基于不精确信息实例检索模型的研究

传统实例检索模型缺乏对不确定环境中不精确信息的适应性。采用构造因果网络的分阶段实例检索模型,则可以有效地处理实例检索中的不精确性,并能提高基于实例推理系统的性能。     

基于灰色模糊推理的油料消耗预测

为了克服传统预测方法的弊端,提出了基于灰色模糊推理的油料消耗预测方法。首先,构建了基于加权灰色关联分析的案例检索模型,且运用信息熵理论确定灰色关联系数的权重;其次,构建了模糊集理论的案例检索模型,且运用改进的层次分析法确定特征属性的权重;最后,基于上述2种检索结果,运用灰色关联分析方法构建了组合检索模型,并且基于检索结果对油料消耗进行预测。通过算例仿真,证明了上述检索方法具有较高的准确度,验证了预测方法的可行性和实用性。     

基于对等模型的分布式协同设计系统

为了解决分布式协同设计系统中的信息快速检索以及多副本同步等问题,引入了对等模型,给出了该系统的功能模型、信息检索模型等。提出了DHT的对等信息检索方法,保证了用户能够在分布式协同设计系统中快速共享资源。采用基于DHT的分布式互斥等算法作为协同数据的一致性维护方法,给出了系统的具体实现方法和实例。     

基于设计草图的零配件检索算法

在机械制造智能化进程中不可避免地产生了海量零配件模型信息,给数据的高效检索带来了巨大的挑战。考虑到设计草图具备用户友好且轻量级的特性,方法通过构造深度跨域表征模型进行基于设计草图的机械零配件模型检索。针对草图和三维模型的跨模态信息关联问题,提出特征联合学习方法,旨在控制检索对象类内及类间差异的过程中,使特征描述符习得单一域特征的同时融合跨域信息,建立跨模态数据在共嵌空间下的一致性关联表征。最后,利用哈希编码构建索引表实现海量数据的快速检索。在零部件数据上的实验结果表明,所提出的基于设计草图的零配件检索方法在同期方法中既能实现最准确的检索结果,也具备较高的检索效率。方法在提升跨模态零配件信息检索准确性的同时提高了数据管理效率,从而间接提升了产品设计的效率和便捷性,相关系统已经在部分企业落地应用且获得良好反馈。     

Automating the construction of CBR systems using kernel methods

Instance-based reasoning systems and, in general, case-based reasoning systems are normally used in problems for which it is difficult to define rules. Although case-based reasoning methods have proved their ability to solve different types of problems, there is still a demand for methods that facilitate their automation during their creation and the retrieval and reuse stages of their reasoning circle. This paper presents one method based on kernels, which can be used to automate some of the reasoning steps of instance-based reasoning systems. Kernels were originally derived in the context of support vector machines, which identify the smallest number of data points necessary to solve a particular problem (e.g., regression or classification). Unsupervised kernel methods have been used successfully to identify the optimal instances to instantiate an instance base. The efficiency of the kernel model is shown on an oceanographic problem. © 2001 John Wiley & Sons, Inc.     

基于子图同构与事例匹配的装配体局部结构相似性分析

基于事例推理是解决面向装配的设计和装配工艺规划问题的有效方法,装配体检索是应用此方法的关键步骤.为此提出一种基于子图同构与事例匹配的装配体局部结构相似性分析方法.该方法将装配体零件间的装配关系用属性邻接图来表示,通过对装配体中联结关系的定性分类及装配体属性邻接图预处理规则的定义,利用Ullmann算法匹配出相似的装配体图(子图)结构,并给出了装配体局部结构相似度计算公式.最后通过实例验证了文中方法的有效性.     

A new full-text indexing model with low space overhead for chinese text retrieval

Text retrieval systems require an index to allow efficient retrieval of documents at the cost of some storage overhead. This paper proposes a novel full-text indexing model for Chinese text retrieval based on the concept of adjacency matrix of directed graph. Using this indexing model, on one hand, retrieval systems need to keep only the indexing data, instead of the indexing data and the original text data as the traditional retrieval systems always do. On the other hand, occurrences of index term are identified by labels of the so-called s-strings where the index term appears, rather than by its positions as in traditional indexing models. Consequently, system space cost as a whole can be reduced drastically while retrieval efficiency is maintained satisfactory. Experiments over several real-world Chinese text collections are carried out to demonstrate the effectiveness and efficiency of this model. In addition to Chinese, The proposed indexing model is also effective and efficient for text retrieval of other Oriental languages, such as Japanese and Korean. It is especially useful for digital library application areas where storage resource is very limited (e.g., e-books and CD-based text retrieval systems).     

