云计算环境下多来源数据最优选取模型仿真

在云计算环境下多来源数据最优选取的研究中,由于来自不同的服务器数据具有不同特征,数据所含信息的融合度也不同,使得数据可选取特征存在较强的关联性.传统的数据最优选取模型,在进行选择时,关联性会给最优数据的选择带来干扰,选出大量的相关数据,不能很好地排除关联性数据,无法实现数据的最优高效选取,提出一种依据数据估计量变化的云计算环境下多来源数据最优选取模型,利用前几点数据拟合直线或曲线,进行云计算环境下多来源数据最优选取中干扰数据的预测,分析干扰数据点删除前后推断统计量的变化,检测该干扰数据点是不是强干扰点,过滤强干扰数据点后的模型则是云计算环境下多来源数据最优选取模型,通过分析数据排除模型引起的估计量的变化,实现云计算环境下多来源数据的最优选取.仿真结果说明,在大量云计算环境下多来源数据的处理中,采用所提模型可有效选取最优数据,并且具有较高的准确率.     

面向移动机器人应用的跨平台自适应软件框架

摘要：自适应软件在移动机器人上具有广泛的应用前景,然而要在具有不同软硬件平台的移动机器人上开发自适应软件是一件非常困难的事情．．为了解决这个问题,提出并实现了一个面向移动机器人应用的跨平台自适应软件框架该框架屏蔽了运行平台的软硬件差异性,并使用规则文件将自适应逻辑分离出来,从而降低了自适应软件开发的难度。为了便于编写及检查规则文件,还实现了一个可视化的规则文件编辑器,用于自动生成模型图,并检查规则中存在的语法及逻辑错误i,最后,通过实验测试了框架的使用情况,并对框架的应用效果进行了评估、     

Rheological behavior of reactive blending of epoxidized natural rubber with cassava starch and epoxidized natural rubber with natural rubber and cassava starch

Epoxidized natural rubbers (ENR‐25 and ENR‐45) were prepared using the performic epoxidation method. Two‐component (ENR–cassava starch) and three‐component (ENR–NR–cassava starch) blends were prepared. ENR‐25 and ENR‐45 were blended with various quantities of gelatinized cassava starch in the latex state. The pure ENR exhibited lower shear stress and shear viscosity than those of the blends with cassava starch. Furthermore, the shear stress and shear viscosity were increased with an increase in the cassava starch concentration. The chemical interaction between the epoxide groups in the ENR and the hydroxyl groups in the cassava starch molecules might be the reason for the increasing trends of the shear stress and shear viscosity. The blends are classified as compatible blends because of the strong chemical bonding between different phases. SEM micrographs were used to clarify the compatibility. Power law behavior with pluglike flow profiles was observed for all sets of ENR–NR–cassava starch blends. Very low power law index values (<0.34) and highly pseudoplastic fluid behavior were also observed. The log additive rule was applied to plots of zero shear viscosity (consistency index) and the shear viscosity versus the concentration of ENR‐25. Positive deviation blending was observed, which indicates compatible blends. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 1752–1762, 2004     

基于软分区的地区电网自动电压控制系统

介绍贵港地区电网基于软分区的三级自动电压控制（AVC）系统,并对该系统的三级控制原理及区域变电站的控制规则进行说明。     

立方型状态方程结合Eyring理论在液体混合物黏度计算中的应用

Cubic equations of state (EOS) have been combined with the absolute rate theory of Eyring to calculate viscosities of liquid mixtures. A modified Huron Vidal gE-mixing rule is employed in the calculation and in comparison with the van Laar and the Redlich-Kister-type mixing rule. The EOS method gives an accurate correlation of liquid viscosities with an overall average deviation less than 1% for 67 binary systems including aqueous solutions. It is also successful in extrapolating viscosity data over a certain temperature range using parameters obtained from the isotherm at a given temperature and in predicting viscosities of ternary solutions from binary parameters for either polar or associated systems.     

