• The Journal of the Korea institute of electronic communication sciences
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• v.1 no.1
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• pp.49-55
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• 2006

Applied by periodic Stimulating Currents in Bonhoeffer -Van der Pol(BVP) model, chaotic and periodic phenomena occured at specific conditions. The conditions of the chaotic motion in BVP comprised 0.7182< $A_1$ <0.792 and 1.09< $A_1$ <1.302 proved by the analysis of phase plane, bifurcation diagram, and lyapunov exponent. To control the chaotic motion, two methods were suggested by the first used the amplitude parameter A1, $A1={\varepsilon}((x-x_s)-(y-y_s))$ and the second used the temperature parameterc, $c=c(1+{\eta}cos{\Omega}t)$ which the values of ${\eta},{\Omega}$ varied respectlvly, and $x_s$, $y_s$ are the periodic signal. As a result of simulating these methods, the chaotic phenomena was controlled with the periodic motion of periodisity. The feasibilities of the chaotic and the periodic phenomena were analysed by phase plane Poincare map and lyapunov exponent.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.12
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• pp.226-235
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• 1998

This study proposes the analysis and evaluation of method of time series of ultrasonic signal using the chaotic feature extraction for ultrasonic pattern recognition. The features are extracted from time series data for analysis of weld defects quantitatively. For this purpose, analysis objectives in this study are fractal dimension, Lyapunov exponent, and strange attractor on hyperspace. The Lyapunov exponent is a measure of rate in which phase space diverges nearby trajectories. Chaotic trajectories have at least one positive Lyapunov exponent, and the fractal dimension appears as a metric space such as the phase space trajectory of a dynamical system. In experiment, fractal(correlation) dimensions and Lyapunov exponents show the mean value of 4.663, and 0.093 relatively in case of learning, while the mean value of 4.926, and 0.090 in case of testing in slag inclusion(weld defects) are shown. Therefore, the proposed chaotic feature extraction can be enhancement of precision rate for ultrasonic pattern recognition in defecting signals of weld zone, such as slag inclusion.

• International Journal of Automotive Technology
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• v.2 no.1
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• pp.17-23
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• 2001

Measurements of the initial values lead to an inverse and mathematically unprecisely formulated problem. A precise definition of an inverse problem is possible. It is to state a mathematical model of a physical process with clearly defined initial and exit values for the system behind the process. One can grasp the idea of an inverse problem by considering the tire as a copy of the objects of nature in a room with observations. Interpretation of nature is generally a result of an inverse problem. On one hand, the tire may be represented through the sensory organs and the nervous system as well as the experiences of the developer's existing apparatus of the projection of reality. On the other hand, it may be represented by a physical law or a model that can be confirmed or is to be refuted with the help of suitable measurements. During reconstruction of a measuring signal and the identification of a black box that can be assumed to be linear and causal, the tire becomes a first type Volterra integral equation of the convolution type. But measurements of the initial values are always fuzzy, the errors grow and the system behavior can no longer be forecasted. Thus, we have to deal with a chaotic system. This chaos produces fractals in a natural way. These are self-similar geometric structures. This self-similarity is clearly visible in the design.

• 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.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.817-819
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• 1995

Applied by periodic Stimulating Currents in Bonhoeffer-Van der Pol(BVP) model, chaotic and periodic phenomena occured at specific conditions. The conditions of the chaotic motion in BVP comprised 0.7182< $A_{1}$ <0.792 and 1.09< $A_{1}$ <1.302 proved by the analysis of phase plane, bifurcation diagram, and lyapunov exponent. To control the chaotic motion, two methods were suggested by the first used the amplitude parameter $A_{1}$,$A_{1}={\varepsilon}((x-x_{s})-(y-y_{s}))$ and the second used the temperature parameter c, c=c$(1+ {\eta}cos{\Omega}t)$ which the values of $\eta$, ${\Omega}$ varied respectlvly, and $x_{s}$, $y_{s}$ are the periodic signal. As a result of simulating these methods, the chaotic phenomena was controlled with the periodic motion of periodisity. The feasibilities of the chaotic and the periodic phenomena were analysed by phase plane and lyapunov exponent.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.5
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• pp.8-14
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• 2009

In the research field of speech recognition, pinpointing the endpoints of speech utterance even with the presence of background noise is of great importance. These noise present during recording introduce disturbances which complicates matters since what we just want is to get the stationary parameters corresponding to each speech section. One major cause of error in automatic recognition of isolated words is the inaccurate detection of the beginning and end boundaries of the test and reference templates, thus the necessity to find an effective method in removing the unnecessary regions of a speech signal. The conventional methods for speech endpoint detection are based on two linear time-domain measurements: the short-time energy, and short-time zero-crossing rate. They perform well for clean speech but their precision is not guaranteed if there is noise present, since the high energy and zero-crossing rate of the noise is mistaken as a part of the speech uttered. This paper proposes a novel approach in finding an apparent threshold between noise and speech based on Lyapunov Exponents (LEs). This proposed method adopts the nonlinear features to analyze the chaos characteristics of the speech signal instead of depending on the unreliable factor-energy. The excellent performance of this approach compared with the conventional methods lies in the fact that it detects the endpoints as a nonlinearity of speech signal, which we believe is an important characteristic and has been neglected by the conventional methods. The proposed method extracts the features based only on the time-domain waveform of the speech signal illustrating its low complexity. Simulations done showed the effective performance of the Proposed method in a noisy environment with an average recognition rate of up 92.85% for unspecified person.

• Korean Journal of Biological Psychiatry
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• v.2 no.2
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• pp.257-265
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• 1995

The object of this study is to apply a chaotic signal analysis method to the EEG research, especially in the aspect of neuropsychiatry, and to get some inspection of the chaotic phenomena according to the brain sites and subjects. We have acquired 21 channel EEG data and one EOG according to the international 10-20 system and calculated the correlation dimension. The subject groups are schizophrenics, bipolar disorder, major depression and normal control. They were all awoke and eye-closed. We have found no distinctive features from our experiments except temporal regions have slightly higher correlation dimension. There is also no specific distinctions between groups. We conjecture that these results are mainly because the subjects were not well controlled. EEG dimension may change in accordance with to the age, sex, medication and the time data were selected to calculate. We have also considered some conditions for a better and more objective research of chaotic analysis to EEG research. Better conditioning and standardizing the calculation of correlation dimension is necessary for the application of the chaotic analysis to neuropsychiatry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1698-1704
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• 2018

This paper presents a implementation of a chaotic Lorenz system for data secure communication applications. Here we have used PIC18F family-based microcontroller to generate the chaotic signal, and simulated waveform patterns confirm that the chaotic behavior of the microcontroller based discrete time chaotic Lorenz system. There are three R-2R ladder type A/D converters have been implemented for conversion of direct microcontroller digital output into analog waveform, utilizing this specific microcontroller relevant to this experiment work, microcontroller ports B, C and D have been utilized for its time waveform outputs X, Y and Z respectively. XC8 compiler used for the compilation of the program. MATLAB and PROTEUS software platforms are used for simulation. Finally, chaotic time wave forms, 2D chaotic attractors were obtained and secure communication analog waveforms were also verified by experimental measurement.