基于模式分析的特征交互检测

旨在提出一种基于模式分析的特征交互检测方法,其基本思想在于从已知的交互中提取具有共性的冲突模式,并以之检测新的特征交互.该方法使用一组谓词公式描述交互发生的条件,使用Java语言对系统建模,借助于一个外部工具,系统模型可以遍历所有的运行场景.在模型运行期间,所有特征的行为将被收集和分析,一旦发现某个交互的条件得到满足,即产生冲突报告.该方法被用于一个E-mail系统的分析.实验结果显示,在处理了超过100万个运行场景后,该方法能够有效地检测出已知和未知的特征交互.     

基于本体的特征模型演化的一致性验证

针对特征模型的演化导致特征间的冲突问题, 从特征模型的演化方面进行研究, 提出了一种基于本体的特征模型演化的一致性验证方法。首先采用本体理论方法对特征模型的演化进行分析和建模, 建立本体的特征模型元模型; 其次基于上述元模型, 为网上购物系统实例建立相应的网上购物的领域特征模型, 根据需求裁剪出产品特征模型; 然后在Eclipse集成开发环境下, 通过Jena推理机加载规则和产品特征模型进行一致性验证, 当检测到冲突时, 采用演化策略来消除冲突; 最后通过实例研究说明了该方法的有效性。     

一种软件特征模型扩展和演化分析方法

特征模型是面向特征的软件开发过程的重要概念和制品,该模型以特征为单位,刻画了领域产品的共性和可变性.在日趋频繁的软件演化过程中,保持特征模型的一致演化,对于支持高效的复用开发和按需配置至关重要.目前,大多数的研究是在需求层面进行共性和可变性分析,对特征模型的研究则集中在对共性和可变性的建模上.但是,由于特征变更在建模过程中存在"涟漪"效应,会导致新的共性和可变性演化.现有的分析方法还无法解决这个问题,会导致丢失一些潜在的产品共性,从而影响复用的效率.提出了一种特征模型扩展和演化分析方法.通过扩展特征关联关系和模型演化元操作,实现对特征变更"涟漪"效应的分析.发现潜在的产品共性,提出重构策略和半自动化的共性提取和特征重构支持方法.该方法还针对典型的配置冲突提出了冲突消解规则和策略.最后,通过案例分析验证了该方法的可用性和有效性.     

面向软件动态演化的需求建模及其模型规范化

针对目前需求工程阶段对软件动态演化考虑不足的现状,在分析软件动态演化面临的挑战的基础上,设计了面向动态演化的需求元模型(dynamic-evolution-orientedrequi rements meta-model,DERM)。该元模型以特征为基本部件,按特征组合的方式建立需求模型,需求模型由行为特征和属性特征组成。行为特征被区分为计算行为特征和交互行为特征,以实现计算和交互的相对隔离;属性特征通过其作用域,指定其在行为特征模型中的作用范围,从而将行为特征模型和属性特征模型统一为需求模型。在此基础上,讨论了需求模型的参照完整性、依赖一致性和互斥一致性,并进一步把需求模型规范化为需求模型第一范式到第三范式,需求模型规范化的过程提供了一种保证需求模型一致性的方法。最后通过案例研究,表明了该方法的可行性及其对软件动态演化的有效支持。     

基于图模型理论的决策者组合行为冲突分析

依据现有4种基本稳定性能够获取冲突均衡解,但该过程通常假设决策者具有相同的行为模式.为了研究各个决策者行为模式的差异性对冲突演化分析与求解的影响,提出一种决策者组合行为冲突分析方法.首先,基于冲突分析图模型4种稳定性概念,通过预见力和风险态度两项指标识别不同决策者的行为模式;其次,定义规范化的组合均衡解概念,以此反映决策者不同行为模式对冲突决策的影响;接着,给出基于矩阵行为模式分析函数的组合均衡求解方法,以此提高均衡解的计算效率;最后,运用新方法解决企业员工体面劳动保障制度实施冲突问题.研究表明,所提出方法能够较好地提高冲突分析图模型理论的战略解析能力和决策水平.     

