The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform
Jianbing Chen - One of the best experts on this subject based on the ideXlab platform.
-
Reduction of random variables in the Stochastic Harmonic Function Representation via spectrum-relative dependent random frequencies
Mechanical Systems and Signal Processing, 2020Co-Authors: Jianbing Chen, Yongbo Peng, Liam Comerford, Michael BeerAbstract:Abstract Two significant developments pertaining to the application of the Stochastic Harmonic Function Representation of stochastic processes are presented. Together, they allow for Gaussian records to be simulated within the bounds of the Representation with the fewest number of random variables. Specifically, independent random frequencies that form a staple component of the Stochastic Harmonic Function are replaced by dependent random frequencies, along with a specific scheme for choosing frequency interval widths. Numerical examples demonstrating spectrum reconstruction accuracy and estimated PDF convergence to the Gaussian are presented to support the work.
-
a stochastic harmonic Function Representation for non stationary stochastic processes
Mechanical Systems and Signal Processing, 2017Co-Authors: Jianbing Chen, Fan Kong, Yongbo PengAbstract:Abstract The time-domain Representation of non-stationary stochastic processes is of paramount importance, in particular for response analysis and reliability evaluation of nonlinear structures. In the present paper a stochastic harmonic Function (SHF) Representation originally developed for stationary processes is extended to evolutionary non-stationary processes. Utilizing the new scheme, the time-domain Representation of non-stationary stochastic processes is expressed as the linear combination of a series of stochastic harmonic components. Different from the classical spectral Representation (SR), not only the phase angles but also the frequencies and their associated amplitudes, are treated as random variables. The proposed method could also be regarded as an extension of the classical spectral Representation method. However, it is rigorously proved that the new scheme well accommodates the target evolutionary power spectral density Function. Compared to the classical spectral Representation method, moreover, the new scheme needs much fewer terms to be retained. The first four moments and the distribution properties, e.g., the asymptotical Gaussianity, of the simulated stochastic process via SHF Representation are studied. Numerical examples are addressed for illustrative purposes, showing the effectiveness of the proposed scheme.
-
Stochastic Harmonic Function Representation of Stochastic Processes
Journal of Applied Mechanics, 2012Co-Authors: Jianbing Chen, Weiling SunAbstract:An approach to represent a stochastic process by the combination of finite stochastic harmonic Functions is proposed. The conditions that should be satisfied to make sure that the power spectral density Function of the stochastic harmonic Function process is identical to the target power spectral density are firstly studied. Then, two kinds of stochastic harmonic Functions, of which the distribution of the amplitudes and the random frequencies are different, are discussed. The probabilistic characteristics of the two kinds of stochastic harmonic Functions, including the asymptotic distribution, the one-dimensional probability density Function, and the rate of approaching the asymptotic distribution, etc., are studied in detail by theoretical treatment and numerical examples. Responses of a nonlinear structure subjected to strong earthquake excitation are investigated. The studies show that the proposed approach can capture the target power spectral density exactly with any number of components. The reduction of the components provides flexibility and reduces the computational cost. Finally, problems that need further investigations are discussed.
Yongbo Peng - One of the best experts on this subject based on the ideXlab platform.
-
Reduction of random variables in the Stochastic Harmonic Function Representation via spectrum-relative dependent random frequencies
Mechanical Systems and Signal Processing, 2020Co-Authors: Jianbing Chen, Yongbo Peng, Liam Comerford, Michael BeerAbstract:Abstract Two significant developments pertaining to the application of the Stochastic Harmonic Function Representation of stochastic processes are presented. Together, they allow for Gaussian records to be simulated within the bounds of the Representation with the fewest number of random variables. Specifically, independent random frequencies that form a staple component of the Stochastic Harmonic Function are replaced by dependent random frequencies, along with a specific scheme for choosing frequency interval widths. Numerical examples demonstrating spectrum reconstruction accuracy and estimated PDF convergence to the Gaussian are presented to support the work.
