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Data compression of very large‐scale structural seismic and typhoon responses by low‐rank representation with matrix reshape
Author(s) -
Yang Yongchao,
Nagarajaiah Satish,
Ni YiQing
Publication year - 2015
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.1737
Subject(s) - tower , rank (graph theory) , dimension (graph theory) , typhoon , data set , singular value decomposition , scale (ratio) , compression (physics) , structural health monitoring , structural engineering , matrix (chemical analysis) , representation (politics) , structural system , computer science , data mining , algorithm , engineering , mathematics , geography , artificial intelligence , cartography , physics , meteorology , materials science , combinatorics , politics , law , political science , composite material , thermodynamics
Summary The intrinsic low‐dimensional structure, which is implicit in the large‐scale data sets of structural seismic and typhoon responses, is exploited for efficient data compression. Such a low‐dimensional structure, empirically, stems from few modes that are active in the structural dynamic responses. Originally, limited to the sensor and time‐history dimension, the structural seismic and typhoon response data set generally does not have an explicit low‐rank representation (e.g., by singular value decomposition or principal component analysis), which is critical in multi‐channel data compression. By the proposed matrix reshape scheme, the low‐rank structure of the large‐scale data set stands out, regardless of the original data dimension. Examples demonstrate that the developed method can significantly compress the large‐scale structural seismic and typhoon response data sets, which were recorded by the structural health monitoring system of the super high‐rise Canton Tower. Copyright © 2015 John Wiley & Sons, Ltd.