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Masanori Arita - One of the best experts on this subject based on the ideXlab platform.

  • escape process and stochastic resonance under Noise Intensity fluctuation
    Physics Letters A, 2011
    Co-Authors: Yoshihiko Hasegawa, Masanori Arita
    Abstract:

    Abstract We study the effects of Noise Intensity fluctuations on the stationary and dynamical properties of an overdamped Langevin model with a bistable potential and external periodical driving force. We calculated the stationary distributions, mean-first passage time (MFPT) and the spectral amplification factor using a complete set expansion (CSE) technique. We found resonant activation (RA) and stochastic resonance (SR) phenomena in the system under investigation. Moreover, the strength of RA and SR phenomena exhibit non-monotonic behavior and their trade-off relation as a function of the squared variation coefficient of the Noise Intensity process. The reliability of CSE is verified with Monte Carlo simulations.

  • Noise Intensity fluctuation in langevin model and its higher order fokker planck equation
    Physica A-statistical Mechanics and Its Applications, 2011
    Co-Authors: Yoshihiko Hasegawa, Masanori Arita
    Abstract:

    In this paper, we investigate a Langevin model subjected to stochastic Intensity Noise (SIN), which incorporates temporal fluctuations in Noise-Intensity. We derive a higher-order Fokker–Planck equation (HFPE) of the system, taking into account the effect of SIN by the adiabatic elimination technique. Stationary distributions of the HFPE are calculated by using the perturbation expansion. We investigate the effect of SIN in three cases: (a) parabolic and quartic bistable potentials with additive Noise, (b) a quartic potential with multiplicative Noise, and (c) a stochastic gene expression model. We find that the existence of Noise-Intensity fluctuations induces an intriguing phenomenon of a bimodal-to-trimodal transition in probability distributions. These results are validated with Monte Carlo simulations.

  • Noise Intensity fluctuation in langevin model and its higher order fokker planck equation
    Physica A-statistical Mechanics and Its Applications, 2011
    Co-Authors: Yoshihiko Hasegawa, Masanori Arita
    Abstract:

    In this paper, we investigate a Langevin model subjected to stochastic Intensity Noise (SIN), which incorporates temporal fluctuations in Noise-Intensity. We derive a higher-order Fokker–Planck equation (HFPE) of the system, taking into account the effect of SIN by the adiabatic elimination technique. Stationary distributions of the HFPE are calculated by using the perturbation expansion. We investigate the effect of SIN in three cases: (a) parabolic and quartic bistable potentials with additive Noise, (b) a quartic potential with multiplicative Noise, and (c) a stochastic gene expression model. We find that the existence of Noise-Intensity fluctuations induces an intriguing phenomenon of a bimodal-to-trimodal transition in probability distributions. These results are validated with Monte Carlo simulations.

Huanfeng Shen - One of the best experts on this subject based on the ideXlab platform.

  • hyperspectral image denoising via Noise adjusted iterative low rank matrix approximation
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
    Co-Authors: Hongyan Zhang, Liangpei Zhang, Huanfeng Shen
    Abstract:

    Due to the low-dimensional property of clean hyperspectral images (HSIs), many low-rank-based methods have been proposed to deNoise HSIs. However, in an HSI, the Noise Intensity in different bands is often different, and most of the existing methods do not take this fact into consideration. In this paper, a Noise-adjusted iterative low-rank matrix approximation (NAILRMA) method is proposed for HSI denoising. Based on the low-rank property of HSIs, the patchwise low-rank matrix approximation (LRMA) is established. To further separate the Noise from the signal subspaces, an iterative regularization framework is proposed. Considering that the Noise Intensity in different bands is different, an adaptive iteration factor selection based on the Noise variance of each HSI band is adopted. This Noise-adjusted iteration strategy can effectively preserve the high-SNR bands and deNoise the low-SNR bands. The randomized singular value decomposition (RSVD) method is then utilized to solve the NAILRMA optimization problem. A number of experiments were conducted in both simulated and real data conditions to illustrate the performance of the proposed NAILRMA method for HSI denoising.

