An improved direct labeling method for the max–flow min–cut computation in large hypergraphs and applications

Algorithms described so far to solve the maximum flow problem on hypergraphs first necessitate the transformation of these hypergraphs into directed graphs. The resulting maximum flow problem is then solved by standard algorithms. This paper describes a new method that solves the maximum flow problem directly on hypergraphs, leading to both reduced run time and lower memory requirements. We compare our approach with a state–of–the–art algorithm that uses a transformation of the hypergraph into a directed graph and an augmenting path algorithm to compute the maximum flow on this directed graph: the run–time complexity as well as the memory space complexity are reduced by a constant factor. Experimental results on large hypergraphs from VLSI applications show that the run time is reduced, on average, by a factor approximately 2, while memory occupation is reduced, on average, by a factor of 10. This improvement is particularly interesting for very large instances, to be solved in practical applications.     

最小割多路径路由算法

在最小割理论基础上提出了最小割多路径(min-cut multi-path,简称MCMP)路由算法,为流量请求选取少量关键路径,并在这些路径间均衡流量,在获得方法易实现性的同时能够有效地控制网络瓶颈链路拥塞通过实际流量数据在北美和欧洲骨干网络中的实验,对比常用的OSPF(open shortest path first)路由算法和模型中的多路径路由算法,MCMP路由算法可降低拥塞链路负载分别达到41％和20％以上.     

结合密度峰值和切边权值的自训练算法

针对自训练迭代过程中错误标记样本对算法性能的影响,提出了基于密度峰值和切边权值的自训练算法。用密度聚类方法发现数据集的空间结构,选出具有代表性的未标记样本进行标签预测。用切边权值作为统计量进行假设检验,判断样本是否被正确标记,进而用正确标记样本逐步扩充有标记样本集合,直至所有未标记样本标签预测完成。新算法既充分利用了样本数据的空间结构信息,又解决了部分样本被标记错误的问题,提高了算法的分类准确率。通过在真实数据集上实验验证了新算法的有效性。     

Controlled integration of the cut rule into connection tableau calculi

In this paper techniques are developed and compared that increase the inferential power of tableau systems for classical first-order logic. The mechanisms are formulated in the framework of connection tableaux, which is an amalgamation of the connection method and the tableau calculus, and a generalization of model elimination. Since connection tableau calculi are among the weakest proof systems with respect to proof compactness, and the (backward) cut rule is not suitable for the first-order case, we study alternative methods for shortening proofs. The techniques we investigate are the folding-up and the folding-down operations. Folding up represents an efficient way of supporting the basic calculus, which is top-down oriented, with lemmata derived in a bottom-up manner. It is shown that both techniques can also be viewed as controlled integrations of the cut rule. To remedy the additional redundancy imported into tableau proof procedures by the new inference rules, we develop and apply an extension of the regularity condition on tableaux and the mechanism of anti-lemmata which realizes a subsumption concept on tableaux. Using the framework of the theorem prover SETHEO, we have implemented three new proof procedures that overcome the deductive weakness of cut-free tableau systems. Experimental results demonstrate the superiority of the systems with folding up over the cut-free variant and the one with folding down.Work supported by the Deutsche Forschungsgemeinschaft and the Esprit Basic Research Action 6471 Medlar II.     

一种自学习阴影检测方法

提出一种实时学习的阴影检测方法,该方法无须用户提供监控场景信息或设定任何阈值,而是根据统计的技巧来自我学习该场景的阴影特性,进而达到阴影检测的目的。仿真结果表明,该方法可有效地实现阴影检测,并具有较强的自适应性和实时性。     

Formulations for an inventory routing problem

In this paper, we present and compare formulations for the inventory routing problem (IRP) where the demand of customers has to be served, over a discrete time horizon, by capacitated vehicles starting and ending their routes at a depot. The objective of the IRP is the minimization of the sum of inventory and transportation costs. The formulations include known and new mathematical programming formulations. Valid inequalities are also presented. The formulations are tested on a large set of benchmark instances. One of the most significant conclusions is that the formulations that use vehicle‐indexed variables are superior to the more compact, aggregate formulations.     

