Reflectance Models with Fast Importance Sampling

We introduce a physically plausible mathematical model for a large class of BRDFs. The new model is as simple as the well-known Phong model, but eliminates its disadvantages. It gives a good visual approximation for many practical materials: coated metals, plastics, ceramics, retro-reflective paints, anisotropic and retro-reflective materials, etc. Because of its illustrative properties it can be used easily in most commercial software and because of its low computational cost it is practical for virtual reality. The model is based on a special basic BRDF definition, which meets the requirements of reciprocity and of energy conservation. Then a class of BRDFs is constructed from this basic BRDF with different weight functions. The definition of such weight functions requires the user to specify the profile of the highlights, from which the weight function is obtained by derivation. It is also demonstrated how importance sampling can be used with the new BRDFs.     

SNS:一种快速无偏的分层图抽样算法

抽样作为一种有效的统计分析方法,常被用于大规模图数据分析领域以提升性能。现有的图抽样算法大多存在高度节点或低度节点过度入样的问题,较大程度地影响了算法的性能。复杂网络具有无标度特性,即节点的度服从幂律分布,节点个体之间存在较大差异。在基于点选择策略的抽样方法的基础上,通过结合节点的近似度分布策略,设计并实现了高效无偏的分层图抽样算法SNS。在3个真实的图数据集上的实验结果表明,SNS算法比其他图抽样算法保留了更多的拓扑属性,且执行效率比FFS更高。SNS算法在度的无偏性、抽样结果拓扑属性近似性方面的表现均优于现有算法。     

A Fast Procedure for Calculating Importance Weights in Bootstrap Sampling

Importance sampling is an efficient strategy for reducing the variance of certain bootstrap estimates. It has found wide applications in bootstrap quantile estimation, proportional hazards regression, bootstrap confidence interval estimation, and other problems. Although estimation of the optimal sampling weights is a special case of convex programming, generic optimization methods are frustratingly slow on problems with large numbers of observations. For instance, interior point and adaptive barrier methods must cope with forming, storing, and inverting the Hessian of the objective function. In this paper, we present an efficient procedure for calculating the optimal importance weights and compare its performance to standard optimization methods on a representative data set. The procedure combines several potent ideas for large-scale optimization.     

基于序贯重点采样粒子滤波和Cholesky分解的分布估计算法

连续域分布估计算法一般假设数据服从Gauss分布,而且大多采用了单峰的概率模型,但是对于一些复杂的优化问题,单峰的Gauss分布模型不能有效地描述解在空间的分布.提出一种基于序贯重点采样粒子滤波的分布估计算法,采用带权粒子描述优选集样本服从的概率分布,Cholesky分解法分解收缩的协方差矩阵并利用其产生下一代样本,不需要假设样本服从Gauss分布.算法采用的概率模型是多峰的.变量之间的相关性通过采样时利用群体的协方差矩阵显式地予以考虑,并对协方差矩阵为零矩阵的情况进行了处理.仿真实验结果验证了方法的正确性和有效性.     

一种估计网络可靠性的蒙特卡洛方法

将K—终端剩余连通可靠度的概念推广到链路存在失效状态的网络模型上,并提出了计算该可靠度的基于RVR的蒙特卡洛方法。首先对网络链路的状态进行抽样。在网络所有链路状态确定的前提下计算网络的K—终端剩余连通可靠度,即等价于计算相同拓扑结构,链路完全可靠网络的K—终端剩余连通可靠度,对于后者可采用H.Cancela等提出的基于RVR的蒙特卡洛算法。实践证明该估计方法是无偏的,且与原始的蒙特卡洛方法相比具有较小的方差。     

顺序蕴含图的状态编码方法

控制器综合中状态编码这个课题,已经有过不少的研究。人们都试图设计出能够得到“最佳编码”的方法或者是算法,来达到节省门电路、触发器电路,或者是起到简化组合逻辑电路的目的。该文在原有基于蕴含图算法的基础上,提出了一种求解状态编码的方法“顺序蕴含图方法(SequentialImplicationGraph,简称SIG)”,利用相邻关系的顺序,改善分配结果。     

基于软件测试的软件可靠性评价技术研究

提出了利用软件测试结果进行软件可靠性评价的技术和方法,并结合具体的软件项目,计算出了软件模块的可靠性以及软件的可靠性.该方法能够有效满足在软件开发的不同阶段对软件可靠性进行评价的需要.     