一种基于CPN的运行时监控服务交互行为的方法

BPEL对Web服务组合的描述特性使得它很难表达和分析服务组合中的交互行为属性.这些行为属性需要遵循一定的交互协议.然而良定义的交互协议还是容易受到侵犯,这将导致服务组合进程产生状态不一致的问题.为了解决这类实际交互行为与其服务组合描述之间的不一致性问题,提出了一种由BPEL描述自动构造运行时监控器的方法.首先提出了一种基于有色Petri网(CPN)的形式化表示模型以及服务组合规约到模型的映射方法,用于精确表达从BPEL流程描述中提取的服务交互行为规约.然后,描述了由CPN形式化表示模型生成运行时监控器的方法.该运行时监控器能够捕获所有进出服务组合进程的交互消息,并且检测是否存在违背交互协议的行为.     

Web应用程序的用户非预期行为测试模型的研究

现有Web应用程序测试模型默认前提是将Web应用程序软件行为等同于其他传统软件,而忽略了Web应用程序中用户的非预期行为将可能影响软件应有的逻辑,导致软件行为失效,针对这一问题,本文在分析现有测试模型,测试策略与算法的基础上,提出一种基于扩展有限状态机(EFSM)的UUBTM测试模型来生成非预期行为的测试序列UTSEQ,实验证明该序列可以有效检测Web应用程序中存在的缺陷.     

化学性服务组合

为解决嵌入式系统服务化集成过程中服务的动态组合和重组等应用问题,借鉴化学合成、分子裂解、化学置换、分子复制等化学反应机制,设计了一套化学性服务组合模型.形式化描述了服务组合过程中服务合成、分解、替换、复制等服务行为及其相应的运算演化机制,并在此基础上结合petri网方法给出了服务演化模型,描述了多服务并行和依赖情况下的复合组合过程.最后以指控系统自适应服务组合应用需求为背景,实现了面向任务的服务组合系统,对化学性服务组合模型的应用效果和性能进行了验证和分析.实验结果表明,化学性服务组合模型满足了指控系统自适应服务组合的应用需求,增强了系统的灵活性、可重构性.     

项目组合交互风险的网络聚类及协同演化研究

将交互关系纳入项目组合风险研究框架,分析项目组合交互风险网络复杂性及其演化规律。从交互关系分类入手,将项目组合表征为由多个子系统相互作用构成的交互耦合网络,并提出了基于直觉模糊生态聚类算法的项目组合交互风险网络聚类识别模型;基于扩展的Lotka-Volterra方程建立项目组合交互风险协同演化模型,对比研究具有竞争、共生和偏利偏害效应的项目组合交互风险演化规律。研究表明,项目组合风险效率最大化及系统稳定性的关键在于项目组合交互关系及其强度,同时合理控制组合配置比例。     

个体交互行为的平滑干预模型

基于交互行为的用户特征提取和身份认证方法是一种重要的身份识别方式,但高频用户的交互行为模式和操作习惯相对稳定,易被欺诈者模仿使得现有模型对此类欺诈行为的误判较高.如何使得用户行为主动平滑变化且可区分成为解决上述问题的关键.针对此问题本文提出一种基于个体交互行为系统平滑干预模型,首先根据用户历史交互行为日志从多个维度得到用户的交互行为变化趋势;然后结合行为的稳定性和偏向性提出行为时域漂移算法（TDDA）,为每个用户确定行为引导的时机,最后基于Petri网提出交互行为重构系统干预模型,在系统中的非关键路径叠加行为触发因素,引导用户产生新的交互行为习惯.实验证明本文提出的方法能够很好的引导用户行为平滑变化,且产生足够的区分性使得行为伪装异常检测场景下模型的准确性显著提高.     