  • hyperspectral image denoising employing a spectral spatial adaptive total variation model
    IEEE Transactions on Geoscience and Remote Sensing, 2012
    Co-Authors: Qiangqiang Yuan, Liangpei Zhang, Huanfeng Shen
    Abstract:

    The amount of Noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the Noise Intensity in different bands is different, to better suppress the Noise in the high-Noise-Intensity bands and preserve the detailed information in the low-Noise-Intensity bands, the denoising strength should be adaptively adjusted with the Noise Intensity in the different bands. Meanwhile, in the same band, there exist different spatial property regions, such as homogeneous regions and edge or texture regions; to better reduce the Noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different spatial property regions should also be different. Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-spatial adaptive total variation (TV) model, in which the spectral Noise differences and spatial information differences are both considered in the process of Noise reduction. To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising. A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-spatial adaptive mechanism in the denoising process, and superior denoising results are produced.

Jiwook Jang - One of the best experts on this subject based on the ideXlab platform.

  • measuring tail dependence for aggregate collateral losses using bivariate compound cox process with shot Noise Intensity
    Social Science Research Network, 2010
    Co-Authors: Jiwook Jang
    Abstract:

    A catastrophic event such as flood, storm, hail, bushfire and earthquake brings about damages in properties, motors and interruption of businesses collaterally. Also a couple of losses incurred collaterally from the World Trade Centre (WTC) catastrophe, Hurricane Katrina and Victorian Bushfire. However it has not been developed a suitable model for insurance companies either to measure tail dependence between these collateral losses or relevant risk measures that can be used as insurance risk premiums. The first aim of this paper is to measure tail dependence between collateral losses as insurance industry is more concerned with dependence between extreme losses. The second is to calculate conditional probabilities and conditional expectations as relevant risk measures. To achieve these aims, we use bivariate compound process where a Cox process with shot Noise Intensity is used to count collateral losses from catastrophic events. Homogeneous Poisson process is also examined as its counterpart for the case where the catastrophic loss frequency rate is deterministic. Using a member of Farlie-Gumbel-Morgenstern copula with exponential margins, we derive explicit expressions of joint Laplace transforms of aggregate collateral losses. Fast Fourier transform is used to obtain the joint distributions of aggregate collateral losses, with which we calculate relevant risk measures. The figures of the joint distributions of collateral losses, their contours and numerical calculations of risk measures are provided.

  • the distribution of the interval between events of a cox process with shot Noise Intensity
    LSE Research Online Documents on Economics, 2008
    Co-Authors: Angelos Dassios, Jiwook Jang
    Abstract:

    Applying piecewise deterministic Markov processes theory, the probability generating function of a Cox process, incorporating with shot Noise process as the claim Intensity, is obtained. We also derive the Laplace transform of the distribution of the shot Noise process at claim jump times, using stationary assumption of the shot Noise process at any times. Based on this Laplace transform and from the probability generating function of a Cox process with shot Noise Intensity, we obtain the distribution of the interval of a Cox process with shot Noise Intensity for insurance claims and its moments, that is, mean and variance.

  • pricing of catastrophe reinsurance and derivatives using the cox process with shot Noise Intensity
    LSE Research Online Documents on Economics, 2003
    Co-Authors: Angelos Dassios, Jiwook Jang
    Abstract:

    We use the Cox process (or a doubly stochastic Poisson process) to model the claim arrival process for catastrophic events. The shot Noise process is used for the claim Intensity function within the Cox process. The Cox process with shot Noise Intensity is examined by piecewise deterministic Markov process theory. We apply the model to price stop-loss catastrophe reinsurance contract and catastrophe insurance derivatives. The asymptotic distribution of the claim Intensity is used to derive pricing formulae for stop-loss reinsurance contract for catastrophic events and catastrophe insurance derivatives. We assume that there is an absence of arbitrage opportunities in the market to obtain the gross premium for stop-loss reinsurance contract and arbitrage-free prices for insurance derivatives. This can be achieved by using an equivalent martingale probability measure in the pricing models. The Esscher transform is used for this purpose.

