Effect of weak randomness flaws on security evaluation of practical quantum key distribution with distinguishable decoy states

Quantum key distribution provides an unconditional secure key sharing method in theory,but the imperfect factors of practical devices will bring security vulnerabilities.In this paper,we characterize the imperfections of the sender and analyze the possible attack strategies of Eve.Firstly,we present a quantized model for distinguishability of decoy states caused by intensity modulation.Besides,considering that Eve may control the preparation of states through hidden variables,we evaluate the security of preparation in practical quantum key distribution(QKD)scheme based on the weak-randomness model.Finally,we analyze the influence of the distinguishability of decoy state to secure key rate,for Eve may conduct the beam splitting attack and control the channel attenuation of different parts.Through the simulation,it can be seen that the secure key rate is sensitive to the distinguishability of decoy state and weak randomness,especially when Eve can control the channel attenuation.

Slowly moving matter-wave gap soliton propagation in weak random nonlinear potential

We systematically investigate the motion of slowly moving matter wave gap solitons in a nonlinear potential, produced by the weak random spatial variation of the atomic scattering length. With the weak randomness, we construct an effective-particle theory to study the motion of gap solitons. Based on the effective-particle theory, the effect of the randomness on gap solitons is obtained, and the motion of gap solitons is finally solved. Moreover, the analytic results for the general behaviours of gap soliton motion, such as the ensemble-average speed and the reflection probability depending on the weak randomness are obtained. We find that with the increase of the random strength the ensemble-average speed of gap solitons decreases slowly where the reduction is proportional to the variance of the weak randomness, and the reflection probability becomes larger. The theoretical results are in good agreement with the numerical simulations based on the Cross-Pitaevskii equation.

A weak equivalence theorem for weak random elements with values in weakly compactly generated Banach spaces and its applications

1 Introduction and preliminaries The aim of this note is to prove the following basic theorem: Let (Ω, σ, u) be a probability space, (B, ‖·‖) a weakly compactly generated Banach space, and a mapping V from Ω to B be a weak random element, then there exists a unique strongly measurable random element V from Ω to B under the sense of almost sure equality such that (?) is weakly equivalent to the weak random dement V. This theorem itself not only removes the limitation that the weak random element considered in a theorem due to Lewis is bounded, but also has many applications to probability theory in Banach spacest. As an example of applications, we give a theorem of properties of the reproducing kernel Hilbert spaces for weak twofold weak random elements.

Complete Convergence and Weak Law of Large Numbers for <i><span style="text-decoration:overline;">ρ</span></i>-Mixing Sequences of Random Variables

In this paper, the complete convergence and weak law of large numbers are established for ρ-mixing sequences of random variables. Our results extend and improve the Baum and Katz complete convergence theorem and the classical weak law of large numbers, etc. from independent sequences of random variables to ρ-mixing sequences of random variables without necessarily adding any extra conditions.

SOLUTIONS FOR A SYSTEM OF NONLINEAR RANDOM INTEGRAL AND DIFFERENTIAL EQUATIONS UNDER WEAK TOPOLOGY

In this paper, a Darbao type random fixed point theorem for a system of weak continuous random operators with random domain is first proved. When, by using the theorem, some existence criteria of random solutions for a systems of nonlinear random Volterra integral equations relative to the weak topology in Banach spaces are given. As applications, some existence theorems of weak random solutions for the random Cauchy problem of a system of nonlinear random differential equations are obtained, as well as the existence of extremal random solutions and random comparison results for these systems of random equations relative to weak topology in Banach spaces. The corresponding results of Szep, Mitchell-Smith, Cramer-Lakshmikantham, Lakshmikantham-Leela and Ding are improved and generalized by these theorems.

