• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.323-330
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• 2004

The purpose of this paper is to experimentally identify the finite element (FE) model of a building structure with magnetorheological (MR) fluid damper. Using FE model based system identification (FEBSI) technique, The model of MR damper having nonlinear characteristics is expressed with equivalent linear properties such as mass, stiffness, and damping. Bingham model is used for MR damper modeling. The equivalent stiffness and damping matrices of MR damper are predicted by applying an equivalent linearization technique, and those values are compared with the experimentally obtained ones.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.127-130
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• 2007

The conventional feature recombination technique is very effective in the band-limited noise condition, but in broad-band noise condition, the conventional feature recombination technique does not produce notable performance improvement compared with the full-band system. To cope with this drawback, we introduce a new technique of sub-band likelihood computation in the feature recombination, and propose a new feature recombination method by using this sub-band likelihood computation. Furthermore, the reliable sub-band selection based on the signal-to-noise ratio is used to improve the performance of this proposed feature recombination. Experimental results shows that the average error reduction rate in various noise condition is more than 27% compared with the conventional full-band speaker identification system.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.1-6
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• 2005

The purpose of this paper is to evaluate the performance of an MR fluid damper for seismic vibration control of a structure in terms of equivalent linear damping based on linearization technique and to experimentally verify the results from linearization technique by comparing them to those from system identification testing of a building structure with the MR damper. First, among various models for the MR damper, the equivalent damping is estimated for the Bingham model which is mathematically simple. Second, the transfer function of a building structure with the MR damper is obtained by performing shaking table tests and the damping matrices of the structure are constructed using the modal information obtained by the transfer function. It is observed that the damping mathematically estimated using linearization technique for the Bingham model matches well with the damping coefficient experimentally obtained by system identification.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.641-647
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• 2008

This paper introduces a new technique that can extract natural frequencies and damping ratios from output-only vibration data. Firstly, the free vibration data is obtained from the cross correlations of the output-only response data using a singular value decomposition process. Secondly, the well-known system identification algorithm is applied to extract the natural frequencies and damping ratios from the extracted free vibration data. Comparing to ERADC technique, the accuracy of the proposed modal parameter identification algorithm has been numerically examined. Furthermore, the practicability of the proposed algorithm has been examined through the output-only acceleration data collected from the Seohae cable-stayed bridge. Using the proposed technique, total 24 modes have been identified for the deck plate motions of the bridge.

• International Journal of Aeronautical and Space Sciences
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• v.6 no.1
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• pp.52-60
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• 2005

The CFD coupled aeroelastic analyses have significant advantages over linear panel methods in their accuracy and usefulness for the simulation of actual aeroelastic motion after specific initial disturbance. However, in spite of their advantages, a heavy computation time is required. In this paper, a method is discussed to save a computational cost in the time domain aeroelastic analysis based on the system identification technique. The coefficients of system identification model are fit to the computed time response obtained from a previously developed aeroelastic analysis code. Because the non-dimensionalized data is only used to construct the model structure, the resulting model of the unsteady CFD solution is independent of dynamic pressure and this independency makes it possible to find the flutter dynamic pressure without the unsteady aerodynamic computation. To confirm the accuracy of the system identification methodology, the system model responses are compared with those of the CFD coupled aeroelastic analysis at the same dynamic pressure.

• Steel and Composite Structures
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• v.9 no.5
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• pp.445-455
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• 2009

This study proposes a system identification technique for a fiber-reinforced polymer deck with neural networks. Neural networks are trained for system identification and the identified structure gives training data in return. This process is repeated until the identified parameters converge. Hence, the proposed algorithm is called an iterative neural network scheme. The proposed algorithm also relies on recent developments in the experimental design of the response surface method. The proposed strategy is verified with known systems and applied to a fiber-reinforced polymer bridge deck with experimental data.

• Structural Monitoring and Maintenance
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• v.7 no.3
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• pp.195-213
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• 2020

In this paper an extension to the method for the identification of mechanical parameters of nonlinear systems proposed in Breccolotti and Materazzi (2007) for MDoF systems is presented. It can be used for damage identification purposes when damage modifies the linear characteristics of the investigated structure. It is based on the following two main features: the solution of the Fokker-Planck equation that describes the response probabilistic properties of the system when it is excited by external Gaussian loads; and a model updating technique that minimizes the differences between the response of the actual system and that of a parametric system used to identify the unknown parameters. Numerical analysis, that simulate virtual experimental tests, are used in the paper to show the capabilities of the method and to analyse the conditions required for its application.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.04a
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• pp.180-189
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• 2001

In this study, recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.117-126
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• 2001

In this study, the recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.928-940
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• 2017

State-of-the-art speaker recognition systems may work better for the English language. However, if the same system is used for recognizing those who speak different languages, the systems may yield a poor performance. In this work, the decisions of a Gaussian mixture model-universal background model (GMM-UBM) and a learning vector quantization (LVQ) are combined to improve the recognition performance of a multilingual speaker identification system. The difference between these classifiers is in their modeling techniques. The former one is based on probabilistic approach and the latter one is based on the fine-tuning of neurons. Since the approaches are different, each modeling technique identifies different sets of speakers for the same database set. Therefore, the decisions of the classifiers may be used to improve the performance. In this study, multitaper mel-frequency cepstral coefficients (MFCCs) are used as the features and the monolingual and cross-lingual speaker identification studies are conducted using NIST-2003 and our own database. The experimental results show that the combined system improves the performance by nearly 10% compared with that of the individual classifier.