Rational model selection in large engineering knowledge bases

It is generally admitted that several models differing along various dimensions are needed for executing complex engineering tasks such as diagnosis and monitoring. A key problem is thus to decide what model to use in a particular situation in front of a specified problem-solving task and reasoning objectives. We address this problem within the Multimodeling framework for reasoning about physical systems that we proposed in a previous work. After having characterized the space of possible models in the Multimodeling approach, we formulate the selection problem using the conceptual tools offered by the economic theory of rationality. In this frame we illustrate a preference-based model selection method that is used to navigate in the universe of available models of a system searching for the model that best matches a given task and reasoning objectives. The method exploits the use of a model map that is a metalevel concept representing the ontology and teleology of each model and the transformational relations (abstractions and approximations) connecting each model to other models. The model map is used to compare models on the basis of their content and to understand what can be gained or lost when switching from one model to another. Finally, some implications of the foregoing selection method in developing action-based diagnostic systems are discussed.     

Modeling term proximity for probabilistic information retrieval models

Proximity among query terms has been found to be useful for improving retrieval performance. However, its application to classical probabilistic information retrieval models, such as Okapi’s BM25, remains a challenging research problem. In this paper, we propose to improve the classical BM25 model by utilizing the term proximity evidence. Four novel methods, namely a window-based N-gram Counting method, Survival Analysis over different statistics, including the Poisson process, an exponential distribution and an empirical function, are proposed to model the proximity between query terms. Through extensive experiments on standard TREC collections, our proposed proximity-based BM25 model, called BM25P, is compared to strong state-of-the-art evaluation baselines, including the original unigram BM25 model, the Markov Random Field model, and the positional language model. According to the experimental results, the window-based N-gram Counting method, and Survival Analysis over an exponential distribution are the most effective among all four proposed methods, which lead to marked improvement over the baselines. This shows that the use of term proximity considerably enhances the retrieval effectiveness of the classical probabilistic models. It is therefore recommended to deploy a term proximity component in retrieval systems that employ probabilistic models.     

Inheritance rules for flexible model retrieval

Convenient model management requires flexible model retrieval. This paper presents a new flexible retrieval approach for mathematical model bases. The approach defines a multi-valued model inheritance relationship among models at a signature level. The inheritance provides a rich semantic information for the retrieval mechanism to refine inexact retrieval requirements. An inheritance rules reasoning system is proposed to enhance the ability and the efficiency of the model retrieval. The interface of the approach includes an SQL-like command, which enables users to retrieve their required models with inexact requirement expressions. The approach has been implemented in a rule-based mathematical model base system RMMBS. Application examples demonstrate the retrieval approach.     

An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation

In this paper, a quantum neuro-fuzzy classifier (QNFC) for classification applications is proposed. The proposed QNFC model is a five-layer structure, which combines the compensatory-based fuzzy reasoning method with the traditional Takagi–Sugeno–Kang (TSK) fuzzy model. The compensatory-based fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic system more adaptive and effective. Layer 2 of the QNFC model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), quantum fuzzy entropy and the backpropagation algorithm, is also proposed. The proposed SCA method is a fast, one-pass algorithm that dynamically estimates the number of clusters in an input data space. Quantum fuzzy entropy is employed to evaluate the information on pattern distribution in the pattern space. With this information, we can determine the number of quantum levels. The backpropagation algorithm is used to tune the adjustable parameters. The simulation results have shown that (1) the QNFC model converges quickly; (2) the QNFC model has a higher correct classification rate than other models.     

Inheritance rules for flexible model retrieval

Convenient model management requires flexible model retrieval. This paper presents a new flexible retrieval approach for mathematical model bases. The approach defines a multi-valued model inheritance relationship among models at a signature level. The inheritance provides a rich semantic information for the retrieval mechanism to refine inexact retrieval requirements. An inheritance rules reasoning system is proposed to enhance the ability and the efficiency of the model retrieval. The interface of the approach includes an SQL-like command, which enables users to retrieve their required models with inexact requirement expressions. The approach has been implemented in a rule-based mathematical model base system RMMBS. Application examples demonstrate the retrieval approach.     

