基于灰色系统理论的图像修复研究

针对结构图像修复中修复质量和时间难以平衡的问题,提出一种基于灰色理论和蚕食控制的快速修复方法。该方法以待修复区域的已知邻域信息为基础,采用基于均值生成时序的单因子系统云灰色预测模型SCGMmv(1,1),挖掘不同方向灰度序列的变化趋势,对单个未知像素点的像素值进行预测,并在修复过程中模仿蚕食桑叶对填充顺序进行控制,在保证边缘结构修复质量基础上,避免了复杂的修复优先级计算。实验结果显示,该算法不仅能有效延伸边缘和平滑区域的拓扑结构,而且能满足实时性要求,获得了较好的视觉效果。     

基于Artifact快照序列的行为一致性检测方法

Artifact行为的一致性检测,是在流程建模、运行之后亟待解决的关键问题之一.针对现有一致性检测技术忽略数据操作方面检测的问题,提出了一种基于Artifact快照序列的行为一致性检测方法.首先,利用全序Artifact快照序列定义了Artifact的行为模式,该行为模式不仅体现了服务的运行轨迹,也描述出了Artifact数据属性赋值的状态变化;然后,将Artifact行为一致性检测问题转换为语言可判定问题,证明了该问题是一个可判定问题,该过程中,设计一台判定该语言的图灵机作为一致性验证模型,该模型不仅检测了Artifact生命周期中服务路径的一致性,同时也检测了生命周期中Artifact属性赋值的正确性;进一步地,利用服务-快照关联矩阵的等价转换,给出了行为一致性量化指标中确切度的精确计算方法;最后,通过实例分析及实验对所提出的方法进行了验证.     

基于图常量条件函数依赖的图修复规则发现

数据一致性是数据质量管理的一个重要内容。为了提升图数据一致性,大量关系型数据库中的数据依赖理论被引入到图数据库,包括图函数依赖、图关联规则等。图修复规则是最新提出的一种针对图数据的数据依赖规则,具有强大的修复能力,但目前尚无有效的挖掘算法。为了自动生成图修复规则并提高图数据修复的可靠性,提出一种将图常量条件函数依赖转化为图修复规则的方法(GenGRR)。通过图模式在图中匹配同构子图并映射成节点-属性二维表,从表中相应属性域中抽取错误模式把图常量条件函数依赖转化成图属性值修复规则;删去图模式中常量条件函数依赖RHS对应的节点与相连边生成图属性补充规则。基于最大公共同构子图筛选并验证生成图修复规则的一致性。在多个真实数据集上进行测试,验证相比图常量条件函数直接修复图数据,通过转化生成的图修复规则具有更好的修复效果。     

基于RAID6编码的校验盘故障修复算法

针对基于RAID6编码的分布式存储系统中校验盘故障修复问题,提出一种快速修复算法。通过对RDP和EVENODD编码的理论分析,利用节点的计算编码能力,传输经过编码的数据块来修复校验盘,减少修复过程中的数据传输量,缩短修复时间。理论分析表明,相比于传统修复算法,该算法可显著减少校验盘故障修复过程中耗费的带宽资源,提高修复效率。     

基于邻域一致性的数字X射线图像局部快速修复算法

针对工业X射线扫描图像中存在微小异物及裂缝缺陷的特点,提出了一种基于邻域一致性的数字X射线图像局部快速修复算法。通过分析图像中封闭的连通区域的大小,设置蒙板并记录其坐标,采用从外到内、由粗到精的非迭代修复方法完成对扫描图像背景的修复过程。实验结果表明,与传统FOE和PDE修复方法相比,该算法在保证修复效率的前提下提高了图像的修复质量。     

