• Speech Sciences
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• v.12 no.2
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• pp.171-182
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• 2005

An efficient GMM (Gaussian mixture modeling) method based on LPCA (local principal component analysis) with VQ (vector quantization) for speaker identification is proposed. To reduce the dimension and correlation of the feature vector, this paper proposes a speaker identification method based on principal component analysis. The proposed method firstly partitions the data space into several disjoint regions by VQ, and then performs PCA in each region. Finally, the GMM for the speaker is obtained from the transformed feature vectors in each region. Compared to the conventional GMM method with diagonal covariance matrix, the proposed method requires less storage and complexity while maintaining the same performance requires less storage and shows faster results.

• Structural Engineering and Mechanics
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• v.57 no.3
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• pp.529-541
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• 2016

Under the interaction between dam body, dam foundation and external environment, the dam structural behavior presents the time-varying nonlinear characteristics. According to the prototypical observations, the correct identification on above nonlinear characteristics is very important for dam safety control. It is difficult to implement the description, analysis and diagnosis for dam structural behavior by use of any linear method. Based on the rescaled range analysis approach, the algorithm is proposed to identify and extract the fractal feature on observed dam structural behavior. The displacement behavior of one actual dam is taken as an example. The fractal long-range correlation for observed displacement behavior is analyzed and revealed. The feasibility and validity of the proposed method is verified. It is indicated that the mechanism evidence can be provided for the prediction and diagnosis of dam structural behavior by using the fractal identification method. The proposed approach has a high potential for other similar applications.

• Korean Journal of Oriental Medicine
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• v.14 no.2
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• pp.155-163
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• 2008

State Administration of Traditional Chinese Medicine of the People's Republic of China lead Research on Chao medicine's Constitutional Treatment project by which could involve the following principles. First, constitution can be identified, second, constitution correlates to ones susceptibility to diseases, third, constitution can be harmonized, fourth, prescription made according to ones constitution and patterns. These ideas is originated by Lee Je-ma's theory of Four Constitution Type in "DongEuiSooSeBoWon"(Longetivity and Life Preservation in Eastern Medicine). This is much similar to currents trends of personalized medicine in western medical sciences. Therefore further developing Lee's theory would have significant value on various fields of medicine.

• Phonetics and Speech Sciences
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• v.1 no.1
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• pp.69-73
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• 2009

This paper proposes an efficient global covariance-based principal component analysis (GCPCA) for speaker identification. Principal component analysis (PCA) is a feature extraction method which reduces the dimension of the feature vectors and the correlation among the feature vectors by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when performing PCA to find the eigenvalue and eigenvector matrix using the full covariance matrix by each speaker. The proposed method first calculates the global covariance matrix using training data of all speakers. It then finds the eigenvalue matrix and the corresponding eigenvector matrix from the global covariance matrix. Compared to conventional PCA and Gaussian mixture model (GMM) methods, the proposed method shows better performance while requiring less storage space and complexity in speaker identification.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.857-860
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• 1993

In this paper, we apply the self generating neuro fuzzy model (SGNFM) to the dimension analysis of the chaotic time series. Firstly, we formulate a nonlinear time series identification problem with nonlinear autoregressive (NARMAX) model. Secondly, we propose an identification algorithm using SGNFM. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of chaotic time series. In this estimation method, identification problems with gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the dimension estimation of a chaotic pulsation in a finger's capillary vessels.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1310-1312
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• 1996

In this study, the algorithm for detection of evoked potentials is proposed. The observed evoked potentials are first preprocessed by blind identification so as to eliminate the ongoing EEG Bile noise. Then, statistic characteristics of the peak components i.e latency and amplitude are detected from prefiltered responses by latency-corrected averaging method. The performance of blind identification is compared with those of adaptive fillers as to deterministic and stochastic EPs, is assessed in terms of NMSE, distortion index, correlation coefficient with original EPs. The estimated deterministic and stochastic EPs restored with peak components are compared and assessed. The results show the superiority of this proposed algorithm using blind identification in detecting deterministic and stochastic EPs.

• Speech Sciences
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• v.7 no.2
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• pp.177-189
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• 2000

This paper investigates how individual speakers' speech can be distinguished using acoustic parameters such as amplitude, pitch, and formant frequencies. Word samples from fifteen male speakers in their 20's in three different regions were recorded in two different modes (i.e., casual and clear speech) in quiet settings, and were analyzed with a Praat macro scrip. In order to determine individual speakers' acoustical values, the total duration of voicing segments was measured in five different timepoints. Results showed that a high correlation coefficient between $F_1\;and\;F_2$ in formant frequency was found among the speakers although there was little correlation coefficient between amplitude and pitch. Statistical grouping shows that individual speakers' voices were not reflected in regional dialects for both casual and clear speech. In addition, the difference of maximum and minimum in amplitude was about 10 dB which indicates a perceptually audible degree. These acoustic data can give some meaningful guidelines for implementing algorithms of speaker identification and speaker verification.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.560-563
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• 1997

Sound intensity method is well known as a visualization technique of sound field and sound propagation in noise control. Sound intensity is a vector quantity that describes the magnitude and the direction of net flow of acoustic energy at a given position. The current measuring method is expensive and difficult to identify the noise source exactly. In this paper, we have studied the noise source identification and the characteristics of noise source of rotary compressor for air conditioner using complex sound intensity method. The new method for instantaneous sound intensity is also proposed and it is useful for transient state and steady state. The criteria of these states select auto correlation coefficient. The advantage, simplicity and economic attribution of this method are verified by analyzing the characteristics of noise source with instantaneous sound intensity compared to mean sound intensity.

• YAKHAK HOEJI
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• v.38 no.4
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• pp.366-371
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• 1994

To improve the identification power of TLC/HPTLC the in situ reflectance spectra obtained directly from plates with commercial scanner are used. The spectrum normalization should be carried out prior to comparing and searching the spectra from library for the identification of compounds. Because the reflectance does not obey the Lambert-Beer's law, there arise some problems in normalization. These problems could be solved to some extent by normalizing the spectra with regression methods. The spectra are manipulated with the regression function of a curve obtained from the correlation plot. When the parabola was used as the manipulating function, the spectra were identified with the accuracy of 97% and this result was better than that of conventionally used the point and area normalization method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.228-232
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• 2004

This paper is to identify the position of random disturbance on flexible dynamic system, and the position of the piezo ceramic actuator 0 minimize tip response. Correlation of the output signals from each parts on flexible system is used to identify the position of random disturbance. Except the correlation with an output signal from the position of random disturbance, other correlations have time delay. This is a base idea to identify the position on this study.