• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.5
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• pp.141-153
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• 2012

In this study, we estimated probable precipitation amounts at the target year (2020, 2030, 2040) of 55 weather stations in Korea using the 24 hour annual maximum precipitation data from 1973 through 2009 which should be useful for management of agricultural reservoirs. Not only trend tests but also non-stationary tests were performed and non-stationary frequency analysis were conducted to all of 55 sites. Gumbel distribution was chosen and probability weighted moment method was used to estimate model parameters. The behavior of the mean of extreme precipitation data, scale parameter, and location parameter were analyzed. The probable precipitation amount at the target year was estimated by a non-stationary frequency analysis using the linear regression analysis for the mean of extreme precipitation data, scale parameter, and location parameter. Overall results demonstrated that the probable precipitation amounts using the non-stationary frequency analysis were overestimated. There were large increase of the probable precipitation amounts of middle part of Korea and decrease at several sites in Southern part. The non-stationary frequency analysis using a linear model should be applicable to relatively short projection periods.

• Structural Engineering and Mechanics
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• v.82 no.3
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• pp.325-341
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• 2022

Previous major earthquakes indicated that the earthquake induced ground motions are typical non-stationary processes, which are non-stationary in both amplification and frequency. For the convenience of aseismic design and analysis, it usually assumes that the ground motions at structural supports are stationary processes. The development of time-frequency analysis technique makes it possible to evaluate the non-stationary responses of engineering structures subjected to non-stationary inputs, which is more general and realistic than the analysis method commonly used in engineering. In this paper, the wavelet-based stochastic vibration analysis methodology is adopted to calculate the non-stationary responses of multi-support structures. For comparison, the stationary response based on the standard random vibration method is also investigated. A frame structure and a two-span bridge are analyzed. The effects of non-stationary spatial ground motion and local site conditions are considered, and the influence of structural property on the structural responses are also considered. The analytical results demonstrate that the non-stationary spatial ground motions have significant influence on the response of multi-support structures.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.29-36
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• 2009

The analysis of Radar signal, telecommunication, bioengineering, seismic, and acoustic signal is consist of the Non-stationary signal which has non-linear phase variation. Non-stationary signal means that the physical properties of signal depend on time variation and the instantaneous frequency represents physical property of these type of signal. Thus estimation of the instantaneous frequency of non-stationary signal is important subject in signal processing. In this work, the instantaneous frequency analysis method utilizing continuous wavelets transform is represented and compared with Hilbert Transform method.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.187-199
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• 2010

This study evaluated applicability and confidence of probability rainfalls estimated by the non-stationary rainfall frequency analysis which was recently developed. Using rainfall data at 4 sites which have an obvious increasing trend in observations, we estimated 3 type probability rainfalls; probability rainfalls from stationary rainfall frequency analysis using data from 1973-1997, probability rainfalls from stationary rainfall frequency analysis using data from 1973-2006, probability rainfalls from non-stationary rainfall frequency analysis assuming that the current year is 1997 and the target year is 2006. Based on the comparison of residuals from 3 probability rainfalls, the non-stationary rainfall frequency analysis provided more effective and well-directed estimates of probability rainfalls in the target year. Using Bootstrap resampling, this study also evaluated the parameter estimation methods for the non-stationary rainfall frequency analysis based on confidence intervals. The confidence interval length estimated by the maximum likelihood estimation (MLE) is narrower than the probability weighted moments (PWM). The results indicated that MLE provides more proper confidence than PWM for non-stationary probability rainfalls.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.499-514
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• 2011

An underlying assumption of traditional hydrologic frequency analysis is that climate, and hence the frequency of hydrologic events, is stationary, or unchanging over time. Under stationary conditions, the distribution of the variable of interest is invariant to temporal translation. Water resources infrastructure planning and design, such as dams, levees, canals, bridges, and culverts, relies on an understanding of past conditions and projection of future conditions. But, Water managers have always known our world is inherently non-stationary, and they routinely deal with this in management and planning. The aim of this paper is to give a brief introduction to non-stationary extreme value analysis methods. In this paper, a non-stationary hydrologic frequency analysis approach is introduced in order to determine probability rainfall consider changing climate. The non-stationary statistical approach is based on the conditional Generalized Extreme Value(GEV) distribution and Maximum Likelihood parameter estimation. This method are applied to the annual maximum 24 hours-rainfall. The results show that the non-stationary GEV approach is suitable for determining probability rainfall for changing climate, sucha sa trend, Moreover, Non-stationary frequency analyzed using SOI(Southern Oscillation Index) of ENSO(El Nino Southern Oscillation).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.9 s.252
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• pp.1154-1159
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• 2006

