Effect of nucleating agents and heat treatments on the crystallization of magnesium aluminosilicate transparent glass-ceramics

The crystallization behavior and transparent property of MgO-Al2O3-SiO2 (MAS) glasses with TiO2 and TiO2+ZrO2 as nucleating agents were discussed by differential thermal analysis, X-ray diffraction, field emission-environment scanning electron microscope, energy dispersive spectrum and UV-VIS-NIR scanning spectrophotometer. It was found that the glass crystallized at 950 ℃ with ZrO2 less than 3% could obtain transparent glass ceramic, which presented purple to colorless. With the nucleating agent additives (5% TiO2+3% ZrO2), the colorless transparent glass-ceramics with spinel as the main crystal phase could be prepared, and the transmittance reached about 80% . As the crystallized temperature increase to 1 000 ℃, besides spinel(MgAl2O4), sapphirine (Mg3.5Al9Si1.5O20) and ZrTiO4 precipitated from matrix glass, and the transmitance of glass-ceramic decreased.     

耦合Schrodinger-KdV方程的行波解

为了得到Schrodinger-KdV方程的行波解,运用平面动力系统理论方法,对其动力学行为进行研究,证明了该方程光滑孤立波解和光滑周期波解的存在性,并在不同的参数条件下,给出了各类解存在的充分条件,求出了所有显式精确行波解。     

一种功能可配置的业务中间件软件产品及其应用

探讨一种功能可配置的业务中间件软件产品及其在业务系统开发中的应用.阐述该系统实现的原理,分析系统体系结构与主要组成,研究系统实现方法及其特点.以国土资源电子政务信息系统为例,说明该软件产品的应用及效果.该研究为众多领域的业务信息系统的高效研发提供了一种新的技术与工具支持.     

一种新的频繁子树挖掘算法研究与实现

为提高频繁子树挖掘算法效率,结合原有频繁子树挖掘算法FSubtreeM的相关技术提出了新的全局树引导结构及其相关引理,并证明了其正确性.最后提出了新的频繁子树挖掘算法FSM_CGTG,并通过实验证明了该算法在现实数据集上的有效性且比现有频繁子树挖掘算法FSubtreeM性能优越.     

Preface

Recently, there has been a dramatic increase in research on data mining on social networks and social media. Theubiquitous nature of Web-enabled devices, including desktops,     

A tunable support vector machine assembly classifier for epileptic seizure detection

Automating the detection of epileptic seizures could reduce the significant human resources necessary for the care of patients suffering from intractable epilepsy and offer improved solutions for closed-loop therapeutic devices such as implantable electrical stimulation systems. While numerous detection algorithms have been published, an effective detector in the clinical setting remains elusive. There are significant challenges facing seizure detection algorithms. The epilepsy EEG morphology can vary widely among the patient population. EEG recordings from the same patient can change over time. EEG recordings can be contaminated with artifacts that often resemble epileptic seizure activity. In order for an epileptic seizure detector to be successful, it must be able to adapt to these different challenges. In this study, a novel detector is proposed based on a support vector machine assembly classifier (SVMA). The SVMA consists of a group of SVMs each trained with a different set of weights between the seizure and non-seizure data and the user can selectively control the output of the SVMA classifier. The algorithm can improve the detection performance compared to traditional methods by providing an effective tuning strategy for specific patients. The proposed algorithm also demonstrates a clear advantage over threshold tuning. When compared with the detection performances reported by other studies using the publicly available epilepsy dataset hosted by the University of BONN, the proposed SVMA detector achieved the best total accuracy of 98.72%. These results demonstrate the efficacy of the proposed SVMA detector and its potential in the clinical setting.     

一种改进的多机器人协作实时FastSLAM算法

针对FastSLAM1.0中机器人缺乏自身定位测量修正引起的累积误差和FastSLAM2.0引入测量修正引起算法复杂度增加的问题,提出一种改进的基于辅助测量的多机器人协作实时FastSLAM算法,使用双机器人协同工作,领头机器人负责完成同时定位与地图构建任务,辅助机器人通过静态相对位置测量为领头机器人提供实时定位测量修正.该辅助测量方法不仅为SLAM任务执行机器人提供较准确的定位测量值,同时也避免了FastSLAM2.0算法中额外的算法复杂度问题.实验结果表明算法既可以获得较高的精度,而且方便可行,具有较高实用价值.     

基于pay-as-you-go模式的Web服务发现

Web服务描述语言是基于模式优先(schema-first)的,Web服务发现方法需要花费较高的前期构建成本,目前的研究没有涉及如何在pay-as-you-go模式下发现Web服务这一问题.提出了一个基于数据空间技术的服务发现框架,支持pay-as-you-go模式下基于关键词匹配和基于相似度的Web服务发现.首先给出一个schema-later数据模型用于描述Web服务及其关系,并讨论了该模型的延迟计算和查询方法,该方法不必事先物化模型就可以提供查询服务;然后给出一个基于相似度的Web服务发现算法及其正确性证明,该算法将相似度看作是极松散结构模型的虚拟边(关系),在计算相似度之前,通过延迟计算得到需要进行比较的服务节点集及其信息,以便在pay-as-you-go模式下计算相似度;最后通过实验表明该方法是可行和有效的.     

基于K-均值聚类的神经切片图像功能束类型识别研究

针对周围神经物理切片图像中不同类型的神经功能束染色后显示出的特异性,提出了一种基于K-均值聚类的神经切片染色图像中神经功能束类型识别的方法。首先通过特征分析与提取决定以灰度均值肛和方差σ2作为纹理特征,然后运用本文算法对神经切片染色图像中的不同类型神经功能束进行聚类和识别。通过在人体周围神经组织切片图像上的实验证明,该算法能对神经切片染色图像中的神经功能柬类型进行有效分类识别。     

一种基于BP神经网络的数字识别新方法

针对标准BP神经网络收敛速度慢、易陷入局部极小点的缺点,提出了一种新的BP神经网络改进算法。该算法通过变步长法和牛顿法来改进BP算法,加快了网络的收敛速度,且收敛速度快于其他的改进算法。在此基础上将BP神经网络应用于数字识别中,为其网络建立识别模型。利用仿真实验观察BP网络的泛化能力以及识别准确性,比较BP算法及其改进方案,提出改进方案中分别需要注意的地方。