Fast and Stable Polynomial Equation Solving and Its Application to Computer Vision

This paper presents several new results on techniques for solving systems of polynomial equations in computer vision. Gröbner basis techniques for equation solving have been applied successfully to several geometric computer vision problems. However, in many cases these methods are plagued by numerical problems. In this paper we derive a generalization of the Gröbner basis method for polynomial equation solving, which improves overall numerical stability. We show how the action matrix can be computed in the general setting of an arbitrary linear basis for ?[x]/I. In particular, two improvements on the stability of the computations are made by studying how the linear basis for ?[x]/I should be selected. The first of these strategies utilizes QR factorization with column pivoting and the second is based on singular value decomposition (SVD). Moreover, it is shown how to improve stability further by an adaptive scheme for truncation of the Gröbner basis. These new techniques are studied on some of the latest reported uses of Gröbner basis methods in computer vision and we demonstrate dramatically improved numerical stability making it possible to solve a larger class of problems than previously possible.     

Sequential and Parallel Approaches to Incorporate Reliability in the Synthesis of Computer Networks

This paper presents a scenario-orientedoptimization model and solution algorithm to solve thejoint routing/capacity assignment problem for computernetworks. The advantage of this model is that failures are directly dealt with at the design stage.The model helps to find a suitable trade-off betweencapacity assignment and performance in the event offailures. As accounting for major failures can be very time consuming, we introduce parallelism as atool to solve this type of problem. Two parallelversions of the algorithm were implemented. Bothparallel versions were found to be extremely efficientin reducing computational time, the one presentingtwo levels of parallelization was found more suitablefor larger networks.     

基于状态转换符的时序电路冗余状态变换设计

在分析了时序电路冗余状态基础上,提出了基于状态转换符的时序电路冗余状态变换设计,可以把时序电路在４ 变量以内产生的冗余状态故障自动变换为合法状态,从而避免了状态验证和测试生成等较为繁琐的步骤,可以方便地设计出各种给定状态的具有自启动能力的时序电路,简洁方便、设计效率高,尤为适应采用Ｍａｔｌａｂ 程序对各类同步时序电路进行分析、化简和设计,具有较为实际的应用价值     

互联网络拓扑结构Cayley图可靠性容错延迟的研究

互联网的可靠性是评估一个网络性能的重要指标,而影响网络可靠性的因素又有很多其中容错性可以验证一个网络在发生故障时剩余网络的重组能力的强弱.在一个容错网络中当网络的结点和(或)连线发生故障时,使数据能畅通有效的传输且延迟时间尽可能短,这就需要有一个设计很好的路由选择ρ.而度量路由选择优劣的重要参数容错延迟的确定显得很重要,本文就证明了某些Cayley图存在路由选择使它的容错延迟能够达到最小值.     

一种不确定图中最可靠最大流问题的解决方案

最可靠最大流是不确定图中可靠性最高的最大流,它是传统最大流问题在不确定图上的自然延伸.现有的最可靠最大流算法SDBA时间复杂性较高,无法满足实际中不同应用的需求,为此,文中提出一种具有普遍适用性的最可靠最大流解决方案.该方案包含面向不同需求的3种算法:基于负权群落消去的NWCE算法、基于时间约束优先单环消去的SPEA-t算法和基于概率阈值约束优先单环消去的SPEA-p算法.其中,NWCE算法借鉴最小费用最大流的"流平移"思想并基于文中提出的负权群落概念,在辅助剩余图中不断地消去可使可靠性增加而流量不变的负权群落,可证当消去所有负权群落时对应的最大流即为最可靠最大流.根据负权群落中由单环组成的群落占很高比例且相对于多环组成的群落更易查找和消去的性质,同时考虑到NWCE算法为了获得最优解,往往为了消去最后少数几个对概率提高贡献很小的负权群落却花费了很长时间的现象,提出SPEA-t和SPEA-p两种快速近似算法,前者是以规定时间内尽可能逼近最优解为目标,后者是以最少时间达到预设的概率阈值为目标,它们都采用了优先消去概率-时间效益较好的单环群落的策略,加快对最优解的逼近速度,减少或放弃时间开销较大的多环群落的消去,以满足那些对算法时间性能要求很高而结果以近似最优即可的应用需求.实验表明,相对于SDBA算法,NWCE算法结合概率剪枝策略在时间性能上有了数量级的提高,而SPEA-t算法和SPEA-p算法则具有更高的性能和更好的适用性.     

