In this paper,we propose a new non-Gaussian quantum state,termed the photon-modulated displaced thermal state(DTS).Using the operator ordering method,we obtain the normal ordering product of its density operator and t...In this paper,we propose a new non-Gaussian quantum state,termed the photon-modulated displaced thermal state(DTS).Using the operator ordering method,we obtain the normal ordering product of its density operator and then investigate its normalization,the negativity of its Wigner function and its non-Gaussianity.展开更多
This research considers the tracking problem of a moving target in distributed sensor networks with a limited sensing range(LSR)affected by non-Gaussian noise.In such sensor networks,observation loss due to LSR is a p...This research considers the tracking problem of a moving target in distributed sensor networks with a limited sensing range(LSR)affected by non-Gaussian noise.In such sensor networks,observation loss due to LSR is a prevalent issue that has received insufficient attention.We introduce a time-varying random variable to describe whether the sensor observes a moving target at each moment.When a single sensor node is unable to receive information from other nodes,it cannot update its state estimation of the moving target once the target moves beyond this node’s observation range.We propose an information flow topology within distributed sensor networks to facilitate the reception of prior state estimation data transmitted by neighboring nodes.Based on this information,a quadratic distributed estimator is designed for each sensor,and an output injection term is introduced to handle unstable systems.Finally,a numerical example is provided to illustrate the effectiveness of the proposed control scheme.展开更多
This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable...This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable to nonlinear systems with Gaussian white noise.The system is reformulated with intermediate variables to expand the application of nonlinear systems under unknown inputs and non-Gaussian noises,which help decompose unknown input estimation into residual tracking and state observation subproblems.By introducing the orthogonal principle of innovation and attenuation factor,the intermediate variables-based filter can improve the estimation performance under non-Gaussian noises and unknown inputs.Simulation results validate the effectiveness of the proposed method.展开更多
A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density （GSPHD） filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of ...A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density （GSPHD） filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of the states is Gaussian or can be identified as a Gaussian sum, the analytical results of the algorithm show that the posterior intensity at any subsequent time step remains a Gaussian sum under the assumption that the state noise, the measurement noise, target spawn intensity, new target birth intensity, target survival probability, and detection probability are all Gaussian sums. The analysis also shows that the existing Gaussian mixture probability hypothesis density （GMPHD） filter, which is unsuitable for handling the non-Gaussian noise cases, is no more than a special case of the proposed algorithm, which fills the shortage of incapability of treating non-Gaussian noise. The multi-target tracking simulation results verify the effectiveness of the proposed GSPHD.展开更多
The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this a...The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this assumption in practice. Since the seismic data （primaries and multiples） have a non-Gaussian distribution, in this paper we present an improved predictive deconvolution algorithm （IPD） by maximizing the non-Gaussianity of the recovered primaries. Applications of the IPD method on synthetic and real seismic datasets show that the proposed method obtains promising results.展开更多
Nonlinear dynamical systems are sometimes under the influence of random fluctuations. It is desirable to examine possible bifurcations for stochastic dynamical systems when a parameter varies.A computational analysis ...Nonlinear dynamical systems are sometimes under the influence of random fluctuations. It is desirable to examine possible bifurcations for stochastic dynamical systems when a parameter varies.A computational analysis is conducted to investigate bifurcations of a simple dynamical system under non-Gaussian a-stable Levy motions, by examining the changes in stationary probability density functions for the solution orbits of this stochastic system. The stationary probability density functions are obtained by solving a nonlocal Fokker-Planck equation numerically. This allows numerically investigating phenomenological bifurcation, or P-bifurcation, for stochastic differential equations with non-Gaussian Levy noises.展开更多
