The Experts below are selected from a list of 314616 Experts worldwide ranked by ideXlab platform
Khaldoon Ghanem - One of the best experts on this subject based on the ideXlab platform.
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extending the average Spectrum Method grid point sampling and density averaging
Physical Review B, 2020Co-Authors: Khaldoon Ghanem, Erik KochAbstract:Analytic continuation of imaginary time or frequency data to the real axis is a crucial step in extracting dynamical properties from quantum Monte Carlo simulations. The average Spectrum Method provides an elegant solution by integrating over all nonnegative spectra weighted by how well they fit the data. In a recent paper, we found that discretizing the functional integral, as in Feynman's path-integrals, does not have a well-defined continuum limit. Instead, the limit depends on the discretization grid whose choice may strongly bias the results. In this paper, we demonstrate that sampling the grid points, instead of keeping them fixed, also changes the functional integral limit and rather helps to overcome the bias considerably. We provide an efficient algorithm for doing the sampling and show how the density of the grid points acts now as a default model with a significantly reduced biasing effect. The remaining bias depends mainly on the width of the grid density, so we go one step further and average also over densities of different widths. For a certain class of densities, including Gaussian and exponential ones, this width averaging can be done analytically, eliminating the need to specify this parameter without introducing any computational overhead.
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average Spectrum Method for analytic continuation efficient blocked mode sampling and dependence on the discretization grid
Physical Review B, 2020Co-Authors: Khaldoon Ghanem, Erik KochAbstract:The average Spectrum Method is a promising approach for the analytic continuation of imaginary time or frequency data to the real axis. It determines the analytic continuation of noisy data from a functional average over all admissible spectral functions, weighted by how well they fit the data. Its main advantage is the apparent lack of adjustable parameters and smoothness constraints, using instead the information on the statistical noise in the data. Its main disadvantage is the enormous computational cost of performing the functional integral. Here we introduce an efficient implementation, based on the singular value decomposition of the integral kernel, eliminating this problem. It allows us to analyze the behavior of the average Spectrum Method in detail. We find that the discretization of the real-frequency grid, on which the spectral function is represented, biases the results. The distribution of the grid points plays the role of a default model while the number of grid points acts as a regularization parameter. We give a quantitative explanation for this behavior, point out the crucial role of the default model and provide a practical Method for choosing it, making the average Spectrum Method a reliable and efficient technique for analytic continuation.
Erik Koch - One of the best experts on this subject based on the ideXlab platform.
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extending the average Spectrum Method grid point sampling and density averaging
Physical Review B, 2020Co-Authors: Khaldoon Ghanem, Erik KochAbstract:Analytic continuation of imaginary time or frequency data to the real axis is a crucial step in extracting dynamical properties from quantum Monte Carlo simulations. The average Spectrum Method provides an elegant solution by integrating over all nonnegative spectra weighted by how well they fit the data. In a recent paper, we found that discretizing the functional integral, as in Feynman's path-integrals, does not have a well-defined continuum limit. Instead, the limit depends on the discretization grid whose choice may strongly bias the results. In this paper, we demonstrate that sampling the grid points, instead of keeping them fixed, also changes the functional integral limit and rather helps to overcome the bias considerably. We provide an efficient algorithm for doing the sampling and show how the density of the grid points acts now as a default model with a significantly reduced biasing effect. The remaining bias depends mainly on the width of the grid density, so we go one step further and average also over densities of different widths. For a certain class of densities, including Gaussian and exponential ones, this width averaging can be done analytically, eliminating the need to specify this parameter without introducing any computational overhead.
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average Spectrum Method for analytic continuation efficient blocked mode sampling and dependence on the discretization grid
Physical Review B, 2020Co-Authors: Khaldoon Ghanem, Erik KochAbstract:The average Spectrum Method is a promising approach for the analytic continuation of imaginary time or frequency data to the real axis. It determines the analytic continuation of noisy data from a functional average over all admissible spectral functions, weighted by how well they fit the data. Its main advantage is the apparent lack of adjustable parameters and smoothness constraints, using instead the information on the statistical noise in the data. Its main disadvantage is the enormous computational cost of performing the functional integral. Here we introduce an efficient implementation, based on the singular value decomposition of the integral kernel, eliminating this problem. It allows us to analyze the behavior of the average Spectrum Method in detail. We find that the discretization of the real-frequency grid, on which the spectral function is represented, biases the results. The distribution of the grid points plays the role of a default model while the number of grid points acts as a regularization parameter. We give a quantitative explanation for this behavior, point out the crucial role of the default model and provide a practical Method for choosing it, making the average Spectrum Method a reliable and efficient technique for analytic continuation.
Yu Yang - One of the best experts on this subject based on the ideXlab platform.
