网络测量中自适应数据采集方法

抽样方法广泛地应用于网络测量与其他领域对被测总体的指标进行估计.研究表明,多种网络指标呈现重尾分布或自相似的特征.这些特性为准确估计总体指标带来了诸多困难.但同时,对被测网络指标进行建模也有着重要的应用.然而,建立精确网络模型是困难的.从时间序列拟合角度出发,提出了一种基于拟合的自适应抽样方法,对被测指标进行基于测量的建模.工作主要体现在: (1) 采用分段线性函数对被测指标进行逼近,建立基于测量的模型; (2) 与常用的抽样方法相比,在相同的样本数情况下,由拟合模型对指标进行的估计更准确、更稳定;通过对两个测量记录的分析表明,在与常用抽样方法保持相同的拟合误差时,自适应抽样方法明显地减少了所需采集的样本数量; (3) 与其他概率抽样方法相比,自适应抽样最终抽取的样本数更稳定、更可靠,并给出了最终样本数的概率分布.     

基于重要抽样法和神经网络的模糊鲁棒性分析

将重要抽样(IS)法与神经网络(NN)用于不确定控制系统的模糊鲁棒性分析中.IS法被用于提高当模糊不可接受性能的概率很小时的抽样效率,而NN被用于预测每次仿真试验中所需计算时间较长的性能指标值.所建议方法降低了标准MonteCarlo仿真(MCS)方法在处理模糊鲁棒性分析中小概率事件以及性能指标计算时间较长所带来的过高计算成本.最后,仿真结果验证了方法的有效性.     

基于时序距离的AS级Internet动态性测量方法

随着域间路由安全问题日益突出,AS(Autonomous System)级互联网的动态性测量开始成为研究热点。针对当前的测量方法无法全面度量AS级互联网演化规律的问题,提出了基于时序距离的AS可达距离(ASRD)、AS连通距离(ASCD)两个特征参数,分别从可达性和连通性两个方面度量AS级互联网在不同时刻的差异。通过分析不同时间跨度和时间粒度的路由表数据集,可以对特定AS的动态性进行测量。实验结果表明,对ASRD和ASCD进行时序分析不仅能够准确检测AS级Internet异常事件,而且可以发现AS级Internet的长期演化规律。     

改进匹配方法的BFG-GMPHD滤波算法

针对高斯混合概率假设密度(GMPHD)滤波算法中的机动目标跟踪问题,提出了一种改进的最佳拟合高斯(BFG)与GMPHD结合的BFG-GMPHD算法.算法对BFG近似方式做出改进,通过匹配状态转移的均值和协方差矩阵来近似多个目标动态模型中的状态转移矩阵和过程噪声的协方差矩阵,实现了滤波器与不同动态模型的匹配;在对BFG分布进行递推时,引入了模型概率更新过程,解决了BFG仅依赖于先验信息的问题.仿真实验表明:改进后的算法能滤除传感器数据中的杂波干扰,有效匹配目标运动模型的变化,更加准确地估计出目标的数目和状态,提高了跟踪的性能.     

基于蜂巢状虚拟结构的单汇聚节点节能重定位策略

提出了一种基于蜂巢状虚拟结构的单汇聚节点节能重定位(HEESR)策略.此策略中整个网络覆盖区域被划分成多个蜂巢状虚拟网格结构.汇聚节点周期性地收集周边虚拟蜂巢网格的平均剩余能量和网格内单个节点的剩余能量,基于这些信息汇聚节点进行重定位.为进一步提高能量效率,HEESR策略还采用基于剩余能量水平的通信半径调整机制.仿真实验表明:与现有几种汇聚节点重定位方法相比,HEESR能够更有效地降低能量消耗、延长网络寿命.     

多输出弹性k-旋转对称布尔函数的存在性与构造

多输出布尔函数可由多个单输出布尔函数表示,在分组密码中有着广泛的应用.多输出k-旋转对称布尔函数(k-RSBF)是多输出旋转对称布尔函数(RSBF)的扩展.本文首先研究多输出旋转对称函数和多输出k-旋转对称函数的轨道分布情况,给出了计算两类函数中长度相同轨道个数的方法.其次研究了平衡多输出k-旋转对称布尔函数的存在性,给出了在选择合适的k的前提下,n=pr、n=2pr和n=2r时,平衡(n,m)k-RSBF的构造方法.之后研究弹性多输出k-旋转对称布尔函数的存在性,分别给出了r≥3,n=2r,2≤m≤2r-r,k=2时1阶弹性(n,m)k-RSBF的构造方法,以及p为奇素数,r≥2,n=pr,2≤m≤p-1,k=p时1阶弹性(n,m)k-RSBF的构造方法.最后我们还对两种方法得到的1阶弹性多输出k-旋转对称布尔函数进行仿真测试.     

