一种基于最少出现文档频的文本特征提取方法

传统特征提取改进方法在特征分布信息的量化方面存在不足,很大程度上影响了其分类效能。针对这一问题,提出一种基于最少出现文档频的特征提取改进方法,即TF-LDF算法。该算法用最少出现文档频来量化特征类间集中度与类内离散度,能够更加准确地反映特征分布情况。通过实验结果比较,可以证明TF-LDF算法分类效果更佳。     

水声信号的混沌特征参数提取与分类研究

研究了水声信号的混沌特征参数提取以及利用混沌特征参数的水声信号分类。讨论了水声信号的关联维数、最大Lyapunov指数以及时间序列h2熵等混沌特征参数的计算以及它们在水声信号特征提取、分类中的应用。通过对不同类别、一定样本数量的实测水声数据计算它们的混沌特征参数,验证了水声信号不仅具有混沌特性,而且它们的某些混沌特征参数具有可分性。     

Dynamic origin-destination demand estimation using automatic vehicle identification data

This paper proposes a dynamic origin-destination (OD) estimation method to extract valuable point-to-point split-fraction information from automatic vehicle identification (AVI) counts without estimating market-penetration rates and identification rates of AVI tags. A nonlinear ordinary least-squares estimation model is presented to combine AVI counts, link counts, and historical demand information into a multiobjective optimization framework. A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors, and the resulting optimization problems are solved by using an iterative bilevel estimation procedure. Based on a synthetic data set, this study shows the effectiveness of the proposed estimation models under different market-penetration rates and identification rates.     

基于AHP的威胁评估与排序模型研究

层次分析法(AnalyticHierarchyProcess,简称AHP)把一个复杂问题表示为有序的递阶层次结构,通过定性判断和定量计算,将经验判断给予量化,对决策方案进行排序,是一种定性分析与定量分析相结合的决策分析方法,适用于多准则决策问题。通过仿真实验用AHP根据威胁程度的大小对敌空袭目标进行排序,介绍了威胁评估和排序的求解过程,以此提供敌空袭目标对我保卫目标或区域所构成的威胁等级。该方法有效地解决了对敌空中目标威胁评估与排序问题。     

基于Modem的远程控制的C++Builder实现方法的研究

文章主要介绍了Windows环境下开发基于C + +Builder的Modem通信软件来实现远程控制 ,着重介绍了如何利用C + +Builder进行串口编程和Modem的基础通信技术。作为实例 ,还介绍了作者自主开发的一个数控切纸机远程诊断系统。     

文化视野中的建筑功能

功能的理解是建筑创作和建筑理解的关键，功能通常被表述为“物质功能”和“精神功能”，这种表术常常造成一持的分离，形成某些混乱，对此，运用文化人类学的观点方法，能使我们得到一个有价值的功能观。     

世界铁路发展状况及其关键力学问题 全国结构工程学术会议特邀报告

论述了世界部分发达国家和我国铁路发展情况、铁路运输的优点及其在经济建设中所发挥的作用。介绍了世界发达国家和我国铁路发展历史和未来轨道交通发展方向。详细讨论了现有铁路运输存在的问题和未来高速铁路亟待解决的关键力学问题。它们主要是:(1)弓网/列车/轨道(桥梁)大系统耦合动力学; (2)车辆结构强度和可靠度问题;(3)轮轨关系(脱轨、粘着、滚动接触疲劳和波浪形磨损、轮轨噪声);(4)高速列车弓网动力学和高速受流条件下摩擦磨损;(5)轨道结构关键力学和严酷环境下轨道工程破坏问题。这些问题直接危及到行车的安全和运行品质.并提出今后解决这些问题的主要方向。     

高铝质陶瓷蓄热材料的研究开发

按蓄热式热交换器使用性能的要求和高铝质耐火材料的特殊性能和优点，选择适当的原料配方、特殊的成型和烧结工艺，研制开发出性能较为优越的陶瓷球蓄热材料。     

Experimental research progress on critical heat flux of Chinese PWR

One of the most important requirements in the design of pressurized water reactor (PWR) is to avoid the occurrence of critical heat flux (CHF). The design criteria for PWR specify that they must be operated at a certain percentage below CHF at all times and locations so as to the cladding temperature of fuel element at safe values. So in the process of safety assessment, CHF is one of important thermal-hydraulic parameters limiting the available power, whose size directly affects safety and economy of PWR nuclear power plant. This paper deals with a summary of experimental research progress on CHF of Chinese PWR. It mainly presents CHF experimental researches of Φ10 fuel assembly, CHF experimental researches of standard fuel assembly, and CHF experimental progress of non-uniform heated rod bundles. It should be emphasized that it also presents experimental research programs on CHF of Chinese advanced fuel assembly with self-reliance copyright. All CHF data obtained will be used for design improvement of Chinese PWR and R&D program of New Generation 1000 MWe PWR.     

Semi-supervised transfer discriminant analysis based on cross-domain mean constraint

In this paper, a novel semi-supervised feature extraction algorithm, i.e., semi-supervised transfer discriminant analysis (STDA) with knowledge transfer capability is proposed, based on the traditional algorithm that cannot get adapted in the change of the learning environment. By using both the pseudo label information from target domain samples and the actual label information from source domain samples in the label iterative refinement process, not only the between-class scatter is maximized while that within-class scatter is minimized, but also the original space structure is maintained via Laplacian matrix, and the distribution difference is reduced by using maximum mean discrepancy as well. Moreover, semi-supervised transfer discriminant analysis based on cross-domain mean constraint (STDA-CMC) is proposed. In this algorithm, the cross-domain mean constraint term is incorporated into STDA, such that knowledge transfer between domains is facilitated by making source and target samples after being projected are located more closely in the low-dimensional feature subspace. The proposed algorithm is proved efficient and feasible from experiments on several datasets.