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application of frequency family separation Method based upon emd and local hilbert energy Spectrum Method to gear fault diagnosis
Mechanism and Machine Theory, 2008Co-Authors: Junsheng Cheng, Jiashi Tang, Yu YangAbstract:Abstract Targeting the advantages of Hilbert–Huang transform (HHT) and the characteristics of gear fault vibration signals, HHT is introduced into gear fault diagnosis. The concept of local Hilbert energy Spectrum is proposed and two gear fault diagnosis approaches, namely, frequency family separation Method based on EMD (empirical mode decomposition) and local Hilbert energy Spectrum Method, are put forward, which are applied to gear fault diagnosis. Considering that the gear fault vibration signal is a multi-component amplitude-demodulated and frequency-demodulated (AM–FM) signal and EMD could exactly decompose the AM–FM signal into a number of intrinsic mode functions (IMFs), each of which can be amplitude-demodulated or frequency-demodulated component, the frequency families could be separated effectively from the gear vibration signal by applying EMD to the gear vibration signal. Furthermore, when faults occur in gear, the energy of the gear vibration signal would change correspondingly, whilst the local Hilbert energy Spectrum can exactly provide the energy distribution of the signal in certain frequency with the change of the time and frequency. Thus, the fault information of the gear vibration signal can be extracted effectively from the local Hilbert energy Spectrum. The analysis results from the experimental signals show that both frequency family separation Method based on EMD and local Hilbert energy Spectrum Method could extract the characteristics information of the gear fault vibration signal effectively.
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application of frequency family separation Method based upon emd and local hilbert energy Spectrum Method to gear fault diagnosis
Mechanism and Machine Theory, 2008Co-Authors: Junsheng Cheng, Jiashi Tang, Dejie Yu, Yu YangAbstract:Abstract Targeting the advantages of Hilbert–Huang transform (HHT) and the characteristics of gear fault vibration signals, HHT is introduced into gear fault diagnosis. The concept of local Hilbert energy Spectrum is proposed and two gear fault diagnosis approaches, namely, frequency family separation Method based on EMD (empirical mode decomposition) and local Hilbert energy Spectrum Method, are put forward, which are applied to gear fault diagnosis. Considering that the gear fault vibration signal is a multi-component amplitude-demodulated and frequency-demodulated (AM–FM) signal and EMD could exactly decompose the AM–FM signal into a number of intrinsic mode functions (IMFs), each of which can be amplitude-demodulated or frequency-demodulated component, the frequency families could be separated effectively from the gear vibration signal by applying EMD to the gear vibration signal. Furthermore, when faults occur in gear, the energy of the gear vibration signal would change correspondingly, whilst the local Hilbert energy Spectrum can exactly provide the energy distribution of the signal in certain frequency with the change of the time and frequency. Thus, the fault information of the gear vibration signal can be extracted effectively from the local Hilbert energy Spectrum. The analysis results from the experimental signals show that both frequency family separation Method based on EMD and local Hilbert energy Spectrum Method could extract the characteristics information of the gear fault vibration signal effectively.
Kai Zheng - One of the best experts on this subject based on the ideXlab platform.
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parameter characterization of high overtone bulk acoustic resonators by resonant Spectrum Method
Ultrasonics, 2005Co-Authors: Hui Zhang, Shuyi Zhang, Kai ZhengAbstract:Abstract Effects of electrode on high-overtone bulk acoustic resonator (HBAR) spectra are analyzed by numerical simulation. The figure of merit (FOM), the effective electromechanical coupling factor, k eff 2 ( m ) , and the quality factor Qs of the unique mode are discussed based on the resonance spectra of the HBAR. It is demonstrated that electrodes with proper acoustic impedance and thickness could improve the performance of the HBAR, or degrade the performance if the electrodes are not properly chosen.
Norden E Huang - One of the best experts on this subject based on the ideXlab platform.
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application of the empirical mode decomposition hilbert Spectrum Method to identify near fault ground motion characteristics and structural responses
Bulletin of the Seismological Society of America, 2004Co-Authors: Tsuchiu Wu, Norden E HuangAbstract:In this article, the empirical mode decomposition Method combined with the Hilbert Spectrum Method (EMD + HHT) is used to analyze the free-field ground motion and to estimate the global structural property of building and bridge structure through the measurement of seismic response data. The EMD + HHT Method provides a powerful tool for signal processing to identify nonlinear and nonstationary data. Based on the decomposed ground-motion signal, the absolute input energy of each decomposed wave was studied (the fling step [pulselike wave] can be separated from the recorded near-fault ground motion). Through application of the EMD + HHT Method to building and bridge seismic response data, the time-varying system natural frequency and damping ratio can also be estimated. Damage identification from seismic response data of buildings and bridges, particularly from the Chi-Chi earthquake data, is also described. Manuscript received 31 July 2000.