We propose three alternative measures for non-Gaussianity of quantum states: sine distance, Bures angle, and Bures distance, which are based on quantum fidelity introduced by Wang [Phys. Lett. A 373 58(2008)]. Using t...We propose three alternative measures for non-Gaussianity of quantum states: sine distance, Bures angle, and Bures distance, which are based on quantum fidelity introduced by Wang [Phys. Lett. A 373 58(2008)]. Using them, we evaluate the non-Gaussianity of some relevant single-mode and two-mode non-Gaussian states and find a good consistency of the three examined measures. In addition, we show that such metrics can exactly quantify the degree of Gaussianity of even Schrödinger-cat-like states of small amplitudes that can not be measured by other known non-Gaussianity measures such as the Hilbert–Schmidt metric and the relative entropy metric. We make a comparative study between all existing nonGaussianity measures according to the metric axioms and point out that the sine distance is the best candidate among them.展开更多
Non-Gaussianity of quantum states is a very important source for quantum information technology and can be quantified by using the known squared Hilbert–Schmidt distance recently introduced by Genoni et al.(Phys. Rev...Non-Gaussianity of quantum states is a very important source for quantum information technology and can be quantified by using the known squared Hilbert–Schmidt distance recently introduced by Genoni et al.(Phys. Rev. A 78 042327(2007)). It is, however, shown that such a measure has many imperfects such as the lack of the swapping symmetry and the ineffectiveness evaluation of even Schr?dinger-cat-like states with small amplitudes. To deal with these difficulties, we propose an improved measure of non-Gaussianity for quantum states and discuss its properties in detail. We then exploit this improved measure to evaluate the non-Gaussianities of some relevant single-mode non-Gaussian states and multi-mode non-Gaussian entangled states. These results show that our measure is reliable. We also introduce a modified measure for Gaussianity following Mandilara and Cerf(Phys. Rev. A 86 030102(R)(2012)) and establish a conservation relation of non-Gaussianity and Gaussianity of a quantum state.展开更多
A control method for Multi-Input Multi-Output（MIMO） non-Gaussian random vibration test with cross spectra consideration is proposed in the paper. The aim of the proposed control method is to replicate the specified ...A control method for Multi-Input Multi-Output（MIMO） non-Gaussian random vibration test with cross spectra consideration is proposed in the paper. The aim of the proposed control method is to replicate the specified references composed of auto spectral densities, cross spectral densities and kurtoses on the test article in the laboratory. It is found that the cross spectral densities will bring intractable coupling problems and induce difficulty for the control of the multioutput kurtoses. Hence, a sequential phase modification method is put forward to solve the coupling problems in multi-input multi-output non-Gaussian random vibration test. To achieve the specified responses, an improved zero memory nonlinear transformation is utilized first to modify the Fourier phases of the signals with sequential phase modification method to obtain one frame reference response signals which satisfy the reference spectra and reference kurtoses. Then, an inverse system method is used in frequency domain to obtain the continuous stationary drive signals. At the same time, the matrix power control algorithm is utilized to control the spectra and kurtoses of the response signals further. At the end of the paper, a simulation example with a cantilever beam and a vibration shaker test are implemented and the results support the proposed method very well.展开更多
During environment testing,the time histories of some dynamic environments follow non-Gaussian distribution.It is always assumed that the random vibration simulated follows Gaussian distribution,because the traditiona...During environment testing,the time histories of some dynamic environments follow non-Gaussian distribution.It is always assumed that the random vibration simulated follows Gaussian distribution,because the traditional digital random vibration control system can only supply the random vibration excitation signal of Gaussian.Yo simulate the real environment of product,a method is developed in this paper that can generate non-Gaussian random signal with specified power spectrum density(PSD),skewness and kurtosis by shot noise.In this way,non-Gaussian random vibration can be produced on traditional electrodynamic shaker.It solves the problems of spectral valley and energy shortage in low frequency on omni-axis shaker.At last,the wavelet is used to analyze the non-Gaussian signal.展开更多
