基于类间分散度和类内集中度的文本特征选择。

特征选择是文本分类的关键步骤之一,所选特征子集的优劣直接影响文本分类的结果。论文首先定义了两种特征分类能力：一种是特征对类间文档的分散程度,该分散度越大越好;另一种是特征对类内文档的聚集程度,该集中度越大越好。然后把这两种特征影响度有机地结合起来设计了一个新的特征选择方法,该方法能够对所选特征进行综合考虑,从而使获得的特征集具有较好的代表性。仿真实验表明所提特征选择方法在一定程度上能够提高文本分类性能。     

C++程序设计教学中的技能培养

本文阐述了知识与技能的关系,以及C++程序设计教学中知识和技能的双重性要求。以选择语句为例,演示了"先知识后技能"的教学过程。实践证明,这样的教学安排行之有效。     

跳数加权DV-Hop定位算法

针对DV-Hop定位算法中距离估计误差对定位结果的影响,提出了一种信标节点优选方案和跳数加权DV-Hop定位算法。首先通过设定跳数阈值,保留跳数较少的信标节点,然后剔除近似在一条直线上的信标节点,完成信标节点优选,避免未知节点无法定位的情形。此外,利用Friis模型推导出距离估计误差与信号传播跳数之间的映射关系,采用传播跳数作为加权因子对定位结果进行了修正。仿真结果表明该算法降低了距离估计误差对定位精度的影响,提高了定位精度。     

膨胀弯管在管道柔性设计中的定性分析

为防止管道因热胀冷缩、管道支撑或端点附加位移造成的应力问题,对管线中设置的膨胀弯管进行定性分析.通过2个参数的变化对膨胀弯管进行编号得到多种设计方案,用ANSYS建立管道的有限元模型,并分析各方案中膨胀管道的变形、应力和弯矩等;根据分析结果对膨胀弯管设计半径、设置位置和形状进行定性分析.该定性分析结果可为膨胀弯管概念设计和管道柔性设计提供参考.     

旱情预报技术进展与展望

受全球气候变化影响和人类活动的加剧,干旱灾害发生频次增加且强度增大,严重威胁着我国的粮食安全和水安全。准确及时的旱情预报,对于制定科学有效的干旱应对策略、减少灾害造成的损失具有重大意义。从基于数理统计模型的预报技术和基于物理机制模型的预报技术两方面入手,梳理回顾了国内外研究进展,揭示了当前预报技术所存在的问题,并提出针对性的解决方案。未来研究应注重提高干旱监测数据的质量、突破核心关键技术、构建全国旱情预报业务化系统,为抗旱减灾事业提供强有力的科技支撑。     

Incorporating statistical information into expert classification systems to reduce classification costs

Interactive expert systems seek relevant information from a user in order to answer a query or to solve a problem that the user has posed. A fundamental design issue for such a system is therefore itsinformation-seeking strategy, which determines the order in which it asks questions or performs experiments to gain the information that it needs to respond to the user. This paper examines the problem of optimal knowledge acquisition through questioning in contexts where it is expensive or time-consuming to obtain the answers to questions. An abstract model of an expert classification system — considered as a set of logical classification rules supplemented by some statistical knowledge about attribute frequencies — is developed and applied to analyze the complexity and to present constructive algorithms for doing probabilistic question-based classification. New heuristics are presented that generalize previous results for optimal identification keys and questionnaires. For an important class of discrete discriminant analysis problems, these heuristics find optimal or near-optimal questioning strategies in a small fraction of the time required by an exact solution algorithm.     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

图形控制器在中小型控制系统中的应用

从选型、设计、调试3个方面比较了中小型控制系统中采用的触摸屏加PLC方案和图形控制器方案的优缺点;以某工厂供粉控制系统为例,给出了一种采用图形控制器来代替触摸屏加PLC的控制方案。实际应用说明了该控制方案的可行性及优势。     

短波chirp探测的遗传退火频率分配算法研究

针对短波chirp探测系统的通信网络频率分配问题,建立了合理的干扰模型;利用系统所提供的测量参数,结合基本遗传算法,同时进行模拟退火操作以克服基本遗传算法易陷入局部最优解的不足,并最终趋于全局最优化;仿真结果表明,该算法在寻优性、收敛速度和稳定性等方面都要优于基本遗传算法。