• Wind and Structures
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• v.17 no.2
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• pp.227-244
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• 2013

Non-stationary extreme winds such as thunderstorm downbursts are responsible for many structural damages. This research presents a time domain approach for estimating along-wind load effects on tall buildings using multiple wind speed time history samples, which are simulated from evolutionary power spectra density (EPSD) functions of non-stationary wind fluctuations using the method developed by the authors' earlier research. The influence of transient wind loads on various responses including time-varying mean, root-mean-square value and peak factor is also studied. Furthermore, a simplified model is proposed to describe the non-stationary wind fluctuation as a uniformly modulated process with a modulation function following the time-varying mean. Finally, the probabilistic extreme response and peak factor are quantified based on the up-crossing theory of non-stationary process. As compared to the time domain response analysis using limited samples of wind record, usually one sample, the analysis using multiple samples presented in this study will provide more statistical information of responses. The time domain simulation also facilitates consideration of nonlinearities of structural and wind load characteristics over previous frequency domain analysis.

• Journal of Korea Water Resources Association
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• v.48 no.7
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• pp.567-579
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• 2015

This study performed the non-stationary flood frequency analysis considering time-varying parameters of a probability density function. Also, return period and risk under non-stationary condition were estimated. A stationary model and three non-stationary models using Generalized Extreme Value(GEV) were developed. The only location parameter was assumed as time-varying parameter in the first model. In second model, the only scale parameter was assumed as time-varying parameter. Finally, the both parameters were assumed as time varying parameter in the last model. Relative likelihood ratio test and Akaike information criterion were used to select appropriate model. The suggested procedure in this study was applied to eight multipurpose dams in South Korea. Using relative likelihood ratio test and Akaike information criterion it is shown that the inflow into the Hapcheon dam and the Seomjingang dam were suitable for non-stationary GEV model but the other six dams were suitable for stationary GEV model. Also, it is shown that the estimated return period under non-stationary condition was shorter than those estimated under stationary condition.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.225-238
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• 2022

In this paper, we propose a clustering method for advanced metering infrastructure (AMI) data in Korea. As AMI data presents non-stationarity, we consider time-dependent frequency domain principal components analysis, which is a proper method for locally stationary time series data. We develop a new clustering method based on time-varying eigenvectors, and our method provides a meaningful result that is different from the clustering results obtained by employing conventional methods, such as K-means and K-centres functional clustering. Simulation study demonstrates the superiority of the proposed approach. We further apply the clustering results to the evaluation of the electricity price system in South Korea, and validate the reform of the progressive electricity tariff system.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1999.04a
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• pp.61-68
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• 1999

In the nonlinear dynamic structural analysis the given ground excitation as an input should be well defined. Because of the lack of recorded accelerograms in Korea it is required to generate an artificial earthquake by a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms. It is well known that earthquake motions are generally non-stationary with time-varying intensity and frequency content. Many researchers have proposed non-stationary random process models. Yeh and Wen (1990) proposed a non-stationary modulation function and a power spectral density function to describe such non-stationary characteristics. Satio and Wen(1994) proposed a non-stationary stochastic process model to generate earthquake ground motions which are compatible with design reponse spectrum at sites in Japan. this paper shows the process to modify power spectrum compatible with target design response spectrum for generating of nonstationary artificial earthquake ground motions. Target reponse spectrum is chosen by ATC14 to calibrate the response spectrum according to a give recurrence period.

• Journal of Korea Water Resources Association
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• v.48 no.6
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• pp.451-461
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• 2015

This study aimed to estimate the future design rainfall through a non-stationary frequency analysis using the rainfall separation technique. First, we classified rainfall in the Korean Peninsula into local downpour and TC-induced rainfall through rainfall separation technique based on the path and size of a typhoon. Furthermore, we performed the analysis of regional rainfall characteristics and trends. In addition, we estimated the future design rainfall through a non-stationary frequency analysis using Gumbel distribution and carried out its quantitative comparison and evaluation. The results of the analysis suggest that the increase and decrease rate of rainfall in the Korean Peninsula were different and the increasing and decreasing tendencies were mutually contradictory at some points. In addition, a non-stationary frequency analysis was carried out by using the rainfall separation technique. The outcome of this analysis suggests that a relatively reasonable future design rainfall can be estimated. Comparing total rainfall with the future design rainfall, differences were found in the southern and eastern regions of the Korean peninsula. This means that climate change may have a different effect on the typhoon and local downpour. Thus, in the future, individual assessment of climate change impacts needs to be done through moisture separation. The results presented here are applicable in future hydraulic structures design, flood control measures related to climate change, and policy establishment.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.499-513
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• 2009

