• Journal of information and communication convergence engineering
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• 제2권4호
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• pp.237-242
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• 2004

In this paper, we introduce a complex systems synchronization method using hyper-chaos circuit consist of State-Controlled Cellular Neural Network (SC-CNN). We make a complex systems using SC-CNN with the n-double scroll. A complex system is created by applying identical n-double scroll or non-identical n-double scroll and Chua's oscillator with weak coupled method to each cell. Complex systems synchronization were achieved using GS(Generalized Synchronization) method between the transmitter and receiver about each state variable in the SC-CNN.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2001년도 추계종합학술대회
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• pp.701-704
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• 2001

Nowadays there are being done many researches on chaos phenomenon among an assortment of group. Currently, already many applications has been developed, applying this phenomenon to engineering problem. now we are to show how we achieved secure communication through hyperchaotic synchronization system using 1-dimensional CNN(Cellular Neural Network). we focused on materializing secure communication.

• Journal of Electrical Engineering and Technology
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• 제2권1호
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• pp.118-122
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• 2007

In this work, experimental investigation is carried out in order to design and fabricate the UHF sensor that is able to detect the partial discharges produced from 10 artificial defects introduced into the real scale 70kV GIS mock-up under high voltage within a well shielded room. As well, in order to verify the on-site applicability of our method, the newly proposed CAPD (chaos analysis of partial discharge) is combined with spectral analysis for identifying the nature of 10 artificial defects under investigation. The PD pattern recognition of each defect has been fulfilled by applying our ANN software. The result indicates that the recognition rate reaches up to 80% by the newly proposed method while the traditional PRPD analysis method allows us to obtain 41%. In consequence, it can be pointed out that the proposed method seems likely to be applicable to the real GIS at the site.

• Journal of the Korean Society for Precision Engineering
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• 제15권11호
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• pp.152-165
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• 1998

This study proposes the analysis and evaluation method of time series ultrasonic signal using the chaoral post-process system for precision rate enhancement of ultrasonic pattern recognition. Chaos features extracted from time series data for analysis quantitatively weld defects For this purpose, feature extraction objectives in this study are fractal dimension, Lyapunov exponent, shape of strange attrator. Trajectory changes in the strange attractor indicated that even same type of defects carried substantial difference in chaoticity resulting from distance shifts such as nearby 0.5, 1.0 skip distance. Such difference in chaoticity enables the evaluation of unique features of defects in the weld zone. In quantitative chaos fenture extraction, feature values of 0.835 and 0.823 in the case of slag inclusion and 0.609 and 0.573 in the case of crack were suggested on the basis of fractal dimension and Lyapunov exponent. Proposed chaoral post-process system in this study can enhances precision rate of ultrasonic pattern recognition results from defect signals of weld zone, such as slag inclusion and crack.

• Journal of Wetlands Research
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• 제21권3호
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• pp.236-243
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• 2019

Hydrologic time series has been analyzed and forecasted by using classical linear models. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. Daily streamflow series at St. Johns river near Cocoa, Florida, USA showed an interesting result of a low dimensional, nonlinear dynamical system but daily inflow at Soyang reservoir, South Korea showed stochastic property. Based on the chaotic dynamical characteristic, DVS (deterministic versus stochastic) algorithm is used for short-term forecasting, as well as for exploring the properties of the system. In addition to the use of DVS algorithm, a neural network scheme for the forecasting of the daily streamflow series can be used and the two techniques are compared in this study. As a result, the daily streamflow which has chaotic property showed much more accurate result in short term forecasting than stochastic data.

• Speech Sciences
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• 제9권2호
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• pp.147-155
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• 2002

In the area of speech processing, raw signals used to be presented into 2D format. However, such kind of presentation methods have limitation to extract characteristics from the signal because of the presentation method. Generally, not much information can be detected from the 2D signal. Strange attractor in the field of chaos theory provides a 3D presentation method. In the area of recognition problem, signal presentation method is very important because good features can be detected from a good presentation. This paper discusses a new feature extraction method that extracts features from a cycle of the strange attractor. A neural network is used to check whether the method extracts suitable features or not. The result shows very good points that can be applied to some areas of signal processing.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.43.1-43
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• 2001

We propose two control methods of the Lyapunov exponents for Jordan-type recurrent neural networks. Both the two methods are formulated by a gradient-based learning method. The first method is derived strictly from the definition of the Lyapunov exponents that are represented by the state transition of the recurrent networks. The first method can control the complete set of the exponents, called the Lyapunov spectrum, however, it is computationally expensive because of its inherent recursive way to calculate the changes of the network parameters. Also this recursive calculation causes an unstable control when, at least, one of the exponents is positive, such as the largest Lyapunov exponent in the recurrent networks with chaotic dynamics. To improve stability in the chaotic situation, we propose a non recursive formulation by approximating ...

• Journal of the Institute of Convergence Signal Processing
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• 제5권1호
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• pp.32-39
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• 2004

Biological neurons generally have chaotic characteristics for permanent or transient period. The effects of chaotic response of biological neuron have not yet been verified by using analytical methods. Even though the transient chaos of neuron could be beneficial to overcoming the local minimum problem, the permanent chaotic response gives adverse effect on optimization problems in general. To solve optimization problems, which are needed in almost all neural network applications such as pattern recognition, identification or prediction, and control, the neuron should have one stable fixed point. In this paper, the dynamic characteristics of the chaotic dynamic neuron and the condition that produces the chaotic response are analyzed, and the convergence conditions are presented.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.494-494
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• 2000

This paper demonstrates that the largest Lyapunov exponent $\lambda$ of recurrent neural networks can be controlled by a gradient method. The method minimizes a square error $e_{\lambda}=(\lambda-\lambda^{obj})^2$ where $\lambda^{obj}$ is desired exponent. The $\lambda$ can be given as a function of the network parameters P such as connection weights and thresholds of neurons' activation. Then changes of parameters to minimize the error are given by calculating their gradients $\partial\lambda/\partialP$. In a previous paper, we derived a control method of $\lambda$via a direct calculation of $\partial\lambda/\partialP$ with a gradient collection through time. This method however is computationally expensive for large-scale recurrent networks and the control is unstable for recurrent networks with chaotic dynamics. Our new method proposed in this paper is based on a stochastic relation between the complexity $\lambda$ and parameters P of the networks configuration under a restriction. Then the new method allows us to approximate the gradient collection in a fashion without time evolution. This approximation requires only $O(N^2)$ run time while our previous method needs $O(N^{5}T)$ run time for networks with N neurons and T evolution. Simulation results show that the new method can realize a "stable" control for larege-scale networks with chaotic dynamics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.18-21
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• 1997

Intelligent control technologies (fuzzy logic, neural network, chaos, and genetic algorithm) have been a great deal of influences and impact, especially in home appliances industry. As a result, products that utilize these technologies are pouring into the market from just about every companies. These products are getting good responses from the consumers, because they offer convenience and amenities through the intelligent self-control. In this article, the functionality of the intelligent control technologies will be explained, and how they are being applied to the consumer products developed in Samsung Electronics Co.