基于Petri网接口变迁的交互流程模型模块网挖掘方法

交互流程模型的模块分解是查找流程模型变化域的核心内容之一,已有的模块分解方法多是基于完整的流程模型,通过挖掘对比流程模型中所有活动的行为关系将流程模型分解为多个模块网。但是在基于单纯的事件日志分解交互流程模型方面,目前的模块分解方法存在一定的局限性。提出基于Petri网接口变迁的交互流程模型模块网挖掘方法,首先基于系统运行所记录的局部有效事件日志确定其中各活动间的前驱后继关系,并得到相应的活动前驱后继关系表。然后,基于前驱后继关系频繁的活动查找接口变迁,同时考虑无后继变迁的活动。其次,通过分析接口变迁的前集变迁查找交互流程模型中各个模块网的初始变迁,并由初始变迁开始,利用活动前驱后继关系表,逐个添加活动,以此挖掘交互流程模型的模块网。论文最后通过实例验证该优化方法的有效性。     

一种离散连续混成的时空事件驱动的CPS体系架构

为描述信息物理融合系统(cyber-physical system,CPS)中离散信息系统和连续物理系统交互融合的混成特性,基于混成自动机理论,结合多智能体系统(multi-agent system,MAS)和时空事件驱动方法,提出一种CPS体系架构.该架构用改进的混成自动机理论刻画了离散空间和连续状态混成的CPS体系架构的主体,同时定义了基于时空事件的CPS事件,设计了基于MAS的CPS端,并运用定义不同功能的CPS端和CPS事件驱动机制解析了CPS体系架构内部信息物理交互反馈过程.最后以车速调控系统为例,采用提出的CPS体系架构分析了协同驾驶过程中单一车辆内部速度调控的信息物理融合过程,并对其进行了形式化描述,实例的形式化过程表明提出的体系架构的有效性.构建的CPS体系架构为刻画CPS的本质特征提供了新的方法.     

Low complexity SEU mitigation technique for SRAM-based FPGAs

An internal single event upset (SEU) mitigation technique is proposed, which reads back the configuration frames from the static random access memory (SRAM)-based field programmable gate array (FPGA) through an internal port and compares them with those stored in the radiation-hardened memory to detect and correct SEUs. Triple modular redundancy (TMR), which triplicates the circuit of the technique and uses majority voters to isolate any single upset within it, is used to enhance the reliability. Performance analysis shows that the proposed technique can satisfy the requirement of ordinary aerospace missions with less power dissipation, size and weight. The fault injection experiment validates that the proposed technique is capable of correcting most errors to protect space-borne facilities from SEUs.     

Wonderful events automatic detection and location of the HCRF and webcast text

Based on the powerful function of the hidden conditional random fields (HCRF) model in the expression and identification of semantic events and combining the webcast text information, a new framework for wonderful events automatic detection is put forward. Firstly, by analyzing and processing the webcast text, keyword combinations corresponding to each exciting event are obtained. Secondly, by classifying the webcast text to be detected, the happening time labels of each wonderful event are obtained. Thirdly, an HCRF model for semantic shot annotation is built to realize the semantic annotation of multiple types of semantic shots simultaneously, and the semantic shot sequence of the video clip is obtained. Finally, combining the multi-modal semantic clues, an HCRF model for the wonderful events detection and localization is effectively built in the case of small-scale training samples. Based on the mapping relationship among video low-level features, the multi-modal semantic clues and the wonderful semantic events, the inherent patterns of the wonderful events are excavated deeply in the multiple dimensions of the semantic structure, and then the wonderful events automatic detection, localization and segmentation are precisely achieved. Experiments show the effectiveness of this model.     

Interactive Evolutionary Multi-Objective Optimization Algorithm Using Cone Dominance

As the number of objectives increases, the performance of the Pareto dominance-based Evolutionary Multi-objective Optimization (EMO) algorithms such as NSGA-II, SPEA2 severely deteriorates due to the drastic increase in the Pareto-incomparable solutions. We propose a sorting method which classifies these incomparable solutions into several ordered classes by using the decision maker's (DM) preference information. This is accomplished by designing an interactive evolutionary algorithm and constructing convex cones. This method allows the DMs to drive the search process toward a preferred region of the Pareto optimal front. The performance of the proposed algorithm is assessed for two, three, and four-objective knapsack problems. The results demonstrate the algorithm's ability to converge to the most preferred point. The evaluation and comparison of the results indicate that the proposed approach gives better solutions than that of NSGA-II. In addition, the approach is more efficient compared to NSGA-II in terms of the number of generations required to reach the preferred point.     