基于粗糙熵加权密度的电网调控系统异常检测

针对调控系统运行过程中出现的异常状态,提出基于粗糙熵加权密度的智能电网调控系统运行异常数据 检测方法。采用粗糙集对电网调控系统运行数据进行分析和推理;利用隶属度的割关系方法,将复杂的不确定关系 转化为布尔数据并排序;基于对象加权密度对智能电网调控系统运行中出现的异常数据进行检测,实现对调控系统 各种功能异常状态数据准确识别。采用真实电网调控系统数据对所提方法进行验证,结果表明：该方法与传统异常 状态识别方法相比,具有更高准确率和更低漏判率。     

粘弹性聚合物溶液孔喉模型流变动力分析

针对多孔介质中聚合物溶液的粘弹特性难描述的问题,通过对其在多孔介质中流动特征的分析,提出将粘弹性流体在孔喉模型中流动过程分为入口收敛阶段、通过孔喉阶段和挤出孔喉阶段,并将各个阶段压降分解为粘性耗散压降和弹性拉伸压降。通过张量分析的方法,综合考虑了聚合物溶液的假塑性、弹性和弹性回复特性以及多孔介质的孔喉比和孔隙因子(喉道长度与喉道直径之比)等因素,推导了各个阶段的粘性耗散压降和弹性拉伸压降的表达式,建立了粘弹性聚合物溶液通过孔喉模型的压降数学模型。实例计算结果表明,建立粘弹性本构模型时,必须考虑通过孔喉阶段和挤出孔喉阶段以及弹性流体的弹性回复;弹性特性是造成压降损失的影响因素,在聚驱过程中不可忽略。     

降水防砂复合材料的研究与应用

针对大港油田疏松砂岩区块含水上升快、出砂加剧,常规防砂工艺技术无法解决既防砂又能有效降水的问题,研制出了一种自身可固结的颗粒型降水防砂复合材料（粒径0.3～0.5,0.5～0.7和0.7～1.4 mm）。采用正交试验进行了材料配方优选,并对优选出的材料进行了固化温度和固化时间对人工岩心固结强度和渗流能力的影响评价、耐酸碱介质性能评价和岩心稳定性评价。结果表明:该系列降水防砂复合材料适用于井温30～80 ℃的油井,人工岩心抗压强度大于3 MPa,液相渗透率0.3～0.8 μm2（根据粒径可以调节）。该降水防砂复合材料在大港油田成功实施了16井次,防砂施工成功率100%,防砂有效率93.8%,平均降水率达14.7%,取得了较好的降水防砂效果。      

孔喉结构对CO2驱储层伤害程度的影响

在CO₂驱提高采收率的过程中,CO₂与原油、基质矿物的相互作用会对储层孔喉结构造成一定的伤害。为了揭示孔喉结构对CO₂驱储层伤害程度的影响,利用高压压汞、扫描电镜结合核磁共振技术,通过室内物理模拟实验确定岩心样品的孔喉堵塞程度,评价了不同孔喉结构的岩心样品在CO₂驱过程中的伤害程度,明确了CO₂驱储层伤害机理。实验结果表明：CO₂驱过程中产生的沥青质沉积及酸化作用对储层孔隙度的影响很小,实验岩心样品的孔隙度降幅为1%左右,而渗透率受到的伤害程度较高,Ⅲ类孔隙结构岩心的渗透率降幅达20.55%,且渗透率越低、孔喉结构越差,渗透率受到伤害的程度越高;孔喉堵塞程度与孔喉结构参数成正相关关系,孔喉结构越差,中值半径越小,越容易发生孔喉堵塞;Ⅰ类孔隙结构岩心的孔喉堵塞程度较低,Ⅲ类孔隙结构岩心的孔喉堵塞程度明显增高,最高可达到34.32%。该研究结果可为CO₂驱现场高效应用提供依据。