The Experts below are selected from a list of 8631 Experts worldwide ranked by ideXlab platform
Guoqing Huang - One of the best experts on this subject based on the ideXlab platform.
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Fast simulation of multivariate Nonstationary Process and its application to extreme winds
Journal of Wind Engineering and Industrial Aerodynamics, 2017Co-Authors: Ning Zhao, Guoqing HuangAbstract:Abstract Simulation of the devastating excitations such as the ground motion and transient extreme winds is an important task in the structural response analysis when it comes to the nonlinearity, system stochasticity, parametric excitations and so on. Although the classic spectral representation method (SRM) is widely used in the Nonstationary Process simulation, it suffers from lower efficiency due to the unavailability of use of fast Fourier transform (FFT). In this study, the classic SRM is extended to the Nonstationary Process with the time-varying coherence. Then, the FFT-aided and almost accurate simulation algorithm for the Nonstationary Process with the time-varying coherence is developed with the help of the proper orthogonal decomposition (POD) which is used to factorize the decomposed evolutionary spectra. Especially, the more efficient simulation for the Nonstationary Process with time-invariant coherence is also proposed, where the spectral matrix decomposition, use of POD and execution of FFT can be reduced significantly. Two examples including downburst and typhoon winds are employed to evaluate the accuracy and efficiency of the proposed method. Results show that the method has the good performance in terms of the efficiency and accuracy.
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Time-Frequency Analysis of Nonstationary Process Based on Multivariate Empirical Mode Decomposition
Journal of Engineering Mechanics-asce, 2015Co-Authors: Guoqing Huang, Ahsan Kareem, Haili LiaoAbstract:Currently, empirical mode decomposition (EMD) has become a popular data-driven time-frequency analysis method for Nonstationary and nonlinear data. However, it is still limited to univariate data due to the number and/or scale misalignment for multivariate data. A newly developed multivariate EMD (MEMD) scheme decomposes multivariate data simultaneously and thus leads to mode alignment and minimizes mode mixing. In addition, an improved amplitude and frequency modulation (AM-FM) decomposition algorithm presented here provides an estimation of a more meaningful instantaneous amplitude and frequency than the widely used Hilbert transform (HT). Both of these facilitate development of a time-frequency analysis framework for multivariate Nonstationary and nonlinear data analysis. In this paper, MEMD-based scalogram and coscalogram, and instantaneous frequency spectra and cospectra are proposed to characterize a multivariate Nonstationary Process. The scalogram and instantaneous frequency spectra capture spectral evolution of each component while the coscalogram and instantaneous frequency cospectra reveal embedded intermittent correlation between two components. Compared with scale-based scalogram and coscalogram, frequency-based instantaneous frequency spectra and cospectra provide a more detailed portrait of multivariate data. The effectiveness of the proposed MEMD-based time-frequency analysis framework is demonstrated by numerical examples of a thunderstorm downburst and an earthquake ground motion. Also, the results from the MEMD-based approach are compared with those based on a continuous wavelet transform, which further reinforces the effectiveness of the proposed framework.
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Application of Proper Orthogonal Decomposition in Fast Fourier Transform—Assisted Multivariate Nonstationary Process Simulation
Journal of Engineering Mechanics-asce, 2015Co-Authors: Guoqing HuangAbstract:AbstractThe classic spectral representation method (SRM)-based Nonstationary Process simulation algorithm is used extensively in the engineering community. However, it is less efficient owing to the unavailability of fast Fourier transform (FFT). In this paper, an efficient, almost accurate, and straightforward algorithm is developed for the simulation of the multivariate Nonstationary Process. In this method, an evolutionary spectral matrix is decomposed via Cholesky method, and then proper orthogonal decomposition (POD) is used to factorize decomposed spectra as the summation of the products of time and frequency functions. Because original time-dependent decomposed spectra are decoupled via factorization, FFT can be used to significantly expedite the simulation efficiency. This POD-based factorization is totally data-driven and optimal, and fewer items are required in matching decomposed spectra. Therefore, the accuracy and efficiency of the factorization can be guaranteed at the same time. Another att...
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application of proper orthogonal decomposition in fast fourier transform assisted multivariate Nonstationary Process simulation
Journal of Engineering Mechanics-asce, 2015Co-Authors: Guoqing HuangAbstract:AbstractThe classic spectral representation method (SRM)-based Nonstationary Process simulation algorithm is used extensively in the engineering community. However, it is less efficient owing to the unavailability of fast Fourier transform (FFT). In this paper, an efficient, almost accurate, and straightforward algorithm is developed for the simulation of the multivariate Nonstationary Process. In this method, an evolutionary spectral matrix is decomposed via Cholesky method, and then proper orthogonal decomposition (POD) is used to factorize decomposed spectra as the summation of the products of time and frequency functions. Because original time-dependent decomposed spectra are decoupled via factorization, FFT can be used to significantly expedite the simulation efficiency. This POD-based factorization is totally data-driven and optimal, and fewer items are required in matching decomposed spectra. Therefore, the accuracy and efficiency of the factorization can be guaranteed at the same time. Another att...
