基于近似计算与多数覆盖技术的电路面积优化

针对基于最小项的近似计算技术不适合解决大规模电路面积优化问题,提出一种采用乘积项和逻辑覆盖的电路面积近似计算技术优化算法.利用基于乘积项的多数覆盖技术实现近似逻辑函数搜索,用逻辑覆盖不相交运算实现近似函数错误率计算,可以有效地避免因输入变量增加和最小项数量激增导致算法效率低下甚至无法工作的问题.文中算法用C编程并经MC...     

Proving Approximate Implementations for Probabilistic I/O Automata

In this paper we introduce the notion of approximate implementations for Probabilistic I/O Automata (PIOA) and develop methods for proving such relationships. We employ a task structure on the locally controlled actions and a task scheduler to resolve nondeterminism. The interaction between a scheduler and an automaton gives rise to a trace distribution—a probability distribution over the set of traces. We define a PIOA to be a (discounted) approximate implementation of another PIOA if the set of trace distributions produced by the first is close to that of the latter, where closeness is measured by the (resp. discounted) uniform metric over trace distributions. We propose simulation functions for proving approximate implementations corresponding to each of the above types of approximate implementation relations. Since our notion of similarity of traces is based on a metric on trace distributions, we do not require the state spaces nor the space of external actions of the automata to be metric spaces. We discuss applications of approximate implementations to verification of probabilistic safety and termination.     

Nonerasing, Counting, and Majority over the Linear Time Hierarchy

In this paper, we investigate several extensions of the linear time hierarchy (denoted by LTH). We first prove that it is not necessary to erase the oracle tape between two successive oracle calls, thereby lifting a common restriction on LTH machines. We also define a natural counting extension of LTH and show that it corresponds to a robust notion of counting bounded arithmetic predicates. Finally, we show that the computational power of the majority operator is equivalent to that of the exact counting operator in both contexts.     

一种基于近似因子的在线概率知识库推理方法

概率知识库中的推理技术是近年来的研究热点。目前大多数系统的推理主要基于批处理的方式实现,并不适用于在线查询场景。针对此本文提出了一种基于近似因子的在线概率知识库推理方法,它可重复利用已推断结果计算查询变量的边缘概率。该算法首先提取查询变量的子图（含已推断变量）;接着在此子图上添加近似因子以模拟子图外其余变量的影响;最后采用团树算法推断查询变量的边缘概率。实验结果表明,相较于已有算法,本文算法可在时间和精度上取得较好的权衡。.     

概率数据库中近似函数依赖挖掘算法

一个近似函数依赖(approximate functional dependency, AFD)是一个几乎成立的函数依赖,目前大部分工作仅限于从一般数据上挖掘近似函数依赖.有时数据是被组织成概率数据的形式,为了从挖掘概率数据中挖掘出可用的近似函数依赖,定义了概率近似函数依赖,它不同于任何一种以往的定义,并给出了在不确定数据中,置信概率的动态规划求解算法,由于动态规划算法复杂度较高,导出了候选依赖的概率下界来进行剪枝,随后给出了基于字典序的挖掘方法以及相应的剪枝策略,最后,在真实和合成的数据集上进行充分的实验,说明了挖掘算法的可扩展性和剪枝策略的高效性,并展示了有趣的挖掘结果.     

Optimal Time Bounds for Approximate Clustering

Clustering is a fundamental problem in unsupervised learning, and has been studied widely both as a problem of learning mixture models and as an optimization problem. In this paper, we study clustering with respect to the k-median objective function, a natural formulation of clustering in which we attempt to minimize the average distance to cluster centers. One of the main contributions of this paper is a simple but powerful sampling technique that we call successive sampling that could be of independent interest. We show that our sampling procedure can rapidly identify a small set of points (of size just O( $k \log \frac{n}{k}$ )) that summarize the input points for the purpose of clustering. Using successive sampling, we develop an algorithm for the k-median problem that runs in O(nk) time for a wide range of values of k and is guaranteed, with high probability, to return a solution with cost at most a constant factor times optimal. We also establish a lower bound of Ω(nk) on any randomized constant-factor approximation algorithm for the k-median problem that succeeds with even a negligible (say $\frac{1}{{100}}$ ) probability. The best previous upper bound for the problem was Õ(nk), where the Õ-notation hides polylogarithmic factors in n and k. The best previous lower bound of Ω(nk) applied only to deterministic k-median algorithms. While we focus our presentation on the k-median objective, all our upper bounds are valid for the k-means objective as well. In this context our algorithm compares favorably to the widely used k-means heuristic, which requires O(nk) time for just one iteration and provides no useful approximation guarantees.     

Probabilistic Duration Calculus for Continuous Time

This paper deals with dependability of imperfect implementations concerning given requirements. The requirements are assumed to be written as formulas in Duration Calculus. Implementations are modelled by continuous semi-Markov processes with finite state space, which are expressed in the paper as finite automata with stochastic delays of state transitions. A probabilistic model for Duration Calculus formulas is introduced, so that the satisfaction probabilities of Duration Calculus formulas with respect to semi-Markov processes can be defined, reasoned about and calculated through a set of axioms and rules of the model. Received November 1994 / Accepted in revised form June 1998     

Partial Order Reduction for Probabilistic Branching Time

In the past, partial order reduction has been used successfully to combat the state explosion problem in the context of model checking for non-probabilistic systems. For both linear time and branching time specifications, methods have been developed to apply partial order reduction in the context of model checking. Only recently, results were published that give criteria on applying partial order reduction for verifying quantitative linear time properties for probabilistic systems. This paper presents partial order reduction criteria for Markov decision processes and branching time properties, such as formulas of probabilistic computation tree logic. Moreover, we provide a comparison of the results established so far about reduction conditions for Markov decision processes.     

