网络主动带宽测量算法设计

本文基于Cruz流量模型.通过主动检测单向时延连续上升的概率.推断探测速率与端到端可用带宽之间的关系,提出一种网络可用带宽主动测量算法PathPCQ.NS2仿真显示,PathPCQ算法测量精度和效率都优于现有的带宽测量工具Pathload.     

基于谱聚类的在线数据库垂直分区多阶段生成方法

垂直数据分区技术从逻辑上将满足一定语义条件的数据库表属性存放在同一个物理块中,进而降低数据访问成本,提高查询效率.数据库查询负载中的每条查询通常只与数据库表中的部分属性有关,因此只需使用数据库表的某个属性子集便可以得到准确的查询结果.合理的垂直数据分区方式可以使大多数查询负载不需要扫描完整数据库就可以完成查询任务,从而达到减少数据访问量,提高查询处理效率的目的.传统的数据库垂直分区方法主要基于专家设置的启发式规则,分区策略粒度较粗,且不能根据负载的特征进行有针对性的分区优化.同时,当负载规模较大或者属性个数较多时,现有垂直分区方法执行时间过长,尤其无法满足数据库在线实时调优的性能需求.为此,提出在线环境下基于谱聚类的垂直数据分区方法（spectral clustering based vertical partitioning,SCVP）,采用分阶段求解的思想,减少算法时间复杂度,加快分区执行速度.首先通过增加约束条件缩小解空间（即根据谱聚类生成初始分区）,然后对解空间设计算法进行精细的搜索（即采用频繁项集和贪心搜索相结合的策略对初始分区进行优化）.为了进一步提升SCVP在高维属性下的性能,提出了SCVP的改进版本SCVP-R （spectral clustering based vertical partitioning redesign）.SCVP-R通过引入同域竞争机制、双败淘汰机制和循环机制,对SCVP在分区优化过程中的合并方案进行了进一步优化.在不同数据集上的实验结果表明,相比于目前最好的垂直分区方法,SCVP和SCVP-R有着更快的执行时间和更好的性能表现.     

基于联合权重超图划分的SNN负载均衡方法

大规模脉冲神经网络并行模拟是探究大脑机能的重要手段。其难点在于合理地将负载映射到并行分布式平台上,提升模拟速度。为解决该问题,提出一种基于联合权重超图划分的SNN负载均衡方法,解决并行计算中进程间计算负载与通信负载的均衡问题,提高SNN模拟速度。并使用稀疏通信的方式替代集体通信,解决事件通信过程中的数据冗余问题,提升通信效率。实验结果表明,该方法使带有STDP突触20%规模的皮质层微电路模型的模拟时间,比标准循环分配算法缩短约64.5%,比普通超图分配算法缩短约57.4%,同时事件通信数据量减少了90%以上。     

Volt-VAR control for distribution networks with high penetration of DGs: An overview

Continuously improving the ability to accept distributed renewable energies is the trend of future grid development, and a large number of papers have been published in recent years to study the problem of Volt-VAR control (VVC) for distribution networks with high penetration of distributed generations. This paper summarizes the relevant modeling and solution methods for VVC problems, mainly including VVC based on multiple time scales, hierarchical partitioning, multi-stage and network reconstruction, in conjunction with the operational characteristics of distribution networks containing distributed renewable energies; meanwhile, it analyzes the advantages and disadvantages of traditional optimization methods, heuristic intelligent algorithms and random variable processing methods used to solve VVC problems, and then introduces the application of model-free deep reinforcement learning as a latest decision method in VVC of distribution networks. Most of the models and methods compiled in this article are from the research results of the last three years and have some reference value.     

Integrated Risk of Progress-Based Costs and Schedule Delays in Construction Projects

This article aims to explore the impact of the integration of risk factors into delayed milestones for construction projects. A simulation model was developed to determine the impact of schedule variability on cost estimation. To generate random scenarios a Monte Carlo Simulation (MCS) technique was applied. The developed model computes the cost impact of delayed milestone in the expected budget. Using a risk integration approach revealed the critical time frame that may lead to a budget deficit for a project. As a result, a number of cost-sensitive risk factors and schedule delays were identified for the critical time period where the risk of budget deficit increases. The method of integration proposed in this article highlights the priority of risk factors and schedule delays for construction contracts involving Payments at Event Occurrences (PEO). Consequently, the developed method can be useful for practitioners in anticipation of potential increase of costs, hence, prevention of failure due to budget deficit.     

On a class of optimal abstractions of finite-state machines

Anabstraction A of an fsmM consists in partitioning its states, inputs, and outputs into groups, thus turning it into a non-deterministic fsmM _A. For fixed sets of states, inputs, and outputs, and abstraction generally maps a number of machinesM defined on these sets into the sameM _A. We would like to find anoptimal abstractionA ^* which minimizes this number, while lumping the states, inputs, and outputs into a specified number of classes. We extend these ideas to an fsmM operating in a random environment, and show that the abstraction results in a probabilistic fsm . Thinking of changes inM's output map as resulting in machinesM≠MM, we want to find anA ^* that minimizes the number ofMM which are such that the transition probabilities of their abstracted version are identical to those of the specification machine . SuchMM arise from statistically-undetectable output faults inM. Abstractions are directly applicable to the monitoring of a complex system by an observer for deviations from correct behavior (faults). Complex systems are usually accessible through restricted interfaces, which do not allow the observer to distinguish among all states, inputs, and outputs, thus rendering some faulty transitions undetectable. An optimal interface design will minimize the number of such undetectable faults. Assuming that only single-transition output faults occur inM, we show that each of the classes into which the abstraction lumps the outputs contributes a number of undetectable output faults. We then show that the problem of partitioning the outputs into a given number of classes that minimizes the maximum of these numbers is NP-complete. However, we give (a) an approximate minimization algorithm, running in time linear in the number of classes and quadratic in the number ofM's outputs, and (b) a lower bound on the minimum, computable in the same amount of time. The concept of optimal abstractions is illustrated by numerical results on combinational logic circuits that perform arithmetical operations. The results shed light on the trade-off between model simplification and the ability to detect erroneous behaviors in complex systems.     

基于时间延迟和掩码的抗DPA方法研究

针对时间延迟技术和掩码技术的不能完全抵御差分功耗攻击的不足,在研究差分功耗攻击防御方法的基础上,提出一种时间延迟和功耗随机化的电路结构,将其应用在AES算法中,并在FPGA开发板、示波器和PC机组成的功耗采集分析平台中进行了验证;通过DPA实验,结果表明,未加防护的AES加密算法IP核能被成功攻击,而增加时间延迟和掩码防护的AES IP核具有抗DPA攻击防护能力;文章的研究方法和思路可为功耗攻击防御研究提供一定的借鉴和参考。     

基于时空划分的数据流挖掘

基于时空划分的思想,设计概要数据结构的在线生成算法。概要数据结构保存流数据不同时刻的分布状态,以支持离线阶段的分类、聚类和关联规则发现等数据挖掘操作。研究时间粒度、量化向量调整和子区域索引等3项内存需求控制策略,以平衡概要数据结构的内存需求和内外存之间的I／O次数。     

网络控制系统时延分布研究

时延是控制网络性能分析中最重要的一个因素.针对网络控制系统中最常用的拓扑结构和数据特性,基于统计网络演算,得出了系统在不同调度策略下的时延模型.利用该模型计算了控制网络中实时数据的时延分布和统计上界,并与确定性网络演算得出的时延上确界进行了比较,评价了该上确界的悲观程度.