基于知识的智能柔性网关研究

本文参照OSI参考模型,提出了一种基于知识的智能柔性网关模型及其总体结构和工作原理.文中对网关主要领域问题进行了分析,提出了建立以规则库、模型库和方法库等多库协同高层协议综合知识库设计方案,并对综合知识库的构成、智能协议转换器和智能服务接口的推理过程等关键技术进行了讨论和阐述.     

基于PKI系统安全等级保护评估准则的评估方法

随着PKI的逐步发展,对PKI进行评估变得越来越重要,安全保护等级评估是PKI评估的一个重要方面。该文结合层次分析法、线性加权评估法,提出了一种PKI安全保护等级评估方法,对PKI系统的安全保护等级评估进行了探讨。     

基于规则库的数据质量评估方法

在当今大数据时代下,数据质量的保证是大数据价值得以发挥的前提,数据质量的评估是其中一个重要的研究课题.本文基于规则库的数据质量评估方法,提出了数据质量评估整体模型,包括规则、规则库、数据质量评估指标、评估模板、评估报告.设计了规则评估模板,组合规则库中的规则,根据数据质量评估指标的重要性设置规则的权重,采用简单比率法和加权平均法相结合的评估方法,计算评估结果并确定数据质量的等级,利用了数据可视化技术来展现数据质量的评估结果.本文既考虑了单个规则的执行合格率,又考虑了各规则在数据质量评估模板中的比重,公正地准确地评估数据质量,并且简洁、直观地呈现评估结果.     

NIDS的攻击知识表达模型与知识库构架

提出基于面向对象的攻击知识表达模型(OOAK),准确描述潜在的复杂攻击和多步骤组合攻击.对网络入侵检测系统(NIDS)的攻击知识库进行构架,以OOAK为基础,以规则库和方法库为核心,融入了层次知识库的设计思想,通过事件处理引擎调配知识库中的规则库和方法库,协同知识库与数据库的通信.     

基于知识和规则的企业工程元方法学

提出一种基于知识和规则的企业工程元方法学,在该元方法学的每个步骤和决策过程中可以参考相关的企业体系结构、方法学、建模框架、参考模型、建模语言、建模工具等建模知识库以及这些建模知识的应用规则库。工程实例验证了上述思想与方法的可行性。     

一种基于表达式的疾病风险评估模型的表示方法

设计了一套可解析、简洁、统一的表达式,用于表示各类疾病风险评估模型中的规则与界面.对疾病风险评估模型中评估规则涉及的各类指标进行归纳和分类,为每一类指标和规则设计了表达式和界面样式.以Caprini评估模型为例,提出了一种适用于各类疾病风险评估模型的,基于表达式的模型表示方法,该方法支持指标解析、规则计算和界面渲染.基于表达式来表示疾病风险评估模型的评估规则和界面,能有效避免硬编码和重复开发工作.     

基于知识的智能柔性网关研究

本文参照OSI参考模型,提出了一种基于知识的智能柔性网关模型及其总体结构和工作原理。文中对网关主要领域问题进行了分析,提出了建立以规则库、模型库和方法库等多库协同高层协议综合知识库设计方案,并对综合知识库的构成、智能协议转换器和智能服务接口的推理过程等关键技术进行了讨论和阐述。     

虚拟导游的行为模型研究

行为模型是虚拟导游的关健内容之一,文章从导游行为的特点出发对虚拟导游的行为模型进行研究,并基于此提出一种由感知器、领域知识库和决策推理器三部分组成的行为模型.感知器感知环境信息,领域知识库记录导游的行为规则,决策推理器基于领域知识库进行行为推理.论文介绍了感知器、领域知识库和决策推理器的作用、原理及其实现方法,并给出了...     

基于T-S模糊推理优化KPI的应用

模糊系统是一种基于知识或基于规则的系统,它的核心就是由所谓的IF-THEN规则所组成的知识库.模糊推理就是针对给定的系统输入,综合运用知识库中的模糊推理规则,获得系统输出的过程.而T-S模糊模型的基本思想是将正常的模糊规则及其推理转换成一种数学表达形式.本文拟将绩效考核与模糊推理的优越性进行有效的结合,研究讨论出T-S模糊推理在绩效考核中的应用.以验证其收敛性及优越性.     

