Symbolic transfer entropy-based premature signal analysis

In this paper, we use symbolic transfer entropy to study the coupling strength between premature signals. Numerical experiments show that three types of signal couplings are in the same direction. Among them, normal signal coupling is the strongest, followed by that of premature ventricular contractions, and that of atrial premature beats is the weakest. The T test shows that the entropies of the three signals are distinct. Symbolic transfer entropy requires less data, can distinguish the three types of signals and has very good computational efficiency.

Multivariate Time Series Forecasting with Transfer Entropy Graph

Multivariate Time Series(MTS)forecasting is an essential problem in many fields.Accurate forecasting results can effectively help in making decisions.To date,many MTS forecasting methods have been proposed and widely applied.However,these methods assume that the predicted value of a single variable is affected by all other variables,ignoring the causal relationship among variables.To address the above issue,we propose a novel end-to-end deep learning model,termed graph neural network with neural Granger causality,namely CauGNN,in this paper.To characterize the causal information among variables,we introduce the neural Granger causality graph in our model.Each variable is regarded as a graph node,and each edge represents the casual relationship between variables.In addition,convolutional neural network filters with different perception scales are used for time series feature extraction,to generate the feature of each node.Finally,the graph neural network is adopted to tackle the forecasting problem of the graph structure generated by the MTS.Three benchmark datasets from the real world are used to evaluate the proposed CauGNN,and comprehensive experiments show that the proposed method achieves state-of-the-art results in the MTS forecasting task.

Characteristics of vapor based on complex networks in China

The uneven spatial distribution of stations providing precipitable water vapor(PWV)observations in China hinders the effective use of these data in assimilation,nowcasting,and prediction.In this study,we proposed a complex network framework for exploring the topological structure and the collective behavior of PWV in the mainland of China.We used the Pearson correlation coefficient and transfer entropy to measure the linear and nonlinear relationships of PWV amongst different stations and to set up the undirected and directed complex networks,respectively.Our findings revealed the statistical and geographical distribution of the variables influencing PWV networks and identified the vapor information source and sink stations.Specifically,the findings showed that the statistical and spatial distributions of the undirected and directed complex vapor networks in terms of degree and distance were similar to each other(the common interaction mode for vapor stations and their locations).The betweenness results displayed different features.The largest betweenness ratio for directed networks tended to be larger than that of the undirected networks,implying that the transfer of directed PWV networks was more efficient than that of the undirected networks.The findings of this study are heuristic and will be useful for constructing the best strategy for the PWV data in applications such as vapor observational networks design and precipitation prediction.

Information flow between stock markets:A Koopman decomposition approach

Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns.

Measuring causality by taking the directional symbolic mutual information approach

We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the other.This provides a new perspective that is different from the conventional conceptions.Based on this point of view,a new causality index is derived that uses the definition of directional symbolic mutual information.This measure presents properties that are different from the time delayed mutual information since the symbolization captures the dynamic features of the analyzed time series.In addition to characterizing the direction and the amplitude of the information flow,it can also detect coupling delays.This method has the property of robustness,conceptual simplicity,and fast computational speed.

Relation Entropy and Transferable Entropy Think of Aggregation on Group Decision Making

In this paper, aggregation question based on group decision making and a single decision making is studied. The theory of entropy is applied to the sets pair analysis. The system of relation entropy and the transferable entropy notion are put. The character is studied. An potential by the relation entropy and transferable entropy are defined. It is the consistency measure on the group between a single decision making. We gained a new aggregation effective definition on the group misjudge.

Numerical investigation of entropy generation and heat transfer of pulsating flow in a horizontal channel with an open cavity

In this study, the entropy generation and the heat transfer of pulsating air flow in a horizontal channel with an open cavity heated from below with uniform temperature distribution are numerically investigated. A numerical method based on finite volume method is used to discretize the governing equations. At the inlet of the channel, pulsating velocity is imposed for a range of Strouhal numbers Stpfrom 0 to 1 and amplitude Apfrom 0 to 0.5. The effects of the governing parameters, such as frequency and amplitude of the pulsation, Richardson number, Ri, and aspect ratio of the cavity, L/H, on the flow field, temperature distribution, average Nusselt number and average entropy generation, are numerically analyzed. The results indicate that the heat transfer and entropy generation are strongly affected by the frequency and amplitude of the pulsation and this depends on the Richardson number and aspect ratio of the cavity. The pulsation is more effective with the aspect ratio of the cavity L/H= 1.5 in terms of heat transfer enhancement and entropy generation minimization.

Study on single-group dynamics in evacuation considering pre-defined and undeclared leadership

Grouping is a common phenomenon that occurs everywhere.The leader-follower relationship inside groups has often been qualitatively characterized in previous models using simple heuristics.However,a general method is lacking to quantitatively explain leadership in an evacuating group.To understand the evolution of single-group dynamics throughout an evacuation,we developed an extended social force model integrated with a group force.A series of single-group evacuations from a room were simulated.An information-theoretic method,transfer entropy(TE),was applied to detect predefined and undeclared leadership among evacuees.The results showed that the predefined leader was correctly detected by TE,suggesting its capability in measuring leadership based on time series of evacuees’movement information(e.g.,velocity and acceleration).When evacuees were grouped together,TE was higher than when they were alone.Leaders presented a monotonically increasing cumulative influence curve over the investigated period,whereas followers showed a diminishing tendency.We found that leadership emergence correlated with evacuees’spatial positions.The individual located in the foremost part of the group was most likely to become a leader of those in the rear,which concurred with the experimental observations.We observed how a large group split into smaller ones with undeclared leadership during evacuation.These observations were quantitatively verified by TE results.This study provides novel insights into quantifying leadership and understanding single-group dynamics during evacuations.

Irreversibility analysis of unsteady couette flow with variable viscosity

This paper investigates numerically the inherent irreversibility in unsteady generalized Couette flow between two parallel plates with variable viscosity. The nonlinear governing equations are derived from the Navier-Stokes equations and solved numerically using a semi-discretization finite difference method together with the Runge-Kutta-Fehlberg integration scheme. The profiles of velocity and the temperature obtained are used to compute the entropy generation number, Bejan number, skin friction and Nusselt number. The effects of embedded parameters on entire flow structure are presented graphically and discussed quantitatively.