• The Bulletin of The Korean Astronomical Society
• /
• v.41 no.1
• /
• pp.81.2-81.2
• /
• 2016

Many signals in the nature have power-law behaviors, namely they are "scale-free". The method of detrended fluctuation analysis (DFA), as one of the popular methods (e.g., Rescaled range analysis and Spectral analysis) for determining scale-free nature of time series, has a very important advantage that the DFA can be applied to both stationary and non-stationary signals. The analysis of time series using the DFA has been broadly used in physiology, finance, hydrology, meteorology, geology, and so on. We performed the DFA of 16 Spaceweather HMI Active Region Patch (SHARP) parameters for 38 HMI Active Region Patches (HARPs) obtained by Solar Dynamics Observatory (SDO) from May 2010 to June 2014. The main results from this study are as follows. (1) The most of the time series data are non-stationary. (2) The DFA scaling exponents of "mean vertical current density" for 38 HARPs have a negative correlation coefficient (-0.41) with flare index. (3) The DFA scaling exponents of parameters such as "Sum of the absolute value of net currents per polarity", "Absolute value of the net current helicity", and "Mean photospheric excess magnetic energy density" for the most active HARPs having more than 10 major flares, have positive correlation coefficients (0.64, 0.59, and 0.53, respectively) with the ratio of "the number of CMEs associated with major flares" to "the number of major flares". Physical interpretations on our results will be discussed.

• The Journal of the Acoustical Society of Korea
• /
• v.29 no.1
• /
• pp.18-23
• /
• 2010

The RLS (Recursive Least Squares) method is a broadly used adaptive algorithm for signal processing in electronic engineering. The RLS algorithm shows a good performance and a fast adaptation within a stationary environment, but it shows a Poor performance within a non-stationary environment because the method has a fixed forgetting factor. In order to enhance 'tracking' performances, BLS methods with an adaptive forgetting factor had been developed. This method shows a good tracking performance, however, it suffers from heavy computational loads. Therefore, we propose a modified AFF-RLS which has relatively low complexity m this paper.

• Earthquakes and Structures
• /
• v.3 no.5
• /
• pp.719-747
• /
• 2012

In this paper a novel approach is proposed to address the problem of deriving non-stationary stochastic processes which are compatible in the mean sense with a given (target) response (uniform hazard) spectrum (UHS) as commonly desired in the aseismic structural design regulated by contemporary codes of practice. The appealing feature of the approach is that it is non-iterative and "one-step". This is accomplished by solving a standard over-determined minimization problem in conjunction with appropriate median peak factors. These factors are determined by a plethora of reported new Monte Carlo studies which on their own possess considerable stochastic dynamics merit. In the proposed approach, generation and treatment of samples of the processes individually on a deterministic basis is not required as is the case with the various "two-step" approaches found in the literature addressing the herein considered task. The applicability and usefulness of the approach is demonstrated by furnishing extensive numerical data associated with the elastic design UHS of the current European (EC8) and the Chinese (GB 50011) aseismic code provisions. Purposely, simple and thus attractive from a practical viewpoint, uniformly modulated processes assuming either the Kanai-Tajimi (K-T) or the Clough-Penzien (C-P) spectral form are employed. The Monte Carlo studies yield damping and duration dependent median peak factor spectra, given in a polynomial form, associated with the first passage problem for UHS compatible K-T and C-P uniformly modulated stochastic processes. Hopefully, the herein derived stochastic processes and median peak factor spectra can be used to facilitate the aseismic design of structures regulated by contemporary code provisions in a Monte Carlo simulation-based or stochastic dynamics-based context of analysis.

