• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.43-48
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• 2007

In this study, an ANN-based damage detection algorithm using acceleration signals is developed for alarming locations of damage in beam-type structures. A new ANN-algorithm using output-only acceleration responses is designed for damage detection in real time. The cross-covariance of two acceleration signals measured at two different locations is selected as the feature representing the structural condition. Neural networks are trained for potential loading patterns and damage scenarios of the target structure for which its actual loadings are unknown. The feasibility and practicality of the proposed method are evaluated from laboratory-model tests on free-free beams for which accelerations were measured before and after several damage cases.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.1-12
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• 2004

In this paper, we consider choosing the appropriate covariance structure for analyzing repeated measures data with three repeat factors from a study of blood pressure data, which is collected from the local residents of Yangpyeong, Gyeonggi-do (2001) and fitted linear mixed models to find the significant covariates on outcome variable(Blood Pressure)

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.133-138
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• 2006

Singular value decomposition (SDV) approach is applied to the observability analysis of GPS/INS in this paper. A measure of observability for a subspace is introduced. It indicates the minimum size of perturbation in the information matrix that makes the subspace unobservable. It is shown that the measure has direct connections with observability of systems, error covariance, and singular structure of the information matrix. The observability measure given in this paper is applicable to the multi-input/multi-output time-varying systems. An example on the observability analysis of GPS/INS is given. The measure of observability is confirmed to be less sensitive to system model perturbation. It is also shown that the estimation error for the vertical component of gyro bias can be considered unobservable for small initial error covariance for a constant velocity horizontal motion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5382-5403
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• 2018

In this paper, for a spectrum sensing purpose, we heuristically established a test statistic (TS) from a sample covariance matrix (SCM) for multiple antennas based cognitive radio. The TS is formulated as a scaled-energy which is calculated as a sum of scaled diagonal entries of a SCM; each of the diagonal entries of a SCM scaled by corresponding row's Euclidean norm. On the top of that, by combining theoretical results together with simulation observations, we have approximated a decision threshold of the TS which does not need prior knowledge of noise power and primary user signal. Furthermore, simulation results - which are obtained in a fading environment and in a spatially correlating channel model - show that the proposed method stands effect of noise power mismatch (non-uniform noise power) and has significant performance improvement compared with state-of-the-art test statistics.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.458-467
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• 2019

The purpose of this study is the suggestion of optimized parameters in OI (Optimal Interpolation) by experimental study. The observation of applying optimal interpolation is ADCP (Acoustic Doppler Current Profiler) data at the southwestern sea of Korea. FVCOM (Finite Volume Coastal Ocean Model) is used for the barotropic model. OI is to the estimation of the gain matrix by a minimum value between the background error covariance and the observation error covariance using the least square method. The scaling factor and correlation radius are very important parameters for OI. It is used to calculate the weight between observation data and model data in the model domain. The optimized parameters from the experiments were found by the Taylor diagram. Constantly each observation point requires optimizing each parameter for the best assimilation. Also, a high accuracy of numerical model means background error covariance is low and then it can decrease all of the parameters in OI. In conclusion, it is expected to have prepared the foundation for research for the selection of ocean observation points and the construction of ocean prediction systems in the future.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.10
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• pp.1382-1387
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• 2017

Objective: During the last decade, genetic evaluation of dairy cows using longitudinal data (test day milk yield or 305-day milk yield) using random regression method has been officially adopted in several countries. The objectives of this study were to estimate covariance functions for genetic and permanent environmental effects and to obtain genetic parameters of 305-day milk yield over seven parities. Methods: Data including 60,279 total 305-day milk yield of 17,309 Iranian Holstein dairy cows in 7 parities calved between 20 to 140 months between 2004 and 2011. Residual variances were modeled by homogeneous and step functions with 7 and 10 classes. Results: The results showed that a third order polynomial for additive genetic and permanent environmental effects plus a step function with 10 classes for the residual variance was the most adequate and parsimonious model to describe the covariance structure of the data. Heritability estimates obtained by this model varied from 0.17 to 0.28. The performance of this model was better than repeatability model. Moreover, 10 classes of residual variance produce the more accurate result than 7 classes or homogeneous residual effect. Conclusion: A quadratic Legendre polynomial for additive genetic and permanent environmental effects with 10 step function residual classes are sufficient to produce a parsimonious model that explained the change in 305-day milk yield over consecutive parities of Iranian Holstein cows.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.35-42
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• 2021

This paper presents an on-line finite element model updating method for in-service structures using measured data. Conventional updating methods, which are based on numerical optimization, are not efficient for on-line updating because they generally require repeated eigenvalue analyses until convergence criteria are met. The proposed method enables fully automated on-line finite element model updating, almost simultaneously with vibration measurement, without any user intervention or off-line procedures. The automated covariance-driven stochastic subspace identification (Cov-SSI) method is utilized to identify modal frequencies and vectors, and the identified modal data is fed to the neural network of the inverse eigenvalue function to produce the updated finite element model parameters. Numerical examples for a wind excited 20-story building structure shows that the proposed method can update the series of finite element model parameters automatically. It is also shown that sudden changes in the structural parameters can be detected and traced successfully.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.463-472
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• 1994

A method of stochastic response analysis of base-isolated fluid-filled pool structures subject to random ground excitations is studied. Fluid-structure interaction effects between the flexible walls and contained fluid are taken into account in the form of added mass matrix derived by FEM modeling of the contained fluid motion. The stationary ground excitation is represented by Modified Clough-Penzien spectral model and the nonstationary one is obtained by imposing an envelope function on the stationary one. The stationary and nonstationary response statistics of the two different isolation systems are obtained by solving the governing Lyapunov covariance matrix differential equations.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.114-117
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• 1990

EEG signal is analyzed by two methods, analysis by visual inspection of EEG recording sheets and analysis by quantative method. Generally visual inspection method is used in the clinical field. But this method has its limitation because EEG signal is random signal. Therefore it is necessary to analyze EEG signals quantatively to obtain more precise and objective information of neural and brain. In this paper, power spectrum of EEG signal was estimated by AR(AutoRegressive) model in the frequency domain. This process is useful as a preprocessing stage for tomographic brain mapping (TBM) at each frequency, band. As a method for estimating power spectral density of EEG signals, periodogram method, autocorrelation method. covariance method, modified covariance method, and Burg method are tested in this paper.

• Journal of Internet Computing and Services
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• v.5 no.3
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• pp.57-62
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• 2004

In this paper, an efficient speaker identification based on robust vector quantizationprincipal component analysis (VQ-PCA) is proposed to solve the problems from outliers and high dimensionality of training feature vectors in speaker identification, Firstly, the proposed method partitions the data space into several disjoint regions by roust VQ based on M-estimation. Secondly, the robust PCA is obtained from the covariance matrix in each region. Finally, our method obtains the Gaussian Mixture model (GMM) for speaker from the transformed feature vectors with reduced dimension by the robust PCA in each region, Compared to the conventional GMM with diagonal covariance matrix, under the same performance, the proposed method gives faster results with less storage and, moreover, shows robust performance to outliers.