In this work we construct a novel dissipaton-equation-of-motion （DEOM） theory in quadratic bath coupling environment, based on an extended algebraic statistical quasi-particle approach. To validate the new ingredien...In this work we construct a novel dissipaton-equation-of-motion （DEOM） theory in quadratic bath coupling environment, based on an extended algebraic statistical quasi-particle approach. To validate the new ingredient of the underlying dissipaton algebra, we derive an extended Zusman equation via a totally different approach. We prove that the new theory, if it starts with the identical setup, constitutes the dynamical resolutions to the extended Zusman equation. Thus, we verify the generalized （non-Gaussian） Wick＇s theorem with dissipatons-pair added. This new algebraic ingredient enables the dissipaton approach being naturally extended to nonlinear coupling environments. Moreover, it is noticed that, unlike the linear bath coupling case, the influence of a non-Gaussian environment cannot be completely characterized with the linear response theory. The new theory has to take this fact into account. The developed DEOM theory manifests the dynamical interplay between dissipatons and nonlinear bath coupling descriptors that will be specified. Numerical demonstrations will be given with the optical line shapes in quadratic coupling environment.展开更多
Based on the target scatterer density, the range-spread target detection of high-resolution radar is addressed in additive non-Gaussian clutter, which is modeled as a spherically invariant random vector. Firstly, for ...Based on the target scatterer density, the range-spread target detection of high-resolution radar is addressed in additive non-Gaussian clutter, which is modeled as a spherically invariant random vector. Firstly, for sparse scatterer density, the detection of target scatterer in each range cell is derived, and then an M/K detector is proposed to detect the whole range-spread target. Se- condly, an integrating detector is devised to detect a range-spread target with dense scatterer density. Finally, to make the best of the advantages of M/K detector and integrating detector, a robust detector based on scatterer density （DBSD） is designed, which can reduce the probable collapsing loss or quantization error ef- fectively. Moreover, the density decision factor of DBSD is also determined. The formula of the false alarm probability is derived for DBSD. It is proved that the DBSD ensures a constant false alarm rate property. Furthermore, the computational results indi- cate that the DBSD is robust to different clutter one-lag correlations and target scatterer densities. It is also shown that the DBSD out- performs the existing scatterer-density-dependent detector.展开更多
To validate the potential space-time adaptive processing （STAP） algorithms for airborne bistatic radar clutter suppression under nonstationary and non-Gaussian clutter environments, a statistically non-Gaussian, spa...To validate the potential space-time adaptive processing （STAP） algorithms for airborne bistatic radar clutter suppression under nonstationary and non-Gaussian clutter environments, a statistically non-Gaussian, space-time clutter model in varying bistatic geometrical scenarios is presented. The inclusive effects of the model contain the range dependency of bistatic clutter spectrum and clutter power variation in range-angle cells. To capture them, a new approach to coordinate system conversion is initiated into formulating bistatic geometrical model, and the bistatic non-Gaussian amplitude clutter representation method based on a compound model is introduced. The veracity of the geometrical model is validated by using the bistatic configuration parameters of multi-channel airborne radar measurement （MCARM） experiment. And simulation results manifest that the proposed model can accurately shape the space-time clutter spectrum tied up with specific airborne bistatic radar scenario and can characterize the heterogeneity of clutter amplitude distribution in practical clutter environments.展开更多
This paper presents a review of the various methods for the stationary non-Gaussian random vibration control.Random vibration tests can be divided,according to the number of exciters,in single-shaker tests and multipl...This paper presents a review of the various methods for the stationary non-Gaussian random vibration control.Random vibration tests can be divided,according to the number of exciters,in single-shaker tests and multiple-shaker tests.In the stationary non-Gaussian random vibration test,the time and frequency domain characteristics of the responses should be controlled independently and simultaneously.Skewness and kurtosis are usually selected as the nonGaussian time control references(targets)while power spectral density is the frequency domain control procedure before it recalls the concepts of non-Gaussianity.Then,the generation of a one frame stationary non-Gaussian random signal for both the single and multiple shakers are reviewed.The commonly used methods for the single non-Gaussian random signal generation in the random vibration test are memoryless nonlinear transformation,phase modification and Filtered Poisson process.For the multiple-shaker case,the sequential phase modification and memoryless nonlinear transformation are used to generate one frame coupled multi-channel non-Gaussian random signal.In order to obtain a stationary and consecutive dynamic input,the time domain randomization procedure is introduced with high computational efficiency and its influences on the skewness and kurtosis are analyzed.Finally,two existing problems in the non-Gaussian random vibration control are addressed.展开更多