• Earthquakes and Structures
• /
• v.3 no.3_4
• /
• pp.581-609
• /
• 2012

In this paper a novel non-iterative approach is proposed to address the problem of deriving non-stationary stochastic processes which are compatible in the mean sense with a given (target) response (uniform hazard) spectrum (UHS) as commonly desired in the aseismic structural design regulated by contemporary codes of practice. This is accomplished by solving a standard over-determined minimization problem in conjunction with appropriate median peak factors. These factors are determined by a plethora of reported new Monte Carlo studies which on their own possess considerable stochastic dynamics merit. In the proposed approach, generation and treatment of samples of the processes individually on a deterministic basis is not required as is the case with the various approaches found in the literature addressing the herein considered task. The applicability and usefulness of the approach is demonstrated by furnishing extensive numerical data associated with the elastic design UHS of the current European (EC8) and the Chinese (GB 50011) aseismic code provisions. Purposely, simple and thus attractive from a practical viewpoint, uniformly modulated processes assuming either the Kanai-Tajimi (K-T) or the Clough-Penzien (C-P) spectral form are employed. The Monte Carlo studies yield damping and duration dependent median peak factor spectra, given in a polynomial form, associated with the first passage problem for UHS compatible K-T and C-P uniformly modulated stochastic processes. Hopefully, the herein derived stochastic processes and median peak factor spectra can be used to facilitate the aseismic design of structures regulated by contemporary code provisions in a Monte Carlo simulation-based or stochastic dynamics-based context of analysis.

• Journal of Wetlands Research
• /
• v.20 no.4
• /
• pp.338-344
• /
• 2018

In this study, the surface air temperature (SAT) and the dew-point temperature (DPT) are applied as the covariance of the location parameter among three parameters of GEV distribution to reflect the non-stationarity of extreme rainfall due to climate change. Busan station is selected as the study site and the monthly maximum daily rainfall depth from May to October is used for analysis. Various models are constructed to select the most appropriate co-variate(SAT and DPT) function for location parameter of GEV distribution, and the model with the smallest AIC(Akaike Information Criterion) is selected as the optimal model. As a result, it is found that the non-stationary GEV distribution with co-variate of exp(DPT) is the best. The selected model is used to analyze the effect of climate change scenarios on extreme rainfall quantile. It is confirmed that the design rainfall depth is highly likely to increase as the future DPT increases.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.631-634
• /
• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.1
• /
• pp.8-15
• /
• 2005

The analysis of Rader signal that have non-linearity variable phase is signal that contact easily in several fields such as radar, telecommunication, seismic, sonar and biomedical applications. In generally, Non-stationary signal means that spectral characteristics are varying with time and instantaneous frequency is only one frequency or narrow range of frequencies varying as a function of time. Therefore, Instantaneous frequency is vary important variable that understanding physical characteristic of signal. This paper was describes continuous wavelet transform to determine instantaneous frequency at non-staionary signal and compare to existing method. When white noise or various frequency is overlapped each other in sign, existing method was can not decide corrected instantaneous frequency, but when used continuous wavelet transform, very well decide correctly frequency regardless of component of signal.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.37 no.3
• /
• pp.11-16
• /
• 2000

In this paper, a real-time implementation of scene-based non-uniformity correction by digital technique is proposed for microscan-mode staring infrared cameras. Most scene-based non-uniformity correction algorithms, without sensor motion, can not be applied to stationary scenes because of image blurring and fading. Using microscanning effect, coupled with a modified version of Scribner's algorithm, the proposed technique can correct the artifacts and non-uniformities in real time Computer simulations and hardware experiments demonstrate substantial Improvement of image qualities in stationary as well as moving scenes.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.1
• /
• pp.232-248
• /
• 2014

A series of kernel regression (KR) algorithms, such as the classic kernel regression (CKR), the 2- and 3-D steering kernel regression (SKR), have been proposed for image and video super-resolution. In existing KR frameworks, a single algorithm is usually adopted and applied for a whole image/video, regardless of region characteristics. However, their performances and computational efficiencies can differ in regions of different characteristics. To take full advantage of the KR algorithms and avoid their disadvantage, this paper proposes a kernel regression framework for video super-resolution. In this framework, each video frame is first analyzed and divided into three types of regions: flat, non-flat-stationary, and non-flat-moving regions. Then different KR algorithm is selected according to the region type. The CKR and 2-D SKR algorithms are applied to flat and non-flat-stationary regions, respectively. For non-flat-moving regions, this paper proposes a similarity-assisted steering kernel regression (SASKR) algorithm, which can give better performance and higher computational efficiency than the 3-D SKR algorithm. Experimental results demonstrate that the computational efficiency of the proposed framework is greatly improved without apparent degradation in performance.

• Communications for Statistical Applications and Methods
• /
• v.15 no.5
• /
• pp.793-798
• /
• 2008

Extreme value inference deals with fitting the generalized extreme value distribution model and the generalized Pareto distribution model, which are recently combined to give a single model, namely a two-dimensional non-homogeneous Poisson exceedance point process model. In this paper, we extend the two-dimensional non-homogeneous Poisson process model to include non-stationary effect or dependence on covariates and then derive the likelihood for the extended model.