弱非线性系统下的耗能减震结构随机地震响应分析

本文研究了结构刚度弱非线性和滞变弱非线性的耗能减震结构的随机地震响应问题。首先利用整体本构关系建立单自由度弱非线性耗能结构的运动方程;然后结合带支撑黏弹性阻尼器的等效阻尼原理和随机等效线性化法对其进行等效;最后基于FPK(Fokker-Planck-Kolmogorov)方程法推导出结构刚度弱非线性耗能结构随机地震响应值的解析解,基于等效Duffing体系法推导出结构刚度滞变弱非线性耗能结构随机地震响应值的解析解,并对这2种方法的误差进行了分析。算例分析表明:计算结果可靠、有效;相较于现有的随机等效线性化法的求解方法,FPK法和等效Duffing体系法具有更高的精度。

考虑大规模风电接入的电网薄弱区域动态无功需求评估

提出考虑大规模风电接入的电网薄弱区域动态无功需求评估方法。首先,采用随机矩阵理论对大规模风电场群接入的电力系统进行电压薄弱节点和薄弱区域辨识,若风电接入地区为电压薄弱区域,则令风电场群进入紧急电压控制预决策环节;然后,基于BP神经网络建立薄弱区域内暂态电压稳定指标计算模型,并在此基础上提出基于灵敏度的薄弱区域动态无功需求评估方法;最后,在风电场群接入的IEEE 10机39节点系统上对该文所提方法的有效性进行验证。

基于随机矩阵理论的电网薄弱环节辨识策略

随着电网规模不断扩大,新型电力系统的运行特性愈加复杂,准确辨识电网存在的薄弱环节对提高系统的监测能力和保障系统的可靠运行具有重要的实际意义。文中基于随机矩阵理论提出了一种电网薄弱环节辨识策略。该策略利用电网运行中的实测数据构造随机矩阵,基于随机矩阵理论构建薄弱环节的判断指标,并设计了薄弱节点和薄弱支路的判断方法。该方法从数据相关性的角度进行分析,不需要考虑实际电网复杂的网络结构和运行机理,因此没有复杂的建模过程。为了验证该方法的可行性,通过IEEE 39节点系统搭建了算例仿真,并与传统方法进行了对比。结果表明,所提策略在辨识薄弱节点和支路上具有较好效果,且准确性相较于传统方法有所提升。

AN ANALYSIS ABOUT BEHAVIOR OF EVOLUTIONARY ALGORITHMS:A KIND OF THEORETICAL DESCRIPTION BASED ON GLOBAL RANDOM SEARCH METHODS

Evolutionary computation is a kind of adaptive non--numerical computation method which is designed tosimulate evolution of nature. In this paper, evolutionary algorithm behavior is described in terms of theconstruction and evolution of the sampling distributions over the space of candidate solutions. Iterativeconstruction of the sampling distributions is based on the idea of the global random search of generationalmethods. Under this frame, propontional selection is characterized as a gobal search operator, and recombination is characerized as the search process that exploits similarities. It is shown-that by properly constraining the search breadth of recombination operators, weak convergence of evolutionary algorithms to aglobal optimum can be ensured.

SOME LIMIT THEOREMS FOR WEIGHTED SUMS OF ARRAYS OF NOD RANDOM VARIABLES

In this paper the authors study the complete, weak and almost sure convergence for weighted sums of NOD random variables and obtain some new limit theorems for weighted sums of NOD random variables, which extend the corresponding theorems of Stout [1], Thrum [2] and Hu et al. [3].

基于弱随机相机位姿图像的三维场景恢复

随着深度学习的快速发展,基于多视图的三维场景恢复研究和应用越来越广泛。许多研究者关注通过优化深度学习网络提高三维场景恢复效果,深度学习使用的训练数据集的相机位姿分布具有规范度高的内在特点。然而在实际应用中,普通用户拍摄目标场景时,相机位姿分布具有较大的随机性,难以保证获取到和训练数据集质量等同或接近的目标场景图像数据,从而影响恢复效果。为了缓解这一问题,该文提出了基于弱随机相机位姿图像的三维场景恢复方法,通过给用户提供目标场景拍摄建议,降低所获取目标场景图像相机位姿分布的随机性,提高场景的三维恢复效果。首先,用户在目标场景拍摄指导下,获得同一场景下不同视角的二维图像数据,然后通过SFM(Structure From Motion)恢复场景的三维稀疏点云和相机位姿,最后在MVS(Multi-View Stereo)网络模型中进行三维点云的稠密重建。实验结果表明,相比拍摄建议前,该方法有效降低了所获取目标场景图像相机位姿分布的随机性,三维场景恢复成功率提高了52.95%。

