• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.3
• /
• pp.348-354
• /
• 2003

This proposes a new algorithm for advanced personal identification system using iris pattern and speech signal. Since the proposed algorithm adopts a fusion scheme to take advantage of iris recognition and speaker identification, it shows robustness for noisy environments. For evaluating the performance of the proposed scheme, we compare it with the iris pattern recognition and speaker identification respectively. In the experiments, the proposed method showed more 56.7% improvements than the iris recognition method and more 10% improvements than the speaker identification method for high quality security level. Also, in noisy environments, the proposed method showed more 30% improvements than the iris recognition method and more 60% improvements than the speaker identification method for high quality security level.

• Smart Structures and Systems
• /
• v.10 no.4_5
• /
• pp.331-351
• /
• 2012

In this study an output-only system identification technique for civil structures under ambient vibrations is carried out, mainly focused on using the Stochastic Subspace Identification (SSI) based algorithms. A newly developed signal processing technique, called Singular Spectrum Analysis (SSA), capable to smooth a noisy signal, is adopted for preprocessing the measurement data. An SSA-based SSI algorithm with the aim of finding accurate and true modal parameters is developed through stabilization diagram which is constructed by plotting the identified system poles with increasing the size of data matrix. First, comparative study between different approaches, with and without using SSA to pre-process the data, on determining the model order and selecting the true system poles is examined in this study through numerical simulation. Finally, application of the proposed system identification task to the real large scale structure: Canton Tower, a benchmark problem for structural health monitoring of high-rise slender structures, using SSA-based SSI algorithm is carried out to extract the dynamic characteristics of the tower from output-only measurements.

• Structural Engineering and Mechanics
• /
• v.29 no.3
• /
• pp.241-258
• /
• 2008

This paper presents a new identification approach to prestress force. Firstly, a bridge deck is modeled as a prestressed Timoshenko beam. The time domain responses of the beam under sinusoidal excitation are studied based on modal superposition. The prestress force is then identified in the time domain by a system identification approach incorporating with the regularization of the solution. The orthogonal polynomial function is used to improve the noise effect and obtain the derivatives of modal responses of the bridge. Good identification results are obtained from only the first few measured modal data under both sinusoidal and impulsive excitations. It is shown that the proposed method is insensitive to the magnitude of force to be identified and can be successfully applied to indirectly identify the prestress force as well as other physical parameters, such as the flexural rigidity and shearing rigidity of a beam even under noisy environment.

• ETRI Journal
• /
• v.30 no.1
• /
• pp.89-100
• /
• 2008

We consider the feature recombination technique in a multiband approach to speaker identification and verification. To overcome the ineffectiveness of conventional feature recombination in broadband noisy environments, we propose a new subband feature recombination which uses subband likelihoods and a subband reliable-feature selection technique with an adaptive noise model. In the decision step of speaker recognition, a few very low unreliable feature likelihood scores can cause a speaker recognition system to make an incorrect decision. To overcome this problem, reliable-feature selection adjusts the likelihood scores of an unreliable feature by comparison with those of an adaptive noise model, which is estimated by the maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. To evaluate the effectiveness of the proposed methods in noisy environments, we use the TIMIT database and the NTIMIT database, which is the corresponding telephone version of TIMIT database. The proposed subband feature recombination with subband reliable-feature selection achieves better performance than the conventional feature recombination system with reliable-feature selection.

• The Journal of the Acoustical Society of Korea
• /
• v.29 no.6
• /
• pp.374-380
• /
• 2010

It is known that the problem of FIR filtering with noisy input and output data can be solved by a total least squares (TLS) estimation. It is also known that the performance of the TLS estimation is very sensitive to the ratio between the variances of the input and output noises. In this paper, we propose a convex combination algorithm between the ordinary recursive LS based TLS (RTLS) and the ordinary recursive LS (RLS). This combined algorithm is robust to the noise variance ratio and has almost the same complexity as the RTLS. Simulation results show that the proposed algorithm performs near TLS in noise variance ratio ${\gamma}{\approx}1$ and that it outperforms TLS and LS in the rage of 2 < $\gamma$ < 20. Consequently, the practical workability of the TLS method applied to noisy data has been significantly broadened.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.2
• /
• pp.18-26
• /
• 1992

