• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.09a
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• pp.36-43
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• 2001

In Korea, we are absolutely short of earthquake data in good quality from moderate and large earthquakes, which are needed fur the study of strong ground motion characteristics. This means that the best use of the available data is needed far the time being. In this respect, several methods are suggested in this paper, which can be applied in the process of data selection and analysis. First, it is shown that the calibration status of seismic stations can be easily checked by comparing the spectra from accelerometer and velocity sensor both of which are located at the same location. Secondly, it is recommended that S/N ratio in the frequency domain should be checked before discarding the data by only look of the data in time domain. Thirdly, the saturated earthquake data caused by ground motion level exceeding the detection limit of a seismograph are considered to see if such data can be used for spectrum analysis by performing numerical simulation. The result reveals that the saturated data can still be used within the dominant frequency range according to the levels of saturation. Finally, a technique to minimize the window effect that distorts the low frequency spectrum is suggested. This technique involves detrending in displacement domain once the displacement data are obtained by integration of low frequency components of the original data in time domain. Especially, the low frequency component can be separated by using discrete wavelet transform among many alternatives. All of these methods mentioned above may increase the available earthquake data and frequency range.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.451-454
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• 2005

Recently, many methods to analyze signal have been proposed and representative methods are the Fourier transform and wavelet transform. In these methods, the Fourier transform represents signal with combination cosine and sine at all locations in the frequency domain. However, it doesn't provide time information that particular frequency occurs in signal and depends on only the global feature of the signal. So, to improve these points the wavelet transform which is capable of multiresolution analysis has been applied to many fields such as speech processing, image processing and computer vision. And the wavelet transform, which uses changing window according to scale parameter, presents time-frequency localization. In this paper, we proposed a new approach using a wavelet of cosine and sine type and analyzed features of signal in a limited point of frequency-time plane.

• Journal of the Korean Society for Railway
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• v.12 no.4
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• pp.465-471
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• 2009

In this paper, a relationship between the noise and vibration of a high-speed train's wheelset is examined by using time-varying frequency analysis with random data analysis which together contributes to a reduction in the number of experimental running. The noise and vibration of the wheelset is mainly caused by an interaction between the wheel and railway which shows in non-stationary characteristics. For the analysis, they are measured by some microphones and accelerometers, and those signals are post-processed by time-varying frequency analysis with random data analysis. From the analysis, their methods are useful for analyzing the noise and vibration of high-speed train's wheelset.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.877-888
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• 2017

Spectral analysis is used to determine the frequency of time series data. We first determine the frequency of the series through the power spectrum or the periodogram and then calculate the period of a cycle that may exist in a time series. Estimating the frequency using a Bayesian technique has been developed and proven to be useful; however, the Bayesian estimator for the frequency cannot be analytically solved through mathematical equations and may be handled numerically or computationally. In this paper, we make an inference on the Bayesian frequency through both resampling a parameter by Markov chain Monte Carlo (MCMC) methods and resampling data by bootstrap methods for a time series. We take the Korean real estate price index as an example for Bayesian frequency estimation. We have found a difference in the periods between the sale price index and the long term rental price index, but the difference is not statistically significant.

• Wind and Structures
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• v.10 no.4
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• pp.347-366
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• 2007

Guyed masts subjected to turbulent winds exhibit complex vibrations featuring many vibration modes, each of which contributes to various structural responses in differing degrees. This dynamic behaviour is further complicated by nonlinear guy cable properties. While previous studies have indicated that conventional frequency domain methods can reliably reproduce load effects within the mast, the system linearization required to perform such an analysis makes it difficult to relate these results directly to corresponding guy forces. As a result, the estimation of peak load effects arising jointly from the structural action of the mast and guys, such as leg loads produced as a result of guy reactions and mast bending moments, is uncertain. A numerical study was therefore undertaken to study peak load effects in a 295 m tall guyed mast acted on by simulated turbulent wind. Responses calculated explicitly from nonlinear time domain finite element analyses were compared with approximate methods in the frequency domain for estimating peak values of selected responses, including guy tension, mast axial loads and mast leg loads. It was found that these peak dynamic load effects could be accurately estimated from frequency domain analysis results by employing simple, slightly conservative assumptions regarding the correlation of related effects.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.30 no.1
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• pp.16-30
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• 1997

