Paper Info
Title
Symbolic dynamics of wavelet images for pattern identification
Abstract
Symbolic time series analysis has been introduced in recent literature for pattern identification in dynamical systems. Relevant information, embedded in the measured time series, is extracted in the form of symbol sequences by partitioning of the data sets, and probabilistic finite state automata are constructed from these symbol sequences to generate pattern vectors. This paper presents a symbolic pattern identification method by partitioning of two-dimensional wavelet (i.e., scale-shift) images of sensor time series data. The proposed method is experimentally validated on a laboratory apparatus for identification of evolving patterns due to fatigue damage in polycrystalline alloy specimens.
Year
DOI
Venue
2010
10.1109/ACC.2010.5531077
American Control Conference
Keywords
DocType
ISSN
feature extraction,finite state machines,image recognition,probabilistic automata,time series,wavelet transforms,data set partitioning,dynamical system identification,fatigue damage,pattern vectors,polycrystalline alloy specimens,probabilistic finite state automata,sensor time series data,symbol sequences,symbolic pattern identification method,symbolic time series analysis,two-dimensional wavelet image partitioning,wavelet image symbolic dynamics,anomaly detection,pattern identification,symbolic dynamics,wavelet images,time series analysis,time series data,artificial neural networks,finite state automata,pixel,dynamic system
Conference
0743-1619
ISBN
Citations 
PageRank 
978-1-4244-7426-4
0
0.34
References 
Authors
4
4
Name
Order
Citations
PageRank
Xin Jin150324.30
Gupta, S.200.34
Mukherjee, K.300.34
Ray, A.4832184.32