棉纺质量控制专家系统的设计

主要研究棉纺质量控制专家系统的体系构成、棉纺质量控制知识的表示策略及知识库的设计.知识库的设计是该系统设计最重要的工作,包括基本数据库和规则库两方面.知识库的核心内容之一是基本数据库,用来存放质量问题的描述、产生原因、防止和处理办法等.规则库由质量问题库、质量问题影响因素、质量问题处理措施三个库构成.知识库的设计方法是先将棉纺质量控制知识抽象成概念、事实和规则,然后用关系数据库来存储这些知识.在此基础上构建一个基于产生式规则的专家系统.本系统采用基于规则的数据和信息驱动正向推理为主,辅之以目标驱动控制策略,算法采用深度优先.合理的知识库设计和有效的推理方法使得该系统效果较好.     

基于粗糙集理论的故障诊断系统知识库设计

故障诊断是与有效决策密切相关的复杂而困难的问题。粗糙集理论可以有效地分析、处理不完备信息。知识库是整个故障诊断系统的核心,利用基于粗糙集的知识约简和决策规则提取算法,将柴油机故障信息值进行约简,求出其决策规则。知识库由事实库和规则库组成。在知识库中采用链表数据结构,以数据文件形式存储,完成知识库设计的程序。采用粗糙集方法进行故障条件属性约简十分有效,得到简化的决策规则,使得知识库的设计更加方便快捷。     

Closed-loop control of depth of anaesthesia: A simulation study using auditory evoked responses

Using wavelet transforms, a system that assesses depth of anaesthesia is proposed using auditory evoked potential measurements. The recorded auditory evoked potential signal is analysed by extracting its features which are fed to a fuzzy logic rule-base, which in turn decides on the depth of the anaesthetic state. The fuzzy logic rule-base is based on the pharmacological knowledge provided by the anaesthetist.     

基于等价压缩快速聚类的Web表格知识抽取

Web表格知识抽取是一种重要的获取高质量知识的途径,在知识图谱、网页挖掘等方面具有广泛的研究意义与应用价值。传统的Web表格知识抽取方法主要依赖于良好的表格结构和足够的先验知识,但在复杂的表格结构以及先验知识不足等情形下难以奏效。针对这类方法的问题,该文通过充分利用表格自身的结构特点,提出了一套可面向大规模数据的基于等价压缩快速聚类的Web表格知识抽取方法,以无监督的聚类方式获得相似形式结构的表格,从而推测其语义结构以抽取知识。实验结果表明,基于等价压缩的快速聚类算法在保持同水平的聚类准确率的前提下,在时间性能上相比传统方法有大幅度的提升,5 000个表格的聚类时间由72小时缩短为20分钟,且在表格聚类后利用表格模板所抽取的知识三元组的准确率也达到了令人满意的结果。     

Iterative learning belief rule-base inference methodology using evidential reasoning for delayed coking unit

The belief rule-base inference methodology using evidential reasoning (RIMER) approach has been proved to be an effective extension of traditional rule-based expert systems and a powerful tool for representing more complicated causal relationships using different types of information with uncertainties. With a predetermined structure of the initial belief rule-base (BRB), the RIMER approach requires the assignment of some system parameters including rule weights, attribute weights, and belief degrees using experts’ knowledge. Although some updating algorithms were proposed to solve this problem, it is still difficult to find an optimal compact BRB. In this paper, a novel updating algorithm is proposed based on iterative learning strategy for delayed coking unit (DCU), which contains both continuous and discrete characteristics. Daily DCU operations under different conditions are modeled by a BRB, which is then updated using iterative learning methodology, based on a novel statistical utility for every belief rule. Compared with the other learning algorithms, our methodology can lead to a more optimal compact final BRB. With the help of this expert system, a feedforward compensation strategy is introduced to eliminate the disturbance caused by the drum-switching operations. The advantages of this approach are demonstrated on the UniSim? Operations Suite platform through the developed DCU operation expert system modeled and optimized from a real oil refinery.     