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An efficient simulation approach for multivariate Nonstationary Process: Hybrid of wavelet and spectral representation method
Probabilistic Engineering Mechanics, 2014Co-Authors: Guoqing HuangAbstract:Abstract Currently, the classical spectral representation method (SRM) for Nonstationary Process simulation is widely used in the engineering community. Although this scheme has the higher accuracy, the time-dependent spectra results in unavailability of fast Fourier transform (FFT) and thus the simulation efficiency is lower. On the other hand, the approach based on stochastic decomposition can apply FFT in the simulation. However, the algorithm including the fitting procedure is relatively complicated and thus limits its use in practice. In this paper, the hybrid efficient simulation method is proposed for the vector-valued Nonstationary Process, which contains the spectra decomposition via wavelets and SRM. This method can take advantage of FFT and is also straightforward to engineering application. Numerical examples are employed to evaluate the proposed method. Results show that the method performs fairly well for the scalar Process and vector-valued Process with real coherence function. In the case of complex coherence function, the majority of the phase in the coherence function cannot be remained in the simulation. In addition, the validity of proper orthogonal decomposition (POD) in Nonstationary Process simulation via the decomposition of the time-dependent Nonstationary spectra is studied. Analysis shows that the direct use of POD in Nonstationary spectra decomposition may not be useful in Nonstationary Process simulations.
Y Takizawa - One of the best experts on this subject based on the ideXlab platform.
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The analysis of Nonstationary Processes based on a kinetic model
Electronics and Communications in Japan Part Iii-fundamental Electronic Science, 1995Co-Authors: Y Takizawa, Atushi Fukasawa, Keisuke Oda, Hiroshi HarashimaAbstract:This paper presents a new analysis method for the Nonstationary phenomenon using modeling. Method efficacy is investigated and effectiveness is demonstrated. Nonstationarity is modeled into the kinetics of the generating Process. the behavior of the Nonstationary Process is represented by the characteristics at a point on the time axis (instantaneous characteristic) and the characteristics of its time variation (time-variant characteristics). By estimating the Nonstationary spectrum, the instantaneous characteristics are determined and then the time-variant characteristics are analyzed by applying the kinetic model. the Nonstationary phenomenon is first modeled using kinetic energy. Characteristics at a point on the time axis are regarded as a point of mass. Position, velocity, and acceleration are determined; these parameters signify a model with time-varying characteristics. the arising time of the event is determined, based on the relations among the parameters. the characteristic plane is constructed using these parameters, and nonstationarity is evaluated on the plane. the proposed algorithm is then evaluated using an artificial signal and the model is validated using a speech signal. This analysis yields a new finding that deals with the characteristics of the phoneme, thus demonstrating the usefulness of the proposed method.
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Spectral estimation method of a multistage Nonstationary Process and its application
Electronics and Communications in Japan Part Iii-fundamental Electronic Science, 1992Co-Authors: Y TakizawaAbstract:This paper considers the time series observed through the Nonstationary Processes and proposes the multistage instantaneous maximum entropy method aiming at the inverse estimation of the behavior of the original Process on the spectrum. The performance of the method is demonstrated and examples of useful applications are presented. To achieve the goal, the cascaded multistage AR Process is considered as the model for the Process from the excitation source of the time series to the observation. The excitation source is assumed as a simple white Gaussian noise. The response of the Process is represented by a time-varying lattice filter. The instantaneous value of the power spectrum is determined from the prediction error estimation function defined at a point on the time axis (instantaneous maximum entropy method, IMEM). Then the nonstationarity (time-varying property) of the Process is noted, and the separation of the connected Processes is discussed. For this purpose, the Nonstationary frequency axis of the two-dimensional spectrum is considered based on IMEM. A filter to separate the considered Process from others is designed. This filter passes the time-varying component of the considered Process but eliminates the time-varying components due to the interference from other Processes, based on the a priori information concerning the time-varying properties of the considered Process. Finally, this paper presents the results of evaluation for the test using the artificial signal as well as the results of evaluation for two application examples. Comparing the result to that of the traditional method, the novelty and the usefulness of the proposed method are demonstrated. The system features are that the accumulation of the error in the multistage prediction can efficiently be suppressed using IMEM with the excellent prediction performance, which results in the high follow-up ability to the time-varying Process, and the excellent spectrum estimation.