The Responsible Designer

Successful software systems often live far longer than their original designers anticipated. And over their lifetime, most of those systems evolve. Developers who make modifications, fix bugs, and add new features to long-lived systems have an easier time of it if they keep the code base habitable ("Creating Sustainable Designs," Rebecca Wirfs-Brock, IEEE Software, May/June 2009) and preserve design integrity. But even so, maintenance can be painful when new requirements invalidate initial design assumptions. This paper discussed about methods and techniques on how to achieve software's flexible design. Flexible design is the byproduct of preparation and continued attention to detail. Where there's a lot of variability in a design problem, a flexible solution will incorporate appropriate design hooks that allow for developers to predictably add planned extensions. Once they've established ways to support specific variations, developers can follow predefined extension recipes rather than hacking in new features that are similar to existing ones.     

An approach to recognize interacting features from B-Rep CAD models of prismatic machined parts using a hybrid (graph and rule based) technique

This paper presents a new hybrid (graph + rule based) approach for recognizing the interacting features from B-Rep CAD models of prismatic machined parts. The developed algorithm considers variable topology features and handles both adjacent and volumetric feature interactions to provide a single interpretation for the latter. The input CAD part model in B-Rep format is preprocessed to create the adjacency graphs for faces and features of associated topological entities and compute their attributes. The developed FR system initially recognizes all varieties of the simple and stepped holes with flat and conical bottoms from the feature graphs. A new concept of Base Explicit Feature Graphs and No-base Explicit Feature Graphs has been proposed which essentially delineates between features having planar base face like pockets, blind slots, etc. and those without planar base faces like passages, 3D features, conical bottom features, etc. Based on the structure of the explicit feature graphs, geometric reasoning rules are formulated to recognize the interacting feature types. Extracted data has been post-processed to compute the feature attributes and their parent-child relationships which are written into a STEP like native feature file format. The FR system was extensively tested with several standard benchmark components and was found to be robust and consistent. The extracted feature file can be used for integration with various downstream applications like CAPP.     

Translating Multi-Agent Autoepistemic Logic into Logic Program

In order to develop a proof procedure of multi-agent autoepistemic Logic (MAEL), a natural framework to formalize belief and reasoning including inheritance, persistence, and causality, we introduce a method that translates a MAEL theory into a logic program with integrity constraints. It is proved that there exists one-to-one correspondence between extensions of a MAEL theory and stable models of a logic program translated from it. Our approach has the following advantages: (1) We can obtain all extensions of a MAEL theory if we compute all stable models of the translated logic program. (2) We can fully use efficient techniques or systems for computing stable models of a logic program. We also investigate the properties of reasoning in MAEL through this translation. The fact that the extension computing problem can be reduced to the stable model computing problem implies that there are close relationships between MAEL and other formalizations of nonmonotonic reasoning.     

Further applications of the non-stationary operational calculus to the synthesis of closed-loop distributed systems A new approach to distributed white noise†

By using a differential polynomial algebra one can obtain a new definition for the distributed transfer functions of distributed systems which apply even when these latter are governed by partial differential equations with space-varying coefficients. We so have a new ‘ system approach ’ to distributed systems, which provides synthesis methods similar to that of Guillemin and Truxal's.

Further extensions of this approach are considered here. After a brief background on previous results by the author, and the statement of new theoretical complements, time sampled-data distributed systems are examined and their digital compensation is solved. Then it is shown that the approach holds also for space sampled-data systems by using the algebra of finite-difference polynomials. A new model is proposed for ditributed noises; it is a compromise between the models of the engineering literature and those of mathematicians, and by using the distributed transfer function above it provides an extension of some results of Wiener's theory. Illustrative examples are given.     

