基于混沌微粒群算法的文本分类研究*

针对中文文本分类问题,将其用于分类规则的抽取。为了避免微粒群算法在全局优化中陷入局部极值,利用混沌运动遍历性、随机性等特点,对标准微粒群算法进行了改进,提出了基于混沌微粒群算法的文本自动分类方法。仿真实验表明本算法对文档进行分类是一种比较可行的分类方法,分类精度高、速度快。     

基于连续型条件属性的模糊规则约简算法*

针对粗糙集对于连续域属性决策表的处理能力差以及不容易获得模糊集之间关系等问题,提出一种将模糊集和粗糙集结合起来的连续型条件属性模糊规则约简算法。该算法首先引入三角隶属度函数将连续属性值转换为模糊值,并使用离散模糊神经网络方法获得数据集之间关系。实例验证表明,采用该算法,用户可以根据实际决策需要和领域知识更改阈值,从而获得满意的模糊规则结果。     

主动授权规则实施支持空间特性的RBAC*

为满足安全策略或者角色定义的变化,系统或模型应该提供一种灵活的机制实施支持空间特性的RBAC。引入了OITE（on-if-then-else）主动授权规则实施支持空间特性的RBAC,定义了支持空间特性的RBAC中各基本元素与OITE之间的映射关系。使用OITE作为实施机制,可以在不同粒度上实施带有空间特性角色约束,并且可以将支持空间特性的RBAC应用在多个领域中。最后简要讨论了授权规则如何从支持空间特性的RBAC安全策略中自动产生。     

CA与生命游戏演化的组合模型*

基于摩尔奇偶型CA,提出一种结合生命游戏的演化模型.首先设置初始构型进行摩尔奇偶型规则演化,再引入生命游戏规则,按游戏规则演化;最后依此重复摩尔奇偶规则和生命游戏规则,直到演化整个元胞空间.证实在摩尔奇偶型CA的基础上,结合生命游戏在很大程度上减小了演化过程对初始构型的依赖,出现更为丰富的图元以及基于图元和演化框架的动态图案,且演化过程仍然符合摩尔奇偶型CA的演化规律.结合模型可以避免演化过度依赖初始构型以及演化过程的单调性,而且组合演化模型可以应用于图案生成,生成保持风格的规则几何纹理图案,丰富图案的生成方法.     

一种基于生成树的分类去除关联规则冗余方法

在信息及数据爆炸的时代,冗余问题已经成为数据挖掘者获得知识的重大障碍,而目前解决的方法会导致关联规则的不完整性。基于此,文中引入了有向超图表示关联规则,重定义了邻接矩阵,介绍了冗余规则分类处理思想,将冗余规则分为从属规则和重复路径规则,通过VB编程去除了从属规则冗余,以及利用生成树算法去除了重复路径规则冗余。实验结果证明,此方法创新性地结合了图论中有向超图、生成树与关联规则的知识,维护了关联规则的完整性和准确性,同时去除了全部冗余规则。     

Artificial intelligence for BIM content management and delivery: Case study of association rule mining for construction detailing

The proliferation of Building Information Modeling (BIM) applications, in tandem with the extensive variation of building products, pose new demands on design and engineering firms to efficiently manage and reuse BIM content (i.e., data-rich parametric model objects and assembly details). Tasks such as classifying BIM objects, indexing them with meta-data (e.g., category), and searching digital libraries to load objects into models still plague practice with inefficient manual workflows. This research aims to improve the productivity of BIM content management and retrieval by developing an AI-backed BIM content recommender system. Using data from a case-study firm, this research extracted content from over 30,000 technical BIM views (e.g., plans, sections, details) in historical projects to build an unsupervised machine-learning prototype with association rule mining. This prototype explicated the strength of relationships among co-occurring BIM objects. Using this prototype as the backbone AI-engine in live BIM sessions, this research developed a context-aware recommender system that dynamically provides BIM users with a set of objects associable with their modeling context (e.g., type of view, existing objects in the model) and human–computer interactions (e.g., objects selected by the user). By mining association data from hundreds of historical projects, this development marks a departure from the existing prototypes that rely on explicit coding, recurring user input, or subjective ratings to recommend BIM content to users. The simulation and experimental implementation of this recommender system yielded high efficacy in predicting content needs and achieved significant savings in the time spent on conventional BIM workflows.     

基于云计算的多路网络流媒体分布式最优存储与分配策略

针对云计算系统在执行任务过程中的能量消耗过多、处理速度不理想等情况,提出一种基于云计算的多路网络流媒体分布式最优存储与分配策略.通过构建基于云计算的数学模型来分析执行任务时的能量消耗情况,采用基于虚拟调度机制的分布式最优存储策略来实现在满足存储需求的情况下减少服务器进行存储时的能量消耗总量,最小化存储成本,并采用基于动态决策规则的分配策略来根据服务器的功率、性能以及负载情况进行任务调度,使云计算系统在满足服务质量要求的条件下,充分利用系统运作能耗,不产生过多空闲能耗.实验及结果分析表明,所提出的分布式最优存储与分配策略在节省能量消耗、提升运行速度上发挥了较好的效果.     

基于文本特征的短文本倾向性分析研究

语篇倾向性分析是倾向性分析的较高层次领域。根据文本篇幅和结构可以将语篇分为短文本和长文本。该文以网络商品评论作为样本研究短文本倾向性分析的特点和策略。根据倾向极性在文中的决定性因素的不同表现,短文本可以分为含显性归总句、含隐性归总句、含特征词以及一般文本四类,针对不同类别文本采用不同的处理策略。在此基础上,运用词典、规则的方法构建了语篇倾向性分析系统CUCsas,该方法在第四届中文倾向性分析评测(COAE2012)中取得了较好成绩。     

一种自整定PID参数的模糊控制系统的设计与仿真

如果无法建立被控对象的数学模型或者建立的数学模型不精确,基于模型的PID控制器就失去了用武之地.而模糊控制器直接采用语言型控制规则,出发点是现场操作人员的控制经验或相关专家的知识,不需要建立被控对象的精确数学模型数学模型,甚至不需要建立数学模型.本文设计了一种将模糊控制和PID控制结合起来的控制系统,利用模糊控制器实时整定PID控制器的参数,并用在MATLAB下仿真,得到阶跃响应曲线.这种模糊PID控制方法起到优化PID控制器的作用,可应用于对控制性能要求很高的控制系统中.     

Optimal design of adaptive type-2 neuro-fuzzy systems: A review

Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system. In the design of type-2 fuzzy logic systems, the early applications were realized in a way that both the antecedent and consequent parameters were chosen by the designer with perhaps some inputs from some experts. Since 2000s, a huge number of papers have been published which are based on the adaptation of the parameters of type-2 fuzzy logic systems using the training data either online or offline. Consequently, the major challenge was to design these systems in an optimal way in terms of their optimal structure and their corresponding optimal parameter update rules. In this review, the state of the art of the three major classes of optimization methods are investigated: derivative-based (computational approaches), derivative-free (heuristic methods) and hybrid methods which are the fusion of both the derivative-free and derivative-based methods.