一种基于序贯估计的直达声区水面舰船 被动测距方法 *

该文利用基于射线的盲解卷积方法，从直达声区的水面舰船噪声中提取出船和锚系于海底的垂直接收阵之间的时域信道响应，并利用直达波在不同阵元相对于参考阵元的到达时间差，通过序贯方法，利用射线模型和声速剖面信息，对水面舰船距接收阵的距离进行了估计。通过处理海深约为580 m的2016年美国圣巴巴拉海峡的实验数据，对1.6~3.5 km直达声区范围内Anna Maersk商船与垂直阵之间的距离进行了估计，验证了测距方法的有效性，并将结果与系统测量值和几何方法的估计值进行了比较。由于该方法不需要对海底参数进行估计，所以在海底参数未知时要优于传统匹配场方法；在声速剖面存在跃层且海底为多层分布的复杂信道条件下，该方法仍能对距离进行有效估计，且与测量值的相对误差在6%以内，小于几何方法的估计误差，测距结果精度较高。     

More efficient time integration for Fourier pseudospectral DNS of incompressible turbulence

Time integration of Fourier pseudospectral DNS is usually performed using the classical fourth-order accurate Runge-Kutta method or other second- or third-order methods, with a fixed step size. We investigate the use of higher-order Runge-Kutta pairs and automatic step size control based on local error estimation. We find that the fifth-order accurate Runge-Kutta pair of Bogacki and Shampine gives much greater accuracy at a significantly reduced computational cost. Specifically, we demonstrate speedups of 2× to 10× for the same accuracy. Numerical tests (including the Taylor-Green vortex, Rayleigh-Taylor instability, and homogeneous isotropic turbulence) confirm the reliability and efficiency of the method. We also show that adaptive time stepping provides a significant computational advantage for some problems (like the development of a Rayleigh-Taylor instability) without compromising accuracy.     

The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate.     

The effect of probe-Ohmic environment coupling type and probe information flow on quantum probing of the cutoff frequency

This work aims to compare results of dissipating and pure dephasing single-qubit probes in characterizing the cutoff frequency of a harmonic reservoir Ohmic spectral density. In particular, we proved that a dissipating single-qubit improves the estimation precision of the cutoff frequency estimator. The information backflow and outflow of the dissipating and the dephasing single-qubit are compared. The relation between the quantum Fisher information and the information outflow and backflow of the probe is obtained. The results show that difference between the values of information outflow and backflow of a dissipating single-qubit is larger than a dephasing single-qubit. So, a single-qubit probe extracts more information from an Ohmic reservoir by increasing the difference between the values of information outflow and backflow of the probe.     

一类改进最大熵方法的可调参数分析

在结构可靠性分析中,引入含可调参数的转换函数能对传统的最大熵方法进行改进,获得更高的失效概率预测精度。但是,此可调参数的最佳取值很难确定。针对这一问题,引入概率守恒方程,从功能函数转换前后所得概率密度函数出发,建立其最大熵值的变化关系,给出转换前后最大熵值之差的理论形式。通过对三种典型单调非线性转换函数开展算例研究,发现功能函数转换前后的最大熵值之差与转换函数的最佳可调参数值有关。改变可调参数值驱使最大熵值之差变化的同时,改进最大熵方法能遍历到更好的失效概率估计值。     

Bayesian Estimation and Statistical Analysis of Risk Measurements

The Bayesian model are established for the VaR and related risk measurements. The relationship between VaR and other risk measurements including expect shortfall, tail condition expectation and conditional value at risk are discussed. Furthermore, the Bayesian estimates and Bayesian predictors of these risk measurement are derived. Thirdly, the consistency and asymptotic normality in the exponential risk model are proved. Finally, the numerical simulation method is used to verify the convergence rate under different sample sizes.     

利用拖线阵运动特性的阵形估计方法

有效估计阵形是提高机动条件下拖线阵声呐探测性能的关键,流体力学类阵形估计方法稳定性和可靠性欠佳,导致其难以应用于工程实际,该文针对此问题提出一种基于拖线阵运动特性的阵形估计方法。利用稳态振荡响应公式计算拖船回转机动时拖线阵稳态阵形特性,将转向机动过程中阵上各点运动状态划分为若干阶段,进而依据偏微分方程特征线理论计算各阶段的分界时刻,探究阵上相邻两点的沿阵切线方向差变化规律,最后通过计算当前阵上各点的沿阵方向实现阵形估计。计算机仿真和海上实验数据验证表明算法可行且有效,与传统的流体力学类阵形估计方法相比具有更高的稳定性和更好的工程应用前景。     

基于数字非线性锁相环的相干态相位估计

基于量子理论获取相位参数的导航机制,理论上可以突破经典物理极限对导航精度的限制.利用量子零拍探测对相干态光场相位进行测量时,通常需要相位与之正交的本振光才能使测量精度达到量子标准极限.由于导航信号相位的高非线性特点,想要利用传统的线性锁相环获取完全满足条件的本振光具有一定的难度.为此,本文设计了一种基于容积准则的非线性锁相环,实现了在非正交本振光的条件下对相干态相位进行精确测量的功能.首先,利用相干态的Wigner函数推导了其相位在量子零拍探测的输出结果,设计了量子相位估计的非线性数字锁相环框架.然后基于正交单纯形容积准则设计了非线性滤波算法实现锁相环功能,该锁相环通过对本振相位进行多次状态更新,最终实现非线性迭代估计.实验结果表明,本文方法突破了本振光相位需与相干态相位正交的局限性,避免了传统量子锁相环方法引入的线性化误差,实现了对相干态相位的准确、稳定估计.     

Statistical inference for Vasicek-type model driven by Hermite processes

Let $Z$ denote a Hermite process of order $q\ge 1$ and self-similarity parameter $H\in (\frac{1}{2},1)$. This process is $H$-self-similar, has stationary increments and exhibits long-range dependence. When $q=1$, it corresponds to the fractional Brownian motion, whereas it is not Gaussian as soon as $q?2$. In this paper, we deal with a Vasicek-type model driven by $Z$, of the form $d{X}_t=a(b?X_t)dt+d{Z}_t$. Here, $a>0$ and $b\in \mathbb{R}$ are considered as unknown drift parameters. We provide estimators for $a$ and $b$ based on continuous-time observations. For all possible values of $H$ and $q$, we prove strong consistency and we analyze the asymptotic fluctuations.