一种基于能量效率的WSN多跳簇生成算法

提出了一种基于能量效率的WSN多跳簇生成方法(MEET).根据节点剩余能量,簇内平均可达功率和节点到基站的平均距离竞争原则选择簇首,并采用最小通信代价的多跳分簇结构.通过仿真结果表明,MEET形成的簇首分布较为均匀,可利用最佳多跳路径节约传输能量,提高网络生存期,与LEACH相比有较大优势.     

基于IUPF的锂离子电池剩余使用寿命预测方法

随着新能源汽车的大力发展,其动力供应装置锂电池的健康评估也逐渐被重视。研究逐渐从关注锂电池的当前剩余电量转移到其剩余使用寿命的计算上。为有效准确地估计锂离子电池剩余使用寿命,本文提出一种基于改进的无迹粒子滤波（IUPF）的锂电池剩余使用寿命预测方法,通过对依托数据统计建立的锂离子电池状态方程和观测方程中的反映电池内阻的2个参数以及反映电池性能退化速率的2个参数进行估计,得到包含有失效时间的锂电池容量公式,并通过该公式计算出锂电池剩余使用寿命。利用美国国家航空航天局(NASA)艾姆斯预测数据库提供的锂离子电池寿命数据做相关的仿真验证,利用3种评价指标对该估计结果进行了性能评价,结果表明本文方法能够实现对锂离子电池剩余使用寿命的估计,而且能够提升UPF方法进行预测时的准确度。     

基于IMM多传感器顺序粒子滤波跟踪机动目标算法

针对交互式多模型粒子滤波在跟踪机动目标时精度受限问题,提出一种基于交互式多模型(IMM)的多传感器顺序粒子滤波算法。采用IMM机制实现目标运动模式的确认;在合理利用单传感器量测和多传感器量测中冗余和互补信息的基础上,引入顺序重抽样方法改善粒子分布,并将改善后的粒子应用于IMM粒子滤波算法框架。仿真实验结果表明:新算法能够估计出强机动目标状态,且精度明显优于标准IMM粒子滤波算法。     

基于节点剩余功率的多中继选择算法

提出一种基于节点剩余功率的多中继放大转发协同节点选择算法,根据节点信道状态信息(CSI)和剩余能量信息对网络生存时间进行优化,使用加权函数和信道容量增益门限进行多协同节点选择。仿真结果表明,对于动态和固定功率分配,该算法选择三四个中继可使协同通信系统性能达到最优;相对基于CSI的单中继选择算法,当中继数为4时,其在动态功率分配时的网络生存时间最高可延长82%。     

Two-layer symbolic representation for stochastic models with phase-type distributed events

Among the techniques that have been proposed for the analysis of non-Markovian models, the state space expansion approach showed great flexibility in terms of modelling capacities.The principal drawback is the explosion of the state space. This paper proposes a two-layer symbolic method for efficiently storing the expanded reachability graph of a non-Markovian model in the case in which continuous phase-type distributions are associated with the firing times of system events, and different memory policies are considered. At the lower layer, the reachability graph is symbolically represented in the form of a set of Kronecker matrices, while, at the higher layer, all the information needed to correctly manage event memory is stored in a multi-terminal multi-valued decision diagram. Such an information is collected by applying a symbolic algorithm, which is based on a couple of theorems. The efficiency of the proposed approach, in terms of memory occupation and execution time, is shown by applying it to a set of non-Markovian stochastic Petri nets and comparing it with a classical explicit expansion algorithm. Moreover, a comparison with a classical symbolic approach is performed whenever possible.     

Improved sampling techniques for the direct simulation Monte Carlo method

The direct simulation Monte Carlo (DSMC) method is a widely used approach for flow simulations having rarefied or nonequilibrium effects. It involves heavily to sample instantaneous values from prescribed distributions using random numbers. In this note, we briefly review the sampling techniques typically employed in the DSMC method and present two techniques to speedup related sampling processes. One technique is very efficient for sampling geometric locations of new particles and the other is useful for the Larsen-Borgnakke energy distribution.     

Computational system identification for Bayesian NARMAX modelling

In this contribution we derive a computational Bayesian approach to NARMAX model identification. The identification algorithm exploits continuing advances in computational processing power to numerically obtain posterior distributions for both model structure and parameters via sampling methods. The main advantage of this approach over other NARMAX identification algorithms is that for the first time model uncertainty is characterised as a byproduct of the identification procedure. The algorithm is based on the reversible jump Markov chain Monte Carlo (RJMCMC) procedure. Key features of the approach are (i) sampling of unselected model terms for testing for inclusion in the model (the birth move), which encourages global searching of the model term space, (ii) sampling of previously selected model terms for testing for exclusion from the model—a naturally incorporated pruning step (the death move), which leads to model parsimony, and (iii) estimation of model and parameter distributions, which are naturally generated in the Bayesian framework. We present a numerical example to demonstrate the algorithm and a comparison with a forward regression method: the results show that the RJMCMC approach is competitive and gives useful additional information regarding uncertainty in both model parameters and structure.     