This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities.The stochastic nonlinearities,which take th...This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities.The stochastic nonlinearities,which take the form of statemultiplicative noises,are introduced in systems to describe the phenomenon of nonlinear disturbances.To resist non-Gaussian noises,we consider a new performance index called maximum correntropy criterion(MCC)which describes the similarity between two stochastic variables.To enhance the“robustness”of the kernel parameter selection on the resultant filtering performance,the Cauchy kernel function is adopted to calculate the corresponding correntropy.The goal of this paper is to design a Kalman-type filter for the underlying systems via maximizing the correntropy between the system state and its estimate.By taking advantage of an upper bound on the one-step prediction error covariance,a modified MCC-based performance index is constructed.Subsequently,with the assistance of a fixed-point theorem,the filter gain is obtained by maximizing the proposed cost function.In addition,a sufficient condition is deduced to ensure the uniqueness of the fixed point.Finally,the validity of the filtering method is tested by simulating a numerical example.展开更多
Dynamical behavior of a tumor-growth model with coupling between non-Gaussian and Gaussian noise terms is investigated. The departure from the Gaussian noise can not only reduce the probability of tumor cells in the a...Dynamical behavior of a tumor-growth model with coupling between non-Gaussian and Gaussian noise terms is investigated. The departure from the Gaussian noise can not only reduce the probability of tumor cells in the active state, induce the minimum of the average tumor-cell population to move toward a smaller non-Gaussian noise, but also decrease the mean first-passage time. The increase of white-noise intensity can increase the tumor-cell population and shorten the mean first-passage time, while the coupling strength between noise terms has opposite effects, and the noise correlation time has a very small effect.展开更多
A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model ident...A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification. The tracked object is the statistical information of a given target probability density function （PDF）, rather than a deterministic signal. Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics. Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities （LMI） algorithms are proposed to fulfil the tracking problem. Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance. Simulations are given to demonstrate the efficiency of the proposed approach.展开更多
A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling...A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.展开更多
This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the ...This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the maximum correntropy criterion(MCC)is chosen to replace the conventional minimum mean square error criterion.Furthermore,the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function.Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.展开更多
Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality,gene flow(through pollen drift),and plant disease transmission(spore dispersion).Although Lagrangian stochas...Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality,gene flow(through pollen drift),and plant disease transmission(spore dispersion).Although Lagrangian stochastic(LS)models describe stochastic wind behaviors,such models assume that wind velocities follow Gaussian distributions.However,measured surface-layer wind velocities show a strong skewness and kurtosis.This paper presents an improved model,a non-Gaussian LS model,which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and kurtosis.Wind velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study,October 1999 experimental dataset and comparing the data with statistics from the original Gaussian model.Results show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model,such as the accuracy in the mean and variance of simulated velocities.This improvement also leads to better accuracy in friction velocity(i.e.,a coupling of three-dimensional velocities).The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis combinations.Moreover,improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle simulations.Thus,the non-Gaussian model is worth applying to wind field simulation in the surface layer.展开更多
基金supported by the Collaborative Innovation Project of University,Anhui Province(Grant No.GXXT-2022-088)。
文摘In this paper,we propose a new non-Gaussian quantum state,termed the photon-modulated displaced thermal state(DTS).Using the operator ordering method,we obtain the normal ordering product of its density operator and then investigate its normalization,the negativity of its Wigner function and its non-Gaussianity.
