论文标题
数据驱动的最佳传感器位置,用于使用退火机的高维系统
Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine
论文作者
论文摘要
我们提出了一种新的方法,用于使用退火机解决高维系统的最佳传感器位置问题。传感器点被计算为图的最大集合问题,其边缘重量是由从数据获得的正确正交分解(POD)模式确定的,因为高维系统通常具有低维表示。由于最大集团问题等于补体图的独立集问题,因此使用富士通数字退火器解决了独立集问题。作为提出方法的证明,基于计算出的传感器点的压力数据,对方形缸后的Kármán涡流街诱导的压力分布进行了重建。压力分布是通过压力敏感涂料(PSP)技术测量的,这是一种光流诊断方法。在同一位置比较了由压力传感器和重建压力(根据所提出的方法和现有贪婪方法计算得出的压力)之间的均方根误差(RMS)。结果,使用现有方法获得的大约1/5个传感器点的传感器点可以通过提议的方法实现相似的RMSE。作为最佳传感器放置问题和退火机的新工程应用的新方法,这种方法非常重要。
We propose a novel method for solving optimal sensor placement problem for high-dimensional system using an annealing machine. The sensor points are calculated as a maximum clique problem of the graph, the edge weight of which is determined by the proper orthogonal decomposition (POD) mode obtained from data based on the fact that a high-dimensional system usually has a low-dimensional representation. Since the maximum clique problem is equivalent to the independent set problem of the complement graph, the independent set problem is solved using Fujitsu Digital Annealer. As a demonstration of the proposed method, the pressure distribution induced by the Kármán vortex street behind a square cylinder is reconstructed based on the pressure data at the calculated sensor points. The pressure distribution is measured by pressure-sensitive paint (PSP) technique, which is an optical flow diagnose method. The root mean square errors (RMSEs) between the pressure measured by pressure transducer and the reconstructed pressures (calculated from the proposed method and an existing greedy method) at the same place are compared. As the result, the similar RMSE is achieved by the proposed method using approximately 1/5 number of sensor points obtained by the existing method. This method is of great importance as a novel approach for optimal sensor placement problem and a new engineering application of an annealing machine.