基于曲波变换 联合双边滤波的二维地震数据随机噪声去除方法研究

在地震数据去噪处理中,曲波变换硬阈值法容易导致弱同相轴模糊,为此,提出了一种基于曲波变换联合双边滤波的地震随机噪声去除方法。曲波系数尺度分析结果表明,粗尺度信号主要表征地震数据构造特征;中尺度信号通常包含大量的弱同相轴的信息;细尺度信号通常包含噪声信息。曲波变换联合双边滤波方法对粗尺度信号进行双边滤波处理,对中间尺度信号进行联合双边滤波处理,对细尺度信号应用硬阈值处理。曲波变换可以较好地处理多方向的线状变化特征,而联合双边滤波具备引导图,可以修复有效信息的关键特征,提高弱同相轴的连续性。叠前和叠后数值模拟测试和实际地震数据处理结果表明,曲波变换联合双边滤波方法的去噪结果优于曲波变换硬阈值法和小波变换方法,具有更高的信噪比和峰值信噪比。该方法克服了曲波变换硬阈值法的局限性,不仅能够较好地去除随机噪声,而且增强了弱同相轴的能量,提高了弱同相轴的连续性。

樽海鞘群行为启发的多机器人自主羽流寻源方法

针对室内复杂的气流模式和湍流污染物扩散,提出一种基于改进樽海鞘群算法的多机器人室内自主羽流寻源方法。该方法受到樽海鞘行为模式的启发,根据机器人不同的羽流检测浓度设置弱感知机器人在食物源位置局部高斯变异,提高搜索行为的多样性,以降低领导者机器人的搜索盲区;同时为追随者机器人位置更新引入随机扰动策略,增强寻源机器人在室内动态羽流环境中的适应性,以提高机器人极值区域的逃逸能力。通过数值模拟和真实室内有障碍物场景测试所提方法的性能,模拟结果表明,相较于其他方法,当机器人数量由3个增加到10个时,所提方法的羽流寻源成功率提高6%~80%,效率提高0.16~1.5;真实场景中的实验结果显示,3台机器人的羽流寻源成功率为73.3%。该方法在室内复杂和动态气流环境中具有可行性和适用性。

分级随机采样弱随机RRT算法及在移动机器人运动规划中的应用

针对移动机器人运动规划过程中,采用快速扩展随机树(RRT)算法采样效率低,寻找临近节点计算量大,及非线性反馈控制器不受系统模型动态约束的问题,提出一种新的基于分级随机采样与扩展的弱随机RRT算法,同时设计快速限幅非线性反馈控制器,保证运动规划过程中机器人始终满足系统模型动态约束。首先,在迭代伊始结合节点评价策略建立节点的选取集合;其次,按照规定顺序选取扩展节点并随机选择扩展方向,将计算得到的新子节点连接到随机树完成扩展;然后,对初始路径进行规划,采用快速限幅非线性反馈控制器计算机器人在路径点上的控制序列和位姿,实现移动机器人的运动规划;最后,通过仿真验证了该算法的有效性。结果表明:提出的分级随机采样弱随机RRT算法不依赖最近节点的选取,相比RRT算法缩短了求解时间,提高了迭代速度。