Zhen Jin - One of the best experts on this subject based on the ideXlab platform.

  • pattern dynamics of a spatial predator prey model with Noise
    Nonlinear Dynamics, 2012
    Co-Authors: Zhen Jin
    Abstract:

    A spatial predator–prey model with colored Noise is investigated in this paper. We find that the number of the spotted pattern is increased as the Noise Intensity is increased. When the Noise Intensity and temporal correlation are in appropriate levels, the model exhibits phase transition from spotted to stripe pattern. Moreover, we show the number of the spotted and stripe pattern, with respect to both Noise Intensity and temporal correlation. These studies raise important questions on the role of Noise in the pattern formation of the populations, which may well explain some data obtained in the ecosystems.

  • the role of Noise in a predator prey model with allee effect
    Journal of Biological Physics, 2009
    Co-Authors: Guiquan Sun, Zhen Jin, Quanxing Liu
    Abstract:

    The existence and implications of alternative stable states in ecological systems have been investigated extensively within deterministic models. However, it is known that natural systems are undeniably subject to random fluctuations, arising from either environmental variability or internal effects. Thus, in this paper, we study the role of Noise on the pattern formation of a spatial predator–prey model with Allee effect. The obtained results show that the spatially extended system exhibits rich dynamic behavior. More specifically, the stationary pattern can be induced to be a stable target wave when the Noise Intensity is small. As the Noise Intensity is increased, patchy invasion emerges. These results indicate that the dynamic behavior of predator–prey models may be partly due to stochastic factors instead of deterministic factors, which may also help us to understand the effects arising from the undeniable susceptibility to random fluctuations of real ecosystems.

Jianguo Tan - One of the best experts on this subject based on the ideXlab platform.

  • Stochastic resonance in a piecewise nonlinear model driven by multiplicative non-Gaussian Noise and additive white Noise
    Communications in Nonlinear Science and Numerical Simulation, 2016
    Co-Authors: Yongfeng Guo, Ya-jun Shen, Jianguo Tan
    Abstract:

    The phenomenon of stochastic resonance (SR) in a piecewise nonlinear model driven by a periodic signal and correlated Noises for the cases of a multiplicative non-Gaussian Noise and an additive Gaussian white Noise is investigated. Applying the path integral approach, the unified colored Noise approximation and the two-state model theory, the analytical expression of the signal-to-Noise ratio (SNR) is derived. It is found that conventional stochastic resonance exists in this system. From numerical computations we obtain that: (i) As a function of the non-Gaussian Noise Intensity, the SNR is increased when the non-Gaussian Noise deviation parameter q is increased. (ii) As a function of the Gaussian Noise Intensity, the SNR is decreased when q is increased. This demonstrates that the effect of the non-Gaussian Noise on SNR is different from that of the Gaussian Noise in this system. Moreover, we further discuss the effect of the correlation time of the non-Gaussian Noise, cross-correlation strength, the amplitude and frequency of the periodic signal on SR.

  • suprathreshold stochastic resonance in multilevel threshold system driven by multiplicative and additive Noises
    Communications in Nonlinear Science and Numerical Simulation, 2013
    Co-Authors: Yongfeng Guo, Jianguo Tan
    Abstract:

    Abstract The suprathreshold stochastic resonance in multithreshold neuronal networks system driven by multiplicative Gaussian Noise and additive Gaussian Noise is studied. The expression of the mutual information is derived, and the effects of the Noise Intensity and system parameter on mutual information are discussed. It is found that adjusting the additive Noise Intensity is more effective than adjusting the multiplicative Noise Intensity to enhance information transmission, and the more the number of devices, the more apparent the phenomenon of suprathreshold stochastic resonance. Moreover, we also found that the selection of threshold is very important in the process of information transmission.