An experimental study of adaptive control for evolutionary algorithms

In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm.     

Friction compensation controller for load varying machine tool feed drive

The load applied to a machine tool feed drive changes during the machining process as material is removed. This load change alters the Coulomb friction of the feed drive. Because Coulomb friction accounts for a large part of the total friction the friction compensation control accuracy of the feed drives is limited if this nonlinear change in the applied load is not considered. This paper presents a new friction compensation method that estimates the machine tool load in real time and considers its effect on friction characteristics. A friction observer based on a Kalman filter with load estimation is proposed for friction compensation control considering the applied load change. A specially designed feed drive testbed that enables the applied load to be modified easily was constructed for experimental verification. Control performance and friction estimation accuracy are demonstrated experimentally using the testbed.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

基于混合智能的多Agent个性化信息推荐

在基于Agent的个性化网页推荐中，目前主要有两种过滤方法：基于内容的过滤和基于多Agent合作的过滤。在分析了单独使用这两种方法存在的不足之后，提出了基于改进蚁群算法的聚类分析建立用户模型的算法，并给出了结合两种方法优点的用户模型主动学习算法，最后给出了个性化信息推荐模型及相关算法。     

嵌人式与独立存储器设计上的差异分析和研究

进入21世纪,随着集成电路的发展,SoC(System on Chip)片上系统应运而生。而作为SoC重要组成部分的嵌入式存储器,在SoC中所占的比重正逐步增加,并起着越来越重要的作用,那么嵌入式存储器与独立的存储器芯片在设计上存在着哪些差异?对此本文将以NOR型闪存为例在制造工艺的选取、衍生产品的设计、功耗与噪声、后端功能仿真、测试与修复等方面进行分析和研究。     

构建媒体网络信息安全体系

通过对广州市电视台综合信息平台网络系统的应用现状及安全风险进行分析,提出基于网络层、系统层、用户层和安全策略管理全方位的网络信息安全体系.     

提高托盘利用效率的重要手段——电子信息化管理

托盘能否有效运用对物流的畅通与否关系重大,这已经引起了国内外专家和学者们的重视,并就此制定了托盘电子信息化管理系统,旨在更合理地使用托盘,推进托盘一体化和共用系统的建立。而RFID技术在托盘的电子信息化管理的应用更将大大提高物流的效率与准确性,托盘电子信息化管理将成为今后托盘管理的趋势。     

数字电视用户管理系统

课题实现了数字电视用户管理系统的设计与开发,以Oracle数据库作为后台数据库,以C Builder 6作为前台开发工具,包括表的设计与界面的设计.实现了数字电视用户管理系统基本功能,基本上满足了实际商业运营的需要.     

阴,阳离子聚合物地层内凝胶化改善水驱效果的研究

研究了一杆阴离子聚合物（Ａｃ５３０）和一种阳离子聚合物（Ｍｂ５８１）在水溶液中形成凝胶的条件和过程、凝胶化学结构、形态、稳定性和力学性能。在模拟地层的二维微观模型内观测了两种聚合物驱替渗流、相逼、形成凝胶、凝胶封堵大孔道的机理。在亲水填砂模型内测定了阴、阳离子聚合物凝胶体系降低渗透率的能力。