• 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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.07b
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• pp.939-944
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• 2002

PD phenomena can be regarded as a deterministic dynamical process where PD should be occurred if the local electric field be reached to be sufficiently high. And thus, its mathematical model can be described by either difference equations or differential equations using several state variables obtained from the time sequential measured data of PD signals. These variables can provide rich and complex behavior of detectable time series, for which Chaos theory can be employed. In this respect, a new PD pattern recognition method is proposed and named as 'Chaotic Analysis of Partial Discharges (CAPD)' for this work. For this purpose, six types of specimen are designed and made as the models of the possible defects that may cause sudden failures of the underground power transmission cables under service, and partial discharge signals, generated from those samples, are detected and then analyzed by means of CAPD. Throughout the work, qualitative and quantitative properties related to the PD signals from different defects are analyzed by use of attractor in phase space, information dimensions ($D_0$ and D2), Lyapunov exponents and K-S entropy as well. Based on these results, it could be pointed out that the nature of defect seems to be identified more distinctively when the CAPD is combined with traditional statistical method such as PRPDA. Furthermore, the relationship between PD magnitude and the occurrence timing is investigated with a view to simulating PD phenomena.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1850-1852
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• 2000

In this paper, new Partial Discharge (PD) detection technique using Pockels cell was proposed and considerable apparent chaotic characteristics were discussed. For this purpose, PD was generated from needle-plane electrode in air and detected by optical measuring system using Pockels cell, based on Mach-Zehnder interferometer, consisting of He-Ne laser, single mode optical fiber, 50/50 beam splitter and photo detector. A qualitative analysis was carried out by drawing Return map for the normalized time series of the detected PD signals. The results are as follows:(a) Fixed points, between 0.7 and 1.0, are appeared clearly in the right upper area of the return map as the increase in the number of obtained data.(b) Considerable periodicity have been remarked even though exact period and length can not be determined.(c) The self-similarity can be also observed inasmuch as the late paths do not follow the previous ones. Accordingly, exact quantitative analysis such as embedding dimension, fractal dimension, and Lyapunov exponents should be carried out for deducing the quantitative properties regarding PD phenomena.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.2
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• pp.137-143
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• 2011

The pitch motion of a gravity-gradient satellite can be chaotic, depending on the ratio of mass moments of inertia and the eccentricity of the satellite orbit. For a precise prediction of motion, chaotic pitch motion has to be changed to non-chaotic motion. Feedback control can be used to obtain nonchaotic pitch motion. For chaos control and stabilization of the pitch motion of a gravity-gradient satellite, a feedback control system is designed, based on the linear nonautonomous system obtained by linearizing the nonlinear pitch motion. The control law obtained has two parameters and is applied to chaotic nonlinear pitch motion. The nonlinear control system satisfies the proposed control objectives in the range of the nonchaotic parameter space.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Journal for History of Mathematics
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• v.25 no.4
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• pp.53-67
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• 2012

Today, it is necessary to calculate orbits with high accuracy in space flight. The key words of Poincar$\acute{e}$ in celestial mechanics are periodic solutions, invariant integrals, asymptotic solutions, characteristic exponents and the non existence of new single-valued integrals. Poincar$\acute{e}$ define an invariant integral of the system as the form which maintains a constant value at all time $t$, where the integration is taken over the arc of a curve and $Y_i$ are some functions of $x$, and extend 2 dimension and 3 dimension. Eigenvalues are classified as the form of trajectories, as corresponding to nodes, foci, saddle points and center. In periodic solutions, the stability of periodic solutions is dependent on the properties of their characteristic exponents. Poincar$\acute{e}$ called bifurcation that is the possibility of existence of chaotic orbit in planetary motion. Existence of near exceptional trajectories as Hadamard's accounts, says that there are probabilistic orbits. In this context we study the eigenvalue problem in early 20th century in three body problem by analyzing the works of Darwin, Bruns, Gyld$\acute{e}$n, Sundman, Hill, Lyapunov, Birkhoff, Painlev$\acute{e}$ and Hadamard.

• Sleep Medicine and Psychophysiology
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• v.5 no.2
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• pp.185-193
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• 1998

Objectives : The hypothesis that the brain is a nonlinear dynamical system exhibiting deterministic chaos has offered new perspectives to the investigation of information processing in the brain of schizophrenic patients. It seemed worthwhile to estimate nonlinear measures of the electroencephalogram (EEG) in positive and negative schizophrenics, because nonlinear measures might serve as indicators of the specific brain function in schizophrenia according to specific psychopathologies. Method : Previous studies which estimated the chaoticity in the brain of schizophrenia with nonlinear methods recorded the EEGs at limited electrodes, so we tried to record EEGs from 16 channels for nonlinear analysis in 8 positive and 9 negative schizophrenics and 8 healthy control subjects. We employed a new method to calculate the nonlinear invariant measures. For limited noisy data, this algorithm was strikingly faster and more accurate than previous ones. Results : Our results showed that the patients with negative schizophrenia had lower the first positive Lyapunov exponents ($L_1$) than the positive schizophrnics and control subjects at $T_3$ lead. Positive symptoms were positively correlated with $L_1$ in $C_3,\;O_1$ leads, and negatively correlated with $C_4$ lead. Conclusion : These results suggest that if clinical variables such as psychopathology or neuroleptic medications would be well controlled, the nonlinear analysis of the EEGs in patients with schizophrenia seems to be a useful tool in analyzing EEG data to explore the neurodynamics.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.