• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.1
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• pp.22-27
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• 2001

본 논문에서는 복잡한 하이퍼카오스 신호를 발생시키는 HVPM (Hyperchaotic Volume Preserving Maps) 모델의 회로를 제안하고, 보드상에서 구현하고자 한다. 제안한 HVPM 모델은 3차원 이산시간(discrete-time) 연립차분방정식으로 구성되어 있으며, 비선형 사상(maps)과 모듈러(modulus) 함수를 사용하여 랜덤한 카오스 어트랙터(attractor)를 발생시킨다. 이러한 HVPM 모델을 하드웨로 구현하기 위하여 연산 부분은 연산증폭기를 사용하고, 매핑(mapping) 부분은 N형 함수와 비교기를 사용하여 설계한다. 특히, N형의 비선형 함수는 CMOS 전달특성과 선형증폭기의 출력특성을 조합하여 독특하게 구현하였다. 구현한 보드상의 실험에서 카오스 시스템 파라미터 값에 대응하는 가변저항기를 조절하여 비주기적인 하이퍼카오스 신호를 발생시킴을 입증하였다. 또한 출력된 카오스 신호들간의 오실로스코프 사진에서 위상공간(phase space)의 동적응답은 랜덤한 어트랙터를 발생시킴을 확인할 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2E
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• pp.26-31
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• 1994

A new feature extraction technique utilizing strange attractor and artificial neural network for speaker recognition is presented. Since many signals change their characteristics over long periods of time, simple time-domain processing techniques should e capable of providing useful information of signal features. In many cases, normal time series can be viewed as a dynamical system with a low-dimensional attractor that can be reconstructed from the time series using time delay. The reconstruction of strange attractor is described. In the technique, the raw signal will be reproduced into a geometric three dimensional attractor. Classification decision for speaker recognition is based upon the processing or sets of feature vectors that are derived from the attractor. Three different methods for feature extraction will be discussed. The methods include box-counting dimension, natural measure with regular hexahedron and plank-type box. An artificial neural network is designed for training the feature data generated by the method. The recognition rates are about 82%-96% depending on the extraction method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1486-1491
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• 1999

This paper presents a method of recognizing high impedance fault(HIF) of electrical power systems and classifying fault patterns based on chaos attractors. Two dimensional chaos attractors are reconstructed from neutral point current waveforms. Reliable features for HIF pattern classification are obtained from the chaos attractors. Radial basis function network, trained with two types of HIF data generated by the electromagnetic transient program and measured form actual faults. The RBFN successfully classifies normal and the three types of fault patterns according to the features generated from the chaos attractors.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.45-51
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• 2000

This study proposes that analysis and evaluation method of time series ultrasonic signal using the chaotic feature extrac-tion for degradation extent. Features extracted from time series data using the chaotic time series signal analyze quantitatively material degradation extent. For this purpose analysis objective in this study if fractal dimension lyapunov exponent and strange attractor on hyperspace. The lyapunov exponent is a measure of the rate at which nearby trajectories in phase space diverge. Chaotic trajectories have at least one positive lyapunov exponent. The fractal dimension appears as a metric space such as the phase space trajectory of a dynamical syste, In experiment fractal(correlation) dimensions and lyapunov experiments showed values of mean 3.837-4.211 and 0.054-0.078 in case of degradation material The proposed chaotic feature extraction in this study can enhances ultrasonic pattern recognition results from degrada-tion signals.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.2
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• pp.158-163
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• 2010

In this paper, we implemened the simplified Chua's circuit which is replace L to C by real hardware implementation. Because L element has a difficult problem to make a real hardware, L has a saturation characteristic and we also compare with previous Chua's circuit as the result of chaostic attractors creation.

• Proceedings of the KWS Conference
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• 1998.10a
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• pp.284-287
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• 1998

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3238-3240
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• 2000

본 논문의 목적은 음주섭취로 인한 혈중 알코올 농도에 따른 뇌의 활동도변화를 측정, 분석하는데 있다. 1차원 시계열데이터인 EEG신호는 생체 비선형 동역학 시스템으로부터 발생하는 Deterministic Nonlinear Chaos신호로써 무작위적인 신호와는 구분되어질 수 있다. EEG시계열데이터를 위상공간에 적절한 어트랙터로 재구성하여 상관차원 최대발산지수 등의 카오스 지수들을 추출하여보면 EEG시계열데이터가 무작위적인 계에서 발생하는 랜덤한 신호가 아닌 카오스계에서 기인함을 알 수 있고, 인간의 정신상태에 따른 뇌의 활동도를 정성적, 정량적으로 판별해 볼 수 있다. 이러한 카오스 분석방법을 토대로 음주전의 뇌의 활동도와 음주후 혈중알코올 농도에 따른 뇌의 활동도변화를 EEG의 카오스 지수들의 변화를 통해 분석해 보았다.

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

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.315-317
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• 1999

미세한 차이를 고감도 식별하는 카오스 이론의 프랙탈 차원과 스트레인즈 어트랙터를 생성하는 수정된 에농 함수를 이용하여, 한글 2,350자에 대한 시계열 데이터의 혼도도를 분석하기 위해, 각각의 문자 0트랙터를 구성한 후, 프랙탈 차원을 나타내는 Box-counting Dimension 및 Natural Measure, Information Bit, Information Dimension 등을 구하여 문자 특징을 추출하는 새로운 알고리즘을 제시하였다. 실험결과 한글 2,350자에 대하여 99.23%의 분류율을 나타내어 제안된 방법의 유효성을 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.487-489
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• 2018

This paper presents chaotic Lorenz system implementation for secure data communication applications. In this work chaotic signal is generated by a PIC18F family based microcontroller, XC8 compilers have been utilized for the compilation of C code of microcontroller program. For simulation work Matlab and Proteus platforms were utilized and finally, chaotic time waveforms, 2D and 3D chaotic attractor were obtained and secure communication waveforms were achieved successfully.