基金National Natural Science Foundation of China(No.61803081)。
文摘This research considers the tracking problem of a moving target in distributed sensor networks with a limited sensing range(LSR)affected by non-Gaussian noise.In such sensor networks,observation loss due to LSR is a prevalent issue that has received insufficient attention.We introduce a time-varying random variable to describe whether the sensor observes a moving target at each moment.When a single sensor node is unable to receive information from other nodes,it cannot update its state estimation of the moving target once the target moves beyond this node’s observation range.We propose an information flow topology within distributed sensor networks to facilitate the reception of prior state estimation data transmitted by neighboring nodes.Based on this information,a quadratic distributed estimator is designed for each sensor,and an output injection term is introduced to handle unstable systems.Finally,a numerical example is provided to illustrate the effectiveness of the proposed control scheme.
基金Supported by the Foreign Experts Project of the Belt and Road Innovative Talent Exchange(No.DL2023016005L).
文摘This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable to nonlinear systems with Gaussian white noise.The system is reformulated with intermediate variables to expand the application of nonlinear systems under unknown inputs and non-Gaussian noises,which help decompose unknown input estimation into residual tracking and state observation subproblems.By introducing the orthogonal principle of innovation and attenuation factor,the intermediate variables-based filter can improve the estimation performance under non-Gaussian noises and unknown inputs.Simulation results validate the effectiveness of the proposed method.
基金National Natural Science Foundation of China (60572023)
文摘A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density （GSPHD） filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of the states is Gaussian or can be identified as a Gaussian sum, the analytical results of the algorithm show that the posterior intensity at any subsequent time step remains a Gaussian sum under the assumption that the state noise, the measurement noise, target spawn intensity, new target birth intensity, target survival probability, and detection probability are all Gaussian sums. The analysis also shows that the existing Gaussian mixture probability hypothesis density （GMPHD） filter, which is unsuitable for handling the non-Gaussian noise cases, is no more than a special case of the proposed algorithm, which fills the shortage of incapability of treating non-Gaussian noise. The multi-target tracking simulation results verify the effectiveness of the proposed GSPHD.
基金National 863 Foundation of China(No.2006AA09A102-10)National Natural Science Foundation of China(No.40874056)NCET Fund
文摘The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this assumption in practice. Since the seismic data （primaries and multiples） have a non-Gaussian distribution, in this paper we present an improved predictive deconvolution algorithm （IPD） by maximizing the non-Gaussianity of the recovered primaries. Applications of the IPD method on synthetic and real seismic datasets show that the proposed method obtains promising results.
基金supported by the NSFC(10971225, 11171125, 91130003 and 11028102)the NSFH (2011CDB289)+1 种基金HPDEP (20114503 and 2011B400)the Cheung Kong Scholars Program and the Fundamental Research Funds for the Central Universities, HUST(2010ZD037)
文摘Nonlinear dynamical systems are sometimes under the influence of random fluctuations. It is desirable to examine possible bifurcations for stochastic dynamical systems when a parameter varies.A computational analysis is conducted to investigate bifurcations of a simple dynamical system under non-Gaussian a-stable Levy motions, by examining the changes in stationary probability density functions for the solution orbits of this stochastic system. The stationary probability density functions are obtained by solving a nonlocal Fokker-Planck equation numerically. This allows numerically investigating phenomenological bifurcation, or P-bifurcation, for stochastic differential equations with non-Gaussian Levy noises.
