• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.7
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• pp.287-294
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• 2001

The identification of connection stiffnesses of bolted structures is presented using FRT-based substructural sensitivity analysis. The substructural design sensitivity formula is derived and plugged into the optimization module of MATLAB to identify connection stiffnesses of an air-conditioner compressor or passenger Car. The air-conditioner composed of a compressor and a bracket, is analysed by using the FRT-based substructural(FBS) method to obtain FTRs an FE model is generated for the bracket, and the impact hammer test is performed for the compressor, Obtained FRTs are combined to calculate the reaction force at the connection point and the system response. By minimizing the difference between a target FRT and calculated one the connection element properties of the air-conditioner syste are identified It is shown that the proposed identification method is effective for a real problem.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3E
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• pp.93-100
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• 2003

We present a recursive total least squares (RTLS) algorithm for adaptive system identification. So far, recursive least squares (RLS) has been successfully applied in solving adaptive system identification problem. But, when input data contain additive noise, the results from RLS could be biased. Such biased results can be avoided by using the recursive total least squares (RTLS) algorithm. The RTLS algorithm described in this paper gives better performance than RLS algorithm over a wide range of SNRs and involves approximately the same computational complexity of O(N²).

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.131-140
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• 1994

In order to find the identification function from data and to apply the identification function for the pattern classification, we consider the existing problem of the number of patterns in such data. In this paper, a new criteria for the identification of Gaussaian mixture distribution could be established as a charateristic of the sample variance, which is a bootstrap estimate of the sample variance. We examine the properties and fittness of the criteria through a large scale of computer simulations.

• Structural Engineering and Mechanics
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• v.76 no.1
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• pp.101-114
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• 2020

In this paper, an improved experience-based learning algorithm (EBL), termed as IEBL, is proposed to solve the nonlinear hysteretic parameter identification problem with Bouc-Wen model. A quasi-opposition-based learning mechanism and new updating equations are introduced to improve both the exploration and exploitation abilities of the algorithm. Numerical studies on a single-degree-of-freedom system without/with viscous damping are conducted to investigate the efficiency and robustness of the proposed algorithm. A laboratory test of seven lead-filled steel tube dampers is presented and their hysteretic parameters are also successfully identified with normalized mean square error values less than 2.97%. Both numerical and laboratory results confirm that, in comparison with EBL, CMFOA, SSA, and Jaya, the IEBL is superior in nonlinear hysteretic parameter identification in terms of convergence and accuracy even under measurement noise.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.869-877
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• 2011

In this paper, we propose an efficient two-flow zero-knowledge blind identification protocol on the elliptic curve cryptographic (ECC) system. A. Saxena et al. first proposed a two-flow blind identification protocol in 2005. But it has a weakness of the active-intruder attack and uses the pairing operation that causes slow implementation in smart cards. But our protocol is secure under such attacks because of using the hash function. In particular, it is fast because we don't use the pairing operation and consists of only two message flows. It does not rely on any underlying signature or encryption scheme. Our protocol is secure assuming the hardness of the Discrete-Logarithm Problem in bilinear groups.

• Journal of the Korean Mathematical Society
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• v.47 no.5
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• pp.1077-1095
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• 2010

The paper considers the identifiability (i.e., the unique identification) of a composite string in the class of piecewise constant parameters. The 1-D string vibration is measured at finitely many observation points. The observations are processed to obtain the first eigenvalue and a constant multiple of the first eigenfunction at the observation points. It is shown that the identification by the Marching Algorithm is continuous with respect to the mean convergence in the admissible set. The result is based on the continuous dependence of eigenvalues, eigenfunctions, and the solutions on the parameters. A numerical algorithm for the identification in the presence of noise is proposed and implemented.

• Speech Sciences
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• v.11 no.2
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• pp.237-245
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• 2004

In general, Gaussian mixture model(GMM) is used to estimate the speaker model for speaker identification. The parameter estimates of the GMM are obtained by using the expectation-maximization (EM) algorithm for the maximum likelihood(ML) estimation. However, if the number of mixtures isn't defined well in the GMM, those parameters are obtained inappropriately. The problem to find the number of components is significant to estimate the optimal parameter in mixture model. In this paper, to estimate the optimal number of mixtures, we propose the method that starts from the sufficient mixtures, after, the number is reduced by investigating the mutual information between mixtures for GMM. In result, we can estimate the optimal number of mixtures. The effectiveness of the proposed method is shown by the experiment using artificial data. Also, we performed the speaker identification applying the proposed method comparing with other approaches.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.156-156
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.384-391
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• 2003

Blind channel identification and equalization attempt to identify the communication channel and to remove the inter-symbol interference caused by a communication channel without using any known trainning sequences. In this paper, we propose a blind adaptive channel identification and equalization algorithm with phase offset compensation for single-input multiple-output (SIMO) channel. It is based on the one-step forward multichannel linear prediction error method and can be implemented by an RLS algorithm. Phase offset problem, we use a blind adaptive algorithm called the constant modulus derotator (CMD) algorithm based on condtant modulus algorithm (CMA). Moreover, unlike many known subspace (SS) methods or cross relation (CR) methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch.