Data-Driven Determinant-Based Greedy Under/Oversampling Vector Sensor Placement

作     者：Yuji Saito Keigo Yamada Naoki Kanda Kumi Nakai Takayuki Nagata Taku Nonomura Keisuke Asai 

作者机构：Tohoku UniversitySendaiMiyagi980-8579Japan 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2021年第129卷第10期

页      面：1-30页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by JST ACT-X(JPMJAX20AD) JST CREST(JPMJCR1763) JST FOREST(JPMJFR202C),Japan 

主　　题：Sparse sensor selection vector-sensor measurement 

摘      要：A vector-measurement-sensor-selection problem in the undersampled and oversampled cases is considered by extending the previous novel approaches:a greedy method based on D-optimality and a noise-robust greedy method in this *** of the vector-measurement-sensor selection of the greedy algorithms are proposed and applied to randomly generated systems and practical datasets of flowfields around the airfoil and global climates to reconstruct the full state given by the vector-sensor measurement.