New cumulative damage models for failure using stochastic processes as initial damage

Based on a generalized cumulative damage approach with a stochastic process describing initial damage for a material specimen, a broad class of statistical models for material strength is developed. Plausible choices of stochastic processes for the initial damage include Brownian motion, geometric Brownian motion, and the gamma process; and additive & multiplicative cumulative damage functions are considered. The resulting general statistical model gives an accelerated test form of the inverse Gaussian distribution, special cases of which include some existing models in addition to several new models. Model parameterizations & estimation by maximum likelihood from accelerated test data are discussed, and the applicability of the general model is illustrated for three sets of strength data. The proposed models are compared with the power-law Weibull model, and the inverse Gaussian generalized linear models.     

Constructive action of noise for impulsive noise removal in scalar images

A nonlinear variational approach to remove impulsive noise in scalar images is proposed. Taking benefit from recent studies on the use of stochastic resonance and the constructive role of noise in nonlinear processes, the process is based on the classical restoration process of Perona-Malik in which a Gaussian noise is purposely injected. It is shown that this new process can outperform the original restoration process of Perona-Malik.     

A fourth-order homogeneous random field in oceanography (Corresp.)

A mathematical treatment of the random field which we have used as a stochastic model for wind pressure exciting the ocean surface is presented. It is a composite stochastic process consisting of an infinite series of ordinary processes and point processes. It is shown that this is a well-defined homogeneous fourth-order random field.     

探测人体呼吸的多普勒搜救雷达数字信号处理

描述了相干多普勒雷达人体呼吸回波信号的试验研究结果,给出了信息处理的频谱与时域实现及其统计特性。试验表明,在微波频段的声音信号中,人体呼吸引起的回波信号相位调制产生的过程具有周期性关联随机过程的特性。基于等距栅格对该过程的统计特性进行计算,其周期与过程关联周期相等。文中提出了一种随机准则来估算关联周期。对基于等距栅格的过程的遍历性作了理论验证和试验证实。     

Interpretation of responses of visual cells of limulus

Assuming that visual responses are due to the action of particles on the membrane of the visual cells, the stochastic variability of the response should be a function of the number of particles producing it. Quantitative predictions can be made with the aid of a model proposed in previous articles. It is found that responses produced in visual cells of Limulus by absorption of a single photon have the stochastic properties which would be expected if the response to one photon were brought about by 25 particles. It is concluded from this that the processes leading to visual responses produce multiplication of particles. The effects of temperature and of metabolic poisons suggest that these processes are of chemical nature.     

Wavelet representations of stochastic processes and multiresolutionstochastic models

Deterministic signal analysis in a multiresolution framework through the use of wavelets has been extensively studied very successfully in recent years. In the context of stochastic processes, the use of wavelet bases has not yet been fully investigated. We use compactly supported wavelets to obtain multiresolution representations of stochastic processes with paths in L² defined in the time domain. We derive the correlation structure of the discrete wavelet coefficients of a stochastic process and give new results on how and when to obtain strong decay in correlation along time as well as across scales. We study the relation between the wavelet representation of a stochastic process and multiresolution stochastic models on trees proposed by Basseville et al. (see IEEE Trans. Inform. Theory, vol.38, p.766-784, Mar. 1992). We propose multiresolution stochastic models of the discrete wavelet coefficients as approximations to the original time process. These models are simple due to the strong decorrelation of the wavelet transform. Experiments show that these models significantly improve the approximation in comparison with the often used assumption that the wavelet coefficients are completely uncorrelated     

On the representation of a continuous stochastic intensity by Poisson shot noise

In many applications a Poisson shot noise (PSN) process is said to statistically "represent" its intensity process. In this paper an investigation is made of the relationship between a PSN process and its intensity, when the latter is a sample function of a continuous stochastic process. The difference of the moments and the mean-square difference between the two processes are examined. The continuity assumption on the intensity permits the development of a sequence of moment relationships in which the effect of the PSN parameters can be seen. The results simplify and afford some degree of physical interpretation when the component functions of the PSN are "rectangular," or when the intensity process does not vary appreciably over their time width. An integral equation is derived that defines the component function that minimizes the mean-square difference between the two processes. It is shown that a "degenerate" form of component function induces complete statistical equality of the two processes. The problem has application to optical communication systems using photodetectors.     