无人驾驶规则的自动提取方法研究

无人驾驶汽车的测试技术主要是通过虚拟测试和场地测试的方法来检测无人驾驶汽车的智能化水平,通过不断的学习和总结经验来提高无人驾驶的安全性,这种方法耗费大量的人力物力以及时间。为此,从文本的角度出发,利用驾驶行为相关文本,通过NLP技术和基于规则的方法构建一个无人驾驶规则库,依据这个规则库来辅助测试无人驾驶汽车能否满足道路安全要求。实验结果表明,对《中华人民共和国道路交通安全法实施条例》处理后规则提取的正确率为89.85%,驾考题库文本的正确率为87.33%。     

Context-aware cognitive design assistant: Implementation and study of design rules recommendations

Design rules are an essential interface to facilitate the information exchange between designers and experts. Despite many innovations in Knowledge-Based Engineering and Knowledge Management, unstructured design rules documents are still widely used in the manufacturing industry. Due to the complexity of the design process, these documents often contain hundreds of design rules, applicable in varying design contexts. Searching for the right rules according to a design context is demanding in time and cognitive resources. In this paper, we propose a Context-Aware Cognitive Design Assistant (CACDA) to capture the design context and perform tasks such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Contrary to existing works in model quality testing, the CACDA uses a proactive approach of design rules application and helps designers to provide error-free designs on first attempt. In this paper, we present the design rules recommendation system of the CACDA, its capabilities and its implementation. Then, to measure the impact of design rules recommendations on the design process, we compare our approach with the use of traditional design rules documents. Results show that the use of the CACDA’s design rules recommendations lower the perceived difficulty of design rules retrieval from 75 to 43.5 on a scale of 100. On average, participants that used the demonstrator successfully applied 8.6 design rules on the 25 applicable design rules of our set. Participants that used unstructured documentation correctly applied 4.3 design rules. The global cognitive weight of the design activity as well as the design rules retrieval performances appear to be unchanged. These results demonstrate the usability of the demonstrator and show a positive impact on the design process and on the quality of CAD models. Future works will focus on the overcome of the main limitations of our current experiments, with a panel of professional designers, a lager design rules set and the implementation of several lacking features of the CACDA into the demonstrator.     

基于频繁项挖掘的贝叶斯网络结构学习算法BNSL-FIM

贝叶斯网络能够表示不确定知识并进行推理计算表达,但由于实际样本数据存在噪声和大小限制以及网络空间搜索的复杂性,贝叶斯网络结构学习始终会存在一定的误差。为了提高贝叶斯网络结构学习的准确度,提出了以最大频繁项集和关联规则分析结果为先验知识的贝叶斯网络结构学习算法BNSL-FIM 。首先从数据中挖掘出最大频繁项集并对该项集进行结构学习,之后使用关联规则分析结果对其进行校正,从而确定基于频繁项挖掘和关联规则分析的先验知识。然后提出一种融合先验知识的BDeu评分算法进行贝叶斯网络结构学习。最后在6个公开标准的数据集上开展了实验,并对比引入先验/不引入先验的结构与原始网络结构的汉明距离,结果表明所提算法与未引入先验的BDeu评分算法相比显著提高了贝叶斯网络结构学习的准确度。     

基于计算机技术在医院信息化管理系统设计

针对医院信息管理工作难度大,数据种类复杂并且对于医院管理数据利用率低等问题,设计一种医院信息管理系统,该系统软件设计采用C/S架构记性设计;针对医院数据挖掘技术,通过改进Apriori算法和增量决策树算法对数据进行处理,提高医院信息利用率;并通过设计模拟实验方案对设计的算法进行验证,其中对于改进Apriori算法与原始的Apriori算法相比起处理速度提升了 10倍;对于增量决策树算法分类的准确率比C4.5算法和ID3算法高5％以上,并且在增量学习中耗时是C4.5算法和ID3算法的40％以下.     

利用置信规则库构建WSN节点故障检测模型

在无线传感器网络(wireless sensor network, WSN)节点故障检测领域的研究过程中,故障检测准确率会受节点数据的不确定性和专家知识模糊性的影响。针对这一问题,本文提出了一种基于置信规则库(belief rule base, BRB)的WSN节点故障检测方法。首先,根据WSN工作原理及节点工作特性描述WSN节点故障检测过程;然后,从空间和时间2个维度对节点数据提取特征,建立基于空间和时间相关性的WSN节点故障检测模型;最后,利用Intel Lab Data无线传感器数据集进行案例研究以验证模型的有效性。结果证明,本文方法能够统筹利用专家知识和节点数据实现WSN节点故障检测。