Refine
Year of publication
Document Type
- Article (285)
- Postprint (11)
- Preprint (9)
- Monograph/Edited Volume (8)
- Other (1)
Keywords
- Complex networks (5)
- Event synchronization (4)
- precipitation (3)
- synchronization (3)
- Amazon rainforest (2)
- Extreme rainfall (2)
- Synchronization (2)
- channel (2)
- classification (2)
- climate networks (2)
- complex networks (2)
- diffusion (2)
- droughts (2)
- events (2)
- identifying influential nodes (2)
- models (2)
- prediction (2)
- rainfall (2)
- space-dependent diffusivity (2)
- streamflow (2)
- 3D medical image analysis (1)
- African climate (1)
- Algebraic geometry (1)
- Anisotropy (1)
- Baiu (1)
- Bifurcation parameters (1)
- Biomass (1)
- Calvin cycle (1)
- Chaotic System (1)
- Cold air surges (1)
- Complex network (1)
- Convective storms (1)
- Dominant link directions (1)
- EEG (1)
- Escherichia-coli (1)
- Extreme events (1)
- Extreme precipitation (1)
- Holocene (1)
- Hypothesis Test (1)
- India (1)
- Indian monsoon (1)
- Indian summer monsoon (1)
- Isochrones (1)
- K-means technique (1)
- Mesoscale systems (1)
- Multistationarity (1)
- North-Atlantic climate (1)
- Partial wavelet coherence (1)
- Phase Synchronization (1)
- Planetary Rings (1)
- Plio-Pleistocene (1)
- Precipitation (1)
- Precipitation events (1)
- Rainfall (1)
- Rainfall network (1)
- Rainfall patterns (1)
- Regionalization (1)
- Self-organizing map (1)
- South American monsoon system (1)
- Statistical and Nonlinear Physics (1)
- Subtropical cyclones (1)
- Surrogate Data (1)
- Teleconnection patterns (1)
- Time-varying Delay (1)
- Tropical storms (1)
- Ungauged catchments (1)
- Wavelet power spectrum (1)
- Wavelets (1)
- Z-P approach (1)
- algorithms (1)
- anatomical connectivity (1)
- bifurcation analysis (1)
- bifurcations (1)
- climate-driven evolution (1)
- cluster-analysis (1)
- coherence (1)
- complex systems (1)
- cortical network (1)
- desynchronization (1)
- dynamical cluster (1)
- dynamical transitions (1)
- functional connectivity (1)
- high-frequency force (1)
- inference (1)
- interdependences (1)
- intermittency (1)
- low-frequency force (1)
- mean residence time (1)
- monsoon (1)
- mutual information (1)
- networks (1)
- noise (1)
- nonlinear dynamics (1)
- nonlinear time series analysis (1)
- pQCT (1)
- patient immobilization (1)
- period doubling (1)
- periods (1)
- phase (1)
- proteasome (1)
- protein translocation (1)
- ratchets (1)
- recognition (1)
- records (1)
- recurrence plot (1)
- series (1)
- statistical physics (1)
- stochastic process (1)
- stochastic resonance (1)
- teleconnections (1)
- time (1)
- topological community (1)
- trabecular bone (1)
- unferring cellular networks (1)
- variability (1)
- variables (1)
- vibrational resonance (1)
- Æ Recurrence Plots (1)
Institute
- Institut für Physik und Astronomie (234)
- Interdisziplinäres Zentrum für Dynamik komplexer Systeme (48)
- Institut für Geowissenschaften (21)
- Department Psychologie (17)
- Institut für Biochemie und Biologie (4)
- Department Linguistik (3)
- Extern (3)
- Institut für Informatik und Computational Science (2)
- Mathematisch-Naturwissenschaftliche Fakultät (2)
- Department Sport- und Gesundheitswissenschaften (1)
- Institut für Umweltwissenschaften und Geographie (1)
Noise-sustained and controlled synchronization of stirred excitable media by external forcing
(2005)
Most of the previous studies on constructive effects of noise in spatially extended systems have focused on static media, e.g., of the reaction diffusion type. Because many active chemical or biological processes occur in a fluid environment with mixing, we investigate here the interplay among noise, excitability, mixing and external forcing in excitable media advected by a chaotic flow, in a two-dimensional FitzHugh-Nagumo model described by a set of reaction- advection-diffusion equations. In the absence of external forcing, noise may generate sustained coherent oscillations of the media in a range of noise intensities and stirring rates. We find that these noise-sustained oscillations can be synchronized by external periodic signals much smaller than the threshold. Analysis of the locking regions in the parameter space of the signal period, stirring rate and noise intensity reveals that the mechanism underlying the synchronization behaviour is a matching between the time scales of the forcing signal and the noise-sustained oscillations. The results demonstrate that, in the presence of a suitable level of noise, the stirred excitable media act as self-sustained oscillatory systems and become much easier to be entrained by weak external forcing. Our results may be verified in experiments and are useful to understand the synchronization of population dynamics of oceanic ecological systems by annual cycles
In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated firstly from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE-algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and get again promising results. The thermally inducedaccuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems. errors can be estimated with 1-2 micrometer
In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated firstly from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE-algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and get again promising results. The thermally induced errors can be estimated with 1-2${mu m}$ accuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems.
We analyze synchronization between two interacting populations of different phase oscillators. For the important case of asymmetric coupling functions, we find a much richer dynamical behavior compared to that of symmetrically coupled populations of identical oscillators. It includes three types of bistabilities, higher order entrainment and the existence of states with unusual stability properties. All possible routes to synchronization of the populations are presented and some stability boundaries are obtained analytically. The impact of these findings for neuroscience is discussed.
A method for the multivariate analysis of statistical phase synchronization phenomena in empirical data is presented. A first statistical approach is complemented by a stochastic dynamic model, to result in a data analysis algorithm which can in a specific sense be shown to be a generic multivariate statistical phase synchronization analysis. The method is applied to EEG data from a psychological experiment, obtaining results which indicate the relevance of this method in the context of cognitive science as well as in other fields
We present results of physical experiments where we measure the autocorrelation function (ACF) and the spectral linewidth of the basic frequency of a spiral chaotic attractor in a generator with inertial nonlinearity both without and in the presence of external noise. It is shown that the ACF of spiral attractors decays according to an exponential law with a decrement which is defined by the phase diffusion coefficient. It is also established that the evolution of the instantaneous phase can be approximated by a Wiener random process
Untitled
(2004)
We present a study of ocean convection parameterization based on a novel approach which includes both eddy diffusion and advection and consists of a two-dimensional lattice of bistable maps. This approach retains important features of usual grid models and allows to assess the relative roles of diffusion and advection in the spreading of convective cells. For large diffusion our model exhibits a phase transition from convective patterns to a homogeneous state over the entire lattice. In hysteresis experiments we find staircase behavior depending on stability thresholds of local convection patterns. This nonphysical behavior is suspected to induce spurious abrupt changes in the spreading of convection in ocean models. The final steady state of convective cells depends not only on the magnitude of the advective velocity but also on its direction, implying a possible bias in the development of convective patterns. Such bias points to the need for an appropriate choice of grid geometry in ocean modeling
We present different tests for phase synchronization which improve the procedures currently used in the literature. This is accomplished by using a two-sample test setup and by utilizing insights and methods from directional statistics and bootstrap theory. The tests differ in the generality of the situation in which they can be applied as well as in their complexity, including computational cost. A modification of the resampling technique of the bootstrap is introduced, making it possible to fully utilize data from time series