Importance Sampling Microfacet‐Based BSDFs using the Distribution of Visible Normals

We present a new approach to microfacet‐based BSDF importance sampling. Previously proposed sampling schemes for popular analytic BSDFs typically begin by choosing a microfacet normal at random in a way that is independent of direction of incident light. To sample the full BSDF using these normals requires arbitrarily large sample weights leading to possible fireflies. Additionally, at grazing angles nearly half of the sampled normals face away from the incident ray and must be rejected, making the sampling scheme inefficient. Instead, we show how to use the distribution of visible normals directly to generate samples, where normals are weighted by their projection factor toward the incident direction. In this way, no backfacing normals are sampled and the sample weights contain only the shadowing factor of outgoing rays (and additionally a Fresnel term for conductors). Arbitrarily large sample weights are avoided and variance is reduced. Since the BSDF depends on the microsurface model, we describe our sampling algorithm for two models: the V‐cavity and the Smith models. We demonstrate results for both isotropic and anisotropic rough conductors and dielectrics with Beckmann and GGX distributions.     

基于互K近邻图的自动图像标注与快速求解算法

图像语义具有模糊性、复杂性、抽象性等特点,在提取图像语义时仅用低层特征进行描述是不够的,需要结合图像相关内容,以便提高图像标注的精确度。为此,提出了基于互K近部图的图像标注方法,该方法用一个互K近部图融合了图像的低层特征之间、标注词之间以及图像与标注词间的相互关系。利用互K近部图实现了根据两个节点间的相互关系来提取语义信息,弥补了基于K近邻图的方法中单方向挖掘节点信息的不足,有效地提高了图像标注的性能。在对互K近部图结构分析的基础上,结合重启随机游走,提出了一种快速求解算法,该算法在不明显降低图像标注精度下,实现了快速求解。在Cord图像数据集上进行了实验,结果验证了所提方法的有效性。     

基于GVG采样系统的农作物估产可信度分析--以四川盆地为例

以四川盆地的农作物估产为例,对GVG（GIS＆VIDEO＆GPS）采样系统的采样结果进行可信度分析。在试验区随机选取具有代表性的采样线,利用GVG采样系统提取样区内农作物种植比例。然后结合野外实地调绘结果对该地区进行遥感影像解译,在解译影像上对采样线不同缓冲区范围内的农作物面积进行提取,计算出农作物的种植比例。把遥感解译影像缓冲区分析结果为检验数据,采用误差最小可信度最高的原则对GVG采样系统采样结果的精度进行可信度分析。并基于可信度分析初步确定GVG采样系统的沿线采样最佳范围。     

Evaluation of the Probability of Functional Failure of a Redundant System by Importance Sampling Method

A redundant system whose structure is determined by the fault tree with efficiencies is considered. External load on the system is the function of state of a semi-Markov process. A fast simulation method enabling to evaluate the probability of functional failure when the real system efficiency becomes less than required ones is proposed. The conditions that guarantee the boundedness of the relative error estimate as the reliability of elements increases are found. The numerical example illustrates the the accuracy of estimates obtained by the proposed method.     

From the Sup-Decomposition to Sequential Decompositions

W-operators are discrete set operators that are both translation invariant and locally defined within a finite window W. A particularly interesting property of W-operators is that they have a sup-decomposition in terms of a family sup-generating operators, that are parameterized by the operator basis. The sup-decomposition has a parallel structure that usually is not efficient for computation in conventional sequential machines. In this paper, we formalize the problem of transforming sup-decompositions into purely sequential decompositions (when they exist). The techniques were developed for general W-operators, specialized for increasing W-operators and applied on operators built by compositions of dilations and erosions.     

输入函数的参数偏离时最优简化模型的一类误差估计

当大系统的输入函数的初始参数发生偏离时,其最优简化模型的近似模型可以利用输入函数的渐近展开得到。本文提出高阶模型和其最优简化模型的近似模型之间近似程度的一种估计。     

一个带簇个数估计的推广K-means算法

目前已有大量关于聚类方法的研究.然而,绝大部分现有方法都要求输入簇的个数,该数目在很多情形下都无法事先确定.提出了一个能估计簇的个数的推广的K-means算法.新算法无需对不同的输入参数重复运行,而是在划分过程中分裂某些簇直到满足一定的终止条件.划分过程应用了Ward的最小方差方法(Ward's minimum variance method),最佳的簇个数则由一个基于组间方差的指标决定.实验结果表明,新算法不仅能正确估计实际的簇个数,而且运行效率高.     

Learning the Graph Edit Costs Based on a Learning Model Applied to Sub-optimal Graph Matching

Neural Processing Letters - Attributed graphs are used to represent patterns composed of several parts in pattern recognition. The nature of these patterns can be diverse, from images, to...