适体和蛋白质解离常数检测方法比较分析

对基于石英晶体微天平（QCM）生物传感器的两种检测核酸适体与蛋白质解离常数方法进行了比较,提出来一种更加精确合理的检测流程。以凝血酶和凝血酶适体 TBA15为模型,耗散型石英晶体微天平（QCM-D）为传感器,实时检测末端修饰巯基适体的固定、表面封闭剂对非特异性结合位点封闭以及两种不同的蛋白进样方式引起的频率响应,实验数据拟合得到解离常数。不同蛋白的进样方式得到的解离常数不同,非特异性位点的封闭也同样影响解离常数的检测。从低浓度到高浓度依次通入固定体积蛋白的进样方式,实验重复性高且消耗样品量小于1μg,是较为理想的检测方式。     

Block-wise discretization accounting for structural constraints

This paper presents an approximate discretization method, named Mixed Euler-ZOH  (mE-ZOH), which can be applied to any continuous-time linear system. This method has been explicitly developed to preserve the system sparsity, a property that is particularly important when dealing with the analysis and design of distributed controllers for large-scale systems. In terms of stability preservation as a function of the sampling interval, we show that mE-ZOH  outperforms the classical forward Euler (fE) approach, which is the only known discretization method guaranteeing the preservation of sparsity for all possible sampling times. It is then shown that this new discretization method is capable of preserving stability for all sampling times for a wide classes of dynamical systems, including the important class of positive systems. Besides stability, also positivity of the resulting discrete-time system is preserved, contrarily to what happens for the fE approach. A couple of examples are reported to illustrate the main theoretical results of the paper.     

Synthesizing advanced transaction models using the situation calculus

The situation calculus is a versatile logic for reasoning about actions and formalizing dynamic domains. Using the non-Markovian action theories formulated in the situation calculus, one can specify and reason about the effects of database actions under the constraints of the classical, flat database transactions, which constitute the state of the art in database systems. Classical transactions are characterized by the so-called ACID properties. With non-Markovian action theories, one can also specify, reason about, and even synthesize various extensions of the flat transaction model, generally called advanced transaction models (ATMs). In this paper, we show how to use non-Markovian theories of the situation calculus to specify and reason about the properties of ATMs. In these theories, one may refer to past states other than the previous one. ATMs are expressed as such non-Markovian theories using the situation calculus. We illustrate our method by specifying (and sometimes reasoning about the properties of) several classical models and known ATMs.     

Model validation software for classification models using repeated partitioning: MVREP

The process of assessing the prediction ability of a computational model is called model validation. For models predicting a categorical response, the prediction ability is usually quantified by prediction measures such as sensitivity, specificity, and accuracy. This paper presents a software Model Validation using Repeated Partitioning (MVREP) that implements a computer-intensive, nonparametric approach to model validation, which we call the re-partitioning method. MVREP, developed using the SAS Macro language, repeats the process of randomly partitioning a dataset and subsequently performing standard model validation procedures, such as cross-validation, a large number of times and generates the empirical sampling distributions of prediction measures. The means of the sampling distributions serve as the point estimates of prediction measures of the model. The variances of the sampling distributions provide a direct assessment of variability for the point estimates of prediction measures. An example is presented using a mouse developmental toxicity chemical dataset to illustrate how the software can be used for the assessment of structure-activity relationships models.     

干涉型光纤陀螺的性能评价

为了减少干涉型光纤陀螺(IFOG)的误差并提高其精度,需要首先对陀螺仪进行性能评估,了解内部影响其性能的各种误差源。Allan方差分析是一种评估IFOG性能的有效方法,能对陀螺的各种误差源进行辨识。分析了目前国内常用的光纤陀螺性能评价方法,对Allan方差法在光纤陀螺的性能分析中的原理与作用进行了深入研究,并且提出了基于Allan方差法的FCST(fastclustersamplingtechnique)快速算法。最后,通过对实际干涉型光纤陀螺的采样数据进行了不同分析方法的性能评价,得出了令人满意的结论。     

Adaptive Hamiltonian MCMC sampling for robust visual tracking

Recent researches on visual tracking have shown significant improvement in accuracy by handling the large uncertainties induced by appearance variation and abrupt motion. Most studies concentrate on random walk based Markov chain Monte Carlo(MCMC) tracking methods which have shown inefficiency in sampling from complex and high-dimensional distributions. This paper proposes an adaptive Hamiltonian Monte Carlo sampling based tracking method within the Bayesian filtering framework. In order to suppress the random walk behavior in Gibbs sampling stage, the ordered over-relaxation method is used to draw the momentum item for the joint state variable. An adaptive step-size based scheme is used to simulate the Hamiltonian dynamics in order to reduce the simulation error and improve acceptance rate of the proposed samples. Furthermore, in designing the appearnce model, we introduce the locality sensitive histogram (LSH) to deal with appearance changes induced by illumination change. The proposed tracking method is compared with several state-of-the-art trackers using different quantitative measures: success rate and abruption capture rate. Extensive experimental results have shown its superiority to several other trackers.