• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.04a
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• pp.145-150
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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 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 paper shows the process to generate nonstationary artificial earthquake ground motions considering target design response spectrum chosen by ATC14.

• Korean Management Science Review
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• v.10 no.2
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• pp.27-42
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• 1993

Markov chain models can be used to predict the state of the system in the future. We extend the existing Markov chain models in two ways. For the stationary model, we propose a procedure that obtains the transition probabilities by appling the empirical Bayes method, in which the parameters of the prior distribution in the Bayes estimator are obtained on the collaternal micro data. For non-stationary model, we suggest a procedure that obtains a time-varying transition probabilities as a function of the exogenous variables. To illustrate the effectiveness of our extended models, the models are applied to the macro and micro time-series data generated from actual survey. Our stationary model yields reliable parameter values of the prior distribution. And our non-stationary model can predict the variable transition probabilities effectively.

• Wind and Structures
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• v.35 no.6
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• pp.369-383
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• 2022

Wind tunnel experiment was carried out to study the cross-wind layer forces on a square cross-section building model using a synchronous multi-pressure sensing system. The stationarity of measured wind loadings are firstly examined, revealing the non-stationary feature of cross-wind forces. By converting the measured non-stationary wind forces into an energetically equivalent stationary process, the characteristics of local wind forces are studied, such as power spectrum density and spanwise coherence function. Mathematical models to describe properties of cross-wind forces at different layers are thus established. Then, a conditional simulation method, which is able to ex-tend pressure measurements starting from experimentally measured points, is proposed for the cross-wind loading. The method can reproduce the non-stationary cross-wind force by simulating a stationary process and the corresponding time varying amplitudes independently; in this way the non-stationary wind forces can finally be obtained by combining the two parts together. The feasibility and reliability of the proposed method is highlighted by an ex-ample of across wind loading simulation, based on the experimental results analyzed in the first part of the paper.

• Korean Management Science Review
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• v.8 no.1
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• pp.3-11
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• 1991

Non-Stationary transition probabilities models which is incorporated into a Markov framework with exogenous variables to account for some of variability are discussed, and extended for alternative procedure. Also as an application of the methodology, the size change of aggregate time-series data on the planted system of agricultural crops is estimated, and evaluated for the precision of time-varying evolution statistically.

• 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.

• Wind and Structures
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• v.26 no.3
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• pp.129-146
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• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.

• Wind and Structures
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• v.28 no.5
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• pp.315-329
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• 2019

Non-synoptic winds have distinctive statistical properties compared to synoptic winds and can produce different wind loads on buildings and structures. The current study uses the new capabilities of the WindEEE Dome at Western University to replicate a stationary non-Gaussian wind event recorded at the Port of La Spezia in Italy. These stationary non-Gaussian wind events are also known as intermediate wind events as they differ from non-stationary non-Gaussian events (e.g., downbursts) as well as stationary Gaussian events (e.g., atmospheric boundary layer (ABL) flows). In the present study, the wind loads on a typical low-rise building are investigated for an intermediate wind event reproduced using a continuous radial impinging jet (IJ) at the WindEEE Dome. For the same building model, differences in wind loads between ABL and IJ are also examined. Wind loads on different surface zones on the building, as defined in the ASCE code for design loads, are also calculated and compared with the code.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.508-517
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• 2003

This paper presents perceptual model with a stochastic multiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embeds at the texture and edge region for more strongly embedded watermark by the SSQ. The watermark embedding is based on the computation of a NVF that has local image properties. This method uses non- stationary Gaussian and stationary Generalized Gaussian models because watermark has noise properties. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model uses the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark 3.1 benchmark test.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.3
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• pp.329-337
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• 2001

This simulator is developed for the analysis of a MOSFET based on Thermally Coupled Energy Transport Model(TCETM). The simulator has the ability to calculate not only stationary characteristics but also non - stationary characteristics of a MOSFET. It solves basic semiconductor devices equations including Possion equation, current continuity equations for electrons and holes, energy balance equation for electrons and heat flow equation, using finite difference method. The conventional semiconductor device simulation technique, based on the Drift-Diffusion Model (DDM), neglects the thermal and other energy-related properties of a miniaturized device. I, therefore, developed a simulator based on the Thermally Coupled Energy Transport Model (TCETM) which treats not only steady-state but also transient phenomena of such a small-size MOSFET. In particular, the present paper investigates the breakdown characteristics in transient conditions. As a result, we found that the breakdown voltage has been largely underestimated by the DDM in transient conditions.

• Water for future
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• v.28 no.5
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• pp.219-233
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• 1995

A non-stationary multivariate model is selected in which the mean and variance of rainfall are not temporally or spatially constant. And the rainfall prediction system is constructed which uses the recursive estimation algorithm, Kalman filter, to estimate system states and parameters of rainfall model simulataneously. The on-line, real-time, multivariate short-term, rainfall prediction for multi-stations and lead-times is carried out through the estimation of non-stationary mean and variance by the storm counter method, the normalized residual covariance and rainfall speed. The results of rainfall prediction system model agree with those generated by non-stationary multivariate model. The longer the lead time is, the larger the root mean square error becomes and the further the model efficiency decreases form 1. Thus, the accuracy of the rainfall prediction decreases as the lead time gets longer. Also it shows that the mean obtained by storm counter method constitutes the most significant part of the rainfall structure.