本文旨在为在有限的传感器部署下更精确地重建基于二维物联网（IoT）的爆炸波监控提供新的见解，并进一步提供了一种新的超压重建方案。