Using bidimensional regression to assess face similarity

Face recognition is the identification of humans by the unique characteristics of their faces and forms the basis for many biometric systems. In this research the problem of feature-based face recognition is considered. Bidimensional regression (BDR) is an extension of standard regression to 2D variables. Bidimensional regression can be used to determine the degree of resemblance between two planar configurations of points and for assessing the nature of their geometry. A primary advantage of this approach is that no training is needed. The goal of this research is to explore the suitability of BDR for 2D matching. Specifically, we explore if bidimensional regression can be used as a basis for a similarity measure to compare faces. The approach is tested using standard datasets. The results show that BDR can be effective in recognizing faces and hence can be used as an effective 2D matching technique.     

On transforming statistical models for non-frontal face verification

We address the pose mismatch problem which can occur in face verification systems that have only a single (frontal) face image available for training. In the framework of a Bayesian classifier based on mixtures of gaussians, the problem is tackled through extending each frontal face model with artificially synthesized models for non-frontal views. The synthesis methods are based on several implementations of maximum likelihood linear regression (MLLR), as well as standard multi-variate linear regression (LinReg). All synthesis techniques rely on prior information and learn how face models for the frontal view are related to face models for non-frontal views. The synthesis and extension approach is evaluated by applying it to two face verification systems: a holistic system (based on PCA-derived features) and a local feature system (based on DCT-derived features). Experiments on the FERET database suggest that for the holistic system, the LinReg-based technique is more suited than the MLLR-based techniques; for the local feature system, the results show that synthesis via a new MLLR implementation obtains better performance than synthesis based on traditional MLLR. The results further suggest that extending frontal models considerably reduces errors. It is also shown that the local feature system is less affected by view changes than the holistic system; this can be attributed to the parts based representation of the face, and, due to the classifier based on mixtures of gaussians, the lack of constraints on spatial relations between the face parts, allowing for deformations and movements of face areas.     

A theory of independent fuzzy probability for system reliability

Fuzzy fault trees provide a powerful and computationally efficient technique for developing fuzzy probabilities based on independent inputs. The probability of any event that can be described in terms of a sequence of independent unions, intersections, and complements may be calculated by a fuzzy fault tree. Unfortunately, fuzzy fault trees do not provide a complete theory: many events of substantial practical interest cannot be described only by independent operations. Thus, the standard fuzzy extension (based on fuzzy fault trees) is not complete since not all events are assigned a fuzzy probability. Other complete extensions have been proposed, but these extensions are not consistent with the calculations from fuzzy fault trees. We propose a new extension of crisp probability theory. Our model is based on n independent inputs, each with a fuzzy probability. The elements of our sample space describe exactly which of the n input events did and did not occur. Our extension is complete since a fuzzy probability is assigned to every subset of the sample space. Our extension is also consistent with all calculations that can be arranged as a fault tree. Our approach allows the reliability analyst to develop complete and consistent fuzzy reliability models from existing crisp reliability models. This allows a comprehensive analysis of the system. Computational algorithms are provided both to extend existing models and develop new models. The technique is demonstrated on a reliability model of a three-stage industrial process     

Finding alternatives and reduced formulations for process-based models

This paper addresses the problem of model complexity commonly arising in constructing and using process-based models with intricate interactions. Apart from complex process details the dynamic behavior of such systems is often limited to a discrete number of typical states. Thus, models reproducing the system's processes in all details are often too complex and over-parameterized. In order to reduce simulation times and to get a better impression of the important mechanisms, simplified formulations are desirable. In this work a data adaptive model reduction scheme that automatically builds simple models from complex ones is proposed. The method can be applied to the transformation and reduction of systems of ordinary differential equations. It consists of a multistep approach using a low dimensional projection of the model data followed by a Genetic Programming/Genetic Algorithm hybrid to evolve new model systems. As the resulting models again consist of differential equations, their process-based interpretation in terms of new state variables becomes possible. Transformations of two simple models with oscillatory dynamics, simulating a mathematical pendulum and predator-prey interactions respectively, serve as introductory examples of the method's application. The resulting equations of force indicate the predator-prey system's equivalence to a nonlinear oscillator. In contrast to the simple pendulum it contains driving and damping forces that produce a stable limit cycle.