• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.291-296
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• 2014

Most of the statistical signal analysis processed in the time domain and the frequency domain are based on the assumption that the signal is weakly stationary(wide sense stationary). Therefore, it is necessary to know whether the surface EMG signals processed in the statistical basis satisfy the condition of weak stationarity. The purpose of this study is to analyze the accuracy of the Run-test, modified Run-test, RA(reverse arrangement)-test, and modified RA-test for assessing surface EMG signal stationarity. Six stationary and three non-stationary signals were simulated by using sine wave, AR(autoregressive) modeling, and real surface EMG. The simulated signals were tested for stationarity using nine different methods of Run-test and RA-test. The results showed that the modified Run-test method2 (mRT2) classified exactly the surface EMG signals by stationarity with 100% accuracy. This finding indicates that the mRT2 may be the best way for assessing stationarity in surface EMG signals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1047-1062
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• 2018

In this paper the problem of massive multiple input multiple output (MIMO) channel estimation with sparsity aware adaptive algorithms for $5^{th}$ generation mobile systems is investigated. These channels are shown to be non-stationary along with being sparse. Non-stationarity is a feature that implies channel taps change with time. Up until now most of the adaptive algorithms that have been presented for channel estimation, have only considered sparsity and very few of them have been tested in non-stationary conditions. Therefore we investigate the performance of several newly proposed sparsity aware algorithms in these conditions and finally propose an enhanced version of RZA-LMS/F algorithm with variable threshold namely VT-RZA-LMS/F. The results show that this algorithm has better performance than all other algorithms for the next generation channel estimation problems, especially when the non-stationarity gets high. Overall, in this paper for the first time, we estimate a non-stationary Rayleigh fading channel with sparsity aware algorithms and show that by increasing non-stationarity, the estimation performance declines.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1128-1133
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• 2014

Most of the statistical signal analysis processed in the time domain and the frequency domain are based on the assumption that the signal is weakly stationary(wide sense stationary). Therefore, it is necessary to know whether the surface EMG signals processed in the statistical basis satisfy the condition of the weak stationarity. The purpose of this study is to find optimal segment length of surface EMG signal for assessing stationarity with the modified Run-test and RA-test. Ten stationary surface EMG signals were simulated by AR(autoregressive) modeling, and ten real surface EMG signals were recorded from biceps brachii muscle and then modified to have non-stationary structures. In condition of varying segment length from 20ms to 100ms, stationarity of the signals was tested by using six different methods of modified Run-test and RA-test. The results indicate that the optimal segment length for the surface EMG is 30ms~35ms, and the best way for assessing surface EMG signal stationarity is the modified Run-test (Run2) method using this optimal length.

• Water for future
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• v.46 no.11
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• pp.90-94
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• 2013

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1221-1226
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• 2006

The use of Artificial Neural Networks (ANN) has received increasing attention in the analysis and prediction of financial time series. Stationarity of the observed financial time series is the basic underlying assumption in the practical application of ANN on financial time series. In this paper, we will investigate whether it is feasible to relax the stationarity condition to non-stationary time series. Our result discusses the range of complexities caused by non-stationary behavior and finds that overfitting by ANN could be useful in the analysis of such non-stationary complex financial time series.

• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1135-1148
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• 2018

Previous studies on reservoir operation have been assumed that the climate in the future would be similar to that in the past. However, in the presence of climate non-stationarity, Robust Optimization (RO) which finds the feasible solutions under broader uncertainty is necessary. RO improves the existing optimization method by adding a robust term to the objective function that controls the uncertainty inherent due to input data instability. This study proposed Robust-SDP that combines Stochastic Dynamic Programming (SDP) and RO to estimate dam operation rules while coping with climate non-stationarity. The future inflow series that reflect climate non-stationarity were synthetically generated. We then evaluated the capacity of the dam operation rules obtained from the past inflow series based on six evaluation indicators and two decision support schemes. Although Robust-SDP was successful in reducing the incidence of extreme water scarcity events under climate non-stationarity, there was a trade-off between the number of extreme water scarcity events and the water scarcity ratio. Thus, it is proposed that decision-makers choose their optimal rules in reference to the evaluation results and decision support illustrations.

• Wind and Structures
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• v.31 no.5
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• pp.445-457
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• 2020

The buffeting response is a vital consideration for long-span bridges in typhoon-prone areas. In the conventional analysis, the turbulence and structural vibrations are assumed as stationary processes, which are, however, inconsistent with the non-stationary features observed in typhoon winds. This poses a question on how the stationary assumption would affect the evaluation of buffeting responses under non-stationary wind actions in nature. To figure out this problem, this paper presents a comparative study on buffeting responses of a long-span cable-stayed bridge based on stationary and non-stationary perspectives. The stationary and non-stationary buffeting analysis frameworks are firstly reviewed. Then, a modal analysis of the example bridge, Sutong Cable-stayed Bridge (SCB), is conducted, and stationary and non-stationary spectral models are derived based on measured typhoon winds. On this condition, the buffeting responses of SCB are finally analyzed by following stationary and non-stationary approaches. Although the stationary results are almost identical with the non-stationary results in the mean sense, the root-mean-square value of buffeting responses are underestimated by the stationary assumption as the time-varying features existing in the spectra of turbulence are neglected. The analytical results highlights a transition from stationarity to non-stationarity in the buffeting analysis of long-span bridges.

• 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 Hazard Mitigation
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• v.7 no.5
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• pp.89-97
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• 2007

This study proposed new simple methodologies for testing the stationarity of rainfall quantiles, and applied to the rainfall data at Seoul. The methodologies in this study are based on the analysis of frequency change of rainfall quantiles, different from previous studies like Ahn et al. (2001) who analyzed the change of rainfall quantiles themselves. The different types of methodologies are proposed in this study; one is to evaluate the occurrence frequency of rainfall with its return period more than the data length, and the other is to evaluate the effect of new observation on the highest rainfall data recorded. The application of these methodologies shows that the rainfall quantiles at Seoul have no significant proof leading their non-stationarity.

• Wind and Structures
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• v.24 no.1
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• pp.59-78
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• 2017

Non-stationarity and non-Gaussian property are two of the most important characteristics of wind. These two features are studied in this study based on wind speed records measured at different heights from a 325 m high meteorological tower during the synoptic wind storms. By using the time-frequency analysis tools, it is found that after removing the low frequency trend of the longitudinal wind, the retained fluctuating wind speeds remain to be asymmetrically non-Gaussian distributed. Results show that such non-Gaussianity is due to the weak-stationarity of the detrended fluctuating wind speed. The low frequency components of the fluctuating wind speeds mainly contribute to the non-zero skewness, while distribution of the high frequency component is found to have high kurtosis values. By further studying the decomposed wind speed, the mechanisms of the non-Gaussian distribution are examined from the phase, turbulence energy point of view.