-
a stochastic harmonic Function Representation for non stationary stochastic processes
Mechanical Systems and Signal Processing, 2017Co-Authors: Jianbing Chen, Fan Kong, Yongbo PengAbstract:Abstract The time-domain Representation of non-stationary stochastic processes is of paramount importance, in particular for response analysis and reliability evaluation of nonlinear structures. In the present paper a stochastic harmonic Function (SHF) Representation originally developed for stationary processes is extended to evolutionary non-stationary processes. Utilizing the new scheme, the time-domain Representation of non-stationary stochastic processes is expressed as the linear combination of a series of stochastic harmonic components. Different from the classical spectral Representation (SR), not only the phase angles but also the frequencies and their associated amplitudes, are treated as random variables. The proposed method could also be regarded as an extension of the classical spectral Representation method. However, it is rigorously proved that the new scheme well accommodates the target evolutionary power spectral density Function. Compared to the classical spectral Representation method, moreover, the new scheme needs much fewer terms to be retained. The first four moments and the distribution properties, e.g., the asymptotical Gaussianity, of the simulated stochastic process via SHF Representation are studied. Numerical examples are addressed for illustrative purposes, showing the effectiveness of the proposed scheme.
Kees Wapenaar - One of the best experts on this subject based on the ideXlab platform.
-
Elastodynamic single-sided homogeneous Green’s Function Representation: Theory and numerical examples
Wave Motion, 2019Co-Authors: Christian Reinicke, Kees WapenaarAbstract:The homogeneous Green’s Function is the difference between an impulse response and its time-reversal. According to existing Representation theorems, the homogeneous Green’s Function associated with source–receiver pairs inside a medium can be computed from measurements at a boundary enclosing the medium. However, in many applications such as seismic imaging, time-lapse monitoring, medical imaging, non-destructive testing, etc., media are only accessible from one side. A recent development of wave theory has provided a Representation of the homogeneous Green’s Function in an elastic medium in terms of wavefield recordings at a single (open) boundary. Despite its single-sidedness, the elastodynamic homogeneous Green’s Function Representation accounts for all orders of scattering inside the medium. We present the theory of the elastodynamic single-sided homogeneous Green’s Function Representation and illustrate it with numerical examples for 2D laterally-invariant media. For propagating waves, the resulting homogeneous Green’s Functions match the exact ones within numerical precision, demonstrating the accuracy of the theory. In addition, we analyse the accuracy of the single-sided Representation of the homogeneous Green’s Function for evanescent wave tunnelling.
-
A single-sided homogeneous Green's Function Representation for holographic imaging, inverse scattering, time-reversal acoustics and interferometric Green's Function retrieval
Geophysical Journal International, 2016Co-Authors: Kees Wapenaar, Jan Thorbecke, Joost Van Der NeutAbstract:Green's theorem plays a fundamental role in a diverse range of wavefield imaging applications, such as holographic imaging, inverse scattering, time-reversal acoustics and interferometric Green's Function retrieval. In many of those applications, the homogeneous Green's Function (i.e. the Green's Function of the wave equation without a singularity on the right-hand side) is represented by a closed boundary integral. In practical applications, sources and/or receivers are usually present only on an open surface, which implies that a significant part of the closed boundary integral is by necessity ignored. Here we derive a homogeneous Green's Function Representation for the common situation that sources and/or receivers are present on an open surface only. We modify the integrand in such a way that it vanishes on the part of the boundary where no sources and receivers are present. As a consequence, the remaining integral along the open surface is an accurate single-sided Representation of the homogeneous Green's Function. This single-sided Representation accounts for all orders of multiple scattering. The new Representation significantly improves the aforementioned wavefield imaging applications, particularly in situations where the first-order scattering approximation breaks down.
-
Green's Function Representation for seismic interferometry by deconvolution
SEG Technical Program Expanded Abstracts 2010, 2010Co-Authors: Kees Wapenaar, Jan Thorbecke, Joost Van Der Neut, Evert Slob, Deyan Draganov, Elmer Ruigrok, Jürg Hunziker, R. SniederAbstract:Despite the strength of seismic interferometry to retrieve new seismic responses by crosscorrelating observations at different receiver locations, the method relies on a number of assumptions which are not always fulfilled in practice. Some practical circumstances that may hamper interferometry by crosscorrelation are: one-sided illumination, irregular source distribution, varying source spectra, multiples in the illuminating wave field, intrinsic losses, etc. To account for some or more of these effects, several authors have proposed interferometry by deconvolution, using a variety of approaches (Schuster and Zhou, 2006; Snieder et al., 2006; Mehta et al., 2007; Vasconcelos and Snieder, 2008; Wapenaar et al., 2008a, 2008b; van der Neut and Bakulin, 2009; Berkhout and Verschuur, 2009; van Groenestijn and Verschuur, 2010). Here we discuss a Green’s Function Representation of the convolution type as a unifying framework for seismic interferometry by multi-dimensional deconvolution (MDD).