基于马尔可夫决策过程的维修决策模型研究

提出了一种基于设备状况进行维修的模型.该模型包括随机失效和针对失效进行有计划检查、维修的过程.运用马尔可夫决策模型,提出了以费用-效果为指标的维修策略.     

Wavelet decomposition of harmonizable random processes

The discrete wavelet decomposition of second-order harmonizable random processes is considered. The deterministic wavelet decomposition of a complex exponential function is examined, where its pointwise and bounded convergence to the function is proved. This result is then used for establishing the stochastic wavelet decomposition of harmonizable processes. The similarities and differences between the wavelet decompositions of general harmonizable processes and a subclass of processes having no spectral mass at zero frequency, e.g., those that are wide-sense stationary and have continuous power spectral densities, are also investigated. The relationships between the harmonization of a process and that of its wavelet decomposition are examined. Finally, certain linear operations such as addition, differentiation, and linear filtering on stochastic wavelet decompositions are considered. It is shown that certain linear operations can be performed term by term with the decomposition     

Modeling and analysis of stochastic differential equations driven by point processes

The modeling and analysis of nonlinear systems described by differential equations driven by point process noise are considered. The stochastic calculus of McShane is generalized to include such differential equations, and a more general canonical extension is defined. It is proved that this canonical extension possesses the same desirable properties for point process noise that it does for the noise processes, such as Brownian motion, considered by McShane. In addition, a new stochastic integral with respect to a point process is defined; this alternative integral obeys the rules of ordinary calculus. As a special case of the analysis of such systems, linear systems with multiplicative point process noise are investigated. The consistency of the canonical extension is studied by means of the product integral. Finally, moment equations and criteria for the stochastic stability of linear systems with multiplicative Poisson noise are derived.     

临近空间高超声速滑翔目标地理位置估计

为了提高临近空间高超声速滑翔目标在地心地固(ECEF)坐标系下地理位置的跟踪精度,基于目标的三自由度弹道方程对其轨迹进行分析,提出将轨迹投影到高度,经度,纬度三个坐标方向,在高度方向的轨迹衰减震荡,将加速度描述为零均值的正弦衰减震荡自相关随机过程,构造了一个位置滤波器,在经度和纬度方向的轨迹线性偏移,将角度的变化率描述为零均值的指数自相关随机过程,构造了两个角度子滤波器,将三个滤波器并行运行以实现目标的三维跟踪,仿真表明该算法具有较好的跟踪精度。     

Stochastic differential equations: an approach to the generation ofcontinuous non-Gaussian processes

The generation of continuous random processes with jointly specified probability density and covariation functions is considered. The proposed approach is based on the interpretation of the simulated process as a stationary output of a nonlinear dynamic system, excited by white Gaussian noise and described by a system of a first-order stochastic differential equations (SDE). The authors explore how the statistical characteristics of the equation's solution depends on the form of its operator and on the intensity of the input noise. Some aspects of the approximate synthesis of stochastic differential equations and examples of their application to the generation of non-Gaussian continuous processes are considered. The approach should be useful in signal processing when it is necessary to translate the available a priori information on the real random process into the language of its Markov model as well as in simulation of continuous correlated processes with the known probability density function     

Multiscale statistical signal processing: identification of amultiscale AR process from a sample of an ordinary signal

The theory of stochastic processes on homogeneous trees aims at contributing to the theory of multiresolution stochastic modeling and associated techniques of multiscale statistical signal processing. The author solves the problem of identifying such a multiscale process indexed by the nodes of a tree from the observation of this process on one single level of resolution. In particular he considers multiscale autoregressive processes, which are evolving by descending on a “hanging” homogeneous tree     

Relative Entropy Rate Based Multiple Hidden Markov Model Approximation

This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimization problem on the RER between joint laws describing the state and output processes (rather than the more usual RER between output processes). A suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the RER is reduced. These filter consistency and convergence bounds are the first results characterizing multiple HMM approximation performance and suggest that joint RER concepts provide a useful model selection criteria. The proposed model design process and MHMM filter are demonstrated on an important image processing dim-target detection problem.     