基金Project supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2021JJ30535)the Science and Technology Innovation Foundation for College Students in Hunan Province of China (Grant No. 2020RC1013)the Research Foundation for Young Teachers from the Education Department of Hunan Province of China (Grant No. 20B460)。
文摘We propose three alternative measures for non-Gaussianity of quantum states: sine distance, Bures angle, and Bures distance, which are based on quantum fidelity introduced by Wang [Phys. Lett. A 373 58(2008)]. Using them, we evaluate the non-Gaussianity of some relevant single-mode and two-mode non-Gaussian states and find a good consistency of the three examined measures. In addition, we show that such metrics can exactly quantify the degree of Gaussianity of even Schrödinger-cat-like states of small amplitudes that can not be measured by other known non-Gaussianity measures such as the Hilbert–Schmidt metric and the relative entropy metric. We make a comparative study between all existing nonGaussianity measures according to the metric axioms and point out that the sine distance is the best candidate among them.
基金the Natural Science Foundation of Hunan Province of China (Grant No. 2021JJ30535)the Research Foundation for Young Teachers from the Education Department of Hunan Province of China (Grant No. 20B460)。
文摘Non-Gaussianity of quantum states is a very important source for quantum information technology and can be quantified by using the known squared Hilbert–Schmidt distance recently introduced by Genoni et al.(Phys. Rev. A 78 042327(2007)). It is, however, shown that such a measure has many imperfects such as the lack of the swapping symmetry and the ineffectiveness evaluation of even Schr?dinger-cat-like states with small amplitudes. To deal with these difficulties, we propose an improved measure of non-Gaussianity for quantum states and discuss its properties in detail. We then exploit this improved measure to evaluate the non-Gaussianities of some relevant single-mode non-Gaussian states and multi-mode non-Gaussian entangled states. These results show that our measure is reliable. We also introduce a modified measure for Gaussianity following Mandilara and Cerf(Phys. Rev. A 86 030102(R)(2012)) and establish a conservation relation of non-Gaussianity and Gaussianity of a quantum state.
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutionsthe Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX17_0234)
文摘A control method for Multi-Input Multi-Output（MIMO） non-Gaussian random vibration test with cross spectra consideration is proposed in the paper. The aim of the proposed control method is to replicate the specified references composed of auto spectral densities, cross spectral densities and kurtoses on the test article in the laboratory. It is found that the cross spectral densities will bring intractable coupling problems and induce difficulty for the control of the multioutput kurtoses. Hence, a sequential phase modification method is put forward to solve the coupling problems in multi-input multi-output non-Gaussian random vibration test. To achieve the specified responses, an improved zero memory nonlinear transformation is utilized first to modify the Fourier phases of the signals with sequential phase modification method to obtain one frame reference response signals which satisfy the reference spectra and reference kurtoses. Then, an inverse system method is used in frequency domain to obtain the continuous stationary drive signals. At the same time, the matrix power control algorithm is utilized to control the spectra and kurtoses of the response signals further. At the end of the paper, a simulation example with a cantilever beam and a vibration shaker test are implemented and the results support the proposed method very well.
文摘During environment testing,the time histories of some dynamic environments follow non-Gaussian distribution.It is always assumed that the random vibration simulated follows Gaussian distribution,because the traditional digital random vibration control system can only supply the random vibration excitation signal of Gaussian.Yo simulate the real environment of product,a method is developed in this paper that can generate non-Gaussian random signal with specified power spectrum density(PSD),skewness and kurtosis by shot noise.In this way,non-Gaussian random vibration can be produced on traditional electrodynamic shaker.It solves the problems of spectral valley and energy shortage in low frequency on omni-axis shaker.At last,the wavelet is used to analyze the non-Gaussian signal.
基金This work was supported by the Ministry of Science and Technology of China （No.2017YFA0204904 and No.2016YFA0400904）, the National Natural Science Foundation of China （No.21633006 and No.21373191）, and the Fundamental Research Funds for Central Universities （No.2030020028）.
