Narrow Results

We consider a version of large population games whose players compete for resources using strategies with adaptable preferences. The system efficiency is measured by the variance of the decisions. In the regime where the system can be plagued by the maladaptive behavior of the players, we find that diversity among the players improves the system efficiency, though it slows the convergence to the steady state. Diversity causes a mild spread of resources at the transient state, but reduces the uneven distribution of resources in the steady state.

The chaotic complexity properties of semiconductor lasers in the chaotic synchronization systems are investigated numerically, based on the information theory based quantifier, the permutation entropy (PE). We find that, on the one hand, the degree of complexity for the master laser increases with the feedback strength firstly and then saturate at higher feedback strength, but are hardly affected by the feedback delay. On the other hand, for the slave laser, the complexity degree is closer to that for the master laser when the high quality chaos synchronization is obtained, which shows that, the PE method is a successful quantifier to evaluate the degree of complexity for the chaotic signals in chaos synchronization systems, and can be considered as a complementary tool to observe the synchronization quality.

In this paper, we propose a new method of Rényi entropy and surrogate data analysis as a new measure to assess the complexity of a complex dynamical system. Simulations are conducted over artificial sequence and stock market series to provide model test and empirical analysis. The results show that the new method has a strong identification for different series and the $\mathrm{\Delta }R(q)$ curves of stock markets are all successfully fitted by exponential functions. These results can be well identified and analyzed in depth.

Legs are the contact point of humans during walking. In fact, leg muscles react when we walk in different conditions (such as different speeds and paths). In this research, we analyze how walking path affects leg muscles’ reaction. In fact, we investigate how the complexity of muscle reaction is related to the complexity of path of movement. For this purpose, we employ fractal theory. In the experiment, subjects walk on different paths that have different fractal dimensions and then we calculate the fractal dimension of Electromyography (EMG) signals obtained from both legs. The result of our analysis showed that the complexity of EMG signal increases with the increment of complexity of path of movement. The conducted statistical analysis also supported the result of analysis. The method of analysis used in this research can be further applied to find the relation between complexity of path of movement and other physiological signals of humans such as respiration and Electroencephalography (EEG) signal.

In this research, for the first time, we analyze the relationship between facial muscles and brain activities when human receives different dynamic visual stimuli. We present different moving visual stimuli to the subjects and accordingly analyze the complex structure of electromyography (EMG) signal versus the complex structure of electroencephalography (EEG) signal using fractal theory. Based on the obtained results from analysis, presenting the stimulus with greater complexity causes greater change in the complexity of EMG and EEG signals. Statistical analysis also supported the results of analysis and showed that visual stimulus with greater complexity has greater effect on the complexity of EEG and EMG signals. Therefore, we showed the relationship between facial muscles and brain activities in this paper. The method of analysis in this research can be further employed to investigate the relationship between other human organs’ activities and brain activity.

The rapid and accurate diagnosis of power grid faults plays a vital role in speeding up the process of accident handling and system recovery and ensuring the safe operation of the power system. This paper proposes to apply the ensemble empirical mode decomposition (EEMD) method and scale-related intrinsic entropy to diagnose the type of fault for the transmission line. First, the electrical data collected by the power system is decomposed by using the EEMD method. Then by eliminating some intrinsic mode functions, the signal is reconstructed by inspecting the correlation coefficient. Finally, the complexity of the reconstructed signal is calculated by using the scale-dependent intrinsic entropy. Since the scale-dependent intrinsic entropy reflects the complexity of one-dimensional time series, it is susceptible to signal changes. The complexity is helpful in the power system for fault signal analysis. The results show the combined method’s effectiveness and practicability through failure analysis using the IEEE 14-bus system as the simulation model.