微弱回波条件下差分合成孔径激光雷达成像实验演示

本文针对差分合成孔径激光雷达(DSAL)未来应用场景具有回波信号微弱、平台与目标之间存在运动误差的特点,在微弱回波条件下进行了随机活塞运动目标的DSAL成像演示实验。采用波长为1 550nm的激光源,搭建了DSAL成像系统,接收孔径间距为188μm,目标距离为2.4m。通过在发射端加入偏振片对发射功率进行衰减,并利用步进线性平移台给目标到雷达之间的光程引入随机活塞运动误差。在激光发射功率约为50nW和20nW的情况下,对光程变化在[-5μm,5μm]的随机活塞运动目标进行DSAL成像实验,此时合成孔径激光雷达(SAL)图像由于随机相位误差而完全散焦,DSAL图像则聚焦良好。实验结果表明,在微弱回波条件下,DSAL系统仍能较好地消除相位误差,实现稳定成像。

考虑非期望产出弱可处置性的随机DEA模型

针对大量生产过程中存在非期望产出的现象及效率评价中数据包含随机性误差的事实,建立非期望产出的随机DEA模型.该模型通过引入风险的概念定义决策单元的占优,并应用统计学中的"相关性"概念刻画非期望产出的弱可处置性,在最优化理论的框架下,将两者结合起来,构建了可以同时考察两者的评价模型.在实证分析中,考察了该模型在不同随机误差水平下,在考虑与不考虑弱可处置性的情况下,模型评价结果的异同.结论表明:该模型可以同时解决非期望产出存在和数据包含随机误差的问题,有着广泛的适用性,模型的分析能力优于既有的模型.

基于高维随机矩阵理论的电网薄弱点评估方法

电网薄弱节点的识别是电力系统安全稳定分析领域的重要内容之一。为了准确辨识电网薄弱环节,从数据驱动的角度出发,提出一种综合高维随机矩阵理论及熵理论的评估方法。分析利用电网实时数据构成高维随机矩阵的处理方法,将随机矩阵理论的统计规律映射到电网实际的运行状态,最后综合随机矩阵理论中M-P定理(TheMarcenkoPastur Law)、圆环率、线性特征值统计量及熵理论评估电网受扰后的脆弱性,构造了电网薄弱点判断指标。该方法不需要识别系统结构,避免了对系统的建模过程,且准确性较现有文献有所提升。通过IEEE39节点系统的仿真结果,验证了所提识别方法的有效性。

自相关滤波法提取重磁场中弱异常

在每条测线内自动寻找剩余局部异常最大点处的观测值,将其作为参考模型,并沿整条测线进行滑动自相关处理,对重磁异常处理中的自相关滤波因子进行了改造。理论模型研究表明,改造后的滤波结果中零等值线能较好的划分异常边界,对横向叠加异常的分辨能力很强,能够提取常规方法很难识别的弱异常。对滤波窗口的研究表明,自相关半径选择3~6个网格节点距为佳。利用改造后的自相关滤波因子进行实际资料处理,发现了新的找矿远景区。

应用混沌系统实现弱信号的检测

在分析了混沌弱信号检测原理的基础上,提出了采用一组非线性微分方程组检测随机相位弱正弦信号的方法,并给出了理论证明.计算机模拟表明对随相弱正弦信号可以用16个方程检测到.

微弱次同步振荡模态参数的在线实时辨识方法

提出一种基于环境噪声激励响应的微弱次同步振荡模态参数在线辨识方法。将其用于电力系统正常运行中的转子旋转振动响应和电网侧电气响应,从中提取出反映次同步振荡各模态特征的单自度自由响应分量;进而利用Ibrahim时域模态辨识法计算出各模态的频率和阻尼系数等参数。研究了用于获取自由响应分量的随机减量技术的关键参数,给出门槛值和子样本长度的推荐值,从而提高了模态阻尼的辨识精度。通过对多模态微弱响应信号进行模态预分离,从而保证幅值相差很大时的小幅值模态无缺失,提高了模态辨识的可靠性。利用上述方法和步骤,从新疆哈密地区机网正常运行中的环境激励响应中辨识出微弱次同步振荡模态的阻尼因子等参数,并实现对模态阻尼趋势的在线连续实时辨识,尤其在当前大规模新能源并网背景下,能够及时发现影响系统稳定运行的关键环节,为切实做好广域动态稳定工作奠定基础。