• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.179-190
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• 2017

Hand Accelerometers are widely used to detect human motion patterns in real-time. It is essential to reliably identify which type of activity is performed by human subjects. This rests on having accurate template of each activity. Many human activities are represented as a set of multiple time-series data from such sensors, which are mostly non-stationary and non-linear in nature. This requires a method which can effectively extract patterns from non-stationary and non-linear data. To achieve such a goal, we propose the method to apply Hilbert-Huang Transform which is known to be an effective way of extracting non-stationary and non-linear components from time-series data. It is applied on samples of accelerometer data to determine its effectiveness.

• The Journal of the Acoustical Society of Korea
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• v.22 no.4E
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• pp.161-166
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• 2003

We present an adaptive wavelet packet based speech enhancement method with robust voice activity detection (VAD) in non-stationary noise environment. The proposed method can be divided into two main procedures. The first procedure is a VAD with adaptive wavelet packet transform. And the other is a speech enhancement procedure based on the proposed VAD method. The proposed VAD method shows remarkable performance even in low SNRs and non-stationary noise environment. And subjective evaluation shows that the performance of the proposed speech enhancement method with wavelet bases is better than that with Fourier basis.

• Structural Engineering and Mechanics
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• v.44 no.4
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• pp.487-499
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• 2012

Dynamic instability of beams subjected to periodic axial forces is studied using the discrete singular convolution (DSC) method with the regularized Shannon's delta kernel. The principal regions of dynamic instability under different boundary conditions are examined in detail, and the non-stationary vibrations near the stability-instability critical regions have been investigated. It is found that the results obtained by using the DSC method are consistent with the analytical solutions, which shows that the DSC algorithm is suitable for the problems considered in this study. It was found that there is a narrow region of beat vibration existed in the vicinity of one side (${\theta}/{\Omega}$ > 1) of the boundaries of the instable region for each condition.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.398-402
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• 2018

비정상(non-stationary) 장기 시계열 안에서도, 단기적으로 추세의 변화가 일시적인 것인지, 아니면 구조적으로 변한 것인지를 적시에 판단하는 것은 중요하다. 이는 시계열 추세의 변화를 상시 감지하여, 변화에 맞는 적정한 수준의 대응을 할 필요가 있기 때문이다. 본 연구에서는 장기 시계열이 주어진 상황에서, 단위근 검정법을 기반으로 단기적으로 구조변화를 감지하여, 이러한 변화가 얼마나 지속될 것인지를 시각적으로 판단할 수 있는 방법을 제시하고자 한다.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.225-238
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• 2022

In this paper, we propose a clustering method for advanced metering infrastructure (AMI) data in Korea. As AMI data presents non-stationarity, we consider time-dependent frequency domain principal components analysis, which is a proper method for locally stationary time series data. We develop a new clustering method based on time-varying eigenvectors, and our method provides a meaningful result that is different from the clustering results obtained by employing conventional methods, such as K-means and K-centres functional clustering. Simulation study demonstrates the superiority of the proposed approach. We further apply the clustering results to the evaluation of the electricity price system in South Korea, and validate the reform of the progressive electricity tariff system.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.508-517
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• 2003

This paper presents perceptual model with a stochastic multiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embeds at the texture and edge region for more strongly embedded watermark by the SSQ. The watermark embedding is based on the computation of a NVF that has local image properties. This method uses non- stationary Gaussian and stationary Generalized Gaussian models because watermark has noise properties. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model uses the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark 3.1 benchmark test.

• Journal of Astronomy and Space Sciences
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• v.33 no.2
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• pp.75-92
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• 2016

It is generally believed that the high energy emissions from isolated pulsars are emitted from relativistic electrons/positrons accelerated in outer magnetospheric accelerators (outergaps) via a curvature radiation mechanism, which has a simple exponential cut-off spectrum. However, many gamma-ray pulsars detected by the Fermi LAT (Large Area Telescope) cannot be fitted by simple exponential cut-off spectrum, and instead a sub-exponential is more appropriate. It is proposed that the realistic outergaps are non-stationary, and that the observed spectrum is a superposition of different stationary states that are controlled by the currents injected from the inner and outer boundaries. The Vela and Geminga pulsars have the largest fluxes among all targets observed, which allows us to carry out very detailed phase-resolved spectral analysis. We have divided the Vela and Geminga pulsars into 19 (the off pulse of Vela was not included) and 33 phase bins, respectively. We find that most phase resolved spectra still cannot be fitted by a simple exponential spectrum: in fact, a sub-exponential spectrum is necessary. We conclude that non-stationary states exist even down to the very fine phase bins.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1626-1633
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• 2021

In an industrial field, non-destructive testing (NDT) is commonly used to inspect industrial products. Among NDT methods using radiation sources, X-ray laminography has several advantages, such as high depth resolution and low computational costs. Moreover, an X-ray laminography system with stationary source array and compact detector is able to reduce mechanical motion artifacts and improve inspection efficiency. However, this system, called stationary inverse-geometry X-ray laminography (s-IGXL), causes truncation artifacts in reconstructed images due to limited fields-of-view (FOVs). In this study, we proposed a projection data correction (PDC) method to reduce the truncation artifacts arisen in s-IGXL images, and the performance of the proposed method was evaluated with the different number of focal spots in terms of quantitative accuracy. Comparing with conventional techniques, the PDC method showed superior performance in reducing truncation artifacts and improved the quantitative accuracy of s-IGXL images for all the number of focal spots. In conclusion, the PDC method can improve the accuracy of s-IGXL images and allow precise NDT measurements.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.239-247
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• 2018

Adaptive Beamforming (ABF) algorithm, which is a typical jammer suppression algorithm, guarantees the performance on the assumption that the jamming characteristics of the TDS (Training Data Sample) are stationary, which are obtained immediately before and after transmitting the pulse signal. Therefore, effective jammer suppression can not be expected when the jamming characteristics are non-stationary. In this paper, we propose a new jammer suppression algorithm, of which power spectrum fluctuates fast. In this case, we assume that the location of the jammer station is fixed during the processing time. By applying the MPM (Matrix Pencil Method) to the jamming signal in TDS, we can estimate jammer parameters such as power and incident angle, of which the power will vary fast in time or range bins after TDS. Though we assume that the jammer station is fixed, the estimated jammer's incident angle has an error due to the noise, which degrades the performance of the jammer suppression as the jammer power increases fast. Therefore, the jammer's incident angle should be re-estimated at each range bin after TDS. By using the re-estimated jammer's incident angle, we can construct new covariance matrix under the non-stationary jamming environment. Then, the optimum weight for the jammer suppression is obtained by inversing matrix estimation method based on the matrix projection with the estimated jammer parameters as variables. To verify the performance of the proposed algorithm, the SINR (signal-to-interference plus noise ratio) loss of the proposed algorithm is compared with that of the conventional ABF algorithm.