• Bulletin of the Korean Mathematical Society
• /
• v.51 no.5
• /
• pp.1469-1484
• /
• 2014

In this paper we are concerned with a class of stochastic partial functional differential equations with infinite delay. Supposing that the linear part is a Hille-Yosida operator but not necessarily densely defined and employing the integrated semigroup and random dynamics theory, we present some appropriate conditions to guarantee the existence of a random attractor.

• Journal of the Korean Mathematical Society
• /
• v.60 no.2
• /
• pp.255-271
• /
• 2023

In this paper, we deal with random attractors for dynamical systems forced by a deterministic noise. These kind of systems are modeled as skew products where the dynamics of the forcing process are described by the base transformation. Here, we consider skew products over the Bernoulli shift with the unit interval fiber. We study the geometric structure of maximal attractors, the orbit stability and stability of mixing of these skew products under random perturbations of the fiber maps. We show that there exists an open set U in the space of such skew products so that any skew product belonging to this set admits an attractor which is either a continuous invariant graph or a bony graph attractor. These skew products have negative fiber Lyapunov exponents and their fiber maps are non-uniformly contracting, hence the non-uniform contraction rates are measured by Lyapnnov exponents. Furthermore, each skew product of U admits an invariant ergodic measure whose support is contained in that attractor. Additionally, we show that the invariant measure for the perturbed system is continuous in the Hutchinson metric.

• Bulletin of the Korean Mathematical Society
• /
• v.41 no.2
• /
• pp.327-335
• /
• 2004

In this paper, we reformulate a snapshot attractor([5]), ($K,\;\={\mu_{\iota}}$) generated by a random baker's map with a sequence of probability measures {\={\mu_{\iota}}} on K. We obtain bounds of the correlation dimensions of ($K,\;\={\mu_{\iota}}$) for all ${\iota}\;{\geq}\;1$.

• KIEE International Transactions on Power Engineering
• /
• v.4A no.1
• /
• pp.33-41
• /
• 2004

Knowledge of partial discharge (PD) is important to accurately diagnose and predict the condition of insulation. The PD phenomenon is highly complex and seems to be random in its occurrence. This paper indicates the possible use of chaos theory for the recognition and distinction concerning PD signals. Chaos refers to a state where the predictive abilities of a systems future are lost and the system is rendered aperiodic. The analysis of PD using deterministic chaos comprises of the study of the basic system dynamics of the PD phenomenon. This involves the construction of the PD attractor in state space. The simulation results show that the variance of an orthogonal axis in an attractor of chaos theory increases according to the magnitude and the number of PDs. However, it is difficult to clearly identify the characteristics of the PDs. Thus, we calculated the magnitude on an orthogonal axis in an attractor using singular value decomposition (SVD) and principal component analysis (PCA) to extract the numerical characteristics. In this paper, we proposed the condition monitoring method for gas insulated switchgear (GIS) using SVD for efficient calculation of the variance. Thousands of simulations have proven the accuracy and effectiveness of the proposed algorithm.

• Speech Sciences
• /
• v.1
• /
• pp.285-293
• /
• 1997

This paper was constructed to investigate strange attractor in considering speech which is regarded as chaos in that the random signal appears in the deterministic raising system. This paper searches for the delay time from AR model power spectrum for constructing fit attractor for speech signal. As a result of applying Taken's embedding theory to the delay time, an exact correlation dimension solution is obtained. As a result of this consideration of speech, it is found that it has more speaker recognition characteristic parameter, and gains a large speaker discrimination recognition rate.

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.4
• /
• pp.42-48
• /
• 1997

This paper has apparaised ability of speaker recognition and speech recognition using correlation dimension and Lyapunov dimension. In this method, speech was regarded the cahos that the random signal is appeared in determinisitic raising system. we deduced exact correlation dimension and Lyapunov dimension with searching important orbit from AR model power spectrum when reconstruct strange attractor using Taken's embedding theory. We considered a usefulness of speech recognition and speaker recognition using correlation dimension and Lyapunov dimension that characterized reconstruction attractor. As a result of consideration, which were of use more the speaker recognition than speech recognition, and in case of speaker recognition using Lyapunov dimension were much recognition rate more than speaker recognitions using correlation dimension.

• Journal of Korea Society of Industrial Information Systems
• /
• v.6 no.4
• /
• pp.75-80
• /
• 2001

In this paper, we propose a new chaotic synchronization algorithm of using HVPM(Hyperchaotic Volume Preserving Maps) model. The proposed chaotic equation, that is, HVPM model which consists of three dimensional discrete-time simultaneous difference equations and shows uniquely random chaotic attractor using nonlinear maps and modulus function. Pecora and Carrol have recently shown that it is possible to synchronize a chaotic system by sending a signal from the drive chaotic system to the response subsystem. We proposed coupled synchronization algorithm in order to accomplish discrete time hyperchaotic HVPM signals. In the numerical results, two hyperchaotic signals are coupled and driven for accomplishing to the chaotic synchronization systems. And it is demonstrated that HVPM signals have shown the chaotic behavior and chaotic coupled synchronization.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.4 no.2
• /
• pp.379-388
• /
• 2000

Previous studies on high impedance faults assumed that the erratic behavior of fault current would be random. In this paper, we prove that the nature of the high impedance faults is indeed a deterministic chaos, not a random motion. Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents in order to evaluate the orbital instability peculiar to deterministic chaos dynamically, and fractal dimensions of fault currents, which represent geometrical self-similarity are calculated. In addition, qualitative analysis such as phase planes, Poincare maps obtained from fault currents indicate that the irregular behavior is described by strange attractor.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.5
• /
• pp.545-549
• /
• 1999

Previous studies on high impedance faults assumed that the erratic behavior of fault current would be random. In this paper we prove that the nature of the high impedance faults is indeed a deterministic chaos not a random motion. Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents in order to evaluate the orbital instability peculiar to deterministic chaos dynamically and fractal dimensions of fault currents which represent geometrical self-similarity are calculated. In addition qualitative analysis such a s phase planes Poincare maps obtained from fault currents indicate that the irregular behavior is described by strange attractor.

• Computational Structural Engineering
• /
• v.10 no.4
• /
• pp.239-249
• /
• 1997

The method to distinguish chaotic attractors in the perturbed response behaviors of a piecewise-linear system under combined regular and external randomness is provided and examined. In the noisy fields such as the ocean environment, excitation forces induced by wind, waves and currents contain a finite degree of randomness. Under external random perturbations, the system responses are disturbed, and consequently chaotic signatures in the response attractors are not distinguishable, but rather look just random-like. Mean Poincare map can be utilized to identify such chaotic responses veiled due to the random noise by averaging the noise effect out of the perturbed responses. In this study, the procedure to create mean Poincare map combined with the direct numerical simulations is provided and examined. It is found that mean Poincare maps can successfully distinguish chaotic attractors under stochastic excitations, and also can give the information of limit value of noise intensity with which the chaos signature in system responses vanishes.