基于切片技术获取系统测试用例的方法

针对如何获取局部和全部的系统测试用例,确保其正确性和完备性以及与需求的一致性问题,提出利用先进的建模技术,建立可模拟验证的过程模型,并应用切片技术获取测试用例,并利用基于过程模型结构分析的归纳法证明了该法的正确性,根据过程模型切片准则获取系统测试用例的算法,介绍了实现工具的构成和应用实例。     

ERDAS LPS与TerraSolid软件相结合快速制作低空数码遥感正射影像图的方法

分析了ERDAS的LPS模块制作DOM的方法及其存在的缺点,介绍了一种结合ERDAS LPS与TerraSolid软件,快速处理超轻型飞机低空数码遥感系统获取的数码航摄影像,制作DOM的技术流程,并通过实验验证了此方法在超轻型飞机低空数码大比例尺航空正射影像图制作中的适应性.     

自适应动态信任关系量化模型的研究

提出一个新的自适应动态信任关系量化模型。该模型在建立基于历史函数的直接信任计算方法和基于路径函数的反馈信任聚合模型的基础上,引入自信因子和反馈因子来自动建立和动态调节直接信任与反馈信任的权重。仿真实验结果表明,与现有模型相比,该模型能够有效提高可信决策的准确性。     

基于DSL的装备保障领域建模

基于领域专用语言构建了装备保障领域的元模型,描述了实体、关系、行为、约束、流程等基本要素,在该模型中,二次开发人员能够方便地对该领域的各类软件系统进行建模,并通过模型精化来自动生成最终的可执行系统。实验证明,该模型是有效的。     

基于PDA的无线局域网测试系统的设计与实现

分析了早期无线局域网测试方法的特点,从面向应用的角度,提出了一种全面综合的测试分析方法,并在此基础上设计了无线局域网测试系统体系结构模型及应包含的功能模块,实现了基于PDA的无线局域网测试系统.     

可扩展的Web日志采集框架的设计与实现

以某网站所需的日志分析需求为背景,设计并实现了一种Web日志分析系统的前端日志采集框架.该日志采集方案没有采用传统的写日志文件然后轮训的方式进行日志收集,而是采用客户端/服务器的模式进行"自定义"格式的日志采集.该框架同样完全兼容传统的日志记录格式和记录方式.     

RoR开发框架研究

近年来,随着Web应用的逐渐普及和需求的多样化,现有的Web应用开发框架已经不能满足开发者的需求,开发者们迫切需要一款能够简化开发过程、降低开发难度、提高开发效率的框架。而Web应用开发框架Ruby on Rails（RoR）的突起在极大程度上满足了这种需求。在简要介绍了RoR开发框架以及涉及到的关键技术的基础上,着重在框架性能上提出了改进的解决方案。     

The generalization of a class of impulse stochastic control models of a geometric Brownian motion

Recently, international academic circles advanced a class of new stochastic control models of a geometric Brownian motion which is an important kind of impulse control models whose cost structure is different from the others before, and it has a broad applying background and important theoretical significance in financial control and management of investment. This paper generalizes substantially the above stochastic control models under quite extensive conditions and describes the models more exactly under more normal theoretical system of stochastic process. By establishing a set of proper variational equations and proving the existence of its solution, and applying the means of stochastic analysis, this paper proves that the generalized stochastic control models have optimal controls. Meanwhile, we also analyze the structure of optimal controls carefully. Besides, we study the solution function of variational equations in a relatively deep-going way, which constitutes the value function of control models to some extent. Because the analysis methods of this paper are greatly different from those of original reference, this paper possesses considerable originality to some extent. In addition, this paper gives the strict proof to the part of original reference which is not fairly well-knit in analyses, and makes analyses and discussions of the model have the exactitude of mathematical sense. Supported by the National Natural Science Foundation of China (Grant No. 19671004)     

Real-time reliability prediction for dynamic systems with both deteriorating and unreliable components

As an important technology for predictive maintenance, failure prognosis has attracted more and more attentions in recent years. Real-time reliability prediction is one effective solution to failure prognosis. Considering a dynamic system that is composed of normal, deteriorating and unreliable components, this paper proposes an integrated approach to perform real-time reliability prediction for such a class of systems. For a deteriorating component, the degradation is modeled by a time-varying fault process which is a linear or approximately linear function of time. The behavior of an unreliable component is described by a random variable which has two possible values corresponding to the operating and malfunction conditions of this component. The whole proposed approach contains three algorithms. A modified interacting multiple model particle filter is adopted to estimate the dynamic system’s state variables and the unmeasurable time-varying fault. An exponential smoothing algorithm named the Holt’s method is used to predict the fault process. In the end, the system’s reliability is predicted in real time by use of the Monte Carlo strategy. The proposed approach can effectively predict the impending failure of a dynamic system, which is verified by computer simulations based on a three-vessel water tank system.     

OCSVM集成模型解决支持向量机的大规模训练问题

该文针对集成方法实现支持向量机大规模训练的相关问题进行了深入研究,提出了一种称为"DD-Boosting"的成员分类器产生算法,能够在大规模数据集情况下利用类似Boosting技术产生稳定、高泛化性能的成员分类器。在此基础上,推导出基于OCSVM的分类器集成模型,实验仿真表明,该集成模型能够获得比主投票方法更好的泛化性能,且通过调整正则参数避免了训练过拟合问题。