基于多模型假设检验的认知无线电频谱感知方法

提出一种基于序贯概率似然比多模型假设检验的认知无线电协作频谱感知方法,用于检测可能含有不同结构和参数不确定性的未知信号.传统的认知无线电协作频谱感知方法(如基于序贯概率似然比的单模型假设检验、M元假设检验等),仅限于处理已知信号分布,不考虑信号分布的不确定性,可能会造成检测误判.所提出方法不仅可以处理认知无线电信号分布模型的不确定性问题,而且可以得到满足错误概率约束的有效检测.对频谱感知的一个典型场景进行仿真实验,结果表明所提出基于序贯概率似然比多模型假设检验方法相对于传统方法的检测有效性.     

基于序贯蒙特卡罗方法的自适应红外目标提取算法

本文将红外目标提取阈值的计算问题看作系统状态估计问题,提出了一种基于序贯蒙特卡罗方法的红外目标提取算法。在序贯蒙特卡罗方法框架下,建立关于灰度—方差加权信息熵和像素点灰度值的阈值状态空间;建立随机漂移状态转移模型;综合了红外图像中灰度、信息熵、梯度、像素点的空间位置等信息,提出了定量描述红外目标分割效果的评价函数,并以此作为系统的观测模型;最后,将粒子的加权平均做为分割阈值的估计值。实验结果表明,该方法是有效且稳健的。     

一类快速匹配算法的模拟与比较

本文对于平均绝对差算法,序贯相似性检测算法以及分层序贯匹配法的匹配定位精度,运算速度和抗干扰性能进行了分析和比较。给出了序贯相似性检测算法中,自适应门限的选择方法。最后,在DPS8/52计算机系统上用三幅不同的航空照片进行了大量计算机模拟加以验证,为在实际应用过程中选用适当的算法提供了方便。     

基于相对熵的概率假设密度滤波器序贯蒙特卡罗实现方式

概率假设密度滤波器的典型序贯蒙特卡罗实现方式与粒子滤波类似,均是利用大量加权粒子估计多目标状态,典型实现方式是为每个期望目标分配固定数目的粒子,这导致较大的算法时间开销.鉴于此,建立了基于相对熵的序贯蒙特卡罗实现方式.首先计算两个不同规模粒子集合的相对熵,与预设阈值进行比较以确定粒子数目,从而动态调整粒子数目.仿真结果表明,所提出的实现方式提高了跟踪效率,在大部分时间步上优于典型实现方式.     

自适应伪蒙特卡罗算法及其在拟合太阳电池I-V曲线中的应用

文章分析了序贯数论优化算法用于太阳电池I-V曲线拟合时的不足之处,将遗传算法中自适应搜索的思想和序贯数论优化算法相结合提出了一种自适应伪蒙特卡罗算法。该算法的基本过程是:以太阳电池等效电路的数学模型为基础建立目标函数;在拟合时,利用自适应搜索算法来随机地确定收缩比,即,用第t次拟合中的计算结果来确定第t+1次的最优参数可取值范围,使各步中目标函数的参数空间构成一个最优决策序列。拟合结果表明,该算法比直接采用伪蒙特卡罗算法具有更少的计算量、更高的收敛性和鲁棒性。     

安全协议验证模型的高效自动生成

为了能高效地利用模型检测技术对安全协议进行分析与验证,提高工作效率,提出了一种适用范围广,自动化程度及验证效率均较高的建模算法。开发了一个基于该建模算法“网络安全协议验证模型生成系统”,该系统可高效地对安全协议进行分析与验证,系统在对攻击者建模时采用偏序规约、语法重定序及类型检查等优化策略以提高验证效率,有效地缓解了模型检测过程中的状态爆炸问题。     

基于序贯变化检测的DDoS攻击检测方法

给出了一种有效的DDoS攻击检测方法,将DDoS攻击的检测作为序贯变化检测的一个具体实例来分析,采用序贯变化检测算法--非参数CUSUM算法进行检测.方法具有计算量小、检测迅速准确、适用于不同网络环境和攻击模式的优点,有一定的实用价值.文章最后对两种典型的攻击模式进行了实际检测,全面评估了检测算法在不同DDoS攻击场景下的性能.     