基于模糊聚类的粗糙集决策表简化方法研究

决策表是一种特殊而重要的知识系统,在决策支持和数据挖掘等领域有着重要的应用。该文给出了一种基于模糊聚类的粗糙集决策表分析方法。该方法结合模糊集和粗糙集理论,由模糊聚类得出模糊决策表,并可以方便地构造决策表和对决策规则表进行简化。     

基于列重合度的网络表格一致性扩展

网络表格的扩展是根据已知信息扩展与主列相关的其他属性列,以满足人们通过表格获取感兴趣信息的需求。目前的研究工作主要针对由主列和待扩展列组成的实体-属性二元表,并将主列视为其他属性列扩展的唯一依据,但该技术运用到具有多个待扩展列的网络表格时,由多个二元表拼接而成的结果表很容易出现实体不一致现象。综合考虑各属性列间以及元组行间的关系,提出一致性支持度概念,设计并实现了基于列重合度的表格一致性扩展系统CCA,其既能保证候选值的高匹配分数,又能使结果表中填值所使用的数据源表数目最小化,有效地避免了实体不一致问题。实验表明,与现有方法相比CCA系统有更高的精确度、覆盖率、一致性,以及更低的查询时间代价。     

Neighborhood systems and approximate retrieval

An approximate retrieval model is proposed based on the notion of neighborhood systems. The knowledge used in the model consists of an information table, in which each object is represented by its values on a finite set of attributes, and neighborhood systems on attribute values, which provide semantic similarity or closeness of different values. An information table can be used for exact retrieval. With the introduction of neighborhood systems to information tables, one is able to perform approximate retrieval. Operations on neighborhood systems are introduced based on power algebras. An ordering relation representing the information of a neighborhood system is suggested and examined. Approximate retrieval is carried out by the relaxation of the original query using neighborhood systems, and the combination of intermediate results using neighborhood system operations. The final retrieval results are presented according to the proposed ordering relation. In contrast to many existing systems, a main advantage of the proposed model is that the retrieval results are a non-linear ordering of objects.     

一种基于时间戳的简单表缩减算法

表约束是一种外延的知识表示方法,每个约束在对应的变量集上列举出所有支持或禁止的元组.广义弧相容（generalized arc consistency,简称GAC）是求解约束满足问题应用最广泛的相容性.Simple Tabular Reduction（STR）是一类高效的维持GAC的算法.在回溯搜索中,STR动态地删除无效元组,降低了查找支持的开销,并拥有单位时间的回溯代价,在高元表约束上获得了广泛运用,并有大量基于STR的改进算法被提出,其中,元组集的压缩表示是目前研究较多的方法.同样基于动态维持元组集有效部分的思想,为STR提出一种检测并删除无效元组和为变量更新支持的算法,作用于原始表约束并拥有单位时间的回溯代价.实验结果表明,该算法在表约束上维持GAC的效率普遍高于现有的非基于压缩表示的STR算法,并且在一些实例上的效率高于最新的基于元组集压缩表示的STR算法.     

Belief rule-base inference methodology using the evidential reasoning Approach-RIMER

In this paper, a generic rule-base inference methodology using the evidential reasoning (RIMER) approach is proposed. Existing knowledge-base structures are first examined, and knowledge representation schemes under uncertainty are then briefly analyzed. Based on this analysis, a new knowledge representation scheme in a rule base is proposed using a belief structure. In this scheme, a rule base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule base is capable of capturing vagueness, incompleteness, and nonlinear causal relationships, while traditional if-then rules can be represented as a special case. Other knowledge representation parameters such as the weights of both attributes and rules are also investigated in the scheme. In an established rule base, an input to an antecedent attribute is transformed into a belief distribution. Subsequently, inference in such a rule base is implemented using the evidential reasoning (ER) approach. The scheme is further extended to inference in hierarchical rule bases. A numerical study is provided to illustrate the potential applications of the proposed methodology.     

A multi-level conceptual data reduction approach based on the Lukasiewicz implication

Starting from fuzzy binary data represented as tables in the fuzzy relational database, in this paper, we use fuzzy formal concept analysis to reduce the tables size to only keep the minimal rows in each table, without losing knowledge (i.e., association rules extracted from reduced databases are identical at given precision level). More specifically, we develop a fuzzy extension of a previously proposed algorithm for crisp data reduction without loss of knowledge. The fuzzy Galois connection based on the Lukasiewicz implication is mainly used in the definition of the closure operator according to a precision level, which makes data reduction sensitive to the variation of this precision level.