文摘In this work we construct a novel dissipaton-equation-of-motion （DEOM） theory in quadratic bath coupling environment, based on an extended algebraic statistical quasi-particle approach. To validate the new ingredient of the underlying dissipaton algebra, we derive an extended Zusman equation via a totally different approach. We prove that the new theory, if it starts with the identical setup, constitutes the dynamical resolutions to the extended Zusman equation. Thus, we verify the generalized （non-Gaussian） Wick＇s theorem with dissipatons-pair added. This new algebraic ingredient enables the dissipaton approach being naturally extended to nonlinear coupling environments. Moreover, it is noticed that, unlike the linear bath coupling case, the influence of a non-Gaussian environment cannot be completely characterized with the linear response theory. The new theory has to take this fact into account. The developed DEOM theory manifests the dynamical interplay between dissipatons and nonlinear bath coupling descriptors that will be specified. Numerical demonstrations will be given with the optical line shapes in quadratic coupling environment.
基金supported by the National Natural Science Foundation of China (61102166)the Scientific Research Foundation of Naval Aeronautical and Astronautical University for Young Scholars (HY2012)
文摘Based on the target scatterer density, the range-spread target detection of high-resolution radar is addressed in additive non-Gaussian clutter, which is modeled as a spherically invariant random vector. Firstly, for sparse scatterer density, the detection of target scatterer in each range cell is derived, and then an M/K detector is proposed to detect the whole range-spread target. Se- condly, an integrating detector is devised to detect a range-spread target with dense scatterer density. Finally, to make the best of the advantages of M/K detector and integrating detector, a robust detector based on scatterer density （DBSD） is designed, which can reduce the probable collapsing loss or quantization error ef- fectively. Moreover, the density decision factor of DBSD is also determined. The formula of the false alarm probability is derived for DBSD. It is proved that the DBSD ensures a constant false alarm rate property. Furthermore, the computational results indi- cate that the DBSD is robust to different clutter one-lag correlations and target scatterer densities. It is also shown that the DBSD out- performs the existing scatterer-density-dependent detector.
基金supported by the National Defense Advanced Research Foundation of China (51407020304DZ0223).
文摘To validate the potential space-time adaptive processing （STAP） algorithms for airborne bistatic radar clutter suppression under nonstationary and non-Gaussian clutter environments, a statistically non-Gaussian, space-time clutter model in varying bistatic geometrical scenarios is presented. The inclusive effects of the model contain the range dependency of bistatic clutter spectrum and clutter power variation in range-angle cells. To capture them, a new approach to coordinate system conversion is initiated into formulating bistatic geometrical model, and the bistatic non-Gaussian amplitude clutter representation method based on a compound model is introduced. The veracity of the geometrical model is validated by using the bistatic configuration parameters of multi-channel airborne radar measurement （MCARM） experiment. And simulation results manifest that the proposed model can accurately shape the space-time clutter spectrum tied up with specific airborne bistatic radar scenario and can characterize the heterogeneity of clutter amplitude distribution in practical clutter environments.
基金co-supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,china。
文摘This paper presents a review of the various methods for the stationary non-Gaussian random vibration control.Random vibration tests can be divided,according to the number of exciters,in single-shaker tests and multiple-shaker tests.In the stationary non-Gaussian random vibration test,the time and frequency domain characteristics of the responses should be controlled independently and simultaneously.Skewness and kurtosis are usually selected as the nonGaussian time control references(targets)while power spectral density is the frequency domain control procedure before it recalls the concepts of non-Gaussianity.Then,the generation of a one frame stationary non-Gaussian random signal for both the single and multiple shakers are reviewed.The commonly used methods for the single non-Gaussian random signal generation in the random vibration test are memoryless nonlinear transformation,phase modification and Filtered Poisson process.For the multiple-shaker case,the sequential phase modification and memoryless nonlinear transformation are used to generate one frame coupled multi-channel non-Gaussian random signal.In order to obtain a stationary and consecutive dynamic input,the time domain randomization procedure is introduced with high computational efficiency and its influences on the skewness and kurtosis are analyzed.Finally,two existing problems in the non-Gaussian random vibration control are addressed.