基于序贯概率映射的组合导航自适应容错算法

基于联邦滤波的容错组合导航系统通常在判定故障时刻对故障子系统进行整体隔离,未充分考虑缓变故障影响的渐近变化与状态分量间的差异。为此,提出一种基于序贯概率映射的组合导航自适应容错算法。该算法通过子滤波器新息动态映射建立局部估计状态统计模型,通过序贯概率比检测在线估计缓变故障影响下的局部状态质量,并据此对融合过程进行自适应调节。仿真结果表明,所提出的方法能有效提高组合导航系统对缓变故障的自适应容错调节能力。     

自适应伪蒙特卡罗算法及其在拟合太阳电池Ⅰ-Ⅴ曲线中的应用

文章分析了序贯数论优化算法用于太阳电池Ⅰ-Ⅴ曲线拟合时的不足之处,将遗传算法中自适应搜索的思想和序贯数论优化算法相结合提出了一种自适应伪蒙特卡罗算法.该算法的基本过程是以太阳电池等效电路的数学模型为基础建立目标函数;在拟合时,利用自适应搜索算法来随机地确定收缩比,即,用第t次拟合中的计算结果来确定第t+1次的最优参数可取值范围,使各步中目标函数的参数空间构成一个最优决策序列.拟合结果表明,该算法比直接采用伪蒙特卡罗算法具有更少的计算量、更高的收敛性和鲁棒性.     

认知无线电中基于分簇的能量序贯协作检测算法

在认知无线电系统中,频谱感知技术是保证频谱资源分配以及重构网络参数的前提条件。针对复杂信道环境低信噪比下传统频谱感知技术感知性能恶化、感知时间变长和对授权用户位置不固定适应性差的问题,提出基于分簇的并行能量序贯检测的方法。该方法按照地理位置信息划分簇,按照减少传输误差和节省传输功率原则选取簇头,各簇分段并行计算似然比统计量的方法进行序贯检测,任一簇内判决成功即可结束检测。仿真结果表明,在低信噪比情况下,该算法相较现有的多节点分段序贯检测和单节点序贯检测可以有效缩短检测时间。分析分簇数对所提算法系统综合性能的影响,结果表明信噪比越低,选择分簇数越大,越能降低检测时间。     

基于重要抽样的软件统计测试加速

本文提出一种基于重要抽样的软件统计测试加速方法，该方法通过调整软件Markov链使用模型的迁移概率，在根据统计测试结果得到软件可靠性无偏估计的前提下，可以有效提高安全攸关软件的测试效率，部分解决了安全攸关软件统计测试时间和费用开销过大的问题。同时，本文给出了计算优化迁移概率的模拟退火算法。实验仿真结果表明，该方法可以有效地提高安全攸关软件统计测试的效率。     

一种面向CPS的自适应统计模型检测方法

随着计算机与物理环境的交互日益密切,信息物理融合系统（cyber physical systems,CPSs）在健康医疗、航空电子、智能建筑等领域有着广泛的应用前景,CPSs的正确性、可靠性分析已引起人们的广泛关注.统计模型检测（statistical model checking,SMC）技术能够对CPSs进行有效验证,并为系统的性能提供定量评估.然而,随着系统规模的日益扩大,如何提高统计模型检测技术验证CPSs的效率,是目前所面临的主要困难之一.针对此问题,本文首先对现有SMC技术进行实验分析,总结各种SMC技术的受限适用范围和性能缺陷,并针对贝叶斯区间估计算法（Bayesian Interval Estimate,BIE）在实际概率接近0.5时需要大量路径才能完成验证的缺陷,提出一种基于抽象和学习的统计模型检测方法AL-SMC.该方法采用了主成分分析、前缀树约减等技术,对仿真路径进行学习和抽象,以减少样本空间.接着,提出了一个面向CPS的自适应SMC算法框架,可根据不同的概率区间自动选择AL-SMC算法或者BIE算法,有效应对不同情况下的验证问题.最后,结合经典案例进行实验分析,实验结果表明自适应SMC算法框架能够在一定误差范围内有效提高CPSs统计模型检测的效率,为CPSs的分析验证提供了一种有效的途径.     

面向复杂随机系统的启发式统计模型检测方法 *

统计模型检测是一种高效的验证技术,常用于复杂的随机系统验证,如分布式算法等。而在超长路径上对性质进行验证时,其验证效率会急剧降低。为解决这个问题,这里提出一种启发式的统计模型检测算法。在对路径进行验证时,我们会查找帮助剪枝的最短前缀。并在后续抽样时,利用前缀信息直接判定路径是否满足给定性质,避免进入费时的路径验证阶段。在与PRISM的比较中,它的路径验证次数相对更少且平均抽样路径长度更短。因此使统计模型检测技术可应用于超长路径上的性质验证。     

MCMC methods to approximate conditional predictive distributions

Sampling from conditional distributions is a problem often encountered in statistics when inferences are based on conditional distributions which are not of closed-form. Several Markov chain Monte Carlo (MCMC) algorithms to simulate from them are proposed. Potential problems are pointed out and some suitable modifications are suggested. Approximations based on conditioning sets are also explored. The issues are illustrated within a specific statistical tool for Bayesian model checking, and compared in an example. An example in frequentist conditional testing is also given.     

