• Journal of Communications and Networks
• /
• v.9 no.1
• /
• pp.18-27
• /
• 2007

The conventional normalized least mean square (NLMS) algorithm is the most widely used for adaptive identification within a non-stationary input context. The convergence of the NLMS algorithm is independent of environmental changes. However, its steady state performance is impaired during input sequences with low dynamics. In this paper, we propose a new NLMS algorithm which is, in the steady state, insensitive to the time variations of the input dynamics. The square soot (SR)-NLMS algorithm is based on a normalization of the LMS adaptive filter input by the Euclidean norm of the tap-input. The tap-input power of the SR-NLMS adaptive filter is then equal to one even during sequences with low dynamics. Therefore, the amplification of the observation noise power by the tap-input power is cancelled in the misadjustment time evolution. The harmful effect of the low dynamics input sequences, on the steady state performance of the LMS adaptive filter are then reduced. In addition, the square root normalized input is more stationary than the base input. Therefore, the robustness of LMS adaptive filter with respect to the input non stationarity is enhanced. A performance analysis of the first- and the second-order statistic behavior of the proposed SR-NLMS adaptive filter is carried out. In particular, an analytical expression of the step size ensuring stability and mean convergence is derived. In addition, the results of an experimental study demonstrating the good performance of the SR-NLMS algorithm are given. A comparison of these results with those obtained from a standard NLMS algorithm, is performed. It is shown that, within a non-stationary input context, the SR-NLMS algorithm exhibits better performance than the NLMS algorithm.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 1999.04a
• /
• pp.61-68
• /
• 1999

In the nonlinear dynamic structural analysis the given ground excitation as an input should be well defined. Because of the lack of recorded accelerograms in Korea it is required to generate an artificial earthquake by a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms. It is well known that earthquake motions are generally non-stationary with time-varying intensity and frequency content. Many researchers have proposed non-stationary random process models. Yeh and Wen (1990) proposed a non-stationary modulation function and a power spectral density function to describe such non-stationary characteristics. Satio and Wen(1994) proposed a non-stationary stochastic process model to generate earthquake ground motions which are compatible with design reponse spectrum at sites in Japan. this paper shows the process to modify power spectrum compatible with target design response spectrum for generating of nonstationary artificial earthquake ground motions. Target reponse spectrum is chosen by ATC14 to calibrate the response spectrum according to a give recurrence period.

• Earthquakes and Structures
• /
• v.4 no.4
• /
• pp.383-396
• /
• 2013

In earthquake engineering area, the critical excitation method is an approach to find the most severe earthquake subjected to the structure. However, given some earthquake constraints, such as intensity and power, the critical excitations have spectral density functions that often resonate with the first modes of the structure. This paper presents a non-stationary critical excitation that is capable of exciting the main modes of the structure using a non-uniform power spectral density (PSD) that is similar to natural earthquakes. Thus, this paper proposes a new method to estimate the power and intensity of earthquakes. Finally, a new method for the linear seismic design of structures using a modified non-stationary critical excitation is proposed.

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

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1999.04a
• /
• pp.145-150
• /
• 1999

In the nonlinear dynamic structural analysis, the given ground excitation as an input should be well defined. Because of the lack of recorded accelerograms in Korea, it is required to generate an artificial earthquake by a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms. It is well known that earthquake motions are generally non-stationary with time-varying intensity and frequency content. Many researchers have proposed non-stationary random process models. Yeh and Wen (1990) proposed a non-stationary stochastic process model which can be modeled as components with an intensity function, a frequency modulation function and a power spectral density function to describe such non-stationary characteristics. This paper shows the process to generate nonstationary artificial earthquake ground motions considering target design response spectrum chosen by ATC14.

• Journal of the Korean GEO-environmental Society
• /
• v.12 no.4
• /
• pp.53-63
• /
• 2011

Selection or generation of appropriate input ground motion is very important in performing a dynamic analysis. In Korea, it is a common practice to use recorded strong ground motions or artificial motions. The recorded motions show non-stationary characteristics, which is a distinct property of all earthquake motions, but have the problem of not matching the design response spectrum. The artificial motions match the design spectrum, but show stationary characteristics. This study generated ground motions that preserve the non-stationary characteristics of a real earthquake motion, but also matches the design spectrum. In the process, an impulse function-based algorithm that adjusts a given time series in time domain such that it matches the target response spectrum is used. Application of the algorithm showed that it can successfully adjust any recorded motions to match the target spectrum and also preserve the non-stationary characteristics. The modified motions are used to perform a series of nonlinear site response analyses. It is shown that the results using the adjusted motions result in more reliable estimates of ground vibration. It is thus recommended that the newly adjusted motions be used in practice instead of original recorded motions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.6
• /
• pp.2838-2858
• /
• 2019

Traditional channel models for vehicle-to-vehicle (V2V) communication usually assume fixed velocity in static scattering environment. In the realistic scenarios, however, time-variant velocity for V2V results in non-stationary statistical properties of wireless channels. Dynamic scatterers with random velocities and directions have been always utilized to depict the non-stationary statistical properties of the channel. In this paper, a non-stationary geometry-based cooperative scattering channel model is proposed for multiple-input multiple-output (MIMO) V2V communication systems, where a birth-death process is used to capture the appearance and disappearance dynamic properties of moving scatterers that reflect the time-variant time correlation and Doppler spectrum characteristics. Moreover, our model has more straight and concise to study the impact of the vehicular traffic density on channel characteristics and thus avoid complicated procedure in deriving the analytical expressions of the channel parameters and functions. The numerical results validate our analysis and demonstrate that setting important parameters of our model can appropriately build up more purposeful measurement campaigns in the future.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.2
• /
• pp.168-175
• /
• 1999

In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained.

• Proceedings of the KIEE Conference
• /
• 1998.07g
• /
• pp.2299-2301
• /
• 1998

In this paper, a modified RBF (Radial Basis Function) neural network structure is suggested for the prediction of time series with non-linear, non-stationary characteristics. Conventional RBF neural network predicting time series by using past outputs is for sensing the trajectory of the time series and for reacting when there exists strong relation between input and hidden neuron's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden neurons are modified to react to the increments of input variable and multiplied by increments(or decrements) of out puts for prediction. When the suggested structure is applied to prediction of Lorenz equation, and Rossler equation, improved performances are obtainable.

• Journal of Information Processing Systems
• /
• v.14 no.2
• /
• pp.455-468
• /
• 2018

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.