ORCID Profile
0000-0002-3241-3139
Current Organisation
East China Normal University
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Publisher: AIP Publishing
Date: 24-08-2012
DOI: 10.1063/1.4747707
Abstract: Chaotic attractors are known to often exhibit not only complex dynamics but also a complex geometry in phase space. In this work, we provide a detailed characterization of chaotic electrochemical oscillations obtained experimentally as well as numerically from a corresponding mathematical model. Power spectral density and recurrence time distributions reveal a considerable increase of dynamic complexity with increasing temperature of the system, resulting in a larger relative spread of the attractor in phase space. By allowing for feasible coordinate transformations, we demonstrate that the system, however, remains phase-coherent over the whole considered parameter range. This finding motivates a critical review of existing definitions of phase coherence that are exclusively based on dynamical characteristics and are thus potentially sensitive to projection effects in phase space. In contrast, referring to the attractor geometry, the gradual changes in some fundamental properties of the system commonly related to its phase coherence can be alternatively studied from a purely structural point of view. As a prospective ex le for a corresponding framework, recurrence network analysis widely avoids undesired projection effects that otherwise can lead to ambiguous results of some existing approaches to studying phase coherence. Our corresponding results demonstrate that since temperature increase induces more complex chaotic chemical reactions, the recurrence network properties describing attractor geometry also change gradually: the bimodality of the distribution of local clustering coefficients due to the attractor’s band structure disappears, and the corresponding asymmetry of the distribution as well as the average path length increase.
Publisher: Elsevier BV
Date: 2018
Publisher: American Geophysical Union (AGU)
Date: 28-04-2022
DOI: 10.1029/2021GL097647
Abstract: Understanding the direct and indirect impact of the Pacific and Atlantic Oceans on precipitation in the region of Northeast Brazil (NEB) is crucial for monitoring unprecedented drought events. We propose nonlinear methods of phase coherence and generalized event synchronization analysis to understand the underlying mechanism. In particular, the relationships between sea surface temperature (SST) variability and the standard precipitation index are interpreted as direct interactions, while the relationships between surrounding oceans are interpreted as indirect effects on the precipitation. Our results reveal a dominant role of tropical North Atlantic for precipitation deficit and droughts, particularly in recent decades. Meanwhile, the indirect Pacific‐North Atlantic phase synchronizations have significant influence on and reinforcement of the droughts in NEB. Furthermore, we find that the instantaneous angular frequencies of precipitation and SST are drastically changed after very strong El Niño and La Niña events, therefore resulting in a higher probability of phase coherence.
Publisher: IOP Publishing
Date: 30-01-2014
Publisher: Copernicus GmbH
Date: 24-11-2014
Abstract: Abstract. Solar activity is characterized by complex dynamics superimposed onto an almost periodic, approximately 11-year cycle. One of its main features is the presence of a marked, time-varying hemispheric asymmetry, the deeper reasons for which have not yet been completely uncovered. Traditionally, this asymmetry has been studied by considering litude and phase differences. Here, we use visibility graphs, a novel tool of nonlinear time series analysis, to obtain complementary information on hemispheric asymmetries in dynamical properties. Our analysis provides deep insights into the potential and limitations of this method, revealing a complex interplay between factors relating to statistical and dynamical properties, i.e., effects due to the probability distribution and the regularity of observed fluctuations. We demonstrate that temporal changes in the hemispheric predominance of the graph properties lag those directly associated with the total hemispheric sunspot areas. Our findings open a new dynamical perspective on studying the north–south sunspot asymmetry, which is to be further explored in future work.
Publisher: Springer Science and Business Media LLC
Date: 10-08-2017
DOI: 10.1038/S41598-017-08245-X
Abstract: A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.
Publisher: World Scientific Pub Co Pte Lt
Date: 04-2011
DOI: 10.1142/S0218127411029021
Abstract: Complex networks are an important paradigm of modern complex systems sciences which allows quantitatively assessing the structural properties of systems composed of different interacting entities. During the last years, intensive efforts have been spent on applying network-based concepts also for the analysis of dynamically relevant higher-order statistical properties of time series. Notably, many corresponding approaches are closely related to the concept of recurrence in phase space. In this paper, we review recent methodological advances in time series analysis based on complex networks, with a special emphasis on methods founded on recurrence plots. The potentials and limitations of the in idual methods are discussed and illustrated for paradigmatic ex les of dynamical systems as well as for real-world time series. Complex network measures are shown to provide information about structural features of dynamical systems that are complementary to those characterized by other methods of time series analysis and, hence, substantially enrich the knowledge gathered from other existing (linear as well as nonlinear) approaches.
No related grants have been discovered for Yong Zou.