Statistical model checking of Timed Rebeca models

The actor-based language, Timed Rebeca, was introduced to model distributed and asynchronous systems with timing constraints and message passing communication. A toolset was developed for automated translation of Timed Rebeca models to Erlang. The translated code can be executed using a timed extension of McErlang for model checking and simulation. In this work, we added a new toolset that provides statistical model checking of Timed Rebeca models. Using statistical model checking, we are now able to verify larger models against safety properties compared to McErlang model checking. We examine the typical case studies of elevators and ticket service to show the efficiency of statistical model checking and applicability of our toolset.     

Open-Source Model Checking

We present GMC², a software model checker for GCC, the open-source compiler from the Free Software Foundation (FSF). GMC², which is part of the GMC static-analysis and model-checking tool suite for GCC under development at SUNY Stony Brook, can be seen as an extension of Monte Carlo model checking to the setting of concurrent, procedural programming languages. Monte Carlo model checking is a newly developed technique that utilizes the theory of geometric random variables, statistical hypothesis testing, and random sampling of lassos in Büchi automata to realize a one- sided error, randomized algorithm for LTL model checking. To handle the function call/return mechanisms inherent in procedural languages such as C/C++, the version of Monte Carlo model checking implemented in GMC² is optimized for pushdown-automaton models. Our experimental results demonstrate that this approach yields an efficient and scalable software model checker for GCC.     

Improved algorithm for tolerance allocation based on Monte Carlo simulation and discrete optimization

The allocation of design and manufacturing tolerances has a significant effect on both manufacturing cost and quality. This paper considers nonlinearly constrained tolerance allocation problems. The purpose is to minimize the ratio between the sum of the manufacturing costs (tolerances costs) and the risk (probability of the respect of geometrical requirements). The techniques of Monte Carlo simulation and genetic algorithm are adopted to solve these problems. As the simplest and the popular method for non-linear statistical tolerance analysis, the Monte Carlo simulation is introduced into the frame. Moreover, in order to make the frame efficient, the genetic algorithm is improved according to the features of the Monte Carlo simulation. An illustrative example (hyperstatic mechanism) is given to demonstrate the efficiency of the proposed approach.     

Formal modeling,performance estimation,and model checking of wireless sensor network algorithms in Real-Time Maude

The purpose of this paper is to show how the rewriting-logic-based Real-Time Maude language and tool can be used to formally model, simulate, and model check advanced wireless sensor network (WSN) algorithms. This is done by first proposing some general techniques for modeling and analyzing WSN algorithms, and then by showing how these techniques have been applied to the modeling, performance estimation, and model checking of the state-of-the-art optimal geographical density control (OGDC) density control algorithm. Wireless sensor networks in general, and the OGDC algorithm in particular, pose many challenges to their formal specification and analysis, including novel communication forms, spatial entities, time-dependent and probabilistic features, and the need to analyze both correctness and performance. We focus on Monte Carlo simulations to evaluate the performance of OGDC. Extensive simulations with up to 800 sensor nodes, and comparison with the ns-2 simulations of OGDC, indicate that Real-Time Maude simulations provide fairly accurate performance estimates of WSN algorithms. As a consequence, simulating the high-level Real-Time Maude model of a WSN algorithm eliminates the need for implementing it on a simulation tool to get a faithful estimate of its performance, while providing much greater flexibility in defining the appropriate simulation scenario; in addition, Real-Time Maude model checking can search for “corner case” bugs and evaluate best-case and worst-case performance. Some of the techniques presented in this paper are also used in an ongoing analysis effort of another state-of-the-art WSN algorithm.     

一种新变步长LMS算法及在自适应波束形成中的应用

LMS算法是智能天线自适应波束形成算法中的经典算法,由于其步长固定,造成收敛速度和稳态失调之间的矛盾。为了解决这一问题,提出一种新的变步长LMS算法,并在算法中引入误差信号的自相关估计,大大降低了噪声的干扰,对算法进行仿真,得到了最优的参数设置。利用蒙特卡罗方法对算法进行了性能评估,与传统的LMS算法、NLMS算法相比,新的变步长LMS算法具有更快的收敛速度和较小的稳态误差及优良的抗噪性能。     

Computer simulation of two continuous spin models using Wang-Landau-Transition-Matrix Monte Carlo algorithm

Monte Carlo simulation using a combination of Wang-Landau (WL) and Transition Matrix (TM) Monte Carlo algorithms to simulate two lattice spin models with continuous energy is described. One of the models, the one-dimensional Lebwohl-Lasher model has an exact solution and we have used this to test the performance of the mixed algorithm (WLTM). The other system we have worked on is the two-dimensional XY-model. The purpose of the present work is to test the performance of the WLTM algorithm in continuous models and to suggest methods for obtaining best results in such systems using this algorithm.