Method for Overflow Attack Defense of SDN Network Flow Table Based on Stochastic Differential Equation

In order to avoid the overflow problem of network flow table caused by hackers attacking the network in the process of using the network, a method for overflow attack defense of SDN network flow table based on stochastic differential equation is proposed. In this method, the stochastic differential equation is first proposed, and the drift coefficient and diffusion coefficient of the equation are expanded and adjusted by Taylor. By using the limit theorem, the spillover attack of SDN network is weakly converged to an approximate two-dimensional Markov diffusion process, and the improved stochastic differential equation is obtained. Then, according to the stochastic nature of SDN network attack, the stochastic differential equation is transformed into an amplitude equation, which is based on the amplitude. The equation establishes a SDN attack detection scheme based on flow table statistics, which detects the spillover attacks of SDN network flow tables. Finally, according to the test results, it is proposed to use other switches instead of network flow table overflow switches to control the data upload rate, thus reducing the possibility of network crash and meeting the attack defense requirements of flow table overflow. The simulation results show that the proposed method has better detection performance and shorter running time, and can provide help for network security related work.

     

Computer simulation of radio channels using a harmonicdecomposition technique

Based on a harmonic decomposition of stochastic processes, a new approach for approximating the time variant radio channel impulse response, given an arbitrary Doppler power density spectrum is presented. The basic idea is to use a Fourier expansion with uncorrelated coefficients to approximate the actual radio fading process. An upper bound on the mean square error between the actual and approximated fading as a function of the number of terms in the expansion is also given. The proposed method is particularly adapted for simulating broadband radio channels     

基于FDTD方法的无线信道衰落特性模拟研究

为了表述无线信道衰落的时变及远区分布特性，提出了利用时域有限差分(FDTD)方法研究由多径效应引起的时变无线信道衰落的方法。在此将无线信道的衰落建模为随机过程，而不是通常的确定性随机变量。首先基于Clarke统计模型，利用FDTD方法研究了时变无线信道的小尺度平坦衰落特征；然后利用Monte Carlo方法和FDTD方法产生了具有信道多普勒功率谱特性的色高斯随机过程，再基于无线信道的时变统计特性建模，用Suzuki模型研究了时变无线信道的快衰落；最后将无线信道衰落特性分布和理论分布做了比较，验证了方法的有效性。     

SOI MOSFET structure with a junction-type body contact forsuppression of pass gate leakage

A SOI MOSFET structure with a junction-type body contact [body-junctioned-to-gate (BJG)] is proposed to effectively suppress the parasitic bipolar effect in all kinds of MOSFETs including the pass transistor, which can be realized with compact design and simple processes. It utilizes the buried contact process to minimize the area consumption. Various on-chip test circuits have been fabricated to verify the BJG characteristics and it is shown that the proposed structure provides nearly perfect immunity against the circuit failure caused by the parasitic bipolar effect and an excellent speed performance, so that it can be a promising candidate for the SOI circuits     

随机工艺变化下互连线ABCD参数建模与仿真

集成电路的不断发展使得互连线的随机工艺变化问题已经成为影响集成电路设计与制造的重要因素。基于电报方程建立了工艺变化下互连线的分布参数随机模型,推导出互连线ABCD参数满足的随机微分方程组,并提出了基于蒙特卡洛法的互连线ABCD参数统计分析方法,通过对ABCD参数各参量系数的正态性进行偏度-峰度检验,给出了最差情况估计。实验结果表明所提出的互连线随机模型及统计分析方法可以对工艺变化下的互连线传输性能进行有效的评估。