基于IBL算法的CBR系统中索引与检索机制研究

文章介绍了基于示例学习算法IBL的概况,并对其加以改进,提出了一个新的算法IBL－Cluster。它主要由概念描述形成算法和概念描述修改算法构成。在此基础上建立了应用在基于事例的推理系统CBR中的基于IBL算法的索引与检索机制。实验表明新算法IBL－Cluster在存储空间及测试正确率方面均有改善。     

Augmenting the aho-corasick pattern matching machine

The Aho-Corasick algorithm is a well-known method of determining the occurrences of one of several given pattern strings in a given text string. We address the question of augmenting the pattern matching machine constructed by this algorithm with a new pattern string, both on-line and off-line. We show that augmenting a machine of N nodes with a new pattern string of length m takes Θ(mN) time on-line and Θ(N) time off-line.     

An efficient approach for the maintenance of user behaviours

Mining sequential patterns is to discover sequential purchasing behaviours for most of the customers from a large number of customer transactions. The strategy of mining sequential patterns focuses on discovering frequent sequences. A frequent sequence is an ordered list of the itemsets purchased by a sufficient number of customers. The previous approaches for mining sequential patterns need to repeatedly scan the database so that they take a large amount of computation time to find frequent sequences. The customer transactions will grow rapidly in a short time, and some of the customer transactions may be antiquated. Consequently, the frequent sequences may be changed due to the insertion of new customer transactions or the deletion of old customer transactions from the database. It may require rediscovering all the patterns by scanning the entire updated customer transaction database. In this paper, we propose an incremental updating technique to maintain the discovered sequential patterns when transactions are inserted into or deleted from the database. Our approach partitions the database into some segments and scans the database segment by segment. For each segment scan, our approach prunes those sequences that cannot be frequent sequences any more to accelerate the finding process of the frequent sequences. Therefore, the number of database scans can be significantly reduced by our approach. The experimental results show that our algorithms are more efficient than other algorithms for the maintenance of mining sequential patterns.     

基于关联规则的智能点餐推荐系统设计

餐饮信息化的快速发展将使用智能推荐系统分析客户的消费偏好,向客户提供更具针对性的点餐意见成为现实。推荐系统的运行既可以帮助客户更加合理地点餐、提升客户满意度,又可以有效提升餐饮企业的销售水平。文章在对基于关联规则的推荐系统进行分析讨论的基础上,提出面向分类预测的增量关联规则算法获取推荐规则,并进行实验验证。最后,在此基础上对点餐推荐系统的结构设计进行了研究。     

Constraint graph-based frequent pattern updating from temporal databases

There have been many kinds of association rule mining (ARM) algorithms, e.g., Apriori and FP-tree, to discover meaningful frequent patterns from a large dataset. Particularly, it is more difficult for such ARM algorithms to be applied for temporal databases which are continuously changing over time. Such algorithms are generally based on repeating time-consuming tasks, e.g., scanning databases. To deal with this problem, in this paper, we propose a constraint graph-based method for maintaining frequent patterns (FP) discovered from the temporal databases. Particularly, the constraint graph, which is represented as a set of constraint between two items, can be established by temporal persistency of the patterns. It means that some patterns can be used to build the constraint graph, when the patterns have been shown in a set of the FP. Two types of constraints can be generated by users and adaptation. Based on our scheme, we find that a large number of dataset has been efficiently reduced during mining process and the gathering information while updating.     

在内核下的BootLoader自动更新方法

目前的BootLoader更新方法仅适用于单块目标板的调试阶段,无法实现批量更新。为此,提出一种在内核下的BootLoader自动更新方法。该方法提取新BootLoader镜像文件中的有效数据,并将其保存到以“.h”结尾的文件中,利用特制的Flash驱动程序及该类型文件生成特制的内核,通过外部移动存储设备,将特制内核更新到嵌入式系统中,特制内核启动后将自动更新BootLoader,从而完成BootLoader的批量更新。     

基于信任评估带权重的动态秘密共享方案

已有的秘密共享方案中参与方权重固定不变,不能满足实际应用的问题。为此,基于信任评估模型,提出一种参与方权重可以动态变化的秘密共享方案。根据参与方的不同行为对其子秘密数量进行调整,并改变其秘密分享职能。在删除不诚实成员后可以加入新的成员,在不改变参与方共享秘密的条件下,可以先验式地更新参与方所拥有的秘密份额。分析结果表明,该方案是一个安全的、参与方权重以及子秘密可动态改变的秘密共享方案。     

一种有效的道路背景提取与更新算法

针对复杂环境中道路背景图像的快速获取问题,提出了一种快速有效的道路背景提取和更新算法。应用改进的多帧平均算法提取背景,采用改进的Surendra算法对背景进行更新。实验结果表明,该算法能够减轻初始静止车辆对背景建立的影响,能及时消除由于初始帧中目标移动而造成的鬼影,对光线变化鲁棒性高、速度快、更新效果好。     

含无序量测的多传感器信息融合算法研究

由于通信时间的延迟性,在多传感器系统中常常会出现无序量测情况.为提高估计的精度,系统须对无序量测进行处理,状态更新法是处理无序量测问题的一种有效方法.对于多传感器信息融合问题,给出了一种处理无序量测的状态估计更新算法.仿真计算表明该算法能有效的提高融合估计的精度.     

用不完全模态试验数据修正粘性阻尼矩阵

假定质量矩阵、刚度矩阵、复振型是精确的或已修正的，研究用不完全复模态试验数据修正粘性阻尼矩阵。依据特征方程、粘性阻尼矩阵的对称性和半正定性，利用代数特征值反问题的理论和方法，给出了问题解的存在性和唯一性，提出了修正粘性阻尼矩阵的一个新方法。用这个方法修正的粘性阻尼矩阵不仅满足特征方程，而且是唯一的最优对称半正定矩阵。最后用两个例子说明本文方法的有效性。