Application of digital filter to the wave analysis is studied using the observed data by wave gauge. Sea wave data obtained from wave gauge always include long wave frequency components. In order to estimate the sea wave parameters, we must re-analyzed wave data by using a digital filter and the concept of mean sea level correction method. By the wave by wave analysis and spectral methods, sea wave parameters on the basis of wave data obtained by the conventional method and digital filter are compared. The best-fitted design filter determined by the necessary conditions of frequency responses, can be obtained by calculating various transfer functions. Thus, to get the best the digital filter design, both Butterworth filter and Savitzky-Golay filter of digital filter are used in the frequency and time domain, respectively. Three cases of observation wave data are calculated by applying digital filter. The components of different frequency bands in the surf zone are coexisted in three cases. The wave data for wind wave components is computed using the digital filter the surf zone and off-surf zone, and based on the filtered data, wave parameters are calculated by the spectral analysis and wave by wave analysis methods, respectively. As a results, when sea wave data observed by wave gauge are analyzed, the Savitzky-Golay method is recommended which can well appear cut-off frequency by experimental choosing filter length in the time domain. The better mean sea level correction method is the Butterworth filter in the frequency domain.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.04a
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• pp.136-143
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• 1995

The problem of the structural identification becomes important, particularly with relation to the rapid increase of the number of the damaged or deteriorated structures, such as highway bridges, buildings, and industrial facilities. This paper summarizes the recent studies related to those problems by the present authors. The system identfication methods are generally classified as the time domain and the frequency domain methods. As time doamin methods, the sequential algorithms such as the extended Kalman filter and the sequential prediction error method are studied. Several techniques for improving the convergences are incorporated. As frequency domain methods, a new frequency response function estimator is introduced. For damage estimation of existing structures, the modal perturbation and the sensitivity matrix methods are studied. From the example analysis, it has been found that the combined utilization of the measurement data for the static response and the dynamic (modal) properties are very effictive for the damage estimation.

• Annals of Clinical Neurophysiology
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• v.16 no.2
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• pp.49-54
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• 2014

Heart rate variability is significantly associated with cardiovascular complications in various neurological disorders with cardiac impairment. Measures of spontaneous heart rate variability might be different from provocating tests of heart rate variability such as deep breathing and Valsava maneuver. Methods for analysis are divided into time domain methods and frequency domain methods. There are standard deviation of NN interval, standard deviation of average NN interval, root mean square of the successive differences, NN50, and pNN50 in time domain methods. Frequency domain bands can be divided into very low, low, and high frequency. Each variables are influenced by sympathetic and/or parasympathetic activity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1078-1086
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• 2009

This paper presents a Fourier based algorithm for estimating the parameters of the low frequency oscillating modes. The proposed methods estimates various parameters(frequency, damping factor, mode magnitude, phase) by fitting Fourier spectrum and phase with a damped exponential cosine function. Dominant frequency is selected by taking frequency corresponding to the peak spectrum, and damping factor is estimated using the left/right spectra of Fourier spectrum. In addition, mode magnitude is calculated by the normalized peak spectrum, and phase is estimated from spectrum phase. Also, we introduce an accuracy index in order to determine the accuracy of the estimated parameters, and the index is calculated using the deviations of the peak spectrum and the left/right spectra. The parameter estimation methods proposed in this paper include very simple arithmetical processes, so the algorithms are simple and the calculation speed is very fast. The proposed methods are applied to test functions with two dominant modes. The results show that the proposed methods are highly applicable to low frequency parameter estimation.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.67-72
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• 2000

Demand for now nondestructive evaluation are growing to detect fatigue crack growth behavior to predict long term performance of materials and structure in aggressive environments especially when they are In non-visible area. Acoustic emission technique is well suited to these problems and has drawn a keen interests because of its dynamic detection ability, extreme sensitivity and location of growing defects. In this study, we analysed acoustic emission signals obtained in fatigue and tensile test of high strength fire resistance steel for frame structure with time-frequency analysis methods. The main frequency range is different in the noise and the fatigue crack propagation. It could be classified that it were also generated by composite fracture mechanics of cleavage, dimple, inclusion separation etc.