-
Green’s Function Representation of Virtual Reflectors (VR)
71st EAGE Conference and Exhibition incorporating SPE EUROPEC 2009, 2009Co-Authors: Flavio Poletto, Kees Wapenaar, Biancamaria FarinaAbstract:Virtual seismic signals are synthesized by composing traces recorded by a plurality of sources and receivers. Seismic interferometry typically uses cross-correlation to compose the recorded traces, and to get virtual sources at the position of receivers.
-
Unified Green's Function Representation for Interferometry
69th EAGE Conference and Exhibition incorporating SPE EUROPEC 2007, 2007Co-Authors: Kees Wapenaar, Jan Thorbecke, Joost Van Der Neut, Evert Slob, R. Snieder, Deyan Draganov, S. A. L. De RidderAbstract:B031 Unified Green's Function Representation for Interferometry K. Wapenaar* (Delft University of Technology) E. Slob (Delft University of Technology) R. Snieder (Colorado School of Mines) D. Draganov (Delft University of Technology) J. Thorbecke (Delft University of Technology) J. van der Neut (Delft University of Technology) & S. de Ridder (Delft University of Technology) SUMMARY It has been shown by many authors that the cross-correlation of two acoustic wave field recordings yields the Green's Function between these receivers (in geophysics this is also known as ‘seismic interferometry’). Recently the theory has been extended for situations where time-reversal invariance does not hold
Michael Beer - One of the best experts on this subject based on the ideXlab platform.
-
Reduction of random variables in the Stochastic Harmonic Function Representation via spectrum-relative dependent random frequencies
Mechanical Systems and Signal Processing, 2020Co-Authors: Jianbing Chen, Yongbo Peng, Liam Comerford, Michael BeerAbstract:Abstract Two significant developments pertaining to the application of the Stochastic Harmonic Function Representation of stochastic processes are presented. Together, they allow for Gaussian records to be simulated within the bounds of the Representation with the fewest number of random variables. Specifically, independent random frequencies that form a staple component of the Stochastic Harmonic Function are replaced by dependent random frequencies, along with a specific scheme for choosing frequency interval widths. Numerical examples demonstrating spectrum reconstruction accuracy and estimated PDF convergence to the Gaussian are presented to support the work.
Yang Xiang - One of the best experts on this subject based on the ideXlab platform.
-
poster vulnerability discovery with Function Representation learning from unlabeled projects
Computer and Communications Security, 2017Co-Authors: Guanjun Lin, Jun Zhang, Wei Luo, Lei Pan, Yang XiangAbstract:In cybersecurity, vulnerability discovery in source code is a fundamental problem. To automate vulnerability discovery, Machine learning (ML) based techniques has attracted tremendous attention. However, existing ML-based techniques focus on the component or file level detection, and thus considerable human effort is still required to pinpoint the vulnerable code fragments. Using source code files also limit the generalisability of the ML models across projects. To address such challenges, this paper targets at the Function-level vulnerability discovery in the cross-project scenario. A Function Representation learning method is proposed to obtain the high-level and generalizable Function Representations from the abstract syntax tree (AST). First, the serialized ASTs are used to learn project independence features. Then, a customized bi-directional LSTM neural network is devised to learn the sequential AST Representations from the large number of raw features. The new Function-level Representation demonstrated promising performance gain, using a unique dataset where we manually labeled 6000+ Functions from three open-source projects. The results confirm that the huge potential of the new AST-based Function Representation learning.
-
CCS - POSTER: Vulnerability Discovery with Function Representation Learning from Unlabeled Projects
Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017Co-Authors: Guanjun Lin, Jun Zhang, Wei Luo, Lei Pan, Yang XiangAbstract:In cybersecurity, vulnerability discovery in source code is a fundamental problem. To automate vulnerability discovery, Machine learning (ML) based techniques has attracted tremendous attention. However, existing ML-based techniques focus on the component or file level detection, and thus considerable human effort is still required to pinpoint the vulnerable code fragments. Using source code files also limit the generalisability of the ML models across projects. To address such challenges, this paper targets at the Function-level vulnerability discovery in the cross-project scenario. A Function Representation learning method is proposed to obtain the high-level and generalizable Function Representations from the abstract syntax tree (AST). First, the serialized ASTs are used to learn project independence features. Then, a customized bi-directional LSTM neural network is devised to learn the sequential AST Representations from the large number of raw features. The new Function-level Representation demonstrated promising performance gain, using a unique dataset where we manually labeled 6000+ Functions from three open-source projects. The results confirm that the huge potential of the new AST-based Function Representation learning.