基于条件冲突分析的主动规则集汇流性判定方法

主动规则使数据库具备自反应能力,在数据库、知识库和无线传感器网络等领域得到了广泛应用。汇流性分析是保持数据一致性的重要手段,但有效判定汇流性仍是一个难题。现有的方法基于规则可交换性进行判定,没有考虑规则之间是否存在条件冲突而导致它们不可能同时出现在同一个执行序列中;同时基于触发关系的集合分析没有考虑在规则触发序列中的先后时序性。为此,提出了规则的触发序列概念,分析了单个触发序列上的条件冲突和一对无序规则所在的两条触发序列之间的条件冲突,从而提出了更有效的汇流性先决条件及其判定定理,同时给出了新的判定算法的完整描述及其可终止性、正确性证明和复杂度分析。     

基于生成式对抗网络的裂缝图像修复方法

提出一种基于生成式对抗网络的裂缝图像修复方法。在修复过程中,对障碍物所在位置进行信息擦除获得待修复图像。使用生成式对抗网络生成相应的裂缝图像,为待修复图像和生成图像分别覆盖距离加权掩膜,并计算获得修复块。对修复块与待修复图像的拼接图像进行优化获得最终修复结果。实验结果表明,该方法可对裂缝图像进行了准确修复。与传统的修复方法相比,使用该方法修复后的裂缝图像较之前方法峰值信噪比提升了0.6～0.9 dB,实现了在有限的裂缝数据集条件下,生成大量还原度较高的裂缝图像。     

计算主动数据库中不可归约规则集的有效算法

主动数据库中规则集的可终止性判定是一个重要问题,已经成为一个研究热点．有些研究工作提出了在编译阶段运用触发图和活化图的方法解决这个问题,其中的一个关键技术就是计算主动规则集的不可归约规则集．现有的计算方法由于具有一定保守性,使得计算出的不可归约规则集仍可进一步地归约,这无疑将影响到规则集的可终止性判定的准确性和运行阶段规则分析的效率．经过深入分析活化规则可无限执行的特点,提出了活化路径等概念．基于这些概念,提出了一个计算主动规则集的不可归约规则集的有效算法,使现有方法求得的不可归约规则集得到进一步的归约．     

时间序列相似性度量的研究

时间序列的相似性度量是时间序列数据挖掘研究中的一个重要问题,是进行序列查询、分类、预测的一项基础工作。寻求一种好的度量对提高挖掘任务的效率和准确性有着至关重要的意义。目前从事这方面的研究除了少许理论论述外,几乎都采用一种固定的方法,即提出具体要求并提供实验数据。然而,大多数实验方法不是使用范围有限就是侧重点不同。为了提供一个比较全面的实验验证,用1NN分类算法进行了大量的时间序列交叉验证实验,重新评估了其中的弹性度量,并使用不同应用领域的28个时间序列数据集进行比较,结果表明,该方法具有更高的准确性。     

基于DLT-Kmedoids算法的用电负荷聚类分析

针对高校用电负荷中传统聚类算法直接应用于时间序列聚类效果准确性较低的问题,提出一种融合DTW距离、LB＿Keogh距离以及时间窗口的DLT-Kmedoids算法,以提高聚类算法应用于时间序列的准确性以及算法效率。DLT-Kmedoids算法使用DTW计算时序数据之间的距离取代传统的欧氏距离度量方式,提高了相似性度量算法精度,同时也提高了聚类算法的准确性和复杂度,引入LB＿Keogh距离在计算DTW距离之前过滤掉大部分不可能是最优匹配序列的序列,对于剩下的序列再使用DTW逐个比较,进一步降低算法的复杂度。最后结合高校建筑用电负荷时间序列数据进行分析,通过与主流聚类算法进行比较,表明该算法对于高校用电负荷数据的聚类任务,能够更准确地识别相似的负荷模式,并以更高的效率进行聚类分析。     

Discovering context-aware conditional functional dependencies

Conditional functional dependencies(CFDs) are important techniques for data consistency. However, CFDs are limited to 1) provide the reasonable values for consistency repairing and 2) detect potential errors. This paper presents context-aware conditional functional dependencies(CCFDs) which contribute to provide reasonable values and detect potential errors. Especially, we focus on automatically discovering minimal CCFDs. In this paper, we present context relativity to measure the relationship of CFDs. The overlap of the related CFDs can provide reasonable values which result in more accuracy consistency repairing, and some related CFDs are combined into CCFDs.Moreover,we prove that discovering minimal CCFDs is NP-complete and we design the precise method and the heuristic method. We also present the dominating value to facilitate the process in both the precise method and the heuristic method. Additionally, the context relativity of the CFDs affects the cleaning results. We will give an approximate threshold of context relativity according to data distribution for suggestion. The repairing results are approvedmore accuracy, even evidenced by our empirical evaluation.     