基于概率时间Petri网的流程推荐方法*

为了向用户推荐结构相似且时间效率较高的流程,提出了一种基于流程中活动发生的概率和时间的流程推荐方法。首先,定义了一个模型PTN(Probabilistic Time Petri Net )来表示流程;然后,改进了一个现有的流程相似度方法并且将它命名为MDS(Matrix Distance Similarity),用于找出流程库中与查询模型结构相似的流程集合;最后,提出了一个最小加权时间方法MWT(Minimum Weighted Time),用于找出流程集合中时间效率较高的业务流程。在此基础上设计了相关算法,并且分析了时间复杂度。实验数据证明了本文方法的有效性和高效性。     

多粒度时间下的近似周期挖掘研究

研究时态数据库中多粒度时间下的近似周期的挖掘问题。在多粒度时间、多粒度时问格式的基础上引入多粒度时间间隔的定义以及相关性质,构造多粒度近似周期模型,提出一个基于SOM聚类的多粒度近似周期的挖掘算法。利用高频股票数据580000宝钢JBT1进行实验,证明了该算法的有效性。     

多粒度时间下的近似周期挖掘研究

研究时态数据库中多粒度时间下的近似周期的挖掘问题。在多粒度时间、多粒度时间格式的基础上引入多粒度时间间隔的定义以及相关性质,构造多粒度近似周期模型,提出一个基于SOM聚类的多粒度近似周期的挖掘算法。利用高频股票数据580000宝钢JBT1进行实验,证明了该算法的有效性。     

Approximate Controllabilty of Semilinear Dynamic Equations on Time Scale

In this paper we study the approximate controllability of semilinear systems on time scale. In order to do so, we first give a complete characterization for the controllability of linear systems on time scale in terms of surjective linear operators in Hilbert spaces. Then we will prove that, under certain conditions on the nonlinear term, if the corresponding linear system is exactly controllable on , for any , then semilinear system on time scale is approximately controllable on .     

静态优先级实时任务的多项式时间近似分析

经典的硬实时任务响应时间分析及其各种基于初始值的递归改进无法适用交互的实时设计环境.高效的近似分析方法是一种有效的选择,提出能高效计算任务最差响应时间上限的方法并给出与精确调度的误差量化分析,定义响应时间分析的线性近似请求约束函数并由此提出一个具有ε参数多项式时间复杂度的死线约束分析方法.针对死线约束分析方法本文将采用经典的近似比率技术和资源增值技术来分析该方法所提供的性能保证的程度.随机任务集的相关实验证明了所提出近似方法的有效性.     

离散时滞系统的近似最优扰动抑制

研究了状态变量合有时滞的离散系统在外部扰动下的最优控制问题．通过引入一个灵敏度参数,将原系统的最优扰动抑制问题转化为一族不含超前项和时滞项的两点边值问题,并由此导出了最优扰动抑制控制器的这代近似设计方法．得到的最优扰动抑制控制律由解析的前馈一反馈项和伴随向量级数和形式的补偿项组成,截取伴随向量级数的有限和得到原系统的次优扰动抑制控制律．数值仿真表明该近似最优控制器对外部持续扰动具有良好的鲁棒性。     

通过自适应随机数据包标记实现实时IP回溯

随机数据包标记(PPM)是对拒绝服务攻击进行IP回溯的一种实用而有效的方法.提供了一种自适应的PPM算法:一个路由器按一个与路过的数据包已传输距离自适应的概率标记该数据包,从而被攻击者可以以最短的收敛时间重构一个攻击路径.通过一个新的称为标注片段编码的IP重载方案,实现了实时的重构,从而能同时回溯数千条路径.与以前的PPM方案相比,收敛时间减少了50%,同时大大减少了重构计算量和伪证性.     

On Approximate GCDs of Univariate Polynomials

In this paper, we consider computations involving polynomials with inexact coefficients, i.e. with bounded coefficient errors. The presence of input errors changes the nature of questions traditionally asked in computer algebra. For instance, given two polynomials, instead of trying to compute their greatest common divisor, one might now try to compute a pair of polynomials with a non-trivial common divisor close to the input polynomials. We consider the problem of finding approximate common divisors in the context of inexactly specified polynomials. We develop efficient algorithms for the so-called nearest common divisor problem and several of its variants.     

声探测网络延时概率多假设跟踪算法

针对利用同步固定声探测网络中的纯方位角延时量测数据对低空机动目标进行探测与跟踪的问题,为提高跟踪精度,根据期望最大化算法提出一种延时概率多假设目标跟踪算法,对网络中多个时刻各个传感器数据间的组合进行延时概率多假设建模,形成多假设联合量测,利用内嵌合适的贝叶斯平滑器对目标状态进行递归优化,最终得到目标状态的极大后验估计值.仿真结果表明算法能够有效解决声探测网络延时数据难以利用的问题,对低空机动目标的状态进行有效跟踪,满足估计精度要求,收敛速度快,鲁棒性强的特点.