Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

Database Encoding and A New Algorithm for Association Rules Mining

This paper proposes a method to encode database. By this way, a record is denoted by only one binary number, so the size of the database is reduced sharply. If some known modified algorithms are used on the database encoded, the efficiency will be improved significantly. At the meantime, a new algorithm based on the proposed encoding method is introduced too. By using some properties of numbers, the itemsets of the database can be converted into numerical fields. Different from the Apriori, the new algorithm discovers the association rules from the largest frequent itemset at first, and then all subitemsets, which are also frequent, will be gotten without any calculation, and all the other small none-frequent itemsets that must be generated in the Apriori will be omitted, and the scan times of the database are also reduced. Test results show the new algorithm based on the encoding database has a lower complexity of time and space.     

Designing Templates for Mining Association Rules

Current approaches to data mining usually address specific userrequests, while no general design criteria for the extraction of associationrules are available for the end-user. In this paper, we propose aclassification of association rule types, which provides a general frameworkfor the design of association rule mining applications. Based on theidentified association rule types, we introduce predefined templates as ameans to capture the user specification of mining applications. Furthermore,we propose a general language to design templates for the extraction ofarbitrary association rule types.     

网络法律浅析

随着当今网络技术的迅猛发展,人们的行为和生活越来越受到其影响,发生着改变,故调整其行为规范的网络相关法律法规也越来越受到人们的关注。从法理学角度出发对网络法律进行构建与研究,在此基础上规范其基本理念和适用原则,对于建立完善网络法律体系尤为重要。本文从法理学角度出发,对网络法律的基本理念和原则进行了简要探析。     

Inference rules using local contexts

We construct equivalent localized versions of a formula, adding assumptions simultaneously to various locations, where the particular location determines what is added. Inference rules that take advantage of localized formulas are presented for sequent calculi in which the left hand side of sequents can be used to accumulate the background assumptions (or contexts) of assertions. The intended application is to the automatic generation of tractable justifying lemmas for substitution operations for interactive proof development systems, especially those concerned with mathematical topics where manipulation of deeply embedded terms is desirable.     

基于云计算的物联网数据挖掘

物联网是一个巨大的、分布广泛的物与物相连的网络,其上产生的海量数据通常是与时间和空间相关的,具有动态、异构、分布的特性,因此对这些数据的挖掘非常困难,而且耗费时间和内存,效率低下。为了解决这些问题,提出了一种基于云计算的物联网数据挖掘系统:将这些海量数据转化为PML数据文件,并存储在HDFS中,同时把挖掘任务分配到多台节点服务器上并行处理。采用Hadoop平台,将关联规则Apriori算法Map/Reduce化,提高了挖掘的效率。另外,采用副本策略将计算向存储迁移,将失效节点的计算迁移到副本数据存储节点就地执行,降低了数据传输的时间,大为提高了挖掘的效率。     

兴趣度在基于关联分析的选课指导系统中的研究与应用*

简要地介绍了数据挖掘技术,通过对关联分析的经典算法Apriori在学生选课指导系统中的应用分析,发现了Apriori不适合学生选课指导系统的缺陷.提出了增加兴趣度阈值以减少产生的无用规则,提高挖掘精度,克服原系统缺陷的新算法,为学生选课辅助决策提供了良好的理论依据和实现方法.     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

一种基于支持向量机的模糊施肥预测方法研究

提出了一种利用支持向量机(SVM)学习算法提取模糊规则进而实现施肥预测的方法.对于农业施肥中常用的正交实验法,由于其数据均衡分散使得曲线拟合的回归预测方法效果不佳.提出了一种利用SVM学习样本数据,再利用隶属度来提取模糊规则,通过阈值和可信度来控制规则的激活和准确性的预测方法,这一方法不仅避免了回归预测所产生的误差,并且模糊规则更具有实际意义,从而大大提高了知识获取的能力.     

基于情景感知的相关推荐信息挖掘研究

通过对移动用户在位置、时间、业务需求即用户所处情景的综合挖掘分析,本文分三个步骤为移动用户提供当前情景下与用户当前业务需求相关性最强的业务信息以及下一情景下与当前情景下业务需求相关性最强的业务信息的主动推荐.