LALR(1)分析器快速生成

根据LR(0)自动机的构造理论及Deremer和Pennello的LALR(1)向前看符号集计算公式,提出求解公式中的lookback关系和includes关系的高效算法. 研究过程表明,LR(0)项目集闭包计算和项目集的查找是LR(0)分析器构造过程中的主要性能瓶颈.对这两个计算过程给出了高效的数据结构和算法设计,实现了LALR(1)分析器的快速生成.系统实现及实验数据表明,LALR(1)分析器的生成速度超过了自由软件基金会的LALR(1)分析器生成器Bison.      

基于SVM的浮动车行驶模式判断模型

浮动车在低速情况下存在两种行驶模式,如不能对上述模式进行准确区分,将严重影响浮动车实时路况计算的精度和效率.研究和设计了一个基于支持向量机(SVM,Support Vector Machine)的浮动车行驶模式判断模型,并针对性地提出了一种简单的基于隶属度矩阵的特征评价和选择方法.实验表明通过上述方法选择的特征子集所训练的分类器在测试样本集上具有92.6%的分类准确性;经过行驶模式分析后,浮动车系统的准确性有显著提升.      

Introduction to the Payloads and the Initial Observation Results of Chang＇E-1

Chang'E-1,the orbiter circling the moon 200km above the moon surface,is the first Chinese Lunar exploration satellite.The satellite was successfully launched on 24th October 2007.There are 8 kinds of scientific payloads onboard,including the stereo camera,the laser altimeter,the Sagnac-based interferometer image spectrometer,the Gamma ray spectrometer,the X-ray spectrom-eter,the microwave radiometer,the high energy particle detector,the solar wind plasma detector and a supporting payload data management system.Chang'E-1 opened her eyes to look at the moon and took the first batch of lunar pictures after her stereo camera was switched on in 20th November 2007.Henceforth all the instruments are successfully switched on one by one.After a period of parameter adjustment and initial check out,all scientific instruments are now in their normal operating phase.In this paper,the payloads and the initial observation results are introduced.     

基于L1范数的k平面聚类算法设计

基于L2范数度量的k平面聚类(k-Plane Clustering,k PC)设计思想,本文提出了一种采用L1范数度量的聚类算法。由于在平面更新步骤中,所导出的优化问题是非凸的,文中给出了一种求解方法,即将非凸问题转化为有限个子集上的凸问题,为避免求解多个优化问题导致训练时间过长问题,本文还设计了一种新的优选策略,有限个子集的搜索任务可在线性时间内完成。本文所提出的方法只需要求解k个线性规划,而不再是k PC的求解特征值问题。在人工和UCI数据集上的实验结果表明:基于L1范数平面聚类算法的训练和测试时间更短,且在大多数数据集上均表现出了更好的聚类性能。     

A new global version of M(3000)F2 prediction model based on artificial neural networks

A new version of global empirical model for the ionospheric propagation factor, M(3000)F2 prediction is presented. Artificial neural network (ANN) technique was employed by considering the relevant geophysical input parameters which are known to influence the M(3000)F2 parameter. This new version is an update to the previous neural network based M(3000)F2 global model developed by Oyeyemi et al. (2007), and aims to address the inadequacy of the International Reference Ionosphere (IRI) M(3000)F2 model (the International Radio Consultative Committee (CCIR) M(3000)F2 model). The M(3000)F2 has been found to be relatively inaccurate in representing the diurnal structure of the low latitude region and the equatorial ionosphere. In particular, the existing hmF2 IRI model is unable to reproduce the sharp post-sunset drop in M(3000)F2 values, which correspond to a sharp post-sunset peak in the peak height of the F2 layer, hmF2. Data from 80 ionospheric stations globally, including a good number of stations in the low latitude region were considered for this work. M(3000)F2 hourly values from 1987 to 2008, spanning all periods of low and high solar activity were used for model development and verification process. The ability of the new model to predict the M(3000)F2 parameter especially in the low latitude and equatorial regions, which is known to be problematic for the existing IRI model is demonstrated.     

高精度UT1-UTC差分预报方法研究

针对卫星导航所需的高精度地球定向参数(EOP)中的UT1-UTC预报问题,提出了基于双差分LS+AR的UT1-UTC参数预报方法。对UT1-UTC观测数据进行跳秒检测、固体地球带谐潮汐项改正,然后对改正后的UT1-UTC数据进行双差分处理,增强数据平稳性;采用最小二乘拟合(LS)与自回归(AR)分析方法对差分处理后的数据进行分析与预报;对预报结果进行逆差分处理与潮汐项改正外推、跳秒恢复,获取高精度的UT1-UTC预报值。通过与国际EOP_PCC预报结果对比表明,UT1-UTC短期预报精度与EOP_PCC较优的预报精度相当,其中1天UT1-UTC预报精度优于0.03ms,优于EOP_PCC预报结果。介绍了北京航天飞行控制中心的UT1-UTC每日例行预报情况。     

高速列车绕流的数值模拟

给出了用低速N－S方程求解高速列车绕流的数值计算方法，采用显隐式结合的方法来加快收敛速度，基于该方法编制了计算软件系统Train3D，用该软件系统对高速列车外形的外流场进行了数值模拟，获得了该外形的空气动力特性和流场结构。     

有机高分子絮凝剂处理高浊度原水效果和机理研究

本文研究了有机高分子絮凝剂ＰＡＭ、ＣＴＡＢ及它们的复配ＰＡＭ＋ＣＴＡＢ处理高浊度原水的最佳实验条件、絮凝性能、效果和机理，为有机高分子絮凝剂的选择和管式絮凝器的研制提供了理论依据。     

随机疲劳裂纹扩展及动态可靠性分析

在确定性袭纹扩展模型的基础上，建立了疲劳裂纹扩展的随机模型。考虑裂纹扩展引起的强度衰减，建立了实用的动态可靠性分析模型。     

优化的GM(1,1)模型及其适用范围

在已有灰色理论的基础上,利用“最小二乘法”确定GM(1,1)白化权函数的时间响应函数中的常数c,摈弃了传统GM(1,1)把原始序列中x^(0)(1)作为初始条件的欠科学的做法,构建了时间响应函数的优化模型。经大量的数据模拟和预测,发现优化的GM(1,1)模型各项指标均优于传统的GM(1,1)模型,且拓宽了其适用范围。