Content-Related Repairing of Inconsistencies in Distributed Data

Conditional functional dependencies (CFDs) are a critical technique for detecting inconsistencies while they may ignore some potential inconsistencies without considering the content relationship of data. Content-related conditional functional dependencies (CCFDs) are a type of special CFDs, which combine content-related CFDs and detect potential inconsistencies by putting content-related data together. In the process of cleaning inconsistencies, detection and repairing are interactive: 1) detection catches inconsistencies, 2) repairing corrects caught inconsistencies while may bring new inconsistencies. Besides, data are often fragmented and distributed into multiple sites. It consequently costs expensive shipment for inconsistencies cleaning. In this paper, our aim is to repair inconsistencies in distributed content-related data. We propose a framework consisting of an inconsistencies detection method and an inconsistencies repairing method, which work iteratively. The detection method marks the violated CCFDs for computing the inconsistencies which should be repaired preferentially. Based on the repairing-cost model presented in this paper, we prove that the minimum-cost repairing using CCFDs is NP-complete. Therefore, the repairing method heuristically repairs the inconsistencies with minimum cost. To improve the efficiency and accuracy of repairing, we propose distinct values and rules sequences. Distinct values make less data shipments than real data for communication. Rules sequences determine appropriate repairing sequences to avoid some incorrect repairs. Our solution is proved to be more effective than CFDs by empirical evaluation on two real-life datasets.     

条件依赖理论及其应用展望

介绍了条件函数依赖理论及如何用于检测不一致数据.首先介绍了条件函数依赖的概念及其推理系统,以及如何通过依赖传播实现视图的规范化;阐述了条件函数依赖的一致性和蕴含判定问题,并在此基础上介绍了基于条件函数依赖检测关系数据库数据一致性的技术;最后讨论了条件函数依赖的扩展及应用.     

基于水利普查数据的函数依赖关系算法

条件函数依赖(Conditional Functional Dependencies,CFDs)在数据库一致性的检测上应用广泛。为检测水利普查数据的一致性,本文针对水利普查数据特点,将普查数据分为度量、维度2部分,并对度量数据进行聚类,引入条件函数依赖的概念,同时重新定义条件函数依赖,改进发现条件函数依赖的算法(即CTANE算法);以水库工程数据为例,验证本文改进的算法能准确高效地发现水利普查数据中的条件函数依赖,为检测数据一致性做好准备。     

Structured firewall design

A firewall is a security guard placed at the point of entry between a private network and the outside Internet such that all incoming and outgoing packets have to pass through it. The function of a firewall is to examine every incoming or outgoing packet and decide whether to accept or discard it. This function is conventionally specified by a sequence of rules, where rules often conflict. To resolve conflicts, the decision for each packet is the decision of the first rule that the packet matches. The current practice of designing a firewall directly as a sequence of rules suffers from three types of major problems: (1) the consistency problem, which means that it is difficult to order the rules correctly; (2) the completeness problem, which means that it is difficult to ensure thorough consideration for all types of traffic; (3) the compactness problem, which means that it is difficult to keep the number of rules small (because some rules may be redundant and some rules may be combined into one rule).To achieve consistency, completeness, and compactness, we propose a new method called structured firewall design, which consists of two steps. First, one designs a firewall using a firewall decision diagram instead of a sequence of often conflicting rules. Second, a program converts the firewall decision diagram into a compact, yet functionally equivalent, sequence of rules. This method addresses the consistency problem because a firewall decision diagram is conflict-free. It addresses the completeness problem because the syntactic requirements of a firewall decision diagram force the designer to consider all types of traffic. It also addresses the compactness problem because in the second step we use two algorithms (namely FDD reduction and FDD marking) to combine rules together, and one algorithm (namely firewall compaction) to remove redundant rules. Moreover, the techniques and algorithms presented in this paper are extensible to other rule-based systems such as IPsec rules.     

