• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.3
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• pp.291-297
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• 2012

Conventional Fourier analysis can give only limited information about the dynamic characteristics of nonstationary signals. Instead, time-frequency analysis is widely used to investigate the nonstationary signal in detail. Several time-frequency analysis methods are compared for a typical acoustic emission burst generated during the impact between a ferrite ceramic and aluminum plate. This AE burst is inherently nonstationary and random containing many frequency contents, which leads to severe interference between cross terms in bilinear convolution type distributions. The smoothing and reassignment processes can improve the readability and resolution of the results. Spectrogram and scalogram of the AE burst are obtained and compared to get the characteristics information. Renyi entropies are computed for various bilinear time-frequency transforms to evaluate the randomness. These bilinear transforms are reassigned by using the improved algorithm in discrete computation.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.15-15
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• 2016

In South Korea, annual maximum precipitation often occurs in association with mature typhoons in the western Pacific and from summer monsoon rains. In addition, certain years have no significant typhoon activity. Therefore, the characteristics of frequency distributions differ between extreme typhoons and monsoon events. Those extremes are also influenced from climate conditions in a different way. Application of nonstationary frequency analysis to the AMP data combined with typhoon and monsoon events might not always be reasonable. Therefore, we propose a novel approach of nonstationary frequency analysis to integrate extreme events of AMP induced from two main sources such as typhoons and monsoon in the current study. In this way, we were able to model the nonstationarity of extreme events from tropical storms and monsoon separately.

• International Journal of Automotive Technology
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• v.5 no.4
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• pp.257-268
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• 2004

The wavelet analysis method is introduced in this paper to study the nonstationary vibration of vehicles. A new road model, a so-called time domain correlated four-wheel road roughness, which considers the coherence relationships between the four wheels of a vehicle, has been newly developed. Based on a vehicle model with eight degrees of freedom, the analysis of nonstationary random vibration responses was carried out in a time domain on a computer. Verification of the simulation results show that the proposed road model is more accurate than previous ones and that the simulated responses are credible enough when compared with some references. Furthermore, by taking wavelet analysis on simulated signals, some substantial rules of vehicle nonstationary vibration, such as the relationship between each vibration level, and how the vibration energy flows on a time-frequency map, beyond those from conventional spectral analysis, were revealed, and these will be of much benefit to vehicle design.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.581-585
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.8
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• pp.589-597
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• 2002

In this paper the nonstationary response of accelerating vehicle is firstly obtained by using nonstationary road roughness model in time domain. To get the result of nonstationary response in frequency domain, the maximum entropy method is used for Processing nonstationary response of vehicle in frequency domain. The three-dimensional transient maximum entropy spectrum (MES) of response is given.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.3 s.108
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• pp.255-262
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• 2006

Frequency spectrum using the conventional Fourier analysis gives adequate information about the dynamic characteristics of the slider air bearing for the linear and stationary cases. The intermittent contacts for the extremely low flying height, however, generate nonlinear and nonstationary vibration at the instant of contact. Nonlinear dynamic model should be developed to simulate the impulse response of the air bearing during slider-disk contact. Time-frequency analysis is widely used to investigate the nonstationary signal. Several time-frequency analysis methods are employed and compared for the slider vibration signal caused by the impact against an artificially induced scratch on the disk. The representative Wigner-Ville distribution leads to the severe interference problem by cross terms even though it gives good resolution both in time and frequency. The smoothing process improves the interference problem at the expense of resolution. In order to get the results with good resolution and little interference, the reassignment method is proposed. Among others the reassigned Gabor spectrogram shows the best resolution and readability with negligible interference.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.667-673
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• 2019

Global warming may accelerate climate change and may increase disaster caused by strong winds. This research studied a method for a nonstationary frequency analysis considering the linear trend over time. The Bayesian method was used to estimate the posterior distribution of the parameters for the extreme value distribution of the annual maximum wind speed at Jeju Airport. The nonstationary frequency analysis was performed based on the Monte Carlo Markov Chain simulation and the Gibbs sampling. The estimated wind speeds by nonstationary frequency analysis was larger than those by stationary analysis. The conventional frequency analysis procedure assuming stationarity is likely to underestimate the future design wind speed in the region where statistically significant trend exists.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.674-679
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• 1994

Acoustic signal such as speech and scattered sound, are generally a nonstationary process whose frequency contents vary at any instant of time. For time-varying signal, whether a nonstationary or a deterministic transient signal, a traditional frequency domain representation does not reveal the contents of signal characteristics and may lead to erroneous results such as the loss of desired characteristics features or the mis-interpretation for a wrong conclusion. A time-frequency domain representation is needed to characterize such signatures. Pseudo Wigner-Ville distribution (PWVD) is ideally suited for portraying nonstationary signal time-frequency domain and carried out by adapting the fast Fourier transform algorithm. In this paper, the important properties of PWVD were investigated using both stationary and nonstationry signatures by numerical examples PWVD was applied to acoustic sigtnatures to demonstrate its application for time-ferquency domain analysis.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1067-1085
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• 2016

Nonstationary features existing in tropical storms have been frequently captured in recent field measurements, and the applicability of the stationary theory to the analysis of wind characteristics needs to be discussed. In this study, a tropical storm called Nakri measured at Taizhou Bridge site based on structural health monitoring (SHM) system in 2014 is analyzed to give a comparison of the stationary and nonstationary characteristics. The stationarity of the wind records in the view of mean and variance is first evaluated with the run test method. Then the wind data are respectively analyzed with the traditional stationary model and the wavelet-based nonstationary model. The obtained wind characteristics such as the mean wind velocity, turbulence intensity, turbulence integral scale and power spectral density (PSD) are compared accordingly. Also, the stationary and nonstationary PSDs are fitted to present the turbulence energy distribution in frequency domain, among which a modulating function is included in the nonstationary PSD to revise the non-monotonicity. The modulated nonstationary PSD can be utilized to unconditionally simulate the turbulence presented by the nonstationary wind model. The results of this study recommend a transition from stationarity to nonstationarity in the analysis of wind characteristics, and further in the accurate prediction of wind-induced vibrations for engineering structures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.165-174
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• 2019

As a nonstationarity is observed in hydrological data, various studies on nonstationary frequency analysis for hydraulic structure design have been actively conducted. Although the inherent diversity in the atmosphere-ocean system is known to be related to the nonstationary phenomena, a nonstationary frequency analysis is generally performed based on the linear trend. In this study, a nonstationary frequency analysis was performed using climate indices as covariates to consider the climate variability and the long-term trend of the extreme rainfall. For 11 weather stations where the trend was detected, the long-term trend within the annual maximum rainfall data was extracted using the ensemble empirical mode decomposition. Then the correlation between the extracted data and various climate indices was analyzed. As a result, autumn-averaged AMM, autumn-averaged AMO, and summer-averaged NINO4 in the previous year significantly influenced the long-term trend of the annual maximum rainfall data at almost all stations. The selected seasonal climate indices were applied to the generalized extreme value (GEV) model and the best model was selected using the AIC. Using the model diagnosis for the selected model and the nonstationary GEV model with the linear trend, we identified that the selected model could compensate the underestimation of the rainfall quantiles.