Abstract
Visibility graph has established itself as a powerful tool for analyzing time series. We in this paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG). We use nonlinear time series from two typical complex systems, i.e., EEG signals and two-phase flow signals, to demonstrate the effectiveness of our method. Combining MLPHVG and support vector machine, we detect epileptic seizures from the EEG signals recorded from healthy subjects and epilepsy patients and the classification accuracy is 100%. In addition, we derive MLPHVGs from oil-water two-phase flow signals and find that the average clustering coefficient at different scales allows faithfully identifying and characterizing three typical oil-water flow patterns. These findings render our MLPHVG method particularly useful for analyzing nonlinear time series from the perspective of multiscale network analysis.
Highlights
Visibility graph has established itself as a powerful tool for analyzing time series
We in this paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG) to analyze nonlinear time series from the perspective of multiscale and complex network analysis
We use two examples to demonstrate the validity of our method, i.e., (a) EEG signals recorded from healthy subjects and epilepsy patients; (b) experimental flow signals from oil-water two-phase flows
Summary
The mixture flow of immiscible oil-water can be viewed as a complex system with typical features of instability, transient and randomness. The interest in oil-water two-phase flows has greatly increased due to the development of petroleum industry. Different flow patterns exhibit distinct local flow behaviors, how to identify and uncover the underlying dynamics of different flow patterns from experimental measurements has represented a challenge of significant importance. We carry out oil-water two-phase flow experiment to obtain the flow signals and use our proposed method to identify and characterize different flow patterns from the experimental measurements. The results suggest that our method enables to identify distinct flow behaviors underlying three typical oil-water flow patterns. The above findings render our MLPHVG method powerful for characterizing a dynamical process underlying a given nonlinear time series of time dependent complex system
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