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spectral deconvolution of a multistage Nonstationary Process based on instantaneous maximum entropy estimation
International Conference on Acoustics Speech and Signal Processing, 1991Co-Authors: Y Takizawa, A FukasawaAbstract:A method to estimate the spectrum of a multistage Nonstationary Process is developed. A cascaded autoregressive Process is adopted as the model of a Process for an observed signal. Each Process is supposed to be Nonstationary. An instantaneous maximum entropy method based on an instantaneously defined evaluation function and a time-variant lattice filter is proposed. A novel approach is proposed for the problem of Process separation based on the nonstationarity of each Process using a priori knowledge. The algorithm is proved to be efficient through evaluations using an artificially synthesized test signal and a speech signal. >
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ICASSP - Spectral deconvolution of a multistage Nonstationary Process based on instantaneous maximum entropy estimation
[Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991Co-Authors: Y Takizawa, A FukasawaAbstract:A method to estimate the spectrum of a multistage Nonstationary Process is developed. A cascaded autoregressive Process is adopted as the model of a Process for an observed signal. Each Process is supposed to be Nonstationary. An instantaneous maximum entropy method based on an instantaneously defined evaluation function and a time-variant lattice filter is proposed. A novel approach is proposed for the problem of Process separation based on the nonstationarity of each Process using a priori knowledge. The algorithm is proved to be efficient through evaluations using an artificially synthesized test signal and a speech signal. >
A Fukasawa - One of the best experts on this subject based on the ideXlab platform.
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spectral deconvolution of a multistage Nonstationary Process based on instantaneous maximum entropy estimation
International Conference on Acoustics Speech and Signal Processing, 1991Co-Authors: Y Takizawa, A FukasawaAbstract:A method to estimate the spectrum of a multistage Nonstationary Process is developed. A cascaded autoregressive Process is adopted as the model of a Process for an observed signal. Each Process is supposed to be Nonstationary. An instantaneous maximum entropy method based on an instantaneously defined evaluation function and a time-variant lattice filter is proposed. A novel approach is proposed for the problem of Process separation based on the nonstationarity of each Process using a priori knowledge. The algorithm is proved to be efficient through evaluations using an artificially synthesized test signal and a speech signal. >
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ICASSP - Spectral deconvolution of a multistage Nonstationary Process based on instantaneous maximum entropy estimation
[Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991Co-Authors: Y Takizawa, A FukasawaAbstract:A method to estimate the spectrum of a multistage Nonstationary Process is developed. A cascaded autoregressive Process is adopted as the model of a Process for an observed signal. Each Process is supposed to be Nonstationary. An instantaneous maximum entropy method based on an instantaneously defined evaluation function and a time-variant lattice filter is proposed. A novel approach is proposed for the problem of Process separation based on the nonstationarity of each Process using a priori knowledge. The algorithm is proved to be efficient through evaluations using an artificially synthesized test signal and a speech signal. >
C. F. Sirmans - One of the best experts on this subject based on the ideXlab platform.
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Nonstationary multivariate Process modeling through spatially varying coregionalization
Test, 2004Co-Authors: Alan E. Gelfand, Alexandra M. Schmidt, Sudipto Banerjee, C. F. SirmansAbstract:Models for the analysis of multivariate spatial data are receiving increased attention these days. In many applications it will be preferable to work with multivariate spatial Processes to specify such models. A critical specification in providing these models is the cross covariance function. Constructive approaches for developing valid cross-covariance functions offer the most practical strategy for doing this. These approaches include separability, kernel convolution or moving average methods, and convolution of covariance functions. We review these approaches but take as our main focus the computationally manageable class referred to as the linear model of coregionalization (LMC). We introduce a fully Bayesian development of the LMC. We offer clarification of the connection between joint and conditional approaches to fitting such models including prior specifications. However, to substantially enhance the usefulness of such modelling we propose the notion of a spatially varying LMC (SVLMC) providing a very rich class of multivariate Nonstationary Processes with simple interpretation. We illustrate the use of our proposed SVLMC with application to more than 600 commercial property transactions in three quite different real estate markets, Chicago, Dallas and San Diego. Bivariate Nonstationary Process inodels are developed for income from and selling price of the property.
Zhiping Lin - One of the best experts on this subject based on the ideXlab platform.
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modeling general distributed Nonstationary Process and identifying time varying autoregressive system by wavelets theory and application
Signal Processing, 2001Co-Authors: Yuanjin Zheng, D B H Tay, Zhiping LinAbstract:In this paper, some new techniques for time-varying parametric autoregressive (AR) system identification by wavelets are presented. Firstly, we derive a new multiresolution least squares (MLS) algorithm for Gaussian time-varying AR model identification employing wavelet operator matrix representation. This method can optimally balance between the over-fitted solution and the poorly represented identification. The main features of the time-varying model parameters are estimated by a multiresoulution method, which represents the smooth trends as well as the rapidly changing components. Combining the total least squares algorithm with the MLS algorithm, a new method is presented which can make the identification of a noisy time-varying AR model. Finally, we deal with a non-Gaussian time-varying AR model for modeling Nonstationary Processes in a non-Gaussian distribution. A pseudo-maximum likelihood estimation algorithm is proposed for this model identification. The time-varying AR parameters as well as the non-Gaussian probability density (approximated by Gaussian mixture density) parameters of the driving noise sequence (DNS) are simultaneously estimated. Simulation results verify that our methods can effectively identify time-varying AR systems with general distributed DNS. A realistic application of the proposed technique in blind equalization of time-varying fading channel will be explored.
