Data-driven optimal sensor placement for high-dimensional system using annealing machine
Data-driven optimal sensor placement for high-dimensional system using annealing machine
复制标题
使用退火机的高维系统数据驱动的最佳传感器放置
DOI：
10.1016/j.ymssp.2022.109957
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发表时间：
2023
影响因子：
8.4
通讯作者：
Yu Matsuda
中科院分区：
文献类型：
--
作者：
Tomoki Inoue;Tsubasa Ikami;Yasuhiro Egami;Hiroki Nagai;Yasuo Naganuma;Koichi Kimura;Yu Matsuda
提出了一种利用退火机求解高维系统传感器优化配置问题的新方法。传感器点的计算作为一个最大团问题的图，其边的权重是由适当的正交分解模式从数据的基础上，一个高维系统通常有一个低维的表示。由于最大团问题等价于补图的独立集问题，因此使用Fujitsu Digital Annealer解决了独立集问题。与现有的贪婪方法相比，该方法在每一步都选择最佳点，并且从不重新考虑先前选择的点，所提出的方法是上级的，因为它能够找到最佳的点集。作为高维系统的演示，压力敏感涂料方法，这是一种光流诊断方法，测量的压力分布，从计算的传感器点的压力数据重建。比较了压力传感器测量的压力与该方法、现有贪婪方法和随机选择方法重建的压力之间的均方根误差（RMSE）。该方法使用现有方法计算的约1/5数量的传感器点来实现类似的RMSE。该方法是解决传感器优化配置问题的一种新方法，也是退火机的一种新的工程应用。
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 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. In contrast to existing greedy methods, which select the optimal point at each step and never reconsider the point selected previously, the proposed method is superior because it is able to find the optimal set of points. As a demonstration of high dimensional system, the pressure distribution measured by the pressure-sensitive paint method, which is an optical flow diagnose method, is reconstructed from the pressure data at the calculated sensor points. The root mean square errors (RMSEs) between the pressures measured by pressure transducers and the pressures reconstructed from the proposed method, an existing greedy method, and random selection method are compared. The similar RMSE is achieved by the proposed method using approximately 1/5 number of sensor points calculated 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.
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DOI：
--
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期刊：
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影响因子：
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DOI：
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发表时间：
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期刊：
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影响因子：
--
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