• 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.

• 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.

• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.667-671
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• 2010

Runs test is a mathematical tool to test the stationarity of electromyographic (EMG) signals. The purpose of this study is to investigate the effects of segmentation size on the stationarity of EMG signals in runs test. Six subjects participated in this experiment and performed isometric trunk exertions for twenty seconds at the load level of 25% and 50% MVC. The signals extracted from the erector spinae muscles were divided into the intervals of 1000ms and the stationarity of the signal in each interval was tested by the runs test. In this test, seven segmentation sizes such as 1.0, 2.0, 3.9, 7.8, 15.6, 31.3 and 62.5ms were applied. Additionally, two stationarity tests of reverse arrangements test and modified reverse arrangements test were used to verify the results of the runs test. In results, the segmentation size of 62.5ms showed the similar results with the other stationarity tests. However, the stationarity values among there tests were different each other when segmentation sizes other than 62.5ms were used. These results indicated the effect of segmentation size in runs test that needs to be considered to have consistent and sensitive result in stationarity test.

• Journal of the Ergonomics Society of Korea
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• v.29 no.2
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• pp.183-188
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• 2010

The purpose of this study is to investigate the stationarity of the electromyographic signal in various flexion angles, loads, and window sizes, which influence the result of the mean power frequency (MPF) and median frequency (MNF) analysis. Six healthy subjects participated in the experiment. They were tested in the combination of 3-level flexion angles (0 degree, 22.5 degree, 45 degree) and 3-level loads (0Nm, 30Nm, 60Nm). Electromyographic data were collected for 20 seconds during isometric contraction. The stationarity of collected data were analyzed with four different window sizes including 250, 500, 1000 and 2000ms. Two test methods for stationarity such as Reverse Arrangements Test and Modified Reverse Arrangements Test were used. In order to show the effect of nonstationarity, the increasing/decreasing trend of MPF and MNF trend were discussed. In results, the stationarity of the electromyographic signal decreased as flexion angle increased and load decreased while window size decreased based on Reverse Arrangements Test. The highest stationarity was shown at 500 ms window in Modified Reverse Arrangements Test. The inclination of MNF and MPF indicated 3.6-6.3%, 3.8-5.1% discrepancy compared to the result from stationary data.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.499-513
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• 2009

An economic signal in the real world usually reflects complex phenomena. One may have difficulty both extracting and interpreting information embedded in such a signal. A natural way to reduce complexity is to decompose the original signal into several simple components, and then analyze each component. Spectral analysis (Priestley, 1981) provides a tool to analyze such signals under the assumption that the time series is stationary. However when the signal is subject to non-stationary and nonlinear characteristics such as amplitude and frequency modulation along time scale, spectral analysis is not suitable. Huang et al. (1998b, 1999) proposed a data-adaptive decomposition method called empirical mode decomposition and then applied Hilbert spectral analysis to decomposed signals called intrinsic mode function. Huang et al. (1998b, 1999) named this two step procedure the Hilbert-Huang transform(HHT). Because of its robustness in the presence of nonlinearity and non-stationarity, HHT has been used in various fields. In this paper, we discuss the applications of the HHT and demonstrate its promising potential for non-stationary financial time series data provided through a Korean stock price index.

• International Journal of Contents
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• v.3 no.2
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• pp.35-39
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• 2007

The cognitive radio communication is taking the attentions because the development of the technique came to be possible to analyze wireless signals. In the IEEE 802.22 WRAN Systems[1], how to detect a spectrum and signals is continuously studied. In this paper, we propose the efficient signal detection method using SCF (Spectral Correlation Function). It is easy to detect the signal feature when we are using the SCF. Because most modulated signals have the cyclo-stationarity which is unique for each signal. But the fading channel effected serious influence even though it detects the feature of the signal. We applied LMS(Least Mean Square) filter for the compensation of the signal which is effected the serious influence in the fading channel. And we analyze some signal patterns through the SCF. And we show the unique signal feature of each signal through the SCF method. It is robust for low SNR(Signal to Noise Ratio) environment and we can distinguish it in the fading channel using LMS Filter.

• Journal of Global Scholars of Marketing Science
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• v.16 no.1
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• pp.13-26
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• 2006

In this research, we develope a procedure for detecting a random non-stationarity to the individual's purchasing rate in a stationary NED model. On this purpose, we derive the likelihood ratio statistic for a testing null and alternative hypotheses defined as whether there is no significant structural change in a stationary NED model or any. Where the structural change comes from a random non-stationarity(marketing mix activities or seasonality, for example) to the individual's purchasing rate. We also apply the developed method to a panel data for a frequently purchased good. This research could be a solution to include the non-stationarity in a stationary NED model. We also expect that the developed model could give a signal for an early detection of significant changes in marketing environment, and a mean for a measurement of the effects of marketing mix activities.

• 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.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.12
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• pp.152-158
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• 2008

In shallow water environment, the detection performance of an active sonar using matched filter with LFM(linear frequency modulation) pulse can be seriously degraded by reverberation which is considered as non-white noise. To reduce the effect of reverberation, a whitening filter preceding the matched fitter, is usually adopted. In the conventional pre-whitening filter, it is assumed that local stationarity is preserved between detection block and its right ahead block. And then by using the characteristics of the reverberation of preceding block, the reverberation of detection block is estimated and whitened. According to the environment of shallow water, the stationarity of reverberation may be preserved for more blocks. In this case, the reverberation of the detection block can be estimated more accurately if more blocks are used. In this paper, the real reverberation signal which is obtained from shallow sea is analyzed and its proper region of estimation block is examined. And the performance of pre-whitening matched filter is compared and analyzed according to the region of estimation block.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2435-2453
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• 2015

Independent component analysis (ICA) is a signal processing technique used for un-mixing of the mixed recorded signals. In wireless communication, ICA is mainly used in multiple input multiple output (MIMO) systems. Most of the existing work regarding the ICA applications in MIMO systems assumed static or quasi static wireless channels. Performance of the ICA algorithms degrades in case of time varying wireless channels and is further degraded if the data block lengths are reduced to get the quasi stationarity. In this paper, we propose an ICA based MIMO transceiver that performs well in time varying wireless channels, even for smaller data blocks. Simulation is performed over quadrature amplitude modulated (QAM) signals. Results show that the proposed transceiver system outperforms the existing MIMO system utilizing the FastICA and the OBAICA algorithms in both the transceiver systems for time varying wireless channels. Performance improvement is observed for different data blocks lengths and signal to noise ratios (SNRs).