• Journal of Mechanical Science and Technology
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• v.14 no.9
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• pp.968-977
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• 2000

The motion of an aircraft landing gear over a rough runway can be modeled by a nonclassically damped system subject to nonstationary random excitations. In this paper, the approximate analysis methods based on either the real or complex normal modes for the computation of nonstationary response covariances are proposed. It has been found by simulation involving a realistic example that, for the nonclassically damped random vibrational systems, the approximate solution method based on the complex normal mode is superior to other approaches with respect to the accuracy and computation time.

• ETRI Journal
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• v.38 no.2
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• pp.366-375
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• 2016

This paper describes an algorithm to suppress composite noise in a two-microphone speech enhancement system for robust hands-free speech communication. The proposed algorithm has four stages. The first stage estimates the power spectral density of the residual stationary noise, which is based on the detection of nonstationary signal-dominant time-frequency bins (TFBs) at the generalized sidelobe canceller output. Second, speech-dominant TFBs are identified among the previously detected nonstationary signal-dominant TFBs, and power spectral densities of speech and residual nonstationary noise are estimated. In the final stage, the bin-wise output signal-to-noise ratio is obtained with these power estimates and a Wiener post-filter is constructed to attenuate the residual noise. Compared to the conventional beamforming and post-filter algorithms, the proposed speech enhancement algorithm shows significant performance improvement in terms of perceptual evaluation of speech quality.

• International Journal of Automotive Technology
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• v.3 no.3
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• pp.101-109
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• 2002

A time domain method for solving nonstationary random vibration caused by vehicle acceleration is first proposed in which a time changing model is established for representing nonstationary excitation of a rough road. Furthermore a novel frequency domain method called the transient power spectral density with spatial frequency (TPSD) is presented to obtain a response of vehicle system in frequency domain. This method has been proved to be valid by comparing numerical results with the exact solution.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.67-90
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• 1998

In this paper we analytically study model misspecification that arises in regression analysis of nonstationary seasonal time series. We assume the underlying data generating process is a seasonally or a regularly and seasonally integrated process. We first study consequences of totally misspecified cases where seasonal indicator variables, a linear time trend, or another statistically independent seasonally integrated process are used as predictor variables in order to model the nonstationary seasonal behavior of the dependent variable. Then we study consequences of partially misspecified cases where the dependent variable and a predictor variable are cointegrated at some, but not all of the frequencies corresponding to the nonstationary roots.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.04a
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• pp.60-68
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• 1997

The motion of an aircraft landing gear over rough runway at variable speed is nonstationary. In this paper a method for the computation of nonstationary response variance is presented which uses a state space form for the combination of landing gear and runway excitation. The dynamic characteristics of the landing gear under nonstationary random excitations has also been analyzed using the proposed method. The formulation is for linear systems of arbitrary order and allows any deterministic velocity history. It has been found by a series of simulation that correlation parameter, damping coefficients of landing gear and tire, and velocity profiles plays a prominent role on the dynamic characteristics.

• Journal of the Korea Society for Simulation
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• v.9 no.2
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• pp.75-87
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• 2000

In this paper, nonstationary multiple sampling plans are discussed which are difficult to solve by analytical method when there exists dependency between the sample data. The initial solution is found by the sequential sampling plan using the sequential probability ration test. The number of acceptance and rejection in each step of the multiple sampling plan are found by grouping the sequential sampling plan's solution initially. The optimal multiple sampling plans are found by simulation. Four search methods are developed U and the optimum sampling plans satisfying the Type I and Type ll error probabilities. The performance of the sampling plans is measured and their algorithms are also shown. To consider the nonstationary property of the dependent sampling plan, simulation method is used for finding the lot rejection and acceptance probability function. As a numerical example Markov chain model is inspected. Effects of the dependency factor and search methods are compared to analyze the sampling results by changing their parameters.

• 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.

• Journal of Broadcast Engineering
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• v.8 no.3
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• pp.278-287
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• 2003

Noise in an image degrades image quality and deteriorates coding efficiency. Recently, various edge-preserving noise filtering methods based on the nonstationary image model have been proposed to overcome this problem. In most conventional nonstationary image models, however, pixels are assumed to be uncorrelated to each other in order not to Increase the computational burden too much. As a result, some detailed information is lost in the filtered results. In this paper, we propose a computationally feasible adaptive noise smoothing algorithm which considers the nonstationary correlation characteristics of images. We assume that an image has a nonstationary mean and can be segmented into subimages which have individually different stationary correlations. Taking advantage of the special structure of the covariance matrix that results from the proposed image model, we derive a computationally efficient FFT-based adaptive linear minimum mean-square-error filter. Justification for the proposed image model is presented and effectiveness of the proposed algorithm is demonstrated experimentally.

• Structural Engineering and Mechanics
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• v.26 no.6
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• pp.709-723
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• 2007

Considering the vast usage of time-history dynamic analyses to calculate structural responses and lack of sufficient and suitable earthquake records, generation of artificial accelerograms is very necessary. The main target of this paper is to present a novel method based on nonstationary Kanai-Tajimi model and wavelet transform to generate more artificial earthquake records, which are compatible with target spectrum. In this regard, the generalized nonstationary Kanai-Tajimi model to include the nonstationary evaluation of amplitude and dominant frequency of ground motion and properties of wavelet transform is used to generate ground acceleration time history. Application of the method for El Centro 1940 earthquake and two Iranian earthquakes (Tabas 1978 and Manjil 1990) is presented. It is shown that the model and identification algorithms are able to accurately capture the nonstationary features of these earthquake accelerograms. The statistical characteristics of the spectral response of the generated accelerograms are compared with those for the actual records to demonstrate the effectiveness of the method. Also, for comparison of the presented method with other methods, the response spectra of the synthetic accelerograms compared with the models of Fan and Ahmadi (1990) and Rofooei et al. (2001) and it is shown that the response spectra of the synthetic accelerograms with the method of this paper are close to those of actual earthquakes.

• Wind and Structures
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• v.31 no.3
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• pp.269-285
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• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.