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

• Structural Engineering and Mechanics
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• v.50 no.5
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• pp.653-664
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• 2014

Wind fragility analysis provides a quantitative instrument for delineating the safety performance of civil structures under hazardous wind loading conditions such as cyclones and tornados. It has attracted and would be expected to continue to attract intensive research spotlight particularly in the nowadays worldwide context of adapting to the changing climate. One of the challenges encumbering efficacious assessment of the safety performance of existing civil structures is the possible incompleteness of the structural appraisal data. Addressing the issue of the data missingness, the study presented in this paper forms a first attempt to investigate the feasibility of using the expectation-maximization (EM) algorithm and Bayesian techniques to predict the wind fragilities of existing civil structures. Numerical examples of typical linear or hysteretic shear frames are introduced with the wind loads derived from a widely used power spectral density function. Specifically, the application of the maximum a posteriori estimates of the distribution parameters for the story stiffness is examined, and a surrogate model is developed and applied to facilitate the nonlinear response computation when studying the fragilities of the hysteretic shear frame involved.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.5
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• pp.261-269
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• 2018

This study examines earthquake-induced sloshing effects on liquid storage tanks using computation fluid dynamics. To achieve this goal, this study selects an existing square steel tank tested by Seismic Simulation Test Center at Pusan National University as a case study. The model validation was firstly performed through the comparison of shaking table test data and simulated results for the water tank subjected to a harmonic excitation. For a realistic estimation of the wall pressure response of the water tank, three recorded earthquakes with similar peak ground acceleration are applied:1940 El Centro earthquake, 2016 Gyeongju earthquake, and 2017 Pohang earthquake. Wall pressures monitored during the dynamic analyses are examined and compared for different earthquake motions and monitoring points, using power spectrum density. Finally, the maximum dynamic pressure for three earthquakes is compared with the design pressure calculated from a seismic design code. Results indicated that the maximum pressure from the El Centro earthquake exceeds the design pressure although its peak ground acceleration is less than 0.4 g, which is the design acceleration. On the other hand, the maximum pressure due to two Korean earthquakes does not reach the design pressure. Thus, engineers should not consider only the peak ground acceleration when determining the design pressure of water tanks.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.12
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• pp.1040-1047
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• 2012

This paper presents a fault diagnosis algorithm of control surfaces of small fixed-wing aircraft to reduce maintenance cost or to improve repair efficiency by estimation of fault occurrence or part replacement periods. The proposed fault diagnosis algorithm consists of ANPSD (Averaged Normalized Power Spectral Density), PCA (Principle Component Analysis), and GC (Geometric Classifier). ANPSD is used for frequency-domain vibration testing. PCA has advantage to extract compressed information from ANPSD. GC has good properties to minimize errors of the fault detection and isolation. The algorithm was verified by the accelerometer measurements of the scaled normal and faulty ailerons and the test results show that the algorithm is suitable for the detection and isolation of the control surface faults. This paper also proposes solutions for some kind of implementation problems.