An economic signal in the real world usually reflects complex phenomena. One may have difficulty both extracting and interpreting information embedded in such a signal. A natural way to reduce complexity is to decompose the original signal into several simple components, and then analyze each component. Spectral analysis (Priestley, 1981) provides a tool to analyze such signals under the assumption that the time series is stationary. However when the signal is subject to non-stationary and nonlinear characteristics such as amplitude and frequency modulation along time scale, spectral analysis is not suitable. Huang et al. (1998b, 1999) proposed a data-adaptive decomposition method called empirical mode decomposition and then applied Hilbert spectral analysis to decomposed signals called intrinsic mode function. Huang et al. (1998b, 1999) named this two step procedure the Hilbert-Huang transform(HHT). Because of its robustness in the presence of nonlinearity and non-stationarity, HHT has been used in various fields. In this paper, we discuss the applications of the HHT and demonstrate its promising potential for non-stationary financial time series data provided through a Korean stock price index.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.10 s.175
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• pp.80-89
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• 2005

In this paper, time-frequency analysis using wavelet packet transform and advanced-MDSA (Multiple Dimensional Spectral Analysis) which based on wavelet packet transform is applied fur fault source identification and diagnosis of early detection of fault non-stationary sound/vibration signals. This method is analyzing the signal in the plane of instantaneous time and instantaneous frequency. The results of ordinary coherence function, which obtained by wavelet packet analysis, showed the possibility of early fault detection by analysis at the instantaneous time. So, by checking the coherence function trend, it is possible to detect which signal contains the major fault signal and to know how much the system is damaged. Finally, It is impossible to monitor the system is damaged or undamaged by using conventional method, because crest factor is almost constant under the range of magnitude of fault signal as its approach to normal signal. However instantaneous coherence function showed that a little change of fault signal is possible to monitor the system condition. And it is possible to predict the maintenance time by condition based maintenance for any stationary or non-stationary signals.

• Journal of Mechanical Science and Technology
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• v.18 no.9
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• pp.1565-1571
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• 2004

Ride characteristics of a vehicle moving on a rough ground with changing travel velocity are analyzed in this paper. The solution is difficult due to the non-stationary characteristics of the problem. Hence a new technique has been proposed to overcome this difficulty. This new technique is employed in the analysis of ride characteristics of a vehicle with changing velocity in the time/frequency domain. It is found that the proposed technique gives successful results in modelling non-stationary responses in terms of a series of stationary responses.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.497-504
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• 1998

In the nonlinear dynamic structural analysis, the given ground excitation as an input should be well defined. Because of the lack of recorded accelerograms in Korea, it is required to generate an artificial earthquake by a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms. It is well own that earthquake motions are generally non-stationary with time-varying intensity and frequency content. Many researchers have proposed non-stationary random process models. Yeh and Wen (1990) proposed a non-stationary stochastic process model which can be modeled as components with an intensity function, a frequency modulation function and a power spectral density function to describe such non-stationary characteristics. This model is based on the simulation for the strong-motion earthquakes with magnitude greater than approximately 5.0~6.0, because it will be not only expected to cause structural damage but also involved the characteristics of earthquake motions. Also, the recorded earthquake motion within this range are still very scarce in Korea. Thus, it is necessary to verify the model by the application of it to the mid-magnitude (approximately 4.0~6.0) earthquakes actually recorded in domestic or foreign area. The purpose of the paper is to generate an artificial earthquake using the model of Yeh and Wen in the area with low seismicity.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1454-1462
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• 2008

This paper is focused on the analysis of the noise and vibration measured near the wheelset of the high-speed trains using a time-varying frequency transform as a preliminary research of running monitoring. Due to the non-stationary characteristics, it is necessary to examine noise and vibration of the train with time-varying frequency transforms. In this paper, the short-time Fourier transform method is utilized - the stored data is localized by modulating with a window function, and Fourier transform is taken to each localized data. For the examination, the non-stationary noise and vibration of the high-speed train's wheelset are measured by using some microphones and accelerometers, and those signals are stored in a on-board data acquisition system. The non-stationary random signal analyses with the short-time Fourier transform are performed, and the result are classified as follows; auto-spectral density, cross-spectral density, frequency response, and coherence functions. From those functions, it is possible to observe the frequency characteristics of sleepers, switchers, tunnels, and steel bridges. Also, some distinct peaks, which are not dependent upon the train's speed, are identified from the results.