MR(Model Reduction) is presented in order to estimate the frequency of multiple sinusoids from the finite noisy data with the white or colored noises. MR, using the reduced rank models, is designed, appling the approximation of linear system to LP(Linear Prediction). The MR method is analyzed. Monte-carlo simulations are conducted for MR and Lp. The results are compared with in terms of mean, root-mean square and relative bias. MR eliminates effectevely the extremeous and exceptional poles appearing in LP and improves the accuracy of LP. Especially, MR gives promising results in short noisy measurements, low SNR's and colored noises. Power spectral density and angular frequency position are showed by figures, for examples. Finally, the new method is utilized to the communication and biomedical systems estimating the characteristics of the signal and the system identification modelling the dynamic systems from experimental data.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.10
• /
• pp.2819-2830
• /
• 2009

Load bearing structural members in a wide variety of applications accumulate damage over their service life. During experiment much effort and cost is needed for measuring structural safety assessment. The sparseness and errors of measured data have to be considered during the safety estimation of structures. This paper introduces parameter estimation and damage identification algorithm by a system identification using static and dynamic response. The equation error estimator and response error widely used in system identification are based on the minimization of least squared error between measured and calculated responses by a mathematical model of a structure. Since each estimator has a specific form of application in noisy environment and proposes different definitions for these forms. To study the behaviour of the estimators in noisy environment Using Monte Carlo simulation, and a data measured pertubation scheme is adopted to investigate the influence of measurement errors on identification results. The assessment result by static and dynamic response were compared, and the efficiency and applicabilities of the proposed algorithm are demonstrated through simulated static and dynamic responses of a dimensional truss type structures.

• Journal of the Korean Society of Safety
• /
• v.20 no.2 s.70
• /
• pp.119-126
• /
• 2005

Load bearing structural members in a wide variety of applications accumulate damage over their service life. From a standpoint of both safety and performance, it is desirable to monitor the occurrence, location, and extent of such damage. Structures require complicated element models with a number of degrees of freedom in structural analysis. During experiment much effort and cost is needed for measuring structural parameters. The sparseness and errors of measured data have to be considered during the parameter estimation Of Structures. In this paper we introduces damage identification algorithm by a system identification(S.I) using static and dynamic response. To study the behaviour of the estimators in noisy environment Using Monte Carlo simulation and a data measured perturbation scheme is adopted to investigate the influence of measurement errors on identification results. The assessment result by static and dynamic response were compared, and the efficiency and applicabilities of the proposed algorithm are demonstrated through simulated static and dynamic responses of a truss bridge. The assessment results by each method were compared and we could observe that the 5.1 method is superior to the other conventional methods.

• Smart Structures and Systems
• /
• v.9 no.5
• /
• pp.441-459
• /
• 2012

Identification of vibration parameters from the analysis of the dynamic response of a structure plays a key role in current health monitoring systems. This study evaluates the capabilities of the recently developed Synchrosqueezed Wavelet Transform (SWT) to extract instant frequencies and damping values from the simulated noise-contaminated response of a structure. Two approaches to estimate the modal damping ratio from the results of the SWT are presented. The results obtained are compared to other signal processing methods based on Continuous Wavelet (CWT) and Hilbert-Huang (HHT) transforms. It was found that the time-frequency representation obtained via SWT is sharped than the obtained using just the CWT and it allows a more robust extraction of the individual modal responses than using the HHT. However, the identification of damping ratios is more stable when the CWT coefficients are employed.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.1
• /
• pp.95-113
• /
• 2006

The data resulting from a deterministic dynamic system may often appear to be random. However, it is important to distinguish a deterministic and a random processes for statistical analysis. In this paper, we propose a nonparametric test procedure to distinguish a noisy chaos from i.i.d. random process. The proposed procedure can be easily implemented by computer. We notice that the test is very effective to identify a low dimensional chaos process in some cases.