基金supported in part by the National Natural Science Foundation of China(62273088,62273087)the Shanghai Pujiang Program of China(22PJ1400400)the Program of Shanghai Academic/Technology Research Leader(20XD1420100)。
文摘This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities.The stochastic nonlinearities,which take the form of statemultiplicative noises,are introduced in systems to describe the phenomenon of nonlinear disturbances.To resist non-Gaussian noises,we consider a new performance index called maximum correntropy criterion(MCC)which describes the similarity between two stochastic variables.To enhance the“robustness”of the kernel parameter selection on the resultant filtering performance,the Cauchy kernel function is adopted to calculate the corresponding correntropy.The goal of this paper is to design a Kalman-type filter for the underlying systems via maximizing the correntropy between the system state and its estimate.By taking advantage of an upper bound on the one-step prediction error covariance,a modified MCC-based performance index is constructed.Subsequently,with the assistance of a fixed-point theorem,the filter gain is obtained by maximizing the proposed cost function.In addition,a sufficient condition is deduced to ensure the uniqueness of the fixed point.Finally,the validity of the filtering method is tested by simulating a numerical example.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11005077, 11105095, and 11074184)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (Grant No. 10KJD140003)
文摘Dynamical behavior of a tumor-growth model with coupling between non-Gaussian and Gaussian noise terms is investigated. The departure from the Gaussian noise can not only reduce the probability of tumor cells in the active state, induce the minimum of the average tumor-cell population to move toward a smaller non-Gaussian noise, but also decrease the mean first-passage time. The increase of white-noise intensity can increase the tumor-cell population and shorten the mean first-passage time, while the coupling strength between noise terms has opposite effects, and the noise correlation time has a very small effect.
基金supported by National Natural Science Foundationof China (No. 60472065, No. 60774013).
文摘A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification. The tracked object is the statistical information of a given target probability density function （PDF）, rather than a deterministic signal. Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics. Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities （LMI） algorithms are proposed to fulfil the tracking problem. Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance. Simulations are given to demonstrate the efficiency of the proposed approach.
基金the Korea Research Foundation Grant Funded by the Korean Government (MOEHRD) (KRF-2007-331-D00089) Funded by Seoul Development Institute (CS070160)
文摘A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.
基金Rahul Radhakrishnan received the B.Tech.degree in Applied Electronics and Instrumentation from the Government Engineering College,Calicut,India,in 2010 and the M.Tech.degreein Control Systems from the Department of Electrical Engineering,National Institute of Technology Kurukshetra,India,in 2013.He received the Ph.D.degree from the Department of Electrical Engineering,Indian Institute of Technology Patna,India,in 2018.Currently,he is workingasan Assistant Professor in the Department of Electrical Engineering,Sardar Vallabhbhai National Institute of Technology,Surat,Gujarat,India.His main research interests include nonlinear filtering,aerospace,and underwater target tracking.
文摘This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the maximum correntropy criterion(MCC)is chosen to replace the conventional minimum mean square error criterion.Furthermore,the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function.Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.
基金financial support for this research from a USDA-AFRI Foundational Grant (Grant No. 2012-67013-19687)from the Illinois State Water Survey at the University of Illinois at Urbana—Champaign
文摘Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality,gene flow(through pollen drift),and plant disease transmission(spore dispersion).Although Lagrangian stochastic(LS)models describe stochastic wind behaviors,such models assume that wind velocities follow Gaussian distributions.However,measured surface-layer wind velocities show a strong skewness and kurtosis.This paper presents an improved model,a non-Gaussian LS model,which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and kurtosis.Wind velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study,October 1999 experimental dataset and comparing the data with statistics from the original Gaussian model.Results show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model,such as the accuracy in the mean and variance of simulated velocities.This improvement also leads to better accuracy in friction velocity(i.e.,a coupling of three-dimensional velocities).The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis combinations.Moreover,improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle simulations.Thus,the non-Gaussian model is worth applying to wind field simulation in the surface layer.