A decision procedure for detecting atomicity violations for communicating processes with locks

The problem of interest is to verify data consistency of a concurrent Java program. In particular, we present a new decision procedure for verifying that a class of data races caused by inconsistent accesses on multiple fields of an object cannot occur (so-called atomic-set serializability). Atomic-set serializability generalizes the ordinary notion of a data race (i.e., inconsistent coordination of accesses on a single memory location) to a broader class of races that involve accesses on multiple memory locations. Previous work by some of the authors presented a technique to abstract a concurrent Java program into an EML program, a modeling language based on pushdown systems and a finite set of reentrant locks. Our previous work used only a semi-decision procedure, and hence provides a definite answer only some of the time. In this paper, we rectify this shortcoming by developing a decision procedure for verifying data consistency, i.e., atomic-set serializability, of an EML program. When coupled with the previous work, it provides a decision procedure for verifying data consistency of a concurrent Java program. We implemented the decision procedure, and applied it to detect both single-location and multi-location data races in models of concurrent Java programs. Compared with the prior method based on a semi-decision procedure, not only was the decision procedure 34 times faster overall, but the semi-decision procedure timed out on about 50% of the queries, whereas the decision procedure timed out on none of the queries.     

基于输入样本和主数据的编辑规则挖掘算法

基于编辑规则和主数据的数据修复技术能自动地、确切地修复不一致数据,但目前编辑规则的获取主要依靠专业人员的定义. 为了实现数据清洗全自动化,数据规则的挖掘技术近年来成为研究热点,针对条件函数依赖提出的挖掘算法主要有CFDMiner,CTANE,FastCFD. 在此基础上,扩展条件函数依赖（CFD）的定义,在编辑规则的定义下提出了一种基于输入样本和主数据的编辑规则挖掘算法,主要思路是从输入样本中挖掘出CFD,然后根据输入样本与主数据在属性上的定义域相似性求出输入样本在主数据中的对应属性,从而形成带模式组的编辑规则,此算法能有效地挖掘编辑规则. 且所挖掘的编辑规则按照编辑规则语义能有效地进行数据修复.     

条件函数依赖及其在领域无关数据清洗中的应用

条件函数依赖(Conditional Functional Dependeny,CFD)是对函数依赖(Functional Depencency,FD)加入语义约束扩展而来,它在数据库一致性检测、数据清洗方面更优于后者.讨论了条件函数依赖的相关概念及其基本性质,讨论如何将它应用于数据清洗,并对已提出的基于CFD的数据清洗方案提出改进措施,并通过实验说明改进措施的可行性.     

Group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency

In this paper, we present a new method for group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency. We estimate unknown preference values based on the additive consistency and then construct the consistency matrix which satisfies the additive consistency and the order consistency simultaneously for aggregation. The existing group decision making methods may not satisfy the order consistency for aggregation in some situations. The proposed method can overcome the drawback of the existing methods. It provides us with a useful way for group decision making with incomplete fuzzy preference relations based on the additive consistency and the order consistency.     

基于SPN模型的可生存性DBMS中恶意事务修复算法的研究

在传统的数据库恶意事务修复方案的基础上,采用Petri网模型分析事务撤销冲突和操作执行序列异常检测;进而结合可生存性DBMS特征提出恶意事务静态和on-the-fly修复算法,并在此基础上给出随机Petri网恶意事务修复模型;在分析恶意事务修复随机Petri网模型和连续时间Markov链的一致性后,给出了连续时间Markov链的恶意事务修复模型求解．