A comparison of Kernel methods for instantiating case based reasoning systems

Instance based reasoning systems and in general case based reasoning systems are normally used in problems for which it is difficult to define rules. Instance based reasoning is the term which tends to be applied to systems where there are a great amount of data (often of a numerical nature). The volume of data in such systems leads to difficulties with respect to case retrieval and matching. This paper presents a comparative study of a group of methods based on Kernels, which attempt to identify only the most significant cases with which to instantiate a case base. Kernels were originally derived in the context of Support Vector Machines which identify the smallest number of data points necessary to solve a particular problem (e.g. regression or classification). We use unsupervised Kernel methods to identify the optimal cases to instantiate a case base. The efficiencies of the Kernel models measured as Mean Absolute Percentage Error are compared on an oceanographic problem.     

基于本体的语义网检索模型及关键技术研究

为解决传统的基于题,构建了一个基于本体的语义网检索模型.提出了一种领域本体库和应用本体库的构建方法,给出了查询本体的生成以及相似本体匹配推理的方法,实现了以该模型为基础的试验性检索系统.实验结果表明,该模型能够进行本体的语义推理,在一定程度上增强信息检索系统的语义处理能力,检索效率得到了改善.     

基于互信息案例推理的氧气脱碳效率预测模型

提出基于互信息案例推理的氧气脱碳效率预测模型,并依据预测结果计算转炉炼钢静态和动态阶段吹氧量.首先提出一种新的吹氧量预测方法,将氧气脱碳效率作为案例推理的解属性;然后将互信息引入属性权重的确定过程中,解决了传统案例检索方法忽略问题属性与解属性之间信息量的不足.将所提方法用于一座150t转炉的实际生产数据中,仿真结果表明该模型预测精度较高.该方法能够实现对转炉炼钢吹氧量的准确计算,满足实际生产的要求.     

基于模糊聚类和直觉模糊推理的合成旅油料需求预测

需求预测是合成旅组织油料保障的基础环节,对合成旅成功遂行军事行动有着比较重要的影响.由于合成旅组成结构的特殊性,传统预测方法存在较大弊端,因此,提出了基于模糊聚类和直觉模糊推理的合成旅油料需求预测方法.首先,通过模糊C均值聚类算法实现对历史案例的初步筛选,以提高案例检索速度.然后,构建了案例特征属性的主客观综合权重模型和基于直觉模糊集的案例检索模型,保证了案例检索的准确度.最后,构建了基于整体数据特征的合成旅油料需求预测模型.通过算例分析验证上述预测方法的可行性和实用性,证明了该方法有助于提高检索速度和预测准确度.     

知识网格中基于领域本体的智能检索

本文提出知识网格环境下基于领域本体的智能检索模型,采用OWL DL语言对领域知识进行形式化描述,支持推理和深层语义检索.＂标注＂和＂查询优化＂是检索的两个关键技术.通过规范的概念和概念间语义关系对文档片段进行标注,并针对＂一词多义＂问题提出＂主题-概念＂两阶段消歧算法.＂查询优化＂过程中,基于OWL DL推理的优化算法实现查询概念的自动扩展,提高了查全率和查准率.基于以上方法,建立航天领域本体,利用网上数据库开放资源作为测试集进行评测.实验显示,与传统基于     

一种基于切片的三维模型检索算法*

为了更加高效地从三维模型库中检索出相似模型,提出了一种基于切片的三维模型检索算法。首先应用主元分析对三维模型进行预处理;然后对预处理后的模型在不同位置和不同方向上进行切片,根据所得切片断面的相对面积进行三维模型检索。基于该算法设计并实现了三维模型检索系统。实验采用PSB(PrincetonShape Benchmark)提供的三维模型数据库,结果表明把模型的切片截面作为三维模型的特征描述,能有效地区分不同模型,并取得了较好的检索效果。     

基于二分图的装配体检索研究

基于事例推理是解决DFA和装配工艺规划问题的有效方法,装配体检索是应用此方法的关键步骤．对装配体中的联结关系进行表达和分类,提出联结关系定量的比较方法,基于二分图理论建立了装配体之间的相似计算模型,给出了装配体检索过程和计算实例．     

企业数据空间中关联推理机制研究

阐述了企业数据空间的特点,指出了企业数据管理中存在着关联关系繁杂的问题,为了使企业数据空间提供数据检索服务时更具有语义关联性,在分析个人数据空间和知识图谱中推理方式的基础上,提出了基于最短路径的推理过程方法,分别进行了基于单源点的最短路径的推理过程和基于一对源点的最短路径推理过程设计实验,对搜索范围进行了初次缩小,并结...