In this paper, time-frequency analysis and multi-dimensional spectral analysis methods are applied to source identification and diagnostic of non-stationary sound vibration signals. By checking the coherences for concerned time, this simulation is very well coincident to expected results. The proposed method analyzes the signal instantaneously in both time and frequency domains. The MDSA (Multiple Dimensional Spectral Analysis) analyzes the signal in the plane of instantaneous time and instantaneous frequency at the same time. And it was verified by using the 1500cc passenger car which is accelerated from 70Hz to 95Hz in 4 seconds, the proposed method is effective in determining the vehicle diagnostic problems.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.26-30
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• 2005

A rotor run-up or run-down process provide more useful information for modal analysis than normal operation conditions. A traditional difficulty associated with rotor run-up or run-down analysis is the non-stationary nature of vibration data. This paper compares Short-Time Fourier Transform (STFT) and the wavelets analysis in these non-stationary signal analyses. An Adaptive Wavelet Analysis (AWT) is proposed to analyze these signals. Although simulations and experiments in a simple rotor-bearing system show that both STFT and AWT can be used to analyze non-stationary vibration signals in rotor dynamics, proposed AWT provides better results than STFT analysis. From the amplitude-frequency curve obtained by AWT, the modal frequency and damping ratio are calculated. This paper also analyzes the characteristics of signals when the shaft touches the outer hoop in a run-up process. The AWT can give a good result in this complex dynamic analysis of the touching process.

• Wind and Structures
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• v.32 no.2
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• pp.89-104
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• 2021

Non-synoptic winds generated by tornadoes, downbursts or gust fronts exhibit significant non-stationarity and can cause significant wind load effect on flexible structures such as long-span bridges. However, conventional assumptions on stationarity used to evaluate the structural wind-induced vibration are inadequate. In this paper, an efficient frequency domain scheme based on fast CQC method, which can predict non-stationary buffeting random responses of long-span bridges, is presented, and then this approach is applied to evaluate the buffeting response of a long-span suspension bridge located in a complex mountainous wind environment as an example. In this study, the data-driven method based on one available measured wind speed sample is firstly presented to establish non-stationary wind models, including time-varying mean wind speed, time-varying intensity envelope function and uniformly modulated fluctuating spectrum. Then, a linear time-variant (LTV) system based on the proposed scheme can be generally applied to calculate the non-stationary buffeting responses. The effectiveness and accuracy of the proposed scheme are verified through Monte Carlo time domain simulation implemented in ANSYS platform. Also, the transient effect nature of the bridge responses is further illustrated by comparison of the non-stationary, quasistationary and steady-state cases. Finally, buffeting response analysis with traditional stationary treatment (10 min constant mean plus stationary wind fluctuation) is performed to illustrate the importance of the non-stationary characteristics embedded in original wind speed samples.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.4
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• pp.29-39
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• 2014

In this study, we performed a non-stationary frequency analysis using a power model and the model was applied for Seoul, Daegu, Daejeon, Mokpo sites in Korea to estimate the probable precipitation amount at the target years (2020, 2050, 2080). We used the annual maximum precipitation of 24 hours duration of precipitation using data from 1973 to 2009. We compared results to that of non-stationary analyses using the linear and logistic regression. The probable precipitation amounts using linear regression showed very large increase in the long term projection, while the logistic regression resulted in similar amounts for different target years because the logistic function converges before 2020. But the probable precipitation amount for the target years using a power model showed reasonable results suggesting that power model be able to reflect the increase of hydrologic extremes reasonably well.

• 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.