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

• Structural Engineering and Mechanics
• /
• v.18 no.2
• /
• pp.137-150
• /
• 2004

The optimization of active bars' placement and feedback gains of closed loop control system for random intelligent truss structures under non-stationary random excitation is presented. Firstly, the optimal mathematical model with the reliability constraints on the mean square value of structural dynamic displacement and stress response are built based on the maximization of dissipation energy due to control action. In which not only the randomness of the physics parameters of structural materials, geometric dimensions and structural damping are considered simultaneously, but also the applied force are considered as non-stationary random excitation. Then, the numerical characteristics of the stationary random responses of random intelligent structure are developed. Finally, the rationality and validity of the presented model are demonstrated by an engineering example and some useful conclusions are obtained.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.42 no.1
• /
• pp.19-28
• /
• 2017

Moments of stationary intervals and those of the counting process can be used for moment fittings of the point processes. As for the Markovian arrival processes, the moments of stationary intervals are given as a polynomial function of parameters whereas the moments of the counting process involve exponential terms. Therefore, moment fittings are more complicated with the counting process than with stationary intervals. However, in queueing network analysis, cross-correlation between point processes can be modeled more conveniently with counting processes than with stationary intervals. A Laplace-Stieltjies transform of the stationary intervals of MAP (3)s is recently proposed in minimal number of parameters. We extend the results and present the Laplace transform of the counting process of MAP (3)s. We also show how moments of the counting process such as index of dispersions for counts, IDC, and limiting IDC can be used for moment fittings. Examples of exact MAP (3) moment fittings are also presented on the basis of moments of stationary intervals and those of the counting process.

• Wind and Structures
• /
• v.17 no.2
• /
• pp.227-244
• /
• 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 Advanced Navigation Technology
• /
• v.20 no.6
• /
• pp.539-543
• /
• 2016

In this paper, we study the technique to improve the performance of the aerial images taken by UAV using daubechies stationary wavelet transform. When aerial images taken by UAV were damaged by gaussian noise very commonly applied, the experiment for image performance improvement was performed. It was known that stationary wavelet transform is the transferring solution to the problem occurred by down sampling from DWT also more efficient to remove noise than DWT. Also haar wavelet is discontinuous function so not efficient for smooth signal and image processing. Therefore, this study is confirmed that the noise can be removed by daubechies stationary wavelet and the performance is improved by haar stationary wavelet.

• Proceedings of the KOSOMBE Conference
• /
• v.1989 no.05
• /
• pp.27-28
• /
• 1989

In conventional line scan angiography, flow signal has been enhanced by the time_of_flight effect while the signal from stationary tissues has been suppressed by the saturation rf pulse followed by spoiling gradients. Due to the inhomogeneous rf field and the tissue dependent T1 relaxation time, however, stationary tissues can not be suppressed completely or uniformly, and the remnant stationary signal deteriorates the resultant angiogram. Here, the complete cancellation of stationary tissues is made possible by the spectral analysis of a series of repetitive line images of the same slice. The Fourier transformation of a set of line images results in the spectrum images, where stationary tissues are collected into the dc component while arteries are included in harmonic components because of the variation of the flow velocity and the resultant flow signal in arteries according to the cardiac cycle. The summation of harmonic components excluding the dc component results in the angiogram of arteries with the complete cancellation of stationary tissues.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.6
• /
• pp.520-525
• /
• 2009

The conventional studies about an adaptive beamformer assumed that the interference signals are stationary, so they used time-average of signals or Least Mean Squares. However, these methods showed low performance of canceling the non-stationary interferences. In this paper, the MAFF-RLS algorithm is developed in order to cancel non-stationary interferences, and the GSC structure using this algorithm is proposed. Furthermore, the performance of the MAFF-RLS beamformer is verified by simulation using MATLAB. This simulation results show the performance of the proposed beamformer is better than that of the SMI and the conventional RLS beamformer.

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

• Korean Chemical Engineering Research
• /
• v.56 no.4
• /
• pp.469-473
• /
• 2018

When pure hydrogen was supplied to the stationary PEMFC generally using the reforming gas, its characteristics were compared with the vehicle PEMFC. The effect of varying the amount of hydrogen supply to the anode on the overall performance was compared. The variation of hydrogen supply in the range of 1.0~1.7 excess (stoi.) had little effect on the OCV of stationary and vehicle MEA (Membrane and Electrode Assembly). At 0.7 V, the current density of the stationary MEA was about 16% higher than that of the vehicle MEA. I-V performance, impedance, and LSV were measured with varying relative humidity. Both OCV and electrolyte membrane resistances decreased with increasing relative humidity. The hydrogen permeability of the stationary MEA was lower than that of the vehicle MEA, showing that the durability of the stationary membrane could be higher than that of the vehicle membrane.

• Journal of the Korean Society of Radiology
• /
• v.9 no.7
• /
• pp.515-521
• /
• 2015

This study assessed the degradation of image quality caused by grid artifacts and $moir{\acute{e}}$ pattern artifacts in a stationary grid, and the degradation of image quality caused by cut off artifacts in a moving grid. X-ray images were acquired in a stationary grid and a moving grid with X-ray exposure conditions of 100 cm, 80 kVp, and 30 mA using a CDRAD phantom and a 24 cm thickness acrylic phantom. Observer's perception of X-ray imaging using CDRAD Analyzer was mean 49.36, standard deviation 3.76, maximum 55.56, and minimum 38.67 in the stationary grid, and 47.04, 12.69, 55.56, and 20.89, respectively, in the moving grid. The stationary grid was superior to the moving grid in terms of the mean and standard deviation of observer's perception.