基于混沌优化和网格筛选策略的多目标分布估计算法

为了解决多目标分布估计算法中进化速度慢、解精度和分布不佳等问题,提出一种基于混沌优化和网格筛选策略的多目标分布估计算法.该算法首先利用混沌模型进行种群的初始化,以获得较理想的初始化结果;然后运用混沌的局部优化策略对每代产生的非支配个体进行寻优,加速种群向Pareto最优前沿的逼近;最后利用简单的网格筛选策略保持个体的均匀分布,从而增强精英种群的多样性.3种评价标准在8个测试问题上的实验表明:与目前最具代表性的RM-MEDA算法相比,该算法不仅在接近真实的最优前沿和保持种群的多样性方面具有一定优势,而且在进化速度上也有较大提高.     

求解4G网络建站问题进化多目标算法

根据4G建站模型,设计了一种针对重点区域进行杂交、变异的进化多目标算法.该算法能有效减少个体进行杂交和变异时编码的长度、降低复杂度,使处理4G建站模型的进化多目标算法性能大大提高;在杂交变异之后,再根据约束条件对非重点区域进行搜索,在非重点区域中,激活能够最大程度覆盖了未被覆盖的测试点的候选基站,以此类推,直到全部激活的基站的覆盖率满足模型覆盖率约束为止,既保证了生成的解均为有效解,又使得在激活尽可能少基站的情况下覆盖到尽可能多的测试点.最后用极大极小方法求解出了一组最优解集.模拟4G建站问题的计算机仿真表明该算法非常有效.     

公共文化PPP项目中承包商机会主义行为奖惩机制演化博弈

在公共文化PPP项目管理模式中,承包商为了追求自身利益在一定情况下可能会选择利用不对称信息采取机会主义行为转移项目利益,这将会损害政府建设单位的利益,影响公众的享受公共文化服务的权利,扰乱政府建设市场,因此建设单位需采取一定的管理手段来抑制承包商的机会主义行为.运用演化博弈理论分析政府建设单位应对承包商机会主义行为的演化路径,分别建立惩罚机制和补偿机制2种状态下的演化博弈模型,分别探究甲乙双方在惩罚机制和补偿机制下的演化均衡策略.结果表明,惩罚机制并未对承包商机会主义行为产生实质性的抑制效果,而补偿机制在一定条件下可显著影响承包商的机会主义行为.基于此,政府建设单位可充分利用补偿机制手段应对承包商的机会主义行为.     

ENSO事件与渭河径流变异的响应关系

为研究ENSO事件对径流变异的影响,以渭河干流华县水文站和支流北洛河状头水文站的还原年径流流量之和作为渭河流域出口流量,采用其1956—2010年径流系列,分析了ENSO事件与渭河流域径流变异的响应关系.结果表明:渭河流域径流量的丰枯变化和变异与ENSO事件都有明显的对应关系,El Nio事件发生年径流偏枯概率较大,La Nia事件发生年径流偏丰概率较大.采用曼-肯德尔(Mann-Kendall)非参数统计检验方法,对渭河流域径流序列进行了变异诊断,得到变异点为1991—1992年;径流变异年份也是El Nio事件发生强烈年;采用Pearson、Kendall、Spearman法对变异点前ENSO事件的不同强度发生年月径流序列与变异年份月径流序列相关性进行检验,表明径流变异年份1991—1992年与El Nio事件年1987年,与La Nia事件年1970年、1956年和1988年在0.01水平(双侧)上显著相关,有明显的响应关系.     

高校工会主席胜任力模型研究

胜任力是工作表现背后所蕴含的个人能力和行为总和,它能够有效反映出个体对于某一岗位的适应性及耦合度.运用行为事件访谈技术、质性研究及问卷调查方法构建了高校工会主席胜任力模型,研究结果显示个人特质、领导能力、协调变通能力、战略管理能力、服务关怀意识、学习创新能力是高校工会主席的核心胜任力.